Concomitant modulation of BOLD responses in white matter pathways and cortex

Characteristic BOLD signals are detectable in white matter of the spinal cord at rest and after a stimulus

We report the reliable detection of reproducible patterns of blood-oxygenation-level-dependent (BOLD) MRI signals within the white matter (WM) of the spinal cord during a task and in a resting state. Previous functional MRI studies have shown that BOLD signals are robustly detectable not only in gray matter (GM) in the brain but also in cerebral WM as well as the GM within the spinal cord, but similar signals in WM of the spinal cord have been overlooked. In this study, we detected BOLD signals in the WM of the spinal cord in squirrel monkeys and studied their relationships with the locations and functions of ascending and descending WM tracts. Tactile sensory stimulus -evoked BOLD signal changes were detected in the ascending tracts of the spinal cord using a general-linear model. Power spectral analysis confirmed that the amplitude at the fundamental frequency of the response to a periodic stimulus was significantly higher in the ascending tracts than the descending ones. Independent component analysis of resting-state signals identified coherent fluctuations from eight WM hubs which correspond closely to the known anatomical locations of the major WM tracts. Resting-state analyses showed that the WM hubs exhibited correlated signal fluctuations across spinal cord segments in reproducible patterns that correspond well with the known neurobiological functions of WM tracts in the spinal cord. Overall, these findings provide evidence of a functional organization of intraspinal WM tracts and confirm that they produce hemodynamic responses similar to GM both at baseline and under stimulus conditions.

Quantification of mediation effects of white matter functional characteristics on cognitive decline in aging

Cognitive decline with aging involves multifactorial processes, including changes in brain structure and function. This study focuses on the role of white matter functional characteristics, as reflected in blood oxygenation level-dependent signals, in age-related cognitive deterioration. Building on previous research confirming the reproducibility and age-dependence of blood oxygenation level-dependent signals acquired via functional magnetic resonance imaging, we here employ mediation analysis to test if aging affects cognition through white matter blood oxygenation level-dependent signal changes, impacting various cognitive domains and specific white matter regions. We used independent component analysis of resting-state blood oxygenation level-dependent signals to segment white matter into coherent hubs, offering a data-driven view of white matter's functional architecture. Through correlation analysis, we constructed a graph network and derived metrics to quantitatively assess regional functional properties based on resting-state blood oxygenation level-dependent fluctuations. Our analysis identified significant mediators in the age-cognition relationship, indicating that aging differentially influences cognitive functions by altering the functional characteristics of distinct white matter regions. These findings enhance our understanding of the neurobiological basis of cognitive aging, highlighting the critical role of white matter in maintaining cognitive integrity and proposing new approaches to assess interventions targeting cognitive decline in older populations.

Alteration of the large-scale white-matter functional networks in autism spectrum disorder

Autism spectrum disorder is a neurodevelopmental disorder whose core deficit is social dysfunction. Previous studies have indicated that structural changes in white matter are associated with autism spectrum disorder. However, few studies have explored the alteration of the large-scale white-matter functional networks in autism spectrum disorder. Here, we identified ten white-matter functional networks on resting-state functional magnetic resonance imaging data using the K-means clustering algorithm. Compared with the white matter and white-matter functional network connectivity of the healthy controls group, we found significantly decreased white matter and white-matter functional network connectivity mainly located within the Occipital network, Middle temporo-frontal network, and Deep network in autism spectrum disorder. Compared with healthy controls, findings from white-matter gray-matter functional network connectivity showed the decreased white-matter gray-matter functional network connectivity mainly distributing in the Occipital network and Deep network. Moreover, we compared the spontaneous activity of white-matter functional networks between the two groups. We found that the spontaneous activity of Middle temporo-frontal and Deep network was significantly decreased in autism spectrum disorder. Finally, the correlation analysis showed that the white matter and white-matter functional network connectivity between the Middle temporo-frontal network and others networks and the spontaneous activity of the Deep network were significantly correlated with the Social Responsiveness Scale scores of autism spectrum disorder. Together, our findings indicate that changes in the white-matter functional networks are associated behavioral deficits in autism spectrum disorder.

Detection and characterization of resting state functional networks in squirrel monkey brain

Resting-state fMRI based on analyzing BOLD signals is widely used to derive functional networks in the brain and how they alter during disease or injury conditions. Resting-state networks can also be used to study brain functional connectomes across species, which provides insights into brain evolution. The squirrel monkey (SM) is a non-human primate (NHP) that is widely used as a preclinical model for experimental manipulations to understand the organization and functioning of the brain. We derived resting-state networks from the whole brain of anesthetized SMs using Independent Component Analysis of BOLD acquisitions. We detected 15 anatomically constrained resting-state networks localized in the cortical and subcortical regions as well as in the white-matter. Networks encompassing visual, somatosensory, executive control, sensorimotor, salience and default mode regions, and subcortical networks including the Hippocampus-Amygdala, thalamus, basal-ganglia and brainstem region correspond well with previously detected networks in humans and NHPs. The connectivity pattern between the networks also agrees well with previously reported seed-based resting-state connectivity of SM brain. This study demonstrates that SMs share remarkable homologous network organization with humans and other NHPs, thereby providing strong support for their suitability as a translational animal model for research and additional insight into brain evolution across species.

Changes in white matter functional networks across late adulthood

Introduction

The aging brain is characterized by decreases in not only neuronal density but also reductions in myelinated white matter (WM) fibers that provide the essential foundation for communication between cortical regions. Age-related degeneration of WM has been previously characterized by histopathology as well as T2 FLAIR and diffusion MRI. Recent studies have consistently shown that BOLD (blood oxygenation level dependent) effects in WM are robustly detectable, are modulated by neural activities, and thus represent a complementary window into the functional organization of the brain. However, there have been no previous systematic studies of whether or how WM BOLD signals vary with normal aging. We therefore performed a comprehensive quantification of WM BOLD signals across scales to evaluate their potential as indicators of functional changes that arise with aging.

Methods

By using spatial independent component analysis (ICA) of BOLD signals acquired in a resting state, WM voxels were grouped into spatially distinct functional units. The functional connectivities (FCs) within and among those units were measured and their relationships with aging were assessed. On a larger spatial scale, a graph was reconstructed based on the pair-wise connectivities among units, modeling the WM as a complex network and producing a set of graph-theoretical metrics.

Results

The spectral powers that reflect the intensities of BOLD signals were found to be significantly affected by aging across more than half of the WM units. The functional connectivities (FCs) within and among those units were found to decrease significantly with aging. We observed a widespread reduction of graph-theoretical metrics, suggesting a decrease in the ability to exchange information between remote WM regions with aging.

Discussion

Our findings converge to support the notion that WM BOLD signals in specific regions, and their interactions with other regions, have the potential to serve as imaging markers of aging.

White matter BOLD signals at 7 Tesla reveal visual field maps in optic radiation and vertical occipital fasciculus

There is growing evidence that blood-oxygen-level-dependent (BOLD) activity in the white matter (WM) can be detected by functional magnetic resonance imaging (fMRI). However, the functional relevance and significance of WM BOLD signals remain controversial. Here we investigated whether 7T BOLD fMRI can reveal fine-scale functional organizations of a WM bundle. Population receptive field (pRF) analyses of the 7T retinotopy dataset from the Human Connectome Project revealed clear contralateral retinotopic organizations of two visual WM bundles: the optic radiation (OR) and the vertical occipital fasciculus (VOF). The retinotopic maps of OR are highly consistent with post-mortem dissections and diffusion tractographies, while the VOF maps are compatible with the dorsal and ventral visual areas connected by the WM. Similar to the grey matter (GM) visual areas, both WM bundles show over-representations of the central visual field and increasing pRF size with eccentricity. Hemodynamic response functions of visual WM were slower and wider compared with those of GM areas. These findings clearly demonstrate that WM BOLD at 7 Tesla is closely coupled with neural activity related to axons, encoding highly specific information that can be used to characterize fine-scale functional organizations of a WM bundle.

Identification of synchronous BOLD signal patterns in white matter of primate spinal cord

Functional MRI studies of the brain have shown that blood-oxygenation-level-dependent (BOLD) signals are robustly detectable not only in gray matter (GM) but also in white matter (WM). Here, we report the detection and characteristics of BOLD signals in WM of spinal cord (SC) of squirrel monkeys. Tactile stimulus-evoked BOLD signal changes were detected in the ascending sensory tracts of SC using a General-Linear Model (GLM) as well as Independent Component Analysis (ICA). ICA of resting state signals identified coherent fluctuations from eight WM hubs which correspond closely with known anatomical locations of SC WM tracts. Resting state analyses showed that the WM hubs exhibited correlated signal fluctuations within and between SC segments in specific patterns that correspond well with the known neurobiological functions of WM tracts in SC. Overall, these findings suggest WM BOLD signals in SC show similar features as GM both at baseline and under stimulus conditions.

Inefficient white matter activity in Schizophrenia evoked during intra and inter-hemispheric communication

Intensive cognitive tasks induce inefficient regional and network responses in schizophrenia (SCZ). fMRI-based studies have naturally focused on gray matter, but appropriately titrated visuo-motor integration tasks reliably activate inter- and intra-hemispheric white matter pathways. Such tasks can assess network inefficiency without demanding intensive cognitive effort. Here, we provide the first application of this framework to the study of white matter functional responses in SCZ. Event-related fMRI data were acquired from 28 patients (nine females, mean age 43.3, ±11.7) and 28 age- and gender-comparable controls (nine females, mean age 42.1 ± 10.1), using the Poffenberger paradigm, a rapid visual detection task used to induce intra- (ipsi-lateral visual and motor cortex) or inter-hemispheric (contra-lateral visual and motor cortex) transfer. fMRI data were pre- and post-processed to reliably isolate activations in white matter, using probabilistic tractography-based white matter tracts. For intra- and inter-hemispheric transfer conditions, SCZ evinced hyper-activations in longitudinal and transverse white matter tracts, with hyper-activation in sub-regions of the corpus callosum primarily observed during inter-hemispheric transfer. Evidence for the functional inefficiency of white matter was observed in conjunction with small (~50 ms) but significant increases in response times. Functional inefficiencies in SCZ are (1) observable in white matter, with the degree of inefficiency contextually related to task-conditions, and (2) are evoked by simple detection tasks without intense cognitive processing. These cumulative results while expanding our understanding of this dys-connection syndrome, also extend the search of biomarkers beyond the traditional realm of fMRI studies of gray matter.

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

PURPOSE

Previous 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.

METHODS

We 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-covariances 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.

RESULTS

Projections 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.

CONCLUSIONS

These findings provide additional insight into the character and roles of white matter BOLD signals in brain functions.

Tonic pain alters functional connectivity of the descending pain modulatory network involving amygdala, periaqueductal gray, parabrachial nucleus and anterior cingulate cortex

INTRODUCTION

Resting state functional connectivity (FC) is widely used to assess functional brain alterations in patients with chronic pain. However, reports of FC accompanying tonic pain in pain-free persons are rare. A network we term the Descending Pain Modulatory Network (DPMN) is implicated in healthy and pathologic pain modulation. Here, we evaluate the effect of tonic pain on FC of specific nodes of this network: anterior cingulate cortex (ACC), amygdala (AMYG), periaqueductal gray (PAG), and parabrachial nuclei (PBN).

METHODS

In 50 pain-free participants (30F), we induced tonic pain using a capsaicin-heat pain model. functional MRI measured resting BOLD signal during pain-free rest with a 32°C thermode and then tonic pain where participants experienced a previously warm temperature combined with capsaicin. We evaluated FC from ACC, AMYG, PAG, and PBN with correlation of self-report pain intensity during both states. We hypothesized tonic pain would diminish FC dyads within the DPMN.

RESULTS

Of all hypothesized FC dyads, only PAG and subgenual ACC was weakly altered during pain (F=3.34; p=0.074; pain-free>pain d=0.25). After pain induction sACC-PAG FC became positively correlated with pain intensity (R=0.38; t=2.81; p=0.007). Right PBN-PAG FC during pain-free rest positively correlated with subsequently experienced pain (R=0.44; t=3.43; p=0.001). During pain, this connection's FC was diminished (paired t=-3.17; p=0.0026). In whole-brain analyses, during pain-free rest, FC between left AMYG and right superior parietal lobule and caudate nucleus were positively correlated with subsequent pain. During pain, FC between left AMYG and right inferior temporal gyrus negatively correlated with pain. Subsequent pain positively correlated with right AMYG FC with right claustrum; right primary visual cortex and right temporo-occipitoparietal junction Conclusion: We demonstrate sACC-PAG tonic pain FC positively correlates with experienced pain and resting right PBN-PAG FC correlates with subsequent pain and is diminished during tonic pain. Finally, we reveal PAG- and right AMYG-anchored networks which correlate with subsequently experienced pain intensity. Our findings suggest specific connectivity patterns within the DPMN at rest are associated with subsequently experienced pain and modulated by tonic pain. These nodes and their functional modulation may reveal new therapeutic targets for neuromodulation or biomarkers to guide interventions.

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 signal 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, 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.

Characterization of resting functional MRI activity alterations across epileptic foci and networks

Brain network alterations have been studied extensively in patients with mesial temporal lobe epilepsy (mTLE) and other focal epilepsies using resting-state functional magnetic resonance imaging (fMRI). However, little has been done to characterize the basic fMRI signal alterations caused by focal epilepsy. Here, we characterize how mTLE affects the fMRI signal in epileptic foci and networks. Resting-state fMRI and diffusion MRI were collected from 47 unilateral mTLE patients and 96 healthy controls. FMRI activity, quantified by amplitude of low-frequency fluctuations, was increased in the epileptic focus and connected regions in mTLE. Evidence for spread of this epileptic fMRI activity was found through linear relationships of regional activity across subjects, the association of these relationships with functional connectivity, and increased activity along white matter tracts. These fMRI activity increases were found to be dependent on the epileptic focus, where the activity was related to disease severity, suggesting the focus to be the origin of these pathological alterations. Furthermore, we found fMRI activity decreases in the default mode network of right mTLE patients with different properties than the activity increases found in the epileptic focus. This work provides insights into basic fMRI signal alterations and their potential spread across networks in focal epilepsy.

Imaging functional neuroplasticity in human white matter tracts

Magnetic resonance imaging (MRI) studies are sensitive to biological mechanisms of neuroplasticity in white matter (WM). In particular, diffusion tensor imaging (DTI) has been used to investigate structural changes. Historically, functional MRI (fMRI) neuroplasticity studies have been restricted to gray matter, as fMRI studies have only recently expanded to WM. The current study evaluated WM neuroplasticity pre-post motor training in healthy adults, focusing on motor learning in the non-dominant hand. Neuroplasticity changes were evaluated in two established WM regions-of-interest: the internal capsule and the corpus callosum. Behavioral improvements following training were greater for the non-dominant hand, which corresponded with MRI-based neuroplasticity changes in the internal capsule for DTI fractional anisotropy, fMRI hemodynamic response functions, and low-frequency oscillations (LFOs). In the corpus callosum, MRI-based neuroplasticity changes were detected in LFOs, DTI, and functional correlation tensors (FCT). Taken together, the LFO results converged as significant amplitude reductions, implicating a common underlying mechanism of optimized transmission through altered myelination. The structural and functional neuroplasticity findings open new avenues for direct WM investigations into mapping connectomes and advancing MRI clinical applications.

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

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.

Diffusion-informed spatial smoothing of fMRI data in white matter using spectral graph filters

Brain activation mapping using functional magnetic resonance imaging (fMRI) has been extensively studied in brain gray matter (GM), whereas in large disregarded for probing white matter (WM). This unbalanced treatment has been in part due to controversies in relation to the nature of the blood oxygenation level-dependent (BOLD) contrast in WM and its detectability. However, an accumulating body of studies has provided solid evidence of the functional significance of the BOLD signal in WM and has revealed that it exhibits anisotropic spatio-temporal correlations and structure-specific fluctuations concomitant with those of the cortical BOLD signal. In this work, we present an anisotropic spatial filtering scheme for smoothing fMRI data in WM that accounts for known spatial constraints on the BOLD signal in WM. In particular, the spatial correlation structure of the BOLD signal in WM is highly anisotropic and closely linked to local axonal structure in terms of shape and orientation, suggesting that isotropic Gaussian filters conventionally used for smoothing fMRI data are inadequate for denoising the BOLD signal in WM. The fundamental element in the proposed method is a graph-based description of WM that encodes the underlying anisotropy observed across WM, derived from diffusion-weighted MRI data. Based on this representation, and leveraging graph signal processing principles, we design subject-specific spatial filters that adapt to a subject's unique WM structure at each position in the WM that they are applied at. We use the proposed filters to spatially smooth fMRI data in WM, as an alternative to the conventional practice of using isotropic Gaussian filters. We test the proposed filtering approach on two sets of simulated phantoms, showcasing its greater sensitivity and specificity for the detection of slender anisotropic activations, compared to that achieved with isotropic Gaussian filters. We also present WM activation mapping results on the Human Connectome Project's 100-unrelated subject dataset, across seven functional tasks, showing that the proposed method enables the detection of streamline-like activations within axonal bundles.

CHARACTERIZATION OF SPATIAL DYNAMICS OF FMRI DATA IN WHITE MATTER USING DIFFUSION-INFORMED WHITE MATTER HARMONICS

In this work, we leverage the Laplacian eigenbasis of voxel-wise white matter (WM) graphs derived from diffusion-weighted MRI data, dubbed WM harmonics, to characterize the spatial structure of WM fMRI data. Our motivation for such a characterization is based on studies that show WM fMRI data exhibit a spatial correlational anisotropy that coincides with underlying fiber patterns. By quantifying the energy content of WM fMRI data associated with subsets of WM harmonics across multiple spectral bands, we show that the data exhibits notable subtle spatial modulations under functional load that are not manifested during rest. WM harmonics provide a novel means to study the spatial dynamics of WM fMRI data, in such way that the analysis is informed by the underlying anatomical structure.

Identification of White Matter Networks Engaged in Object (Face) Recognition Showing Differential Responses to Modulated Stimulus Strength

Blood-oxygenation-level-dependent (BOLD) signals in magnetic resonance imaging indirectly reflect neural activity in cortex, but they are also detectable in white matter (WM). BOLD signals in WM exhibit strong correlations with those in gray matter (GM) in a resting state, but their interpretation and relationship to GM activity in a task are unclear. We performed a parametric visual object recognition task designed to modulate the BOLD signal response in GM regions engaged in higher order visual processing, and measured corresponding changes in specific WM tracts. Human faces embedded in different levels of random noise have previously been shown to produce graded changes in BOLD activation in for example, the fusiform gyrus, as well as in electrophysiological (N170) evoked potentials. The magnitudes of BOLD responses in both GM regions and selected WM tracts varied monotonically with the stimulus strength (noise level). In addition, the magnitudes and temporal profiles of signals in GM and WM regions involved in the task coupled strongly across different task parameters. These findings reveal the network of WM tracts engaged in object (face) recognition and confirm that WM BOLD signals may be directly affected by neural activity in GM regions to which they connect.