Evidence for Functional Networks within the Human Brain's White Matter

Abnormal structure and function of white matter in obsessive-compulsive disorder

BACKGROUND

Abnormal structure and function of gray matter (GM) have been discovered in the cortico-striatal-thalamic-cortical (CSTC) circuit in obsessive-compulsive disorder (OCD). The GM structure and function may be influenced by the structure and function of the white matter (WM). Therefore, it is crucial to explore the characteristics of WM in OCD.

METHODS

Diffusion tensor imaging and resting-state functional magnetic resonance imaging data of 52 patients with OCD and 39 healthy controls (HCs) were collected. The tract-based spatial statistics, amplitude of low-frequency fluctuations (ALFF), and structural-functional coupling approaches were utilized to explore the WM structure and function. Furthermore, the relationship between the abnormal WM structure and function and clinical symptoms of OCD was investigated using Pearson's correlation. Support vector machine was performed to evaluate whether patients with OCD could be identified with the changed WM structure and function.

RESULTS

Compared to HCs, the lower fractional anisotropy (FA) values of four clusters including the superior corona radiata, anterior corona radiata, right superior longitudinal fasciculus, corpus callosum, left posterior corona radiata, fornix, and the right anterior limb of internal capsule, reduced ALFF/FA ratio in the left anterior thalamic radiation (ATR), and the decreased functional connectivity between the left ATR and the left dorsal lateral prefrontal cortex within CSTC circuit at rest were observed in OCD. The decreased ALFF/FA ratio in the left ATR negatively correlated with Yale-Brown Obsessive-Compulsive Scale obsessive thinking scores and Hamilton Anxiety Rating Scale scores in OCD. Furthermore, the features that combined the abnormal WM structure and function performed best in distinguishing OCD from HCs with the appropriate accuracy (0.80), sensitivity (0.82), as well as specificity (0.80).

CONCLUSION

Current research discovered changed WM structure and function in OCD. Furthermore, abnormal WM structural-functional coupling may lead to aberrant GM connectivity within the CSTC circuit at rest in OCD.

TRIAL REGISTRATION

Study on the mechanism of brain network in obsessive-compulsive disorder with multi-model magnetic resonance imaging (ChiCTR-COC-17013301).

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.

Eigenvector centrality mapping reveals volatility of functional brain dynamics in anti-NMDA receptor encephalitis

BACKGROUND

Anti-N-methyl-D-aspartate receptor encephalitis (NMDARE) causes long-lasting cognitive deficits associated with altered functional connectivity. Eigenvector centrality (EC) mapping represents a powerful new method for data-driven voxel-wise and time-resolved estimation of network importance - beyond changes in classical 'static' functional connectivity.

METHODS

To assess changes in functional brain network organization, we applied EC mapping in 73 patients with NMDARE and 73 matched healthy controls. Areas with significant group differences were further investigated using (i) spatial clustering analyses, (ii) time series correlation to assess synchronicity between the hippocampus and cortical brain regions, and (iii) correlation with cognitive and clinical parameters.

RESULTS

Dynamic, time-resolved EC showed significantly higher variability in 13 cortical areas (p(FWE)<0.05) in patients with NMDARE compared to HC. Areas with dynamic EC group differences were spatially organized in centrality clusters resembling resting-state networks. Importantly, variability of dynamic EC in the frontotemporal cluster was associated with impaired verbal episodic memory in patients (r=-0.25, p=0.037). EC synchronicity between the hippocampus and the medial prefrontal cortex was reduced in patients compared to HC (p(FWE)<0.05, t(max)=3.76), and associated with verbal episodic memory in patients (r=0.28, p=0.019). Static EC analyses showed group differences in only one brain region (left intracalcarine cortex).

CONCLUSIONS

Widespread changes in network dynamics and reduced hippocampal-medial prefrontal synchronicity were associated with verbal episodic memory deficits and may thus represent a functional neural correlate of cognitive dysfunction in NMDARE. Importantly, dynamic EC detected substantially more network alterations than traditional static approaches, highlighting the potential of this method to explain long-term deficits in NMDARE.

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.

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.

Narcissus reflected: Grey and white matter features joint contribution to the default mode network in predicting narcissistic personality traits

Despite the clinical significance of narcissistic personality, its neural bases have not been clarified yet, primarily because of methodological limitations of the previous studies, such as the low sample size, the use of univariate techniques and the focus on only one brain modality. In this study, we employed for the first time a combination of unsupervised and supervised machine learning methods, to identify the joint contributions of grey matter (GM) and white matter (WM) to narcissistic personality traits (NPT). After preprocessing, the brain scans of 135 participants were decomposed into eight independent networks of covarying GM and WM via parallel ICA. Subsequently, stepwise regression and Random Forest were used to predict NPT. We hypothesized that a fronto-temporo parietal network, mainly related to the default mode network, may be involved in NPT and associated WM regions. Results demonstrated a distributed network that included GM alterations in fronto-temporal regions, the insula and the cingulate cortex, along with WM alterations in cerebellar and thalamic regions. To assess the specificity of our findings, we also examined whether the brain network predicting narcissism could also predict other personality traits (i.e., histrionic, paranoid and avoidant personalities). Notably, this network did not predict such personality traits. Additionally, a supervised machine learning model (Random Forest) was used to extract a predictive model for generalization to new cases. Results confirmed that the same network could predict new cases. These findings hold promise for advancing our understanding of personality traits and potentially uncovering brain biomarkers associated with narcissism.

Abnormal functional connectivity of white-matter networks and gray-white matter functional networks in patients with NMOSD

Cognitive impairment (CI) has been reported in 29-70% of patients with neuromyelitis optica spectrum disorder (NMOSD). Abnormal white matter (WM) functional networks that correlate with cognitive functions have not been studied well in patients with NMOSD. The aim of the current study was to investigate functional connectivity (FC), spontaneous activity, and functional covariance connectivity (FCC) abnormalities of WM functional networks in patients with NMOSD and their correlation with cognitive performance. Twenty-four patients with NMOSD and 24 healthy controls (HCs) were included in the study. Participants underwent brain resting-state functional magnetic resonance imaging (fMRI) and the Montreal Cognitive Assessment (MoCA). Eight WM networks and nine gray matter (GM) networks were created. In patients, WM networks, including WM1-4, WM1-8, WM2-6, WM2-7, WM2-8, WM4-8, WM5-8 showed reduced FC (P < 0.05). All WM networks except WM1 showed decreased spontaneous activity (P < 0.05). The major GM networks demonstrated increased/decreased FC (P < 0.05), whereas GM7-WM7, GM8-WM4, GM8-WM6 and GM8-WM8 displayed decreased FC (P < 0.05). The MoCA results showed that two-thirds (16/24) of the patients had CI. FC and FCC in WM networks were correlated negatively with the MoCA scores (P < 0.05). WM functional networks are multi-layered. Abnormal FC of WM functional networks and GM functional networks may be responsible for CI.

Rich-club organization of whole-brain spatio-temporal multilayer functional connectivity networks

Objective

In this work, we propose a novel method for constructing whole-brain spatio-temporal multilayer functional connectivity networks (FCNs) and four innovative rich-club metrics.

Methods

Spatio-temporal multilayer FCNs achieve a high-order representation of the spatio-temporal dynamic characteristics of brain networks by combining the sliding time window method with graph theory and hypergraph theory. The four proposed rich-club scales are based on the dynamic changes in rich-club node identity, providing a parameterized description of the topological dynamic characteristics of brain networks from both temporal and spatial perspectives. The proposed method was validated in three independent differential analysis experiments: male-female gender difference analysis, analysis of abnormality in patients with autism spectrum disorders (ASD), and individual difference analysis.

Results

The proposed method yielded results consistent with previous relevant studies and revealed some innovative findings. For instance, the dynamic topological characteristics of specific white matter regions effectively reflected individual differences. The increased abnormality in internal functional connectivity within the basal ganglia may be a contributing factor to the occurrence of repetitive or restrictive behaviors in ASD patients.

Conclusion

The proposed methodology provides an efficacious approach for constructing whole-brain spatio-temporal multilayer FCNs and conducting analysis of their dynamic topological structures. The dynamic topological characteristics of spatio-temporal multilayer FCNs may offer new insights into physiological variations and pathological abnormalities in neuroscience.

Decoupling of gray and white matter functional networks in cognitive impairment induced by occupational aluminum exposure

Aluminum (Al) is a low-toxic, accumulative substance with neurotoxicity properties that adversely affect human cognitive function. This study aimed to investigate the neurobiological mechanisms underlying cognitive impairment resulting from occupational Al exposure. Resting-state functional magnetic resonance imaging was conducted on 54 individuals with over 10 years of Al exposure. Al levels were measured, and cognitive function was assessed using the Montreal Cognitive Assessment (MoCA). Subsequently, the K-means clustering algorithm was employed to identify functional gray matter (GM) and white matter (WM) networks. Two-sample t-tests were conducted between the cognition impairment group and the control group. Al exhibited a negative correlation with MoCA scores. Participants with cognitive impairment demonstrated reduced functional connectivity (FC) between the middle cingulum network (WM1) and anterior cingulum network (WM2), as well as between the executive control network (WM6) and limbic network (WM10). Notably, decreased FCs were observed between the executive control network (GM5) and WM1, WM4, WM6, and WM10. Additionally, the FC of GM5-GM4 and WM1-WM2 negatively correlated with Trail Making Test Part A (TMT-A) scores. Prolonged Al accumulation detrimentally affects cognition, primarily attributable to executive control and limbic network disruptions.

Brain activation and connection across resting and motor-task states in patients with generalized tonic-clonic seizures

AIMS

Motor abnormalities have been identified as one common symptom in patients with generalized tonic-clonic seizures (GTCS) inspiring us to explore the disease in a motor execution condition, which might provide novel insight into the pathomechanism.

METHODS

Resting-state and motor-task fMRI data were collected from 50 patients with GTCS, including 18 patients newly diagnosed without antiepileptic drugs (ND_GTCS) and 32 patients receiving antiepileptic drugs (AEDs_GTCS). Motor activation and its association with head motion and cerebral gradients were assessed. Whole-brain network connectivity across resting and motor states was further calculated and compared between groups.

RESULTS

All patients showed over-activation in the postcentral gyrus and the ND_GTCS showed decreased activation in putamen. Specifically, activation maps of ND_GTCS showed an abnormal correlation with head motion and cerebral gradient. Moreover, we detected altered functional network connectivity in patients within states and across resting and motor states by using repeated-measures analysis of variance. Patients did not show abnormal connectivity in the resting state, while distributed abnormal connectivity in the motor-task state. Decreased across-state network connectivity was also found in all patients.

CONCLUSION

Convergent findings suggested the over-response of activation and connection of the brain to motor execution in GTCS, providing new clues to uncover motor susceptibility underlying the disease.

The T1-weighted/T2-weighted ratio as a biomarker of anti-NMDA receptor encephalitis

BACKGROUND

Anti-N-methyl-D-aspartate receptor (NMDAR) encephalitis rarely causes visible lesions in conventional MRI, yet advanced imaging detects extensive white matter damage. To improve prognostic capabilities, we evaluate the T1-weighted/T2-weighted (T1w/T2w) ratio, a measure of white matter integrity computable from clinical MRI sequences, in NMDAR encephalitis and examine its associations with cognitive impairment.

METHODS

T1-weighted and T2-weighted MRI were acquired cross-sectionally at 3 Tesla in 53 patients with NMDAR encephalitis (81% women, mean age 29 years) and 53 matched healthy controls. Quantitative and voxel-wise group differences in T1w/T2w ratios and associations with clinical and neuropsychological outcomes were assessed. P-values were false discovery rate (FDR) adjusted where multiple tests were conducted.

RESULTS

Patients with NMDAR encephalitis had significantly lower T1w/T2w ratios across normal appearing white matter (p=0.009, Hedges' g=-0.51), which was associated with worse verbal episodic memory performance (r=0.39, p=0.005, p(FDR)=0.026). White matter integrity loss was observed in the corticospinal tract, superior longitudinal fascicle, optic radiation and callosal body with medium to large effects (Cohen's d=[0.42-1.17]). In addition, patients showed decreased T1w/T2w ratios in the hippocampus (p=0.002, p(FDR)=0.005, Hedges' g=-0.62), amygdala (p=0.002, p(FDR)=0.005, Hedges' g=-0.63) and thalamus (p=0.010, p(FDR)=0.019, Hedges' g=-0.51).

CONCLUSIONS

The T1w/T2w ratio detects microstructural changes in grey and white matter of patients with NMDAR encephalitis that correlate with cognitive performance. Computable from conventional clinical MRI sequences, this measure shows promise in bridging the clinico-radiological dissociation in NMDAR encephalitis and could serve as an imaging outcome measure in clinical trials.

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.

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.

Comparing data-driven physiological denoising approaches for resting-state fMRI: implications for the study of aging

Introduction

Physiological nuisance contributions by cardiac and respiratory signals have a significant impact on resting-state fMRI data quality. As these physiological signals are often not recorded, data-driven denoising methods are commonly used to estimate and remove physiological noise from fMRI data. To investigate the efficacy of these denoising methods, one of the first steps is to accurately capture the cardiac and respiratory signals, which requires acquiring fMRI data with high temporal resolution.

Methods

In this study, we used such high-temporal resolution fMRI data to evaluate the effectiveness of several data-driven denoising methods, including global-signal regression (GSR), white matter and cerebrospinal fluid regression (WM-CSF), anatomical (aCompCor) and temporal CompCor (tCompCor), ICA-AROMA. Our analysis focused on the consequence of changes in low-frequency, cardiac and respiratory signal power, as well as age-related differences in terms of functional connectivity (fcMRI).

Results

Our results confirm that the ICA-AROMA and GSR removed the most physiological noise but also more low-frequency signals. These methods are also associated with substantially lower age-related fcMRI differences. On the other hand, aCompCor and tCompCor appear to be better at removing high-frequency physiological signals but not low-frequency signal power. These methods are also associated with relatively higher age-related fcMRI differences, whether driven by neuronal signal or residual artifact. These results were reproduced in data downsampled to represent conventional fMRI sampling frequency. Lastly, methods differ in performance depending on the age group.

Discussion

While this study cautions direct comparisons of fcMRI results based on different denoising methods in the study of aging, it also enhances the understanding of different denoising methods in broader fcMRI applications.

Aberrant interhemispheric structural and functional connectivity within whole brain in schizophrenia

OBJECTIVE

Schizophrenia is a serious mental disorder whose etiology remains unclear. Although numerous studies have analyzed the abnormal gray matter functional activity and whole-brain anatomical changes in schizophrenia, fMRI signal fluctuations from white matter have usually been ignored and rarely reported in the literature.

METHODS

We employed 45 schizophrenia subjects and 75 healthy controls (HCs) from a publicly available fMRI dataset. By combining the voxel-mirrored homotopic connectivity (VMHC) measure and fiber tracking method, we investigated the interhemispheric functional and structural connectivity within whole brain in schizophrenia.

RESULTS

Compared to HCs, patients with schizophrenia exhibited significantly reduced VMHC in the bilateral middle occipital gyrus, precentral gyrus, postcentral gyrus and corpus callosum. Fiber tracking results showed the changes in structural connectivity for the bilateral precentral gyrus, and the bilateral corpus callosum, and the fiber bundles connecting bilateral precentral gyrus and connecting the bilateral corpus callosum passed through the posterior midbody, isthmus and splenium of mid-sagittal corpus callosum, which closely related to the interhemispheric integration of visual and auditory information. More importantly, we observed a negative correlation between averaged VMHC values in the postcentral gyrus and SAPS scores, and a positive correlation between the fractional anisotropy of fiber bundle connecting the bilateral precentral gyrus and Matrix Reasoning scores in schizophrenia.

CONCLUSION

Our findings provide a novel perspective of white matter functional images on understanding abnormal interhemispheric visual and auditory information transfer in schizophrenia.

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.

Identification of functional white matter networks in BOLD fMRI

White matter signals in resting state blood oxygen level dependent functional magnetic resonance (BOLD-fMRI) have been largely discounted, yet there is growing evidence that these signals are indicative of brain activity. Understanding how these white matter signals capture function can provide insight into brain physiology. Moreover, functional signals could potentially be used as early markers for neurological changes, such as in Alzheimer's Disease. To investigate white matter brain networks, we leveraged the OASIS-3 dataset to extract white matter signals from resting state BOLD-FMRI data on 711 subjects. The imaging was longitudinal with a total of 2,026 images. Hierarchical clustering was performed to investigate clusters of voxel-level correlations on the timeseries data. The stability of clusters was measured with the average Dice coefficients on two different cross fold validations. The first validated the stability between scans, and the second validated the stability between subject populations. Functional clusters at hierarchical levels 4, 9, 13, 18, and 24 had local maximum stability, suggesting better clustered white matter. In comparison with JHU-DTI-SS Type-I Atlas defined regions, clusters at lower hierarchical levels identified well defined anatomical lobes. At higher hierarchical levels, functional clusters mapped motor and memory functional regions, identifying 50.00%, 20.00%, 27.27%, and 35.14% of the frontal, occipital, parietal, and temporal lobe regions respectively.

rTMS concurrent with cognitive training rewires AD brain by enhancing GM-WM functional connectivity: a preliminary study

Repetitive transcranial magnetic stimulation (rTMS) and cognitive training for patients with Alzheimer's disease (AD) can change functional connectivity (FC) within gray matter (GM). However, the role of white matter (WM) and changes of GM-WM FC under these therapies are still unclear. To clarify this problem, we applied 40 Hz rTMS over angular gyrus (AG) concurrent with cognitive training to 15 mild-moderate AD patients and analyzed the resting-state functional magnetic resonance imaging before and after treatment. Through AG-based FC analysis, corona radiata and superior longitudinal fasciculus (SLF) were identified as activated WM tracts. Compared with the GM results with AG as seed, more GM regions were found with activated WM tracts as seeds. The averaged FC, fractional amplitude of low-frequency fluctuation (fALFF), and regional homogeneity (ReHo) of the above GM regions had stronger clinical correlations (r/P = 0.363/0.048 vs 0.299/0.108, 0.351/0.057 vs 0.267/0.153, 0.420/0.021 vs 0.408/0.025, for FC/fALFF/ReHo, respectively) and better classification performance to distinguish pre-/post-treatment groups (AUC = 0.91 vs 0.88, 0.65 vs 0.63, 0.87 vs 0.82, for FC/fALFF/ReHo, respectively). Our results indicated that rTMS concurrent with cognitive training could rewire brain network by enhancing GM-WM FC in AD, and corona radiata and SLF played an important role in this process.

Assessing White Matter Engagement in Brain Networks through Functional and Structural Connectivity Mapping

Understanding the intricate interplay between gray matter (GM) and white matter (WM) is crucial for deciphering the complex activities of the brain. While diffusion tensor imaging (DTI) has advanced the mapping of these structural pathways, the relationship between structural connectivity (SC) and functional connectivity (FC) remains inadequately understood. This study addresses the need for a more integrative approach by mapping the importance of the inter-GM functional link to its structural counterparts in WM. This mapping yields a spatial distribution of engagement that is not only highly reproducible but also aligns with direct structural, functional, and bioenergetic measures within WM, illustrating a notable interdependence between the function of GM and the characteristics of WM. Additionally, our research has uncovered a set of unique engagement modes through a clustering analysis of window-wise engagement maps, highlighting the dyanmic nature of the engagement. The engagement along with their temporal variations revealed significant differences across genders and age groups. These findings suggest the potential of WM engagement as a biomarker for neurological and cognitive conditions, offering a more nuanced understanding of individualized brain activity and connectivity patterns.

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.

Structural-functional connectivity deficits of callosal-white matter-cortical circuits in schizophrenia

Schizophrenia is increasingly recognized as a disorder with altered integration between large-scale functional networks and cortical-subcortical pathways. This spatial long-distance information communication must be associated with white matter (WM) fiber bundles. With accumulating evidence that WM functional signals reflect the intrinsic neural activities, how the deep callosal organization modulates cortical functional activities through WM remains unclear in schizophrenia. Using a data-driven method, we identified nine WM and gray matter (GM) functional networks, and then parcellated corpus callosum into distinct sub-regions. Combining functional connectivity and fiber tracking analysis, we estimated the structural and functional connectivity changes of callosal-WM-cortical circuits in schizophrenia. We observed higher structural and functional connectivity between corpus callosum, WM and GM functional networks involving visual network (visual processing), executive control network (executive controls), ventral attention network (processing of salience), and limbic network (emotion processing) in schizophrenia compared to healthy controls. We also found nine abnormal pathways of callosal-WM-cortical circuits involving the above networks and default mode network (self-related thought). These results highlight the role of connectivity deficits in callosal-WM-cortical circuits may play in understanding the delusions, hallucinations and cognitive impairment of schizophrenia.

Characterizing the spatiotemporal features of functional connectivity across the white matter and gray matter during the naturalistic condition

Introduction

The naturalistic stimuli due to its ease of operability has attracted many researchers in recent years. However, the influence of the naturalistic stimuli for whole-brain functions compared with the resting state is still unclear.

Methods

In this study, we clustered gray matter (GM) and white matter (WM) masks both at the ROI- and network-levels. Functional connectivity (FC) and inter-subject functional connectivity (ISFC) were calculated in GM, WM, and between GM and WM under the movie-watching and the resting-state conditions. Furthermore, intra-class correlation coefficients (ICC) of FC and ISFC were estimated on different runs of fMRI data to denote the reliability of them during the two conditions. In addition, static and dynamic connectivity indices were calculated with Pearson correlation coefficient to demonstrate the associations between the movie-watching and the resting-state.

Results

As the results, we found that the movie-watching significantly affected FC in whole-brain compared with the resting-state, but ISFC did not show significant connectivity induced by the naturalistic condition. ICC of FC and ISFC was generally higher during movie-watching compared with the resting-state, demonstrating that naturalistic stimuli could promote the reliability of connectivity. The associations between static and dynamic ISFC were weakly negative correlations in the naturalistic stimuli while there is no correlation between them under resting-state condition.

Discussion

Our findings confirmed that compared to resting-state condition, the connectivity indices under the naturalistic stimuli were more reliable and stable to investigate the normal functional activities of the human brain, and might promote the applications of FC in the cerebral dysfunction in various mental disorders.

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.

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.

Connectivity gradients in spontaneous brain activity at multiple frequency bands

The intrinsic organizational structure of the brain is reflected in spontaneous brain oscillations. Its functional integration and segregation hierarchy have been discovered in space by leveraging gradient approaches to low-frequency functional connectivity. This hierarchy of brain oscillations has not yet been fully understood, since previous studies have mainly concentrated on the brain oscillations from a single limited frequency range (~ 0.01-0.1 Hz). In this work, we extended the frequency range and performed gradient analysis across multiple frequency bands of fast resting-state fMRI signals from the Human Connectome Project and condensed a frequency-rank cortical map of the highest gradient. We found that the coarse skeletons of the functional organization hierarchy are generalizable across the multiple frequency bands. Beyond that, the highest integration levels of connectivity vary in the frequency domain across different large-scale brain networks. These findings are replicated in another independent dataset and demonstrated that different brain networks can integrate information at varying rates, indicating the significance of examining the intrinsic architecture of spontaneous brain activity from the perspective of multiple frequency bands.

Functional Changes of White Matter Are Related to Human Pain Sensitivity during Sustained Nociception

Pain is considered an unpleasant perceptual experience associated with actual or potential somatic and visceral harm. Human subjects have different sensitivity to painful stimulation, which may be related to different painful response pattern. Excellent studies using functional magnetic resonance imaging (fMRI) have found the effect of the functional organization of white matter (WM) on the descending pain modulatory system, which suggests that WM function is feasible during pain modulation. In this study, 26 pain sensitive (PS) subjects and 27 pain insensitive (PIS) subjects were recruited based on cold pressor test. Then, all subjects underwent the cold bottle test (CBT) in normal (26 degrees temperature stimulating) and cold (8 degrees temperature stimulating) conditions during fMRI scan, respectively. WM functional networks were obtained using K-means clustering, and the functional connectivity (FC) was assessed among WM networks, as well as gray matter (GM)-WM networks. Through repeated measures ANOVA, decreased FC was observed between the GM-cerebellum network and the WM-superior temporal network, as well as the WM-sensorimotor network in the PS group under the cold condition, while this difference was not found in PIS group. Importantly, the changed FC was positively correlated with the state and trait anxiety scores, respectively. This study highlighted that the WM functional network might play an integral part in pain processing, and an altered FC may be related to the descending pain modulatory system.

Identification and analysis of autism spectrum disorder via large-scale dynamic functional network connectivity

Autism spectrum disorder (ASD) is a prevalent neurodevelopmental disorder with severe cognitive impairment. Several studies have reported that brain functional network connectivity (FNC) has great potential for identifying ASD from healthy control (HC) and revealing the relationships between the brain and behaviors of ASD. However, few studies have explored dynamic large-scale FNC as a feature to identify individuals with ASD. This study used a time-sliding window method to study the dynamic FNC (dFNC) on the resting-state fMRI. To avoid arbitrarily determining the window length, we set a window length range of 10-75 TRs (TR = 2 s). We constructed linear support vector machine classifiers for all window length conditions. Using a nested 10-fold cross-validation framework, we obtained a grand average accuracy of 94.88% across window length conditions, which is higher than those reported in previous studies. In addition, we determined the optimal window length using the highest classification accuracy of 97.77%. Based on the optimal window length, we found that the dFNCs were located mainly in dorsal and ventral attention networks (DAN and VAN) and exhibited the highest weight in classification. Specifically, we found that the dFNC between DAN and temporal orbitofrontal network (TOFN) was significantly negatively correlated with social scores of ASD. Finally, using the dFNCs with high classification weights as features, we construct a model to predict the clinical score of ASD. Overall, our findings demonstrated that the dFNC could be a potential biomarker to identify ASD and provide new perspectives to detect cognitive changes in ASD.

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.

Large-scale brain networks and intra-axial tumor surgery: a narrative review of functional mapping techniques, critical needs, and scientific opportunities

In recent years, a paradigm shift in neuroscience has been occurring from "localizationism," or the idea that the brain is organized into separately functioning modules, toward "connectomics," or the idea that interconnected nodes form networks as the underlying substrates of behavior and thought. Accordingly, our understanding of mechanisms of neurological function, dysfunction, and recovery has evolved to include connections, disconnections, and reconnections. Brain tumors provide a unique opportunity to probe large-scale neural networks with focal and sometimes reversible lesions, allowing neuroscientists the unique opportunity to directly test newly formed hypotheses about underlying brain structural-functional relationships and network properties. Moreover, if a more complete model of neurological dysfunction is to be defined as a "disconnectome," potential avenues for recovery might be mapped through a "reconnectome." Such insight may open the door to novel therapeutic approaches where previous attempts have failed. In this review, we briefly delve into the most clinically relevant neural networks and brain mapping techniques, and we examine how they are being applied to modern neurosurgical brain tumor practices. We then explore how brain tumors might teach us more about mechanisms of global brain dysfunction and recovery through pre- and postoperative longitudinal connectomic and behavioral analyses.

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.

Structure-function coupling in white matter uncovers the abnormal brain connectivity in Schizophrenia

Schizophrenia is characterized by dysconnectivity syndrome. Evidence of widespread impairment of structural and functional integration has been demonstrated in schizophrenia. Although white matter (WM) microstructural abnormalities have been commonly reported in schizophrenia, the dysfunction of WM as well as the relationship between structure and function in WM remains uncertain. In this study, we proposed a novel structure-function coupling measurement to reflect neuronal information transfer, which combined spatial-temporal correlations of functional signals with diffusion tensor orientations in the WM circuit from functional and diffusion magnetic resonance images (MRI). By analyzing MRI data from 75 individuals with schizophrenia (SZ) and 89 healthy volunteers (HV), the associations between structure and function in WM regions in schizophrenia were examined. Randomized validation of the measurement was performed in the HV group to confirm the capacity of the neural signal transferring along the WM tracts, referring to quantifying the association between structure and function. Compared to HV, SZ showed a widespread decrease in the structure-function coupling within WM regions, involving the corticospinal tract and the superior longitudinal fasciculus. Additionally, the structure-function coupling in the WM tracts was found to be significantly correlated with psychotic symptoms and illness duration in schizophrenia, suggesting that abnormal signal transfer of neuronal fiber pathways could be a potential mechanism of the neuropathology of schizophrenia. This work supports the dysconnectivity hypothesis of schizophrenia from the aspect of circuit function, and highlights the critical role of WM networks in the pathophysiology of schizophrenia.

Patterns of gray and white matter functional networks involvement in glioblastoma patients: indirect mapping from clinical MRI scans

Background

Resting-state functional-MRI studies identified several cortical gray matter functional networks (GMNs) and white matter functional networks (WMNs) with precise anatomical localization. Here, we aimed at describing the relationships between brain's functional topological organization and glioblastoma (GBM) location. Furthermore, we assessed whether GBM distribution across these networks was associated with overall survival (OS).

Materials and methods

We included patients with histopathological diagnosis of IDH-wildtype GBM, presurgical MRI and survival data. For each patient, we recorded clinical-prognostic variables. GBM core and edema were segmented and normalized to a standard space. Pre-existing functional connectivity-based atlases were used to define network parcellations: 17 GMNs and 12 WMNs were considered in particular. We computed the percentage of lesion overlap with GMNs and WMNs, both for core and edema. Differences between overlap percentages were assessed through descriptive statistics, ANOVA, post-hoc tests, Pearson's correlation tests and canonical correlations. Multiple linear and non-linear regression tests were employed to explore relationships with OS.

Results

99 patients were included (70 males, mean age 62  years). The most involved GMNs included ventral somatomotor, salient ventral attention and default-mode networks; the most involved WMNs were ventral frontoparietal tracts, deep frontal white matter, and superior longitudinal fasciculus system. Superior longitudinal fasciculus system and dorsal frontoparietal tracts were significantly more included in the edema (p < 0.001). 5 main patterns of GBM core distribution across functional networks were found, while edema localization was less classifiable. ANOVA showed significant differences between mean overlap percentages, separately for GMNs and WMNs (p-values<0.0001). Core-N12 overlap predicts higher OS, although its inclusion does not increase the explained OS variance.

Discussion and conclusion

Both GBM core and edema preferentially overlap with specific GMNs and WMNs, especially associative networks, and GBM core follows five main distribution patterns. Some inter-related GMNs and WMNs were co-lesioned by GBM, suggesting that GBM distribution is not independent of the brain's structural and functional organization. Although the involvement of ventral frontoparietal tracts (N12) seems to have some role in predicting survival, network-topology information is overall scarcely informative about OS. fMRI-based approaches may more effectively demonstrate the effects of GBM on brain networks and survival.

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.

Role of lipocalin 2 in stroke

Stroke is the second leading cause of death worldwide; however, the treatment choices available to neurologists are limited in clinical practice. Lipocalin 2 (LCN2) is a secreted protein, belonging to the lipocalin superfamily, with multiple biological functions in mediating innate immune response, inflammatory response, iron-homeostasis, cell migration and differentiation, energy metabolism, and other processes in the body. LCN2 is expressed at low levels in the brain under normal physiological conditions, but its expression is significantly up-regulated in multiple acute stimulations and chronic pathologies. An up-regulation of LCN2 has been found in the blood/cerebrospinal fluid of patients with ischemic/hemorrhagic stroke, and could serve as a potential biomarker for the prediction of the severity of acute stroke. LCN2 activates reactive astrocytes and microglia, promotes neutrophil infiltration, amplifies post-stroke inflammation, promotes blood-brain barrier disruption, white matter injury, and neuronal death. Moreover, LCN2 is involved in brain injury induced by thrombin and erythrocyte lysates, as well as microvascular thrombosis after hemorrhage. In this paper, we review the role of LCN2 in the pathological processes of ischemic stroke; intracerebral hemorrhage; subarachnoid hemorrhage; and stroke-related brain diseases, such as vascular dementia and post-stroke depression, and their underlying mechanisms. We hope that this review will help elucidate the value of LCN2 as a therapeutic target in stroke.

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.

Altered white matter functional network in nicotine addiction

Nicotine addiction is a neuropsychiatric disorder with dysfunction in cortices as well as white matter (WM). The nature of the functional alterations in WM remains unclear. The small-world model can well characterize the structure and function of the human brain. In this study, we utilized the small-world model to compare the WM functional connectivity between 62 nicotine addiction participants (called the discovery sample) and 66 matched healthy controls (called the control sample). We also recruited an independent sample comprising 32 nicotine addicts (called the validation sample) for clinical application. The WM functional network data at the network level showed that the nicotine addiction group revealed decreased small-worldness index (σ) and normalized clustering coefficient (γ) compared with healthy controls. For clinical application, the small-world topology of WM functional connectivity could distinguish nicotine addicts from healthy controls (classification accuracy=0.59323, p = 0.0464). We trained abnormal small-world properties on the discovery sample to identify the severity of nicotine addiction, and the identification was successfully applied to the validation sample (classification accuracy=0.65625, p = 0.0106). Our neuroimaging findings provide direct evidence for WM functional changes in nicotine addiction and suggest that the small-world properties of WM function could be qualified as potential biomarkers in nicotine addiction.

Olfactory functional covariance connectivity in Parkinson's disease: Evidence from a Chinese population

Introduction

Central anosmia is a potential marker of the prodrome and progression of Parkinson's disease (PD). Resting-state functional magnetic resonance imaging studies have shown that olfactory dysfunction is related to abnormal changes in central olfactory-related structures in patients with early PD.

Methods

This study, which was conducted at Guanyun People's Hospital, analyzed the resting-state functional magnetic resonance data using the functional covariance connection strength method to decode the functional connectivity between the white-gray matter in a Chinese population comprising 14 patients with PD and 13 controls.

Results

The following correlations were observed in patients with PD: specific gray matter areas related to smell (i.e., the brainstem, right cerebellum, right temporal fusiform cortex, bilateral superior temporal gyrus, right Insula, left frontal pole and right superior parietal lobule) had abnormal connections with white matter fiber bundles (i.e., the left posterior thalamic radiation, bilateral posterior corona radiata, bilateral superior corona radiata and right superior longitudinal fasciculus); the connection between the brainstem [region of interest (ROI) 1] and right cerebellum (ROI2) showed a strong correlation. Right posterior corona radiation (ROI11) showed a strong correlation with part 2 of the Unified Parkinson's Disease Rating Scale, and right superior longitudinal fasciculus (ROI14) showed a strong correlation with parts 1, 2, and 3 of the Unified Parkinson's Disease Rating Scale and Hoehn and Yahr Scale.

Discussion

The characteristics of olfactory-related brain networks can be potentially used as neuroimaging biomarkers for characterizing PD states. In the future, dynamic testing of olfactory function may help improve the accuracy and specificity of olfactory dysfunction in the diagnosis of neurodegenerative diseases.

Functional Connectivity of White Matter and Its Association with Sleep Quality

Purpose

Functional magnetic resonance imaging (fMRI) has been widely adopted to investigate the neural activity in gray matter (GM) in the field of sleep research, but the neural activity in white matter (WM) has received much less attention. The current study set out to test our hypothesis that WM functional abnormality is associated with poor sleep quality.

Participants and Methods

K-means clustering analysis was performed on 78 healthy adults drawn from the Human Connectome Project dataset to extract stable WM functional networks (WM-FNs) and GM-FNs. The differences in functional connectivity within WM-FNs and between WM- and GM-FNs, as well as the power spectrum between good sleep quality group (Pittsburgh Sleep Quality Index (PSQI) <6, daytime dysfunction = 0) and poor sleep quality group (PSQI >6, daytime dysfunction >0) were examined between groups with good and poor sleep quality. Additionally, linear relationships between sleep quality and altered functional characteristics of WM-FNs were evaluated.

Results

Functional connectivity between middle and superficial WM-FNs, short- and long-range functional connectivity between WM- and GM-FNs were decreased in poor sleepers and negatively correlated with PSQI score. The mean amplitudes of right sensorimotor WM networks at whole, high and low frequency bands were higher in poor sleepers and were positively correlated with PSQI score.

Conclusion

WM functional abnormality is associated with poor sleep quality. The neurobiological mechanisms that underlie the functional alterations of WM-FNs in poor sleepers need to be investigated in future studies.

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.

The influence of visual deprivation on the development of the thalamocortical network: Evidence from congenitally blind children and adults

The thalamus is heavily involved in relaying sensory signals to the cerebral cortex. A relevant issue is how the deprivation of congenital visual sensory information modulates the development of the thalamocortical network. The answer is unclear because previous studies on this topic did not investigate network development, structure-function combinations, and cognition-related behaviors in the same study. To overcome these limitations, we recruited 30 congenitally blind subjects (8 children, 22 adults) and 31 sighted subjects (10 children, 21 adults), and conducted multiple analyses [i.e., gray matter volume (GMV) analysis using the voxel-based morphometry (VBM) method, resting-state functional connectivity (FC), and brain-behavior correlation]. We found that congenital blindness elicited significant changes in the development of GMV in visual and somatosensory thalamic regions. Blindness also resulted in significant changes in the development of FC between somatosensory thalamic regions and visual cortical regions as well as advanced information processing regions. Moreover, the somatosensory thalamic regions and their FCs with visual cortical regions were reorganized to process high-level tactile language information in blind individuals. These findings provide a refined understanding of the neuroanatomical and functional plasticity of the thalamocortical network.

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.

Frequency-dependent white-matter functional network changes associated with cognitive deficits in subcortical vascular cognitive impairment

Vascular cognitive impairment (VCI) refers to all forms of cognitive decline associated with cerebrovascular diseases, in which white matter (WM) is highly vulnerable. Although previous studies have shown that blood oxygen level-dependent (BOLD) signals inside WM can effectively reflect neural activities, whether WM BOLD signal alterations are present and their roles underlying cognitive impairment in VCI remain largely unknown. In this study, 36 subcortical VCI (SVCI) patients and 36 healthy controls were enrolled to evaluate WM dysfunction. Specifically, fourteen distinct WM networks were identified from resting-state functional MRI using K-means clustering analysis. Subsequently, between-network functional connectivity (FC) and within-network BOLD signal amplitude of WM networks were calculated in three frequency bands (band A: 0.01-0.15 Hz, band B: 0.08-0.15 Hz, and band C: 0.01-0.08 Hz). Patients with SVCI manifested decreased FC mainly in bilateral parietal WM regions, forceps major, superior and inferior longitudinal fasciculi. These connections extensively linked with distinct WM networks and with gray-matter networks such as frontoparietal control, dorsal and ventral attention networks, which exhibited frequency-specific alterations in SVCI. Additionally, extensive amplitude reductions were found in SVCI, showing frequency-dependent properties in parietal, anterior corona radiate, pre/post central, superior and inferior longitudinal fasciculus networks. Furthermore, these decreased FC and amplitudes showed significant positive correlations with cognitive performances in SVCI, and high diagnostic performances for SVCI especially combining all bands. Our study indicated that VCI-related cognitive deficits were characterized by frequency-dependent WM functional abnormalities, which offered novel applicable neuromarkers for VCI.