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Explaining Individual Differences in Motor Behavior by Intrinsic Functional Connectivity and Corticospinal Excitability.

Wed, 03/04/2020 - 03:00
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Explaining Individual Differences in Motor Behavior by Intrinsic Functional Connectivity and Corticospinal Excitability.

Front Neurosci. 2020;14:76

Authors: Herszage J, Dayan E, Sharon H, Censor N

Abstract
Motor performance varies substantially between individuals. This variance is rooted in individuals' innate motor abilities, and should thus have a neural signature underlying these differences in behavior. Could these individual differences be detectable with neural measurements acquired at rest? Here, we tested the hypothesis that motor performance can be predicted by resting motor-system functional connectivity and motor-evoked-potentials (MEPs) induced by non-invasive brain stimulation. Twenty healthy right handed subjects performed structural and resting-state fMRI scans. On a separate day, MEPs were measured using transcranial magnetic stimulation (TMS) over the contrateral primary motor cortex (M1). At the end of the session, participants performed a finger-tapping task using their left non-dominant hand. Resting-state functional connectivity between the contralateral M1 and the supplementary motor area (SMA) predicted motor task performance, indicating that individuals with stronger resting M1-SMA functional connectivity exhibit better motor performance. This prediction was neither improved nor reduced by the addition of corticospinal excitability to the model. These results confirm that motor behavior can be predicted from neural measurements acquired prior to task performance, primarily relying on resting functional connectivity rather than corticospinal excitability. The ability to predict motor performance from resting neural markers, provides an opportunity to identify the extent of successful rehabilitation following neurological damage.

PMID: 32116520 [PubMed]

Hypernetwork Construction and Feature Fusion Analysis Based on Sparse Group Lasso Method on Functional fMRI Dataset.

Wed, 03/04/2020 - 03:00
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Hypernetwork Construction and Feature Fusion Analysis Based on Sparse Group Lasso Method on Functional fMRI Dataset.

Front Neurosci. 2020;14:60

Authors: Li Y, Sun C, Li P, Zhao Y, Mensah GK, Xu Y, Guo H, Chen J

Abstract
Recent works have shown that the resting-state brain functional connectivity hypernetwork, where multiple nodes can be connected, are an effective technique for brain disease diagnosis and classification research. The lasso method was used to construct hypernetworks by solving sparse linear regression models in previous research. But, constructing a hypernetwork based on the lasso method simply selects a single variable, in that it lacks the ability to interpret the grouping effect. Considering the group structure problem, the previous study proposed to create a hypernetwork based on the elastic net and the group lasso methods, and the results showed that the former method had the best classification performance. However, the highly correlated variables selected by the elastic net method were not necessarily in the active set in the group. Therefore, we extended our research to address this issue. Herein, we propose a new method that introduces the sparse group lasso method to improve the construction of the hypernetwork by solving the group structure problem of the brain regions. We used the traditional lasso, group lasso method, and sparse group lasso method to construct a hypernetwork in patients with depression and normal subjects. Meanwhile, other clustering coefficients (clustering coefficients based on pairs of nodes) were also introduced to extract features with traditional clustering coefficients. Two types of features with significant differences obtained after feature selection were subjected to multi-kernel learning for feature fusion and classification using each method, respectively. The network topology results revealed differences among the three networks, where hypernetwork using the lasso method was the strictest; the group lasso, most lenient; and the sgLasso method, moderate. The network topology of the sparse group lasso method was similar to that of the group lasso method but different from the lasso method. The classification results show that the sparse group lasso method achieves the best classification accuracy by using multi-kernel learning, which indicates that better classification performance can be achieved when the group structure exists and is properly extended.

PMID: 32116508 [PubMed]

Multi-Level Clustering of Dynamic Directional Brain Network Patterns and Their Behavioral Relevance.

Wed, 03/04/2020 - 03:00
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Multi-Level Clustering of Dynamic Directional Brain Network Patterns and Their Behavioral Relevance.

Front Neurosci. 2019;13:1448

Authors: Deshpande G, Jia H

Abstract
Dynamic functional connectivity (DFC) obtained from resting state functional magnetic resonance imaging (fMRI) data has been shown to provide novel insights into brain function which may be obscured by static functional connectivity (SFC). Further, DFC, and by implication how different brain regions may engage or disengage with each other over time, has been shown to be behaviorally relevant and more predictive than SFC of behavioral performance and/or diagnostic status. DFC is not a directional entity and may capture neural synchronization. However, directional interactions between different brain regions is another putative mechanism by which neural populations communicate. Accordingly, static effective connectivity (SEC) has been explored as a means of characterizing such directional interactions. But investigation of its dynamic counterpart, i.e., dynamic effective connectivity (DEC), is still in its infancy. Of particular note are methodological insufficiencies in identifying DEC configurations that are reproducible across time and subjects as well as a lack of understanding of the behavioral relevance of DEC obtained from resting state fMRI. In order to address these issues, we employed a dynamic multivariate autoregressive (MVAR) model to estimate DEC. The method was first validated using simulations and then applied to resting state fMRI data obtained in-house (N = 21), wherein we performed dynamic clustering of DEC matrices across multiple levels [using adaptive evolutionary clustering (AEC)] - spatial location, time, and subjects. We observed a small number of directional brain network configurations alternating between each other over time in a quasi-stable manner akin to brain microstates. The dominant and consistent DEC network patterns involved several regions including inferior and mid temporal cortex, motor and parietal cortex, occipital cortex, as well as part of frontal cortex. The functional relevance of these DEC states were determined using meta-analyses and pertained mainly to memory and emotion, but also involved execution and language. Finally, a larger cohort of resting-state fMRI and behavioral data from the Human Connectome Project (HCP) (N = 232, Q1-Q3 release) was used to demonstrate that metrics derived from DEC can explain larger variance in 70 behaviors across different domains (alertness, cognition, emotion, and personality traits) compared to SEC in healthy individuals.

PMID: 32116487 [PubMed]

Acute and long-term effects of electroacupuncture alter frontal and insular cortex activity and functional connectivity during resting state.

Wed, 03/04/2020 - 03:00
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Acute and long-term effects of electroacupuncture alter frontal and insular cortex activity and functional connectivity during resting state.

Psychiatry Res Neuroimaging. 2020 Feb 20;298:111047

Authors: Ren Y, Xu M, von Deneen KM, He Y, Li G, Zheng Y, Zhang W, Li X, Han Y, Cui G, Ji G, Nie Y, Zhang Y

Abstract
Electroacupuncture (EA) is a safe method for treating obesity; however, its underlying neural mechanisms remain unclear. We employed resting-state-functional-magnetic-resonance-imaging (RS-fMRI) and amplitude-of-low-frequency-fluctuation (ALFF) to investigate acute/long-term effects of EA on brain activity and resting-state-functional-connectivity (RSFC) in overweight/obesity subjects who received real/Sham stimulation. For acute effects, 26 and 19 overweight/obesity subjects were included in EA and Sham groups respectively. There were significant time effects on ALFF in the right insula (INS) and left dorsolateral-prefrontal-cortex (DLPFC) due to decreases/increases in INS/DLPFC in both groups. There were weaker positive RSFC between INS and supplementary-motor-area (SMA)/right DLPFC and weaker negative RSFC between INS and precuneus (PCUN); stronger negative RSFC between DLPFC and dorsomedial-prefrontal-cortex (DMPFC) in both groups. For long-term study, body-mass-index (BMI) had significant reduction in EA (n = 17) and Sham (15) groups; EA had higher BMI reduction than in Sham. There were significant time effects on ALFF in right ventrolateral-prefrontal-cortex (VLPFC) due to significant increases in EA group, and stronger positive RSFC between VLPFC and orbitofrontal-cortex and negative RSFC between VLPFC and left thalamus (THA) in both groups after long-term treatment. These findings suggest that changes in resting-activity and RSFC implicated in inhibitory-control, gastric-motility and satiety-control are associated with EA-induced weight-loss.

PMID: 32114310 [PubMed - as supplied by publisher]

Estimating individual scores of inattention and impulsivity based on dynamic features of intrinsic connectivity network.

Wed, 03/04/2020 - 03:00
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Estimating individual scores of inattention and impulsivity based on dynamic features of intrinsic connectivity network.

Neurosci Lett. 2020 Feb 27;:134874

Authors: Wang XH, Xu J, Li L

Abstract
Inattention and impulsivity are the two most important indices for evaluations of ADHD. Currently, inattention and impulsivity were evaluated by clinical scales. The intelligent evaluation of the two indices using machine learning remains largely unexplored. This paper aimed to build regression modes for inattention and impulsivity based on resting state fMRI and additional measures, and discover the associating features for the two indices. To achieve these goals, a cohort of 95 children with ADHD as well as 105 healthy controls were selected from the ADHD-200 database. The raw features were consisted of univariate dynamic estimators of intrinsic connectivity network (ICNs), head motion, and additional measures. The regression models were solved using support vector regression (SVR). The performance of the regression models was evaluated by cross-validations. The performance of regression models based on ICNs outperformed that based on regional measures. The estimated clinical scores were significantly correlated to inattention (r = 0.4 ± 0.02, p < 0.01) and impulsivity (r = 0.31 ± 0.02, p < 0.01). The most associating ICNs are sensorimotor network (SMN) for inattention and executive control network (ECN) for impulsivity. The results suggested that inattention and impulsivity could be estimated using machine learning, and the intra-ICN dynamics could be supplementary features for regression models of clinical scores of ADHD.

PMID: 32114120 [PubMed - as supplied by publisher]

Brain functional connectome-based prediction of individual decision impulsivity.

Sun, 03/01/2020 - 20:57
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Brain functional connectome-based prediction of individual decision impulsivity.

Cortex. 2020 Feb 11;125:288-298

Authors: Cai H, Chen J, Liu S, Zhu J, Yu Y

Abstract
Extensive neuroimaging research has attempted to identify neural correlates and predictors of decision impulsivity. However, the nature and extent of decision impulsivity-brain association have varied substantially across studies, likely due to small sample sizes, limited image quality, different imaging measurement selections, and non-specific methodologies. The objective of this study was to develop a reliable predictive model of decision impulsivity-brain relationship in a large sample by applying connectome-based predictive modeling (CPM), a recently developed machine learning approach, to whole-brain functional connectivity data ("neural fingerprints"). For 809 healthy young participants from the Human Connectome Project, high-quality resting-state functional MRI data were utilized to construct brain functional connectome and delay discounting test was used to assess decision impulsivity. Then, CPM with leave-one-out cross-validation was conducted to predict individual decision impulsivity from whole-brain functional connectivity. We found that CPM successfully and reliably predicted the delay discounting scores in novel individuals. Moreover, different feature selection thresholds, parcellation strategies and cross-validation approaches did not significantly influence the prediction results. At the neural level, we observed that the decision impulsivity-associated functional networks included brain regions within default-mode, subcortical, somato-motor, dorsal attention, and visual systems, suggesting that decision impulsivity emerges from highly integrated connections involving multiple intrinsic networks. Our findings not only may expand existing knowledge regarding the neural mechanism of decision impulsivity, but also may present a workable route towards translation of brain imaging findings into real-world economic decision-making.

PMID: 32113043 [PubMed - as supplied by publisher]

Training on Abacus-based Mental Calculation Enhances Resting State Functional Connectivity of Bilateral Superior Parietal Lobules.

Sun, 03/01/2020 - 20:57
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Training on Abacus-based Mental Calculation Enhances Resting State Functional Connectivity of Bilateral Superior Parietal Lobules.

Neuroscience. 2020 Feb 26;:

Authors: Zhou H, Geng F, Wang T, Wang C, Xie Y, Hu Y, Chen F

Abstract
Accumulating evidence indicates a positive effect of abacus-based mental calculation (AMC) training on various cognitive functions including short-term memory (STM). Our previous work has shown AMC training-induced activation changes in the frontal-parietal network (FPN) using task fMRI. However, whether AMC training-induced functional plasticity in the same brain network can be detected at resting state remains unknown. The current study aimed to address this question using resting state functional connectivity in a longitudinal AMC training experiment engaging a training group (18 subjects, age = 21.439±0.565) and a control group (18 subjects, age = 21.113±1.140). Our results revealed that the average functional connectivity strength within the FPN showing task activation changes was significantly enhanced after training in the AMC group, whereas it remained stable in the control group. Further analysis indicated that such connectivity increase in the AMC group was primarily driven by the enhanced coupling of bilateral superior parietal lobules (SPL). In addition, a significant and positive correlation between letter forward memory span and SPL connectivity was found at post-training session in the AMC group. While the weakest quartile of SPL connections ranking by pre-training connectivity strength showed the largest effect of enhancement after training, it was the strongest quartile of SPL connectivity that correlated the most with memory span at post-training session. These findings suggest that AMC training may enhance bilateral SPL functional connectivity, through which AMC training might exert a transfer effect to improve short-term memory capacity.

PMID: 32112920 [PubMed - as supplied by publisher]

What's in a hub?-Representing Identity in Language and Mathematics.

Sun, 03/01/2020 - 20:57
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What's in a hub?-Representing Identity in Language and Mathematics.

Neuroscience. 2020 Feb 26;:

Authors: Arora A, Pletzer B, Aichhorn M, Perner J

Abstract
Hubs emerge in structural and resting state network analysis as areas highly connected to other parts of the brain and have been shown to respond to several task domains in functional imaging studies. A cognitive explanation for this multi-functionality is still wanting. We propose, that hubs subserve domain-general meta-cognitive functions, relevant to a variety of domain-specific networks and test this hypothesis for the example of processing explicit identity information. To isolate this meta-cognitive function from the processing of domain-specific context, we investigate the overlapping activations to linguistic identity processes (e.g. Mr. Dietrich is the dentist) on the one hand and numerical identity processes (e.g. do "3 × 8" and "36 - 12" give the same number) on the other hand. The main question was, whether these overlapping activations would fall within areas, consistently identified as hubs by network-based analyses. Indeed, the two contrasts showed significant conjunctions in the left inferior parietal lobe (IPL), precuneus, and posterior cingulate. Accordingly, identity processing may well be one domain-general meta-cognitive function that hub-areas provide to domain-specific networks. For the parietal lobe we back up our hypothesis further with existing reports of activation peaks for other tasks that depend on identity processing, e.g., episodic recollection, theory of mind, and visual perspective taking.

PMID: 32112913 [PubMed - as supplied by publisher]

Deep Spatial-Temporal Feature Fusion from Adaptive Dynamic Functional Connectivity for MCI Identification.

Sun, 03/01/2020 - 20:57
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Deep Spatial-Temporal Feature Fusion from Adaptive Dynamic Functional Connectivity for MCI Identification.

IEEE Trans Med Imaging. 2020 Feb 27;:

Authors: Li Y, Liu J, Tang Z, Lei B

Abstract
Dynamic functional connectivity (dFC) analysis using resting-state functional Magnetic Resonance Imaging (rs-fMRI) is currently an advanced technique for capturing the dynamic changes of neural activities in brain disease identification. Most existing dFC modeling methods extract dynamic interaction information by using the sliding window-based correlation, whose performance is very sensitive to window parameters. Because few studies can convincingly identify the optimal combination of window parameters, sliding window-based correlation may not be the optimal way to capture the temporal variability of brain activity. In this paper, we propose a novel adaptive dFC model, aided by a deep spatial-temporal feature fusion method, for mild cognitive impairment (MCI) identification. Specifically, we adopt an adaptive Ultra-weighted-lasso recursive least squares algorithm to estimate the adaptive dFC, which effectively alleviates the problem of parameter optimization. Then, we extract temporal and spatial features from the adaptive dFC. In order to generate coarser multi-domain representations for subsequent classification, the temporal and spatial features are further mapped into comprehensive fused features with a deep feature fusion method. Experimental results show that the classification accuracy of our proposed method is reached to 87.7%, which is at least 5.5% improvement than the state-of-the-art methods. These results elucidate the superiority of the proposed method for MCI classification, indicating its effectiveness in the early identification of brain abnormalities.

PMID: 32112678 [PubMed - as supplied by publisher]

Parietal memory network and default mode network in first-episode drug-naïve schizophrenia: Associations with auditory hallucination.

Sun, 03/01/2020 - 20:57
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Parietal memory network and default mode network in first-episode drug-naïve schizophrenia: Associations with auditory hallucination.

Hum Brain Mapp. 2020 Feb 29;:

Authors: Guo Q, Hu Y, Zeng B, Tang Y, Li G, Zhang T, Wang J, Northoff G, Li C, Goff D, Wang J, Yang Z

Abstract
Atypical spontaneous activities in resting-state networks may play a role in auditory hallucinations (AHs), but networks relevant to AHs are not apparent. Given the debating role of the default mode network (DMN) in AHs, a parietal memory network (PMN) may better echo cognitive theories of AHs in schizophrenia, because PMN is spatially adjacent to the DMN and more relevant to memory processing or information integration. To examine whether PMN is more relevant to AHs than DMN, we characterized these intrinsic networks in AHs with 59 first-episode, drug-naïve schizophrenics (26 AH+ and 33 AH-) and 60 healthy participants in resting-state fMRI. We separated the PMN, DMN, and auditory network (AN) using independent component analysis, and compared their functional connectivity across the three groups. We found that only AH+ patients displayed dysconnectivity in PMN, both AH+ and AH- patients exhibited dysfunctions of AN, but neither patient group showed abnormal connectivity within DMN. The connectivity of PMN significantly correlated with memory performance of the patients. Further region-of-interest analyses confirmed that the connectivity between the core regions of PMN, the left posterior cingulate gyrus and the left precuneus, was significantly lower only in the AH+ group. In exploratory correlation analysis, this functional connectivity metric significantly correlated with the severity of AH symptoms. The results implicate that compared to the DMN, the PMN is more relevant to the AH symptoms in schizophrenia, and further provides a more precise potential brain modulation target for the intervention of AH symptoms.

PMID: 32112506 [PubMed - as supplied by publisher]

Psilocybin Induces Time-Dependent Changes in Global Functional Connectivity.

Sun, 03/01/2020 - 20:57
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Psilocybin Induces Time-Dependent Changes in Global Functional Connectivity.

Biol Psychiatry. 2020 Jan 13;:

Authors: Preller KH, Duerler P, Burt JB, Ji JL, Adkinson B, Stämpfli P, Seifritz E, Repovš G, Krystal JH, Murray JD, Anticevic A, Vollenweider FX

Abstract
BACKGROUND: The use of psilocybin in scientific and experimental clinical contexts has triggered renewed interest in the mechanism of action of psychedelics. However, its time-dependent systems-level neurobiology remains sparsely investigated in humans.
METHODS: We conducted a double-blind, randomized, counterbalanced, crossover study comprising 23 healthy human participants who received placebo and 0.2 mg/kg of psilocybin orally on 2 different test days. Participants underwent magnetic resonance imaging at 3 time points between administration and peak effects: 20 minutes, 40 minutes, and 70 minutes after administration. Resting-state functional connectivity was quantified via a data-driven global brain connectivity method and compared with cortical gene expression maps.
RESULTS: Psilocybin reduced associative, but concurrently increased sensory, brain-wide connectivity. This pattern emerged over time from administration to peak effects. Furthermore, we showed that baseline connectivity is associated with the extent of psilocybin-induced changes in functional connectivity. Lastly, psilocybin-induced changes correlated in a time-dependent manner with spatial gene expression patterns of the 5-HT2A (5-hydroxytryptamine 2A) and 5-HT1A (5-hydroxytryptamine 1A) receptors.
CONCLUSIONS: These results suggest that the integration of functional connectivity in sensory regions and the disintegration in associative regions may underlie the psychedelic state and pinpoint the critical role of the serotonin 2A and 1A receptor systems. Furthermore, baseline connectivity may represent a predictive marker of the magnitude of changes induced by psilocybin and may therefore contribute to a personalized medicine approach within the potential framework of psychedelic treatment.

PMID: 32111343 [PubMed - as supplied by publisher]

Functional connectivity of the dorsolateral prefrontal cortex contributes to different components of executive functions.

Sat, 02/29/2020 - 20:56
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Functional connectivity of the dorsolateral prefrontal cortex contributes to different components of executive functions.

Int J Psychophysiol. 2020 Feb 25;:

Authors: Panikratova YR, Vlasova RM, Akhutina TV, Korneev AA, Sinitsyn VE, Pechenkova EV

Abstract
OBJECTIVE: The dorsolateral prefrontal cortex (DLPFC) orchestrates other brain regions and plays a vital role for "the most uniquely human" executive functions (EFs), which are divided into distinct components. Components of EFs have been localized to different brain regions and at the same time the DLPFC was found to be involved in a majority of EF components. The possible mechanism of the DLPFC's contribution to EF components might be found in DLPFC functional connectivity (FC): this FC of the DLPFC with other brain regions contributes to different EF components.
METHOD: To explore the DLPFC FC contribution to different EFs, we used an integrative approach involving analysis of fMRI and neuropsychological assessment of EFs. Fifty healthy adults (27 females, mean age 34.5 ± 16.6 years) underwent neuropsychological assessment of EFs as well as task-based and resting-state fMRI. Task-based fMRI was applied as a functional localizer for individually defined DLPFC ROIs that were further used for the FC seed-based correlation analysis of the resting-state data. Then we looked for associations between individual scores of different EF components and the whole-brain resting-state FC of the DLPFC.
RESULTS: Resting-state correlates of DLPFC FC were revealed for three out of the seven EF components derived from an extensive neuropsychological assessment: inhibition, switching, and the verbal EF component.
CONCLUSIONS: Our study is the first to reveal the contribution of the DLPFC FC to several distinct EF components. The obtained results give insight into the brain mechanisms of EFs.

PMID: 32109499 [PubMed - as supplied by publisher]

Resting-state fMRI Detects Alterations in Whole Brain Connectivity Related to Tumor Biology in Glioma Patients.

Sat, 02/29/2020 - 20:56
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Resting-state fMRI Detects Alterations in Whole Brain Connectivity Related to Tumor Biology in Glioma Patients.

Neuro Oncol. 2020 Feb 28;:

Authors: Stoecklein VM, Stoecklein S, Galiè F, Ren J, Schmutzer M, Unterrainer M, Albert NL, Kreth FW, Thon N, Liebig T, Ertl-Wagner B, Tonn JC, Liu H

Abstract
BACKGROUND: Systemic infiltration of the brain by tumor cells is a hallmark of glioma pathogenesis which may cause disturbances in functional connectivity. We hypothesized that aggressive high-grade tumors cause more damage to functional connectivity than low-grade tumors.
METHODS: We designed an imaging tool based on resting-state functional MRI to individually quantify abnormality of functional connectivity and tested it in a prospective cohort of patients with newly diagnosed glioma.
RESULTS: 34 patients (WHO II: 13; WHO III: 6; WHO IV: 15; mean age 48,7 years) were analyzed. Connectivity abnormality could be observed not only in the lesioned brain area but also in the contralateral hemisphere with a close correlation between connectivity abnormality and aggressiveness of the tumor as indicated by WHO grade. IDH 1 mutation status was also associated with abnormal connectivity, with more alterations in IDH 1 wildtype tumors independent of tumor size. Finally, deficits in neuropsychological performance were correlated with connectivity abnormality.
CONCLUSIONS: Here, we suggested an individually applicable resting-state fMRI marker in glioma patients. Analysis of the functional connectome using this marker revealed that abnormalities of functional connectivity could be detected not only adjacent to the visible lesion but also in distant brain tissue, even in the contralesional hemisphere. These changes were associated with tumor biology and cognitive function. The ability of our novel method to capture tumor effects in non-lesional brain suggests a potential clinical value for both individualizing and monitoring glioma therapy.

PMID: 32107555 [PubMed - as supplied by publisher]

Modified models and simulations for estimating dynamic functional connectivity in resting state functional magnetic resonance imaging.

Fri, 02/28/2020 - 20:55
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Modified models and simulations for estimating dynamic functional connectivity in resting state functional magnetic resonance imaging.

Stat Med. 2020 Feb 27;:

Authors: Behboudi M, Farnoosh R

Abstract
As understanding the nature of brain networks through dynamic functional connectivity (dFC) estimation is of paramount significant, the introduction and revision of blood-oxygen-level dependent (BOLD) signal simulation methods in brain regions and dFC estimation methods have gained significant ground in recent years. Based on the observation of BOLD signals with multivariate nonnormal distribution in functional magnetic resonance imaging (fMRI) images, we first propose a copula-based method for the production of these signals, in which nonnormal data are generated with a selected time-varying covariance matrix. Therefore, we can compare the performance of models in the cases where brain signals have a multivariate nonnormal distribution. Then, two kendallized exponentially weighted moving average (KEWMA) and kendallized dynamic conditional correlation (KDCC) multivariate volatility models are introduced which are based on two well-known and commonly used exponentially weighted moving average (EMWA) and dynamic conditional correlation (DCC) models. The results show that KDCC model can estimate conditional correlation significantly far better than the former ones (ie, DCC, standardized dynamic conditional correlation, EWMA, and standardized exponentially weighted moving average) on both types of data (ie, multivariate normal and nonnormal). In the next step, the bivariate normal distribution in Iranian resting state fMRI data is confirmed by using statistical tests, and it is shown that the dynamic nature of FC is not optimally detected using prevalent methods. Two alternative Portmanteau and rank-based tests are proposed for the examination of conditional heteroscedasticity in data. Finally, dFC in these data is estimated by employing the KDCC model.

PMID: 32106335 [PubMed - as supplied by publisher]

Whole-brain electrophysiological functional connectivity dynamics in resting-state EEG.

Fri, 02/28/2020 - 20:55
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Whole-brain electrophysiological functional connectivity dynamics in resting-state EEG.

J Neural Eng. 2020 Feb 27;:

Authors: Shou G, Yuan H, Li C, Chen Y, Chen Y, Ding L

Abstract
OBJECTIVE: Functional connectivity (FC) dynamics have been studied in functional magnetic resonance imaging (fMRI) data, while it is largely unknown in electrophysiological data, e.g., EEG.
APPROACH: The present study proposed a novel analytic framework to study spatiotemporal dynamics of FC (dFC) in resting-state human EEG data, including independent component analysis, cortical source imaging, sliding-window correlation analysis, and k-means clustering.
MAIN RESULTS: Our results confirm that major fMRI intrinsic connectivity networks (ICNs) can be successfully reconstructed from EEG using our analytic framework. Prominent spatial and temporal variability were revealed in these ICNs. The mean dFC spatial patterns of individual ICNs resemble their corresponding static FC (sFC) patterns but show fewer cross-talks among distinct ICNs. Our investigation unveils evidences of time-domain variations in individual ICNs comparable to their mean FC level in terms of magnitude. The major contributors to these variations are from the frequency below 0.0156 Hz, in the similar range of FC dynamics from fMRI data. Among different ICNs, larger temporal variabilities are observed in the frontal attention and auditory/visual ICNs, while sensorimotor, salience, and default model networks showed less. Our analytic framework for the first time revealed quasi-stable states within individual EEG ICNs, with various strengths or spatial patterns that were reliably detected at both group and individual levels. These states all together reveal a more complete picture of EEG ICNs: 1) quasi-stable state spatial patterns as a whole for each EEG ICN are more consistent with the corresponding fMRI ICN in terms of the bilateral distribution and multi-nodes structure; 2) EEG ICNs reveal more transient patterns about within-ICN between-node communications than fMRI ICNs.
SIGNIFICANCE: The present findings highlight the fact that rich temporal and spatial dynamics exist in ICN that can be detected from EEG data. Future studies might extend investigations towards spectral dynamics of EEG ICNs.

PMID: 32106106 [PubMed - as supplied by publisher]

Functional dedifferentiation of associative resting state networks in older adults - A longitudinal study.

Fri, 02/28/2020 - 20:55
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Functional dedifferentiation of associative resting state networks in older adults - A longitudinal study.

Neuroimage. 2020 Feb 24;:116680

Authors: Malagurski B, Liem F, Oschwald J, Mérillat S, Jäncke L

Abstract
Healthy aging is associated with weaker functional connectivity within resting state brain networks and stronger functional interaction between these networks. This phenomenon has been characterized as reduced functional segregation and has been investigated mainly in cross-sectional studies. Here, we used a longitudinal dataset which consisted of four occasions of resting state fMRI and psychometric cognitive ability data, collected from a sample of healthy older adults (baseline N = 232, age range: 64-87 y, age M = 70.8 y), to investigate the functional segregation of several well-defined resting state networks encompassing the whole brain. We characterized the ratio of within-network and between-network correlations via the well-established segregation index. Our findings showed a decrease over a 4-year interval in the functional segregation of the default mode, frontoparietal control and salience ventral attention networks. In contrast, we showed an increase in the segregation of the limbic network over the same interval. More importantly, the rate of change in functional segregation of the frontoparietal control network was associated with the rate of change in processing speed. These findings support the hypothesis of functional dedifferentiation in healthy aging as well as its role in cognitive function in elderly.

PMID: 32105885 [PubMed - as supplied by publisher]

Effects of galvanic vestibular stimulation on resting state brain activity in patients with bilateral vestibulopathy.

Fri, 02/28/2020 - 20:55
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Effects of galvanic vestibular stimulation on resting state brain activity in patients with bilateral vestibulopathy.

Hum Brain Mapp. 2020 Feb 27;:

Authors: Helmchen C, Machner B, Rother M, Spliethoff P, Göttlich M, Sprenger A

Abstract
We examined the effect of galvanic vestibular stimulation (GVS) on resting state brain activity using fMRI (rs-fMRI) in patients with bilateral vestibulopathy. Based on our previous findings, we hypothesized that GVS, which excites the vestibular nerve fibers, (a) increases functional connectivity in temporoparietal regions processing vestibular signals, and (b) alleviates abnormal visual-vestibular interaction. Rs-fMRI of 26 patients and 26 age-matched healthy control subjects was compared before and after GVS. The stimulation elicited a motion percept in all participants. Using different analyses (degree centrality, DC; fractional amplitude of low frequency fluctuations [fALFF] and seed-based functional connectivity, FC), group comparisons revealed smaller rs-fMRI in the right Rolandic operculum of patients. After GVS, rs-fMRI increased in the right Rolandic operculum in both groups and in the patients' cerebellar Crus 1 which was related to vestibular hypofunction. GVS elicited a fALFF increase in the visual cortex of patients that was inversely correlated with the patients' rating of perceived dizziness. After GVS, FC between parietoinsular cortex and higher visual areas increased in healthy controls but not in patients. In conclusion, short-term GVS is able to modulate rs-fMRI in healthy controls and BV patients. GVS elicits an increase of the reduced rs-fMRI in the patients' right Rolandic operculum, which may be an important contribution to restore the disturbed visual-vestibular interaction. The GVS-induced changes in the cerebellum and the visual cortex were associated with lower dizziness-related handicaps in patients, possibly reflecting beneficial neural plasticity that might subserve visual-vestibular compensation of deficient self-motion perception.

PMID: 32103579 [PubMed - as supplied by publisher]

Primary functional brain connections associated with melancholic major depressive disorder and modulation by antidepressants.

Fri, 02/28/2020 - 20:55
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Primary functional brain connections associated with melancholic major depressive disorder and modulation by antidepressants.

Sci Rep. 2020 Feb 26;10(1):3542

Authors: Ichikawa N, Lisi G, Yahata N, Okada G, Takamura M, Hashimoto RI, Yamada T, Yamada M, Suhara T, Moriguchi S, Mimura M, Yoshihara Y, Takahashi H, Kasai K, Kato N, Yamawaki S, Seymour B, Kawato M, Morimoto J, Okamoto Y

Abstract
The limited efficacy of available antidepressant therapies may be due to how they affect the underlying brain network. The purpose of this study was to develop a melancholic MDD biomarker to identify critically important functional connections (FCs), and explore their association to treatments. Resting state fMRI data of 130 individuals (65 melancholic major depressive disorder (MDD) patients, 65 healthy controls) were included to build a melancholic MDD classifier, and 10 FCs were selected by our sparse machine learning algorithm. This biomarker generalized to a drug-free independent cohort of melancholic MDD, and did not generalize to other MDD subtypes or other psychiatric disorders. Moreover, we found that antidepressants had a heterogeneous effect on the identified FCs of 25 melancholic MDDs. In particular, it did impact the FC between left dorsolateral prefrontal cortex (DLPFC)/inferior frontal gyrus (IFG) and posterior cingulate cortex (PCC)/precuneus, ranked as the second 'most important' FC based on the biomarker weights, whilst other eight FCs were normalized. Given that left DLPFC has been proposed as an explicit target of depression treatments, this suggest that the limited efficacy of antidepressants might be compensated by combining therapies with targeted treatment as an optimized approach in the future.

PMID: 32103088 [PubMed - in process]

Motor Imagery Training After Stroke Increases Slow-5 Oscillations and Functional Connectivity in the Ipsilesional Inferior Parietal Lobule.

Fri, 02/28/2020 - 20:55
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Motor Imagery Training After Stroke Increases Slow-5 Oscillations and Functional Connectivity in the Ipsilesional Inferior Parietal Lobule.

Neurorehabil Neural Repair. 2020 Feb 26;:1545968319899919

Authors: Wang X, Wang H, Xiong X, Sun C, Zhu B, Xu Y, Fan M, Tong S, Sun L, Guo X

Abstract
Background. Reorganization in motor areas have been suggested after motor imagery training (MIT). However, motor imagery involves a large-scale brain network, in which many regions, andnot only the motor areas, potentially constitute the neural substrate for MIT. Objective. This study aimed to identify the targets for MIT in stroke rehabilitation from a voxel-based whole brain analysis of resting-state functional magnetic resonance imaging (fMRI). Methods. Thirty-four chronic stroke patients were recruited and randomly assigned to either an MIT group or a control group. The MIT group received a 4-week treatment of MIT plus conventional rehabilitation therapy (CRT), whereas the control group only received CRT. Before and after intervention, the Fugl-Meyer Assessment Upper Limb subscale (FM-UL) and resting-state fMRI were collected. The fractional amplitude of low-frequency fluctuations (fALFF) in the slow-5 band (0.01-0.027 Hz) was calculated across the whole brain to identify brain areas with distinct changes between 2 groups. These brain areas were then targeted as seeds to perform seed-based functional connectivity (FC) analysis. Results. In comparison with the control group, the MIT group exhibited more improvements in FM-UL and increased slow-5 fALFF in the ipsilesional inferior parietal lobule (IPL). The change of the slow-5 oscillations in the ipsilesional IPL was positively correlated with the improvement of FM-UL. The MIT group also showed distinct alternations in FCs of the ipsilesional IPL, which were correlated with the improvement of FM-UL. Conclusions. The rehabilitation efficiency of MIT was associated with increased slow-5 oscillations and altered FC in the ipsilesional IPL. Clinical Trial Registration. http://www.chictr.org.cn . Unique Identifier. ChiCTR-TRC-08003005.

PMID: 32102610 [PubMed - as supplied by publisher]

Sleep and resting-state functional magnetic resonance imaging connectivity in middle-aged adults and the elderly: A population-based study.

Thu, 02/27/2020 - 20:55
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Sleep and resting-state functional magnetic resonance imaging connectivity in middle-aged adults and the elderly: A population-based study.

J Sleep Res. 2020 Feb 26;:e12999

Authors: Lysen TS, Zonneveld HI, Muetzel RL, Ikram MA, Luik AI, Vernooij MW, Tiemeier H

Abstract
Sleep problems increase with ageing. Increasing evidence suggests that sleep problems are not only a consequence of age-related processes, but may independently contribute to developing vascular or neurodegenerative brain disease. Yet, it remains unclear what mechanisms underlie the impact sleep problems may have on brain health in the general middle-aged and elderly population. Here, we studied sleep's relation to brain functioning in 621 participants (median age 62 years, 55% women) from the population-based Rotterdam Study. We investigated cross-sectional associations of polysomnographic and subjectively measured aspects of sleep with intrinsic neural activity measured with resting-state functional magnetic resonance imaging on a different day. We investigated both functional connectivity between regions and brain activity (blood-oxygen-level-dependent signal amplitude) within regions, hierarchically towards smaller topographical levels. We found that longer polysomnographic total sleep time is associated with lower blood-oxygen-level-dependent signal amplitude in (pre)frontal regions. No objective or subjective sleep parameters were associated with functional connectivity between or within resting-state networks. The findings may indicate a pathway through which sleep, in a 'real-life' population setting, impacts brain activity or regional brain activity determines total sleep time.

PMID: 32100903 [PubMed - as supplied by publisher]