Biology subjects

Admon, R.

Publications and source records attributed to Admon, R..

3 recordsLinked to original sources

bioRxiv2019

Neuroanatomical Risk Factors for Post Traumatic Stress Disorder (PTSD) in Recent Trauma Survivors

BackgroundLow hippocampal volume could serve as an early risk factor for Post-traumatic Stress Disorder (PTSD) in interaction with other brain anomalies of developmental origin. One such anomaly may well be a presence of large Cavum Septum Pellucidum (CSP), which has been loosely associated with PTSD. Here, we performed a longitudinal prospective study of recent trauma survivors. We hypothesized that at one-month after trauma exposure, the relation between hippocampal volume and PTSD symptom severity will be moderated by CSP volume, and that this early interaction will account for persistent PTSD symptoms at subsequent time-points.\n\nMethods171 adults (87 females, average age=34.22, range=18-65) admitted to a general hospitals emergency department following a traumatic event, underwent clinical assessment and structural MRI within one-month after trauma. Follow-up clinical evaluations were conducted at six (n=97) and fourteen (n=78) months after trauma. Hippocampus and CSP volumes were measured automatically by FreeSurfer software and verified manually by a neuroradiologist.\n\nResultsAt one-month following trauma, CSP volume significantly moderated the relation between hippocampal volume and PTSD severity (p=0.026), and this interaction further predicted symptom severity at fourteen months post-trauma (p=0.018). Specifically, individuals with smaller hippocampus and larger CSP at one-month post-trauma, showed more severe symptoms at one-and fourteen months following trauma exposure.\n\nConclusionsOur study provides evidence for an early neuroanatomical risk factors for PTSD, which could also predict the progression of the disorder in the year following trauma exposure. Such a simple-to-acquire neuroanatomical signature for PTSD could guide early management, as well as long-term monitoring.\n\nTrial RegistrationNeurobehavioral Moderators of Post-traumatic Disease Trajectories. ClinicalTrials.gov registration: NCT03756545. https://clinicaltrials.gov/ct2/show/NCT03756545

neuroscience↗

bioRxiv2019

Multi-Domain Potential Biomarkers for Post Traumatic Stress Disorder (PTSD) Severity in Recent Trauma Survivors

Contemporary symptom-based diagnosis of Post-traumatic Stress Disorder (PTSD) largely overlooks related neurobehavioral findings and rely entirely on subjective interpersonal reporting. Previous studies associating objective biomarkers with PTSD have mostly used the disorders symptom-based diagnosis as main outcome measure, overlooking the actual clustering and richness of phenotypical features associated with PTSD. Here, we aimed to computationally derive potential neurocognitive biomarkers that could efficiently differentiate PTSD subtypes, based on an observational cohort study of recent trauma survivors. A three-staged semi-unsupervised method ("3C") was used to categorize trauma survivors based on current PTSD diagnostics, derive clusters of PTSD based on features related to symptom load, and to classify participants cluster membership using objective features. A total of 256 features were extracted from psychometrics, cognitive, structural and functional neuroimaging data, obtained from 101 adult civilians (age=34.80{+/-}11.95, 51 females) evaluated within a month of trauma exposure. Multi-domain features that best differentiated cluster membership were indicated by using importance analysis, classification trees, and ANOVA. Results revealed that entorhinal and rostral anterior cingulate cortices volumes (structural domain), in-task amygdalas functional connectivity with the insula and thalamus (functional domain), executive function and cognitive flexibility (cognitive domain) best differentiated between two clusters related to PTSD severity. Cross-validation established the results robustness and consistency within this sample. Multi-domain biomarkers revealed by the 3C analytics offer objective classifiers of post-traumatic morbidity shortly following trauma. They also map onto previously documented neurobehavioral PTSD features, supporting the future use of standardized and objective measurements to more precisely identify psychopathology subgroups shortly after trauma.

neuroscience↗

bioRxiv2019

Machine learning identifies large-scale reward-related activity modulated by dopaminergic enhancement in major depression

BackgroundTheoretical models have emphasized systems-level abnormalities in Major Depressive Disorder (MDD). For unbiased yet rigorous evaluations of pathophysiological mechanisms underlying MDD, it is critically important to develop data-driven approaches that harness whole-brain data to classify MDD and evaluate possible normalizing effects of targeted interventions. Here, using an experimental therapeutics approach coupled with machine-learning we investigated the effect of a pharmacological challenge aiming to enhance dopaminergic signaling on whole-brains response to reward-related stimuli in MDD.\n\nMethodsUsing a double-blind placebo-controlled design, functional magnetic resonance imaging (fMRI) data from 31 unmedicated MDD participants receiving a single dose of 50 mg amisulpride (MDDAmisulpride), 26 MDD participants receiving placebo (MDDPlacebo), and 28 healthy controls receiving placebo (HCPlacebo) were analyzed. An importance-guided machine learning technique for model selection was used on whole-brain fMRI data probing reward anticipation and consumption to identify features linked to MDD (MDDPlacebo vs. HCPlacebo) and dopaminergic enhancement (MDDAmisulpride vs. MDDPlacebo).\n\nResultsHighly predictive classification models emerged that distinguished MDDPlacebo from HCPlacebo (AUC=0.87) and MDDPlacebo from MDDAmisulpride (AUC=0.89). Although reward-related striatal activation and connectivity were among the most predictive features, the best truncated models based on whole-brain features were significantly better relative to models trained using striatal features only.\n\nConclusionsResults indicate that, in MDD, enhanced dopaminergic signaling restores abnormal activation and connectivity in a widespread network of regions. These findings provide new insights into the pathophysiology of MDD and pharmacological mechanism of antidepressants at the system level in addressing reward processing deficits among depressed individuals.\n\nClinicalTrials.gov identifierNCT01253421 and NCT01701258

neuroscience↗