Research Review: Brain network connectivity and the heterogeneity of depression in adolescence – a precision mental health perspective.
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| Title: | Research Review: Brain network connectivity and the heterogeneity of depression in adolescence – a precision mental health perspective. |
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| Authors: | Chahal, Rajpreet, Gotlib, Ian H., Guyer, Amanda E. |
| Source: | Journal of Child Psychology & Psychiatry. Dec2020, Vol. 61 Issue 12, p1282-1298. 17p. 1 Diagram. |
| Subjects: | Brain physiology, Mental depression risk factors, Biomarkers, Mental depression, Mental health, Treatment effectiveness, Neural pathways, Functional connectivity, Adolescence |
| Abstract: | Background: Adolescence is a period of high risk for the onset of depression, characterized by variability in symptoms, severity, and course. During adolescence, the neurocircuitry implicated in depression continues to mature, suggesting that it is an important period for intervention. Reflecting the recent emergence of 'precision mental health' – a person‐centered approach to identifying, preventing, and treating psychopathology – researchers have begun to document associations between heterogeneity in features of depression and individual differences in brain circuitry, most frequently in resting‐state functional connectivity (RSFC). Methods: In this review, we present emerging work examining pre‐ and post‐treatment measures of network connectivity in depressed adolescents; these studies reveal potential intervention‐specific neural markers of treatment efficacy. We also review findings from studies examining associations between network connectivity and both types of depressive symptoms and response to treatment in adults, and indicate how this work can be extended to depressed adolescents. Finally, we offer recommendations for research that we believe will advance the science of precision mental health of adolescence. Results: Nascent studies suggest that linking RSFC‐based pathophysiological variation with effects of different types of treatment and changes in mood following specific interventions will strengthen predictions of prognosis and treatment response. Studies with larger sample sizes and direct comparisons of treatments are required to determine whether RSFC patterns are reliable neuromarkers of treatment response for depressed adolescents. Although we are not yet at the point of using RSFC to guide clinical decision‐making, findings from research examining the stability and reliability of RSFC point to a favorable future for network‐based clinical phenotyping. Conclusions: Delineating the correspondence between specific clinical characteristics of depression (e.g., symptoms, severity, and treatment response) and patterns of network‐based connectivity will facilitate the development of more tailored and effective approaches to the assessment, prevention, and treatment of depression in adolescents. [ABSTRACT FROM AUTHOR] |
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| Database: | Psychology and Behavioral Sciences Collection |
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| Abstract: | Background: Adolescence is a period of high risk for the onset of depression, characterized by variability in symptoms, severity, and course. During adolescence, the neurocircuitry implicated in depression continues to mature, suggesting that it is an important period for intervention. Reflecting the recent emergence of 'precision mental health' – a person‐centered approach to identifying, preventing, and treating psychopathology – researchers have begun to document associations between heterogeneity in features of depression and individual differences in brain circuitry, most frequently in resting‐state functional connectivity (RSFC). Methods: In this review, we present emerging work examining pre‐ and post‐treatment measures of network connectivity in depressed adolescents; these studies reveal potential intervention‐specific neural markers of treatment efficacy. We also review findings from studies examining associations between network connectivity and both types of depressive symptoms and response to treatment in adults, and indicate how this work can be extended to depressed adolescents. Finally, we offer recommendations for research that we believe will advance the science of precision mental health of adolescence. Results: Nascent studies suggest that linking RSFC‐based pathophysiological variation with effects of different types of treatment and changes in mood following specific interventions will strengthen predictions of prognosis and treatment response. Studies with larger sample sizes and direct comparisons of treatments are required to determine whether RSFC patterns are reliable neuromarkers of treatment response for depressed adolescents. Although we are not yet at the point of using RSFC to guide clinical decision‐making, findings from research examining the stability and reliability of RSFC point to a favorable future for network‐based clinical phenotyping. Conclusions: Delineating the correspondence between specific clinical characteristics of depression (e.g., symptoms, severity, and treatment response) and patterns of network‐based connectivity will facilitate the development of more tailored and effective approaches to the assessment, prevention, and treatment of depression in adolescents. [ABSTRACT FROM AUTHOR] |
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| ISSN: | 00219630 |
| DOI: | 10.1111/jcpp.13250 |
