Exploring the functional connectome in white matter

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.

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.

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.

Longitudinal development of the human white matter structural connectome and its association with brain transcriptomic and cellular architecture

From childhood to adolescence, the spatiotemporal development pattern of the human brain white matter connectome and its underlying transcriptomic and cellular mechanisms remain largely unknown. With a longitudinal diffusion MRI cohort of 604 participants, we map the developmental trajectory of the white matter connectome from global to regional levels and identify that most brain network properties followed a linear developmental trajectory. Importantly, connectome-transcriptomic analysis reveals that the spatial development pattern of white matter connectome is potentially regulated by the transcriptomic architecture, with positively correlated genes involve in ion transport- and development-related pathways expressed in excitatory and inhibitory neurons, and negatively correlated genes enriches in synapse- and development-related pathways expressed in astrocytes, inhibitory neurons and microglia. Additionally, the macroscale developmental pattern is also associated with myelin content and thicknesses of specific laminas. These findings offer insights into the underlying genetics and neural mechanisms of macroscale white matter connectome development from childhood to adolescence.

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.

A metabolism-functional connectome sparse coupling method to reveal imaging markers for Alzheimer's disease based on simultaneous PET/MRI scans

Abnormal glucose metabolism and hemodynamic changes in the brain are closely related to cognitive function, providing complementary information from distinct biochemical and physiological processes. However, it remains unclear how to effectively integrate these two modalities across distinct brain regions. In this study, we developed a connectome-based sparse coupling method for hybrid PET/MRI imaging, which could effectively extract imaging markers of Alzheimer's disease (AD) in the early stage. The FDG-PET and resting-state fMRI data of 56 healthy controls (HC), 54 subjective cognitive decline (SCD), and 27 cognitive impairment (CI) participants due to AD were obtained from SILCODE project (NCT03370744). For each participant, the metabolic connectome (MC) was constructed by Kullback-Leibler divergence similarity estimation, and the functional connectome (FC) was constructed by Pearson correlation. Subsequently, we measured the coupling strength between MC and FC at various sparse levels, assessed its stability, and explored the abnormal coupling strength along the AD continuum. Results showed that the sparse MC-FC coupling index was stable in each brain network and consistent across subjects. It was more normally distributed than other traditional indexes and captured more SCD-related brain areas, especially in the limbic and default mode networks. Compared to other traditional indices, this index demonstrated best classification performance. The AUC values reached 0.748 (SCD/HC) and 0.992 (CI/HC). Notably, we found a significant correlation between abnormal coupling strength and neuropsychological scales (p < .05). This study provides a clinically relevant tool for hybrid PET/MRI imaging, allowing for exploring imaging markers in early stage of AD and better understanding the pathophysiology along the AD continuum.

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.

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

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

Changes in white matter functional networks across late adulthood

Introduction

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

Methods

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

Results

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

Discussion

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

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.

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.

Abnormal white matter changes in Alzheimer's disease based on diffusion tensor imaging: A systematic review

Alzheimer's disease (AD) is a degenerative neurological disease in elderly individuals. Subjective cognitive decline (SCD), mild cognitive impairment (MCI) and further development to dementia (d-AD) are considered to be major stages of the progressive pathological development of AD. Diffusion tensor imaging (DTI), one of the most important modalities of MRI, can describe the microstructure of white matter through its tensor model. It is widely used in understanding the central nervous system mechanism and finding appropriate potential biomarkers for the early stages of AD. Based on the multilevel analysis methods of DTI (voxelwise, fiberwise and networkwise), we summarized that AD patients mainly showed extensive microstructural damage, structural disconnection and topological abnormalities in the corpus callosum, fornix, and medial temporal lobe, including the hippocampus and cingulum. The diffusion features and structural connectomics of specific regions can provide information for the early assisted recognition of AD. The classification accuracy of SCD and normal controls can reach 92.68% at present. And due to the further changes of brain structure and function, the classification accuracy of MCI, d-AD and normal controls can reach more than 97%. Finally, we summarized the limitations of current DTI-based AD research and propose possible future research directions.

Altered static and dynamic indices of intrinsic brain activity in patients with subcortical ischemic vascular disease: a resting-state functional magnetic resonance imaging analysis

PURPOSE

To explore the static and dynamic characteristics of intrinsic brain activity (IBA) in subcortical ischemic vascular disease (SIVD) patients with or without cognitive impairment.

METHODS

In total, 90 participants were recruited, including 32 SIVD patients with cognitive impairment (SIVD-CI, N = 32), 26 SIVD patients with no cognitive impairment (SIVD-NCI, N = 26), and 32 healthy controls (HC, N = 32) matched for age, gender, and education. All subjects underwent resting-state functional magnetic resonance imaging (rs-fMRI) scanning and neuropsychological tests. Amplitude of low-frequency fluctuation (ALFF) was calculated to reflect static alterations of regional IBA. Sliding window analysis was conducted in order to explore the dynamic characteristics.

RESULTS

Both SIVD-CI and SIVD-NCI group showed significantly decreased ALFF in left angular gyrus (ANG), whereas SIVD-CI group showed increased ALFF in right superior frontal gyrus (SFG), compared with HCs. Furthermore, SIVD-CI group showed significantly decreased ALFF dynamics (dALFF) in right precuneus (PreCu) and left dorsal anterior cingulate cortex (dACC), compared with HC and SIVD-NCI groups (Gaussian random field-corrected, voxel-level P < 0.001, cluster-level P < 0.05). No dynamic changes were detected between SIVD-NCI group and HC group. The mean ALFF value in left ANG of SIVD-CI group was correlated with the score of delayed memory scale.

CONCLUSION

ANG may be a vulnerable brain region in SIVD patients. Temporal dynamic analysis could serve as a sensitive and promising method to investigate IBA alterations in SIVD patients.

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.

Localized activity alternations in periventricular nodular heterotopia-related epilepsy

OBJECTIVE

Periventricular nodular heterotopia (PNH) is a common type of heterotopia usually characterized by epilepsy. Previous studies have identified alterations in structural and functional connectivity related to this disorder, but its local functional neural basis has received less attention. The purpose of this study was to combine univariate analysis and a Gaussian process classifier (GPC) to assess local activity and further explore neuropathological mechanisms in PNH-related epilepsy.

METHODS

We used a 3.0-T scanner to acquire resting-state data and measure local regional homogeneity (ReHo) alterations in 38 patients with PNH-related epilepsy and 38 healthy controls (HCs). We first assessed ReHo alterations by comparing the PNH group to the HC group using traditional univariate analysis. Next, we applied a GPC to explore whether ReHo could be used to differentiate PNH patients from healthy patients at an individual level.

RESULTS

Compared to HCs, PNH-related epilepsy patients exhibited lower ReHo in the left insula extending to the putamen as well as in the subgenual anterior cingulate cortex (sgACC) extending to the orbitofrontal cortex (OFC) [p < 0.05, family-wise error corrected]. Both of these regions were also correlated with epilepsy duration. Furthermore, the ReHo GPC classification yielded a 76.32% accuracy (sensitivity = 71.05% and specificity = 81.58%) with p < 0.001 after permutation testing.

INTERPRETATION

Using the resting-state approach, we identified localized activity alterations in the left insula extending to the putamen and the sgACC extending to the OFC, providing pathophysiological evidence of PNH. These local connectivity patterns may provide a means to differentiate PNH patients from HCs.

Multivariate associations between behavioural dimensions and white matter across children and adolescents with and without attention-deficit/hyperactivity disorder

BACKGROUND

Attention deficit/hyperactivity disorder (ADHD) is a heterogeneous neurodevelopmental disorder. Integrity of white matter microstructure plays a key role in the neural mechanism of ADHD presentations. However, the relationships between specific behavioural dimensions and white matter microstructure are less well known. This study aimed to identify associations between white matter and a broad set of clinical features across children and adolescent with and without ADHD using a data-driven multivariate approach.

METHOD

We recruited a total of 130 children (62 controls and 68 ADHD) and employed regularized generalized canonical correlation analysis to characterize the associations between white matter and a comprehensive set of clinical measures covering three domains, including symptom, cognition and behaviour. We further applied linear discriminant analysis to integrate these associations to explore potential developmental effects.

RESULTS

We delineated two brain-behaviour dimensional associations in each domain resulting a total of six multivariate patterns of white matter microstructural alterations linked to hyperactivity-impulsivity and mild affected; executive functions and working memory; externalizing behaviour and social withdrawal, respectively. Apart from executive function and externalizing behaviour sharing similar white matter patterns, all other dimensions linked to a specific pattern of white matter microstructural alterations. The multivariate dimensional association scores showed an overall increase and normalization with age in ADHD group while remained stable in controls.

CONCLUSIONS

We found multivariate neurobehavioral associations exist across ADHD and controls, which suggested that multiple white matter patterns underlie ADHD heterogeneity and provided neural bases for more precise diagnosis and individualized treatment.

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.

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

Background

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

Methods

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

Results

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

Conclusions

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

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

Background

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

Materials and methods

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

Results

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

Conclusion

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

Driving brain state transitions in major depressive disorder through external stimulation

Major depressive disorder (MDD) as a dysfunction of neural circuits and brain networks has been established in modern neuroimaging sciences. However, the brain state transitions between MDD and health through external stimulation remain unclear, which limits translation to clinical contexts and demonstrable clinical utility. We propose a framework of the large-scale whole-brain network model for MDD linking the underlying anatomical connectivity with functional dynamics obtained from diffusion tensor imaging (DTI) and functional magnetic resonance imaging (fMRI). Then, we further explored the optimal brain regions to promote the transition of brain states between MDD and health through external stimulation of the model. Based on the whole-brain model successfully fitting the brain state space in MDD and the health, we demonstrated that the transition from MDD to health is achieved by the excitatory activation of the limbic system and from health to MDD by the inhibitory stimulation of the reward circuit. Our finding provides novel biophysical evidence for the neural mechanism of MDD and its recovery and allows the discovery of new stimulation targets for MDD recovery.

Attenuated brain white matter functional network interactions in Parkinson's disease

Parkinson's disease (PD) is a neurodegenerative disorder characterized by extensive structural abnormalities in cortical and subcortical brain areas. However, an association between changes in the functional networks in brain white matter (BWM) and Parkinson's symptoms remains unclear. With confirming evidence that resting‐state functional magnetic resonance imaging (rs‐fMRI) of BWM signals can effectively describe neuronal activity, this study investigated the interactions among BWM functional networks in PD relative to healthy controls (HC). Sixty‐eight patients with PD and sixty‐three HC underwent rs‐fMRI. Twelve BWM functional networks were identified by K‐means clustering algorithm, which were further classified as deep, middle, and superficial layers. Network‐level interactions were examined via coefficient Granger causality analysis. Compared with the HC, the patients with PD displayed significantly weaker functional interaction strength within the BWM networks, particularly excitatory influences from the superficial to deep networks. The patients also showed significantly weaker inhibitory influences from the deep to superficial networks. Additionally, the sum of the absolutely positive/negative regression coefficients of the tri‐layered networks in the patients was lower relative to HC (p < .05, corrected for false discovery rate). Moreover, we found the functional interactions involving the deep BWM networks negatively correlated with part III of the Unified Parkinson's Disease Rating Scales and Hamilton Depression Scales. Taken together, we demonstrated attenuated BWM interactions in PD and these abnormalities were associated with clinical motor and nonmotor symptoms. These findings may aid understanding of the neuropathology of PD and its progression throughout the nervous system from the perspective of BWM function.

Changes in white matter functional networks during wakefulness and sleep

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

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

BACKGROUND

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

METHODS

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

RESULTS

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

CONCLUSIONS

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

Pallidal volume reduction and prefrontal-striatal-thalamic functional connectivity disruption in pediatric bipolar disorders

BACKGROUND

As a crucial node of the corticolimbic model, the striatum has been demonstrated in modulating emotional cues in pediatric bipolar disorders (PBD), the striatal distinction in structure and function between PBD-I and PBD-II remains unclear.

METHODS

MRI data of 36 patients in PBD-I, 22 patients in PBD-II and 19 age-gender matched healthy controls (HCs) were processed. Here, we investigated structural and functional alterations of 8 subregions of striatum (bilateral nucleus accumbens, caudate, putamen and globus pallidus) by analyzing MRI data.

RESULTS

We found volume reduction of the right pallidum, the significant positive correlation between the number of episodes and the functional connectivity between left pallidum and right caudate in PBD-I patients, abrupted prefrontal-striatal-thalamic functional connectivity in PBD-I group and decreased functional connectivity in PBD-II relative to HCs and PBD-I.

LIMITATIONS

Future studies should enroll more subjects and adopt a longitudinal perspective, which could help to discover striatum structural or functional alterations during subject-specific clinical progress in different states.

CONCLUSIONS

Results of the present study confirmed that structural and functional abnormality of striatum may be helpful in identifying PBD clinical types as distinctive biomarkers. The interruptions of the prefrontal-striatal-thalamic circuits may provide advantageous evidence for expounding the role of striatum in bipolar disorders etiology. Thus, potential mechanisms of dysfunction striatum need to be formulated and reconceptualized with multimodal neuroimaging studies in future.

Correlated functional connectivity and glucose metabolism in brain white matter revealed by simultaneous MRI/positron emission tomography

PURPOSE

There has been converging evidence of reliable detections of blood oxygenation level dependent (BOLD) signals evoked by neural stimulation and in a resting state in white matter (WM), within which few studies examined the relationship between BOLD functional signals and tissue metabolism. The purpose of the present study was to explore whether such relationship exists using combined functional MRI and positron emission tomography (PET) measurements of glucose uptake.

METHODS

Functional and metabolic imaging data from 25 right-handed healthy human adults (aged 18-23 years, 18 females) were analyzed. Measures, including average resting state functional connectivity (FC) with respect to 82 Brodmann areas, fractional amplitude of low-frequency fluctuations (FALFF), and average fluorodeoxyglucose (FDG) uptake by PET, were computed for 48 predefined WM bundles. Pearson correlations across the bundles and 25 subjects studied were calculated among these measures. Linear mixed effects models were used to estimate the variance explainable by a predictor variable in the absence of inter-subject variations.

RESULTS

Analysis of six separate imaging intervals found that average FC the bundles was significantly correlated with local FDG uptake (r = 0.25, p < 0.001), and the FC also covaried significantly with FALFF (r = 0.41, p < 0.001). When random effects from inter-subject variations were controlled, these correlations appeared to be medium to strong (r = 0.41 for FC vs. FDG uptake, and r = 0.65 for FALFF vs. FC).

CONCLUSION

This study indicates that BOLD signals in WM are directly related to variations in metabolic demand and engagement with cortical processing and suggests they should be incorporated into more complete models of brain function.

Hippocampus-Based Dynamic Functional Connectivity Mapping in the Early Stages of Alzheimer's Disease

BACKGROUND

The hippocampus with varying degrees of atrophy was a crucial neuroimaging feature resulting in the declining memory and cognitive function in Alzheimer's disease (AD). However, the abnormal dynamic functional connectivity (DFC) in both white matter (WM) and gray matter (GM) from the left and right hippocampus remains unclear.

OBJECTIVE

To explore the abnormal DFC within WM and GM from the left and right hippocampus across the different stages of AD.

METHODS

Current study employed the OASIS-3 dataset including 43 mild cognitive impairment (MCI), 71 pre-mild cognitive impairment (pre-MCI), and matched 87 normal cognitive (NC). Adopting the FMRIB's Integrated Registration and Segmentation Tool, we obtained the left and right hippocampus mask. Based on above hippocampus mask as seed point, we calculated the DFC between left/right hippocampus and all voxel time series within whole brain. One-way ANOVA analysis was performed to estimate the abnormal DFC among MCI, pre-MCI, and NC groups.

RESULTS

We found that MCI and pre-MCI groups showed the common abnormalities of DFC in the Temporal_Mid_L, Cingulum_Mid_L, and Thalamus_L. Specific abnormalities were found in the Cerebelum_9_L and Precuneus of MCI group and Vermis_8 and Caudate_L of pre-MCI group. In addition, we found that DFC within WM regions also showed the common low DFC for the Cerebellum anterior lobe-WM, Corpus callosum, and Frontal lobe-WM in MCI and pre-MCI group.

CONCLUSION

Our findings provided a novel information for discover the pathophysiological mechanisms of AD and indicate WM lesions were also an important cause of cognitive decline in AD.

Transcriptomic and macroscopic architectures of intersubject functional variability in human brain white-matter

Intersubject variability is a fundamental characteristic of brain organizations, and not just "noise". Although intrinsic functional connectivity (FC) is unique to each individual and varies across brain gray-matter, the underlying mechanisms of intersubject functional variability in white-matter (WM) remain unknown. This study identified WMFC variabilities and determined the genetic basis and macroscale imaging in 45 healthy subjects. The functional localization pattern of intersubject variability across WM is heterogeneous, with most variability observed in the heteromodal cortex. The variabilities of heteromodal regions in expression profiles of genes are related to neuronal cells, involved in synapse-related and glutamic pathways, and associated with psychiatric disorders. In contrast, genes overexpressed in unimodal regions are mostly expressed in glial cells and were related to neurological diseases. Macroscopic variability recapitulates the functional and structural specializations and behavioral phenotypes. Together, our results provide clues to intersubject variabilities of the WMFC with convergent transcriptomic and cellular signatures, which relate to macroscale brain specialization.

A novel stroke lesion network mapping approach: improved accuracy yet still low deficit prediction

Lesion network mapping estimates functional network abnormalities caused by a focal brain lesion. The method requires embedding the volume of the lesion into a normative functional connectome and using the average functional magnetic resonance imaging signal from that volume to compute the temporal correlation with all other brain locations. Lesion network mapping yields a map of potentially functionally disconnected regions. Although promising, this approach does not predict behavioural deficits well. We modified lesion network mapping by using the first principal component of the functional magnetic resonance imaging signal computed from the voxels within the lesioned area for temporal correlation. We measured potential improvements in connectivity strength, anatomical specificity of the lesioned network and behavioural prediction in a large cohort of first-time stroke patients at 2-weeks post-injury (n = 123). This principal component functional disconnection approach localized mainly cortical voxels of high signal-to-noise; and it yielded networks with higher anatomical specificity, and stronger behavioural correlation than the standard method. However, when examined with a rigorous leave-one-out machine learning approach, principal component functional disconnection approach did not perform better than the standard lesion network mapping in predicting neurological deficits. In summary, even though our novel method improves the specificity of disconnected networks and correlates with behavioural deficits post-stroke, it does not improve clinical prediction. Further work is needed to capture the complex adjustment of functional networks produced by focal damage in relation to behaviour.

The structural connectome in ADHD

Attention-deficit/hyperactivity disorder (ADHD) has been conceptualized as a brain dysconnectivity disorder. In the past decade, noninvasive diffusion magnetic resonance imaging (dMRI) studies have demonstrated that individuals with ADHD have alterations in the white matter structural connectome, and that these alterations are associated with core symptoms and cognitive deficits in patients. This review aims to summarize recent dMRI-based structural connectome studies in ADHD from voxel-, tractography-, and network-based perspectives. Voxel- and tractography-based studies have demonstrated disrupted microstructural properties predominantly located in the frontostriatal tracts, the corpus callosum, the corticospinal tracts, and the cingulum bundle in patients with ADHD. Network-based studies have suggested abnormal global and local efficiency as well as nodal properties in the prefrontal and parietal regions in the ADHD structural connectomes. The altered structural connectomes in those with ADHD provide significant signatures for prediction of symptoms and diagnostic classification. These studies suggest that abnormalities in the structural connectome may be one of the neural underpinnings of ADHD psychopathology and show potential for establishing imaging biomarkers in clinical evaluation. However, given that there are inconsistent findings across studies due to sample heterogeneity and analysis method variations, these ADHD-related white matter alterations are still far from informing clinical practice. Future studies with larger and more homogeneous samples are needed to validate the consistency of current results; advanced dMRI techniques can help to generate much more precise estimation of white matter pathways and assure specific fiber configurations; and finally, dimensional analysis frameworks can deepen our understanding of the neurobiology underlying ADHD.

White Matter Functional Connectivity in Resting-State fMRI: Robustness, Reliability, and Relationships to Gray Matter

A comprehensive characterization of the spatiotemporal organization in the whole brain is critical to understand both the function and dysfunction of the human brain. Resting-state functional connectivity (FC) of gray matter (GM) has helped in uncovering the inherent baseline networks of brain. However, the white matter (WM), which composes almost half of brain, has been largely ignored in this characterization despite studies indicating that FC in WM does change during task and rest functional magnetic resonance imaging (fMRI). In this study, we identify 9 white matter functional networks (WM-FNs) and 9 gray matter functional networks (GM-FNs) of resting fMRI. Intraclass correlation coefficient (ICC) was calculated on multirun fMRI data to estimate the reliability of static functional connectivity (SFC) and dynamic functional connectivity (DFC). Associations between SFC, DFC, and their respective ICCs are estimated for GM-FNs, WM-FNs, and GM-WM-FNs. SFC of GM-FNs were stronger than that of WM-FNs, but the corresponding DFC of GM-FNs was lower, indicating that WM-FNs were more dynamic. Associations between SFC, DFC, and their ICCs were similar in both GM- and WM-FNs. These findings suggest that WM fMRI signal contains rich spatiotemporal information similar to that of GM and may hold important cues to better establish the functional organization of the whole brain.

Altered Homotopic Functional Connectivity Within White Matter in the Early Stages of Alzheimer's Disease

Alzheimer's disease (AD) is a progressive neurodegenerative disorder with memory loss and cognitive impairment. The white matter (WM) BOLD signal has recently been shown to provide an important role in understanding the intrinsic cerebral activity. Although the altered homotopic functional connectivity within gray matter (GM-HFC) has been examined in AD, the abnormal HFC to WM remains unknown. The present study sought to identify changes in the WM-HFC and anatomic characteristics by combining functional magnetic resonance imaging with diffusion tensor imaging (DTI). Resting-state and DTI magnetic resonance images were collected from the OASIS-3 dataset and consisted of 53 mild cognitive impairment (MCI) patients, 90 very MCI (VMCI), and 100 normal cognitive (NC) subjects. Voxel-mirrored HFC was adopted to examine whether WM-HFC was disrupted in VMCI and MCI participants. Moreover, the DTI technique was used to investigate whether specific alterations of WM-HFC were associated with anatomic characteristics. Support vector machine analyses were used to identify the MCI and VMCI participants using the abnormal WM-HFC as the features. Compared with NC, MCI, and VMCI participants showed significantly decreased GM-HFC in the middle occipital gyrus and inferior parietal gyrus and decreased WM-HFC in the bilateral middle occipital and parietal lobe-WM. In addition, specific WM-functional network alteration for the bilateral sub-lobar-WM was found in MCI subjects. MCI subjects showed abnormal anatomic characteristics for bilateral sub-lobar and parietal lobe-WM. Results of GM-HFC mainly showed common neuroimaging features for VMCI and MCI subjects, whereas analysis of WM-HFC showed specific clinical neuromarkers and effectively compensated for the lack of GM-HFC to distinguish NC, VMCI, and MCI subjects.

Decline in executive function in patients with white matter hyperintensities from the static and dynamic perspectives of amplitude of low-frequency fluctuations

Cognitive impairments are characteristics of patients with white matter hyperintensities (WMHs), and hypoperfusion is currently a relatively recognized mechanism of WMHs. Brain activity is closely coupled to the regulation of local blood flow. This study aimed to investigate the abnormal local brain activity of patients with WMHs from the viewpoint of the static amplitude of low-frequency fluctuations (sALFF) and dynamic amplitude of low-frequency fluctuations (dALFF). Seventy-four patients with WMHs and 35 healthy controls (HCs) were included. Based on the Fazekas scale, patients with WMHs were further divided into a mild WMH group (n = 33, Fazekas score 1-2) and moderate-severe WMH group (n = 41, Fazekas score 3-6). The sALFF and dALFF values were calculated separately and neuropsychological tests including the Montreal Cognitive Assessment (MoCA), Auditory Verbal Learning Test (AVLT), Trail Making Test (TMT), and Boston Naming Test (BNT) were completed by all participants. Patients with WMHs showed increased sALFF and dALFF values in the bilateral thalamus and decreased performance in the MoCA test, AVLT-immediate, AVLT-delay, AVLT-recognition, TMT-A, and BNT. The dALFF values in the bilateral thalamus was correlated with the MoCA in HCs. The sALFF values in the bilateral thalamus correlated with TMT-B in patients with WMHs. Patients with WMHs showed abnormal brain activity and decreased functional stability of the bilateral thalamus, which may be a potential mechanism of decreased executive function.

Disrupted functional connectivity in white matter resting-state networks in unilateral temporal lobe epilepsy

Unilateral temporal lobe epilepsy (TLE) is the most common type of focal epilepsy characterized by foci in the unilateral temporal lobe grey matters of regions such as the hippocampus. However, it remains unclear how the functional features of white matter are altered in TLE. In the current study, resting-state functional magnetic resonance imaging (fMRI) was performed on 71 left TLE (LTLE) patients, 79 right TLE (RTLE) patients and 47 healthy controls (HC). Clustering analysis was used to identify fourteen white matter networks (WMN). The functional connectivity (FC) was calculated among WMNs and between WMNs and grey matter. Furthermore, the FC laterality of hemispheric WMNs was assessed. First, both patient groups showed decreased FCs among WMNs. Specifically, cerebellar white matter illustrated decreased FCs with the cerebral superficial WMNs, implying a dysfunctional interaction between the cerebellum and the cerebral cortex in TLE. Second, the FCs between WMNs and the ipsilateral hippocampus (grey matter foci) were also reduced in patient groups, which may suggest insufficient functional integration in unilateral TLE. Interestingly, RTLE showed more severe abnormalities of white matter FCs, including links to the bilateral hippocampi and temporal white matter, than LTLE. Taken together, these findings provide functional evidence of white matter abnormalities, extending the understanding of the pathological mechanism of white matter impairments in unilateral TLE.

Influence of epileptogenic region on brain structural changes in Rolandic epilepsy

To investigate the influence of epileptogenic cortex (Rolandic areas) with executive functions in Rolandic epilepsy using structural covariance analysis of structural magnetic resonance imaging (MRI). Structural MRI data of drug-naive patients with Rolandic epilepsy (n = 70) and typically developing children as healthy controls (n = 83) were analyzed using voxel-based morphometry. Gray matter volumes in the patients were compared with those of healthy controls, and were further correlated with epilepsy duration and cognitive score of executive function, respectively. By applying Granger causal analysis to the sequenced morphometric data according to disease progression information, causal network of structural covariance was constructed to assess the causal influence of structural changes from Rolandic cortices to the regions engaging executive function in the patients. Compared with healthy controls, epilepsy patients showed increased gray matter volume in the Rolandic regions, and also the regions engaging in executive function. Covariance network analyses showed that along with disease progression, the Rolandic regions imposed positive causal influence on the regions engaging in executive function. In the patients with Rolandic epilepsy, epileptogenic regions have causal influence on the structural changes in the regions of executive function, implicating damaging effects of Rolandic epilepsy on human brain.

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

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

Superficial white-matter functional networks changes in bipolar disorder patients during depressive episodes

BACKGROUND

Bipolar disorder is a common psychiatric disorder characterized by insufficient or ineffective connections associated with white-matter (WM) abnormalities. Previous studies have detected the structural attributes of WM using magnetic resonance imaging (MRI) or diffusion tensor imaging, however, they failed to disentangle the dysfunctional organization within the WM.

METHODS

This study aimed to uncover the WM functional connectivity (FC) in 45 bipolar disorder patients during depressive episodes (BDD) and 45 healthy controls based on resting-state functional MRI. Eight WM functional networks were identified by using a clustering analysis of voxel-based correlation profiles, which were further classified into superficial, middle and deep layers of networks.

RESULTS

Group comparisons on the FCs among 8 WM networks showed that the superficial tempofrontal network (TFN) in BDD patients had increased FC with the superficial cerebellar network (CN) and with the superficial pre/post-central network (PCN). Further, support vector regression prediction analysis results revealed that the increased FCs of CN-TFN and PCN-TFN could be served as features to predict the numbers of depressive episode in BDD patients.

CONCLUSIONS

The current study extended our knowledge about the impaired WM functional connections associated with emotional and sensory-motor perception processing in BDD, which may facilitate the interpretation of the pathophysiology mechanisms underlying BDD.

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

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

Distributed Functional Connectome of White Matter in Patients With Functional Dyspepsia

Purpose: We aimed to find out the distributed functional connectome of white matter in patients with functional dyspepsia (FD). Methods: 20 patients with FD and 24 age- and gender-matched healthy controls were included into the study. The functional connectome of white matter and graph theory were used to these participants. Two-sample t-test was used for the detection the abnormal graph properties in FD. Pearson correlation was used for the relationship between properties and the clinical and neuropshychological information. Results: Patients with FD and healthy controls showed small-world properties in functional connectome of white matter. Compared with healthy controls, the FD group showed decreased global properties (Cp, S, Eglobal, and Elocal). Four pairs of fiber bundles that are connected to the frontal lobe, insula, and thalamus were affected in the FD group. Duration and Pittsburgh Sleep Quality Index positively correlated with the betweenness centrality of white matter regions of interest. Conclusion: FD patients turned to a non-optimized functional organization of WM brain network. Frontal lobe, insula, and thalamus were key regions in brain information exchange of FD. It provided some novel imaging evidences for the mechanism of FD.

Abnormal white matter functional connectivity density in antipsychotic-naive adolescents with schizophrenia

OBJECTIVES

This study aimed to assess the white matter (WM) functional hubs and abnormal functional connectivity pattern in adolescents with schizophrenia (AOS) and to explore the potential mechanisms.

METHODS

Based on resting-state fMRI data, we measured the WM functional connectivity density (FCD) at local- and long- ranges in 39 AOS and 31 healthy controls (HCs). Group comparison was conducted between the two groups. Spearman rank correlation analysis between the altered WM FCD and clinical PANSS scores was performed.

RESULTS

In the local scale, the functional hubs of the WM were mainly located in the corona radiata and cerebellum. Compared with HCs, AOS patients exhibited decreased FCD in the superior corona radiata. In the long-range, the functional hubs of the WM were mainly located in the external capsule and pons. AOS patients exhibited increased FCD in the cingulum but decreased FCD in the right dorsal raphe nuclei (DR). Furthermore, the aberrant long-range FCD in the right DR was inversely proportional to the clinical symptoms.

CONCLUSION

These findings indicated that the pathophysiology of schizophrenia may also lie in WM functional dysconnectivity.

SIGNIFICANCE

The current results provided initial evidence for the hypothesis of abnormal WM functional connectivity in schizophrenia.

Different posterior hippocampus and default mode network modulation in young APOE ε4 carriers: a functional connectome-informed phenotype longitudinal study

To determine the functional connectome change pattern based on subregions of the hippocampus in young APOEε4 carriers during a 3-year follow-up. All the participants (n = 213) were tested for resting-state functional MRI, neuropsychological scales, and APOE genotype. The age- and sex-matched APOE ε4/ε3 (23.9 ± 3.2 years old, 6 female/7 male) carriers and APOE ε3/ε3 (22.9 ± 1.6 years old, 7 female/12 male) carriers were finally followed up. The hippocampus and its anterior/middle/posterior subregion-based functional connectivity (FC) patterns were compared between APOEε4 and APOEε3 groups by a two-sample t-test at baseline and mixed-effect analysis at follow-up. The effective connectivity (EC) patterns among the altered regions of interaction effect were examined in the APOEε4 groups. APOEε4 carries displayed saliently enhanced FC in the right anterior/middle hippocampus and core regions of the default mode network (DMN) (P < 0.05 by Gaussian Random Fields (GRF) correction). However, the APOEε4-by-time interaction was evident in the middle/posterior hippocampus with connection to the lateral temporal lobe and anterior cingulate cortex (ACC) (P < 0.05 by GRF correction). Moreover, the APOEε4 group at follow-up showed increased EC separately from both the left middle hippocampus and lateral temporal lobe to the left posterior hippocampus, and its changes of FC/EC significantly correlated with altered memory function. The posterior hippocampus might be especially vulnerable to early modulation in young APOEε4 carriers. Its connection with the lateral temporal lobe, rather than with DMN, might be the early compensative mechanism of memory function regulation influenced by APOE ε4 in the young adults.

Dynamics and Concordance Abnormalities Among Indices of Intrinsic Brain Activity in Individuals With Subjective Cognitive Decline: A Temporal Dynamics Resting-State Functional Magnetic Resonance Imaging Analysis

Individuals with subjective cognitive decline (SCD) are more likely to develop into Alzheimer disease (AD) in the future. Resting-state functional magnetic resonance imaging (rs-fMRI) studies have shown alterations of intrinsic brain activity (IBA) in SCD individuals. However, rs-fMRI studies to date have mainly focused on static characteristics of IBA, with few studies reporting dynamics- and concordance-related changes in IBA indices in SCD individuals. To investigate these aberrant changes, a temporal dynamic analysis of rs-fMRI data was conducted on 94 SCD individuals (71.07 ± 6.18 years, 60 female), 75 (74.36 ± 8.42 years, 35 female) mild cognitive impairment (MCI) patients, and 82 age-, gender-, and education-matched controls (NCs; 73.88 ± 7.40 years, 49 female) from the Alzheimer's Disease Neuroimaging Initiative database. The dynamics and concordance of the rs-fMRI indices were calculated. The results showed that SCD individuals had a lower amplitude of low-frequency fluctuations dynamics in bilateral hippocampus (HP)/parahippocampal gyrus (PHG)/fusiform gyrus (FG) and bilateral cerebellum, a lower fractional amplitude of low-frequency fluctuation dynamics in bilateral precuneus (PreCu) and paracentral lobule, and a lower regional homogeneity dynamics in bilateral cerebellum, vermis, and left FG compared with the other two groups, whereas those in MCI patients were higher (Gaussian random field-corrected, voxel-level P < 0.001, cluster-level P < 0.05). Furthermore, SCD individuals had higher concordance in bilateral HP/PHG/FG, temporal lobe, and left midcingulate cortex than NCs, but those in MCI were lower than those in NCs. No correlation between concordance values and neuropsychological scale scores was found. SCD individuals showed both dynamics and concordance-related alterations in IBA, which indicates a compensatory mechanism in SCD individuals. Temporal dynamics analysis offers a novel approach to capturing brain alterations in individuals with SCD.

The effect of conscientiousness on procrastination: The interaction between the self-control and motivation neural pathways

Procrastination is a prevalent and universal problematic behavior, largely impairing individual's health, wealth and well-being. Substantial studies have confirmed that conscientiousness, one of the big five personality, showed markedly inverse relation with procrastination. However, it is hitherto unknown about the neural basis underlying the impact of conscientiousness on procrastination. To address this issue, we employed the voxel-based morphometry (VBM) and resting-state functional connectivity (RSFC) methods to explore the neural substrates of conscientiousness responsible for procrastination (N = 330). In line with previous findings, the behavioral results showed a strong negative correlation between conscientiousness and procrastination (r = -.75). The VBM analysis found that conscientiousness was positively correlated with gray matter (GM) volumes in the left dorsal-lateral prefrontal cortex (dlPFC), right orbital frontal cortex (OFC) and right putamen, but negatively correlated with that in the left insula. Moreover, the RSFC results revealed that both dlPFC-IPL (inferior parietal lobule) and dlPFC-PCC (posterior cingulate gyrus) functional connectivity were positively associated with conscientiousness, while the functional connectivity of parahippocampal gyrus (PHC)-putamen and insula-IPL were negatively associated with conscientiousness. More importantly, the structural equation modeling (SEM) integrating RSFC results were well fitted for the influence process of conscientiousness on procrastination by both self-control (i.e., dlPFC-IPL, dlPFC-PCC) and motivation pathways (i.e., PHC-putamen, insula-IPL). The current findings suggest that self-control and motivation could be the two neural pathways underlying the impact of conscientiousness on procrastination, which provides a new perspective to understand the relationship between conscientiousness and procrastination.

Aberrant state-related dynamic amplitude of low-frequency fluctuations of the emotion network in major depressive disorder

Major depressive disorder (MDD) is a highly prevalent mental disorder that is typically characterized by pervasive and persistent low mood. This durable emotional disturbance may represent a key aspect of the neuropathology of MDD, typified by the wide-ranging distribution of brain alterations involved in emotion processing. However, little is known about whether these alterations are represented as the state properties of dynamic amplitude of low-frequency fluctuation (dALFF) variability in the emotion network. To address this question, we investigated the time-varying intrinsic brain activity derived from resting-state functional magnetic resonance imaging (R-fMRI). Data were obtained from 50 MDD patients and 37 sex- and age-matched healthy controls; a sliding-window method was used to assess dALFF in the emotion network, and two reoccurring dALFF states throughout the entire R-fMRI scan were then identified using a k-means clustering method. The results showed that MDD patients had a significant decrease in dALFF variability in the emotion network and its three modules located in the lateral paralimbic, media posterior, and visual association regions. Altered state-wise dALFF was also observed in MDD patients. Specifically, we found that these altered dALFF measurements in the emotion network were related to scores on the Hamilton Rating Scale for Depression (HAMD) among patients with MDD. The detection and estimation of these temporal dynamic alterations could advance our knowledge about the brain mechanisms underlying emotional dysfunction in MDD.

Desynchronized Functional Activities Between Brain White and Gray Matter in Major Depression Disorder

BACKGROUND

Alterations in gray matter (GM) have been recognized as playing an important role in the neurobiological mechanism underlying major depressive disorder (MDD) and antidepressant responses. However, little is known about white matter (WM) connectivity in MDD, leaving an incomplete understanding of the pathophysiology of the disorder.

PURPOSE

To examine the functional connectivity (FC) of WM, GM, and WM-GM in MDD patients and explore the relationship between FC and antidepressant response.

STUDY TYPE

Longitudinal study.

SUBJECTS

In all, 129 MDD patients and 89 healthy controls (HC).

FIELD STRENGTH/SEQUENCE

Whole-brain blood oxygen level-dependent (BOLD) single-shot echo planar imaging was acquired at 3.0T.

ASSESSMENT

At baseline, all participants received Hamilton depression rating scale (HAMD) assessment and an fMRI scan. After 2- and 8-week antidepressant treatment, patients completed the HAMD again. The HAMD reductive rate of 2- and 8-weeks were calculated.

STATISTICAL TESTS

The comparisons of age, education, HAMD scores, and FC values (false discovery rate correction) between patients and controls were calculated with a two-sample t-test. The chi-square test was employed to compare the differences of gender between these two groups. Correlations between FC and HAMD, as well as the reductive rate of HAMD, were analyzed with Pearson or Spearman correlation. Receiver operator curve analysis was performed to predict the antidepressant response.

RESULTS

Compared to HC, MDD patients exhibited widespread decreases in FC of WM-GM. Furthermore, 28 GM regions and 11 WM bundles had lower connectivity in MDD patients. At baseline, four FC of WM-GM showed negative correlations with the HAMD scores. Six FC of WM-GM correlated with the 2-week reductive rate of HAMD. Moreover, FC in GM, WM, and WM-GM also exhibited significantly positive correlations with an 8-week reductive rate of HAMD.

DATA CONCLUSION

The FC of WM-GM was decreased in MDD and may play a role in its pathophysiology and antidepressant responses.

LEVEL OF EVIDENCE

2.

TECHNICAL EFFICACY STAGE

2.

Greater Dynamic and Lower Static Functional Brain Connectivity Prospectively Predict Placebo Response in Pediatric Generalized Anxiety Disorder

Objectives: Placebo response is one of the most significant barriers to detecting treatment effects in pediatric (and adult) clinical trials focusing on affective and anxiety disorders. We sought to identify neurofunctional predictors of placebo response in adolescents with generalized anxiety disorder (GAD) by examining dynamic and static functional brain connectivity. Methods: Before randomization to blinded placebo, adolescents, aged 12-17 years, with GAD (N = 25) underwent resting state functional magnetic resonance imaging. Whole brain voxelwise correlation analyses were used to determine the relationship between change in anxiety symptoms from baseline to week 8 and seed-based dynamic and static functional connectivity maps of regions in the salience and ventral attention networks (amygdala, dorsal anterior cingulate cortex [dACC], and ventrolateral prefrontal cortex [VLPFC]). Results: Greater dynamic functional connectivity variability in amygdala, dACC, VLPFC, and regions within salience, default mode, and frontoparietal networks was associated with greater placebo response. Lower static functional connectivity between amygdala and dorsolateral prefrontal cortex, amygdala and medial prefrontal cortex, dACC and posterior cingulate cortex and greater static functional connectivity between VLPFC and inferior parietal lobule were associated with greater placebo response. Conclusion: Placebo response is associated with a distinct dynamic and static connectivity fingerprint characterized by "variable" dynamic but "weak" static connectivity in the salience, default mode, frontoparietal, and ventral attention networks. These data provide granular evidence of how circuit-based biotypes mechanistically relate to placebo response. Finding biosignatures that predict placebo response is critically important in clinical psychopharmacology and to improve our ability to detect medication-placebo differences in clinical trials.