New resting-state fMRI related studies at PubMed

Alteration of spatial patterns at the network-level in facial synkinesis: an independent component and connectome analysis

Fri, 03/12/2021 - 11:00

Ann Transl Med. 2021 Feb;9(3):240. doi: 10.21037/atm-20-4865.

ABSTRACT

BACKGROUND: The treatment of post-facial palsy synkinesis (PFPS) remains inadequate. Previous studies have confirmed that brain plasticity is involved in the process of functional restoration. Isolated activation has been well studied, however, the brain works as an integrity of several isolated regions. This study aimed to assess the alteration of the brain network topology with overall and local characteristics of information dissemination. Understanding the neural mechanisms of PFPS could help to improve therapy options and prognosis.

METHODS: Patients with facial synkinesis and healthy controls (HCs) were estimated using functional magnetic resonance imaging (fMRI) of resting-state. Subsequently, an independent component analysis (ICA) was used to extract four subnets from the whole brain. Then we used the measurements of graph theory and calculated in the whole-brain network and each sub-network.

RESULTS: We found no significant difference between the patient group and the HCs on the whole-brain scale. Then we identified four subnetworks from the resting-state data. In the sub-network property analysis, patients' locally distributed properties in the sensorimotor network (SMN) and ventral default mode network (vDMN) were reduced. It revealed that γ (10,000 permutations, P=0.048) and S (10,000 permutations, P=0.022) within the SMN progressively decreased in patients with PFPS. For the analysis of vDMN, significant differences were found in γ (10,000 permutations, P=0.019), Elocal (10,000 permutations, P=0.008), and β (10,000 permutations, P=0.011) between the groups.

CONCLUSIONS: Our results demonstrated a reduction in local network processing efficiency in patients with PFPS. Therefore, we speculate that decreased characteristics in the intra-vDMN and intra-SMN, rather than the whole-brain network, may serve distinct symptoms such as facial nerve damage or more synkinetic movements. This finding of the alteration of network properties is a small step forward to help uncover the underlying mechanism.

PMID:33708867 | PMC:PMC7940883 | DOI:10.21037/atm-20-4865

Dynamic Structural and Functional Reorganizations Following Motor Stroke

Fri, 03/12/2021 - 11:00

Med Sci Monit. 2021 Mar 11;27:e929092. doi: 10.12659/MSM.929092.

ABSTRACT

BACKGROUND The combined effects of bilateral corticospinal tract (CST) reorganization and interhemispheric functional connectivity (FC) reorganization on motor recovery of upper and lower limbs after stroke remain unknown. MATERIAL AND METHODS A total of 34 patients underwent magnetic resonance imaging (MRI) examination at weeks 1, 4, and 12 after stroke, with a control group of 34 healthy subjects receiving 1 MRI examination. Interhemispheric FC in the somatomotor network (SMN) was calculated using the resting-state functional MRI (rs-fMRI). Fractional anisotropy (FA) of bilateral CST was recorded as a measure of reorganization obtained from diffusion tensor imaging (DTI). After intergroup comparisons, multiple linear regression analysis was used to explore the effects of altered FA and interhemispheric FC on motor recovery. RESULTS Interhemispheric FC restoration mostly occurred within 4 weeks after stroke, and FA in ipsilesional remained CST consistently elevated within 12 weeks. Multivariate linear regression analysis showed that the increase in both interhemispheric FC and ipsilesional CST-FA were significantly correlated with greater motor recovery from week 1 to week 4 following stroke. Moreover, only increased FA of ipsilesional CST was significantly correlated with greater motor recovery during weeks 4 to 12 after stroke compared to interhemispheric FC. CONCLUSIONS Our results show dynamic structural and functional reorganizations following motor stroke, and structure reorganization may be more related to motor recovery at the late subacute phase. These results may play a role in guiding neurological rehabilitation.

PMID:33707406 | DOI:10.12659/MSM.929092

The effect of effort-reward imbalance on brain structure and resting-state functional connectivity in individuals with high levels of schizotypal traits

Fri, 03/12/2021 - 11:00

Cogn Neuropsychiatry. 2021 Mar 11:1-17. doi: 10.1080/13546805.2021.1899906. Online ahead of print.

ABSTRACT

INTRODUCTION: Effort-reward imbalance (ERI) is a typical psychosocial stress. Schizotypal traits are attenuated features of schizophrenia in the general population. According to the diathesis-stress model, schizotypal traits and psychosocial stress contribute to the onset of schizophrenia. However, few studies examined the effects of these factors on brain alterations. This study aimed to examine relationships between ERI, schizotypal traits and brain structures and functions.

METHODS: We recruited 37 (13 male, 24 female) participants with high levels of schizotypal traits and 36 (12 male, 24 female) participants with low levels of schizotypal traits by the Schizotypal Personality Questionnaire (SPQ). The Chinese school version of the effort-reward imbalance questionnaire (C-ERI-S) was used to measure ERI. We conducted the voxel-based morphometry (VBM) and whole brain resting-state functional connectivity (rsFC) analysis using reward or stress-related regions as seeds.

RESULTS: Participants with high levels of schizotypal traits were more likely to perceive ERI. The severity of ERI was correlated with grey matter volume (GMV) reduction of the left pallidum and altered rsFC among the prefrontal, striatum and cerebellum in participants with high levels of schizotypal traits.

CONCLUSION: ERI is associated with GMV reduction and altered rsFC in individuals with high levels of schizotypal traits.

PMID:33706673 | DOI:10.1080/13546805.2021.1899906

Gender-related differences in frontal-parietal modular segregation and altered effective connectivity in internet gaming disorder

Thu, 03/11/2021 - 11:00

J Behav Addict. 2021 Mar 10. doi: 10.1556/2006.2021.00015. Online ahead of print.

ABSTRACT

BACKGROUND: Although previous studies have revealed gender-related differences in executive function in internet gaming disorder (IGD), neural mechanisms underlying these processes remain unclear, especially in terms of brain networks.

METHODS: Resting-state fMRI data were collected from 78 subjects with IGD (39 males, 20.8 ± 2.16 years old) and 72 with recreational game use (RGU) (39 males, 21.5 ± 2.56 years old). By utilizing graph theory, we calculated participation coefficients among brain network modules for all participants and analyzed the diagnostic-group-by-gender interactions. We further explored possible causal relationships between networks through spectral dynamic causal modeling (spDCM) to assess differences in between-network connections.

RESULTS: Compared to males with RGU, males with IGD demonstrated reduced modular segregation of the frontal-parietal network (FPN). Male IGD subjects also showed increased connections between the FPN and cingulo-opercular network (CON); however, these differences were not found in female subjects. Further spDCM analysis indicated that the causal influence from CON to FPN in male IGD subjects was enhanced relative to that of RGU males, while this influence was relatively reduced in females with IGD.

CONCLUSIONS: These results suggest poor modular segmentation of the FPN and abnormal FPN/CON connections in males with IGD, suggesting a mechanism for male vulnerability to IGD. An increased "bottom-up" effect from the CON to FPN in male IGD subjects could reflect dysfunction between the brain networks. Different mechanisms may underlie in IGD, suggesting that different interventions may be optimal in males and females with IGD.

PMID:33704084 | DOI:10.1556/2006.2021.00015

Machine Learning Analytics of Resting-State Functional Connectivity Predicts Survival Outcomes of Glioblastoma Multiforme Patients

Thu, 03/11/2021 - 11:00

Front Neurol. 2021 Feb 22;12:642241. doi: 10.3389/fneur.2021.642241. eCollection 2021.

ABSTRACT

Glioblastoma multiforme (GBM) is the most frequently occurring brain malignancy. Due to its poor prognosis with currently available treatments, there is a pressing need for easily accessible, non-invasive techniques to help inform pre-treatment planning, patient counseling, and improve outcomes. In this study we determined the feasibility of resting-state functional connectivity (rsFC) to classify GBM patients into short-term and long-term survival groups with respect to reported median survival (14.6 months). We used a support vector machine with rsFC between regions of interest as predictive features. We employed a novel hybrid feature selection method whereby features were first filtered using correlations between rsFC and OS, and then using the established method of recursive feature elimination (RFE) to select the optimal feature subset. Leave-one-subject-out cross-validation evaluated the performance of models. Classification between short- and long-term survival accuracy was 71.9%. Sensitivity and specificity were 77.1 and 65.5%, respectively. The area under the receiver operating characteristic curve was 0.752 (95% CI, 0.62-0.88). These findings suggest that highly specific features of rsFC may predict GBM survival. Taken together, the findings of this study support that resting-state fMRI and machine learning analytics could enable a radiomic biomarker for GBM, augmenting care and planning for individual patients.

PMID:33692747 | PMC:PMC7937731 | DOI:10.3389/fneur.2021.642241

Altered Regional Cerebral Blood Flow and Brain Function Across the Alzheimer's Disease Spectrum: A Potential Biomarker

Thu, 03/11/2021 - 11:00

Front Aging Neurosci. 2021 Feb 22;13:630382. doi: 10.3389/fnagi.2021.630382. eCollection 2021.

ABSTRACT

Objective: To investigate variation in the characteristics of regional cerebral blood flow (rCBF), brain activity, and intrinsic functional connectivity (FC) across the Alzheimer's disease spectrum (ADS). Methods: The study recruited 20 individuals in each of the following categories: Alzheimer's disease (AD), mild cognitive impairment (MCI), subjective cognitive decline (SCD), and healthy control (HC). All participants completed the 3.0T resting-state functional MRI (rs-fMRI) and arterial spin labeling scans in addition to neuropsychological tests. Additionally, the normalized CBF, regional homogeneity (ReHo), and amplitude of low-frequency fluctuation (ALFF) of individual subjects were compared in the ADS. Moreover, the changes in intrinsic FC were investigated across the ADS using the abnormal rCBF regions as seeds and behavioral correlations. Finally, a support-vector classifier model of machine learning was used to distinguish individuals with ADS from HC. Results: Compared to the HC subjects, patients with AD showed the poorest level of rCBF in the left precuneus (LPCUN) and right middle frontal gyrus (RMFG) among all participants. In addition, there was a significant decrease in the ALFF in the bilateral posterior cingulate cortex (PCC) and ReHo in the right PCC. Moreover, RMFG- and LPCUN-based FC analysis revealed that the altered FCs were primarily located in the posterior brain regions. Finally, a combination of altered rCBF, ALFF, and ReHo in posterior cingulate cortex/precuneus (PCC/PCUN) showed a better ability to differentiate ADS from HC, AD from SCD and MCI, but not MCI from SCD. Conclusions: The study demonstrated the significance of an altered rCBF and brain activity in the early stages of ADS. These findings, therefore, present a potential diagnostic neuroimaging-based biomarker in ADS. Additionally, the study provides a better understanding of the pathophysiology of AD.

PMID:33692680 | PMC:PMC7937726 | DOI:10.3389/fnagi.2021.630382

Assessing Uncertainty and Reliability of Connective Field Estimations From Resting State fMRI Activity at 3T

Thu, 03/11/2021 - 11:00

Front Neurosci. 2021 Feb 22;15:625309. doi: 10.3389/fnins.2021.625309. eCollection 2021.

ABSTRACT

Connective Field (CF) modeling estimates the local spatial integration between signals in distinct cortical visual field areas. As we have shown previously using 7T data, CF can reveal the visuotopic organization of visual cortical areas even when applied to BOLD activity recorded in the absence of external stimulation. This indicates that CF modeling can be used to evaluate cortical processing in participants in which the visual input may be compromised. Furthermore, by using Bayesian CF modeling it is possible to estimate the co-variability of the parameter estimates and therefore, apply CF modeling to single cases. However, no previous studies evaluated the (Bayesian) CF model using 3T resting-state fMRI data. This is important since 3T scanners are much more abundant and more often used in clinical research compared to 7T scanners. Therefore in this study, we investigate whether it is possible to obtain meaningful CF estimates from 3T resting state (RS) fMRI data. To do so, we applied the standard and Bayesian CF modeling approaches on two RS scans, which were separated by the acquisition of visual field mapping data in 12 healthy participants. Our results show good agreement between RS- and visual field (VF)- based maps using either the standard or Bayesian CF approach. In addition to quantify the uncertainty associated with each estimate in both RS and VF data, we applied our Bayesian CF framework to provide the underlying marginal distribution of the CF parameters. Finally, we show how an additional CF parameter, beta, can be used as a data-driven threshold on the RS data to further improve CF estimates. We conclude that Bayesian CF modeling can characterize local functional connectivity between visual cortical areas from RS data at 3T. Moreover, observations obtained using 3T scanners were qualitatively similar to those reported for 7T. In particular, we expect the ability to assess parameter uncertainty in individual participants will be important for future clinical studies.

PMID:33692669 | PMC:PMC7937930 | DOI:10.3389/fnins.2021.625309

Hippocampal functional connectivity development during the first two years indexes 4-year working memory performance

Wed, 03/10/2021 - 11:00

Cortex. 2021 Feb 17;138:165-177. doi: 10.1016/j.cortex.2021.02.005. Online ahead of print.

ABSTRACT

The hippocampus is a key limbic region involved in higher-order cognitive processes including learning and memory. Although both typical and atypical functional connectivity patterns of the hippocampus have been well-studied in adults, the developmental trajectory of hippocampal connectivity during infancy and how it relates to later working memory performance remains to be elucidated. Here we used resting state fMRI (rsfMRI) during natural sleep to examine the longitudinal development of hippocampal functional connectivity using a large cohort (N = 202) of infants at 3 weeks (neonate), 1 year, and 2 years of age. Next, we used multivariate modeling to investigate the relationship between both cross-sectional and longitudinal growth in hippocampal connectivity and 4-year working memory outcome. Results showed robust local functional connectivity of the hippocampus in neonates with nearby limbic and subcortical regions, with dramatic maturation and increasing connectivity with key default mode network (DMN) regions resulting in adult-like topology of the hippocampal functional connectivity by the end of the first year. This pattern was stabilized and further consolidated by 2 years of age. Importantly, cross-sectional and longitudinal measures of hippocampal connectivity in the first year predicted subsequent behavioral measures of working memory at 4 years of age. Taken together, our findings provide insight into the development of hippocampal functional circuits underlying working memory during this early critical period.

PMID:33691225 | DOI:10.1016/j.cortex.2021.02.005

Common and Distinct Neural Connectivity in Attention Deficit/Hyperactivity Disorder and Alcohol Use Disorder: A Study Using Resting-State Functional Magnetic Resonance Imaging

Wed, 03/10/2021 - 11:00

Alcohol Clin Exp Res. 2021 Mar 9. doi: 10.1111/acer.14593. Online ahead of print.

ABSTRACT

BACKGROUND: The relation between Attention Deficit/Hyperactivity Disorder (ADHD) and Alcohol Use Disorder (AUD) has been widely demonstrated. In this study, we aimed to investigate the connectivity traits that would help to understand the strong link between both disorders using a neuroimaging perspective.

METHODS: The study included an AUD group (N = 18), an ADHD group (N = 17), a group with AUD+ADHD comorbidity individuals (N = 12) and a control group (N = 18). We used resting-state functional connectivity in a seed-based approach in the Default Mode Networks, the Dorsal Attention Network and the Salience Network.

RESULTS: Within the Default Mode Networks, all groups shared increased connectivity towards the Temporal Gyrus when compared to the control group. Regarding the Dorsal Attention Network, the Brodmann Area 6 presented increased connectivity for each disorder group in comparison with the control group, displaying the strongest aberrations in the AUD+ADHD group. In the Salience Network, the Prefrontal Cortex showed decreased connectivity in every disorder group compared to the control group.

CONCLUSIONS: Despite the small and unequal sample size, our study suggests common neurobiological alterations in AUD and ADHD, supporting the hypothesis that ADHD might be a risk factor for the development of an AUD. The results highlight the importance of an early ADHD diagnosis and treatment to reduce this risk for a subsequent AUD.

PMID:33690916 | DOI:10.1111/acer.14593

Differential contributions of static and time-varying functional connectivity to human behavior

Wed, 03/10/2021 - 11:00

Netw Neurosci. 2021 Feb 1;5(1):145-165. doi: 10.1162/netn_a_00172. eCollection 2021.

ABSTRACT

Measures of human brain functional connectivity acquired during the resting-state track critical aspects of behavior. Recently, fluctuations in resting-state functional connectivity patterns-typically averaged across in traditional analyses-have been considered for their potential neuroscientific relevance. There exists a lack of research on the differences between traditional "static" measures of functional connectivity and newly considered "time-varying" measures as they relate to human behavior. Using functional magnetic resonance imagining (fMRI) data collected at rest, and a battery of behavioral measures collected outside the scanner, we determined the degree to which each modality captures aspects of personality and cognitive ability. Measures of time-varying functional connectivity were derived by fitting a hidden Markov model. To determine behavioral relationships, static and time-varying connectivity measures were submitted separately to canonical correlation analysis. A single relationship between static functional connectivity and behavior existed, defined by measures of personality and stable behavioral features. However, two relationships were found when using time-varying measures. The first relationship was similar to the static case. The second relationship was unique, defined by measures reflecting trialwise behavioral variability. Our findings suggest that time-varying measures of functional connectivity are capable of capturing unique aspects of behavior to which static measures are insensitive.

PMID:33688610 | PMC:PMC7935045 | DOI:10.1162/netn_a_00172

Graph convolutional network for fMRI analysis based on connectivity neighborhood

Wed, 03/10/2021 - 11:00

Netw Neurosci. 2021 Feb 1;5(1):83-95. doi: 10.1162/netn_a_00171. eCollection 2021.

ABSTRACT

There have been successful applications of deep learning to functional magnetic resonance imaging (fMRI), where fMRI data were mostly considered to be structured grids, and spatial features from Euclidean neighbors were usually extracted by the convolutional neural networks (CNNs) in the computer vision field. Recently, CNN has been extended to graph data and demonstrated superior performance. Here, we define graphs based on functional connectivity and present a connectivity-based graph convolutional network (cGCN) architecture for fMRI analysis. Such an approach allows us to extract spatial features from connectomic neighborhoods rather than from Euclidean ones, consistent with the functional organization of the brain. To evaluate the performance of cGCN, we applied it to two scenarios with resting-state fMRI data. One is individual identification of healthy participants and the other is classification of autistic patients from normal controls. Our results indicate that cGCN can effectively capture functional connectivity features in fMRI analysis for relevant applications.

PMID:33688607 | PMC:PMC7935029 | DOI:10.1162/netn_a_00171

A unified approach for characterizing static/dynamic connectivity frequency profiles using filter banks

Wed, 03/10/2021 - 11:00

Netw Neurosci. 2021 Feb 1;5(1):56-82. doi: 10.1162/netn_a_00155. eCollection 2021.

ABSTRACT

Static and dynamic functional network connectivity (FNC) are typically studied separately, which makes us unable to see the full spectrum of connectivity in each analysis. Here, we propose an approach called filter-banked connectivity (FBC) to estimate connectivity while preserving its full frequency range and subsequently examine both static and dynamic connectivity in one unified approach. First, we demonstrate that FBC can estimate connectivity across multiple frequencies missed by a sliding-window approach. Next, we use FBC to estimate FNC in a resting-state fMRI dataset including schizophrenia patients (SZ) and typical controls (TC). The FBC results are clustered into different network states. Some states showed weak low-frequency strength and as such were not captured in the window-based approach. Additionally, we found that SZs tend to spend more time in states exhibiting higher frequencies compared with TCs who spent more time in lower frequency states. Finally, we show that FBC enables us to analyze static and dynamic connectivity in a unified way. In summary, FBC offers a novel way to unify static and dynamic connectivity analyses and can provide additional information about the frequency profile of connectivity patterns.

PMID:33688606 | PMC:PMC7935048 | DOI:10.1162/netn_a_00155

Functional MRI Correlates of Sleep Quality in HIV

Wed, 03/10/2021 - 11:00

Nat Sci Sleep. 2021 Mar 2;13:291-301. doi: 10.2147/NSS.S291544. eCollection 2021.

ABSTRACT

OBJECTIVE: To examine resting-state functional MRI (rs-fMRI) networks related to sleep in the context of HIV infection.

METHODS: rs-fMRI data were collected in 40 HIV-infected (HIV+) individuals at baseline (treatment-naive), 12 week (post-treatment) and one year timepoints. A group of 50 age-matched HIV-negative (HIV-) individuals were also imaged at baseline and one year timepoints. The Pittsburgh Sleep Quality Index (PSQI) questionnaire was administered at all timepoints. Using group independent component analysis (ICA), maps of functional networks were generated from fMRI data; from these, sleep-related networks were selected. A generalized linear model (GLM) was used to analyze if these networks were significantly associated with the PSQI score, and if this relationship was influenced by HIV status/treatment or timepoint.

RESULTS: HIV+ individuals had significantly lower PSQI score after treatment (p=0.022). Networks extracted from group ICA analysis included the anterior and posterior default mode network (DMN), central executive network (CEN), bilateral frontoparietal networks (FPNs), and the anterior cingulate cortex salience network (ACC SN). We found the posterior DMN, right FPN, and ACC SN GLMs showed significantly higher goodness-of-fit after incorporating PSQI data (p = 0.0204, 0.044, 0.044, respectively). Furthermore, the correlation between ACC SN and posterior DMN connectivity was significantly decreased in the HIV+ cohort.

CONCLUSION: Functional networks such as the DMN, FPN, CEN, and ACC SN are altered in poor sleep, as measured by the PSQI score. Furthermore, the relationship between these networks and PSQI is different in the HIV+ and HIV- populations.

PMID:33688288 | PMC:PMC7936696 | DOI:10.2147/NSS.S291544

Disrupted Brain Network Topology in Drug-naive Essential Tremor Patients with and Without Depression : A Resting State Functional Magnetic Resonance Imaging Study

Tue, 03/09/2021 - 11:00

Clin Neuroradiol. 2021 Mar 9. doi: 10.1007/s00062-021-01002-8. Online ahead of print.

ABSTRACT

PURPOSE: This study was carried out to investigate brain functional connectome and its potential relationships with the disease severity and emotion function in patients with essential tremor with and without depressive symptoms by using resting-state functional magnetic resonance imaging and graph theory approaches.

METHODS: In this study 33 essential tremor patients with depression, 45 essential tremor patients without depression and 79 age and gender-matched healthy controls were recruited to undergo a 3.0‑T imaging scan. The whole brain functional connectome was constructed by thresholding the partial correlation matrices of 116 brain regions, and the topologic properties were analyzed by using graph theory approaches and network-based statistic approaches. Nonparametric permutation test was also used for group comparisons of topological metrics. Correlation analyses between topographic features and the clinical characteristics were performed.

RESULTS: The functional connectome in both essential tremor patients with and without depression showed abnormalities at the global level (decrease in clustering coefficient, global efficiency, and local efficiency but increase in characteristic path length) and at the nodal level (decrease nodal centralities in the cerebellum, motor cortex, prefrontal-limbic regions, default mode network) (p < 0.05, false discovery rate corrected). Moreover, essential tremor patients with depression showed higher node efficiency in superior frontal gyrus and posterior cingulate gyrus compared to essential tremor without depression.

CONCLUSION: Our results may provide insights into the underlying pathophysiology of essential tremor patients with and without depression and aid the development of some potential biomarkers of the depressive symptoms in patients with essential tremor.

PMID:33687483 | DOI:10.1007/s00062-021-01002-8

Altered frontal connectivity after sleep deprivation predicts sustained attentional impairment: A resting-state functional magnetic resonance imaging study

Tue, 03/09/2021 - 11:00

J Sleep Res. 2021 Mar 8:e13329. doi: 10.1111/jsr.13329. Online ahead of print.

ABSTRACT

A series of studies have shown that sleep loss impairs one's capability for sustained attention. However, the underlying neurobiological mechanism linking sleep loss with sustained attention has not been elucidated. The present study aimed to investigate the effect of sleep deprivation on the resting-state brain and explored whether the magnitude of vigilance impairment after acute sleep deprivation can be predicted by measures of spontaneous fluctuations and functional connectivity. We implemented resting-state functional magnetic resonance imaging with 42 participants under both normal sleep and 24-hr sleep-deprivation conditions. The amplitude of low-frequency fluctuations (ALFF) and functional connectivity was used to investigate the neurobiological change caused by sleep deprivation, and the psychomotor vigilance task (PVT) was used to measure sustained attention in each state. Correlation analysis was used to investigate the relationship between the change in ALFF/functional connectivity and vigilance performance. Sleep deprivation induced significant reductions in ALFF in default mode network nodes and frontal-parietal network nodes, while inducing significant increments of ALFF in the bilateral thalamus, motor cortex, and visual cortex. The increased ALFF in the visual cortex was correlated with increased PVT lapses. Critically, decreased frontal-thalamus connectivity was correlated with increased PVT lapses, while increased frontal-visual connectivity was correlated with increased PVT lapses. The findings indicated that acute sleep deprivation induced a robust alteration in the resting brain, and sustained attentional impairment after sleep deprivation could be predicted by altered frontal connectivity with crucial neural nodes of stimulus input, such as the thalamus and visual cortex.

PMID:33686744 | DOI:10.1111/jsr.13329

A resting-state functional MRI study in patients with vestibular migraine during interictal period

Mon, 03/08/2021 - 11:00

Acta Neurol Belg. 2021 Mar 8. doi: 10.1007/s13760-021-01639-9. Online ahead of print.

ABSTRACT

To evaluate the spontaneous neuronal activities and the changes of brain functional network in patients with vestibular migraine (VM). Three groups including18 patients with VM, 21 patients with migraine without aura (MWoA) and 21 healthy controls (HCs) underwent the scanning of the resting-state fMRI. Covariance analysis and bonferroni multiple comparisons were used to obtain brain regions with significant differences in amplitude of low-frequency fluctuation (ALFF) values. Furthermore, the brain regions with the most significant differences of ALFF values were recognized as a region of interest (ROI) and functional connectivity (FC) analysis was performed in these regions. (1) ALFF: Compared with HCs, patients with VM showed significantly lower ALFF in the right putamen (P < 0.05), and significantly higher ALFF in the right lingual gyrus (P < 0.05). In addition, compared with MWoA patients, patients with VM showed significantly higher ALFF in the right lingual gyrus (P < 0.05). (2) Compared with HCs, VM patients showed significantly higher FC among the cerebellum, the left dorsolateral superior frontal gyrus and the right putamen (P < 0.05) but significantly lower FC among the left median cingulate, paracingulate gyri and the right putamen (P < 0.05). Compared with MWoA patients, VM patients showed significantly higher FC between the cerebellum and the right putamen (P < 0.05) but significantly lower FC among the left median cingulate, paracingulate gyri and the right putamen (P < 0.05). There are functional abnormalities in nociceptive, vestibular and visual cortex regions in patients with VM during the interictal period.

PMID:33683634 | DOI:10.1007/s13760-021-01639-9

The cerebellum and its network: Disrupted static and dynamic functional connectivity patterns and cognitive impairment in multiple sclerosis

Mon, 03/08/2021 - 11:00

Mult Scler. 2021 Mar 8:1352458521999274. doi: 10.1177/1352458521999274. Online ahead of print.

ABSTRACT

BACKGROUND: The impact of cerebellar damage and (dys)function on cognition remains understudied in multiple sclerosis.

OBJECTIVE: To assess the cognitive relevance of cerebellar structural damage and functional connectivity (FC) in relapsing-remitting multiple sclerosis (RRMS) and secondary progressive multiple sclerosis (SPMS).

METHODS: This study included 149 patients with early RRMS, 81 late RRMS, 48 SPMS and 82 controls. Cerebellar cortical imaging included fractional anisotropy, grey matter volume and resting-state functional magnetic resonance imaging (MRI). Cerebellar FC was assessed with literature-based resting-state networks, using static connectivity (that is, conventional correlations), and dynamic connectivity (that is, fluctuations in FC strength). Measures were compared between groups and related to disability and cognition.

RESULTS: Cognitive impairment (CI) and cerebellar damage were worst in SPMS. Only SPMS showed cerebellar connectivity changes, compared to early RRMS and controls. Lower static FC was seen in fronto-parietal and default-mode networks. Higher dynamic FC was seen in dorsal and ventral attention, default-mode and deep grey matter networks. Cerebellar atrophy and higher dynamic FC together explained 32% of disability and 24% of cognitive variance. Higher dynamic FC was related to working and verbal memory and to information processing speed.

CONCLUSION: Cerebellar damage and cerebellar connectivity changes were most prominent in SPMS and related to worse CI.

PMID:33683158 | DOI:10.1177/1352458521999274

Dysfunction of anterior insula in the non- affected hemisphere in patients with post- stroke depression: A resting-state fMRI study

Mon, 03/08/2021 - 11:00

Technol Health Care. 2021 Feb 26. doi: 10.3233/THC-218004. Online ahead of print.

ABSTRACT

BACKGROUND: Post-stroke depression (PSD) is a consequential neuropsychiatric sequela that occurs after stroke. However, the pathophysiology of PSD are not well understood yet.

OBJECTIVE: To explore alterations in functional connectivity (FC) between anterior insula and fronto-cortical and other subcortical regions in the non-affected hemisphere in patients with PSD compared to without PSD and healthy control.

METHODS: Resting-state FC was estimated between the anterior insula and cortical and subcortical brain regions in the non-affected hemisphere in 13 patients with PSD, 12 patients without PSD, and 13 healthy controls. The severity of depressive mood was measured by the Beck Depression Inventory (BDI)-II.

RESULTS: Patients with PSD showed significant differences in FC scores between the anterior insula and the superior frontal, middle frontal, and orbitofrontal gyrus in the non-affected hemisphere than healthy control or patients without PSD (P< 0.05). In post-hoc, patients with PSD showed higher FC scores between the anterior insula and the superior frontal region than patients without PSD (P< 0.05). Furthermore, alterations in FC of the superior frontal, middle frontal, and orbitofrontal gyrus were positively correlated with depression severity, as measured with the BDI-II (P< 0.001).

PMID:33682743 | DOI:10.3233/THC-218004

Glutamate- and GABA-Modulated Connectivity in Auditory Hallucinations-A Combined Resting State fMRI and MR Spectroscopy Study

Mon, 03/08/2021 - 11:00

Front Psychiatry. 2021 Feb 17;12:643564. doi: 10.3389/fpsyt.2021.643564. eCollection 2021.

ABSTRACT

Background: Auditory verbal hallucinations (AVH) have been linked to aberrant interhemispheric connectivity between the left and the right superior temporal gyrus (STG), labeled the interhemispheric miscommunication theory. The present study investigated if interhemispheric miscommunication is modulated at the neurochemical level by glutamate (Glu) and gamma-aminobutyric acid (GABA) concentrations in temporal and prefrontal lobe areas, as proposed by the theory. Methods: We combined resting-state fMRI connectivity with MR spectroscopy (MRS) in a sample of 81 psychosis patients, comparing patients with high hallucination severity (high-AVH) and low hallucination severity (low-AVH) groups. Glu and GABA concentrations were acquired from the left STG and the anterior cingulate cortex (ACC), an area of cognitive control that has been proposed to modulate STG functioning in AVH. Results: Functional connectivity showed significant interaction effects between AVH Group and ACC-recorded Glu and GABA metabolites. Follow-up tests showed that there was a significant positive association for Glu concentration and interhemispheric STG connectivity in the high-AVH group, while there was a significant negative association for GABA concentration and interhemispheric STG connectivity in the low-AVH group. Conclusion: The results show neurochemical modulation of STG interhemispheric connectivity, as predicted by the interhemispheric miscommunication hypothesis. Furthermore, the findings are in line with an excitatory/inhibitory imbalance model for AVH. By combining different neuroimaging modalities, the current results provide a more comprehensive insight into the neural correlates of AVH.

PMID:33679491 | PMC:PMC7925618 | DOI:10.3389/fpsyt.2021.643564

Disrupted Regional Homogeneity in Melancholic and Non-melancholic Major Depressive Disorder at Rest

Mon, 03/08/2021 - 11:00

Front Psychiatry. 2021 Feb 16;12:618805. doi: 10.3389/fpsyt.2021.618805. eCollection 2021.

ABSTRACT

Background: Melancholic depression has been viewed as one severe subtype of major depressive disorder (MDD). However, it is unclear whether melancholic depression has distinct changes in brain imaging. We aimed to explore specific or distinctive alterations in melancholic MDD and whether the alterations could be used to separate melancholic MDD from non-melancholic MDD or healthy controls. Materials and Methods: Thirty-one outpatients with melancholic MDD and thirty-three outpatients with non-melancholic MDD and thirty-two age- and gender-matched healthy controls were recruited. All participants were scanned by resting-state functional magnetic resonance imaging (fMRI). Imaging data were analyzed with the regional homogeneity (ReHo) and support vector machine (SVM) methods. Results: Melancholic MDD patients exhibited lower ReHo in the right superior occipital gyrus/middle occipital gyrus than non-melancholic MDD patients and healthy controls. Merely for non-melancholic MDD patients, decreased ReHo in the right middle frontal gyrus was negatively correlated with the total HRSD-17 scores. SVM analysis results showed that a combination of abnormal ReHo in the right fusiform gyrus/cerebellum Crus I and the right superior occipital gyrus/middle occipital gyrus exhibited the highest accuracy of 83.05% (49/59), with a sensitivity of 90.32% (28/31), and a specificity of 75.00% (21/28) for discriminating patients with melancholic MDD from patients with non-melancholic MDD. And a combination of abnormal ReHo in the right fusiform gyrus/cerebellum VI and left postcentral gyrus/precentral gyrus exhibited the highest accuracy of 98.41% (62/63), with a sensitivity of 96.77% (30/31), and a specificity of 100.00%(32/32) for separating patients with melancholic MDD from healthy controls. Conclusion: Our findings showed the distinctive ReHo pattern in patients with melancholic MDD and found brain area that may be associated with the pathophysiology of non-melancholic MDD. Potential imaging markers for discriminating melancholic MDD from non-melancholic MDD or healthy controls were reported.

PMID:33679477 | PMC:PMC7928375 | DOI:10.3389/fpsyt.2021.618805

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