New resting-state fMRI related studies at PubMed

Functional Connectivity of Ipsilateral Striatum in Rats with Ischemic Stroke Increased by Electroacupuncture

Fri, 11/25/2022 - 11:00

J Integr Neurosci. 2022 Sep 27;21(6):162. doi: 10.31083/j.jin2106162.


BACKGROUND: This study aimed to investigate the effects of electroacupuncture (EA) treatment at Zusanli (ST36) and Quchi (LI11) on cortico-striatal network connectivity after ischemia stroke by resting-state functional magnetic resonance imaging (fMRI).

METHODS: A rat model of middle cerebral artery occlusion (MCAO) was established. Rats were randomly assigned into a sham-operated control group (SC group, n = 8), untreated MCAO model group (MCAO group, n = 8), and MCAO group receiving EA treatment at ST36 and LI11 (MCAO + EA group, n = 8). Rats in the SC and the MCAO groups received no treatment. The MCAO + EA group was treated with EA from the 1st day to the 7th day after surgery. The behavioral tests including Zea Longa test and modified neurologic severity score (mNSS) for all rats were performed before and after treatment for MCAO + EA group. fMRI scans were performed after behavioral tests on the 7th day after surgery.

RESULTS: The neurologic severity scores estimated by Zea Longa and mNSS were significantly improved in the rat ischemic stroke model of MCAO within 1 week after EA treatment at acupoints ST36 and LI11. Besides, voxel-wise analysis showed that EA could increase the functional connectivity of the left striatum with the bilateral sensory cortex, bilateral motor cortex, left retrosplenial cortex, right cerebellum, bilateral hippocampus, bilateral auditory cortex, bilateral visual cortex, left parietal cortex, left cingulate gyrus, and left superior colliculus. Further graph theory analysis showed that EA significantly decreased the characteristic path length and increased the global efficiency of the cortico-striatal network.

CONCLUSIONS: EA at ST36 and LI11 could improve the cortico-striatal network to impact the brain's protective in MCAO, which is a potential treatment for ischemia stroke.

PMID:36424737 | DOI:10.31083/j.jin2106162

Microstate analysis in infancy

Thu, 11/24/2022 - 11:00

Infant Behav Dev. 2022 Nov 21;70:101785. doi: 10.1016/j.infbeh.2022.101785. Online ahead of print.


BACKGROUND: Microstate analysis is an emerging method for investigating global brain connections using electroencephalography (EEG). Microstates have been colloquially referred to as the "atom of thought," meaning that from these underlying networks comes coordinated neural processing and cognition. The present study examined microstates at 6-, 8-, and 10-months of age. It was hypothesized that infants would demonstrate distinct microstates comparable to those identified in adults that also parallel resting-state networks using fMRI. An additional exploratory aim was to examine the relationship between microstates and temperament, assessed via parent reports, to further demonstrate microstate analysis as a viable tool for examining the relationship between neural networks, cognitive processes as well as emotional expression embodied in temperament attributes.

METHODS: The microstates analysis was performed with infant EEG data when the infant was either 6- (n = 12), 8- (n = 16), or 10-months (n = 6) old. The resting-state task involved watching a 1-minute video segment of Baby Einstein while listening to the accompanying music. Parents completed the IBQ-R to assess infant temperament.

RESULTS: Four microstate topographies were extracted. Microstate 1 had an isolated posterior activation; Microstate 2 had a symmetric occipital to prefrontal orientation; Microstate 3 had a left occipital to right frontal orientation; and Microstate 4 had a right occipital to left frontal orientation. At 10-months old, Microstate 3, thought to reflect auditory/language processing, became activated more often, for longer periods of time, covering significantly more time across the task and was more likely to be transitioned into. This finding is interpreted as consistent with language acquisition and phonological processing that emerges around 10-months. Microstate topographies and parameters were also correlated with differing temperament broadband and narrowband scales on the IBQ-R.

CONCLUSION: Three microstates emerged that appear comparable to underlying networks identified in adult and infant microstate literature and fMRI studies. Each of the temperament domains was related to specific microstates and their parameters. These networks also correspond with auditory and visual processing as well as the default mode network found in prior research and can lead to new investigations examining differences across stimulus presentations to further explain how infants begin to recognize, respond to, and engage with the world around them.

PMID:36423552 | DOI:10.1016/j.infbeh.2022.101785

Multi-label classification of Alzheimer's disease stages from resting-state fMRI-based correlation connectivity data and deep learning

Thu, 11/24/2022 - 11:00

Comput Biol Med. 2022 Nov 9;151(Pt A):106240. doi: 10.1016/j.compbiomed.2022.106240. Online ahead of print.


Alzheimer's disease is a neurodegenerative condition that gradually impairs cognitive abilities. Recently, various neuroimaging modalities and machine learning methods have surfaced to diagnose Alzheimer's disease. Resting-state fMRI is a neuroimaging modality that has been widely utilized to study brain activity related to neurodegenerative diseases. In literature, the previous studies are limited to the binary classification of Alzheimer's disease and Mild Cognitive Impairment. The application of computer-aided diagnosis for the numerous advancing phases of Alzheimer's disease, on the other hand, remains understudied. This research analyzes and presents methods for multi-label classification of six Alzheimer's stages using rs-fMRI and deep learning. The proposed model solves the multi-class classification problem by extracting the brain's functional connectivity networks from rs-fMRI data and employing two deep learning approaches, Stacked Sparse Autoencoder and Brain Connectivity Graph Convolutional Network. The suggested models' results were assessed using the k-fold cross-validation approach, and an average accuracy of 77.13% and 84.03% was reached for multi-label classification using Stacked Sparse Autoencoders and Brain Connectivity Based Convolutional Network, respectively. An analysis of brain regions was also performed by using the network's learned weights, leading to the conclusion that the precentral gyrus, frontal gyrus, lingual gyrus, and supplementary motor area are the significant brain regions of interest.

PMID:36423532 | DOI:10.1016/j.compbiomed.2022.106240

SD-CNN: A static-dynamic convolutional neural network for functional brain networks

Thu, 11/24/2022 - 11:00

Med Image Anal. 2022 Nov 12;83:102679. doi: 10.1016/ Online ahead of print.


Static functional connections (sFCs) and dynamic functional connections (dFCs) have been widely used in the resting-state functional MRI (rs-fMRI) analysis. sFCs, calculated based on entire rs-fMRI scans, can accurately describe the static topology of the brain network. dFCs, estimated by dividing rs-fMRI scans into a series of short sliding windows, are used to reveal time-varying changes in FC patterns. Currently, how to jointly use sFCs and dFCs to identify brain diseases under the framework of deep learning is still a hot issue. To this end, we propose a static-dynamic convolutional neural network for functional brain networks, which involves a static pathway and a dynamic pathway for taking full advantages of sFCs and dFCs. Specifically, the static pathway, using high-resolution convolution filters (i.e., convolution filters with a high number of channels) at a single adjacency matrix of sFCs, is performed to capture static FC patterns. The dynamic pathway, using low-resolution convolution filters at each adjacency matrix of dFCs, is performed to capture time-varying FC patterns. Two types of diffusion connections are used in this model for encouraging the transfer of information between the static pathway and the dynamic pathway, which can make the learned features more discriminative. Furthermore, a static and dynamic combination classifier is introduced to combine features from two pathways for identifying brain diseases. Experiments on two real datasets demonstrate the effectiveness and advantages of our proposed method.

PMID:36423466 | DOI:10.1016/

Altered Functional Connectivity of Basal Ganglia in Mild Cognitive Impairment and Alzheimer's Disease

Thu, 11/24/2022 - 11:00

Brain Sci. 2022 Nov 15;12(11):1555. doi: 10.3390/brainsci12111555.


(1) Background: Alzheimer's disease (AD), an age-progressive neurodegenerative disease that affects cognitive function, causes changes in the functional connectivity of the default-mode network (DMN). However, the question of whether AD-related changes occur in the functional connectivity of the basal ganglia has rarely been specifically analyzed. This study aimed to measure the changes in basal ganglia functional connectivity among patients with AD and mild cognitive impairment (MCI) in their resting state using the functional connectivity density (FCD) value, the functional connectivity (FC) intensity, and the graph theory index, and to confirm their influence on clinical manifestations. (2) Methods: Resting-state functional MRI (rs-fMRI) and neuropsychological data from 48 participants in the Alzheimer's Disease Neuroimaging Initiative (ADNI) were used for analyses. The 48 ADNI participants comprised 16 patients with AD, 16 patients with MCI, and 16 normal controls (NCs). The functional connectivity of basal ganglia was evaluated by FCDs, FC strength, and graph theory index. We compared voxel-based FCD values between groups to show specific regions with significant variation and significant connectivity from ROI conduction to ROI analysis. Pearson's correlation analyses between functional connectivity and several simultaneous clinical variables were also conducted. Additionally, receiver operating characteristic (ROC) analyses associated with classification were conducted for both FCD values and graph theory indices. (3) Results: The level of FCD in patients with cognitive impairment showed obvious abnormalities (including short-range and long-range FCD). In addition to DMN-related regions, aberrant functional connectivity was also found to be present in the basal ganglia, especially in the caudate and amygdala. The FCD values of the basal ganglia (involving the caudate and amygdala) were closely related to scores from the Mini-Mental State Examination (MMSE) and the Functional Activities Questionnaire (FAQ); meanwhile, the graph theory indices (involving global efficiency and degree) of the basal ganglia (involving the caudate, amygdala, and putamen) were also found to be closely correlated with MMSE scores. In ROC analyses of both FCD and graph theory, the amygdala was of the utmost importance in the early-stage detection of MCI; additionally, the caudate nucleus was found to be crucial in the progression of cognitive decline and AD diagnosis. (4) Conclusions: It was systematically confirmed that there is a phenomenon of change in the functional connections in the basal ganglia during cognitive decline. The findings of this study could improve our understanding of AD and MCI pathology in the basal ganglia and make it possible to propose new targets for AD treatment in further studies.

PMID:36421879 | DOI:10.3390/brainsci12111555

Connectivity between default mode and frontoparietal networks mediates the association between global amyloid-β and episodic memory

Thu, 11/24/2022 - 11:00

Hum Brain Mapp. 2022 Nov 24. doi: 10.1002/hbm.26148. Online ahead of print.


Βeta-amyloid (Aβ) is a neurotoxic protein that deposits early in the pathogenesis of preclinical Alzheimer's disease. We aimed to identify network connectivity that may alter the negative effect of Aβ on cognition. Following assessment of memory performance, resting-state fMRI, and mean cortical PET-Aβ, a total of 364 older adults (286 with clinical dementia rating [CDR-0], 59 with CDR-0.5 and 19 with CDR-1, mean age: 74.0 ± 6.4 years) from the OASIS-3 sample were included in the analysis. Across all participants, a partial least squares regression showed that lower connectivity between posterior medial default mode and frontoparietal networks, higher within-default mode, and higher visual-motor connectivity predict better episodic memory. These connectivities partially mediate the effect of Aβ on episodic memory. These results suggest that connectivity strength between the precuneus cortex and the superior frontal gyri may alter the negative effect of Aβ on episodic memory. In contrast, education was associated with different functional connectivity patterns. In conclusion, functional characteristics of specific brain networks may help identify amyloid-positive individuals with a higher likelihood of memory decline, with implications for AD clinical trials.

PMID:36420978 | DOI:10.1002/hbm.26148

Functional reconfiguration of task-active frontoparietal control network facilitates abstract reasoning

Thu, 11/24/2022 - 11:00

Cereb Cortex. 2022 Nov 24:bhac457. doi: 10.1093/cercor/bhac457. Online ahead of print.


While the brain's functional network architecture is largely conserved between resting and task states, small but significant changes in functional connectivity support complex cognition. In this study, we used a modified Raven's Progressive Matrices Task to examine symbolic and perceptual reasoning in human participants undergoing fMRI scanning. Previously, studies have focused predominantly on discrete symbolic versions of matrix reasoning, even though the first few trials of the Raven's Advanced Progressive Matrices task consist of continuous perceptual stimuli. Our analysis examined the activation patterns and functional reconfiguration of brain networks associated with resting state and both symbolic and perceptual reasoning. We found that frontoparietal networks, including the cognitive control and dorsal attention networks, were significantly activated during abstract reasoning. We determined that these same task-active regions exhibited flexibly-reconfigured functional connectivity when transitioning from resting state to the abstract reasoning task. Conversely, we showed that a stable network core of regions in default and somatomotor networks was maintained across both resting and task states. We propose that these regionally-specific changes in the functional connectivity of frontoparietal networks puts the brain in a "task-ready" state, facilitating efficient task-based activation.

PMID:36420534 | DOI:10.1093/cercor/bhac457

Altered hippocampal functional connectivity after the rupture of anterior communicating artery aneurysm

Thu, 11/24/2022 - 11:00

Front Aging Neurosci. 2022 Nov 7;14:997231. doi: 10.3389/fnagi.2022.997231. eCollection 2022.


BACKGROUND AND PURPOSE: Aneurysmal subarachnoid hemorrhage (SAH) predisposes hippocampal injury, a major cause of follow-up cognitive impairment. Our previous study has revealed an abnormal resting-state brain network in patients after the rupture of anterior communicating artery (ACoA) aneurysm. However, the functional connectivity (FC) characteristics of the hippocampus and its relationship with cognitive performance in these patients remain unknown.

METHODS: This study ultimately included 26 patients and 19 age- and sex-matched controls who completed quality control for resting-state functional magnetic resonance imaging (fMRI). The mean time series for each side of the hippocampus was extracted from individuals and then a seed-to-voxel analysis was performed. We compared the difference in FC strength between the two groups and subsequently analyzed the correlations between abnormal FC and their cognitive performance.

RESULTS: The results of bilateral hippocampus-based FC analysis were largely consistent. Compared with the healthy controls, patients after the rupture of ACoA aneurysm exhibited significantly decreased FC between the hippocampus and other brain structures within the Papez circuit, including bilateral anterior and middle cingulate cortex (MCC), bilateral medial superior frontal gyrus, and left inferior temporal gyrus (ITG). Instead, increased FC between the hippocampus and bilateral insula was observed. Correlation analyses showed that more subjective memory complaints or lower total cognitive scores were associated with decreased connectivity in the hippocampus and several brain regions such as left anterior cingulate cortex (ACC) and frontotemporal cortex.

CONCLUSION: These results extend our previous findings and suggest that patients with ruptured ACoA aneurysm exist hypoconnectivity between the hippocampus and multiple brain regions within the Papez circuit. Deactivation of the Papez circuit may be a crucial neural mechanism related to cognitive deficits in patients after the rupture of ACoA aneurysm.

PMID:36420312 | PMC:PMC9677126 | DOI:10.3389/fnagi.2022.997231

Intrinsic brain functional connectivity patterns in alcohol use disorder

Thu, 11/24/2022 - 11:00

Brain Commun. 2022 Nov 4;4(6):fcac290. doi: 10.1093/braincomms/fcac290. eCollection 2022.


Alcohol use disorder is associated with damaging effects to the brain. This study aimed to examine differences in static and dynamic intrinsic functional connectivity patterns in individuals with a history of alcohol use disorder in comparison to those with no history of alcohol abuse. A total of 55 participants consisting of 23 patients and 32 control individuals underwent neuropsychological assessments and resting-state functional magnetic resonance imaging on a 3 Tesla MRI scanner. Differences in functional connectivity between the two groups were determined using static and dynamic independent component analysis. Differences in static functional connectivity between the two groups were identified in the default mode network, attention network, frontoparietal network, frontal cortical network and cerebellar network. Furthermore, the analyses revealed specific differences in the dynamic temporal characteristics of functional connectivity between the two groups of participants, in a cluster involving key regions in reward, sensorimotor and frontal cortical functional networks, with some connections correlating with the length of sobriety and some others with the severity of drinking. The findings altogether suggest dysregulation in the intrinsic connectivity of cortico-basal ganglia-thalamo-cortical loops that may reflect persistent alcohol use disorder-related network abnormalities, compensatory recovery-related processes whereby additional neural resources are recruited to achieve normal levels of performance, or a predisposition toward developing alcohol use disorder.

PMID:36419966 | PMC:PMC9679426 | DOI:10.1093/braincomms/fcac290

The brain structure and function abnormalities of migraineurs: A systematic review and neuroimaging meta-analysis

Thu, 11/24/2022 - 11:00

Front Neurol. 2022 Nov 7;13:1022793. doi: 10.3389/fneur.2022.1022793. eCollection 2022.


OBJECTIVES: To quantitatively summarize the specific changes in brain structure and function in migraine patients.

METHODS: A literature screening of migraine was conducted from inception to Sept 1, 2022, in PubMed, Web of Science, Cochrane Library, and Medline databases using the keyword combination of "migraine and MRI." Activation likelihood estimation (ALE) was performed to assess the differentiation of functional connectivity (FC), regional homogeneity (ReHo), and gray matter volume (GMV) of migraine patients.

RESULTS: Eleven voxel-based morphometry (VBM) studies and 25 resting-state fMRI (rs-fMRI) studies (16 FC and 9 ReHo studies) were included in this study. ALE analysis revealed the ReHo increase in the brainstem and left thalamus, with no decreased area. Neither increased nor decreased regions were detected in FC and GMV of migraine patients.

CONCLUSIONS: The left thalamus and brainstem were the significantly activated regions of migraine. It is a meaningful insights into the pathophysiology of migraine. The consistent alterated brain areas of morphometrical and functional in migraine patients were far from reached based on current studies.

PMID:36419535 | PMC:PMC9676357 | DOI:10.3389/fneur.2022.1022793

Automatic diagnosis of late-life depression by 3D convolutional neural networks and cross-sample Entropy analysis from resting-state fMRI

Wed, 11/23/2022 - 11:00

Brain Imaging Behav. 2022 Nov 24. doi: 10.1007/s11682-022-00748-0. Online ahead of print.


Resting-state fMRI has been widely used in investigating the pathophysiology of late-life depression (LLD). Unlike the conventional linear approach, cross-sample entropy (CSE) analysis shows the nonlinear property in fMRI signals between brain regions. Moreover, recent advances in deep learning, such as convolutional neural networks (CNNs), provide a timely application for understanding LLD. Accurate and prompt diagnosis is essential in LLD; hence, this study aimed to combine CNN and CSE analysis to discriminate LLD patients and non-depressed comparison older adults based on brain resting-state fMRI signals. Seventy-seven older adults, including 49 patients and 28 comparison older adults, were included for fMRI scans. Three-dimensional CSEs with volumes corresponding to 90 seed regions of interest of each participant were developed and fed into models for disease classification and depression severity prediction. We obtained a diagnostic accuracy > 85% in the superior frontal gyrus (left dorsolateral and right orbital parts), left insula, and right middle occipital gyrus. With a mean root-mean-square error (RMSE) of 2.41, three separate models were required to predict depressive symptoms in the severe, moderate, and mild depression groups. The CSE volumes in the left inferior parietal lobule, left parahippocampal gyrus, and left postcentral gyrus performed best in each respective model. Combined complexity analysis and deep learning algorithms can classify patients with LLD from comparison older adults and predict symptom severity based on fMRI data. Such application can be utilized in precision medicine for disease detection and symptom monitoring in LLD.

PMID:36418676 | DOI:10.1007/s11682-022-00748-0

Default mode network mechanisms of repeated transcranial magnetic stimulation in heroin addiction

Wed, 11/23/2022 - 11:00

Brain Imaging Behav. 2022 Nov 23. doi: 10.1007/s11682-022-00741-7. Online ahead of print.


Repetitive transcranial magnetic stimulation (rTMS) over the left dorsolateral prefrontal cortex (DLPFC) has been shown to reduce cravings in heroin-dependent (HD) individuals, but the mechanisms underlying the anti-craving effects of rTMS are unknown. Abnormalities in the default mode network (DMN) are known to be consistent findings in HD individuals and are involved in cravings. We assessed the effect of rTMS on DMN activity and its relationship to the treatment response. Thirty HD individuals were included in this self-controlled study, and all HD participants received 10-Hz rTMS 7-session during a week. Data for cravings and withdrawal symptoms and resting-state functional magnetic resonance imaging data were collected before and after rTMS treatment. Thirty demographically matched healthy individuals who did not receive rTMS were included as controls. We focused on changes in coupling seeded from the medial prefrontal cortex (MPFC), posterior cingulate cortex (PCC), and bilateral inferior parietal lobe (IPL), which are the core regions of the DMN. The craving and withdrawal symptom score of HD individuals decreased significantly after rTMS treatment. The left IPL-left middle frontal gyrus coupling and the left IPL-right inferior occipital gyrus coupling decreased significantly, and the changes in the left IPL-left middle frontal gyrus coupling were positively correlated with changes in drug-cue induced cravings. rTMS could modulate the coupling between the DMN and executive control network (ECN). Alterations of the left IPL-left middle frontal gyrus coupling may play an important mechanistic role in reducing drug cue-induced cravings.

PMID:36418675 | DOI:10.1007/s11682-022-00741-7

Unravelling the relationship between amyloid accumulation and brain network function in normal aging and very mild cognitive decline: a longitudinal analysis

Wed, 11/23/2022 - 11:00

Brain Commun. 2022 Nov 2;4(6):fcac282. doi: 10.1093/braincomms/fcac282. eCollection 2022.


Pathological changes in the brain begin accumulating decades before the appearance of cognitive symptoms in Alzheimer's disease. The deposition of amyloid beta proteins and other neurotoxic changes occur, leading to disruption in functional connections between brain networks. Discrete characterization of the changes that take place in preclinical Alzheimer's disease has the potential to help treatment development by targeting the neuropathological mechanisms to prevent cognitive decline and dementia from occurring entirely. Previous research has focused on the cross-sectional differences in the brains of patients with mild cognitive impairment or Alzheimer's disease and healthy controls or has concentrated on the stages immediately preceding cognitive symptoms. The present study emphasizes the early preclinical phases of neurodegeneration. We use a longitudinal approach to examine the brain changes that take place during the early stages of cognitive decline in the Open Access Series of Imaging Studies-3 data set. Among 1098 participants, 274 passed the inclusion criteria (i.e. had at least two cognitive assessments and two amyloid scans). Over 90% of participants were healthy at baseline. Over 8-10 years, some participants progressed to very mild cognitive impairment (n = 48), while others stayed healthy (n = 226). Participants with cognitive decline show faster amyloid accumulation in the lateral temporal, motor and parts of the lateral prefrontal cortex. These changes in amyloid levels were linked to longitudinal increases in the functional connectivity of select networks, including default mode, frontoparietal and motor components. Our findings advance the understanding of amyloid staging and the corresponding changes in functional organization of large-scale brain networks during the progression of early preclinical Alzheimer's disease.

PMID:36415665 | PMC:PMC9678202 | DOI:10.1093/braincomms/fcac282

Neuromodulatory effects of theta burst stimulation to the prefrontal cortex

Tue, 11/22/2022 - 11:00

Sci Data. 2022 Nov 21;9(1):717. doi: 10.1038/s41597-022-01820-6.


Theta burst stimulation (TBS) is a new form of repetitive transcranial magnetic stimulation (TMS) capable of non-invasively modulating cortical excitability. In recent years TBS has been increasingly used as a neuroscientific investigative tool and therapeutic intervention for psychiatric disorders, in which the dorsolateral prefrontal cortex (DLPFC) is often the primary target. However, the neuromodulatory effects of TBS on prefrontal regions remain unclear. Here we share EEG and ECG recordings and structural MRI scans, including high-resolution DTI, from twenty-four healthy participants who received intermittent TBS (two sessions), continuous TBS (two sessions), and sham stimulation (one session) applied to the left DLPFC using a single-blinded crossover design. Each session includes eyes-open resting-state EEG and single-pulse TMS-EEG obtained before TBS and 2-, 15-, and 30-minutes post-stimulation. This dataset enables foundational basic science investigations into the neuromodulatory effects of TBS on the DLPFC.

PMID:36414684 | DOI:10.1038/s41597-022-01820-6

Occupational therapy using a robotic-assisted glove ameliorates finger dexterity and modulates functional connectivity in amyotrophic lateral sclerosis

Mon, 11/21/2022 - 11:00

J Clin Neurosci. 2022 Nov 18:S0967-5868(22)00446-5. doi: 10.1016/j.jocn.2022.11.004. Online ahead of print.


INTRODUCTION: Although rehabilitation is recommended for amyotrophic lateral sclerosis (ALS), improvement of functional decline has hardly been achieved. We investigated the effect of occupational therapy that uses a robotic-assisted glove (RAG) on hand dexterity and the functional connectivities found in the brain of ALS patients.

METHOD: Ten patients diagnosed with ALS and admitted to the Shiga University of Medical Science (SUMS) Hospital from December 2018 to December 2021 participated in the study. These participants chose the hand side to wear RAG and exercised for two weeks. A sham movement was performed on the other side. We administered several functional assessments, including the Simple Test for Evaluating Hand Function (STEF), grip strength, pinch meter for grip strength, Canadian occupational performance measure (COPM), as well as nerve conduction study (NCS) before and after the exercise, and evaluated the results. We also analyzed six patients' resting-state functional magnetic resonance imaging (rs-fMRI).

RESULTS: Two-week robotic rehabilitation improved the STEF, grip strength, and COPM scores when compared with those of the other side. However, no significant effect was observed in the pinch meter and the NCS results. The rs-fMRI data analysis revealed that the robotic rehabilitation augmented two functional connectivities between the left pallidum-right supplementary motor cortex and right insular cortex-right sensorimotor network among the patients, which had beneficial effects.

CONCLUSION: The occupational therapy using RAG displayed improved hand dexterity. The enhanced functional connectivities around the sensorimotor network might be associated with the improvement in hand dexterity because of the RAG.

PMID:36411175 | DOI:10.1016/j.jocn.2022.11.004

Longitudinal alterations of modular functional-metabolic coupling in first-episode schizophrenia

Mon, 11/21/2022 - 11:00

J Psychiatr Res. 2022 Nov 7;156:705-712. doi: 10.1016/j.jpsychires.2022.10.067. Online ahead of print.


Altered network organization and aberrant neurometabolic levels have been associated with schizophrenia. However, modular alterations of functional-neurometabolic coupling in various stages of schizophrenia remain unclear. This longitudinal study enrolled 34 drug-naïve first-episode schizophrenia (FES) patients and 30 healthy controls (HC). The FES patients underwent resting-state functional magnetic resonance imaging (rs-fMRI) and proton magnetic resonance spectroscopy (1H-MRS) at baseline, 2 months, and 6 months of treatment. For 1H-MRS, the concentrations of γ-aminobutyric acid (GABA), N-acetylaspartate (NAA) and glutamate + glutamine in the ventromedial prefrontal cortex region were measured. A graph theoretical approach was applied for functional connectivity-based modular parcellation. We found that intra-default mode network (DMN) connectivity, inter-modular connectivity between the DMN and the hippocampus, and inter-modular connectivity between the DMN and the frontoparietal module were significantly different across 6-month treatment in the FES patients. The inter-module connectivity of the DMN and hippocampus correlated positively with NAA concentration in the HC group, while this correlation was absent in FES patients. This exploratory study suggests an altered modular connectivity in association with neurometabolite concentrations in FES patients and provides insights into multimodal neuroimaging biomarkers in schizophrenia. Future studies with larger sample sizes are needed to consolidate our findings.

PMID:36410309 | DOI:10.1016/j.jpsychires.2022.10.067

Functional and Morphological Brain Alterations in Dysthyroid Optic Neuropathy: A Combined Resting-State fMRI and Voxel-Based Morphometry Study

Mon, 11/21/2022 - 11:00

J Magn Reson Imaging. 2022 Nov 21. doi: 10.1002/jmri.28534. Online ahead of print.


BACKGROUND: Increasing evidence has indicated that the entire visual pathway from retina to visual cortex may be involved in dysthyroid optic neuropathy (DON) pathological mechanisms.

PURPOSE: To explore the functional and morphological brain characteristics in DON and their relationship with ophthalmologic performance.

STUDY TYPE: Retrospective.

POPULATION: A total of 30 DON patients, 40 thyroid-associated ophthalmopathy (TAO) without DON patients and 21 healthy-controls (HCs).

FIELD STRENGTH/SEQUENCE: A 3.0 T, 3D T1-weighted spoiled gradient-recalled echo and gradient-recalled echo-planar imaging.

ASSESSMENT: Functional and structural alterations in brain regions were evaluated with fractional amplitude of low-frequency fluctuations, degree centrality (DC), and gray matter volume (GMV). Clinical activity score (CAS) is assessed across patients.

STATISTICAL TEST: One-way analysis of variance with post hoc two sample t-tests (GRF-corrected, voxel level: P < 0.005, cluster level: P < 0.05) and correlation analysis (significance level: P < 0.05).

RESULTS: Compared to HCs, DON patients had significantly decreased DC values in the bilateral BA17 and BA18 regions. Compared to the TAO group, DON patients had decreased GMV in the left anterior cingulate cortex, left middle frontal gyrus, left lingual gyrus, left parietal gyrus, right Rolandic operculum, left supplementary motor area, and right middle temporal gyrus. In addition, GMV in the right Rolandic operculum was significantly positively correlated with CAS (correlation coefficient: r = 0.448).

DATA CONCLUSION: This study showed significant morphological and functional alterations in visual cortex and morphological alterations in partial default mode network regions of DON patients, which may provide insights into the mechanism of vision loss and may facilitate the diagnosis and treatment of DON.



PMID:36408884 | DOI:10.1002/jmri.28534

Effects of hypertension and aging on brain function in spontaneously hypertensive rats: a longitudinal resting-state functional magnetic resonance imaging study

Mon, 11/21/2022 - 11:00

Cereb Cortex. 2022 Nov 20:bhac436. doi: 10.1093/cercor/bhac436. Online ahead of print.


To investigate the dynamic evolution of brain function under the comorbidities of hypertension and aging. Resting-state functional magnetic resonance imaging scans were longitudinally acquired at 10, 24, and 52 weeks in spontaneously hypertensive rats (SHRs) and Wistar-Kyoto rats. We computed the mean amplitude of low-frequency fluctuation (mALFF), mean regional homogeneity (mReHo), and functional connectivity (FC). There was no interaction between hypertension and aging on brain function. The main effect of aging reflects primarily the cumulative increase of brain activity, especially the increase of mALFF in amygdala and mReHo in cingulate cortex, accompanied by the decrease of brain activity. The main effect of hypertension reflects primarily decreased brain activity in default modal network, accompanied by increased brain activity. The main effect of aging shows reduced brain FC as early as 24 weeks, and the main effect of hypertension shows higher brain FC in SHRs. The novel discovery is that 1 brain FC network increased linearly with age in SHRs, in addition to the linearly decreasing FC. Hypertension and aging independently contribute to spatiotemporal alterations in brain function in SHRs following ongoing progression and compensation. This study provides new insight into the dynamic characteristics of brain function.

PMID:36408643 | DOI:10.1093/cercor/bhac436

Altered cortical thickness, degree centrality, and functional connectivity in middle-age type 2 diabetes mellitus

Mon, 11/21/2022 - 11:00

Front Neurol. 2022 Nov 4;13:939318. doi: 10.3389/fneur.2022.939318. eCollection 2022.


PURPOSE: This study aimed to investigate the changes in brain structure and function in middle-aged patients with type 2 diabetes mellitus (T2DM) using morphometry and blood oxygen level-dependent functional magnetic resonance imaging (BOLD-fMRI).

METHODS: A total of 44 middle-aged patients with T2DM and 45 matched healthy controls (HCs) were recruited. Surface-based morphometry (SBM) was used to evaluate the changes in brain morphology. Degree centrality (DC) and functional connectivity (FC) were used to evaluate the changes in brain function.

RESULTS: Compared with HCs, middle-aged patients with T2DM exhibited cortical thickness reductions in the left pars opercularis, left transverse temporal, and right superior temporal gyri. Decreased DC values were observed in the cuneus and precuneus in T2DM. Hub-based FC analysis of these regions revealed lower connectivity in the bilateral hippocampus and parahippocampal gyrus, left precuneus, as well as left frontal sup.

CONCLUSION: Cortical thickness, degree centrality, as well as functional connectivity were found to have significant changes in middle-aged patients with T2DM. Our observations provide potential evidence from neuroimaging for analysis to examine diabetes-related brain damage.

PMID:36408505 | PMC:PMC9672081 | DOI:10.3389/fneur.2022.939318