Most recent paper

Subscribe to Most recent paper feed Most recent paper
NCBI: db=pubmed; Term="resting"[All Fields] AND "fMRI"[All Fields]
Updated: 4 hours 2 min ago

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

Wed, 10/23/2019 - 20:52
Related Articles

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
Related Articles

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
Related Articles

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
Related Articles

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
Related Articles

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
Related Articles

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
Related Articles

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
Related Articles

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
Related Articles

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
Related Articles

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
Related Articles

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
Related Articles

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
Related Articles

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]

Brain networks, dimensionality, and global signal averaging in resting-state fMRI: Hierarchical network structure results in low-dimensional spatiotemporal dynamics.

Mon, 10/21/2019 - 20:48
Related Articles

Brain networks, dimensionality, and global signal averaging in resting-state fMRI: Hierarchical network structure results in low-dimensional spatiotemporal dynamics.

Neuroimage. 2019 Oct 17;:116289

Authors: Gotts SJ, Gilmore AW, Martin A

Abstract
One of the most controversial practices in resting-state fMRI functional connectivity studies is whether or not to regress out the global average brain signal (GS) during artifact removal. Some groups have argued that it is absolutely essential to regress out the GS in order to fully remove head motion, respiration, and other global imaging artifacts. Others have argued that removing the GS distorts the resulting correlation matrices and inappropriately alters the results of group comparisons and relationships to behavior. At the core of this argument is the assessment of dimensionality in terms of the number of brain networks with uncorrelated time series. If the dimensionality is high, then the distortions due to GS removal could be effectively negligible. In the current paper, we examine the dimensionality of resting-state fMRI data using principal component analyses (PCA) and network clustering analyses. In two independent datasets (Set 1: N = 62, Set 2: N = 32), scree plots of the eigenvalues level off at or prior to 10 principal components, with prominent elbows at 3 and 7 components. While network clustering analyses have previously demonstrated that numerous networks can be distinguished with high thresholding of the voxel-wise correlation matrices, lower thresholding reveals a lower-dimensional hierarchical structure, with the first prominent branch at 2 networks (corresponding to the previously described "task-positive"/"task-negative" distinction) and further stable subdivisions at 4, 7 and 17. Since inter-correlated time series within these larger branches do not cancel to zero when averaged, the hierarchical nature of the correlation structure results in low effective dimensionality. Consistent with this, partial correlation analyses revealed that network-specific variance remains present in the GS at each level of the hierarchy, accounting for at least 14-18% of the overall GS variance in each dataset. These results demonstrate that GS regression is expected to remove substantial portions of network-specific brain signals along with artifacts, not simply whole-brain signals corresponding to arousal levels. We highlight alternative means of controlling for residual global artifacts when not removing the GS.

PMID: 31629827 [PubMed - as supplied by publisher]

Test-retest reliability of spatial patterns from resting-state functional MRI using the restricted Boltzmann machine and hierarchically organized spatial patterns from the deep belief network.

Sat, 10/19/2019 - 20:45
Related Articles

Test-retest reliability of spatial patterns from resting-state functional MRI using the restricted Boltzmann machine and hierarchically organized spatial patterns from the deep belief network.

J Neurosci Methods. 2019 Oct 15;:108451

Authors: Kim HC, Jang H, Lee JH

Abstract
BACKGROUND: Restricted Boltzmann machines (RBMs), including greedy layer-wise trained RBMs as part of a deep belief network (DBN), have the ability to identify spatial patterns (SPs; functional networks) in resting-state fMRI (rfMRI) data. However, there has been little research on (1) the reproducibility and test-retest reliability of SPs derived from RBMs and on (2) hierarchical SPs derived from DBNs.
METHODS: We applied a weight sparsity-controlled RBM and DBN to whole-brain rfMRI data from the Human Connectome Project. We evaluated the within-session reproducibility and between-session test-retest reliability of the SPs derived from the RBM approach and compared them both with those identified using independent component analysis (ICA) and with three voxel-wise statistical measures-the Hurst exponent, entropy, and kurtosis-of the rfMRI data. We also assessed the potential hierarchy of the SPs from the DBN.
RESULTS: An increase in the sparsity level of the RBM weights enhanced the reproducibility of the SPs. The SPs deriving from a stringent weight sparsity level were predominantly found in the cortical gray matter and substantially overlapped with the SPs obtained from the Hurst exponent. A hierarchical representation was shown by constructed using the default-mode network obtained from the DBN.
COMPARISON WITH EXISTING METHODS: The test-retest reliability of the SPs from the RBM was superior to that of the SPs from the voxel-wise statistics.
CONCLUSIONS: The SPs from the RBM were reproducible within sessions and reliable across sessions. The hierarchically organized SPs from the DBN could possibly be applied to research based on rfMRI data.

PMID: 31626847 [PubMed - as supplied by publisher]

Decreased regional homogeneity and increased functional connectivity of default network correlated with neurocognitive deficits in subjects with genetic high-risk for schizophrenia: A resting-state fMRI study.

Fri, 10/18/2019 - 23:44
Related Articles

Decreased regional homogeneity and increased functional connectivity of default network correlated with neurocognitive deficits in subjects with genetic high-risk for schizophrenia: A resting-state fMRI study.

Psychiatry Res. 2019 Oct 07;281:112603

Authors: Ma X, Zheng W, Li C, Li Z, Tang J, Yuan L, Ouyang L, Jin K, He Y, Chen X

Abstract
The complex symptoms of schizophrenia (SCZ) have been associated with dysfunction of the default mode network (DMN). Subjects at genetic high risk (GHR) for SCZ exhibit similar but milder brain abnormalities. This study aimed to investigate functional alterations of DMN from the local to the whole and their relationships with cognitive deficits in GHR subjects. 42 GHR subjects and 38 matched healthy controls (HC) were studied by resting-state functional magnetic resonance imaging (rs-fMRI). Regional homogeneity (ReHo) analysis was performed to measure the local brain function of the DMN, derived by the group independent component analysis, and areas with aberrant ReHo were used as seeds in functional connectivity (FC). Compared with the HC group, the GHR group exhibited significantly decreased ReHo and increased FC in the fronto-limbic-striatal system within the DMN. Furthermore, a significant negative correlation was found between decreased ReHo in the right superior frontal gyrus and the delayed recall in GHR subjects. Our findings revealed decreased local function and hyper-connectivity in the fronto-limbic-striatal system of the DMN in GHR subjects, which is associated with cognitive deficits. This may improve our understanding of the neurophysiological endophenotypes of SCZ and the neural substrate underlying the cognitive deficits of the disease.

PMID: 31622873 [PubMed - as supplied by publisher]

Dysfunction between dorsal caudate and salience network associated with impaired cognitive flexibility in obsessive-compulsive disorder: A resting-state fMRI study.

Fri, 10/18/2019 - 23:44
Related Articles

Dysfunction between dorsal caudate and salience network associated with impaired cognitive flexibility in obsessive-compulsive disorder: A resting-state fMRI study.

Neuroimage Clin. 2019 Sep 16;24:102004

Authors: Tomiyama H, Nakao T, Murayama K, Nemoto K, Ikari K, Yamada S, Kuwano M, Hasuzawa S, Togao O, Hiwatashi A, Kanba S

Abstract
BACKGROUND: Impaired cognitive flexibility has been implicated in the genetic basis of obsessive-compulsive disorder (OCD). Recent endophenotype studies of OCD showed neural inefficiency in the cognitive control network and interference by the limbic network of the cognitive control network. Exploring the relationship between the functional brain network and impaired cognitive flexibility may provide novel information about the neurobiological basis of OCD.
METHODS: We obtained resting-state functional magnetic resonance imaging (rsfMRI) scans and measured the cognitive flexibility of 37 medication-free OCD patients and 40 healthy control (HC) participants using the Wisconsin Card Sorting Test (WCST). We explored the difference between OCD and HC groups in the functional brain network related to impaired cognitive flexibility from the amygdala and dorsal striatal regions of interest (ROIs) by using a seed-based approach.
RESULTS: Significant differences between the OCD and HC groups were identified in the resting state functional network from the dorsal caudate. Increased functional connectivity from the dorsal caudate to the dorsal anterior cingulate cortex (dACC) and anterior insula (AI) was associated with poorer cognitive flexibility in the OCD group, but better cognitive flexibility in the HC group.
CONCLUSIONS: These results provide evidence that the impaired cognitive flexibility of OCD may be associated with dysfunctions of the brain network from the dorsal caudate (DC) to important nodes of the salience network. Our results extend the neuropsychological model of OCD by showing intrinsically different associations between OCD and HC in functional network and cognitive flexibility.

PMID: 31622840 [PubMed - as supplied by publisher]

Apomorphine-induced reorganization of striato-frontal connectivity in patients with tremor-dominant Parkinson's disease.

Fri, 10/18/2019 - 23:44
Related Articles

Apomorphine-induced reorganization of striato-frontal connectivity in patients with tremor-dominant Parkinson's disease.

Parkinsonism Relat Disord. 2019 Sep 08;67:14-20

Authors: Nigro S, Bordier C, Cerasa A, Nisticò R, Olivadese G, Vescio B, Bianco MG, Fiorillo A, Barbagallo G, Crasà M, Quattrone A, Morelli M, Arabia G, Augimeri A, Nicolini C, Bifone A, Quattrone A

Abstract
INTRODUCTION: Apomorphine is a dopamine agonist used in Parkinson's disease (PD), which matches levodopa in terms of the magnitude of effect on the cardinal motor features, such as tremor and bradykinesia. The beneficial effect of this treatment on PD patients with tremor-dominant has widely been demonstrated, although the underlying neural correlates are unknown. We sought to examine the effects of apomorphine on topological characteristics of resting-state functional connectivity networks in tremor-dominant PD (tdPD) patients.
METHODS: Sixteen tdPD patients were examined using a combined electromyography-functional magnetic resonance imaging approach. Patients were scanned twice following either placebo (subcutaneous injection of 1 mL saline solution) or 1 mg of apomorphine injection. Graph analysis methods were employed to investigate the modular organization of functional connectivity networks before and after drug treatment.
RESULTS: After injection of apomorphine, evident reduction of tremor symptoms was mirrored by a significant increase in overall connectivity strength and reorganization of the modular structure of the basal ganglia and of the fronto-striatal module. Moreover, we found an increase in the centrality of motor and premotor regions. No differences were found between pre- and post-placebo sessions.
CONCLUSION: These results provide new evidence about the effects of apomorphine at a large-scale neural network level showing that drug treatment modifies the brain functional organization of tdPD, increasing the overall resting-state functional connectivity strength, the segregation of striato-frontal regions and the integrative role of motor areas.

PMID: 31621599 [PubMed - as supplied by publisher]

Increased Functional Connectivity Within Intrinsic Neural Networks in Chronic Stroke Following Treatment With Red/Near-Infrared Transcranial Photobiomodulation: Case Series With Improved Naming in Aphasia.

Fri, 10/18/2019 - 23:44
Related Articles

Increased Functional Connectivity Within Intrinsic Neural Networks in Chronic Stroke Following Treatment With Red/Near-Infrared Transcranial Photobiomodulation: Case Series With Improved Naming in Aphasia.

Photobiomodul Photomed Laser Surg. 2019 Oct 17;:

Authors: Naeser MA, Ho MD, Martin PI, Hamblin MR, Koo BB

Abstract
Objective: To examine effects of four different transcranial, red/near-infrared (NIR), light-emitting diode (tLED) protocols on naming ability in persons with aphasia (PWA) due to left hemisphere (LH) stroke. This is the first study to report beneficial effects from tLED therapy in chronic stroke, and parallel changes on functional magnetic resonance imaging (fMRI). Materials and methods: Six PWA, 2-18 years poststroke, in whom 18 tLED treatments were applied (3 × /week, 6 weeks) using LED cluster heads: 500 mW, red (633 nm) and NIR (870 nm), 22.48 cm2, 22.2 mW/cm2. Results: After Protocol A with bilateral LED placements, including midline, at scalp vertex over left and right supplementary motor areas (L and R SMAs), picture naming was not improved. P1 underwent pre-/postovert, picture-naming task-fMRI scans; P2 could not. After Protocol A, P1 showed increased activation in LH and right hemisphere, including L and R SMAs. After Protocol B with LEDs only on ipsilesional, LH side, naming ability significantly improved for P1 and P2; the fMRI scans for P1 then showed activation only on the ipsilesional LH side. After Protocol C with LED placements on ipsilesional LH side, plus one midline placement over mesial prefrontal cortex (mPFC) at front hairline, a cortical node of the default mode network (DMN), P3 and P4 had only moderate/poor response, and no increase in functional connectivity on resting-state functional-connectivity MRI. After Protocol D, however, with LED placements on ipsilesional LH side, plus over two midline nodes of DMN, mPFC, and precuneus (high parietal) simultaneously, P5 and P6 each had good response with significant increase in functional connectivity within DMN, p < 0.0005; salience network, p < 0.0005; and central executive network, p < 0.05. Conclusions: NIR photons can affect surface brain cortex areas subjacent to where LEDs are applied on the scalp. Improved naming ability was present with optimal Protocol D. Transcranial photobiomodulation may be an additional noninvasive therapy for stroke.

PMID: 31621498 [PubMed - as supplied by publisher]

Abnormal Functional Connectivity in Cognitive Control Network, Default Mode Network, and Visual Attention Network in Internet Addiction: A Resting-State fMRI Study.

Fri, 10/18/2019 - 23:44
Related Articles

Abnormal Functional Connectivity in Cognitive Control Network, Default Mode Network, and Visual Attention Network in Internet Addiction: A Resting-State fMRI Study.

Front Neurol. 2019;10:1006

Authors: Wang Y, Qin Y, Li H, Yao D, Sun B, Li Z, Li X, Dai Y, Wen C, Zhang L, Zhang C, Zhu T, Luo C

Abstract
Internet addiction (IA) has become a global mental and social problem, which may lead to a series of psychiatric symptoms including uncontrolled use of internet, and lack of concentration. However, the exact pathophysiology of IA remains unclear. Most of functional connectivity studies were based on pre-selected regions of interest (ROI), which could not provide a comprehensive picture of the communication abnormalities in IA, and might lead to limited or bias observations. Using local functional connectivity density (lFCD), this study aimed to explore the whole-brain abnormalities of functional connectivity in IA. We evaluated the whole-brain lFCD resulting from resting-state fMRI data in 28 IA individuals and 30 demographically matched healthy control subjects (HCs). The correlations between clinical characteristics and aberrant lFCD were also assessed. Compared with HCs, subjects with IA exhibited heightened lFCD values in the right dorsolateral prefrontal cortex (DLPFC), left parahippocampal gyrus (PHG), and cerebellum, and the bilateral middle cingulate cortex (MCC) and superior temporal pole (STP), as well as decreased lFCD values in the right inferior parietal lobe (IPL), and bilateral calcarine and lingual gyrus. Voxel-based correlation analysis revealed the significant correlations between the Young's Internet Addiction Test (IAT) score and altered lFCD values in the left PHG and bilateral STP. These findings revealed the hyper-connectivity in cognitive control network and default mode network as well as the hypo-connectivity in visual attention network, verifying the common mechanism in IA and substance addiction, and the underlying association between IA, and attention deficit/hyperactivity disorder in terms of neurobiology.

PMID: 31620077 [PubMed]