BrainIAK: The Brain Imaging Analysis Kit.

Manoj Kumar,J Benjamin Hutchinson,Ming Bo Cai,Hejia Zhang,Anne C Mennen,Michael Shvartsman,James W Antony,Paula P Brooks,Kai Li,Samuel A Nastase,Uri Hasson,Hugo Richard,Nicolas W Schuck,Theodore L Willke,Kenneth A Norman,Peter J Ramadge,Po-Hsuan Cameron Chen,David Huberdeau,Cameron T Ellis,Jamal A Williams,Christopher Baldassano,Grant Wallace,Michael J Anderson,Yida Wang,Y Peeta Li,Javier S Turek,Daniel Suo,Gregory Henselman-Petrusek,Jeremy R Manning,Jonathan D Cohen,Narayanan Sundaram,Anna C Schapiro,Mihai Capotă ,Xiaoyan Zhu ,D T Turner ,Vy Vo ,Nicholas B Turk‐Browne ,Qingming Lu

doi:10.52294/31bb5b68-2184-411b-8c00-a1dacb61e1da

Abstract

Functional magnetic resonance imaging (fMRI) offers a rich source of data for studying the neural basis of cognition. Here, we describe the Brain Imaging Analysis Kit (BrainIAK), an open-source, free Python package that provides computationally optimized solutions to key problems in advanced fMRI analysis. A variety of techniques are presently included in BrainIAK: intersubject correlation (ISC) and intersubject functional connectivity (ISFC), functional alignment via the shared response model (SRM), full correlation matrix analysis (FCMA), a Bayesian version of representational similarity analysis (BRSA), event segmentation using hidden Markov models, topographic factor analysis (TFA), inverted encoding models (IEMs), an fMRI data simulator that uses noise characteristics from real data (fmrisim), and some emerging methods. These techniques have been optimized to leverage the efficiencies of high-performance compute (HPC) clusters, and the same code can be se amlessly transferred from a laptop to a cluster. For each of the aforementioned techniques, we describe the data analysis problem that the technique is meant to solve and how it solves that problem; we also include an example Jupyter notebook for each technique and an annotated bibliography of papers that have used and/or described that technique. In addition to the sections describing various analysis techniques in BrainIAK, we have included sections describing the future applications of BrainIAK to real-time fMRI, tutorials that we have developed and shared online to facilitate learning the techniques in BrainIAK, computational innovations in BrainIAK, and how to contribute to BrainIAK. We hope that this manuscript helps readers to understand how BrainIAK might be useful in their research.

Highlights

Many modern fMRI studies of the human brain use data from multiple subjects
The successful aggregation of fMRI brain imaging data across subjects requires resolving the major problem that both anatomical structure and functional topography vary across subjects [1, 2, 3, 4]
We have shown that by tuning this dimensionality, the data-driven aggregation achieved by shared response model (SRM) demonstrates higher sensitivity in distinguishing multivariate functional responses across cognitive states

Summary

Introduction

Many modern fMRI studies of the human brain use data from multiple subjects. The use of multiple subjects is critical for assessing the generality and validity of the findings across subjects. It is increasingly important since from one subject one can gather at most a few thousand noisy instances of functional response patterns. To increase the power of multivariate statistical analysis, one needs to aggregate response data across multiple subjects. The successful aggregation of fMRI brain imaging data across subjects requires resolving the major problem that both anatomical structure and functional topography vary across subjects [1, 2, 3, 4]. A more radical approach learns a latent multivariate feature that models the shared component of each subject’s response [10, 11, 12]

Objectives

Methods

Findings

Conclusion