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The Nathan S. Kline Institute for Psychiatric Research

Center for Biomedical Imaging and Neuromodulation (C-BIN)

Computational Neuroimaging Laboratories

 
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Research Scientist
Research Scientist
Research Scientist

The Computational Neuroimaging Lab’s research agenda involves the development of novel computational analysis and experimental techniques for determining how brain function and structure are impacted by mental illness and development. Ongoing projects involve developing real-time fMRI experiments to evaluate the interaction between brain networks, applying machine learning and signal processing methods to map inter-individual variation in the human connectome, and optimizing MRI acquisition for pediatric and psychiatric populations. Additionally, the CNL is a strong supporter of open science as exemplified by developing the Configurable Pipeline for the Analysis of Connectomes open source software package, openly sharing data through the International Neuroimaging Datasharing Initiative, and spearheading the Preprocessed Connectomes Project.

Current Projects

Real-time fMRI Neurofeedback Based Stratification of Default Network Regulation

This project capitalizes on recent innovations in real-time fMRI (RT-fMRI) based neurofeedback to provide a dimensional profile of DN regulation that can be linked to cognitive and psychiatric phenotyping profiles, as well as underlying brain architecture. This project is funded by NIMH BRAINS RO1: 1R01MH101555-01.

 

Configurable Pipeline for the Analysis of Connectomes

The Configurable Pipeline for the Analysis of Connectomes (C-PAC) is an open-source software pipeline for automated preprocessing and analysis of resting-state fMRI data. C-PAC builds upon a robust set of existing software packages including AFNI, FSL, and FreeSurfer, and makes it easy for both novice users and experts to explore their data using a wide array of analytic tools.

 

Preprocessed Connectomes Project

The goal of the Preprocessed Connectomes Project (PCP) is to systematically preprocess datasets available in neuroimaging datasharing repositories and to share the results. The hope is to engage a broad audience of data scientists and neuroscientists in neuroimaging research by reducing the barrier to entry.

 

Characterizing inter-individual variation in the functional connectome

The Computational Neuroimaging Laboratory has several previous and current research projects aimed at developing methods for mapping inter-individual variation in the functional connectome: