Publications by authors named "Stavros I Dimitriadis"

50 Publications

An automatic sleep disorder detection model based on EEG cross-frequency coupling and Random Forest model.

J Neural Eng 2021 Apr 13. Epub 2021 Apr 13.

Research and Innovation Development, Brussels, Belgium, Intrasoft International S.A., Place du Champ de Mars 5/10 1050 Brussels, BRUSSELS, 1050, BELGIUM.

Sleep disorders are medical disorders of a subject's sleep architecture and based on their severity, they can interfere with mental, emotional and physical functioning. The most common ones are insomnia, narcolepsy, sleep apnea, bruxism, etc. There is an increased risk of developing sleep disorders in elderly like insomnia, periodic leg movements, rapid eye movement (REM) behaviour disorders, sleep disorder breathing, etc. Consequently, their accurate diagnosis and classification are important steps towards an early stage treatment that could save the life of a patient. The Electroencephalographic (EEG) signal is the most sensitive and important biosignal, which is able to capture the brain sleep activity that is sensitive to sleep. In this study, we attempt to analyse EEG sleep activity via complementary cross-frequency coupling (CFC) estimates, which further feed a classifier, aiming to discriminate sleep disorders. We adopted an open EEG Database with recordings that were grouped into seven sleep disorders and a healthy control. The EEG brain activity from common sensors has been analysed with two basic types of cross-frequency coupling (CFC). Finally, a Random Forest (RF) classification model was built on CFC patterns, which were extracted from non-cyclic alternating pattern (CAP) epochs. Our RFCFC model achieved a 74% multiclass accuracy. Both types of CFC, phase-to-amplitude (PAC) and amplitude-amplitude coupling (AAC) patterns contribute to the accuracy of the RF model, thus supporting their complementary information. CFC patterns, in conjunction with the RF classifier proved a valuable biomarker for the classification of sleep disorders.
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http://dx.doi.org/10.1088/1741-2552/abf773DOI Listing
April 2021

Global Brain Flexibility During Working Memory is reduced in a High Genetic Risk Group for Schizophrenia.

Biol Psychiatry Cogn Neurosci Neuroimaging 2021 Jan 29. Epub 2021 Jan 29.

Neuroscience and Mental Health Research Institute, Cardiff University, Cardiff, UK; Cardiff University Brain Research Imaging Centre (CUBRIC), School of Psychology, Cardiff University, Cardiff, UK; MRC Centre for Neuropsychiatric Genetics and Genomics, Institute of Psychological Medicine and Clinical Neurosciences, Cardiff School of Medicine, Cardiff University, Cardiff, UK; School for Mental Health and Neuroscience, Faculty of Health, Medicine and Life Sciences, Maastricht University, Maastricht, the Netherlands.

Background: Altered functional brain connectivity has been proposed as an intermediate phenotype between genetic risk loci and clinical expression of schizophrenia. Genetic high-risk groups of healthy subjects are particularly suited for the investigation of this proposition because they can be tested in the absence of medication or other secondary effects of schizophrenia.

Methods: Here we applied dFC analysis to functional MRI data in order to reveal the reconfiguration of brain networks during a cognitive task. We recruited healthy carriers of common risk variants using the recall-by-genotype design. We assessed 197 individuals: 99 individuals (52 female, 47 male) with low polygenic risk scores (Schizophrenia risk profile scores, SCZ-PRS) and 98 individuals (52 female, 46 male) with high SCZ-PRS from both tails of the SCZ-PRS distribution from a genotyped population cohort, the Avon Longitudinal Study of Parents And Children (ALSPAC) (N=8169). We compared groups both on conventional brain activation profiles, using the general linear model of the experiment, and on the neural Flexibility Index (FI) which quantifies how frequent a brain region's community affiliation changes over experimental time.

Results: Behavioral performance and standard brain activation profiles did not differ significantly between groups. High SCZ-PRS was associated with reduced FI and network modularity across n-back levels. The whole-brain FI and that of the fronto-parietal working memory network was associated with n-back performance. We identified a dynamic network phenotype related to high SCZ-PRS.

Conclusions: Such neurophysiological markers can become important for the elucidation of biological mechanisms of schizophrenia and particularly the associated cognitive deficit.
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http://dx.doi.org/10.1016/j.bpsc.2021.01.007DOI Listing
January 2021

Quantitative Identification of Functional Connectivity Disturbances in Neuropsychiatric Lupus Based on Resting-State fMRI: A Robust Machine Learning Approach.

Brain Sci 2020 Oct 25;10(11). Epub 2020 Oct 25.

Computational Bio-Medicine Laboratory, Institute of Computer Science, Foundation for Research and Technology-Hellas, 70013 Heraklion, Greece.

Neuropsychiatric systemic lupus erythematosus (NPSLE) is an autoimmune entity comprised of heterogenous syndromes affecting both the peripheral and central nervous system. Research on the pathophysiological substrate of NPSLE manifestations, including functional neuroimaging studies, is extremely limited. The present study examined person-specific patterns of whole-brain functional connectivity in NPSLE patients ( = 44) and age-matched healthy control participants ( = 39). Static functional connectivity graphs were calculated comprised of connection strengths between 90 brain regions. These connections were subsequently filtered through rigorous surrogate analysis, a technique borrowed from physics, novel to neuroimaging. Next, global as well as nodal network metrics were estimated for each individual functional brain network and were input to a robust machine learning algorithm consisting of a random forest feature selection and nested cross-validation strategy. The proposed pipeline is data-driven in its entirety, and several tests were performed in order to ensure model robustness. The best-fitting model utilizing nodal graph metrics for 11 brain regions was associated with 73.5% accuracy (74.5% sensitivity and 73% specificity) in discriminating NPSLE from healthy individuals with adequate statistical power. Closer inspection of graph metric values suggested an increased role within the functional brain network in NSPLE (indicated by higher nodal degree, local efficiency, betweenness centrality, or eigenvalue efficiency) as compared to healthy controls for seven brain regions and a reduced role for four areas. These findings corroborate earlier work regarding hemodynamic disturbances in these brain regions in NPSLE. The validity of the results is further supported by significant associations of certain selected graph metrics with accumulated organ damage incurred by lupus, with visuomotor performance and mental flexibility scores obtained independently from NPSLE patients.
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http://dx.doi.org/10.3390/brainsci10110777DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7692139PMC
October 2020

Population neuroimaging: generation of a comprehensive data resource within the ALSPAC pregnancy and birth cohort.

Wellcome Open Res 2020 28;5:203. Epub 2020 Aug 28.

MRC Integrative Epidemiology Unit, Population Health Sciences, University of Bristol, Bristol, BS8 2BN, UK.

Neuroimaging offers a valuable insight into human brain development by allowing assessment of structure, connectivity and function. Multimodal neuroimaging data have been obtained as part of three sub-studies within the Avon Longitudinal Study of Parents and Children, a prospective multigenerational pregnancy and birth cohort based in the United Kingdom. Brain imaging data were acquired when offspring were between 18 and 24 years of age, and included acquisition of structural, functional and magnetization transfer magnetic resonance, diffusion tensor, and magnetoencephalography imaging. This resource provides a unique opportunity to combine neuroimaging data with extensive phenotypic and genotypic measures from participants, their mothers, and fathers.
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http://dx.doi.org/10.12688/wellcomeopenres.16060.1DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7531050PMC
August 2020

Reconfiguration of αmplitude driven dominant coupling modes (DoCM) mediated by α-band in adolescents with schizophrenia spectrum disorders.

Prog Neuropsychopharmacol Biol Psychiatry 2021 Jun 14;108:110073. Epub 2020 Aug 14.

Cardiff University Brain Research Imaging Centre (CUBRIC), School of Psychology, College of Biomedical and Life Sciences, Cardiff University, Cardiff, United Kingdom; Neuroinformatics Group, Cardiff University Brain Research Imaging Centre (CUBRIC), School of Psychology, College of Biomedical and Life Sciences, Cardiff University, Cardiff, United Kingdom; Division of Psychological Medicine and Clinical Neurosciences, School of Medicine, College of Biomedical and Life Sciences, Cardiff University, Cardiff, United Kingdom; School of Psychology, College of Biomedical and Life Sciences,Cardiff University, Cardiff, United Kingdom; Neuroscience and Mental Health Research Institute, School of Medicine, College of Biomedical and Life Sciences,Cardiff University, Cardiff, United Kingdom; MRC Centre for Neuropsychiatric Genetics and Genomics, School of Medicine, College of Biomedical and Life Sciences, Cardiff University, Cardiff, United Kingdom. Electronic address:

Electroencephalography (EEG) based biomarkers have been shown to correlate with the presence of psychotic disorders. Increased delta and decreased alpha power in psychosis indicate an abnormal arousal state. We investigated brain activity across the basic EEG frequencies and also dynamic functional connectivity of both intra and cross-frequency coupling that could reveal a neurophysiological biomarker linked to an aberrant modulating role of alpha frequency in adolescents with schizophrenia spectrum disorders (SSDs). A dynamic functional connectivity graph (DFCG) has been estimated using the imaginary part of phase lag value (iPLV) and correlation of the envelope (corrEnv). We analyzed DFCG profiles of electroencephalographic resting state (eyes closed) recordings of healthy controls (HC) (n = 39) and SSDs subjects (n = 45) in basic frequency bands {δ,θ,α,α,β,β,γ}. In our analysis, we incorporated both intra and cross-frequency coupling modes. Adopting our recent Dominant Coupling Mode (DοCM) model leads to the construction of an integrated DFCG (iDFCG) that encapsulates the functional strength and the DοCM of every pair of brain areas. We revealed significantly higher ratios of delta/alpha, power spectrum in SSDs subjects versus HC. The probability distribution (PD) of amplitude driven DoCM mediated by alpha frequency differentiated SSDs from HC with absolute accuracy (100%). The network Flexibility Index (FI) was significantly lower for subjects with SSDs compared to the HC group. Our analysis supports the central role of alpha frequency alterations in the neurophysiological mechanisms of SSDs. Currents findings open up new diagnostic pathways to clinical detection of SSDs and support the design of rational neurofeedback training.
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http://dx.doi.org/10.1016/j.pnpbp.2020.110073DOI Listing
June 2021

Aberrant Whole-Brain Transitions and Dynamics of Spontaneous Network Microstates in Mild Traumatic Brain Injury.

Front Comput Neurosci 2019 15;13:90. Epub 2020 Jan 15.

Biomedical Imaging Lab, Departments of Engineering Technology, Computer Science, Biomedical Engineering, and Electrical and Computer Engineering, University of Houston, Houston, TX, United States.

Dynamic Functional Connectivity (DFC) analysis is a promising approach for the characterization of brain electrophysiological activity. In this study, we investigated abnormal alterations due to mild Traumatic Brain Injury (mTBI) using DFC of the source reconstructed magnetoencephalographic (MEG) resting-state recordings. Brain activity in several well-known frequency bands was first reconstructed using beamforming of the MEG data to determine ninety anatomical brain regions of interest. A DFC graph was formulated using the imaginary part of phase-locking values, which were obtained from 30 mTBI patients and 50 healthy controls (HC). Subsequently, we estimated normalized Laplacian transformations of individual, statistically and topologically filtered quasi-static graphs. The corresponding eigenvalues of each node synchronization were then computed and through the neural-gas algorithm, we quantized the evolution of the eigenvalues resulting in distinct network microstates (NMstates). The discrimination level between the two groups was assessed using an iterative cross-validation classification scheme with features either the NMstates in each frequency band, or the combination of the so-called chronnectomics (flexibility index, occupancy time of NMstate, and Dwell time) with the complexity index over the evolution of the NMstates across all frequency bands. Classification performance based on chronnectomics showed 80% accuracy, 99% sensitivity, and 49% specificity. However, performance was much higher (accuracy: 91-97%, sensitivity: 100%, and specificity: 77-93%) when focusing on the microstates. Exploring the mean node degree within and between brain anatomical networks (default mode network, frontoparietal, occipital, cingulo-opercular, and sensorimotor), a reduced pattern occurred from lower to higher frequency bands, with statistically significant stronger degrees for the HC than the mTBI group. A higher entropic profile on the temporal evolution of the modularity index was observed for both NMstates for the mTBI group across frequencies. A significant difference in the flexibility index was observed between the two groups for the β frequency band. The latter finding may support a central role of the thalamus impairment in mTBI. The current study considers a complete set of frequency-dependent connectomic markers of mTBI-caused alterations in brain connectivity that potentially could serve as markers to assess the return of an injured subject back to normality.
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http://dx.doi.org/10.3389/fncom.2019.00090DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6974679PMC
January 2020

Typical and Aberrant Functional Brain Flexibility: Lifespan Development and Aberrant Organization in Traumatic Brain Injury and Dyslexia.

Brain Sci 2019 Dec 16;9(12). Epub 2019 Dec 16.

Division of Clinical Neurosciences, Department of Pediatrics, University of Tennessee Health Science Center, Memphis, TN 38103, USA.

Intrinsic functional connectivity networks derived from different neuroimaging methods and connectivity estimators have revealed robust developmental trends linked to behavioural and cognitive maturation. The present study employed a dynamic functional connectivity approach to determine dominant intrinsic coupling modes in resting-state neuromagnetic data from 178 healthy participants aged 8-60 years. Results revealed significant developmental trends in three types of dominant intra- and inter-hemispheric neuronal population interactions (amplitude envelope, phase coupling, and phase-amplitude synchronization) involving frontal, temporal, and parieto-occipital regions. Multi-class support vector machines achieved 89% correct classification of participants according to their chronological age using dynamic functional connectivity indices. Moreover, systematic temporal variability in functional connectivity profiles, which was used to empirically derive a composite flexibility index, displayed an inverse U-shaped curve among healthy participants. Lower flexibility values were found among age-matched children with reading disability and adults who had suffered mild traumatic brain injury. The importance of these results for normal and abnormal brain development are discussed in light of the recently proposed role of cross-frequency interactions in the fine-grained coordination of neuronal population activity.
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http://dx.doi.org/10.3390/brainsci9120380DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6956162PMC
December 2019

Aberrant MEG multi-frequency phase temporal synchronization predicts conversion from mild cognitive impairment-to-Alzheimer's disease.

Neuroimage Clin 2019 8;24:101972. Epub 2019 Aug 8.

Laboratory of Cognitive and Computational Neuroscience, Center for Biomedical Technology, Universidad Complutense and Universidad Politécnica de Madrid, Madrid, Spain; Department of Experimental Psychology, Universidad Complutense de Madrid, Madrid, Spain; Networking Research Center on Bioengineering, Biomaterials and Nanomedicine (CIBER-BBN), Madrid, Spain.

Many neuroimaging studies focus on a frequency-specific or a multi-frequency network analysis showing that functional brain networks are disrupted in patients with Alzheimer's disease (AD). Although those studies enriched our knowledge of the impact of AD in brain's functionality, our goal is to test the effectiveness of combining neuroimaging with network neuroscience to predict with high accuracy subjects with mild cognitive impairment (MCI) that will convert to AD. In this study, eyes-closed resting-state magnetoencephalography (MEG) recordings from 27 stable MCI (sMCI) and 27 progressive MCI (pMCI) from two scan sessions (baseline and follow-up after approximately 3 years) were projected via beamforming onto an atlas-based set of regions of interest (ROIs). Dynamic functional connectivity networks were constructed independently for the five classical frequency bands while a multivariate phase-based coupling metric was adopted. Thus, computing the distance between the fluctuation of functional strength of every pair of ROIs between the two conditions with dynamic time wrapping (DTW), a large set of features was extracted. A machine learning algorithm revealed 30 DTW-based features in the five frequency bands that can distinguish the sMCI from pMCI with absolute accuracy (100%). Further analysis of the selected links revealed that most of the connected ROIs were part of the default mode network (DMN), the cingulo-opercular (CO), the fronto-parietal and the sensorimotor network. Overall, our dynamic network multi-frequency analysis approach provides an effective framework of constructing a sensitive MEG-based connectome biomarker for the prediction of conversion from MCI to Alzheimer's disease.
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http://dx.doi.org/10.1016/j.nicl.2019.101972DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6745514PMC
September 2020

Is brain connectome research the future frontier for subjective cognitive decline? A systematic review.

Clin Neurophysiol 2019 10 19;130(10):1762-1780. Epub 2019 Jul 19.

Information Technologies Institute, Centre for Research and Technology Hellas (CERTH-ITI), Greece; 1(st) Department of Neurology, "AHEPA" UGH", School of Medicine, Faculty of Health Sciences, Aristotle University of Thessaloniki (AUTH), Greece; Greek Alzheimer's Association and Related Disorders (GAADRD), Greece.

Objective: We performed a systematic literature review on Subjective Cognitive Decline (SCD) in order to examine whether the resemblance of brain connectome and functional connectivity (FC) alterations in SCD with respect to MCI, AD and HC can help us draw conclusions on the progression of SCD to more advanced stages of dementia.

Methods: We searched for studies that used any neuroimaging tool to investigate potential differences/similarities of brain connectome in SCD with respect to HC, MCI, and AD.

Results: Sixteen studies were finally included in the review. Apparent FC connections and disruptions were observed in the white matter, default mode and gray matter networks in SCD with regards to HC, MCI, and AD. Interestingly, more apparent connections in SCD were located over the posterior regions, while an increase of FC over anterior regions was observed as the disease progressed.

Conclusions: Elders with SCD display a significant disruption of the brain network, which in most of the cases is worse than HC across multiple network parameters.

Significance: The present review provides comprehensive and balanced coverage of a timely target research activity around SCD with the intention to identify similarities/differences across patient groups on the basis of brain connectome properties.
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http://dx.doi.org/10.1016/j.clinph.2019.07.004DOI Listing
October 2019

Modeling the Switching Behavior of Functional Connectivity Microstates (FCμstates) as a Novel Biomarker for Mild Cognitive Impairment.

Front Neurosci 2019 11;13:542. Epub 2019 Jun 11.

Laboratory of Cognitive and Computational Neuroscience, Center for Biomedical Technology, Universidad Complutense de Madrid - Universidad Politécnica de Madrid, Madrid, Spain.

The need for designing and validating novel biomarkers for the detection of mild cognitive impairment (MCI) is evident. MCI patients have a high risk of developing Alzheimer's disease (AD), and for that reason the introduction of novel and reliable biomarkers is of significant clinical importance. Motivated by recent findings on the rich information of dynamic functional connectivity graphs (DFCGs) about brain (dys) function, we introduced a novel approach of identifying MCI based on magnetoencephalographic (MEG) resting state recordings. The activity of different brain rhythms {δ, 𝜃, α1, α2, β1, β2, γ1, γ2} was first beamformed with linear constrained minimum norm variance in the MEG data to determine 90 anatomical regions of interest (ROIs). A DFCG was then estimated using the imaginary part of phase lag value (iPLV) for both intra-frequency coupling (8) and cross-frequency coupling pairs (28). We analyzed DFCG profiles of neuromagnetic resting state recordings of 18 MCI patients and 22 healthy controls. We followed our model of identifying the dominant intrinsic coupling mode (DICM) across MEG sources and temporal segments, which further leads to the construction of an integrated DFCG (iDFCG). We then filtered statistically and topologically every snapshot of the iDFCG with data-driven approaches. An estimation of the normalized Laplacian transformation for every temporal segment of the iDFCG and the related eigenvalues created a 2D map based on the network metric time series of the eigenvalues (NMTS). The NMTS preserves the non-stationarity of the fluctuated synchronizability of iDCFG for each subject. Employing the initial set of 20 healthy elders and 20 MCI patients, as training set, we built an overcomplete dictionary set of network microstates (n μstates). Afterward, we tested the whole procedure in an extra blind set of 20 subjects for external validation. We succeeded in gaining a high classification accuracy on the blind dataset (85%), which further supports the proposed Markovian modeling of the evolution of brain states. The adaptation of appropriate neuroinformatic tools that combine advanced signal processing and network neuroscience tools could properly manipulate the non-stationarity of time-resolved FC patterns revealing a robust biomarker for MCI.
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http://dx.doi.org/10.3389/fnins.2019.00542DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6579926PMC
June 2019

Optimization of graph construction can significantly increase the power of structural brain network studies.

Neuroimage 2019 10 6;199:495-511. Epub 2019 Jun 6.

Cardiff University Brain Research Imaging Centre (CUBRIC), School of Psychology, Cardiff University, Maindy Road, Cardiff, CF24 4HQ, UK; School of Psychology, Cardiff University, 70 Park Place, Cardiff, CF10 3AT, UK; Mary McKillop Institute for Health Research, Faculty of Health Sciences, Australian Catholic University, Melbourne, Australia.

Structural brain networks derived from diffusion magnetic resonance imaging data have been used extensively to describe the human brain, and graph theory has allowed quantification of their network properties. Schemes used to construct the graphs that represent the structural brain networks differ in the metrics they use as edge weights and the algorithms they use to define the network topologies. In this work, twenty graph construction schemes were considered. The schemes use the number of streamlines, the fractional anisotropy, the mean diffusivity or other attributes of the tracts to define the edge weights, and either an absolute threshold or a data-driven algorithm to define the graph topology. The test-retest data of the Human Connectome Project were used to compare the reproducibility of the graphs and their various attributes (edges, topologies, graph theoretical metrics) derived through those schemes, for diffusion images acquired with three different diffusion weightings. The impact of the scheme on the statistical power of the study and on the number of participants required to detect a difference between populations or an effect of an intervention was also calculated. The reproducibility of the graphs and their attributes depended heavily on the graph construction scheme. Graph reproducibility was higher for schemes that used thresholding to define the graph topology, while data-driven schemes performed better at topology reproducibility (mean similarities of 0.962 and 0.984 respectively, for graphs derived from diffusion images with b=2000 s/mm). Additionally, schemes that used thresholding resulted in better reproducibility for local graph theoretical metrics (intra-class correlation coefficients (ICC) of the order of 0.8), compared to data-driven schemes. Thresholded and data-driven schemes resulted in high (0.86 or higher) ICCs only for schemes that use exclusively the number of streamlines to construct the graphs. Crucially, the number of participants required to detect a difference between populations or an effect of an intervention could change by a factor of two or more depending on the scheme used, affecting the power of studies to reveal the effects of interest.
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http://dx.doi.org/10.1016/j.neuroimage.2019.05.052DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6693529PMC
October 2019

A Novel, Fast, Reliable, and Data-Driven Method for Simultaneous Single-Trial Mining and Amplitude-Latency Estimation Based on Proximity Graphs and Network Analysis.

Front Neuroinform 2018 19;12:59. Epub 2018 Nov 19.

Cardiff University Brain Research Imaging Centre, School of Psychology, Cardiff University, Cardiff, United Kingdom.

Both amplitude and latency of single-trial EEG/MEG recordings provide valuable information regarding functionality of the human brain. In this article, we provided a data-driven graph and network-based framework for mining information from multi-trial event-related brain recordings. In the first part, we provide the general outline of the proposed methodological approach. In the second part, we provide a more detailed illustration, and present the obtained results on every step of the algorithmic procedure. To justify the proposed framework instead of presenting the analytic data mining and graph-based steps, we address the problem of response variability, a prerequisite to reliable estimates for both the amplitude and latency on specific N/P components linked to the nature of the stimuli. The major question addressed in this study is the selection of representative single-trials with the aim of uncovering a less noisey averaged waveform elicited from the stimuli. This graph and network-based algorithmic procedure increases the signal-to-noise (SNR) of the brain response, a key pre-processing step to reveal significant and reliable amplitude and latency at a specific time after the onset of the stimulus and with the right polarity (N or P). We demonstrated the whole approach using electroencephalography (EEG) auditory mismatch negativity (MMN) recordings from 42 young healthy controls. The method is novel, fast and data-driven succeeding first to reveal the true waveform elicited by MMN on different conditions (frequency, intensity, duration, etc.). The proposed graph-oriented algorithmic pipeline increased the SNR of the characteristic waveforms and the reliability of amplitude and latency within the adopted cohort. We also demonstrated how different EEG reference schemes (REST vs. average) can influence amplitude-latency estimation. Simulation results revealed robust amplitude-latency estimations under different SNR and amplitude-latency variations with the proposed algorithm.
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http://dx.doi.org/10.3389/fninf.2018.00059DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6252329PMC
November 2018

Complexity of brain activity and connectivity in functional neuroimaging.

J Neurosci Res 2018 11;96(11):1741-1757

Cardiff University Brain Research Imaging Centre, School of Psychology, Cardiff University, Cardiff, United Kingdom.

Understanding the complexity of human brain dynamics and brain connectivity across the repertoire of functional neuroimaging and various conditions, is of paramount importance. Novel measures should be designed tailored to the input focusing on multichannel activity and dynamic functional brain connectivity (DFBC). Here, we defined a novel complexity index (CI) from the field of symbolic dynamics that quantifies patterns of different words up to a length from a symbolic sequence. The CI characterizes the complexity of the brain activity. We analysed DFBC by adopting the sliding window approach using imaginary part of phase locking value (iPLV) for EEG/ECoG/MEG and wavelet coherence (WC) for fMRI. Both intra and cross-frequency couplings (CFC) namely phase-to-amplitude were estimated using iPLV/WC at every snapshot of the DFBC. Using proper surrogate analysis, we defined the dominant intrinsic coupling mode (DICM) per pair of regions-of-interest (ROI). The spatiotemporal probability distribution of DICM were reported to reveal the most prominent coupling modes per condition and modality. Finally, a novel flexibility index is defined that quantifies the transition of DICM per pair of ROIs between consecutive time windows. The whole methodology was demonstrated using four neuroimaging datasets (EEG/ECoG/MEG/fMRI). Finally, we succeeded to totally discriminate healthy controls from schizophrenic using FI and dynamic reconfiguration of DICM. Anaesthesia independently of the drug caused a global decreased of complexity in all frequency bands with the exception in δ and alters the dynamic reconfiguration of DICM. CI and DICM of MEG/fMRI resting-state recordings in two spatial scales were high reliable.
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http://dx.doi.org/10.1002/jnr.24316DOI Listing
November 2018

Reliability of Static and Dynamic Network Metrics in the Resting-State: A MEG-Beamformed Connectivity Analysis.

Front Neurosci 2018 3;12:506. Epub 2018 Aug 3.

Cardiff University Brain Research Imaging Centre, School of Psychology, Cardiff University, Cardiff, United Kingdom.

The resting activity of the brain can be described by so-called intrinsic connectivity networks (ICNs), which consist of spatially and temporally distributed, but functionally connected, nodes. The coordinated activity of the resting state can be explored via magnetoencephalography (MEG) by studying frequency-dependent functional brain networks at the source level. Although many algorithms for the analysis of brain connectivity have been proposed, the reliability of network metrics derived from both static and dynamic functional connectivity is still unknown. This is a particular problem for studies of associations between ICN metrics and personality variables or other traits, and for studies of differences between patient and control groups, which both depend critically on the reliability of the metrics used. A detailed investigation of the reliability of metrics derived from resting-state MEG repeat scans is therefore a prerequisite for the development of connectomic biomarkers. Here, we first estimated both static (SFC) and dynamic functional connectivity (DFC) after beamforming source reconstruction using the imaginary part of the phase locking index (iPLV) and the correlation of the amplitude envelope (CorEnv). Using our approach, functional network microstates (FCμstates) were derived from the DFC and chronnectomics were computed from the evolution of FCμstates across experimental time. In both temporal scales, the reliability of network metrics (SFC), the FCμstates and the related chronnectomics were evaluated for every frequency band. Chronnectomic statistics and FCμstates were generally more reliable than node-wise static network metrics. CorEnv-based network metrics were more reproducible at the static approach. The reliability of chronnectomics have been evaluated also in a second dataset. This study encourages the analysis of MEG resting-state via DFC.
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http://dx.doi.org/10.3389/fnins.2018.00506DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6088195PMC
August 2018

How random is the random forest? Random forest algorithm on the service of structural imaging biomarkers for Alzheimer's disease: from Alzheimer's disease neuroimaging initiative (ADNI) database.

Neural Regen Res 2018 Jun;13(6):962-970

High Performance Computing Center Stuttgart (HLRS), University of Stuttgart, Stuttgart, Germany; Department of Informatics, Aristotle University of Thessaloniki, Thessaloniki, Greece.

Neuroinformatics is a fascinating research field that applies computational models and analytical tools to high dimensional experimental neuroscience data for a better understanding of how the brain functions or dysfunctions in brain diseases. Neuroinformaticians work in the intersection of neuroscience and informatics supporting the integration of various sub-disciplines (behavioural neuroscience, genetics, cognitive psychology, etc.) working on brain research. Neuroinformaticians are the pathway of information exchange between informaticians and clinicians for a better understanding of the outcome of computational models and the clinical interpretation of the analysis. Machine learning is one of the most significant computational developments in the last decade giving tools to neuroinformaticians and finally to radiologists and clinicians for an automatic and early diagnosis-prognosis of a brain disease. Random forest (RF) algorithm has been successfully applied to high-dimensional neuroimaging data for feature reduction and also has been applied to classify the clinical label of a subject using single or multi-modal neuroimaging datasets. Our aim was to review the studies where RF was applied to correctly predict the Alzheimer's disease (AD), the conversion from mild cognitive impairment (MCI) and its robustness to overfitting, outliers and handling of non-linear data. Finally, we described our RF-based model that gave us the 1 position in an international challenge for automated prediction of MCI from MRI data.
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http://dx.doi.org/10.4103/1673-5374.233433DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6022472PMC
June 2018

How to Build a Functional Connectomic Biomarker for Mild Cognitive Impairment From Source Reconstructed MEG Resting-State Activity: The Combination of ROI Representation and Connectivity Estimator Matters.

Front Neurosci 2018 1;12:306. Epub 2018 Jun 1.

Laboratory of Cognitive and Computational Neuroscience, Center for Biomedical Technology, Madrid, Spain.

Our work aimed to demonstrate the combination of machine learning and graph theory for the designing of a connectomic biomarker for mild cognitive impairment (MCI) subjects using eyes-closed neuromagnetic recordings. The whole analysis based on source-reconstructed neuromagnetic activity. As ROI representation, we employed the principal component analysis (PCA) and centroid approaches. As representative bi-variate connectivity estimators for the estimation of intra and cross-frequency interactions, we adopted the phase locking value (PLV), the imaginary part (iPLV) and the correlation of the envelope (CorrEnv). Both intra and cross-frequency interactions (CFC) have been estimated with the three connectivity estimators within the seven frequency bands (intra-frequency) and in pairs (CFC), correspondingly. We demonstrated how different versions of functional connectivity graphs single-layer (SL-FCG) and multi-layer (ML-FCG) can give us a different view of the functional interactions across the brain areas. Finally, we applied machine learning techniques with main scope to build a reliable connectomic biomarker by analyzing both SL-FCG and ML-FCG in two different options: as a whole unit using a tensorial extraction algorithm and as single pair-wise coupling estimations. We concluded that edge-weighed feature selection strategy outperformed the tensorial treatment of SL-FCG and ML-FCG. The highest classification performance was obtained with the centroid ROI representation and edge-weighted analysis of the SL-FCG reaching the 98% for the CorrEnv in α:α and 94% for the iPLV in α. Classification performance based on the multi-layer participation coefficient, a multiplexity index reached 52% for iPLV and 52% for CorrEnv. Selected functional connections that build the multivariate connectomic biomarker in the edge-weighted scenario are located in default-mode, fronto-parietal, and cingulo-opercular network. Our analysis supports the notion of analyzing FCG simultaneously in intra and cross-frequency whole brain interactions with various connectivity estimators in beamformed recordings.
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http://dx.doi.org/10.3389/fnins.2018.00306DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5992286PMC
June 2018

Enhancing Performance and Bit Rates in a Brain-Computer Interface System With Phase-to-Amplitude Cross-Frequency Coupling: Evidences From Traditional c-VEP, Fast c-VEP, and SSVEP Designs.

Front Neuroinform 2018 8;12:19. Epub 2018 May 8.

Brain Innovation B. V., Maastricht, Netherlands.

A brain-computer interface (BCI) is a channel of communication that transforms brain activity into specific commands for manipulating a personal computer or other home or electrical devices. In other words, a BCI is an alternative way of interacting with the environment by using brain activity instead of muscles and nerves. For that reason, BCI systems are of high clinical value for targeted populations suffering from neurological disorders. In this paper, we present a new processing approach in three publicly available BCI data sets: (a) a well-known multi-class ( = 6) coded-modulated Visual Evoked potential (c-VEP)-based BCI system for able-bodied and disabled subjects; (b) a multi-class ( = 32) c-VEP with slow and fast stimulus representation; and (c) a steady-state Visual Evoked potential (SSVEP) multi-class ( = 5) flickering BCI system. Estimating cross-frequency coupling (CFC) and namely δ-θ [δ: (0.5-4 Hz), θ: (4-8 Hz)] phase-to-amplitude coupling (PAC) within sensor and across experimental time, we succeeded in achieving high classification accuracy and Information Transfer Rates (ITR) in the three data sets. Our approach outperformed the originally presented ITR on the three data sets. The bit rates obtained for both the disabled and able-bodied subjects reached the fastest reported level of with the PAC estimator. Additionally, our approach outperformed alternative signal features such as the relative power (29.73 bits/min) and raw time series analysis (24.93 bits/min) and also the original reported bit rates of . In the second data set, we succeeded in achieving an average ITR of 124.40 ± 11.68 for the slow 60 Hz and an average ITR of 233.99 ± 15.75 for the fast 120 Hz. In the third data set, we succeeded in achieving an average ITR of 106.44 ± 8.94. Current methodology outperforms any previous methodologies applied to each of the three free available BCI datasets.
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http://dx.doi.org/10.3389/fninf.2018.00019DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5952007PMC
May 2018

Fronto-Parietal Subnetworks Flexibility Compensates For Cognitive Decline Due To Mental Fatigue.

Hum Brain Mapp 2018 09 24;39(9):3528-3545. Epub 2018 Apr 24.

Singapore Institute for Neurotechnology (SINAPSE), Centre for Life Sciences, National University of Singapore, Singapore.

Fronto-parietal subnetworks were revealed to compensate for cognitive decline due to mental fatigue by community structure analysis. Here, we investigate changes in topology of subnetworks of resting-state fMRI networks due to mental fatigue induced by prolonged performance of a cognitively demanding task, and their associations with cognitive decline. As it is well established that brain networks have modular organization, community structure analyses can provide valuable information about mesoscale network organization and serve as a bridge between standard fMRI approaches and brain connectomics that quantify the topology of whole brain networks. We developed inter- and intramodule network metrics to quantify topological characteristics of subnetworks, based on our hypothesis that mental fatigue would impact on functional relationships of subnetworks. Functional networks were constructed with wavelet correlation and a data-driven thresholding scheme based on orthogonal minimum spanning trees, which allowed detection of communities with weak connections. A change from pre- to posttask runs was found for the intermodule density between the frontal and the temporal subnetworks. Seven inter- or intramodule network metrics, mostly at the frontal or the parietal subnetworks, showed significant predictive power of individual cognitive decline, while the network metrics for the whole network were less effective in the predictions. Our results suggest that the control-type fronto-parietal networks have a flexible topological architecture to compensate for declining cognitive ability due to mental fatigue. This community structure analysis provides valuable insight into connectivity dynamics under different cognitive states including mental fatigue.
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http://dx.doi.org/10.1002/hbm.24192DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6866441PMC
September 2018

A novel, fast and efficient single-sensor automatic sleep-stage classification based on complementary cross-frequency coupling estimates.

Clin Neurophysiol 2018 04 31;129(4):815-828. Epub 2018 Jan 31.

Division of Psychological Medicine and Clinical Neurosciences, School of Medicine, Cardiff University, Cardiff, United Kingdom; Cardiff University Brain Research Imaging Centre, School of Psychology, Cardiff University, Cardiff, United Kingdom; School of Psychology, Cardiff University, Cardiff, United Kingdom; Neuroscience and Mental Health Research Institute, Cardiff University, Cardiff, United Kingdom.

Objective: Limitations of the manual scoring of polysomnograms, which include data from electroencephalogram (EEG), electro-oculogram (EOG), electrocardiogram (ECG) and electromyogram (EMG) channels have long been recognized. Manual staging is resource intensive and time consuming, and thus considerable effort must be spent to ensure inter-rater reliability. As a result, there is a great interest in techniques based on signal processing and machine learning for a completely Automatic Sleep Stage Classification (ASSC).

Methods: In this paper, we present a single-EEG-sensor ASSC technique based on the dynamic reconfiguration of different aspects of cross-frequency coupling (CFC) estimated between predefined frequency pairs over 5 s epoch lengths. The proposed analytic scheme is demonstrated using the PhysioNet Sleep European Data Format (EDF) Database with repeat recordings from 20 healthy young adults. We validate our methodology in a second sleep dataset.

Results: We achieved very high classification sensitivity, specificity and accuracy of 96.2 ± 2.2%, 94.2 ± 2.3%, and 94.4 ± 2.2% across 20 folds, respectively, and also a high mean F1 score (92%, range 90-94%) when a multi-class Naive Bayes classifier was applied. High classification performance has been achieved also in the second sleep dataset.

Conclusions: Our method outperformed the accuracy of previous studies not only on different datasets but also on the same database.

Significance: Single-sensor ASSC makes the entire methodology appropriate for longitudinal monitoring using wearable EEG in real-world and laboratory-oriented environments.
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http://dx.doi.org/10.1016/j.clinph.2017.12.039DOI Listing
April 2018

Aberrant resting-state functional brain networks in dyslexia: Symbolic mutual information analysis of neuromagnetic signals.

Int J Psychophysiol 2018 04 21;126:20-29. Epub 2018 Feb 21.

Division of Clinical Neurosciences, Department of Pediatrics, University of Tennessee Health Science Center, Memphis, TN, USA; Le Bonheur Neuroscience Institute, Le Bonheur Children's Hospital, Memphis, TN, USA.

Neuroimaging studies have identified a variety of structural and functional connectivity abnormalities in students experiencing reading difficulties. The present study adopted a novel approach to assess the dynamics of resting-state neuromagnetic recordings in the form of symbolic sequences (i.e., repeated patterns of neuromagnetic fluctuations within and/or between sensors). Participants were 25 students experiencing severe reading difficulties (RD) and 27 age-matched non-impaired readers (NI) aged 7-14 years. Sensor-level data were first represented as symbolic sequences in eight conventional frequency bands. Next, dominant types of sensor-to-sensor interactions in the form of intra and cross-frequency coupling were computed and subjected to graph modeling to assess group differences in global network characteristics. As a group RD students displayed predominantly within-frequency interactions between neighboring sensors which may reflect reduced overall global network efficiency and cost-efficiency of information transfer. In contrast, sensor networks among NI students featured a higher proportion of cross-frequency interactions. Brain-reading achievement associations highlighted the role of left hemisphere temporo-parietal functional networks, at rest, for reading acquisition and ability.
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http://dx.doi.org/10.1016/j.ijpsycho.2018.02.008DOI Listing
April 2018

Improving the Reliability of Network Metrics in Structural Brain Networks by Integrating Different Network Weighting Strategies into a Single Graph.

Front Neurosci 2017 19;11:694. Epub 2017 Dec 19.

Cardiff University Brain Research Imaging Centre, School of Psychology, Cardiff University, Cardiff, United Kingdom.

Structural brain networks estimated from diffusion MRI (dMRI) via tractography have been widely studied in healthy controls and patients with neurological and psychiatric diseases. However, few studies have addressed the reliability of derived network metrics both node-specific and network-wide. Different network weighting strategies (NWS) can be adopted to weight the strength of connection between two nodes yielding structural brain networks that are almost fully-weighted. Here, we scanned five healthy participants five times each, using a diffusion-weighted MRI protocol and computed edges between 90 regions of interest (ROI) from the Automated Anatomical Labeling (AAL) template. The edges were weighted according to nine different methods. We propose a linear combination of these nine NWS into a single graph using an appropriate diffusion distance metric. We refer to the resulting weighted graph as an Integrated Weighted Structural Brain Network (ISWBN). Additionally, we consider a topological filtering scheme that maximizes the information flow in the brain network under the constraint of the overall cost of the surviving connections. We compared each of the nine NWS and the ISWBN based on the improvement of: (a) intra-class correlation coefficient (ICC) of well-known network metrics, both node-wise and per network level; and (b) the recognition accuracy of each subject compared to the remainder of the cohort, as an attempt to access the uniqueness of the structural brain network for each subject, after first applying our proposed topological filtering scheme. Based on a threshold where the network level ICC should be >0.90, our findings revealed that six out of nine NWS lead to unreliable results at the network level, while all nine NWS were unreliable at the node level. In comparison, our proposed ISWBN performed as well as the best performing individual NWS at the network level, and the ICC was higher compared to all individual NWS at the node level. Importantly, both network and node-wise ICCs of network metrics derived from the topologically filtered ISBWN (ISWBN), were further improved compared to the non-filtered ISWBN. Finally, in the recognition accuracy tests, we assigned each single ISWBN to the correct subject. We also applied our methodology to a second dataset of diffusion-weighted MRI in healthy controls and individuals with psychotic experience. Following a binary classification scheme, the classification performance based on ISWBN outperformed the nine different weighting strategies and the ISWBN. Overall, these findings suggest that the proposed methodology results in improved characterization of genuine between-subject differences in connectivity leading to the possibility of network-based structural phenotyping.
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http://dx.doi.org/10.3389/fnins.2017.00694DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5742099PMC
December 2017

Mnemonic strategy training of the elderly at risk for dementia enhances integration of information processing via cross-frequency coupling.

Alzheimers Dement (N Y) 2016 Nov 15;2(4):241-249. Epub 2016 Sep 15.

Health-IS Lab, Chair of Information Management, Department of Management, ETH Zurich, Zurich, Switzerland.

Introduction: We sought to identify whether intensive 10-week mobile health mnemonic strategy training (MST) could shift the resting-state brain network more toward cortical-level integration, which has recently been proven to reflect the reorganization of the brain networks compensating the cognitive decline.

Methods: One hundred fifty-eight patients with mild cognitive impairment (MCI) were selected and participated in 10-week training lasting 90 min/d of memory training. They benefited from an initial and a follow-up neuropsychological evaluation and resting-state electroencephalography (EEG).

Results: At follow-up, MST revealed an extensive significant training effect that changed the network with an increase of synchronization between parietotemporal and frontal areas; frontal-parietal causal strengthening as part of top-down inhibitory control; enhancement of sensorimotor connections in β band; and a general increase of cortical-level integration. More precisely, MST induced gain as an increase of the global cost efficiency (GCE) of the whole cortical network and a neuropsychological performance improvement, which was correlated with it (r = 0.32,  = .0001). The present study unfolded intervention changes based on EEG source activity via novel neuroinformatic tools for revealing intrinsic coupling modes in both amplitude-phase representations and in the mixed spectrospatiotemporal domain.

Discussion: Further work should identify whether the GCE enhancement of the functional cortical brain networks is a compensation mechanism to the brain network dysfunction or a more permanent neuroplasticity effect.
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http://dx.doi.org/10.1016/j.trci.2016.08.004DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5651360PMC
November 2016

Mining Time-Resolved Functional Brain Graphs to an EEG-Based Chronnectomic Brain Aged Index (CBAI).

Front Hum Neurosci 2017 7;11:423. Epub 2017 Sep 7.

Department of Informatics and Telecommunications Engineering, University of Western MacedoniaKozani, Greece.

The brain at rest consists of spatially and temporal distributed but functionally connected regions that called intrinsic connectivity networks (ICNs). Resting state electroencephalography (rs-EEG) is a way to characterize brain networks without confounds associated with task EEG such as task difficulty and performance. A novel framework of how to study dynamic functional connectivity under the notion of functional connectivity microstates (FCμstates) and symbolic dynamics is further discussed. Furthermore, we introduced a way to construct a single integrated dynamic functional connectivity graph (IDFCG) that preserves both the strength of the connections between every pair of sensors but also the type of dominant intrinsic coupling modes (DICM). The whole methodology is demonstrated in a significant and unexplored task for EEG which is the definition of an objective Chronnectomic Brain Aged index (CBAI) extracted from resting-state data ( = 94 subjects) with both eyes-open and eyes-closed conditions. Novel features have been defined based on symbolic dynamics and the notion of DICM and FCμstates. The transition rate of FCμstates, the symbolic dynamics based on the evolution of FCμstates (the Markovian Entropy, the complexity index), the probability distribution of DICM, the novel Flexibility Index that captures the dynamic reconfiguration of DICM per pair of EEG sensors and the relative signal power constitute a valuable pool of features that can build the proposed CBAI. Here we applied a feature selection technique and Extreme Learning Machine (ELM) classifier to discriminate young adults from middle-aged and a Support Vector Regressor to build a linear model of the actual age based on EEG-based spatio-temporal features. The most significant type of features for both prediction of age and discrimination of young vs. adults age groups was the dynamic reconfiguration of dominant coupling modes derived from a subset of EEG sensor pairs. Specifically, our results revealed a very high prediction of age for eyes-open ( = 0.60; y = 0.79x + 8.03) and lower for eyes-closed ( = 0.48; y = 0.71x + 10.91) while we succeeded to correctly classify young vs. middle-age group with 97.8% accuracy in eyes-open and 87.2% for eyes-closed. Our results were reproduced also in a second dataset for further external validation of the whole analysis. The proposed methodology proved valuable for the characterization of the intrinsic properties of dynamic functional connectivity through the age untangling developmental differences using EEG resting-state recordings.
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http://dx.doi.org/10.3389/fnhum.2017.00423DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5594081PMC
September 2017

Altered Rich-Club and Frequency-Dependent Subnetwork Organization in Mild Traumatic Brain Injury: A MEG Resting-State Study.

Front Hum Neurosci 2017 30;11:416. Epub 2017 Aug 30.

Biomedical Imaging Lab, Departments of Engineering Technology, Computer Science, Biomedical Engineering, and Electrical and Computer Engineering, University of HoustonHouston, TX, United States.

Functional brain connectivity networks exhibit "small-world" characteristics and some of these networks follow a "rich-club" organization, whereby a few nodes of high connectivity (hubs) tend to connect more densely among themselves than to nodes of lower connectivity. The Current study followed an "attack strategy" to compare the rich-club and small-world network organization models using Magnetoencephalographic (MEG) recordings from mild traumatic brain injury (mTBI) patients and neurologically healthy controls to identify the topology that describes the underlying intrinsic brain network organization. We hypothesized that the reduction in global efficiency caused by an attack targeting a model's hubs would reveal the "true" underlying topological organization. Connectivity networks were estimated using mutual information as the basis for cross-frequency coupling. Our results revealed a prominent rich-club network organization for both groups. In particular, mTBI patients demonstrated hyper-synchronization among rich-club hubs compared to controls in the δ band and the δ-γ, θ-γ, and β-γ frequency pairs. Moreover, rich-club hubs in mTBI patients were overrepresented in right frontal brain areas, from θ to γ frequencies, and underrepresented in left occipital regions in the δ-β, δ-γ, θ-β, and β-γ frequency pairs. These findings indicate that the rich-club organization of resting-state MEG, considering its role in information integration and its vulnerability to various disorders like mTBI, may have a significant predictive value in the development of reliable biomarkers to help the validation of the recovery from mTBI. Furthermore, the proposed approach might be used as a validation tool to assess patient recovery.
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http://dx.doi.org/10.3389/fnhum.2017.00416DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5582079PMC
August 2017

Data-Driven Topological Filtering Based on Orthogonal Minimal Spanning Trees: Application to Multigroup Magnetoencephalography Resting-State Connectivity.

Brain Connect 2017 12;7(10):661-670

10 Department of Pediatrics, University of Tennessee Health Science Center, Memphis, Tennessee.

In the present study, a novel data-driven topological filtering technique is introduced to derive the backbone of functional brain networks relying on orthogonal minimal spanning trees (OMSTs). The method aims to identify the essential functional connections to ensure optimal information flow via the objective criterion of global efficiency minus the cost of surviving connections. The OMST technique was applied to multichannel, resting-state neuromagnetic recordings from four groups of participants: healthy adults (n = 50), adults who have suffered mild traumatic brain injury (n = 30), typically developing children (n = 27), and reading-disabled children (n = 25). Weighted interactions between network nodes (sensors) were computed using an integrated approach of dominant intrinsic coupling modes based on two alternative metrics (symbolic mutual information and phase lag index), resulting in excellent discrimination of individual cases according to their group membership. Classification results using OMST-derived functional networks were clearly superior to results using either relative power spectrum features or functional networks derived through the conventional minimal spanning tree algorithm.
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http://dx.doi.org/10.1089/brain.2017.0512DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6435350PMC
December 2017

Reconfiguration of dominant coupling modes in mild traumatic brain injury mediated by δ-band activity: A resting state MEG study.

Neuroscience 2017 07 31;356:275-286. Epub 2017 May 31.

Biomedical Imaging Lab, Departments of Engineering Technology, Computer Science, Biomedical Engineering, and Electrical and Computer Engineering, University of Houston, 4730 Calhoun Road Room 300, Houston, TX, USA. Electronic address:

During the last few years, rich-club (RC) organization has been studied as a possible brain-connectivity organization model for large-scale brain networks. At the same time, empirical and simulated data of neurophysiological models have demonstrated the significant role of intra-frequency and inter-frequency coupling among distinct brain areas. The current study investigates further the importance of these couplings using recordings of resting-state magnetoencephalographic activity obtained from 30 mild traumatic brain injury (mTBI) subjects and 50 healthy controls. Intra-frequency and inter-frequency coupling modes are incorporated in a single graph to detect group differences within individual rich-club subnetworks (type I networks) and networks connecting RC nodes with the rest of the nodes (type II networks). Our results show a higher probability of inter-frequency coupling for (δ-γ), (δ-γ), (θ-β), (θ-γ), (α-γ), (γ-γ) and intra-frequency coupling for (γ-γ) and (δ-δ) for both type I and type II networks in the mTBI group. Additionally, mTBI and control subjects can be correctly classified with high accuracy (98.6%), whereas a general linear regression model can effectively predict the subject group using the ratio of type I and type II coupling in the (δ, θ), (δ, β), (δ, γ), and (δ, γ) frequency pairs. These findings support the presence of an RC organization simultaneously with dominant frequency interactions within a single functional graph. Our results demonstrate a hyperactivation of intrinsic RC networks in mTBI subjects compared to controls, which can be seen as a plausible compensatory mechanism for alternative frequency-dependent routes of information flow in mTBI subjects.
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http://dx.doi.org/10.1016/j.neuroscience.2017.05.032DOI Listing
July 2017

Topological Filtering of Dynamic Functional Brain Networks Unfolds Informative Chronnectomics: A Novel Data-Driven Thresholding Scheme Based on Orthogonal Minimal Spanning Trees (OMSTs).

Front Neuroinform 2017 26;11:28. Epub 2017 Apr 26.

Institute of Psychological Medicine and Clinical Neurosciences, School of Medicine, Cardiff UniversityCardiff, UK.

The human brain is a large-scale system of functionally connected brain regions. This system can be modeled as a network, or graph, by dividing the brain into a set of regions, or "nodes," and quantifying the strength of the connections between nodes, or "edges," as the temporal correlation in their patterns of activity. Network analysis, a part of graph theory, provides a set of summary statistics that can be used to describe complex brain networks in a meaningful way. The large-scale organization of the brain has features of complex networks that can be quantified using network measures from graph theory. The adaptation of both bivariate (mutual information) and multivariate (Granger causality) connectivity estimators to quantify the synchronization between multichannel recordings yields a fully connected, weighted, (a)symmetric functional connectivity graph (FCG), representing the associations among all brain areas. The aforementioned procedure leads to an extremely dense network of tens up to a few hundreds of weights. Therefore, this FCG must be filtered out so that the "true" connectivity pattern can emerge. Here, we compared a large number of well-known topological thresholding techniques with the novel proposed data-driven scheme based on orthogonal minimal spanning trees (OMSTs). OMSTs filter brain connectivity networks based on the optimization between the global efficiency of the network and the cost preserving its wiring. We demonstrated the proposed method in a large EEG database ( = 101 subjects) with eyes-open (EO) and eyes-closed (EC) tasks by adopting a time-varying approach with the main goal to extract features that can totally distinguish each subject from the rest of the set. Additionally, the reliability of the proposed scheme was estimated in a second case study of fMRI resting-state activity with multiple scans. Our results demonstrated clearly that the proposed thresholding scheme outperformed a large list of thresholding schemes based on the recognition accuracy of each subject compared to the rest of the cohort (EEG). Additionally, the reliability of the network metrics based on the fMRI static networks was improved based on the proposed topological filtering scheme. Overall, the proposed algorithm could be used across neuroimaging and multimodal studies as a common computationally efficient standardized tool for a great number of neuroscientists and physicists working on numerous of projects.
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http://dx.doi.org/10.3389/fninf.2017.00028DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5405139PMC
April 2017

A high performing EEG approach for the automated scoring of the sleep stages of neonates.

Clin Neurophysiol 2017 06 8;128(6):1039-1040. Epub 2017 Apr 8.

Institute of Psychological Medicine and Clinical Neurosciences, Cardiff University School of Medicine, Maindy Road, Cardiff, CF24 4HQ, Wales, United Kingdom; School of Psychology, Cardiff University School of Medicine, 70 Park Place, Cardiff, CF10 3A,Wales, United Kingdom; Cardiff University Brain Research Imaging Center (CUBRIC), School of Psychology, Cardiff University, Maindy Road, Cardiff, CF24 4HQ, Wales, United Kingdom; Neuroinformatics Group, (CUBRIC), School of Psychology, Cardiff University, Maindy Road, Cardiff, CF24 4HQ, Wales, United Kingdom. Electronic address:

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http://dx.doi.org/10.1016/j.clinph.2017.04.001DOI Listing
June 2017

Mining cross-frequency coupling microstates (CFCμstates) from EEG recordings during resting state and mental arithmetic tasks.

Annu Int Conf IEEE Eng Med Biol Soc 2016 Aug;2016:5517-5520

The functional brain connectivity has been studied by analyzing synchronization between dynamic oscillations of identical frequency or between different frequencies of distinct brain areas. It has been hypothesized that cross-frequency coupling (CFC) between different frequency bands is the carrier mechanism for the coordination of global and local neural processes and hence supports the distributed information processing in the brain. In the present study, we attempt to study the dynamic evolution of CFC at resting-state and during a mental task. The concept of CFC microstates (CFCμstates) is introduced as emerged short-lived patterns of CFC. We analyzed dynamic CFC (dCFC) at resting-state and during a comparison task by adopting a phase-amplitude coupling (PAC) estimator for [δ phase-γ-amplitude] coupling at every sensor. Modifying a well-established framework for mining brain dynamics, we show that a small sized repertoire of CFCμstates can be derived so as to encapsulate connectivity variations and further provide novel insights into network's functional reorganization. By analyzing the transition dynamics among CFCμstates, in both tasks, we provided a clear evidence about intrinsic networks that may play a crucial role in information integration.
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http://dx.doi.org/10.1109/EMBC.2016.7591976DOI Listing
August 2016

Mining cross-frequency coupling microstates from resting state MEG: An application to mild traumatic brain injury.

Annu Int Conf IEEE Eng Med Biol Soc 2016 Aug;2016:5513-5516

Recent studies have investigated the possible role of dynamic functional connectivity and the role of cross-frequency coupling (CFC) to provide the substrate for reliable biomarkers of brain disorders. In this study, we analyzed time-varying CFC profiles from resting state Magnetoencephal-ographic recordings of 30 mild Traumatic Brain Injury (mTBI) patients and 50 normal controls. Interactions among sensors at specific pairs of frequency bands were computed via estimation of phase-to-amplitude couplings. We then computed time-varying functional connectivity graphs that were described in terms of segregation (local efficiency, LE) and integration (global efficiency, GE) and mapped those graphs to time series of GE/LE estimates. The resulting dynamic network revealed transitions between a limited number of microstates for mTBI subjects compared to controls. The significant differences in transition probability between the two groups, along with the limited repertoire of possible states, can form the basis for a robust dynamic connectomic biomarker for the diagnosis of mTBI.
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http://dx.doi.org/10.1109/EMBC.2016.7591975DOI Listing
August 2016