Mentor/s: Dr Khusbu Agarwal <khusbu.agarwal@nbrc.ac.in>
Project Synopsis: This project aims to build NeuroSim, an open-source “In-Silico Stimulation” engine. Unlike standard tools that simply analyze static functional connectivity, NeuroSim integrates Network Control Theory (NCT) with Effective Connectivity modeling to quantify the energy dynamics of brain state transitions. The pipeline will be validated by identifying “Stuck States” (Attractor Basins) in Alcohol Use Disorder (AUD), Alzheimer’s Disease and Epilepsy, effectively creating a mimic framework for virtual therapeutic stress-testing.
The Problem: Current neuroinformatics workflows largely focus on static functional connectivity (correlations). However, understanding complex pathologies—from neurodegeneration to addiction—requires quantifying the dynamic cost of brain state transitions. While wet lab approaches like intracranial stimulation (TMS/DBS) can probe these dynamics, they are invasive and limited to pre-surgical patients. There is a critical unmet need for a pre-emptive computational framework that can simulate these dynamics non-invasively to identify biomarkers before physical intervention is attempted.
The Objectives: The aim is to build a robust, modular Python pipeline to provide an effective solution to the problem, utilizing Network Control Theory and Manifold Learning. The project has three specific technical goals:
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Automate In-Silico Stimulation: Develop a workflow to calculate “Control Energy” landscapes. This allows researchers to simulate how hard it is for a brain to switch between cognitive states.
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Ensure Physical Validity: Implement Effective Connectivity estimation. This allows Network Control Theory to be validly applied to functional (fMRI) data, therefore bridging the gap between structural and functional analysis.
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Validate via Case Studies: Demonstrate the tool’s utility by isolating dynamical biomarkers in three distinct regimes with the first one as control. They are: the healthy baseline (HCP), the entropic collapse of neurodegeneration (ADNI), the rigid attractor states of addiction (AUD) and the facilitator nodes that drive seizure propagation in Epilepsy (OpenNeuro).
Methodology & Implementation Plan: The pipeline will be developed as a Python library, consisting of three core modules:
a) Collection, Cleaning & Harmonization: Standardization of BIDS-formatted data from different sources (HCP, ADNI, OpenNeuro). To account for multi-site scanner effects, this module will incorporate neuroCombat. This guarantees that true biological variance, not site noise and artifacts, which is reflected in downstream physical modeling.
b) Network Control Theory: This module constitutes the computational engine. It will first estimate Effective Connectivity (e.g., via spectral inversion or regression methods). This will construct directed adjacency matrices. It will then compute key control metrics:
• Average Controllability: To Quantify the brain’s general capacity to navigate state space.
• Modal Controllability: To identify nodes that drive difficult state transitions (potential “Facilitator Nodes” in epilepsy or addiction).
c) Trajectory Inference & Visualization A visualization engine using Manifold Learning (UMAP) and Pseudo-Time Inference. This will project high-dimensional control energy profiles onto a low-dimensional manifold, allowing clinicians to visualize a patient’s position on a disease trajectory.
Expected Outcomes:
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A fully documented NeuroSim Python library.
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Jupyter Notebook Tutorials hosted on GitHub, that demonstrate how to run an “In-Silico Stimulation” on patient data.
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Validation Report: A benchmark comparison of Control Energy biomarkers vs. standard Static Connectivity in distinguishing AUD patients from healthy controls.
Skills Required: Python (Advanced), Neuroimaging (Nilearn, Nibabel), Linear Algebra (SciPy), Graph Theory, Basic Machine Learning (Scikit-learn, UMAP).