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

Microbiome's effect on white matter in autism

Autism spectrum disorder (ASD) is characterized by deficits in social communication and restricted, repetitive behavioral patterns. Although other physiological presentations in individuals with ASD are heterogeneous, neuroimaging studies have consistently revealed a developmental pattern of initial white matter hypermyelination followed by reduced myelination compared with typically developing peers. Multiple studies have demonstrated that core ASD symptoms, including impairments in social skills, language acquisition, learning capabilities, motor performance, and sensory processing, correlate significantly with white matter dysregulation measured through diffusion tensor imaging (DTI). Longitudinal studies have shown that decreased gut microbiome diversity, particularly reductions in beneficial bacteria such as Bifidobacterium and Lactobacillus, correlates with symptom severity. Emerging mechanistic evidence suggests bidirectional relationships between microbiome composition and white matter development, both directly through metabolites like short-chain fatty acids (SCFAs) that regulate oligodendrocyte function and subsequent myelination, and indirectly through modulation of neuroinflammatory pathways. By integrating molecular-level gut physiology findings with macro-level brain imaging data, we may identify novel therapeutic approaches targeting the gut-brain axis in ASD management.

Functional Reorganization of White Matter Supporting the Transhemispheric Mechanism of Mirror Therapy After Stroke: A Multimodal MRI Study

Mirror therapy (MT) is an effective approach in stroke recovery, but its impact on subcortical neural reorganization remains unclear. Thus, we aimed to investigate the neuroplastic effects on white matter due to MT. In this study, thirty-three participants with stroke were recruited and randomly assigned into the MT group (n = 16) or the control group (n = 17) for a 4-week intervention. Before and after the intervention, motor recovery was evaluated using the Fugl-Meyer Assessment upper limb subscale (FMA-UL), and the white matter structure and function were investigated using DTI and resting-state fMRI, focusing on the corticospinal tract and the corpus callosum. Significant correlations between the improvements of the FMA-UL and the baseline fractional anisotropy of ipsilesional corticospinal tract ( ) and corpus callosum ( ) were observed only in the MT group. Additionally, no significant structural alterations were found between the two groups after the intervention. The fractional amplitude of low-frequency fluctuation of ipsilesional corticospinal tract ( ) and corpus callosum ( ) were significantly enhanced only in the MT group, which were correlated with the improvements of the FMA-UL ( ). Furthermore, partial correlation analysis and subsequent mediation model analysis suggested that the changes of fractional amplitude of low-frequency fluctuation in corpus callosum partially mediated the effect of the baseline fractional anisotropy of ipsilesional corticospinal tract on the FMA-UL improvements in the MT group. This study provided neuroimaging evidence on white matter reorganization after MT, specifically the corpus callosum, suggesting a potential interhemispheric transcallosal neuroplastic mechanism of MT.

Apparent Diffusion Coefficient fMRI shines light on white matter resting-state connectivity compared to BOLD

Resting-state functional magnetic resonance imaging (fMRI) is used to derive functional connectivity (FC) between brain regions. Typically, blood oxygen level-dependent (BOLD) contrast is used. However, BOLD's reliance on neurovascular coupling poses challenges in reflecting brain activity accurately, leading to reduced sensitivity in white matter (WM). WM BOLD signals have long been considered physiological noise, although recent evidence shows that both stimulus-evoked and resting-state WM BOLD signals resemble those in gray matter (GM), albeit smaller in amplitude. We introduce apparent diffusion coefficient fMRI (ADC-fMRI) as a promising functional contrast for GM and WM FC, capturing activity-driven neuromorphological fluctuations. Our study compares BOLD-fMRI and ADC-fMRI FC in GM and WM, showing that ADC-fMRI mirrors BOLD-fMRI connectivity in GM, while capturing more robust FC in WM. ADC-fMRI displays higher average clustering and average node strength in WM, and higher inter-subject similarity, compared to BOLD. Taken together, this suggests that ADC-fMRI is a reliable tool for exploring FC that incorporates gray and white matter nodes in a novel way.

Functional Alterations in Gray Matter Networks Mediated by White Matter During the Aging Process

BACKGROUND AND PURPOSE

Extensive research has been carried out to investigate changes in various gray matter (GM) regions during the aging process using resting-state functional MRI. However, the impact of aging on the functional connectivity (FC) between white matter (WM) and GM, particularly white matter-gray matter functional connectivity (WM-GM FC), remains largely unknown. This study proposes a novel method for constructing functional networks that integrate both WM and GM.

METHODS

By utilizing data from a lifespan cohort of 439 healthy adults, we devised a covariance-based approach to establish a gray matter-white matter-gray matter (GM-WM-GM) mediated network. The FC between GM and WM was quantified using the Johns Hopkins University International Consortium of Brain Mapping-Diffusion Tensor Imaging-81 WM atlas in combination with the Automated Anatomical Labeling atlas. First, the WM-GM FC was calculated via Pearson correlation coefficients between WM and GM regions, followed by the standardization of the resulting matrix. The GM-WM-GM FC was then constructed using the covariance matrix. Furthermore, topological properties were calculated for GM-WM-GM networks. Finally, the age effect of GM-WM-GM and its topology were explored.

RESULTS

Our findings reveal a significant age-related decline in intranetwork connectivity and global network efficiency, while internetwork connectivity followed an inverted U-shaped pattern, suggesting functional dedifferentiation in the aging brain. Despite relatively stable local efficiency, the observed reduction in global efficiency indicates a weakening of long-range neural connections. Additionally, a decrease in network modularity further supports this trend.

CONCLUSION

These results offer novel insights into the age-associated reorganization of brain networks, enhancing our understanding of the neural mechanisms underlying normal aging.

White matter functional and structural alterations of spinocerebellar ataxia type 3: A longitudinal MRI study

Widespread white matter (WM) microstructural abnormalities have been reported in patients with spinocerebellar ataxia type 3 (SCA3) using diffusion tensor imaging (DTI), whereas the ability of DTI to detect WM degeneration over short-term period remains insufficiently explored. Additionally, WM dysfunction remains entirely unknown in this disease. This study aims to investigate WM structural and functional alterations in SCA3, and provide promising progression biomarkers for short-term clinical trials. DTI and resting-state functional magnetic resonance imaging data of 52 SCA3 patients and 56 healthy controls (HCs) were collected at baseline. After a mean follow-up of 1 year, MRI scans were performed on a subset of 28 SCA3 patients. Compared with HCs, widespread WM structural and functional abnormalities were observed in patients with SCA3. Between-group differences of both structural and functional MR metrics showed remarkable similarities, with large differences located in pons and corticospinal tracts, involving cerebellar WM, cerebellar and cerebral peduncles, medial lemniscus and bilateral posterior limb of internal capsule (PLIC). The longitudinal analysis further showed decreased ALFF in the right PLIC and increased mean diffusivity in the left inferior cerebellar peduncle and right medial lemniscus over time in SCA3 patients. These findings emphasized that pons and the CST were the most vulnerable WM areas in SCA3, and have the potential to become therapeutic targets of SCA3 for upcoming interventional trials. In addition, both DT metrics and WM ALFF were efficient progression biomarkers for SCA3 even in short-term period.

Cortical modulation of BOLD signals in white matter

The relationship of BOLD signals in white matter to cortical neural activity remains unclear. We quantified the degree to which spontaneous neural activities in the cortex, which are reflected in low frequency fluctuations in cortical BOLD signals, modulate BOLD signals in white matter. From measurements of resting state correlations we find cortical networks of more basic level functions tend to contribute more to correlated fluctuations in white matter than those of higher level functions. In addition, each cortical network exhibits distinct, structurally interpretable spatial distribution patterns of white matter projections. Moreover, the myelination level of cortical networks is found to be strongly correlated with the white matter projection of cortical BOLD signals. Our findings confirm that BOLD signals in white matter encode neural activity in proportion to the spontaneous activity of individual cortical networks, and with network-specific spatial distribution patterns, which could be mediated by the microstructure of the brain cortex.

Investigation of white matter functional networks in young smokers

AIMS

This study investigated the changes in the organizational and intrinsical activities of the white matter functional networks (WMFNs) in young smokers using resting-state functional magnetic resonance imaging.

METHODS

A data-driven approach was used to characterize the WMFNs of 30 young smokers and 30 non-smokers. We applied K-means clustering to the neuroimaging data to delineate the WMFNs. Functional neural activities of the WMFNs were compared between the two groups. Correlation analyses were also conducted for the WMFNs neural activities of and clinical indicators of smoking.

RESULTS

Eight WMFNs were identified in both groups. Compared to non-smokers, young smokers demonstrated a different dorsal attention network and lack of a frontostriatal network. The neural activities in the frontal network, deep frontoparietal network, and visual network were reduced in young smokers. Further correlation analyses showed that the decreased neural activity in the deep frontal network and deep frontoparietal network were significantly negatively correlated with the Fagerström Test for Nicotine Dependence.

CONCLUSION

Young smokers exhibited differences in the organizational structure and neural activity intensities of the WMFNs. The present findings may indicate the importance of WMFNs in young smokers, which can help in obtaining a comprehensive understanding of the neural mechanisms underlying smoking addiction.

Prevalence and Related Factors of Hypokalemia in Patients with Acute Ischemic Stroke

Aim

This study aimed to investigate the prevalence and associated factors of hypokalemia in patients with acute ischemic stroke.

Methods

A cohort of 996 patients was assessed using a general data questionnaire, laboratory indicators, the NIH Stroke Scale (NIHSS), the Barthel Index (BI), the Frail scale, Nutritional Risk Screening (NRS-2002), and the Kubota drinking water test.

Results

Among the 996 patients, 205 (20.6%) were found to have hypokalemia. Logistic regression analysis identified several independent predictors of hypokalemia: age (OR 1.020, 95% CI 1.001-1.039, P=0.041), hypertension (OR 2.691, 95% CI 1.190-6.089, P=0.017), Frail score (OR 1.756, 95% CI 1.034-2.981, P=0.037), Kubota drinking water test grade 3 (OR 2.124, 95% CI 1.055-4.276, P=0.035), Kubota drinking water test grade 4 (OR 3.016, 95% CI 1.113-8.174, P=0.037), NIHSS score (OR 1.135, 95% CI 1.018-1.264, P=0.022), platelet count (OR 0.997, 95% CI 0.994-0.999, P=0.021), and urea nitrogen levels (OR 0.833, 95% CI 0.750-0.926, P=0.001).

Conclusion

The prevalence of hypokalemia is high in patients with acute ischemic stroke. Independent risk factors included age, hypertension, frailty, neurological function, swallowing function, platelet count and blood urea level.

White matter engagement in brain networks assessed by integration of functional and structural connectivity

Current models of brain networks may potentially be improved by integrating our knowledge of structural connections, within and between circuits, with metrics of functional interactions between network nodes. The former may be obtained from diffusion MRI of white matter (WM), while the latter may be derived by measuring correlations between resting state BOLD signals from pairs of gray matter (GM) regions. From inspection of diffusion MRI data, it is clear that each WM voxel within a 3D image array may be traversed by multiple WM structural tracts, each of which connects a pair of GM nodes. We hypothesized that by appropriately weighting and then integrating the functional connectivity of each such connected pair, the overall engagement of any WM voxel in brain functions could be evaluated. This model introduces a structural constraint to earlier studies of WM engagement and addresses some limitations of previous efforts to relate structure and function. Using concepts derived from graph theory, we obtained spatial maps of WM engagement which highlight WM regions critical for efficient communications across the brain. The distributions of WM engagement are highly reproducible across subjects and depict a notable interdependence between the distribution of GM activities and the detailed organization of WM. Additionally, we provide evidence that the engagement varies over time and shows significant differences between genders. These findings suggest the potential of WM engagement as a measure of the integrity of normal brain functions and as a biomarker for neurological and cognitive disorders.

Structure-function coupling in white matter uncovers the hypoconnectivity in autism spectrum disorder

BACKGROUND

Autism Spectrum Disorder (ASD) is a neurodevelopmental disorder associated with alterations in structural and functional coupling in gray matter. However, despite the detectability and modulation of brain signals in white matter, the structure-function coupling in white matter in autism remains less explored.

METHODS

In this study, we investigated structural-functional coupling in white matter (WM) regions, by integrating diffusion tensor data that contain fiber orientation information from WM tracts, with functional connectivity tensor data that reflect local functional anisotropy information. Using functional and diffusion magnetic resonance images, we analyzed a cohort of 89 ASD and 63 typically developing (TD) individuals from the Autism Brain Imaging Data Exchange II (ABIDE-II). Subsequently, the associations between structural-functional coupling in WM regions and ASD severity symptoms assessed by Autism Diagnostic Observation Schedule-2 were examined via supervised machine learning in an independent test cohort of 29 ASD individuals. Furthermore, we also compared the performance of multi-model features (i.e. structural-functional coupling) with single-model features (i.e. functional or structural models alone).

RESULTS

In the discovery cohort (ABIDE-II), individuals with ASD exhibited widespread reductions in structural-functional coupling in WM regions compared to TD individuals, particularly in commissural tracts (e.g. corpus callosum), association tracts (sagittal stratum), and projection tracts (e.g. internal capsule). Notably, supervised machine learning analysis in the independent test cohort revealed a significant correlation between these alterations in structural-functional coupling and ASD severity scores. Furthermore, compared to single-model features, the integration of multi-model features (i.e., structural-functional coupling) performed best in predicting ASD severity scores.

CONCLUSION

This work provides novel evidence for atypical structural-functional coupling in ASD in white matter regions, further refining our understanding of the critical role of WM networks in the pathophysiology of ASD.

Functional correlation tensors in brain white matter and the effects of normal aging

Resting state correlations between blood oxygenation level dependent (BOLD) MRI signals from voxels in white matter (WM) are demonstrably anisotropic, so that functional correlation tensors (FCT) may be used to quantify the underlying microstructure of BOLD effects in WM tracts. However, the overall spatial distribution of FCTs and their metrics in specific populations has not yet been established, and the factors that affect their precise arrangements remain unclear. Changes in WM occur with normal aging, and these may be expected to affect FCTs. We hypothesized that FCTs exhibit a characteristic spatial pattern and may show systematic changes with aging or other factors. Here we report our analyses of the FCT characteristics of fMRI images of a large cohort of 461 cognitively normal subjects (190 females, 271 males) sourced from the Open Access Series of Imaging Studies (OASIS), with age distributions of 42 y/o - 95 y/o. Group averages and statistics of FCT indices, including axial functional correlations, radial functional correlations, mean functional correlations and fractional anisotropy, were quantified in WM bundles defined by the JHU ICBM-DTI-81 WM atlas. In addition, their variations with normal aging were examined. The results revealed a dimorphic distribution of changes in FCT metrics with age, with decreases of the functional correlations in some regions and increases in others. Supplementary analysis revealed that females exhibited significant age effects on a greater number of WM areas, but the interaction between age and sex was not significant. The findings demonstrate the reproducibility of the spatial distribution of FCT metrics and reveal subtle regional changes with age.

Association of Compensatory Mechanisms in Prefrontal Cortex and Impaired Anatomical Correlates in Semantic Verbal Fluency: A Functional Near-Infrared Spectroscopy Study

OBJECTIVE

Semantic verbal fluency (SVF) engages cognitive functions such as executive function, mental flexibility, and semantic memory. Left frontal and temporal lobes, particularly the left inferior frontal gyrus (IFG), are crucial for SVF. This study investigates SVF and associated neural processing in older adults with mild SVF impairment and the relationship between structural abnormalities in the left IFG and functional activation during SVF in those individuals.

METHODS

Fifty-four elderly individuals with modest level of mild cognitive impairment whose global cognition were preserved to normal but exhibited mild SVF impairment were participated. Prefrontal oxyhemoglobin (HbO2) activation and frontal cortical thickness were collected from the participants using functional near-infrared spectroscopy (fNIRS) and brain MRI, respectively. We calculated the β coefficient of HbO2 activation induced by tasks, and performed correlation analysis between SVF induced HbO2 activation and cortical thickness in frontal areas.

RESULTS

We observed increased prefrontal activation during SVF task compared to the resting and control task. The activation distinct to SVF was identified in the midline superior and left superior prefrontal regions (p<0.05). Correlation analysis revealed an inverse relationship between SVF-specific activation and cortical thickness in the left IFG, particularly in pars triangularis (r(54)=-0.304, p=0.025).

CONCLUSION

The study contributes to understanding the relationship between reduced cortical thickness in left IFG and increased functional activity in cognitively normal individuals with mild SVF impairment, providing implications on potential compensatory mechanisms for cognitive preservation.

A Comparison of Denoising Approaches for Spoken Word Production Related Artefacts in Continuous Multiband fMRI Data

It is well-established from fMRI experiments employing gradient echo echo-planar imaging (EPI) sequences that overt speech production introduces signal artefacts compromising accurate detection of task-related responses. Both design and post-processing (denoising) techniques have been proposed and implemented over the years to mitigate the various noise sources. Recently, fMRI studies of speech production have begun to adopt multiband EPI sequences that offer better signal-to-noise ratio (SNR) and temporal resolution allowing adequate sampling of physiological noise sources (e.g., respiration, cardiovascular effects) and reduced scanner acoustic noise. However, these new sequences may also introduce additional noise sources. In this study, we demonstrate the impact of applying several noise-estimation and removal approaches to continuous multiband fMRI data acquired during a naming-to-definition task, including rigid body motion regression and outlier censoring, principal component analysis for removal of cerebrospinal fluid (CSF)/edge-related noise components, and global fMRI signal regression (using two different approaches) compared to a baseline of realignment and unwarping alone. Our results show the strongest and most spatially extensive sources of physiological noise are the global signal fluctuations arising from respiration and muscle action and CSF/edge-related noise components, with residual rigid body motion contributing relatively little variance. Interestingly, denoising approaches tended to reduce and enhance task-related BOLD signal increases and decreases, respectively. Global signal regression using a voxel-wise linear model of the global signal estimated from unmasked data resulted in dramatic improvements in temporal SNR. Overall, these findings show the benefits of combining continuous multiband EPI sequences and denoising approaches to investigate the neurobiology of speech production.

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

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

Sex differences in patterns of white matter neuroplasticity after balance training in young adults

Introduction

In past work we demonstrated different patterns of white matter (WM) plasticity in females versus males associated with learning a lab-based unilateral motor skill. However, this work was completed in neurologically intact older adults. The current manuscript sought to replicate and expand upon these WM findings in two ways: (1) we investigated biological sex differences in neurologically intact young adults, and (2) participants learned a dynamic full-body balance task.

Methods

24 participants (14 female, 10 male) participated in the balance training intervention, and 28 were matched controls (16 female, 12 male). Correlational tractography was used to analyze changes in WM from pre- to post-training.

Results

Both females and males demonstrated skill acquisition, yet there were significant differences in measures of WM between females and males. These data support a growing body of evidence suggesting that females exhibit increased WM neuroplasticity changes relative to males despite comparable changes in motor behavior (e.g., balance).

Discussion

The biological sex differences reported here may represent an important factor to consider in both basic research (e.g., collapsing across females and males) as well as future clinical studies of neuroplasticity associated with motor function (e.g., tailored rehabilitation approaches).

A practical guide for combining functional regions of interest and white matter bundles

Diffusion-weighted imaging (DWI) is the primary method to investigate macro- and microstructure of neural white matter in vivo. DWI can be used to identify and characterize individual-specific white matter bundles, enabling precise analyses on hypothesis-driven connections in the brain and bridging the relationships between brain structure, function, and behavior. However, cortical endpoints of bundles may span larger areas than what a researcher is interested in, challenging presumptions that bundles are specifically tied to certain brain functions. Functional MRI (fMRI) can be integrated to further refine bundles such that they are restricted to functionally-defined cortical regions. Analyzing properties of these Functional Sub-Bundles (FSuB) increases precision and interpretability of results when studying neural connections supporting specific tasks. Several parameters of DWI and fMRI analyses, ranging from data acquisition to processing, can impact the efficacy of integrating functional and diffusion MRI. Here, we discuss the applications of the FSuB approach, suggest best practices for acquiring and processing neuroimaging data towards this end, and introduce the FSuB-Extractor, a flexible open-source software for creating FSuBs. We demonstrate our processing code and the FSuB-Extractor on an openly-available dataset, the Natural Scenes Dataset.

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.

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

RATIONALE AND OBJECTIVES

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

METHODS

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

RESULTS

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

CONCLUSION

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

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

BACKGROUND

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

NEW METHOD

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

RESULTS

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

COMPARISON WITH EXISTING METHOD(S)

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

CONCLUSIONS

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

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

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

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

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

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

Objective

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

Methods

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

Results

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

Conclusion

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

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

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

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

BACKGROUND

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

AIM

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

METHODS

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

RESULTS

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

CONCLUSION

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

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

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

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

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

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

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

White matter functional gradients and their formation in adolescence

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

Seasonal variations of functional connectivity of human brains

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

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

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

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

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

Changes in white matter functional networks across late adulthood

Introduction

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

Methods

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

Results

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

Discussion

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

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

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

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

BACKGROUND

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

OBJECTIVE

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

METHODS

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

RESULTS

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

CONCLUSION

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

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

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

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

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

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

BACKGROUND

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

METHODS

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

RESULTS

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

CONCLUSIONS

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

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

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

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

BACKGROUND AND PURPOSE

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

METHODS

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

RESULTS

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

CONCLUSION

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

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

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

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

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

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

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

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

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

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

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

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

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

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

Background

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

Methods

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

Results

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

Conclusion

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