Detection of functional networks within white matter using independent component analysis

White matter dysfunction in Alzheimer's disease is associated with disease-related transcriptomic signatures

While anatomical white matter (WM) alterations in Alzheimer's disease (AD) are well-established, functional WM dysregulation remains rarely investigated. The current study examines WM functional connectivity and network properties alterations in AD and mild cognitive impairment (MCI) and further describes their spatially correlated genes. AD and MCI shared decreased functional connectivity, clustering coefficient, and local efficiency within WM regions involved in impaired sensory-motor, visual-spatial, language, or memory functions. AD-specific dysfunction (i.e., AD vs. MCI and cognitively unimpaired participants) was predominantly located in WM, including anterior and posterior limb of internal capsule, corona radiata, and left tapetum. This WM dysfunction spatially correlates with specific genes, which are enriched in multiple biological processes related to synaptic function and development, and are mostly active in neurons and astrocytes. These findings may contribute to understanding molecular, cellular, and functional signatures associated with WM damage in AD.

White matter functional connectome gradient dysfunction in major depressive disorder

Background

Major depressive disorder (MDD) is a prevalent psychiatric disorder with disruptions in brain white matter (WM). While much research has focused on WM structure, the dysfunctional organization of WM in MDD remains poorly understood.

Methods

Using resting-state functional magnetic resonance imaging data from 48 MDD patients and 68 healthy controls (HC), we characterized the WM functional connectome gradients across participants and identified both global and regional alterations in MDD. Furthermore, we examined the relationship between gradient properties and depressive symptom severity. External validation and sensitivity analyses were finally conducted to ensure the reliability of results.

Results

The principal WM connectome gradient extended from the forceps major and superior longitudinal fasciculus to the uncinate fasciculus (UF) and anterior thalamic radiation (ATR), exhibiting a superficial-to-deep pattern in both groups. Compared to HC, MDD patients displayed a narrower gradient range and lower spatial variation, indicating a contracted WM hierarchy. At the tract-specific level, MDD patients exhibited lower gradient scores in the forceps minor, left ATR and UF, and bilateral cingulate gyrus and cingulum hippocampus, but higher gradient scores in the forceps major, bilateral inferior longitudinal fasciculus and superior longitudinal fasciculus. WM tract gradient patterns explained 37.2% of the variance in clinical severity, with the strongest contributions from the inferior fronto-occipital fasciculus, cingulum hippocampus, ATR, UF, and corticospinal tract.

Conclusions

These findings highlight altered WM functional connectome gradient in MDD and their association with clinical severity, offering novel insights into the neurobiological mechanisms of the disorder and potential biomarkers for symptom evaluation.

Disrupted topology of the functional white matter connectome in thyroid-associated ophthalmopathy

BACKGROUND

This study aims to investigate the changes in the topological organization of WM functional connectivity in individuals with TAO, providing a novel and insightful perspective on the functional disruptions that characterize this condition.

METHODS

This study utilized resting-state functional Magnetic Resonance Imaging (rs-fMRI) to capture blood-oxygen-level-dependent (BOLD) signals and T1-weighted images from patients with TAO and healthy control subjects. Group-level masks for white matter were created to extract WM-related BOLD signals, facilitating the construction of a functional white matter network. Graph theory analysis was subsequently conducted to evaluate global metrics, nodal metrics, and modularity, alongside network-based analysis. Finally, support vector machines (SVM) were employed for classification.

RESULTS

A functional white matter network comprising 128 nodes and their respective connections was identified. The graph theory analysis revealed significant differences primarily in the sigma characteristic of the global small-world metrics, with a notable decrease in betweenness centrality observed in the splenium of the corpus callosum. Modularity analysis indicated significant intra-module variations in modules 03 and 05, while strong inter-module connections were observed between modules 01 and 03, as well as between modules 02 and 04. Furthermore, network-based statistics (NBS) highlighted 13 networks that exhibited significant alterations in the TAO group compared to healthy controls, underscoring the potential impact of TAO on the organization of white matter networks.

CONCLUSION

In our study, we found that patients with TAO exhibited abnormalities in the white matter functional network regarding small-world metrics and modularity, which are related to visual and cognitive functions.

A 4D atlas of diffusion-informed spatial smoothing windows for BOLD signal in white matter

Typical methods for preprocessing functional magnetic resonance images (fMRI) involve applying isotropic Gaussian smoothing windows to denoise blood oxygenation level-dependent (BOLD) signals, a process which spatially smooths white matter signals that occur along anisotropically-oriented fibers. Abramian et al. have proposed diffusion-informed spatial smoothing (DSS) filters to smooth white matter in a physiologically-informed manner. However, these filters rely on paired diffusion MRI and fMRI data, which are not always available. Here, we create DSS windows for smoothing fMRI data in the white matter based on the Human Connectome Project Young Adult population-averaged atlas of fiber orientation distribution functions. We smooth fMRI data from 63 subjects using the atlas-based DSS windows and compare the results with fMRI data smoothed with isotropic Gaussian windows at 1.04 mm full-width half-max (FWHM) and 3 mm FWHM. Compared to isotropic Gaussian windows, the atlas-based DSS windows result in fMRI data with a significantly higher local functional connectivity measured with regional homogeneity (ReHo, p < 0.001). The DSS atlas results in biologically informed regions of interest identified through independent component analysis that more closely agree with regions from a diffusion MRI-based white matter atlas. The DSS atlas generated here allows for diffusion-informed smoothing of fMRI data when additional diffusion MRI data are not available. The DSS atlas and code are available online (https://github.com/MASILab/dss_fmri_atlas).

Vasculature-informed spatial smoothing of white matter functional magnetic resonance imaging

Blood oxygenation level-dependent (BOLD) signals in white matter in the brain are anisotropically oriented, so that typical isotropic Gaussian spatial smoothing (GSS) of functional magnetic resonance images (fMRI) blurs across anatomical distributions. Abramian et al. developed a graph signal processing approach to smooth fMRI data along white matter fibers using diffusion MRI (diffusion-informed spatial smoothing, DSS). BOLD signals are modulated by the volume and oxygenation of blood carried by the vasculature, so we extend this method to provide vasculature-informed spatial smoothing (VSS). We collected susceptibility-weighted images and applied a Frangi filter to identify the peak vasculature direction in each voxel, alongside co-registered diffusion MRI and resting-state fMRI, weighting the VSS graph by the agreement of the vasculature directions aligned onto the graph's edges. We acquired resting-state fMRI at 7T using a repetition time of 1.5 seconds and 400 time points. Applying the DSS and VSS filters significantly increased the local functional connectivity measured using regional homogeneity (ReHo) compared to GSS (p < 0.01 using a paired t-test), but not when comparing DSS and VSS (p = 0.06). Independent component analysis resulted in less noisy components that agree better with labels from a white matter atlas with a significantly higher Dice score from the VSS filter compared to GSS (p < 0.05 using the Mann-Whitney U-test), and the VSS filter and DSS filter performed comparably (p = 0.06). In this pilot analysis, we find that fMRI data smoothed using VSS are comparable to results generated using DSS. The filtering code is available online (https://github.com/MASILab/vss_fmri).

Static and dynamic connectivity structure of white-matter functional networks across the adult lifespan

Aging of the human brain involves intricate biological processes, resulting in complex changes in structure and function. While the effects of aging on gray matter (GM) connectivity are extensively studied, white matter (WM) functional changes have received comparatively less attention. This study examines age-related WM functional dynamics using resting-state fMRI across the adult lifespan. We identified GM and WM functional networks (FNs) using k-means clustering. Static and dynamic analyses of WM functional network connectivity (FNC) were performed to explore age effects on WM-FNs and recurrent patterns of dynamic FNC. We identified 9 WM and 12 GM FNs. Age-related effects on WM FNC strength and WM-GM FNC dynamics included linear positive and U-shaped age trajectories in static FNC strength, and linear negative and inverted U-shaped trajectories in FNC temporal variability. Three distinct brain states with significant age-related differences were identified and validated. These findings were largely replicated in the validation analysis. High integration and low temporal variability in WM-GM FNC may indicate reduced adaptability of the network system in older adults, offering insights into brain aging processes.

White Matter-Gray Matter Correlation Analysis Based on White Matter Functional Gradient

BACKGROUND

The spontaneous fluctuations in functional magnetic resonance imaging (fMRI) signals of the brain's gray matter (GM) have been interpreted as representations of neural activity variations. In previous research, white matter (WM) signals, often considered noise, have also been demonstrated to reflect characteristics of functional activity and interactions among different brain regions. Recently, functional gradients have gained significant attention due to their success in characterizing the functional organization of the whole brain. However, previous studies on brain functional gradients have predominantly focused on GM, neglecting valuable functional information within WM.

METHODS

In this paper, we have elucidated the symmetrical nature of the functional hierarchy in the left and right brain hemispheres in healthy individuals, utilizing the principal functional gradient of the whole-brain WM while also accounting for gender differences.

RESULTS

Interestingly, both males and females exhibit a similar degree of asymmetry in their brain regions, albeit with distinct regional variations. Additionally, we have thoroughly examined and analyzed the distribution of functional gradient values in the spatial structure of the corpus callosum (CC) independently, revealing that a simple one-to-one correspondence between structure and function is absent. This phenomenon may be associated with the intricacy of their internal structural connectivity.

CONCLUSIONS

We suggest that the functional gradients within the WM regions offer a fresh perspective for investigating the structural and functional characteristics of WM and may provide insights into the regulation of neural activity between GM and WM.

White Matter Function and Network Abnormalities in Patients with Diabetic Retinopathy

Background

This study aims to explore changes in white matter function and network connectivity in individuals with DR.

Methods

This study included 46 patients with DR and 43 age- and gender-matched healthy control (HC) participants were enrolled in the study. The aim was to investigate inter-group differences in white matter (WM) function and to analyze changes in the WM network among DR patients.

Results

Increased degree centrality (DC) values were observed in the middle cerebellar peduncle and genu of the corpus callosum, while higher fractional amplitude of low-frequency fluctuations (fALFF) values were found in the left superior corona radiata, right anterior corona radiata, and right superior longitudinal fasciculus. Conversely, reduced regional homogeneity (ReHo) values were noted in the left posterior thalamic radiation among patients with DR compared to HC, with statistical correction applied The SVM classification accuracy for distinguishing between DR and HC patients based on WM measures indicated values of 81.52%, 80.43%, and 89.13% for DC, fALFF, and ReHo, respectively, with respective area under the curve (AUC) values of 0.87, 0.85, and 0.93. Furthermore, alterations were detected within specific brain regions including the body of corpus callosum (BCC), splenium of corpus callosum (SCC), genu of corpus callosum (GCC), left posterior thalamic radiation (PTR), right anterior corona radiata (ACR), and right posterior corona radiata (PCR) in the DR group compared to HCs, with an intra-network decrease in connectivity. Interestingly, the left superior longitudinal fasciculus (SLF) within the DR group exhibited an intra-network increase compared to the HC group.

Conclusion

DR exhibited abnormal white matter functional alterations, particularly affecting the fiber pathways linking the visual network to the sensory-motor network.

White matter functional networks in the developing brain

Background

Functional magnetic resonance imaging (fMRI) is widely used to depict neural activity and understand human brain function. Studies show that functional networks in gray matter undergo complex transformations from neonatal age to childhood, supporting rapid cognitive development. However, white matter functional networks, given the much weaker fMRI signal, have not been characterized until recently, and changes in white matter functional networks in the developing brain remain unclear.

Purpose

Aims to examine and compare white matter functional networks in neonates and 8-year-old children.

Methods

We acquired resting-state fMRI data on 69 full-term healthy neonates and 38 healthy 8-year-old children using a same imaging protocol and studied their brain white matter functional networks using a similar pipeline. First, we utilized the ICA method to extract white matter functional networks. Next, we analyzed the characteristics of the white matter functional networks from both time-domain and frequency-domain perspectives, specifically, intra-network functional connectivity (intra-network FC), inter-network functional connectivity (inter-network FC), and fractional amplitude of low-frequency fluctuation (fALFF). Finally, the differences in the above functional networks' characteristics between the two groups were evaluated. As a supplemental measure and to confirm with literature findings on gray matter functional network changes in the developing brain, we also studied and reported functional networks in gray matter.

Results

White matter functional networks in the developing brain can be depicted for both the neonates and the 8-year-old children. White matter intra-network FC within the optic radiations, corticospinal tract, and anterior corona radiata was lower in 8-year-old children compared to neonates (p < 0.05). Inter-network FC between cerebral peduncle (CP) and anterior corona radiation (ACR) was higher in 8-year-olds (p < 0.05). Additionally, 8-year-olds showed a greater distribution of brain activity energy in the high-frequency range of 0.01-0.15 Hz. Significant developmental differences in brain white matter functional networks exist between the two group, characterized by increased inter-network FC, decreased intra-network FC, and higher high-frequency energy distribution. Similar findings were also observed in gray matter functional networks.

Conclusion

White matter functional networks can be reliably measured in the developing brain, and the differences in these networks reflect functional differentiation and integration in brain development.

Morphological Brain Networks of White Matter: Mapping, Evaluation, Characterization, and Application

Although white matter (WM) accounts for nearly half of adult brain, its wiring diagram is largely unknown. Here, an approach is developed to construct WM networks by estimating interregional morphological similarity based on structural magnetic resonance imaging. It is found that morphological WM networks showed nontrivial topology, presented good-to-excellent test-retest reliability, accounted for phenotypic interindividual differences in cognition, and are under genetic control. Through integration with multimodal and multiscale data, it is further showed that morphological WM networks are able to predict the patterns of hamodynamic coherence, metabolic synchronization, gene co-expression, and chemoarchitectonic covariance, and associated with structural connectivity. Moreover, the prediction followed WM functional connectomic hierarchy for the hamodynamic coherence, is related to genes enriched in the forebrain neuron development and differentiation for the gene co-expression, and is associated with serotonergic system-related receptors and transporters for the chemoarchitectonic covariance. Finally, applying this approach to multiple sclerosis and neuromyelitis optica spectrum disorders, it is found that both diseases exhibited morphological dysconnectivity, which are correlated with clinical variables of patients and are able to diagnose and differentiate the diseases. Altogether, these findings indicate that morphological WM networks provide a reliable and biologically meaningful means to explore WM architecture in health and disease.

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.

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.

Altered white matter functional pathways in Alzheimer's disease

Alzheimer's disease (AD) is associated with functional disruption in gray matter (GM) and structural damage to white matter (WM), but the relationship to functional signal in WM is unknown. We performed the functional connectivity (FC) and graph theory analysis to investigate abnormalities of WM and GM functional networks and corpus callosum among different stages of AD from a publicly available dataset. Compared to the controls, AD group showed significantly decreased FC between the deep WM functional network (WM-FN) and the splenium of corpus callosum, between the sensorimotor/occipital WM-FN and GM visual network, but increased FC between the deep WM-FN and the GM sensorimotor network. In the clinical groups, the global assortativity, modular interaction between occipital WM-FN and visual network, nodal betweenness centrality, degree centrality, and nodal clustering coefficient in WM- and GM-FNs were reduced. However, modular interaction between deep WM-FN and sensorimotor network, and participation coefficients of deep WM-FN and splenium of corpus callosum were increased. These findings revealed the abnormal integration of functional networks in different stages of AD from a novel WM-FNs perspective. The abnormalities of WM functional pathways connect downward to the corpus callosum and upward to the GM are correlated with AD.

Functional magnetic resonance imaging study of group independent components underpinning item responses to paranoid-depressive scale

BACKGROUND

Our study expand upon a large body of evidence in the field of neuropsychiatric imaging with cognitive, affective and behavioral tasks, adapted for the functional magnetic resonance imaging (MRI) (fMRI) experimental environment. There is sufficient evidence that common networks underpin activations in task-based fMRI across different mental disorders.

AIM

To investigate whether there exist specific neural circuits which underpin differential item responses to depressive, paranoid and neutral items (DN) in patients respectively with schizophrenia (SCZ) and major depressive disorder (MDD).

METHODS

60 patients were recruited with SCZ and MDD. All patients have been scanned on 3T magnetic resonance tomography platform with functional MRI paradigm, comprised of block design, including blocks with items from diagnostic paranoid (DP), depression specific (DS) and DN from general interest scale. We performed a two-sample t-test between the two groups-SCZ patients and depressive patients. Our purpose was to observe different brain networks which were activated during a specific condition of the task, respectively DS, DP, DN.

RESULTS

Several significant results are demonstrated in the comparison between SCZ and depressive groups while performing this task. We identified one component that is task-related and independent of condition (shared between all three conditions), composed by regions within the temporal (right superior and middle temporal gyri), frontal (left middle and inferior frontal gyri) and limbic/salience system (right anterior insula). Another component is related to both diagnostic specific conditions (DS and DP) e.g. It is shared between DEP and SCZ, and includes frontal motor/language and parietal areas. One specific component is modulated preferentially by to the DP condition, and is related mainly to prefrontal regions, whereas other two components are significantly modulated with the DS condition and include clusters within the default mode network such as posterior cingulate and precuneus, several occipital areas, including lingual and fusiform gyrus, as well as parahippocampal gyrus. Finally, component 12 appeared to be unique for the neutral condition. In addition, there have been determined circuits across components, which are either common, or distinct in the preferential processing of the sub-scales of the task.

CONCLUSION

This study has delivers further evidence in support of the model of trans-disciplinary cross-validation in psychiatry.

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.

Aberrant dynamic functional network connectivity in type 2 diabetes mellitus individuals

An increasing number of recent brain imaging studies are dedicated to understanding the neuro mechanism of cognitive impairment in type 2 diabetes mellitus (T2DM) individuals. In contrast to efforts to date that are limited to static functional connectivity, here we investigate abnormal connectivity in T2DM individuals by characterizing the time-varying properties of brain functional networks. Using group independent component analysis (GICA), sliding-window analysis, and k-means clustering, we extracted thirty-one intrinsic connectivity networks (ICNs) and estimated four recurring brain states. We observed significant group differences in fraction time (FT) and mean dwell time (MDT), and significant negative correlation between the Montreal Cognitive Assessment (MoCA) scores and FT/MDT. We found that in the T2DM group the inter- and intra-network connectivity decreases and increases respectively for the default mode network (DMN) and task-positive network (TPN). We also found alteration in the precuneus network (PCUN) and enhanced connectivity between the salience network (SN) and the TPN. Our study provides evidence of alterations of large-scale resting networks in T2DM individuals and shed light on the fundamental mechanisms of neurocognitive deficits in T2DM.

An automated pipeline for obtaining labeled ICA-templates corresponding to functional brain systems

The complexity of our actions and thinking is likely reflected in functional brain networks. Independent component analysis (ICA) is a popular data-driven method to compute group differences between such networks. A common way to investigate network differences is based on ICA maps which are generated from study-specific samples. However, this approach limits the generalizability and reproducibility of the results. Alternatively, network ICA templates can be used, but up to date, few such templates exist and are limited in terms of the functional systems they cover. Here, we propose a simple two-step procedure to obtain ICA-templates corresponding to functional brain systems of the researcher's choice: In step 1, the functional system of interest needs to be defined by means of a statistical parameter map (input), which one can generate with open-source software such as NeuroSynth or BrainMap. In step 2, that map is correlated to group-ICA maps provided by the Human Connectome Project (HCP), which is based on a large sample size and uses high quality and standardized acquisition procedures. The HCP-provided ICA-map with the highest correlation to the input map is then used as an ICA template representing the functional system of interest, for example, for subsequent analyses such as dual regression. We provide a toolbox to complete step 2 of the suggested procedure and demonstrate the usage of our pipeline by producing an ICA templates that corresponds to "motor function" and nine additional brain functional systems resulting in an ICA maps with excellent alignment with the gray matter/white matter boundaries of the brain. Our toolbox generates data in two different file formats: volumetric-based (NIFTI) and combined surface/volumetric files (CIFTI). Compared to 10 existing templates, our procedure output component maps with systematically stronger contribution of gray matter to the ICA z-values compared to white matter voxels in 9/10 cases by at least a factor of 2. The toolbox allows users to investigate functional networks of interest, which will enhance interpretability, reproducibility, and standardization of research investigating functional brain networks.

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.

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.

Atlasing white matter and grey matter joint contributions to resting-state networks in the human brain

Over the past two decades, the study of resting-state functional magnetic resonance imaging has revealed that functional connectivity within and between networks is linked to cognitive states and pathologies. However, the white matter connections supporting this connectivity remain only partially described. We developed a method to jointly map the white and grey matter contributing to each resting-state network (RSN). Using the Human Connectome Project, we generated an atlas of 30 RSNs. The method also highlighted the overlap between networks, which revealed that most of the brain's white matter (89%) is shared between multiple RSNs, with 16% shared by at least 7 RSNs. These overlaps, especially the existence of regions shared by numerous networks, suggest that white matter lesions in these areas might strongly impact the communication within networks. We provide an atlas and an open-source software to explore the joint contribution of white and grey matter to RSNs and facilitate the study of the impact of white matter damage to these networks. In a first application of the software with clinical data, we were able to link stroke patients and impacted RSNs, showing that their symptoms aligned well with the estimated functions of the networks.

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.

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.

The human brain networks mediating the vestibular sensation of self-motion

Vestibular Agnosia - where peripheral vestibular activation triggers the usual reflex nystagmus response but with attenuated or no self-motion perception - is found in brain disease with disrupted cortical network functioning, e.g. traumatic brain injury (TBI) or neurodegeneration (Parkinson's Disease). Patients with acute focal hemispheric lesions (e.g. stroke) do not manifest vestibular agnosia. Thus, brain network mapping techniques, e.g. resting state functional MRI (rsfMRI), are needed to interrogate functional brain networks mediating vestibular agnosia. Hence, we prospectively recruited 39 acute TBI patients with preserved peripheral vestibular function and obtained self-motion perceptual thresholds during passive yaw rotations in the dark and additionally acquired whole-brain rsfMRI in the acute phase. Following quality-control checks, 26 patient scans were analyzed. Using self-motion perceptual thresholds from a matched healthy control group, 11 acute TBI patients were classified as having vestibular agnosia versus 15 with normal self-motion perception thresholds. Using independent component analysis on the rsfMRI data, we found altered functional connectivity in bilateral lingual gyrus and temporo-occipital fusiform cortex in the vestibular agnosia patients. Moreover, regions of interest analyses showed both inter-hemispheric and intra-hemispheric network disruption in vestibular agnosia. In conclusion, our results show that vestibular agnosia is mediated by bilateral anterior and posterior network dysfunction and reveal the distributed brain mechanisms mediating vestibular self-motion perception.

Mapping white matter structural covariance connectivity for single subject using wavelet transform with T1-weighted anatomical brain MRI

Introduction

Current studies of structural covariance networks were focused on the gray matter in the human brain. The structural covariance connectivity in the white matter remains largely unexplored. This paper aimed to build novel metrics that can infer white matter structural covariance connectivity, and to explore the predictive power of the proposed features.

Methods

To this end, a cohort of 315 adult subjects with the anatomical brain MRI datasets were obtained from the publicly available Dallas Lifespan Brain Study (DLBS) project. The 3D wavelet transform was applied on the individual voxel-based morphology (VBM) volume to obtain the white matter structural covariance connectivity. The predictive models for cognitive functions were built using support vector regression (SVR).

Results

The predictive models exhibited comparable performance with previous studies. The novel features successfully predicted the individual ability of digit comparison (DC) (r = 0.41 ± 0.01, p < 0.01) and digit symbol (DSYM) (r = 0.5 ± 0.01, p < 0.01). The sensorimotor-related white matter system exhibited as the most predictive network node. Furthermore, the node strengths of sensorimotor mode were significantly correlated to cognitive scores.

Discussion

The results suggested that the white matter structural covariance connectivity was informative and had potential for predictive tasks of brain-behavior research.

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.

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.

Intrinsic network activity reflects the ongoing experience of chronic pain

Analyses of intrinsic network activity have been instrumental in revealing cortical processes that are altered in chronic pain patients. In a novel approach, we aimed to elucidate how intrinsic functional networks evolve in regard to the fluctuating intensity of the experience of chronic pain. In a longitudinal study with 156 fMRI sessions, 20 chronic back pain patients and 20 chronic migraine patients were asked to continuously rate the intensity of their endogenous pain. We investigated the relationship between the fluctuation of intrinsic network activity with the time course of subjective pain ratings. For chronic back pain, we found increased cortical network activity for the salience network and a local pontine network, as well as decreased network activity in the anterior and posterior default mode network for higher pain intensities. Higher pain intensities in chronic migraine were accompanied with lower activity in a prefrontal cortical network. By taking the perspective of the individual, we focused on the variability of the subjective perception of pain, which include phases of relatively low pain and phases of relatively high pain. The present design of the assessment of ongoing endogenous pain can be a powerful and promising tool to assess the signature of a patient's endogenous pain encoding.