Brain connectivity networks and longitudinal trajectories of depression symptoms in adolescence

Association of lifetime major depressive disorder with enhanced attentional sensitivity measured with P3 response in young adult twins

Major depression is associated with alterations in the auditory P3 event-related potential (ERP). However, the persistence of these abnormalities after recovery from depressive episodes, especially in young adults, is not well known. Furthermore, the potential influence of substance use on this association is poorly understood. Young adult twin pairs (N=177) from the longitudinal FinnTwin16 study were studied with a psychiatric interview, and P3a and P3b ERPs elicited by task-irrelevant novel sounds and targets, respectively. Dyadic linear mixed-effect models were used to distinguish the effects of lifetime major depressive disorder from familial factors and effects of alcohol problem drinking and tobacco smoking. P3a amplitude was significantly increased and P3b latency decreased, in individuals with a history of lifetime major depression, when controlling the fixed effects of alcohol abuse, tobacco, gender, twins' birth order, and zygosity. These results suggest that past lifetime major depressive disorder may be associated with enhanced attentional sensitivity.

Functional Profiling and Future Research Direction of Rice Bran Oil in Bangladesh

Rice bran oil (RBO) has been demonstrated to affect complex malfunctioned conditions such as oxidative stress, hyperlipidemia, hyperglycemia, hypertension, inflammation, abnormal cell growth (cancer), ulceration, immune and cognitive modulation. This unique effect of RBO is due to the presence of well-balanced fatty acid composition and several bioactive compounds, γ- oryzanol (cycloartenyl ferulate, 24-methylenecycloartanyl ferulate, campesterol ferulate, and β-sitosteryl ferulate), vitamin E (tocopherol and tocotrienol), phytosterols (β-sitosterol, campesterol and stigmasterol) and other nutrients. The RBO composition of bioactive compounds varied geographically, thus the clear-cut mechanisms of action on complex disease cascades are still required. This review article summarized the RBO compositional profiling and compared it with other edible oils. This article also summarized Bangladesh RBO profiling and their proposed mechanism of action as well as the first line of defense in the prevention, management, and control of complex disease conditions. This review indicates how Bangladesh RBO increase their opportunity to be functional food for 21st century's ailment.

Girls' brain structural connectivity in late adolescence relates to history of depression symptoms

BACKGROUND

Girls' depressive symptoms typically increase in adolescence, with individual differences in course and severity being key risk factors for impaired emotional functioning in young adulthood. Given the continued brain white matter (WM) maturation that occurs in adolescence, the present study tested whether structural connectivity patterns in late adolescence are associated with variation in the course of depression symptom severity throughout adolescence.

METHOD

Participants were girls (N = 115) enrolled in a multiyear prospective cohort study of risk for depression. Initial depression severity (intercept) at age 10 and change in severity (linear slope) across ages 10-19 were examined in relation to WM tractography collected at age 19. Network-based statistic analyses were used to identify clusters showing variation in structural connectivity in association with depressive symptom intercept, slope, and their interaction.

RESULTS

Higher initial depressive severity and steeper positive slope (separately) were associated with greater structural connectivity between temporal, subcortical socioaffective, and occipital regions. Intercept showed more connectivity associations than slope. The interaction effect indicated that higher initial symptom severity and a steeper negative slope (i.e., alleviating symptoms) were related to greater connectivity between cognitive control regions. Moderately severe symptoms that worsened over time were followed by greater connectivity between self-referential and cognitive regions (e.g., posterior cingulate and frontal gyrus).

CONCLUSIONS

Higher depressive symptom severity in early adolescence and increasing symptom severity over time may forecast structural connectivity differences in late adolescence, particularly in pathways involving cognitive and emotion-processing regions. Understanding how clinical course relates to neurobiological correlates may inform new treatment approaches to adolescent depression.

MRI Insights Into Adolescent Neurocircuitry-A Vision for the Future

Adolescence is the time of onset of many psychiatric disorders. Half of pediatric patients present with comorbid psychiatric disorders that complicate both their medical and psychiatric care. Currently, diagnosis and treatment decisions are based on symptoms. The field urgently needs brain-based diagnosis and personalized care. Neuroimaging can shed light on how aberrations in brain circuits might underlie psychiatric disorders and their development in adolescents. In this perspective article, we summarize recent MRI literature that provides insights into development of psychiatric disorders in adolescents. We specifically focus on studies of brain structural and functional connectivity. Ninety-six included studies demonstrate the potential of MRI to assess psychiatrically relevant constructs, diagnose psychiatric disorders, predict their development or predict response to treatment. Limitations of the included studies are discussed, and recommendations for future research are offered. We also present a vision for the role that neuroimaging may play in pediatrics and primary care in the future: a routine neuropsychological and neuropsychiatric imaging (NPPI) protocol for adolescent patients, which would include a 30-min brain scan, a quality control and safety read of the scan, followed by computer-based calculation of the structural and functional brain network metrics that can be compared to the normative data by the pediatrician. We also perform a cost-benefit analysis to support this vision and provide a roadmap of the steps required for this vision to be implemented.

Application of machine learning to structural connectome to predict symptom reduction in depressed adolescents with cognitive behavioral therapy (CBT)

Purpose

Adolescent major depressive disorder (MDD) is a highly prevalent, incapacitating and costly illness. Many depressed teens do not improve with cognitive behavioral therapy (CBT), a first-line treatment for adolescent MDD, and face devastating consequences of increased risk of suicide and many negative health outcomes. “Who will improve with CBT?” is a crucial question that remains unanswered, and treatment planning for adolescent depression remains biologically unguided. The purpose of this study was to utilize machine learning applied to patients' brain imaging data in order to help predict depressive symptom reduction with CBT.

Methods

We applied supervised machine learning to diffusion MRI-based structural connectome data in order to predict symptom reduction in 30 depressed adolescents after three months of CBT. A set of 21 attributes was chosen, including the baseline depression score, age, gender, two global network properties, and node strengths of brain regions previously implicated in depression. The practical and robust J48 pruned tree classifier was utilized with a 10-fold cross-validation.

Results

The classification resulted in an 83% accuracy of predicting depressive symptom reduction. The resulting tree of size seven with only three attributes highlights the role of the right thalamus in predicting depressive symptom reduction with CBT. Additional analysis showed a significant negative correlation between the change in the depressive symptoms and the node strength of the right thalamus.

Conclusions

Our results demonstrate that a machine learning algorithm that exclusively uses structural connectome data and the baseline depression score can predict with a high accuracy depressive symptom reduction in adolescent MDD with CBT. This knowledge can help improve treatment planning for adolescent depression.

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Machine learning predicted symptom reduction in depressed teens with 83% accuracy.

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Resulting prunned classification tree size was 7, with only 3 attributes.

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Change in depression symptoms correlated with node strength of the right thalamus.