TWave: high-order analysis of functional MRI

Scalable and Robust Tensor Decomposition of Spontaneous Stereotactic EEG Data

OBJECTIVE

Identification of networks from resting brain signals is an important step in understanding the dynamics of spontaneous brain activity. We approach this problem using a tensor-based model.

METHODS

We develop a rank-recursive scalable and robust sequential canonical polyadic decomposition (SRSCPD) framework to decompose a tensor into several rank-1 components. Robustness and scalability are achieved using a warm start for each rank based on the results from the previous rank.

RESULTS

In simulations we show that SRSCPD consistently outperforms the multi-start alternating least square (ALS) algorithm over a range of ranks and signal-to-noise ratios (SNRs), with lower computation cost. When applying SRSCPD to resting in-vivo stereotactic EEG (SEEG) data from two subjects with epilepsy, we found components corresponding to default mode and motor networks in both subjects. These components were also highly consistent within subject between two sessions recorded several hours apart. Similar components were not obtained using the conventional ALS algorithm.

CONCLUSION

Consistent brain networks and their dynamic behaviors were identified from resting SEEG data using SRSCPD.

SIGNIFICANCE

SRSCPD is scalable to large datasets and therefore a promising tool for identification of brain networks in long recordings from single subjects.

Examining stability of independent component analysis based on coefficient and component matrices for voxel-based morphometry of structural magnetic resonance imaging

Independent component analysis (ICA) on group-level voxel-based morphometry (VBM) produces the coefficient matrix and the component matrix. The former contains variability among multiple subjects for further statistical analysis, and the latter reveals spatial maps common for all subjects. ICA algorithms converge to local optimization points in practice and the mostly applied stability investigation approach examines the stability of the extracted components. We found that the practically stable components do not guarantee to produce the practically stable coefficients of ICA decomposition for the further statistical analysis. Consequently, we proposed a novel approach including two steps: (1), the stability index for the coefficient matrix and the stability index for the component matrix were examined, respectively; (2) the two indices were multiplied to analyze the stability of ICA decomposition. The proposed approach was used to study the sMRI data of Type II diabetes mellitus group and the healthy control group (HC). Group differences in VBM were found in the superior temporal gyrus. Besides, it was revealed that the VBMs of the region of the HC group were significantly correlated with Montreal Cognitive Assessment (MoCA) describing the level of cognitive disorder. In contrast to the widely applied approach to investigating the stability of the extracted components for ICA decomposition, we proposed to examine the stability of ICA decomposition by fusion the stability of both coefficient matrix and the component matrix. Therefore, the proposed approach can examine the stability of ICA decomposition sufficiently.

Multidimensional encoding of brain connectomes

The ability to map brain networks in living individuals is fundamental in efforts to chart the relation between human behavior, health and disease. Advances in network neuroscience may benefit from developing new frameworks for mapping brain connectomes. We present a framework to encode structural brain connectomes and diffusion-weighted magnetic resonance (dMRI) data using multidimensional arrays. The framework integrates the relation between connectome nodes, edges, white matter fascicles and diffusion data. We demonstrate the utility of the framework for in vivo white matter mapping and anatomical computing by evaluating 1,490 connectomes, thirteen tractography methods, and three data sets. The framework dramatically reduces storage requirements for connectome evaluation methods, with up to 40x compression factors. Evaluation of multiple, diverse datasets demonstrates the importance of spatial resolution in dMRI. We measured large increases in connectome resolution as function of data spatial resolution (up to 52%). Moreover, we demonstrate that the framework allows performing anatomical manipulations on white matter tracts for statistical inference and to study the white matter geometrical organization. Finally, we provide open-source software implementing the method and data to reproduce the results.

Ranking brain areas encoding the perceived level of pain from fMRI data

Pain perception is thought to emerge from the integrated activity of a distributed brain system, but the relative contribution of the different network nodes is still incompletely understood. In the present functional magnetic resonance imaging (fMRI) study, we aimed to identify the more relevant brain regions to explain the time profile of the perceived pain intensity in healthy volunteers, during noxious chemical stimulation (ascorbic acid injection) of the left hand. To this end, we performed multi-way partial least squares regression of fMRI data from twenty-two a-priori defined brain regions of interest (ROI) in each hemisphere, to build a model that could efficiently reproduce the psychophysical pain profiles in the same individuals; moreover, we applied a novel three-way extension of the variable importance in projection (VIP) method to summarize each ROI contribution to the model. Brain regions showing the highest VIP scores included the bilateral mid-cingulate, anterior and posterior insular, and parietal operculum cortices, the contralateral paracentral lobule, bilateral putamen and ipsilateral medial thalamus. Most of these regions, with the exception of medial thalamus, were also identified by a statistical analysis on mean ROI beta values estimated using the time course of the psychophysical rating as a regressor at the voxel level. Our results provide the first rank-ordering of brain regions involved in coding the perceived level of pain. These findings in a model of acute prolonged pain confirm and extend previous data, suggesting that a bilateral array of cortical areas and subcortical structures is involved in pain perception.