Manish Saggar

Efficient Deep Learning Models for Predicting Individualized Task Activation from Resting-State Functional Connectivity

This study evaluates deep learning models for predicting task activation from resting-state fMRI, finding that architectural changes improve efficiency without sacrificing accuracy.

Global topology of brain-wide co-fluctuations links task states, personality, and behavioral symptom dimensions

The paper uses topological analysis of fMRI dynamics to show that pairwise co-fluctuations better capture task states and link to personality and symptom dimensions than other interaction representations.

Topological data analysis reveals rigid brain-state dynamics during self-viewing in trait rumination

This paper uses topological data analysis to show that higher trait rumination is linked to reduced brain-state variability during self-relevant tasks.

Refining RDoC Using Individual-Level Task fMRI Factor Models RevealsReproducible and Clinically Relevant Brain-Wide Motifs

This paper validates data-driven factor models over RDoC-based models using fMRI and behavioral data, showing better reproducibility and clinical relevance across neural and behavioral domains.

The Topological Architecture of Brain Identity

This paper introduces a topological fingerprinting method using homological scaffolds from resting-state fMRI data, achieving near-perfect individual identification accuracy.

Excitation-Inhibition Balance and Fronto-Limbic Connectivity Drive TMS Treatment Outcomes in Refractory Depression

This study identifies neurophysiological signatures predicting response to TMS in treatment-resistant depression using combined EEG and computational modeling.

Revealing Changes in Linear and Nonlinear Functional Connectivity After Psilocybin and Escitalopram Treatment in Patients with Depression

This study compares changes in linear and nonlinear functional connectivity in MDD patients after psilocybin or escitalopram treatment, finding distinct patterns between responders and non-responders.

A Data-Driven Latent Variable Approach to Validating the Research Domain Criteria Framework

This study validates the RDoC framework using a latent variable approach, showing a bifactor model better fits fMRI data than the current framework and highlights underrepresentation of the arousal and regulatory systems domain.

The control costs of human brain dynamics

This paper quantifies the control costs of human brain dynamics using network control theory and finds that brain state transitions are more efficient within hierarchical groups.

Concurrent single-pulse (sp) TMS/fMRI to reveal the causal connectome in healthy and patient populations

This study uses concurrent sp-TMS/fMRI to map brain circuits in healthy and depressed individuals, investigating how spTMS modulates neural connectivity.

Densely sampled stimulus-response map of human cortex with single pulse TMS-EEG and its relation to whole brain neuroimaging measures

The paper maps stimulus-response patterns in the human cortex using TMS-EEG and relates them to whole-brain neuroimaging measures from a single densely-sampled individual.

Deconstructing the Mapper algorithm to extract richer topological and temporal features from functional neuroimaging data

The paper examines Mapper parameter choices in functional neuroimaging data to improve the extraction of topological and temporal features using synthetic and real fMRI data.

Cross-attractor modeling of resting-state functional connectivity in psychiatric disorders

This study uses cross-attractor brain network modeling to compare resting-state functional connectivity across schizophrenia, bipolar disorder, ADHD, and healthy controls, finding global and local differences in network dynamics.

Development of Top-Down Cortical Propagations in Youth

This study tracks cortical activity propagations in youth, finding increased top-down propagations with cognitive control demands and development.

Temporal Mapper: transition networks in simulated and real neural dynamics

The paper introduces Temporal Mapper, a method to reconstruct attractor transition networks from time-series data, validated on simulated and fMRI data, showing associations between network structure and behavioral performance.

Precision dynamical mapping using topological data analysis reveals a unique hub-like transition state at rest

The paper uses topological data analysis to reveal a hub-like transition state in resting brain activity, showing consistent dynamics within individuals and potential for precision medicine.

Cross-attractor repertoire provides new perspective on structure-function relationship in the brain

This paper introduces a novel dynamical systems framework to analyze cross-attractor coordination in the brain and its relation to functional connectivity and energy costs.

Spontaneous and deliberate modes of creativity: Multitask eigen-connectivity analysis captures latent cognitive modes during creative thinking

The paper uses multitask eigen-connectivity analysis to identify latent cognitive modes during creative thinking, showing that both deliberate and spontaneous thinking contribute to creativity.

Dynamic autonomic nervous system patterns differentiate human emotions and manifest in resting physiology

Brain mitochondrial diversity and network organization predict anxiety-like behavior in mice

The paper examines mitochondrial respiratory chain capacity and its association with stress-related behaviors in mice, identifying brain networks linked to anxiety-like behavior.

Simplicial and Topological Descriptions of Human Brain Dynamics

This study compares topological and traditional metrics for segmenting brain states from fMRI data, finding topological methods more accurate and generalizable.

Finding the neural correlates of collaboration using a three-person fMRI hyperscanning paradigm

This study uses a 3-person fMRI hyperscanning paradigm to identify neural correlates of collaboration, finding increased activation and synchrony in theory of mind-related brain regions during a triadic task.