1
|
Coolen T, Mihai Dumitrescu A, Wens V, Bourguignon M, Rovai A, Sadeghi N, Urbain C, Goldman S, De Tiège X. Spectrotemporal cortical dynamics and semantic control during sentence completion. Clin Neurophysiol 2024; 163:90-101. [PMID: 38714152 DOI: 10.1016/j.clinph.2024.04.012] [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: 01/23/2024] [Revised: 03/27/2024] [Accepted: 04/14/2024] [Indexed: 05/09/2024]
Abstract
OBJECTIVE To investigate cortical oscillations during a sentence completion task (SC) using magnetoencephalography (MEG), focusing on the semantic control network (SCN), its leftward asymmetry, and the effects of semantic control load. METHODS Twenty right-handed adults underwent MEG while performing SC, consisting of low cloze (LC: multiple responses) and high cloze (HC: single response) stimuli. Spectrotemporal power modulations as event-related synchronizations (ERS) and desynchronizations (ERD) were analyzed: first, at the whole-brain level; second, in key SCN regions, posterior middle/inferior temporal gyri (pMTG/ITG) and inferior frontal gyri (IFG), under different semantic control loads. RESULTS Three cortical response patterns emerged: early (0-200 ms) theta-band occipital ERS; intermediate (200-700 ms) semantic network alpha/beta-band ERD; late (700-3000 ms) dorsal language stream alpha/beta/gamma-band ERD. Under high semantic control load (LC), pMTG/ITG showed prolonged left-sided engagement (ERD) and right-sided inhibition (ERS). Left IFG exhibited heightened late (2500-2550 ms) beta-band ERD with increased semantic control load (LC vs. HC). CONCLUSIONS SC involves distinct cortical responses and depends on the left IFG and asymmetric engagement of the pMTG/ITG for semantic control. SIGNIFICANCE Future use of SC in neuromagnetic preoperative language mapping and for understanding the pathophysiology of language disorders in neurological conditions.
Collapse
Affiliation(s)
- Tim Coolen
- Université Libre de Bruxelles (ULB), ULB Neuroscience Institute (UNI), Laboratoire de Neuroanatomie et Neuroimagerie Translationnelles (LN(2)T), Brussels, Belgium; Université Libre de Bruxelles, Hôpital Universitaire de Bruxelles (HUB), CUB Hôpital Erasme, Department of Radiology, Brussels, Belgium.
| | - Alexandru Mihai Dumitrescu
- Université Libre de Bruxelles (ULB), ULB Neuroscience Institute (UNI), Laboratoire de Neuroanatomie et Neuroimagerie Translationnelles (LN(2)T), Brussels, Belgium
| | - Vincent Wens
- Université Libre de Bruxelles (ULB), ULB Neuroscience Institute (UNI), Laboratoire de Neuroanatomie et Neuroimagerie Translationnelles (LN(2)T), Brussels, Belgium
| | - Mathieu Bourguignon
- Université Libre de Bruxelles (ULB), ULB Neuroscience Institute (UNI), Laboratoire de Neuroanatomie et Neuroimagerie Translationnelles (LN(2)T), Brussels, Belgium; Université Libre de Bruxelles (ULB), ULB Neuroscience Institute (UNI), Laboratory of Neurophysiology and Movement Biomechanics, Brussels, Belgium
| | - Antonin Rovai
- Université Libre de Bruxelles (ULB), ULB Neuroscience Institute (UNI), Laboratoire de Neuroanatomie et Neuroimagerie Translationnelles (LN(2)T), Brussels, Belgium
| | - Niloufar Sadeghi
- Université Libre de Bruxelles, Hôpital Universitaire de Bruxelles (HUB), CUB Hôpital Erasme, Department of Radiology, Brussels, Belgium
| | - Charline Urbain
- Université Libre de Bruxelles (ULB), ULB Neuroscience Institute (UNI), Laboratoire de Neuroanatomie et Neuroimagerie Translationnelles (LN(2)T), Brussels, Belgium; Université Libre de Bruxelles (ULB), ULB Neuroscience Institute (UNI), Centre for Research in Cognition and Neurosciences (CRCN), Neuropsychology and Functional Neuroimaging Research Unit (UR2NF), Brussels, Belgium
| | - Serge Goldman
- Université Libre de Bruxelles (ULB), ULB Neuroscience Institute (UNI), Laboratoire de Neuroanatomie et Neuroimagerie Translationnelles (LN(2)T), Brussels, Belgium
| | - Xavier De Tiège
- Université Libre de Bruxelles (ULB), ULB Neuroscience Institute (UNI), Laboratoire de Neuroanatomie et Neuroimagerie Translationnelles (LN(2)T), Brussels, Belgium
| |
Collapse
|
2
|
Nagy P, Tóth B, Winkler I, Boncz Á. The effects of spatial leakage correction on the reliability of EEG-based functional connectivity networks. Hum Brain Mapp 2024; 45:e26747. [PMID: 38825981 PMCID: PMC11144954 DOI: 10.1002/hbm.26747] [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] [Received: 07/04/2023] [Revised: 03/28/2024] [Accepted: 05/16/2024] [Indexed: 06/04/2024] Open
Abstract
Electroencephalography (EEG) functional connectivity (FC) estimates are confounded by the volume conduction problem. This effect can be greatly reduced by applying FC measures insensitive to instantaneous, zero-lag dependencies (corrected measures). However, numerous studies showed that FC measures sensitive to volume conduction (uncorrected measures) exhibit higher reliability and higher subject-level identifiability. We tested how source reconstruction contributed to the reliability difference of EEG FC measures on a large (n = 201) resting-state data set testing eight FC measures (including corrected and uncorrected measures). We showed that the high reliability of uncorrected FC measures in resting state partly stems from source reconstruction: idiosyncratic noise patterns define a baseline resting-state functional network that explains a significant portion of the reliability of uncorrected FC measures. This effect remained valid for template head model-based, as well as individual head model-based source reconstruction. Based on our findings we made suggestions how to best use spatial leakage corrected and uncorrected FC measures depending on the main goals of the study.
Collapse
Affiliation(s)
- Péter Nagy
- HUN‐REN Research Centre for Natural SciencesBudapestHungary
- Faculty of Electrical Engineering and Informatics, Department of Measurement and Information SystemsBudapest University of Technology and EconomicsBudapestHungary
| | - Brigitta Tóth
- HUN‐REN Research Centre for Natural SciencesBudapestHungary
| | - István Winkler
- HUN‐REN Research Centre for Natural SciencesBudapestHungary
| | - Ádám Boncz
- HUN‐REN Research Centre for Natural SciencesBudapestHungary
| |
Collapse
|
3
|
Maddaluno O, Della Penna S, Pizzuti A, Spezialetti M, Corbetta M, de Pasquale F, Betti V. Encoding Manual Dexterity through Modulation of Intrinsic α Band Connectivity. J Neurosci 2024; 44:e1766232024. [PMID: 38538141 PMCID: PMC11097277 DOI: 10.1523/jneurosci.1766-23.2024] [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: 09/18/2023] [Revised: 01/21/2024] [Accepted: 02/20/2024] [Indexed: 05/18/2024] Open
Abstract
The human hand possesses both consolidated motor skills and remarkable flexibility in adapting to ongoing task demands. However, the underlying mechanisms by which the brain balances stability and flexibility remain unknown. In the absence of external input or behavior, spontaneous (intrinsic) brain connectivity is thought to represent a prior of stored memories. In this study, we investigated how manual dexterity modulates spontaneous functional connectivity in the motor cortex during hand movement. Using magnetoencephalography, in 47 human participants (both sexes), we examined connectivity modulations in the α and β frequency bands at rest and during two motor tasks (i.e., finger tapping or toe squeezing). The flexibility and stability of such modulations allowed us to identify two groups of participants with different levels of performance (high and low performers) on the nine-hole peg test, a test of manual dexterity. In the α band, participants with higher manual dexterity showed distributed decreases of connectivity, specifically in the motor cortex, increased segregation, and reduced nodal centrality. Participants with lower manual dexterity showed an opposite pattern. Notably, these patterns from the brain to behavior are mirrored by results from behavior to the brain. Indeed, when participants were divided using the median split of the dexterity score, we found the same connectivity patterns. In summary, this experiment shows that a long-term motor skill-manual dexterity-influences the way the motor systems respond during movements.
Collapse
Affiliation(s)
- Ottavia Maddaluno
- Department of Psychology, Sapienza University of Rome, Rome 00185, Italy
- IRCCS Santa Lucia Foundation, Rome 00179, Italy
| | - Stefania Della Penna
- Department of Neuroscience, Imaging and Clinical Sciences and ITAB - Institute of Advanced Biomedical Technologies, "G. d'Annunzio" University of Chieti and Pescara, Chieti 66013, Italy
| | - Alessandra Pizzuti
- Department of Psychology, Sapienza University of Rome, Rome 00185, Italy
- IRCCS Santa Lucia Foundation, Rome 00179, Italy
| | - Matteo Spezialetti
- Department of Psychology, Sapienza University of Rome, Rome 00185, Italy
- IRCCS Santa Lucia Foundation, Rome 00179, Italy
| | - Maurizio Corbetta
- Department of Neuroscience and Padova Neuroscience Center, University of Padua, Padua 35131, Italy
- Veneto Institute of Molecular Medicine (VIMM), Padova 35129, Italy
| | | | - Viviana Betti
- Department of Psychology, Sapienza University of Rome, Rome 00185, Italy
- IRCCS Santa Lucia Foundation, Rome 00179, Italy
| |
Collapse
|
4
|
Roshchupkina L, Wens V, Coquelet N, Urbain C, de Tiege X, Peigneux P. Motor learning- and consolidation-related resting state fast and slow brain dynamics across wake and sleep. Sci Rep 2024; 14:7531. [PMID: 38553500 PMCID: PMC10980824 DOI: 10.1038/s41598-024-58123-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/09/2023] [Accepted: 03/26/2024] [Indexed: 04/02/2024] Open
Abstract
Motor skills dynamically evolve during practice and after training. Using magnetoencephalography, we investigated the neural dynamics underpinning motor learning and its consolidation in relation to sleep during resting-state periods after the end of learning (boost window, within 30 min) and at delayed time scales (silent 4 h and next day 24 h windows) with intermediate daytime sleep or wakefulness. Resting-state neural dynamics were investigated at fast (sub-second) and slower (supra-second) timescales using Hidden Markov modelling (HMM) and functional connectivity (FC), respectively, and their relationship to motor performance. HMM results show that fast dynamic activities in a Temporal/Sensorimotor state network predict individual motor performance, suggesting a trait-like association between rapidly recurrent neural patterns and motor behaviour. Short, post-training task re-exposure modulated neural network characteristics during the boost but not the silent window. Re-exposure-related induction effects were observed on the next day, to a lesser extent than during the boost window. Daytime naps did not modulate memory consolidation at the behavioural and neural levels. These results emphasise the critical role of the transient boost window in motor learning and memory consolidation and provide further insights into the relationship between the multiscale neural dynamics of brain networks, motor learning, and consolidation.
Collapse
Affiliation(s)
- Liliia Roshchupkina
- UR2NF - Neuropsychology and Functional Neuroimaging Research Unit Affiliated at CRCN - Centre for Research in Cognition and Neurosciences, Université Libre de Bruxelles (ULB), Brussels, Belgium.
- UNI - ULB Neuroscience Institute, Brussels, Belgium.
- LN2T - Laboratoire de Neuroanatomie et Neuroimagerie Translationnelles, ULB, Brussels, Belgium.
- Faculté des Sciences Psychologiques et de l'Éducation, Campus du Solbosch - CP 191, Avenue F.D. Roosevelt, 50, 1050, Brussels, Belgium.
| | - Vincent Wens
- UNI - ULB Neuroscience Institute, Brussels, Belgium
- LN2T - Laboratoire de Neuroanatomie et Neuroimagerie Translationnelles, ULB, Brussels, Belgium
- Department of Functional Neuroimaging, Service of Nuclear Medicine, HUB - Hôpital Universitaire de Bruxelles, Hospital Erasme, Brussels, Belgium
| | - Nicolas Coquelet
- UNI - ULB Neuroscience Institute, Brussels, Belgium
- LN2T - Laboratoire de Neuroanatomie et Neuroimagerie Translationnelles, ULB, Brussels, Belgium
- Department of Functional Neuroimaging, Service of Nuclear Medicine, HUB - Hôpital Universitaire de Bruxelles, Hospital Erasme, Brussels, Belgium
| | - Charline Urbain
- UR2NF - Neuropsychology and Functional Neuroimaging Research Unit Affiliated at CRCN - Centre for Research in Cognition and Neurosciences, Université Libre de Bruxelles (ULB), Brussels, Belgium
- UNI - ULB Neuroscience Institute, Brussels, Belgium
- LN2T - Laboratoire de Neuroanatomie et Neuroimagerie Translationnelles, ULB, Brussels, Belgium
| | - Xavier de Tiege
- UNI - ULB Neuroscience Institute, Brussels, Belgium
- LN2T - Laboratoire de Neuroanatomie et Neuroimagerie Translationnelles, ULB, Brussels, Belgium
- Department of Functional Neuroimaging, Service of Nuclear Medicine, HUB - Hôpital Universitaire de Bruxelles, Hospital Erasme, Brussels, Belgium
| | - Philippe Peigneux
- UR2NF - Neuropsychology and Functional Neuroimaging Research Unit Affiliated at CRCN - Centre for Research in Cognition and Neurosciences, Université Libre de Bruxelles (ULB), Brussels, Belgium
- UNI - ULB Neuroscience Institute, Brussels, Belgium
| |
Collapse
|
5
|
Fernández-Martín R, Feys O, Juvené E, Aeby A, Urbain C, De Tiège X, Wens V. Towards the automated detection of interictal epileptiform discharges with magnetoencephalography. J Neurosci Methods 2024; 403:110052. [PMID: 38151188 DOI: 10.1016/j.jneumeth.2023.110052] [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: 09/14/2023] [Revised: 12/08/2023] [Accepted: 12/21/2023] [Indexed: 12/29/2023]
Abstract
BACKGROUND The analysis of clinical magnetoencephalography (MEG) in patients with epilepsy traditionally relies on visual identification of interictal epileptiform discharges (IEDs), which is time consuming and dependent on subjective criteria. NEW METHOD Here, we explore the ability of Independent Components Analysis (ICA) and Hidden Markov Modeling (HMM) to automatically detect and localize IEDs. We tested our pipelines on resting-state MEG recordings from 10 school-aged children with (multi)focal epilepsy. RESULTS In focal epilepsy patients, both pipelines successfully detected visually identified IEDs, but also revealed unidentified low-amplitude IEDs. Success was more mitigated in patients with multifocal epilepsy, as our automated pipeline missed IED activity associated with some foci-an issue that could be alleviated by post-hoc manual selection of epileptiform ICs or HMM states. COMPARISON WITH EXISTING METHODS We compared our results with visual IED detection by an experienced clinical magnetoencephalographer, getting heightened sensitivity and requiring minimal input from clinical practitioners. CONCLUSIONS IED detection based on ICA or HMM represents an efficient way to identify IED localization and timing. The development of these automatic IED detection algorithms provide a step forward in clinical MEG practice by decreasing the duration of MEG analysis and enhancing its sensitivity.
Collapse
Affiliation(s)
- Raquel Fernández-Martín
- Université libre de Bruxelles (ULB), ULB Neuroscience Institute (UNI), Laboratoire de Neuroanatomie et de Neuroimagerie translationnelles (LNbT), Brussels, Belgium.
| | - Odile Feys
- Université libre de Bruxelles (ULB), ULB Neuroscience Institute (UNI), Laboratoire de Neuroanatomie et de Neuroimagerie translationnelles (LNbT), Brussels, Belgium; Université libre de Bruxelles (ULB), Hôpital Universitaire de Bruxelles (H.U.B.), Hôpital Erasme, Department of Neurology, Brussels, Belgium
| | - Elodie Juvené
- Université libre de Bruxelles (ULB), Hôpital Universitaire de Bruxelles (H.U.B.), Department of Pediatric Neurology, Brussels, Belgium
| | - Alec Aeby
- Université libre de Bruxelles (ULB), Hôpital Universitaire de Bruxelles (H.U.B.), Department of Pediatric Neurology, Brussels, Belgium
| | - Charline Urbain
- Université libre de Bruxelles (ULB), ULB Neuroscience Institute (UNI), Laboratoire de Neuroanatomie et de Neuroimagerie translationnelles (LNbT), Brussels, Belgium; Université libre de Bruxelles (ULB), ULB Neuroscience Institute (UNI), Centre for Research in Cognition and Neurosciences (CRCN), Neuropsychology and Functional Neuroimaging Research Unit (UR2NF), Brussels, Belgium
| | - Xavier De Tiège
- Université libre de Bruxelles (ULB), ULB Neuroscience Institute (UNI), Laboratoire de Neuroanatomie et de Neuroimagerie translationnelles (LNbT), Brussels, Belgium; Université libre de Bruxelles (ULB), Hôpital Universitaire de Bruxelles (H.U.B.), Hôpital Erasme, Service of translational Neuroimaging, Brussels, Belgium
| | - Vincent Wens
- Université libre de Bruxelles (ULB), ULB Neuroscience Institute (UNI), Laboratoire de Neuroanatomie et de Neuroimagerie translationnelles (LNbT), Brussels, Belgium; Université libre de Bruxelles (ULB), Hôpital Universitaire de Bruxelles (H.U.B.), Hôpital Erasme, Service of translational Neuroimaging, Brussels, Belgium
| |
Collapse
|
6
|
Gosti G, Milanetti E, Folli V, de Pasquale F, Leonetti M, Corbetta M, Ruocco G, Della Penna S. A recurrent Hopfield network for estimating meso-scale effective connectivity in MEG. Neural Netw 2024; 170:72-93. [PMID: 37977091 DOI: 10.1016/j.neunet.2023.11.027] [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: 02/17/2023] [Revised: 11/07/2023] [Accepted: 11/09/2023] [Indexed: 11/19/2023]
Abstract
The architecture of communication within the brain, represented by the human connectome, has gained a paramount role in the neuroscience community. Several features of this communication, e.g., the frequency content, spatial topology, and temporal dynamics are currently well established. However, identifying generative models providing the underlying patterns of inhibition/excitation is very challenging. To address this issue, we present a novel generative model to estimate large-scale effective connectivity from MEG. The dynamic evolution of this model is determined by a recurrent Hopfield neural network with asymmetric connections, and thus denoted Recurrent Hopfield Mass Model (RHoMM). Since RHoMM must be applied to binary neurons, it is suitable for analyzing Band Limited Power (BLP) dynamics following a binarization process. We trained RHoMM to predict the MEG dynamics through a gradient descent minimization and we validated it in two steps. First, we showed a significant agreement between the similarity of the effective connectivity patterns and that of the interregional BLP correlation, demonstrating RHoMM's ability to capture individual variability of BLP dynamics. Second, we showed that the simulated BLP correlation connectomes, obtained from RHoMM evolutions of BLP, preserved some important topological features, e.g, the centrality of the real data, assuring the reliability of RHoMM. Compared to other biophysical models, RHoMM is based on recurrent Hopfield neural networks, thus, it has the advantage of being data-driven, less demanding in terms of hyperparameters and scalable to encompass large-scale system interactions. These features are promising for investigating the dynamics of inhibition/excitation at different spatial scales.
Collapse
Affiliation(s)
- Giorgio Gosti
- Center for Life Nano- & Neuro-Science, Istituto Italiano di Tecnologia, Viale Regina Elena, 291, 00161, Rome, Italy; Soft and Living Matter Laboratory, Institute of Nanotechnology, Consiglio Nazionale delle Ricerche, Piazzale Aldo Moro, 5, 00185, Rome, Italy; Istituto di Scienze del Patrimonio Culturale, Sede di Roma, Consiglio Nazionale delle Ricerche, CNR-ISPC, Via Salaria km, 34900 Rome, Italy.
| | - Edoardo Milanetti
- Center for Life Nano- & Neuro-Science, Istituto Italiano di Tecnologia, Viale Regina Elena, 291, 00161, Rome, Italy; Department of Physics, Sapienza University of Rome, Piazzale Aldo Moro, 5, 00185, Rome, Italy.
| | - Viola Folli
- Center for Life Nano- & Neuro-Science, Istituto Italiano di Tecnologia, Viale Regina Elena, 291, 00161, Rome, Italy; D-TAILS srl, Via di Torre Rossa, 66, 00165, Rome, Italy.
| | - Francesco de Pasquale
- Faculty of Veterinary Medicine, University of Teramo, 64100 Piano D'Accio, Teramo, Italy.
| | - Marco Leonetti
- Center for Life Nano- & Neuro-Science, Istituto Italiano di Tecnologia, Viale Regina Elena, 291, 00161, Rome, Italy; Soft and Living Matter Laboratory, Institute of Nanotechnology, Consiglio Nazionale delle Ricerche, Piazzale Aldo Moro, 5, 00185, Rome, Italy; D-TAILS srl, Via di Torre Rossa, 66, 00165, Rome, Italy.
| | - Maurizio Corbetta
- Department of Neuroscience, University of Padova, Via Belzoni, 160, 35121, Padova, Italy; Padova Neuroscience Center (PNC), University of Padova, Via Orus, 2/B, 35129, Padova, Italy; Veneto Institute of Molecular Medicine (VIMM), Via Orus, 2, 35129, Padova, Italy.
| | - Giancarlo Ruocco
- Center for Life Nano- & Neuro-Science, Istituto Italiano di Tecnologia, Viale Regina Elena, 291, 00161, Rome, Italy; Department of Physics, Sapienza University of Rome, Piazzale Aldo Moro, 5, 00185, Rome, Italy.
| | - Stefania Della Penna
- Department of Neuroscience, Imaging and Clinical Sciences, and Institute for Advanced Biomedical Technologies, "G. d'Annunzio" University of Chieti-Pescara, Via Luigi Polacchi, 11, 66100 Chieti, Italy.
| |
Collapse
|
7
|
Vallinoja J, Nurmi T, Jaatela J, Wens V, Bourguignon M, Mäenpää H, Piitulainen H. Functional connectivity of sensorimotor network is enhanced in spastic diplegic cerebral palsy: A multimodal study using fMRI and MEG. Clin Neurophysiol 2024; 157:4-14. [PMID: 38006621 DOI: 10.1016/j.clinph.2023.10.014] [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: 12/23/2022] [Revised: 06/02/2023] [Accepted: 10/15/2023] [Indexed: 11/27/2023]
Abstract
OBJECTIVE To assess the effects to functional connectivity (FC) caused by lesions related to spastic diplegic cerebral palsy (CP) in children and adolescents using multiple imaging modalities. METHODS We used resting state magnetoencephalography (MEG) envelope signals in alpha, beta and gamma ranges and resting state functional magnetic resonance imaging (fMRI) signals to quantify FC between selected sensorimotor regions of interest (ROIs) in 11 adolescents with spastic diplegic cerebral palsy and 24 typically developing controls. Motor performance of the hands was quantified with gross motor, fine motor and kinesthesia tests. RESULTS In fMRI, participants with CP showed enhanced FC within posterior parietal regions; in MEG, they showed enhanced interhemispheric FC between sensorimotor regions and posterior parietal regions both in alpha and lower beta bands. There was a correlation between the kinesthesia score and fronto-parietal connectivity in the control population. CONCLUSIONS CP is associated with enhanced FC in sensorimotor network. This difference is not correlated with hand coordination performance. The effect of the lesion is likely not fully captured by temporal correlation of ROI signals. SIGNIFICANCE Brain lesions can show as increased temporal correlation of activity between remote brain areas. We suggest this effect is likely separate from typical physiological correlates of functional connectivity.
Collapse
Affiliation(s)
- Jaakko Vallinoja
- Department of Neuroscience and Biomedical Engineering, Aalto University School of Science, P.O. BOX 12200, 00076 AALTO Espoo, Finland.
| | - Timo Nurmi
- Department of Neuroscience and Biomedical Engineering, Aalto University School of Science, P.O. BOX 12200, 00076 AALTO Espoo, Finland; Faculty of Sport and Health Sciences, University of Jyväskylä, P.O. BOX 35, FI-40014 Jyväskylä, Finland
| | - Julia Jaatela
- Department of Neuroscience and Biomedical Engineering, Aalto University School of Science, P.O. BOX 12200, 00076 AALTO Espoo, Finland
| | - Vincent Wens
- Laboratoire de Neuroanatomie et Neuroimagerie Translationnelles (LN(2)T), UNI - ULB Neuroscience Institute, Université Libre de Bruxelles (ULB), Brussels, Belgium; Department of Translational Neuroimaging, HUB - Hôpital Erasme, Brussels, Belgium
| | - Mathieu Bourguignon
- Laboratoire de Neuroanatomie et Neuroimagerie Translationnelles (LN(2)T), UNI - ULB Neuroscience Institute, Université Libre de Bruxelles (ULB), Brussels, Belgium; Laboratory of Neurophysiology and Movement Biomechanics, UNI - ULB Neuroscience Institute, Université Libre de Bruxelles (ULB), 1070 Brussels, Belgium; BCBL, Basque Center on Cognition, Brain and Language, 20009 San Sebastian, Spain
| | - Helena Mäenpää
- Department of Child Neurology, New Children's Hospital, University of Helsinki and Helsinki University Hospital, FI-00029 Helsinki, Finland
| | - Harri Piitulainen
- Department of Neuroscience and Biomedical Engineering, Aalto University School of Science, P.O. BOX 12200, 00076 AALTO Espoo, Finland; Faculty of Sport and Health Sciences, University of Jyväskylä, P.O. BOX 35, FI-40014 Jyväskylä, Finland; Aalto NeuroImaging, Aalto University School of Science, Espoo, Finland
| |
Collapse
|
8
|
Paz-Linares D, Gonzalez-Moreira E, Areces-Gonzalez A, Wang Y, Li M, Martinez-Montes E, Bosch-Bayard J, Bringas-Vega ML, Valdes-Sosa M, Valdes-Sosa PA. Identifying oscillatory brain networks with hidden Gaussian graphical spectral models of MEEG. Sci Rep 2023; 13:11466. [PMID: 37454235 PMCID: PMC10349891 DOI: 10.1038/s41598-023-38513-y] [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] [Received: 04/05/2023] [Accepted: 07/11/2023] [Indexed: 07/18/2023] Open
Abstract
Identifying the functional networks underpinning indirectly observed processes poses an inverse problem for neurosciences or other fields. A solution of such inverse problems estimates as a first step the activity emerging within functional networks from EEG or MEG data. These EEG or MEG estimates are a direct reflection of functional brain network activity with a temporal resolution that no other in vivo neuroimage may provide. A second step estimating functional connectivity from such activity pseudodata unveil the oscillatory brain networks that strongly correlate with all cognition and behavior. Simulations of such MEG or EEG inverse problem also reveal estimation errors of the functional connectivity determined by any of the state-of-the-art inverse solutions. We disclose a significant cause of estimation errors originating from misspecification of the functional network model incorporated into either inverse solution steps. We introduce the Bayesian identification of a Hidden Gaussian Graphical Spectral (HIGGS) model specifying such oscillatory brain networks model. In human EEG alpha rhythm simulations, the estimation errors measured as ROC performance do not surpass 2% in our HIGGS inverse solution and reach 20% in state-of-the-art methods. Macaque simultaneous EEG/ECoG recordings provide experimental confirmation for our results with 1/3 times larger congruence according to Riemannian distances than state-of-the-art methods.
Collapse
Affiliation(s)
- Deirel Paz-Linares
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, China
- Department of Neuroinformatics, Cuban Neuroscience Center, Havana, Cuba
| | - Eduardo Gonzalez-Moreira
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, China
- School of Electrical Engineering, Central University "Marta Abreu" of Las Villas, Santa Clara, Cuba
- Center for Biomedical Imaging and Neuromodulation, Nathan Kline Institute for Psychiatric Research, Orangeburg, NY, USA
| | - Ariosky Areces-Gonzalez
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, China
- School of Technical Sciences, University of Pinar del Río "Hermanos Saiz Montes de Oca", Pinar del Rio, Cuba
| | - Ying Wang
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, China
| | - Min Li
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, China
| | | | - Jorge Bosch-Bayard
- Department of Neuroinformatics, Cuban Neuroscience Center, Havana, Cuba
- McGill Centre for Integrative Neurosciences MCIN, Ludmer Centre for Mental Health, Montreal Neurological Institute, McGill University, Montreal, Canada
| | - Maria L Bringas-Vega
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, China
- Department of Neuroinformatics, Cuban Neuroscience Center, Havana, Cuba
| | - Mitchell Valdes-Sosa
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, China
- Department of Neuroinformatics, Cuban Neuroscience Center, Havana, Cuba
| | - Pedro A Valdes-Sosa
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, China.
- Department of Neuroinformatics, Cuban Neuroscience Center, Havana, Cuba.
| |
Collapse
|
9
|
Wens V. Exploring the limits of MEG spatial resolution with multipolar expansions. Neuroimage 2023; 270:119953. [PMID: 36842521 DOI: 10.1016/j.neuroimage.2023.119953] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/14/2022] [Accepted: 02/17/2023] [Indexed: 02/26/2023] Open
Abstract
The advent of scalp magnetoencephalography (MEG) based on optically pumped magnetometers (OPMs) may represent a step change in the field of human electrophysiology. Compared to cryogenic MEG based on superconducting quantum interference devices (SQUIDs, placed 2-4 cm above scalp), scalp MEG promises significantly higher spatial resolution imaging but it also comes with numerous challenges regarding how to optimally design OPM arrays. In this context, we sought to provide a systematic description of MEG spatial resolution as a function of the number of sensors (allowing comparison of low- vs. high-density MEG), sensor-to-brain distance (cryogenic SQUIDs vs. scalp OPM), sensor type (magnetometers vs. gradiometers; single- vs. multi-component sensors), and signal-to-noise ratio. To that aim, we present an analytical theory based on MEG multipolar expansions that enables, once supplemented with experimental input and simulations, quantitative assessment of the limits of MEG spatial resolution in terms of two qualitatively distinct regimes. In the regime of asymptotically high-density MEG, we provide a mathematically rigorous description of how magnetic field smoothness constraints spatial resolution to a slow, logarithmic divergence. In the opposite regime of low-density MEG, it is sensor density that constraints spatial resolution to a faster increase following a square-root law. The transition between these two regimes controls how MEG spatial resolution saturates as sensors approach sources of neural activity. This two-regime model of MEG spatial resolution integrates known observations (e.g., the difficulty of improving spatial resolution by increasing sensor density, the gain brought by moving sensors on scalp, or the usefulness of multi-component sensors) and gathers them under a unifying theoretical framework that highlights the underlying physics and reveals properties inaccessible to simulations. We propose that this framework may find useful applications to benchmark the design of future OPM-based scalp MEG systems.
Collapse
Affiliation(s)
- Vincent Wens
- LN(2)T - Laboratoire de Neuroanatomie et Neuroimagerie translationnelles, UNI - ULB Neuroscience Institute, Université libre de Bruxelles (ULB), Brussels, Belgium; Department of Translational Neuroimaging, H.U.B. - Hôpital Erasme, Brussels, Belgium.
| |
Collapse
|
10
|
Kida T, Tanaka E, Kakigi R, Inui K. Brain-wide network analysis of resting-state neuromagnetic data. Hum Brain Mapp 2023; 44:3519-3540. [PMID: 36988453 DOI: 10.1002/hbm.26295] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/11/2022] [Revised: 03/16/2023] [Accepted: 03/20/2023] [Indexed: 03/30/2023] Open
Abstract
The present study performed a brain-wide network analysis of resting-state magnetoencephalograms recorded from 53 healthy participants to visualize elaborate brain maps of phase- and amplitude-derived graph-theory metrics at different frequencies. To achieve this, we conducted a vertex-wise computation of threshold-independent graph metrics by combining proportional thresholding and a conjunction analysis and applied them to a correlation analysis of age and brain networks. Source power showed a frequency-dependent cortical distribution. Threshold-independent graph metrics derived from phase- and amplitude-based connectivity showed similar or different distributions depending on frequency. Vertex-wise age-brain correlation maps revealed that source power at the beta band and the amplitude-based degree at the alpha band changed with age in local regions. The present results indicate that a brain-wide analysis of neuromagnetic data has the potential to reveal neurophysiological network features in the human brain in a resting state.
Collapse
Affiliation(s)
- Tetsuo Kida
- Higher Brain Function Unit, Department of Functioning and Disability, Institute for Developmental Research, Aichi Developmental Disability Center, Kasugai, Japan
- Department of Functioning and Disability, Institute for Developmental Research, Aichi Developmental Disability Center, Kasugai, Japan
- Department of Integrative Physiology, National Institute for Physiological Sciences, Okazaki, Japan
- Section of Brain Function Information, Supportive Center for Brain Research, National Institute for Physiological Sciences, Okazaki, Japan
| | - Emi Tanaka
- Brain and Mind Research Center, Nagoya University, Nagoya, Japan
| | - Ryusuke Kakigi
- Department of Integrative Physiology, National Institute for Physiological Sciences, Okazaki, Japan
| | - Koji Inui
- Department of Functioning and Disability, Institute for Developmental Research, Aichi Developmental Disability Center, Kasugai, Japan
- Department of Integrative Physiology, National Institute for Physiological Sciences, Okazaki, Japan
- Section of Brain Function Information, Supportive Center for Brain Research, National Institute for Physiological Sciences, Okazaki, Japan
| |
Collapse
|
11
|
Paz-Linares D, Gonzalez-Moreira E, Areces-Gonzalez A, Wang Y, Li M, Vega-Hernandez M, Wang Q, Bosch-Bayard J, Bringas-Vega ML, Martinez-Montes E, Valdes-Sosa MJ, Valdes-Sosa PA. Minimizing the distortions in electrophysiological source imaging of cortical oscillatory activity via Spectral Structured Sparse Bayesian Learning. Front Neurosci 2023; 17:978527. [PMID: 37008210 PMCID: PMC10050575 DOI: 10.3389/fnins.2023.978527] [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: 06/26/2022] [Accepted: 02/07/2023] [Indexed: 03/17/2023] Open
Abstract
Oscillatory processes at all spatial scales and on all frequencies underpin brain function. Electrophysiological Source Imaging (ESI) is the data-driven brain imaging modality that provides the inverse solutions to the source processes of the EEG, MEG, or ECoG data. This study aimed to carry out an ESI of the source cross-spectrum while controlling common distortions of the estimates. As with all ESI-related problems under realistic settings, the main obstacle we faced is a severely ill-conditioned and high-dimensional inverse problem. Therefore, we opted for Bayesian inverse solutions that posited a priori probabilities on the source process. Indeed, rigorously specifying both the likelihoods and a priori probabilities of the problem leads to the proper Bayesian inverse problem of cross-spectral matrices. These inverse solutions are our formal definition for cross-spectral ESI (cESI), which requires a priori of the source cross-spectrum to counter the severe ill-condition and high-dimensionality of matrices. However, inverse solutions for this problem were NP-hard to tackle or approximated within iterations with bad-conditioned matrices in the standard ESI setup. We introduce cESI with a joint a priori probability upon the source cross-spectrum to avoid these problems. cESI inverse solutions are low-dimensional ones for the set of random vector instances and not random matrices. We achieved cESI inverse solutions through the variational approximations via our Spectral Structured Sparse Bayesian Learning (ssSBL) algorithm https://github.com/CCC-members/Spectral-Structured-Sparse-Bayesian-Learning. We compared low-density EEG (10-20 system) ssSBL inverse solutions with reference cESIs for two experiments: (a) high-density MEG that were used to simulate EEG and (b) high-density macaque ECoG that were recorded simultaneously with EEG. The ssSBL resulted in two orders of magnitude with less distortion than the state-of-the-art ESI methods. Our cESI toolbox, including the ssSBL method, is available at https://github.com/CCC-members/BC-VARETA_Toolbox.
Collapse
Affiliation(s)
- Deirel Paz-Linares
- MOE Key Lab for Neuroinformation, The Clinical Hospital of Chengdu Brain Science Institute, University of Electronic Science and Technology of China, Chengdu, China
- Neuroinformatics Department, Cuban Neuroscience Center, Havana, Cuba
| | - Eduardo Gonzalez-Moreira
- MOE Key Lab for Neuroinformation, The Clinical Hospital of Chengdu Brain Science Institute, University of Electronic Science and Technology of China, Chengdu, China
- Center for Biomedical Imaging and Neuromodulation, Nathan Kline Institute for Psychiatric Research, Orangeburg, NY, United States
- Research Unit for Neurodevelopment, Institute of Neurobiology, Autonomous University of Mexico, Querétaro, Mexico
- Faculty of Electrical Engineering, Central University “Marta Abreu” of Las Villas, Santa Clara, Cuba
| | - Ariosky Areces-Gonzalez
- MOE Key Lab for Neuroinformation, The Clinical Hospital of Chengdu Brain Science Institute, University of Electronic Science and Technology of China, Chengdu, China
- Faculty of Technical Sciences, University of Pinar del Río “Hermanos Saiz Montes de Oca”, Pinar del Rio, Cuba
| | - Ying Wang
- MOE Key Lab for Neuroinformation, The Clinical Hospital of Chengdu Brain Science Institute, University of Electronic Science and Technology of China, Chengdu, China
| | - Min Li
- MOE Key Lab for Neuroinformation, The Clinical Hospital of Chengdu Brain Science Institute, University of Electronic Science and Technology of China, Chengdu, China
| | | | - Qing Wang
- MOE Key Lab for Neuroinformation, The Clinical Hospital of Chengdu Brain Science Institute, University of Electronic Science and Technology of China, Chengdu, China
- McGill Centre for Integrative Neurosciences MCIN, Montreal Neurological Institute, McGill University, Montreal, QC, Canada
- Ludmer Centre for Mental Health, Montreal Neurological Institute, McGill University, Montreal, QC, Canada
| | - Jorge Bosch-Bayard
- MOE Key Lab for Neuroinformation, The Clinical Hospital of Chengdu Brain Science Institute, University of Electronic Science and Technology of China, Chengdu, China
- McGill Centre for Integrative Neurosciences MCIN, Montreal Neurological Institute, McGill University, Montreal, QC, Canada
- Ludmer Centre for Mental Health, Montreal Neurological Institute, McGill University, Montreal, QC, Canada
| | - Maria L. Bringas-Vega
- MOE Key Lab for Neuroinformation, The Clinical Hospital of Chengdu Brain Science Institute, University of Electronic Science and Technology of China, Chengdu, China
- Neuroinformatics Department, Cuban Neuroscience Center, Havana, Cuba
| | | | - Mitchel J. Valdes-Sosa
- MOE Key Lab for Neuroinformation, The Clinical Hospital of Chengdu Brain Science Institute, University of Electronic Science and Technology of China, Chengdu, China
- Neuroinformatics Department, Cuban Neuroscience Center, Havana, Cuba
| | - Pedro A. Valdes-Sosa
- MOE Key Lab for Neuroinformation, The Clinical Hospital of Chengdu Brain Science Institute, University of Electronic Science and Technology of China, Chengdu, China
- Neuroinformatics Department, Cuban Neuroscience Center, Havana, Cuba
| |
Collapse
|
12
|
Niesen M, Bourguignon M, Bertels J, Vander Ghinst M, Wens V, Goldman S, De Tiège X. Cortical tracking of lexical speech units in a multi-talker background is immature in school-aged children. Neuroimage 2023; 265:119770. [PMID: 36462732 DOI: 10.1016/j.neuroimage.2022.119770] [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: 03/28/2022] [Revised: 11/09/2022] [Accepted: 11/23/2022] [Indexed: 12/03/2022] Open
Abstract
Children have more difficulty perceiving speech in noise than adults. Whether this difficulty relates to an immature processing of prosodic or linguistic elements of the attended speech is still unclear. To address the impact of noise on linguistic processing per se, we assessed how babble noise impacts the cortical tracking of intelligible speech devoid of prosody in school-aged children and adults. Twenty adults and twenty children (7-9 years) listened to synthesized French monosyllabic words presented at 2.5 Hz, either randomly or in 4-word hierarchical structures wherein 2 words formed a phrase at 1.25 Hz, and 2 phrases formed a sentence at 0.625 Hz, with or without babble noise. Neuromagnetic responses to words, phrases and sentences were identified and source-localized. Children and adults displayed significant cortical tracking of words in all conditions, and of phrases and sentences only when words formed meaningful sentences. In children compared with adults, the cortical tracking was lower for all linguistic units in conditions without noise. In the presence of noise, the cortical tracking was similarly reduced for sentence units in both groups, but remained stable for phrase units. Critically, when there was noise, adults increased the cortical tracking of monosyllabic words in the inferior frontal gyri and supratemporal auditory cortices but children did not. This study demonstrates that the difficulties of school-aged children in understanding speech in a multi-talker background might be partly due to an immature tracking of lexical but not supra-lexical linguistic units.
Collapse
Affiliation(s)
- Maxime Niesen
- Université libre de Bruxelles (ULB), UNI - ULB Neurosciences Institute, Laboratoire de Neuroanatomie et de Neuroimagerie translationnelles (LN2T), 1070 Brussels, Belgium; Université libre de Bruxelles (ULB), Hôpital Universitaire de Bruxelles (HUB), CUB Hôpital Erasme, Department of Otorhinolaryngology, 1070 Brussels, Belgium.
| | - Mathieu Bourguignon
- Université libre de Bruxelles (ULB), UNI - ULB Neurosciences Institute, Laboratoire de Neuroanatomie et de Neuroimagerie translationnelles (LN2T), 1070 Brussels, Belgium; Université libre de Bruxelles (ULB), UNI-ULB Neuroscience Institute, Laboratory of Neurophysiology and Movement Biomechanics, 1070 Brussels, Belgium.; BCBL, Basque Center on Cognition, Brain and Language, 20009 San Sebastian, Spain
| | - Julie Bertels
- Université libre de Bruxelles (ULB), UNI - ULB Neurosciences Institute, Laboratoire de Neuroanatomie et de Neuroimagerie translationnelles (LN2T), 1070 Brussels, Belgium; Université libre de Bruxelles (ULB), UNI-ULB Neuroscience Institute, Cognition and Computation group, ULBabyLab - Consciousness, Brussels, Belgium
| | - Marc Vander Ghinst
- Université libre de Bruxelles (ULB), UNI - ULB Neurosciences Institute, Laboratoire de Neuroanatomie et de Neuroimagerie translationnelles (LN2T), 1070 Brussels, Belgium; Université libre de Bruxelles (ULB), Hôpital Universitaire de Bruxelles (HUB), CUB Hôpital Erasme, Department of Otorhinolaryngology, 1070 Brussels, Belgium
| | - Vincent Wens
- Université libre de Bruxelles (ULB), UNI - ULB Neurosciences Institute, Laboratoire de Neuroanatomie et de Neuroimagerie translationnelles (LN2T), 1070 Brussels, Belgium; Université libre de Bruxelles (ULB), Hôpital Universitaire de Bruxelles (HUB), CUB Hôpital Erasme, Department of translational Neuroimaging, 1070 Brussels, Belgium
| | - Serge Goldman
- Université libre de Bruxelles (ULB), UNI - ULB Neurosciences Institute, Laboratoire de Neuroanatomie et de Neuroimagerie translationnelles (LN2T), 1070 Brussels, Belgium; Université libre de Bruxelles (ULB), Hôpital Universitaire de Bruxelles (HUB), CUB Hôpital Erasme, Department of Nuclear Medicine, 1070 Brussels, Belgium
| | - Xavier De Tiège
- Université libre de Bruxelles (ULB), UNI - ULB Neurosciences Institute, Laboratoire de Neuroanatomie et de Neuroimagerie translationnelles (LN2T), 1070 Brussels, Belgium; Université libre de Bruxelles (ULB), Hôpital Universitaire de Bruxelles (HUB), CUB Hôpital Erasme, Department of translational Neuroimaging, 1070 Brussels, Belgium
| |
Collapse
|
13
|
Siems M, Tünnerhoff J, Ziemann U, Siegel M. Multistage classification identifies altered cortical phase- and amplitude-coupling in Multiple Sclerosis. Neuroimage 2022; 264:119752. [PMID: 36400377 PMCID: PMC9771829 DOI: 10.1016/j.neuroimage.2022.119752] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/25/2021] [Revised: 10/28/2022] [Accepted: 11/14/2022] [Indexed: 11/16/2022] Open
Abstract
Distinguishing groups of subjects or experimental conditions in a high-dimensional feature space is a common goal in modern neuroimaging studies. Successful classification depends on the selection of relevant features as not every neuronal signal component or parameter is informative about the research question at hand. Here, we developed a novel unsupervised multistage analysis approach that combines dimensionality reduction, bootstrap aggregating and multivariate classification to select relevant neuronal features. We tested the approach by identifying changes of brain-wide electrophysiological coupling in Multiple Sclerosis. Multiple Sclerosis is a demyelinating disease of the central nervous system that can result in cognitive decline and physical disability. However, related changes in large-scale brain interactions remain poorly understood and corresponding non-invasive biomarkers are sparse. We thus compared brain-wide phase- and amplitude-coupling of frequency specific neuronal activity in relapsing-remitting Multiple Sclerosis patients (n = 17) and healthy controls (n = 17) using magnetoencephalography. Changes in this dataset included both, increased and decreased phase- and amplitude-coupling in wide-spread, bilateral neuronal networks across a broad range of frequencies. These changes allowed to successfully classify patients and controls with an accuracy of 84%. Furthermore, classification confidence predicted behavioral scores of disease severity. In sum, our results unravel systematic changes of large-scale phase- and amplitude coupling in Multiple Sclerosis. Furthermore, our results establish a new analysis approach to efficiently contrast high-dimensional neuroimaging data between experimental groups or conditions.
Collapse
Affiliation(s)
- Marcus Siems
- Department of Neural Dynamics and Magnetoencephalography, Hertie Institute for Clinical Brain Research, University of Tübingen, Germany,Centre for Integrative Neuroscience, University of Tübingen, Germany,MEG Center, University of Tübingen, Germany,Department of Neurophysiology and Pathophysiology, University Medical Center Hamburg-Eppendorf, Hamburg, Germany,Correspondence author at: Department of Neural Dynamics and Magnetoencephalography, Hertie Institute for Clinical Brain Research, University of Tübingen, Germany.
| | - Johannes Tünnerhoff
- Department of Neurology & Stroke, Hertie Institute for Clinical Brain Research, University of Tübingen, Germany
| | - Ulf Ziemann
- Department of Neurology & Stroke, Hertie Institute for Clinical Brain Research, University of Tübingen, Germany
| | - Markus Siegel
- Department of Neural Dynamics and Magnetoencephalography, Hertie Institute for Clinical Brain Research, University of Tübingen, Germany,Centre for Integrative Neuroscience, University of Tübingen, Germany,MEG Center, University of Tübingen, Germany,Correspondence author at: Department of Neural Dynamics and Magnetoencephalography, Hertie Institute for Clinical Brain Research, University of Tübingen, Germany.
| |
Collapse
|
14
|
Neurodevelopmental oscillatory basis of speech processing in noise. Dev Cogn Neurosci 2022; 59:101181. [PMID: 36549148 PMCID: PMC9792357 DOI: 10.1016/j.dcn.2022.101181] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/28/2022] [Revised: 10/31/2022] [Accepted: 11/25/2022] [Indexed: 11/27/2022] Open
Abstract
Humans' extraordinary ability to understand speech in noise relies on multiple processes that develop with age. Using magnetoencephalography (MEG), we characterize the underlying neuromaturational basis by quantifying how cortical oscillations in 144 participants (aged 5-27 years) track phrasal and syllabic structures in connected speech mixed with different types of noise. While the extraction of prosodic cues from clear speech was stable during development, its maintenance in a multi-talker background matured rapidly up to age 9 and was associated with speech comprehension. Furthermore, while the extraction of subtler information provided by syllables matured at age 9, its maintenance in noisy backgrounds progressively matured until adulthood. Altogether, these results highlight distinct behaviorally relevant maturational trajectories for the neuronal signatures of speech perception. In accordance with grain-size proposals, neuromaturational milestones are reached increasingly late for linguistic units of decreasing size, with further delays incurred by noise.
Collapse
|
15
|
Feys O, Corvilain P, Aeby A, Sculier C, Holmes N, Brookes M, Goldman S, Wens V, De Tiège X. On-Scalp Optically Pumped Magnetometers versus Cryogenic Magnetoencephalography for Diagnostic Evaluation of Epilepsy in School-aged Children. Radiology 2022; 304:429-434. [PMID: 35503013 DOI: 10.1148/radiol.212453] [Citation(s) in RCA: 35] [Impact Index Per Article: 17.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/11/2022]
Abstract
Background Magnetoencephalography (MEG) is an established method used to detect and localize focal interictal epileptiform discharges (IEDs). Current MEG systems house hundreds of cryogenic sensors in a rigid, one-size-fits-all helmet, which results in several limitations, particularly in children. Purpose To determine if on-scalp MEG based on optically pumped magnetometers (OPMs) alleviates the main limitations of cryogenic MEG. Materials and Methods In this prospective single-center study conducted in a tertiary university teaching hospital, participants underwent cryogenic (102 magnetometers, 204 planar gradiometers) and on-scalp (32 OPMs) MEG. The two modalities for the detection and localization of IEDs were compared. The t test was used to compare IED amplitude and signal-to-noise ratio (SNR). Distributed source modeling was performed on OPM-based and cryogenic MEG data. Results Five children (median age, 9.4 years [range, 5-11 years]; four girls) with self-limited idiopathic (n = 3) or refractory (n = 2) focal epilepsy were included. IEDs were identified in all five children with comparable sensor topographies for both MEG devices. IED amplitudes were 2.3 (7.2 of 3.1) to 4.6 (3.2 of 0.7) times higher (P < .001) with on-scalp MEG, and the SNR was 27% (16.7 of 13.2) to 60% (12.8 of 8.0) higher (P value range: .001-.009) with on-scalp MEG in all but one participant (P = .93), whose head movements created pronounced motion artifacts. The neural source of averaged IEDs was located at approximately 5 mm (n = 3) or higher (8.3 mm, n = 1; 15.6 mm, n = 1) between on-scalp and cryogenic MEG. Conclusion Despite the limited number of sensors and scalp coverage, on-scalp magnetoencephalography (MEG) based on optically pumped magnetometers helped detect interictal epileptiform discharges in school-aged children with epilepsy with a higher amplitude, higher signal-to-noise ratio, and similar localization value compared with conventional cryogenic MEG. Online supplemental material is available for this article. © RSNA, 2022 See also the editorial by Widjaja in this issue.
Collapse
Affiliation(s)
- Odile Feys
- From the Departments of Neurology (O.F.), Pediatric Neurology (C.S., F.C.), Nuclear Medicine (S.G.), and Translational Neuroimaging (V.W., X.D.T.), CUB Hôpital Erasme, Université Libre de Bruxelles, Brussels, Belgium; Laboratory of Translational Neuroimaging and Neuroanatomy (Laboratoire de Neuroimagerie et Neuroanatomie translationnelles) (LNT), ULB Neuroscience Institute, Université Libre de Bruxelles, 808 Lennik St, Brussels, Belgium (O.F., P.C., S.G., V.W., X.D.T.); Department of Pediatric Neurology, Hôpital Universitaire des Enfants Reine Fabiola, Université Libre de Bruxelles, Brussels, Belgium (A.A.); and Sir Peter Mansfield Imaging Centre, School of Physics and Astronomy, University of Nottingham, Nottingham, United Kingdom (N.H., M.B.)
| | - Pierre Corvilain
- From the Departments of Neurology (O.F.), Pediatric Neurology (C.S., F.C.), Nuclear Medicine (S.G.), and Translational Neuroimaging (V.W., X.D.T.), CUB Hôpital Erasme, Université Libre de Bruxelles, Brussels, Belgium; Laboratory of Translational Neuroimaging and Neuroanatomy (Laboratoire de Neuroimagerie et Neuroanatomie translationnelles) (LNT), ULB Neuroscience Institute, Université Libre de Bruxelles, 808 Lennik St, Brussels, Belgium (O.F., P.C., S.G., V.W., X.D.T.); Department of Pediatric Neurology, Hôpital Universitaire des Enfants Reine Fabiola, Université Libre de Bruxelles, Brussels, Belgium (A.A.); and Sir Peter Mansfield Imaging Centre, School of Physics and Astronomy, University of Nottingham, Nottingham, United Kingdom (N.H., M.B.)
| | - Alec Aeby
- From the Departments of Neurology (O.F.), Pediatric Neurology (C.S., F.C.), Nuclear Medicine (S.G.), and Translational Neuroimaging (V.W., X.D.T.), CUB Hôpital Erasme, Université Libre de Bruxelles, Brussels, Belgium; Laboratory of Translational Neuroimaging and Neuroanatomy (Laboratoire de Neuroimagerie et Neuroanatomie translationnelles) (LNT), ULB Neuroscience Institute, Université Libre de Bruxelles, 808 Lennik St, Brussels, Belgium (O.F., P.C., S.G., V.W., X.D.T.); Department of Pediatric Neurology, Hôpital Universitaire des Enfants Reine Fabiola, Université Libre de Bruxelles, Brussels, Belgium (A.A.); and Sir Peter Mansfield Imaging Centre, School of Physics and Astronomy, University of Nottingham, Nottingham, United Kingdom (N.H., M.B.)
| | - Claudine Sculier
- From the Departments of Neurology (O.F.), Pediatric Neurology (C.S., F.C.), Nuclear Medicine (S.G.), and Translational Neuroimaging (V.W., X.D.T.), CUB Hôpital Erasme, Université Libre de Bruxelles, Brussels, Belgium; Laboratory of Translational Neuroimaging and Neuroanatomy (Laboratoire de Neuroimagerie et Neuroanatomie translationnelles) (LNT), ULB Neuroscience Institute, Université Libre de Bruxelles, 808 Lennik St, Brussels, Belgium (O.F., P.C., S.G., V.W., X.D.T.); Department of Pediatric Neurology, Hôpital Universitaire des Enfants Reine Fabiola, Université Libre de Bruxelles, Brussels, Belgium (A.A.); and Sir Peter Mansfield Imaging Centre, School of Physics and Astronomy, University of Nottingham, Nottingham, United Kingdom (N.H., M.B.)
| | - Niall Holmes
- From the Departments of Neurology (O.F.), Pediatric Neurology (C.S., F.C.), Nuclear Medicine (S.G.), and Translational Neuroimaging (V.W., X.D.T.), CUB Hôpital Erasme, Université Libre de Bruxelles, Brussels, Belgium; Laboratory of Translational Neuroimaging and Neuroanatomy (Laboratoire de Neuroimagerie et Neuroanatomie translationnelles) (LNT), ULB Neuroscience Institute, Université Libre de Bruxelles, 808 Lennik St, Brussels, Belgium (O.F., P.C., S.G., V.W., X.D.T.); Department of Pediatric Neurology, Hôpital Universitaire des Enfants Reine Fabiola, Université Libre de Bruxelles, Brussels, Belgium (A.A.); and Sir Peter Mansfield Imaging Centre, School of Physics and Astronomy, University of Nottingham, Nottingham, United Kingdom (N.H., M.B.)
| | - Matthew Brookes
- From the Departments of Neurology (O.F.), Pediatric Neurology (C.S., F.C.), Nuclear Medicine (S.G.), and Translational Neuroimaging (V.W., X.D.T.), CUB Hôpital Erasme, Université Libre de Bruxelles, Brussels, Belgium; Laboratory of Translational Neuroimaging and Neuroanatomy (Laboratoire de Neuroimagerie et Neuroanatomie translationnelles) (LNT), ULB Neuroscience Institute, Université Libre de Bruxelles, 808 Lennik St, Brussels, Belgium (O.F., P.C., S.G., V.W., X.D.T.); Department of Pediatric Neurology, Hôpital Universitaire des Enfants Reine Fabiola, Université Libre de Bruxelles, Brussels, Belgium (A.A.); and Sir Peter Mansfield Imaging Centre, School of Physics and Astronomy, University of Nottingham, Nottingham, United Kingdom (N.H., M.B.)
| | - Serge Goldman
- From the Departments of Neurology (O.F.), Pediatric Neurology (C.S., F.C.), Nuclear Medicine (S.G.), and Translational Neuroimaging (V.W., X.D.T.), CUB Hôpital Erasme, Université Libre de Bruxelles, Brussels, Belgium; Laboratory of Translational Neuroimaging and Neuroanatomy (Laboratoire de Neuroimagerie et Neuroanatomie translationnelles) (LNT), ULB Neuroscience Institute, Université Libre de Bruxelles, 808 Lennik St, Brussels, Belgium (O.F., P.C., S.G., V.W., X.D.T.); Department of Pediatric Neurology, Hôpital Universitaire des Enfants Reine Fabiola, Université Libre de Bruxelles, Brussels, Belgium (A.A.); and Sir Peter Mansfield Imaging Centre, School of Physics and Astronomy, University of Nottingham, Nottingham, United Kingdom (N.H., M.B.)
| | - Vincent Wens
- From the Departments of Neurology (O.F.), Pediatric Neurology (C.S., F.C.), Nuclear Medicine (S.G.), and Translational Neuroimaging (V.W., X.D.T.), CUB Hôpital Erasme, Université Libre de Bruxelles, Brussels, Belgium; Laboratory of Translational Neuroimaging and Neuroanatomy (Laboratoire de Neuroimagerie et Neuroanatomie translationnelles) (LNT), ULB Neuroscience Institute, Université Libre de Bruxelles, 808 Lennik St, Brussels, Belgium (O.F., P.C., S.G., V.W., X.D.T.); Department of Pediatric Neurology, Hôpital Universitaire des Enfants Reine Fabiola, Université Libre de Bruxelles, Brussels, Belgium (A.A.); and Sir Peter Mansfield Imaging Centre, School of Physics and Astronomy, University of Nottingham, Nottingham, United Kingdom (N.H., M.B.)
| | - Xavier De Tiège
- From the Departments of Neurology (O.F.), Pediatric Neurology (C.S., F.C.), Nuclear Medicine (S.G.), and Translational Neuroimaging (V.W., X.D.T.), CUB Hôpital Erasme, Université Libre de Bruxelles, Brussels, Belgium; Laboratory of Translational Neuroimaging and Neuroanatomy (Laboratoire de Neuroimagerie et Neuroanatomie translationnelles) (LNT), ULB Neuroscience Institute, Université Libre de Bruxelles, 808 Lennik St, Brussels, Belgium (O.F., P.C., S.G., V.W., X.D.T.); Department of Pediatric Neurology, Hôpital Universitaire des Enfants Reine Fabiola, Université Libre de Bruxelles, Brussels, Belgium (A.A.); and Sir Peter Mansfield Imaging Centre, School of Physics and Astronomy, University of Nottingham, Nottingham, United Kingdom (N.H., M.B.)
| |
Collapse
|
16
|
Monitoring the Chl-a Distribution Details in the Yangtze River Mouth Using Satellite Remote Sensing. WATER 2022. [DOI: 10.3390/w14081295] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
The distribution of chlorophyll-a (Chl-a) in the Yangtze River Mouth area was analyzed using a new Chl-a inversion model (PMS-C) based on the relationship between in situ Chl-a and GF-4 PMS band combinations. Combining GF-4 PMS with HY-C CZI, this paper revealed that: (1) Chl-a concentration in Yangtze River Mouth was in the range of 2–6 µg/L, being higher in the west than in the east. The high Chl-a area was mainly distributed near the mouth of the Yangtze River and the Chl-a value was in the range of 3.7 µg/L to 5.9 µg/L. Chl-a concentration is higher in spring and summer than in autumn and winter, with a Chl-a concentration difference of 1–2 µg/L. Chl-a downstream of islands and bridges increased by 0.5–1.7 µg/L compared with upstream. (2) Short-term (within 3 h) changes of Chl-a concentration were effectively detected. In summer and autumn, Chl-a obtained at 13:30 in the noon was generally lower than Chl-a obtained at around 10:30 in the morning and it decreased by nearly 0.1–4 μg/L within three hours on the same day. In winter, the concentration of Chl-a decreased in the range of 0–1.9 μg/L. Generally, within three hours, Chl-a in the downstream of the island decreased significantly from 5 μg/L to about 3.8 μg/L, and Chl-a downstream of piers decreased from 3.7 μg/L to about 3 μg/L. (3) Environmental factors including seawater temperature, illumination, and nutrients, as well as dynamic factors such as wind and tidal current can induce Chl-a change in the Yangtze River Mouth. Short-term change of Chl-a concentration is closely related to the specific hydrodynamic conditions, nutrients, and lighting conditions.
Collapse
|
17
|
Roshchupkina L, Wens V, Coquelet N, de Tiege X, Peigneux P. Resting state fast brain dynamics predict interindividual variability in motor performance. Sci Rep 2022; 12:5340. [PMID: 35351907 PMCID: PMC8964712 DOI: 10.1038/s41598-022-08767-z] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/12/2021] [Accepted: 03/07/2022] [Indexed: 11/09/2022] Open
Abstract
Motor learning features rapid enhancement during practice then offline post-practice gains with the reorganization of related brain networks. We hypothesised that fast transient, sub-second variations in magnetoencephalographic (MEG) network activity during the resting-state (RS) reflect early learning-related plasticity mechanisms and/or interindividual motor variability in performance. MEG RS activity was recorded before and 20 min after motor learning. Hidden Markov modelling (HMM) of MEG power envelope signals highlighted 8 recurrent topographical states. For two states, motor performance levels were associated with HMM temporal parameters both in pre- and post-learning resting-state sessions. However, no association emerged with offline changes in performance. These results suggest a trait-like relationship between spontaneous transient neural dynamics at rest and interindividual variations in motor abilities. On the other hand, transient RS dynamics seem not to be state-dependent, i.e., modulated by learning experience and reflect neural plasticity, at least on the short timescale.
Collapse
Affiliation(s)
- Liliia Roshchupkina
- UR2NF-Neuropsychology and Functional Neuroimaging Research Unit affiliated at CRCN - Centre for Research in Cognition and Neurosciences, Avenue F.D. Roosevelt 50, 1050, Bruxelles, Belgium. .,UNI-ULB Neuroscience Institute, Université Libre de Bruxelles (ULB), Avenue F.D. Roosevelt 50, 1050, Bruxelles, Belgium. .,Laboratoire de Cartographie Fonctionnelle du Cerveau (LCFC), Université Libre de Bruxelles (ULB), Brussels, Belgium.
| | - Vincent Wens
- Laboratoire de Cartographie Fonctionnelle du Cerveau (LCFC), Université Libre de Bruxelles (ULB), Brussels, Belgium.,Department of Functional Neuroimaging, Service of Nuclear Medicine, CUB Hôpital Erasme, Université Libre de Bruxelles (ULB), Brussels, Belgium
| | - Nicolas Coquelet
- Laboratoire de Cartographie Fonctionnelle du Cerveau (LCFC), Université Libre de Bruxelles (ULB), Brussels, Belgium.,Department of Functional Neuroimaging, Service of Nuclear Medicine, CUB Hôpital Erasme, Université Libre de Bruxelles (ULB), Brussels, Belgium
| | - Xavier de Tiege
- UR2NF-Neuropsychology and Functional Neuroimaging Research Unit affiliated at CRCN - Centre for Research in Cognition and Neurosciences, Avenue F.D. Roosevelt 50, 1050, Bruxelles, Belgium.,Laboratoire de Cartographie Fonctionnelle du Cerveau (LCFC), Université Libre de Bruxelles (ULB), Brussels, Belgium.,Department of Functional Neuroimaging, Service of Nuclear Medicine, CUB Hôpital Erasme, Université Libre de Bruxelles (ULB), Brussels, Belgium
| | - Philippe Peigneux
- UR2NF-Neuropsychology and Functional Neuroimaging Research Unit affiliated at CRCN - Centre for Research in Cognition and Neurosciences, Avenue F.D. Roosevelt 50, 1050, Bruxelles, Belgium.,UNI-ULB Neuroscience Institute, Université Libre de Bruxelles (ULB), Avenue F.D. Roosevelt 50, 1050, Bruxelles, Belgium
| |
Collapse
|
18
|
Destoky F, Bertels J, Niesen M, Wens V, Vander Ghinst M, Rovai A, Trotta N, Lallier M, De Tiège X, Bourguignon M. The role of reading experience in atypical cortical tracking of speech and speech-in-noise in dyslexia. Neuroimage 2022; 253:119061. [PMID: 35259526 DOI: 10.1016/j.neuroimage.2022.119061] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/30/2021] [Revised: 02/28/2022] [Accepted: 03/04/2022] [Indexed: 11/18/2022] Open
Abstract
Dyslexia is a frequent developmental disorder in which reading acquisition is delayed and that is usually associated with difficulties understanding speech in noise. At the neuronal level, children with dyslexia were reported to display abnormal cortical tracking of speech (CTS) at phrasal rate. Here, we aimed to determine if abnormal tracking relates to reduced reading experience, and if it is modulated by the severity of dyslexia or the presence of acoustic noise. We included 26 school-age children with dyslexia, 26 age-matched controls and 26 reading-level matched controls. All were native French speakers. Children's brain activity was recorded with magnetoencephalography while they listened to continuous speech in noiseless and multiple noise conditions. CTS values were compared between groups, conditions and hemispheres, and also within groups, between children with mild and severe dyslexia. Syllabic CTS was significantly reduced in the right superior temporal gyrus in children with dyslexia compared with controls matched for age but not for reading level. Severe dyslexia was characterized by lower rapid automatized naming (RAN) abilities compared with mild dyslexia, and phrasal CTS lateralized to the right hemisphere in children with mild dyslexia and all control groups but not in children with severe dyslexia. Finally, an alteration in phrasal CTS was uncovered in children with dyslexia compared with age-matched controls in babble noise conditions but not in other less challenging listening conditions (non-speech noise or noiseless conditions); no such effect was seen in comparison with reading-level matched controls. Overall, our results confirmed the finding of altered neuronal basis of speech perception in noiseless and babble noise conditions in dyslexia compared with age-matched peers. However, the absence of alteration in comparison with reading-level matched controls demonstrates that such alterations are associated with reduced reading level, suggesting they are merely driven by reduced reading experience rather than a cause of dyslexia. Finally, our result of altered hemispheric lateralization of phrasal CTS in relation with altered RAN abilities in severe dyslexia is in line with a temporal sampling deficit of speech at phrasal rate in dyslexia.
Collapse
Affiliation(s)
- Florian Destoky
- Laboratoire de Neuroanatomie et Neuroimagerie translationnelles, UNI-ULB Neuroscience Institute, Université libre de Bruxelles (ULB), 808 Leenik Street, Brussels 1070, Belgium.
| | - Julie Bertels
- Laboratoire de Neuroanatomie et Neuroimagerie translationnelles, UNI-ULB Neuroscience Institute, Université libre de Bruxelles (ULB), 808 Leenik Street, Brussels 1070, Belgium; Consciousness, Cognition and Computation Group, UNI-ULB Neuroscience Institute, Université libre de Bruxelles (ULB), Brussels, Belgium
| | - Maxime Niesen
- Laboratoire de Neuroanatomie et Neuroimagerie translationnelles, UNI-ULB Neuroscience Institute, Université libre de Bruxelles (ULB), 808 Leenik Street, Brussels 1070, Belgium; Service d'ORL et de Chirurgie Cervico-Faciale, ULB-Hôpital Erasme, Université libre de Bruxelles (ULB), Brussels 1070, Belgium
| | - Vincent Wens
- Laboratoire de Neuroanatomie et Neuroimagerie translationnelles, UNI-ULB Neuroscience Institute, Université libre de Bruxelles (ULB), 808 Leenik Street, Brussels 1070, Belgium; Department of Functional Neuroima ging, Service of Nuclear Medicine, CUB Hôpital Erasme, Université Libre de Bruxelles (ULB), Brussels, Belgium
| | - Marc Vander Ghinst
- Laboratoire de Neuroanatomie et Neuroimagerie translationnelles, UNI-ULB Neuroscience Institute, Université libre de Bruxelles (ULB), 808 Leenik Street, Brussels 1070, Belgium; Service d'ORL et de Chirurgie Cervico-Faciale, ULB-Hôpital Erasme, Université libre de Bruxelles (ULB), Brussels 1070, Belgium
| | - Antonin Rovai
- Laboratoire de Neuroanatomie et Neuroimagerie translationnelles, UNI-ULB Neuroscience Institute, Université libre de Bruxelles (ULB), 808 Leenik Street, Brussels 1070, Belgium; Department of Functional Neuroima ging, Service of Nuclear Medicine, CUB Hôpital Erasme, Université Libre de Bruxelles (ULB), Brussels, Belgium
| | - Nicola Trotta
- Laboratoire de Neuroanatomie et Neuroimagerie translationnelles, UNI-ULB Neuroscience Institute, Université libre de Bruxelles (ULB), 808 Leenik Street, Brussels 1070, Belgium; Department of Functional Neuroima ging, Service of Nuclear Medicine, CUB Hôpital Erasme, Université Libre de Bruxelles (ULB), Brussels, Belgium
| | - Marie Lallier
- BCBL, Basque Center on Cognition, Brain and Language, San Sebastian 20009, Spain
| | - Xavier De Tiège
- Laboratoire de Neuroanatomie et Neuroimagerie translationnelles, UNI-ULB Neuroscience Institute, Université libre de Bruxelles (ULB), 808 Leenik Street, Brussels 1070, Belgium; Department of Functional Neuroima ging, Service of Nuclear Medicine, CUB Hôpital Erasme, Université Libre de Bruxelles (ULB), Brussels, Belgium
| | - Mathieu Bourguignon
- Laboratoire de Neuroanatomie et Neuroimagerie translationnelles, UNI-ULB Neuroscience Institute, Université libre de Bruxelles (ULB), 808 Leenik Street, Brussels 1070, Belgium; BCBL, Basque Center on Cognition, Brain and Language, San Sebastian 20009, Spain; Laboratory of Neurophysiology and Movement Biomechanics, UNI-ULB Neuroscience Institute, Université Libre de Bruxelles (ULB), Brussels, Belgium
| |
Collapse
|
19
|
Van Dyck D, Deconinck N, Aeby A, Baijot S, Coquelet N, Trotta N, Rovai A, Goldman S, Urbain C, Wens V, De Tiège X. Atypical resting-state functional brain connectivity in children with developmental coordination disorder. Neuroimage Clin 2022; 33:102928. [PMID: 34959048 PMCID: PMC8856907 DOI: 10.1016/j.nicl.2021.102928] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/08/2021] [Revised: 12/06/2021] [Accepted: 12/22/2021] [Indexed: 12/21/2022]
Abstract
Atypical connectivity in children with developmental coordination disorder. Stronger connectivity mainly found within the dorsal extrastriate network. May reflect a brain trait of children with developmental coordination disorder. This atypical connectivity is not associated with motor/visual perceptual abilities. Lower visuomotor performance associated with stronger sensorimotor connectivity.
Children with developmental coordination disorder (DCD) present lower abilities to acquire and execute coordinated motor skills. DCD is frequently associated with visual perceptual (with or without motor component) impairments. This magnetoencephalography (MEG) study compares the brain resting-state functional connectivity (rsFC) and spectral power of children with and without DCD. 29 children with DCD and 28 typically developing (TD) peers underwent 2 × 5 min of resting-state MEG. Band-limited power envelope correlation and spectral power were compared between groups using a functional connectome of 59 nodes from eight resting-state networks. Correlation coefficients were calculated between fine and gross motor activity, visual perceptual and visuomotor abilities measures on the one hand, and brain rsFC and spectral power on the other hand. Nonparametric statistics were used. Significantly higher rsFC between nodes of the visual, attentional, frontoparietal, default-mode and cerebellar networks was observed in the alpha (maximum statistics, p = .0012) and the low beta (p = .0002) bands in children with DCD compared to TD peers. Lower visuomotor performance (copying figures) was associated with stronger interhemispheric rsFC within sensorimotor areas and power in the cerebellum (right lobule VIII). Children with DCD showed increased rsFC mainly in the dorsal extrastriate visual brain system and the cerebellum. However, this increase was not associated with their coordinated motor/visual perceptual abilities. This enhanced functional brain connectivity could thus reflect a characteristic brain trait of children with DCD compared to their TD peers. Moreover, an interhemispheric compensatory process might be at play to perform visuomotor task within the normative range.
Collapse
Affiliation(s)
- Dorine Van Dyck
- Laboratoire de Cartographie Fonctionnelle du Cerveau (LCFC), ULB Neuroscience Institute (UNI), Université libre de Bruxelles (ULB), Brussels, Belgium; Department of Neurology, Hôpital Universitaire des Enfants Reine Fabiola (HUDERF), Université libre de Bruxelles (ULB), Brussels, Belgium.
| | - Nicolas Deconinck
- Department of Neurology, Hôpital Universitaire des Enfants Reine Fabiola (HUDERF), Université libre de Bruxelles (ULB), Brussels, Belgium
| | - Alec Aeby
- Department of Neurology, Hôpital Universitaire des Enfants Reine Fabiola (HUDERF), Université libre de Bruxelles (ULB), Brussels, Belgium; Neuropsychology and Functional Neuroimaging Research Group (UR2NF) at Center for Research in Cognition and Neurosciences (CRCN) and ULB Neurosciences Institute (UNI), Université libre de Bruxelles (ULB), Brussels, Belgium
| | - Simon Baijot
- Department of Neurology, Hôpital Universitaire des Enfants Reine Fabiola (HUDERF), Université libre de Bruxelles (ULB), Brussels, Belgium; Neuropsychology and Functional Neuroimaging Research Group (UR2NF) at Center for Research in Cognition and Neurosciences (CRCN) and ULB Neurosciences Institute (UNI), Université libre de Bruxelles (ULB), Brussels, Belgium
| | - Nicolas Coquelet
- Laboratoire de Cartographie Fonctionnelle du Cerveau (LCFC), ULB Neuroscience Institute (UNI), Université libre de Bruxelles (ULB), Brussels, Belgium
| | - Nicola Trotta
- Laboratoire de Cartographie Fonctionnelle du Cerveau (LCFC), ULB Neuroscience Institute (UNI), Université libre de Bruxelles (ULB), Brussels, Belgium; Clinics of Functional Neuroimaging, Service of Nuclear Medicine, CUB Hôpital Erasme, Université libre de Bruxelles (ULB), Brussels, Belgium
| | - Antonin Rovai
- Laboratoire de Cartographie Fonctionnelle du Cerveau (LCFC), ULB Neuroscience Institute (UNI), Université libre de Bruxelles (ULB), Brussels, Belgium; Clinics of Functional Neuroimaging, Service of Nuclear Medicine, CUB Hôpital Erasme, Université libre de Bruxelles (ULB), Brussels, Belgium
| | - Serge Goldman
- Laboratoire de Cartographie Fonctionnelle du Cerveau (LCFC), ULB Neuroscience Institute (UNI), Université libre de Bruxelles (ULB), Brussels, Belgium; Clinics of Functional Neuroimaging, Service of Nuclear Medicine, CUB Hôpital Erasme, Université libre de Bruxelles (ULB), Brussels, Belgium
| | - Charline Urbain
- Laboratoire de Cartographie Fonctionnelle du Cerveau (LCFC), ULB Neuroscience Institute (UNI), Université libre de Bruxelles (ULB), Brussels, Belgium; Neuropsychology and Functional Neuroimaging Research Group (UR2NF) at Center for Research in Cognition and Neurosciences (CRCN) and ULB Neurosciences Institute (UNI), Université libre de Bruxelles (ULB), Brussels, Belgium
| | - Vincent Wens
- Laboratoire de Cartographie Fonctionnelle du Cerveau (LCFC), ULB Neuroscience Institute (UNI), Université libre de Bruxelles (ULB), Brussels, Belgium; Clinics of Functional Neuroimaging, Service of Nuclear Medicine, CUB Hôpital Erasme, Université libre de Bruxelles (ULB), Brussels, Belgium
| | - Xavier De Tiège
- Laboratoire de Cartographie Fonctionnelle du Cerveau (LCFC), ULB Neuroscience Institute (UNI), Université libre de Bruxelles (ULB), Brussels, Belgium; Clinics of Functional Neuroimaging, Service of Nuclear Medicine, CUB Hôpital Erasme, Université libre de Bruxelles (ULB), Brussels, Belgium
| |
Collapse
|
20
|
Rahimi S, Farahibozorg SR, Jackson R, Hauk O. Task modulation of spatiotemporal dynamics in semantic brain networks: An EEG/MEG study. Neuroimage 2022; 246:118768. [PMID: 34856376 PMCID: PMC8784826 DOI: 10.1016/j.neuroimage.2021.118768] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/30/2021] [Revised: 11/09/2021] [Accepted: 11/29/2021] [Indexed: 11/02/2022] Open
Abstract
How does brain activity in distributed semantic brain networks evolve over time, and how do these regions interact to retrieve the meaning of words? We compared spatiotemporal brain dynamics between visual lexical and semantic decision tasks (LD and SD), analysing whole-cortex evoked responses and spectral functional connectivity (coherence) in source-estimated electroencephalography and magnetoencephalography (EEG and MEG) recordings. Our evoked analysis revealed generally larger activation for SD compared to LD, starting in primary visual area (PVA) and angular gyrus (AG), followed by left posterior temporal cortex (PTC) and left anterior temporal lobe (ATL). The earliest activation effects in ATL were significantly left-lateralised. Our functional connectivity results showed significant connectivity between left and right ATL, PTC and right ATL in an early time window, as well as between left ATL and IFG in a later time window. The connectivity of AG was comparatively sparse. We quantified the limited spatial resolution of our source estimates via a leakage index for careful interpretation of our results. Our findings suggest that the different demands on semantic information retrieval in lexical and semantic decision tasks first modulate visual and attentional processes, then multimodal semantic information retrieval in the ATLs and finally control regions (PTC and IFG) in order to extract task-relevant semantic features for response selection. Whilst our evoked analysis suggests a dominance of left ATL for semantic processing, our functional connectivity analysis also revealed significant involvement of right ATL in the more demanding semantic task. Our findings demonstrate the complementarity of evoked and functional connectivity analysis, as well as the importance of dynamic information for both types of analyses.
Collapse
Affiliation(s)
- Setareh Rahimi
- MRC Cognition and Brain Sciences Unit, University of Cambridge, 15 Chaucer Road, Cambridge CB2 7EF, United Kingdom.
| | - Seyedeh-Rezvan Farahibozorg
- Wellcome Centre for Integrative Neuroimaging, Nuffield Department of Neurosciences, University of Oxford, United Kingdom
| | - Rebecca Jackson
- MRC Cognition and Brain Sciences Unit, University of Cambridge, 15 Chaucer Road, Cambridge CB2 7EF, United Kingdom
| | - Olaf Hauk
- MRC Cognition and Brain Sciences Unit, University of Cambridge, 15 Chaucer Road, Cambridge CB2 7EF, United Kingdom
| |
Collapse
|
21
|
Coquelet N, De Tiège X, Roshchupkina L, Peigneux P, Goldman S, Woolrich M, Wens V. Microstates and power envelope hidden Markov modeling probe bursting brain activity at different timescales. Neuroimage 2021; 247:118850. [PMID: 34954027 PMCID: PMC8803543 DOI: 10.1016/j.neuroimage.2021.118850] [Citation(s) in RCA: 15] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/21/2021] [Revised: 12/20/2021] [Accepted: 12/21/2021] [Indexed: 11/29/2022] Open
Abstract
State modeling of whole-brain electroencephalography (EEG) or magnetoencephalography (MEG) allows to investigate transient, recurring neurodynamical events. Two widely-used techniques are the microstate analysis of EEG signals and hidden Markov modeling (HMM) of MEG power envelopes. Both reportedly lead to similar state lifetimes on the 100 ms timescale, suggesting a common neural basis. To investigate whether microstates and power envelope HMM states describe the same neural dynamics, we used simultaneous MEG/EEG recordings at rest and compared the spatial signature and temporal activation dynamics of microstates and power envelope HMM states obtained separately from EEG and MEG. Results showed that microstates and power envelope HMM states differ both spatially and temporally. Microstates reflect sharp events of neural synchronization, whereas power envelope HMM states disclose network-level activity with 100–200 ms lifetimes. Further, MEG microstates do not correspond to the canonical EEG microstates but are better interpreted as split HMM states. On the other hand, both MEG and EEG HMM states involve the (de)activation of similar functional networks. Microstate analysis and power envelope HMM thus appear sensitive to neural events occurring over different spatial and temporal scales. As such, they represent complementary approaches to explore the fast, sub-second scale bursting electrophysiological dynamics in spontaneous human brain activity.
Collapse
Affiliation(s)
- N Coquelet
- Laboratoire de Cartographie fonctionnelle du Cerveau (LCFC), UNI - ULB Neuroscience Institute, Université libre de Bruxelles, Brussels 1070, Belgium.
| | - X De Tiège
- Laboratoire de Cartographie fonctionnelle du Cerveau (LCFC), UNI - ULB Neuroscience Institute, Université libre de Bruxelles, Brussels 1070, Belgium; Magnetoencephalography Unit, Service of Translational Neuroimaging, CUB - Hôpital Erasme, Brussels, Belgium
| | - L Roshchupkina
- Laboratoire de Cartographie fonctionnelle du Cerveau (LCFC), UNI - ULB Neuroscience Institute, Université libre de Bruxelles, Brussels 1070, Belgium; Neuropsychology and Functional Neuroimaging Research Unit (UR2NF), Centre for Research in Cognition and Neurosciences (CRCN), UNI - ULB Neuroscience Institute, Université libre de Bruxelles, Brussels, Belgium
| | - P Peigneux
- Neuropsychology and Functional Neuroimaging Research Unit (UR2NF), Centre for Research in Cognition and Neurosciences (CRCN), UNI - ULB Neuroscience Institute, Université libre de Bruxelles, Brussels, Belgium
| | - S Goldman
- Laboratoire de Cartographie fonctionnelle du Cerveau (LCFC), UNI - ULB Neuroscience Institute, Université libre de Bruxelles, Brussels 1070, Belgium; Magnetoencephalography Unit, Service of Translational Neuroimaging, CUB - Hôpital Erasme, Brussels, Belgium
| | - M Woolrich
- Oxford Centre for Human Brain Activity, Wellcome Centre for Integrative Neuroimaging, Department of Psychiatry, University of Oxford, Oxford, United Kingdom
| | - V Wens
- Laboratoire de Cartographie fonctionnelle du Cerveau (LCFC), UNI - ULB Neuroscience Institute, Université libre de Bruxelles, Brussels 1070, Belgium; Magnetoencephalography Unit, Service of Translational Neuroimaging, CUB - Hôpital Erasme, Brussels, Belgium
| |
Collapse
|
22
|
Vander Ghinst M, Bourguignon M, Wens V, Naeije G, Ducène C, Niesen M, Hassid S, Choufani G, Goldman S, De Tiège X. Inaccurate cortical tracking of speech in adults with impaired speech perception in noise. Brain Commun 2021; 3:fcab186. [PMID: 34541530 PMCID: PMC8445395 DOI: 10.1093/braincomms/fcab186] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/13/2021] [Revised: 06/05/2021] [Accepted: 06/08/2021] [Indexed: 01/17/2023] Open
Abstract
Impaired speech perception in noise despite normal peripheral auditory function is a common problem in young adults. Despite a growing body of research, the pathophysiology of this impairment remains unknown. This magnetoencephalography study characterizes the cortical tracking of speech in a multi-talker background in a group of highly selected adult subjects with impaired speech perception in noise without peripheral auditory dysfunction. Magnetoencephalographic signals were recorded from 13 subjects with impaired speech perception in noise (six females, mean age: 30 years) and matched healthy subjects while they were listening to 5 different recordings of stories merged with a multi-talker background at different signal to noise ratios (No Noise, +10, +5, 0 and −5 dB). The cortical tracking of speech was quantified with coherence between magnetoencephalographic signals and the temporal envelope of (i) the global auditory scene (i.e. the attended speech stream and the multi-talker background noise), (ii) the attended speech stream only and (iii) the multi-talker background noise. Functional connectivity was then estimated between brain areas showing altered cortical tracking of speech in noise in subjects with impaired speech perception in noise and the rest of the brain. All participants demonstrated a selective cortical representation of the attended speech stream in noisy conditions, but subjects with impaired speech perception in noise displayed reduced cortical tracking of speech at the syllable rate (i.e. 4–8 Hz) in all noisy conditions. Increased functional connectivity was observed in subjects with impaired speech perception in noise in Noiseless and speech in noise conditions between supratemporal auditory cortices and left-dominant brain areas involved in semantic and attention processes. The difficulty to understand speech in a multi-talker background in subjects with impaired speech perception in noise appears to be related to an inaccurate auditory cortex tracking of speech at the syllable rate. The increased functional connectivity between supratemporal auditory cortices and language/attention-related neocortical areas probably aims at supporting speech perception and subsequent recognition in adverse auditory scenes. Overall, this study argues for a central origin of impaired speech perception in noise in the absence of any peripheral auditory dysfunction.
Collapse
Affiliation(s)
- Marc Vander Ghinst
- Laboratoire de Cartographie fonctionnelle du Cerveau, UNI-ULB Neuroscience Institute, Université Libre de Bruxelles (ULB), Brussels 1070, Belgium.,Service, d'ORL et de chirurgie cervico-faciale, CUB Hôpital Erasme, Université Libre de Bruxelles (ULB), Brussels 1070, Belgium
| | - Mathieu Bourguignon
- Laboratoire de Cartographie fonctionnelle du Cerveau, UNI-ULB Neuroscience Institute, Université Libre de Bruxelles (ULB), Brussels 1070, Belgium.,Laboratory of Neurophysiology and Movement Biomechanics, UNI-ULB Neuroscience Institute, Université Libre de Bruxelles (ULB), Brussels 1070, Belgium.,Basque Center on Cognition, Brain and Language (BCBL), Donostia/San Sebastian 20009, Spain
| | - Vincent Wens
- Laboratoire de Cartographie fonctionnelle du Cerveau, UNI-ULB Neuroscience Institute, Université Libre de Bruxelles (ULB), Brussels 1070, Belgium.,Clinics of Functional Neuroimaging, Service of Nuclear Medicine, CUB Hôpital Erasme, Université Libre de Bruxelles (ULB), Brussels 1070, Belgium
| | - Gilles Naeije
- Laboratoire de Cartographie fonctionnelle du Cerveau, UNI-ULB Neuroscience Institute, Université Libre de Bruxelles (ULB), Brussels 1070, Belgium.,Service de Neurologie, ULB-Hôpital Erasme, Université libre de Bruxelles (ULB), Brussels 1070, Belgium
| | - Cecile Ducène
- Laboratoire de Cartographie fonctionnelle du Cerveau, UNI-ULB Neuroscience Institute, Université Libre de Bruxelles (ULB), Brussels 1070, Belgium.,Service, d'ORL et de chirurgie cervico-faciale, CUB Hôpital Erasme, Université Libre de Bruxelles (ULB), Brussels 1070, Belgium
| | - Maxime Niesen
- Laboratoire de Cartographie fonctionnelle du Cerveau, UNI-ULB Neuroscience Institute, Université Libre de Bruxelles (ULB), Brussels 1070, Belgium.,Service, d'ORL et de chirurgie cervico-faciale, CUB Hôpital Erasme, Université Libre de Bruxelles (ULB), Brussels 1070, Belgium
| | - Sergio Hassid
- Service, d'ORL et de chirurgie cervico-faciale, CUB Hôpital Erasme, Université Libre de Bruxelles (ULB), Brussels 1070, Belgium
| | - Georges Choufani
- Service, d'ORL et de chirurgie cervico-faciale, CUB Hôpital Erasme, Université Libre de Bruxelles (ULB), Brussels 1070, Belgium
| | - Serge Goldman
- Laboratoire de Cartographie fonctionnelle du Cerveau, UNI-ULB Neuroscience Institute, Université Libre de Bruxelles (ULB), Brussels 1070, Belgium.,Clinics of Functional Neuroimaging, Service of Nuclear Medicine, CUB Hôpital Erasme, Université Libre de Bruxelles (ULB), Brussels 1070, Belgium
| | - Xavier De Tiège
- Laboratoire de Cartographie fonctionnelle du Cerveau, UNI-ULB Neuroscience Institute, Université Libre de Bruxelles (ULB), Brussels 1070, Belgium.,Clinics of Functional Neuroimaging, Service of Nuclear Medicine, CUB Hôpital Erasme, Université Libre de Bruxelles (ULB), Brussels 1070, Belgium
| |
Collapse
|
23
|
Naeije G, Coquelet N, Wens V, Goldman S, Pandolfo M, De Tiège X. Age of onset modulates resting-state brain network dynamics in Friedreich Ataxia. Hum Brain Mapp 2021; 42:5334-5344. [PMID: 34523778 PMCID: PMC8519851 DOI: 10.1002/hbm.25621] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/02/2021] [Revised: 07/19/2021] [Accepted: 07/20/2021] [Indexed: 02/06/2023] Open
Abstract
This magnetoencephalography (MEG) study addresses (i) how Friedreich ataxia (FRDA) affects the sub‐second dynamics of resting‐state brain networks, (ii) the main determinants of their dynamic alterations, and (iii) how these alterations are linked with FRDA‐related changes in resting‐state functional brain connectivity (rsFC) over long timescales. For that purpose, 5 min of resting‐state MEG activity were recorded in 16 FRDA patients (mean age: 27 years, range: 12–51 years; 10 females) and matched healthy subjects. Transient brain network dynamics was assessed using hidden Markov modeling (HMM). Post hoc median‐split, nonparametric permutations and Spearman rank correlations were used for statistics. In FRDA patients, a positive correlation was found between the age of symptoms onset (ASO) and the temporal dynamics of two HMM states involving the posterior default mode network (DMN) and the temporo‐parietal junctions (TPJ). FRDA patients with an ASO <11 years presented altered temporal dynamics of those two HMM states compared with FRDA patients with an ASO > 11 years or healthy subjects. The temporal dynamics of the DMN state also correlated with minute‐long DMN rsFC. This study demonstrates that ASO is the main determinant of alterations in the sub‐second dynamics of posterior associative neocortices in FRDA patients and substantiates a direct link between sub‐second network activity and functional brain integration over long timescales.
Collapse
Affiliation(s)
- Gilles Naeije
- Laboratoire de Cartographie fonctionnelle du Cerveau (LCFC), UNI-ULB Neuroscience Institute, Université libre de Bruxelles (ULB), Brussels, Belgium.,Department of Neurology, CUB Hôpital Erasme, Université libre de Bruxelles (ULB), Brussels, Belgium
| | - Nicolas Coquelet
- Laboratoire de Cartographie fonctionnelle du Cerveau (LCFC), UNI-ULB Neuroscience Institute, Université libre de Bruxelles (ULB), Brussels, Belgium
| | - Vincent Wens
- Laboratoire de Cartographie fonctionnelle du Cerveau (LCFC), UNI-ULB Neuroscience Institute, Université libre de Bruxelles (ULB), Brussels, Belgium
| | - Serge Goldman
- Laboratoire de Cartographie fonctionnelle du Cerveau (LCFC), UNI-ULB Neuroscience Institute, Université libre de Bruxelles (ULB), Brussels, Belgium.,Department of Functional Neuroimaging, CUB Hôpital Erasme, Université libre de Bruxelles (ULB), Brussels, Belgium
| | - Massimo Pandolfo
- Department of Neurology and Neurosurgery, McGill University, Montreal, Canada
| | - Xavier De Tiège
- Laboratoire de Cartographie fonctionnelle du Cerveau (LCFC), UNI-ULB Neuroscience Institute, Université libre de Bruxelles (ULB), Brussels, Belgium.,Department of Functional Neuroimaging, CUB Hôpital Erasme, Université libre de Bruxelles (ULB), Brussels, Belgium
| |
Collapse
|
24
|
Van Dyck D, Deconinck N, Aeby A, Baijot S, Coquelet N, Trotta N, Rovai A, Goldman S, Urbain C, Wens V, De Tiège X. Resting-state functional brain connectivity is related to subsequent procedural learning skills in school-aged children. Neuroimage 2021; 240:118368. [PMID: 34242786 DOI: 10.1016/j.neuroimage.2021.118368] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/18/2021] [Revised: 06/30/2021] [Accepted: 07/05/2021] [Indexed: 10/20/2022] Open
Abstract
This magnetoencephalography (MEG) study investigates how procedural sequence learning performance is related to prior brain resting-state functional connectivity (rsFC), and to what extent sequence learning induces rapid changes in brain rsFC in school-aged children. Procedural learning was assessed in 30 typically developing children (mean age ± SD: 9.99 years ± 1.35) using a serial reaction time task (SRTT). During SRTT, participants touched as quickly and accurately as possible a stimulus sequentially or randomly appearing in one of the quadrants of a touchscreen. Band-limited power envelope correlation (brain rsFC) was applied to MEG data acquired at rest pre- and post-learning. Correlation analyses were performed between brain rsFC and sequence-specific learning or response time indices. Stronger pre-learning interhemispheric rsFC between inferior parietal and primary somatosensory/motor areas correlated with better subsequent sequence learning performance and faster visuomotor response time. Faster response time was associated with post-learning decreased rsFC within the dorsal extra-striate visual stream and increased rsFC between temporo-cerebellar regions. In school-aged children, variations in functional brain architecture at rest within the sensorimotor network account for interindividual differences in sequence learning and visuomotor performance. After learning, rapid adjustments in functional brain architecture are associated with visuomotor performance but not sequence learning skills.
Collapse
Affiliation(s)
- Dorine Van Dyck
- Laboratoire de Cartographie fonctionnelle du Cerveau (LCFC), ULB Neuroscience Institute (UNI), Université libre de Bruxelles (ULB), Brussels, Belgium; Department of Neurology, Hôpital Universitaire des Enfants Reine Fabiola (HUDERF), Université libre de Bruxelles (ULB), Brussels, Belgium.
| | - Nicolas Deconinck
- Department of Neurology, Hôpital Universitaire des Enfants Reine Fabiola (HUDERF), Université libre de Bruxelles (ULB), Brussels, Belgium
| | - Alec Aeby
- Department of Neurology, Hôpital Universitaire des Enfants Reine Fabiola (HUDERF), Université libre de Bruxelles (ULB), Brussels, Belgium; Neuropsychology and Functional Neuroimaging Research Unit (UR2NF), Center for Research in Cognition and Neurosciences (CRCN) and ULB Neurosciences Institute (UNI), Université libre de Bruxelles (ULB), Brussels, Belgium
| | - Simon Baijot
- Department of Neurology, Hôpital Universitaire des Enfants Reine Fabiola (HUDERF), Université libre de Bruxelles (ULB), Brussels, Belgium; Neuropsychology and Functional Neuroimaging Research Unit (UR2NF), Center for Research in Cognition and Neurosciences (CRCN) and ULB Neurosciences Institute (UNI), Université libre de Bruxelles (ULB), Brussels, Belgium
| | - Nicolas Coquelet
- Laboratoire de Cartographie fonctionnelle du Cerveau (LCFC), ULB Neuroscience Institute (UNI), Université libre de Bruxelles (ULB), Brussels, Belgium
| | - Nicola Trotta
- Laboratoire de Cartographie fonctionnelle du Cerveau (LCFC), ULB Neuroscience Institute (UNI), Université libre de Bruxelles (ULB), Brussels, Belgium; Department of Functional Neuroimaging, Service of Nuclear Medicine, CUB Hôpital Erasme, Université libre de Bruxelles (ULB), Brussels, Belgium
| | - Antonin Rovai
- Laboratoire de Cartographie fonctionnelle du Cerveau (LCFC), ULB Neuroscience Institute (UNI), Université libre de Bruxelles (ULB), Brussels, Belgium; Department of Functional Neuroimaging, Service of Nuclear Medicine, CUB Hôpital Erasme, Université libre de Bruxelles (ULB), Brussels, Belgium
| | - Serge Goldman
- Laboratoire de Cartographie fonctionnelle du Cerveau (LCFC), ULB Neuroscience Institute (UNI), Université libre de Bruxelles (ULB), Brussels, Belgium; Department of Functional Neuroimaging, Service of Nuclear Medicine, CUB Hôpital Erasme, Université libre de Bruxelles (ULB), Brussels, Belgium
| | - Charline Urbain
- Laboratoire de Cartographie fonctionnelle du Cerveau (LCFC), ULB Neuroscience Institute (UNI), Université libre de Bruxelles (ULB), Brussels, Belgium; Neuropsychology and Functional Neuroimaging Research Unit (UR2NF), Center for Research in Cognition and Neurosciences (CRCN) and ULB Neurosciences Institute (UNI), Université libre de Bruxelles (ULB), Brussels, Belgium
| | - Vincent Wens
- Laboratoire de Cartographie fonctionnelle du Cerveau (LCFC), ULB Neuroscience Institute (UNI), Université libre de Bruxelles (ULB), Brussels, Belgium; Department of Functional Neuroimaging, Service of Nuclear Medicine, CUB Hôpital Erasme, Université libre de Bruxelles (ULB), Brussels, Belgium
| | - Xavier De Tiège
- Laboratoire de Cartographie fonctionnelle du Cerveau (LCFC), ULB Neuroscience Institute (UNI), Université libre de Bruxelles (ULB), Brussels, Belgium; Department of Functional Neuroimaging, Service of Nuclear Medicine, CUB Hôpital Erasme, Université libre de Bruxelles (ULB), Brussels, Belgium
| |
Collapse
|
25
|
Intra-Brain Connectivity vs. Inter-Brain Connectivity in Gestures Reproduction: What Relationship? Brain Sci 2021; 11:brainsci11050577. [PMID: 33947101 PMCID: PMC8145238 DOI: 10.3390/brainsci11050577] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/14/2021] [Revised: 04/26/2021] [Accepted: 04/27/2021] [Indexed: 11/16/2022] Open
Abstract
Recently, the neurosciences have become interested in the investigation of neural responses associated with the use of gestures. This study focuses on the relationship between the intra-brain and inter-brain connectivity mechanisms underlying the execution of different categories of gestures (positive and negative affective, social, and informative) characterizing non-verbal interactions between thirteen couples of subjects, each composed of an encoder and a decoder. The study results underline a similar modulation of intra- and inter-brain connectivity for alpha, delta, and theta frequency bands in specific areas (frontal or posterior regions) depending on the type of gesture. Moreover, taking into account the gestures' valence (positive or negative), a similar modulation of intra- and inter-brain connectivity in the left and right sides was observed. This study showed congruence in the intra-brain and inter-brain connectivity trend during the execution of different gestures, underlining how non-verbal exchanges might be characterized by intra-brain phase alignment and implicit mechanisms of mirroring and synchronization between the two individuals involved in the social exchange.
Collapse
|
26
|
Van Rinsveld A, Wens V, Guillaume M, Beuel A, Gevers W, De Tiège X, Content A. Automatic Processing of Numerosity in Human Neocortex Evidenced by Occipital and Parietal Neuromagnetic Responses. Cereb Cortex Commun 2021; 2:tgab028. [PMID: 34296173 PMCID: PMC8152830 DOI: 10.1093/texcom/tgab028] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/20/2021] [Revised: 03/20/2021] [Accepted: 04/05/2021] [Indexed: 01/23/2023] Open
Abstract
Humans and other animal species are endowed with the ability to sense, represent, and mentally manipulate the number of items in a set without needing to count them. One central hypothesis is that this ability relies on an automated functional system dedicated to numerosity, the perception of the discrete numerical magnitude of a set of items. This system has classically been associated with intraparietal regions, however accumulating evidence in favor of an early visual number sense calls into question the functional role of parietal regions in numerosity processing. Targeting specifically numerosity among other visual features in the earliest stages of processing requires high temporal and spatial resolution. We used frequency-tagged magnetoencephalography to investigate the early automatic processing of numerical magnitudes and measured the steady-state brain responses specifically evoked by numerical and other visual changes in the visual scene. The neuromagnetic responses showed implicit discrimination of numerosity, total occupied area, and convex hull. The source reconstruction corresponding to the implicit discrimination responses showed common and separate sources along the ventral and dorsal visual pathways. Occipital sources attested the perceptual salience of numerosity similarly to both other implicitly discriminable visual features. Crucially, we found parietal responses uniquely associated with numerosity discrimination, showing automatic processing of numerosity in the parietal cortex, even when not relevant to the task. Taken together, these results provide further insights into the functional roles of parietal and occipital regions in numerosity encoding along the visual hierarchy.
Collapse
Affiliation(s)
- Amandine Van Rinsveld
- Center for Research in Cognition and Neurosciences (CRCN), UNI – ULB Neuroscience Institute, Université libre de Bruxelles (ULB), Brussels 1050, Belgium
| | - Vincent Wens
- Laboratoire de Cartographie fonctionnelle du Cerveau (LCFC), UNI – ULB Neuroscience Institute, Université libre de Bruxelles (ULB), Brussels 1070, Belgium
- Magnetoencephalography Unit, Department of Functional Neuroimaging, Service of Nuclear Medicine, CUB – Hôpital Erasme, Brussels 1070, Belgium
| | - Mathieu Guillaume
- Center for Research in Cognition and Neurosciences (CRCN), UNI – ULB Neuroscience Institute, Université libre de Bruxelles (ULB), Brussels 1050, Belgium
| | - Anthony Beuel
- Center for Research in Cognition and Neurosciences (CRCN), UNI – ULB Neuroscience Institute, Université libre de Bruxelles (ULB), Brussels 1050, Belgium
| | - Wim Gevers
- Center for Research in Cognition and Neurosciences (CRCN), UNI – ULB Neuroscience Institute, Université libre de Bruxelles (ULB), Brussels 1050, Belgium
| | - Xavier De Tiège
- Laboratoire de Cartographie fonctionnelle du Cerveau (LCFC), UNI – ULB Neuroscience Institute, Université libre de Bruxelles (ULB), Brussels 1070, Belgium
- Magnetoencephalography Unit, Department of Functional Neuroimaging, Service of Nuclear Medicine, CUB – Hôpital Erasme, Brussels 1070, Belgium
| | - Alain Content
- Center for Research in Cognition and Neurosciences (CRCN), UNI – ULB Neuroscience Institute, Université libre de Bruxelles (ULB), Brussels 1050, Belgium
| |
Collapse
|
27
|
de Pasquale F, Spadone S, Betti V, Corbetta M, Della Penna S. Temporal modes of hub synchronization at rest. Neuroimage 2021; 235:118005. [PMID: 33819608 DOI: 10.1016/j.neuroimage.2021.118005] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/18/2020] [Revised: 03/12/2021] [Accepted: 03/17/2021] [Indexed: 10/21/2022] Open
Abstract
The brain is a dynamic system that generates a broad repertoire of perceptual, motor, and cognitive states by the integration and segregation of different functional domains represented in large-scale brain networks. However, the fundamental mechanisms underlying brain network integration remain elusive. Here, for the first time to our knowledge, we found that in the resting state the brain visits few synchronization modes defined as clusters of temporally aligned functional hubs. These modes alternate over time and their probability of switching leads to specific temporal loops among them. Notably, although each mode involves a small set of nodes, the brain integration seems highly vulnerable to a simulated attack on this temporal synchronization mechanism. In line with the hypothesis that the resting state represents a prior sculpted by the task activity, the observed synchronization modes might be interpreted as a temporal brain template needed to respond to task/environmental demands .
Collapse
Affiliation(s)
- F de Pasquale
- Faculty of Veterinary Medicine, University of Teramo, Teramo, Italy.
| | - S Spadone
- Department of Neuroscience, Imaging and Clinical Sciences, and Institute for Advanced Biomedical Technologies, "G. d'Annunzio" University of Chieti-Pescara, Chieti, Italy
| | - V Betti
- Department of Psychology, Sapienza University of Rome, 00185, Rome, Italy; IRCCS Fondazione Santa Lucia, 00142, Rome, Italy
| | - M Corbetta
- Department of Neuroscience and Padova Neuroscience Center (PNC), University of Padua, Padua, Italy; Venetian Institute of Molecular Medicine (VIMM), Padua, Italy; Department of Neurology, Radiology, and Neuroscience, Washington University St. Louis
| | - S Della Penna
- Department of Neuroscience, Imaging and Clinical Sciences, and Institute for Advanced Biomedical Technologies, "G. d'Annunzio" University of Chieti-Pescara, Chieti, Italy
| |
Collapse
|
28
|
de Lange P, Boto E, Holmes N, Hill RM, Bowtell R, Wens V, De Tiège X, Brookes MJ, Bourguignon M. Measuring the cortical tracking of speech with optically-pumped magnetometers. Neuroimage 2021; 233:117969. [PMID: 33744453 DOI: 10.1016/j.neuroimage.2021.117969] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/17/2020] [Revised: 01/08/2021] [Accepted: 03/04/2021] [Indexed: 11/25/2022] Open
Abstract
During continuous speech listening, brain activity tracks speech rhythmicity at frequencies matching with the repetition rate of phrases (0.2-1.5 Hz), words (2-4 Hz) and syllables (4-8 Hz). Here, we evaluated the applicability of wearable MEG based on optically-pumped magnetometers (OPMs) to measure such cortical tracking of speech (CTS). Measuring CTS with OPMs is a priori challenging given the complications associated with OPM measurements at frequencies below 4 Hz, due to increased intrinsic interference and head movement artifacts. Still, this represents an important development as OPM-MEG provides lifespan compliance and substantially improved spatial resolution compared with classical MEG. In this study, four healthy right-handed adults listened to continuous speech for 9 min. The radial component of the magnetic field was recorded simultaneously with 45-46 OPMs evenly covering the scalp surface and fixed to an additively manufactured helmet which fitted all 4 participants. We estimated CTS with reconstruction accuracy and coherence, and determined the number of dominant principal components (PCs) to remove from the data (as a preprocessing step) for optimal estimation. We also identified the dominant source of CTS using a minimum norm estimate. CTS estimated with reconstruction accuracy and coherence was significant in all 4 participants at phrasal and word rates, and in 3 participants (reconstruction accuracy) or 2 (coherence) at syllabic rate. Overall, close-to-optimal CTS estimation was obtained when the 3 (reconstruction accuracy) or 10 (coherence) first PCs were removed from the data. Importantly, values of reconstruction accuracy (~0.4 for 0.2-1.5-Hz CTS and ~0.1 for 2-8-Hz CTS) were remarkably close to those previously reported in classical MEG studies. Finally, source reconstruction localized the main sources of CTS to bilateral auditory cortices. In conclusion, t his study demonstrates that OPMs can be used for the purpose of CTS assessment. This finding opens new research avenues to unravel the neural network involved in CTS across the lifespan and potential alterations in, e.g., language developmental disorders. Data also suggest that OPMs are generally suitable for recording neural activity at frequencies below 4 Hz provided PCA is used as a preprocessing step; 0.2-1.5-Hz being the lowest frequency range successfully investigated here.
Collapse
Affiliation(s)
- Paul de Lange
- Laboratoire de Cartographie fonctionnelle du Cerveau, UNI - ULB Neuroscience Institute, Université libre de Bruxelles (ULB), 808 Lennik Street, Brussels 1070, Belgium
| | - Elena Boto
- Sir Peter Mansfield Imaging Centre, School of Physics and Astronomy, University of Nottingham, University Park, Nottingham NG7 2RD, United Kingdom
| | - Niall Holmes
- Sir Peter Mansfield Imaging Centre, School of Physics and Astronomy, University of Nottingham, University Park, Nottingham NG7 2RD, United Kingdom
| | - Ryan M Hill
- Sir Peter Mansfield Imaging Centre, School of Physics and Astronomy, University of Nottingham, University Park, Nottingham NG7 2RD, United Kingdom
| | - Richard Bowtell
- Sir Peter Mansfield Imaging Centre, School of Physics and Astronomy, University of Nottingham, University Park, Nottingham NG7 2RD, United Kingdom
| | - Vincent Wens
- Laboratoire de Cartographie fonctionnelle du Cerveau, UNI - ULB Neuroscience Institute, Université libre de Bruxelles (ULB), 808 Lennik Street, Brussels 1070, Belgium; Department of Functional Neuroimaging, Service of Nuclear Medicine, CUB Hôpital Erasme, Université libre de Bruxelles (ULB), Brussels, Belgium
| | - Xavier De Tiège
- Laboratoire de Cartographie fonctionnelle du Cerveau, UNI - ULB Neuroscience Institute, Université libre de Bruxelles (ULB), 808 Lennik Street, Brussels 1070, Belgium; Department of Functional Neuroimaging, Service of Nuclear Medicine, CUB Hôpital Erasme, Université libre de Bruxelles (ULB), Brussels, Belgium
| | - Matthew J Brookes
- Sir Peter Mansfield Imaging Centre, School of Physics and Astronomy, University of Nottingham, University Park, Nottingham NG7 2RD, United Kingdom
| | - Mathieu Bourguignon
- Laboratoire de Cartographie fonctionnelle du Cerveau, UNI - ULB Neuroscience Institute, Université libre de Bruxelles (ULB), 808 Lennik Street, Brussels 1070, Belgium; Laboratory of neurophysiology and movement biomechanics, UNI - ULB Neuroscience Institute, Université libre de Bruxelles (ULB), Brussels, Belgium; BCBL, Basque Center on Cognition, Brain and Language, San Sebastian 20009, Spain.
| |
Collapse
|
29
|
Hindriks R. Relation between the phase-lag index and lagged coherence for assessing interactions in EEG and MEG data. NEUROIMAGE: REPORTS 2021. [DOI: 10.1016/j.ynirp.2021.100007] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/21/2022]
|
30
|
Favaretto C, Spadone S, Sestieri C, Betti V, Cenedese A, Della Penna S, Corbetta M. Multi-band MEG signatures of BOLD connectivity reorganization during visuospatial attention. Neuroimage 2021; 230:117781. [PMID: 33497772 DOI: 10.1016/j.neuroimage.2021.117781] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/30/2020] [Revised: 01/12/2021] [Accepted: 01/15/2021] [Indexed: 02/07/2023] Open
Abstract
The functional architecture of the resting brain, as measured with the blood oxygenation level-dependent functional connectivity (BOLD-FC), is slightly modified during task performance. In previous work, we reported behaviorally relevant BOLD-FC modulations between visual and dorsal attention regions when subjects performed a visuospatial attention task as compared to central fixation (Spadone et al., 2015). Here we use magnetoencephalography (MEG) in the same group of subjects to identify the electrophysiological correlates of the BOLD-FC modulation found in our previous work. While BOLD-FC topography, separately at rest and during visual attention, corresponded to neuromagnetic Band-Limited Power (BLP) correlation in the alpha and beta bands (8-30 Hz), BOLD-FC modulations evoked by performing the visual attention task (Spadone et al. 2015) did not match any specific oscillatory band BLP modulation. Conversely, following the application of an orthogonal spatial decomposition that identifies common inter-subject co-variations, we found that attention-rest BOLD-FC modulations were recapitulated by multi-spectral BLP-FC components. Notably, individual variability of alpha connectivity between Frontal Eye Fields and visual occipital regions, jointly with decreased interaction in the Visual network, correlated with visual discrimination accuracy. In summary, task-rest BOLD connectivity modulations match multi-spectral MEG BLP connectivity.
Collapse
Affiliation(s)
- Chiara Favaretto
- Department of Neuroscience and Padova Neuroscience Center, University of Padova, 35128 Padova, Italy; Padova Neuroscience Center, PNC, 35131 Padova, Italy.
| | - Sara Spadone
- Department of Neuroscience, Imaging and Clinical Sciences - and ITAB, Institute for Advanced Biomedical Technologies, G. d'Annunzio University of Chieti-Pescara, 66100 Chieti, Italy
| | - Carlo Sestieri
- Department of Neuroscience, Imaging and Clinical Sciences - and ITAB, Institute for Advanced Biomedical Technologies, G. d'Annunzio University of Chieti-Pescara, 66100 Chieti, Italy
| | - Viviana Betti
- Department of Psychology, Sapienza University of Rome, 00185 Rome, Italy; IRCCS Fondazione Santa Lucia, 00179 Rome, Italy
| | - Angelo Cenedese
- Department of Information Engineering, University of Padova, 35131 Padova, Italy
| | - Stefania Della Penna
- Department of Neuroscience, Imaging and Clinical Sciences - and ITAB, Institute for Advanced Biomedical Technologies, G. d'Annunzio University of Chieti-Pescara, 66100 Chieti, Italy
| | - Maurizio Corbetta
- Department of Neuroscience and Padova Neuroscience Center, University of Padova, 35128 Padova, Italy; Department of Neurology, Radiology, Neuroscience, and Biomedical Engineering Washington University Saint Louis, MO 63110, USA; Venetian Institute of Molecular Medicine, VIMM, 35128 Padova, Italy; Padova Neuroscience Center, PNC, 35131 Padova, Italy.
| |
Collapse
|
31
|
Puttaert D, Coquelet N, Wens V, Peigneux P, Fery P, Rovai A, Trotta N, Sadeghi N, Coolen T, Bier JC, Goldman S, De Tiège X. Alterations in resting-state network dynamics along the Alzheimer's disease continuum. Sci Rep 2020; 10:21990. [PMID: 33319785 PMCID: PMC7738511 DOI: 10.1038/s41598-020-76201-3] [Citation(s) in RCA: 18] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/18/2020] [Accepted: 10/26/2020] [Indexed: 12/26/2022] Open
Abstract
Human brain activity is intrinsically organized into resting-state networks (RSNs) that transiently activate or deactivate at the sub-second timescale. Few neuroimaging studies have addressed how Alzheimer's disease (AD) affects these fast temporal brain dynamics, and how they relate to the cognitive, structural and metabolic abnormalities characterizing AD. We aimed at closing this gap by investigating both brain structure and function using magnetoencephalography (MEG) and hybrid positron emission tomography-magnetic resonance (PET/MR) in 10 healthy elders, 10 patients with subjective cognitive decline (SCD), 10 patients with amnestic mild cognitive impairment (aMCI) and 10 patients with typical Alzheimer's disease with dementia (AD). The fast activation/deactivation state dynamics of RSNs were assessed using hidden Markov modeling (HMM) of power envelope fluctuations at rest measured with MEG. Correlations were sought between temporal properties of HMM states and participants' cognitive test scores, whole hippocampal grey matter volume and regional brain glucose metabolism. The posterior default-mode network (DMN) was less often activated and for shorter durations in AD patients than matched healthy elders. No significant difference was found in patients with SCD or aMCI. The time spent by participants in the activated posterior DMN state did not correlate significantly with cognitive scores, nor with the whole hippocampal volume. However, it correlated positively with the regional glucose consumption in the right dorsolateral prefrontal cortex (DLPFC). AD patients present alterations of posterior DMN power activation dynamics at rest that identify an additional electrophysiological correlate of AD-related synaptic and neural dysfunction. The right DLPFC may play a causal role in the activation of the posterior DMN, possibly linked to the occurrence of mind wandering episodes. As such, these data might suggest a neural correlate of the decrease in mind wandering episodes reported in pathological aging.
Collapse
Affiliation(s)
- D Puttaert
- Laboratoire de Cartographie fonctionnelle du Cerveau (LCFC), UNI-ULB Neuroscience Institute, Université libre de Bruxelles (ULB), Brussels, Belgium. .,Neuropsychology and Functional Neuroimaging Research Unit (UR2NF), Center for Research in Cognition and Neurosciences (CRCN), UNI-ULB Neuroscience Institute, Université libre de Bruxelles (ULB), Brussels, Belgium.
| | - N Coquelet
- Laboratoire de Cartographie fonctionnelle du Cerveau (LCFC), UNI-ULB Neuroscience Institute, Université libre de Bruxelles (ULB), Brussels, Belgium
| | - V Wens
- Laboratoire de Cartographie fonctionnelle du Cerveau (LCFC), UNI-ULB Neuroscience Institute, Université libre de Bruxelles (ULB), Brussels, Belgium.,Department of Functional Neuroimaging, Service of Nuclear Medicine, CUB Hôpital Erasme, Université libre de Bruxelles (ULB), Brussels, Belgium
| | - P Peigneux
- Neuropsychology and Functional Neuroimaging Research Unit (UR2NF), Center for Research in Cognition and Neurosciences (CRCN), UNI-ULB Neuroscience Institute, Université libre de Bruxelles (ULB), Brussels, Belgium
| | - P Fery
- Neuropsychology and Functional Neuroimaging Research Unit (UR2NF), Center for Research in Cognition and Neurosciences (CRCN), UNI-ULB Neuroscience Institute, Université libre de Bruxelles (ULB), Brussels, Belgium.,Service of Neuropsychology and Speech Therapy, CUB Hôpital Erasme, Université libre de Bruxelles (ULB), Brussels, Belgium
| | - A Rovai
- Laboratoire de Cartographie fonctionnelle du Cerveau (LCFC), UNI-ULB Neuroscience Institute, Université libre de Bruxelles (ULB), Brussels, Belgium.,Department of Functional Neuroimaging, Service of Nuclear Medicine, CUB Hôpital Erasme, Université libre de Bruxelles (ULB), Brussels, Belgium
| | - N Trotta
- Laboratoire de Cartographie fonctionnelle du Cerveau (LCFC), UNI-ULB Neuroscience Institute, Université libre de Bruxelles (ULB), Brussels, Belgium.,Department of Functional Neuroimaging, Service of Nuclear Medicine, CUB Hôpital Erasme, Université libre de Bruxelles (ULB), Brussels, Belgium
| | - N Sadeghi
- Department of Radiology, CUB Hôpital Erasme, Université libre de Bruxelles (ULB), Brussels, Belgium
| | - T Coolen
- Department of Radiology, CUB Hôpital Erasme, Université libre de Bruxelles (ULB), Brussels, Belgium
| | - J-C Bier
- Department of Neurology, CUB Hôpital Erasme, Université libre de Bruxelles (ULB), Brussels, Belgium
| | - S Goldman
- Laboratoire de Cartographie fonctionnelle du Cerveau (LCFC), UNI-ULB Neuroscience Institute, Université libre de Bruxelles (ULB), Brussels, Belgium.,Department of Functional Neuroimaging, Service of Nuclear Medicine, CUB Hôpital Erasme, Université libre de Bruxelles (ULB), Brussels, Belgium
| | - X De Tiège
- Laboratoire de Cartographie fonctionnelle du Cerveau (LCFC), UNI-ULB Neuroscience Institute, Université libre de Bruxelles (ULB), Brussels, Belgium.,Department of Functional Neuroimaging, Service of Nuclear Medicine, CUB Hôpital Erasme, Université libre de Bruxelles (ULB), Brussels, Belgium
| |
Collapse
|
32
|
Hindriks R. Lag-invariant detection of interactions in spatially-extended systems using linear inverse modeling. PLoS One 2020; 15:e0242715. [PMID: 33306719 PMCID: PMC7732350 DOI: 10.1371/journal.pone.0242715] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/13/2020] [Accepted: 11/07/2020] [Indexed: 11/18/2022] Open
Abstract
Measurements on physical systems result from the systems' activity being converted into sensor measurements by a forward model. In a number of cases, inversion of the forward model is extremely sensitive to perturbations such as sensor noise or numerical errors in the forward model. Regularization is then required, which introduces bias in the reconstruction of the systems' activity. One domain in which this is particularly problematic is the reconstruction of interactions in spatially-extended complex systems such as the human brain. Brain interactions can be reconstructed from non-invasive measurements such as electroencephalography (EEG) or magnetoencephalography (MEG), whose forward models are linear and instantaneous, but have large null-spaces and high condition numbers. This leads to incomplete unmixing of the forward models and hence to spurious interactions. This motivated the development of interaction measures that are exclusively sensitive to lagged, i.e. delayed interactions. The drawback of such measures is that they only detect interactions that have sufficiently large lags and this introduces bias in reconstructed brain networks. We introduce three estimators for linear interactions in spatially-extended systems that are uniformly sensitive to all lags. We derive some basic properties of and relationships between the estimators and evaluate their performance using numerical simulations from a simple benchmark model.
Collapse
Affiliation(s)
- Rikkert Hindriks
- Department of Mathematics, VU University Amsterdam, Amsterdam, The Netherlands
| |
Collapse
|
33
|
Sjøgård M, Wens V, Van Schependom J, Costers L, D'hooghe M, D'haeseleer M, Woolrich M, Goldman S, Nagels G, De Tiège X. Brain dysconnectivity relates to disability and cognitive impairment in multiple sclerosis. Hum Brain Mapp 2020; 42:626-643. [PMID: 33242237 PMCID: PMC7814767 DOI: 10.1002/hbm.25247] [Citation(s) in RCA: 20] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/16/2019] [Revised: 09/10/2020] [Accepted: 09/29/2020] [Indexed: 12/27/2022] Open
Abstract
The pathophysiology of cognitive dysfunction in multiple sclerosis (MS) is still unclear. This magnetoencephalography (MEG) study investigates the impact of MS on brain resting-state functional connectivity (rsFC) and its relationship to disability and cognitive impairment. We investigated rsFC based on power envelope correlation within and between different frequency bands, in a large cohort of participants consisting of 99 MS patients and 47 healthy subjects. Correlations were investigated between rsFC and outcomes on disability, disease duration and 7 neuropsychological scores within each group, while stringently correcting for multiple comparisons and possible confounding factors. Specific dysconnections correlating with MS-induced physical disability and disease duration were found within the sensorimotor and language networks, respectively. Global network-level reductions in within- and cross-network rsFC were observed in the default-mode network. Healthy subjects and patients significantly differed in their scores on cognitive fatigue and verbal fluency. Healthy subjects and patients showed different correlation patterns between rsFC and cognitive fatigue or verbal fluency, both of which involved a shift in patients from the posterior default-mode network to the language network. Introducing electrophysiological rsFC in a regression model of verbal fluency and cognitive fatigue in MS patients significantly increased the explained variance compared to a regression limited to structural MRI markers (relative thalamic volume and lesion load). This MEG study demonstrates that MS induces distinct changes in the resting-state functional brain architecture that relate to disability, disease duration and specific cognitive functioning alterations. It highlights the potential value of electrophysiological intrinsic rsFC for monitoring the cognitive impairment in patients with MS.
Collapse
Affiliation(s)
- Martin Sjøgård
- Laboratoire de Cartographie fonctionnelle du Cerveau, UNI-ULB Neuroscience Institute, Université libre de Bruxelles (ULB), Brussels, Belgium
| | - Vincent Wens
- Laboratoire de Cartographie fonctionnelle du Cerveau, UNI-ULB Neuroscience Institute, Université libre de Bruxelles (ULB), Brussels, Belgium.,Department of Functional Neuroimaging, Service of Nuclear Medicine, CUB-Hôpital Erasme, Université libre de Bruxelles (ULB), Brussels, Belgium
| | - Jeroen Van Schependom
- Center for Neurosciences, Vrije Universiteit Brussel, Brussels, Belgium.,National MS Center, Belgium
| | - Lars Costers
- Center for Neurosciences, Vrije Universiteit Brussel, Brussels, Belgium
| | - Marie D'hooghe
- Center for Neurosciences, Vrije Universiteit Brussel, Brussels, Belgium.,National MS Center, Belgium
| | - Miguel D'haeseleer
- Center for Neurosciences, Vrije Universiteit Brussel, Brussels, Belgium.,National MS Center, Belgium
| | - Mark Woolrich
- Oxford Centre for Human Brain Activity, Wellcome Centre for Integrative Neuroimaging, Department of Psychiatry, University of Oxford, Oxford, UK
| | - Serge Goldman
- Laboratoire de Cartographie fonctionnelle du Cerveau, UNI-ULB Neuroscience Institute, Université libre de Bruxelles (ULB), Brussels, Belgium.,Department of Functional Neuroimaging, Service of Nuclear Medicine, CUB-Hôpital Erasme, Université libre de Bruxelles (ULB), Brussels, Belgium
| | - Guy Nagels
- Center for Neurosciences, Vrije Universiteit Brussel, Brussels, Belgium.,National MS Center, Belgium.,St Edmund Hall, University of Oxford, Oxford, UK
| | - Xavier De Tiège
- Laboratoire de Cartographie fonctionnelle du Cerveau, UNI-ULB Neuroscience Institute, Université libre de Bruxelles (ULB), Brussels, Belgium.,Department of Functional Neuroimaging, Service of Nuclear Medicine, CUB-Hôpital Erasme, Université libre de Bruxelles (ULB), Brussels, Belgium
| |
Collapse
|
34
|
Changes in electrophysiological static and dynamic human brain functional architecture from childhood to late adulthood. Sci Rep 2020; 10:18986. [PMID: 33149179 PMCID: PMC7642359 DOI: 10.1038/s41598-020-75858-0] [Citation(s) in RCA: 14] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/17/2020] [Accepted: 10/20/2020] [Indexed: 12/15/2022] Open
Abstract
This magnetoencephalography study aimed at characterizing age-related changes in resting-state functional brain organization from mid-childhood to late adulthood. We investigated neuromagnetic brain activity at rest in 105 participants divided into three age groups: children (6-9 years), young adults (18-34 years) and healthy elders (53-78 years). The effects of age on static resting-state functional brain integration were assessed using band-limited power envelope correlation, whereas those on transient functional brain dynamics were disclosed using hidden Markov modeling of power envelope activity. Brain development from childhood to adulthood came with (1) a strengthening of functional integration within and between resting-state networks and (2) an increased temporal stability of transient (100-300 ms lifetime) and recurrent states of network activation or deactivation mainly encompassing lateral or medial associative neocortical areas. Healthy aging was characterized by decreased static resting-state functional integration and dynamic stability within the primary visual network. These results based on electrophysiological measurements free of neurovascular biases suggest that functional brain integration mainly evolves during brain development, with limited changes in healthy aging. These novel electrophysiological insights into human brain functional architecture across the lifespan pave the way for future clinical studies investigating how brain disorders affect brain development or healthy aging.
Collapse
|
35
|
Pascucci D, Rubega M, Plomp G. Modeling time-varying brain networks with a self-tuning optimized Kalman filter. PLoS Comput Biol 2020; 16:e1007566. [PMID: 32804971 PMCID: PMC7451990 DOI: 10.1371/journal.pcbi.1007566] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/20/2019] [Revised: 08/27/2020] [Accepted: 07/03/2020] [Indexed: 12/14/2022] Open
Abstract
Brain networks are complex dynamical systems in which directed interactions between different areas evolve at the sub-second scale of sensory, cognitive and motor processes. Due to the highly non-stationary nature of neural signals and their unknown noise components, however, modeling dynamic brain networks has remained one of the major challenges in contemporary neuroscience. Here, we present a new algorithm based on an innovative formulation of the Kalman filter that is optimized for tracking rapidly evolving patterns of directed functional connectivity under unknown noise conditions. The Self-Tuning Optimized Kalman filter (STOK) is a novel adaptive filter that embeds a self-tuning memory decay and a recursive regularization to guarantee high network tracking accuracy, temporal precision and robustness to noise. To validate the proposed algorithm, we performed an extensive comparison against the classical Kalman filter, in both realistic surrogate networks and real electroencephalography (EEG) data. In both simulations and real data, we show that the STOK filter estimates time-frequency patterns of directed connectivity with significantly superior performance. The advantages of the STOK filter were even clearer in real EEG data, where the algorithm recovered latent structures of dynamic connectivity from epicranial EEG recordings in rats and human visual evoked potentials, in excellent agreement with known physiology. These results establish the STOK filter as a powerful tool for modeling dynamic network structures in biological systems, with the potential to yield new insights into the rapid evolution of network states from which brain functions emerge. During normal behavior, brains transition between functional network states several times per second. This allows humans to quickly read a sentence, and a frog to catch a fly. Understanding these fast network dynamics is fundamental to understanding how brains work, but up to now it has proven very difficult to model fast brain dynamics for various methodological reasons. To overcome these difficulties, we designed a new Kalman filter (STOK) by innovating on previous solutions from control theory and state-space modelling. We show that STOK accurately models fast network changes in simulations and real neural data, making it an essential new tool for modelling fast brain networks in the time and frequency domain.
Collapse
Affiliation(s)
- D Pascucci
- Perceptual Networks Group, University of Fribourg, Fribourg, Switzerland.,Laboratory of Psychophysics, Brain Mind Institute, École Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland
| | - M Rubega
- Functional Brain Mapping Lab, Department of Fundamental Neurosciences, University of Geneva, Geneva, Switzerland.,Department of Neurosciences, University of Padova, Padova, Italy
| | - G Plomp
- Perceptual Networks Group, University of Fribourg, Fribourg, Switzerland
| |
Collapse
|
36
|
Glomb K, Mullier E, Carboni M, Rubega M, Iannotti G, Tourbier S, Seeber M, Vulliemoz S, Hagmann P. Using structural connectivity to augment community structure in EEG functional connectivity. Netw Neurosci 2020; 4:761-787. [PMID: 32885125 PMCID: PMC7462431 DOI: 10.1162/netn_a_00147] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/05/2019] [Accepted: 05/12/2020] [Indexed: 11/17/2022] Open
Abstract
Recently, EEG recording techniques and source analysis have improved, making it feasible to tap into fast network dynamics. Yet, analyzing whole-cortex EEG signals in source space is not standard, partly because EEG suffers from volume conduction: Functional connectivity (FC) reflecting genuine functional relationships is impossible to disentangle from spurious FC introduced by volume conduction. Here, we investigate the relationship between white matter structural connectivity (SC) and large-scale network structure encoded in EEG-FC. We start by confirming that FC (power envelope correlations) is predicted by SC beyond the impact of Euclidean distance, in line with the assumption that SC mediates genuine FC. We then use information from white matter structural connectivity in order to smooth the EEG signal in the space spanned by graphs derived from SC. Thereby, FC between nearby, structurally connected brain regions increases while FC between nonconnected regions remains unchanged, resulting in an increase in genuine, SC-mediated FC. We analyze the induced changes in FC, assessing the resemblance between EEG-FC and volume-conduction- free fMRI-FC, and find that smoothing increases resemblance in terms of overall correlation and community structure. This result suggests that our method boosts genuine FC, an outcome that is of interest for many EEG network neuroscience questions.
Collapse
Affiliation(s)
- Katharina Glomb
- Connectomics Lab, Department of Radiology, University Hospital of Lausanne and University of Lausanne, Lausanne (CHUV-UNIL), Vaud, Switzerland
| | - Emeline Mullier
- Connectomics Lab, Department of Radiology, University Hospital of Lausanne and University of Lausanne, Lausanne (CHUV-UNIL), Vaud, Switzerland
| | - Margherita Carboni
- EEG and Epilepsy, Neurology, University Hospitals of Geneva and University of Geneva, Geneva, Switzerland
- Functional Brain Mapping Lab, Department of Fundamental Neurosciences, University of Geneva, Geneva, Switzerland
| | - Maria Rubega
- Department of Neurosciences, University of Padova, Padova, Italy
| | - Giannarita Iannotti
- Functional Brain Mapping Lab, Department of Fundamental Neurosciences, University of Geneva, Geneva, Switzerland
| | - Sebastien Tourbier
- Connectomics Lab, Department of Radiology, University Hospital of Lausanne and University of Lausanne, Lausanne (CHUV-UNIL), Vaud, Switzerland
| | - Martin Seeber
- Functional Brain Mapping Lab, Department of Fundamental Neurosciences, University of Geneva, Geneva, Switzerland
| | - Serge Vulliemoz
- EEG and Epilepsy, Neurology, University Hospitals of Geneva and University of Geneva, Geneva, Switzerland
| | - Patric Hagmann
- Connectomics Lab, Department of Radiology, University Hospital of Lausanne and University of Lausanne, Lausanne (CHUV-UNIL), Vaud, Switzerland
| |
Collapse
|
37
|
Neocortical activity tracks the hierarchical linguistic structures of self-produced speech during reading aloud. Neuroimage 2020; 216:116788. [DOI: 10.1016/j.neuroimage.2020.116788] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/19/2019] [Revised: 02/19/2020] [Accepted: 03/20/2020] [Indexed: 11/19/2022] Open
|
38
|
Coolen T, Wens V, Vander Ghinst M, Mary A, Bourguignon M, Naeije G, Peigneux P, Sadeghi N, Goldman S, De Tiège X. Frequency-Dependent Intrinsic Electrophysiological Functional Architecture of the Human Verbal Language Network. Front Integr Neurosci 2020; 14:27. [PMID: 32528258 PMCID: PMC7264165 DOI: 10.3389/fnint.2020.00027] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/05/2020] [Accepted: 04/16/2020] [Indexed: 11/13/2022] Open
Abstract
Functional magnetic resonance imaging (fMRI) allowed the spatial characterization of the resting-state verbal language network (vLN). While other resting-state networks (RSNs) were matched with their electrophysiological equivalents at rest and could be spectrally defined, such correspondence is lacking for the vLN. This magnetoencephalography (MEG) study aimed at defining the spatio-spectral characteristics of the neuromagnetic intrinsic functional architecture of the vLN. Neuromagnetic activity was recorded at rest in 100 right-handed healthy adults (age range: 18-41 years). Band-limited power envelope correlations were performed within and across frequency bands (θ, α, β, and low γ) from a seed region placed in the left Broca's area, using static orthogonalization as leakage correction. K-means clustering was used to segregate spatio-spectral clusters of resting-state functional connectivity (rsFC). Remarkably, unlike other RSNs, within-frequency long-range rsFC from the left Broca's area was not driven by one main carrying frequency but was characterized by a specific spatio-spectral pattern segregated along the ventral (predominantly θ and α) and dorsal (β and low-γ bands) vLN streams. In contrast, spatial patterns of cross-frequency vLN functional integration were spectrally more widespread and involved multiple frequency bands. Moreover, the static intrinsic functional architecture of the neuromagnetic human vLN involved clearly left-hemisphere-dominant vLN interactions as well as cross-network interactions with the executive control network and postero-medial nodes of the DMN. Overall, this study highlighted the involvement of multiple modes of within and cross-frequency power envelope couplings at the basis of long-range electrophysiological vLN functional integration. As such, it lays the foundation for future works aimed at understanding the pathophysiology of language-related disorders.
Collapse
Affiliation(s)
- Tim Coolen
- Laboratoire de Cartographie fonctionnelle du Cerveau, ULB Neuroscience Institute (UNI), Université libre de Bruxelles (ULB), Brussels, Belgium.,Department of Radiology, CUB-Hôpital Erasme, Université libre de Bruxelles (ULB), Brussels, Belgium
| | - Vincent Wens
- Laboratoire de Cartographie fonctionnelle du Cerveau, ULB Neuroscience Institute (UNI), Université libre de Bruxelles (ULB), Brussels, Belgium.,Magnetoencenphalography Unit, Department of Functional Neuroimaging, Service of Nuclear Medicine, CUB-Hôpital Erasme, Université libre de Bruxelles (ULB), Brussels, Belgium
| | - Marc Vander Ghinst
- Laboratoire de Cartographie fonctionnelle du Cerveau, ULB Neuroscience Institute (UNI), Université libre de Bruxelles (ULB), Brussels, Belgium
| | - Alison Mary
- Neuropsychology & Functional Neuroimaging Research Unit (UR2NF), Center for Research in Cognition and Neurosciences, ULB-Neuroscience Institute (UNI), Université libre de Bruxelles (ULB), Brussels, Belgium
| | - Mathieu Bourguignon
- Laboratoire de Cartographie fonctionnelle du Cerveau, ULB Neuroscience Institute (UNI), Université libre de Bruxelles (ULB), Brussels, Belgium.,BCBL-Basque Center on Cognition, Brain and Language, San Sebastian, Spain.,Laboratoire Cognition Langage et Développement, ULB Neuroscience Institute (UNI), Université libre de Bruxelles (ULB), Brussels, Belgium
| | - Gilles Naeije
- Laboratoire de Cartographie fonctionnelle du Cerveau, ULB Neuroscience Institute (UNI), Université libre de Bruxelles (ULB), Brussels, Belgium
| | - Philippe Peigneux
- Neuropsychology & Functional Neuroimaging Research Unit (UR2NF), Center for Research in Cognition and Neurosciences, ULB-Neuroscience Institute (UNI), Université libre de Bruxelles (ULB), Brussels, Belgium
| | - Niloufar Sadeghi
- Department of Radiology, CUB-Hôpital Erasme, Université libre de Bruxelles (ULB), Brussels, Belgium
| | - Serge Goldman
- Laboratoire de Cartographie fonctionnelle du Cerveau, ULB Neuroscience Institute (UNI), Université libre de Bruxelles (ULB), Brussels, Belgium.,Magnetoencenphalography Unit, Department of Functional Neuroimaging, Service of Nuclear Medicine, CUB-Hôpital Erasme, Université libre de Bruxelles (ULB), Brussels, Belgium
| | - Xavier De Tiège
- Laboratoire de Cartographie fonctionnelle du Cerveau, ULB Neuroscience Institute (UNI), Université libre de Bruxelles (ULB), Brussels, Belgium.,Magnetoencenphalography Unit, Department of Functional Neuroimaging, Service of Nuclear Medicine, CUB-Hôpital Erasme, Université libre de Bruxelles (ULB), Brussels, Belgium
| |
Collapse
|
39
|
Shu S, Qin L, Yin Y, Han M, Cui W, Gao JH. Cortical electrophysiological evidence for individual-specific temporal organization of brain functional networks. Hum Brain Mapp 2020; 41:2160-2172. [PMID: 31961469 PMCID: PMC7267903 DOI: 10.1002/hbm.24937] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/23/2019] [Revised: 01/02/2020] [Accepted: 01/13/2020] [Indexed: 12/21/2022] Open
Abstract
The human brain has been demonstrated to rapidly and continuously form and dissolve networks on a subsecond timescale, offering effective and essential substrates for cognitive processes. Understanding how the dynamic organization of brain functional networks on a subsecond level varies across individuals is, therefore, of great interest for personalized neuroscience. However, it remains unclear whether features of such rapid network organization are reliably unique and stable in single subjects and, therefore, can be used in characterizing individual networks. Here, we used two sets of 5‐min magnetoencephalography (MEG) resting data from 39 healthy subjects over two consecutive days and modeled the spontaneous brain activity as recurring networks fast shifting between each other in a coordinated manner. MEG cortical maps were obtained through source reconstruction using the beamformer method and subjects' temporal structure of recurring networks was obtained via the Hidden Markov Model. Individual organization of dynamic brain activity was quantified with the features of the network‐switching pattern (i.e., transition probability and mean interval time) and the time‐allocation mode (i.e., fractional occupancy and mean lifetime). Using these features, we were able to identify subjects from the group with significant accuracies (~40% on average in 0.5–48 Hz). Notably, the default mode network displayed a distinguishable pattern, being the least frequently visited network with the longest duration for each visit. Together, we provide initial evidence suggesting that the rapid dynamic temporal organization of brain networks achieved in electrophysiology is intrinsic and subject specific.
Collapse
Affiliation(s)
- Su Shu
- Beijing City Key Lab for Medical Physics and Engineering, Institution of Heavy Ion Physics, School of Physics, Peking University, Beijing, China.,Center for MRI Research, Academy for Advanced Interdisciplinary Studies, Peking University, Beijing, China.,McGovern Institute for Brain Research, Peking University, Beijing, China
| | - Lang Qin
- Center for MRI Research, Academy for Advanced Interdisciplinary Studies, Peking University, Beijing, China.,Department of Linguistics, The University of Hong Kong, Hong Kong, China
| | - Yayan Yin
- Department of Radiology, Xuanwu Hospital of Capital Medical University, Beijing, China
| | - Meizhen Han
- Beijing City Key Lab for Medical Physics and Engineering, Institution of Heavy Ion Physics, School of Physics, Peking University, Beijing, China.,Center for MRI Research, Academy for Advanced Interdisciplinary Studies, Peking University, Beijing, China.,McGovern Institute for Brain Research, Peking University, Beijing, China
| | - Wei Cui
- Center for MRI Research, Academy for Advanced Interdisciplinary Studies, Peking University, Beijing, China.,Center for Biomedical Engineering, University of Science and Technology of China, Hefei, Anhui, China
| | - Jia-Hong Gao
- Beijing City Key Lab for Medical Physics and Engineering, Institution of Heavy Ion Physics, School of Physics, Peking University, Beijing, China.,Center for MRI Research, Academy for Advanced Interdisciplinary Studies, Peking University, Beijing, China.,McGovern Institute for Brain Research, Peking University, Beijing, China
| |
Collapse
|
40
|
Coquelet N, De Tiège X, Destoky F, Roshchupkina L, Bourguignon M, Goldman S, Peigneux P, Wens V. Comparing MEG and high-density EEG for intrinsic functional connectivity mapping. Neuroimage 2020; 210:116556. [DOI: 10.1016/j.neuroimage.2020.116556] [Citation(s) in RCA: 47] [Impact Index Per Article: 11.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/14/2019] [Revised: 12/11/2019] [Accepted: 01/14/2020] [Indexed: 01/22/2023] Open
|
41
|
Nunes AS, Moiseev A, Kozhemiako N, Cheung T, Ribary U, Doesburg SM. Multiple constrained minimum variance beamformer (MCMV) performance in connectivity analyses. Neuroimage 2020; 208:116386. [DOI: 10.1016/j.neuroimage.2019.116386] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/04/2019] [Revised: 09/27/2019] [Accepted: 11/19/2019] [Indexed: 01/08/2023] Open
|
42
|
Niesen M, Vander Ghinst M, Bourguignon M, Wens V, Bertels J, Goldman S, Choufani G, Hassid S, De Tiège X. Tracking the Effects of Top-Down Attention on Word Discrimination Using Frequency-tagged Neuromagnetic Responses. J Cogn Neurosci 2020; 32:877-888. [PMID: 31933439 DOI: 10.1162/jocn_a_01522] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/04/2022]
Abstract
Discrimination of words from nonspeech sounds is essential in communication. Still, how selective attention can influence this early step of speech processing remains elusive. To answer that question, brain activity was recorded with magnetoencephalography in 12 healthy adults while they listened to two sequences of auditory stimuli presented at 2.17 Hz, consisting of successions of one randomized word (tagging frequency = 0.54 Hz) and three acoustically matched nonverbal stimuli. Participants were instructed to focus their attention on the occurrence of a predefined word in the verbal attention condition and on a nonverbal stimulus in the nonverbal attention condition. Steady-state neuromagnetic responses were identified with spectral analysis at sensor and source levels. Significant sensor responses peaked at 0.54 and 2.17 Hz in both conditions. Sources at 0.54 Hz were reconstructed in supratemporal auditory cortex, left superior temporal gyrus (STG), left middle temporal gyrus, and left inferior frontal gyrus. Sources at 2.17 Hz were reconstructed in supratemporal auditory cortex and STG. Crucially, source strength in the left STG at 0.54 Hz was significantly higher in verbal attention than in nonverbal attention condition. This study demonstrates speech-sensitive responses at primary auditory and speech-related neocortical areas. Critically, it highlights that, during word discrimination, top-down attention modulates activity within the left STG. This area therefore appears to play a crucial role in selective verbal attentional processes for this early step of speech processing.
Collapse
|
43
|
Antonakakis M, Dimitriadis SI, Zervakis M, Papanicolaou AC, Zouridakis G. Aberrant Whole-Brain Transitions and Dynamics of Spontaneous Network Microstates in Mild Traumatic Brain Injury. Front Comput Neurosci 2020; 13:90. [PMID: 32009921 PMCID: PMC6974679 DOI: 10.3389/fncom.2019.00090] [Citation(s) in RCA: 16] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/22/2019] [Accepted: 12/19/2019] [Indexed: 12/18/2022] Open
Abstract
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.
Collapse
Affiliation(s)
- Marios Antonakakis
- Institute for Biomagnetism and Biosignal Analysis, University of Muenster, Muenster, Germany
- Digital Image and Signal Processing Laboratory, School of Electronic and Computer Engineering, Technical University of Crete, Chania, Greece
- Neuroinformatics Group, Cardiff University Brain Research Imaging Center (CUBRIC), School of Psychology, Cardiff University, Cardiff, United Kingdom
| | - Stavros I. Dimitriadis
- Institute of Psychological Medicine and Clinical Neurosciences, Cardiff University School of Medicine, Cardiff, United Kingdom
- Cardiff University Brain Research Imaging Center (CUBRIC), 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
- MRC Centre for Neuropsychiatric Genetics and Genomics, School of Medicine, Cardiff University, Cardiff, United Kingdom
| | - Michalis Zervakis
- Digital Image and Signal Processing Laboratory, School of Electronic and Computer Engineering, Technical University of Crete, Chania, Greece
| | - Andrew C. Papanicolaou
- Departments of Pediatrics, and Anatomy and Neurobiology, Neuroscience Institute, University of Tennessee Health Science Center, Le Bonheur Children's Hospital, Memphis, TN, United States
| | - George Zouridakis
- Biomedical Imaging Lab, Departments of Engineering Technology, Computer Science, Biomedical Engineering, and Electrical and Computer Engineering, University of Houston, Houston, TX, United States
| |
Collapse
|
44
|
Naeije G, Wens V, Coquelet N, Sjøgård M, Goldman S, Pandolfo M, De Tiège XP. Age of onset determines intrinsic functional brain architecture in Friedreich ataxia. Ann Clin Transl Neurol 2020; 7:94-104. [PMID: 31854120 PMCID: PMC6952309 DOI: 10.1002/acn3.50966] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/13/2019] [Revised: 10/30/2019] [Accepted: 11/18/2019] [Indexed: 12/12/2022] Open
Abstract
OBJECTIVE Friedreich ataxia (FRDA) is the commonest hereditary ataxia in Caucasians. Most patients are homozygous for expanded GAA triplet repeats in the first intron of the frataxin (FXN) gene, involved in mitochondrial iron metabolism. Here, we used magnetoencephalography (MEG) to characterize the main determinants of FRDA-related changes in intrinsic functional brain architecture. METHODS Five minutes of MEG signals were recorded at rest from 18 right-handed FRDA patients (mean age 27 years, 9 females; mean SARA score: 21.4) and matched healthy individuals. The MEG connectome was estimated as resting-state functional connectivity (rsFC) matrices involving 37 nodes from six major resting state networks and the cerebellum. Source-level rsFC maps were computed using leakage-corrected broad-band (3-40 Hz) envelope correlations. Post hoc median-split was used to contrast rsFC in FRDA patients with different clinical characteristics. Nonparametric permutations and Spearman rank correlation test were used for statistics. RESULTS High rank correlation levels were found between rsFC and age of symptoms onset in FRDA mostly between the ventral attention, the default-mode, and the cerebellar networks; patients with higher rsFC developing symptoms at an older age. Increased rsFC was found in FRDA with later age of symptoms onset compared to healthy subjects. No correlations were found between rsFC and other clinical parameters. CONCLUSION Age of symptoms onset is a major determinant of FRDA patients' intrinsic functional brain architecture. Higher rsFC in FRDA patients with later age of symptoms onset supports compensatory mechanisms for FRDA-related neural network dysfunction and position neuromagnetic rsFC as potential marker of FRDA neural reserve.
Collapse
Affiliation(s)
- Gilles Naeije
- Laboratoire de Cartographie fonctionnelle du CerveauULB Neuroscience Institute (UNI)Université libre de Bruxelles (ULB)BrusselsBelgium
- Department of NeurologyCUB Hôpital ErasmeUniversité libre de Bruxelles (ULB)BrusselsBelgium
| | - Vincent Wens
- Laboratoire de Cartographie fonctionnelle du CerveauULB Neuroscience Institute (UNI)Université libre de Bruxelles (ULB)BrusselsBelgium
- Department of Functional NeuroimagingService of Nuclear MedicineCUB Hôpital ErasmeUniversité libre de Bruxelles (ULB)BrusselsBelgium
| | - Nicolas Coquelet
- Laboratoire de Cartographie fonctionnelle du CerveauULB Neuroscience Institute (UNI)Université libre de Bruxelles (ULB)BrusselsBelgium
| | - Martin Sjøgård
- Laboratoire de Cartographie fonctionnelle du CerveauULB Neuroscience Institute (UNI)Université libre de Bruxelles (ULB)BrusselsBelgium
| | - Serge Goldman
- Laboratoire de Cartographie fonctionnelle du CerveauULB Neuroscience Institute (UNI)Université libre de Bruxelles (ULB)BrusselsBelgium
- Department of Functional NeuroimagingService of Nuclear MedicineCUB Hôpital ErasmeUniversité libre de Bruxelles (ULB)BrusselsBelgium
| | - Massimo Pandolfo
- Department of NeurologyCUB Hôpital ErasmeUniversité libre de Bruxelles (ULB)BrusselsBelgium
| | - Xavier P. De Tiège
- Laboratoire de Cartographie fonctionnelle du CerveauULB Neuroscience Institute (UNI)Université libre de Bruxelles (ULB)BrusselsBelgium
- Department of Functional NeuroimagingService of Nuclear MedicineCUB Hôpital ErasmeUniversité libre de Bruxelles (ULB)BrusselsBelgium
| |
Collapse
|
45
|
Della Penna S, Corbetta M, Wens V, de Pasquale F. The Impact of the Geometric Correction Scheme on MEG Functional Topology at Rest. Front Neurosci 2019; 13:1114. [PMID: 31708725 PMCID: PMC6823434 DOI: 10.3389/fnins.2019.01114] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/30/2019] [Accepted: 10/02/2019] [Indexed: 01/07/2023] Open
Abstract
Spontaneous activity is correlated across brain regions in large scale networks (RSN) closely resembling those recruited during several behavioral tasks and characterized by functional specialization and dynamic integration. Specifically, MEG studies revealed a set of central regions (dynamic core) possibly facilitating communication among differently specialized brain systems. However, source projected MEG signals, due to the fundamentally ill-posed inverse problem, are affected by spatial leakage, leading to the estimation of spurious, blurred connections that may affect the topological properties of brain networks and their integration. To reduce leakage effects, several correction schemes have been proposed including the Geometric Correction Scheme (GCS) whose theory, simulations and empirical results on topography of a few RSNs were already presented. However, its impact on the estimation of fundamental graph measures used to describe the architecture of interactions among brain regions has not been investigated yet. Here, we estimated dense, MEG band-limited power connectomes in theta, alpha, beta, and gamma bands from 13 healthy subjects (all young adults). We compared the connectivity and topology of MEG uncorrected and GCS-corrected connectomes. The use of GCS considerably reorganized the topology of connectivity, reducing the local, within-hemisphere interactions mainly in the beta and gamma bands and increasing across-hemisphere interactions mainly in the alpha and beta bands. Moreover, the number of hubs decreased in the alpha and beta bands, but the centrality of some fundamental regions such as the Posterior Cingulate Cortex (PCC), Supplementary Motor Area (SMA) and Middle Prefrontal Cortex (MPFC) remained strong in all bands, associated to an increase of the Global Efficiency and a decrease of Modularity. As a comparison, we applied orthogonalization on connectomes and ran the same topological analyses. The correlation values were considerably reduced, and orthogonalization mainly decreased local within-hemisphere interactions in all bands, similarly to GCS. Notably, the centrality of the PCC, SMA and MPFC was preserved in all bands, as for GCS, together with other hubs in the posterior parietal regions. Overall, leakage correction removes spurious local connections, but confirms the role of dynamic hub regions, specifically the anterior and posterior cingulate, in integrating information in the brain at rest.
Collapse
Affiliation(s)
- Stefania Della Penna
- Department of Neuroscience, Imaging and Clinical Sciences, and Institute for Advanced Biomedical Technologies, "G. d'Annunzio" University of Chieti-Pescara, Chieti, Italy
| | - Maurizio Corbetta
- Department of Neuroscience and Padova Neuroscience Center, University of Padua, Padua, Italy.,Department of Neurology, Radiology, and Anatomy and Neurobiology, Washington University, St. Louis, MO, United States
| | - Vincent Wens
- Laboratoire de Cartographie Fonctionnelle du Cerveau, UNI-ULB Neuroscience Institute, Université Libre de Bruxelles, Brussels, Belgium.,Magnetoencephalography Unit, Department of Functional Neuroimaging, Service of Nuclear Medicine, CUB-Hôpital Erasme, Brussels, Belgium
| | | |
Collapse
|
46
|
Sjøgård M, De Tiège X, Mary A, Peigneux P, Goldman S, Nagels G, van Schependom J, Quinn AJ, Woolrich MW, Wens V. Do the posterior midline cortices belong to the electrophysiological default-mode network? Neuroimage 2019; 200:221-230. [DOI: 10.1016/j.neuroimage.2019.06.052] [Citation(s) in RCA: 16] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/06/2018] [Revised: 04/30/2019] [Accepted: 06/21/2019] [Indexed: 12/22/2022] Open
|
47
|
Wens V, Bourguignon M, Vander Ghinst M, Mary A, Marty B, Coquelet N, Naeije G, Peigneux P, Goldman S, De Tiège X. Synchrony, metastability, dynamic integration, and competition in the spontaneous functional connectivity of the human brain. Neuroimage 2019; 199:313-324. [PMID: 31170458 DOI: 10.1016/j.neuroimage.2019.05.081] [Citation(s) in RCA: 28] [Impact Index Per Article: 5.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/12/2018] [Revised: 04/25/2019] [Accepted: 05/29/2019] [Indexed: 11/25/2022] Open
Abstract
The human brain is functionally organized into large-scale neural networks that are dynamically interconnected. Multiple short-lived states of resting-state functional connectivity (rsFC) identified transiently synchronized networks and cross-network integration. However, little is known about the way brain couplings covary as rsFC states wax and wane. In this magnetoencephalography study, we explore the synchronization structure among the spontaneous interactions of well-known resting-state networks (RSNs). To do so, we extracted modes of dynamic coupling that reflect rsFC synchrony and analyzed their spatio-temporal features. These modes identified transient, sporadic rsFC changes characterized by the widespread integration of RSNs across the brain, most prominently in the β band. This is in line with the metastable rsFC state model of resting-state dynamics, wherein our modes fit as state transition processes. Furthermore, the default-mode network (DMN) stood out as being structured into competitive cross-network couplings with widespread DMN-RSN interactions, especially among the β-band modes. These results substantiate the theory that the DMN is a core network enabling dynamic global brain integration in the β band.
Collapse
Affiliation(s)
- Vincent Wens
- LCFC - Laboratoire de Cartographie fonctionnelle du Cerveau, UNI - ULB Neuroscience Institute, Université libre de Bruxelles (ULB), Brussels, Belgium; Magnetoencephalography Unit, Department of Functional Neuroimaging, Service of Nuclear Medicine, CUB - Hôpital Erasme, Brussels, Belgium.
| | - Mathieu Bourguignon
- LCFC - Laboratoire de Cartographie fonctionnelle du Cerveau, UNI - ULB Neuroscience Institute, Université libre de Bruxelles (ULB), Brussels, Belgium; Laboratoire Cognition Langage et Développement, UNI - ULB Neuroscience Institute, Université libre de Bruxelles (ULB), Brussels, Belgium; BCBL - Basque Center on Cognition, Brain and Language, 20009 San Sebastian, Spain
| | - Marc Vander Ghinst
- LCFC - Laboratoire de Cartographie fonctionnelle du Cerveau, UNI - ULB Neuroscience Institute, Université libre de Bruxelles (ULB), Brussels, Belgium
| | - Alison Mary
- UNICAEN, PSL Research University, EPHE, INSERM, U1077, CHU de Caen, Neuropsychologie et Imagerie de la Mémoire Humaine, Caen, France
| | - Brice Marty
- LCFC - Laboratoire de Cartographie fonctionnelle du Cerveau, UNI - ULB Neuroscience Institute, Université libre de Bruxelles (ULB), Brussels, Belgium
| | - Nicolas Coquelet
- LCFC - Laboratoire de Cartographie fonctionnelle du Cerveau, UNI - ULB Neuroscience Institute, Université libre de Bruxelles (ULB), Brussels, Belgium
| | - Gilles Naeije
- LCFC - Laboratoire de Cartographie fonctionnelle du Cerveau, UNI - ULB Neuroscience Institute, Université libre de Bruxelles (ULB), Brussels, Belgium
| | - Philippe Peigneux
- UR2NF - Neuropsychology and Functional Neuroimaging Research Unit at CRCN - Centre de Recherches Cognition et Neurosciences, UNI - ULB Neuroscience Institute, Université libre de Bruxelles (ULB), Brussels, Belgium
| | - Serge Goldman
- LCFC - Laboratoire de Cartographie fonctionnelle du Cerveau, UNI - ULB Neuroscience Institute, Université libre de Bruxelles (ULB), Brussels, Belgium; Magnetoencephalography Unit, Department of Functional Neuroimaging, Service of Nuclear Medicine, CUB - Hôpital Erasme, Brussels, Belgium
| | - Xavier De Tiège
- LCFC - Laboratoire de Cartographie fonctionnelle du Cerveau, UNI - ULB Neuroscience Institute, Université libre de Bruxelles (ULB), Brussels, Belgium; Magnetoencephalography Unit, Department of Functional Neuroimaging, Service of Nuclear Medicine, CUB - Hôpital Erasme, Brussels, Belgium
| |
Collapse
|
48
|
Altered neocortical tactile but preserved auditory early change detection responses in Friedreich ataxia. Clin Neurophysiol 2019; 130:1299-1310. [PMID: 31176929 DOI: 10.1016/j.clinph.2019.05.003] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/31/2019] [Revised: 04/16/2019] [Accepted: 05/01/2019] [Indexed: 12/17/2022]
Abstract
OBJECTIVE To study using magnetoencephalography (MEG) the spatio-temporal dynamics of neocortical responses involved in sensory processing and early change detection in Friedreich ataxia (FRDA). METHODS Tactile (TERs) and auditory (AERs) evoked responses, and early neocortical change detection responses indexed by the mismatch negativity (MMN) were recorded using tactile and auditory oddballs in sixteen FRDA patients and matched healthy subjects. Correlations between the maximal amplitude of each response, genotype and clinical parameters were investigated. RESULTS Evoked responses were detectable in all FRDA patients but one. In patients, TERs were delayed and reduced in amplitude, while AERs were only delayed. Only tactile MMN responses at the contralateral secondary somatosensory cortex were altered in FRDA patients. Maximal amplitudes of TERs, AERs and tactile MMN correlated with genotype, but did not correlate with clinical parameters. CONCLUSIONS In FRDA, theamplitude of tactile MMN responses at SII cortex are reduced and correlate with the genotype, whileauditory MMN responses are not altered. SIGNIFICANCE Somatosensory pathways and tactile early change detection are selectively impaired in FRDA.
Collapse
|
49
|
Leung RC, Pang EW, Brian JA, Taylor MJ. Happy and Angry Faces Elicit Atypical Neural Activation in Children With Autism Spectrum Disorder. BIOLOGICAL PSYCHIATRY: COGNITIVE NEUROSCIENCE AND NEUROIMAGING 2019; 4:1021-1030. [PMID: 31171500 DOI: 10.1016/j.bpsc.2019.03.013] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/23/2018] [Revised: 03/29/2019] [Accepted: 03/29/2019] [Indexed: 10/27/2022]
Abstract
BACKGROUND Autism spectrum disorder (ASD) is a neurodevelopmental disorder characterized by significant impairments in social interactions and communication. The ability to accurately perceive and interpret emotional faces is critical to successful social interactions. However, few studies have investigated the spatiotemporal profile of the neural mechanisms underlying emotional face processing in ASD, particularly in children. The current study fills this important gap. METHODS Participants were 55 children: 28 children with ASD (mean age = 9.5 ± 1.3 years) and 27 control children (mean age = 8.5 ± 1.3 years). All children completed an implicit emotional face task while magnetoencephalography was recorded. We examined spatiotemporal differences between the groups in neural activation during implicit processing of emotional faces. RESULTS Within-group analyses demonstrated greater right middle temporal (300-375 ms) and superior temporal (300-400 ms) activation to angry faces than to happy faces in control children, while children with ASD showed greater activation from 250 to 500 ms to happy faces than to angry faces across frontal and temporal regions. Between-group analyses demonstrated that children with ASD showed similar patterns of late (425-500 ms) posterior cingulate and thalamic underactivity to both angry and happy faces relative to control children, suggesting general atypical processing of emotional information. CONCLUSIONS Atypical posterior cingulate cortex and thalamus recruitment in children with ASD to emotional faces suggests poor modulation of toggling between the default mode network and task-based processing. Increased neural activity to happy faces compared with angry faces in children with ASD suggests reduced salience or immature response to anger, which in turn could contribute to deficits in social cognition in ASD.
Collapse
Affiliation(s)
- Rachel C Leung
- Department of Diagnostic Imaging, Hospital for Sick Children, Toronto, Ontario, Canada; Department of Psychology, University of Toronto, Toronto, Ontario, Canada.
| | - Elizabeth W Pang
- Division of Neurology, Hospital for Sick Children, Toronto, Ontario, Canada; Neurosciences and Mental Health Program, Research Institute, Hospital for Sick Children, Toronto, Ontario, Canada; Department of Pediatrics, University of Toronto, Toronto, Ontario, Canada
| | - Jessica A Brian
- Department of Pediatrics, University of Toronto, Toronto, Ontario, Canada; Autism Research Centre, Bloorview Research Institute, Holland Bloorview Rehabilitation Hospital, Toronto, Ontario, Canada
| | - Margot J Taylor
- Department of Diagnostic Imaging, Hospital for Sick Children, Toronto, Ontario, Canada; Neurosciences and Mental Health Program, Research Institute, Hospital for Sick Children, Toronto, Ontario, Canada; Department of Psychology, University of Toronto, Toronto, Ontario, Canada; Department of Pediatrics, University of Toronto, Toronto, Ontario, Canada
| |
Collapse
|
50
|
Cortical Tracking of Speech-in-Noise Develops from Childhood to Adulthood. J Neurosci 2019; 39:2938-2950. [PMID: 30745419 DOI: 10.1523/jneurosci.1732-18.2019] [Citation(s) in RCA: 31] [Impact Index Per Article: 6.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/10/2018] [Revised: 01/08/2019] [Accepted: 01/12/2019] [Indexed: 11/21/2022] Open
Abstract
In multitalker backgrounds, the auditory cortex of adult humans tracks the attended speech stream rather than the global auditory scene. Still, it is unknown whether such preferential tracking also occurs in children whose speech-in-noise (SiN) abilities are typically lower compared with adults. We used magnetoencephalography (MEG) to investigate the frequency-specific cortical tracking of different elements of a cocktail party auditory scene in 20 children (age range, 6-9 years; 8 females) and 20 adults (age range, 21-40 years; 10 females). During MEG recordings, subjects attended to four different 5 min stories, mixed with different levels of multitalker background at four signal-to-noise ratios (SNRs; noiseless, +5, 0, and -5 dB). Coherence analysis quantified the coupling between the time courses of the MEG activity and attended speech stream, multitalker background, or global auditory scene, respectively. In adults, statistically significant coherence was observed between MEG signals originating from the auditory system and the attended stream at <1, 1-4, and 4-8 Hz in all SNR conditions. Children displayed similar coupling at <1 and 1-4 Hz, but increasing noise impaired the coupling more strongly than in adults. Also, children displayed drastically lower coherence at 4-8 Hz in all SNR conditions. These results suggest that children's difficulties to understand speech in noisy conditions are related to an immature selective cortical tracking of the attended speech streams. Our results also provide unprecedented evidence for an acquired cortical tracking of speech at syllable rate and argue for a progressive development of SiN abilities in humans.SIGNIFICANCE STATEMENT Behaviorally, children are less proficient than adults at understanding speech-in-noise. Here, neuromagnetic signals were recorded while healthy adults and typically developing 6- to 9-year-old children attended to a speech stream embedded in a multitalker background noise with varying intensity. Results demonstrate that auditory cortices of both children and adults selectively track the attended speaker's voice rather than the global acoustic input at phrasal and word rates. However, increments of noise compromised the tracking significantly more in children than in adults. Unexpectedly, children displayed limited tracking of both the attended voice and the global acoustic input at the 4-8 Hz syllable rhythm. Thus, both speech-in-noise abilities and cortical tracking of speech syllable repetition rate seem to mature later in adolescence.
Collapse
|