Biology subjects

Abdallah, C. G.

Publications and source records attributed to Abdallah, C. G..

4 recordsLinked to original sources

bioRxiv2025

Volumetric Differences of Thalamic Nuclei are Associated with Post-Trauma Psychopathology

Previous investigations of whole thalamus and thalamic nuclei volumes in post-trauma psychopathology have been sparse, limited in scope, and yielded inconsistent results. To address this, volumetric estimates of whole thalamus and thalamic nuclei were obtained from structural brain MRI scans from 2,058 participants across 20 worldwide sites in the ENIGMA PTSD working group. Thalamic volumes were compared between trauma-exposed participants with posttraumatic stress disorder (PTSD) (n=238), major depressive disorder (MDD) (n=184), comorbid PTSD+MDD (n=618), and trauma-exposed control participants (n=1,018). PTSD and MDD symptom severity, PTSD symptom clusters, and childhood trauma were similarly examined for associations with thalamic volume. Participants with PTSD only compared to controls had smaller thalamic nuclei volumes in sensorimotor nuclei, including the parafascicular (Pf), ventral anterior magnocellular (VAmc), medial pulvinar (PuM), and anterior pulvinar (PuA) nuclei of the thalamus. MDD only and comorbid PTSD+MDD participants exhibited smaller mediodorsal thalamus volumes compared to controls. Overall PTSD and MDD symptom severity negatively correlated with the volume of the mediodorsal thalamus. A significant interaction between PTSD and MDD severity was found, such that MDD severity was positively associated with thalamic volume only among individuals with high PTSD severity. Avoidance and hyperarousal symptoms of PTSD were positively associated with thalamic volume, while re- experiencing and negative mood/cognition symptoms were negatively associated with thalamic volume. Childhood physical and emotional abuse were positively and negatively associated with thalamic volume, respectively. Whole thalamus volume and volumes of the sensorimotor and limbic thalamus may play an important role in the development of PTSD and MDD in the aftermath of trauma exposure. The interaction between PTSD and MDD symptoms and contrasting effects across PTSD symptom clusters and types of childhood adversity suggests multiple neurobiological mechanisms are involved in shaping thalamic volume post-trauma.

neuroscience↗

bioRxiv2023

BrainLM: A foundation model for brain activity recordings

AO_SCPLOWBSTRACTC_SCPLOWWe introduce the Brain Language Model (BrainLM), a foundation model for brain activity dynamics trained on 6,700 hours of fMRI recordings. Utilizing self-supervised masked-prediction training, BrainLM demonstrates proficiency in both fine-tuning and zero-shot inference tasks. Fine-tuning allows for the accurate prediction of clinical variables like age, anxiety, and PTSD as well as forecasting of future brain states. Critically, the model generalizes well to entirely new external cohorts not seen during training. In zero-shot inference mode, BrainLM can identify intrinsic functional networks directly from raw fMRI data without any network-based supervision during training. The model also generates interpretable latent representations that reveal relationships between brain activity patterns and cognitive states. Overall, BrainLM offers a versatile and interpretable framework for elucidating the complex spatiotemporal dynamics of human brain activity. It serves as a powerful "lens" through which massive repositories of fMRI data can be analyzed in new ways, enabling more effective interpretation and utilization at scale. The work demonstrates the potential of foundation models to advance computational neuroscience research.

neuroscience↗

bioRxiv2022

Multimodal Imaging-Based Classification of PTSD Using Data-Driven Computational Approaches: A Multisite Big Data Study from the ENIGMA-PGC PTSD Consortium

BackgroundCurrent clinical assessments of Posttraumatic stress disorder (PTSD) rely solely on subjective symptoms and experiences reported by the patient, rather than objective biomarkers of the illness. Recent advances in data-driven computational approaches have been helpful in devising tools to objectively diagnose psychiatric disorders. Here we aimed to classify individuals with PTSD versus controls using heterogeneous brain datasets from the ENIGMA-PGC PTSD Working group. MethodsWe analyzed brain MRI data from 3,527 structural-MRI; 2,502 resting state-fMRI; and 1,953 diffusion-MRI. First, we identified the brain features that best distinguish individuals with PTSD from controls (TEHC and HC) using traditional machine learning methods. Second, we assessed the utility of the denoising variational autoencoder (DVAE) and evaluated its classification performance. Third, we assessed the generalizability and reproducibility of both models using leave-one-site-out cross-validation procedure for each modality. ResultsWe found lower performance in classifying PTSD vs. controls with data from over 20 sites (60% test AUC for s-MRI, 59% for rs-fMRI and 56% for d-MRI), as compared to other studies run on single-site data. The performance increased when classifying PTSD from HC without trauma history across all three modalities (75% AUC). The classification performance remained intact when applying the DVAE framework, which reduced the number of features. Finally, we found that the DVAE framework achieved better generalization to unseen datasets compared with the traditional machine learning frameworks, albeit performance was slightly above chance. ConclusionOur findings highlight the promise offered by machine learning methods for the diagnosis of patients with PTSD. The utility of brain biomarkers across three MRI modalities and the contribution of DVAE models for improving generalizability offers new insights into neural mechanisms involved in PTSD. Significance Classifying PTSD from trauma-unexposed healthy controls (HC) using three imaging modalities performed well ([~]75% AUC), but performance suffered markedly when classifying PTSD from trauma-exposed healthy controls (TEHC) using three imaging modalities ([~]60% AUC). Using deep learning for feature reduction (denoising variational auto-encoder; DVAE) dramatically reduced the number of features with no concomitant performance degradation. Utilizing denoising variational autoencoder (DVAE) models improves generalizability across heterogeneous multi-site data compared with the traditional machine learning frameworks

neuroscience↗

bioRxiv2018

Determining the Hierarchical Architecture of the Human Brain Using Subject-Level Clustering of Functional Networks

Optimal integration and segregation of neuronal connections are necessary for efficient large-scale network communication between distributed cortical regions while allowing for modular specialization. This dynamic in the cortex is enabled at the network mesoscale--the organization of nodes into communities. Previous in vivo efforts to map the mesoscale architecture in humans had several limitations. Here we characterize a consensus multiscale community organization of the functional cortical network. We derive this consensus from the clustering of subject-level networks. We show that this subject-derived consensus framework yields clusters that better map to the individual, compared to the widely-used group-derived consensus approach. We applied this analysis to magnetic resonance imaging data from 1003 healthy individuals part of the Human Connectome Project. The hierarchical atlas and code will be made publicly available for future investigators.

neuroscience↗