Function in the human connectome: Task-fMRI and individual differences in behavior

Abstract

The primary goal of the Human Connectome Project (HCP) is to delineate the typical patterns of structural and functional connectivity in the healthy adult human brain. However, we know that there are important individual differences in such patterns of connectivity, with evidence that this variability is associated with alterations in important cognitive and behavioral variables that affect real world function. The HCP data will be a critical stepping-off point for future studies that will examine how variation in human structural and functional connectivity play a role in adult and pediatric neurological and psychiatric disorders that account for a huge amount of public health resources. Thus, the HCP is collecting behavioral measures of a range of motor, sensory, cognitive and emotional processes that will delineate a core set of functions relevant to understanding the relationship between brain connectivity and human behavior. In addition, the HCP is using task-fMRI (tfMRI) to help delineate the relationships between individual differences in the neurobiological substrates of mental processing and both functional and structural connectivity, as well as to help characterize and validate the connectivity analyses to be conducted on the structural and functional connectivity data. This paper describes the logic and rationale behind the development of the behavioral, individual difference, and tfMRI batteries and provides preliminary data on the patterns of activation associated with each of the fMRI tasks, at both a group and individual level.

Mentalizing in schizophrenia: A multivariate functional MRI study

“…As the Triangles Task is part of the social cognition battery used in the Human Connectome Project (Barch, et al, 2013;Hillebrandt, Friston, & Blakemore, 2014) it is likely to receive considerable attention. Therefore understanding the deficits of clinical groups such as those with schizophrenia and the underlying neurophysiological differences is of paramount importance.…”

Sparse and smooth canonical correlation analysis through rank-1 matrix approximation

“…The single subject (id 100307) rsfMRI dataset used in this section was obtained from the Human Connectome Project Q1 release [33]. The acquisition parameters of rsfMRI data are 90 × 104 matrix, 220 mm FOV, 72 slices, TR = 0.72 s, TE = 33.1 ms, flip angle = 52 • , BW = 2290Hz/Px, in-plane FOV = 208 × 180 mm with 2.0 mm isotropic voxels.…”

“…The obtained data was already preprocessed with the preprocessing pipeline consisting of motion correction, temporal pre-whitening, slice time correction and global drift removal, and the scans were spatially normalized to a standard MNI152 template and were resampled to 2 mm × 2 mm × 2 mm voxels. The reader is referred to [33,34] for more details regarding data acquisition and preprocessing.…”

Distributed rank-1 dictionary learning: Towards fast and scalable solutions for fMRI big data analytics

“…The acquisition parameters of the fMRI are as follows: 90×104 matrix, 220mm FOV, 72 slices, TR=0.72s, TE=33.1ms, flip angle=52°, BW=2290 Hz/Px, 2.0mm isotropic voxels. Data preprocessing followed the protocols detailed in [13], including motion correction, spatial smoothing, temporal pre-whitening, slice time correction, and global drift removal. The tfMRI data was then registered to the standard MNI152 2mm space using FSL FLIRT to enable group-wise analysis.…”