scholarly journals Spatiotemporal empirical mode decomposition of resting-state fMRI signals: application to global signal regression

2019 ◽  
Author(s):  
Narges Moradi ◽  
Mehdy Dousty ◽  
Roberto C. Sotero
Keyword(s):  
Functional Connectivity ◽  
Empirical Mode Decomposition ◽  
Resting State ◽  
Large Scale ◽  
Pearson Correlation ◽  
Brain Regions ◽  
Connectivity Matrix ◽  
Correlation Pattern ◽  
Mode Decomposition ◽  
Global Signal

AbstractResting-state functional connectivity MRI (rs-fcMRI) is a common method for mapping functional brain networks. However, estimation of these networks is affected by the presence of a common global systemic noise, or global signal (GS). Previous studies have shown that the common preprocessing steps of removing the GS may create spurious correlations between brain regions. In this paper, we decompose fMRI signals into 5 spatial and 3 temporal intrinsic mode functions (SIMF and TIMF, respectively) by means of the empirical mode decomposition (EMD), which is an adaptive data-driven method widely used to analyze nonlinear and nonstationary phenomena. For each SIMF, brain connectivity matrices were computed by means of the Pearson correlation between TIMFs of different brain areas. Thus, instead of a single connectivity matrix, we obtained 5 × 3 = 15 functional connectivity matrices. Given the high value obtained for large-scale topological measures such as transitivity, in the low spatial maps (SIMF3, SIMF4, and SIMF5), our results suggest that these maps can be considered as spatial global signal masks. Thus, the spatiotemporal EMD of fMRI signals automatically regressed out the GS, although, interestingly, the removed noisy component was voxel-specific. We compared the performance of our method with the conventional GS regression and to the results when the GS was not removed. While the correlation pattern identified by the other methods suffers from a low level of precision, our approach demonstrated a high level of accuracy in extracting the correct correlation between different brain regions.

scholarly journals Dynamic Expression of Brain Functional Systems Disclosed by Fine-Scale Analysis of Edge Time Series

2020 ◽  
Author(s):  
Olaf Sporns ◽  
Joshua Faskowitz ◽  
Andreia Sofia Teixera ◽  
Richard F. Betzel
Keyword(s):  
Time Series ◽  
Functional Connectivity ◽  
Resting State ◽  
Pearson Correlation ◽  
Resting State Fmri ◽  
Brain Regions ◽  
Statistical Dependence ◽  
Fine Scale ◽  
Time Step ◽  
Functional Systems

AbstractFunctional connectivity (FC) describes the statistical dependence between brain regions in resting-state fMRI studies and is usually estimated as the Pearson correlation of time courses. Clustering reveals densely coupled sets of regions constituting a set of resting-state networks or functional systems. These systems manifest most clearly when FC is sampled over longer epochs lasting many minutes but appear to fluctuate on shorter time scales. Here, we propose a new approach to track these temporal fluctuations. Un-wrapping FC signal correlations yields pairwise co-fluctuation time series, one for each node pair/edge, and reveals fine-scale dynamics across the network. Co-fluctuations partition the network, at each time step, into exactly two communities. Sampled over time, the overlay of these bipartitions, a binary decomposition of the original time series, very closely approximates functional connectivity. Bipartitions exhibit characteristic spatiotemporal patterns that are reproducible across participants and imaging sessions and disclose fine-scale profiles of the time-varying levels of expression of functional systems. Our findings document that functional systems appear transiently and intermittently, and that FC results from the overlay of many variable instances of system expression. Potential applications of this decomposition of functional connectivity into a set of binary patterns are discussed.

scholarly journals Heterogeneity of EEG resting-state brain networks in absolute pitch

2020 ◽  
Author(s):  
Marielle Greber ◽  
Carina Klein ◽  
Simon Leipold ◽  
Silvano Sele ◽  
Lutz Jäncke
Keyword(s):  
Functional Connectivity ◽  
Auditory Cortex ◽  
Resting State ◽  
Large Scale ◽  
Absolute Pitch ◽  
Brain Regions ◽  
Brain Network ◽  
Higher Order ◽  
Neural Basis ◽  
Whole Brain

AbstractThe neural basis of absolute pitch (AP), the ability to effortlessly identify a musical tone without an external reference, is poorly understood. One of the key questions is whether perceptual or cognitive processes underlie the phenomenon as both sensory and higher-order brain regions have been associated with AP. One approach to elucidate the neural underpinnings of a specific expertise is the examination of resting-state networks.Thus, in this paper, we report a comprehensive functional network analysis of intracranial resting-state EEG data in a large sample of AP musicians (n = 54) and non-AP musicians (n = 51). We adopted two analysis approaches: First, we applied an ROI-based analysis to examine the connectivity between the auditory cortex and the dorsolateral prefrontal cortex (DLPFC) using several established functional connectivity measures. This analysis is a replication of a previous study which reported increased connectivity between these two regions in AP musicians. Second, we performed a whole-brain network-based analysis on the same functional connectivity measures to gain a more complete picture of the brain regions involved in a possibly large-scale network supporting AP ability.In our sample, the ROI-based analysis did not provide evidence for an AP-specific connectivity increase between the auditory cortex and the DLPFC. In contrast, the whole-brain analysis revealed three networks with increased connectivity in AP musicians comprising nodes in frontal, temporal, subcortical, and occipital areas. Commonalities of the networks were found in both sensory and higher-order brain regions of the perisylvian area. Further research will be needed to confirm these exploratory results.

scholarly journals Sex-specific differences in resting-state functional connectivity of large-scale networks in postconcussion syndrome

2020 ◽  
Vol 10 (1) ◽  
Author(s):  
Reema Shafi ◽  
Adrian P. Crawley ◽  
Maria Carmela Tartaglia ◽  
Charles H. Tator ◽  
Robin E. Green ◽  
...  
Keyword(s):  
Functional Connectivity ◽  
Resting State ◽  
Large Scale ◽  
Brain Regions ◽  
Healthy Controls ◽  
Postconcussion Syndrome ◽  
Resting State Functional Connectivity ◽  
Post Injury ◽  
Network Nodes ◽  
Specific Impact

AbstractConcussions are associated with a range of cognitive, neuropsychological and behavioral sequelae that, at times, persist beyond typical recovery times and are referred to as postconcussion syndrome (PCS). There is growing support that concussion can disrupt network-based connectivity post-injury. To date, a significant knowledge gap remains regarding the sex-specific impact of concussion on resting state functional connectivity (rs-FC). The aims of this study were to (1) investigate the injury-based rs-FC differences across three large-scale neural networks and (2) explore the sex-specific impact of injury on network-based connectivity. MRI data was collected from a sample of 80 concussed participants who fulfilled the criteria for postconcussion syndrome and 31 control participants who did not have any history of concussion. Connectivity maps between network nodes and brain regions were used to assess connectivity using the Functional Connectivity (CONN) toolbox. Network based statistics showed that concussed participants were significantly different from healthy controls across both salience and fronto-parietal network nodes. More specifically, distinct subnetwork components were identified in the concussed sample, with hyperconnected frontal nodes and hypoconnected posterior nodes across both the salience and fronto-parietal networks, when compared to the healthy controls. Node-to-region analyses showed sex-specific differences across association cortices, however, driven by distinct networks. Sex-specific network-based alterations in rs-FC post concussion need to be examined to better understand the underlying mechanisms and associations to clinical outcomes.

scholarly journals Cognitive enhancement: Effects of methylphenidate, modafinil and caffeine on latent memory and resting state functional connectivity in healthy adults

2021 ◽  
Author(s):  
Maxi Becker ◽  
Dimitris Repantis ◽  
Martin Dresler ◽  
Simone Kuehn
Keyword(s):  
Functional Connectivity ◽  
Resting State ◽  
Medial Temporal Lobe ◽  
Large Scale ◽  
Cognitive Enhancement ◽  
Brain Regions ◽  
Controlled Study ◽  
Attention And Memory ◽  
Resting State Functional Connectivity ◽  
Double Blind

Stimulants like methylphenidate, modafinil and caffeine have repeatedly shown to enhance cognitive processes such as attention and memory. However, brain-functional mechanisms underlying such cognitive enhancing effects of stimulants are still poorly characterized. Here, we utilized behavioral and resting-state fMRI data from a double-blind randomized placebo-controlled study of methylphenidate, modafinil and caffeine in 48 healthy male adults. The results show that performance in different memory tasks is enhanced, and functional connectivity (FC) specifically between the fronto-parietal (FPN) and default mode (DMN) network is modulated by the stimulants in comparison to placebo. Decreased negative connectivity between right prefrontal and medial parietal but also between medial temporal lobe and visual brain regions predicted stimulant-induced latent memory enhancement. We discuss dopamine's role in attention and memory as well as its ability to modulate FC between large-scale neural networks (e.g. FPN and DMN) as a potential cognitive enhancement mechanism.

scholarly journals Dynamic expression of brain functional systems disclosed by fine-scale analysis of edge time series

2021 ◽  
pp. 1-34
Author(s):  
Olaf Sporns ◽  
Joshua Faskowitz ◽  
Andreia Sofia Teixeira ◽  
Sarah A Cutts ◽  
Richard F. Betzel
Keyword(s):  
Time Series ◽  
Functional Connectivity ◽  
Resting State ◽  
Pearson Correlation ◽  
Resting State Fmri ◽  
Brain Regions ◽  
Statistical Dependence ◽  
Fine Scale ◽  
Time Step ◽  
Functional Systems

Functional connectivity (FC) describes the statistical dependence between neuronal populations or brain regions in resting-state fMRI studies and is commonly estimated as the Pearson correlation of time courses. Clustering or community detection reveals densely coupled sets of regions constituting resting-state networks or functional systems. These systems manifest most clearly when FC is sampled over longer epochs but appear to fluctuate on shorter time scales. Here, we propose a new approach to reveal temporal fluctuations in neuronal time series. Un-wrapping FC signal correlations yields pairwise co-fluctuation time series, one for each node pair or edge, and allows tracking of fine-scale dynamics across the network. Co-fluctuations partition the network, at each time step, into exactly two communities. Sampled over time, the overlay of these bipartitions, a binary decomposition of the original time series, very closely approximates functional connectivity. Bipartitions exhibit characteristic spatiotemporal patterns that are reproducible across participants and imaging runs, capture individual differences, and disclose fine-scale temporal expression of functional systems. Our findings document that functional systems appear transiently and intermittently, and that FC results from the overlay of many variable instances of system expression. Potential applications of this decomposition of functional connectivity into a set of binary patterns are discussed.

scholarly journals Networks Are Associated With Acupuncture Treatment in Patients With Diarrhea-Predominant Irritable Bowel Syndrome: A Resting-State Imaging Study

2021 ◽  
Vol 15 ◽  
Author(s):  
Tingting Zhao ◽  
Lixia Pei ◽  
Houxu Ning ◽  
Jing Guo ◽  
Yafang Song ◽  
...  
Keyword(s):  
Irritable Bowel Syndrome ◽  
Functional Connectivity ◽  
Resting State ◽  
Large Scale ◽  
Acupuncture Treatment ◽  
Brain Regions ◽  
Attention Network ◽  
Frontoparietal Network ◽  
Before And After ◽  
Irritable Bowel

Background: Irritable Bowel Syndrome (IBS), as a functional gastrointestinal disorder, is characterized by abdominal pain and distension. Recent studies have shown that acupuncture treatment improves symptoms of diarrhea-predominant irritable bowel syndrome (IBS-D) by altering networks in certain brain regions. However, few studies have used resting-state functional magnetic resonance imaging (fMRI) to compare altered resting-state inter-network functional connectivity in IBS-D patients before and after acupuncture treatment.Objective: To analyze altered resting-state inter-network functional connectivity in IBS-D patients before and after acupuncture treatment.Methods: A total of 74 patients with IBS-D and 31 healthy controls (HCs) were recruited for this study. fMRI examination was performed in patients with IBS-D before and after acupuncture treatment, but only at baseline in HCs. Data on the left frontoparietal network (LFPN), default mode network (DMN), salience network (SN), ventral attention network (VAN), auditory network (AN), visual network (VN), sensorimotor network (SMN), dorsal attention network (DAN), and right frontoparietal network (RFPN) were subjected to independent component analysis (ICA). The functional connectivity values of inter-network were explored.Results: Acupuncture decreased irritable bowel syndrome symptom severity score (IBS-SSS) and Hamilton Anxiety Scale (HAMA). It also ameliorated symptoms related to IBS-D. Notably, functional connectivity between AN and VAN, SMN and DMN, RFPN and VAN in IBS-D patients after acupuncture treatment was different from that in HCs. Furthermore, there were differences in functional connectivity between DMN and DAN, DAN and LFPN, DMN and VAN before and after acupuncture treatment. The inter-network changes in DMN-VAN were positively correlated with changes in HAMA, life influence degree, and IBS-SSS in IBS-D.Conclusion: Altered inter-network functional connectivity is involved in several important hubs in large-scale networks. These networks are altered by acupuncture stimulation in patients with IBS-D.

scholarly journals Altered Temporal Variability of Local and Large-scale Resting-state Brain Functional Connectivity Patterns in Schizophrenia and Bipolar Disorder

2020 ◽  
Author(s):  
Yicheng Long ◽  
Zhening Liu ◽  
Calais Kin-yuen Chan ◽  
Guowei Wu ◽  
Zhimin Xue ◽  
...  
Keyword(s):  
Bipolar Disorder ◽  
Functional Connectivity ◽  
Clinical Features ◽  
Resting State ◽  
Large Scale ◽  
Brain Regions ◽  
Type I ◽  
Healthy Controls ◽  
Resting State Functional Connectivity ◽  
Brain Functional Connectivity

AbstractSchizophrenia and bipolar disorder share some common clinical features and are both characterized by aberrant resting-state functional connectivity (FC). However, little is known about the common and specific aberrant features of the dynamic FC patterns in these two disorders. In this study, we explored the differences in dynamic FC among schizophrenia patients (n = 66), type I bipolar disorder patients (n = 53) and healthy controls (n = 66), by comparing temporal variabilities of FC patterns involved in specific brain regions and large-scale brain networks. Compared with healthy controls, both patient groups showed significantly increased regional FC variabilities in subcortical areas including the thalamus and basal ganglia, as well as increased inter-network FC variability between the thalamus and sensorimotor areas. Specifically, more widespread changes were found in the schizophrenia group, involving increased FC variabilities in sensorimotor, visual, attention, limbic and subcortical areas at both regional and network levels, as well as decreased regional FC variabilities in the default-mode areas. The observed alterations shared by schizophrenia and bipolar disorder may help to explain their overlapped clinical features; meanwhile, the schizophrenia-specific abnormalities in a wider range may support that schizophrenia is associated with more severe functional brain deficits than bipolar disorder.

scholarly journals Heterogeneous Acupuncture Effects of Taixi (KI3) on Functional Connectivity in Healthy Youth and Elder: A Functional MRI Study Using Regional Homogeneity and Large-Scale Functional Connectivity Analysis

2020 ◽  
Vol 2020 ◽  
pp. 1-9
Author(s):  
Linlu He ◽  
Guangxiang Chen ◽  
Ruwen Zheng ◽  
Yan Hu ◽  
Xiu Chen ◽  
...  
Keyword(s):  
Functional Connectivity ◽  
Spontaneous Activity ◽  
Resting State ◽  
Large Scale ◽  
Age Groups ◽  
Brain Regions ◽  
Regional Homogeneity ◽  
Supporting Evidence ◽  
The Right ◽  
Acupuncture Effects

Heterogeneous neurological responses of acupuncture between different groups have been proposed by previous studies but rarely studied. The study described here was designed to explore the divergence of acupuncture at Taixi (KI3) on spontaneous activity of brain regions and functional connectivity (FC) between healthy youth and elder with functional magnetic resonance imaging (fMRI). 20 healthy young volunteers and 20 healthy elders underwent 10-minute-resting-state fMRI before acupuncture, and then acupuncture at Taixi (KI3) for 3 minutes; after withdrawing the needles, volunteers underwent a second fMRI scan for 10 minutes. Regional homogeneity (ReHo) and large-scale FC analysis using Power 264 atlas were utilized to analyze the changes of brain spontaneous activity. Compared with the resting state, the decreased ReHo after acupuncture at KI3 in both groups were concentrated in the left postcentral, right paracentral lobule, and right SMA. Moreover, the subjects in the HY group showed declined ReHo in brain regions involving the right lingual and precentral. However, those subjects in the HE group presented decreased ReHo in the right postcentral and precentral, left supramarginal gyrus and SMA, and both cingulum middle after needling in KI3. Compared with the resting state, the HY group in the postneedling state showed lower mean intranetwork FC in sensory/somatomotor and subcortical network. And the internetwork FC between sensory/somatomotor and dorsal attention had significantly decreased after acupuncture. Furthermore, the internetwork FC between subcortical and dorsal attention and between subcortical and cerebellar showed the most obvious elevations after needling in the HY group. In the elder group, both FCs of internetwork and intranetwork primarily involving sensory/somatomotor, cingulo-opercular, and dorsal attention were declined after acupuncture. These results indicated that acupuncture at KI3 had heterogeneous acupuncture effects in different age groups. Our study led to converging evidence supporting the acupuncture effect segregation of different condition subjects and supporting evidence for prevention and treatment with acupuncture in the future.

scholarly journals New graph-theoretical-multimodal approach using temporal and structural correlations reveal disruption in the thalamo-cortical network in patients with schizophrenia

2018 ◽  
Author(s):  
Paolo Finotteli ◽  
Caroline Garcia Forlim ◽  
Paolo Dulio ◽  
Leonie Klock ◽  
Alessia Pini ◽  
...  
Keyword(s):  
Functional Connectivity ◽  
Resting State ◽  
Brain Connectivity ◽  
Brain Networks ◽  
Structural Connectivity ◽  
Network Models ◽  
Brain Regions ◽  
Cortical Network ◽  
Future Research ◽  
Connectivity Matrix

Schizophrenia has been understood as a network disease with altered functional and structural connectivity in multiple brain networks compatible to the extremely broad spectrum of psychopathological, cognitive and behavioral symptoms in this disorder. When building brain networks, functional and structural networks are typically modelled independently: functional network models are based on temporal correlations among brain regions, whereas structural network models are based on anatomical characteristics. Combining both features may give rise to more realistic and reliable models of brain networks. In this study, we applied a new flexible graph-theoretical-multimodal model called FD (F, the functional connectivity matrix, and D, the structural matrix) to construct brain networks combining functional, structural and topological information of MRI measurements (structural and resting state imaging) to patients with schizophrenia (N=35) and matched healthy individuals (N=41). As a reference condition, the traditional pure functional connectivity (pFC) analysis was carried out. By using the FD model, we found disrupted connectivity in the thalamo-cortical network in schizophrenic patients, whereas the pFC model failed to extract group differences after multiple comparison correction. We interpret this observation as evidence that the FD model is superior to conventional connectivity analysis, by stressing relevant features of the whole brain connectivity including functional, structural and topological signatures. The FD model can be used in future research to model subtle alterations of functional and structural connectivity resulting in pronounced clinical syndromes and major psychiatric disorders. Lastly, FD is not limited to the analysis of resting state fMRI, and can be applied to EEG, MEG etc.

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