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(p. 3) Defining Depression and Bipolar Disorder 

(p. 3) Defining Depression and Bipolar Disorder
Chapter:
(p. 3) Defining Depression and Bipolar Disorder
Author(s):

Rachel Neuhut

, Tami Benton

, Paul Crits-Christoph

, Marivel Davila

, Myrna Weissman

, and Charles Nemeroff

DOI:
10.1093/med-psych/9780199928163.003.0001
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date: 28 March 2020

Introduction

(p. 4) The etiology of mood disorders involves a complex, multifactorial model (e.g., Akiskal & McKinney, 1975; Cicchetti & Toth, 1998; Kendler, Gardner, & Prescott, 2002). No single risk factor accounts for all or even most of the variance. The most likely causal model will include individual biological and psychological diatheses that interact with various environmental stressors. There is little question that early-onset, like adult-onset, major depressive disorder (MDD) is highly recurrent, whether the data derive from clinical samples (Kovacs, 1994), long-term population studies (Kessler & Walters, 1998), studies of high school students (Lewinsohn et al., 1998), or depressed patients (Garber et al., 1988; Rao et al., 1995). Over 50% of depressed adolescents had a recurrence within 5 years (Birmaher et al., 1996; Lewinsohn et al., 2000), although only small portions continue to have significant psychopathology in any one year. The few studies of depressed adolescents followed into adulthood show strong continuity between adolescent and adult depression (Frombonne et al., 2001; Harrington et al., 1990, 1998; Weissman et al., 1999a, 1999b), as well as an increased rate of suicide attempts and psychiatric and medical hospitalization, and studies of prepubertal depression show continuity onto adolescence (Kovacs, 1994). The most serious outcome is suicide, which is the third leading cause of death among adolescents. The recent Healthy People 2020: A Report Card on the Health of the Nation described how major depressive episodes in adolescents predict worsening outcomes and suicide deaths in adolescents; the rates have been increasing over time (2014).

Other outcomes include lack of social development and skills, withdrawal from peers, poor school performance, less-than-optimal career and marriage choices, and substance abuse (Frost et al., 1999; Rao et al., 1995, 1999; Weissman et al., 1999a).

This first chapter reviews the epidemiology, the definition of the disorder, psychological factors, social factors, and biological factors that have been shown to increase the risk of mood disorders in children and adolescents. Two major developments have taken place since the publication of the previous edition of this volume, which include new population-based studies as well as the publication of the DSM-5. The previous edition of this book reported epidemiologic data based on studies of adults and extrapolated rates for youth from retrospective reports of age of onset. Since the preparation of the original report, a number of large-scale, population-based studies of children and adolescents have been conducted.

The DSM-5 was published in May 2013, and therefore no epidemiologic studies have yet used DSM-5 criteria. This chapter will present these new data directly derived from U.S. epidemiologic studies of youth either nationally or based on a selected community/school and will update risk factors and speculate on the implications of DSM-5 changes.

Table 1.1 lists the relevant national and community/school-based studies reviewed in this chapter.

Table 1.1 National and Community/School-Based Studies (Children and Adolescents)

Study Authors

Location

N

Age

Diagnostic Interview

Period

National Health and Nutrition Examination Survey (NHANES) (Merikangas et al., 2010a)

National

3,042

8–15

Diagnostic Interview Schedule for Children, Version IV, based on DSM-IV criteria

12-month prevalence

National Comorbidity Survey Replication—Adolescent Supplement NCS—A (Kessler et al., 2012)

National

10,123 (6,483 adolescent–parent pairs)

13–18

Composite International Diagnostic Interview Version 3.0—DSM-IV

Lifetime and 12-month prevalence

Costello et al., 1996, 2003;

Great Smoky Mountains Study

North Carolina

1,420 at baseline assessed annually until age 16 over eight waves

9–13

Child and Adolescent Psychiatric Assessment (CAPA)—DSM-III-R;

Child and Adolescent Psychiatric Assessment—DSM-IV

3-month prevalence

Lewinsohn et al., 2000;

Oregon Adolescent Depression Project Study

Oregon

1,709 (Time 1)

1,507 (Time 2)

14–18

Schedule for Affective Disorders and Schizophrenia for School-Age Children;

Longitudinal Interval Follow-up Evaluation

Lifetime and point prevalence

Angold et al., 2002;

Caring for Children Study

North Carolina

920

9–17

Child and Adolescent Psychiatric Assessment—DSM-IV

3-month prevalence

Canino et al., 2004

Puerto Rico

1,886

4–17

Diagnostic Interview Schedule for Children, Version IV, based on DSM-IV criteria

12-month prevalence

Roberts et al., 2007;

Teen Health 2000 Study

Texas

4,175

11–17

Diagnostic Interview Schedule for Children, Version IV, based on DSM-IV criteria

12-month prevalence

Major Depression

Overview

For many years, children and adolescents were considered theoretically incapable of experiencing depression due to the psychoanalytic concept of the underdeveloped superego. Thus, depression was considered “an adult disease.” However, case reports as early as the 17th century have described adolescents exhibiting symptoms resembling those observed in adults with depressive disorders. The National Institute of Mental Health (NIMH) convened a meeting of thought leaders in 1975, followed by a book published by Shulterbrant and Ruskin, that finally made the diagnosis of depression acceptable in this population.

The past two decades have produced a radical change in understanding the age of onset of mood disorders. MDD is no longer seen primarily as a disorder of the middle-aged and elderly. Epidemiologic and clinical research from the United States and elsewhere has clearly documented that the age of first onset of major (p. 5) (p. 6) depression is commonly in adolescence and young adulthood and that prepubertal onsets, while less common, do occur. There may be symptoms of depression that manifest as early as preschool age. In a recent longitudinal prospective study of preschool children, the authors concluded that preschool-onset depression is a significant predictor of MDD in later childhood, even after accounting for the effect of maternal history of depression and other risk factors (Luby et al., 2014).

It is also clear that adolescent depression is a chronic, recurrent, and serious illness. The offspring of depressed, as compared to nondepressed, parents have over a twofold to fourfold increased risk of depression. Depressions occurring in adolescents share similar features to depression at other ages, including symptom patterns; a higher rates in females (twofold risk); high comorbidity with anxiety disorders, substance abuse, and suicidal behaviors; and high social, occupational, and educational disability. In contrast, childhood MDD tends to be male predominant and mood reactive and is commonly associated with high levels of irritability and dysphoria and high rates of comorbidity with the disruptive behavior disorders (Biederman et al., 1995; Leibenluft et al., 2003).

The epidemiologic data on bipolar disorder in childhood and adolescence are considerably sparser than for MDD. This is in part based on the earlier beliefs that bipolar disorder begins in adulthood; that it is difficult to assess boundaries between normal mood and mood irritability in youth, especially in community studies; and that the first signs of bipolar disorder are uncertain (Nottelman & Jensen, 1998). Most evidence on juvenile bipolar disorder comes from clinical samples where efforts, especially recently, have been made to characterize early clinical presentations of bipolar disorder.

Unfortunately, until recently, persons under age 18 have been excluded from epidemiologic studies, so empirically based information on prevalence, risk factors, course, and treatment are scanty, especially for bipolar disorder. This situation is changing, but not rapidly enough, because mood disorders in youth have a long-term effect on school, work, marriage, and the next generation. This chapter will highlight the empirical basis for understanding the epidemiology, phenomenology, course, and comorbidity of youth with MDD and bipolar disorder. Because a sharp distinction between childhood and adolescent onset cannot be readily made, information on childhood (prepubertal)-onset disorder will be included when relevant.

Diagnosis: Prepubertal- and Adolescent-Onset Depressive Disorders

Evidence suggests that depressive disorders often present during childhood and that there are distinct differences in depressive disorders that present in prepubertal youth and adolescents (Harrington et al., 1990; Weissman et al., 1999). Evidence further suggests that they may be distinctly different conditions rather than continuous (Kauffman, 2001). While some studies have demonstrated continuity of depression among preschoolers through the early school years (Luby, 2010), and continuity through adolescence (Kovacs et al., 1994), other studies of prepubertally depressed children have not found continuity into adulthood (Copeland et al., 2009; Harrington et al., 1990; Weissman et al., 1999a). As many as 1% to 5% of children and 3% to 8% of adolescents are estimated to be affected by a depressive disorder, with a lifetime prevalence of approximately 20% by the end of adolescence (Costello et al., 2003; Lewinsohn et al., 1998; Reinherz et al., 1993).

In contrast to adolescent-onset disorders, childhood-onset depressive disorders are characterized by male predominance, or gender equivalence, and are commonly comorbid with other neurodevelopmental disorders and disruptive impulse control disorders (Biederman et al., 1995; Birmaher et al., 1996; Costello et al., 1996; Egger & Angold, 2006; Rutter, 1996). Pubertal status has been shown to be a strong predictor for the emergence of major depression in girls (Angold et al., 1998).

Another feature of pediatric-onset depression that differs from adult-onset depression is the frequent presentation of irritable rather than depressed mood. The occurrence of irritability associated with disruptive behaviors among (p. 7) prepubertal youths with depression is so common that it has been a focus of much investigation. In the recent past, many practitioners viewed the persistent irritability present in prepubertal depression as characteristic of bipolar disorder, despite requirements for a discrete manic or hypomanic episode as defined by the DSM-IV and DSM-5. By combining these two distinct presentations (persistent, nonepisodic irritable mood and the discrete episodes of mania occurring with classical bipolar disorder), excessively high rates of pediatric bipolar disorder were reported. To strengthen diagnostic clarity and discrimination from bipolar disorder, a new diagnostic category, disruptive mood dysregulation disorder (DMDD), was established in DSM-5. This new category will capture children who present with the hallmark symptoms of severe, nonepisodic irritability. Although the prevalence estimates of this disorder in the community are unclear, the overall 6-month to 1-year period prevalence rates among children and adolescents are estimated to be 2% to 5%. It appears to be male predominant, persistent, impairing, and associated with later development of depression and anxiety disorders in adulthood (DSM-5; Axelson, Birmaher, Strober, et al., 2011; Axelson et al., 2012; Liebenluft, 2011).

DMDD is typified by the following:

  • Chronic, severe, persistent irritability manifesting as frequent verbal or behavioral outbursts (aggression against others, self or property) occurring at least three times per week, over the course of one year.

  • The outbursts must be developmentally inappropriate and occur in at least two settings such as home or school. The other required manifestation of severe irritability is its persistence between the severe temper outbursts. The angry or irritable mood must be present most of the day, nearly every day and evident to others.

  • The onset of this disorder must occur prior to 10 years of age and should not be applied to children whose developmental age is less than 6 years or after 18 years.

  • There cannot be more than one day during which mania/hypomania has been present or during an episode of depression or another mental disorder and cannot co-occur with oppositional defiant disorder, intermittent explosive disorder, or bipolar disorder.

This diagnosis should not be given if the criteria for a manic/hypomanic episode have ever been met and it cannot be explained by another medical condition or substance. If DMDD occurs in the presence of oppositional defiant disorder (ODD) or intermittent explosive disorder, then the diagnosis of DMDD should be given. Distinguishing features of DMDD in comparison to intermittent explosive disorder are the absence of disruptive mood between episodes and duration of only 3 months compared with the 12 months required for DMDD.

The prevalence of dysthymic disorders, now classified as persistent depressive disorder, has been less well studied among children and adolescents. The few existing studies suggest a prevalence of 0.6% to 1.7% among prepubertal children and 1.6% to 8.0% among adolescents (Birmaher, 1996).

Studies examining prepubertally affected youths with depression are conflicting. While some studies demonstrate higher rates of MDD among prepubertal boys compared with girls, other studies (Spencer et al., 1999) suggest that higher male than female representation in prepubertal MDD was due to the comorbidity with attention-deficit/hyperactivity disorder (ADHD); youth without this comorbidity had a similar gender representation before and after puberty. Studies suggest that prepubertally depressed children often grow up to have a variety of psychiatric disorders, especially increased rates of bipolar, anxiety, and substance use disorders (Kovacs et al., 1996; Weissman et al., 1999).

Among preschoolers, comorbidities of ADHD, ODD, and anxiety have been found with depression (Eggar & Angold, 2006). These findings add to a growing literature that suggests that the observed associations between disorders may in fact represent overlap among disorders. Investigation has focused upon (p. 8) understanding whether depressive disorders among prepubertal children represent a state of pervasive emotional and behavioral dysregulation that might represent a different syndrome from the syndrome of depression seen later in development (Egger & Angold, 2006).

Diagnostic Criteria for Depressive Disorders

MDD

The same criteria defined in the DSM-5 for adults are used for adolescents, as follows:

Five or more of the following symptoms must be present nearly every day during the same 2-week period, representing a marked change in function in order to diagnose an adolescent with a major depressive episode:

  • Depressed or irritable mood most of the day

  • Markedly diminished interest or pleasure in almost all activities, most of the day

  • Weight loss or weight gain or increase/decrease in appetite or for children, failure to make weight gains

  • Insomnia or hypersomnia

  • Psychomotor agitation or retardation

  • Fatigue or loss of energy

  • Feelings of inappropriate guilt or hope lessness

  • Indecisiveness or diminished ability to concentrate

  • Recurrent thoughts of death or suicidal ideation, suicide attempt or a specific plan

Depressed or irritable mood or markedly diminished interest or pleasure in almost all activities must be present, and must represent a clear change in affect, cognition and neurovegetative functions from the adolescent’s usual state. The symptoms must cause clinical impairment in important areas of functioning and cannot be due to the direct physiological effect of substance abuse or a general medical condition. There cannot ever have been a manic/hypomanic episode and it cannot be accounted for by bereavement or schizoaffective disorder. A major depressive episode cannot be superimposed on schizophrenia, schizophreniform disorder, delusional disorder, specified and unspecified schizophrenia spectrum, and other psychotic disorders. When recording the diagnosis of depression, one should include whether it is a single or recurrent episode and a severity specifier, mild, moderate or severe. When describing depressive disorders, greater specificity and clarity of risk and prognostic factors may be obtained by specifying accompanying features. For example, “with melancholic features” applies if there is an almost complete loss of pleasure in almost all activities, including previous highly desired events that would normally generate brightened mood, in addition to at least three of the following:

  • Depressed mood characterized by profound despondency, despair, or moroseness or by “empty mood”

  • Depression is typically worse in the morning

  • Morning waking at least 2 hours before usual waking (early morning)

  • Change in psychomotor activity (retardation or agitation)

  • Weight loss or anorexia

  • Guilt that is inappropriate or excessive

If hallucinations or delusional thoughts are present, then the “psychotic features” specifiers should be used in reference to the mood congruency (consistent with depressive themes) or incongruency (not involving typical themes of inadequacy) of those psychotic features. In addition to psychotic features, catatonia may be associated with depressive disorders, and is defined by three or more of the following psychomotor symptoms:

  • Waxy flexibility, catalepsy, or stupor

  • Motor overactivity that is purposeless and not in response to external stimuli (p. 9)

  • Negativism or mutism

  • Peculiarities of movement (posturing, grimacing, stereotypy, and mannerisms)

  • Echolalia or echopraxia

When depression is accompanied by high levels of anxiety, the specifier “with anxious distress” should be used. In addition, the following must be present:

  • Feeling tense or keyed up

  • Feeling unusually restless

  • Difficulty concentrating because of worry

  • Fear that something awful may happen

  • Feeling that the individual might lose control of himself or herself

“With mixed features” should be used if manic or hypomanic symptoms are present with at least three of the following:

  • Elevated or expansive mood

  • Inflated self-esteem or grandiosity

  • More talkative than usual or pressure to keep talking

  • Flight of ideas or subjective experience that thoughts are racing

  • Increase in energy or goal-directed activities that have a high potential for painful consequences

  • Decreased need for sleep

When mood brightens during positive events, called “mood reactivity,” the “atypical features” specifier should be used. Two or more of the following must be present:

  • Significant weight gain or increase in appetite

  • Heavy leaden feeling in arms and legs

  • A longstanding pattern of interpersonal rejection, not occurring exclusively during the mood episode, that results in occupational and social impairment

  • Hypersomnia

Depressive disorders may also present in a seasonal pattern in children and adolescents. Diagnostic challenges may be imposed by the frequent predictable seasonal stressors that occur during particular times of the year, for adolescents, such as the start of the new school year. Furthermore, a major depressive episode can present initially as seasonal affective disorder in children and adolescents. To establish the presence of a seasonal mood disorder, there must be a regular temporal relationship between the mood disorder (depression or mania) and a particular time of the year. A full remission or switching from depression to mania must occur within that particular time of the year. There must be at least two episodes of mood disturbance during the last 2 years and the seasonal episodes must outnumber nonseasonal episodes. This specifier does not apply when a predictable psychosocial stressor, such as the start of the school year, is an explanatory factor.

The specifier “with peripartum onset” should be considered in female adolescents when the onset of depression occurs during pregnancy or within 4 weeks of giving birth.

Dysthymia

Persistent depressive disorder (dysthymia) is a chronic illness often beginning insidiously during childhood, adolescence, and young adulthood. It is commonly comorbid with ADHD and conduct disorder. Predisposing factors are early parental loss or separation, and a chaotic home environment. Kovacs et al. (1984) report that children with persistent depressive disorder (dysthymic) are at risk for developing depression and mania on follow-up. Adolescents who have persistent depressive disorder and who subsequently develop a major depressive episode should be diagnosed with persistent depressive disorder with the specifier “intermittent major depressive episode.” Several features distinguish persistent depressive disorder from major depression in children and adolescents.

(p. 10)

Dysthymia, a diagnosis that was often undetected in adolescents, has been consolidated with chronic major depressive disorder into a new diagnostic category, Persistent Depressive Disorder. This disorder is defined in adolescents as depressed or irritable mood which must be present for a year or longer and the youth must never be symptom-free for more than 2 months. During the depressive episode, two or more of the following symptoms must be present:

  • Increase or decrease in appetite

  • Increased or decreased sleep

  • Decrease in energy or fatigue

  • Low self-esteem

  • Poor concentration or difficulty with decision making

  • Hopelessness

With this disorder, criteria for a major depressive episode may be continually present for 1 year. Major depressive episode may precede Persistent Depressive Disorder and may occur during persistent depressive disorder. Persistent Depressive Disorder (dysthymia) should not be diagnosed if it is a direct result of a substance or medication, is a general medical condition, or is occurring during the course of a psychotic disorder. Persistent Depressive Disorder utilizes the same specifiers as depressive disorders. Specifiers exclusive to this disorder are:

  1. 1. with pure dysthymic syndrome, suggesting that full criteria for a major depressive episode have not been met in at least the preceding 1 year;

  2. 2. with persistent major depressive episode: MDE criteria have been met in the preceding 1-year period;

  3. 3. with intermittent major depressive episodes, with current episode, meaning that full criteria for major depression are currently met, but there have been subthreshold symptoms for at least 8 weeks in the preceding year;

  4. 4. with intermittent major depressive episodes, without current episode, meaning that full MDE criteria are not currently met but there has been an episode of MDD in the preceding 1 year.

It should be further specified if onset is early (before 21 years) or late (after 21 years).

Premenstrual Dysphoric Disorder

The core features of this disorder are the occurrence of irritability, mood lability, anxiety, and dysphoric mood, occurring consistently during the premenstrual phase of the menstrual cycle and remitting proximal to the period of menses. The symptoms must have occurred in most of the cycles during the past year and must impact social and occupational functioning. Prevalence estimates for 12 months suggest that 1.8% to 5.8% of women are affected by this disorder. Limited data exist regarding the prevalence of this disorder among adolescents. MDD is the most commonly associated premorbid condition among women presenting with premenstrual dysphoric disorder.

Bereavement Exclusion

Depressive symptoms lasting less than 2 months after the death of a loved one (referred to as the bereavement exclusion) was excluded in DSM-5 for a number of reasons, including the recognition that bereavement usually lasts 1 to 2 years. A comprehensive footnote has replaced the DSM-IV exclusion to assist clinicians in deciding whether symptoms are consistent with normal bereavement versus those of MDD. Specific to children and adolescents, this change is not expected to impact rates of depression greatly.

Summary of Implications of DSM-5 Changes in Criteria

A number of changes specific to depressive disorders were made in DSM-5, but as noted above (p. 11) no epidemiologic studies have yet included these changes. We can only speculate on the changes their incorporation will make on rates.

  1. 1. Due to concern about overdiagnosis of bipolar disorder in children, disruptive mood dysregulation disorder has been included for children up to age 18 years who report frequent episodes of extreme behavioral dyscontrol and persistent irritability.

  2. 2. Premenstrual dysphoric disorder has been moved from Appendix B, “Criteria Sets and Axes Provided for Further Study,” to the main body of DSM-5.

  3. 3. Persistent depressive disorder replaces what was referred to as dysthymia, a mild but chronic form of depression in DSM-IV.

  4. 4. Within a major depressive episode, the presence of at least three manic symptoms has now been recognized by the specifier “with mixed features.”

  5. 5. The bereavement exclusion was excluded in DSM-5.

  6. 6. A new specifier signifying the presence of mixed symptoms has been included across both bipolar and depressive disorders.

Epidemiology

Previously, epidemiologic data from community surveys directly studying children and adolescents were sparse due to the long-held view that MDD was rare before adulthood or a self-limiting and normal part of growing up. There are now a number of national as well as community studies specific to depression and dysthymia in children and adolescents.

Major Depression

National Studies

Table 1.2 reports the results from national samples of studies conducted among children and adolescents specific to depression.

Table 1.2 Rates of DSM Major Depression and Dysthymia in National Samples of Children and Adolescents

National Studies

Prevalence Rates/100 (SE)

NHANES, 2001–2004, Merikangas et al., 2010a

12-Month Prevalence of Major Depression (without Impairment)

2.7 (0.6)

Females

3.7** (0.8)

Males

1.8** (0.6)

8–11 years

1.6** (0.5)

12–15 years

3.8** (0.8)

12-Month Prevalence of Major Depression (with Severe Impairment)

2.4 (0.5)

Females

3.2 (0.7)

Males

1.6 (0.5)

8–11 years

1.4** (0.4)

12–15 years

3.2** (0.7)

12-Month Prevalence of Dysthymia (without Impairment)

1.0 (0.3)

Females

1.2 (0.4)

Males

0.7 (0.3)

8–11 years

0.8 (0.4)

12–15 years

1.1 (0.3)

12-Month Prevalence of Dysthymia (with Severe Impairment)

0.5 (0.2)

Females

0.9** (0.4)

Males

0.1** (0.1)

8–11 years

0.4 (0.2)

12–15 years

0.7 (0.3)

** Significant difference

National Study Based on NCS-A

Prevalence Rates/100 (SE)

NCS-A, Kessler et al., 2012

Total Major Depressive Disorder—Lifetime Prevalence

10.6 (0.8)

Females

14.2* (1.2)

Males

7.2* (0.8)

Single episode—females

3.4 (0.5)

Single episode—males

2.4 (0.5)

Recurrent episodes—females

10.8* (1.1)

Recurrent episodes—males

4.8* (0.7)

Lifetime morbid risk (LMR) and ratio of lifetime prevalence to morbid risk (LT/LMR) of Major depressive episode among 13- to 17-year-olds

0.4

* Significant gender difference within subsample

The 2001–2004 National Health and Nutrition Examination Survey (NHANES) reported 12-month prevalence estimates for 3,042 participants ages 8 to 15 years based on DSM-IV criteria (Merikangas, He, Brody, Fisher, Bourdon, & Koretz, 2010a), with results broken down by gender and age. Specific to major depression, girls had a significantly higher prevalence than boys (3.7% vs. 1.8%, respectively, χ‎2 = 4.65, p = .04). Children ages 12 to 15 years also had a significantly higher 12-month prevalence than children ages 8 to 11 years (3.8% vs. 1.6%, respectively, χ‎2 = 10.00, p = .004). The 12-month prevalence estimate for major depression with severe impairment was 2.4% (see Table 1.2).

Kessler, Petukhova, Sampson, Zaslavsky, and Wittchen (2012), using the National Comorbidity Survey Replication—Adolescent Supplement (NCS-A) sample, compared lifetime prevalence rates of major depressive episodes and MDD, further broken down into single and recurrent episodes based on DSM-IV-TR criteria, among youth ages 13 to 17 years. Among youth, 16.8% of females met the criteria for lifetime major depressive episode compared to 8.5% of males. On the other hand, 14.2% of females met the criteria for lifetime MDD compared to 7.2% of males. Further lifetime prevalence estimates for single and recurrent episodes are also reported in Table 1.2. In addition, a lifetime morbid risk (LMR), defined as the proportion of individuals who are expected to experience a major depressive episode, using a survival model for projections irrespective of whether or not they report a lifetime history of the disorder at the time of interview, were also presented. In this analysis, the adolescent sample (NCS-A) was combined with the adult sample of the National Comorbidity Survey Replication (NCS-R) in order to project LMR as of age 75 based on age-of-onset distributions (Kessler et al., 2012). While results for LMR were reported for the entire sample (adults and adolescents), the ratio of lifetime prevalence to morbid risk (LT/LMR) of major depressive episode among 13- to 17-year-olds was reported as 0.4 (see Table 1.2). (p. 12)

(p. 13) A previous analysis, also using the NCS-A sample, reported lifetime prevalence of MDD or dysthymia (Merikangas, He, Burstein, Swanson, Avenevoli, Cui, Benjet, Georgiades, & Swendsen, 2010b), which differed slightly from the results shown above by Kessler et al. (2012). While the lifetime prevalence for MDD or dysthymia was 11.7%, 15.9% of adolescent females met criteria for DSM-IV MDD or dysthymia compared to 7.7% of adolescent males. Broken down by age group, 8.4% of youth ages 13 or 14 years met the criteria, compared to 12.6% of 15- and 16-year-olds, and 15.4% of 17- and 18-year-olds. In this analysis (results not shown), the lifetime prevalence of MDD or dysthymia with severe impairment was 8.7%. As noted by Kessler et al. (2012) in their analysis using the same dataset, minor discrepancies in results are most likely due to changes in data coding and weighting in an effort to improve estimates.

Community Studies

Table 1.3 presents the rates of depression in community samples of studies conducted among children and adolescents.

Table 1.3 Rates of DSM Depression in Community Samples of Children and Adolescents

Authors

Major Depression

Dysthymia

Minor Depression

All Depression

Depression NOS**

Great Smoky Mountains Study (Baseline Results) Three-Month Prevalence Estimates—DSM-III-R

Prevalence Rate/100 (SE)

Prevalence Rate/100 (SE)

Prevalence Rate/100 (SE)

Prevalence Rate/100 (SE)

Prevalence Rate/100 (SE)

Costello et al. (1996)

0.03 (0.03) §

0.13 (.07) §

1.52 (0.46)

1.45 (.46)

Female

0.07 (.07) §

0.07 (.07) §

1.36 (0.65)

1.22 (.65)

Male

0 §

0.20 (.12) §

1.68 (0.66)

1.68 (.66)

Caring for Children in the Community Study

Three-Month Prevalence Estimates—DSM-IV

Prevalence Rate/100 (95% CI)

Prevalence Rate/100 (95% CI)

Prevalence Rate/100 (95% CI)

Prevalence Rate/100 (95% CI)

Prevalence Rate/100 (95% CI)

Angold et al. (2002)

1.0 (0.5–1.8)

0.3 (0.1–0.6)

1.7 (1.0–2.8)

2.9 (2.0–4.2)

White

1.5 (0.7–3.1)

0.3 (0.1–1.0)

2.8 (1.5–5.3)

4.6 (2.9–7.3)

African-American

0.5 (0.2–1.5)

0.2 (< 0.1–0.9)

0.7 (0.3–1.5) *

1.4 (0.8–2.5) *

Female

1.2 (0.5–2.8)

0.2 (<0.1–0.9)

1.4 (0.7–2.7)

2.8 (1.7–4.6)

Male

0.7 (0.3–1.6)

0.3 (0.1–1.0)

2.0 (0.9–4.1)

3.0 (1.7–5.1)

Great Smoky Mountains Study

Three-Month Prevalence Estimates—DSM-IV

Prevalence Rate/100 (95% CI)

Prevalence Rate/100 (95% CI)

Prevalence Rate/100 (95% CI)

Prevalence Rate/100 (95% CI)

Prevalence Rate/100 (95% CI)

Costello et al. (2003)

0.4 (0.2–0.7)

0.3 (0.1–0.6)

2.2 (1.6–3.0)

1.5 (1.1–2.2)

Females

0.5 (0.2–0.9)

0.3 (0.1–0.8)

2.8 (1.8–4.3)

2.1 (1.3–3.4)

Males

0.4 (0.2–0.9)

0.3 (0.1–0.8)

1.6 (1.0–2.5)

1.0 (0.6–1.5)

* p < .01 significant difference between white and African-American youth

** Not otherwise specified

§ <5 cases in interviewed sample

Authors

Any Depressive Disorder

Major Depression

Dysthymia

Child–Caretaker Dyads in Puerto Rico Last-Year Prevalence Estimates—DSM-IV

Prevalence Rate/100 (95% CI)

Prevalence Rate/100 (95% CI)

Prevalence Rate/100 (95% CI)

Canino et al. (2004)

  Partial DSM-IV/DISC-IV

3.6 (2.5–5.1)

0.6 (0.30–1.2)

  Full DSM-IV/DISC-IV

4.1 (2.9–5.6)

3.0 (2.0–4.5)

0.5 (0.23–1.0)

  Partial DSM-IV/DISC-IV + PIC-GAS < 69

3.4 (2.4–4.9)

  Full DSM-IV/DISC-IV + PIC–GAS < 69

1.8 (1.0–3.0)

0.4 (0.16–0.97)

2.1 (1.3–3.4)

1.4 (0.75–2.8)

0.3 (0.10–0.87)

1.7 (0.92–3.0)

Teen Health 2000 Past-Year Prevalence Estimates—DSM-IV

Prevalence Rate/100 (95% CI)

Prevalence Rate/100 (95% CI)

Prevalence Rate/100 (95% CI)

Roberts et al. (2007)

  Prevalence of Disorder

1.70 (1.27–2.12)

0.33 (0.13–0.52)

  Prevalence with DISC Impairment

1.54 (1.14–1.95)

0.29 (0.11–0.48)

  Prevalence with CGAS69

0.67 (0.41–0.93)

0.20 (0.04–0.39)

Costello et al. (1996) reported 3-month weighted prevalence estimates using DSM-III-R diagnoses in the baseline results for the Great Smoky Mountains Study of Youth (longitudinal) conducted among children ages 9, 11, and 13 years. In this study, three cohorts of children ages 9, 11, and 13 years at baseline were assessed annually over the course of eight waves. In the baseline results, among all children, 1.52% met the criteria for any depressive disorder, with 1.68% and 1.36% of females meeting the criteria. Specific to depression not otherwise specified, 1.45% of all children met the criteria, with 1.68% of males and 1.22% of females meeting the criteria. In this sample, 0.03% of all children met the criteria for DSM-III-R major depression. In the 2003 analysis, Costello et al. (2003) reported the results from all eight waves up to age 16 from the three age cohorts with results broken down by each age (9–10 through 16). Results were also presented by gender. The 3-month prevalence estimate for any depressive disorder was 2.2% among all respondents (2.8% among female children and adolescents and 1.6% among male children and adolescents). Three-month prevalence estimates for major depression and depression not otherwise specified, further broken down by age group and gender, are presented in Table 1.3, including results from the 1996 baseline analysis.

Angold et al. (2002), in the Caring for Children in the Community Study, reported the prevalence of DSM-IV psychiatric disorders, including depression, among 920 youth ages 9 to 17 years, further broken down by gender and ethnicity. A significantly higher prevalence of depressive disorder (odds ratio [OR] = 3.4, 95% confidence interval [CI], 1.6–7.4) was found among white youth. Three-month prevalence estimates are presented in Table 1.3, with results broken down by gender and white versus African-American youth.

Canino et al. (2004) sampled 1,886 child–caretaker dyads in Puerto Rico and conducted Spanish-language interviews among children ages 4 to 17 years. DSM-IV last-year prevalence rates for any depressive disorder, including major depression, were reported. Results were further broken down four ways: (1) Diagnostic Interview Schedule for Children (DISC) criteria for any depressive disorder met in either parent or child reports excluding the DISC impairment criteria (4.1%); (2) met DISC criteria for any depressive disorder including the DISC-specific impairment criterion in either parent or child reports (3.4%); (3) met the DISC criteria for any depressive disorder including significant impairment based on a cutoff less than 69 on the impairment rating scale, Parent-Interview—Children’s Global Assessment Scale (PIC-GAS) (2.1%); and (4) met full DISC criteria including the DISC-specific impairment criterion in either parent or child reports and a cutoff score less than 69 on the PIC-GAS (1.7%). Results are also presented for major depression in Table 1.3. In logistic regression analyses, girls were found to have significantly more depressive disorders in general (specifically major depression). Rates for depressive disorders and major depression increased significantly with (p. 14) (p. 15) (p. 16) age. Children whose parents were not married were significantly more likely to meet criteria for MDD.

Roberts et al. (2007), in the Teen Health 2000 Study of 4,175 youths ages 11 to 17 years in the Houston area, also reported prevalence estimates based on the DISC. The past-year prevalence of major depression was 1.70%, past-year prevalence of major depression with DISC impairment was 1.54%, and past-year prevalence of major depression with a CGAS score less than or equal to 69 was 0.67% (see Table 1.3).

Dysthymia

National Studies

Tables 1.2 and 1.3 also report the results from national and community samples of studies conducted among children and adolescents that assessed dysthymia.

Twelve-month prevalence estimates for dysthymia, with and without severe impairment, were included in the 2001–2004 NHANES (Merikangas et al., 2010a). There were no statistically significant differences in prevalence estimates between males and females, or between older and younger children: 1.2% of females and 0.7% of males met the criteria for 12-month dysthymia (χ‎2 = 1.53, p = .225). Likewise, 1.1% of children ages 12 to 15 years and 0.8% of children ages 8 to 11 years met the criteria for 12-month dysthymia without impairment (χ‎2 = 0.28, p = .601). The 12-month prevalence estimate for dysthymia with severe impairment was 0.5% (see Table 1.1).

As previously noted, Merikangas et al. (2010b) assessed the prevalence of MDD or dysthymia together as one entity (results not presented in tables).

Community Studies

Table 1.3 reports the results from community studies that included dysthymia in their assessments.

Costello et al. (1996), in the Great Smoky Mountains Study, reported 3-month weighted prevalence estimates using DSM-III-R diagnoses for dysthymia at baseline. Among children ages 9, 11, and 13 years, 0.13% of children met the criteria for dysthymia. Broken down by gender, 0.20% of males and 0.07% of females met the criteria (see Table 1.3). In 2003, Costello et al. (2003) presented 3-month prevalence estimates for dysthymia. Results were also presented by gender. The 3-month prevalence estimate for dysthymia was 0.3% among all respondents (0.3% among female children and adolescents and 0.3% among male children and adolescents). Three-month prevalence estimates for dysthymia further broken down by age group and gender are presented in Table 1.3.

Angold et al. (2002), in the Caring for Children in the Community Study, reported the prevalence of DSM-IV psychiatric disorders, including dysthymia, among 920 youth ages 9 to 17 years. The 3-month prevalence estimate for dysthymia among all youth was 0.3%; it was 0.3% in white youth and 0.2% in African-American youth. The 3-month prevalence estimate was 0.2% in female youth and 0.3% in male youth (see Table 1.3).

Canino et al. (2004) reported DSM-IV last-year prevalence rates for dysthymia among Puerto Rican children. Results were further broken down four ways: (1) DISC criteria for dysthymia met in either parent or child reports excluding the DISC impairment criterion (0.6%); (2) met DISC criteria for dysthymia including the DISC-specific impairment criterion in either parent or child reports (0.5%); (3) met the DISC criteria for dysthymia including significant impairment based on a cutoff less than 69 on the PIC-GAS (0.4%); and (4) met full DISC criteria including the DISC-specific impairment criterion in either parent or child reports and a cutoff score less than 69 on the PIC-GAS (0.3%). Results are presented in Table 1.3.

Roberts et al. (2007), in the Teen Health 2000 Study, reported past-year prevalence of dysthymia (0.33%), past-year prevalence of dysthymia with DISC impairment (0.29%), and past-year prevalence of dysthymia with CGAS score less than or equal to 69 (0.20%) (see Table 1.3).

(p. 17) Comparison of Rates Based on Age of Onset to Rates Based on Child/Adolescent Samples

Specific to depression, the lifetime prevalence for 15- to 18-year-olds based on the National Comorbidity Survey (NCS; Kessler & Walters, 1998) was found to be roughly 14%, with an additional 11% having a lifetime history of minor depression. Using the NCS-A adolescent sample, the lifetime prevalence of MDD or dysthymia was found to be 11.7% (Merikangas et al., 2010b). Kessler et al. (2012), also using the NCS-A sample of adolescents, reported a lifetime prevalence of MDD to be 10.6%. These statistics are consistent with data derived from clinical samples documenting that more that 30% of children referred to clinical centers have major depression and that, in many of these cases, the disorder starts in the preschool years. Moreover, reports from student health services on college campuses note a marked increase in requests for counseling for depression over the past decade and list suicide as the second leading cause of death among students (American College Health Association, 2008).

In summary, there is increasing evidence that the first onset of MDD frequently occurs during adolescence and is not uncommon in childhood.

Comorbidity

Depressive disorders among youth are commonly comorbid with other psychiatric and medical conditions. Prevalence estimates across varied treatment settings suggest that 40% to 90% of depressed youths have another psychiatric condition, with as many as 50% having two or more comorbid conditions. The most common comorbid conditions are anxiety disorders, estimated to occur in as many as 60% of youths with depression (Angold et al., 1999; Birmaher et al., 1996, 2002; Pine et al., 1998).

Disruptive behavior disorders and ADHD follow anxiety disorders in frequency, often emerging before puberty. Among adolescents, substance use disorders are common. Depressive disorders presenting during childhood are often preceded by another psychiatric condition such as an anxiety disorder or ADHD. Furthermore, the presence of a depressive disorder increases the risks for the onset of other nonaffective psychiatric disorders. Depressive disorders are also highly comorbid with pediatric diseases across many conditions, suggesting potential shared etiologies for these illnesses, including epilepsy (Caplan, 2012; Caplan et al., 1998; Davies et al., 2003; Weisbrot & Ettinger, 2001), end-stage renal disease (Bakr et al., 2007), obesity (Pine et al., 2001), headaches (Pine et al., 1996), asthma (Katon et al., 2007; Mrazek et al., 1998), HIV disease (Mellins et al., 2009; Pao et al., 2005), cancer (Kersun & Elia, 2007), and diabetes (Kovacs, 1997; Lustman, 2000).

Risk Factors

Information on risk factors for adolescent MDD comes both from epidemiologic and clinical studies. The two most consistent risk factors for MDD in studies of adolescents and adults are female gender (twofold to threefold increased risk) and a family history of MDD. The offspring of depressed parents are at a twofold to fourfold increased risk of experiencing MDD and show an earlier age of onset and recurrent episodes (Rice et al., 2002). The risk is transmitted across generations to grandchildren (Weissman et al., 2016). One large-scale longitudinal study has identified age 12.5 as the developmental point at which a significant gender difference in risk emerges (Hankin et al., 2015).

Other risk factors that contribute both to the onset and recurrence of adolescent MDD are adverse family environments characterized by absence of supportive interactions; poor parental bonding; poor primary attachments; and harsh discipline (Fendrich et al., 1990; Garber & Little, 1999; Hakim, Larson, & Essau, 1999; Sheeber et al., 2001). A recent study involving a Norwegian community sample of 345 adolescents found that adolescent depression was significantly linked to financial concerns, physical illness or disability, and internalizing and externalizing issues among mothers but not fathers (Agerup, Lydersen, Wallander, & Sund, 2015). (p. 18) Of these maternal risk factors, only internalizing issues significantly predicted adolescent depression after controlling for the other maternal characteristics. Separating out the effects of parental MDD from other risk factors is problematic because parental MDD is frequently associated with other risk factors (e.g., divorce, poor parental bonding). One study of offspring at high and low risk of depression found that parental depression was the strongest risk factor for offspring depression, over and above other family risks, such as divorce or poor parental bonding. The rates of MDD were considerably lower in the offspring of nondepressed parents (low risk), but when MDD was present in the low-risk offspring it was associated with poor parental bonding, parent–child conflict, and parental divorce (Fendrich et al., 1990; Nomura et al., 2002).

Although initial attempts to link childhood adversity (such as early parental death, poverty, and single-parent households) specifically to MDD using twin and epidemiologic data yielded disappointing results (Kendler et al., 1992; Kessler et al., 1997), recent evidence suggests that childhood adversity may elevate MDD risk among adolescents and/or adults. The influence of childhood adversity may be limited to particular developmental stages and may depend on the nature and severity of the adverse experiences. In one study, severe and hazardous adverse experiences during the preschool years had a significant impact on age 14 depressive symptoms in both boys and girls, but by age 17, these effects were only present in girls (St. Clair et al., 2015). In boys, maturational effects may be partially responsible for dampening the effects of severe early childhood adversity after age 14. Another study found that adult MDD was significantly associated with both prospective and retrospective reports of childhood adversity (Patten et al., 2015).

Notwithstanding the significant impact of involuntary risk factors (including gender, family history, family environment, and adversity), numerous studies have also pointed to an array of risk and protective factors that adolescents themselves may have the power to modify. In a recent meta-analysis of self-modifiable traits and behaviors, substance use, dieting, negative coping strategies, and unhealthy weight were identified as significant risk factors for depression in adolescents, while healthy eating and sleep habits were found to have protective effects (Cairns, Yap, Pilkington, & Jorm, 2014).

Personality/Temperament

Several theorists have hypothesized a heritable trait vulnerability factor common to most, if not all, emotional disorders. This trait has been defined slightly differently and given various labels by different theorists, including harm avoidance (Cloninger, 1987), neuroticism (Eysenck, 1947), trait anxiety (Gray, 1982), behavioral inhibition (Kagan, Reznick, & Snidman, 1987), and negative affectivity (Watson & Tellegen, 1985), though the conceptual and empirical overlap among these constructs far outweighs the differences. Each implies a trait disposition to experience negative affect. The term neuroticism often is used to refer to this trait, and is consistent with the emergence of the “Big 5” model of personality as the dominant model of personality structure in children (e.g., Digman & Inouye, 1986; Digman & Shmelyov, 1996), adolescents (e.g., Digman, 1989; Graziano & Ward, 1992) and adults (e.g., Goldberg, 1992; John, 1990; McCrae & Costa, 1987).

Longitudinal studies have shown that neuroticism predicts later negative affect and symptoms of emotional distress (Costa & McCrae, 1980; Larson, 1992; Levenson, Aldwin, Bosse, & Spiro, 1988), even after controlling for initial symptom levels (Gershuny & Sher, 1998; Jorm, Christensen, Henderson, & Jacomb, 2000). Clark, Watson, and Mineka (1994) reviewed several longitudinal studies showing that neuroticism predicts both subsequent diagnoses and chronicity of major depression. Since this review, studies reported by Hayward, Killen, Kraemer, and Taylor (2000), Kendler and colleagues (Kendler et al., 1993, 2002; Roberts & Kendler, 1999), and Krueger et al. (1996) have each obtained results consistent with the conclusions of Clark et al. (1994). For example, in a large adult female twin sample, Kendler et al. (p. 19) (1993) found that neuroticism predicted the onset of MDD over a 1-year period, and Kendler et al. (2002) tested a multifactorial model and showed that after stressful life events, neuroticism was the strongest predictor of the onset of major depression.

The relation between neuroticism and depression may vary somewhat by age. Hirschfeld et al. (1989) found that whereas among older individuals (31–41 years old) neurotic-like characteristics of decreased emotional strength, increased interpersonal dependency, and increased thoughtfulness predicted the first onset of depression, this was not the case for younger individuals (17–30 years old). Similarly, Rohde, Lewinsohn, and Seeley (1990) found that adult participants who experienced a first episode of MDD had exhibited elevated levels of dependent traits 2 to 3 years earlier, whereas Rohde, Lewinsohn, and Seeley (1994) found no differences with regard to prior levels of dependency between adolescents who later developed a first MDD and adolescents who were depression-free during a 1-year follow-up period.

In contrast, studies using other measures of neurotic-like traits in children have found evidence of a link with vulnerability for depression. Elevated levels of behavioral inhibition have been observed in laboratory tasks with young offspring of depressed parents (Kochanska & Kuczynski, 1991; Rosenbaum et al., 2000). Caspi, Moffit, Newman, and Silva (1996) reported that children who had been rated as inhibited, socially reticent, and easily upset at age 3 had elevated rates of depressive disorders at age 21. Similarly, van Os, Jones, Wadsworth, and Murray (1997) found that physicians’ ratings of behavioral apathy at ages 6, 7, and 11 predicted both adolescent mood disorders and chronic depression in middle adulthood. St. Clair et al. (2015) identified higher overall emotionality in childhood as a direct predictor of depressive symptoms at age 14 and an indirect predictor of depressive symptoms at age 17, with stronger associations in girls than boys. Finally, Gjerde (1995) reported that gender may moderate the relation between temperament and mood disorders. Whereas females with higher levels of chronic depression during young adulthood had been described as shy and withdrawn at ages 3 to 4, males with chronic depression had exhibited higher levels of undercontrolled behaviors as young children. Thus, there is some evidence of an association between neurotic-like traits during childhood and subsequent depression, although it may depend on gender as well as how these traits are measured.

Neuroticism also has been found to be a risk factor for other forms of psychopathology, however, and thus it is not specific to mood disorders. For example, neuroticism has been shown to be a risk factor in the development of posttraumatic stress disorder (PTSD; e.g., Breslau & Davis, 1992; Breslau et al., 1995; Helzer et al., 1987). Behaviorally inhibited children are at greater risk for the development of multiple phobias and various anxiety disorders in later childhood (Biederman et al., 1990; Hirshfeld et al., 1992), and social phobias in adolescence (Hayward et al., 1998). Hayward et al. (2000) also found that neuroticism predicted the development of panic attacks in a 4-year prospective study in adolescents. Thus, neuroticism appears to be a significant predictor of depression, although it might not be a specific vulnerability marker. Moreover, it is still difficult to distinguish among common cause, precursor, predisposition, and scar models of the relation between neuroticism and mental disorders (Klein, Durbin, Shankman, & Santiago, 2002; Ormel et al., 2013).

General negative emotionality (rather than neuroticism per se) has also been targeted as a vulnerability factor in several recent studies. In particular, exaggerated emotional responses to negative stimuli have been explored in relation to depression heritability. In one study, boys ages 7 to 13 with high familial risk for depression exhibited elevated sensitivity to sadness in a facial emotion recognition task (Lopez-Duran, Kuhlman, George, & Kovacs, 2013). This effect was specific to sadness (boys in the high-risk group were no different than their low-risk peers in their responses to angry faces) and did not appear to be present in girls at high familial risk. However, these trends have not (p. 20) been consistent across all studies of emotion recognition: in a sample of youth ages 7 to 16, including currently depressed, never depressed, and remitted depressed participants, Jenness, Hankin, Young, and Gibb (2015) found that those with current depression more often mistook happy and sad faces for angry faces. This finding suggests a potential bias toward threat detection, but no apparent difference in terms of sensitivity to sadness or happiness.

Notably, the vulnerability conferred by negative emotionality may depend on other cognitive and affective traits. Vasey et al. (2013) found that a three-way interaction among high negative emotionality, low positive emotionality, and low self-regulation significantly predicted depressive symptoms in four out of five independent youth samples. This interaction did not predict levels of anxiety, suggesting potential diagnostic specificity.

Cognitive Vulnerability

According to cognitive theories of depression (Abramson, Metalsky, & Alloy, 1989; Abramson, Seligman, & Teasdale, 1978; Beck, 1967), depressed individuals have more negative beliefs about themselves, the world, and their future, and tend to make global, stable, and internal attributions for negative events. These negative cognitions are expected to be concurrently associated with depression and to contribute to the onset and exacerbation of depressive symptoms. Cognitive theories of depression are inherently diathesis-stress theories. When confronted with stressful life events, individuals who have such negative cognitive tendencies will appraise the stressors and their consequences negatively and hence are more likely to become depressed than are individuals who do not have such cognitive styles

Several types of cognitions have been proposed to be related to depression, including low self- esteem, negative automatic thoughts, dysfunctional attitudes, and cognitive distortions (Beck, 1967), self-control (Rehm, 1977), control-related beliefs (Weisz & Stipek, 1982), self-efficacy (Bandura, 1977), depressive attributional style (Abramson et al., 1978), hopelessness (Abramson et al., 1989), and a ruminative response style (Nolen-Hoeksema, 2000). Cross-sectional studies with clinic and community samples of children consistently have shown a significant relation between negative cognitions, particularly low self-esteem and a pessimistic attributional style, and depression (Garber & Hilsman, 1992). A recent study of 647 Mexican-origin adolescents ages 10 to 12 supported the conceptualization of low self-esteem as a vulnerability factor rather than a product of depression. A vulnerability effect was present across genders and developmental stages, even after controlling for social support, maternal depression, life stress, and other risk factors (Orth, Robins, Widaman, & Conger, 2014). Meta-analyses of studies reporting on attributional style and depression have demonstrated moderate to large effect sizes in cross-sectional studies, suggesting a strong concurrent association between negative attributional style and higher levels of depressive symptoms in children and adolescents (Gladstone & Kaslow, 1995; Joiner & Wagner, 1995).

Neurocognitive studies point to altered response to reward in adolescent depression. Potential neural correlates of this altered response include disrupted corticostriatal circuit function and high medial prefrontal activity in the presence of reward stimuli (as reviewed by Forbes & Dahl, 2012). Different types of rewards may have differential relevance to the altered neural responses associated with depression. Among boys, status-related rewards appear to be most provocative of depressotypic neural responses, while among girls, social rewards are most associated with these responses (Morgan, Olino, McMakin, Ryan, & Forbes, 2013). Developmental status also appears to moderate the relationship between altered reward response and depressive symptom severity, with the strongest association evident among pubertal adolescents (Morgan et al., 2013). Longitudinal investigations of the role of cognitions in the prediction of childhood depression have yielded varying results. Global self-worth (Allgood-Merton (p. 21) et al., 1990; Garber, Martin, & Keiley, 2002; Vitaro, Pelletier, Gagnon, & Baron, 1995) and perceived self-competence in specific domains (Hoffman, Cole, Martin, Tiam, & Seroczynski, 2001; Vitaro et al., 1995) have predicted child and adolescent depressive symptoms (e.g., Allgood-Merton et al., 1990; Vitaro et al., 1995) and diagnoses (Garber et al., 2002), controlling for prior levels of depression. On the other hand, these same cognitive constructs also have failed to predict depressive symptoms (Dubbis, Felner, Brand, & George, 1999) and onset of new episodes (Goodyer & Kyte, 2008). However, in one of these null studies (Robertson & Simons, 1989), participants were selected from a drug and alcohol treatment clinic. The mean depression score, in this sample, was lower at the second assessment. Treatment procedures may have reduced depression levels during the assessment interval, making it difficult to predict maintenance or exacerbation of depression.

Attributional style generally has been investigated in the context of stress, although several studies have tested main effects models or reported main effects in the absence of interactions. Significant prospective relations have been observed between attributional style and later depressive symptoms in children and young adolescents (Nolen-Hoeksema, Girgus, & Seligman, 1986; 1992; Panak & Garber, 1992), although a few studies have failed to find this relation (Bennett, Pendley, & Bates, 1995; Hammen et al., 1988). In a longitudinal study of the developmental trajectories of negative attributions and depressive symptoms, Garber, Keiley, and Martin (2002) showed that attributional styles that were increasingly negative across time were associated with significantly higher initial levels and increasing growth of depressive symptoms during adolescence.

Prospective studies in children and adolescents also have found support for the cognitive diathesis-stress model of depression (Dixon & Ahrens, 1992; Hilsman & Garber, 1995; Lewinsohn, Joiner, & Rohde, 2001; Nolen-Hoeksema et al., 1992; Panak & Garber, 1992; Robinson et al., 1995). Garber and colleagues showed in three different short-term longitudinal studies, using different stressors (grades, peer rejection, and school transition) and different time periods, that cognitions (attributions, self-worth) measured before the stressors occurred moderated the effect of the stressors on depressive symptoms in children. Among children who experienced high levels of stress, the relation between negative cognitions about the self or causes of events and depressive symptoms was stronger compared to those without such negative cognitions. Lewinsohn et al. (2001) found that among adolescents who had experienced negative life events, intermediate levels of dysfunctional attitudes predicted the onset of depressive disorders a year later.

Developmental theorists (Cole & Turner, 1993; Nolen-Hoeksema et al., 1992; Weisz, Southam-Gero, & McCarty, 2001) have suggested that negative cognitions emerge over time and that their relation with depression becomes stronger with development. For example, in a longitudinal study of children in grades three through eight, Nolen-Hoeksema et al. (1992) showed that attributional style alone and in conjunction with stress significantly predicted depressive symptoms in the older but not in the younger children. Similarly, in a cross-sectional comparison of children in grades four, six, and eight, Turner and Cole (1994) found that negative cognitions contributed to the prediction of depressive symptoms for the oldest children, but not for the two younger groups. Thus, the relation between the cognition–stress interaction and depressive symptoms may increase from middle childhood to early adolescence. However, a more recent study found that cognitive vulnerability factors measured in early childhood (including self-referent encoding and depressotypic attributional style) demonstrated modest stability over time and predicted depressive symptoms in middle childhood (Hayden et al., 2013).

If negative cognitions contribute to the development of mood disorders, then “high risk” offspring of depressed parents should be more likely to exhibit a cognitive vulnerability than children whose parents have not (p. 22) experienced mood disorders. Indeed, children of depressed mothers report significantly lower perceived self-worth and a more depressive attributional style than do children of well mothers (Garber & Robinson, 1997; Goodman, Adamson, Riniti, & Cole, 1994, Jaenicke et al., 1987). Hayden et al. (2013) similarly report that parental depression and maternal criticism are predictive of cognitive vulnerability in early childhood. Thus, children who are at risk for depression, but who have not yet experienced depression themselves, have been found to report a more negative cognitive style, which might be a vulnerability to later depression.

In summary, correlational, predictive, and offspring studies have provided evidence that there is a cognitive style that may be a vulnerability to depressive symptoms and disorders in children. This cognitive style involves beliefs about the self and explanations about the causes of negative events. Future studies need to examine the development of this cognitive vulnerability over time (Cole & Turner, 1993) and whether or not it needs to be primed in children (Ingram, Miranda, & Segal, 1998).

Stress

Common to all definitions of stress is a focus on environmental conditions that threaten to harm the biological and/or psychological well-being of the individual (Grant et al., 2003). Stress may occur either as an acute event or as chronic adversity, and as a major life event or as minor events with accumulated effects (either additive or multiplicative) (Grant et al., 2003; Monroe & Simons, 1991). Stressful events may be normative (e.g., school transition) or atypical (e.g., abuse) and may be independent of, or directly related to and thus dependent on, an individual’s actions. Objective environmental consequences of a stressor (i.e., can be reliably rated by objective observers) are hypothesized to have a direct effect on the development of depression. The subjective threat of a stressor involves individuals’ appraisals of an event as stressful, which then may impact their psychological well-being (Lazarus, DeLongis, Folkman, & Gruen, 1985). Finally, there may be specificity in the relation between stress and psychopathology such that certain subdomains of stressors may be more highly related to depression than others (Beck, 1967; Grant et al., 2003; Hammen, Ellicott, & Gitlin, 1989; Monroe & Simons, 1991).

Stress plays a prominent role in most theories of depression, and a clear empirical link exists between stressful life events and depression in children and adolescents (Compas, 1987; Compas, Grant, & Ey, 1994). In infants, depressive symptoms have been associated with stressful life circumstances and often are responsive to changes in the environment. One stressor particularly linked with depression in infants is separation. Spitz and Wolf (1946) noted that a common feature in depressed infants ages 6 to 8 months is separation from the mother. Separation in young children has been found to be associated with grief responses characterized by negative changes in sleep patterns, activity, heart rate, temperature, monoamine systems, immune function, and endocrine function (Kalin & Carnes, 1984). Spitz (1945) noted the phenomenon of hospitalism, referring to evidence that infants subjected to long hospital stays experienced a number of psychological difficulties. Longer and more frequent hospital stays and earlier age of entering the hospital were associated with more depressive symptoms in infants (Moreau, 1996).

In school-aged children, cross-sectional studies using either life events checklists or interview methods consistently have shown that depressive symptoms and disorders are significantly associated with both minor and major undesirable life events in children, particularly cumulative or chronic stressors, and negative life events are more prevalent among depressed compared to nondepressed children (e.g., Compas, 1987; Goodyer, Wright, & Altham, 1988). Cross-sectional studies, however, are not informative about the direction of the relation between stress and depression. Given the association between dependent stressors and depression (Garber, Martin, & Keiley, 2002), it is possible that depression contributes to the occurrence of stressors. Depressed individuals have been found to generate many of the (p. 23) stressors they encounter, and these stressors then serve to exacerbate and maintain the depressive symptoms (Bennett & Bates, 1995; Coyne, Kessler, Tal, Turnbull, Wortman, & Greden, 1987; Hammen, 1991).

Among both adolescents and adults, self-reported stressful life events have been found to longitudinally predict an increase in rumination, which is a known risk factor and clinical feature of MDD (Michl, McLaughlin, Shepherd, & Nolen-Hoeksema, 2013). In adults, rumination appears to mediate the relationship between stressful live events and depressive symptoms; however, in adolescents, rumination has acted as a mediator between stress and anxiety but not depression (Michl et al., 2013).

Animal studies that manipulate stress in the laboratory have shown that antenatal stress impacts the developing physiology of the fetus and later physiological and behavioral outcomes in the offspring of stressed rat and primate mothers. Henry, Kabbaj, Simon, Le Moal, and Maccari (1994) showed that prenatally stressed rat pups had an elevated corticosterone response to novel environments and reduced corticosteroid receptors in the hippocampus, suggesting that prenatal stress may affect the neurobiological development of systems associated with depression (i.e., the hypothalamic–pituitary–adrenal [HPA] axis). Behaviorally, rat pups stressed in utero had greater distress and defensive behavior (Fride & Weinstock, 1988; Takahashi, Baker, & Kalin, 1990), and reduced environmental exploration when they were exposed to aversive or stressful conditions (Fride, Dan, Feldon, Halevy, & Weinstock, 1986; Poltyrev, Keshet, Kay, & Weinstock, 1996).

Prepartum exposure to stress also may result in hyperresponsiveness to later stressors. Clarke and Schneider (Clarke & Schneider, 1993; Clarke, Wittwer, Abbott, & Schneider, 1994) randomly assigned pregnant rhesus monkeys to stress and control conditions. The prenatally stressed offspring were less likely than control offspring to play and explore the environment and more likely to engage in clinging, which is associated with distress in primates. In addition, the prenatally stressed monkeys had significantly higher levels of cortisol and tended to have higher levels of adrenocorticotropin (ACTH) when blood levels were taken while the monkeys were anesthetized. In stressful situations while awake, the prenatally stressed offspring had marginally higher levels of ACTH but did not differ significantly from control offspring on levels of cortisol. Clarke and Schneider suggested that HPA axis functioning is implicated in the hyperresponsiveness to later environmental stressors of prenatally stressed rhesus monkeys.

Thus, animal models indicate that stress that occurs as early as conception can influence outcomes that have been associated with depression in humans. In human infants, stress during pregnancy is associated with negative outcomes for offspring (e.g., Lou et al., 1994). Although the mechanisms by which stress impacts the developing fetus are still unknown, Glover (1997) hypothesized that fetal neurophysiological development may be sensitive to the intrauterine hormonal environment, and neurophysiological vulnerability (e.g., HPA axis dysregulation) may make these offspring more sensitive to stress and thereby predispose them to depression as they mature. One study has found that mothers with more total lifetime anxiety have children with higher morning cortisol levels at age 6, suggesting an effect on the physiological stress response, and lifetime maternal depression may also interact with children’s positive affectivity at age 3 (a protective factor) to predict morning cortisol levels at age 6 (Dougherty et al., 2013).

Other studies have further elucidated the role of stress, the HPA axis, and psychiatric disorders in human subjects (Goldman-Mellor et al., 2012; Heim & Nemeroff, 2001; Heim et al., 2010). This concept will be discussed in more detail in the biology section of the chapter.

Longitudinal studies in which stressors are assessed prior to the onset of symptoms can be informative about the temporal relation between stress and depression. Prospective studies have found that stress predicts depressive symptoms, controlling for prior symptom levels in children (Goodyer, Herbert, & Altham,1998; Hammen, 1991; Nolen-Hoeksema et al., 1992; (p. 24) Panak & Garber, 1992; Velez, Johnson, & Cohen, 1989) and adolescents (e.g., Allgood-Merten, Lewinsohn, Hops, 1990; Aseltine, Gore, & Colten, 1994; Garrison et al., 1990; Ge, Conger, Lorenz, & Simons, 1994; Leadbeater, Kuperminc, Blatt, & Hertzog, 1999). The relations tend to be stronger predicting children’s self-reports compared to parents’ reports of children’s depressive symptoms (Compas, Howell, Phares, Williams, & Giunta, 1989; Stanger, McConaughy, & Achenbach, 1992).

Fewer studies have examined the contribution of negative life events to the first onset of depressive disorders in children. Stress has predicted the onset of depressive symptoms in previously asymptomatic children (Aseltine et al., 1994) and the onset of clinically significant depressive episodes, controlling for prior symptom levels in samples comprising both children and adolescents (Hammen, 1991) and adolescents alone (Garber et al., 2002; Monroe, Rohde, Seeley, & Lewinsohn, 1999). Only three of these studies (Aseltine et al., 1994; Monroe et al., 1999) controlled for lifetime history of MDD to rule out the possibility that earlier depressive disorder contributed to onset.

Reports of stressful life events have been shown to increase for both boys and girls from childhood through adolescence, with increases being greater for girls (Ge et al., 1994), paralleling increases in rates of depression for boys and girls (Hankin et al., 1998). Cohen et al. (1987) reported that negative events predicted depressive symptoms in girls who had experienced minimal positive events in the same time interval, and Ge et al. (1994) showed that growth of stressful life events over time predicted growth in depressive symptoms for girls but not boys.

Although no one specific type of stressful event invariably leads to depression in children and adolescents, certain stressors consistently have been found to be associated with depression. Childhood abuse/maltreatment is an especially potent predictor of depression (Andrews, 1995; Bifulco, Brown, & Adler, 1991; Browne & Finkelhor, 1986; Levitan et al., 1998; McCauley et al., 1997; Pribor & Dinwiddie, 1992; Trad, 1994), and this is particularly true for women (Weiss, Longhurst, & Mazure, 1999; Whiffen & Clark, 1997). Childhood maltreatment has also been linked to more recurrent and persistent depressive episodes across 16 studies, as well as greater treatment nonresponsiveness across 10 studies (Nanni, Uher, & Danese, 2012). Sexual assault during childhood or adulthood has been found to increase the risk of depression by 2.4 in women (Burnam et al., 1988). Poverty also has been shown to be a significant correlate of depression (Bruce, Takeuchi, & Leaf, 1991; Grant et al., 2003; McLoyd, 1998). For example, the rates of depression among low-income mothers are about twice as high as in the general population (Bassuk, Buckner, Perloff, & Bassuk, 1998; Brown & Moran, 1997). Caspi et al. (2003) demonstrated a relationship between a genetic variable, polymorphism of the serotonin transporter (SERT), and the development of depression after exposure to child abuse.

Events such as disappointments, loss, separation, and interpersonal conflict or rejection also are particularly linked with depression (Aseltine et al., 1994; Monroe et al., 1999; Panak & Garber, 1992; Reinherz et al., 1999; Rueter, Scaramella, Wallace, & Conger, 1999; Shirk, Boergers, Eason, & Van Horn, 1998). This is especially probable for individuals who tend to be more socially dependent or sociotropic. According to the specific vulnerability hypothesis (Beck, 1983; Blatt, Quinlan, Chevron, McDonald, & Zuroff, 1982), individuals whose self-esteem is derived from interpersonal relationships (sociotropy) are at increased risk for depression when they experience stressors within the social domain; in contrast, those who derive their self-worth from achievement-related goals are at greater risk for depression when they encounter failure. Studies investigating this specific vulnerability hypothesis in children have been supportive (Little & Garber, 2000).

In summary, a clear link exists between stress and depression. But by what mechanisms does stress increase an individual’s vulnerability to depression? Although stressors often precede mood disorders, not all individuals exposed to stressors become depressed. There is no perfect correspondence between exposure to negative life events and the onset of depressive symptoms or disorders. Rather, how individuals interpret and respond to events differentiates who (p. 25) does and does not become depressed. Much of the individual variability is due to differences in appraisals of the meaning of the events with regard to the self and future.

Interpersonal Relationships

Interpersonal perspectives on depression emphasize the importance of the social environment and the development of secure attachments (Gotlib & Hammen, 1992). Vulnerability to depression presumably arises in early family environments in which the children’s needs for security, comfort, and acceptance are not met. Bowlby (1980) argued that children with caretakers who are consistently accessible and supportive will develop cognitive representations, or “working models,” of the self and others as positive and trustworthy. In contrast, caretakers who are unresponsive or inconsistent will produce insecure attachments, leading to working models that include abandonment, self-criticism, and excessive dependency. Such working models may contribute to the development of negative cognitions about the self and others, and presumably increase individuals’ vulnerability to depression, particularly when exposed to new interpersonal stressors. Although most studies of the family process as it relates to depression have focused on dyadic interactions, a recent study found that triads involving depressed adolescents also display a variety of affective differences compared to healthy triads, including less time spent in matched affective states (particularly during problem-solving interactions) and more time spent in mismatched affective states (Hollenstein, Allen, & Sheeber, 2016). Thus, multiple types of family structures involving adolescents and caretakers may be impacted in depression and may in turn have a negative impact on the working model.

Reviews of the literature on the relation between the family environment and depression (e.g., Beardslee et al., 1998; Downey & Coyne, 1990; Rapee, 1997) indicate that families of depressed individuals are characterized by problems with attachment, communication, conflict, cohesion, and social support, as well as poor childrearing practices. Security in attachments help infants cope with the environment, and a lack of such attachments may lead infants to seek protection by withdrawing from the environment altogether (Bowlby, 1980; Trad, 1994). Two-year-old children with secure attachments have been found to be more cooperative, persistent, and enthusiastic, to show more positive affect, and to function better overall than those with insecure attachments (Matas, Arend, & Sroufe, 1978). In adolescents, depression has been linked with less secure attachments to parents (Kenny, Moilanen, Lomax, & Brabeck, 1993). Moreover, adolescents undergoing stressful life events are more likely to become depressed if they had insecure attachments to their parents than adolescents with more secure attachments (e.g., Kobak et al., 1991).

Beyond attachment, other kinds of dysfunctional family patterns have been found to be associated with depression in children (Kaslow et al., 1994; Rapee, 1997). Serious abuse and neglect interfere with normal expressions of infants’ emotions and lead to avoidant or resistant attachments, especially if the mother is the perpetrator of the abuse. Maltreatment also leads to withdrawal behaviors in infants and self-esteem deficits later in childhood (Gaensbauer & Sands, 1979; Trad, 1987). The parent–infant relationship is inevitably worsened from such abuse, which in turn puts the infant in higher danger of being abused again (Trad, 1987).

Two main parenting dimensions particularly associated with depression in children are acceptance/rejection and psychological control/autonomy (e.g., Barber, 1996; Parker, Tupling, & Brown, 1979; Schwarz, Barton-Henry, & Pruzinsky, 1985). In retrospective studies, currently depressed adults recalled their parents to have been critical, rejecting, controlling, and intrusive (Parker, 1993). Currently depressed children have described their parents as authoritarian, controlling, rejecting, and unavailable (Stein et al., 2000), and they tend to perceive their families to be less cohesive and more conflictual than do nondepressed youth (e.g., Stark, Humphrey, Crook, & Lewis, 1990; Walker, Garber, & Greene, 1993; although see (p. 26) Asarnow, Carlson, & Guthrie, 1987 for contrary findings). A meta-analysis of 164 studies involving adolescent depression identified deficient warmth, high interparental conflict, overinvolvement, aversiveness, insufficient autonomy granting, and monitoring as parental characteristics that increase the risk of depression for adolescents ages 12 to 18 (Yap, Pilkington, Ryan, & Jorm, 2014).

Mothers of depressed children describe themselves as more rejecting, less communicative, and less affectionate than do mothers of both normal and psychiatric controls (Lefkowitz & Tesiny, 1985; %Puig-Antich et al., 1985), and in observational studies, mothers of depressed children have been described as less rewarding (Cole & Rehm, 1986) and more dominant and controlling (Amanat & Butler, 1984) than mothers of nondepressed children. Conversely, maternal warmth may act as a protective factor. Among boys who are at socioeconomic risk for depression and are exposed to maternal depression, maternal warmth and affection during adolescence may reduce depressotypic neural responses to reward during young adulthood (Morgan, Shaw, & Forbes, 2014). Several longitudinal studies have found a significant relation between the family environment and subsequent depressive symptoms (e.g., Barber, 1996; Garrison et al., 1990; Ge, Best, Conger, & Simons, 1996; Rueter et al., 1999; Sheeber, Hops, Alpert, Davis, & Andrews, 1997), although others have reported only cross-sectional analyses despite having longitudinal data available and others have reported null findings (Burge & Hammen, 1991; Burge et al., 1997). Barber (1996) showed that children’s ratings of parents’ psychologically controlling behavior predicted their depressive symptoms, controlling for prior levels of depression, although children’s prior depressive symptoms also predicted their ratings of their parents’ behavior. Burt, Cohen, and Bjorck (1988) found that for girls, ratings of family expressiveness predicted depression after controlling for prior depressive symptoms. Other studies have shown that adolescents’ reports of family adaptability and cohesion (Garrison et al., 1990; McKeown et al., 1997) and perceptions of family support (McFarlane et al., 1995) made significant prospective contributions to adolescent depressive symptoms, controlling for prior symptom levels. In addition, maternal hostile childrearing attitudes have been found to significantly predict increases in children’s depressive symptoms (Katainen, Raikkonen, Keskivaara, & Keltikangas-Jarvinen, 1999). Using observational data of parental warmth, hostility, and disciplinary skills, Ge et al. (1996) reported that increases in adolescent internalizing symptoms were predicted by lower levels of parental warmth and higher levels of maternal hostility. In this same sample, Rueter et al. (1999) found that escalating parent–adolescent conflict predicted increases in adolescent internalizing symptoms, which in turn increased the risk of the onset of internalizing disorders. However, the effects of depressogenic parenting may differ by gender, and some evidence suggests that they subside by late adolescence. In one study that examined “aberrant parenting” as a form of childhood adversity, negative parenting styles were associated with elevated depression risk in girls but not boys, and for girls, the effect was present at age 14 but not age 17 (St. Clair et al., 2015).

Depressed children also have significant peer difficulties and social skills deficits (e.g., Altmann & Gotlib, 1988). Self-reported depression significantly correlates with teachers’ reports of peer rejection in children (Rudolph, Hammen & Burge, 1994). In laboratory studies, children with depressive symptoms were rated by their peers more negatively than were children without symptoms (Peterson, Mullins, & Ridley-Johnson, 1985). French, Conrad, and Turner (1995) noted that rejection by peers predicted higher levels of self-reported depressive symptoms among antisocial but not among non-antisocial youth. Panak and Garber (1992) found a significant relation between peer-rated rejection and self-reported depression, and this relation was mediated by perceived rejection. Kistner, Balthazor, Risi, and Burton (1999) similarly found that perceived rejection predicted increases in depressive symptoms during middle childhood. Finally, in a longitudinal study of children in sixth grade, Nolan, Flynn, and Garber (2003) (p. 27) found that a composite measure of rejection by peers, family, and teachers significantly predicted depressive symptoms across 3 years. Thus, depression in children is generally associated with high levels of interpersonal conflict and rejection.

However, the effects of interpersonal stress—particularly peer stress—may differ according to gender and depend significantly on gene–environment interactions. In a recent longitudinal study of 665 youth, female adolescents reported significantly greater peer stress than their male counterparts, and gender interacted with peer stress such that chronic peer stress was significantly more predictive of depression in female adolescents than in males (Hankin et al., 2015). There was also an interaction between chronic peer stress (lasting at least 3 years) and genetic vulnerability from the 5-HTTLPR serotonin transporter polymorphism among older adolescents, even after controlling for depression history and other (non-peer) forms of chronic stress. Notably, gender did not moderate the interaction between 5-HTTLPR vulnerability and chronic peer stress.

Finally, relationships between depressed parents and their children also consistently have been found to be disrupted (Goodman & Gotlib, 1999). Depressed parents report more conflict and less coherence in their families (Billings & Moos, 1983), are less involved and affectionate with their children, and experience poorer communication in parent–child relationships than nondepressed parents (Weissman, Paykel, Siegal, & Klerman, 1971). Moreover, depressed mothers tend to feel more hostile toward their children and less positive and competent about their parenting than do well mothers (Webster-Stratton & Hammond, 1988).

Observations of depressed mothers interacting with their children reveal that these mothers are more negative (Garber et al., 1991; Lovejoy, 1991), more controlling (Kochanska, Kuczynski, Radke-Yarrow, & Welsh, 1987), and less responsive and affectively involved (Cohn & Tronick, 1989), and use less productive communications (Gordon et al., 1989). Depressed mothers spend less time talking to and touching their infants, and show more negative affect in their interactions with their infants, who themselves show less positive affect, less activity, and more frequent protests (Field, 1995). Parental depression also can lead to disturbed attachment behavior and an inability by the infant to regulate emotions, thereby putting the infant at greater risk for developing depression (Gaensbauer, Harmon, Cytryn, & McKnew, 1984). Offspring of depressed parents have more insecure attachments compared to offspring of well mothers (DeMulder & Radke-Yarrow, 1991; Teti, Gelfand, Messinger, & Isabella, 1995). Moreover, insecurely attached offspring of depressed mothers tend to have difficulties in their relationships with peers (Rubin, Booth, Zahn-Waxler, Cummings, & Wilkinson, 1991). Finally, negative reciprocal interaction patterns have been observed between depressed mothers and their children. The quality of parental relationships may moderate adolescents’ responses to other forms of stress. For example, in adolescents with positive parental relationships, the depressogenic effects of peer stress appear to be substantially reduced (Hazel, Oppenheimer, Technow, Young, & Hankin, 2014).

In summary, two important findings emerge regarding the link between interpersonal vulnerability and depression. First, families with a depressed member tend to be characterized by less support and more conflict, and such family dysfunction increases children’s risk of developing depression. Second, depressed individuals are themselves more interpersonally difficult, which results in greater problems in their social network. Thus, the link between interpersonal vulnerability and depression likely is bidirectional (Gotlib & Hammen, 1992). Longitudinal studies examining the contribution of family dysfunction, parent–child conflict, peer difficulties, and interpersonal rejection to increases in and maintenance of depressive symptoms in children have shown both that social problems temporally precede depression, and that depression contributes to interpersonal difficulties. Moreover, interpersonal difficulties appear to persist after depressive symptoms have remitted (Puig-Antich et al., 1985). In addition, social adversities such as persistent poor friendships, low involvement of fathers, negative attitudes (p. 28) by family members, and stressful family environments can contribute to the maintenance or relapse of depressive disorders in youth (e.g., Asarnow, Goldstein, Tompson, & Guthrie, 1993; Goodyer, Germany, Gowrusankur, & Altham, 1991; McCauley et al., 1993).

The interpersonal environment clearly is an important and sometimes stressful context in which children develop schema about themselves and others, which then can serve as a vulnerability to depression. In addition, children’s own reactions to these environments can exacerbate and perpetuate negative social exchanges, which furthers the interpersonal vicious cycle, thereby resulting in more rejection and depression (Coyne, 1976). Thus, a transactional model of mutual influence probably best characterizes the association between depressed individuals and their social environment.

Socioeconomic Status

A relationship between low socioeconomic status (SES) and increased depression risk has been well documented in children and adolescents (Bird et al., 1988; Costello et al., 1996; Gilman et al., 2003; Reinherz et al., 1993). In a sample of 875 youth ages 19 to 21, the relationship between low SES and depressive symptoms was fully accounted for by family-related stress and emotional support (Miller & Taylor, 2012). Similarly, another study reported that parental support (particularly on the part of the mother) mediated the relationship between socioeconomic status and depression in adolescents, possibly by influencing the degree of optimism transmitted through the parenting style (Piko, Luszczynska, & Fitzpatrick, 2013).

Some studies have failed to document a consistent relationship between SES and depression, however (Costello et al., 1988, 2003; Whitaker et al., 1990), and one meta-analysis of 310 child samples found no association between social class and depression based on the Children’s Depression Inventory (CDI) (Twenge & Nolen-Hoeksema, 2002). Mixed findings may be the result of a temporary equalization effect in middle adolescence. A longitudinal study of 14,000 North American youth showed that family SES during early adolescence substantially impacted depressive symptoms during the same timeframe (Wickrama, Noh, & Elder, 2009). The effects of early adolescent SES faded during middle and late adolescence and re-emerged during early adulthood. SES has also displayed a reliable relationship with depression among adults: a meta-analysis of 60 adult studies found that both education level and income displayed a dose–response relationship with depression odds (Lorant et al., 2003).

Race/Ethnicity

Due to differences in samples, reporting techniques, and measures used to assess depressive disorders, comparisons across racial/ethnic groups have been difficult (Merikangas & Nakamura, 2011). There is evidence to suggest that both African- and Latino-American youth may experience more symptoms of depression than their white counterparts (Guiao & Thompson, 2004; McLaughlin, Hilt & Nolen-Hoesksema, 2007; Merikangas & Nakamura, 2011; Roberts & Chen, 1995; Roberts, Roberts & Chen, 1997; Twenge & Nolen-Hoeksema, 2002), including two studies that specifically found African-American males reporting higher rates of depressive symptoms than European-American males (Schoenbach et al., 1982). In the period following high school, African-American and Hispanic youth appear to experience significantly more depression than Caucasian and Asian-American youth, with differences explained partly by underrepresentation in universities and partly by conflict in peer and family relationships (Gore & Aseltine, 2003). However, some studies have reported lower rates of depression among African-American and Latino youth (Angold et al., 2002; Roberts, Roberts & Chen, 1995; Roberts, Roberts & Chen, 1997). Interactions between race, family stress, and emotional support may help to explain discrepant findings. Miller and Taylor (2012) reported that in a large sample of young adults, depressive symptoms were significantly more prevalent among African-Americans than among Caucasians, and this difference (p. 29) was partly mediated by family-related stress. In addition, Caucasians experienced more depressive symptoms when family support was lacking, suggesting greater resilience among African-Americans, but African-Americans experienced more depressive symptoms when family support was high, indicating that family support did not have as much of a protective effect as it did for Caucasians.

Bipolar Disorder

Overview

The view that mania in younger people is extremely rare or nonexistent has been increasingly challenged by many case reports and by large-scale community surveys of adults. For example, Akiskal et al. (1985), in a case history of adolescent relatives of “classic” adult bipolar patients, found that despite frank symptoms of depression and mania, and frequent mental health contacts, none of these youth had been diagnosed with an affective disorder. Weller et al. (1986) reviewed over 200 articles published between 1809 and 1982 and identified 157 cases that would likely be considered manic by modern standards. However, 48% of those subjects retrospectively diagnosed as manic according to DSM-III criteria were not considered so at the time of referral. Wozniak et al. (1999) reported that 16% of psychiatrically referred prepubertal children satisfied diagnostic criteria for bipolar disorder. Biederman et al. (1996) reported that a sizeable minority of children with ADHD has bipolar disorder. These reports suggested that pediatric mania may not be rare, but is difficult to diagnose. Despite continued debate and controversy over the validity of the diagnosis of mania in children (Biederman, 1998; Klein et al 1998), there is a growing consensus that many seriously disturbed children are afflicted with severe affective dysregulation and high levels of agitation, aggression, and dyscontrol that may be early bipolar disorder. These children have received increased scientific attention, as is evident in the multiple NIMH workshops on bipolar disorder in children and adolescents and in exhaustive reviews that have supported the validity of the disorder in youth (Faedda et al., 1995; Geller & Luby 1997; Weller et al., 1995). The NIMH Strategic Research Plan for Mood Disorder Research recommended the establishment of multisite network programs on pediatric-onset bipolar disorder (Costello et al., 2002).

Agreement about what is the first presentation of bipolar illness is critical for epidemiologic studies to obtain the true age of onset and estimate of prevalence and risk. The questions include: Does juvenile bipolar disorder differ from the adult form? What are the early signs and symptoms? What is the relationship of ADHD and other disruptive disorders to juvenile-onset bipolar disorder? Answers to these questions are complicated by the uncertainty regarding the appropriate duration of a manic episode, since youth more frequently report manic symptoms that last only a few hours or days (Carlson & Kelly, 1998; Geller et al., 1995) and therefore do not meet adult criteria.

Studies suggest that bipolar disorder may be more accurately characterized as a spectrum disorder, as many people with the illness are not receiving appropriate treatment due to subthreshold symptoms and inappropriate diagnosis. Three subtypes of the illness have been identified: bipolar type I disorder, bipolar type II disorder, and bipolar disorder not otherwise specified (BD-NOS), which includes patients who have manic and depressive symptoms but do not meet strict criteria for type I or type II. The results of the 2007 Merikangas et al. study, which analyzed data from the NCS-R, indicate that bipolar I and bipolar II each occur in about 1% of the population, while BD-NOS occurs in about 2.4% of the population. Only 69% of BD-NOS patients receive treatment, and they are often prescribed inappropriate medications; in contrast, 89% to 95% of those with bipolar type I or type II are in treatment. These findings suggest that bipolar disorder may be better characterized as bipolar spectrum disorder (BPSD), which will include these individuals with subthreshold symptoms.

As part of the Longitudinal Assessment of Manic Symptoms (LAMS) Study, Findling (p. 30) et al. (2010) found that although elevated symptoms of mania (+ESM) were associated with higher rates of bipolar spectrum disorder than those without ESM, 75% of children with ESM did not meet criteria for bipolar spectrum disorder. This study suggests that longitudinal assessment is needed to examine which factors are associated with diagnostic evolution to bipolar spectrum disorder in children with ESM.

Another study resulting from the LAMS data has shown that in many cases, obtaining repeated parent report of mania symptoms significantly altered the probability of a bipolar spectrum disorder diagnosis being made and may be a useful adjunct in forming a clinical diagnosis (Frazier et al., 2011).

As a part of the Course of Subthreshold Bipolar Disorder in Youth (COBY) study, Axelson et al. (2011) found that children and adolescents presenting with mood symptoms who meet criteria for BD-NOS, particularly those with a family history of bipolar disorder, frequently progress to bipolar disorder type I or bipolar disorder type II. Identifying these children and effectively intervening may have the potential to alter the progression of mood disorders in this high-risk population.

In February 2009 the NIMH convened a meeting of experts to discuss diagnostic issues regarding classification of bipolar disorder in children and adolescents. Suggestions for defining a subthreshold diagnosis of bipolar disorder included the following:

  • The patient meets DSM-IV Criterion B for manic or hypomanic episode (i.e., all the symptoms), except for duration criteria (<4 consecutive days).

  • Four hours/day of manic symptoms to count as a day; require lifetime occurrence of >20 days meeting DSM-IV criteria for mania or hypomania

  • There is a distinct change in functioning (but not necessarily functional impairment).

  • The episode is not substance or medication induced and symptoms are not better accounted for by other disorders.

  • The patient does not meet DSM-IV criteria for cyclothymic disorder.

The group agreed that future studies need to further explore subthreshold bipolar disorder and other BD-NOS subgroups. Investigators need to explicitly document how BD-NOS is defined in their studies.

Diagnosis

Despite the growing body of evidence demonstrating that bipolar disorder can be diagnosed in children and adolescents, the diagnosis continues to evoke controversy. Further complicating this problem is that experts around the world disagree about the criteria and symptoms of pediatric mania and hypomania (Dubicka et al., 2008). Most investigators agree that pediatric mania can be diagnosed using current DSM criteria for adults (Axelson et al., 2006; Birmaher et al., 2007; Carlson, 2011; Findling et al., 2010; Kowatch et al., 2005; Youngstrom et al., 2008). Although the majority of studies have shown that mania in youth presents episodically, as with adults, other studies suggest that pediatric bipolar disorder is characterized by chronic continuous symptoms, continuous cycling, and long-duration episodes (Geller, 2000; Mick et al., 2003; Wozniak et al., 1995) and fewer episodes of remission (Carlson et al., 2000). Findings from other studies suggest that children with bipolar disorder have high rates of rapid cycling and low interepisode recovery rates (Findling et al., 2001). Taken together, the existing studies suggest that children and adolescents have more mixed presentations and rapid shifts in polarity of their mood episodes than bipolar adults, although continuity between pediatric mania and adult mania has not been confirmed (Harrington & Maat, 2003; Judd & Akiskal, 2003). There has been general consensus among experts that adult criteria may be used to diagnose bipolar disorder among youths. The DSM-5 criteria for bipolar disorders in youth specify the following:

A. A distinct period of abnormally and persistently elevated, expansive, or (p. 31) irritable mood, and abnormally and persistently increased goal-directed activity or energy lasting at least one week (or any duration if hospitalization is necessary).

B. During the period of mood disturbance, three (or more) of the following symptoms have persisted (four if the mood is only irritable) and have been present to a significant degree representing a noticeable change in behavior:

  • Inflated self-esteem or grandiosity

  • Decreased need for sleep

  • More talkative than usual or pressure to keep talking

  • Flight of ideas or subjective experience that thoughts are racing

  • Distractibility

  • Increase in goal-directed activity

  • Excessive involvement in pleasurable activities that have a high potential for painful consequences

In addition, the symptoms do not meet the criteria of a Mixed Mood Episode, where both criteria for a manic episode and for MDD (except for duration) are met with symptoms nearly every day during at least a 1-week period. For hypomania the elevated or irritable mood lasts for four days. The mood disturbance must cause significant impairment and is not better accounted for by other psychiatric disorders or medical conditions.

As with adults, children and adolescents with bipolar disorder can meet criteria for bipolar I disorder, bipolar II disorder, mixed-manic episodes, cyclothymia, and specified/unspecified bipolar and related disorders (Axelson et al., 2006; Birmaher et al., 2004, 2007; Findling et al., 2010; Youngstom et al., 2008). While youth may present initially with either a manic or depressive episode, pediatric bipolar individuals most frequently present with depression (Birmaher, 2007). Furthermore, bipolar youths present with more mixed episodes and are more likely to present with psychotic symptoms (delusions or hallucinations) (Axelson et al., 2006; Birmaher et al., 2012).

Although the diagnostic criteria for adults and youths are the same, making the diagnosis in youth can be challenging. Difficulties differentiating manic symptoms from normative mood and behaviors, and overlap with symptoms of other pathologies can confound diagnosis. Difficulties expressing mood states due to cognitive and developmental immaturity may also complicate diagnosis (Birmaher et al., 2013). Thus, the criteria must be used considering the following when diagnosing mania or hypomania:

  • The symptoms must exceed those expected for normal developmental age and stage. This can be challenging. For example, it can be difficult to distinguish grandiosity from the normative overestimation of abilities that occurs among children and young adolescents.

  • The symptoms should cluster in episodes so that their onset and intensity increase with the onset of the abnormal mood

  • If other psychiatric comorbidities are present, as is commonly the case with ADHD and ODD, the symptoms must worsen during the episode of mania or hypomania.

  • The symptoms cannot be better explained by environmental or cultural context, medical illnesses, or use of medications or substances.

Summary of Implications of Changes in Criteria

The diagnostic criteria for bipolar disorders in DSM-5 now include changes in both mood and activity or energy. More specifically, a new specifier “with mixed features” replaces the diagnosis criteria for bipolar I disorder, mixed episodes, which required that an individual meet full criteria for both mania and major depressive episode.

Individuals with a past history of MDD whose symptoms meet all criteria for hypomania, with the exception of the duration criteria (e.g., an episode lasts less than the required 4 consecutive days or more) are now diagnosed (p. 32) under the “other specified bipolar and related disorder.” For individuals with too few symptoms of hypomania present to meet the criteria for full bipolar II syndrome, a second category of “other specified bipolar and related disorder variant” has been created.

As in the depressive disorders category, an anxious specifier has been defined.

Longitudinal studies will track how these changes to criteria impact rates of psychopathology among children and adolescents.

Epidemiology

Epidemiology data on juvenile bipolar disorder must be seen within these diagnostic uncertainties. The 1980s Epidemiologic Catchment Area (ECA) study, based on over 18,000 adults age 18 and over in five U.S. communities, provided the first epidemiologic clue about the youthful onset of bipolar disorder (Robins & Price, 1991). The lifetime prevalence of bipolar disorder was about 1/100, with little sex differences in rates and an overall median age of onset of 18 years.

The 1990 National Comorbidity Survey (NCS) included a representative national sample in the United States of over 8,000 subjects ages 15 to 54 (Kessler et al., 1994) and provided the best epidemiologic information at the time. The younger age included in the NCS was based on the ECA findings that many psychiatric disorders have a youthful age of onset. The overall lifetime prevalence of bipolar I was 1.7% in the full sample and 1.3% in the sample ages 15 to 17, with equal sex rates and a median age of onset of 21 years. Both the ECA and the NCS suggested that the onset of bipolar disorder often occurs during adolescence and childhood.

In the 1990s, the Cross National Collaborative Group was formed to directly compare rates and risk of psychiatric disorders by standardizing analysis to overcome the problem of disparate presentation of data between studies. Seven countries (United States, Canada, Puerto Rico, Germany, Taiwan, Korea, and New Zealand) provided data on bipolar disorder. The lifetime prevalence rates for bipolar I ranged from 0.3% in Taiwan to 1.5% in New Zealand (Weissman et al., 1996), with equal sex ratios across sites (with the exception of Korea) and median ages of onset of 18 to 25 years.

More recent studies include national and community samples.

More Recent Studies Include National and Community Samples

National Studies

Table 1.4 reports the results from national studies specific to bipolar disorder among children and adolescents.

Table 1.4 Rates of Bipolar Disorders in National Samples of Children and Adolescents

National Study

Prevalence Rates/100 (SE)

NCS-A, Kessler et al., 2012

Bipolar Disorder I or II—Lifetime Prevalence

3.0 (0.4)

Females

3.1 (0.5)

Males

2.8 (0.5)

Bipolar Disorder I—Lifetime Prevalence

0.2 (0.1)

Females

0.3 (0.2)

Males

0.1 (0.0)

Bipolar Disorder II—Lifetime Prevalence

2.8 (0.4)

Females

2.8 (0.5)

Males

2.8 (0.5)

Lifetime morbid risk (LMR) and ratio of lifetime prevalence to morbid risk (LT/LMR) of:

Bipolar Disorder I among 13- to 17-year-olds

0.1

Bipolar Disorder II among 13- to 17-year-olds

1.0

Bipolar Disorder I or II among 13- to 17-year-olds

0.7

Kessler, Petukhova, Sampson, Zaslavsky, and Wittchen, (2012), combining results from the NCS-R (Kessler & Merikangas, 2004) and the NCS-A (Merikangas et al., 2009), also compared lifetime prevalence rates of bipolar I or II based on DSM-IV-TR criteria. Among youth ages 13 to 17 years, 0.3% of females and 0.1% of males met the criteria for bipolar I disorder. However, 2.8% of both sexes met the criteria for bipolar II disorder. The overall lifetime prevalence estimates for either bipolar I or II was 3.0%, with 3.1% of females and 2.8% of males meeting the criteria for bipolar I or II (see Table 1.4).

In this analysis, Kessler et al. (2012), also reported an LMR for bipolar I disorder, bipolar II disorder, and bipolar I or II disorder (see previous discussion regarding calculation). Among 13- to 17-year-olds, the LT/LMR ratio was 0.1 for bipolar I disorder; 1.0 for bipolar II disorder, and 0.7 for bipolar I or II disorder (see Table 1.4).

Merikangas, He, Burstein, Swanson, Avenevoli, Cui, Benjet, Georgiades, and Swendsen (2010b), also using the NCS-A sample, reported a lifetime prevalence of bipolar I or II disorder of 2.9%. Presented by gender, 3.3% of adolescent females and 2.6% of adolescent males met criteria for DSM-IV bipolar I or II. Broken down by age group, 1.9% of youth ages 13 and 14 met the criteria, compared to 3.1% of 15- and 16-year-olds and 4.3% of 17- and 18-year-olds (results not reported in Table 1.4).

Community Studies

Table 1.5 reports the results from community studies of children and adolescents.

Table 1.5 Rates of Bipolar Disorders in Community and School-Based Samples of Children and Adolescents

Authors

Mania

Hypomania

Bipolar

Great Smoky Mountains Study, Three-Month Prevalence Estimates—DSM-III-R

Prevalence Rate/100 (SE)

Prevalence Rate/100 (SE)

Prevalence Rate/ 100 (SE)

Costello et al. (1996)

0.10 (0.06)§

  Females

0.07 (0.07)§

  Males

0.13 (0.09)§

Great Smoky Mountains Study, Three-Month Prevalence Estimates—DSM-IV

Prevalence Rate/100 (95% CI)

Prevalence Rate/100 (95% CI)

Prevalence Rate/100 (95% CI)

Costello et al. (2003)

< 0.1 (< 0.1–0.1)

  Females

< 0.1 (< 0.1–0.1)

  Males

< 0.1 (< 0.1–0.1)

Oregon Adolescent Depression Project, Lifetime Prevalence Estimates—DSM-III-R

Prevalence Rate/100

Prevalence Rate/100

Prevalence Rate/100

Lewinsohn et al., 2000

0.9% (T1, LT)

0.6% (T1, Point)

1.0% (T2, LT)

0.5% (T2, Point)

Teen Health 2000 Past-Year Prevalence Estimates—DSM-IV

Prevalence Rate/100 (95% CI)

Prevalence Rate/100 (95% CI)

Prevalence Rate/100 (95% CI)

Roberts et al. (2007)

  Prevalence of Disorder

0.39 (0.18–0.61)

0.81 (0.50–1.12)

  Prevalence with DISC Impairment

0.31 (0.12–0.51)

  Prevalence with CGAS ≤ 69

0.22 (0.05–0.39)

0.09 (0–0.20)

§ <5 cases in interviewed sample

The Great Smoky Mountains Study of Youth (Costello et al., 1996) reported a 3-month prevalence for hypomania (0.10%), with 0.07% of females and 0.13% of males meeting the DSM-III-R criteria (see Table 1.5). In the 2003 report that followed the same subjects, the 3-month prevalence of bipolar disorder was less than 0.01% among all respondents, using DSM-IV criteria (Costello et al., 2003).

Lewinsohn et al. (2000), in the Oregon Adolescent Depression Project based on a school sample of adolescents, reported lifetime and point prevalence results for bipolar disorder (see Table 1.5). At Time 1, the lifetime prevalence for bipolar disorder was 0.9%; at Time 2 (approximately 1 year later), it was found to be 1.0%.

Roberts et al. (2007) reported past-year prevalence of mania and hypomania in the Teen Health 2000 Study. While the past-year prevalence of mania was 0.39%, the past-year prevalence of hypomania was 0.81% (see Table 1.5).

Differences in Reporting Prevalence

Among both national and community studies, differences in how rates are reported render it (p. 33) difficult to compare across such studies. While 12-month and lifetime prevalence rates are common, 3-month prevalence rates were used among community studies. Such differences in reporting have been noted in the literature, in turn leading to a lack of synthesis across studies (Kessler et al., 2012).

Comparison of Rates Based on Age of Onset to Rates Based on Child/Adolescent Samples

The lifetime prevalence of bipolar I disorder in the full sample, ages 15 to 54 (Kessler et al., 1994), of the 1990 NCS was 1.7%; in the younger sample (ages 15–17) it was found to be 1.3%. The ECA (Robins & Price, 1991), based on more than 18,000 adults age 18 and over in five U.S. communities, reported a lifetime prevalence of 0.9% for bipolar I disorder. In comparison, based on the NCS-A sample, the lifetime prevalence of bipolar I or II disorder was reported to be 2.9% (Merikangas et al., 2010). Kessler et al. (2012) reported a lifetime prevalence rate of 0.2% for bipolar I disorder, also based on the NCS-A sample.

(p. 34) In summary, the epidemiologic studies of adolescents and adults show an early age of onset of bipolar disorder and prevalence in adolescents close to what is found in studies of adults.

Risk Factors

Among risk factors, family history has been demonstrated to be one of the strongest and most consistent risk factors for the development of bipolar disorder. Family studies of bipolar disorder have found adult relatives of probands with bipolar disorder to have a 10-fold increased risk of the disorder when compared to family members of control subjects (Merikangas & Yu, 2002). Further evidence for the role of genetic factors in the development of bipolar disorder among family members have come from a small number of twin studies that have found a an aggregate estimate of threefold risk among monozygotic versus dizygotic twins (Smoller & Gardner-Schuster, 2007). However, inheritance of bipolar disorder appears to be complex, as results from twin studies have found an average concordance rate of 40% for monozygotic twins compared to 5% for dizygotic twins (Smoller & Finn, 2003).

Despite these findings, there are few data on susceptibility genes that have been shown to (p. 35) have a consistent, significant predictive value for developing bipolar disorder (Merikangas & Pato, 2009). Thus, while family history of bipolar disorder represents a risk factor due to either genetics or environment, it remains an important predictor for the development of bipolar disorder among youth (Merikangas & Pato, 2009).

No sex differential in rates of bipolar disorder among youth has been found (Soutullo et al., 2005), mirroring findings from U.S. population surveys conducted among adults (Grant et al., 2005; Jonas et al., 2003; Merikangas et al., 2007); however, some studies have reported that women are more likely to exhibit the bipolar II subtype (Benazzi, 2006). Caution regarding conclusions specific to the lack of a sex differential among youth is warranted because females may be more likely to exhibit depression whereas males may be more likely to present with mania (Duax, Youngstrom, Calabrese, & Finding, 2007).

Among adults, individuals with lower educational and income levels have been found to be at higher risk for bipolar disorder (Grant et al., 2005; Jonas et al., 2003; Merikangas et al., 2007). In the three U.S. population surveys (NHANES, NCS-R, and the National Epidemiological Survey on Alcohol and Related Conditions [NESARC]), only the NESARC had a large enough sample to compare across several ethnic subgroups. In this survey, Native Americans were found to report higher rates of the bipolar I subtype when compared to other ethnic groups (Grant et al., 2005).

Similar to depression, early life trauma and stressful life events have been shown to be a major risk factor for bipolar disorder, and many studies have examined this correlation (Horesh et al., 2011; Romero et al., 2009; Tillman et al., 2003). Gilman et al. (2014) further investigated the role of childhood adversities and adulthood stressors in liability for bipolar disorder using NESARC data (n = 33,375). Risk was analyzed for initial-onset and recurrent DSM-IV manic episodes during a 3-year follow-up period. Childhood physical abuse and sexual maltreatment were associated with significantly higher risks of both first-onset mania and recurrent mania. Stressors within the past year in the domains of interpersonal instability and financial hardship were associated with a significantly higher risk of incident and recurrent mania.

Comorbidity

Studies of bipolar disorders in youth report high rates of comorbidities with other psychiatric disorders. Estimates suggest that 20% to 80% of bipolar youths have comorbidities, with variations due to different clinical populations and methods of ascertainment (Birmaher, 2013). The most common comorbidities are ADHD; disruptive, impulse control, and conduct disorders; anxiety disorders; and substance use disorders (Axelson et al., 2006; Goldstein et al., 2008; Kowatch & Youngstrom, 2005; Sala et al., 2010). Among adolescents, conduct disorders and substance use disorders are more common.

ADHD

ADHD is the psychiatric disorder of childhood most often confused with mania due to overlapping symptoms. Bipolar spectrum disorder and ADHD share symptoms of impulsivity, distractibility, hyperactivity, and overproductive, rapid speech (Milberger et al., 1995). Further complicating diagnostic clarity are the high rates of reported comorbidity of these two disorders. Estimates suggest that 11% to 98% of pediatric patients with mania will also have ADHD (Biederman, 1996; Geller, 1997; Kowatch, 2005; Lewinsohn, 1995; Wozniak, 1995). Several research groups suggest that ADHD is associated with an earlier onset of bipolar spectrum disorder (Egeland, 2003; Henin, 2007; Masi et al., 2006; Tillman, 2003), consistent with previous studies demonstrating higher rates of ADHD among children presenting with mania (90%) compared with 57% of adolescents with mania (West et al., 1995; Wozniak, 1995a).

The Longitudinal Assessment of Manic Symptoms Study (LAM) attempted to clarify some of the diagnostic issues relating to ADHD and bipolar spectrum disorder. The researchers compared a sample of 6- to 12-year-olds (n = 621) whose parents reported manic symptoms, with a lower-scoring comparison group (n = 86). Among (p. 36) the 707 children in the sample, 59.5% met criteria for ADHD without bipolar spectrum disorder; 6.4% had bipolar spectrum disorder without ADHD; 16.5% had ADHD and bipolar spectrum disorder, and 17.5% did not meet criteria for either disorder. For those meeting criteria for bipolar spectrum disorder, the sample was evenly divided between type I and NOS. Seventy-two percent of the sample who had bipolar spectrum disorder (n = 162) also had ADHD. Comorbidities with other psychiatric disorders were highest for the children with ADHD and bipolar spectrum disorder; the rate was higher for those who had only ADHD than for those who had only bipolar spectrum disorder. The authors found no difference in age of symptom onset between the children who had both ADHD and bipolar spectrum disorder and those who had bipolar spectrum disorder alone. The dually affected group exhibited more impaired functioning than those with either diagnosis (Arnold et al., 2011). The authors concluded that most children presenting with manic symptoms did not have bipolar spectrum disorder, consistent with prior studies (Carlson & Blader, 2011), and that children presenting with both disorders suffer greater functional impairment and disability than those having either disorder alone.

Retrospective studies of adults with bipolar disorder suggest that childhood anxiety disorders and ODD are more common among adults with bipolar spectrum disorder (Hennin & Biederman, 20007). Other studies focused on the developmental aspects of pediatric mania have found associations between anxiety disorders in youth and elevated rates of bipolar spectrum disorder in adulthood (Goldstein, 2007). Several studies have found comorbid ADHD and bipolar spectrum disorder in adults who had the onset of bipolar spectrum disorder during childhood and adolescence (Chang et al., 2000; Sachs, 2000), suggesting that the onset of mania rather than the chronological age at diagnosis might be a predictor for a subtype of bipolar spectrum disorder that is highly comorbid with ADHD and that might have a worse prognosis (Chang et al., 2000).

To further understand the relationship between ADHD and bipolar spectrum disorder, family studies have been used to study this comorbidity in youth. These findings suggest that offspring of parents with bipolar spectrum disorder have higher rates of ADHD (Faraone et al., 1999). Relatives of children with mania were at higher risk for ADHD, similar to the risk in relatives of children with ADHD without bipolar spectrum disorder (Wozniak, 1995b). Moreover, mania, and mania with ADHD, aggregated among relatives of manic youth compared with ADHD and controls (Faraone et al., 1998; Wozniak, 1995), suggesting that mania in children might be a distinct subtype of either bipolar disorder or ADHD.

Goldstein and colleagues approached the question of bipolar spectrum disorder and comorbidities from another perspective in the Bipolar Offspring Study (BIOS). They studied 388 offspring (ages 7–17 years) of 233 parents with bipolar spectrum disorder (type I/II) using structured diagnostic interviews for diagnostic accuracy. A cohort of 41 offspring were identified with bipolar spectrum disorder (type I, n = 9; type II, n = 5; NOS, n = 27). They identified several clinical, demographic, and familial correlates of bipolar spectrum disorder. They found a significantly greater prevalence of ADHD, anxiety disorders, ODD, and conduct disorders among offspring with bipolar spectrum disorder than those without bipolar spectrum disorder (Goldstein, 2010).

Some investigators suggest that disruptive disorders (ADHD and ODD) are early manifestations of bipolar spectrum disorder and not separate diagnoses (Goldstein, 2009). Tillman et al. (2006) reported a 28.5% rate of conversion from ADHD to bipolar disorder during a 6-year prospective follow-up study, whereas other studies have found no conversion during longitudinal follow-up (Biederman et al., 1996; Mannuza, 1993), suggesting that children with ADHD and ODD who go on to develop bipolar spectrum disorder may represent a different diagnostic group.

Many factors contribute to the challenges for studies of ADHD and bipolar spectrum disorder in youth. Many of the existing studies use differing definitions for bipolar disorder. Some research groups, for example, use a technique of (p. 37) counting overlapping symptoms, while other groups do not (Biederman et al., 1996; Geller et al., 2006). Some groups require episodes to make the diagnosis of bipolar spectrum disorder, while others do not. Furthermore, three of the seven criteria for a manic episode are also criteria for ADHD: distractibility, excessive talkativeness, and physical hyperactivity.

Conduct Disorder

Studies of pediatric bipolar disorder document high rates of comorbidity with conduct disorder, similar to those studies that suggest higher rates of ADHD.

Kutcher et al. (1989), in a study of hospitalized youth with mania, reported that 42% also met diagnostic criteria for conduct disorder. Similarly, Wozniak et al. (1995) found that prepubertal children with mania exhibited high rates of comorbid conduct disorder.

A growing number of studies demonstrate elevated risks for conduct disorder among youth with bipolar disorder (Biederman, Faraone, Chu, & Wozniak, 1999; Carlson, 1999; Kovacs & Pollock, 1995). Kovacs and Pollack (1995) suggest an episode prevalence of conduct disorder of 54% for bipolar youths and a 69% lifetime comorbidity. Lewinsohn et al. (1995) have also shown strong associations between pediatric bipolar disorder and disruptive behavior disorders.

In the only empirical study to date investigating the overlap between mania and conduct disorder, Biederman et al. (1999) studied a sample of consecutively referred youth who met diagnostic criteria for either mania (n = 186), conduct disorder (n = 192), or both (n = 76). The investigators found that 40% of youth with conduct disorder and 41% of youth with mania had both disorders, demonstrating that they met the criteria for two distinct disorders, true comorbidities and not disorders that mimic each other.

On further examination of the sample, distinct phenotypic characteristics for each disorder persisted regardless of comorbidity. For example, among children with conduct disorder and mania, both groups demonstrated primarily irritable mood, chronicity, and a mixed presentation (Biederman et al., 1999, 2003). Characteristics that were identified as unique among the comorbid (mania + conduct disorder) group included physical restlessness and poor judgment. In comparison with the conduct disorder group, the comorbid group exhibited higher rates and levels of aggressiveness. Overall, the comorbid groups were found to have more severe and impairing symptoms than those with either conduct disorder or mania alone (Biederman et al., 1999). Along these same lines, Biederman et al. (2003) synthesized the findings of their prior pediatric bipolar disorder and comorbidities studies, suggesting that overall, children with comorbid pediatric bipolar disorder and conduct disorder experience an increased risk for poor outcomes, including increased psychiatric hospitalizations and drug and alcohol dependence.

Evidence suggests that pediatric bipolar disorder can be distinguished from disruptive behavior disorders using existing diagnostic criteria; other studies, however, suggest that this is not conclusive, specifically as it relates to distinguishing manic symptoms from the symptoms of disruptive behavior disorders (Carlson, Loney, Salisbury, & Volpe, 1998). Though complex, efforts should be made to clarify the diagnosis, as both disorders are distinct clinical conditions that require different interventions. When both conditions exist together, treatment of both is indicated to optimize outcomes.

Anxiety Disorders

Anxiety disorders are common comorbidities among youth with bipolar spectrum disorders. While many studies exist documenting the high rates of comorbid anxiety disorders in adult bipolar spectrum disorder, few studies have examined these comorbidities in pediatric populations. Existing studies of this comorbidity for pediatric bipolar spectrum disorder have shown lifetime prevalence rates of 14% to 56%, with a weighted average of 27% (Axelson, 2006; DelBello, 2007; Biederman, 1997; Dickstein, 2005; Kowatch, 2005; Tillman, 2003). Furthermore, family studies of offspring of parents with bipolar spectrum disorder consistently find high rates of anxiety disorders (Birmaher et al., 2009; Henin et al., 2005; (p. 38) Simeonova et al., 2009). In a retrospective study of adults with bipolar spectrum disorder, those reporting the onset of bipolar spectrum disorder prior to 13 years of age had a 70% rate of a comorbid lifetime anxiety disorder diagnosis, compared with 54% for those with onset between 13 and 18 years of age and 38% for those with onset after 18 years of age (Perliss et al., 2004).

To determine the prevalence and correlates of comorbid anxiety disorders among youth with bipolar spectrum disorder, investigators studied 446 youth ages 7 to 17 who met criteria for bipolar spectrum disorder as part of the COBY study. The sample consisted of 260 subjects with type I, 32 with type II, and 154 with NOS. They found that 44% of the sample met the criteria for at least one lifetime anxiety disorder, primarily separation anxiety disorder and generalized anxiety disorder. Approximately 18% of the sample met the criteria for two or more lifetime anxiety disorders. The onset of anxiety generally predated the onset of bipolar spectrum disorder, after adjusting for demographics and subtypes. After adjusting for significant demographic factors and bipolar subtypes, bipolar youth with anxiety showed significantly higher rates of type II disorder, longer duration of mood symptoms, higher current depression scores, lower likelihood of reporting an index episode of the mania, and higher rates of familial depression and reported a worst lifetime depressive episode, characterized by greater severity of hopelessness, and aches and pains.

These findings are consistent with prior studies in which anxiety disorders have been found at high rates among youth and adults with bipolar spectrum disorder, along with the higher prevalence rates of type II bipolar disorder in bipolar youths with anxiety. These findings have important implications for treatment and deserve increased clinical and scientific study.

Substance Use Disorders

Epidemiologic and clinical studies suggest higher rates of substance use disorders among youth with bipolar spectrum disorders when compared with adolescents without bipolar spectrum disorder (Lewinsohn et al., 1995; Wilens et al., 1999, 2004). Evidence further supports the bidirectional overlap between pediatric mania and substance use disorders (Biederman et al., 1997c; West et al., 1996; Wilens et al., 1997b, 1999, 2000, 2004). Pediatric mania may be a risk factor for substance use disorders; several studies have demonstrated overrepresentation of bipolar youth among youth with these disorders (Biederman et al., 1997c, 2000; West et al., 1995; Wilens, 1997a, 1999). Investigators have further demonstrated that mania significantly increases the risk for substance use disorders, independent of conduct disorder and ADHD (Wilens, 1999; Biederman, 1997, 2000).

Wilens et al. (2004), as part of an ongoing, controlled family-based study of adolescents with bipolar spectrum disorder, examined risks for substance use disorders among adolescents with bipolar spectrum disorder (n = 57; mean age 13.3 ± 2.4) compared with adolescents without bipolar spectrum disorder (n = 46; mean age 13.6 ± 2.2). Bipolar spectrum disorder was associated with a highly significant risk for substance use disorders in comparison to the non-mood-disordered group, even after controlling for conduct disorder. Furthermore, adolescent-onset bipolar spectrum disorder was associated with a higher risk of substance use disorder than for youths with childhood-onset bipolar spectrum disorder. Some investigators suggest that developmental heterogeneity may be relevant in juvenile bipolar spectrum disorder based upon onset in childhood or adolescence (Faraone et al., 1997). The authors suggested that further study of the developmental relationships between substance use and mood disorders is needed.

A study aimed to document the prevalence and correlates of substance use disorders among youths with bipolar spectrum disorder. The COBY study is a long-term naturalistic study of youth with bipolar spectrum disorder consisting of 446 children and adolescents, ages 7 to 17 years. The investigators sought to determine the prevalence of substance use disorders in this cohort and to identify clinical and demographic factors associated with substance use (p. 39) disorders. For this study, ages were restricted to 12 to 17 years due to absence of substance use disorders among children (n = 249). Subjects met criteria for type I or type II bipolar disorder or NOS. Lifetime prevalence rates of substance use disorders were 16% among the 249 adolescent subjects. Eighteen (45%) met full criteria for an active substance use disorder at intake. Cannabis use disorders were most common, with a lifetime prevalence of 12% among all adolescents and 73% among adolescents with substance use disorders. Eight percent of all adolescents had a lifetime alcohol use disorder compared with 50% of subjects with any substance use disorder. There was a trend, yet not statistically significant, toward an increased prevalence of substance use disorder among those with adolescent-onset bipolar disorder (20%) versus childhood-onset bipolar disorder (12%). The presence of substance use disorders among youth with bipolar disorder was associated with significant health risks, including suicide attempts, police involvement, teen pregnancy, and abortion. Identification and intervention may have important public health implications.

Biology of Child and Adolescent Mood Disorders

Overview

There has been an exponential increase in our understanding of the pathophysiology of mood disorders in adults, and initially we were largely dependent on extrapolating from these adult findings to inform us about the biology of childhood and adolescent mood disorders. Recently, considerably more research on the biology of depression and bipolar disorder in children has been conducted.

Genetics

Serious mood disorders are known to have their onset in childhood and adolescence and to persist into adulthood. Thus, much of the information that has accrued concerning the pathophysiology of mood disorders in adulthood would appear to be applicable to the childhood-onset mood disorders. One important difference, however, is the apparent lack of efficacy of tricyclic antidepressants in youth as compared to the selective serotonin reuptake inhibitors, whereas both classes are effective in adults (Wagner & Ambrosini, 2001).

As highlighted earlier, heritable factors appear to be the most consistent predictors of risk, though environmental factors also play an important role. Thus, twin, family, and adoption studies have shown that heritable factors are substantial predictors of risk, especially with regard to bipolar disorder. Studies of children and adolescents with depression show a twofold increase in risk to first-degree relatives and a threefold to fourfold increased risk for offspring of depressed parents (Rice et al., 2002). Overall heritability estimates for depression have been shown to be about 35% throughout one’s lifetime (Uher, 2014).

Studies have also consistently shown a higher rate of bipolar disorder among first-degree relatives of youths with bipolar disorder, and the offspring of parents with bipolar disorder have up to a 25-fold increase in rates of bipolar disorder (Birmaher et al., 2009). This increased risk in offspring of bipolar parents also extends to other psychiatric disorders, including MDD, anxiety, ADHD, and behavioral problems, as well as earlier onset of mood symptoms (Wozniak et al., 2012). Heritability estimates have been shown to be about 80% (Uher, 2014).

While considerable research has focused on genetic contributions in adult-onset mood disorders, much less is known about the genetic influences on early-onset bipolar disorder and depression. Genetic studies have mainly focused on the allelic associations found in adult studies, such as brain-derived neurotrophic factor (BDNF) val66 alleles, and the serotonin transporter-linked promoter region short and long alleles (SERT) in bipolar disorder (Geller & Cook, 1999; Geller et al., 2004; Ospina-Duque et al., 2000) and also in major depressive disorder (Goodyer et al., 2009; Kaufman et al., 2006). Unlike adult studies, which have shown correlations (Craddock et al., 2001), results in the pediatric population reveal conflicting evidence. Some studies show (p. 40) a relationship between risk of childhood depression conferred by BDNF and SERT genetic vulnerability combined with stressful life events (Gutierrez et al., 2015), while others do not show a correlation (Rimay et al., 2015).

In one study, the catechol-O-methyltransferase (COMT), a dopamine metabolizing enzyme, lacked linkage disequilibrium with ultradian rapid cycling pediatric bipolar disorder (Geller & Cook, 2000), which also differs from adult data (Craddock et al., 2001) These discrepancies between pediatric and adult populations may be the result of phenotypic differences or developmental influences on gene expression over time. These genes of interest are mainly expressed in limbic areas and represent areas of future study (Faraone et al., 2003)). There is also some evidence indicating that early-onset bipolar disorder may be associated with genetic anticipation, as evidenced by trinucleotide repeats (CAG/CTG) coding for polyglutamine tracts (Schürhoff et al., 2000; Vincent et al., 2000) and the fact that genotypes in FKBP5 that encodes subsensitivity of the glucocorticoid receptor are associated with suicidal events and behavior in adolescent depression (Brent et al., 2010; Tatro et al., 2009). Studies have also found that pediatric bipolar disorder, along with other affective and psychotic disorders, is significantly associated with microdeletion of chromosome 22 based on data from patients with velo-cardio-facial syndrome (Jolin et al., 2012; Scambler et al., 1992).

One study found the familial transmission of mania and major depressive episodes to be independent of each other, despite common comorbidity of mood states. This suggests that bipolar disorder may have its own distinct biological pathway, which is separate from that of MDD and even bipolar type II disorder, rather than representing a more severe manifestation of other mood disorders (Merikangas, 2014).

Longitudinal studies of community and high-risk groups are needed to further explore the genetic influences on childhood depression and bipolar disorder. Genetic influences affect neurobiological processes that modulate susceptibility to environmental risk via gene–environment interaction.

Early Life Stress

Despite the preeminent significance of genetic influences, environmental factors are clearly substantial in the development of mood disorders. Consistent with predictions from early psychoanalytic models, losses early in life, shameful experiences, maternal deprivation, and physical and sexual abuse appear to be major risk factors for the development of mood disorders. Research has highlighted the seminal importance of early adverse life events into the mainstream of the neurobiological processes thought to underlie the pathophysiology of mood disorders (Heim et al., 2010). The pronounced adverse effects of early life stress are believed to be mediated by the substantial plasticity of the developing central nervous system as a function of experience. It has been proposed that stress and emotional trauma during development permanently shape the brain regions that mediate stress and emotion, leading to altered emotional processing and heightened responsiveness to stress, which in the genetically vulnerable individual may then evolve into syndromal psychiatric disorders, such as depression and bipolar disorder (Gilman et al., 2014; Heim & Nemeroff, 2001).

HPA Axis

The system that has been most closely scrutinized in depression is the HPA axis. Upon stress exposure, neurons in the hypothalamic paraventricular nucleus (PVN) secrete corticotropin-releasing factor (CRF) into the hypothalamic-hypophyseal portal circulation, which stimulates the production and release of ACTH from the anterior pituitary. ACTH in turn stimulates the release of glucocorticoids from the adrenal cortex. Glucocorticoids have marked effects on metabolism, immune function, and the brain, adjusting physiological functions and behavior in response to the stressor. Glucocorticoids exert negative feedback control on the HPA axis by regulating hippocampal and PVN neurons. Persistent glucocorticoid exposure exerts adverse effects on hippocampal neurons, including reduction in dendritic branching, loss of dendritic spines, (p. 41) and possibly impairment of neurogenesis. Such damage might progressively reduce inhibitory control of the HPA axis. CRF neurons integrate information relevant to stress not only at the hypothalamic PVN but also in a widespread circuit throughout the limbic system and brain stem. Direct central nervous system administration of CRF to laboratory animals produces integrated endocrine, autonomic, and behavioral responses that parallel signs of stress, depression, and anxiety, including loss of appetite, sleep disruption, decreased sexual behavior, despair, increased motor activity, neophobia, and enhanced startle reactivity.

Laboratory animal studies have provided direct evidence that early life stress leads to heightened stress reactivity and alterations in the aforementioned neural circuits that persist into adulthood. For example, adult rats that were separated from their dams for 180 minutes a day on postnatal days 2 through 14 exhibit up to threefold increases in ACTH and corticosterone responses to a variety of psychological stressors when compared to control rats (Ladd et al., 2000; Plotsky & Meaney, 1993). Maternally separated rats also develop marked behavioral changes, including increased anxiety-like behavior, anhedonia, alcohol and cocaine preference, sleep disruption, decreased appetite, and cognitive impairment. Subsequent studies revealed multiple central nervous system changes that likely underlie physiological and behavioral sensitization to stress after maternal separation or lack of maternal care. These findings include increased activity (increased CRF mRNA expression) and sensitization of CRF neurons in hypothalamic and limbic regions, decreased glucocorticoid receptor density in the hippocampus and prefrontal cortex, increased mineralocorticoid receptors in the hippocampus, decreased mossy fiber development and neurogenesis in the hippocampus, as well as alternations in norepinephrine, GABA, oxytocin sensitization of the norepinephrine system, and behavioral sensitization to fear stimuli in non-human primates reared by mothers exposed to unpredictable conditions with respect to food access over 3 months (Coplan et al., 1996). Taken together, early life stress induces manifold changes in multiple neurologic circuits that are involved in neuroendocrine, autonomic, and behavioral responses to stress. If similar changes also occurred in humans exposed to early life stress, these changes likely confer an enhanced risk for depression.

As noted earlier, several retrospective clinical studies have evaluated the long-term consequences of early life stress in adult humans. In an astonishing parallel to findings in rodents, women who were abused as children, including those with and those without current depression, exhibit greater plasma ACTH responses than controls. The increase was more pronounced in abused women with current depression, and these women also showed greater cortisol and heart rate responses than controls (Heim et al., 2000). Several studies have reported similar neuroendocrine and neurochemical changes in abused children (Harkness et al., 2011; Heim & Nemeroff, 2001, Heim et al., 2010). A more recent study found two divergent patterns of cortisol activity in adults with a history of childhood maltreatment. Those exposed to early life stress with a history of recurrent psychological distress during adulthood had significantly blunted cortisol reactivity, while subjects who were exposed to early life stress but who had no notable distress during adulthood had significantly elevated baseline cortisol levels, prolonged responses, and greater total cortisol production (Goldman-Mellor et al., 2012).

One recent study showed that young daughters of depressed mothers had shorter telomere length than those of never-depressed mothers, putting them at higher risk of developing MDD and other age-related medical illnesses. This shorter telomere length is a sign of accelerated biological aging, and it is also associated with greater cortisol reactivity to stress (Gotlib, 2015). Cortisol reactivity has proven to be a crucial part of the diathesis-stress model in the development of MDD.

Neuroimaging of Childhood Mood Disorders

Brain imaging studies are beginning to provide replicable findings of informative differences between controls and those with early-onset (p. 42) mood disorders. Similar to adult neuroimaging, pediatric depression and bipolar disorder appear to involve abnormalities in the prefrontal cortex, hippocampus, and amygdala networks. Neuroimaging of pediatric MDD has revealed multiple findings. Volumetric studies have highlighted anatomic changes such as significant reductions in frontal lobe volume, overall gray matter volume loss and thinning, and increased ventricular volume in large cohorts of children and adolescents with depressive disorders (Luby et al., 2016; Steingard et al., 2002).

Magnetic resonance imaging (MRI) studies have reported decreased hippocampal volumes in adults exposed to various types of early life stress (Vythilingam et al., 2002). Because hippocampal volume loss is not observed in abused children or young adults (Teicher, 2002) (although corpus callosum, amygdala, and cortical development seems to be impaired), some have suggested that repeated bursts of cortisol secretion over the course of time may eventually result in smaller hippocampi. Enhanced CRF secretion during development may also contribute to progressive hippocampal volume loss (Brunson et al., 2001). The fact that adult patients with major depression exhibit HPA axis hyperactivity and profound CRF hypersecretion, as evidenced in studies of cerebrospinal fluid and postmortem brain tissue (Flores et al., 2004; Merali et al., 2004), and that these findings are also observed after early life stress, may provide additional evidence that this critical stress system plays a role in the pathogenesis of childhood mood disorders.

One review paper examined functional MRI findings in 28 different studies focusing on five functional imaging domains in depressed youth: emotional processing, cognitive control, affective cognition, reward processing, and resting-state functional connectivity. The findings illuminated differences in activation of the ventromedial frontal regions, the anterior cingulate, and the amygdala of depressed and control youths across all five domains (Kerestes et al., 2013). Another review compared functional MRI differences between bipolar youths (12 studies) and bipolar adults (73 studies) and found significantly greater convergence of hyperactivation of the amygdala, inferior frontal, gyrus, and precuneus when using emotional stimuli among bipolar youths versus bipolar adults. This review also showed greater hypoactivation in the anterior cingulate cortex when employing nonemotional cognitive tasks among bipolar youths versus bipolar adults. These developmental differences may represent more emotional dysfunction in bipolar youths caused by amygdala, prefrontal, and visual system hyperactivation and more cognitive deficits related to anterior cingulate cortex hypoactivation when compared to bipolar adults (Wegbreit et al., 2014).

A preliminary diffusion tensor imaging study found lower fractional anisotropy in white matter tracts between the amygdala and subgenual anterior cingulate cortex in depressed youth (Cullen, Klimes-Dougan, et al., 2010). A similar functional circuit also involving the subgenual ACC was identified using functional connectivity methods acquired at rest (Cullen, Gee, et al., 2009).

A review of 12 magnetic resonance spectroscopy studies in children with depression showed changes in glutamate and choline levels in areas of emotional regulation, though there are discordant findings (Kondo et al., 2014).

Neuroimaging studies examining pediatric bipolar disorder have also explored similar areas of the brain involved in mood regulation. Volumetric studies have revealed a decrease in total cerebral volume in youths with pediatric bipolar disorder (DelBello et al., 2004; Frazier et al., 2005). Studies have also shown decreased volume of the amygdala, hippocampus, and nucleus accumbens compared to healthy controls (DelBello et al., 2004; Dickstein et al., 2005; Frazier et al., 2005). One recent study showed depressed left hippocampal volume in pediatric bipolar disorder when associated with low family cohesion and the BDNF val66met polymorphism (Zeni et al., 2016).

A review of 26 magnetic resonance spectroscopy studies in children with bipolar disorder has shown changes in glutamate neurons and mitochondrial functioning in areas of emotional regulation (Kondo et al., 2014).

Neuroimaging in children with mood disorders has shown changes in the areas of the brain that control emotional regulation. Spectroscopy (p. 43) has also revealed possible impaired cellular function in these areas. Similar to genetic studies of pediatric mood disorders, further investigation of the neuroimaging findings is warranted.

Gender

Gender is well known to be an important but poorly understood factor influencing the risk of mood disorders. The prevalence of MDD, while equal between boys and girls prior to puberty, doubles in young women after puberty. This same phenomenon occurs across many different nations, cultures, and ethnicities (Weisman et al., 1996). This increase in females has been hypothesized to be secondary to hormonal changes occurring during puberty, exposure to different stressors, and gender differences in the stress response. One hypothesis is that women are more likely than men to have a dysregulated HPA response to stress, which makes them more likely to develop depression in response to stress (Weiss et al., 1999). Women may be more likely to have a dysregulated HPA response because they are more likely to have suffered traumatic events, which are known to contribute to HPA dysregulation (Heim et al., 2000). Twin studies also suggest that the impact of genetic risk factors becomes more prominent as girls pass through puberty and enter adolescence (Silberg et al., 1999). While these changes may influence brain function, the attendant social/psychological factors of puberty may also play an important role.

The prevalence of bipolar disorder has consistently been shown to be equal in males and females, yet some clinical differences have been noted. A cohort study of 604 subjects showed that bipolar women are more likely than men to show a predominance of depressive polarity as well as a depressive onset while bipolar men are more likely to have comorbid substance use disorders. Women have a significantly higher lifetime prevalence of psychotic depression and a higher prevalence of Axis II comorbid disorders. Bipolar women are also more likely to have a family history of suicide and a lifetime history of attempted suicide (Nivoli et al., 2011).

Another major distinction between men and women with regard to bipolar disorder is the impact of reproductive life events, particularly during the postpartum period (Diflorio & Jones, 2010).

Conclusion

Accurate epidemiologic data are useful for determining the magnitude of the problem, identifying risk factors, monitoring changes in rates (epidemics), and identifying the underserved. There are no data yet available on how the new DSM-5 criteria will affect rates, because DSM-5 was only released in May 2013; longitudinal studies will track how these changes to criteria impact rates of psychopathology among children and adolescents. Accurate estimates rest on accurate diagnosis, yet another challenge is stigma and underreporting in this population. In a recent New York Times article, two high school students wrote about the stigma of psychiatric problems among their classmates and how school administrators did not allow them to publish articles revealing personal accounts of mental health issues in their school district. They reported that students are not comfortable discussing mental health issues, despite the fact that many of them are receiving psychiatric treatment (Halpert & Rosenfeld, 2014). Stigma of mental illness must be decreased in this population in order to accurately identify the rates of these disorders and to improve treatment-seeking behavior; this topic will be discussed in further detail in a later chapter.

The explosive developments in neurosciences, genetics, and neuroimaging will undoubtedly help refine the diagnostic process of these complex mood disorders afflicting the young. Such advances can shed light on the etiology and underlying pathophysiology, including the identification of dysfunctional brain circuits and ultimately treatment of afflicted youth and their families. These data will also likely identify subtypes of depression and bipolar disorder, and ideally this will allow us to predict treatment response. Longitudinal studies focusing on the biology and treatment response of childhood and adolescent mood disorders are sorely needed. (p. 44)