Most recent paper

Aberrant Dynamic Functional Connectivity of Posterior Cingulate Cortex Subregions in Major Depressive Disorder With Suicidal Ideation

Fri, 08/05/2022 - 18:00

Front Neurosci. 2022 Jul 19;16:937145. doi: 10.3389/fnins.2022.937145. eCollection 2022.


Accumulating evidence indicates the presence of structural and functional abnormalities of the posterior cingulate cortex (PCC) in patients with major depressive disorder (MDD) with suicidal ideation (SI). Nevertheless, the subregional-level dynamic functional connectivity (dFC) of the PCC has not been investigated in MDD with SI. We therefore sought to investigate the presence of aberrant dFC variability in PCC subregions in MDD patients with SI. We analyzed resting-state functional magnetic resonance imaging (fMRI) data from 31 unmedicated MDD patients with SI (SI group), 56 unmedicated MDD patients without SI (NSI group), and 48 matched healthy control (HC) subjects. The sliding-window method was applied to characterize the whole-brain dFC of each PCC subregion [the ventral PCC (vPCC) and dorsal PCC (dPCC)]. In addition, we evaluated associations between clinical variables and the aberrant dFC variability of those brain regions showing significant between-group differences. Compared with HCS, the SI and the NSI groups exhibited higher dFC variability between the left dPCC and left fusiform gyrus and between the right vPCC and left inferior frontal gyrus (IFG). The SI group showed higher dFC variability between the left vPCC and left IFG than the NSI group. Furthermore, the dFC variability between the left vPCC and left IFG was positively correlated with Scale for Suicidal Ideation (SSI) score in patients with MDD (i.e., the SI and NSI groups). Our results indicate that aberrant dFC variability between the vPCC and IFG might provide a neural-network explanation for SI and may provide a potential target for future therapeutic interventions in MDD patients with SI.

PMID:35928017 | PMC:PMC9344055 | DOI:10.3389/fnins.2022.937145

Respective Involvement of the Right Cerebellar Crus I and II in Syntactic and Semantic Processing for Comprehension of Language

Thu, 08/04/2022 - 18:00

Cerebellum. 2022 Aug 4. doi: 10.1007/s12311-022-01451-y. Online ahead of print.


The right posterolateral portions of the cerebellum (crus-I/II) are involved in language processing. However, their functional role in language remains unknown. The cerebellum is hypothesized to acquire an internal model that is a functional copy of mental representations in the cerebrum and to contribute to cognitive function. In this research, based on the cerebellar internal model hypothesis, we conducted task-based and resting-state functional magnetic resonance imaging (fMRI) experiments to investigate the role of the cerebellum in the syntactic and semantic aspects of comprehension of sentences. In a syntactic task, participants read sentences with center-embedded hierarchical structures. The hierarchical level-dependent activity was found in the right crus-I as well as Broca's area (p < 0.05, voxel-based small volume correction (SVC)). In a semantic task, the participants read three types of sentences for investigation of sentence-level, phrase-level, and word-level semantic processing. The semantic level-dependent activity was found in the right crus-II as well as in the left anterior temporal lobe and the left angular gyrus (p < 0.05, voxel-based SVC). Moreover, the right crus-I/II showed significant activity when the cognitive load was high. Resting-state fMRI demonstrated intrinsic functional connectivity between the right crus-I/II and language-related regions in the left cerebrum (p < 0.05, voxel-based SVC). These findings suggest that the right crus-I and crus-II are involved, respectively, in the syntactic and semantic aspects of sentence processing. The cerebellum assists processing of language in the cerebrum when the cognitive load is high.

PMID:35927417 | DOI:10.1007/s12311-022-01451-y

Neuroimaging in Breast Implant Illness, an fMRI Pilot Study

Thu, 08/04/2022 - 18:00

Aesthet Surg J. 2022 Aug 4:sjac216. doi: 10.1093/asj/sjac216. Online ahead of print.


BACKGROUND: Some women with breast implants report systemic and cognitive symptoms known as breast implant illness (BII), which are very similar to those of fibromyalgia. Functional MRI has shown altered brain activity in fibromyalgia patients.

OBJECTIVES: In this pilot study, we investigated whether brain alterations could be observed in BII patients using fMRI.

METHODS: Women aged 18 to 76 with silicone breast implants for cosmetic reasons were recruited through a Dutch online BII support organization (MKS) and through Maastricht University Medical Center. Twelve women with BII and twelve women without symptoms were included. Participants completed questionnaires regarding demographic characteristics, medical history, psychosocial complaints (4DSQ), cognitive failure (MSSE), pain intensity and pain-related disability (CPGS). Subsequently, brain images of all participants were obtained using resting-state fMRI (rs-fMRI) and Diffusion Tensor Imaging (DTI) at a 3 Tesla MRI scanner (Siemens Medical System, Erlangen, Germany).

RESULTS: Eleven BII patients and 12 healthy controls were included for analysis. Baseline characteristics were similar in the two groups and the mean silicone exposure was 15 years. Patients scored significantly higher on both pain intensity and disability than controls. Patients scored worse on depression, somatization, distress, and anxiety compared to asymptomatic women. MMSE scores were normal. However, the analyses of both functional connectivity and structural integrity showed no significant differences between the two groups.

CONCLUSIONS: This pilot study showed no evidence of brain alterations in BII patients. However, patients scored significantly worse on psychosocial symptoms than controls. Psychological factors appear to play an important role in BII and should be further investigated.

PMID:35926836 | DOI:10.1093/asj/sjac216

Electroconvulsive therapy changes temporal dynamics of intrinsic brain activity in depressed patients

Thu, 08/04/2022 - 18:00

Psychiatry Res. 2022 Jul 21;316:114732. doi: 10.1016/j.psychres.2022.114732. Online ahead of print.


Electroconvulsive therapy (ECT) has been demonstrated to be effective in treating depressed patients. Previous neuroimaging studies have focused mainly on alterations in static brain activity and connectivity to study the effects of ECT in depressed patients. However, it remains unclear whether the temporal dynamics of brain activity are associated with mechanisms of ECT in depressed patients. We measured the dynamics of spontaneous brain activity using dynamic amplitude of low-frequency fluctuation (dALFF) in healthy controls (n = 40) and patients diagnosed with unipolar depression (UD, n = 36) or bipolar disorder (BD, n = 9) before and after ECT. Furthermore, the temporal variability of intrinsic brain activity (iBA) was quantified as the variance of dALFF across sliding window. In addition, correlation analysis was performed to investigate the relationships among dALFF, depressive symptoms, and cognitive function in depressed patients. We lack second resting-state functional magnetic resonance imaging (rs-fMRI) data for healthy controls. After ECT, patients showed decreased brain dynamics (less temporal variability) in the right dorsal anterior cingulate cortex (dACC) and the right precuneus, whereas they showed increased brain dynamics in the bilateral superior medial frontal cortex (mSFC). No significant correlation was found between the dALFF and clinical variables in depressed patients. Our findings suggest that right dACC, right precuneus, and bilateral mSFC play an important role in response to ECT depressed patients from the perspective of dynamic local brain activity, indicating that the dALFF variability may be useful in further understanding the mechanisms of ECT's antidepressant effects.

PMID:35926361 | DOI:10.1016/j.psychres.2022.114732

Comorbid depressive symptoms can aggravate the functional changes of the pain matrix in patients with chronic back pain: A resting-state fMRI study

Thu, 08/04/2022 - 18:00

Front Aging Neurosci. 2022 Jul 18;14:935242. doi: 10.3389/fnagi.2022.935242. eCollection 2022.


OBJECTIVE: The purposes of this study are to explore (1) whether comorbid depressive symptoms in patients with chronic back pain (CBP) affect the pain matrix. And (2) whether the interaction of depression and CBP exacerbates impaired brain function.

METHODS: Thirty-two patients with CBP without comorbid depressive symptoms and thirty patients with CBP with comorbid depressive symptoms were recruited. All subjects underwent functional magnetic resonance imaging (fMRI) scans. The graph theory analysis, mediation analysis, and functional connectivity (FC) analysis were included in this study. All subjects received the detection of clinical depressive symptoms and pain-related manifestations.

RESULT: Compared with the CBP group, subjects in the CBP with comorbid depressive symptoms (CBP-D) group had significantly increased FC in the left medial prefrontal cortex and several parietal cortical regions. The results of the graph theory analyses showed that the area under the curve of small-world property (t = -2.175, p = 0.034), gamma (t = -2.332, p = 0.023), and local efficiency (t = -2.461, p = 0.017) in the CBP-D group were significantly lower. The nodal efficiency in the ventral posterior insula (VPI) (t = -3.581, p = 0.0007), and the network efficiency values (t = -2.758, p = 0.008) in the pain matrix were significantly lower in the CBP-D group. Both the topological properties and the FC values of these brain regions were significantly correlated with self-rating depression scale (SDS) scores (all FDR corrected) but not with pain intensity. Further mediation analyses demonstrated that pain intensity had a mediating effect on the relationship between SDS scores and Pain Disability Index scores. Likewise, the SDS scores mediated the relationship between pain intensity and PDI scores.

CONCLUSION: Our study found that comorbid depressive symptoms can aggravate the impairment of pain matrix function of CBP, but this impairment cannot directly lead to the increase of pain intensity, which may be because some brain regions of the pain matrix are the common neural basis of depression and CBP.

PMID:35923542 | PMC:PMC9340779 | DOI:10.3389/fnagi.2022.935242

Connectome-based predictive models using resting-state fMRI for studying brain aging

Wed, 08/03/2022 - 18:00

Exp Brain Res. 2022 Aug 4. doi: 10.1007/s00221-022-06430-7. Online ahead of print.


Changes in the brain with age can provide useful information regarding an individual's chronological age. studies have suggested that functional connectomes identified via resting-state functional magnetic resonance imaging (fMRI) could be a powerful feature for predicting an individual's age. We applied connectome-based predictive modeling (CPM) to investigate individual chronological age predictions via resting-state fMRI using open-source datasets. The significant feature for age prediction was confirmed in 168 subjects from the Southwest University Adult Lifespan Dataset. The higher contributing nodes for age production included a positive connection from the left inferior parietal sulcus and a negative connection from the right middle temporal sulcus. On the network scale, the subcortical-cerebellum network was the dominant network for age prediction. The generalizability of CPM, which was constructed using the identified features, was verified by applying this model to independent datasets that were randomly selected from the Autism Brain Imaging Data Exchange I and the Open Access Series of Imaging Studies 3. CPM via resting-state fMRI is a potential robust predictor for determining an individual's chronological age from changes in the brain.

PMID:35922524 | DOI:10.1007/s00221-022-06430-7

Separation of memory span and learning rate: Evidence from behavior and spontaneous brain activity in older adults

Wed, 08/03/2022 - 18:00

Psych J. 2022 Aug 3. doi: 10.1002/pchj.550. Online ahead of print.


It is unclear how the ability to initially acquire information in a first learning trial relates to learning rate in subsequent repeated trials. The separation of memory span and learning rate is an important psychological dilemma that remains unaddressed. Given the potential effects of aging on memory and learning, this study investigated the separation of memory span and learning rate from behavior and spontaneous brain activity in older adults. We enrolled a total of 758 participants, including 707 healthy older adults and 51 mild cognitive impairment (MCI) patients. Sixty-five participants out of 707 completed resting-state functional magnetic resonance imaging (fMRI) scanning. Behaviorally, memory span and learning rate were not correlated with each other in the paired-associative learning test (PALT) but were negatively correlated in the auditory verbal learning test (AVLT). This indicated that the relationship between memory span and learning rate for item memory might be differentially affected by aging. Interaction analysis confirmed that these two capacities were differentially affected by test type (associative memory vs. item memory). Additionally, at three progressive brain activity indexes (ALFF, ReHo, and DC), the right brain regions (right inferior temporal gyrus and right middle frontal gyrus) were more negatively correlated with memory span, whereas, the left precuneus was more positively correlated with learning rate. Regarding pathological aging, none of the correlations between memory span and learning rate were significant in either PALT or AVLT in MCI. This study provides novel evidence for the dissociation of memory span and learning rate at behavioral and brain activity levels, which may have useful applications in detecting cognitive deficits or conducting cognitive interventions.

PMID:35922140 | DOI:10.1002/pchj.550

A sex-dependent computer aided diagnosis system for autism spectrum disorder using connectivity of resting-state fMRI

Wed, 08/03/2022 - 18:00

J Neural Eng. 2022 Aug 3. doi: 10.1088/1741-2552/ac86a4. Online ahead of print.


Autism spectrum disorder (ASD) is a prevalent neurodevelopmental disorder with the main symptoms of social communication disabilities. ASD is more than four times more common among males than females. The diagnosis of ASD is currently a subjective process by experts the same for males and females. Various studies have suggested the use of brain connectivity features for diagnosis of ASD. Also, sex-related biological factors have been shown to play a role in ASD etiology and influence the brain connectivity. Therefore, proposing an accurate computer aided diagnosis system (CADS) for ASD which consider the sex of subjects seems necessary. In this study, we present a sex-dependent connectivity-based CADS for ASD using resting-state functional magnetic resonance imaging (rs-fMRI). The proposed CADS classifies ASD males from normal males, and ASD females from normal females. After data preprocessing, group independent component analysis (GICA) was applied to obtain the resting-state networks (RSNs) followed by applying dual-regression to obtain the time course of each RSN for each subject. Afterwards, functional connectivity (FC) measures of full correlation and partial correlation and effective connectivity (EC) measure of bivariate granger causality were computed between time series of RSNs. To consider the role of sex differences in the classification process, male, female, and mixed groups were taken into account, and feature selection and classification were designed for each sex group separately. At the end, the classification accuracy was computed for each sex group. In the female group, the classification accuracy of 93.3% was obtained using full correlation while in the male group the classification accuracy of 86.7% was achieved using each of full correlation and bivariate granger causality. Also, in the mixed group, the classification accuracy of 83.3% was obtained using full correlation. This supports the importance of considering sex in diagnosing ASD patients from normal controls (NCs).

PMID:35921809 | DOI:10.1088/1741-2552/ac86a4

Amplitude of low-frequency fluctuations in multiple-frequency bands in patients with intracranial tuberculosis: a prospective cross-sectional study

Wed, 08/03/2022 - 18:00

Quant Imaging Med Surg. 2022 Aug;12(8):4120-4134. doi: 10.21037/qims-22-17.


BACKGROUND: Resting-state functional magnetic resonance imaging (rs-fMRI) is widely used to study brain functional alteration, but there have been no reports of research regarding the application of rs-fMRI in intracranial tuberculosis. The purpose of this prospective, cross-sectional study was to investigate spontaneous neural activity at different frequency bands in patients with intracranial tuberculosis using rs-fMRI with amplitude of low-frequency fluctuation (ALFF) and fractional ALFF (fALFF) methods.

METHODS: The rs-fMRI data of 31 patients with intracranial tuberculosis and 30 gender-, age-, and education-matched healthy controls (HCs) were included. The ALFF and fALFF values in the conventional frequency band (0.01-0.08 Hz) and 2 sub-frequency bands (slow-4: 0.027-0.073 Hz; slow-5: 0.01-0.027 Hz) were calculated and compared between the groups. The resultant T-maps were corrected using the Gaussian random field (GRF) theory (voxel P<0.01, cluster P<0.05). Correlations between the ALFF and fALFF values and neurocognitive scores were assessed.

RESULTS: Compared with the HCs, patients with intracranial tuberculosis showed decreased ALFF in the right paracentral lobule (T=-4.69) in the conventional frequency band, in the right supplementary motor area (T=-4.85) in the slow-4 band, and in the left supplementary motor area (T=-3.76) in the slow-5 band. Compared to the slow-5 band, the voxels with decreased ALFF were spatially more extensive in the slow-4 band. Compared with HCs, patients with intracranial tuberculosis showed decreased fALFF in the opercular parts of the right inferior frontal gyrus (T=-4.50) and the left inferior parietal lobe (T=-4.86) and increased fALFF in the left inferior cerebellum (T=5.84) in the conventional frequency band. In the slow-4 band, fALFF decreased in the opercular parts of the right inferior frontal gyrus (T=-5.29) and right precuneus (T=-4.34). In the slow-5 band, fALFF decreased in the left middle occipital gyrus (T=-4.65) and right middle frontal gyrus (T=-5.05).

CONCLUSIONS: Patients with intracranial tuberculosis showed abnormal intrinsic brain activity at different frequency bands, and ALFF abnormalities in different brain regions could be better detected in the slow-4 band. This preliminary study might provide new insights into understanding the pathophysiological mechanism in intracranial tuberculosis.

PMID:35919063 | PMC:PMC9338357 | DOI:10.21037/qims-22-17

Mindfulness meditation increases default mode, salience, and central executive network connectivity

Tue, 08/02/2022 - 18:00

Sci Rep. 2022 Aug 2;12(1):13219. doi: 10.1038/s41598-022-17325-6.


Recent research has begun to identify the neural mechanisms underlying the beneficial impact of mindfulness meditation training (MMT) on health and cognition. However, little is known about the effects of MMT on the global interplay of large-scale networks (LSNs) in the brain. In the present study, healthy, meditation-naïve adults (N = 46) underwent resting state fMRI prior to and upon completing 31 days of MMT or an active control intervention. Independent component analysis, sliding time window, and seed-based correlation analyses were performed to assess training-related changes in functional connectivity (FC) within and between networks with relevance to mindfulness meditation. Across sliding time window analyses and seed-based correlation analyses, we found increased FC between nodes of the default mode network (DMN) and nodes of the salience network (SN) in participants of the MMT. Seed-based correlation analyses revealed further connectivity increases between the SN and key regions of the central executive network (CEN). These results indicate, that, among multiple LSNs, one month of mindfulness meditation effectively increases interconnectivity between networks of the triple network model (DMN, SN, CEN), hereby introducing a potential mechanistic concept underlying the beneficial impact of MMT.Clinical trial registration: This study is listed as a clinical trial on the ISRCTN registry with trial ID ISRCTN95197731 (date of first registration: 15/02/2022).

PMID:35918449 | DOI:10.1038/s41598-022-17325-6

Abnormal percent amplitude of fluctuation and functional connectivity within and between networks in benign epilepsy with centrotemporal spikes

Tue, 08/02/2022 - 18:00

Epilepsy Res. 2022 Jul 20;185:106989. doi: 10.1016/j.eplepsyres.2022.106989. Online ahead of print.


BACKGROUND: Benign epilepsy with centrotemporal spikes (BECTS) is one of the most common childhood epilepsy syndromes. The neural basis of BECTS is still poorly understood. This study aimed to further investigate the possible neural mechanisms of BECTS by comparing percent amplitude of fluctuation (PerAF) of resting-state functional magnetic resonance imaging (RS-fMRI) signal of each brain voxel and connectivity within and between related networks in children with BECTS and healthy controls (HCs).

METHODS: Firstly, we used PerAF method to investigate brain functional alteration and defined the regions of interest (ROIs) where children with BECTS exhibited significant PerAF alterations compared to HCs. We then divided these ROIs into different networks based on previous findings and investigated alterations of functional connectivity within and between networks in children with BECTS. Receiver operating characteristic (ROC) curve was employed to assess the reliable biomarker for distinguishing children with BECTS from HCs based on the intergroup PerAF differences.

RESULTS: Children with BECTS showed decreased PerAF in the left middle frontal cortex (MFC), right precentral gyrus, left precuneus (PCUN), bilateral posterior cingulate cortex (PCC), left angular gyrus, left inferior parietal lobule (IPL), right supplementary motor area (SMA) and left primary somatosensory cortex (S1) compared to HCs. The IPL and PCC exhibited higher classification power by ROC analysis. Moreover, our findings exhibited increased Intra-network connectivity in the default mode network (DMN), and increased inter-network connectivity of the sensorimotor network (SMN) with Broca's area and DMN.

CONCLUSIONS: Our study investigated the abnormal PerAF and functional brain networks in children with BECTS, which might provide new insights into the pathological mechanisms of BECTS.

PMID:35917746 | DOI:10.1016/j.eplepsyres.2022.106989

Functional connectivity in the dorsal network of the cervical spinal cord is correlated with diffusion tensor imaging indices in relapsing-remitting multiple sclerosis

Tue, 08/02/2022 - 18:00

Neuroimage Clin. 2022 Jul 27;35:103127. doi: 10.1016/j.nicl.2022.103127. Online ahead of print.


Focal lesions may affect functional connectivity (FC) of the ventral and dorsal networks in the cervical spinal cord of people with relapsing-remitting multiple sclerosis (RRMS). Resting-state FC can be measured using functional MRI (fMRI) at 3T. This study sought to determine whether alterations in FC may be related to the degree of damage in the normal-appearing tissue. Tissue integrity and FC in the cervical spinal cord were assessed with diffusion tensor imaging (DTI) and resting-state fMRI, respectively, in a group of 26 RRMS participants with high cervical lesion load, low disability, and minimally impaired sensorimotor function, and healthy controls. Lower fractional anisotropy (FA) and higher radial diffusivity (RD) were observed in the normal-appearing white matter in the RRMS group relative to controls. Average FC in ventral and dorsal networks was similar between groups. Significant associations were found between higher FC in the dorsal sensory network and several DTI markers of pathology in the normal-appearing tissue. In the normal-appearing grey matter, dorsal FC was positively correlated with axial diffusivity (AD) (r = 0.46, p = 0.020) and mean diffusivity (MD) (r = 0.43, p = 0.032). In the normal-appearing white matter, dorsal FC was negatively correlated with FA (r = -0.43, p = 0.028) and positively correlated with RD (r = 0.49, p = 0.012), AD (r = 0.42, p = 0.037) and MD (r = 0.53, p = 0.006). These results suggest that increased connectivity, while remaining within the normal range, may represent a compensatory mechanism in response to structural damage in support of preserved sensory function in RRMS.

PMID:35917721 | DOI:10.1016/j.nicl.2022.103127

Imaging the neural underpinnings of freezing of gait in Parkinson's disease

Tue, 08/02/2022 - 18:00

Neuroimage Clin. 2022 Jul 25;35:103123. doi: 10.1016/j.nicl.2022.103123. Online ahead of print.


Freezing of gait (FoG) is a paroxysmal and sporadic gait impairment that severely affects PD patients' quality of life. This review summarizes current neuroimaging investigations that characterize the neural underpinnings of FoG in PD. The review presents and discusses the latest advances across multiple methodological domains that shed light on structural correlates, connectivity changes, and activation patterns associated with the different pathophysiological models of FoG in PD. Resting-state fMRI studies mainly report cortico-striatal decoupling and disruptions in connectivity along the dorsal stream of visuomotor processing, thus supporting the 'interference' and the 'perceptual dysfunction' models of FoG. Task-based MRI studies employing virtual reality and motor imagery paradigms reveal a disruption in functional connectivity between cortical and subcortical regions and an increased recruitment of parieto-occipital regions, thus corroborating the 'interference' and 'perceptual dysfunction' models of FoG. The main findings of fNIRS studies of actual gait primarily reveal increased recruitment of frontal areas during gait, supporting the 'executive dysfunction' model of FoG. Finally, we discuss how identifying the neural substrates of FoG may open new avenues to develop efficient treatment strategies.

PMID:35917720 | DOI:10.1016/j.nicl.2022.103123

Covariance-based vs. Correlation-based Functional Connectivity Dissociates Healthy Aging from Alzheimer Disease

Mon, 08/01/2022 - 18:00

Neuroimage. 2022 Jul 29:119511. doi: 10.1016/j.neuroimage.2022.119511. Online ahead of print.


Prior studies of aging and Alzheimer disease have evaluated resting state functional connectivity (FC) using either seed-based correlation (SBC) or independent component analysis (ICA), with a focus on particular functional systems. SBC and ICA both are insensitive to differences in signal amplitude. At the same time, accumulating evidence indicates that the amplitude of spontaneous BOLD signal fluctuations is physiologically meaningful. We systematically compared covariance-based FC, which is sensitive to amplitude, vs. correlation-based FC, which is not, in affected individuals and controls drawn from two cohorts of participants including autosomal dominant Alzheimer disease (ADAD), late onset Alzheimer disease (LOAD), and age-matched controls. Functional connectivity was computed over 222 regions of interest and group differences were evaluated in terms of components projected onto a space of lower dimension. Our principal observations are: (1) Aging is associated with global loss of resting state fMRI signal amplitude that is approximately uniform across resting state networks. (2) Thus, covariance FC measures decrease with age whereas correlation FC is relatively preserved in healthy aging. (3) In contrast, symptomatic ADAD and LOAD both lead to loss of spontaneous activity amplitude as well as severely degraded correlation structure. These results demonstrate a double dissociation between age vs. Alzheimer disease and the amplitude vs. correlation structure of resting state BOLD signals. Modeling results suggest that the AD-associated loss of correlation structure is attributable to a relative increase in the fraction of locally restricted as opposed to widely shared variance.

PMID:35914670 | DOI:10.1016/j.neuroimage.2022.119511

Altered Brain Function Activity in Patients With Dysphagia After Cerebral Infarction: A Resting-State Functional Magnetic Resonance Imaging Study

Mon, 08/01/2022 - 18:00

Front Neurol. 2022 Jul 14;13:782732. doi: 10.3389/fneur.2022.782732. eCollection 2022.


OBJECTIVE: Dysphagia after cerebral infarction (DYS) has been detected in several brain regions through resting-state functional magnetic resonance imaging (rs-fMRI). In this study, we used two rs-fMRI measures to investigate the changes in brain function activity in DYS and their correlations with dysphagia severity.

METHOD: In this study, a total of 22 patients with DYS were compared with 30 patients without dysphagia (non-DYS) and matched for baseline characteristics. Then, rs-fMRI scans were performed in both groups, and regional homogeneity (ReHo) and fractional amplitude of low-frequency fluctuation (fALFF) values were calculated in both groups. The two-sample t-test was used to compare ReHo and fALFF between the groups. Pearson's correlation analysis was used to determine the correlations between the ReHo and fALFF of the abnormal brain regions and the scores of the Functional Oral Intake Scale (FOIS), the Standardized Bedside Swallowing Assessment (SSA), the Videofluoroscopic Swallowing Study (VFSS), and the Penetration-Aspiration Scale (PAS).

RESULTS: Compared with the non-DYS group, the DYS group showed decreased ReHo values in the left thalamus, the left parietal lobe, and the right temporal lobe and significantly decreased fALFF values in the right middle temporal gyrus and the inferior parietal lobule. In the DYS group, the ReHo of the right temporal lobe was positively correlated with the SSA score and the PAS score (r = 0.704, p < 0.001 and r = 0.707, p < 0.001, respectively) but negatively correlated with the VFSS score (r = -0.741, p < 0.001). The ReHo of the left parietal lobe was positively correlated with SSA and PAS (r = 0.621, p = 0.002 and r = 0.682, p < 0.001, respectively) but negatively correlated with VFSS (r = -0.679, p = 0.001).

CONCLUSION: The changes in the brain function activity of these regions are related to dysphagia severity. The DYS group with high ReHo values in the right temporal and left parietal lobes had severe dysphagia.

PMID:35911901 | PMC:PMC9329512 | DOI:10.3389/fneur.2022.782732

Multi-View Feature Enhancement Based on Self-Attention Mechanism Graph Convolutional Network for Autism Spectrum Disorder Diagnosis

Mon, 08/01/2022 - 18:00

Front Hum Neurosci. 2022 Jul 15;16:918969. doi: 10.3389/fnhum.2022.918969. eCollection 2022.


Functional connectivity (FC) network based on resting-state functional magnetic resonance imaging (rs-fMRI) has become an important tool to explore and understand the brain, which can provide objective basis for the diagnosis of neurodegenerative diseases, such as autism spectrum disorder (ASD). However, most functional connectivity (FC) networks only consider the unilateral features of nodes or edges, and the interaction between them is ignored. In fact, their integration can provide more comprehensive and crucial information in the diagnosis. To address this issue, a new multi-view brain network feature enhancement method based on self-attention mechanism graph convolutional network (SA-GCN) is proposed in this article, which can enhance node features through the connection relationship among different nodes, and then extract deep-seated and more discriminative features. Specifically, we first plug the pooling operation of self-attention mechanism into graph convolutional network (GCN), which can consider the node features and topology of graph network at the same time and then capture more discriminative features. In addition, the sample size is augmented by a "sliding window" strategy, which is beneficial to avoid overfitting and enhance the generalization ability. Furthermore, to fully explore the complex connection relationship among brain regions, we constructed the low-order functional graph network (Lo-FGN) and the high-order functional graph network (Ho-FGN) and enhance the features of the two functional graph networks (FGNs) based on SA-GCN. The experimental results on benchmark datasets show that: (1) SA-GCN can play a role in feature enhancement and can effectively extract more discriminative features, and (2) the integration of Lo-FGN and Ho-FGN can achieve the best ASD classification accuracy (79.9%), which reveals the information complementarity between them.

PMID:35911592 | PMC:PMC9334869 | DOI:10.3389/fnhum.2022.918969

Prediction of Facial Emotion Recognition Ability in Patients With First-Episode Schizophrenia Using Amplitude of Low-Frequency Fluctuation-Based Support Vector Regression Model

Mon, 08/01/2022 - 18:00

Front Psychiatry. 2022 Jul 13;13:905246. doi: 10.3389/fpsyt.2022.905246. eCollection 2022.


OBJECTIVE: There were few studies that had attempted to predict facial emotion recognition (FER) ability at the individual level in schizophrenia patients. In this study, we developed a model for the prediction of FER ability in Chinese Han patients with the first-episode schizophrenia (FSZ).

MATERIALS AND METHODS: A total of 28 patients with FSZ and 33 healthy controls (HCs) were recruited. All subjects underwent resting-state fMRI (rs-fMRI). The amplitude of low-frequency fluctuation (ALFF) method was selected to analyze voxel-level spontaneous neuronal activity. The visual search experiments were selected to evaluate the FER, while the support vector regression (SVR) model was selected to develop a model based on individual rs-fMRI brain scan.

RESULTS: Group difference in FER ability showed statistical significance (P < 0.05). In FSZ patients, increased mALFF value were observed in the limbic lobe and frontal lobe, while decreased mALFF value were observed in the frontal lobe, parietal lobe, and occipital lobe (P < 0.05, AlphaSim correction). SVR analysis showed that abnormal spontaneous activity in multiple brain regions, especially in the right posterior cingulate, right precuneus, and left calcarine could effectively predict fearful FER accuracy (r = 0.64, P = 0.011) in patients.

CONCLUSION: Our study provides an evidence that abnormal spontaneous activity in specific brain regions may serve as a predictive biomarker for fearful FER ability in schizophrenia.

PMID:35911229 | PMC:PMC9326045 | DOI:10.3389/fpsyt.2022.905246

Abnormal spontaneous neural activity in hippocampal-cortical system of patients with obsessive-compulsive disorder and its potential for diagnosis and prediction of early treatment response

Mon, 08/01/2022 - 18:00

Front Cell Neurosci. 2022 Jul 15;16:906534. doi: 10.3389/fncel.2022.906534. eCollection 2022.


Early brain functional changes induced by pharmacotherapy in patients with obsessive-compulsive disorder (OCD) in relation to drugs per se or because of the impact of such drugs on the improvement of OCD remain unclear. Moreover, no neuroimaging biomarkers are available for diagnosis of OCD and prediction of early treatment response. We performed a longitudinal study involving 34 patients with OCD and 36 healthy controls (HCs). Patients with OCD received 5-week treatment with paroxetine (40 mg/d). Resting-state functional magnetic resonance imaging (fMRI), regional homogeneity (ReHo), support vector machine (SVM), and support vector regression (SVR) were applied to acquire and analyze the imaging data. Compared with HCs, patients with OCD had higher ReHo values in the right superior temporal gyrus and bilateral hippocampus/parahippocampus/fusiform gyrus/cerebellum at baseline. ReHo values in the left hippocampus and parahippocampus decreased significantly after treatment. The reduction rate (RR) of ReHo values was positively correlated with the RRs of the scores of Yale-Brown Obsessive-Compulsive Scale (Y-BOCS) and obsession. Abnormal ReHo values at baseline could serve as potential neuroimaging biomarkers for OCD diagnosis and prediction of early therapeutic response. This study highlighted the important role of the hippocampal-cortical system in the neuropsychological mechanism underlying OCD, pharmacological mechanism underlying OCD treatment, and the possibility of building models for diagnosis and prediction of early treatment response based on spontaneous activity in the hippocampal-cortical system.

PMID:35910254 | PMC:PMC9334680 | DOI:10.3389/fncel.2022.906534

The posterior middle temporal gyrus serves as a hub in syntactic comprehension: A model on the syntactic neural network

Sun, 07/31/2022 - 18:00

Brain Lang. 2022 Jul 28;232:105162. doi: 10.1016/j.bandl.2022.105162. Online ahead of print.


Neuroimaging studies have revealed a distributed neural network involving multiple fronto-temporal regions that are active during syntactic processing. Here, we investigated how these regions work collaboratively to support syntactic comprehension by examining the behavioral relevance of the global functional integration of the syntax network (SN). We found that individuals with a stronger resting-state within-network integration in the left posterior middle temporal gyrus (lpMTG) were better at syntactic comprehension. Furthermore, the pair-wise functional connectivity between the lpMTG and the Broca's area, the middle frontal gyrus, and the angular and supramarginal gyri was positively correlated with participants' syntactic processing ability. In short, our study reveals the behavioral significance of intrinsic functional integration of the SN in syntactic comprehension, and provides empirical evidence for the hub-like role of the lpMTG. We proposed a neural model for syntactic comprehension highlighting the hub of the SN and its interactions with other regions in the network.

PMID:35908340 | DOI:10.1016/j.bandl.2022.105162

Predicting escitalopram treatment response from pre-treatment and early response resting state fMRI in a multi-site sample: A CAN-BIND-1 report

Sun, 07/31/2022 - 18:00

Neuroimage Clin. 2022 Jul 16;35:103120. doi: 10.1016/j.nicl.2022.103120. Online ahead of print.


Many previous intervention studies have used functional magnetic resonance imaging (fMRI) data to predict the antidepressant response of patients with major depressive disorder (MDD); however, practical constraints have limited many of those attempts to small, single centre studies which may not adequately reflect how these models will generalize when used in clinical practice. Not only does the act of collecting data at multiple sites generally increase sample sizes (a critical point in machine learning development) it also generates a more heterogeneous dataset due to systematic differences in scanners at different sites, and geographical differences in patient populations. As part of the Canadian Biomarker Integration Network in Depression (CAN-BIND-1) study, 144 MDD patients from six sites underwent resting state fMRI prior to starting escitalopram treatment, and again two weeks after the start. Here, we consider ways to use machine learning techniques to produce models that can predict response (measured at eight weeks after initiation), based on various parcellations, functional connectivity (FC) metrics, dimensionality reduction algorithms, and base learners, and also whether to use scans from one or both time points. Models that use only baseline (pre-treatment) or only week 2 (early-response) whole-brain FC features consistently failed to perform significantly better than default models. Utilizing the change in FC between these two time points, however, yielded significant results, with the best performing analytical pipeline achieving 69.6% (SD 10.8) accuracy. These results appear contrary to findings from many smaller single-site studies, which report substantially higher predictive accuracies from models trained on only baseline resting state FC features, suggesting these models may not generalize well beyond data used for development. Further, these results indicate the potential value of collecting data both before and shortly after treatment initiation.

PMID:35908308 | DOI:10.1016/j.nicl.2022.103120