New resting-state fMRI related studies at PubMed

The effect of creative expression program in neurocognitive networks performance measured by task and resting-state functional MRI

Mon, 05/16/2022 - 10:00

Int Psychogeriatr. 2022 May 16:1-10. doi: 10.1017/S1041610222000382. Online ahead of print.

ABSTRACT

BACKGROUND: The current study examined the effects of a 16-week creative expression program on brain activity during a story creating task and resting-state functional network connectivity in mild cognitive impairment (MCI) adults.

METHOD: Thirty-six MCI adults were allocated to either the creative expression program (CrExp, n = 18) or control group (CG,n = 18). Before and after intervention, all participants were scanned with functional magnetic resonance imaging (fMRI) during story creating task performance and a resting state. The two-group comparison was calculated between the blood oxygenation level-dependent (BOLD) signal changes for each cluster to investigate the differences in fMRI activation and functional connectivity (FC) between two groups.

RESULTS: Task activation analyses showed an increase in the right anterior cingulate gyrus (ACG), right medial frontal gyrus (MFG), right lentiform nucleus (LN), left hippocampus (HIP), left middle occipital gyrus (MOG), and left cerebellum posterior lobe (CPL) (p < 0.05). Story creating performance improvements were associated with greater activation in the left HIP region. Resting-state functional connectivity (FC) between left HIP and certain other brain areas shown a significant interaction of creative expression group versus control group. Moreover, connectivity between the right angular gyrus (ANG), right inferior temporal gyrus (ITG), right superior occipital gyrus (SOG), left ANG, and left MFG were related to improved cognitive performance (p < 0.05).

CONCLUSION: These data extend current knowledge by indicating that the creative expression program can improve cognitive activation in MCI, and these enhancements may be related to the neurocognitive network plasticity changes induced by creative expression training.

PMID:35575053 | DOI:10.1017/S1041610222000382

Regional Neural Activity Abnormalities and Whole-Brain Functional Connectivity Reorganization in Bulimia Nervosa: Evidence From Resting-State fMRI

Mon, 05/16/2022 - 10:00

Front Neurosci. 2022 Apr 26;16:858717. doi: 10.3389/fnins.2022.858717. eCollection 2022.

ABSTRACT

The management of eating behavior in bulimia nervosa (BN) patients is a complex process, and BN involves activity in multiple brain regions that integrate internal and external functional information. This functional information integration occurs in brain regions involved in reward, cognition, attention, memory, emotion, smell, taste, vision and so on. Although it has been reported that resting-state brain activity in BN patients is different from that of healthy controls, the neural mechanisms remain unclear and need to be further explored. The fractional amplitude of low-frequency fluctuation (fALFF) analyses are an important data-driven method that can measure the relative contribution of low-frequency fluctuations within a specific frequency band to the whole detectable frequency range. The fALFF is well suited to reveal the strength of interregional cooperation at the single-voxel level to investigate local neuronal activity power. FC is a brain network analysis method based on the level of correlated dynamics between time series, which establishes the connection between two spatial regions of interest (ROIs) with the assistance of linear temporal correlation. Based on the psychological characteristics of patients with BN and the abnormal brain functional activities revealed by previous neuroimaging studies, in this study, we investigated alterations in regional neural activity by applying fALFF analysis and whole-brain functional connectivity (FC) in patients with BN in the resting state and to explore correlations between brain activities and eating behavior. We found that the left insula and bilateral inferior parietal lobule (IPL), as key nodes in the reorganized resting-state neural network, had altered FC with other brain regions associated with reward, emotion, cognition, memory, smell/taste, and vision-related functional processing, which may have influenced restrained eating behavior. These results could provide a further theoretical basis and potential effective targets for neuropsychological treatment in patients with BN.

PMID:35573287 | PMC:PMC9100949 | DOI:10.3389/fnins.2022.858717

Toward Precise Localization of Abnormal Brain Activity: 1D CNN on Single Voxel fMRI Time-Series

Mon, 05/16/2022 - 10:00

Front Comput Neurosci. 2022 Apr 27;16:822237. doi: 10.3389/fncom.2022.822237. eCollection 2022.

ABSTRACT

Functional magnetic resonance imaging (fMRI) is one of the best techniques for precise localization of abnormal brain activity non-invasively. Machine-learning approaches have been widely used in neuroimaging studies; however, few studies have investigated the single-voxel modeling of fMRI data under cognitive tasks. We proposed a hybrid one-dimensional (1D) convolutional neural network (1D-CNN) based on the temporal dynamics of single-voxel fMRI time-series and successfully differentiated two continuous task states, namely, self-initiated (SI) and visually guided (VG) motor tasks. First, 25 activation peaks were identified from the contrast maps of SI and VG tasks in a blocked design. Then, the fMRI time-series of each peak voxel was transformed into a temporal-frequency domain by using continuous wavelet transform across a broader frequency range (0.003-0.313 Hz, with a step of 0.01 Hz). The transformed time-series was inputted into a 1D-CNN model for the binary classification of SI and VG continuous tasks. Compared with the univariate analysis, e.g., amplitude of low-frequency fluctuation (ALFF) at each frequency band, including, wavelet-ALFF, the 1D-CNN model highly outperformed wavelet-ALFF, with more efficient decoding models [46% of 800 models showing area under the curve (AUC) > 0.61] and higher decoding accuracies (94% of the efficient models), especially on the high-frequency bands (>0.1 Hz). Moreover, our results also demonstrated the advantages of wavelet decompositions over the original fMRI series by showing higher decoding performance on all peak voxels. Overall, this study suggests a great potential of single-voxel analysis using 1D-CNN and wavelet transformation of fMRI series with continuous, naturalistic, steady-state task design or resting-state design. It opens new avenues to precise localization of abnormal brain activity and fMRI-guided precision brain stimulation therapy.

PMID:35573265 | PMC:PMC9094401 | DOI:10.3389/fncom.2022.822237

Effects of the Intermittent Theta Burst Stimulation of the Cerebellar Vermis on Balance Recovery After Stroke: A Study Protocol for a Randomized Controlled Trial

Mon, 05/16/2022 - 10:00

Front Aging Neurosci. 2022 Apr 29;14:881311. doi: 10.3389/fnagi.2022.881311. eCollection 2022.

ABSTRACT

BACKGROUND: The recovery of balance function is a critical segment in the rehabilitation treatment of stroke. The cerebellum is considered as the key structure involved in balance and motor control. The cerebellar vermis plays an important role in integrating vision, proprioception, and sensory skin input and may be a candidate stimulation target for regulating the motor network related with balance. However, evidence that the intermittent theta burst stimulation (iTBS) of cerebellar vermis can promote the recovery of balance function after stroke remains insufficient. Therefore, this study aims to explore the efficacy of the cerebellar vermis iTBS for the treatment of balance function in patients with stroke.

METHODS AND ANALYSIS: Forty patients with stroke will be recruited in this prospective, randomized, sham-controlled trial. Participants will be randomized in a 1:1 ratio to receive either 15 sessions of cerebellar vermis iTBS (600 pulses) or sham stimulation. Additionally, a routine rehabilitation therapy follows the intervention. The primary outcome is the Berg Balance Scale, and the secondary outcomes are the Fugl-Meyer assessment of the lower extremity and modified Barthel index. The above outcomes will be assessed before intervention and at the end of each week. Pre- and post-iTBS resting-state functional magnetic resonance imaging (rs-fMRI) will be acquired, and the regional homogeneity, fractional amplitude of low-frequency fluctuation and functional connectivity will be calculated and analyzed.

DISCUSSION: This protocol holds promise as a potential method to improve balance function in patients with stroke. If the outcomes of patients improve after the intervention, the study will provide new insights into improving balance function.

ETHICS AND DISSEMINATION: This study has been approved by the Medical Research Ethics Committee of Wuxi Mental Health Center (Wuxi Tongren Rehabilitation Hospital). Results will be disseminated through (open-access) peer-reviewed publications, networks of scientists, professionals, and the public and presented at conferences.

CLINICAL TRIAL REGISTRATION NUMBER: www.chictr.org.cn, identifier ChiCTR2100052590.

PMID:35572148 | PMC:PMC9099377 | DOI:10.3389/fnagi.2022.881311

The Effect of Repetitive Transcranial Magnetic Stimulation of Cerebellar Swallowing Cortex on Brain Neural Activities: A Resting-State fMRI Study

Mon, 05/16/2022 - 10:00

Front Hum Neurosci. 2022 Apr 27;16:802996. doi: 10.3389/fnhum.2022.802996. eCollection 2022.

ABSTRACT

OBJECTIVE: The effects and possible mechanisms of cerebellar high-frequency repetitive transcranial magnetic stimulation (rTMS) on swallowing-related neural networks were studied using resting-state functional magnetic resonance imaging (rs-fMRI).

METHOD: A total of 23 healthy volunteers were recruited, and 19 healthy volunteers were finally included for the statistical analysis. Before stimulation, the cerebellar hemisphere dominant for swallowing was determined by the single-pulse TMS. The cerebellar representation of the suprahyoid muscles of this hemisphere was selected as the target for stimulation with 10 Hz rTMS, 100% resting motor threshold (rMT), and 250 pulses, with every 1 s of stimulation followed by an interval of 9 s. The motor evoked potential (MEP) amplitude of the suprahyoid muscles in the bilateral cerebral cortex was measured before and after stimulation to evaluate the cortical excitability. Forty-eight hours after elution, rTMS was reapplied on the dominant cerebellar representation of the suprahyoid muscles with the same stimulation parameters. Rs-fMRI was performed before and after stimulation to observe the changes in amplitude of low-frequency fluctuation (ALFF) and regional homology (ReHo) at 0.01-0.08 Hz, 0.01-0.027 Hz, and 0.027-0.073 Hz.

RESULTS: After cerebellar high-frequency rTMS, MEP recorded from swallowing-related bilateral cerebral cortex was increased. The results of rs-fMRI showed that at 0.01-0.08 Hz, ALFF was increased at the pons, right cerebellum, and medulla and decreased at the left temporal lobe, and ReHo was decreased at the left insular lobe, right temporal lobe, and corpus callosum. At 0.01-0.027 Hz, ALFF was decreased at the left temporal lobe, and ReHo was decreased at the right temporal lobe, left putamen, and left supplementary motor area.

CONCLUSION: Repetitive transcranial magnetic stimulation of the swallowing cortex in the dominant cerebellar hemisphere increased the bilateral cerebral swallowing cortex excitability and enhanced pontine, bulbar, and cerebellar spontaneous neural activity, suggesting that unilateral high-frequency stimulation of the cerebellum can excite both brainstem and cortical swallowing centers. These findings all provide favorable support for the application of cerebellar rTMS in the clinical practice.

PMID:35572005 | PMC:PMC9094708 | DOI:10.3389/fnhum.2022.802996

Altered Resting Brain Functions in Patients With Irritable Bowel Syndrome: A Systematic Review

Mon, 05/16/2022 - 10:00

Front Hum Neurosci. 2022 Apr 29;16:851586. doi: 10.3389/fnhum.2022.851586. eCollection 2022.

ABSTRACT

BACKGROUND: The neural activity of irritable bowel syndrome (IBS) patients in the resting state without any intervention has not been systematically studied. The purpose of this study was to compare the resting-state brain functions of IBS patients with healthy controls (HCs).

METHODS: The published neuroimage studies were obtained from electronic databases including PubMed, EMBASE, PsycINFO, Web of Science Core, CNKI Database, Wanfang Database, VIP Database, and CBMdisc. Search dates were from inception to March 14th, 2022. The studies were identified by the preidentified inclusion and exclusion criteria. Two independent reviewers compiled the studies and evaluated them for quality and bias.

RESULTS: Altogether 22 fMRI studies were included in this review. The risk of bias of the included studies was generally low. The findings indicated that in IBS patients, increased or decreased brain areas were mostly associated with visceral sensations, emotional processing, and pain processing. According to brain network research, IBS may exhibit anomalies in the DMN, CEN, and emotional arousal networks. The fluctuations in emotion (anxiety, sadness) and symptoms in IBS patients were associated with alterations in the relevant brain regions.

CONCLUSION: This study draws a preliminary conclusion that there are insufficient data to accurately distinguish the different neurological features of IBS in the resting state. Additional high-quality research undertaken by diverse geographic regions and teams is required to reach reliable results regarding resting-state changed brain regions in IBS.

PMID:35572000 | PMC:PMC9105452 | DOI:10.3389/fnhum.2022.851586

Adaptive Multimodal Neuroimage Integration for Major Depression Disorder Detection

Mon, 05/16/2022 - 10:00

Front Neuroinform. 2022 Apr 29;16:856175. doi: 10.3389/fninf.2022.856175. eCollection 2022.

ABSTRACT

Major depressive disorder (MDD) is one of the most common mental health disorders that can affect sleep, mood, appetite, and behavior of people. Multimodal neuroimaging data, such as functional and structural magnetic resonance imaging (MRI) scans, have been widely used in computer-aided detection of MDD. However, previous studies usually treat these two modalities separately, without considering their potentially complementary information. Even though a few studies propose integrating these two modalities, they usually suffer from significant inter-modality data heterogeneity. In this paper, we propose an adaptive multimodal neuroimage integration (AMNI) framework for automated MDD detection based on functional and structural MRIs. The AMNI framework consists of four major components: (1) a graph convolutional network to learn feature representations of functional connectivity networks derived from functional MRIs, (2) a convolutional neural network to learn features of T1-weighted structural MRIs, (3) a feature adaptation module to alleviate inter-modality difference, and (4) a feature fusion module to integrate feature representations extracted from two modalities for classification. To the best of our knowledge, this is among the first attempts to adaptively integrate functional and structural MRIs for neuroimaging-based MDD analysis by explicitly alleviating inter-modality heterogeneity. Extensive evaluations are performed on 533 subjects with resting-state functional MRI and T1-weighted MRI, with results suggesting the efficacy of the proposed method.

PMID:35571867 | PMC:PMC9100686 | DOI:10.3389/fninf.2022.856175

Disruptions in Structural and Functional Connectivity Relate to Poststroke Fatigue

Mon, 05/16/2022 - 10:00

Brain Connect. 2022 May 16. doi: 10.1089/brain.2022.0021. Online ahead of print.

ABSTRACT

INTRODUCTION: Poststroke fatigue (PSF) is a disabling condition with unclear etiology. The brain lesion is thought to be an important causal factor in PSF, though focal lesion characteristics such as size and location have not proven to be predictive. Given that the stroke lesion results not only in focal tissue death, but also widespread changes in brain networks that are structurally and functionally connected to damaged tissue, we hypothesized that PSF relates to disruptions in structural and functional connectivity.

METHODS: Twelve patients who incurred an ischemic stroke in the middle cerebral artery (MCA) territory 1-3 years prior, and currently experiencing a range of fatigue severity, were enrolled. The patients underwent structural and resting-state functional magnetic resonance imaging (MRI). The structural MRI data were used to measure structural disconnection of gray matter resulting from lesion to white matter pathways. The functional MRI data were used to measure network functional connectivity.

RESULTS: The patients showed structural disconnection in varying cortical and subcortical regions. Fatigue severity correlated significantly with structural disconnection of several frontal cortex regions in the ipsilesional and contralesional hemispheres. Fatigue-related structural disconnection was most severe in the ipsilesional rostral middle frontal cortex. Greater structural disconnection of a subset of fatigue-related frontal cortex regions, including the ipsilesional rostral middle frontal cortex, trended toward correlating significantly with greater loss in functional connectivity. Among identified fatigue-related frontal cortex regions, only the ipsilesional rostral middle frontal cortex showed loss in functional connectivity correlating significantly with fatigue severity.

CONCLUSION: Our results provide evidence that loss in structural and functional connectivity of bihemispheric frontal cortex regions play a role in PSF after MCA stroke, with connectivity disruptions of the ipsilesional rostral middle frontal cortex having a central role.

PMID:35570655 | DOI:10.1089/brain.2022.0021

Classification of Functional Movement Disorders with Resting State fMRI

Mon, 05/16/2022 - 10:00

Brain Connect. 2022 May 16. doi: 10.1089/brain.2022.0001. Online ahead of print.

ABSTRACT

INTRODUCTION: Functional movement disorder (FMD) is a type of functional neurological disorder (FND) characterized by abnormal movements that patients do not perceive as self-generated. Prior imaging studies show a complex pattern of altered activity, linking regions of the brain involved in emotional responses, motor control, and agency. This study aimed to better characterize these relationships by building a classifier via support vector machine (SVM) to accurately distinguish between 61 FMD patients and 59 healthy controls using features derived from resting state functional MRI (rs-fMRI).

METHODS: First, we selected 66 seed regions based on prior related studies, then calculated the full correlation matrix between them, before performing recursive feature elimination to winnow the feature set to the most predictive features and building the classifier.

RESULTS: We identified 29 features of interest that were highly predictive of FMD condition, classifying patients from controls with 80% accuracy. Several key features included regions in the right sensorimotor cortex, the left dorsolateral prefrontal cortex (dlPFC), the left cerebellum and the left posterior insula.

CONCLUSION: The features selected by the model highlight the importance of the interconnected relationship between areas associated with emotion, reward and sensorimotor integration, potentially mediating communication between regions associated with motor function, attention, and executive function. Exploratory machine learning was able to identify this distinctive, abnormal pattern, suggesting that alterations in functional linkages between these regions may be a consistent feature of the condition in many FMD patients.

PMID:35570651 | DOI:10.1089/brain.2022.0001

Gender-related differences in involvement of addiction brain networks in internet gaming disorder: Relationships with craving and emotional regulation

Sun, 05/15/2022 - 10:00

Prog Neuropsychopharmacol Biol Psychiatry. 2022 May 12:110574. doi: 10.1016/j.pnpbp.2022.110574. Online ahead of print.

ABSTRACT

BACKGROUND: Abnormal interactions among addiction brain networks associated with intoxication, negative affect, and anticipation may have relevance for internet gaming disorder (IGD). Despite prior studies having identified gender-related differences in the neural correlates of IGD, gender-related differences in the involvement of brain networks remain unclear.

METHODS: One-hundred-and-nine individuals with IGD (54 males) and 111 with recreational game use (RGU; 58 males) provided resting-state fMRI data. We examined gender-related differences in involvement of addiction brain networks in IGD versus RGU subjects. We further compared the strength between and within addiction brain networks and explored possible relationships between the strength of functional connectivities within and between addiction brain networks and several relevant behavioral measures.

RESULTS: The addiction brain networks showed high correct classification rates in distinguishing IGD and RGU subjects in men and women. Male subjects with versus without IGD showed stronger functional connectivities between and within addiction brain networks. Moreover, the strength of the connectivity within the anticipation network in male IGD subjects was positively related to subjective craving. However, female subjects with versus without IGD showed decreased functional connections between and within addiction brain networks. The strength of connectivity between the anticipation and negative-affect brain networks in female IGD subjects was negatively related to maladaptive cognitive emotion-regulation strategies.

CONCLUSIONS: Addiction brain networks have potential for distinguishing IGD and RGU individuals. Importantly, this study identified novel gender-related differences in brain-behavior relationships in IGD. These results help advance current neuroscientific theories of IGD and may inform gender-informed treatment strategies.

PMID:35569619 | DOI:10.1016/j.pnpbp.2022.110574

Neuroplastic changes in anterior cingulate cortex gray matter volume and functional connectivity following attention bias modification in high trait anxious individuals

Sun, 05/15/2022 - 10:00

Biol Psychol. 2022 May 12:108353. doi: 10.1016/j.biopsycho.2022.108353. Online ahead of print.

ABSTRACT

Attention bias modification (ABM) was developed to alleviate anxious symptoms by way of a reduction in anxiety-linked attentional bias to threat. Central to the rational of ABM is a learning-related reconfiguration of attentional biases. Yet, the neuroplastic changes in brain structure that underlie this learning are unresolved. The amygdala, anterior cingulate cortex, and lateral prefrontal cortex are part of a system linked to attentional bias to threat and its modification with ABM. We assessed the extent to which ABM modulates gray matter volume and resting-state functional connectivity. Sixty-one individuals selected for attentional bias to threat and heightened trait anxiety completed a 6-week multi-session ABM protocol with 7200 total training trials. Participants were assigned to either an ABM (n = 30) or a control (n = 31) condition. We found that participants' levels of attentional bias and anxiety did not differ following ABM and control training interventions. However, the ABM group displayed greater levels of anterior cingulate cortex gray matter volume as well as greater superior frontal gyrus resting-state functional connectivity with the anterior cingulate cortex and insula. Changes in anterior cingulate cortex gray matter volume were linked to reduced anxious symptoms in the ABM, but not control, group. These findings suggest that ABM distinctively impacts structural and functional neural mechanisms associated with emotion reactivity and cognitive control processes.

PMID:35569575 | DOI:10.1016/j.biopsycho.2022.108353

Markers of emotion regulation processes: a neuroimaging and behavioral study of reappraising abilities

Sun, 05/15/2022 - 10:00

Biol Psychol. 2022 May 12:108349. doi: 10.1016/j.biopsycho.2022.108349. Online ahead of print.

ABSTRACT

Emotion regulation (ER) is a core element for individual well-being, and dysregulated emotional states are prominent in several mental disorders. Moreover, dispositional use of adaptive ER strategies, such as cognitive reappraisal, is usually associated to better psychological outcomes and less emotional problems. Thus, identifying markers of emotion dysregulation could serve as a key point for developing treatments against risks of psychopathological outcomes. Neuroimaging techniques could represent a useful tool within these aims, focusing on neurobiological markers of psychopathological illness. Given the well known gender differences in using ER strategies, we examined behavioral and neuroimaging patterns associated with dispositional use of reappraisal among a non-clinical female sample. We found that the individual predisposition to use cognitive reappraisal as an emotion regulation strategy was associated with decreased levels of dysregulation. From a neurobiological perspective, difficulties in using reappraisal were associated with decreased resting-state functional connectivity (rs-FC) between the Middle Temporal Gyrus and occipito-parietal regions. Moreover, rs-FC between prefrontal and occipito-parietal brain regions was negatively associated with emotion dysregulation levels. Microstructural anomalies across white matter tracts connecting temporal, parietal, and occipital brain regions were associated to difficulties in using reappraisal. Our findings suggest that specific behavioral and neurobiological substrates are linked to reappraising abilities. Furthermore, the ability to implement adaptive ER strategies could serve as protective factor against developing emotion dysregulation.

PMID:35569572 | DOI:10.1016/j.biopsycho.2022.108349

Graph Convolutional Networks Reveal Network-Level Functional Dysconnectivity in Schizophrenia

Sun, 05/15/2022 - 10:00

Schizophr Bull. 2022 May 15:sbac047. doi: 10.1093/schbul/sbac047. Online ahead of print.

ABSTRACT

BACKGROUND AND HYPOTHESIS: Schizophrenia is increasingly understood as a disorder of brain dysconnectivity. Recently, graph-based approaches such as graph convolutional network (GCN) have been leveraged to explore complex pairwise similarities in imaging features among brain regions, which can reveal abstract and complex relationships within brain networks.

STUDY DESIGN: We used GCN to investigate topological abnormalities of functional brain networks in schizophrenia. Resting-state functional magnetic resonance imaging data were acquired from 505 individuals with schizophrenia and 907 controls across 6 sites. Whole-brain functional connectivity matrix was extracted for each individual. We examined the performance of GCN relative to support vector machine (SVM), extracted the most salient regions contributing to both classification models, investigated the topological profiles of identified salient regions, and explored correlation between nodal topological properties of each salient region and severity of symptom.

STUDY RESULTS: GCN enabled nominally higher classification accuracy (85.8%) compared with SVM (80.9%). Based on the saliency map, the most discriminative brain regions were located in a distributed network including striatal areas (ie, putamen, pallidum, and caudate) and the amygdala. Significant differences in the nodal efficiency of bilateral putamen and pallidum between patients and controls and its correlations with negative symptoms were detected in post hoc analysis.

CONCLUSIONS: The present study demonstrates that GCN allows classification of schizophrenia at the individual level with high accuracy, indicating a promising direction for detection of individual patients with schizophrenia. Functional topological deficits of striatal areas may represent a focal neural deficit of negative symptomatology in schizophrenia.

PMID:35569019 | DOI:10.1093/schbul/sbac047

The functional and structural neural correlates of dynamic balance impairment and recovery in persons with acquired brain injury

Sat, 05/14/2022 - 10:00

Sci Rep. 2022 May 14;12(1):7990. doi: 10.1038/s41598-022-12123-6.

ABSTRACT

Dynamic balance control is associated with the function of multiple brain networks and is impaired following Acquired Brain Injury (ABI). This study aims to characterize the functional and structural correlates of ABI-induced dynamic balance impairments and recovery following a rehabilitation treatment. Thirty-one chronic participants with ABI participated in a novel rehabilitation treatment composed of 22 sessions of a perturbation-based rehabilitation training. Dynamic balance was assessed using the Community Balance and Mobility scale (CB&M) and the 10-Meter Walking Test (10MWT). Brain function was estimated using resting-state fMRI imaging that was analysed using independent component analysis (ICA), and regions-of-interest analyses. Brain morphology was also assessed using structural MRI. ICA revealed a reduction in component-related activation within the sensorimotor and cerebellar networks post-intervention. Improvement in CB&M scale was associated with a reduction in FC within the cerebellar network and with baseline FC within the cerebellar-putamen and cerebellar-thalamic networks. Improvement in 10MWT was associated with baseline FC within the cerebellar-putamen and cerebellar-cortical networks. Brain volume analysis did not reveal structural correlates of dynamic balance, but dynamic balance was correlated with time since injury. Our results show that dynamic balance recovery is associated with FC reduction within and between the cerebellar and sensorimotor networks. The lack of global structural correlates of dynamic balance may point to the involvement of specific networks in balance control.

PMID:35568728 | DOI:10.1038/s41598-022-12123-6

Language network self-inhibition and semantic similarity in first-episode schizophrenia: A computational-linguistic and effective connectivity approach

Sat, 05/14/2022 - 10:00

Schizophr Res. 2022 May 11:S0920-9964(22)00160-8. doi: 10.1016/j.schres.2022.04.007. Online ahead of print.

ABSTRACT

INTRODUCTION: A central feature of schizophrenia is the disorganization and impoverishment of language. Recently, we observed higher semantic similarity in first-episode-schizophrenia (FES) patients. In this study, we investigate if this aberrant similarity relates to the 'causal' connectivity between two key nodes of the word production system: inferior frontal gyrus (IFG) and the semantic-hub at the ventral anterior temporal lobe (vATL).

METHODS: Resting-state fMRI scans were collected from 60 participants (30 untreated FES and 30 healthy controls). The semantic distance was measured with the CoVec semantic tool based on GloVe. A spectral dynamic causal model with Parametrical Empirical Bayes was constructed modelling the intrinsic self-inhibitory and extrinsic-excitatory connections within the brain regions. We estimated the parameters of a fully connected model with the semantic distance as a covariate.

RESULTS: FES patients chose words with higher semantic similarity when describing the pictures compared to the HC group. Among patients, an increased semantic similarity was related with an increase in intrinsic connections within both the vATL and IFG, suggesting that reduced 'synaptic gain' in these regions likely contribute to aberrant sampling of the semantic space during discourse in schizophrenia.

CONCLUSIONS: Lexical impoverishment relates to increased self-inhibition in both the IFG and vATL. The associated reduction in synaptic gain may relate to reduced precision of locally generated neural activity, forcing the choice of words that are already 'activated' in a lexical network. One approach to improve word sampling may be via promoting synaptic gain via supra-physiological stimulation within the Broca's-vATL network; this proposal needs verification.

PMID:35568676 | DOI:10.1016/j.schres.2022.04.007

Comparing the Effectiveness of Brain Structural Imaging, Resting-state fMRI, and Naturalistic fMRI in Recognizing Social Anxiety Disorder in Children and Adolescents

Sat, 05/14/2022 - 10:00

Psychiatry Res Neuroimaging. 2022 Apr 26;323:111485. doi: 10.1016/j.pscychresns.2022.111485. Online ahead of print.

ABSTRACT

Social anxiety disorder (SAD) is a common anxiety disorder in childhood and adolescence. Studies on SAD in adults have reported both structural and functional aberrancies of the brain at the group level. However, evidence has shown differences in anxiety-related brain abnormalities between adolescents and adults. Since children and adolescents can afford limited scan time, optimizing the scan tasks is essential for SAD research in children and adolescents. Thus, we need to address whether brain structure, resting-state fMRI, and naturalistic imaging enable individualized identification of SAD in children and adolescents, which measurement is more effective, and whether pooling multi-modal features can improve the identification of SAD. We comprehensively addressed these questions by building machine learning models based on parcel-wise brain features. We found that naturalistic fMRI yielded higher classification accuracy (69.17%) than the other modalities and the classification performance showed dependence on the contents of the movie. The classification models also identified contributing brain regions, some of which exhibited correlations with the symptoms scores of SAD. However, pooling brain features from the three modalities did not help enhance the classification accuracy. These results support the application of carefully designed naturalistic imaging in recognizing children and adolescents at risk of SAD.

PMID:35567906 | DOI:10.1016/j.pscychresns.2022.111485

Extracting electrophysiological correlates of functional magnetic resonance imaging data using the canonical polyadic decomposition

Sat, 05/14/2022 - 10:00

Hum Brain Mapp. 2022 May 14. doi: 10.1002/hbm.25902. Online ahead of print.

ABSTRACT

The relation between electrophysiology and BOLD-fMRI requires further elucidation. One approach for studying this relation is to find time-frequency features from electrophysiology that explain the variance of BOLD time-series. Convolution of these features with a canonical hemodynamic response function (HRF) is often required to model neurovascular coupling mechanisms and thus account for time shifts between electrophysiological and BOLD-fMRI data. We propose a framework for extracting the spatial distribution of these time-frequency features while also estimating more flexible, region-specific HRFs. The core component of this method is the decomposition of a tensor containing impulse response functions using the Canonical Polyadic Decomposition. The outputs of this decomposition provide insight into the relation between electrophysiology and BOLD-fMRI and can be used to construct estimates of BOLD time-series. We demonstrated the performance of this method on simulated data while also examining the effects of simulated measurement noise and physiological confounds. Afterwards, we validated our method on publicly available task-based and resting-state EEG-fMRI data. We adjusted our method to accommodate the multisubject nature of these datasets, enabling the investigation of inter-subject variability with regards to EEG-to-BOLD neurovascular coupling mechanisms. We thus also demonstrate how EEG features for modelling the BOLD signal differ across subjects.

PMID:35567768 | DOI:10.1002/hbm.25902

Resting State Functional Connectivity between Dorsal Attentional Network and Right Inferior Frontal Gyrus in Concussed and Control Adolescents

Sat, 05/14/2022 - 10:00

J Clin Med. 2022 Apr 20;11(9):2293. doi: 10.3390/jcm11092293.

ABSTRACT

Concussion among adolescents continues to be a public health concern. Yet, the differences in brain function between adolescents with a recent concussion and adolescents with no history of concussion are not well understood. Although resting state functional magnetic resonance imaging (fMRI) can be a useful tool in examining these differences, few studies have used this technique to examine concussion in adolescents. Here, we investigate the differences in the resting state functional connectivity of 52 adolescents, 38 with a concussion in the previous 10 days (mean age = 15.6; female = 36.8%), and 14 controls with no concussion history (mean age = 15.1; female = 57.1%). Independent component analysis and dual regression revealed that control adolescents had significantly greater functional connectivity between the dorsal attention network (DAN) and right inferior frontal gyrus (RIFG) compared to concussed adolescents (p-corrected &lt; 0.001). Specifically, there was a positive DAN-RIFG connectivity in control, but not concussed, adolescents. Our findings indicate that concussion is associated with disrupted DAN-RIFG connectivity, which may reflect a general, nonspecific response to injury.

PMID:35566427 | DOI:10.3390/jcm11092293

Accounting for motion in resting-state fMRI: What part of the spectrum are we characterizing in autism spectrum disorder?

Fri, 05/13/2022 - 10:00

Neuroimage. 2022 May 10:119296. doi: 10.1016/j.neuroimage.2022.119296. Online ahead of print.

ABSTRACT

The exclusion of high-motion participants can reduce the impact of motion in functional Magnetic Resonance Imaging (fMRI) data. However, the exclusion of high-motion participants may change the distribution of clinically relevant variables in the study sample, and the resulting sample may not be representative of the population. Our goals are two-fold: 1) to document the biases introduced by common motion exclusion practices in functional connectivity research and 2) to introduce a framework to address these biases by treating excluded scans as a missing data problem. We use a study of autism spectrum disorder in children without an intellectual disability to illustrate the problem and the potential solution. We aggregated data from 545 children (8-13 years old) who participated in resting-state fMRI studies at Kennedy Krieger Institute (173 autistic and 372 typically developing) between 2007 and 2020. We found that autistic children were more likely to be excluded than typically developing children, with 28.5% and 16.1% of autistic and typically developing children excluded, respectively, using a lenient criterion and 81.0% and 60.1% with a stricter criterion. The resulting sample of autistic children with usable data tended to be older, have milder social deficits, better motor control, and higher intellectual ability than the original sample. These measures were also related to functional connectivity strength among children with usable data. This suggests that the generalizability of previous studies reporting naïve analyses (i.e., based only on participants with usable data) may be limited by the selection of older children with less severe clinical profiles because these children are better able to remain still during an rs-fMRI scan. We adapt doubly robust targeted minimum loss based estimation with an ensemble of machine learning algorithms to address these data losses and the resulting biases. The proposed approach selects more edges that differ in functional connectivity between autistic and typically developing children than the naïve approach, supporting this as a promising solution to improve the study of heterogeneous populations in which motion is common.

PMID:35561944 | DOI:10.1016/j.neuroimage.2022.119296

Discrimination of motor and sensorimotor effects of phencyclidine and MK-801: Involvement of GluN2C-containing NMDA receptors in psychosis-like models

Fri, 05/13/2022 - 10:00

Neuropharmacology. 2022 May 10:109079. doi: 10.1016/j.neuropharm.2022.109079. Online ahead of print.

ABSTRACT

Non-competitive NMDA receptor (NMDA-R) antagonists like ketamine, phencyclidine (PCP) and MK-801 are routinely used as pharmacological models of schizophrenia. However, the NMDA-R subtypes, neuronal types (e.g., GABA vs. glutamatergic neurons) and brain regions involved in psychotomimetic actions are not fully understood. PCP activates thalamo-cortical circuits after NMDA-R blockade in reticular thalamic GABAergic neurons. GluN2C subunits are densely expressed in thalamus and cerebellum. Therefore, we examined their involvement in the behavioral and functional effects elicited by PCP and MK-801 using GluN2C knockout (GluN2CKO) and wild-type mice, under the working hypothesis that psychotomimetic effects should be attenuated in mutant mice. PCP and MK-801 induced a disorganized and meandered hyperlocomotion in both genotypes. Interestingly, stereotyped behaviors like circling/rotation, rearings and ataxia signs were dramatically reduced in GluN2CKO mice, indicating a better motor coordination in absence of GluN2C subunits. In contrast, other motor or sensorimotor (pre-pulse inhibition of the startle response) aspects of the behavioral syndrome remained unaltered by GluN2C deletion. PCP and MK-801 evoked a general pattern of c-fos activation in mouse brain (including thalamo-cortical networks) but not in the cerebellum, where they markedly reduced c-fos expression, with significant genotype differences paralleling those in motor coordination. Finally, resting-state fMRI showed an enhanced cortico-thalamic-cerebellar connectivity in GluN2CKO mice, less affected by MK-801 than controls. Hence, the GluN2C subunit allows the dissection of the behavioral alterations induced by PCP and MK-801, showing that some motor effects (in particular, motor incoordination), but not deficits in sensorimotor gating, likely depend on GluN2C-containing NMDA-R blockade in cerebellar circuits.

PMID:35561792 | DOI:10.1016/j.neuropharm.2022.109079

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