Structural connectomics of anxious arousal in early adolescence: Translating clinical and ethological findings

An interpersonal neurobiology perspective on the mind and mental health: personal, public, and planetary well-being

This article outlines an Interpersonal Neurobiology (IPNB) perspective on the fundamental components that comprise mental health and promote well-being. The central aim of this paper is to answer essential but often overlooked questions related to the field of mental health, such as: What is the mind? What is the basis of well-being? What is the self and how does it develop? We will offer scientific support for the IPNB position that the mind is relational and embodied and that integration is the basis of mental health. It will also describe how the self extends beyond the individual, arising from and inextricably connected to the social, cultural and planetary systems in which we exist. IPNB is not a form of therapy; rather, it is a framework that focuses on deepening our understanding of the mind and human development across the lifespan. Drawing from interdisciplinary principles from a range of fields including physics, mathematics, neuroscience, and psychology, we will provide a practical view of the underlying basis of mental suffering and the scientific mechanisms of change to improve mental well-being. These core principles are building blocks of clinical evaluation and treatment that can be applied across multiple theoretical orientations and client populations. The special emphasis in this article is on the issue of psychache as an underlying cause of suicide and its relationship to personal, public and planetary health.

Cingulate and hippocampal subregion abnormalities in combat-exposed veterans with PTSD

BACKGROUND

The hippocampus and cingulate gyrus are strongly interconnected brain regions that have been implicated in the neurobiology of post-traumatic stress disorder (PTSD). These brain structures are comprised of functionally distinct subregions that may contribute to the expression of PTSD symptoms or associated cardio-metabolic markers, but have not been well investigated in prior studies.

METHODS

Two divisions of the cingulate cortex (i.e., rostral and caudal) and 11 hippocampal subregions were investigated in 22 male combat-exposed veterans with PTSD and 22 male trauma-exposed veteran controls (TC). Cardio-metabolic measures included cholesterol, body mass index, and mean arterial pressure.

RESULTS

Individuals with PTSD had less caudal cingulate area compared to TC even after controlling for caudal cingulate thickness. Total hippocampus volume was lower in PTSD compared to TC, accounted for by differences in CA1-CA4, granule cell layer of the dentate gyrus, molecular layer, and subiculum. Individuals with PTSD had higher mean arterial pressure compared to TC, which correlated with hippocampus volume only in the PTSD group.

LIMITATIONS

Sample size, cross-sectional analysis, no control for medications and findings limited to males.

CONCLUSIONS

These data demonstrate preferential involvement of caudal cingulate area (vs. thickness) and hippocampus subregions in PTSD. The inverse association between hippocampus volume and mean arterial pressure may contribute to accelerated aging known to be associated with PTSD.

Reduced anxiety and changes in amygdala network properties in adolescents with training for awareness, resilience, and action (TARA)

Mindfulness-based approaches show promise to improve emotional health in youth and may help treat and prevent adolescent depression and anxiety. However, there is a fundamental gap in understanding the neural reorganization that takes place as a result of such interventions. The Training for Awareness, Resilience, and Action (TARA) program, initially developed for depressed adolescents, uses a framework drawn from neuroscience, mindfulness, yoga, and modern psychotherapeutic techniques to promote emotional health. The goal of this study was to assess the effects of the TARA training on emotional health and structural white matter brain networks in healthy youth. We analyzed data from 23 adolescents who underwent the 12-week TARA training in a controlled within-subject study design and whose brain networks were assessed using diffusion MRI connectomics. Compared to the control time period, adolescents showed a significant decrease in anxiety symptoms with TARA (Cohen's d = -0.961, p = 0.006); moreover, the node strength of the Right Amygdala decreased significantly after TARA (Cohen's d = -1.026, p = 0.004). Post-hoc analyses indicated that anxiety at baseline before TARA was positively correlated with Right Amygdala node strength (r = 0.672, p = 0.001). While change in Right Amygdala node strength with TARA was not correlated with change in anxiety (r = 0.146, p = 0.51), it was associated with change in depression subscale of Anhedonia / Negative Affect (r = 0.575, p = 0.004, exploratory analysis), possibly due to overlapping constructs captured in our anxiety and depression scales. Our results suggest that increased structural connectivity of Right Amygdala may underlie increased anxiety in adolescents and be lowered through anxiety-reducing training such as TARA. The results of this study contribute to our understanding of the neural mechanisms of TARA and may facilitate neuroscience-based prevention and treatment of adolescent anxiety and 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.

Posttraumatic stress symptom persistence across 24 years: association with brain structures

Posttraumatic stress disorder (PTSD) is known to persist, eliciting early medical co-morbidity, and accelerated aging. Although PTSD diagnosis has been found to be associated with smaller volume in multiple brain regions, posttraumatic stress (PTS) symptoms and their associations with brain morphometry are rarely assessed over long periods of time. We predicted that persistent PTS symptoms across ~24 years would be inversely associated with hippocampal, amygdala, anterior cingulate volumes, and hippocampal occupancy (HOC = hippocampal volume/[hippocampal volume + inferior lateral ventricle volume]) in late middle age. Exploratory analyses examined prefrontal regions. We assessed PTS symptoms in 247 men at average ages 38 (time 1) and 62 (time 2). All were trauma-exposed prior to time 1. Brain volumes were assessed at time 2 using 3 T structural magnetic resonance imaging. Symptoms were correlated over time (r = 0.46 p < .0001). Higher PTS symptoms averaged over time and symptoms at time 1 were both associated with lower hippocampal, amygdala, rostral middle frontal gyrus (MFG), and medial orbitofrontal cortex (OFC) volumes, and a lower HOC ratio at time 2. Increased PTS symptomatology from time 1 to time 2 was associated with smaller hippocampal volume. Results for hippocampal, rostral MFG and medial OFC remained significant after omitting individuals above the threshold for PTSD diagnosis. Even at sub-diagnostic threshold levels, PTS symptoms were present decades after trauma exposure in parallel with highly correlated structural deficits in brain regions regulating stress responsivity and adaptation.

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.