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.

Download Full-text

Hemodynamic Response Function in Brain White Matter in a Resting State

Cerebral Cortex Communications ◽

10.1093/texcom/tgaa056 ◽

2020 ◽

Vol 1 (1) ◽

Cited By ~ 1

Author(s):

Ting Wang ◽

D Mitchell Wilkes ◽

Muwei Li ◽

Xi Wu ◽

John C Gore ◽

...

Keyword(s):

White Matter ◽

Response Function ◽

Gray Matter ◽

Resting State ◽

Hemodynamic Response ◽

Temporal Variations ◽

Blood Oxygenation ◽

Hemodynamic Response Function ◽

Brain White Matter ◽

Oxygenation Level

Abstract The hemodynamic response function (HRF) characterizes temporal variations of blood oxygenation level-dependent (BOLD) signals. Although a variety of HRF models have been proposed for gray matter responses to functional demands, few studies have investigated HRF profiles in white matter particularly under resting conditions. In the present work we quantified the nature of the HRFs that are embedded in resting state BOLD signals in white matter, and which modulate the temporal fluctuations of baseline signals. We demonstrate that resting state HRFs in white matter could be derived by referencing to intrinsic avalanches in gray matter activities, and the derived white matter HRFs had reduced peak amplitudes and delayed peak times as compared with those in gray matter. Distributions of the time delays and correlation profiles in white matter depend on gray matter activities as well as white matter tract distributions, indicating that resting state BOLD signals in white matter encode neural activities associated with those of gray matter. This is the first investigation of derivations and characterizations of resting state HRFs in white matter and their relations to gray matter activities. Findings from this work have important implications for analysis of BOLD signals in the brain.

Download Full-text

Disrupted White Matter Functional Connectivity With the Cerebral Cortex in Migraine Patients

Frontiers in Neuroscience ◽

10.3389/fnins.2021.799854 ◽

2022 ◽

Vol 15 ◽

Author(s):

Zhaoxia Qin ◽

Huai-Bin Liang ◽

Muwei Li ◽

Yue Hu ◽

Jing Wu ◽

...

Keyword(s):

White Matter ◽

Functional Connectivity ◽

Gray Matter ◽

Resting State ◽

Correlation Coefficients ◽

Blood Oxygenation ◽

Healthy Controls ◽

Functional Connections ◽

Time Courses ◽

Cortical Regions

Background: In attempts to understand the migraine patients’ overall brain functional architecture, blood oxygenation level-dependent (BOLD) signals in the white matter (WM) and gray matter (GM) were considered in the current study. Migraine, a severe and multiphasic brain condition, is characterized by recurrent attacks of headaches. BOLD fluctuations in a resting state exhibit similar temporal and spectral profiles in both WM and GM. It is feasible to explore the functional interactions between WM tracts and GM regions in migraine.Methods: Forty-eight migraineurs without aura (MWoA) and 48 healthy controls underwent resting-state functional magnetic resonance imaging. Pearson’s correlations between the mean time courses of 48 white matter (WM) bundles and 82 gray matter (GM) regions were computed for each subject. Two-sample t-tests were performed on the Pearson’s correlation coefficients (CC) to compare the differences between the MWoA and healthy controls in the GM-averaged CC of each bundle and the WM-averaged CC of each GM region.Results: The MWoAs exhibited an overall decreased average temporal CC between BOLD signals in 82 GM regions and 48 WM bundles compared with healthy controls, while little was increased. In particular, WM bundles such as left anterior corona radiata, left external capsule and bilateral superior longitudinal fasciculus had significantly decreased mean CCs with GM in MWoA. On the other hand, 16 GM regions had significantly decreased mean CCs with WM in MWoA, including some areas that are parts of the somatosensory regions, auditory cortex, temporal areas, frontal areas, cingulate cortex, and parietal cortex.Conclusion: Decreased functional connections between WM bundles and GM regions might contribute to disrupted functional connectivity between the parts of the pain processing pathway in MWoAs, which indicated that functional and connectivity abnormalities in cortical regions may not be limited to GM regions but are instead associated with functional abnormalities in WM tracts.

Download Full-text

Declined functional connectivity of white matter during rest and working memory tasks associates with cognitive impairments in schizophrenia

10.1101/2020.05.16.20091397 ◽

2020 ◽

Cited By ~ 1

Author(s):

Yurui Gao ◽

Muwei Li ◽

Anna S Huang ◽

Adam W Anderson ◽

Zhaohua Ding ◽

...

Keyword(s):

Working Memory ◽

White Matter ◽

Functional Connectivity ◽

Resting State ◽

Spatial Working Memory ◽

Cognitive Impairments ◽

Memory Task ◽

Brain Network ◽

Blood Oxygenation ◽

Cerebral Peduncle

BACKGROUND: Schizophrenia, characterized by cognitive impairments, arises from a disturbance of brain network. Pathological changes in white matter (WM) have been indicated as playing a role in disturbing neural connectivity in schizophrenia. However, deficits of functional connectivity (FC) in individual WM bundles in schizophrenia have never been explored; neither have cognitive correlates with those deficits. METHODS: Resting-state and spatial working memory task fMRI images were acquired on 67 healthy subjects and 84 patients with schizophrenia. The correlations in blood-oxygenation-level-dependent (BOLD) signals between 46 WM and 82 gray matter regions were quantified, analyzed and compared between groups under three scenarios (i.e., resting state, retention period and entire time of a spatial working memory task). Associations of FC in WM with cognitive assessment scores were evaluated for three scenarios. RESULTS: FC deficits were significant (p<.05) in external capsule, cingulum, uncinate fasciculus, genu and body of corpus callosum under all three scenarios. Deficits were also present in the anterior limb of the internal capsule and cerebral peduncle in task scenario. Decreased FCs in specific WM bundles associated significantly (p<.05) with cognitive impairments in working memory, processing speed and/or cognitive control. CONCLUSIONS: Decreases in FC are evident in several WM bundles in patients with schizophrenia and are significantly associated with cognitive impairments during both rest and working memory tasks. Furthermore, working memory tasks expose FC deficits in more WM bundles and more cognitive associates in schizophrenia than resting state does.

Download Full-text

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

10.1101/2021.04.16.440245 ◽

2021 ◽

Author(s):

Bin Guo ◽

Fugen Zhou ◽

Muwei Li ◽

John C Gore ◽

Zhaohua Ding

Keyword(s):

White Matter ◽

Resting State ◽

Low Frequency ◽

Blood Oxygenation ◽

Vascular Density ◽

Metabolic Demand ◽

Spatially Correlated ◽

Brain White Matter ◽

Oxygenation Level ◽

The Relationship

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.

Download Full-text

Detection of synchronous brain activity in white matter tracts at rest and under functional loading

Proceedings of the National Academy of Sciences ◽

10.1073/pnas.1711567115 ◽

2017 ◽

Vol 115 (3) ◽

pp. 595-600 ◽

Cited By ~ 66

Author(s):

Zhaohua Ding ◽

Yali Huang ◽

Stephen K. Bailey ◽

Yurui Gao ◽

Laurie E. Cutting ◽

...

Keyword(s):

White Matter ◽

Neural Activity ◽

Resting State ◽

Brain Activity ◽

Blood Oxygenation ◽

Task Specificity ◽

Temporal Correlations ◽

Neural Activities ◽

Oxygenation Level ◽

Functional Loading

Functional MRI based on blood oxygenation level-dependent (BOLD) contrast is well established as a neuroimaging technique for detecting neural activity in the cortex of the human brain. While detection and characterization of BOLD signals, as well as their electrophysiological and hemodynamic/metabolic origins, have been extensively studied in gray matter (GM), the detection and interpretation of BOLD signals in white matter (WM) remain controversial. We have previously observed that BOLD signals in a resting state reveal structure-specific anisotropic temporal correlations in WM and that external stimuli alter these correlations and permit visualization of task-specific fiber pathways, suggesting variations in WM BOLD signals are related to neural activity. In this study, we provide further strong evidence that BOLD signals in WM reflect neural activities both in a resting state and under functional loading. We demonstrate that BOLD signal waveforms in stimulus-relevant WM pathways are synchronous with the applied stimuli but with various degrees of time delay and that signals in WM pathways exhibit clear task specificity. Furthermore, resting-state signal fluctuations in WM tracts show significant correlations with specific parcellated GM volumes. These observations support the notion that neural activities are encoded in WM circuits similarly to cortical responses.

Download Full-text

The Effect of CO2 on Resting-State Functional Connectivity: Isocapnia vs. Poikilocapnia

Frontiers in Physiology ◽

10.3389/fphys.2021.639782 ◽

2021 ◽

Vol 12 ◽

Author(s):

Larissa McKetton ◽

Kevin Sam ◽

Julien Poublanc ◽

Adrian P. Crawley ◽

Olivia Sobczyk ◽

...

Keyword(s):

Resting State ◽

Hippocampal Formation ◽

Blood Oxygenation ◽

Hemodynamic Response Function ◽

Resting State Functional Connectivity ◽

Measured Variable ◽

Functional Connections ◽

Bivariate Correlation ◽

Normal Breathing ◽

End Tidal

The normal variability in breath size and frequency results in breath-to-breath variability of end-tidal PCO2 (PETCO2), the measured variable, and arterial partial pressure of carbon dioxide (PaCO2), the independent variable affecting cerebral blood flow (CBF). This study examines the effect of variability in PaCO2 on the pattern of resting-state functional MRI (rs-fMRI) connectivity. A region of interest (ROI)-to-ROI and Seed-to-Voxel first-level bivariate correlation, hemodynamic response function (hrf)-weighted analysis for measuring rs-fMRI connectivity was performed during two resting-state conditions: (a) normal breathing associated with breath-to-breath variation in PaCO2 (poikilocapnia), and (b) normal breathing with breath-to-breath variability of PETCO2 dampened using sequential rebreathing (isocapnia). End-tidal PCO2 (PETCO2) was used as a measurable surrogate for fluctuations of PaCO2. During poikilocapnia, enhanced functional connections were found between the cerebellum and inferior frontal and supramarginal gyrus (SG), visual cortex and occipital fusiform gyrus; and between the primary visual network (PVN) and the hippocampal formation. During isocapnia, these associations were not seen, rather enhanced functional connections were identified in the corticostriatal pathway between the putamen and intracalacarine cortex, supracalcarine cortex (SCC), and precuneus cortex. We conclude that vascular responses to variations in PETCO2, account for at least some of the observed resting state synchronization of blood oxygenation level-dependent (BOLD) signals.

Download Full-text

Resting State Networks in empirical and simulated dynamic functional connectivity

10.1101/089516 ◽

2016 ◽

Author(s):

Katharina Glomb ◽

Adrián Ponce-Alvarez ◽

Matthieu Gilson ◽

Petra Ritter ◽

Gustavo Deco

Keyword(s):

Resting State ◽

Field Model ◽

Temporal Dynamics ◽

Mean Field ◽

Tensor Decomposition ◽

Resting State Networks ◽

Mean Field Model ◽

Spatio Temporal ◽

Time Courses ◽

Over Time

AbstractIt is well-established that patterns of functional connectivity (FC) - measures of correlated activity between pairs of voxels or regions observed in the human brain using neuroimaging - are robustly expressed in spontaneous activity during rest. These patterns are not static, but exhibit complex spatio-temporal dynamics. Over the last years, a multitude of methods have been proposed to reveal these dynamics on the level of the whole brain. One finding is that the brain transitions through different FC configurations over time, and substantial effort has been put into characterizing these configurations. However, the dynamics governing these transitions are more elusive, specifically, the contribution of stationary vs. non-stationary dynamics is an active field of inquiry. In this study, we use a whole-brain approach, considering FC dynamics between 66 ROIs covering the entire cortex. We combine an innovative dimensionality reduction technique, tensor decomposition, with a mean field model which possesses stationary dynamics. It has been shown to explain resting state FC averaged over time and multiple subjects, however, this average FC summarizes the spatial distribution of correlations while hiding their temporal dynamics. First, we apply tensor decomposition to resting state scans from 24 healthy controls in order to characterize spatio-temporal dynamics present in the data. We simultaneously utilize temporal and spatial information by creating tensors that are subsequently decomposed into sets of brain regions (“communities”) that share similar temporal dynamics, and their associated time courses. The tensors contain pairwise FC computed inside of overlapping sliding windows. Communities are discovered by clustering features pooled from all subjects, thereby ensuring that they generalize. We find that, on the group level, the data give rise to four distinct communities that resemble known resting state networks (RSNs): default mode network, visual network, control networks, and somatomotor network. Second, we simulate data with our stationary mean field model whose nodes are connected according to results from DTI and fiber tracking. In this model, all spatio-temporal structure is due to noisy fluctuations around the average FC. We analyze the simulated data in the same way as the empirical data in order to determine whether stationary dynamics can explain the emergence of distinct FC patterns (RSNs) which have their own time courses. We find that this is the case for all four networks using the spatio-temporal information revealed by tensor decomposition if nodes in the simulation are connected according to model-based effective connectivity. Furthermore, we find that these results require only a small part of the FC values, namely the highest values that occur across time and ROI pair. Our findings show that stationary dynamics can account for the emergence of RSNs. We provide an innovative method that does not make strong assumptions about the underlying data and is generally applicable to resting state or task data from different subject populations.

Download Full-text

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

10.1101/2021.02.24.432346 ◽

2021 ◽

Author(s):

Muwei Li ◽

Yurui Gao ◽

Zhaohua Ding ◽

John C. Gore

Keyword(s):

White Matter ◽

Resting State ◽

Functional Integration ◽

Power Spectra ◽

Spectral Measurements ◽

State Time ◽

Power Spectral ◽

Time Courses ◽

Two Peaks

AbstractAccurate 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.

Download Full-text

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

Proceedings of the National Academy of Sciences ◽

10.1073/pnas.2103104118 ◽

2021 ◽

Vol 118 (44) ◽

pp. e2103104118

Author(s):

Muwei Li ◽

Yurui Gao ◽

Zhaohua Ding ◽

John C. Gore

Keyword(s):

White Matter ◽

Resting State ◽

Functional Integration ◽

Power Spectra ◽

Blood Oxygen Level Dependent ◽

Blood Oxygen Level ◽

State Time ◽

Components Analysis ◽

Time Courses

Accurate characterization of the time courses of blood-oxygen-level–dependent (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, and 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 interregional connections based on the two categories separately. Taken together, these findings suggest WM signals are heterogeneous in nature and depend on local structural-vascular-functional associations.

Download Full-text

Improvement in prefrontal thalamic connectivity during the early course of the illness in recent-onset psychosis: a 12-month longitudinal follow-up resting-state fMRI study

Psychological Medicine ◽

10.1017/s0033291720004808 ◽

2020 ◽

pp. 1-9

Author(s):

Daniel Bergé ◽

Tyler A. Lesh ◽

Jason Smucny ◽

Cameron S. Carter

Keyword(s):

Resting State ◽

Resting State Fmri ◽

Functional Changes ◽

Recent Onset ◽

Fmri Study ◽

Clinical Meaning ◽

Mixed Pattern ◽

The Right ◽

Over Time

Abstract Background Previous research in resting-state functional magnetic resonance imaging (rs-fMRI) has shown a mixed pattern of disrupted thalamocortical connectivity in psychosis. The clinical meaning of these findings and their stability over time remains unclear. We aimed to study thalamocortical connectivity longitudinally over a 1-year period in participants with recent-onset psychosis. Methods To this purpose, 129 individuals with recent-onset psychosis and 87 controls were clinically evaluated and scanned using rs-fMRI. Among them, 43 patients and 40 controls were re-scanned and re-evaluated 12 months later. Functional connectivity between the thalamus and the rest of the brain was calculated using a seed to voxel approach, and then compared between groups and correlated with clinical features cross-sectionally and longitudinally. Results At baseline, participants with recent-onset psychosis showed increased connectivity (compared to controls) between the thalamus and somatosensory and temporal regions (k = 653, T = 5.712), as well as decreased connectivity between the thalamus and left cerebellum and right prefrontal cortex (PFC; k = 201, T = −4.700). Longitudinal analyses revealed increased connectivity over time in recent-onset psychosis (relative to controls) in the right middle frontal gyrus. Conclusions Our results support the concept of abnormal thalamic connectivity as a core feature in psychosis. In agreement with a non-degenerative model of illness in which functional changes occur early in development and do not deteriorate over time, no evidence of progressive deterioration of connectivity during early psychosis was observed. Indeed, regionally increased connectivity between thalamus and PFC was observed.

Download Full-text