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Shan X, Wang P, Yin Q, Li Y, Wang X, Feng Y, Xiao J, Li L, Huang X, Chen H, Duan X. Atypical dynamic neural configuration in autism spectrum disorder and its relationship to gene expression profiles. Eur Child Adolesc Psychiatry 2024:10.1007/s00787-024-02476-w. [PMID: 38861168 DOI: 10.1007/s00787-024-02476-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/06/2024] [Accepted: 05/18/2024] [Indexed: 06/12/2024]
Abstract
Although it is well recognized that autism spectrum disorder (ASD) is associated with atypical dynamic functional connectivity patterns, the dynamic changes in brain intrinsic activity over each time point and the potential molecular mechanisms associated with atypical dynamic temporal characteristics in ASD remain unclear. Here, we employed the Hidden Markov Model (HMM) to explore the atypical neural configuration at every scanning time point in ASD, based on resting-state functional magnetic resonance imaging (rs-fMRI) data from the Autism Brain Imaging Data Exchange. Subsequently, partial least squares regression and pathway enrichment analysis were employed to explore the potential molecular mechanism associated with atypical neural dynamics in ASD. 8 HMM states were inferred from rs-fMRI data. Compared to typically developing, individuals on the autism spectrum showed atypical state-specific temporal characteristics, including number of states and occurrences, mean life time and transition probability between states. Moreover, these atypical temporal characteristics could predict communication difficulties of ASD, and states assoicated with negative activation in default mode network and frontoparietal network, and positive activation in somatomotor network, ventral attention network, and limbic network, had higher predictive contribution. Furthermore, a total of 321 genes was revealed to be significantly associated with atypical dynamic brain states of ASD, and these genes are mainly enriched in neurodevelopmental pathways. Our study provides new insights into characterizing the atypical neural dynamics from a moment-to-moment perspective, and indicates a linkage between atypical neural configuration and gene expression in ASD.
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Affiliation(s)
- Xiaolong Shan
- The Clinical Hospital of Chengdu Brain Science Institute, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, 611731, PR China
- High-Field Magnetic Resonance Brain Imaging Key Laboratory of Sichuan Province, MOE Key Lab for Neuro information, University of Electronic Science and Technology of China, Chengdu, 611731, PR China
| | - Peng Wang
- The Clinical Hospital of Chengdu Brain Science Institute, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, 611731, PR China
- High-Field Magnetic Resonance Brain Imaging Key Laboratory of Sichuan Province, MOE Key Lab for Neuro information, University of Electronic Science and Technology of China, Chengdu, 611731, PR China
| | - Qing Yin
- The Clinical Hospital of Chengdu Brain Science Institute, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, 611731, PR China
- High-Field Magnetic Resonance Brain Imaging Key Laboratory of Sichuan Province, MOE Key Lab for Neuro information, University of Electronic Science and Technology of China, Chengdu, 611731, PR China
| | - Youyi Li
- The Clinical Hospital of Chengdu Brain Science Institute, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, 611731, PR China
- High-Field Magnetic Resonance Brain Imaging Key Laboratory of Sichuan Province, MOE Key Lab for Neuro information, University of Electronic Science and Technology of China, Chengdu, 611731, PR China
| | - Xiaotian Wang
- The Clinical Hospital of Chengdu Brain Science Institute, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, 611731, PR China
- High-Field Magnetic Resonance Brain Imaging Key Laboratory of Sichuan Province, MOE Key Lab for Neuro information, University of Electronic Science and Technology of China, Chengdu, 611731, PR China
| | - Yu Feng
- The Clinical Hospital of Chengdu Brain Science Institute, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, 611731, PR China
- High-Field Magnetic Resonance Brain Imaging Key Laboratory of Sichuan Province, MOE Key Lab for Neuro information, University of Electronic Science and Technology of China, Chengdu, 611731, PR China
| | - Jinming Xiao
- The Clinical Hospital of Chengdu Brain Science Institute, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, 611731, PR China
- High-Field Magnetic Resonance Brain Imaging Key Laboratory of Sichuan Province, MOE Key Lab for Neuro information, University of Electronic Science and Technology of China, Chengdu, 611731, PR China
| | - Lei Li
- The Clinical Hospital of Chengdu Brain Science Institute, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, 611731, PR China
- High-Field Magnetic Resonance Brain Imaging Key Laboratory of Sichuan Province, MOE Key Lab for Neuro information, University of Electronic Science and Technology of China, Chengdu, 611731, PR China
| | - Xinyue Huang
- The Clinical Hospital of Chengdu Brain Science Institute, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, 611731, PR China
- High-Field Magnetic Resonance Brain Imaging Key Laboratory of Sichuan Province, MOE Key Lab for Neuro information, University of Electronic Science and Technology of China, Chengdu, 611731, PR China
| | - Huafu Chen
- The Clinical Hospital of Chengdu Brain Science Institute, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, 611731, PR China.
- High-Field Magnetic Resonance Brain Imaging Key Laboratory of Sichuan Province, MOE Key Lab for Neuro information, University of Electronic Science and Technology of China, Chengdu, 611731, PR China.
| | - Xujun Duan
- The Clinical Hospital of Chengdu Brain Science Institute, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, 611731, PR China.
- High-Field Magnetic Resonance Brain Imaging Key Laboratory of Sichuan Province, MOE Key Lab for Neuro information, University of Electronic Science and Technology of China, Chengdu, 611731, PR China.
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Carrasco-Gómez M, García-Colomo A, Nebreda A, Bruña R, Santos A, Maestú F. Dynamic functional connectivity is modulated by the amount of p-Tau231 in blood in cognitively intact participants. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.05.29.596323. [PMID: 38854147 PMCID: PMC11160744 DOI: 10.1101/2024.05.29.596323] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/11/2024]
Abstract
INTRODUCTION Electrophysiology and plasma biomarkers are early and non-invasive candidates for Alzheimer's disease detection. The purpose of this paper is to evaluate changes in dynamic functional connectivity measured with magnetoencephalography, associated with the plasma pathology marker p-tau231 in unimpaired adults. METHODS 73 individuals were included. Static and dynamic functional connectivity were calculated using leakage corrected amplitude envelope correlation. Each source's strength entropy across trials was calculated. A data-driven statistical analysis was performed to find the association between functional connectivity and plasma p-tau231 levels. Regression models were used to assess the influence of other variables over the clusters' connectivity. RESULTS Frontotemporal dynamic connectivity positively associated with p-tau231 levels. Linear regression models identified pathological, functional and structural factors that influence dynamic functional connectivity. DISCUSSION These results expand previous literature on dynamic functional connectivity in healthy individuals at risk of AD, highlighting its usefulness as an early, non-invasive and more sensitive biomarker.
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Gohil C, Huang R, Roberts E, van Es MWJ, Quinn AJ, Vidaurre D, Woolrich MW. osl-dynamics, a toolbox for modeling fast dynamic brain activity. eLife 2024; 12:RP91949. [PMID: 38285016 PMCID: PMC10945565 DOI: 10.7554/elife.91949] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/30/2024] Open
Abstract
Neural activity contains rich spatiotemporal structure that corresponds to cognition. This includes oscillatory bursting and dynamic activity that span across networks of brain regions, all of which can occur on timescales of tens of milliseconds. While these processes can be accessed through brain recordings and imaging, modeling them presents methodological challenges due to their fast and transient nature. Furthermore, the exact timing and duration of interesting cognitive events are often a priori unknown. Here, we present the OHBA Software Library Dynamics Toolbox (osl-dynamics), a Python-based package that can identify and describe recurrent dynamics in functional neuroimaging data on timescales as fast as tens of milliseconds. At its core are machine learning generative models that are able to adapt to the data and learn the timing, as well as the spatial and spectral characteristics, of brain activity with few assumptions. osl-dynamics incorporates state-of-the-art approaches that can be, and have been, used to elucidate brain dynamics in a wide range of data types, including magneto/electroencephalography, functional magnetic resonance imaging, invasive local field potential recordings, and electrocorticography. It also provides novel summary measures of brain dynamics that can be used to inform our understanding of cognition, behavior, and disease. We hope osl-dynamics will further our understanding of brain function, through its ability to enhance the modeling of fast dynamic processes.
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Affiliation(s)
- Chetan Gohil
- Oxford Centre for Human Brain Activity, Wellcome Centre for Integrative Neuroimaging, Department of Psychiatry, University of OxfordOxfordUnited Kingdom
| | - Rukuang Huang
- Oxford Centre for Human Brain Activity, Wellcome Centre for Integrative Neuroimaging, Department of Psychiatry, University of OxfordOxfordUnited Kingdom
| | - Evan Roberts
- Oxford Centre for Human Brain Activity, Wellcome Centre for Integrative Neuroimaging, Department of Psychiatry, University of OxfordOxfordUnited Kingdom
| | - Mats WJ van Es
- Oxford Centre for Human Brain Activity, Wellcome Centre for Integrative Neuroimaging, Department of Psychiatry, University of OxfordOxfordUnited Kingdom
| | - Andrew J Quinn
- Oxford Centre for Human Brain Activity, Wellcome Centre for Integrative Neuroimaging, Department of Psychiatry, University of OxfordOxfordUnited Kingdom
- Centre for Human Brain Health, School of Psychology, University of BirminghamBirminghamUnited Kingdom
| | - Diego Vidaurre
- Oxford Centre for Human Brain Activity, Wellcome Centre for Integrative Neuroimaging, Department of Psychiatry, University of OxfordOxfordUnited Kingdom
- Center for Functionally Integrative Neuroscience, Department of Clinical Medicine, Aarhus UniversityAarhusDenmark
| | - Mark W Woolrich
- Oxford Centre for Human Brain Activity, Wellcome Centre for Integrative Neuroimaging, Department of Psychiatry, University of OxfordOxfordUnited Kingdom
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Khazaei M, Raeisi K, Vanhatalo S, Zappasodi F, Comani S, Tokariev A. Neonatal cortical activity organizes into transient network states that are affected by vigilance states and brain injury. Neuroimage 2023; 279:120342. [PMID: 37619792 DOI: 10.1016/j.neuroimage.2023.120342] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/18/2023] [Revised: 08/11/2023] [Accepted: 08/21/2023] [Indexed: 08/26/2023] Open
Abstract
Early neurodevelopment is critically dependent on the structure and dynamics of spontaneous neuronal activity; however, the natural organization of newborn cortical networks is poorly understood. Recent adult studies suggest that spontaneous cortical activity exhibits discrete network states with physiological correlates. Here, we studied newborn cortical activity during sleep using hidden Markov modeling to determine the presence of such discrete neonatal cortical states (NCS) in 107 newborn infants, with 47 of them presenting with a perinatal brain injury. Our results show that neonatal cortical activity organizes into four discrete NCSs that are present in both cardinal sleep states of a newborn infant, active and quiet sleep, respectively. These NCSs exhibit state-specific spectral and functional network characteristics. The sleep states exhibit different NCS dynamics, with quiet sleep presenting higher fronto-temporal activity and a stronger brain-wide neuronal coupling. Brain injury was associated with prolonged lifetimes of the transient NCSs, suggesting lowered dynamics, or flexibility, in the cortical networks. Taken together, the findings suggest that spontaneously occurring transient network states are already present at birth, with significant physiological and pathological correlates; this NCS analysis framework can be fully automatized, and it holds promise for offering an objective, global level measure of early brain function for benchmarking neurodevelopmental or clinical research.
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Affiliation(s)
- Mohammad Khazaei
- Department of Neurosciences, Imaging and Clinical Sciences, University "Gabriele d'Annunzio" of Chieti-Pescara, ITAB building, 3rd floor, room 314, Chieti, Via dei Vestini, Italy.
| | - Khadijeh Raeisi
- Department of Neurosciences, Imaging and Clinical Sciences, University "Gabriele d'Annunzio" of Chieti-Pescara, ITAB building, 3rd floor, room 314, Chieti, Via dei Vestini, Italy
| | - Sampsa Vanhatalo
- BABA center, Pediatric Research Center, Departments of Clinical Neurophysiology and Physiology, Children's Hospital, Helsinki University Hospital and University of Helsinki, Helsinki, Finland
| | - Filippo Zappasodi
- Department of Neurosciences, Imaging and Clinical Sciences, University "Gabriele d'Annunzio" of Chieti-Pescara, ITAB building, 3rd floor, room 314, Chieti, Via dei Vestini, Italy; Institute for Advanced Biomedical Technologies, University "Gabriele d'Annunzio" of Chieti-Pescara, Chieti, Italy
| | - Silvia Comani
- Department of Neurosciences, Imaging and Clinical Sciences, University "Gabriele d'Annunzio" of Chieti-Pescara, ITAB building, 3rd floor, room 314, Chieti, Via dei Vestini, Italy; Behavioral Imaging and Neural Dynamics Center, University "Gabriele d'Annunzio" of Chieti-Pescara, Chieti, Italy
| | - Anton Tokariev
- BABA center, Pediatric Research Center, Departments of Clinical Neurophysiology and Physiology, Children's Hospital, Helsinki University Hospital and University of Helsinki, Helsinki, Finland
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5
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Castaldo F, Páscoa Dos Santos F, Timms RC, Cabral J, Vohryzek J, Deco G, Woolrich M, Friston K, Verschure P, Litvak V. Multi-modal and multi-model interrogation of large-scale functional brain networks. Neuroimage 2023; 277:120236. [PMID: 37355200 PMCID: PMC10958139 DOI: 10.1016/j.neuroimage.2023.120236] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/15/2022] [Revised: 06/14/2023] [Accepted: 06/16/2023] [Indexed: 06/26/2023] Open
Abstract
Existing whole-brain models are generally tailored to the modelling of a particular data modality (e.g., fMRI or MEG/EEG). We propose that despite the differing aspects of neural activity each modality captures, they originate from shared network dynamics. Building on the universal principles of self-organising delay-coupled nonlinear systems, we aim to link distinct features of brain activity - captured across modalities - to the dynamics unfolding on a macroscopic structural connectome. To jointly predict connectivity, spatiotemporal and transient features of distinct signal modalities, we consider two large-scale models - the Stuart Landau and Wilson and Cowan models - which generate short-lived 40 Hz oscillations with varying levels of realism. To this end, we measure features of functional connectivity and metastable oscillatory modes (MOMs) in fMRI and MEG signals - and compare them against simulated data. We show that both models can represent MEG functional connectivity (FC), functional connectivity dynamics (FCD) and generate MOMs to a comparable degree. This is achieved by adjusting the global coupling and mean conduction time delay and, in the WC model, through the inclusion of balance between excitation and inhibition. For both models, the omission of delays dramatically decreased the performance. For fMRI, the SL model performed worse for FCD and MOMs, highlighting the importance of balanced dynamics for the emergence of spatiotemporal and transient patterns of ultra-slow dynamics. Notably, optimal working points varied across modalities and no model was able to achieve a correlation with empirical FC higher than 0.4 across modalities for the same set of parameters. Nonetheless, both displayed the emergence of FC patterns that extended beyond the constraints of the anatomical structure. Finally, we show that both models can generate MOMs with empirical-like properties such as size (number of brain regions engaging in a mode) and duration (continuous time interval during which a mode appears). Our results demonstrate the emergence of static and dynamic properties of neural activity at different timescales from networks of delay-coupled oscillators at 40 Hz. Given the higher dependence of simulated FC on the underlying structural connectivity, we suggest that mesoscale heterogeneities in neural circuitry may be critical for the emergence of parallel cross-modal functional networks and should be accounted for in future modelling endeavours.
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Affiliation(s)
- Francesca Castaldo
- Wellcome Centre for Human Neuroimaging, UCL Queen Square Institute of Neurology, London, United Kingdom.
| | - Francisco Páscoa Dos Santos
- Eodyne Systems SL, Barcelona, Spain; Department of Information and Communication Technologies, Universitat Pompeu Fabra, Barcelona, Spain
| | - Ryan C Timms
- Wellcome Centre for Human Neuroimaging, UCL Queen Square Institute of Neurology, London, United Kingdom
| | - Joana Cabral
- Life and Health Sciences Research Institute (ICVS), School of Medicine, University of Minho, Braga, Portugal; ICVS/3B's - Portuguese Government Associate Laboratory, Braga/Guimarães, Portugal; Centre for Eudaimonia and Human Flourishing, Linacre College, University of Oxford, United United Kingdom
| | - Jakub Vohryzek
- Centre for Eudaimonia and Human Flourishing, Linacre College, University of Oxford, United United Kingdom; Centre for Brain and Cognition, Computational Neuroscience Group, Universitat Pompeu Fabra, Barcelona, Spain
| | - Gustavo Deco
- Centre for Brain and Cognition, Computational Neuroscience Group, Universitat Pompeu Fabra, Barcelona, Spain; Institució Catalana de la Recerca i Estudis Avançats (ICREA), Barcelona, Spain; Department of Neuropsychology, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany; School of Psychological Sciences, Monash University, Melbourne, Australia
| | - Mark Woolrich
- Wellcome Centre for Integrative Neuroimaging, University of Oxford, Oxford, United Kingdom
| | - Karl Friston
- Wellcome Centre for Human Neuroimaging, UCL Queen Square Institute of Neurology, London, United Kingdom
| | - Paul Verschure
- Donders Institute for Brain, Cognition and Behaviour, Radboud University, Nijmegen, The Netherlands
| | - Vladimir Litvak
- Wellcome Centre for Human Neuroimaging, UCL Queen Square Institute of Neurology, London, United Kingdom
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Cabrera-Álvarez J, Doorn N, Maestú F, Susi G. Modeling the role of the thalamus in resting-state functional connectivity: Nature or structure. PLoS Comput Biol 2023; 19:e1011007. [PMID: 37535694 PMCID: PMC10426958 DOI: 10.1371/journal.pcbi.1011007] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/07/2023] [Revised: 08/15/2023] [Accepted: 07/10/2023] [Indexed: 08/05/2023] Open
Abstract
The thalamus is a central brain structure that serves as a relay station for sensory inputs from the periphery to the cortex and regulates cortical arousal. Traditionally, it has been regarded as a passive relay that transmits information between brain regions. However, recent studies have suggested that the thalamus may also play a role in shaping functional connectivity (FC) in a task-based context. Based on this idea, we hypothesized that due to its centrality in the network and its involvement in cortical activation, the thalamus may also contribute to resting-state FC, a key neurological biomarker widely used to characterize brain function in health and disease. To investigate this hypothesis, we constructed ten in-silico brain network models based on neuroimaging data (MEG, MRI, and dwMRI), and simulated them including and excluding the thalamus, and raising the noise into thalamus to represent the afferences related to the reticular activating system (RAS) and the relay of peripheral sensory inputs. We simulated brain activity and compared the resulting FC to their empirical MEG counterparts to evaluate model's performance. Results showed that a parceled version of the thalamus with higher noise, able to drive damped cortical oscillators, enhanced the match to empirical FC. However, with an already active self-oscillatory cortex, no impact on the dynamics was observed when introducing the thalamus. We also demonstrated that the enhanced performance was not related to the structural connectivity of the thalamus, but to its higher noisy inputs. Additionally, we highlighted the relevance of a balanced signal-to-noise ratio in thalamus to allow it to propagate its own dynamics. In conclusion, our study sheds light on the role of the thalamus in shaping brain dynamics and FC in resting-state and allowed us to discuss the general role of criticality in the brain at the mesoscale level.
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Affiliation(s)
- Jesús Cabrera-Álvarez
- Department of Experimental Psychology, Complutense University of Madrid, Madrid, Spain
- Centre for Cognitive and Computational Neuroscience, Madrid, Spain
| | - Nina Doorn
- Department of Clinical Neurophysiology, University of Twente, Enschede, The Netherlands
| | - Fernando Maestú
- Department of Experimental Psychology, Complutense University of Madrid, Madrid, Spain
- Centre for Cognitive and Computational Neuroscience, Madrid, Spain
| | - Gianluca Susi
- Centre for Cognitive and Computational Neuroscience, Madrid, Spain
- Department of Structure of Matter, Thermal Physics and Electronics, Complutense University of Madrid, Madrid, Spain
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Liu L, Ren J, Li Z, Yang C. A review of MEG dynamic brain network research. Proc Inst Mech Eng H 2022; 236:763-774. [PMID: 35465768 DOI: 10.1177/09544119221092503] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
Abstract
The dynamic description of neural networks has attracted the attention of researchers for dynamic networks may carry more information compared with resting-state networks. As a non-invasive electrophysiological data with high temporal and spatial resolution, magnetoencephalogram (MEG) can provide rich information for the analysis of dynamic functional brain networks. In this review, the development of MEG brain network was summarized. Several analysis methods such as sliding window, Hidden Markov model, and time-frequency based methods used in MEG dynamic brain network studies were discussed. Finally, the current research about multi-modal brain network analysis and their applications with MEG neurophysiology, which are prospected to be one of the research directions in the future, were concluded.
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Affiliation(s)
- Lu Liu
- Faculty of Environment and Life, Beijing University of Technology, Beijing, China
| | - Jiechuan Ren
- Department of Internal Neurology, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
| | - Zhimei Li
- Department of Internal Neurology, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
| | - Chunlan Yang
- Faculty of Environment and Life, Beijing University of Technology, Beijing, China
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8
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Uncovering hidden resting state dynamics: A new perspective on auditory verbal hallucinations. Neuroimage 2022; 255:119188. [PMID: 35398281 DOI: 10.1016/j.neuroimage.2022.119188] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/01/2021] [Revised: 02/25/2022] [Accepted: 03/13/2022] [Indexed: 11/24/2022] Open
Abstract
In the absence of sensory stimulation, the brain transits between distinct functional networks. Network dynamics such as transition patterns and the time the brain stays in each network link to cognition and behavior and are subject to much investigation. Auditory verbal hallucinations (AVH), the temporally fluctuating unprovoked experience of hearing voices, are associated with aberrant resting state network activity. However, we lack a clear understanding of how different networks contribute to aberrant activity over time. An accurate characterization of latent network dynamics and their relation to neurocognitive changes necessitates methods that capture the sub-second temporal fluctuations of the networks' functional connectivity signatures. Here, we critically evaluate the assumptions and sensitivity of several approaches commonly used to assess temporal dynamics of brain connectivity states in M/EEG and fMRI research, highlighting methodological constraints and their clinical relevance to AVH. Identifying altered brain connectivity states linked to AVH can facilitate the detection of predictive disease markers and ultimately be valuable for generating individual risk profiles, differential diagnosis, targeted intervention, and treatment strategies.
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Basti A, Chella F, Guidotti R, Ermolova M, D'Andrea A, Stenroos M, Romani GL, Pizzella V, Marzetti L. Looking through the windows: a study about the dependency of phase-coupling estimates on the data length. J Neural Eng 2022; 19. [PMID: 35147515 DOI: 10.1088/1741-2552/ac542f] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/06/2021] [Accepted: 02/08/2022] [Indexed: 11/11/2022]
Abstract
OBJECTIVE Being able to characterize functional connectivity (FC) state dynamics in a real-time setting, such as in brain-computer interface, neurofeedback or closed-loop neurostimulation frameworks, requires the rapid detection of the statistical dependencies that quantify FC in short windows of data. The aim of this study is to characterize, through extensive realistic simulations, the reliability of FC estimation as a function of the data length. In particular, we focused on FC as measured by phase-coupling (PC) of neuronal oscillations, one of the most functionally relevant neural coupling modes. APPROACH We generated synthetic data corresponding to different scenarios by varying the data length, the signal-to-noise ratio, the phase difference value, the spectral analysis approach (Hilbert or Fourier) and the fractional bandwidth. We compared seven PC metrics, i.e. imaginary part of phase locking value (PLV), PLV of orthogonalized signals, phase lag index (PLI), debiased weighted PLI, imaginary part of coherency, coherence of orthogonalized signals and lagged coherence. MAIN RESULTS Our findings show that, for a signal-to-noise-ratio of at least 10 dB, a data window that contains 5 to 8 cycles of the oscillation of interest (e.g. a 500-800ms window at 10Hz) is generally required to achieve reliable PC estimates. In general, Hilbert-based approaches were associated with higher performance than Fourier-based approaches. Furthermore, the results suggest that, when the analysis is performed in a narrow frequency range, a larger window is required. SIGNIFICANCE The achieved results pave the way to the introduction of best-practice guidelines to be followed when a real-time frequency-specific PC assessment is at target.
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Affiliation(s)
- Alessio Basti
- NEUROSCIENCE. IMAGING AND CLINICAL SCIENCE, Universita degli Studi Gabriele d\'Annunzio Chieti e Pescara, Via Luigi Polacchi 11, Chieti, Chieti, 66100, ITALY
| | - Federico Chella
- Neuroscience, Imaging and Clinical Sciences, Gabriele d'Annunzio University of Chieti and Pescara, Via Luigi Polacchi 11, Chieti, Abruzzo, 66100, ITALY
| | - Roberto Guidotti
- Neuroscience, Imaging and Clinical Sciences, Universita degli Studi Gabriele d'Annunzio Chieti e Pescara, Via Luigi Polacchi 11, Chieti Scalo, CH, 66100, ITALY
| | - Maria Ermolova
- Eberhard Karls University Tubingen Hertie Institute for Clinical Brain Research, Hoppe-Seyler Str. 3, Tubingen, Baden-Württemberg, 72076, GERMANY
| | - Antea D'Andrea
- Department of Neuroscience, Imaging and Clinical Sciences, Gabriele d'Annunzio University of Chieti and Pescara, Via Luigi Polacchi 11, Chieti, Abruzzo, 66100, ITALY
| | - Matti Stenroos
- Department of Biomedical Engineering and Computational Science, Aalto University, PO Box 12200, FI-00076 AALTO, Espoo, 00076, FINLAND
| | - Gian-Luca Romani
- Institute for Advanced Biomedical Technologies, Gabriele d'Annunzio University of Chieti and Pescara, Via Luigi Polacchi 11, 66013 Chieti, Chieti, Abruzzo, 66100, ITALY
| | - Vittorio Pizzella
- Neuroscience, Imaging and Clinical Sciences, Gabriele d'Annunzio University of Chieti and Pescara, Via Luigi Polacchi 11, Chieti, 66100, ITALY
| | - Laura Marzetti
- NEUROSCIENCE. IMAGING AND CLINICAL SCIENCE, Universita degli Studi Gabriele d\'Annunzio Chieti e Pescara, Via Luigi Polacchi 11, Chieti, Chieti, 66100, ITALY
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Abstract
The study of functional connectivity from magnetoecenphalographic (MEG) data consists of quantifying the statistical dependencies among time series describing the activity of different neural sources from the magnetic field recorded outside the scalp. This problem can be addressed by utilizing connectivity measures whose computation in the frequency domain often relies on the evaluation of the cross-power spectrum of the neural time series estimated by solving the MEG inverse problem. Recent studies have focused on the optimal determination of the cross-power spectrum in the framework of regularization theory for ill-posed inverse problems, providing indications that, rather surprisingly, the regularization process that leads to the optimal estimate of the neural activity does not lead to the optimal estimate of the corresponding functional connectivity. Along these lines, the present paper utilizes synthetic time series simulating the neural activity recorded by an MEG device to show that the regularization of the cross-power spectrum is significantly correlated with the signal-to-noise ratio of the measurements and that, as a consequence, this regularization correspondingly depends on the spectral complexity of the neural activity.
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11
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Tabbal J, Kabbara A, Khalil M, Benquet P, Hassan M. Dynamics of task-related electrophysiological networks: a benchmarking study. Neuroimage 2021; 231:117829. [PMID: 33549758 DOI: 10.1016/j.neuroimage.2021.117829] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/13/2020] [Revised: 01/25/2021] [Accepted: 01/29/2021] [Indexed: 12/29/2022] Open
Abstract
Motor, sensory and cognitive functions rely on dynamic reshaping of functional brain networks. Tracking these rapid changes is crucial to understand information processing in the brain, but challenging due to the great variety of dimensionality reduction methods used at the network-level and the limited evaluation studies. Using Magnetoencephalography (MEG) combined with Source Separation (SS) methods, we present an integrated framework to track fast dynamics of electrophysiological brain networks. We evaluate nine SS methods applied to three independent MEG databases (N=95) during motor and memory tasks. We report differences between these methods at the group and subject level. We seek to help researchers in choosing objectively the appropriate SS method when tracking fast reconfiguration of functional brain networks, due to its enormous benefits in cognitive and clinical neuroscience.
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Affiliation(s)
- Judie Tabbal
- Univ Rennes, LTSI - U1099, F-35000 Rennes, France; Azm Center for Research in Biotechnology and Its Applications, EDST, Lebanese University, Beirut, Lebanon.
| | - Aya Kabbara
- Univ Rennes, LTSI - U1099, F-35000 Rennes, France
| | - Mohamad Khalil
- Azm Center for Research in Biotechnology and Its Applications, EDST, Lebanese University, Beirut, Lebanon; CRSI Lab, Engineering Faculty, Lebanese University, Beirut, Lebanon
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12
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Liu W, Zhang C, Wang X, Xu J, Chang Y, Ristaniemi T, Cong F. Functional connectivity of major depression disorder using ongoing EEG during music perception. Clin Neurophysiol 2020; 131:2413-2422. [PMID: 32828045 DOI: 10.1016/j.clinph.2020.06.031] [Citation(s) in RCA: 28] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/25/2019] [Revised: 05/07/2020] [Accepted: 06/29/2020] [Indexed: 12/14/2022]
Abstract
OBJECTIVE The functional connectivity (FC) of major depression disorder (MDD) has not been well studied under naturalistic and continuous stimuli conditions. In this study, we investigated the frequency-specific FC of MDD patients exposed to conditions of music perception using ongoing electroencephalogram (EEG). METHODS First, we applied the phase lag index (PLI) method to calculate the connectivity matrices and graph theory-based methods to measure the topology of brain networks across different frequency bands. Then, classification methods were adopted to identify the most discriminate frequency band for the diagnosis of MDD. RESULTS During music perception, MDD patients exhibited a decreased connectivity pattern in the delta band but an increased connectivity pattern in the beta band. Healthy people showed a left hemisphere-dominant phenomenon, but MDD patients did not show such a lateralized effect. Support vector machine (SVM) achieved the best classification performance in the beta frequency band with an accuracy of 89.7%, sensitivity of 89.4% and specificity of 89.9%. CONCLUSIONS MDD patients exhibited an altered FC in delta and beta bands, and the beta band showed a superiority in the diagnosis of MDD. SIGNIFICANCE Our study provided a promising reference for the diagnosis of MDD, and revealed a new perspective for understanding the topology of MDD brain networks during music perception.
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Affiliation(s)
- Wenya Liu
- School of Biomedical Engineering, Faculty of Electronic and Electrical Engineering, Dalian University of Technology, 116024 Dalian, China; Faculty of Information Technology, University of Jyväskylä, 40014 Jyväskylä, Finland
| | - Chi Zhang
- School of Biomedical Engineering, Faculty of Electronic and Electrical Engineering, Dalian University of Technology, 116024 Dalian, China
| | - Xiaoyu Wang
- School of Biomedical Engineering, Faculty of Electronic and Electrical Engineering, Dalian University of Technology, 116024 Dalian, China
| | - Jing Xu
- Department of Neurology and Psychiatry, First Affiliated Hospital, Dalian Medical University, 116011 Dalian, China.
| | - Yi Chang
- Department of Neurology and Psychiatry, First Affiliated Hospital, Dalian Medical University, 116011 Dalian, China.
| | - Tapani Ristaniemi
- Faculty of Information Technology, University of Jyväskylä, 40014 Jyväskylä, Finland
| | - Fengyu Cong
- School of Biomedical Engineering, Faculty of Electronic and Electrical Engineering, Dalian University of Technology, 116024 Dalian, China; Faculty of Information Technology, University of Jyväskylä, 40014 Jyväskylä, Finland; School of Artificial Intelligence, Faculty of Electronic Information and Electrical Engineering, Dalian University of Technology, 116024 Dalian, China; Key Laboratory of Integrated Circuit and Biomedical Electronic System, Liaoning Province. Dalian University of Technology, 116024 Dalian, China.
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13
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Padilla-Buritica JI, Ferrandez-Vicente JM, Castaño GA, Acosta-Medina CD. Non-stationary Group-Level Connectivity Analysis for Enhanced Interpretability of Oddball Tasks. Front Neurosci 2020; 14:446. [PMID: 32431593 PMCID: PMC7214628 DOI: 10.3389/fnins.2020.00446] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/26/2019] [Accepted: 04/09/2020] [Indexed: 11/13/2022] Open
Abstract
Neural responses of oddball tasks can be used as a physiological biomarker to evaluate the brain potential of information processing under the assumption that the differential contribution of deviant stimuli can be assessed accurately. Nevertheless, the non-stationarity of neural activity causes the brain networks to fluctuate hugely in time, deteriorating the estimation of pairwise synergies. To deal with the time variability of neural responses, we have developed a piecewise multi-subject analysis that is applied over a set of time intervals within the stationary assumption holds. To segment the whole stimulus-locked epoch into multiple temporal windows, we experimented with two approaches for piecewise segmentation of EEG recordings: a fixed time-window, at which the estimates of FC measures fulfill a given confidence level, and variable time-window, which is segmented at the change points of the time-varying classifier performance. Employing the weighted Phase Lock Index as a functional connectivity metric, we have presented the validation in a real-world EEG data, proving the effectiveness of variable time segmentation for connectivity extraction when combined with a supervised thresholding approach. Consequently, we performed a piecewise group-level analysis of electroencephalographic data that deals with non-stationary functional connectivity measures, evaluating more carefully the contribution of a link node-set in discriminating between the labeled oddball responses.
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Affiliation(s)
- Jorge I. Padilla-Buritica
- Signal Processing and Recognition Group, Universidad Nacional de Colombia, Manizales, Colombia
- Diseño Electrónico y Técnicas de Tratamiento de Señales, Universidad Politécnica de Cartagena, Cartagena, Spain
- Grupo de Automática, Universidad Autónoma, Manizales, Colombia
- *Correspondence: Jorge I. Padilla-Buritica
| | - Jose M. Ferrandez-Vicente
- Diseño Electrónico y Técnicas de Tratamiento de Señales, Universidad Politécnica de Cartagena, Cartagena, Spain
| | - German A. Castaño
- Grupo de Trabajo Academico Cultura de la Calidad en la Educacion, Universidad Nacional de Colombia, Manizales, Colombia
| | - Carlos D. Acosta-Medina
- Signal Processing and Recognition Group, Universidad Nacional de Colombia, Manizales, Colombia
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Candelaria-Cook FT, Stephen JM. Test-Retest Reliability of Magnetoencephalography Resting-State Functional Connectivity in Schizophrenia. Front Psychiatry 2020; 11:551952. [PMID: 33391043 PMCID: PMC7772354 DOI: 10.3389/fpsyt.2020.551952] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/14/2020] [Accepted: 11/23/2020] [Indexed: 12/17/2022] Open
Abstract
The reliability of magnetoencephalography (MEG) resting-state functional connectivity in schizophrenia (SZ) is unknown as previous research has focused on healthy controls (HC). Here, we examined reliability in 26 participants (13-SZ, 13-HC). Eyes opened and eyes closed resting-state data were collected on 4 separate occasions during 2 visits, 1 week apart. For source modeling, we used minimum norm software to apply dynamic statistical parametric mapping. Source analyses compared the following functional connectivity metrics from each data run: coherence (coh), imaginary coherence (imcoh), pairwise phase consistency (ppc), phase-locking value (plv), phase lag index (pli), weighted phase lag index (wpli), and weighted phase lag index debiased (wpli2). Intraclass correlation coefficients (ICCs) were calculated for whole brain, network, and network pair averages. For reliability, ICCs above 0.75 = excellent, above 0.60 = good, above 0.40 = fair, and below 0.40 = poor reliability. We found the reliability of these metrics varied greatly depending on frequency band, network, network pair, and participant group examined. Broadband (1-58 Hz) whole brain averages in both HC and SZ showed excellent reliability for wpli2, and good to fair reliability for ppc, plv, and coh. Broadband network averages showed excellent to good reliability across 1 hour and 1 week for coh, imcoh, ppc, plv, wpli within default mode, cognitive control, and visual networks in HC, while the same metrics had excellent to fair reliability in SZ. Regional network pair averages showed good to fair reliability for coh, ppc, plv within default mode, cognitive control and visual network pairs in HC and SZ. In general, HC had higher reliability compared to SZ, and the default mode, cognitive control, and visual networks had higher reliability compared to somatosensory and auditory networks. Similar reliability levels occurred for both eyes opened and eyes closed resting-states for most metrics. The functional connectivity metrics of coh, ppc, and plv performed best across 1 hour and 1 week in HC and SZ. We also found that SZ had reduced coh, plv, and ppc in the dmn average and pair values indicating dysconnectivity in SZ. These findings encourage collecting both eyes opened and eyes closed resting-state MEG, while demonstrating that clinical populations may differ in reliability.
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