Does functional MRI detect activation in white matter? A review of emerging evidence, issues, and future directions

fMRI signals in white matter rewire gray matter community organization

Human brain gray matter (GM) has usually been clustered into multiple functional networks. The white matter (WM) fiber bundles are known to interconnect these networks simultaneously, engaging in numerous cognitive functions. However, the exact interconnections between GM and WM are still unclear, whether functional signals in WM rewires GM community organization remains to be explored. In this study, we divided brain functional connections into three types by using edge-centric method, including intra-GM, intra-WM and GM-WM connections, and calculated the edge community evaluation indexes for quantifying GM community engagement. The results showed that the involvement of WM significantly enhanced community entropy in the heteromodal system, while the sensory-attention system remained barely changed. In addition, delta community entropy showed a significant correlation with clinical cognitive scale. Our results suggested that WM rewired GM community organization, enhancing the community engagement of brain regions in the heteromodal system. This involvement was observed to be disrupted in disease groups. Our study revealed that considering the functional signals of GM and WM simultaneously could better understand the brain's functional organization.

Sub-bundle based analysis reveals the role of human optic radiation in visual working memory

White matter (WM) functional activity has been reliably detected through functional magnetic resonance imaging (fMRI). Previous studies have primarily examined WM bundles as unified entities, thereby obscuring the functional heterogeneity inherent within these bundles. Here, for the first time, we investigate the function of sub-bundles of a prototypical visual WM tract-the optic radiation (OR). We use the 7T retinotopy dataset from the Human Connectome Project (HCP) to reconstruct OR and further subdivide the OR into sub-bundles based on the fiber's termination in the primary visual cortex (V1). The population receptive field (pRF) model is then applied to evaluate the retinotopic properties of these sub-bundles, and the consistency of the pRF properties of sub-bundles with those of V1 subfields is evaluated. Furthermore, we utilize the HCP working memory dataset to evaluate the activations of the foveal and peripheral OR sub-bundles, along with LGN and V1 subfields, during 0-back and 2-back tasks. We then evaluate differences in 2bk-0bk contrast between foveal and peripheral sub-bundles (or subfields), and further examine potential relationships between 2bk-0bk contrast and 2-back task d-prime. The results show that the pRF properties of OR sub-bundles exhibit standard retinotopic properties and are typically similar to the properties of V1 subfields. Notably, activations during the 2-back task consistently surpass those under the 0-back task across foveal and peripheral OR sub-bundles, as well as LGN and V1 subfields. The foveal V1 displays significantly higher 2bk-0bk contrast than peripheral V1. The 2-back task d-prime shows strong correlations with 2bk-0bk contrast for foveal and peripheral OR fibers. These findings demonstrate that the blood oxygen level-dependent (BOLD) signals of OR sub-bundles encode high-fidelity visual information, underscoring the feasibility of assessing WM functional activity at the sub-bundle level. Additionally, the study highlights the role of OR in the top-down processes of visual working memory beyond the bottom-up processes for visual information transmission. Conclusively, this study innovatively proposes a novel paradigm for analyzing WM fiber tracts at the individual sub-bundle level and expands understanding of OR function.

Integrating brain function and structure in the study of the human attentional networks: a functionnectome study

Attention is a heterogeneous function theoretically divided into different systems. While functional magnetic resonance imaging (fMRI) has extensively characterized their functioning, the role of white matter in cognitive function has gained recent interest due to diffusion-weighted imaging advancements. However, most evidence relies on correlations between white matter properties and behavioral or cognitive measures. This study used a new method that combines the signal from distant voxels of fMRI images using the probability of structural connection given by high-resolution normative tractography. We analyzed three fMRI datasets with a visual perceptual task and three attentional manipulations: phasic alerting, spatial orienting, and executive attention. The phasic alerting network engaged temporal areas and their communication with frontal and parietal regions, with left hemisphere dominance. The orienting network involved bilateral fronto-parietal and midline regions communicating by association tracts and interhemispheric fibers. The executive attention network engaged a broad set of brain regions and white matter tracts connecting them, with a particular involvement of frontal areas and their connections with the rest of the brain. These results partially confirm and extend previous knowledge on the neural substrates of the attentional system, offering a more comprehensive understanding through the integration of structure and function.

Altered White-Matter Functional Network in Children with Idiopathic Generalized Epilepsy

RATIONALE AND OBJECTIVES

The white matter (WM) functional network changes offers insights into the potential pathological mechanisms of certain diseases, the alterations of WM functional network in idiopathic generalized epilepsy (IGE) remain unclear. We aimed to explore the topological characteristics changes of WM functional network in childhood IGE using resting-state functional Magnetic resonance imaging (MRI) and T1-weighted images.

METHODS

A total of 84 children (42 IGE and 42 matched healthy controls) were included in this study. Functional and structural MRI data were acquired to construct a WM functional network. Group differences in the global and regional topological characteristics were assessed by graph theory and the correlations with clinical and neuropsychological scores were analyzed. A support vector machine algorithm model was employed to classify individuals with IGE using WM functional connectivity as features, and the model's accuracy was evaluated using leave-one-out cross-validation.

RESULTS

In IGE group, at the network level, the WM functional network exhibited increased assortativity; at the nodal level, 17 nodes presented nodal disturbances in WM functional network, and nodal disturbances of 11 nodes were correlated with cognitive performance scores, disease duration and age of onset. The classification model achieved the 72.6% accuracy, 0.746 area under the curve, 69.1% sensitivity, 76.2% specificity.

CONCLUSION

Our study demonstrated that the WM functional network topological properties changes in childhood IGE, which were associated with cognitive function, and WM functional network may help clinical classification for childhood IGE. These findings provide novel information for understanding the pathogenesis of IGE and suggest that the WM function network might be qualified as potential biomarkers.

Multimodal active subspace analysis for computing assessment oriented subspaces from neuroimaging data

BACKGROUND

For successful biomarker discovery, it is essential to develop computational frameworks that summarize high-dimensional neuroimaging data in terms of involved sub-systems of the brain, while also revealing underlying heterogeneous functional and structural changes covarying with specific cognitive and biological traits. However, unsupervised decompositions do not inculcate clinical assessment information, while supervised approaches extract only individual feature importance, thereby impeding qualitative interpretation at the level of subspaces.

NEW METHOD

We present a novel framework to extract robust multimodal brain subspaces associated with changes in a given cognitive or biological trait. Our approach involves active subspace learning on the gradients of a trained machine learning model followed by clustering to extract and summarize the most salient and consistent subspaces associated with the target variable.

RESULTS

Through a rigorous cross-validation procedure on an Alzheimer's disease (AD) dataset, our framework successfully extracts multimodal subspaces specific to a given clinical assessment (e.g., memory and other cognitive skills), and also retains predictive performance in standard machine learning algorithms. We also show that the salient active subspace directions occur consistently across randomly sub-sampled repetitions of the analysis.

COMPARISON WITH EXISTING METHOD(S)

Compared to existing unsupervised decompositions based on principle component analysis, the subspace components in our framework retain higher predictive information.

CONCLUSIONS

As an important step towards biomarker discovery, our framework not only uncovers AD-related brain regions in the associated brain subspaces, but also enables automated identification of multiple underlying structural and functional sub-systems of the brain that collectively characterize changes in memory and proficiency in cognitive skills related to brain disorders like AD.

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.

Impact of the day/night cycle on functional connectome in ageing male and female mice

To elucidate how time of day, sex, and age affect functional connectivity (FC) in mice, we aimed to examine whether the mouse functional connectome varied with the day/night cycle and whether it depended on sex and age. We explored C57Bl6/J mice (6♀ and 6♂) at mature age (5 ± 1 months) and middle-age (14 ± 1 months). Each mouse underwent Blood Oxygen-Level-Dependent (BOLD) resting-state functional MRI (rs-fMRI) on a 7T scanner at four different times of the day, two under the light condition and two under the dark condition. Data processing consisted of group independent component analysis (ICA) and region-level analysis using resting-state networks (RSNs) derived from literature. Linear mixed-effect models (LMEM) were used to assess the effects of sex, lighting condition and their interactions for each RSN obtained with group-ICA (RSNs-GICA) and six bilateral RSNs adapted from literature (RSNs-LIT). Our study highlighted new RSNs in mice related to day/night alternation in addition to other networks already reported in the literature. In mature mice, we found sex-related differences in brain activation only in one RSNs-GICA comprising the cortical, hippocampal, midbrain and cerebellar regions of the right hemisphere. In males, brain activity was significantly higher in the left hippocampus, the retrosplenial cortex, the superior colliculus, and the cerebellum regardless of lighting condition; consistent with the role of these structures in memory formation and integration, sleep, and sex-differences in memory processing. Experimental constraints limited the analysis to the impact of light/dark cycle on the RSNs for middle-aged females. We detected significant activation in the pineal gland during the dark condition, a finding in line with the nocturnal activity of this gland. For the analysis of RSNs-LIT, new variables "sexage" (sex and age combined) and "edges" (pairs of RSNs) were introduced. FC was calculated as the Pearson correlation between two RSNs. LMEM revealed no effect of sexage or lighting condition. The FC depended on the edges, but there were no interaction effects between sexage, lighting condition and edges. Interaction effects were detected between i) sex and lighting condition, with higher FC in males under the dark condition, ii) sexage and edges with higher FC in male brain regions related to vision, memory, and motor action. We conclude that time of day and sex should be taken into account when designing, analyzing, and interpreting functional imaging studies in rodents.

Machine Learning Analysis Classifies Patients with Primary Angle-Closure Glaucoma Using Abnormal Brain White Matter Function

Objective

Primary angle-closure glaucoma (PACG) is a globally prevalent, irreversible eye disease leading to blindness. Previous neuroimaging studies demonstrated that PACG patients were associated with gray matter function changes. However, whether the white matter(WM) function changes in PACG patients remains unknown. The purpose of the study is to investigate WM function changes in the PACG patients.

Methods

In total, 40 PACG patients and 40 well-matched HCs participated in our study and underwent resting-state functional magnetic resonance imaging (rs-fMRI) scans. We compared between-group differences between PACG patients and HC in the WM function using amplitude of low-frequency fluctuations (ALFF). In addition, the SVM method was applied to the construction of the PACG classification model.

Results

Compared with the HC group, ALFF was attenuated in right posterior thalamic radiation (include optic radiation), splenium of corpus callosum, and left tapetum in the PACG group, the results are statistically significant (GRF correction, voxel-level P < 0.001, cluster-level P < 0.05). Furthermore, the SVM classification had an accuracy of 80.0% and an area under the curve (AUC) of 0.86 for distinguishing patients with PACG from HC.

Conclusion

The findings of our study uncover abnormal WM functional alterations in PACG patients and mainly involves vision-related regions. These findings provide new insights into widespread brain damage in PACG from an alternative WM functional perspective.

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.

Neuropathological characteristics of abnormal white matter functional signaling in adolescents with major depression

BACKGROUND

Major depression disorder (MDD) constitutes a significant mental health concern. Epidemiological surveys indicate that the lifetime prevalence of depression in adolescents is much higher than that in adults, with a corresponding increased risk of suicide. In studying brain dysfunction associated with MDD in adole-scents, research on brain white matter (WM) is sparse. Some researchers even mistakenly regard the signals generated by the WM as noise points. In fact, studies have shown that WM exhibits similar blood oxygen level-dependent signal fluctuations. The alterations in WM signals and their relationship with disease severity in adolescents with MDD remain unclear.

AIM

To explore potential abnormalities in WM functional signals in adolescents with MDD.

METHODS

This study involved 48 adolescent patients with MDD and 31 healthy controls (HC). All participants were assessed using the Patient Health Questionnaire-9 Scale and the mini international neuropsychiatric interview (MINI) suicide inventory. In addition, a Siemens Skyra 3.0T magnetic resonance scanner was used to obtain the subjects' image data. The DPABI software was utilized to calculate the WM signal of the fractional amplitude of low frequency fluctuations (fALFF) and regional homogeneity, followed by a two-sample t-test between the MDD and HC groups. Independent component analysis (ICA) was also used to evaluate the WM functional signal. Pearson's correlation was performed to assess the relationship between statistical test results and clinical scales.

RESULTS

Compared to HC, individuals with MDD demonstrated a decrease in the fALFF of WM in the corpus callosum body, left posterior limb of the internal capsule, right superior corona radiata, and bilateral posterior corona radiata [P < 0.001, family-wise error (FWE) voxel correction]. The regional homogeneity of WM increased in the right posterior limb of internal capsule and left superior corona radiata, and decreased in the left superior longitudinal fasciculus (P < 0.001, FWE voxel correction). The ICA results of WM overlapped with those of regional homo-geneity. The fALFF of WM signal in the left posterior limb of the internal capsule was negatively correlated with the MINI suicide scale (P = 0.026, r = -0.32), and the right posterior corona radiata was also negatively correlated with the MINI suicide scale (P = 0.047, r = -0.288).

CONCLUSION

Adolescents with MDD involves changes in WM functional signals, and these differences in brain regions may increase the risk of suicide.

The missing third dimension-Functional correlations of BOLD signals incorporating white matter

Correlations between magnetic resonance imaging (MRI) blood oxygenation level-dependent (BOLD) signals from pairs of gray matter areas are used to infer their functional connectivity, but they are unable to describe how white matter is engaged in brain networks. Recently, evidence that BOLD signals in white matter are robustly detectable and are modulated by neural activities has accumulated. We introduce a three-way correlation between BOLD signals from pairs of gray matter volumes (nodes) and white matter bundles (edges) to define the communication connectivity through each white matter bundle. Using MRI images from publicly available databases, we show, for example, that the three-way connectivity is influenced by age. By integrating functional MRI signals from white matter as a third component in network analyses, more comprehensive descriptions of brain function may be obtained.

Whole-brain, gray, and white matter time-locked functional signal changes with simple tasks and model-free analysis

Recent studies have revealed the production of time-locked blood oxygenation level-dependent (BOLD) functional MRI (fMRI) signals throughout the entire brain in response to tasks, challenging the existence of sparse and localized brain functions and highlighting the pervasiveness of potential false negative fMRI findings. "Whole-brain" actually refers to gray matter, the only tissue traditionally studied with fMRI. However, several reports have demonstrated reliable detection of BOLD signals in white matter, which have previously been largely ignored. Using simple tasks and analyses, we demonstrate BOLD signal changes across the whole brain, in both white and gray matters, in similar manner to previous reports of whole brain studies. We investigated whether white matter displays time-locked BOLD signals across multiple structural pathways in response to a stimulus in a similar manner to the cortex. We find that both white and gray matter show time-locked activations across the whole brain, with a majority of both tissue types showing statistically significant signal changes for all task stimuli investigated. We observed a wide range of signal responses to tasks, with different regions showing different BOLD signal changes to the same task. Moreover, we find that each region may display different BOLD responses to different stimuli. Overall, we present compelling evidence that, just like all gray matter, essentially all white matter in the brain shows time-locked BOLD signal changes in response to multiple stimuli, challenging the idea of sparse functional localization and the prevailing wisdom of treating white matter BOLD signals as artifacts to be removed.

Females exhibit smaller volumes of brain activation and lower inter-subject variability during motor tasks

Past work has shown that brain structure and function differ between females and males. Males have larger cortical and sub-cortical volume and surface area (both total and subregional), while females have greater cortical thickness in most brain regions. Functional differences are also reported in the literature, yet to date little work has systematically considered whether patterns of brain activity indexed with functional magnetic resonance imaging (fMRI) differ between females and males. The current study sought to remediate this issue by employing task-based whole brain motor mapping analyses using an openly available dataset. We tested differences in patterns of functional brain activity associated with 12 voluntary movement patterns in females versus males. Results suggest that females exhibited smaller volumes of brain activation across all 12 movement tasks, and lower patterns of variability in 10 of the 12 movements. We also observed that females had greater cortical thickness, which is in alignment with previous analyses of structural differences. Overall, these findings provide a basis for considering biological sex in future fMRI research and provide a foundation of understanding differences in how neurological pathologies present in females vs males.

White matter functional gradients and their formation in adolescence

It is well known that functional magnetic resonance imaging (fMRI) is a widely used tool for studying brain activity. Recent research has shown that fluctuations in fMRI data can reflect functionally meaningful patterns of brain activity within the white matter. We leveraged resting-state fMRI from an adolescent population to characterize large-scale white matter functional gradients and their formation during adolescence. The white matter showed gray-matter-like unimodal-to-transmodal and sensorimotor-to-visual gradients with specific cognitive associations and a unique superficial-to-deep gradient with nonspecific cognitive associations. We propose two mechanisms for their formation in adolescence. One is a "function-molded" mechanism that may mediate the maturation of the transmodal white matter via the transmodal gray matter. The other is a "structure-root" mechanism that may support the mutual mediation roles of the unimodal and transmodal white matter maturation during adolescence. Thus, the spatial layout of the white matter functional gradients is in concert with the gray matter functional organization. The formation of the white matter functional gradients may be driven by brain anatomical wiring and functional needs.

Seasonal variations of functional connectivity of human brains

Seasonal variations have long been observed in various aspects of human life. While there is an abundance of research that has characterized seasonality effects in, for example, cognition, mood, and behavior, including studies of underlying biophysical mechanisms, direct measurements of seasonal variations of brain functional activities have not gained wide attention. We have quantified seasonal effects on functional connectivity as derived from MRI scans. A cohort of healthy human subjects was divided into four groups based on the seasons of their scanning dates as documented in the image database of the Human Connectome Project. Sinusoidal functions were used as regressors to determine whether there were significant seasonal variations in measures of brain activities. We began with the analysis of seasonal variations of the fractional amplitudes of low frequency fluctuations of regional functional signals, followed by the seasonal variations of functional connectivity in both global- and network-level. Furthermore, relevant environmental factors, including average temperature and daylength, were found to be significantly associated with brain functional activities, which may explain how the observed seasonal fluctuations arise. Finally, topological properties of the brain functional network also showed significant variations across seasons. All the observations accumulated revealed seasonality effects of human brain activities in a resting-state, which may have important practical implications for neuroimaging research.

BOLD Response is more than just magnitude: Improving detection sensitivity through capturing hemodynamic profiles

Typical fMRI analyses often assume a canonical hemodynamic response function (HRF) that primarily focuses on the peak height of the overshoot, neglecting other morphological aspects. Consequently, reported analyses often reduce the overall response curve to a single scalar value. In this study, we take a data-driven approach to HRF estimation at the whole-brain voxel level, without assuming a response profile at the individual level. We then employ a roughness penalty at the population level to estimate the response curve, aiming to enhance predictive accuracy, inferential efficiency, and cross-study reproducibility. By examining a fast event-related FMRI dataset, we demonstrate the shortcomings and information loss associated with adopting the canonical approach. Furthermore, we address the following key questions: 1) To what extent does the HRF shape vary across different regions, conditions, and participant groups? 2) Does the data-driven approach improve detection sensitivity compared to the canonical approach? 3) Can analyzing the HRF shape help validate the presence of an effect in conjunction with statistical evidence? 4) Does analyzing the HRF shape offer evidence for whole-brain response during a simple task?

Whole-brain structure-function coupling abnormalities in mild cognitive impairment: a study combining amplitude of low-frequency fluctuations and voxel-based morphometry

Alzheimer's disease (AD), one of the leading diseases of the nervous system, is accompanied by symptoms such as loss of memory, thinking and language skills. Both mild cognitive impairment (MCI) and very mild cognitive impairment (VMCI) are the transitional pathological stages between normal aging and AD. While the changes in whole-brain structural and functional information have been extensively investigated in AD, The impaired structure-function coupling remains unknown. The current study employed the OASIS-3 dataset, which includes 53 MCI, 90 VMCI, and 100 Age-, gender-, and education-matched normal controls (NC). Several structural and functional parameters, such as the amplitude of low-frequency fluctuations (ALFF), voxel-based morphometry (VBM), and The ALFF/VBM ratio, were used To estimate The whole-brain neuroimaging changes In MCI, VMCI, and NC. As disease symptoms became more severe, these regions, distributed in the frontal-inf-orb, putamen, and paracentral lobule in the white matter (WM), exhibited progressively increasing ALFF (ALFFNC < ALFFVMCI < ALFFMCI), which was similar to the tendency for The cerebellum and putamen in the gray matter (GM). Additionally, as symptoms worsened in AD, the cuneus/frontal lobe in the WM and the parahippocampal gyrus/hippocampus in the GM showed progressively decreasing structure-function coupling. As the typical focal areas in AD, The parahippocampal gyrus and hippocampus showed significant positive correlations with the severity of cognitive impairment, suggesting the important applications of the ALFF/VBM ratio in brain disorders. On the other hand, these findings from WM functional signals provided a novel perspective for understanding the pathophysiological mechanisms involved In cognitive decline in AD.

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.

Intracranial electrophysiological and structural basis of BOLD functional connectivity in human brain white matter

While functional MRI (fMRI) studies have mainly focused on gray matter, recent studies have consistently found that blood-oxygenation-level-dependent (BOLD) signals can be reliably detected in white matter, and functional connectivity (FC) has been organized into distributed networks in white matter. Nevertheless, it remains unclear whether this white matter FC reflects underlying electrophysiological synchronization. To address this question, we employ intracranial stereotactic-electroencephalography (SEEG) and resting-state fMRI data from a group of 16 patients with drug-resistant epilepsy. We find that BOLD FC is correlated with SEEG FC in white matter, and this result is consistent across a wide range of frequency bands for each participant. By including diffusion spectrum imaging data, we also find that white matter FC from both SEEG and fMRI are correlated with white matter structural connectivity, suggesting that anatomical fiber tracts underlie the functional synchronization in white matter. These results provide evidence for the electrophysiological and structural basis of white matter BOLD FC, which could be a potential biomarker for psychiatric and neurological disorders.

White Matter Changes as an Independent Predictor of Alzheimer's Disease

BACKGROUND

Abnormalities in white matter (WM) may be a crucial physiologic feature of Alzheimer's disease (AD). However, neuroimaging's ability to visualize the underlying functional degradation of the WM region in AD is unclear.

OBJECTIVE

This study aimed to explore the differences in amplitude of low-frequency fluctuation (ALFF) and fractional ALFF (fALFF) in the WM region of patients with AD and healthy controls (HC) and to investigate further whether these values can provide supplementary information for diagnosing AD.

METHODS

Forty-eight patients with AD and 46 age-matched HC were enrolled and underwent resting-state functional magnetic resonance imaging and a neuropsychological battery assessment. We analyzed the differences in WM activity between the two groups and further explored the correlation between WM activity in the different regions and cognitive function in the AD group. Finally, a machine learning algorithm was adopted to construct a classifier in detecting the clinical classification ability of the values of ALFF/ALFF in the WM.

RESULTS

Compared with HCs, patients with AD had lower WM activity in the right anterior thalamic radiation, left frontal aslant tract, and left forceps minor, which are all positively related to global cognitive function, memory, and attention function (all p < 0.05). Based on the combined WM ALFF and fALFF characteristics in the different regions, individuals not previously assessed were classified with moderate accuracy (75%), sensitivity (71%), specificity (79%), and area under the receiver operating characteristic curve (85%).

CONCLUSION

Our results suggest that WM activity is reduced in AD and can be used for disease classification.

A comparison of how deep brain stimulation in two targets with anti-compulsive efficacy modulates brain activity using fMRI in awake rats

Deep brain stimulation (DBS) is an established neuromodulatory intervention against otherwise treatment-refractory obsessive-compulsive disorder (OCD). Several DBS targets, all of which are part of brain networks connecting basal ganglia and prefrontal cortex, alleviate OCD symptoms. Stimulation of these targets is thought to unfold its therapeutic effect by modulation of network activity through internal capsule (IC) connections. Research into DBS-induced network changes and the nature of IC-related effects of DBS in OCD is needed to further improve DBS. Here, we studied the effects of DBS at the ventral medial striatum (VMS) and IC on blood-oxygen level dependent (BOLD) responses in awake rats using functional magnetic resonance imaging (fMRI). BOLD-signal intensity was measured in five regions of interest (ROIs): medial and orbital prefrontal cortex, nucleus accumbens (NAc), IC area, and mediodorsal thalamus. In previous rodent studies, stimulation at both target locations resulted in a reduction of OCD-like behavior and activation of prefrontal cortical areas. Therefore, we hypothesized that stimulation at both targets would result in partially overlapping BOLD responses. Both differential and overlapping activity between VMS and IC stimulation was found. Stimulating the caudal part of the IC resulted in activation around the electrode, while stimulating the rostral part of the IC resulted in increased cross-correlations between the IC area, orbitofrontal cortex, and NAc. Stimulation of the dorsal part of the VMS resulted in increased activity in the IC area, suggesting this area is activated during both VMS and IC stimulation. This activation is also indicative of VMS-DBS impacting corticofugal fibers running through the medial caudate into the anterior IC, and both VMS and IC DBS might act on these fibers to induce OCD-reducing effects. These results show that rodent fMRI with simultaneous electrode stimulation is a promising approach to study the neural mechanisms of DBS. Comparing the effects of DBS in different target areas has the potential to improve our understanding of the neuromodulatory changes that take place across various networks and connections in the brain. Performing this research in animal disease models will lead to translational insights in the mechanisms underlying DBS, and can aid improvement and optimization of DBS in patient populations.

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.

Disrupted functional networks within white-matter served as neural features in adolescent patients with conduct disorder

BACKGROUND

Conduct disorder (CD) has been conceptualized as a psychiatric disorder associated with white-matter (WM) structural abnormalities. Although diffusion tensor imaging could identify WM structural architecture changes, it cannot characterize functional connectivity (FC) within WM. Few studies have focused on disentangling the WM dysfunctions in CD patients by using functional magnetic resonance imaging (fMRI).

METHODS

The resting-state fMRI data were first obtained from both adolescent CD and typically developing (TD) controls. A voxel-based clustering analysis was utilized to identify the large-scale WM FC networks. Then, we examined the disrupted WM network features in CD, and further investigated whether these features could predict the impulsive symptoms in CD using support vector regression prediction model.

RESULTS

We identified 11 WM functional networks. Compared with TDs, CD patients showed increased FCs between occipital network (ON) and superior temporal network (STN), between orbitofrontal network (OFN) and corona radiate network (CRN), as well as between deep network and CRN. Further, the disrupted FCs between ON and STN and between OFN and CRN were significantly negatively associated with non-planning impulsivity scores in CD. Moreover, the disrupted WM networks could be served as features to predict the motor impulsivity scores in CD.

CONCLUSIONS

Our results provided further support on the existence of WM functional networks and could extended our knowledge about the WM functional abnormalities related with emotional and perception processing in CD patients from the view of WM dysfunction.

Distance-dependent reconfiguration of hubs in Alzheimer's disease: a cross-tissue functional network study

The hubs of the intra-grey matter (GM) network were sensitive to anatomical distance and susceptible to neuropathological damage. However, few studies examined the hubs of cross-tissue distance-dependent networks and their changes in Alzheimer's disease (AD). Using resting-state fMRI data of 30 AD patients and 37 normal older adults (NC), we constructed the cross-tissue networks based on functional connectivity (FC) between GM and white matter (WM) voxels. In the full-ranged and distance-dependent networks (characterized by gradually increased Euclidean distances between GM and WM voxels), their hubs were identified with weight degree metrics (frWD and ddWD). We compared these WD metrics between AD and NC; using the resultant abnormal WDs as the seeds, we performed seed-based FC analysis. With increasing distance, the GM hubs of distance-dependent networks moved from the medial to lateral cortices, and the WM hubs spread from the projection fibers to longitudinal fascicles. Abnormal ddWD metrics in AD were primarily located in the hubs of distance-dependent networks around 20-100mm. Decreased ddWDs were located in the left corona radiation (CR), which had decreased FCs with the executive network's GM regions in AD. Increased ddWDs were located in the posterior thalamic radiation (PTR) and the temporal-parietal-occipital junction (TPO), and their FCs were larger in AD. Increased ddWDs were shown in the sagittal striatum, which had larger FCs with the salience network's GM regions in AD. The reconfiguration of cross-tissue distance-dependent networks possibly reflected the disruption in the neural circuit of executive function and the compensatory changes in the neural circuits of visuospatial and social-emotional functions in AD.

Baseline white matter function predicts short-term treatment response in first-episode schizophrenia

BACKGROUND AND PURPOSE

The detection and characterization of functional activities in the gray matter of schizophrenia (SZ) have been widely explored. However, the relationship between resting-state functional signals in the white matter of first-episode SZ and short-term treatment response remains unclear.

METHODS

Thirty-six patients with first-episode SZ and 44 matched healthy controls were recruited in this study. Patients were classified as nonresponders and responders based on response to antipsychotic medication during a single hospitalization. The fractional amplitude of low-frequency fluctuation (fALFF), regional homogeneity (ReHo), and functional connectivity (FC) of white matter were calculated. The relationships between functional changes and clinical features were analyzed. In addition, voxel-based morphometry was performed to analyze the white matter volume.

RESULTS

One-way analysis of variance showed significant differences of fALFF and ReHo in the left posterior thalamic radiation and left cingulum (hippocampus) in the patient group, and the areas were regarded as seeds. The FC was calculated between seeds and other white matter networks. Compared with responders, nonresponders showed significantly increased FC between the left cingulum (hippocampus) and left posterior thalamic radiation, splenium of corpus callosum, and left tapetum, and were associated with the changes of clinical assessment. However, there was no difference in white matter volume between groups.

CONCLUSION

Our work provides a novel insight that psycho-neuroimaging-based white matter function holds promise for influencing the clinical diagnosis and treatment of SZ.

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.

Neuronal nitric oxide synthase positive neurons in the human corpus callosum: a possible link with the callosal blood-oxygen-level dependent (BOLD) effect

Brain functions have been investigated in the past decades via the blood-oxygen-level dependent (BOLD) effect using functional magnetic resonance imaging. One hypothesis explaining the BOLD effect involves the Nitric Oxide (NO) gaseous neurotransmitter, possibly released also by cells in the corpus callosum (CC). The eventual presence of NO releasing neurons and/or glial cells in the CC can be assessed by immunohistochemistry. Serial sections both from paraffin-embedded and frozen samples of CC obtained from adult human brains autopsy were studied with immunohistochemistry and immunofluorescence analysis, using an antibody against the neuronal isoform of Nitric Oxide Synthase (nNOS), the enzyme synthetizing the NO. The staining revealed the presence of many nNOS-immunopositive cells in the CC, shown to be neurons with immunofluorescence. Neuronal NOS-positive neurons presented different morphologies, were more numerous 4 mm apart from the midline, and displayed a peak in the body of the CC. In some cases, they were located at the upper boundary of the CC, more densely packed in the proximity of the callosal arterioles. The significant presence of nNOS-immunopositive neurons within the commissure suggests their probable role in the CC neurovascular regulation in the adult brain and could explain the BOLD effect detected in human CC.

Whole-brain, gray and white matter time-locked functional signal changes with simple tasks and model-free analysis

Recent studies have revealed the production of time-locked blood oxygenation-level dependent (BOLD) functional MRI (fMRI) signals throughout the entire brain in response to a task, challenging the idea of sparse and localized brain functions, and highlighting the pervasiveness of potential false negative fMRI findings. In these studies, 'whole-brain' refers to gray matter regions only, which is the only tissue traditionally studied with fMRI. However, recent reports have also demonstrated reliable detection and analyses of BOLD signals in white matter which have been largely ignored in previous reports. Here, using model-free analysis and simple tasks, we investigate BOLD signal changes in both white and gray matters. We aimed to evaluate whether white matter also displays time-locked BOLD signals across all structural pathways in response to a stimulus. We find that both white and gray matter show time-locked activations across the whole-brain, with a majority of both tissue types showing statistically significant signal changes for all task stimuli investigated. We observed a wide range of signal responses to tasks, with different regions showing very different BOLD signal changes to the same task. Moreover, we find that each region may display different BOLD responses to different stimuli. Overall, we present compelling evidence that the whole brain, including both white and gray matter, show time-locked activation to multiple stimuli, not only challenging the idea of sparse functional localization, but also the prevailing wisdom of treating white matter BOLD signals as artefacts to be removed.

Neurofeedback-dependent influence of the ventral striatum using a working memory paradigm targeting the dorsolateral prefrontal cortex

Executive functions and motivation have been established as key aspects for neurofeedback success. However, task-specific influence of cognitive strategies is scarcely explored. In this study, we test the ability to modulate the dorsolateral prefrontal cortex, a strong candidate for clinical application of neurofeedback in several disorders with dysexecutive syndrome, and investigate how feedback contributes to better performance in a single session. Participants of both neurofeedback (n = 17) and sham-control (n = 10) groups were able to modulate DLPFC in most runs (with or without feedback) while performing a working memory imagery task. However, activity in the target area was higher and more sustained in the active group when receiving feedback. Furthermore, we found increased activity in the nucleus accumbens in the active group, compared with a predominantly negative response along the block in participants receiving sham feedback. Moreover, they acknowledged the non-contingency between imagery and feedback, reflecting the impact on motivation. This study reinforces DLPFC as a robust target for neurofeedback clinical implementations and enhances the critical influence of the ventral striatum, both poised to achieve success in the self-regulation of brain activity.

A multimodal neuroimaging study of brain abnormalities and clinical correlates in post treatment Lyme disease

Lyme disease is the most common vector-borne infectious disease in the United States. Post-treatment Lyme disease (PTLD) is a condition affecting 10-20% of patients in which symptoms persist despite antibiotic treatment. Cognitive complaints are common among those with PTLD, suggesting that brain changes are associated with the course of the illness. However, there has been a paucity of evidence to explain the cognitive difficulties expressed by patients with PTLD. This study administered a working memory task to a carefully screened group of 12 patients with well-characterized PTLD and 18 healthy controls while undergoing functional MRI (fMRI). A subset of 12 controls and all 12 PTLD participants also received diffusion tensor imaging (DTI) to measure white matter integrity. Clinical variables were also assessed and correlated with these multimodal MRI findings. On the working memory task, the patients with PTLD responded more slowly, but no less accurately, than did controls. FMRI activations were observed in expected regions by the controls, and to a lesser extent, by the PTLD participants. The PTLD group also hypoactivated several regions relevant to the task. Conversely, novel regions were activated by the PTLD group that were not observed in controls, suggesting a compensatory mechanism. Notably, three activations were located in white matter of the frontal lobe. DTI measures applied to these three regions of interest revealed that higher axial diffusivity correlated with fewer cognitive and neurological symptoms. Whole-brain DTI analyses revealed several frontal lobe regions in which higher axial diffusivity in the patients with PTLD correlated with longer duration of illness. Together, these results show that the brain is altered by PTLD, involving changes to white matter within the frontal lobe. Higher axial diffusivity may reflect white matter repair and healing over time, rather than pathology, and cognition appears to be dynamically affected throughout this repair process.

The White Matter Functional Abnormalities in Patients with Transient Ischemic Attack: A Reinforcement Learning Approach

Background

Transient ischemic attack (TIA) is a known risk factor for stroke. Abnormal alterations in the low-frequency range of the gray matter (GM) of the brain have been studied in patients with TIA. However, whether there are abnormal neural activities in the low-frequency range of the white matter (WM) in patients with TIA remains unknown. The current study applied two resting-state metrics to explore functional abnormalities in the low-frequency range of WM in patients with TIA. Furthermore, a reinforcement learning method was used to investigate whether altered WM function could be a diagnostic indicator of TIA.

Methods

We enrolled 48 patients with TIA and 41 age- and sex-matched healthy controls (HCs). Resting-state functional magnetic resonance imaging (rs-fMRI) and clinical/physiological/biochemical data were collected from each participant. We compared the group differences between patients with TIA and HCs in the low-frequency range of WM using two resting-state metrics: amplitude of low-frequency fluctuation (ALFF) and fractional ALFF (fALFF). The altered ALFF and fALFF values were defined as features of the reinforcement learning method involving a Q-learning algorithm.

Results

Compared with HCs, patients with TIA showed decreased ALFF in the right cingulate gyrus/right superior longitudinal fasciculus/left superior corona radiata and decreased fALFF in the right cerebral peduncle/right cingulate gyrus/middle cerebellar peduncle. Based on these two rs-fMRI metrics, an optimal Q-learning model was obtained with an accuracy of 82.02%, sensitivity of 85.42%, specificity of 78.05%, precision of 82.00%, and area under the curve (AUC) of 0.87.

Conclusion

The present study revealed abnormal WM functional alterations in the low-frequency range in patients with TIA. These results support the role of WM functional neural activity as a potential neuromarker in classifying patients with TIA and offer novel insights into the underlying mechanisms in patients with TIA from the perspective of WM function.

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.

Large-scale dysfunctional white matter and grey matter networks in patients with social anxiety disorder

Dysfunction of large-scale brain networks has been implicated in social anxiety disorder (SAD); most work has focused on grey matter (GM) functional connectivity (FC) abnormalities, whereas white matter (WM) FC alterations remain unclear. Here, using a K-means clustering algorithm, we obtained 8 GM and 10 WM functional networks from a cohort dataset (48 SAD patients and 48 healthy controls). By calculating and comparing FC matrices between SAD group and healthy controls, we demonstrated disrupted connections between the limbic and dorsal prefrontal, lateral temporal, and sensorimotor networks, and between the visual and sensorimotor networks. Furthermore, there were negative correlations between HAMD scores and limbic-dorsal prefrontal and limbic-sensorimotor networks, and between illness duration and sensorimotor-visual networks. These findings reflect the critical role of limbic network, with its extensive connections to other networks, and the neurobiology of disordered cognition processing and emotional regulation in SAD.

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Anomalous interactions between large-scale functional networks were identified in SAD

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The limbic, prefrontal, and perceptual networks underlie the neurobiology of SAD

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The white matter functional network is physiologically important

Altered functional synchrony between gray and white matter as a novel indicator of brain system dysconnectivity in schizophrenia

BACKGROUND

There is increasing evidence that blood oxygenation level-dependent signaling in white matter (WM) reflects WM functional activity. Whether this activity is altered in schizophrenia remains uncertain, as does whether it is related to established alterations of gray matter (GM) or the microstructure of WM tracts.

METHODS

A total of 153 antipsychotic-naïve schizophrenia patients and 153 healthy comparison subjects were assessed by resting-state functional magnetic resonance imaging, diffusion tensor imaging, and high-resolution T1-weighted imaging. We tested for case-control differences in the functional activity of WM, and examined their relation to the functional activity of GM and WM microstructure. The relations between fractional anisotropy (FA) in WM and GM-WM functional synchrony were investigated as well. Then, we examined the associations of identified abnormalities to age, duration of untreated psychosis (DUP), and symptom severity.

RESULTS

Schizophrenia patients displayed reductions of the amplitude of low-frequency fluctuations (ALFF), GM-WM functional synchrony, and FA in widespread regions. Specifically, the genu of corpus callosum not only had weakening in the synchrony of functional activity but also had reduced ALFF and FA. Positive associations were found between FA and functional synchrony in the genu of corpus callosum as well. No significant association was found between identified abnormalities and DUP, and symptom severity.

CONCLUSIONS

The widespread weakening in the synchrony of functional activity of GM and WM provided novel evidence for functional alterations in schizophrenia. Regarding the WM function as a component of brain systems and investigating its alternation represent a promising direction for future research.

Increased myelination plays a central role in white matter neuroplasticity

White matter (WM) neuroplasticity in the human brain has been tracked non-invasively using advanced magnetic resonance imaging techniques, with increasing evidence for improved axonal transmission efficiency as a central mechanism. The current study is the culmination of a series of studies, which characterized the structure-function relationship of WM transmission efficiency in the cortico-spinal tract (CST) during motor learning. Here, we test the hypothesis that increased transmission efficiency is linked directly to increased myelination using myelin water imaging (MWI). MWI was used to evaluate neuroplasticity-related improvements in the CST. The MWI findings were then compared to diffusion tensor imaging (DTI) results, with the secondary hypothesis that radial diffusivity (RD) would have a stronger relationship than axial diffusivity (AD) if the changes were due to increased myelination. Both MWI and RD data showed the predicted pattern of significant results, strongly supporting that increased myelination plays a central role in WM neuroplasticity.

Detection of functional activity in brain white matter using fiber architecture informed synchrony mapping

A general linear model is widely used for analyzing fMRI data, in which the blood oxygenation-level dependent (BOLD) signals in gray matter (GM) evoked in response to neural stimulation are modeled by convolving the time course of the expected neural activity with a canonical hemodynamic response function (HRF) obtained a priori. The maps of brain activity produced reflect the magnitude of local BOLD responses. However, detecting BOLD signals in white matter (WM) is more challenging as the BOLD signals are weaker and the HRF is different, and may vary more across the brain. Here we propose a model-free approach to detect changes in BOLD signals in WM by measuring task-evoked increases of BOLD signal synchrony in WM fibers. The proposed approach relies on a simple assumption that, in response to a functional task, BOLD signals in relevant fibers are modulated by stimulus-evoked neural activity and thereby show greater synchrony than when measured in a resting state, even if their magnitudes do not change substantially. This approach is implemented in two technical stages. First, for each voxel a fiber-architecture-informed spatial window is created with orientation distribution functions constructed from diffusion imaging data. This provides the basis for defining neighborhoods in WM that share similar local fiber architectures. Second, a modified principal component analysis (PCA) is used to estimate the synchrony of BOLD signals in each spatial window. The proposed approach is validated using a 3T fMRI dataset from the Human Connectome Project (HCP) at a group level. The results demonstrate that neural activity can be reliably detected as increases in fMRI signal synchrony within WM fibers that are engaged in a task with high sensitivities and reproducibility.

Investigation of white matter functional networks underlying different behavioral profiles in attention-deficit/hyperactivity disorder

Background

Cortical functional network alterations have been widely accepted as the neural basis of attention-deficit/hyperactivity disorder (ADHD). Recently, white matter has also been recognized as a novel neuroimaging marker of psychopathology and has been used as a complement to cortical functional networks to investigate brain-behavior relationships. However, disorder-specific features of white matter functional networks (WMFNs) are less well understood than those of gray matter functional networks. In the current study, we constructed WMFNs using a new strategy to characterize behavior-related network features in ADHD.

Methods

We recruited 46 drug-naïve boys with ADHD and 46 typically developing (TD) boys, and used clustering analysis on resting-state functional magnetic resonance imaging data to generate WMFNs in each group. Intrinsic activity within each network was extracted, and the associations between network activity and behavior measures were assessed using correlation analysis.

Results

Nine WMFNs were identified for both ADHD and TD participants. However, boys with ADHD showed a splitting of the inferior corticospinal-cerebellar network and lacked a cognitive control network. In addition, boys with ADHD showed increased activity in the dorsal attention network and somatomotor network, which correlated positively with attention problems and hyperactivity symptom scores, respectively, while they presented decreased activity in the frontoparietal network and frontostriatal network in association with poorer performance in response inhibition, working memory, and verbal fluency.

Conclusions

We discovered a dual pattern of white matter network activity in drug-naïve ADHD boys, with hyperactive symptom-related networks and hypoactive cognitive networks. These findings characterize two distinct types of WMFN in ADHD psychopathology.

Anomalous and heterogeneous characteristics of the BOLD hemodynamic response function in white matter

Detailed knowledge of the BOLD hemodynamic response function (HRF) is crucial for accurate analyses and interpretation of functional MRI data. Considerable efforts have been made to characterize the HRF in gray matter (GM), but much less attention has been paid to BOLD effects in white matter (WM). However, several recent reports have demonstrated reliable detection and analyses of WM BOLD signals both after stimulation and in a resting state. WM and GM differ in composition, energy requirements, and blood flow, so their neurovascular couplings also may well be different. We aimed to derive a comprehensive characterization of the HRF in WM across a population, including accurate measurements of its shape and its variation along and between WM pathways, using resting-state fMRI acquisitions. Our results show that the HRF is significantly different between WM and GM. Features of the HRF, such as a prominent initial dip, show strong relationships with features of the tissue microstructure derived from diffusion imaging, and these relationships differ between WM and GM, consistent with BOLD signal fluctuations reflecting different energy demands and neurovascular couplings in tissues of different composition and function. We also show that the HRF varies in shape significantly along WM pathways and is different between different WM pathways, suggesting the temporal evolution of BOLD signals after an event vary in different parts of the WM. These features of the HRF in WM are especially relevant for interpretation of the biophysical basis of BOLD effects in WM.

Combined functional and structural imaging of brain white matter reveals stage-dependent impairment in multiple system atrophy of cerebellar type

Advances in fMRI of brain white matter (WM) have established the feasibility of understanding how functional signals of WM evolve with brain diseases. By combining functional signals with structural features of WM, the current study characterizes functional and structural impairments of WM in cerebelar type multiple system atrophy, with the goal to derive new mechanistic insights into the pathological progression of this disease. Our analysis of 30 well-diagnosed patients revealed pronounced decreases in functional connectivity in WM bundles of the cerebellum and brainstem, and concomitant local structural alterations that depended on the disease stage. The novel findings implicate a critical time point in the pathological evolution of the disease, which could guide optimal therapeutic interventions. Furthermore, fMRI signals of impaired WM bundles exhibited superior sensitivity in differentiating initial disease development, which demonstrates great potential of using these signals to inform disease management.

Increased functional connectivity of white-matter in myotonic dystrophy type 1

Background

Myotonic dystrophy type 1 (DM1) is the most common and dominant inherited neuromuscular dystrophy disease in adults, involving multiple organs, including the brain. Although structural measurements showed that DM1 is predominantly associated with white-matter damage, they failed to reveal the dysfunction of the white-matter. Recent studies have demonstrated that the functional activity of white-matter is of great significance and has given us insights into revealing the mechanisms of brain disorders.

Materials and methods

Using resting-state fMRI data, we adopted a clustering analysis to identify the white-matter functional networks and calculated functional connectivity between these networks in 16 DM1 patients and 18 healthy controls (HCs). A two-sample t-test was conducted between the two groups. Partial correlation analyzes were performed between the altered white-matter FC and clinical MMSE or HAMD scores.

Results

We identified 13 white-matter functional networks by clustering analysis. These white-matter functional networks can be divided into a three-layer network (superficial, middle, and deep) according to their spatial distribution. Compared to HCs, DM1 patients showed increased FC within intra-layer white-matter and inter-layer white-matter networks. For intra-layer networks, the increased FC was mainly located in the inferior longitudinal fasciculus, prefrontal cortex, and corpus callosum networks. For inter-layer networks, the increased FC of DM1 patients is mainly located in the superior corona radiata and deep networks.

Conclusion

Results demonstrated the abnormalities of white-matter functional connectivity in DM1 located in both intra-layer and inter-layer white-matter networks and suggested that the pathophysiology mechanism of DM1 may be related to the white-matter functional dysconnectivity. Furthermore, it may facilitate the treatment development of DM1.

The optimized combination of aCompCor and ICA-AROMA to reduce motion and physiologic noise in task fMRI data

One of the main challenges in fMRI processing is filtering the task BOLD signals from the noise. Independent component analysis with automatic removal of motion artifacts (ICA-AROMA) reduces motion artifacts by identifying ICA noise components based on their location at the brain edges and cerebrospinal fluid (CSF), high frequency content and correlation with motion regressors. In anatomical component correction (aCompCor), physiological noise regressors extracted from CSF were regressed out from the fMRI time series. In this study, we compared three methods to combine aCompCor and ICA-AROMA denoising in one denoising step. In the first analysis, we regressed the temporal signals of the ICA components identified as noise by ICA-AROMA together with the noise signals determined by aCompCor from the fMRI signals. For the second and third analyses, the correlation between the temporal signals of the ICA components and the aCompCor noise signals was used as an additional criterion to identify the noise components. In the second analysis, the temporal signals of the ICA components classified as noise were regressed from the fMRI signals. In the third analysis, the noise components were removed. To compare the denoising strategies, we examined the fractional amplitude of low-frequency fluctuations (fALFF) and the overlap between the contrast maps. Our results revealed that including the aCompCor noise signals as regressors in ICA-AROMA resulted in more correctly identified noise components, higher fALFF values, and larger activation maps. Moreover, combining the temporal signals of the noise components identified by ICA-AROMA with the aCompCor signals in a noise regression matrix resulted in deactivations. These results suggest that using the correlation between the ICA component temporal signals and the aCompCor signals as noise identification criteria in ICA-AROMA is the best approach for combining both denoising methods.

White matter substrates of functional connectivity dynamics in the human brain

The contribution of structural connectivity to functional connectivity dynamics is still far from being elucidated. Herein, we applied track-weighted dynamic functional connectivity (tw-dFC), a model integrating structural, functional, and dynamic connectivity, on high quality diffusion weighted imaging and resting-state fMRI data from two independent repositories. The tw-dFC maps were analyzed using independent component analysis, aiming at identifying spatially independent white matter components which support dynamic changes in functional connectivity. Each component consisted of a spatial map of white matter bundles that show consistent fluctuations in functional connectivity at their endpoints, and a time course representative of such functional activity. These components show high intra-subject, inter-subject, and inter-cohort reproducibility. We provided also converging evidence that functional information about white matter activity derived by this method can capture biologically meaningful features of brain connectivity organization, as well as predict higher-order cognitive performance.

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.

Changes in white matter functional networks during wakefulness and sleep

Blood oxygenation level‐dependent (BOLD) signals in the white matter (WM) have been demonstrated to encode neural activities by showing structure‐specific temporal correlations during resting‐state and task‐specific imaging of fiber pathways with various degrees of correlations in strength and time delay. Previous neuroimaging studies have shown state‐dependent functional connectivity and regional amplitude of signal fluctuations in brain gray matter across wakefulness and nonrapid eye movement (NREM) sleep cycles. However, the functional characteristics of WM during sleep remain unknown. Using simultaneous electroencephalography and functional magnetic resonance imaging data during wakefulness and NREM sleep collected from 66 healthy participants, we constructed 10 stable WM functional networks using clustering analysis. Functional connectivity between these WM functional networks and regional amplitude of WM signal fluctuations across multiple low‐frequency bands were evaluated. In general, decreased WM functional connectivity between superficial and middle layer WM functional networks was observed from wakefulness to sleep. In addition, functional connectivity between the deep and cerebellar networks was higher during light sleep and lower during both wakefulness and deep sleep. The regional fluctuation amplitude was always higher during light sleep and lower during deep sleep. Importantly, slow‐wave activity during deep sleep negatively correlated with functional connectivity between WM functional networks but positively correlated with fluctuation strength in the WM. These observations provide direct physiological evidence that neural activities in the WM are modulated by the sleep–wake cycle. This study provided the initial mapping of functional changes in WM during sleep.

Aberrant Cross-Tissue Functional Connectivity in Alzheimer’s Disease: Static, Dynamic, and Directional Properties

Background: BOLD signals in the gray matter (GM) and white matter (WM) are tightly coupled. However, our understanding of the cross-tissue functional network in Alzheimer’s disease (AD) is limited. Objective: We investigated the changes of cross-tissue functional connectivity (FC) metrics for the GM regions susceptible to AD damage. Methods: For each GM region in the default mode (DMN) and limbic networks, we obtained its low-order static FC with any WM region, and the high-order static FC between any two WM regions based on their FC pattern similarity with multiple GM regions. The dynamic and directional properties of cross-tissue FC were then acquired, specifically for the regional pairs whose low- or high-order static FCs showed significant differences between AD and normal control (NC). Moreover, these cross-tissue FC metrics were correlated with voxel-based GM volumes and MMSE in all participants. Results: Compared to NC, AD patients showed decreased low-order static FCs between the intra-hemispheric GM-WM pairs (right ITG-right fornix; left MoFG-left posterior corona radiata), and increased low-order static, dynamic, and directional FCs between the inter-hemispheric GM-WM pairs (right MTG-left superior/posterior corona radiata). The high-order static and directional FCs between the left cingulate bundle-left tapetum were increased in AD, based on their FCs with the GMs of DMN. Those decreased and increased cross-tissue FC metrics in AD had opposite correlations with memory-related GM volumes and MMSE (positive for the decreased and negative for the increased). Conclusion: Cross-tissue FC metrics showed opposite changes in AD, possibly as useful neuroimaging biomarkers to reflect neurodegenerative and compensatory mechanisms.

Characteristics of disrupted topological organization in white matter functional connectome in schizophrenia

BACKGROUND

Neuroimaging characteristics have demonstrated disrupted functional organization in schizophrenia (SZ), involving large-scale networks within grey matter (GM). However, previous studies have ignored the role of white matter (WM) in supporting brain function.

METHODS

Using resting-state functional MRI and graph theoretical approaches, we investigated global topological disruptions of large-scale WM and GM networks in 93 SZ patients and 122 controls. Six global properties [clustering coefficient (Cp), shortest path length (Lp), local efficiency (Eloc), small-worldness (σ), hierarchy (β) and synchronization (S) and three nodal metrics [nodal degree (Knodal), nodal efficiency (Enodal) and nodal betweenness (Bnodal)] were utilized to quantify the topological organization in both WM and GM networks.

RESULTS

At the network level, both WM and GM networks exhibited reductions in Eloc, Cp and S in SZ. The SZ group showed reduced σ and β only for the WM network. Furthermore, the Cp, Eloc and S of the WM network were negatively correlated with negative symptoms in SZ. At the nodal level, the SZ showed nodal disturbances in the corpus callosum, optic radiation, posterior corona radiata and tempo-occipital WM tracts. For GM, the SZ manifested increased nodal centralities in frontoparietal regions and decreased nodal centralities in temporal regions.

CONCLUSIONS

These findings provide the first evidence for abnormal global topological properties in SZ from the perspective of a substantial whole brain, including GM and WM. Nodal centralities enhance GM areas, along with a reduction in adjacent WM, suggest that WM functional alterations may be compensated for adjacent GM impairments in SZ.

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.