Measuring the functional connectome "on-the-fly": towards a new control signal for fMRI-based brain-computer interfaces

by   Ricardo Pio Monti, et al.

There has been an explosion of interest in functional Magnetic Resonance Imaging (MRI) during the past two decades. Naturally, this has been accompanied by many major advances in the understanding of the human connectome. These advances have served to pose novel challenges as well as open new avenues for research. One of the most promising and exciting of such avenues is the study of functional MRI in real-time. Such studies have recently gained momentum and have been applied in a wide variety of settings; ranging from training of healthy subjects to self-regulate neuronal activity to being suggested as potential treatments for clinical populations. To date, the vast majority of these studies have focused on a single region at a time. This is due in part to the many challenges faced when estimating dynamic functional connectivity networks in real-time. In this work we propose a novel methodology with which to accurately track changes in functional connectivity networks in real-time. We adapt the recently proposed SINGLE algorithm for estimating sparse and temporally homo- geneous dynamic networks to be applicable in real-time. The proposed method is applied to motor task data from the Human Connectome Project as well as to real-time data ob- tained while exploring a virtual environment. We show that the algorithm is able to estimate significant task-related changes in network structure quickly enough to be useful in future brain-computer interface applications.


Rank-adaptive covariance changepoint detection for estimating dynamic functional connectivity from fMRI data

The analysis of functional connectivity (FC) networks in resting-state f...

Dictionary Learning and Sparse Coding-based Denoising for High-Resolution Task Functional Connectivity MRI Analysis

We propose a novel denoising framework for task functional Magnetic Reso...

Real-time fMRI-based Brain Computer Interface: A Review

In recent years, the rapid development of neuroimaging technology has be...

BICNet: A Bayesian Approach for Estimating Task Effects on Intrinsic Connectivity Networks in fMRI Data

Intrinsic connectivity networks (ICNs) are specific dynamic functional b...

Nonparametric Modeling of Dynamic Functional Connectivity in fMRI Data

Dynamic functional connectivity (FC) has in recent years become a topic ...

Identification of temporal transition of functional states using recurrent neural networks from functional MRI

Dynamic functional connectivity analysis provides valuable information f...

DELMAR: Deep Linear Matrix Approximately Reconstruction to Extract Hierarchical Functional Connectivity in the Human Brain

The Matrix Decomposition techniques have been a vital computational appr...

Please sign up or login with your details

Forgot password? Click here to reset