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Resting state fMRI based multilayer network configuration in patients with schizophrenia.

Sat, 02/08/2020 - 23:25
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Resting state fMRI based multilayer network configuration in patients with schizophrenia.

Neuroimage Clin. 2020 Jan 11;25:102169

Authors: Gifford G, Crossley N, Kempton MJ, Morgan S, Dazzan P, Young J, McGuire P

Abstract
Novel methods for measuring large-scale dynamic brain organisation are needed to provide new biomarkers of schizophrenia. Using a method for modelling dynamic modular organisation (Mucha et al., 2010), evidence suggests higher 'flexibility' (switching between multilayer network communities) to be a feature of schizophrenia (Braun et al., 2016). The current study compared flexibility between 55 patients with schizophrenia and 72 controls (the COBRE Dataset). In addition, novel methods of 'between resting state network synchronisation' (BRSNS) and the probability of transition from one community to another were used to further describe group differences in dynamic community structure. There was significantly higher schizophrenia group flexibility scores in cerebellar (F (1124) = 9.33, p (FDR) = 0.017), subcortical (F (1124) = 13.14, p (FDR) = 0.005), and fronto-parietal task control (F (1124) = 7.19, p (FDR) = 0.033) resting state networks (RSNs), as well as in the left thalamus (MNI XYZ: -2, -13, 12; F(1, 124) = 17.1, p (FDR) < 0.001) and the right crus I (MNI XYZ: 35, -67, -34; F (1, 124) = 19.65, p (FDR) < 0.001). Flexibility in the left thalamus reflected transitions between communities covering default mode and sensory-somatomotor RSNs. BRSNS scores suggested altered dynamic inter-RSN modular configuration in schizophrenia. This study suggests less stable community structure in a schizophrenia group at an RSN and node level and provides novel methods of exploring dynamic community structure. Mediation of group differences by mean time window correlation did however suggest flexibility to be no better as a schizophrenia biomarker than simpler measures and a range of methodological choices affected results.

PMID: 32032819 [PubMed - as supplied by publisher]

Functional Dissociations of the Left Anterior and Posterior Occipitotemporal Cortex for Semantic and Non-semantic Phonological Access.

Sat, 02/08/2020 - 23:25
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Functional Dissociations of the Left Anterior and Posterior Occipitotemporal Cortex for Semantic and Non-semantic Phonological Access.

Neuroscience. 2020 Feb 04;:

Authors: Dong J, Lu C, Chen C, Li H, Liu X, Mei L

Abstract
Previous studies have identified the ventral and dorsal brain regions that respectively support semantic and non-semantic phonological access. Nevertheless, the specific role of the left occipitotemporal cortex (lOTC) in the two pathways of phonological access is ambiguous. To address that question, the present study compared word reading in Chinese (presumably relying on the semantic pathway) with that in English (presumably relying on the non-semantic pathway). Results revealed a clear dissociation in the involvement of the anterior and posterior lOTC in semantic and non-semantic phonological access. Specifically, the anterior lOTC showed greater activation for Chinese than for English, whereas the posterior lOTC showed greater activation for English than for Chinese. More importantly, both psychophysiological interaction analysis and resting-state functional connectivity analysis showed that the anterior lOTC was functionally connected to the ventral brain regions (e.g., left anterior fusiform gyrus, anterior temporal lobe, and ventral inferior frontal gyrus), whereas the posterior lOTC was functionally connected to the dorsal brain regions (e.g., left posterior superior temporal gyrus, supramarginal gyrus, and dorsal inferior frontal gyrus). These results suggest that the anterior and posterior lOTC are involved in semantic and non-semantic phonological access, respectively.

PMID: 32032670 [PubMed - as supplied by publisher]

Intrinsic brain activity of subcortical-cortical sensorimotor system and psychomotor alterations in schizophrenia and bipolar disorder: A preliminary study.

Sat, 02/08/2020 - 23:25
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Intrinsic brain activity of subcortical-cortical sensorimotor system and psychomotor alterations in schizophrenia and bipolar disorder: A preliminary study.

Schizophr Res. 2020 Feb 03;:

Authors: Magioncalda P, Martino M, Conio B, Lee HC, Ku HL, Chen CJ, Inglese M, Amore M, Lane TJ, Northoff G

Abstract
OBJECTIVE: Alterations in psychomotor dimension cut across different psychiatric disorders, such as schizophrenia (SCZ) and bipolar disorder (BD). This preliminary study aimed to investigate the organization of intrinsic brain activity in the subcortical-cortical sensorimotor system in SCZ (and BD) as characterized according to psychomotor dimension.
METHOD: In this resting-state functional magnetic resonance imaging (fMRI) study, functional connectivity (FC) between thalamus and sensorimotor network (SMN), along with FC from substantia nigra (SN) and raphe nuclei (RN) to basal ganglia (BG) and thalamic regions, were investigated by using an a-priori-driven and dimensional approach. This was done in two datasets: SCZ patients showing inhibited psychomotricity (n = 18) vs. controls (n = 19); SCZ patients showing excited psychomotricity (n = 20) vs. controls (n = 108). Data from a third dataset of BD in inhibited depressive or manic phases (reflecting inhibited or excited psychomotricity) were used as control.
RESULTS: SCZ patients suffering from psychomotor inhibition showed decreased thalamus-SMN FC toward around-zero values paralleled by a concomitant reduction of SN-BG/thalamus FC and RN-BG/thalamus FC (as BD patients in inhibited depression). By contrast, SCZ patients suffering from psychomotor excitation exhibited increased thalamus-SMN FC toward positive values paralleled by a concomitant reduction of RN-BG/thalamus FC (as BD patients in mania).
CONCLUSIONS: These findings suggest that patients exhibiting low or high levels of psychomotor activity show distinct patterns of thalamus-SMN coupling, which could be traced to specific deficit in SN- or RN-related connectivity. Notably, this was independent from the diagnosis of SCZ or BD, supporting an RDoC-like dimensional approach to psychomotricity.

PMID: 32029353 [PubMed - as supplied by publisher]

Resting-state Functional Connectivity of the Right Temporoparietal Junction Relates to Belief Updating and Reorienting during Spatial Attention.

Sat, 02/08/2020 - 02:24
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Resting-state Functional Connectivity of the Right Temporoparietal Junction Relates to Belief Updating and Reorienting during Spatial Attention.

J Cogn Neurosci. 2020 Feb 06;:1-13

Authors: Käsbauer AS, Mengotti P, Fink GR, Vossel S

Abstract
Whereas multiple studies characterized the resting-state functional connectivity (rsFC) of the right temporoparietal junction (rTPJ), little is known about the link between rTPJ rsFC and cognitive functions. Given a putative involvement of rTPJ in both reorienting of attention and the updating of probabilistic beliefs, this study characterized the relationship between rsFC of rTPJ with dorsal and ventral attention systems and these two cognitive processes. Twenty-three healthy young participants performed a modified location-cueing paradigm with true and false prior information about the percentage of cue validity to assess belief updating and attentional reorienting. Resting-state fMRI was recorded before and after the task. Seed-based correlation analysis was employed, and correlations of each behavioral parameter with rsFC before the task, as well as with changes in rsFC after the task, were assessed in an ROI-based approach. Weaker rsFC between rTPJ and right intraparietal sulcus before the task was associated with relatively faster updating of the belief that the cue will be valid after false prior information. Moreover, relatively faster belief updating, as well as faster reorienting, were related to an increase in the interhemispheric rsFC between rTPJ and left TPJ after the task. These findings are in line with task-based connectivity studies on related attentional functions and extend results from stroke patients demonstrating the importance of interhemispheric parietal interactions for behavioral performance. The present results not only highlight the essential role of parietal rsFC for attentional functions but also suggest that cognitive processing during a task changes connectivity patterns in a performance-dependent manner.

PMID: 32027583 [PubMed - as supplied by publisher]

Large-scale dynamic causal modeling of major depressive disorder based on resting-state functional magnetic resonance imaging.

Sat, 02/08/2020 - 02:24
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Large-scale dynamic causal modeling of major depressive disorder based on resting-state functional magnetic resonance imaging.

Hum Brain Mapp. 2020 Mar;41(4):865-881

Authors: Li G, Liu Y, Zheng Y, Li D, Liang X, Chen Y, Cui Y, Yap PT, Qiu S, Zhang H, Shen D

Abstract
Major depressive disorder (MDD) is a serious mental illness characterized by dysfunctional connectivity among distributed brain regions. Previous connectome studies based on functional magnetic resonance imaging (fMRI) have focused primarily on undirected functional connectivity and existing directed effective connectivity (EC) studies concerned mostly task-based fMRI and incorporated only a few brain regions. To overcome these limitations and understand whether MDD is mediated by within-network or between-network connectivities, we applied spectral dynamic causal modeling to estimate EC of a large-scale network with 27 regions of interests from four distributed functional brain networks (default mode, executive control, salience, and limbic networks), based on large sample-size resting-state fMRI consisting of 100 healthy subjects and 100 individuals with first-episode drug-naive MDD. We applied a newly developed parametric empirical Bayes (PEB) framework to test specific hypotheses. We showed that MDD altered EC both within and between high-order functional networks. Specifically, MDD is associated with reduced excitatory connectivity mainly within the default mode network (DMN), and between the default mode and salience networks. In addition, the network-averaged inhibitory EC within the DMN was found to be significantly elevated in the MDD. The coexistence of the reduced excitatory but increased inhibitory causal connections within the DMNs may underlie disrupted self-recognition and emotional control in MDD. Overall, this study emphasizes that MDD could be associated with altered causal interactions among high-order brain functional networks.

PMID: 32026598 [PubMed - in process]

Abnormal Baseline Brain Activity in Neuromyelitis Optica Patients Without Brain Lesion Detected by Resting-State Functional Magnetic Resonance Imaging.

Thu, 02/06/2020 - 20:20
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Abnormal Baseline Brain Activity in Neuromyelitis Optica Patients Without Brain Lesion Detected by Resting-State Functional Magnetic Resonance Imaging.

Neuropsychiatr Dis Treat. 2020;16:71-79

Authors: Liu Y, Xiong H, Li X, Zhang D, Yang C, Yu J, Liao R, Zhou B, Huang X, Tang Z

Abstract
Objective: To investigate the baseline brain activity in neuromyelitis optica patients without brain lesion using the regional amplitude of low-frequency fluctuation (ALFF) and fractional amplitude of low-frequency fluctuation (fALFF) as indexes.
Materials and methods: Forty-two patients of NMO with normal performance in conventional MRI and 42 healthy controls, matched in gender and age, were enrolled in this study. Resting-state functional magnetic resonance imaging (rs-fMRI) data acquired using the rs-fMRI Data Analysis Toolkit. The relationships between expanded disability states scale (EDSS) scores, abnormal baseline brain activity and disease duration were explored.
Results: The left inferior temporal, left cerebellum_4_5, bilateral superior temporal pole, left caudate, right superior temporal, left middle frontal and left superior occipital showed significantly increased ALFF in the NMO. Regions of abnormal fALFF were similar to those of ALFF except that increased fALFF were also indicated in the right cerebellum crus2, right hippocampus, left parahippocampal gyrus and left supplementary motor area. Furthermore, a significant correlation between EDSS scores and ALFF/fALFF was noted in the left inferior temporal gyrus.
Conclusion: Results confirmed the disturbances in NMO-related neural networks, which probably be related to spinal cord damage.

PMID: 32021200 [PubMed]

The assessment dimension of regulatory mode mediates the relation between frontoparietal connectivity and risk-taking: Evidence from voxel-base morphometry and functional connectivity analysis.

Thu, 02/06/2020 - 08:19
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The assessment dimension of regulatory mode mediates the relation between frontoparietal connectivity and risk-taking: Evidence from voxel-base morphometry and functional connectivity analysis.

Brain Cogn. 2020 Feb 01;140:105533

Authors: Huo H, Seger CA, Zhou D, Chen Z, Xu T, Zhang R, Feng T, Chen Q

Abstract
We used voxel-based morphometry and resting-state functional magnetic resonance imaging (rs-fMRI) to investigate whether the regulatory mode orientation of assessment (the tendency of each individual to self-regulate by critically evaluating alternatives) interacts with neural systems underlying risk-taking. Across a sample of 112 participants, propensity for risk-taking (measured using the Wheel of Fortune task) was negatively correlated with assessment orientation, such that a greater tendency to critically evaluate alternatives was associated with a lower tendency for risk-taking. VBM revealed a negative correlation between assessment orientation and right inferior parietal lobe (RIPL) gray matter volume. Resting-state functional connectivity (rs-FC) between this same RIPL region and the left inferior frontal gyrus (LIFG) was positively correlated with assessment orientation in an independent sample of 41 participants. Most importantly, based on the rs-FC results, a mediation analysis indicated that assessment orientation played a completely mediating role in the relation between the functional connectivity of RIPL-LIFG and risk-taking. These results suggest that assessment orientation may affect risk-taking via the RIPL and its connectivity with LIFG. On the whole, the present study yields the insights into how the assessment dimension of regulatory mode affects risk-taking, and provides a novel account of the neural substrate of this relationship.

PMID: 32018217 [PubMed - as supplied by publisher]

Reorganization of rich-clubs in functional brain networks during propofol-induced unconsciousness and natural sleep.

Thu, 02/06/2020 - 08:19
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Reorganization of rich-clubs in functional brain networks during propofol-induced unconsciousness and natural sleep.

Neuroimage Clin. 2020 Jan 21;25:102188

Authors: Wang S, Li Y, Qiu S, Zhang C, Wang G, Xian J, Li T, He H

Abstract
BACKGROUND: General anesthesia (GA) provides an invaluable experimental tool to understand the essential neural mechanisms underlying consciousness. Previous neuroimaging studies have shown the functional integration and segregation of brain functional networks during anesthetic-induced alteration of consciousness. However, the organization pattern of hubs in functional brain networks remains unclear. Moreover, comparisons with the well-characterized physiological unconsciousness can help us understand the neural mechanisms of anesthetic-induced unconsciousness.
METHODS: Resting-state functional magnetic resonance imaging was performed during wakefulness, mild propofol-induced sedation (m-PIS), and deep PIS (d-PIS) with clinical unconsciousness on 8 healthy volunteers and wakefulness and natural sleep on 9 age- and sex-matched healthy volunteers. Large-scale functional brain networks of each volunteer were constructed based on 160 regions of interest. Then, rich-club organizations in brain functional networks and nodal properties (nodal strength and efficiency) were assessed and analyzed among the different states and groups.
RESULTS: Rich-clubs in the functional brain networks were reorganized during alteration of consciousness induced by propofol. Firstly, rich-club nodes were switched from the posterior cingulate cortex (PCC), angular gyrus, and anterior and middle insula to the inferior parietal lobule (IPL), inferior parietal sulcus (IPS), and cerebellum. When sedation was deepened to unconsciousness, the rich-club nodes were switched to the occipital and angular gyrus. These results suggest that the rich-club nodes were switched among the high-order cognitive function networks (default mode network [DMN] and fronto-parietal network [FPN]), sensory networks (occipital network [ON]), and cerebellum network (CN) from consciousness (wakefulness) to propofol-induced unconsciousness. At the same time, compared with wakefulness, local connections were switched to rich-club connections during propofol-induced unconsciousness, suggesting a strengthening of the overall information commutation of networks. Nodal efficiency of the anterior and middle insula and ventral frontal cortex was significantly decreased. Additionally, from wakefulness to natural sleep, a similar pattern of rich-club reorganization with propofol-induced unconsciousness was observed: rich-club nodes were switched from the DMN (including precuneus and PCC) to the sensorimotor network (SMN, including part of the frontal and temporal gyrus). Compared with natural sleep, nodal efficiency of the insula, frontal gyrus, PCC, and cerebellum significantly decreased during propofol-induced unconsciousness.
CONCLUSIONS: Our study demonstrated that the rich-club reorganization in functional brain networks is characterized by switching of rich-club nodes between the high-order cognitive and sensory and motor networks during propofol-induced alteration of consciousness and natural sleep. These findings will help understand the common neurological mechanism of pharmacological and physiological unconsciousness.

PMID: 32018124 [PubMed - as supplied by publisher]

Closer to critical resting-state neural dynamics in individuals with higher fluid intelligence.

Thu, 02/06/2020 - 08:19
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Closer to critical resting-state neural dynamics in individuals with higher fluid intelligence.

Commun Biol. 2020 Feb 03;3(1):52

Authors: Ezaki T, Fonseca Dos Reis E, Watanabe T, Sakaki M, Masuda N

Abstract
According to the critical brain hypothesis, the brain is considered to operate near criticality and realize efficient neural computations. Despite the prior theoretical and empirical evidence in favor of the hypothesis, no direct link has been provided between human cognitive performance and the neural criticality. Here we provide such a key link by analyzing resting-state dynamics of functional magnetic resonance imaging (fMRI) networks at a whole-brain level. We develop a data-driven analysis method, inspired from statistical physics theory of spin systems, to map out the whole-brain neural dynamics onto a phase diagram. Using this tool, we show evidence that neural dynamics of human participants with higher fluid intelligence quotient scores are closer to a critical state, i.e., the boundary between the paramagnetic phase and the spin-glass (SG) phase. The present results are consistent with the notion of "edge-of-chaos" neural computation.

PMID: 32015402 [PubMed - in process]

Effects of repetitive transcranial magnetic stimulation on resting-state connectivity: A systematic review.

Thu, 02/06/2020 - 08:19
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Effects of repetitive transcranial magnetic stimulation on resting-state connectivity: A systematic review.

Neuroimage. 2020 Jan 31;:116596

Authors: Beynel L, Powers JP, Appelbaum LG

Abstract
The brain is organized into networks that reorganize dynamically in response to cognitive demands and exogenous stimuli. In recent years, repetitive transcranial magnetic stimulation (rTMS) has gained increasing use as a noninvasive means to modulate cortical physiology, with effects both proximal to the stimulation site and in distal areas that are intrinsically connected to the proximal target. In light of these network-level neuromodulatory effects, there has been a rapid growth in studies attempting to leverage information about network connectivity to improve neuromodulatory control and intervention outcomes. However, the mechanisms-of-action of rTMS on network-level effects remain poorly understood and is based primarily on heuristics from proximal stimulation findings. To help bridge this gap, the current paper presents a systematic review of 33 rTMS studies with baseline and post-rTMS measures of fMRI resting-state functional connectivity (RSFC). Literature synthesis revealed variability across studies in stimulation parameters, studied populations, and connectivity analysis methodology. Despite this variability, it is observed that active rTMS induces significant changes on RSFC, but the prevalent low-frequency-inhibition/high-frequency-facilitation heuristic endorsed for proximal rTMS effects does not fully describe distal connectivity findings. This review also points towards other important considerations, including that the majority of rTMS-induced changes were found outside the stimulated functional network, suggesting that rTMS effects tend to spread across networks. Future studies may therefore wish to adopt conventions and systematic frameworks, such as the Yeo functional connectivity parcellation atlas adopted here, to better characterize network-level effect that contribute to the efficacy of these rapidly developing noninvasive interventions.

PMID: 32014552 [PubMed - as supplied by publisher]

Exploring memory function in earthquake trauma survivors with resting-state fMRI and machine learning.

Thu, 02/06/2020 - 08:19
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Exploring memory function in earthquake trauma survivors with resting-state fMRI and machine learning.

BMC Psychiatry. 2020 Feb 03;20(1):43

Authors: Li Y, Zhu H, Ren Z, Lui S, Yuan M, Gong Q, Yuan C, Gao M, Qiu C, Zhang W

Abstract
BACKGROUND: Traumatized earthquake survivors may develop poor memory function. Resting-state functional magnetic resonance imaging (rs-fMRI) and machine learning techniques may one day aid the clinical assessment of individual psychiatric patients. This study aims to use machine learning with Rs-fMRI from the perspectives of neurophysiology and neuroimaging to explore the association between it and the individual memory function of trauma survivors.
METHODS: Rs-fMRI data was acquired for eighty-nine survivors (male (33%), average age (SD):45.18(6.31) years) of Wenchuan earthquakes in 2008 each of whom was screened by experienced psychiatrists based on the clinician-administered post-traumatic stress disorder (PTSD) scale (CAPS), and their memory function scores were determined by the Wechsler Memory Scale-IV (WMS-IV). We explored which memory function scores were significantly associated with CAPS scores. Using simple multiple kernel learning (MKL), Rs-fMRI was used to predict the memory function scores that were associated with CAPS scores. A support vector machine (SVM) was also used to make classifications in trauma survivors with or without PTSD.
RESULTS: Spatial addition (SA), which is defined by spatial working memory function, was negatively correlated with the total CAPS score (r = - 0.22, P = 0.04). The use of simple MKL allowed quantitative association of SA scores with statistically significant accuracy (correlation = 0.28, P = 0.03; mean squared error = 8.36; P = 0.04). The left middle frontal gyrus and the left precuneus contributed the largest proportion to the simple MKL association frame. The SVM could not make a quantitative classification of diagnosis with statistically significant accuracy.
LIMITATIONS: The use of the cross-sectional study design after exposure to an earthquake and the leave-one-out cross-validation (LOOCV) increases the risk of overfitting.
CONCLUSION: Spontaneous brain activity of the left middle frontal gyrus and the left precuneus acquired by rs-fMRI may be a brain mechanism of visual working memory that is related to PTSD symptoms. Machine learning may be a useful tool in the identification of brain mechanisms of memory impairment in trauma survivors.

PMID: 32013935 [PubMed - in process]

Resting-state Amplitude of Low-frequency Fluctuation is a Potentially Useful Prognostic Functional Biomarker in Cervical Myelopathy.

Thu, 02/06/2020 - 08:19
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Resting-state Amplitude of Low-frequency Fluctuation is a Potentially Useful Prognostic Functional Biomarker in Cervical Myelopathy.

Clin Orthop Relat Res. 2020 Jan 30;:

Authors: Takenaka S, Kan S, Seymour B, Makino T, Sakai Y, Kushioka J, Tanaka H, Watanabe Y, Shibata M, Yoshikawa H, Kaito T

Abstract
BACKGROUND: Cervical MRI is the standard diagnostic imaging technique for patients with cervical myelopathy. However, the utility of conventional cervical MRI as a predictive biomarker for surgical recovery remains unclear, partly because of the limited information obtained from this anatomically small area. Brain resting-state functional MRI (rs-fMRI) may help identify candidate predictive biomarkers. Two analytical methods that assess local spontaneous brain activity are widely used for rs-fMRI: functional connectivity between two brain regions and amplitude of low-frequency fluctuation (ALFF). In our previous analysis of functional connectivity, we discovered that brain functional connectivity may be a predictive biomarker for neurologic recovery in patients with cervical myelopathy; however, the functional connectivity analysis identified a correlation with only one clinical outcome (the 10-second test). To establish a comprehensive prediction measure, we need to explore other brain biomarkers that can predict recovery of other clinical outcomes in patients with cervical myelopathy.
QUESTIONS/PURPOSES: We aimed to (1) elucidate preoperative ALFF alterations in patients with cervical myelopathy and how ALFF changes after surgery, with a focus on postoperative normalization and (2) establish a predictive model using preoperative ALFF by investigating the correlation between preoperative ALFF and postoperative clinical recovery in patients with cervical myelopathy.
METHODS: Between August 2015 and June 2017, we treated 40 patients with cervical myelopathy. Thirty patients met our prespecified inclusion criteria, all were invited to participate, and 28 patients opted to do so (93%; 14 men and 14 women; mean age: 67 years). The 28 patients and 28 age- and sex-matched controls underwent rs-fMRI (twice for patients with cervical myelopathy: before and 6 months after cervical decompression surgery). We analyzed the same study population that was used in our earlier study investigating functional connectivity. Controls had none of the following abnormalities: neck or arm pain, visual or auditory disorders, cognitive disorder, structural brain disorder, a history of brain surgery, mental and neurologic disorders, and medications for the central nervous system. We performed ALFF comparisons between preoperative patients with cervical myelopathy and controls, analyzed postoperative ALFF changes in patients with cervical myelopathy, and performed a correlation analysis between preoperative ALFF and clinical recovery in these patients. Clinical outcomes in the cervical myelopathy group were assessed using the 10-second test, the Japanese Orthopaedic Association upper-extremity motor (JOA-UEM) score, JOA upper-extremity sensory score (JOA-UES), and Japanese Orthopaedic Association Cervical Myelopathy Evaluation Questionnaire for upper-extremity function (JOACMEQ-UEF) score before and 6 months after surgery, which is when we believe these scores generally reach a plateau. A total of 93% of those enrolled (26 of 28 patients) were analyzed both preoperatively and postoperatively; the other two were lost to follow-up.
RESULTS: The cervical myelopathy group had an increase in ALFF in the bilateral primary sensorimotor cortices (right, cluster size = 850 voxels, t-value = 6.10; left, cluster size = 370 voxels, t-value = 4.84) and left visual cortex (cluster size = 556 voxels, t-value = 4.21) compared with the control group. The cervical myelopathy group had a decrease in ALFF in the bilateral posterior supramarginal gyrus (right, cluster size = 222 voxels, t-value = 5.09; left, cluster size = 436 voxels, t-value = 5.28). After surgery, the bilateral sensorimotor cortices (right, cluster size = 468 voxels, t-value = 6.74; left, cluster size = 167 voxels, t-value = 5.40) and left visual cortex (cluster size = 3748 voxels, t-value = 6.66) showed decreased ALFF compared with preoperative ALFF, indicating postoperative normalization of spontaneous brain activities in these regions. However, the bilateral posterior supramarginal gyrus did not show an increase in ALFF postoperatively, although ALFF in this region decreased preoperatively. Greater levels of ALFF at the left and right frontal pole and left pars opercularis of the inferior frontal gyrus before surgery in the cervical myelopathy group were correlated with larger improvements in the JOACMEQ-UEF score 6 months after surgery (r = 0.784; p < 0.001, r = 0.734; p < 0.001 and r = 0.770, respectively; p < 0.001). The prediction formula, based on preoperative ALFF values in the left frontal pole, was as follows: the predicted postoperative improvement in the JOACMEQ-UEF score = 34.6 × preoperative ALFF value - 7.0 (r = 0.614; p < 0.001).
CONCLUSIONS: Our findings suggest that preoperative ALFF may be a biomarker for postoperative recovery in that it predicted postoperative JOACMEQ-UEF scores. To establish a comprehensive prediction measure for neurologic recovery in patients with cervical myelopathy, a multicenter study is underway.
LEVEL OF EVIDENCE: Level II, diagnostic study.

PMID: 32011371 [PubMed - as supplied by publisher]

Schizophrenia Identification Using Multi-View Graph Measures of Functional Brain Networks.

Thu, 02/06/2020 - 08:19
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Schizophrenia Identification Using Multi-View Graph Measures of Functional Brain Networks.

Front Bioeng Biotechnol. 2019;7:479

Authors: Xiang Y, Wang J, Tan G, Wu FX, Liu J

Abstract
Schizophrenia (SZ) is a functional mental disorder that seriously affects the social life of patients. Therefore, accurate diagnosis of SZ has raised extensive attention of researchers. At present, study of brain network based on resting-state functional magnetic resonance imaging (rs-fMRI) has provided promising results for SZ identification by studying functional network alteration. However, previous studies based on brain network analysis are not very effective for SZ identification. Therefore, we propose an improved SZ identification method using multi-view graph measures of functional brain networks. Firstly, we construct an individual functional connectivity network based on Brainnetome atlas for each subject. Then, multi-view graph measures are calculated by the brain network analysis method as feature representations. Next, in order to consider the relationships between measures within the same brain region in feature selection, multi-view measures are grouped according to the corresponding regions and Sparse Group Lasso is applied to identify discriminative features based on this feature grouping structure. Finally, a support vector machine (SVM) classifier is employed to perform SZ identification task. To evaluate our proposed method, computational experiments are conducted on 145 subjects (71 schizophrenic patients and 74 healthy controls) using a leave-one-out cross-validation (LOOCV) scheme. The results show that our proposed method can obtain an accuracy of 93.10% for SZ identification. By comparison, our method is more effective for SZ identification than some existing methods.

PMID: 32010682 [PubMed]

Evaluation of Resting Spatio-Temporal Dynamics of a Neural Mass Model Using Resting fMRI Connectivity and EEG Microstates.

Thu, 02/06/2020 - 08:19
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Evaluation of Resting Spatio-Temporal Dynamics of a Neural Mass Model Using Resting fMRI Connectivity and EEG Microstates.

Front Comput Neurosci. 2019;13:91

Authors: Endo H, Hiroe N, Yamashita O

Abstract
Resting-state brain activities have been extensively investigated to understand the macro-scale network architecture of the human brain using non-invasive imaging methods such as fMRI, EEG, and MEG. Previous studies revealed a mechanistic origin of resting-state networks (RSNs) using the connectome dynamics modeling approach, where the neural mass dynamics model constrained by the structural connectivity is simulated to replicate the resting-state networks measured with fMRI and/or fast synchronization transitions with EEG/MEG. However, there is still little understanding of the relationship between the slow fluctuations measured with fMRI and the fast synchronization transitions with EEG/MEG. In this study, as a first step toward evaluating experimental evidence of resting state activity at two different time scales but in a unified way, we investigate connectome dynamics models that simultaneously explain resting-state functional connectivity (rsFC) and EEG microstates. Here, we introduce empirical rsFC and microstates as evaluation criteria of simulated neuronal dynamics obtained by the Larter-Breakspear model in one cortical region connected with those in other cortical regions based on structural connectivity. We optimized the global coupling strength and the local gain parameter (variance of the excitatory and inhibitory threshold) of the simulated neuronal dynamics by fitting both rsFC and microstate spatial patterns to those of experimental ones. As a result, we found that simulated neuronal dynamics in a narrow optimal parameter range simultaneously reproduced empirical rsFC and microstates. Two parameter groups had different inter-regional interdependence. One type of dynamics was synchronized across the whole brain region, and the other type was synchronized between brain regions with strong structural connectivity. In other words, both fast synchronization transitions and slow BOLD fluctuation changed based on structural connectivity in the two parameter groups. Empirical microstates were similar to simulated microstates in the two parameter groups. Thus, fast synchronization transitions correlated with slow BOLD fluctuation based on structural connectivity yielded characteristics of microstates. Our results demonstrate that a bottom-up approach, which extends the single neuronal dynamics model based on empirical observations into a neural mass dynamics model and integrates structural connectivity, effectively reveals both macroscopic fast, and slow resting-state network dynamics.

PMID: 32009922 [PubMed]

Short-Term Classification Learning Promotes Rapid Global Improvements of Information Processing in Human Brain Functional Connectome.

Thu, 02/06/2020 - 08:19
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Short-Term Classification Learning Promotes Rapid Global Improvements of Information Processing in Human Brain Functional Connectome.

Front Hum Neurosci. 2019;13:462

Authors: Zippo AG, Castiglioni I, Lin J, Borsa VM, Valente M, Biella GEM

Abstract
Classification learning is a preeminent human ability within the animal kingdom but the key mechanisms of brain networks regulating learning remain mostly elusive. Recent neuroimaging advancements have depicted human brain as a complex graph machinery where brain regions are nodes and coherent activities among them represent the functional connections. While long-term motor memories have been found to alter functional connectivity in the resting human brain, a graph topological investigation of the short-time effects of learning are still not widely investigated. For instance, classification learning is known to orchestrate rapid modulation of diverse memory systems like short-term and visual working memories but how the brain functional connectome accommodates such modulations is unclear. We used publicly available repositories (openfmri.org) selecting three experiments, two focused on short-term classification learning along two consecutive runs where learning was promoted by trial-by-trial feedback errors, while a further experiment was used as supplementary control. We analyzed the functional connectivity extracted from BOLD fMRI signals, and estimated the graph information processing in the cerebral networks. The information processing capability, characterized by complex network statistics, significantly improved over runs, together with the subject classification accuracy. Instead, null-learning experiments, where feedbacks came with poor consistency, did not provoke any significant change in the functional connectivity over runs. We propose that learning induces fast modifications in the overall brain network dynamics, definitely ameliorating the short-term potential of the brain to process and integrate information, a dynamic consistently orchestrated by modulations of the functional connections among specific brain regions.

PMID: 32009918 [PubMed]

Differential tDCS and tACS Effects on Working Memory-Related Neural Activity and Resting-State Connectivity.

Thu, 02/06/2020 - 08:19
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Differential tDCS and tACS Effects on Working Memory-Related Neural Activity and Resting-State Connectivity.

Front Neurosci. 2019;13:1440

Authors: Abellaneda-Pérez K, Vaqué-Alcázar L, Perellón-Alfonso R, Bargalló N, Kuo MF, Pascual-Leone A, Nitsche MA, Bartrés-Faz D

Abstract
Transcranial direct and alternating current stimulation (tDCS and tACS, respectively) entail capability to modulate human brain dynamics and cognition. However, the comparability of these approaches at the level of large-scale functional networks has not been thoroughly investigated. In this study, 44 subjects were randomly assigned to receive sham (N = 15), tDCS (N = 15), or tACS (N = 14). The first electrode (anode in tDCS) was positioned over the left dorsolateral prefrontal cortex, the target area, and the second electrode (cathode in tDCS) was placed over the right supraorbital region. tDCS was delivered with a constant current of 2 mA. tACS was fixed to 2 mA peak-to-peak with 6 Hz frequency. Stimulation was applied concurrently with functional magnetic resonance imaging (fMRI) acquisitions, both at rest and during the performance of a verbal working memory (WM) task. After stimulation, subjects repeated the fMRI WM task. Our results indicated that at rest, tDCS increased functional connectivity particularly within the default-mode network (DMN), while tACS decreased it. When comparing both fMRI WM tasks, it was observed that tDCS displayed decreased brain activity post-stimulation as compared to online. Conversely, tACS effects were driven by neural increases online as compared to post-stimulation. Interestingly, both effects primarily occurred within DMN-related areas. Regarding the differences in each fMRI WM task, during the online fMRI WM task, tACS engaged distributed neural resources which did not overlap with the WM-dependent activity pattern, but with some posterior DMN regions. In contrast, during the post-stimulation fMRI WM task, tDCS strengthened prefrontal DMN deactivations, being these activity reductions associated with faster responses. Furthermore, it was observed that tDCS neural responses presented certain consistency across distinct fMRI modalities, while tACS did not. In sum, tDCS and tACS modulate fMRI-derived network dynamics differently. However, both effects seem to focus on DMN regions and the WM network-DMN shift, which are highly affected in aging and disease. Thus, albeit exploratory and needing further replication with larger samples, our results might provide a refined understanding of how the DMN functioning can be externally modulated through commonly used non-invasive brain stimulation techniques, which may be of eventual clinical relevance.

PMID: 32009896 [PubMed]

Detecting the Information of Functional Connectivity Networks in Normal Aging Using Deep Learning From a Big Data Perspective.

Thu, 02/06/2020 - 08:19
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Detecting the Information of Functional Connectivity Networks in Normal Aging Using Deep Learning From a Big Data Perspective.

Front Neurosci. 2019;13:1435

Authors: Wen X, Dong L, Chen J, Xiang J, Yang J, Li H, Liu X, Luo C, Yao D

Abstract
A resting-state functional connectivity (rsFC)-constructed functional network (FN) derived from functional magnetic resonance imaging (fMRI) data can effectively mine alterations in brain function during aging due to the non-invasive and effective advantages of fMRI. With global health research focusing on aging, several open fMRI datasets have been made available that combine deep learning with big data and are a new, promising trend and open issue for brain information detection in fMRI studies of brain aging. In this study, we proposed a new method based on deep learning from the perspective of big data, named Deep neural network (DNN) with Autoencoder (AE) pretrained Functional connectivity Analysis (DAFA), to deeply mine the important functional connectivity changes in fMRI during brain aging. First, using resting-state fMRI data from 421 subjects from the CamCAN dataset, functional connectivities were calculated using sliding window method, and the complex functional patterns were mined by an AE. Then, to increase the statistical power and reliability of the results, we used an AE-pretrained DNN to relabel the functional connectivities of each subject to classify them as belonging to the attributes of young or old individuals. A method called search-back analysis was performed to find alterations in brain function during aging according to the relabeled functional connectivities. Finally, behavioral data regarding fluid intelligence and response time were used to verify the revealed functional changes. Compared to traditional methods, DAFA revealed additional, important aged-related changes in FC patterns [e.g., FC connections within the default mode (DMN) and the sensorimotor and cingulo-opercular networks, as well as connections between the frontoparietal and cingulo-opercular networks, between the DMN and the frontoparietal/cingulo-opercular/sensorimotor/occipital/cerebellum networks, and between the sensorimotor and frontoparietal/cingulo-opercular networks], which were correlated to behavioral data. These findings demonstrated that the proposed DAFA method was superior to traditional FC-determining methods in discovering changes in brain functional connectivity during aging. In addition, it may be a promising method for exploring important information in other fMRI studies.

PMID: 32009894 [PubMed]

Automated Detection of Autism Spectrum Disorder Using a Convolutional Neural Network.

Thu, 02/06/2020 - 08:19
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Automated Detection of Autism Spectrum Disorder Using a Convolutional Neural Network.

Front Neurosci. 2019;13:1325

Authors: Sherkatghanad Z, Akhondzadeh M, Salari S, Zomorodi-Moghadam M, Abdar M, Acharya UR, Khosrowabadi R, Salari V

Abstract
Background: Convolutional neural networks (CNN) have enabled significant progress in speech recognition, image classification, automotive software engineering, and neuroscience. This impressive progress is largely due to a combination of algorithmic breakthroughs, computation resource improvements, and access to a large amount of data. Method: In this paper, we focus on the automated detection of autism spectrum disorder (ASD) using CNN with a brain imaging dataset. We detected ASD patients using most common resting-state functional magnetic resonance imaging (fMRI) data from a multi-site dataset named the Autism Brain Imaging Exchange (ABIDE). The proposed approach was able to classify ASD and control subjects based on the patterns of functional connectivity. Results: Our experimental outcomes indicate that the proposed model is able to detect ASD correctly with an accuracy of 70.22% using the ABIDE I dataset and the CC400 functional parcellation atlas of the brain. Also, the CNN model developed used fewer parameters than the state-of-art techniques and is hence computationally less intensive. Our developed model is ready to be tested with more data and can be used to prescreen ASD patients.

PMID: 32009868 [PubMed]

Temporal non-local means filtering for studies of intrinsic brain connectivity from individual resting fMRI.

Mon, 02/03/2020 - 23:16
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Temporal non-local means filtering for studies of intrinsic brain connectivity from individual resting fMRI.

Med Image Anal. 2020 Jan 07;61:101635

Authors: Li J, Choi S, Joshi AA, Wisnowski JL, Leahy RM

Abstract
Characterizing functional brain connectivity using resting functional magnetic resonance imaging (fMRI) is challenging due to the relatively small Blood-Oxygen-Level Dependent contrast and low signal-to-noise ratio. Denoising using surface-based Laplace-Beltrami (LB) or volumetric Gaussian filtering tends to blur boundaries between different functional areas. To overcome this issue, a time-based Non-Local Means (tNLM) filtering method was previously developed to denoise fMRI data while preserving spatial structure. The kernel and parameters that define the tNLM filter need to be optimized for each application. Here we present a novel Global PDF-based tNLM filtering (GPDF) algorithm that uses a data-driven kernel function based on a Bayes factor to optimize filtering for spatial delineation of functional connectivity in resting fMRI data. We demonstrate its performance relative to Gaussian spatial filtering and the original tNLM filtering via simulations. We also compare the effects of GPDF filtering against LB filtering using individual in-vivo resting fMRI datasets. Our results show that LB filtering tends to blur signals across boundaries between adjacent functional regions. In contrast, GPDF filtering enables improved noise reduction without blurring adjacent functional regions. These results indicate that GPDF may be a useful preprocessing tool for analyses of brain connectivity and network topology in individual fMRI recordings.

PMID: 32007699 [PubMed - as supplied by publisher]

Identifying a task-invariant cognitive reserve network using task potency.

Mon, 02/03/2020 - 23:16
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Identifying a task-invariant cognitive reserve network using task potency.

Neuroimage. 2020 Jan 30;:116593

Authors: van Loenhoud AC, Habeck C, van der Flier WM, Ossenkoppele R, Stern Y

Abstract
Cognitive reserve (CR) is thought to protect against the consequence of age- or disease-related structural brain changes across multiple cognitive domains. The neural basis of CR may therefore comprise a functional network that is actively involved in many different cognitive processes. To investigate the existence of such a "task-invariant" CR network, we measured functional connectivity in a cognitively normal sample between 20 and 80 years old (N = 265), both at rest and during the performance of 11 separate tasks that aim to capture four latent cognitive abilities (i.e. vocabulary, episodic memory, processing speed, and fluid reasoning). For each individual, we determined the change in functional connectivity from the resting state to each task state, which is referred to as "task potency" (Chauvin et al., 2018, 2019). Task potency was calculated for each pair among 264 nodes (Power et al., 2012) and then summarized across tasks reflecting the same cognitive ability. Subsequently, we established the correlation between task potency and IQ or education (i.e. CR factors). We identified a set of 57 pairs in which task potency showed significant correlations with IQ, but not education, across all four cognitive abilities. These pairs were included in a principal component analysis, from which we extracted the first component to obtain a latent variable reflecting task potency in this task-invariant CR network. This task potency variable was associated with better episodic memory (β = 0.19, p < .01) and fluid reasoning performance (β = 0.17, p < .01) above and beyond the effects of cortical thickness (range β = 0.28-0.32, p < .001). Our identification of this task-invariant network contributes to a better understanding of the mechanism underlying CR, which may facilitate the development of CR-enhancing treatments. Our work also offers a useful alternative operational measure of CR for future studies.

PMID: 32007499 [PubMed - as supplied by publisher]