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Cardiorespiratory fitness predicts effective connectivity between the hippocampus and default mode network nodes in young adults.

Sat, 10/26/2019 - 08:57

Cardiorespiratory fitness predicts effective connectivity between the hippocampus and default mode network nodes in young adults.

Hippocampus. 2019 Oct 24;:

Authors: Kronman CA, Kern KL, Nauer RK, Dunne MF, Storer TW, Schon K

Abstract
Rodent and human studies examining the relationship between aerobic exercise, brain structure, and brain function indicate that the hippocampus (HC), a brain region critical for episodic memory, demonstrates striking plasticity in response to exercise. Beyond the hippocampal memory system, human studies also indicate that aerobic exercise and cardiorespiratory fitness (CRF) are associated with individual differences in large-scale brain networks responsible for broad cognitive domains. Examining network activity in large-scale resting-state brain networks may provide a link connecting the observed relationships between aerobic exercise, hippocampal plasticity, and cognitive enhancement within broad cognitive domains. Previously, CRF has been associated with increased functional connectivity of the default mode network (DMN), specifically in older adults. However, how CRF relates to the magnitude and directionality of connectivity, or effective connectivity, between the HC and other DMN nodes remains unknown. We used resting-state fMRI and conditional Granger causality analysis (CGCA) to test the hypothesis that CRF positively predicts effective connectivity between the HC and other DMN nodes in healthy young adults. Twenty-six participants (ages 18-35 years) underwent a treadmill test to determine CRF by estimating its primary determinant, maximal oxygen uptake (V. O2max ), and a 10-min resting-state fMRI scan to examine DMN effective connectivity. We identified the DMN using group independent component analysis and examined effective connectivity between nodes using CGCA. Linear regression analyses demonstrated that CRF significantly predicts causal influence from the HC to the ventromedial prefrontal cortex, posterior cingulate cortex, and lateral temporal cortex and to the HC from the dorsomedial prefrontal cortex. The observed relationship between CRF and hippocampal effective connectivity provides a link between the rodent literature, which demonstrates a relationship between aerobic exercise and hippocampal plasticity, and the human literature, which demonstrates a relationship between aerobic exercise and CRF and the enhancement of broad cognitive domains including, but not limited to, memory.

PMID: 31647603 [PubMed - as supplied by publisher]

[The brain imaging studies of obstructive sleep apnea: evidence from resting-state EEG and fMRI].

Sat, 10/26/2019 - 08:57
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[The brain imaging studies of obstructive sleep apnea: evidence from resting-state EEG and fMRI].

Sheng Li Xue Bao. 2019 Oct 25;71(5):760-768

Authors: Wan XY, Zhao WR, Wu XR, Chen XY, Lei X

Abstract
Obstructive sleep apnea (OSA) is a common clinic sleep disorder, and characterized by obstruction of upper airway during sleep, resulting in sleep fragmentation and intermittent hypoxemia. We reviewed the brain imaging studies in OSA patients compared with healthy subjects, including studies of functional magnetic resonance imaging (fMRI) and electroencephalography (EEG). The resting-state EEG studies showed increased power of δ and θ in the front and central regions of the cerebral cortex in OSA patients. While resting-state fMRI studies demonstrated altered large-scale networks in default-mode network (DMN), central executive network (CEN) and salience network (SN). Evidence from resting-state studies of both fMRI and EEG focused on the abnormal activity in prefrontal cortex (PFC), which is correlated with OSA severity. These findings suggested that the PFC may play a key role in the abnormal function of OSA patients. Finally, based on the perspectives of treatment effect, multimodal data acquisition, and comorbidities, we discussed the future research direction of the neuroimaging study of OSA.

PMID: 31646330 [PubMed - in process]

Repetitive Transcranial Magnetic Stimulation Delivered With an H-Coil to the Right Insula Reduces Functional Connectivity Between Insula and Medial Prefrontal Cortex.

Thu, 10/24/2019 - 23:53
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Repetitive Transcranial Magnetic Stimulation Delivered With an H-Coil to the Right Insula Reduces Functional Connectivity Between Insula and Medial Prefrontal Cortex.

Neuromodulation. 2019 Oct 23;:

Authors: Lee MR, Caparelli EC, Leff M, Steele VR, Maxwell AM, McCullough K, Salmeron BJ

Abstract
OBJECTIVE: Insula neurocircuitry alterations are reported in a range of neuropsychiatric disorders holding promise for clinical interventions. We measured, in a pilot study, acute neuroplastic modulations resulting from high- and low-frequency stimulation with repetitive transcranial magnetic stimulation (rTMS) delivered via an H-coil that targeted the right insula and overlying prefrontal cortex.
METHODS: Healthy, nonsmoking, adult participants (N = 28), in a within-participant, sham-controlled experiment, received a single rTMS session on four separate days. Participants received one session each of low- (1 Hz) and high (10 Hz)-frequency stimulation and two sessions of sham stimulation matched to each rTMS frequency. After each rTMS session, participants completed a functional magnetic resonance imaging (fMRI) scan while performing two cognitive tasks and a resting-state scan. The effect of rTMS was examined on task behavior as well as blood oxygenated level-dependent (BOLD) response during task performance and resting state. We expected low- and high-frequency stimulation to decrease and increase, respectively, insula and overlying cortical BOLD signal and network connectivity.
RESULTS/CONCLUSIONS: There was no effect of rTMS, regardless of frequency, on task behavior or task-based BOLD response. There was an effect of rTMS compared to sham on rsFC between insula and medial prefrontal cortex, with connectivity reduced after rTMS compared to sham, regardless of frequency. Implications for using rTMS to the insula as a treatment for neuropsychiatric disorders are discussed in light of insula-medial prefrontal cortex connectivity.

PMID: 31645087 [PubMed - as supplied by publisher]

Sex-linked neurofunctional basis of psychological resilience in late adolescence: a resting-state functional magnetic resonance imaging study.

Thu, 10/24/2019 - 23:53
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Sex-linked neurofunctional basis of psychological resilience in late adolescence: a resting-state functional magnetic resonance imaging study.

Eur Child Adolesc Psychiatry. 2019 Oct 22;:

Authors: Wang S, Yang C, Zhao Y, Lai H, Zhang L, Gong Q

Abstract
Psychological resilience refers to the ability to adapt effectively in the face of adversity, which is closely related to an individual's psychological and physical health and well-being. Although previous behavioural studies have shown sex differences in psychological resilience, little is known about the neural basis of sex differences in psychological resilience. Here, we measured amplitude of low-frequency fluctuations (ALFF) via resting-state functional magnetic resonance imaging to investigate the sex-linked neurofunctional basis of psychological resilience in 231 healthy adolescents. At the behavioural level, we replicated previous findings indicating that males are more resilient than females. At the neural level, we found sex differences in the relationship between psychological resilience and ALFF in the right orbitofrontal cortex (OFC). Specifically, males showed a positive correlation between psychological resilience and ALFF in the right OFC, while females showed a negative correlation in this region. The sex-specific association between psychological resilience and spontaneous brain activity might be dependent on differences in hormonal systems and brain development between male and female adolescents. Taken together, the results of our study might provide the first evidence of sex-specific neurofunctional substrates of psychological resilience in adolescents, emphasizing the vital role of sex effects in future psychological resilience-related studies.

PMID: 31641900 [PubMed - as supplied by publisher]

[Comparison between approximate entropy and regional homogeneity for identification of irritable bowel syndrome based on functional magnetic resonance imaging].

Thu, 10/24/2019 - 23:53
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[Comparison between approximate entropy and regional homogeneity for identification of irritable bowel syndrome based on functional magnetic resonance imaging].

Nan Fang Yi Ke Da Xue Xue Bao. 2019 Sep 30;39(9):1023-1029

Authors: Nan J, Zhang L, Zheng Q, Zhang M, Lu Z

Abstract
OBJECTIVE: To compare the effectiveness and sensitivity of entropy and regional homogeneity (ReHo) for identifying irritable bowel syndrome (IBS) based on functional magnetic resonance imaging (fMRI).
METHODS: Voxel-based approximate entropy (ApEn) was calculated based on findings of resting fMRI of 54 patients with IBS and 54 healthy control subjects. Feature selection was performed using independent sample t-test, and support vector machine was then used to classify and identify different groups. The classification performance obtained from ApEn was compared with that from ReHo.
RESULTS: Significant differences between the two groups were found in the left triangle part of inferior prefrontal gyrus, right angular gyrus of the inferior parietal lobule, left inferior temporal gyrus, left middle temporal gyrus, left lingual gyrus, bilateral middle occipital gyrus and bilateral superior occipital gyrus for ReHo (P < 0.05), and in the bilateral postcentral gyrus, right precentral gyrus, right inferior temporal gyrus, bilateral middle temporal gyrus and left superior occipital gyrus for ApEn (P < 0.05). ApEn consistently showed better performance than ReHo regardless of the variations in the number of features. The classification accuracy, specificity and sensitivity of ApEn were 93.5185%, 90.7407% and 96.2963%, respectively, as compared with 86.1111%, 85.1852% and 87.037% of ReHo.
CONCLUSIONS: Entropy analysis based on fMRI can be more sensitive and effective than ReHo for identification of IBS.

PMID: 31640953 [PubMed - in process]

Rich-Club Analysis in Adults With ADHD Connectomes Reveals an Abnormal Structural Core Network.

Thu, 10/24/2019 - 23:53
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Rich-Club Analysis in Adults With ADHD Connectomes Reveals an Abnormal Structural Core Network.

J Atten Disord. 2019 Oct 23;:1087054719883031

Authors: Wang B, Wang G, Wang X, Cao R, Xiang J, Yan T, Li H, Yoshimura S, Toichi M, Zhao S

Abstract
Objective: Whether the abnormal connectome of brain's rich-club structure in adults with attention-deficit hyperactivity disorder (ADHD) remains unclear. Method: The current study used diffusion tensor imaging (DTI) and resting-state functional magnetic resonance imaging (fMRI) to compare the performance of 42 adults with ADHD and 59 typical development (TD) adults. Results: A reduced density of rich-clubs among structural hub nodes, including the bilateral precuneus, the insula, the caudate nucleus, the left putamen, and the right calcarine, was found in adults with ADHD. Moreover, lower global efficiency was found in adults with ADHD than in TD, which might be caused by a reduced density of rich-club connections in ADHD patients. Conclusion: Given that adults with ADHD have greater coupling strength between structural and functional connectivity than TD adults, connectome abnormalities with a reduced rich-club connectivity density might be accompanied by altered functional brain dynamics in ADHD patients.

PMID: 31640493 [PubMed - as supplied by publisher]

Resting-State fMRI Study of ADHD and Internet Gaming Disorder.

Thu, 10/24/2019 - 23:53
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Resting-State fMRI Study of ADHD and Internet Gaming Disorder.

J Atten Disord. 2019 Oct 23;:1087054719883022

Authors: Han DH, Bae S, Hong J, Kim SM, Son YD, Renshaw P

Abstract
Objective: We aimed to understand whether Attention Deficit Hyperactivity Disorder (ADHD) and Internet gaming disorder (IGD) share similar brain functional connectivity (FC) between the frontal and subcortices. Method: We compared changes in clinical symptoms and brain activity using functional magnetic resonance imaging (fMRI) in 26 patients with ADHD but without IGD, 29 patients with ADHD and IGD, and 20 patients with IGD but without ADHD. Results: The functional connectivity (FC) from the cortex to subcortex in both groups was decreased relative to that in age-matched healthy participants. One-year treatment for ADHD and IGD symptoms increased the FC between the cortex and subcortex in all ADHD participants and all IGD participants with good prognoses compared with those in all ADHD participants and all IGD participants with poor prognoses. Conclusion: Patients with ADHD and IGD shared similar brain FC at baseline and FC changes in response to treatment.

PMID: 31640464 [PubMed - as supplied by publisher]

Combining fMRI during resting state and an attention bias task in children.

Wed, 10/23/2019 - 20:52
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Combining fMRI during resting state and an attention bias task in children.

Neuroimage. 2019 Oct 19;:116301

Authors: Harrewijn A, Abend R, Linke J, Brotman MA, Fox NA, Leibenluft E, Winkler AM, Pine DS

Abstract
Neuroimaging studies typically focus on either resting state or task-based fMRI data. Prior research has shown that similarity in functional connectivity between rest and cognitive tasks, interpreted as reconfiguration efficiency, is related to task performance and IQ. Here, we extend this approach from adults to children, and from cognitive tasks to a threat-based attention task. The goal of the current study was to examine whether similarity in functional connectivity during rest and an attention bias task relates to threat bias, IQ, anxiety symptoms, and social reticence. fMRI was measured during resting state and during the dot-probe task in 41 children (M = 13.44, SD = 0.70). Functional connectivity during rest and dot-probe was positively correlated, suggesting that functional hierarchies in the brain are stable. Similarity in functional connectivity between rest and the dot-probe task only related to threat bias (puncorr < .03). This effect did not survive correction for multiple testing. Overall, children who allocate more attention towards threat also may possess greater reconfiguration efficiency in switching from intrinsic to threat-related attention states. Finally, functional connectivity correlated negatively across the two conditions of the dot-probe task. Opposing patterns of modulation of functional connectivity by threat-congruent and threat-incongruent trials may reflect task-specific network changes during two different attentional processes.

PMID: 31639510 [PubMed - as supplied by publisher]

Regression-based machine-learning approaches to predict task activation using resting-state fMRI.

Wed, 10/23/2019 - 20:52
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Regression-based machine-learning approaches to predict task activation using resting-state fMRI.

Hum Brain Mapp. 2019 Oct 22;:

Authors: Cohen AD, Chen Z, Parker Jones O, Niu C, Wang Y

Abstract
Resting-state fMRI has shown the ability to predict task activation on an individual basis by using a general linear model (GLM) to map resting-state network features to activation z-scores. The question remains whether the relatively simplistic GLM is the best approach to accomplish this prediction. In this study, several regression-based machine-learning approaches were compared, including GLMs, feed-forward neural networks, and random forest bootstrap aggregation (bagging). Resting-state and task data from 350 Human Connectome Project subjects were analyzed. First, the effect of the number of training subjects on the prediction accuracy was evaluated. In addition, the prediction accuracy and Dice coefficient were compared across models. Prediction accuracy increased with the training number up to 200 subjects; however, an elbow in the prediction curve occurred around 30-40 training subjects. All models performed well with correlation matrices, which displayed correlation between actual and predicted task activation for all subjects, exhibiting a strong diagonal trend for all tasks. Overall, the neural network and random forest bagging techniques outperformed the GLM. These approaches, however, require additional computing power and processing time. These results show that, while the GLM performs well, resting-state fMRI prediction of task activation could benefit from more complex machine learning approaches.

PMID: 31638304 [PubMed - as supplied by publisher]

Dynamic lag analysis reveals atypical brain information flow in autism spectrum disorder.

Wed, 10/23/2019 - 20:52
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Dynamic lag analysis reveals atypical brain information flow in autism spectrum disorder.

Autism Res. 2019 Oct 22;:

Authors: Raatikainen V, Korhonen V, Borchardt V, Huotari N, Helakari H, Kananen J, Raitamaa L, Joskitt L, Loukusa S, Hurtig T, Ebeling H, Uddin LQ, Kiviniemi V

Abstract
This study investigated whole-brain dynamic lag pattern variations between neurotypical (NT) individuals and individuals with autism spectrum disorder (ASD) by applying a novel technique called dynamic lag analysis (DLA). The use of 3D magnetic resonance encephalography data with repetition time = 100 msec enables highly accurate analysis of the spread of activity between brain networks. Sixteen resting-state networks (RSNs) with the highest spatial correlation between NT individuals (n = 20) and individuals with ASD (n = 20) were analyzed. The dynamic lag pattern variation between each RSN pair was investigated using DLA, which measures time lag variation between each RSN pair combination and statistically defines how these lag patterns are altered between ASD and NT groups. DLA analyses indicated that 10.8% of the 120 RSN pairs had statistically significant (P-value <0.003) dynamic lag pattern differences that survived correction with surrogate data thresholding. Alterations in lag patterns were concentrated in salience, executive, visual, and default-mode networks, supporting earlier findings of impaired brain connectivity in these regions in ASD. 92.3% and 84.6% of the significant RSN pairs revealed shorter mean and median temporal lags in ASD versus NT, respectively. Taken together, these results suggest that altered lag patterns indicating atypical spread of activity between large-scale functional brain networks may contribute to the ASD phenotype. Autism Res 2019. © 2019 The Authors. Autism Research published by International Society for Autism Research published by Wiley Periodicals, Inc. LAY SUMMARY: Autism spectrum disorder (ASD) is characterized by atypical neurodevelopment. Using an ultra-fast neuroimaging procedure, we investigated communication across brain regions in adults with ASD compared with neurotypical (NT) individuals. We found that ASD individuals had altered information flow patterns across brain regions. Atypical patterns were concentrated in salience, executive, visual, and default-mode network areas of the brain that have previously been implicated in the pathophysiology of the disorder.

PMID: 31637863 [PubMed - as supplied by publisher]

Synchronization lag in post stroke: relation to motor function and structural connectivity.

Wed, 10/23/2019 - 20:52
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Synchronization lag in post stroke: relation to motor function and structural connectivity.

Netw Neurosci. 2019;3(4):1121-1140

Authors: Wang X, Seguin C, Zalesky A, Wong WW, Chu WC, Tong RK

Abstract
Stroke is characterized by delays in the resting-state hemodynamic response, resulting in synchronization lag in neural activity between brain regions. However, the structural basis of this lag remains unclear. In this study, we used resting-state functional MRI (rs-fMRI) to characterize synchronization lag profiles between homotopic regions in 15 individuals (14 males, 1 female) with brain lesions consequent to stroke as well as a group of healthy comparison individuals. We tested whether the network communication efficiency of each individual's structural brain network (connectome) could explain interindividual and interregional variation in synchronization lag profiles. To this end, connectomes were mapped using diffusion MRI data, and communication measures were evaluated under two schemes: shortest paths and navigation. We found that interindividual variation in synchronization lags was inversely associated with communication efficiency under both schemes. Interregional variation in lag was related to navigation efficiency and navigation distance, reflecting its dependence on both distance and structural constraints. Moreover, severity of motor deficits significantly correlated with average synchronization lag in stroke. Our results provide a structural basis for the delay of information transfer between homotopic regions inferred from rs-fMRI and provide insight into the clinical significance of structural-functional relationships in stroke individuals.

PMID: 31637341 [PubMed]

Adaptive frequency-based modeling of whole-brain oscillations: Predicting regional vulnerability and hazardousness rates.

Wed, 10/23/2019 - 20:52
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Adaptive frequency-based modeling of whole-brain oscillations: Predicting regional vulnerability and hazardousness rates.

Netw Neurosci. 2019;3(4):1094-1120

Authors: Kaboodvand N, van den Heuvel MP, Fransson P

Abstract
Whole-brain computational modeling based on structural connectivity has shown great promise in successfully simulating fMRI BOLD signals with temporal coactivation patterns that are highly similar to empirical functional connectivity patterns during resting state. Importantly, previous studies have shown that spontaneous fluctuations in coactivation patterns of distributed brain regions have an inherent dynamic nature with regard to the frequency spectrum of intrinsic brain oscillations. In this modeling study, we introduced frequency dynamics into a system of coupled oscillators, where each oscillator represents the local mean-field model of a brain region. We first showed that the collective behavior of interacting oscillators reproduces previously shown features of brain dynamics. Second, we examined the effect of simulated lesions in gray matter by applying an in silico perturbation protocol to the brain model. We present a new approach to map the effects of vulnerability in brain networks and introduce a measure of regional hazardousness based on mapping of the degree of divergence in a feature space.

PMID: 31637340 [PubMed]

Hemodynamic Correlates of Electrophysiological Activity in the Default Mode Network.

Wed, 10/23/2019 - 20:52
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Hemodynamic Correlates of Electrophysiological Activity in the Default Mode Network.

Front Neurosci. 2019;13:1060

Authors: Marino M, Arcara G, Porcaro C, Mantini D

Abstract
Hemodynamic fluctuations in the default mode network (DMN), observed through functional magnetic resonance imaging (fMRI), have been linked to electrophysiological oscillations detected by electroencephalography (EEG). It has been reported that, among the electrophysiological oscillations, those in the alpha frequency range (8-13 Hz) are the most dominant during resting state. We hypothesized that DMN spatial configuration closely depends on the specific neuronal oscillations considered, and that alpha oscillations would mainly correlate with increased blood oxygen-level dependent (BOLD) signal in the DMN. To test this hypothesis, we used high-density EEG (hdEEG) data simultaneously collected with fMRI scanning in 20 healthy volunteers at rest. We first detected the DMN from source reconstructed hdEEG data for multiple frequency bands, and we then mapped the correlation between temporal profile of hdEEG-derived DMN activity and fMRI-BOLD signals on a voxel-by-voxel basis. In line with our hypothesis, we found that the correlation map associated with alpha oscillations, more than with any other frequency bands, displayed a larger overlap with DMN regions. Overall, our study provided further evidence for a primary role of alpha oscillations in supporting DMN functioning. We suggest that simultaneous EEG-fMRI may represent a powerful tool to investigate the neurophysiological basis of human brain networks.

PMID: 31636535 [PubMed]

A set of functionally-defined brain regions with improved representation of the subcortex and cerebellum.

Tue, 10/22/2019 - 20:50
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A set of functionally-defined brain regions with improved representation of the subcortex and cerebellum.

Neuroimage. 2019 Oct 18;:116290

Authors: Seitzman BA, Gratton C, Marek S, Raut RV, Dosenbach NUF, Schlaggar BL, Petersen SE, Greene DJ

Abstract
An important aspect of network-based analysis is robust node definition. This issue is critical for functional brain network analyses, as poor node choice can lead to spurious findings and misleading inferences about functional brain organization. Two sets of functional brain nodes from our group are well represented in the literature: (1) 264 volumetric regions of interest (ROIs) reported in Power et al., 2011) and (2) 333 cortical surface parcels reported in Gordon et al., 2016). However, subcortical and cerebellar structures are either incompletely captured or missing from these ROI sets. Therefore, properties of functional network organization involving the subcortex and cerebellum may be underappreciated thus far. Here, we apply a winner-take-all partitioning method to resting-state fMRI data to generate novel functionally-constrained ROIs in the thalamus, basal ganglia, amygdala, hippocampus, and cerebellum. We validate these ROIs in three datasets using several criteria, including agreement with existing literature and anatomical atlases. Further, we demonstrate that combining these ROIs with established cortical ROIs recapitulates and extends previously described functional network organization. This new set of ROIs is made publicly available for general use, including a full list of MNI coordinates and functional network labels.

PMID: 31634545 [PubMed - as supplied by publisher]

Multiscale Community Detection in Functional Brain Networks Constructed using Dynamic Time Warping.

Tue, 10/22/2019 - 20:50
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Multiscale Community Detection in Functional Brain Networks Constructed using Dynamic Time Warping.

IEEE Trans Neural Syst Rehabil Eng. 2019 Oct 17;:

Authors: Jin D, Li R, Xu J

Abstract
Previous studies have focused on the detection of community structures of brain networks constructed with resting-state functional magnetic resonance imaging (fMRI) data. Pearson correlation is often used to describe the connections between nodes in the construction of functional brain networks, which typically ignores the inherent timing and validity of fMRI time series. To solve this problem, this study applied the Dynamic Time Warp (DTW) algorithm to determine the correlation between two brain regions by comparing the synchronization and asynchrony of the time series. In addition, to determine the best community structure for each subject, we further divided the brain network into different scales, and then detected the different communities in these brain networks by using Modularity, Variation of Information (VI) and Normalized Mutual Information (NMI) as structural monitoring variables. Finally, we affirmed each subject's best community structure based on them. The experiments showed that through the method proposed in this paper, we not only accurately discovered important components of seven basic functional subnetworks, but also found that the putamen and Heschl's gyrus have a relationship with the inferior parietal network. Most importantly, this method can also determine each subjectb's functional brain network density, thus confirming the findings of studies testing real brain networks.

PMID: 31634138 [PubMed - as supplied by publisher]

Functional outcome is tied to dynamic brain states after mild to moderate traumatic brain injury.

Tue, 10/22/2019 - 20:50
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Functional outcome is tied to dynamic brain states after mild to moderate traumatic brain injury.

Hum Brain Mapp. 2019 Oct 21;:

Authors: van der Horn HJ, Vergara VM, Espinoza FA, Calhoun VD, Mayer AR, van der Naalt J

Abstract
The current study set out to investigate the dynamic functional connectome in relation to long-term recovery after mild to moderate traumatic brain injury (TBI). Longitudinal resting-state functional MRI data were collected (at 1 and 3 months postinjury) from a prospectively enrolled cohort consisting of 68 patients with TBI (92% mild TBI) and 20 healthy subjects. Patients underwent a neuropsychological assessment at 3 months postinjury. Outcome was measured using the Glasgow Outcome Scale Extended (GOS-E) at 6 months postinjury. The 57 patients who completed the GOS-E were classified as recovered completely (GOS-E = 8; n = 37) or incompletely (GOS-E < 8; n = 20). Neuropsychological test scores were similar for all groups. Patients with incomplete recovery spent less time in a segregated brain state compared to recovered patients during the second visit. Also, these patients moved less frequently from one meta-state to another as compared to healthy controls and recovered patients. Furthermore, incomplete recovery was associated with disruptions in cyclic state transition patterns, called attractors, during both visits. This study demonstrates that poor long-term functional recovery is associated with alterations in dynamics between brain networks, which becomes more marked as a function of time. These results could be related to psychological processes rather than injury-effects, which is an interesting area for further work. Another natural progression of the current study is to examine whether these dynamic measures can be used to monitor treatment effects.

PMID: 31633256 [PubMed - as supplied by publisher]

Dynamic Alterations of Spontaneous Neural Activity in Parkinson's Disease: A Resting-State fMRI Study.

Tue, 10/22/2019 - 20:50
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Dynamic Alterations of Spontaneous Neural Activity in Parkinson's Disease: A Resting-State fMRI Study.

Front Neurol. 2019;10:1052

Authors: Zhang C, Dou B, Wang J, Xu K, Zhang H, Sami MU, Hu C, Rong Y, Xiao Q, Chen N, Li K

Abstract
Objective: To investigate the dynamic amplitude of low-frequency fluctuations (dALFFs) in patients with Parkinson's disease (PD) and healthy controls (HCs) and further explore whether dALFF can be used to test the feasibility of differentiating PD from HCs. Methods: Twenty-eight patients with PD and 28 demographically matched HCs underwent resting-state functional magnetic resonance imaging (rs-fMRI) scans and neuropsychological tests. A dynamic method was used to calculate the dALFFs of rs-fMRI data obtained from all subjects. The dALFF alterations were compared between the PD and HC groups, and the correlations between dALFF variability and disease duration/neuropsychological tests were further calculated. Then, the statistical differences in dALFF between both groups were selected as classification features to help distinguish patients with PD from HCs through a linear support vector machine (SVM) classifier. The classifier performance was assessed using a permutation test (repeated 5,000 times). Results: Significantly increased dALFF was detected in the left precuneus in patients with PD compared to HCs, and dALFF variability in this region was positively correlated with disease duration. Our results show that 80.36% (p < 0.001) subjects were correctly classified based on the SVM classifier by using the leave-one-out cross-validation method. Conclusion: Patients with PD exhibited abnormal dynamic brain activity in the left precuneus, and the dALFF variability could distinguish PD from HCs with high accuracy. Our results showed novel insights into the pathophysiological mechanisms of PD.

PMID: 31632340 [PubMed]

Altered Resting State Functional Activity and Microstructure of the White Matter in Migraine With Aura.

Tue, 10/22/2019 - 20:50
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Altered Resting State Functional Activity and Microstructure of the White Matter in Migraine With Aura.

Front Neurol. 2019;10:1039

Authors: Faragó P, Tóth E, Kocsis K, Kincses B, Veréb D, Király A, Bozsik B, Tajti J, Párdutz Á, Szok D, Vécsei L, Szabó N, Kincses ZT

Abstract
Introduction: Brain structure and function were reported to be altered in migraine. Importantly our earlier results showed that white matter diffusion abnormalities and resting state functional activity were affected differently in the two subtypes of the disease, migraine with and without aura. Resting fluctuation of the BOLD signal in the white matter was reported recently. The question arising whether the white matter activity, that is strongly coupled with gray matter activity is also perturbed differentially in the two subtypes of the disease and if so, is it related to the microstructural alterations of the white matter. Methods: Resting state fMRI, 60 directional DTI images and high-resolution T1 images were obtained from 51 migraine patients and 32 healthy volunteers. The images were pre-processed and the white matter was extracted. Independent component analysis was performed to obtain white matter functional networks. The differential expression of the white matter functional networks in the two subtypes of the disease was investigated with dual-regression approach. The Fourier spectrum of the resting fMRI fluctuations were compared between groups. Voxel-wise correlation was calculated between the resting state functional activity fluctuations and white matter microstructural measures. Results: Three white matter networks were identified that were expressed differently in migraine with and without aura. Migraineurs with aura showed increased functional connectivity and amplitude of BOLD fluctuation. Fractional anisotropy and radial diffusivity showed strong correlation with the expression of the frontal white matter network in patients with aura. Discussion: Our study is the first to describe changes in white matter resting state functional activity in migraine with aura, showing correlation with the underlying microstructure. Functional and structural differences between disease subtypes suggest at least partially different pathomechanism, which may necessitate handling of these subtypes as separate entities in further studies.

PMID: 31632336 [PubMed]

Parsing heterogeneity in autism spectrum disorder and attention-deficit/hyperactivity disorder with individual connectome mapping.

Tue, 10/22/2019 - 20:50
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Parsing heterogeneity in autism spectrum disorder and attention-deficit/hyperactivity disorder with individual connectome mapping.

Brain Connect. 2019 Oct 21;:

Authors: Dajani D, Burrows C, Nebel MB, Mostofsky S, Gates K, Uddin LQ

Abstract
Traditional diagnostic systems for neurodevelopmental disorders define diagnostic categories that are heterogeneous in behavior and underlying neurobiological alterations. The goal of this study was to parse heterogeneity in a core executive function, cognitive flexibility, in children with a range of abilities (N=132; children with autism spectrum disorder [ASD], attention deficit/hyperactivity disorder [ADHD], and typically developing [TD] children) using directed functional connectivity profiles derived from resting-state fMRI data. Brain regions activated in response to a cognitive flexibility task in adults were used to guide region-of-interest (ROI) selection to estimate individual connectivity profiles in this study. We expected to find subgroups of children who differed in their network connectivity metrics and symptom measures. Unexpectedly, we did not find a stable or valid subgrouping solution, which suggests that categorical models of the neural substrates of cognitive flexibility in children may be invalid. Exploratory analyses revealed dimensional associations between network connectivity metrics and ADHD symptomatology and executive function ability across the entire sample. Results shed light on the validity of conceptualizing the neural substrates of cognitive flexibility categorically in children. Ultimately, this work may provide a foundation for the development of a revised nosology focused on neurobiological substrates as an alternative to traditional symptom-based classification systems.

PMID: 31631690 [PubMed - as supplied by publisher]

Inter-subject P300 variability relates to the efficiency of brain networks reconfigured from resting-to task-state: Evidence from a simultaneous event-related EEG-fMRI study.

Mon, 10/21/2019 - 20:48
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Inter-subject P300 variability relates to the efficiency of brain networks reconfigured from resting-to task-state: Evidence from a simultaneous event-related EEG-fMRI study.

Neuroimage. 2019 Oct 17;:116285

Authors: Li F, Tao Q, Peng W, Zhang T, Si Y, Zhang Y, Yi C, Biswal B, Yao D, Xu P

Abstract
The P300 event-related potential (ERP) varies across individuals, and exploring this variability deepens our knowledge of the event, and scope for its potential applications. Previous studies exploring the P300 have relied on either electroencephalography (EEG) or functional magnetic resonance imaging (fMRI). We applied simultaneous event-related EEG-fMRI to investigate how the network structure is updated from rest to the P300 task so as to guarantee information processing in the oddball task. We first identified 14 widely distributed regions of interest (ROIs) that were task-associated, including the inferior frontal gyrus and the middle frontal gyrus, etc. The task-activated network was found to closely relate to the concurrent P300 amplitude, and moreover, the individuals with optimized resting-state brain architectures experienced the pruning of network architecture, i.e. decreasing connectivity, when the brain switched from rest to P300 task. Our present simultaneous EEG-fMRI study explored the brain reconfigurations governing the variability in P300 across individuals, which provided the possibility to uncover new biomarkers to predict the potential for personalized control of brain-computer interfaces.

PMID: 31629829 [PubMed - as supplied by publisher]