Identification of structural brain alterations in adolescents with depressive symptomatology

Introduction: Depressive symptoms can emerge as early as childhood and may lead to adverse situations in adulthood. Studies have examined structural brain alternations in individuals with depressive symptoms, but findings remain inconclusive. Furthermore, previous studies have focused on adults or used a categorical approach to assess depression. The current study looks to identify grey matter volumes (GMV) that predict depressive symptomatology across a clinically concerning sample of adolescents. Methods: Structural MRI data were collected from 338 clinically concerning adolescents (mean age = 15.30 SD=2.07; mean IQ = 101.01 SD=12.43; 132 F). Depression symptoms were indexed via the Mood and Feelings Questionnaire (MFQ). Freesurfer was used to parcellate the brain into 68 cortical regions and 14 subcortical regions. GMV was extracted from all 82 brain areas. Multiple linear regression was used to look at the relationship between MFQ scores and region-specific GMV parameter. Follow up regressions were conducted to look at potential effects of psychiatric diagnoses and medication intake. Results: Our regression analysis produced a significant model (R2 = 0.446, F(86, 251) = 2.348, p \u3c 0.001). Specifically, there was a negative association between GMV of the left parahippocampal (B = -0.203, p = 0.005), right rostral anterior cingulate (B = -0.162, p = 0.049), and right frontal pole (B = -0.147, p = 0.039) and a positive association between GMV of the left bank of the superior temporal sulcus (B = 0.173, p = 0.029). Follow up analyses produced results proximal to the main analysis. Conclusions: Altered regional brain volumes may serve as biomarkers for the development of depressive symptoms during adolescence. These findings suggest a homogeneity of altered cortical structures in adolescents with depressive symptoms

Neuroimaging alterations of the suicidal brain and its relevance to practice: an updated review of MRI studies

Suicide is a leading cause of death in the United States. Historically, scientific inquiry has focused on psychological theory. However, more recent studies have started to shed light on complex biosignatures using MRI techniques, including task-based and resting-state functional MRI, brain morphometry, and diffusion tensor imaging. Here, we review recent research across these modalities, with a focus on participants with depression and Suicidal Thoughts and Behavior (STB). A PubMed search identified 149 articles specific to our population of study, and this was further refined to rule out more diffuse pathologies such as psychotic disorders and organic brain injury and illness. This left 69 articles which are reviewed in the current study. The collated articles reviewed point to a complex impairment showing atypical functional activation in areas associated with perception of reward, social/affective stimuli, top-down control, and reward-based learning. This is broadly supported by the atypical morphometric and diffusion-weighted alterations and, most significantly, in the network-based resting-state functional connectivity data that extrapolates network functions from well validated psychological paradigms using functional MRI analysis. We see an emerging picture of cognitive dysfunction evident in task-based and resting state fMRI and network neuroscience studies, likely preceded by structural changes best demonstrated in morphometric and diffusion-weighted studies. We propose a clinically-oriented chronology of the diathesis-stress model of suicide and link other areas of research that may be useful to the practicing clinician, while helping to advance the translational study of the neurobiology of suicide

Identification of structural brain alterations in adolescents with depressive symptomatology

Introduction: Depressive symptoms can emerge as early as childhood and may lead to adverse situations in adulthood. Studies have examined structural brain alternations in individuals with depressive symptoms, but findings remain inconclusive. Furthermore, previous studies have focused on adults or used a categorical approach to assess depression. The current study looks to identify grey matter volumes (GMV) that predict depressive symptomatology across a clinically concerning sample of adolescents. Methods: Structural MRI data were collected from 338 clinically concerning adolescents (mean age = 15.30 SD=2.07; mean IQ = 101.01 SD=12.43; 132 F). Depression symptoms were indexed via the Mood and Feelings Questionnaire (MFQ). Freesurfer was used to parcellate the brain into 68 cortical regions and 14 subcortical regions. GMV was extracted from all 82 brain areas. Multiple linear regression was used to look at the relationship between MFQ scores and region-specific GMV parameter. Follow up regressions were conducted to look at potential effects of psychiatric diagnoses and medication intake. Results: Our regression analysis produced a significant model (R2 = 0.446, F(86, 251) = 2.348, p < 0.001). Specifically, there was a negative association between GMV of the left parahippocampal (B = −0.203, p = 0.005), right rostral anterior cingulate (B = −0.162, p = 0.049), and right frontal pole (B = −0.147, p = 0.039) and a positive association between GMV of the left bank of the superior temporal sulcus (B = 0.173, p = 0.029). Follow up analyses produced results proximal to the main analysis. Conclusions: Altered regional brain volumes may serve as biomarkers for the development of depressive symptoms during adolescence. These findings suggest a homogeneity of altered cortical structures in adolescents with depressive symptoms

Machine learning based identification of structural brain alterations underlying suicide risk in adolescents

Suicide is the third leading cause of death for individuals between 15 and 19 years of age. The high suicide mortality rate and limited prior success in identifying neuroimaging biomarkers indicate that it is crucial to improve the accuracy of clinical neural signatures underlying suicide risk. The current study implements machine-learning (ML) algorithms to examine structural brain alterations in adolescents that can discriminate individuals with suicide risk from typically developing (TD) adolescents at the individual level. Structural MRI data were collected from 79 adolescents who demonstrated clinical levels of suicide risk and 79 demographically matched TD adolescents. Region-specific cortical/subcortical volume (CV/SCV) was evaluated following whole-brain parcellation into 1000 cortical and 12 subcortical regions. CV/SCV parameters were used as inputs for feature selection and three ML algorithms (i.e., support vector machine [SVM], K-nearest neighbors, and ensemble) to classify adolescents at suicide risk from TD adolescents. The highest classification accuracy of 74.79% (with sensitivity = 75.90%, specificity = 74.07%, and area under the receiver operating characteristic curve = 87.18%) was obtained for CV/SCV data using the SVM classifier. Identified bilateral regions that contributed to the classification mainly included reduced CV within the frontal and temporal cortices but increased volume within the cuneus/precuneus for adolescents at suicide risk relative to TD adolescents. The current data demonstrate an unbiased region-specific ML framework to effectively assess the structural biomarkers of suicide risk. Future studies with larger sample sizes and the inclusion of clinical controls and independent validation data sets are needed to confirm our findings