Decomposition of individual-specific and individual-shared components from resting-state functional connectivity using a multi-task machine learning method

The Commonality and Individuality of Human Brains When Performing Tasks

It is imperative to study individual brain functioning toward understanding the neural bases responsible for individual behavioral and clinical traits. The complex and dynamic brain activity varies from area to area and from time to time across the entire brain, and BOLD-fMRI measures this spatiotemporal activity at large-scale systems level. We present a novel method to investigate task-evoked whole brain activity that varies not only from person to person but also from task trial to trial within each task type, offering a means of characterizing the individuality of human brains when performing tasks. For each task trial, the temporal correlation of task-evoked ideal time signal with the time signal of every point in the brain yields a full spatial map that characterizes the whole brain's functional co-activity (FC) relative to the task-evoked ideal response. For any two task trials, regardless of whether they are the same task or not, the spatial correlation of their corresponding two FC maps over the entire brain quantifies the similarity between these two maps, offering a means of investigating the variation in the whole brain activity trial to trial. The results demonstrated a substantially varied whole brain activity from trial to trial for each task category. The degree of this variation was task type-dependent and varied from subject to subject, showing a remarkable individuality of human brains when performing tasks. It demonstrates the potential of using the presented method to investigate the relationship of the whole brain activity with individual behavioral and clinical traits.

A Holistic Analysis of Individual Brain Activity Revealed the Relationship of Brain Areal Activity with the Entire Brain's Activity

The relationship between brain areal activity and the entire brain's activity is unknown, and understanding this relationship is imperative for understanding the neural mechanisms of human brain function at systems level. The complex activity of human brains varies from area to area and from time to time across the whole brain. BOLD-fMRI measures this spatiotemporal activity at a large-scale systems level. The BOLD time signal of an area reflects a collective neuronal activity of over one million neurons under that area, and the temporal correlation of this time signal with that of every point in the brain yields a full spatial map that characterizes the entire brain's functional co-activity (FC) relative to that area's activity. Here we show a quantitative relationship between brain areal activity and the activity of the entire brain. The temporal correlation coefficient r of the signal time courses of two areas quantifies the degree of co-activity between the two areas, and the spatial correlation coefficient R of their corresponding two FC maps quantifies the co-activity between these two maps. We found that a modified sigmoid function quantified this R with r, i.e., Rr=1+ra-1-ra1+ra+1-ra, revealing a relationship between the activity of brain areas and that of the entire brain. The parameter a in this equation was found to be associated with the mean degree of the temporal co-activity among all brain areas, and its value was brain functional state dependent too. Our study demonstrated a novel approach for analyzing fMRI data to holistically characterize the entire brain's activity quantitatively for any brain functional state in individual humans.

Person-specific and precision neuroimaging: Current methods and future directions

Most neuroimaging studies of brain function analyze data in normalized space to identify regions of common activation across participants. These studies treat interindividual differences in brain organization as noise, but this approach can obscure important information about the brain's functional architecture. Recently, a number of studies have adopted a person-specific approach that aims to characterize these individual differences and explore their reliability and implications for behavior. A subset of these studies has taken a precision imaging approach that collects multiple hours of data from each participant to map brain function on a finer scale. In this review, we provide a broad overview of how person-specific and precision imaging techniques have used resting-state measures to examine individual differences in the brain's organization and their impact on behavior, followed by how task-based activity continues to add detail to these discoveries. We argue that person-specific and precision approaches demonstrate substantial promise in uncovering new details of the brain's functional organization and its relationship to behavior in many areas of cognitive neuroscience. We also discuss some current limitations in this new field and some new directions it may take.

Structural insight into the individual variability architecture of the functional brain connectome

Human cognition and behaviors depend upon the brain's functional connectomes, which vary remarkably across individuals. However, whether and how the functional connectome individual variability architecture is structurally constrained remains largely unknown. Using tractography- and morphometry-based network models, we observed the spatial convergence of structural and functional connectome individual variability, with higher variability in heteromodal association regions and lower variability in primary regions. We demonstrated that functional variability is significantly predicted by a unifying structural variability pattern and that this prediction follows a primary-to-heteromodal hierarchical axis, with higher accuracy in primary regions and lower accuracy in heteromodal regions. We further decomposed group-level connectome variability patterns into individual unique contributions and uncovered the structural-functional correspondence that is associated with individual cognitive traits. These results advance our understanding of the structural basis of individual functional variability and suggest the importance of integrating multimodal connectome signatures for individual differences in cognition and behaviors.

Multivariate Gaussian Copula Mutual Information to Estimate Functional Connectivity with Less Random Architecture

Recognition of a brain region’s interaction is an important field in neuroscience. Most studies use the Pearson correlation to find the interaction between the regions. According to the experimental evidence, there is a nonlinear dependence between the activities of different brain regions that is ignored by Pearson correlation as a linear measure. Typically, the average activity of each region is used as input because it is a univariate measure. This dimensional reduction, i.e., averaging, leads to a loss of spatial information across voxels within the region. In this study, we propose using an information-theoretic measure, multivariate mutual information (mvMI), as a nonlinear dependence to find the interaction between regions. This measure, which has been recently proposed, simplifies the mutual information calculation complexity using the Gaussian copula. Using simulated data, we show that the using this measure overcomes the mentioned limitations. Additionally using the real resting-state fMRI data, we compare the level of significance and randomness of graphs constructed using different methods. Our results indicate that the proposed method estimates the functional connectivity more significantly and leads to a smaller number of random connections than the common measure, Pearson correlation. Moreover, we find that the similarity of the estimated functional networks of the individuals is higher when the proposed method is used.

Altered voxel-level whole-brain functional connectivity in multiple system atrophy patients with depression symptoms

BACKGROUND

It is yet unknown if the whole-brain resting-state network is altered in multiple system atrophy with symptoms of depression. This study aimed to investigate if and how depression symptoms in multiple system atrophy are associated with resting-state network dysfunction.

METHODS

We assessed the resting-state functional network matric using Degree centrality (DC) coupling with a second ROI-wise functional connectivity (FC) algorithm in a multimodal imaging case-control study that enrolled 32 multiple system atrophy patients with depression symptoms (MSA-D), 30 multiple system atrophy patients without depression symptoms (MSA-ND), and 34 healthy controls (HC).

RESULTS

Compared to HC, MSA-D showed more extensive DC hub dysfunction in the left precentral and right middle frontal cortex than MSA-ND. A direct comparison between MSA-D and MSA-ND detected increased DC in the right anterior cingulum cortex, but decreased DC in the left cerebellum lobule IV and lobule V, left middle pole temporal cortex, and right superior frontal cortex. Only right anterior cingulum cortex mean DC values showed a positive correlation with depression severity, and used ACC as seed, a second ROI-wise functional connectivity further revealed MSA-D patients showed decreased connectivity between the ACC and right thalamus and right middle temporal gyrus (MTG).

CONCLUSIONS

These findings revealed that dysfunction of rACC, right middle temporal lobe and right thalamus involved in depressed MSA. Our study might help to the understanding of the neuropathological mechanism of depression in MSA.