Functional Subdivisions of the Cerebellum in Naturalistic Paradigm Functional Magnetic Resonance Imaging

Compelling evidence has suggested that the human cerebellum is engaged in a wide range of cognitive tasks besides traditional opinions of motor control, and it is organized into a set of distinct functional subregions. The existing model-driven cerebellum parcellations through resting-state functional MRI (rsfMRI) and task-fMRI are relatively coarse, introducing challenges in resolving the functions of the cerebellum especially when the brain is exposed to naturalistic environments. The current study took the advantages of the naturalistic paradigm (i.e., movie viewing) fMRI (nfMRI) to derive fine parcellations via a data-driven dual-regression-like sparse representation framework. The parcellations were quantitatively evaluated by functional homogeneity, and global and local boundary confidence. In addition, the differences of cerebellum–cerebrum functional connectivities between rsfMRI and nfMRI for some exemplar parcellations were compared to provide qualitatively functional validations. Our experimental results demonstrated that the proposed study successfully identified distinct subregions of the cerebellum. This fine parcellation may serve as a complementary solution to existing cerebellum parcellations, providing an alternative template for exploring neural activities of the cerebellum in naturalistic environments.

Computing personalized brain functional networks from fMRI using self-supervised deep learning

ABSTRACTA novel self-supervised deep learning (DL) method is developed for computing bias-free, personalized brain functional networks (FNs) that provide unique opportunities to better understand brain function, behavior, and disease. Specifically, convolutional neural networks with an encoder-decoder architecture are employed to compute personalized FNs from resting-state fMRI data without utilizing any external supervision by optimizing functional homogeneity of personalized FNs in a self-supervised setting. We demonstrate that a DL model trained on fMRI scans from the Human Connectome Project can identify canonical FNs and generalizes well across four different datasets. We further demonstrate that the identified personalized FNs are informative for predicting individual differences in behavior, brain development, and schizophrenia status. Taken together, self-supervised DL allows for rapid, generalizable computation of personalized FNs.

Conditioning team cognition: A meta-analysis

Abundant research supports a cognitive foundation to teamwork. Team cognition describes the mental states that enable team members to anticipate and to coordinate. Having been examined in hundreds of studies conducted in board rooms, cockpits, nuclear power plants, and locker rooms, to name a few, we turn to the question of moderators: Under which conditions is team cognition more and less strongly related to team performance? Random effects meta-analytic moderator analysis of 107 independent studies ( N = 7,778) reveals meaningful variation in effect sizes conditioned on team composition and boundary factors. The overall effect of team cognition on performance is ρ = .35, though examining this effect by these moderators finds the effect can meaningfully vary between ρ = .22 and ρ = .42. This meta-analysis advances team effectiveness theory by moving past the question of “what is important?” to explore the question of “when and why is it important?” Results indicate team cognition is most strongly related to performance for teams with social category heterogeneity ( ρ = .42), high external interdependence ( ρ = .41), as well as low authority differentiation ( ρ = .35), temporal dispersion ( ρ = .36), and geographic dispersion ( ρ = .35). Functional homogeneity and temporal stability (compositional factors) were not meaningful moderators of this relationship. The key takeaway of these findings is that team cognition matters most for team performance when—either by virtue of composition, leadership, structure, or technology—there are few substitute enabling conditions to otherwise promote performance.

Regularized-Ncut: Robust and homogeneous functional parcellation of neonate and adult brain networks

Brain network parcellation based on resting-state functional MRI (rs-fMRI) is affected by noise, resulting in spurious small patches and decreased functional homogeneity within each network. Robust and homogeneous parcellation of neonate brain is more difficult with neonate rs-fMRI associated with higher level of noise and no functional atlas as spatial constraints. To meet these challenges, we developed a novel data-driven Regularized Normalized-cut (RNcut) method. RNcut is formulated by adding two regularization terms, a smoothing term using Markov random fields and a small-patch removal term, to conventional normalized-cut (Ncut) method. The RNcut and competing methods were tested with simulated datasets with known ground truth and then applied to both adult and neonate rs-fMRI datasets. Based on the parcellated networks generated by RNcut, intra-network connectivity was quantified. The test results from simulated datasets demonstrated that the RNcut method is more robust (p<0.01) to noise and can delineate parcellated functional networks with significantly better (p<0.01) spatial contiguity and significantly higher (p<0.01) functional homogeneity than competing methods. Application of RNcut to neonate and adult rs-fMRI dataset revealed distinctive functional brain organization of neonate brains from that of adult brains. Collectively, we developed a novel data-driven RNcut method by integrating conventional Ncut with two regularization terms, generating robust and homogeneous functional parcellation without imposing spatial constraints. A broad range of brain network applications and analyses, especially exploratory investigations of parcellating neonate and infant brain with noisy dataset, can potentially benefit from this RNcut method.

High efficiency Mg2(Si,Sn)-based thermoelectric materials: scale-up synthesis, functional homogeneity, and thermal stability

Considering the need for large quantities of high efficiency thermoelectric materials for industrial applications, a scalable synthesis method for high performance magnesium silicide based materials is proposed.

Regions of Interest as nodes of dynamic functional brain networks

The properties of functional brain networks strongly depend on how their nodes are chosen. Commonly, nodes are defined by Regions of Interest (ROIs), predetermined groupings of fMRI measurement voxels. Earlier, we demonstrated that the functional homogeneity of ROIs, captured by their spatial consistency, varies widely across ROIs in commonly used brain atlases. Here, we ask how ROIs behave as nodes of dynamic brain networks. To this end, we use two measures: spatiotemporal consistency measures changes in spatial consistency across time and network turnover quantifies the changes in the local network structure around an ROI. We find that spatial consistency varies non-uniformly in space and time, which is reflected in the variation of spatiotemporal consistency across ROIs. Furthermore, we see time-dependent changes in the network neighborhoods of the ROIs, reflected in high network turnover. Network turnover is nonuniformly distributed across ROIs: ROIs with high spatiotemporal consistency have low network turnover. Finally, we reveal that there is rich voxel-level correlation structure inside ROIs. Because the internal structure and the connectivity of ROIs vary in time, the common approach of using static node definitions may be surprisingly inaccurate. Therefore, network neuroscience would greatly benefit from node definition strategies tailored for dynamical networks.

Revealing the Neural Mechanisms Underlying the Beneficial Effects of Tai Chi: A Neuroimaging Perspective

Tai Chi Chuan (TCC), a traditional Chinese martial art, is well-documented to result in beneficial consequences in physical and mental health. TCC is regarded as a mind-body exercise that is comprised of physical exercise and meditation. Favorable effects of TCC on body balance, gait, bone mineral density, metabolic parameters, anxiety, depression, cognitive function, and sleep have been previously reported. However, the underlying mechanisms explaining the effects of TCC remain largely unclear. Recently, advances in neuroimaging technology have offered new investigative opportunities to reveal the effects of TCC on anatomical morphologies and neurological activities in different regions of the brain. These neuroimaging findings have provided new clues for revealing the mechanisms behind the observed effects of TCC. In this review paper, we discussed the possible effects of TCC-induced modulation of brain morphology, functional homogeneity and connectivity, regional activity and macro-scale network activity on health. Moreover, we identified possible links between the alterations in brain and beneficial effects of TCC, such as improved motor functions, pain perception, metabolic profile, cognitive functions, mental health and sleep quality. This paper aimed to stimulate further mechanistic neuroimaging studies in TCC and its effects on brain morphology, functional homogeneity and connectivity, regional activity and macro-scale network activity, which ultimately lead to a better understanding of the mechanisms responsible for the beneficial effects of TCC on human health.