Anees Abrol

This paper explores privacy-preserving visualization techniques for brain functional connectivity using differential privacy to protect participant data while maintaining visual utility.

This paper introduces a multimodal vision transformer (MultiViT) to classify schizophrenia using structural and functional MRI data, achieving higher accuracy than unimodal and existing multimodal methods.

This study compares AD and SZ using rs-FNC with confounder control and machine learning, finding shared and distinct functional abnormalities and classification accuracies of 85% and 68%.

This paper introduces a generative deep learning framework to predict functional network connectivity biomarkers from brain structure using a modified vision transformer GAN.

The paper introduces a multimodal active subspace learning framework to identify brain subspaces associated with cognitive and biological traits, validated on Alzheimer's disease data.

The paper introduces a multimodal framework to identify brain subspaces linked to biological age and Schizophrenia, showing improved performance over PCA-based methods.

The paper uses a Bayesian optimization framework to analyze brain functional subdomains in schizophrenia, showing that more complex models better capture discriminatory information in specific subdomains.

This study uses a Siamese CNN framework to predict individual identity from resting-state fMRI data, achieving 88% accuracy in subject discrimination using spatial network pairs.

The paper introduces a toolbox to automatically label and order brain activity maps with high accuracy using a pretrained model and integration of existing techniques.

This study compares deep learning and standard machine learning on brain imaging data, showing deep learning's superior predictive power and robustness in age and gender classification.

This paper proposes a classification-based method to determine the optimal number of states in dynamic functional connectivity analysis, showing improved classification accuracy compared to traditional methods.

The paper detects and corrects label noise in mental health diagnoses using MRI data from mood and psychosis disorders through a multi-label data cleansing approach.

This study identifies 45 structural-functional component maps across 9 functional domains using independent component analysis in two cohorts, showing varying S-F coherence in different brain regions.

This study analyzes age-related changes in brain structure and function across 5967 individuals from 13 to 72 years old, revealing nonlinear patterns in gray matter volume, structural network correlation, and functional network connectivity.

This paper applies deep residual networks to predict Alzheimer’s disease progression from mild cognitive impairment using neuroimaging data, achieving better performance than existing methods.

The paper critiques null models for resting-state fMRI dynamics, highlighting issues with their formulation and suggesting alternative metrics for capturing functionally relevant brain dynamics.

This study evaluates the replicability of dynamic functional network connectivity patterns across 7500 fMRI datasets, finding robust and reproducible connectivity states.