Biology subjects

Senthilkumar, P.

Publications and source records attributed to Senthilkumar, P..

7 recordsLinked to original sources

bioRxiv2026

CALM-VLM: CALIBRATION AND SELECTIVE PREDICTION IN VISION-LANGUAGE MODELS FOR RELIABLE BRAIN MRI CLASSIFICATION

AO_SCPLOWBSTRACTC_SCPLOWRecent advances in vision-language models (VLMs) have demonstrated strong multimodal capabilities for medical image analysis. However, their confidence in diagnostic predictions is often unclear, limiting adoption in clinical settings. We introduce CALM-VLM (CALibration Mechanism for Vision-Language Models), which integrates confidence calibration and selective prediction into a generative 3D VLM. To create CALM-VLM, we fine-tuned the Med3DVLM architecture for Alzheimers disease (AD) and stroke classification as initial test cases. To improve reliability, we incorporated temperature scaling on the VLMs generative outputs. The calibrated model then selectively abstained from predictions when uncertain; this also improved its diagnostic accuracy. Experiments across multi-site MRI datasets, from 10 countries worldwide, show that CALM-VLM improved confidence relative to uncalibrated VLMs. Coverage-adjusted test receiver-operator characteristic curve-area under the curve (ROC-AUC) increased by 5% to 13% for both diagnostic tasks across independent test sets. Our calibrated VLM achieved a test ROC-AUC of 0.951 for AD classification and 0.905 for stroke classification. These findings highlight the importance of calibrated, uncertainty-aware VLMs for trustworthy neuroimaging AI.

neuroscience↗

bioRxiv2025

MAVeRiC-AD: Mixture-of-experts Agentic Vision-Language Ensemble for Robust MRI Classification of Alzheimer's Disease

Robust classification of Alzheimers disease (AD) from structural T1-weighted MRI (T1w) images remains an unmet clinical need, especially when data is acquired at multiple sites that differ in scanning protocols and population demographics. In this paper, we present MAVeRiC-AD (Mixture-of-experts Agent-guided Vision-Language Ensemble for Robust Imaging-based classification of Alzheimers Disease), an agentic framework that dynamically utilizes the optimal inferencing tool for radiological queries to provide relevant answers to the user. In our framework, we tested three specialized models encoded as callable tools: (1) CNN-AD, a 3D DenseNet trained on T1w intensities only; (2) MOE-VLM, a vision-language model that jointly models the T1w with subject-specific demographics (age, sex, site) via a mixture-of-experts (MoE) projection head; (3) Retrieval engine, a similarity-search module that contextualizes a patient against others from the site and reports % prevalence of AD. A light-weight agent analyzes the user request and then routes the input (image, or image + text) to the appropriate tool, aggregates responses and returns the tool response augmented with its confidence derived from conformal prediction. Experiments were conducted using T1w images from the ADNI (N=4,098) and OASIS-3 (N=600) datasets. Single-site training baselines achieved ROC-AUC = 0.79 (CNN) and 0.82 (VLM) on ADNI. When trained jointly on both sites, MOE-VLM surpassed both image-only and standard vision-language models with ROC-AUC = 0.90 on ADNI and 0.81 on OASIS. MAVeRiC-AD demonstrates that agentic orchestration of complementary expert deep models, coupled with explicit demographic conditioning for multi-site data can improve robustness and interpretability of AD image analysis pipelines and serves as a blueprint for scalable, trustworthy clinical AI assistants.

neuroscience↗

bioRxiv2025

Using Diffusion Transformers to Generate Synthetic Diffusion Scalar Maps for Data Augmentation

Generation of high-quality synthetic brain MRI data could be beneficial for advancing neuroimaging research, particularly when access to large-scale, labeled datasets is limited. In this work, we leverage a pretrained Diffusion Transformer (DiT) architecture to synthesize 3D mean diffusivity (MD) scalar maps from the Cam-CAN dataset. To adapt the DiT model--originally trained on 2D natural images--for 3D neuroimaging data, we implemented a preprocessing strategy that tiles 2D slices from 3D volumes into composite 2D images, enabling effective finetuning. The quality of the generated synthetic images was evaluated using Multi-Scale Structural Similarity (MS-SSIM) and Maximum Mean Discrepancy (MMD) metrics, demonstrating high fidelity and anatomical coherence. To assess the utility of synthetic data in downstream tasks, we conducted transfer learning experiments for dementia classification on the ADNI dataset. A sex classification model, trained on both real and synthetic Cam-CAN data, was repurposed for this task, showing that synthetic samples can enhance model performance. These results highlight the potential of diffusion-based generative models for augmenting neuroimaging datasets and supporting clinical applications.

neuroscience↗

bioRxiv2025

Evaluation of Deep Learning Algorithms to Predict Multiple Dementia-Related Neuropathologies from Brain MRI, Clinical and Genetic Data

Alzheimers disease and related dementias (ADRD) involve overlapping neurodegenerative and vascular pathologies--such as amyloid-{beta} (A{beta}), tau, cerebral amyloid angiopathy (CAA), TDP-43, and alpha-synuclein--that complicate diagnosis and treatment. While PET and CSF biomarkers are useful for detecting A{beta} and tau, they are invasive, expensive, and not widely available. In contrast, magnetic resonance imaging (MRI) is non-invasive and widely accessible, offering an opportunity for pathology prediction when combined with deep learning. Most prior studies have focused on single-pathology detection, but there remains a need for models that can jointly predict multiple co-occurring pathologies. In this work, we evaluate deep learning models that integrate structural MRI with demographic, clinical, and genetic data to classify six autopsy-confirmed neuropathologies: A{beta}, tau, CAA, TDP-43, hippocampal sclerosis, and dementia with Lewy bodies. We compare our hybrid deep learning model to AutoGluon, an automated machine learning framework. Our findings support the potential of multimodal AI to enable non-invasive, comprehensive neuropathological profiling in ADRD.

neuroscience↗

bioRxiv2025

Deep Learning to Predict Future Cognitive Decline: A Multimodal Approach Using Brain MRI and Clinical Data

Predicting the trajectory of clinical decline in aging individuals is a pressing challenge, especially for people with mild cognitive impairment, Alzheimers disease, Parkinsons disease, or vascular dementia. Accurate predictions can guide treatment decisions, identify risk factors, and optimize clinical trials. In this study, we compared two deep learning approaches for forecasting changes, over a 2-year interval, in the Clinical Dementia Rating scale sum of boxes score (sobCDR). This is a key metric in dementia research, and scores range from 0 (no impairment) to 18 (severe impairment). To predict decline, we trained a hybrid convolutional neural network that integrates 3D T1-weighted brain MRI scans with tabular clinical and demographic features (including age, sex, body mass index (BMI), and baseline sobCDR). We benchmarked its performance against AutoGluon, an automated multimodal machine learning framework that selects an appropriate neural network architecture. Our results demonstrate the importance of combining image and tabular data in predictive modeling for clinical applications. Deep learning algorithms can fuse image-based brain signatures and tabular clinical data, with potential for personalized prognostics in aging and dementia.

neuroscience↗

bioRxiv2024

Endogenous opioid dynamics in the dorsal striatum sculpt neural activity to control goal-directed action

Endogenous neuropeptides are uniquely poised to regulate neuronal activity and behavior across multiple timescales. Traditional studies ascribing neuropeptide contributions to behavior lack spatiotemporal precision. The endogenous opioid dynorphin is highly enriched in the dorsal striatum, known to be critical for regulating goal-directed behavior. However, the locus, the precise timescale, or functional role of endogenous dyn-KOR signaling on goal-directed behavior is unknown. Here, we report that local, time-locked dynorphin release from the dorsomedial striatum is necessary and sufficient for goal-directed behavior using a suite of high resolution modern approaches including in vivo two-photon imaging, neuropeptide biosensor detection, conditional deletions and time-locked optogenetic manipulations. We discovered that glutamatergic axon terminals from the basolateral amygdala evoke striatal dynorphin release, resulting in retrograde presynaptic GPCR inhibition during behavior. Collectively, our findings isolate a causal role for endogenous neuropeptide release at rapid timescales, and subsequent GPCR activity for tuning and promoting fundamental goal-directed behaviors.

neuroscience↗

bioRxiv2024

Opioid-driven disruption of the septal complex reveals a role for neurotensin- expressing neurons in withdrawal

Because opioid withdrawal is an intensely aversive experience, persons with opioid use disorder (OUD) often relapse to avoid it. The lateral septum (LS) is a forebrain structure that is important in aversion processing, and previous studies have linked the lateral septum (LS) to substance use disorders. It is unclear, however, which precise LS cell types might contribute to the maladaptive state of withdrawal. To address this, we used single-nucleus RNA-sequencing to interrogate cell type specific gene expression changes induced by chronic morphine and withdrawal. We discovered that morphine globally disrupted the transcriptional profile of LS cell types, but Neurotensin-expressing neurons (Nts; LS-Nts neurons) were selectively activated by naloxone. Using two-photon calcium imaging and ex vivo electrophysiology, we next demonstrate that LS-Nts neurons receive enhanced glutamatergic drive in morphine-dependent mice and remain hyperactivated during opioid withdrawal. Finally, we showed that activating and silencing LS-Nts neurons during opioid withdrawal regulates pain coping behaviors and sociability. Together, these results suggest that LS-Nts neurons are a key neural substrate involved in opioid withdrawal and establish the LS as a crucial regulator of adaptive behaviors, specifically pertaining to OUD.

neuroscience↗