Cognitive Control Deficits in Depression: A Novel Target to Improve Suboptimal Outcomes in Childhood
Publication: The Journal of Neuropsychiatry and Clinical Neurosciences
Abstract
Cognitive control deficits are one of three primary endophenotypes in depression, and the enhanced targeting of these deficits in clinical and research work is expected to lead to improved depression outcomes. Cognitive control is a set of self-regulatory processes responsible for goal-oriented behavior that predicts clinical/functional outcomes across the spectrum of brain-based disorders. In depression, cognitive control deficits emerge by the first depressive episode, persist during symptom remission, and worsen over the course of depression. In addition, the presence of these deficits predicts a poor response to evidence-based depression treatments, including psychotherapy and antidepressant medication. This is particularly relevant to childhood depression, as 1%−2% of children are diagnosed with depression, yet there are very limited evidence-based treatment options. Cognitive control deficits may be a previously underaddressed factor contributing to poor outcomes, although there remains a dearth of research examining the topic. The investigators describe the prior literature on cognitive control in depression to argue for the need for increased focus on this endophenotype. They then describe cognitive control-focused clinical and research avenues that would likely lead to improved treatments and outcomes for this historically undertreated aspect of childhood depression.
Depression, or major depressive disorder, is a leading burden and the third most common cause of disability worldwide (1, 2). Psychiatric disorders are also the most costly to treat of any childhood health conditions, with $13.8 billion spent on treatment for psychiatric disorders in the United States in 2011 (3). By adolescence, one in five youths in the United States will experience at least one severely impairing psychiatric disorder (4). With a 3.2% prevalence rate of depression across childhood and adolescence in the United States, this is <1% of 3- to 5-year-old children, 1.7% of 6- to 11-year-old children, and 6.1% of 12- to 17-year-old adolescents (5). Childhood depression is also a precursor to adolescent and adult depression; 72% of children subsequently experience a recurrent depressive episode after initial episode recovery (6).
Despite the established prevalence of childhood depression, essentially no evidence-based treatments exist for this condition. In one recent meta-analysis that compared psychotherapy effects across age groups, psychotherapy had only a small effect on depressive symptoms in children (g=0.35), yet a medium effect was observed in adolescents (g=0.55), older adults (g=0.66), and middle-aged adults (g=0.77); a large effect was observed in young adults (g=0.98) (7). A 2017 meta-analysis of cognitive-behavioral therapy (CBT) for childhood depression found a small effect of CBT on depressive symptoms in children (standardized mean difference=−0.41), although it was not superior to wait list or control interventions (8). In a 2016 meta-analysis, there was inconclusive evidence that psychotherapy was better than no treatment for childhood depression across CBT, family therapy, and psychodynamic therapy modalities (9). Additionally, while CBT and interpersonal psychotherapy are considered well-established interventions for adolescent depression, no psychosocial interventions are well established for childhood depression. CBT was identified as possibly efficacious (10).
The evidence for antidepressant medication in childhood and adolescent depression remains mixed. In a network meta-analysis, fluoxetine was the only medication (out of 14) to be superior to placebo in improving depressive symptoms in childhood and adolescent depression (standardized mean difference=0.51; medium effect) (11). The other 13 medications were not superior to placebo; imipramine, venlafaxine, and duloxetine had a higher rate of adverse event-related discontinuation than placebo. A similar pattern of medication nonsuperiority to placebo and increased adverse event-related discontinuation was found in a separate meta-analysis that examined serotonin-norepinephrine reuptake inhibitors (12). However, these findings are not without controversy and inconsistencies. Compared with industry-funded trials, trials funded by the National Institute of Mental Health (NIMH) are characterized by lower placebo response rates and larger between-group differences in favor of antidepressant efficacy (11, 13).
Diagnostic criteria for major depressive disorder can be met in 227 possible symptom combinations, but only 10% of patients possess all nine of the core criteria (14). Given this significant heterogeneity, research has shifted toward possible traits or endophenotypes. Neuroticism, blunted reward learning, and cognitive control deficits have emerged as three primary endophenotypes of depression (15). Cognitive control (also referred to as executive function) is a collection of self-regulatory processes required when automatic or proponent thoughts and actions are insufficient to meet environmental demands (16). Cognitive control subdomains are somewhat heterogeneous, including processes such as working memory, inhibition, flexibility, self-monitoring, emotion regulation, and problem solving (16–18). To capture the overarching cognitive control construct, we investigated studies that used any cognitive control measures. Cognitive control is a critical function that is one of the strongest predictors of clinical and functional outcomes across the spectrum of psychopathology (e.g., 19, 20). Improved identification and treatment of this previously underaddressed component of the disorder offers an opportunity to improve the clinical and functional outcomes of childhood depression. As there is a limited literature on cognitive control in childhood depressive disorders, this perspective will summarize relevant cognitive control findings in adult depression and describe key clinical and research approaches that could harness cognitive control to improve outcomes.
The Role of Cognitive Control in Depression
Cognitive Control Deficits in Depression
Prior meta-analyses have identified small- to large-sized cognitive control deficits as an established component of adult major depressive disorder and other depressive disorders (21–23). Although depression is associated with lower cognitive control as a diagnostic group, only approximately 21%−62% of adults with major depressive disorder display cognitive control deficits (24–27). In a recent analysis of adult depression, 45% of the sample was labeled “cognitively preserved,” 39% as “cognitively reduced,” and 16% as “cognitively impaired” (27). Regarding the disease course, small- to medium-sized cognitive control deficits are present during the first depressive episode (28) and persist after depressive episode remission (21, 29). Meta-analytic evidence indicates that the time since depression onset and the number of prior episodes are also associated with lower cognitive control (29). Although healthy control subjects and those with bipolar disorder showed age-related improvements in inhibitory control, depression has been associated with age-related declines in inhibitory control (30). This potential progression of cognitive control deficit in depression has led to the theoretical conceptualization that each subsequent depressive episode causes further “repetitive scarring” and subsequent cognitive deterioration (30).
Cognitive Control Predictors of Treatment Outcome
Regarding general treatment predictors, one recent meta-analysis found a medium effect of cognitive control on treatment for older adults with depression across multiple treatment modalities; lower cognitive flexibility was associated with worse treatment response (31). A qualitative review found a similar effect of lower general cognitive control leading to poor depression treatment, although this review noted response inhibition and semantic organization as the primary implicated cognitive control subdomains (32). The literature in adult depression is less robust, although lower cognitive control has predicted a worse response to tertiary outpatient care (33), 3-week treatment with CBT (34), and a combination of psychodynamic therapy and fluoxetine (35). In addition, a change in self-reported problem solving was found to predict subsequent change in depressive symptoms across CBT, supportive psychotherapy, and medication interventions in adults (36).
Regarding antidepressant medication predictors, one meta-analysis in adults and one meta-analysis in older adults found lower cognitive control to be associated with poorer response to antidepressant medication (37, 38). Interestingly, the cognitive control subdomain differed: Planning and organization was identified in older adults, and initiation and perseveration in all adults. More recently, lower working memory at baseline predicted poorer response to fluoxetine for depression during inpatient hospitalization (39). In a machine learning analysis of sertraline response in adult depression, cognitive control was one of five pretreatment variables that moderated treatment response (40). Furthermore, the course of cognitive flexibility and semantic fluency and organization was found to predict the course of depressive symptoms in an antidepressant medication trial (24).
Childhood Depression Findings
The presence of cognitive control deficits in depression extends downward into childhood and adolescence; a prior meta-analysis identified medium-sized deficits in major depressive disorder within the inhibition, phonemic fluency and organization, and planning domains (41). In the only studies in adolescent depression, lower sustained attention, inhibitory control, and planning were associated with a poorer response to a fluoxetine trial (42); baseline self-reported problem solving predicted depressive symptoms across CBT and medication interventions (43).
It is worth noting that well-done meta-analyses are limited in the ability to examine childhood depression due to the lack of available studies in this age range (e.g., 44). While the small number of available studies may not show a strong effect, we have consistently found an association between cognitive control and depression at our children’s psychiatric hospital, specifically within outpatient child and adolescent psychiatry (45), inpatient child psychiatry (46, 47), and inpatient adolescent psychiatry (48, 49) programs. However, this may reflect subgroup or phenotype differences between studies, as our patients receive intensive hospital-based treatment and are referred for evaluation due to neurocognitive concerns. For example, in children referred for evaluation during inpatient admission, only 52% of those with clinically significant depressive symptoms also displayed cognitive control deficits (47). These children were ultimately hospitalized 60% longer than children with elevated depressive symptoms without cognitive control deficits, although the relatively modest rate of deficits in such an intensive setting suggests the rate may be lower in nonhospital treatment settings.
Call to Action
Cognitive control deficits are a core, previously unaddressed component of depression. Preliminary findings show the results also apply to childhood depression. Here, we emphasize clinical and research approaches that can be taken to target these deficits and improve childhood depression outcomes.
Clinical Care
Evaluation.
Neuropsychological evaluation is the application of standardized testing techniques to understand and measure underlying brain-behavior relationships. It is an established, evidence-based clinical service across the spectrum of brain-based disorders (50). In childhood, neuropsychological evaluation has been shown to improve access to relevant therapies and improve the overall severity of psychiatric symptoms (51). However, childhood depression is not a common reason for referral. We previously found children with depression were referred for evaluation less often than children with behavioral disorders, despite identifying cognitive control deficits that were specific to children with depression (46).
Waitlists can limit access to neuropsychology, but options are currently available that primary clinicians can implement. At our hospital, treatment programs now implement a brief cognitive control screener, including the Behavior Rating Inventory of Executive Function–Second Edition, Parent Form (BRIEF-2) and the cognitive control measures of the National Institutes of Health (NIH) Toolbox. Both are established and feasible clinical measures for children (17, 52, 53), and we have previously found that both measures accurately detect cognitive control deficits specific to childhood depression (45). A psychometrician or trainee can administer and score these measures in approximately 30 minutes, without requiring a neuropsychologist. This provides critical, immediate data to the primary psychologist or psychiatrist on possible deficits. The team can then integrate the child’s deficits into the treatment plan, or the child can be referred for a formal neuropsychological evaluation as needed.
Recommendation:
•
We encourage primary clinicians and physicians to implement brief and cost-effective cognitive control screening methods into their standard practice and to refer for a more comprehensive neuropsychological evaluation as needed. Neuropsychologists should improve access to care for children with depression, potentially providing brief or tailored evaluations specific to cognitive control.
Treatment.
To our knowledge, cognitive remediation is the only clinically available, evidence-based treatment specifically targeting cognitive control. Cognitive remediation is a systematic intervention that uses learning principles to teach, train, and improve the neurocognitive functions often impaired in various brain-based disorders. A meta-analysis of cognitive remediation in adult depression found significant small to moderate effects for symptom severity and daily functioning as well as moderate to large effects for attention, working memory, and global functioning (54). Another meta-analysis that examined the effect of various cognitively based therapies (e.g., CBT) found cognitive remediation had a medium-sized effect on repetitive negative thinking in depression (55). Interestingly, this effect size was second only to rumination-focused CBT, and it was stronger than standard CBT, concreteness training, and mindfulness-based cognitive therapy. Not specific to depression, the cognitive remediation research in childhood is generally consistent with adult findings (i.e., a small-medium effect on cognitive control in preschool, school-aged, and adolescents) (56–58).
A cognitive remediation expert working group published an article on the core intervention techniques, which consisted of the presence of a trained therapist, engagement in cognitive exercises, implementation of problem-solving strategies, and procedures to facilitate transfer to real-world functioning (59). Cognitive remediation programs that incorporate these techniques are considered to be an acceptable, sustainable, and effective intervention in psychiatry (60). This is uniformly distinct from computer-only cognitive training programs, which despite their potential appeal have limited clinical utility. Various manuals and programs are available using different approaches to cognitive remediation for children and individuals with psychiatric disorders.
We have piloted a cognitive remediation clinic within our neuropsychology program for children with a range of psychiatric disorders, including depression. We have found the service is highly feasible to implement by providers and well received by families and primary clinicians. While our clinic is within neuropsychology, other related providers such as child psychologists, speech/language pathologists, and occupational therapists can implement cognitive remediation.
Recommendation:
•
We are hopeful that cognitive remediation services can be more consistently integrated into the standard of care for childhood depression to target cognitive control deficits.
Research
Evaluation.
Replicating adult studies, cognitive control measures should become a regular component of all treatment/intervention and outcome studies in childhood depression. Historically, one barrier to this additional procedure was the cost and time commitment associated with lengthy in-person testing and involvement by a neuropsychologist. However, feasibility is now drastically improved with parent-report questionnaires such as the BRIEF-2 and computerized measures such as the NIH Toolbox, as clinical trials and studies can reliably assess cognitive control with a research assistant or psychometrician in a short amount of time. Similar to the adult approach and consistent with the NIMH Research Domain Criteria, studies should quickly move away from examining group differences between children with and without depression (61). Rather, studies should investigate depressive symptoms and cognitive control as continuous constructs and examine their interactions across the spectrum of disease severity (e.g., 44). Data on the course and responsiveness of cognitive control to depression treatments are critical for improved care. Possible topics for investigation include whether cognitive control deficits precede depressive symptom onset in high-risk samples (e.g., offspring of depressed adults), persist during symptom remission, predict adolescent or adult depression severity, and either predict symptom response to depression treatments or improve after depression treatments.
Recommendation:
•
All childhood depression research should include measures targeting cognitive control, as well as the other endophenotypes of blunted reward learning and anhedonia.
Treatment.
One novel approach or avenue is to take an experimental therapeutics approach to developing interventions that can specifically target cognitive control in childhood depression. As is well described elsewhere, the NIMH experimental therapeutics approach specifically calls for three steps in treatment development (62). Target identification involves the identification of a clearly defined mechanism that underlies clinical symptomatology and a novel intervention to modulate the target. Target engagement tests whether an intervention specifically designed for the identified target can in fact modulate the target in a dose-dependent manner. Target validation tests whether engaging this target is associated with actual change in the patient’s observable clinical symptomatology. Cognitive control is neuroanatomically subserved by the frontoparietal or cognitive control network. This includes functional nodes within regions such as the dorsolateral prefrontal cortex, cingulate cortex, and posterior parietal cortex (63, 64). Highly consistent with the cognitive control deficit endophenotype perspective, major depressive disorder during childhood and adolescence is specifically associated with hypoactivation within these regions during cognitive control demands (65). Various neuroimaging approaches, as described here with functional MRI, offer established neural mechanisms that underlie cognitive control and can serve as treatment targets in childhood depression.
Emerging brain-based interventions can also be harnessed to target these cognitive control mechanisms. Noninvasive brain stimulation holds tremendous promise in transforming psychiatry, as it takes a precision medicine approach to treatment. One stimulation approach is transcranial magnetic stimulation (TMS). TMS is a noninvasive method of cortical stimulation based on the principle of electromagnetic induction; repetitive TMS (rTMS), the therapeutic application of TMS, involves applying repeated trains of magnetic pulses at a specific frequency in repeated daily sessions over a course of weeks (66). Depression researchers and clinicians are quite familiar with rTMS, as it has been approved by the U.S. Food and Drug Administration since 2008 for treatment-resistant adult major depressive disorder (67). It has strong clinical efficacy, including a 29%−62% response rate and a 19%−54% remission rate (e.g., 68). At least 70 studies to date have also shown cognition-enhancing effects of rTMS (69, 70), including prior reviews showing effects specific to cognitive control (71–74). As shown in one recent systematic review, rTMS is a safe and well-tolerated tool in childhood (75); preliminary studies have shown cognitive control-enhancing effects of rTMS in children (76–78).
However, rTMS represents just one of many options in an experimental therapeutics approach, including highly promising and less intensive procedures such as transcranial direct current stimulation and neurofeedback. As an example, we are currently targeting the working memory mechanism of theta-gamma coupling via theta burst stimulation in teenagers with attention deficit hyperactivity disorder in a target engagement study (NCT03480737).
Recommendation:
•
Adopting the experimental therapeutics approach to treatment development in childhood depression is expected to lead to novel, brain-based treatments that can specifically target cognitive control.
Conclusions
Cognitive control deficits are one of three primary endophenotypes of depression that predict clinical and functional outcomes. These deficits emerge early in depression onset and persist or even worsen throughout the disease course. Consistent with adult findings, cognitive control deficits are a common component of childhood and adolescent depression. These deficits are consistently linked to a poor response to treatment for adolescent, adult, and older adult depression. From a clinical perspective, evaluation and treatment of cognitive control deficits are established clinical practices that can improve such deficits and psychiatric symptoms, yet they are not a primary component of current childhood depression care. More consistent implementation of these clinically available options into the standard of care is likely to improve the historically poor treatment outcomes of childhood depression. From a research perspective, cognitive control measures that are low cost and less time intensive should become a regular component of assessment in ongoing childhood depression research. In addition, the experimental therapeutics approach is likely to lead to novel, brain-based interventions that can specifically modulate mechanisms underlying cognitive control deficits. This enhanced approach to these deficits in childhood depression is expected to improve the chronically poor treatment outcomes of childhood depression.
References
1.
Murray CJ, Atkinson C, Bhalla K, et al: The state of US health, 1990-2010: burden of diseases, injuries, and risk factors. JAMA 2013; 310:591–608
2.
Ferrari AJ, Charlson FJ, Norman RE, et al: Burden of depressive disorders by country, sex, age, and year: findings from the global burden of disease study 2010. PLoS Med 2013; 10:e1001547
3.
Soni A: The Five Most Costly Children’s Conditions, 2011: Estimates for U.S. Civilian Noninstitutionalized Children, Ages 0–17. Rockville, Md, Agency for Healthcare Research and Quality, 2014. https://meps.ahrq.gov/data_files/publications/st434/stat434.shtml
4.
Merikangas KR, He JP, Brody D, et al: Prevalence and treatment of mental disorders among US children in the 2001-2004 NHANES. Pediatrics 2010; 125:75–81
5.
Ghandour RM, Sherman LJ, Vladutiu CJ, et al: Prevalence and treatment of depression, anxiety, and conduct problems in US children. J Pediatr 2019; 206:256–267.e3
6.
Kovacs M, Obrosky S, George C: The course of major depressive disorder from childhood to young adulthood: recovery and recurrence in a longitudinal observational study. J Affect Disord 2016; 203:374–381
7.
Cuijpers P, Karyotaki E, Eckshtain D, et al: Psychotherapy for depression across different age groups: a systematic review and meta-analysis. JAMA Psychiatry 2020; 77:694–702
8.
Yang L, Zhou X, Zhou C, et al: Efficacy and acceptability of cognitive behavioral therapy for depression in children: a systematic review and meta-analysis. Acad Pediatr 2017; 17:9–16
9.
O’Connor JA, Wiffen B, Diforti M, et al: Neuropsychological, clinical and cognitive insight predictors of outcome in a first episode psychosis study. Schizophr Res 2013; 149:70–76
10.
Weersing VR, Jeffreys M, Do MT, et al: Evidence base update of psychosocial treatments for child and adolescent depression. J Clin Child Adolesc Psychol 2017; 46:11–43
11.
Cipriani A, Zhou X, Del Giovane C, et al: Comparative efficacy and tolerability of antidepressants for major depressive disorder in children and adolescents: a network meta-analysis. Lancet 2016; 388:881–890
12.
Xu Y, Bai SJ, Lan XH, et al: Randomized controlled trials of serotonin-norepinephrine reuptake inhibitor in treating major depressive disorder in children and adolescents: a meta-analysis of efficacy and acceptability. Braz J Med Biol Res 2016; 49:S0100-879X2016000600704
13.
Walkup JT: Antidepressant efficacy for depression in children and adolescents: industry- and NIMH-funded studies. Am J Psychiatry 2017; 174:430–437
14.
Zimmerman M, Ellison W, Young D, et al: How many different ways do patients meet the diagnostic criteria for major depressive disorder? Compr Psychiatry 2015; 56:29–34
15.
Webb CA, Dillon DG, Pechtel P, et al: Neural correlates of three promising endophenotypes of depression: evidence from the EMBARC Study. Neuropsychopharmacology 2016; 41:454–463
16.
Diamond A: Executive functions. Annu Rev Psychol 2013; 64:135–168
17.
Gioia G, Isquith PK, Guy SC, et al: Behavior Rating Inventory of Executive Function, 2nd ed. Lutz, Fla, Psychological Assessment Resources, 2015
18.
Zelazo PD: Executive function and psychopathology: a neurodevelopmental perspective. Annu Rev Clin Psychol 2020; 16:431–454
19.
Miller M, Hinshaw SP: Does childhood executive function predict adolescent functional outcomes in girls with ADHD? J Abnorm Child Psychol 2010; 38:315–326
20.
Cotrena C, Branco LD, Shansis FM, et al: Executive function impairments in depression and bipolar disorder: association with functional impairment and quality of life. J Affect Disord 2016; 190:744–753
21.
Rock PL, Roiser JP, Riedel WJ, et al: Cognitive impairment in depression: a systematic review and meta-analysis. Psychol Med 2014; 44:2029–2040
22.
Wagner S, Doering B, Helmreich I, et al: A meta-analysis of executive dysfunctions in unipolar major depressive disorder without psychotic symptoms and their changes during antidepressant treatment. Acta Psychiatr Scand 2012; 125:281–292
23.
Snyder HR: Major depressive disorder is associated with broad impairments on neuropsychological measures of executive function: a meta-analysis and review. Psychol Bull 2013; 139:81–132
24.
Wagner S, Helmreich I, Wollschläger D, et al: Early improvement of executive test performance during antidepressant treatment predicts treatment outcome in patients with Major Depressive Disorder. PLoS One 2018; 13:e0194574
25.
Gu CZ, He HL, Duan HF, et al: Predictors of neurocognitive impairment at 2years after a first-episode major depressive disorder. Compr Psychiatry 2016; 68:24–33
26.
Godard J, Grondin S, Baruch P, et al: Psychosocial and neurocognitive profiles in depressed patients with major depressive disorder and bipolar disorder. Psychiatry Res 2011; 190:244–252
27.
Martin DM, Wollny-Huttarsch D, Nikolin S, et al: Neurocognitive subgroups in major depressive disorder. Neuropsychology 2020; 34:726–734
28.
Lee RS, Hermens DF, Porter MA, et al: A meta-analysis of cognitive deficits in first-episode major depressive disorder. J Affect Disord 2012; 140:113–124
29.
Semkovska M, Quinlivan L, O’Grady T, et al: Cognitive function following a major depressive episode: a systematic review and meta-analysis. Lancet Psychiatry 2019; 6:851–861
30.
Bessette KL, Karstens AJ, Crane NA, et al: A lifespan model of interference resolution and inhibitory control: risk for depression and changes with illness progression. Neuropsychol Rev 2020; 30:477–498
31.
Tunvirachaisakul C, Gould RL, Coulson MC, et al: Predictors of treatment outcome in depression in later life: a systematic review and meta-analysis. J Affect Disord 2018; 227:164–182
32.
Pimontel MA, Culang-Reinlieb ME, Morimoto SS, et al: Executive dysfunction and treatment response in late-life depression. Int J Geriatr Psychiatry 2012; 27:893–899
33.
Dawson EL, Caveney AF, Meyers KK, et al: Executive functioning at baseline prospectively predicts depression treatment response. Prim Care Companion CNS Disord 2017; 19:16m01949
34.
Kundermann B, Hemmeter-Spernal J, Strate P, et al: Neuropsychological predictors of the clinical response to cognitive-behavioral therapy in patients with major depression. Z Neuropsychol 2015; 26:87–98
35.
Bastos AG, Guimarães LS, Trentini CM: Predictors of response in the treatment of moderate depression. Br J Psychiatry 2017; 39:12–20
36.
Klein DN, Leon AC, Li C, et al: Social problem solving and depressive symptoms over time: a randomized clinical trial of cognitive-behavioral analysis system of psychotherapy, brief supportive psychotherapy, and pharmacotherapy. J Consult Clin Psychol 2011; 79:342–352
37.
Pimontel MA, Rindskopf D, Rutherford BR, et al: A meta-analysis of executive dysfunction and antidepressant treatment response in late-life depression. Am J Geriatr Psychiatry 2016; 24:31–41
38.
McLennan SN, Mathias JL: The depression-executive dysfunction (DED) syndrome and response to antidepressants: a meta-analytic review. Int J Geriatr Psychiatry 2010; 25:933–944
39.
Lin CH, Chou LS, Tang SH, et al: Do baseline WAIS-III subtests predict treatment outcomes for depressed inpatients receiving fluoxetine? Psychiatry Res 2019; 271:279–285
40.
Webb CA, Trivedi MH, Cohen ZD, et al: Personalized prediction of antidepressant v. placebo response: evidence from the EMBARC study. Psychol Med 2019; 49:1118–1127
41.
Wagner S, Müller C, Helmreich I, et al: A meta-analysis of cognitive functions in children and adolescents with major depressive disorder. Eur Child Adolesc Psychiatry 2015; 24:5–19
42.
Maalouf F, Bakhti R, Tamim H, et al: Neurocognitive predictors of clinical improvement in selective serotonin reuptake inhibitor-treated adolescents with major depressive disorder. J Child Adolesc Psychopharmacol 2018; 28:387–394
43.
Becker-Weidman EG, Jacobs RH, Reinecke MA, et al: Social problem-solving among adolescents treated for depression. Behav Res Ther 2010; 48:11–18
44.
Dotson VM, McClintock SM, Verhaeghen P, et al: Depression and cognitive control across the lifespan: a systematic review and meta-analysis. Neuropsychol Rev 2020; 30:461–476
45.
Kavanaugh BC, Cancilliere MK, Fryc A, et al: Measurement of executive functioning with the National Institute of Health Toolbox and the association to anxiety/depressive symptomatology in childhood/adolescence. Child Neuropsychol 2019; 26:754–769
46.
Kavanaugh BC, Gaudet CE, Dupont-Frechette JA, et al: Failure to maintain set as a predictor of childhood depression within a children’s psychiatric inpatient sample. Psychiatry Res 2016; 246:644–649
47.
Weber EB, Studeny J, Kavanaugh BC, et al: Pediatric depression symptoms, executive functioning weaknesses, and associated neuropsychological and psychiatric outcomes. J Child Fam Stud 2018; 27:1661–1670
48.
Holler K, Kavanaugh B, Cook NE: Executive functioning in adolescent depressive disorders. J Child Fam Stud 2013; 23:1315–1324
49.
Kavanaugh B, Holler K: Executive functioning and self-reported depressive symptoms within an adolescent inpatient population. Appl Neuropsychol Child 2014; 3:126–134
50.
Braun M, Tupper D, Kaufmann P, et al: Neuropsychological assessment: a valuable tool in the diagnosis and management of neurological, neurodevelopmental, medical, and psychiatric disorders. Cogn Behav Neurol 2011; 24:107–114
51.
Combs T, Beebe DW, Austin CA, et al: Changes in child functioning pre-to post-neuropsychological evaluation. Child Neuropsychol 2020; 26:711–720
52.
Tulsky DS, Carlozzi NE, Chevalier N, et al: NIH Toolbox Cognition Battery (CB): measuring working memory. Monogr Soc Res Child Dev 2013; 78:70–87
53.
Zelazo PD, Anderson JE, Richler J, et al: NIH Toolbox Cognition Battery (CB): measuring executive function and attention. Monogr Soc Res Child Dev 2013; 78:16–33
54.
Motter JN, Pimontel MA, Rindskopf D, et al: Computerized cognitive training and functional recovery in major depressive disorder: a meta-analysis. J Affect Disord 2016; 189:184–191
55.
Spinhoven P, Klein N, Kennis M, et al: The effects of cognitive-behavior therapy for depression on repetitive negative thinking: a meta-analysis. Behav Res Ther 2018; 106:71–85
56.
Scionti N, Cavallero M, Zogmaister C, et al: Is cognitive training effective for improving executive functions in preschoolers? a systematic review and meta-analysis. Front Psychol 2020; 10:2812
57.
Oldrati V, Corti C, Poggi G, et al: Effectiveness of computerized cognitive training programs (CCTP) with game-like features in children with or without neuropsychological disorders: a meta-analytic investigation. Neuropsychol Rev 2020; 30:126–141
58.
Cortese S, Ferrin M, Brandeis D, et al: Cognitive training for attention-deficit/hyperactivity disorder: meta-analysis of clinical and neuropsychological outcomes from randomized controlled trials. J Am Acad Child Adolesc Psychiatry 2015; 54:164–174
59.
Bowie CR, Bell MD, Fiszdon JM, et al: Cognitive remediation for schizophrenia: an expert working group white paper on core techniques. Schizophr Res 2020; 215:49–53
60.
Medalia A, Saperstein AM, Erlich MD, et al: Cognitive remediation in large systems of psychiatric care. CNS Spectr 2019; 24:163–173
61.
Garvey M, Avenevoli S, Anderson K: The National Institute of Mental Health research domain criteria and clinical research in child and adolescent psychiatry. J Am Acad Child Adolesc Psychiatry 2016; 55:93–98
62.
Lewandowski KE, Ongur D, Keshavan MS: Development of novel behavioral interventions in an experimental therapeutics world: challenges, and directions for the future. Schizophr Res 2018; 192:6–8
63.
Niendam TA, Laird AR, Ray KL, et al: Meta-analytic evidence for a superordinate cognitive control network subserving diverse executive functions. Cogn Affect Behav Neurosci 2012; 12:241–268
64.
Cole MW, Schneider W: The cognitive control network: integrated cortical regions with dissociable functions. Neuroimage 2007; 37:343–360
65.
Miller CH, Hamilton JP, Sacchet MD, et al: Meta-analysis of functional neuroimaging of major depressive disorder in youth. JAMA Psychiatry 2015; 72:1045–1053
66.
McClintock SM, Freitas C, Oberman L, et al: Transcranial magnetic stimulation: a neuroscientific probe of cortical function in schizophrenia. Biol Psychiatry 2011; 70:19–27
67.
McClintock SM, Reti IM, Carpenter LL, et al: National Network of Depression Centers r TMS Task Group; American Psychiatric Association Council on Research Task Force on Novel B, Treatments: Consensus Recommendations for the Clinical Application of Repetitive Transcranial Magnetic Stimulation (rTMS) in the Treatment of Depression. J Clin Psychiatry 2018; 79:16cs10905
68.
Fitzgerald PB, Hoy KE, Anderson RJ, et al: A study of the pattern of response to rTMS treatment in depression. Depress Anxiety 2016; 33:746–753
69.
Demeter E: Enhancing cognition with theta burst stimulation. Curr Behav Neurosci Rep 2016; 3:87–94
70.
Luber B, Lisanby SH: Enhancement of human cognitive performance using transcranial magnetic stimulation (TMS). Neuroimage 2014; 85:961–970
71.
Brunoni AR, Vanderhasselt MA: Working memory improvement with non-invasive brain stimulation of the dorsolateral prefrontal cortex: a systematic review and meta-analysis. Brain Cogn 2014; 86:1–9
72.
Martin DM, McClintock SM, Forster JJ, et al: Cognitive enhancing effects of rTMS administered to the prefrontal cortex in patients with depression: a systematic review and meta-analysis of individual task effects. Depress Anxiety 2017; 34:1029–1039
73.
Ilieva IP, Alexopoulos GS, Dubin MJ, et al: Age-related repetitive transcranial magnetic stimulation effects on executive function in depression: a systematic review. Am J Geriatr Psychiatry 2018; 26:334–346
74.
Lowe CJ, Manocchio F, Safati AB, et al: The effects of theta burst stimulation (TBS) targeting the prefrontal cortex on executive functioning: a systematic review and meta-analysis. Neuropsychologia 2018; 111:344–359
75.
Allen CH, Kluger BM, Buard I: Safety of transcranial magnetic stimulation in children: a systematic review of the literature. Pediatr Neurol 2017; 68:3–17
76.
Abujadi C, Croarkin PE, Bellini BB, et al: Intermittent theta-burst transcranial magnetic stimulation for autism spectrum disorder: an open-label pilot study. Braz J Psychiatry 2017; 40:309–311
77.
Casanova MF, Baruth JM, El-Baz A, et al: Repetitive transcranial magnetic stimulation (rTMS) modulates event-related potential (ERP) indices of attention in autism. Transl Neurosci 2012; 3:170–180
78.
Sokhadze EM, El-Baz AS, Sears LL, et al: rTMS neuromodulation improves electrocortical functional measures of information processing and behavioral responses in autism. Front Syst Neurosci 2014; 8:134
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Received: 10 September 2020
Revision received: 30 January 2021
Accepted: 1 February 2021
Published online: 15 July 2021
Published in print: Fall 2021
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Dr. Kavanaugh is supported by the Thrasher Research Fund (Early Career Award) and the Rhode Island Foundation. The other authors report no financial relationships with commercial interests.
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