Biology subjects

Gore, J. C.

Publications and source records attributed to Gore, J. C..

4 recordsLinked to original sources

bioRxiv2021

Dynamic variations of resting-state BOLD signal spectra in white matter

Recent studies have demonstrated that the mathematical model used for analyzing and interpreting fMRI data in gray matter (GM) is inappropriate for detecting or describing blood-oxygenation-level-dependent (BOLD) signals in white matter (WM). In particular the hemodynamic response function (HRF) which serves as the regressor in general linear models is different in WM compared to GM. We recently reported measurements of the frequency contents of resting-state time courses in WM that showed distinct power spectra which depended on local structural-vascular-functional associations. In addition, multiple studies of GM have revealed how functional connectivity between regions, as measured by the correlation between BOLD time series, varies dynamically over time. We therefore investigated whether and how BOLD signals from WM in a resting state varied over time. We measured voxel-wise spectrograms, which reflect the time-varying spectral patterns of WM time courses. The results suggest that the spectral patterns are non-stationary but could be categorized into five modes that recurred over time. These modes showed distinct spatial distributions of their occurrences and durations, and the distributions were highly consistent across individuals. In addition, one of the modes exhibited a strong coupling of its occurrence between GM and WM across individuals, and two communities of WM voxels were identified according to the hierarchical structures of transitions among modes. Moreover, the total number of transitions in each community predicts specific human behaviors. Last, these modes are coupled to the shape of instantaneous HRFs. Our findings extend previous studies and reveal the non-stationary nature of spectral patterns of BOLD signals over time, providing a spatial-temporal-frequency characterization of resting-state signals in WM.

neuroscience↗

bioRxiv2021

Correlated Functional Connectivity and Glucose Metabolism in Brain White Matter Revealed by Simultaneous MRI/PET

Blood oxygenation level-dependent (BOLD) signals in white matter (WM) have usually been ignored or undetected, consistent with the lower vascular density and metabolic demands in WM than in gray matter (GM). Despite converging evidence demonstrating the reliable detection of BOLD signals in WM evoked by neural stimulation and in a resting state, few studies have examined the relationship between BOLD functional signals and tissue metabolism in WM. By analyzing simultaneous recordings of MRI and PET data, we found that the correlations between low frequency resting state BOLD signals in WM are spatially correlated with local glucose uptake, which also covaried with the amplitude of spontaneous low frequency fluctuations in BOLD signals. These results provide further evidence that BOLD signals in WM reflect variations in metabolic demand associated with neural activity, and suggest they should be incorporated into more complete models of brain function.

neuroscience↗

bioRxiv2021

Lag structure of BOLD signals within white matter in resting-state fMRI

PurposePrevious studies have demonstrated that BOLD signals in gray matter in resting-state functional MRI (RSfMRI) have variable time lags, representing apparent propagations of fMRI BOLD signals in gray matter. We complemented existing findings and explored the corresponding variations of signal latencies in white matter. MethodsWe used data from the Brain Genomics Superstruct Project, consisting of 1412 subjects (both sexes included) and divided the dataset into ten equal groups to study both the patterns and reproducibility of latency estimates within white matter. We constructed latency matrices by computing cross-correlations between voxel pairs. We also applied a clustering analysis to identify functional networks within white matter, based on which latency analysis was also performed to investigate lead/lag relationship at network level. A dataset consisting of various sensory states (eyes closed, eyes open and eyes open with fixation) was also included to examine the relationship between latency structure and different states. ResultsProjections of voxel latencies from the latency matrices were highly correlated (average Pearson correlation coefficient = 0.89) across the subgroups, confirming the reproducibility and structure of signal lags in white matter. Analysis of latencies within and between networks revealed a similar pattern of inter- and intra-network communication to that reported for gray matter. Moreover, a dominant direction, from inferior to superior regions, of BOLD signal propagation was revealed by higher resolution clustering. The variations of lag structure within white matter are associated with different sensory states. ConclusionsThese findings provide additional insight into the character and roles of white matter BOLD signals in brain functions.

neuroscience↗

bioRxiv2021

Power spectra reveal distinct BOLD resting-state time courses in white matter

Accurate characterization of the time courses of BOLD signal changes is crucial for the analysis and interpretation of functional MRI data. While several studies have shown that white matter (WM) exhibits distinct BOLD responses evoked by tasks, there have been no comprehensive investigations into the time courses of spontaneous signal fluctuations in WM. We measured the power spectra of the resting-state time courses in a set of regions within WM identified as showing synchronous signals using independent components analysis. In each component, a clear separation between voxels into two categories was evident, based on their power spectra: one group exhibited a single peak, the other had an additional peak at a higher frequency. Their groupings are location-specific, and their distributions reflect unique neurovascular and anatomical configurations. Importantly, the two categories of voxels differed in their engagement in functional integration, revealed by differences in the number of inter- regional connections based on the two categories separately. Moreover, the power spectral measurements in voxels with two peaks in specific components predict specific human behaviors. Taken together, these findings suggest WM signals are heterogeneous in nature and depend on local structural-vascular-functional associations.

neuroscience↗