scholarly journals Disrupted White Matter Functional Connectivity With the Cerebral Cortex in Migraine Patients

2022 ◽  
Vol 15 ◽  
Author(s):  
Zhaoxia Qin ◽  
Huai-Bin Liang ◽  
Muwei Li ◽  
Yue Hu ◽  
Jing Wu ◽  
...  
Keyword(s):  
White Matter ◽  
Functional Connectivity ◽  
Gray Matter ◽  
Resting State ◽  
Correlation Coefficients ◽  
Blood Oxygenation ◽  
Healthy Controls ◽  
Functional Connections ◽  
Time Courses ◽  
Cortical Regions

Background: In attempts to understand the migraine patients’ overall brain functional architecture, blood oxygenation level-dependent (BOLD) signals in the white matter (WM) and gray matter (GM) were considered in the current study. Migraine, a severe and multiphasic brain condition, is characterized by recurrent attacks of headaches. BOLD fluctuations in a resting state exhibit similar temporal and spectral profiles in both WM and GM. It is feasible to explore the functional interactions between WM tracts and GM regions in migraine.Methods: Forty-eight migraineurs without aura (MWoA) and 48 healthy controls underwent resting-state functional magnetic resonance imaging. Pearson’s correlations between the mean time courses of 48 white matter (WM) bundles and 82 gray matter (GM) regions were computed for each subject. Two-sample t-tests were performed on the Pearson’s correlation coefficients (CC) to compare the differences between the MWoA and healthy controls in the GM-averaged CC of each bundle and the WM-averaged CC of each GM region.Results: The MWoAs exhibited an overall decreased average temporal CC between BOLD signals in 82 GM regions and 48 WM bundles compared with healthy controls, while little was increased. In particular, WM bundles such as left anterior corona radiata, left external capsule and bilateral superior longitudinal fasciculus had significantly decreased mean CCs with GM in MWoA. On the other hand, 16 GM regions had significantly decreased mean CCs with WM in MWoA, including some areas that are parts of the somatosensory regions, auditory cortex, temporal areas, frontal areas, cingulate cortex, and parietal cortex.Conclusion: Decreased functional connections between WM bundles and GM regions might contribute to disrupted functional connectivity between the parts of the pain processing pathway in MWoAs, which indicated that functional and connectivity abnormalities in cortical regions may not be limited to GM regions but are instead associated with functional abnormalities in WM tracts.

scholarly journals Altered Functional Connectivity in White and Gray Matter in Patients With Multiple Sclerosis

2020 ◽  
Vol 14 ◽  
Author(s):  
Jing Huang ◽  
Muwei Li ◽  
Qiongge Li ◽  
Zhipeng Yang ◽  
Bowen Xin ◽  
...  
Keyword(s):  
Multiple Sclerosis ◽  
Functional Connectivity ◽  
Gray Matter ◽  
Correlation Coefficients ◽  
Temporal Correlation ◽  
Resting State Fmri ◽  
Lesion Volume ◽  
Healthy Controls ◽  
Functional Changes ◽  
Time Courses

Background: Functional magnetic resonance imaging (fMRI) has been widely used to assess neural activity changes in gray matter (GM) in patients with multiple sclerosis (MS); however, brain function alterations in white matter (WM) relatively remain under-explored.Purpose: This work aims to identify the functional connectivity in both the WM and the GM of patients with MS using fMRI and the correlations between these functional changes and cumulative disability as well as the lesion ratio.Materials and Methods: For this retrospective study, 37 patients with clinically definite MS and 43 age-matched healthy controls were included between 2010 and 2014. Resting-state fMRI was performed. The WFU Pick and JHU Eve atlases were used to define 82 GM and 48 WM regions in common spaces, respectively. The time courses of blood oxygen level-dependent (BOLD) signals were averaged over each GM or WM region. The averaged time courses for each pair of GM and WM regions were correlated. All 82 × 48 correlations for each subject formed a functional correlation matrix.Results: Compared with the healthy controls, the MS patients had a decreased temporal correlation between the WM and the GM regions. Five WM bundles and four GM regions had significantly decreased mean correlation coefficients (CCs). More specifically, the WM functional alterations were negatively correlated with the lesion volume in the bilateral fornix, and the mean GM-averaged CCs of the WM bundles were inversely correlated with the lesion ratio (r = −0.36, P = 0.012). No significant correlation was found between WM functional alterations and the paced auditory serial addition test score, Expanded Disease Severity Scale score, and Multiple Sclerosis Severity Score (MSSS) in MS.Conclusions: These findings highlight current gaps in our knowledge of the WM functional alterations in patients with MS and may link WM function with pathological mechanisms.

scholarly journals Declined functional connectivity of white matter during rest and working memory tasks associates with cognitive impairments in schizophrenia

2020 ◽  
Author(s):  
Yurui Gao ◽  
Muwei Li ◽  
Anna S Huang ◽  
Adam W Anderson ◽  
Zhaohua Ding ◽  
...  
Keyword(s):  
Working Memory ◽  
White Matter ◽  
Functional Connectivity ◽  
Resting State ◽  
Spatial Working Memory ◽  
Cognitive Impairments ◽  
Memory Task ◽  
Brain Network ◽  
Blood Oxygenation ◽  
Cerebral Peduncle

BACKGROUND: Schizophrenia, characterized by cognitive impairments, arises from a disturbance of brain network. Pathological changes in white matter (WM) have been indicated as playing a role in disturbing neural connectivity in schizophrenia. However, deficits of functional connectivity (FC) in individual WM bundles in schizophrenia have never been explored; neither have cognitive correlates with those deficits. METHODS: Resting-state and spatial working memory task fMRI images were acquired on 67 healthy subjects and 84 patients with schizophrenia. The correlations in blood-oxygenation-level-dependent (BOLD) signals between 46 WM and 82 gray matter regions were quantified, analyzed and compared between groups under three scenarios (i.e., resting state, retention period and entire time of a spatial working memory task). Associations of FC in WM with cognitive assessment scores were evaluated for three scenarios. RESULTS: FC deficits were significant (p<.05) in external capsule, cingulum, uncinate fasciculus, genu and body of corpus callosum under all three scenarios. Deficits were also present in the anterior limb of the internal capsule and cerebral peduncle in task scenario. Decreased FCs in specific WM bundles associated significantly (p<.05) with cognitive impairments in working memory, processing speed and/or cognitive control. CONCLUSIONS: Decreases in FC are evident in several WM bundles in patients with schizophrenia and are significantly associated with cognitive impairments during both rest and working memory tasks. Furthermore, working memory tasks expose FC deficits in more WM bundles and more cognitive associates in schizophrenia than resting state does.

Remitted major depression is related to increased functional coupling between ventral striatum and cortical regions in resting state fMRI

2011 ◽  
Vol 26 (S2) ◽  
pp. 948-948 ◽  
Author(s):  
G. Pail ◽  
C. Scharinger ◽  
K. Kalcher ◽  
W. Huf ◽  
R. Boubela ◽  
...  
Keyword(s):  
Functional Connectivity ◽  
Orbitofrontal Cortex ◽  
Resting State ◽  
Ventral Striatum ◽  
Resting State Fmri ◽  
Anterior Cingulate ◽  
Functional Coupling ◽  
Healthy Controls ◽  
Full Remission ◽  
Cortical Regions

IntroductionDysfunction in the basal ganglia has been related to impaired reward processing and anhedonia, a core symptom of Major Depressive Disorder (MDD). In particular, the ventral striatum including the nucleus accumbens is increasingly implicated in the pathophysiology of MDD, but evidence for a specific role during episodes of full remission is lacking so far.ObjectivesTo investigate functional connectivity patterns of resting-state activity in patients in the remitted phase of MDD (rMDD).AimsTo determine whether rMDD is related to disruptions of functional coupling between the ventral striatum and cortical regions.MethodsForty-three remitted depressed patients and thirty-five healthy controls were recruited at Medical University of Vienna, Vienna, Austria, and performed a six minute resting-state fMRI scan. Seed time series were extracted from the preprocessed data using individual masks for ventral striatum and correlated with all nodes in a surface based analysis using FreeSurfer, AFNI and SUMA. The resulting correlation coefficients were then Fishertransformed, group results were determined by comparing group mean smoothed z-scores with a two-sample ttest.ResultsIncreased resting-state functional connectivity was revealed between ventral striatum (seed region) and anterior cingulate cortex as well as orbitofrontal cortex in the rMDD group compared to healthy controls.ConclusionsOur preliminary data is in accordance with the idea that increased functional coupling between the ventral striatum and two major emotion processing regions, the anterior cingulate cortex and the orbitofrontal cortex, may represent a neural mechanism contributing to the maintenance of full remission of MDD.

scholarly journals Multimodal Classification of Mild Traumatic Brain Injury Reveals Local Coupling Between Structural and Functional Connectomes

2021 ◽  
Author(s):  
Ajay Peddada ◽  
Kevin Holly ◽  
Tejaswi D Sudhakar ◽  
Christina Ledbetter ◽  
Christopher E. Talbot ◽  
...  
Keyword(s):  
White Matter ◽  
Functional Connectivity ◽  
Temporal Lobe ◽  
Resting State ◽  
Classification Accuracy ◽  
Structural Connectivity ◽  
Brain Regions ◽  
Group Differences ◽  
Functional Connections ◽  
White Matter Tractography

Background: Following mild traumatic brain injury (mTBI) compromised white matter structural integrity can result in alterations in functional connectivity of large-scale brain networks and may manifest in functional deficit including cognitive dysfunction . Advanced magnetic resonance neuroimaging techniques, specifically diffusion tensor imaging (DTI) and resting state functional magnetic resonance imaging (rs-fMRI), have demonstrated an increased sensitivity for detecting microstructural changes associated with mTBI. Identification of novel imaging biomarkers can facilitate early detection of these changes for effective treatment. In this study, we hypothesize that feature selection combining both structural and functional connectivity increases classification accuracy. Methods: 16 subjects with mTBI and 20 healthy controls underwent both DTI and resting state functional imaging. Structural connectivity matrices were generated from white matter tractography from DTI sequences. Functional connectivity was measured through pairwise correlations of rs-fMRI between brain regions. Features from both DTI and rs-fMRI were selected by identifying five brain regions with the largest group differences and were used to classify the generated functional and structural connectivity matrices, respectively. Classification was performed using linear support vector machines and validated with leave-one-out cross validation. Results: Group comparisons revealed increased functional connectivity in the temporal lobe and cerebellum as well as decreased structural connectivity in the temporal lobe. After training on structural connections only, a maximum classification accuracy of 78% was achieved when structural connections were selected based on their corresponding functional connectivity group differences. After training on functional connections only, a maximum classification accuracy of 69% was achieved when functional connections were selected based on their structural connectivity group differences. After training on both structural and functional connections, a maximum classification accuracy of 69% was achieved when connections were selected based on their structural connectivity. Conclusions: Our multimodal approach to ROI selection achieves at highest, a classification accuracy of 78%. Our results also implicate the temporal lobe in the pathophysiology of mTBI. Our findings suggest that white matter tractography can serve as a robust biomarker for mTBI when used in tandem with resting state functional connectivity.

scholarly journals Insulin Resistance-Associated Interhemispheric Functional Connectivity Alterations in T2DM: A Resting-State fMRI Study

2015 ◽  
Vol 2015 ◽  
pp. 1-9 ◽  
Author(s):  
Wenqing Xia ◽  
Shaohua Wang ◽  
Andrea M. Spaeth ◽  
Hengyi Rao ◽  
Pin Wang ◽  
...  
Keyword(s):  
Insulin Resistance ◽  
Functional Connectivity ◽  
Resting State ◽  
Correlation Coefficients ◽  
Resting State Fmri ◽  
Brain Regions ◽  
Middle Temporal Gyrus ◽  
Model Assessment ◽  
Healthy Controls ◽  
Resting State Functional Connectivity

We aim to investigate whether decreased interhemispheric functional connectivity exists in patients with type 2 diabetes mellitus (T2DM) by using resting-state functional magnetic resonance imaging (rs-fMRI). In addition, we sought to determine whether interhemispheric functional connectivity deficits associated with cognition and insulin resistance (IR) among T2DM patients. We compared the interhemispheric resting state functional connectivity of 32 T2DM patients and 30 healthy controls using rs-fMRI. Partial correlation coefficients were used to detect the relationship between rs-fMRI information and cognitive or clinical data. Compared with healthy controls, T2DM patients showed bidirectional alteration of functional connectivity in several brain regions. Functional connectivity values in the middle temporal gyrus (MTG) and in the superior frontal gyrus were inversely correlated with Trail Making Test-B score of patients. Notably, insulin resistance (log homeostasis model assessment-IR) negatively correlated with functional connectivity in the MTG of patients. In conclusion, T2DM patients exhibit abnormal interhemispheric functional connectivity in several default mode network regions, particularly in the MTG, and such alteration is associated with IR. Alterations in interhemispheric functional connectivity might contribute to cognitive dysfunction in T2DM patients.

Association Study Between White Matter Microstructure and Intelligence Decline in Schizophrenia

2021 ◽  
pp. 155005942110633
Author(s):  
Junya Matsumoto ◽  
Kenichiro Miura ◽  
Masaki Fukunaga ◽  
Kiyotaka Nemoto ◽  
Daisuke Koshiyama ◽  
...  
Keyword(s):  
White Matter ◽  
Functional Connectivity ◽  
Gray Matter ◽  
Diffusion Tensor ◽  
Region Of Interest ◽  
Healthy Controls ◽  
Resonance Imaging ◽  
White Matter Microstructure ◽  
Magnetic Resonance Imaging Mri ◽  
Diffusion Tensor Imaging Dti

Patients with schizophrenia can exhibit intelligence decline, which is an important element of cognitive impairment. Previous magnetic resonance imaging (MRI) studies have demonstrated that patients with schizophrenia have altered gray matter structures and functional connectivity associated with intelligence decline defined by a difference between premorbid and current intelligence quotients (IQs). However, it has remained unclear whether white matter microstructures are related to intelligence decline. In the present study, the indices of diffusion tensor imaging (DTI) obtained from 138 patients with schizophrenia and 554 healthy controls were analyzed. The patients were classified into three subgroups based on intelligence decline: deteriorated (94 patients), preserved (42 patients), and compromised IQ (2 patients) groups. Given that the DTI of each subject was acquired using either one of two different MRI scanners, we analyzed DTI indices separately for each scanner group. In the comparison between the deteriorated IQ group and the healthy controls, differences in some DTI indices were noted in three regions of interest irrespective of the MRI scanners, whereas differences in only one region of interest were noted between the preserved IQ group and the healthy controls. However, the comparisons between the deteriorated and preserved IQ groups did not show any reproducible differences. Together with the previous findings, it is thought that gray matter structures and functional connectivity are more promising as markers of intelligence decline in schizophrenia than white matter microstructures.

Abnormal functional connectivity of thalamic sub-regions contributes to fatigue in multiple sclerosis

2017 ◽  
Vol 24 (9) ◽  
pp. 1183-1195 ◽  
Author(s):  
Milagros Hidalgo de la Cruz ◽  
Alessandro d’Ambrosio ◽  
Paola Valsasina ◽  
Elisabetta Pagani ◽  
Bruno Colombo ◽  
...  
Keyword(s):  
Multiple Sclerosis ◽  
Functional Connectivity ◽  
Resting State ◽  
Structural Connectivity ◽  
Healthy Controls ◽  
Sensorimotor Network ◽  
Cortical Connections ◽  
Impact Scale ◽  
Fatigue Impact Scale ◽  
Cortical Regions

Objective: To investigate sub-regional thalamic resting-state (RS) functional connectivity (FC) abnormalities in multiple sclerosis (MS) and their correlation with fatigue and its subcomponents (physical, cognitive, and psychosocial). Methods: From 122 MS patients and 94 healthy controls, 5 thalamic sub-regions (frontal, motor, postcentral, occipital, temporal) were parcellated based on their cortico-thalamic structural connectivity and used for a seed-based RS FC analysis. Abnormalities of thalamic RS FC in MS patients and their correlation with Modified Fatigue Impact Scale (MFIS) were assessed. Results: Compared to controls and non-fatigued MS ( n = 86), fatigued MS patients ( n = 36) showed thalamic RS FC abnormalities with middle frontal gyrus, sensorimotor network, precuneus, insula, and cerebellum, which correlated with global MFIS. Higher thalamic RS FC with precuneus and lower RS FC with posterior cerebellum correlated with cognitive MFIS. Higher thalamic RS FC with sensorimotor network in frontal-, motor-, and temporal thalamic sub-regions correlated with physical and psychosocial MFIS. Reduced thalamic RS FC with right insula in motor-, postcentral-, and occipital thalamic sub-regions correlated with psychosocial fatigue. Conclusion: Regional thalamic RS FC abnormalities with different cortical regions, including the frontal lobe, sensorimotor network, precuneus, insular cortices, and cerebellum contribute to fatigue in MS. Abnormal RS FC of selected thalamo-cortical connections explains different components of fatigue.

scholarly journals Dynamic variations of resting-state BOLD signal spectra in white matter

2021 ◽  
Author(s):  
Muwei Li ◽  
Yurui Gao ◽  
Adam W Anderson ◽  
Zhaohua Ding ◽  
John C Gore
Keyword(s):  
White Matter ◽  
Resting State ◽  
Linear Models ◽  
Temporal Frequency ◽  
Hierarchical Structures ◽  
Blood Oxygenation ◽  
Hemodynamic Response Function ◽  
Time Courses ◽  
Spectral Patterns ◽  
Over Time

Recent studies have demonstrated that the mathematical model used for analyzing and interpreting fMRI data in gray matter (GM) is inappropriate for detecting or describing blood-oxygenation-level-dependent (BOLD) signals in white matter (WM). In particular the hemodynamic response function (HRF) which serves as the regressor in general linear models is different in WM compared to GM. We recently reported measurements of the frequency contents of resting-state time courses in WM that showed distinct power spectra which depended on local structural-vascular-functional associations. In addition, multiple studies of GM have revealed how functional connectivity between regions, as measured by the correlation between BOLD time series, varies dynamically over time. We therefore investigated whether and how BOLD signals from WM in a resting state varied over time. We measured voxel-wise spectrograms, which reflect the time-varying spectral patterns of WM time courses. The results suggest that the spectral patterns are non-stationary but could be categorized into five modes that recurred over time. These modes showed distinct spatial distributions of their occurrences and durations, and the distributions were highly consistent across individuals. In addition, one of the modes exhibited a strong coupling of its occurrence between GM and WM across individuals, and two communities of WM voxels were identified according to the hierarchical structures of transitions among modes. Moreover, the total number of transitions in each community predicts specific human behaviors. Last, these modes are coupled to the shape of instantaneous HRFs. Our findings extend previous studies and reveal the non-stationary nature of spectral patterns of BOLD signals over time, providing a spatial-temporal-frequency characterization of resting-state signals in WM.

scholarly journals Hemodynamic Response Function in Brain White Matter in a Resting State

2020 ◽  
Vol 1 (1) ◽  
Author(s):  
Ting Wang ◽  
D Mitchell Wilkes ◽  
Muwei Li ◽  
Xi Wu ◽  
John C Gore ◽  
...  
Keyword(s):  
White Matter ◽  
Response Function ◽  
Gray Matter ◽  
Resting State ◽  
Hemodynamic Response ◽  
Temporal Variations ◽  
Blood Oxygenation ◽  
Hemodynamic Response Function ◽  
Brain White Matter ◽  
Oxygenation Level

Abstract The hemodynamic response function (HRF) characterizes temporal variations of blood oxygenation level-dependent (BOLD) signals. Although a variety of HRF models have been proposed for gray matter responses to functional demands, few studies have investigated HRF profiles in white matter particularly under resting conditions. In the present work we quantified the nature of the HRFs that are embedded in resting state BOLD signals in white matter, and which modulate the temporal fluctuations of baseline signals. We demonstrate that resting state HRFs in white matter could be derived by referencing to intrinsic avalanches in gray matter activities, and the derived white matter HRFs had reduced peak amplitudes and delayed peak times as compared with those in gray matter. Distributions of the time delays and correlation profiles in white matter depend on gray matter activities as well as white matter tract distributions, indicating that resting state BOLD signals in white matter encode neural activities associated with those of gray matter. This is the first investigation of derivations and characterizations of resting state HRFs in white matter and their relations to gray matter activities. Findings from this work have important implications for analysis of BOLD signals in the brain.

Migraine classification using magnetic resonance imaging resting-state functional connectivity data

Cephalalgia ◽  
2016 ◽  
Vol 37 (9) ◽  
pp. 828-844 ◽  
Author(s):  
Catherine D Chong ◽  
Nathan Gaw ◽  
Yinlin Fu ◽  
Jing Li ◽  
Teresa Wu ◽  
...  
Keyword(s):  
Magnetic Resonance ◽  
Functional Connectivity ◽  
Resting State ◽  
Disease Burden ◽  
Brain Mri ◽  
Machine Learning Techniques ◽  
Healthy Controls ◽  
Resting State Functional Connectivity ◽  
Functional Connections ◽  
Healthy Control

Background This study used machine-learning techniques to develop discriminative brain-connectivity biomarkers from resting-state functional magnetic resonance neuroimaging ( rs-fMRI) data that distinguish between individual migraine patients and healthy controls. Methods This study included 58 migraine patients (mean age = 36.3 years; SD = 11.5) and 50 healthy controls (mean age = 35.9 years; SD = 11.0). The functional connections of 33 seeded pain-related regions were used as input for a brain classification algorithm that tested the accuracy of determining whether an individual brain MRI belongs to someone with migraine or to a healthy control. Results The best classification accuracy using a 10-fold cross-validation method was 86.1%. Resting functional connectivity of the right middle temporal, posterior insula, middle cingulate, left ventromedial prefrontal and bilateral amygdala regions best discriminated the migraine brain from that of a healthy control. Migraineurs with longer disease durations were classified more accurately (>14 years; 96.7% accuracy) compared to migraineurs with shorter disease durations (≤14 years; 82.1% accuracy). Conclusions Classification of migraine using rs-fMRI provides insights into pain circuits that are altered in migraine and could potentially contribute to the development of a new, noninvasive migraine biomarker. Migraineurs with longer disease burden were classified more accurately than migraineurs with shorter disease burden, potentially indicating that disease duration leads to reorganization of brain circuitry.

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