Bharat B. Biswal

Neural correlates of cognitive efficiency

Since its inception, experimental psychology has sought to account for individual differences in human performance. Some neuroimaging research, involving complex behavioral paradigms, has suggested that faster-performing individuals show greater neural activity than slower performers. Other research has suggested that faster-performing individuals show less neural activity than slower performers. To examine the neural basis of individual performance differences, we had participants perform a simple speeded-processing task during fMRI scanning. In some prefrontal cortical (PFC) brain regions, faster performers showed less cortical activity than slower performers while in other PFC and parietal regions they showed greater activity. Regional-causality analysis indicated that PFC exerted more influence over other brain regions for slower than for faster individuals. These results suggest that a critical determinant of individual performance differences is the efficiency of interactions between brain regions and that slower individuals may require more prefrontal executive control than faster individuals to perform successfully.

Blind source separation of multiple signal sources of fMRI data sets using independent component analysis.

PURPOSE The objective of this study was to separate multiple signal components present in functional MRI (fMRI) data sets. Blind source separation techniques were applied to the analysis of fMRI data to determine multiple physiologically relevant independent signal sources. METHOD Computer simulations were performed to test the reliability and robustness of the independent component analysis (ICA). Four subjects (3 males and 1 female between 14 and 29 years old) were scanned under various stimulus conditions: (1) rest while breathing room air, (2) bilateral finger tapping while breathing room air, and (3) hypercapnia during bilateral finger tapping. RESULTS Simulations performed on synthetic data sets demonstrated that not only could the algorithm reliably detect the shapes of each of the source signals, but it also preserved their relative amplitudes. The algorithm also performed robustly in the presence of noise. With use of fMRI time series data sets from bilateral finger tapping during hypercapnia, distinct physiologically relevant independent sources were reliably estimated. One independent component corresponded to the hypercapnic cerebrovascular response, and another independent component corresponded to cortical activation from bilateral finger tapping. In three of the four subjects, the underlying fluctuations in signal related to baseline respiratory rate were identified in the third independent component. Principal component analysis (PCA) could not separate these two independent physiological components. CONCLUSION With use of ICA, signals originating from independent sources could be separated from a linear mixture of observed data. Limitations of PCA were also demonstrated.

An open science resource for establishing reliability and reproducibility in functional connectomics.

Efforts to identify meaningful functional imaging-based biomarkers are limited by the ability to reliably characterize inter-individual differences in human brain function. Although a growing number of connectomics-based measures are reported to have moderate to high test-retest reliability, the variability in data acquisition, experimental designs, and analytic methods precludes the ability to generalize results. The Consortium for Reliability and Reproducibility (CoRR) is working to address this challenge and establish test-retest reliability as a minimum standard for methods development in functional connectomics. Specifically, CoRR has aggregated 1,629 typical individuals’ resting state fMRI (rfMRI) data (5,093 rfMRI scans) from 18 international sites, and is openly sharing them via the International Data-sharing Neuroimaging Initiative (INDI). To allow researchers to generate various estimates of reliability and reproducibility, a variety of data acquisition procedures and experimental designs are included. Similarly, to enable users to assess the impact of commonly encountered artifacts (for example, motion) on characterizations of inter-individual variation, datasets of varying quality are included.

Metabolic Brain Covariant Networks as Revealed by FDG-PET with Reference to Resting-State fMRI Networks

The human brain is inherently organized as separate networks, as has been widely revealed by resting-state functional magnetic resonance imaging (fMRI). Although the large-scale functional connectivity can be partially explained by the underlying white-matter structural connectivity, the question of whether the underlying functional connectivity is related to brain metabolic factors is still largely unanswered. The present study investigated the presence of metabolic covariant networks across subjects using a set of fluorodeoxyglucose ((18)F, FDG) positron-emission tomography (PET) images. Spatial-independent component analysis was performed on the subject series of FDG-PET images. A number of networks that were mainly homotopic regions could be identified, including visual, auditory, motor, cerebellar, and subcortical networks. However, the anterior-posterior networks such as the default-mode and left frontoparietal networks could not be observed. Region-of-interest-based correlation analysis confirmed that the intersubject metabolic covariances within the default-mode and left frontoparietal networks were reduced as compared with corresponding time-series correlations using resting-state fMRI from an independent sample. In contrast, homotopic intersubject metabolic covariances observed using PET were comparable to the corresponding fMRI resting-state time-series correlations. The current study provides preliminary illustration, suggesting that the human brain metabolism pertains to organized covariance patterns that might partially reflect functional connectivity as revealed by resting-state blood oxygen level dependent (BOLD). The discrepancy between the PET covariance and BOLD functional connectivity might reflect the differences of energy consumption coupling and ongoing neural synchronization within these brain networks.

Vision therapy in adults with convergence insufficiency: clinical and functional magnetic resonance imaging measures.

Purpose. This research quantified clinical measurements and functional neural changes associated with vision therapy in subjects with convergence insufficiency (CI). Methods. Convergence and divergence 4° step responses were compared between 13 control adult subjects with normal binocular vision and four CI adult subjects. All CI subjects participated in 18 h of vision therapy. Clinical parameters quantified throughout the therapy included: nearpoint of convergence, recovery point of convergence, positive fusional vergence at near, near dissociated phoria, and eye movements that were quantified using peak velocity. Neural correlates of the CI subjects were quantified with functional magnetic resonance imaging scans comparing random vs. predictable vergence movements using a block design before and after vision therapy. Images were quantified by measuring the spatial extent of activation and the average correlation within five regions of interests (ROI). The ROIs were the dorsolateral prefrontal cortex, a portion of the frontal lobe, part of the parietal lobe, the cerebellum, and the brain stem. All measurements were repeated 4 months to 1 year post-therapy in three of the CI subjects. Results. Convergence average peak velocities to step stimuli were significantly slower (p = 0.016) in CI subjects compared with controls; however, significant differences in average peak velocities were not observed for divergence step responses (p = 0.30). The investigation of CI subjects participating in vision therapy showed that the nearpoint of convergence, recovery point of convergence, and near dissociated phoria significantly decreased. Furthermore, the positive fusional vergence, average peak velocity from 4° convergence steps, and the amount of functional activity within the frontal areas, cerebellum, and brain stem significantly increased. Several clinical and cortical parameters were significantly correlated. Conclusions. Convergence peak velocity was significantly slower in CI subjects compared with controls, which may result in asthenopic complaints reported by the CI subjects. Vision therapy was associated with and may have evoked clinical and cortical activity changes.

Dynamic brain functional connectivity modulated by resting-state networks.

Abstract Studies of large-scale brain functional connectivity using the resting-state functional magnetic resonance imaging have advanced our understanding of human brain functions. Although the evidence of dynamic functional connectivity is accumulating, the variations of functional connectivity over time have not been well characterized. In the present study, we aimed to associate the variations of functional connectivity with the intrinsic activities of resting-state networks during a single resting-state scan by comparing functional connectivity differences between when a network had higher and lower intrinsic activities. The activities of the salience network, default mode network (DMN), and motor network were associated with changes of resting-state functional connectivity. Higher activity of the salience network was accompanied by greater functional connectivity between the fronto-parietal regions and the DMN regions, and between the regions within the DMN. Higher DMN activity was associated with less connectivity between the regions within the DMN, and greater connectivity between the regions within the fronto-parietal network. Higher motor network activity was correlated with greater connectivity between the regions within the motor network, and smaller connectivity between the DMN regions and fronto-parietal regions, and between the DMN regions and the motor regions. In addition, the whole brain network modularity was positively correlated with the motor network activity, suggesting that the brain is more segregated as sub-systems when the motor network is intrinsically activated. Together, these results demonstrate the association between the resting-state connectivity variations and the intrinsic activities of specific networks, which can provide insights on the dynamic changes in large-scale brain connectivity and network configurations.

Functional Integration Between Brain Regions at Rest Occurs in Multiple-Frequency Bands

Studies of resting-state fMRI have shown that blood oxygen level dependent (BOLD) signals giving rise to temporal correlation across voxels (or regions) are dominated by low-frequency fluctuations in the range of ∼ 0.01-0.1 Hz. These low-frequency fluctuations have been further divided into multiple distinct frequency bands (slow-5 and -4) based on earlier neurophysiological studies, though low sampling frequency of fMRI (∼ 0.5 Hz) has substantially limited the exploration of other known frequency bands of neurophysiological origins (slow-3, -2, and -1). In this study, we used resting-state fMRI data acquired from 21 healthy subjects at a higher sampling frequency of 1.5 Hz to assess the presence of resting-state functional connectivity (RSFC) across multiple frequency bands: slow-5 to slow-1. The effect of different frequency bands on spatial extent and connectivity strength for known resting-state networks (RSNs) was also evaluated. RSNs were derived using independent component analysis and seed-based correlation. Commonly known RSNs, such as the default mode, the fronto-parietal, the dorsal attention, and the visual networks, were consistently observed at multiple frequency bands. Significant inter-hemispheric connectivity was observed between each seed and its contra lateral brain region across all frequency bands, though overall spatial extent of seed-based correlation maps decreased in slow-2 and slow-1 frequency bands. These results suggest that functional integration between brain regions at rest occurs over multiple frequency bands and RSFC is a multiband phenomenon. These results also suggest that further investigation of BOLD signal in multiple frequency bands for related cognitive processes should be undertaken.

The influence of the amplitude of low-frequency fluctuations on resting-state functional connectivity.

Studies of brain functional connectivity have provided a better understanding of organization and integration of large-scale brain networks. Functional connectivity using resting-state functional magnetic resonance imaging (fMRI) is typically based upon the correlations of the low-frequency fluctuation of fMRI signals. Reproducible spatial maps in the brain have also been observed using the amplitude of low-frequency fluctuations (ALFF) in resting-state. However, little is known about the influence of the ALFF on the functional connectivity measures. In the present study, we analyzed resting-state fMRI data on 79 healthy old individuals. Spatial independent component analysis and regions of interest (ROIs) based connectivity analysis were performed to obtain measures of functional connectivity. ALFF maps were also calculated. First, voxel-matched inter-subject correlations were computed between back-reconstructed IC and ALFF maps. For all the resting-state networks, there was a consistent correlation between ALFF variability and network strengths (within regions that had high IC strengths). Next, inter-subject variance of correlations across 160 functionally defined ROIs were correlated with the corresponding ALFF variance. The connectivity of several ROIs to other regions were more likely to correlate with its own regional ALFF. These regions were mainly located in the anterior cingulate cortex, medial prefrontal cortex, precuneus, insula, basal ganglia, and thalamus. These associations may suggest a functional significance of functional connectivity modulations. Alternatively, the fluctuation amplitudes may arise from physiological noises, and therefore, need to be controlled when studying resting-state functional connectivity.

High prevalence of NMDA receptor IgA/IgM antibodies in different dementia types

To retrospectively determine the frequency of N‐Methyl‐D‐Aspartate (NMDA) receptor (NMDAR) autoantibodies in patients with different forms of dementia.

Modulatory interactions between the default mode network and task positive networks in resting-state

The two major brain networks, i.e., the default mode network (DMN) and the task positive network, typically reveal negative and variable connectivity in resting-state. In the present study, we examined whether the connectivity between the DMN and different components of the task positive network were modulated by other brain regions by using physiophysiological interaction (PPI) on resting-state functional magnetic resonance imaging data. Spatial independent component analysis was first conducted to identify components that represented networks of interest, including the anterior and posterior DMNs, salience, dorsal attention, left and right executive networks. PPI analysis was conducted between pairs of these networks to identify networks or regions that showed modulatory interactions with the two networks. Both network-wise and voxel-wise analyses revealed reciprocal positive modulatory interactions between the DMN, salience, and executive networks. Together with the anatomical properties of the salience network regions, the results suggest that the salience network may modulate the relationship between the DMN and executive networks. In addition, voxel-wise analysis demonstrated that the basal ganglia and thalamus positively interacted with the salience network and the dorsal attention network, and negatively interacted with the salience network and the DMN. The results demonstrated complex modulatory interactions among the DMNs and task positive networks in resting-state, and suggested that communications between these networks may be modulated by some critical brain structures such as the salience network, basal ganglia, and thalamus.