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Marín–García A, Arzate-Mena JD, Corsi-Cabrera M, Muñoz-Torres Z, Olguín–Rodríguez PV, Ríos–Herrera WA, Rivera A, Müller MF. Stationary correlation pattern in highly non-stationary MEG recordings of healthy subjects and its relation to former EEG studies. PLoS One 2024; 19:e0307378. [PMID: 39436944 PMCID: PMC11495582 DOI: 10.1371/journal.pone.0307378] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/23/2023] [Accepted: 07/04/2024] [Indexed: 10/25/2024] Open
Abstract
In this study, we analyze magnetoencephalographic (MEG) recordings from 48 clinically healthy subjects obtained from the Human Connectome Project (HCP) while they performed a working memory task and a motor task. Our results reveal a well-developed, stable interrelation pattern that spans the entire scalp and is nearly universal, being almost task- and subject-independent. Additionally, we demonstrate that this pattern closely resembles a stationary correlation pattern (SCP) observed in EEG signals under various physiological and pathological conditions (the distribution of Pearson correlations are centered at about 0.75). Furthermore, we identify the most effective EEG reference for studying the brain's functional network derived from lag-zero cross-correlations. We contextualize these findings within the theory of complex dynamical systems operating near a critical point of a phase transition.
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Affiliation(s)
- ArlexOscar Marín–García
- Centro de Ciencias de la Complejidad, Universidad Nacional Autónoma de México, Ciudad de México, México
| | - J. Daniel Arzate-Mena
- Centro de Investigación en Ciencias, Universidad Autónoma del Estado de Morelos, Cuernavaca, Morelos, México
| | - Mari Corsi-Cabrera
- Unidad de Neurodesarrollo, Instituto de Neurobiología, Universidad Nacional Autónoma de México, Juriquilla, México
- Facultad de Psicología, Universidad Nacional Autónoma de México, Ciudad de México, México
| | - Zeidy Muñoz-Torres
- Centro de Ciencias de la Complejidad, Universidad Nacional Autónoma de México, Ciudad de México, México
- Facultad de Psicología, Universidad Nacional Autónoma de México, Ciudad de México, México
| | - Paola Vanessa Olguín–Rodríguez
- Centro de Ciencias de la Complejidad, Universidad Nacional Autónoma de México, Ciudad de México, México
- Centro de Investigación en Ciencias, Universidad Autónoma del Estado de Morelos, Cuernavaca, Morelos, México
| | | | - AnaLeonor Rivera
- Centro de Ciencias de la Complejidad, Universidad Nacional Autónoma de México, Ciudad de México, México
- Instituto de Ciencias Nucleares, Universidad Nacional Autónoma 15 de México, Ciudad de México, México
| | - Markus F. Müller
- Centro de Ciencias de la Complejidad, Universidad Nacional Autónoma de México, Ciudad de México, México
- Centro de Investigación en Ciencias, Universidad Autónoma del Estado de Morelos, Cuernavaca, Morelos, México
- Centro Internacional de Ciencias A.C., Cuernavaca, Morelos, México
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2
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Deodato M, Melcher D. Aperiodic EEG Predicts Variability of Visual Temporal Processing. J Neurosci 2024; 44:e2308232024. [PMID: 39168653 PMCID: PMC11450528 DOI: 10.1523/jneurosci.2308-23.2024] [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: 12/11/2023] [Revised: 05/16/2024] [Accepted: 06/19/2024] [Indexed: 08/23/2024] Open
Abstract
The human brain exhibits both oscillatory and aperiodic, or 1/f, activity. Although a large body of research has focused on the relationship between brain rhythms and sensory processes, aperiodic activity has often been overlooked as functionally irrelevant. Prompted by recent findings linking aperiodic activity to the balance between neural excitation and inhibition, we investigated its effects on the temporal resolution of perception. We recorded electroencephalography (EEG) from participants (both sexes) during the resting state and a task in which they detected the presence of two flashes separated by variable interstimulus intervals. Two-flash discrimination accuracy typically follows a sigmoid function whose steepness reflects perceptual variability or inconsistent integration/segregation of the stimuli. We found that individual differences in the steepness of the psychometric function correlated with EEG aperiodic exponents over posterior scalp sites. In other words, participants with flatter EEG spectra (i.e., greater neural excitation) exhibited increased sensory noise, resulting in shallower psychometric curves. Our finding suggests that aperiodic EEG is linked to sensory integration processes usually attributed to the rhythmic inhibition of neural oscillations. Overall, this correspondence between aperiodic neural excitation and behavioral measures of sensory noise provides a more comprehensive explanation of the relationship between brain activity and sensory integration and represents an important extension to theories of how the brain samples sensory input over time.
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Affiliation(s)
- Michele Deodato
- Psychology Program, Division of Science, New York University Abu Dhabi, Abu Dhabi, United Arab Emirates
| | - David Melcher
- Psychology Program, Division of Science, New York University Abu Dhabi, Abu Dhabi, United Arab Emirates
- Center for Brain and Health, NYUAD Research Institute, New York University Abu Dhabi, Abu Dhabi, United Arab Emirates
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3
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Senkowski D, Engel AK. Multi-timescale neural dynamics for multisensory integration. Nat Rev Neurosci 2024; 25:625-642. [PMID: 39090214 DOI: 10.1038/s41583-024-00845-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 07/02/2024] [Indexed: 08/04/2024]
Abstract
Carrying out any everyday task, be it driving in traffic, conversing with friends or playing basketball, requires rapid selection, integration and segregation of stimuli from different sensory modalities. At present, even the most advanced artificial intelligence-based systems are unable to replicate the multisensory processes that the human brain routinely performs, but how neural circuits in the brain carry out these processes is still not well understood. In this Perspective, we discuss recent findings that shed fresh light on the oscillatory neural mechanisms that mediate multisensory integration (MI), including power modulations, phase resetting, phase-amplitude coupling and dynamic functional connectivity. We then consider studies that also suggest multi-timescale dynamics in intrinsic ongoing neural activity and during stimulus-driven bottom-up and cognitive top-down neural network processing in the context of MI. We propose a new concept of MI that emphasizes the critical role of neural dynamics at multiple timescales within and across brain networks, enabling the simultaneous integration, segregation, hierarchical structuring and selection of information in different time windows. To highlight predictions from our multi-timescale concept of MI, real-world scenarios in which multi-timescale processes may coordinate MI in a flexible and adaptive manner are considered.
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Affiliation(s)
- Daniel Senkowski
- Department of Psychiatry and Neurosciences, Charité - Universitätsmedizin Berlin, Berlin, Germany
| | - Andreas K Engel
- Department of Neurophysiology and Pathophysiology, University Medical Center Hamburg-Eppendorf, Hamburg, Germany.
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4
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Gavenas J, Rutishauser U, Schurger A, Maoz U. Slow ramping emerges from spontaneous fluctuations in spiking neural networks. Nat Commun 2024; 15:7285. [PMID: 39179554 PMCID: PMC11344096 DOI: 10.1038/s41467-024-51401-x] [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: 10/13/2023] [Accepted: 08/05/2024] [Indexed: 08/26/2024] Open
Abstract
The capacity to initiate actions endogenously is critical for goal-directed behavior. Spontaneous voluntary actions are typically preceded by slow-ramping activity in medial frontal cortex that begins around two seconds before movement, which may reflect spontaneous fluctuations that influence action timing. However, the mechanisms by which these slow ramping signals emerge from single-neuron and network dynamics remain poorly understood. Here, we developed a spiking neural-network model that produces spontaneous slow ramping activity in single neurons and population activity with onsets ~2 s before threshold crossings. A key prediction of our model is that neurons that ramp together have correlated firing patterns before ramping onset. We confirmed this model-derived hypothesis in a dataset of human single neuron recordings from medial frontal cortex. Our results suggest that slow ramping signals reflect bounded spontaneous fluctuations that emerge from quasi-winner-take-all dynamics in clustered networks that are temporally stabilized by slow-acting synapses.
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Affiliation(s)
- Jake Gavenas
- Institute for Interdisciplinary Brain and Behavioral Sciences, Chapman University, Orange, CA, USA.
- Schmid College of Science and Technology, Chapman University, Orange, CA, USA.
- Department of Neurosurgery, Cedars-Sinai Medical Center, Los Angeles, CA, USA.
| | - Ueli Rutishauser
- Department of Neurosurgery, Cedars-Sinai Medical Center, Los Angeles, CA, USA
- Department of Neurology, Cedars-Sinai Medical Center, Los Angeles, CA, USA
- Center for Neural Science and Medicine, Department of Biomedical Sciences, Cedars-Sinai Medical Center, Los Angeles, CA, USA
- Division of Biology and Biological Engineering, California Institute of Technology, Pasadena, CA, USA
| | - Aaron Schurger
- Institute for Interdisciplinary Brain and Behavioral Sciences, Chapman University, Orange, CA, USA
- Crean College of Health and Behavioral Sciences, Chapman University, Orange, CA, USA
- INSERM U992, Cognitive Neuroimaging Unit, NeuroSpin Center, Gif sur Yvette, 91191, France
- Commissariat à l'Energie Atomique, Direction des Sciences du Vivant, I2BM, NeuroSpin Center, Gif sur Yvette, 91191, France
| | - Uri Maoz
- Institute for Interdisciplinary Brain and Behavioral Sciences, Chapman University, Orange, CA, USA.
- Schmid College of Science and Technology, Chapman University, Orange, CA, USA.
- Division of Biology and Biological Engineering, California Institute of Technology, Pasadena, CA, USA.
- Crean College of Health and Behavioral Sciences, Chapman University, Orange, CA, USA.
- Fowler School of Engineering, Chapman University, Orange, CA, USA.
- Anderson School of Management, University of California, Los Angeles, CA, USA.
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5
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Srinivasan K, Ribeiro TL, Kells P, Plenz D. The recovery of parabolic avalanches in spatially subsampled neuronal networks at criticality. Sci Rep 2024; 14:19329. [PMID: 39164334 PMCID: PMC11335857 DOI: 10.1038/s41598-024-70014-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/29/2024] [Accepted: 08/12/2024] [Indexed: 08/22/2024] Open
Abstract
Scaling relationships are key in characterizing complex systems at criticality. In the brain, they are evident in neuronal avalanches-scale-invariant cascades of neuronal activity quantified by power laws. Avalanches manifest at the cellular level as cascades of neuronal groups that fire action potentials simultaneously. Such spatiotemporal synchronization is vital to theories on brain function yet avalanche synchronization is often underestimated when only a fraction of neurons is observed. Here, we investigate biases from fractional sampling within a balanced network of excitatory and inhibitory neurons with all-to-all connectivity and critical branching process dynamics. We focus on how mean avalanche size scales with avalanche duration. For parabolic avalanches, this scaling is quadratic, quantified by the scaling exponent, χ = 2, reflecting rapid spatial expansion of simultaneous neuronal firing over short durations. However, in networks sampled fractionally, χ is significantly lower. We demonstrate that applying temporal coarse-graining and increasing a minimum threshold for coincident firing restores χ = 2, even when as few as 0.1% of neurons are sampled. This correction crucially depends on the network being critical and fails for near sub- and supercritical branching dynamics. Using cellular 2-photon imaging, our approach robustly identifies χ = 2 over a wide parameter regime in ongoing neuronal activity from frontal cortex of awake mice. In contrast, the common 'crackling noise' approach fails to determine χ under similar sampling conditions at criticality. Our findings overcome scaling bias from fractional sampling and demonstrate rapid, spatiotemporal synchronization of neuronal assemblies consistent with scale-invariant, parabolic avalanches at criticality.
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Affiliation(s)
- Keshav Srinivasan
- Section on Critical Brain Dynamics, National Institute of Mental Health, Porter Neuroscience Research Center, Rm 3A-1000, 35 Convent Drive, Bethesda, MD, 20892, USA
| | - Tiago L Ribeiro
- Section on Critical Brain Dynamics, National Institute of Mental Health, Porter Neuroscience Research Center, Rm 3A-1000, 35 Convent Drive, Bethesda, MD, 20892, USA
| | - Patrick Kells
- Section on Critical Brain Dynamics, National Institute of Mental Health, Porter Neuroscience Research Center, Rm 3A-1000, 35 Convent Drive, Bethesda, MD, 20892, USA
| | - Dietmar Plenz
- Section on Critical Brain Dynamics, National Institute of Mental Health, Porter Neuroscience Research Center, Rm 3A-1000, 35 Convent Drive, Bethesda, MD, 20892, USA.
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6
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Serafim F, Carvalho TTA, Copelli M, Carelli PV. Maximum-entropy-based metrics for quantifying critical dynamics in spiking neuron data. Phys Rev E 2024; 110:024401. [PMID: 39294971 DOI: 10.1103/physreve.110.024401] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/01/2024] [Accepted: 07/08/2024] [Indexed: 09/21/2024]
Abstract
An important working hypothesis to investigate brain activity is whether it operates in a critical regime. Recently, maximum-entropy phenomenological models have emerged as an alternative way of identifying critical behavior in neuronal data sets. In the present paper, we investigate the signatures of criticality from a firing rate-based maximum-entropy approach on data sets generated by computational models, and we compare them to experimental results. We found that the maximum entropy approach consistently identifies critical behavior around the phase transition in models and rules out criticality in models without phase transition. The maximum-entropy-model results are compatible with results for cortical data from urethane-anesthetized rats data, providing further support for criticality in the brain.
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Affiliation(s)
| | - Tawan T A Carvalho
- Departamento de Física, Centro de Ciência Exatas e da Natureza, Universidade Federal de Pernambuco, Recife PE 50670-901, Brazil
- Life and Health Sciences Research Institute (ICVS), School of Medicine, University of Minho, 4710-057 Braga, Portugal
- ICVS/3B's-PT Government Associate Laboratory, 4806-909 Braga/Guimares, Portugal
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7
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Molina JL, Joshi YB, Nungaray JA, Sprock J, Attarha M, Biagianti B, Thomas ML, Swerdlow NR, Light GA. Early auditory processing abnormalities alter individual learning trajectories and sensitivity to computerized cognitive training in schizophrenia. Psychol Med 2024; 54:2669-2676. [PMID: 38587021 DOI: 10.1017/s0033291724000783] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 04/09/2024]
Abstract
BACKGROUND Auditory system plasticity is a promising target for neuromodulation, cognitive rehabilitation and therapeutic development in schizophrenia (SZ). Auditory-based targeted cognitive training (TCT) is a 'bottom up' intervention designed to enhance the speed and accuracy of auditory information processing, which has been shown to improve neurocognition in certain SZ patients. However, the dynamics of TCT learning as a function of training exercises and their impact on neurocognitive functioning and therapeutic outcomes are unknown. METHODS Forty subjects (SZ, n = 21; healthy subjects (HS), n = 19) underwent comprehensive clinical, cognitive, and auditory assessments, including measurements of auditory processing speed (APS) at baseline and after 1-h of TCT. SZ patients additionally completed 30-hours of TCT and repeated assessments ~10-12 weeks later. RESULTS SZ patients were deficient in APS at baseline (d = 0.96, p < 0.005) relative to HS. After 1-h of TCT, analyses revealed significant main effects of diagnosis (d = 1.75, p = 0.002) and time (d = 1.04, p < 0.001), and a diagnosis × time interaction (d = 0.85, p = 0.02) on APS. APS learning effects were robust after 1-h in SZ patients (d = 1.47, p < 0.001) and persisted throughout the 30-h of training. Baseline APS was associated with verbal learning gains after 30-h of TCT (r = 0.51, p = 0.02) in SZ. CONCLUSIONS TCT learning metrics may have prognostic utility and aid in the prospective identification of individuals likely to benefit from TCT. Future experimental medicine studies may advance predictive algorithms that enhance TCT-related clinical, cognitive and functional outcomes.
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Affiliation(s)
- Juan L Molina
- Department of Psychiatry, University of California, San Diego, CA, USA
- VA Desert Pacific Mental Illness Research, Education and Clinical Center (MIRECC), VA San Diego Healthcare System, San Diego, CA, USA
| | - Yash B Joshi
- Department of Psychiatry, University of California, San Diego, CA, USA
- VA Desert Pacific Mental Illness Research, Education and Clinical Center (MIRECC), VA San Diego Healthcare System, San Diego, CA, USA
| | - John A Nungaray
- Department of Psychiatry, University of California, San Diego, CA, USA
| | - Joyce Sprock
- Department of Psychiatry, University of California, San Diego, CA, USA
- VA Desert Pacific Mental Illness Research, Education and Clinical Center (MIRECC), VA San Diego Healthcare System, San Diego, CA, USA
| | - Mouna Attarha
- Department of R&D, Posit Science Corporation, San Francisco, CA, USA
| | - Bruno Biagianti
- Department of Psychology, University of Milano-Bicocca, Milan, Italy
| | - Michael L Thomas
- Department of Psychology, Colorado State University, Fort Collins, CO, USA
| | - Neal R Swerdlow
- Department of Psychiatry, University of California, San Diego, CA, USA
| | - Gregory A Light
- Department of Psychiatry, University of California, San Diego, CA, USA
- VA Desert Pacific Mental Illness Research, Education and Clinical Center (MIRECC), VA San Diego Healthcare System, San Diego, CA, USA
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8
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Srinivasan K, Ribeiro TL, Kells P, Plenz D. The recovery of parabolic avalanches in spatially subsampled neuronal networks at criticality. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.02.26.582056. [PMID: 38464324 PMCID: PMC10925085 DOI: 10.1101/2024.02.26.582056] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 03/12/2024]
Abstract
Scaling relationships are key in characterizing complex systems at criticality. In the brain, they are evident in neuronal avalanches-scale-invariant cascades of neuronal activity quantified by power laws. Avalanches manifest at the cellular level as cascades of neuronal groups that fire action potentials simultaneously. Such spatiotemporal synchronization is vital to theories on brain function yet avalanche synchronization is often underestimated when only a fraction of neurons is observed. Here, we investigate biases from fractional sampling within a balanced network of excitatory and inhibitory neurons with all-to-all connectivity and critical branching process dynamics. We focus on how mean avalanche size scales with avalanche duration. For parabolic avalanches, this scaling is quadratic, quantified by the scaling exponent, χ = 2 , reflecting rapid spatial expansion of simultaneous neuronal firing over short durations. However, in networks sampled fractionally, χ is significantly lower. We demonstrate that applying temporal coarse-graining and increasing a minimum threshold for coincident firing restores χ = 2 , even when as few as 0.1% of neurons are sampled. This correction crucially depends on the network being critical and fails for near sub- and supercritical branching dynamics. Using cellular 2-photon imaging, our approach robustly identifies χ = 2 over a wide parameter regime in ongoing neuronal activity from frontal cortex of awake mice. In contrast, the common 'crackling noise' approach fails to determine χ under similar sampling conditions at criticality. Our findings overcome scaling bias from fractional sampling and demonstrate rapid, spatiotemporal synchronization of neuronal assemblies consistent with scale-invariant, parabolic avalanches at criticality.
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Affiliation(s)
- Keshav Srinivasan
- Section on Critical Brain Dynamics, National Institute of Mental Health, Bethesda, MD 20892, USA
| | - Tiago L. Ribeiro
- Section on Critical Brain Dynamics, National Institute of Mental Health, Bethesda, MD 20892, USA
| | - Patrick Kells
- Section on Critical Brain Dynamics, National Institute of Mental Health, Bethesda, MD 20892, USA
| | - Dietmar Plenz
- Section on Critical Brain Dynamics, National Institute of Mental Health, Bethesda, MD 20892, USA
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9
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Chini M, Hnida M, Kostka JK, Chen YN, Hanganu-Opatz IL. Preconfigured architecture of the developing mouse brain. Cell Rep 2024; 43:114267. [PMID: 38795344 DOI: 10.1016/j.celrep.2024.114267] [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: 12/20/2023] [Revised: 03/13/2024] [Accepted: 05/08/2024] [Indexed: 05/27/2024] Open
Abstract
In the adult brain, structural and functional parameters, such as synaptic sizes and neuronal firing rates, follow right-skewed and heavy-tailed distributions. While this organization is thought to have significant implications, its development is still largely unknown. Here, we address this knowledge gap by investigating a large-scale dataset recorded from the prefrontal cortex and the olfactory bulb of mice aged 4-60 postnatal days. We show that firing rates and spike train interactions have a largely stable distribution shape throughout the first 60 postnatal days and that the prefrontal cortex displays a functional small-world architecture. Moreover, early brain activity exhibits an oligarchical organization, where high-firing neurons have hub-like properties. In a neural network model, we show that analogously right-skewed and heavy-tailed synaptic parameters are instrumental to consistently recapitulate the experimental data. Thus, functional and structural parameters in the developing brain are already extremely distributed, suggesting that this organization is preconfigured and not experience dependent.
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Affiliation(s)
- Mattia Chini
- Institute of Developmental Neurophysiology, Center for Molecular Neurobiology, University Medical Center Hamburg-Eppendorf, Hamburg, Germany.
| | - Marilena Hnida
- Institute of Developmental Neurophysiology, Center for Molecular Neurobiology, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
| | - Johanna K Kostka
- Institute of Developmental Neurophysiology, Center for Molecular Neurobiology, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
| | - Yu-Nan Chen
- Institute of Developmental Neurophysiology, Center for Molecular Neurobiology, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
| | - Ileana L Hanganu-Opatz
- Institute of Developmental Neurophysiology, Center for Molecular Neurobiology, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
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10
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Van Schependom J, Baetens K, Nagels G, Olmi S, Beste C. Neurophysiological avenues to better conceptualizing adaptive cognition. Commun Biol 2024; 7:626. [PMID: 38789522 PMCID: PMC11126671 DOI: 10.1038/s42003-024-06331-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/12/2024] [Accepted: 05/14/2024] [Indexed: 05/26/2024] Open
Abstract
We delve into the human brain's remarkable capacity for adaptability and sustained cognitive functioning, phenomena traditionally encompassed as executive functions or cognitive control. The neural underpinnings that enable the seamless navigation between transient thoughts without detracting from overarching goals form the core of our article. We discuss the concept of "metacontrol," which builds upon conventional cognitive control theories by proposing a dynamic balancing of processes depending on situational demands. We critically discuss the role of oscillatory processes in electrophysiological activity at different scales and the importance of desynchronization and partial phase synchronization in supporting adaptive behavior including neural noise accounts, transient dynamics, phase-based measures (coordination dynamics) and neural mass modelling. The cognitive processes focused and neurophysiological avenues outlined are integral to understanding diverse psychiatric disorders thereby contributing to a more nuanced comprehension of cognitive control and its neural bases in both health and disease.
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Affiliation(s)
- Jeroen Van Schependom
- AIMS Lab, Center for Neurosciences, Vrije Universiteit Brussel, Brussels, Belgium
- Department of Electronics and Informatics (ETRO), Vrije Universiteit Brussel, Brussels, Belgium
| | - Kris Baetens
- Brain, Body and Cognition, Vrije Universiteit Brussel, Brussels, Belgium
| | - Guy Nagels
- AIMS Lab, Center for Neurosciences, Vrije Universiteit Brussel, Brussels, Belgium
- UZ Brussel, Department of Neurology, Brussels, Belgium
- St Edmund Hall, University of Oxford, Oxford, United Kingdom
| | - Simona Olmi
- CNR-Consiglio Nazionale delle Ricerche - Istituto dei Sistemi Complessi, Sesto Fiorentino, Italy
| | - Christian Beste
- Cognitive Neurophysiology, Department of Child and Adolescent Psychiatry, Faculty of Medicine, TU Dresden, Dresden, Germany.
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Gavenas J, Rutishauser U, Schurger A, Maoz U. Slow ramping emerges from spontaneous fluctuations in spiking neural networks. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2023.05.27.542589. [PMID: 37398452 PMCID: PMC10312459 DOI: 10.1101/2023.05.27.542589] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/04/2023]
Abstract
1. We reveal a mechanism for slow-ramping signals before spontaneous voluntary movements. 2. Slow synapses stabilize spontaneous fluctuations in spiking neural network. 3. We validate model predictions in human frontal cortical single-neuron recordings. 4. The model recreates the readiness potential in an EEG proxy signal. 5. Neurons that ramp together had correlated activity before ramping onset. The capacity to initiate actions endogenously is critical for goal-directed behavior. Spontaneous voluntary actions are typically preceded by slow-ramping activity in medial frontal cortex that begins around two seconds before movement, which may reflect spontaneous fluctuations that influence action timing. However, the mechanisms by which these slow ramping signals emerge from single-neuron and network dynamics remain poorly understood. Here, we developed a spiking neural-network model that produces spontaneous slow ramping activity in single neurons and population activity with onsets ∼2 seconds before threshold crossings. A key prediction of our model is that neurons that ramp together have correlated firing patterns before ramping onset. We confirmed this model-derived hypothesis in a dataset of human single neuron recordings from medial frontal cortex. Our results suggest that slow ramping signals reflect bounded spontaneous fluctuations that emerge from quasi-winner-take-all dynamics in clustered networks that are temporally stabilized by slow-acting synapses.
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12
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Proshina E, Martynova O, Portnova G, Khayrullina G, Sysoeva O. Long-range temporal correlations in resting state alpha oscillations in major depressive disorder and obsessive-compulsive disorder. Front Neuroinform 2024; 18:1339590. [PMID: 38450096 PMCID: PMC10914983 DOI: 10.3389/fninf.2024.1339590] [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: 11/16/2023] [Accepted: 01/29/2024] [Indexed: 03/08/2024] Open
Abstract
Introduction Mental disorders are a significant concern in contemporary society, with a pressing need to identify biological markers. Long-range temporal correlations (LRTC) of brain rhythms have been widespread in clinical cohort studies, especially in major depressive disorder (MDD). However, research on LRTC in obsessive-compulsive disorder (OCD) is severely limited. Given the high co-occurrence of OCD and MDD, we conducted a comparative LRTC investigation. We assumed that the LRTC patterns will allow us to compare measures of brain cortical balance of excitation and inhibition in OCD and MDD, which will be useful in the area of differential diagnosis. Methods In this study, we used the 64-channel resting state EEG of 29 MDD participants, 26 OCD participants, and a control group of 37 volunteers. Detrended fluctuation analyzes was used to assess LRTC. Results Our results indicate that all scaling exponents of the three subject groups exhibited persistent LRTC of EEG oscillations. There was a tendency for LRTC to be higher in disorders than in controls, but statistically significant differences were found between the OCD and control groups in the entire frontal and left parietal occipital areas, and between the MDD and OCD groups in the middle and right frontal areas. Discussion We believe that these results indicate abnormalities in the inhibitory and excitatory neurotransmitter systems, predominantly affecting areas related to executive functions.
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Affiliation(s)
- Ekaterina Proshina
- Centre for Cognition and Decision Making, Institute for Cognitive Neuroscience, National Research University Higher School of Economics, Moscow, Russia
| | - Olga Martynova
- Laboratory of Human Higher Nervous Activity, Institute of Higher Nervous Activity and Neurophysiology of RAS, Moscow, Russia
- Faculty of Biology and Biotechnology, National Research University Higher School of Economics, Moscow, Russia
| | - Galina Portnova
- Laboratory of Human Higher Nervous Activity, Institute of Higher Nervous Activity and Neurophysiology of RAS, Moscow, Russia
| | - Guzal Khayrullina
- Centre for Cognition and Decision Making, Institute for Cognitive Neuroscience, National Research University Higher School of Economics, Moscow, Russia
- Laboratory of Human Higher Nervous Activity, Institute of Higher Nervous Activity and Neurophysiology of RAS, Moscow, Russia
| | - Olga Sysoeva
- Laboratory of Human Higher Nervous Activity, Institute of Higher Nervous Activity and Neurophysiology of RAS, Moscow, Russia
- Sirius Center for Cognitive Sciences, Sirius University of Science and Technology, Sochi, Russia
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13
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D’Andrea A, Croce P, O’Byrne J, Jerbi K, Pascarella A, Raffone A, Pizzella V, Marzetti L. Mindfulness meditation styles differently modulate source-level MEG microstate dynamics and complexity. Front Neurosci 2024; 18:1295615. [PMID: 38370436 PMCID: PMC10869546 DOI: 10.3389/fnins.2024.1295615] [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: 09/16/2023] [Accepted: 01/15/2024] [Indexed: 02/20/2024] Open
Abstract
Background The investigation of mindfulness meditation practice, classically divided into focused attention meditation (FAM), and open monitoring meditation (OMM) styles, has seen a long tradition of theoretical, affective, neurophysiological and clinical studies. In particular, the high temporal resolution of magnetoencephalography (MEG) or electroencephalography (EEG) has been exploited to fill the gap between the personal experience of meditation practice and its neural correlates. Mounting evidence, in fact, shows that human brain activity is highly dynamic, transiting between different brain states (microstates). In this study, we aimed at exploring MEG microstates at source-level during FAM, OMM and in the resting state, as well as the complexity and criticality of dynamic transitions between microstates. Methods Ten right-handed Theravada Buddhist monks with a meditative expertise of minimum 2,265 h participated in the experiment. MEG data were acquired during a randomized block design task (6 min FAM, 6 min OMM, with each meditative block preceded and followed by 3 min resting state). Source reconstruction was performed using eLORETA on individual cortical space, and then parcellated according to the Human Connect Project atlas. Microstate analysis was then applied to parcel level signals in order to derive microstate topographies and indices. In addition, from microstate sequences, the Hurst exponent and the Lempel-Ziv complexity (LZC) were computed. Results Our results show that the coverage and occurrence of specific microstates are modulated either by being in a meditative state or by performing a specific meditation style. Hurst exponent values in both meditation conditions are reduced with respect to the value observed during rest, LZC shows significant differences between OMM, FAM, and REST, with a progressive increase from REST to FAM to OMM. Discussion Importantly, we report changes in brain criticality indices during meditation and between meditation styles, in line with a state-like effect of meditation on cognitive performance. In line with previous reports, we suggest that the change in cognitive state experienced in meditation is paralleled by a shift with respect to critical points in brain dynamics.
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Affiliation(s)
- Antea D’Andrea
- Department of Neuroscience, Imaging and Clinical Sciences, University of Chieti-Pescara, Chieti, Abruzzo, Italy
| | - Pierpaolo Croce
- Department of Neuroscience, Imaging and Clinical Sciences, University of Chieti-Pescara, Chieti, Abruzzo, Italy
| | - Jordan O’Byrne
- Department of Psychology, University of Montreal, Montreal, QC, Canada
| | - Karim Jerbi
- Department of Psychology, University of Montreal, Montreal, QC, Canada
| | - Annalisa Pascarella
- Institute for the Applications of Calculus “M. Picone”, National Research Council, Rome, Lazio, Italy
| | - Antonino Raffone
- Department of Psychology, Sapienza University of Rome, Rome, Lazio, Italy
| | - Vittorio Pizzella
- Department of Neuroscience, Imaging and Clinical Sciences, University of Chieti-Pescara, Chieti, Abruzzo, Italy
- Institute for Advanced Biomedical Technologies, University of Chieti-Pescara, Chieti, Abruzzo, Italy
| | - Laura Marzetti
- Department of Neuroscience, Imaging and Clinical Sciences, University of Chieti-Pescara, Chieti, Abruzzo, Italy
- Institute for Advanced Biomedical Technologies, University of Chieti-Pescara, Chieti, Abruzzo, Italy
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14
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Corsi MC, Sorrentino P, Schwartz D, George N, Gollo LL, Chevallier S, Hugueville L, Kahn AE, Dupont S, Bassett DS, Jirsa V, De Vico Fallani F. Measuring neuronal avalanches to inform brain-computer interfaces. iScience 2024; 27:108734. [PMID: 38226174 PMCID: PMC10788504 DOI: 10.1016/j.isci.2023.108734] [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: 08/08/2023] [Revised: 10/18/2023] [Accepted: 12/12/2023] [Indexed: 01/17/2024] Open
Abstract
Large-scale interactions among multiple brain regions manifest as bursts of activations called neuronal avalanches, which reconfigure according to the task at hand and, hence, might constitute natural candidates to design brain-computer interfaces (BCIs). To test this hypothesis, we used source-reconstructed magneto/electroencephalography during resting state and a motor imagery task performed within a BCI protocol. To track the probability that an avalanche would spread across any two regions, we built an avalanche transition matrix (ATM) and demonstrated that the edges whose transition probabilities significantly differed between conditions hinged selectively on premotor regions in all subjects. Furthermore, we showed that the topology of the ATMs allows task-decoding above the current gold standard. Hence, our results suggest that neuronal avalanches might capture interpretable differences between tasks that can be used to inform brain-computer interfaces.
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Affiliation(s)
- Marie-Constance Corsi
- Sorbonne Université, Institut du cerveau - Paris Brain Institute - ICM, CNRS, Inserm, APHP, Hôpital de la Pitié Salpêtrière, Paris, France
- Inria, Aramis Team, Paris, France
| | - Pierpaolo Sorrentino
- Institut de Neuroscience des Systèmes, Aix-Marseille University, Inserm, Marseille, France
| | - Denis Schwartz
- Institut du Cerveau - Paris Brain Institute, ICM, Inserm U 1127, CNRS UMR 7225, Sorbonne Université, CENIR, Centre MEG-EEG, Paris, France
| | - Nathalie George
- Sorbonne Université, Institut du cerveau - Paris Brain Institute - ICM, CNRS, Inserm, APHP, Hôpital de la Pitié Salpêtrière, Paris, France
- Institut du Cerveau - Paris Brain Institute, ICM, Inserm U 1127, CNRS UMR 7225, Sorbonne Université, CENIR, Centre MEG-EEG, Paris, France
| | - Leonardo L. Gollo
- The Turner Institute for Brain and Mental Health, School of Psychological Sciences, and Monash Biomedical Imaging, Monash University, Victoria 3168, Australia
| | | | - Laurent Hugueville
- Institut de Neuroscience des Systèmes, Aix-Marseille University, Inserm, Marseille, France
| | - Ari E. Kahn
- Princeton Neuroscience Institute, Princeton University, Princeton, NJ 08540, USA
| | - Sophie Dupont
- Sorbonne Université, Institut du cerveau - Paris Brain Institute - ICM, CNRS, Inserm, APHP, Hôpital de la Pitié Salpêtrière, Paris, France
| | | | - Viktor Jirsa
- Institut de Neuroscience des Systèmes, Aix-Marseille University, Inserm, Marseille, France
| | - Fabrizio De Vico Fallani
- Sorbonne Université, Institut du cerveau - Paris Brain Institute - ICM, CNRS, Inserm, APHP, Hôpital de la Pitié Salpêtrière, Paris, France
- Inria, Aramis Team, Paris, France
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15
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Wang ZJ, Lee HC, Chuang CH, Hsiao FC, Lee SH, Hsu AL, Wu CW. Traces of EEG-fMRI coupling reveals neurovascular dynamics on sleep inertia. Sci Rep 2024; 14:1537. [PMID: 38233587 PMCID: PMC10794702 DOI: 10.1038/s41598-024-51694-4] [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: 07/13/2023] [Accepted: 01/08/2024] [Indexed: 01/19/2024] Open
Abstract
Upon emergence from sleep, individuals experience temporary hypo-vigilance and grogginess known as sleep inertia. During the transient period of vigilance recovery from prior nocturnal sleep, the neurovascular coupling (NVC) may not be static and constant as assumed by previous neuroimaging studies. Stemming from this viewpoint of sleep inertia, this study aims to probe the NVC changes as awakening time prolongs using simultaneous EEG-fMRI. The time-lagged coupling between EEG features of vigilance and BOLD-fMRI signals, in selected regions of interest, was calculated with one pre-sleep and three consecutive post-awakening resting-state measures. We found marginal changes in EEG theta/beta ratio and spectral slope across post-awakening sessions, demonstrating alterations of vigilance during sleep inertia. Time-varying EEG-fMRI coupling as awakening prolonged was evidenced by the changing time lags of the peak correlation between EEG alpha-vigilance and fMRI-thalamus, as well as EEG spectral slope and fMRI-anterior cingulate cortex. This study provides the first evidence of potential dynamicity of NVC occurred in sleep inertia and opens new avenues for non-invasive neuroimaging investigations into the neurophysiological mechanisms underlying brain state transitions.
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Affiliation(s)
- Zhitong John Wang
- Graduate Institute of Mind, Brain and Consciousness, Taipei Medical University, 5 Floor, 301, Yuantong Rd., Zhonghe Dist, New Taipei, 235040, Taiwan
| | - Hsin-Chien Lee
- Department of Psychiatry, School of Medicine, College of Medicine, Taipei Medical University, Taipei, Taiwan
- Research Center of Sleep Medicine, Taipei Medical University Hospital, Taipei, Taiwan
| | - Chun-Hsiang Chuang
- Research Center for Education and Mind Sciences, College of Education, National Tsing Hua University, Hsinchu, Taiwan
| | - Fan-Chi Hsiao
- Department of Counseling, Clinical and Industrial/Organizational Psychology, Ming Chuan University, Taoyuan, Taiwan
| | - Shwu-Hua Lee
- Department of Psychiatry, Chang Gung Memorial Hospital at Linkou, 259, Wenhua 1St Rd., Guishan Dist., Taoyuan, 33302, Taiwan
- School of Medicine, College of Medicine, Chang Gung University, Taoyuan, Taiwan
| | - Ai-Ling Hsu
- Department of Psychiatry, Chang Gung Memorial Hospital at Linkou, 259, Wenhua 1St Rd., Guishan Dist., Taoyuan, 33302, Taiwan.
- Bachelor Program in Artificial Intelligence, Chang Gung University, Taoyuan, Taiwan.
| | - Changwei W Wu
- Graduate Institute of Mind, Brain and Consciousness, Taipei Medical University, 5 Floor, 301, Yuantong Rd., Zhonghe Dist, New Taipei, 235040, Taiwan.
- Research Center of Sleep Medicine, Taipei Medical University Hospital, Taipei, Taiwan.
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16
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Väyrynen T, Helakari H, Korhonen V, Tuunanen J, Huotari N, Piispala J, Kallio M, Raitamaa L, Kananen J, Järvelä M, Matias Palva J, Kiviniemi V. Infra-slow fluctuations in cortical potentials and respiration drive fast cortical EEG rhythms in sleeping and waking states. Clin Neurophysiol 2023; 156:207-219. [PMID: 37972532 DOI: 10.1016/j.clinph.2023.10.013] [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: 04/18/2023] [Revised: 08/09/2023] [Accepted: 10/23/2023] [Indexed: 11/19/2023]
Abstract
OBJECTIVE Infra-slow fluctuations (ISF, 0.008-0.1 Hz) characterize hemodynamic and electric potential signals of human brain. ISFs correlate with the amplitude dynamics of fast (>1 Hz) neuronal oscillations, and may arise from permeability fluctuations of the blood-brain barrier (BBB). It is unclear if physiological rhythms like respiration drive or track fast cortical oscillations, and the role of sleep in this coupling is unknown. METHODS We used high-density full-band electroencephalography (EEG) in healthy human volunteers (N = 21) to measure concurrently the ISFs, respiratory pulsations, and fast neuronal oscillations during periods of wakefulness and sleep, and to assess the strength and direction of their phase-amplitude coupling. RESULTS The phases of ISFs and respiration were both coupled with the amplitude of fast neuronal oscillations, with stronger ISF coupling being evident during sleep. Phases of ISF and respiration drove the amplitude dynamics of fast oscillations in sleeping and waking states, with different contributions. CONCLUSIONS ISFs in slow cortical potentials and respiration together significantly determine the dynamics of fast cortical oscillations. SIGNIFICANCE We propose that these slow physiological phases play a significant role in coordinating cortical excitability, which is a fundamental aspect of brain function.
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Affiliation(s)
- Tommi Väyrynen
- Oulu Functional NeuroImaging (OFNI), Department of Diagnostic Radiology, Oulu University Hospital, Oulu 90029, Finland; MIPT group to: Research Unit of Health Sciences and Technology (HST), Faculty of Medicine, University of Oulu, Oulu 90220, Finland; Medical Research Center (MRC), Oulu 90220, Finland.
| | - Heta Helakari
- Oulu Functional NeuroImaging (OFNI), Department of Diagnostic Radiology, Oulu University Hospital, Oulu 90029, Finland; MIPT group to: Research Unit of Health Sciences and Technology (HST), Faculty of Medicine, University of Oulu, Oulu 90220, Finland; Medical Research Center (MRC), Oulu 90220, Finland
| | - Vesa Korhonen
- Oulu Functional NeuroImaging (OFNI), Department of Diagnostic Radiology, Oulu University Hospital, Oulu 90029, Finland; MIPT group to: Research Unit of Health Sciences and Technology (HST), Faculty of Medicine, University of Oulu, Oulu 90220, Finland; Medical Research Center (MRC), Oulu 90220, Finland
| | - Johanna Tuunanen
- Oulu Functional NeuroImaging (OFNI), Department of Diagnostic Radiology, Oulu University Hospital, Oulu 90029, Finland; MIPT group to: Research Unit of Health Sciences and Technology (HST), Faculty of Medicine, University of Oulu, Oulu 90220, Finland; Medical Research Center (MRC), Oulu 90220, Finland
| | - Niko Huotari
- Oulu Functional NeuroImaging (OFNI), Department of Diagnostic Radiology, Oulu University Hospital, Oulu 90029, Finland; MIPT group to: Research Unit of Health Sciences and Technology (HST), Faculty of Medicine, University of Oulu, Oulu 90220, Finland; Medical Research Center (MRC), Oulu 90220, Finland
| | - Johanna Piispala
- MIPT group to: Research Unit of Health Sciences and Technology (HST), Faculty of Medicine, University of Oulu, Oulu 90220, Finland; Medical Research Center (MRC), Oulu 90220, Finland; Clinical Neurophysiology, Oulu University Hospital, Oulu 90220, Finland
| | - Mika Kallio
- MIPT group to: Research Unit of Health Sciences and Technology (HST), Faculty of Medicine, University of Oulu, Oulu 90220, Finland; Medical Research Center (MRC), Oulu 90220, Finland; Clinical Neurophysiology, Oulu University Hospital, Oulu 90220, Finland
| | - Lauri Raitamaa
- Oulu Functional NeuroImaging (OFNI), Department of Diagnostic Radiology, Oulu University Hospital, Oulu 90029, Finland; MIPT group to: Research Unit of Health Sciences and Technology (HST), Faculty of Medicine, University of Oulu, Oulu 90220, Finland; Medical Research Center (MRC), Oulu 90220, Finland
| | - Janne Kananen
- Oulu Functional NeuroImaging (OFNI), Department of Diagnostic Radiology, Oulu University Hospital, Oulu 90029, Finland; MIPT group to: Research Unit of Health Sciences and Technology (HST), Faculty of Medicine, University of Oulu, Oulu 90220, Finland; Medical Research Center (MRC), Oulu 90220, Finland; Clinical Neurophysiology, Oulu University Hospital, Oulu 90220, Finland
| | - Matti Järvelä
- Oulu Functional NeuroImaging (OFNI), Department of Diagnostic Radiology, Oulu University Hospital, Oulu 90029, Finland; MIPT group to: Research Unit of Health Sciences and Technology (HST), Faculty of Medicine, University of Oulu, Oulu 90220, Finland; Medical Research Center (MRC), Oulu 90220, Finland
| | - J Matias Palva
- Department of Neuroscience and Biomedical Engineering, Aalto University, 02150 Espoo, Finland; Neuroscience Center, Helsinki Institute of Life Science, University of Helsinki, Finland; Centre for Cognitive Neuroimaging, University of Glasgow, United Kingdom
| | - Vesa Kiviniemi
- Oulu Functional NeuroImaging (OFNI), Department of Diagnostic Radiology, Oulu University Hospital, Oulu 90029, Finland; MIPT group to: Research Unit of Health Sciences and Technology (HST), Faculty of Medicine, University of Oulu, Oulu 90220, Finland; Medical Research Center (MRC), Oulu 90220, Finland; Biocenter Oulu, Faculty of Biochemistry and Molecular Medicine, University of Oulu, Oulu 90220, Finland
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17
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Nadalini G, Borghi F, Košutová T, Falqui A, Ludwig N, Milani P. Engineering the structural and electrical interplay of nanostructured Au resistive switching networks by controlling the forming process. Sci Rep 2023; 13:19713. [PMID: 37953278 PMCID: PMC10641076 DOI: 10.1038/s41598-023-46990-4] [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: 05/23/2023] [Accepted: 11/07/2023] [Indexed: 11/14/2023] Open
Abstract
Networks of random-assembled gold clusters produced in the gas phase show resistive switching (RS) activity at room temperature and they are suitable for the fabrication of devices for neuromorphic data processing and classification. Fully connected cluster-assembled nanostructured Au films are characterized by a granular structure rich of interfaces, grain boundaries and crystalline defects. Here we report a systematic characterization of the electroforming process of the cluster-assembled films demonstrating how this process affects the interplay between the nano- and mesoscale film structure and the neuromorphic characteristics of the resistive switching activity. The understanding and the control of the influence of the resistive switching forming process on the organization of specific structures at different scales of the cluster-assembled films, provide the possibility to engineer random-assembled neuromorphic architectures for data processing task.
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Affiliation(s)
- Giacomo Nadalini
- CIMaINa and Dipartimento di Fisica, Università degli Studi di Milano, Via Celoria 16, 20133, Milan, Italy
| | - Francesca Borghi
- CIMaINa and Dipartimento di Fisica, Università degli Studi di Milano, Via Celoria 16, 20133, Milan, Italy.
| | - Tereza Košutová
- Faculty of Mathematics and Physics, Charles University, V Holešoviˇck ́ ach 2, 18000, Prague 8, Czech Republic
| | - Andrea Falqui
- CIMaINa and Dipartimento di Fisica, Università degli Studi di Milano, Via Celoria 16, 20133, Milan, Italy
| | - Nicola Ludwig
- CIMaINa and Dipartimento di Fisica, Università degli Studi di Milano, Via Celoria 16, 20133, Milan, Italy
| | - Paolo Milani
- CIMaINa and Dipartimento di Fisica, Università degli Studi di Milano, Via Celoria 16, 20133, Milan, Italy.
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18
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Wang SH, Siebenhühner F, Arnulfo G, Myrov V, Nobili L, Breakspear M, Palva S, Palva JM. Critical-like Brain Dynamics in a Continuum from Second- to First-Order Phase Transition. J Neurosci 2023; 43:7642-7656. [PMID: 37816599 PMCID: PMC10634584 DOI: 10.1523/jneurosci.1889-22.2023] [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: 10/05/2022] [Revised: 06/07/2023] [Accepted: 09/25/2023] [Indexed: 10/12/2023] Open
Abstract
The classic brain criticality hypothesis postulates that the brain benefits from operating near a continuous second-order phase transition. Slow feedback regulation of neuronal activity could, however, lead to a discontinuous first-order transition and thereby bistable activity. Observations of bistability in awake brain activity have nonetheless remained scarce and its functional significance unclear. Moreover, there is no empirical evidence to support the hypothesis that the human brain could flexibly operate near either a first- or second-order phase transition despite such a continuum being common in models. Here, using computational modeling, we found bistable synchronization dynamics to emerge through elevated positive feedback and occur exclusively in a regimen of critical-like dynamics. We then assessed bistability in vivo with resting-state MEG in healthy adults (7 females, 11 males) and stereo-electroencephalography in epilepsy patients (28 females, 36 males). This analysis revealed that a large fraction of the neocortices exhibited varying degrees of bistability in neuronal oscillations from 3 to 200 Hz. In line with our modeling results, the neuronal bistability was positively correlated with classic assessment of brain criticality across narrow-band frequencies. Excessive bistability was predictive of epileptic pathophysiology in the patients, whereas moderate bistability was positively correlated with task performance in the healthy subjects. These empirical findings thus reveal the human brain as a one-of-a-kind complex system that exhibits critical-like dynamics in a continuum between continuous and discontinuous phase transitions.SIGNIFICANCE STATEMENT In the model, while synchrony per se was controlled by connectivity, increasing positive local feedback led to gradually emerging bistable synchrony with scale-free dynamics, suggesting a continuum between second- and first-order phase transitions in synchrony dynamics inside a critical-like regimen. In resting-state MEG and SEEG, bistability of ongoing neuronal oscillations was pervasive across brain areas and frequency bands and was observed only with concurring critical-like dynamics as the modeling predicted. As evidence for functional relevance, moderate bistability was positively correlated with executive functioning in the healthy subjects, and excessive bistability was associated with epileptic pathophysiology. These findings show that critical-like neuronal dynamics in vivo involves both continuous and discontinuous phase transitions in a frequency-, neuroanatomy-, and state-dependent manner.
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Affiliation(s)
- Sheng H Wang
- Neuroscience Center, Helsinki Institute of Life Science, University of Helsinki, 00014 Helsinki, Finland
- Doctoral Programme Brain & Mind, University of Helsinki, 00014 Helsinki, Finland
- BioMag Laboratory, HUS Medical Imaging Center, 00290 Helsinki, Finland
- Department of Neuroscience and Biomedical Engineering, Aalto University, 00076 Espoo, Finland
| | - Felix Siebenhühner
- Neuroscience Center, Helsinki Institute of Life Science, University of Helsinki, 00014 Helsinki, Finland
- BioMag Laboratory, HUS Medical Imaging Center, 00290 Helsinki, Finland
| | - Gabriele Arnulfo
- Neuroscience Center, Helsinki Institute of Life Science, University of Helsinki, 00014 Helsinki, Finland
- Department of Informatics, Bioengineering, Robotics and System Engineering, University of Genoa, 16136 Genoa, Italy
| | - Vladislav Myrov
- Neuroscience Center, Helsinki Institute of Life Science, University of Helsinki, 00014 Helsinki, Finland
- Department of Neuroscience and Biomedical Engineering, Aalto University, 00076 Espoo, Finland
| | - Lino Nobili
- Department of Neurosciences, Rehabilitation, Ophthalmology, Genetics and Maternal and Children's Sciences, University of Genoa, 16136 Genoa, Italy
- Child Neuropsychiatry Unit, Istituto Di Ricovero e Cura a Carattere Scientifico Istituto Giannina Gaslini, 16147 Genoa, Italy
- Centre of Epilepsy Surgery "C. Munari," Department of Neuroscience, Niguarda Hospital, 20162 Milan, Italy
| | - Michael Breakspear
- College of Engineering, Science and Environment, College of Health and Medicine, University of Newcastle, Callaghan, 2308 Australia
| | - Satu Palva
- Neuroscience Center, Helsinki Institute of Life Science, University of Helsinki, 00014 Helsinki, Finland
- Centre for Cognitive Neuroimaging, Institute of Neuroscience & Psychology, University of Glasgow, Glasgow G12 8QB, United Kingdom
| | - J Matias Palva
- Neuroscience Center, Helsinki Institute of Life Science, University of Helsinki, 00014 Helsinki, Finland
- Department of Neuroscience and Biomedical Engineering, Aalto University, 00076 Espoo, Finland
- Centre for Cognitive Neuroimaging, Institute of Neuroscience & Psychology, University of Glasgow, Glasgow G12 8QB, United Kingdom
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19
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Yang Y, Tang S, Wang X, Zhen Y, Zheng Y, Zheng H, Liu L, Zheng Z. Eigenmode-based approach reveals a decline in brain structure-function liberality across the human lifespan. Commun Biol 2023; 6:1128. [PMID: 37935762 PMCID: PMC10630517 DOI: 10.1038/s42003-023-05497-4] [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: 07/15/2023] [Accepted: 10/23/2023] [Indexed: 11/09/2023] Open
Abstract
While brain function is supported and constrained by the underlying structure, the connectome-based link estimated by current approaches is either relatively moderate or accompanied by high model complexity, with the essential principles underlying structure-function coupling remaining elusive. Here, by proposing a mapping method based on network eigendecomposition, we present a concise and strong correspondence between structure and function. We show that the explanation of functional connectivity can be significantly improved by incorporating interactions between different structural eigenmodes. We also demonstrate the pronounced advantage of the present mapping in capturing individual-specific information with simple implementation. Applying our methodology to the human lifespan, we find that functional diversity decreases with age, with functional interactions increasingly dominated by the leading functional mode. We also find that structure-function liberality weakens with age, which is driven by the decreases in functional components that are less constrained by anatomy, while the magnitude of structure-aligned components is preserved. Overall, our work enhances the understanding of structure-function coupling from a collective, connectome-oriented perspective and promotes a more refined identification of functional portions relevant to human aging, holding great potential for mechanistic insights into individual differences associated with cognition, development, and neurological disorders.
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Affiliation(s)
- Yaqian Yang
- School of Mathematical Sciences, Beihang University, Beijing, China
- Key laboratory of Mathematics, Informatics and Behavioral Semantics (LMIB), Beihang University, Beijing, China
| | - Shaoting Tang
- Key laboratory of Mathematics, Informatics and Behavioral Semantics (LMIB), Beihang University, Beijing, China.
- Institute of Artificial Intelligence, Beihang University, Beijing, China.
- State Key Lab of Software Development Environment (NLSDE), Beihang University, Beijing, China.
- Zhongguancun Laboratory, Beijing, China.
- Beijing Advanced Innovation Center for Future Blockchain and Privacy Computing, Beihang University, Beijing, China.
- PengCheng Laboratory, Shenzhen, China.
- Institute of Medical Artificial Intelligence, Binzhou Medical University, Yantai, China.
| | - Xin Wang
- Key laboratory of Mathematics, Informatics and Behavioral Semantics (LMIB), Beihang University, Beijing, China.
- Institute of Artificial Intelligence, Beihang University, Beijing, China.
- State Key Lab of Software Development Environment (NLSDE), Beihang University, Beijing, China.
- Zhongguancun Laboratory, Beijing, China.
- Beijing Advanced Innovation Center for Future Blockchain and Privacy Computing, Beihang University, Beijing, China.
- PengCheng Laboratory, Shenzhen, China.
| | - Yi Zhen
- School of Mathematical Sciences, Beihang University, Beijing, China
- Key laboratory of Mathematics, Informatics and Behavioral Semantics (LMIB), Beihang University, Beijing, China
| | - Yi Zheng
- School of Mathematical Sciences, Beihang University, Beijing, China
- Key laboratory of Mathematics, Informatics and Behavioral Semantics (LMIB), Beihang University, Beijing, China
| | - Hongwei Zheng
- Beijing Academy of Blockchain and Edge Computing (BABEC), Beijing, China
| | - Longzhao Liu
- Key laboratory of Mathematics, Informatics and Behavioral Semantics (LMIB), Beihang University, Beijing, China
- Institute of Artificial Intelligence, Beihang University, Beijing, China
- State Key Lab of Software Development Environment (NLSDE), Beihang University, Beijing, China
- Zhongguancun Laboratory, Beijing, China
- Beijing Advanced Innovation Center for Future Blockchain and Privacy Computing, Beihang University, Beijing, China
- PengCheng Laboratory, Shenzhen, China
| | - Zhiming Zheng
- Key laboratory of Mathematics, Informatics and Behavioral Semantics (LMIB), Beihang University, Beijing, China
- Institute of Artificial Intelligence, Beihang University, Beijing, China
- State Key Lab of Software Development Environment (NLSDE), Beihang University, Beijing, China
- Zhongguancun Laboratory, Beijing, China
- Beijing Advanced Innovation Center for Future Blockchain and Privacy Computing, Beihang University, Beijing, China
- PengCheng Laboratory, Shenzhen, China
- Institute of Medical Artificial Intelligence, Binzhou Medical University, Yantai, China
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20
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Rabus A, Curic D, Ivan VE, Esteves IM, Gruber AJ, Davidsen J. Changes in functional connectivity preserve scale-free neuronal and behavioral dynamics. Phys Rev E 2023; 108:L052301. [PMID: 38115411 DOI: 10.1103/physreve.108.l052301] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/09/2023] [Accepted: 09/06/2023] [Indexed: 12/21/2023]
Abstract
Does the brain optimize itself for storage and transmission of information, and if so, how? The critical brain hypothesis is based in statistical physics and posits that the brain self-tunes its dynamics to a critical point or regime to maximize the repertoire of neuronal responses. Yet, the robustness of this regime, especially with respect to changes in the functional connectivity, remains an unsolved fundamental challenge. Here, we show that both scale-free neuronal dynamics and self-similar features of behavioral dynamics persist following significant changes in functional connectivity. Specifically, we find that the psychedelic compound ibogaine that is associated with an altered state of consciousness fundamentally alters the functional connectivity in the retrosplenial cortex of mice. Yet, the scale-free statistics of movement and of neuronal avalanches among behaviorally related neurons remain largely unaltered. This indicates that the propagation of information within biological neural networks is robust to changes in functional organization of subpopulations of neurons, opening up a new perspective on how the adaptive nature of functional networks may lead to optimality of information transmission in the brain.
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Affiliation(s)
- Anja Rabus
- Complexity Science Group, Department of Physics and Astronomy University of Calgary, Calgary, Alberta, Canada T2N 1N4
| | - Davor Curic
- Complexity Science Group, Department of Physics and Astronomy University of Calgary, Calgary, Alberta, Canada T2N 1N4
| | - Victorita E Ivan
- Canadian Centre for Behavioral Neuroscience University of Lethbridge, Lethbridge, Alberta, Canada T1K 3M4
| | - Ingrid M Esteves
- Canadian Centre for Behavioral Neuroscience University of Lethbridge, Lethbridge, Alberta, Canada T1K 3M4
| | - Aaron J Gruber
- Canadian Centre for Behavioral Neuroscience University of Lethbridge, Lethbridge, Alberta, Canada T1K 3M4
| | - Jörn Davidsen
- Complexity Science Group, Department of Physics and Astronomy University of Calgary, Calgary, Alberta, Canada T2N 1N4
- Hotchkiss Brain Institute, University of Calgary, Calgary, Alberta, Canada T2N 4N1
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21
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Lombardi F, Herrmann HJ, Parrino L, Plenz D, Scarpetta S, Vaudano AE, de Arcangelis L, Shriki O. Beyond pulsed inhibition: Alpha oscillations modulate attenuation and amplification of neural activity in the awake resting state. Cell Rep 2023; 42:113162. [PMID: 37777965 PMCID: PMC10842118 DOI: 10.1016/j.celrep.2023.113162] [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: 05/16/2022] [Revised: 06/07/2023] [Accepted: 09/07/2023] [Indexed: 10/03/2023] Open
Abstract
Alpha oscillations are a distinctive feature of the awake resting state of the human brain. However, their functional role in resting-state neuronal dynamics remains poorly understood. Here we show that, during resting wakefulness, alpha oscillations drive an alternation of attenuation and amplification bouts in neural activity. Our analysis indicates that inhibition is activated in pulses that last for a single alpha cycle and gradually suppress neural activity, while excitation is successively enhanced over a few alpha cycles to amplify neural activity. Furthermore, we show that long-term alpha amplitude fluctuations-the "waxing and waning" phenomenon-are an attenuation-amplification mechanism described by a power-law decay of the activity rate in the "waning" phase. Importantly, we do not observe such dynamics during non-rapid eye movement (NREM) sleep with marginal alpha oscillations. The results suggest that alpha oscillations modulate neural activity not only through pulses of inhibition (pulsed inhibition hypothesis) but also by timely enhancement of excitation (or disinhibition).
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Affiliation(s)
- Fabrizio Lombardi
- Institute of Science and Technology Austria, Am Campus 1, 3400 Klosterneuburg, Austria; Department of Biomedical Sciences, University of Padova, Via Ugo Bassi 58B, 35131 Padova, Italy.
| | - Hans J Herrmann
- Departamento de Fisica, Universitade Federal do Ceara, Fortaleza 60451-970, Ceara, Brazil; PMMH, ESPCI, 7 quai St. Bernard, 75005 Paris, France
| | - Liborio Parrino
- Sleep Disorders Center, Department of Neurosciences, University of Parma, 43121 Parma, Italy
| | - Dietmar Plenz
- Section on Critical Brain Dynamics, NIH, Bethesda, MD 20892, USA
| | - Silvia Scarpetta
- Department of Physics, University of Salerno, 84084 Fisciano, Italy; INFN sez, Napoli Gr. Coll, 84084 Fisciano, Italy
| | - Anna Elisabetta Vaudano
- Neurology Unit, Azienda Ospedaliero-Universitaria of Modena, OCB Hospital, 41125 Modena, Italy; Department of Biomedical, Metabolic, and Neural Sciences, University of Modena and Reggio Emilia, 41125 Modena, Italy
| | - Lucilla de Arcangelis
- Department of Mathematics and Physics, University of Campania "Luigi Vanvitelli", Viale Lincoln 5, 81100 Caserta, Italy.
| | - Oren Shriki
- Department of Cognitive and Brain Sciences, Ben-Gurion University of the Negev, Beer-sheva, Israel.
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22
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Rao A, Sanjay S, Dey V, Ahmadi M, Yadav P, Venugopalrao A, Bhat N, Kooi B, Raghavan S, Nukala P. Realizing avalanche criticality in neuromorphic networks on a 2D hBN platform. MATERIALS HORIZONS 2023; 10:5235-5245. [PMID: 37740285 DOI: 10.1039/d3mh01000g] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 09/24/2023]
Abstract
Networks and systems which exhibit brain-like behavior can analyze information from intrinsically noisy and unstructured data with very low power consumption. Such characteristics arise due to the critical nature and complex interconnectivity of the brain and its neuronal network. We demonstrate a system comprising of multilayer hexagonal boron nitride (hBN) films contacted with silver (Ag), which can uniquely host two different self-assembled networks, which are self-organized at criticality (SOC). This system shows bipolar resistive switching between the high resistance state (HRS) and the low resistance state (LRS). In the HRS, Ag clusters (nodes) intercalate in the van der Waals gaps of hBN forming a network of tunnel junctions, whereas the LRS contains a network of Ag filaments. The temporal avalanche dynamics in both these states exhibit power-law scaling, long-range temporal correlation, and SOC. These networks can be tuned from one to another with voltage as a control parameter. For the first time, two different neural networks are realized in a single CMOS compatible, 2D material platform.
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Affiliation(s)
- Ankit Rao
- Centre for Nano Science and Engineering, Indian Institute of Science, Bengaluru 560012, India.
| | - Sooraj Sanjay
- Centre for Nano Science and Engineering, Indian Institute of Science, Bengaluru 560012, India.
| | - Vivek Dey
- Centre for Nano Science and Engineering, Indian Institute of Science, Bengaluru 560012, India.
| | - Majid Ahmadi
- Zernike Institute for Advanced Materials, University of Groningen, Groningen, The Netherlands
| | - Pramod Yadav
- Centre for Nano Science and Engineering, Indian Institute of Science, Bengaluru 560012, India.
| | - Anirudh Venugopalrao
- Centre for Nano Science and Engineering, Indian Institute of Science, Bengaluru 560012, India.
| | - Navakanta Bhat
- Centre for Nano Science and Engineering, Indian Institute of Science, Bengaluru 560012, India.
| | - Bart Kooi
- Zernike Institute for Advanced Materials, University of Groningen, Groningen, The Netherlands
- CogniGron Center, University of Groningen, Groningen, 9747 AG, The Netherlands
| | - Srinivasan Raghavan
- Centre for Nano Science and Engineering, Indian Institute of Science, Bengaluru 560012, India.
| | - Pavan Nukala
- Centre for Nano Science and Engineering, Indian Institute of Science, Bengaluru 560012, India.
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23
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Scarpetta S, Morisi N, Mutti C, Azzi N, Trippi I, Ciliento R, Apicella I, Messuti G, Angiolelli M, Lombardi F, Parrino L, Vaudano AE. Criticality of neuronal avalanches in human sleep and their relationship with sleep macro- and micro-architecture. iScience 2023; 26:107840. [PMID: 37766992 PMCID: PMC10520337 DOI: 10.1016/j.isci.2023.107840] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/25/2022] [Revised: 06/30/2023] [Accepted: 09/03/2023] [Indexed: 09/29/2023] Open
Abstract
Sleep plays a key role in preserving brain function, keeping brain networks in a state that ensures optimal computation. Empirical evidence indicates that this state is consistent with criticality, where scale-free neuronal avalanches emerge. However, the connection between sleep architecture and brain tuning to criticality remains poorly understood. Here, we characterize the critical behavior of avalanches and study their relationship with sleep macro- and micro-architectures, in particular, the cyclic alternating pattern (CAP). We show that avalanches exhibit robust scaling behaviors, with exponents obeying scaling relations consistent with the mean-field directed percolation universality class. We demonstrate that avalanche dynamics is modulated by the NREM-REM cycles and that, within NREM sleep, avalanche occurrence correlates with CAP activation phases-indicating a potential link between CAP and brain tuning to criticality. The results open new perspectives on the collective dynamics underlying CAP function, and on the relationship between sleep architecture, avalanches, and self-organization to criticality.
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Affiliation(s)
- Silvia Scarpetta
- Department of Physics, University of Salerno, 84084 Fisciano, Italy
- INFN sez. Napoli Gr. Coll. Salerno, 84084 Fisciano, Italy
| | - Niccolò Morisi
- Nephrology, Dialysis and Transplant Unit, University Hospital of Modena, 41121 Modena, Italy
| | - Carlotta Mutti
- Sleep Disorders Center, Department of Medicine and Surgery, University of Parma, 43121 Parma, Italy
| | - Nicoletta Azzi
- Sleep Disorders Center, Department of Medicine and Surgery, University of Parma, 43121 Parma, Italy
| | - Irene Trippi
- Sleep Disorders Center, Department of Medicine and Surgery, University of Parma, 43121 Parma, Italy
| | - Rosario Ciliento
- Department of Neurology, University of Wisconsin, Madison, WI 53705, USA
| | - Ilenia Apicella
- INFN sez. Napoli Gr. Coll. Salerno, 84084 Fisciano, Italy
- Department of Physics, University of Naples “Federico II”, 80126 Napoli, Italy
| | - Giovanni Messuti
- Department of Physics, University of Salerno, 84084 Fisciano, Italy
- INFN sez. Napoli Gr. Coll. Salerno, 84084 Fisciano, Italy
| | - Marianna Angiolelli
- Department of Physics, University of Salerno, 84084 Fisciano, Italy
- INFN sez. Napoli Gr. Coll. Salerno, 84084 Fisciano, Italy
- Engineering Department, University Campus Bio-Medico of Rome, 00128 Roma, Italy
| | - Fabrizio Lombardi
- Institute of Science and Technology Austria, Am Campus 1, 3400 Klosterneuburg, Austria
- Department of Biomedical Sciences, University of Padova, Via Ugo Bassi 58B, 35131 Padova, Italy
| | - Liborio Parrino
- Sleep Disorders Center, Department of Medicine and Surgery, University of Parma, 43121 Parma, Italy
| | - Anna Elisabetta Vaudano
- Neurology Unit, Azienda Ospedaliero-Universitaria of Modena, OCB Hospital, 41125 Modena, Italy
- Department of Biomedical, Metabolic and Neural Sciences, University of Modena and Reggio Emilia, 41125 Modena, Italy
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24
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Yang B, Zhang H, Jiang T, Yu S. Natural brain state change with E/I balance shifting toward inhibition is associated with vigilance impairment. iScience 2023; 26:107963. [PMID: 37822500 PMCID: PMC10562778 DOI: 10.1016/j.isci.2023.107963] [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: 04/20/2023] [Revised: 07/25/2023] [Accepted: 09/15/2023] [Indexed: 10/13/2023] Open
Abstract
The delicate balance between cortical excitation and inhibition (E/I) plays a pivotal role in brain state changes. While previous studies have associated cortical hyperexcitability with brain state changes induced by sleep deprivation, whether cortical hypoexcitability is also linked to brain state changes and, if so, how it could affect cognitive performance remain unknown. Here, we address these questions by examining the brain state change occurring after meals, i.e., postprandial somnolence, and comparing it with that induced by sleep deprivation. By analyzing features representing network excitability based on electroencephalogram (EEG) signals, we confirmed cortical hyperexcitability under sleep deprivation but revealed hypoexcitability under postprandial somnolence. In addition, we found that both sleep deprivation and postprandial somnolence adversely affected the level of vigilance. These results indicate that cortical E/I balance toward inhibition is associated with brain state changes, and deviation from the balanced state, regardless of its direction, could impair cognitive performance.
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Affiliation(s)
- Binghao Yang
- Brainnetome Center, Laboratory of Brain Atlas and Brain-inspired Intelligence, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China
- School of Future Technology, University of Chinese Academy of Sciences, Beijing 101408, China
| | - Haoran Zhang
- Brainnetome Center, Laboratory of Brain Atlas and Brain-inspired Intelligence, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China
- School of Future Technology, University of Chinese Academy of Sciences, Beijing 101408, China
| | - Tianzi Jiang
- Brainnetome Center, Laboratory of Brain Atlas and Brain-inspired Intelligence, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China
- School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing 101408, China
- Research Center for Augmented Intelligence, Zhejiang Lab, Hangzhou 311121, China
| | - Shan Yu
- Brainnetome Center, Laboratory of Brain Atlas and Brain-inspired Intelligence, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China
- School of Future Technology, University of Chinese Academy of Sciences, Beijing 101408, China
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25
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Papo D, Bucolo M, Dimitriadis SI, Onton JA, Philippu A, Shannahoff-Khalsa D. Editorial: Advances in brain dynamics in the healthy and psychiatric disorders. Front Psychiatry 2023; 14:1284670. [PMID: 37779613 PMCID: PMC10539585 DOI: 10.3389/fpsyt.2023.1284670] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Key Words] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/28/2023] [Accepted: 08/31/2023] [Indexed: 10/03/2023] Open
Affiliation(s)
- David Papo
- Center for Translational Neurophysiology of Speech and Communication, Fondazione Istituto Italiano di Tecnologia, Ferrara, Italy
- Department of Neuroscience and Rehabilitation, Section of Physiology, University of Ferrara, Ferrara, Italy
| | - Maide Bucolo
- Department of Electrical, Electronic and Informatics, University of Catania, Catania, Italy
| | - Stavros I. Dimitriadis
- Department of Clinical Psychology and Psychobiology, Faculty of Psychology, University of Barcelona, Barcelona, Spain
| | - Julie A. Onton
- Institute of Neural Computation, University of California, San Diego, La Jolla, CA, United States
| | - Athineos Philippu
- Department of Pharmacology and Toxicology, University of Innsbruck, Innsbruck, Austria
| | - David Shannahoff-Khalsa
- BioCircuits Institute, University of California San Diego, La Jolla, CA, United States
- Center for Integrative Medicine, University of California San Diego, La Jolla, CA, United States
- The Khalsa Foundation for Medical Science, Del Mar, CA, United States
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26
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Fuscà M, Siebenhühner F, Wang SH, Myrov V, Arnulfo G, Nobili L, Palva JM, Palva S. Brain criticality predicts individual levels of inter-areal synchronization in human electrophysiological data. Nat Commun 2023; 14:4736. [PMID: 37550300 PMCID: PMC10406818 DOI: 10.1038/s41467-023-40056-9] [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: 12/07/2022] [Accepted: 07/10/2023] [Indexed: 08/09/2023] Open
Abstract
Neuronal oscillations and their synchronization between brain areas are fundamental for healthy brain function. Yet, synchronization levels exhibit large inter-individual variability that is associated with behavioral variability. We test whether individual synchronization levels are predicted by individual brain states along an extended regime of critical-like dynamics - the Griffiths phase (GP). We use computational modelling to assess how synchronization is dependent on brain criticality indexed by long-range temporal correlations (LRTCs). We analyze LRTCs and synchronization of oscillations from resting-state magnetoencephalography and stereo-electroencephalography data. Synchronization and LRTCs are both positively linearly and quadratically correlated among healthy subjects, while in epileptogenic areas they are negatively linearly correlated. These results show that variability in synchronization levels is explained by the individual position along the GP with healthy brain areas operating in its subcritical and epileptogenic areas in its supercritical side. We suggest that the GP is fundamental for brain function allowing individual variability while retaining functional advantages of criticality.
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Affiliation(s)
- Marco Fuscà
- Centre for Cognitive Neuroimaging, School of Psychology and Neuroscience, University of Glasgow, Glasgow, UK
- Neuroscience Center, HiLIFE-Helsinki Institute of Life Science, University of Helsinki, Helsinki, Finland
| | - Felix Siebenhühner
- Neuroscience Center, HiLIFE-Helsinki Institute of Life Science, University of Helsinki, Helsinki, Finland
- BioMag Laboratory, HUS Medical Imaging Center, University of Helsinki, Aalto University, and Helsinki University Hospital, Helsinki, Finland
| | - Sheng H Wang
- Neuroscience Center, HiLIFE-Helsinki Institute of Life Science, University of Helsinki, Helsinki, Finland
- Department of Neuroscience and Biomedical Engineering, Aalto University, Espoo, Finland
- CEA, NeuroSpin, Gif-sur-Yvette, France
- MIND team, Inria, Université Paris-Saclay, Bures-sur-Yvette, France
| | - Vladislav Myrov
- Department of Neuroscience and Biomedical Engineering, Aalto University, Espoo, Finland
| | - Gabriele Arnulfo
- Neuroscience Center, HiLIFE-Helsinki Institute of Life Science, University of Helsinki, Helsinki, Finland
- Dept. of Informatics, Bioengineering, Robotics and System engineering, University of Genoa, Genoa, Italy
| | - Lino Nobili
- Child Neuropsychiatry Unit, IRCCS, Istituto G. Gaslini, Department of Neuroscience (DINOGMI), University of Genoa, Genoa, Italy
- "Claudio Munari" Epilepsy Surgery Centre, Niguarda Hospital, Milan, Italy
| | - J Matias Palva
- Centre for Cognitive Neuroimaging, School of Psychology and Neuroscience, University of Glasgow, Glasgow, UK
- Neuroscience Center, HiLIFE-Helsinki Institute of Life Science, University of Helsinki, Helsinki, Finland
- Department of Neuroscience and Biomedical Engineering, Aalto University, Espoo, Finland
| | - Satu Palva
- Centre for Cognitive Neuroimaging, School of Psychology and Neuroscience, University of Glasgow, Glasgow, UK.
- Neuroscience Center, HiLIFE-Helsinki Institute of Life Science, University of Helsinki, Helsinki, Finland.
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27
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Ponce-Alvarez A, Kringelbach ML, Deco G. Critical scaling of whole-brain resting-state dynamics. Commun Biol 2023; 6:627. [PMID: 37301936 PMCID: PMC10257708 DOI: 10.1038/s42003-023-05001-y] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/28/2022] [Accepted: 05/31/2023] [Indexed: 06/12/2023] Open
Abstract
Scale invariance is a characteristic of neural activity. How this property emerges from neural interactions remains a fundamental question. Here, we studied the relation between scale-invariant brain dynamics and structural connectivity by analyzing human resting-state (rs-) fMRI signals, together with diffusion MRI (dMRI) connectivity and its approximation as an exponentially decaying function of the distance between brain regions. We analyzed the rs-fMRI dynamics using functional connectivity and a recently proposed phenomenological renormalization group (PRG) method that tracks the change of collective activity after successive coarse-graining at different scales. We found that brain dynamics display power-law correlations and power-law scaling as a function of PRG coarse-graining based on functional or structural connectivity. Moreover, we modeled the brain activity using a network of spins interacting through large-scale connectivity and presenting a phase transition between ordered and disordered phases. Within this simple model, we found that the observed scaling features were likely to emerge from critical dynamics and connections exponentially decaying with distance. In conclusion, our study tests the PRG method using large-scale brain activity and theoretical models and suggests that scaling of rs-fMRI activity relates to criticality.
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Affiliation(s)
- Adrián Ponce-Alvarez
- Center for Brain and Cognition, Computational Neuroscience Group, Department of Information and Communication Technologies, Universitat Pompeu Fabra, Barcelona, 08005, Spain.
- Departament de Matemàtiques, Universitat Politècnica de Catalunya, Barcelona, Spain.
| | - Morten L Kringelbach
- Department of Psychiatry, University of Oxford, Oxford, OX3 7JX, UK
- Center for Music in the Brain, Department of Clinical Medicine, Aarhus University, Aarhus, 8000, Denmark
- Centre for Eudaimonia and Human Flourishing, Linacre College, University of Oxford, Oxford, UK
| | - Gustavo Deco
- Center for Brain and Cognition, Computational Neuroscience Group, Department of Information and Communication Technologies, Universitat Pompeu Fabra, Barcelona, 08005, Spain
- Institució Catalana de la Recerca i Estudis Avançats (ICREA), Barcelona, 08010, Spain
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28
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Wei X, Yan Z, Cai L, Lu M, Yi G, Wang J, Dong Y. Aberrant temporal correlations of ongoing oscillations in disorders of consciousness on multiple time scales. Cogn Neurodyn 2023; 17:633-645. [PMID: 37265651 PMCID: PMC10229524 DOI: 10.1007/s11571-022-09852-9] [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: 05/17/2021] [Revised: 05/19/2022] [Accepted: 07/06/2022] [Indexed: 11/27/2022] Open
Abstract
Changes in neural oscillation amplitude across states of consciousness has been widely reported, but little is known about the link between temporal dynamics of these oscillations on different time scales and consciousness levels. To address this question, we analyzed amplitude fluctuation of the oscillations extracted from spontaneous resting-state EEG recorded from the patients with disorders of consciousness (DOC) and healthy controls. Detrended fluctuation analysis (DFA) and measures of life-time and waiting-time were employed to characterize the temporal structure of EEG oscillations on long time scales (1-20 s) and short time scales (< 1 s), in groups with different consciousness states: patients in minimally conscious state (MCS), patients with unresponsive wakefulness syndrome (UWS) and healthy subjects. Results revealed increased DFA exponents that implies higher long-range temporal correlations (LRTC), especially in the central brain area in alpha and beta bands. On short time scales, declined bursts of oscillations were also observed. All the metrics exhibited lower individual variability in the UWS or MCS group, which may be attributed to the reduced spatial variability of oscillation dynamics. In addition, the temporal dynamics of EEG oscillations showed significant correlations with the behavioral responsiveness of patients. In summary, our findings shows that loss of consciousness is accompanied by alternation of temporal structure in neural oscillations on multiple time scales, and thus may help uncover the mechanism of underlying neuronal correlates of consciousness. Supplementary Information The online version contains supplementary material available at 10.1007/s11571-022-09852-9.
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Affiliation(s)
- Xile Wei
- School of Electrical and Information Engineering, Tianjin University, Tianjin, China
| | - Zhuang Yan
- School of Electrical and Information Engineering, Tianjin University, Tianjin, China
| | - Lihui Cai
- School of Electrical and Information Engineering, Tianjin University, Tianjin, China
| | - Meili Lu
- School of Information Technology Engineering, Tianjin University of Technology and Education, Tianjin, 300222 China
| | - Guosheng Yi
- School of Electrical and Information Engineering, Tianjin University, Tianjin, China
| | - Jiang Wang
- School of Electrical and Information Engineering, Tianjin University, Tianjin, China
| | - Yueqing Dong
- Xincheng Hospital of Tianjin University, Tianjin, China
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29
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Bernardi D, Shannahoff-Khalsa D, Sale J, Wright JA, Fadiga L, Papo D. The time scales of irreversibility in spontaneous brain activity are altered in obsessive compulsive disorder. Front Psychiatry 2023; 14:1158404. [PMID: 37234212 PMCID: PMC10208430 DOI: 10.3389/fpsyt.2023.1158404] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/03/2023] [Accepted: 03/30/2023] [Indexed: 05/27/2023] Open
Abstract
We study how obsessive-compulsive disorder (OCD) affects the complexity and time-reversal symmetry-breaking (irreversibility) of the brain resting-state activity as measured by magnetoencephalography (MEG). Comparing MEG recordings from OCD patients and age/sex matched control subjects, we find that irreversibility is more concentrated at faster time scales and more uniformly distributed across different channels of the same hemisphere in OCD patients than in control subjects. Furthermore, the interhemispheric asymmetry between homologous areas of OCD patients and controls is also markedly different. Some of these differences were reduced by 1-year of Kundalini Yoga meditation treatment. Taken together, these results suggest that OCD alters the dynamic attractor of the brain's resting state and hint at a possible novel neurophysiological characterization of this psychiatric disorder and how this therapy can possibly modulate brain function.
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Affiliation(s)
- Davide Bernardi
- Center for Translational Neurophysiology of Speech and Communication, Fondazione Istituto Italiano di Tecnologia, Ferrara, Italy
| | - David Shannahoff-Khalsa
- BioCircuits Institute, University of California, San Diego, La Jolla, CA, United States
- Center for Integrative Medicine, University of California, San Diego, La Jolla, CA, United States
- The Khalsa Foundation for Medical Science, Del Mar, CA, United States
| | - Jeff Sale
- San Diego Supercomputer Center, University of California, San Diego, La Jolla, CA, United States
| | - Jon A. Wright
- BioCircuits Institute, University of California, San Diego, La Jolla, CA, United States
| | - Luciano Fadiga
- Center for Translational Neurophysiology of Speech and Communication, Fondazione Istituto Italiano di Tecnologia, Ferrara, Italy
- Department of Neuroscience and Rehabilitation, Section of Physiology, University of Ferrara, Ferrara, Italy
| | - David Papo
- Center for Translational Neurophysiology of Speech and Communication, Fondazione Istituto Italiano di Tecnologia, Ferrara, Italy
- Department of Neuroscience and Rehabilitation, Section of Physiology, University of Ferrara, Ferrara, Italy
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30
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van Nifterick AM, Mulder D, Duineveld DJ, Diachenko M, Scheltens P, Stam CJ, van Kesteren RE, Linkenkaer-Hansen K, Hillebrand A, Gouw AA. Resting-state oscillations reveal disturbed excitation-inhibition ratio in Alzheimer's disease patients. Sci Rep 2023; 13:7419. [PMID: 37150756 PMCID: PMC10164744 DOI: 10.1038/s41598-023-33973-8] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/23/2023] [Accepted: 04/21/2023] [Indexed: 05/09/2023] Open
Abstract
An early disruption of neuronal excitation-inhibition (E-I) balance in preclinical animal models of Alzheimer's disease (AD) has been frequently reported, but is difficult to measure directly and non-invasively in humans. Here, we examined known and novel neurophysiological measures sensitive to E-I in patients across the AD continuum. Resting-state magnetoencephalography (MEG) data of 86 amyloid-biomarker-confirmed subjects across the AD continuum (17 patients diagnosed with subjective cognitive decline, 18 with mild cognitive impairment (MCI) and 51 with dementia due to probable AD (AD dementia)), 46 healthy elderly and 20 young control subjects were reconstructed to source-space. E-I balance was investigated by detrended fluctuation analysis (DFA), a functional E/I (fE/I) algorithm, and the aperiodic exponent of the power spectrum. We found a disrupted E-I ratio in AD dementia patients specifically, by a lower DFA, and a shift towards higher excitation, by a higher fE/I and a lower aperiodic exponent. Healthy subjects showed lower fE/I ratios (< 1.0) than reported in previous literature, not explained by age or choice of an arbitrary threshold parameter, which warrants caution in interpretation of fE/I results. Correlation analyses showed that a lower DFA (E-I imbalance) and a lower aperiodic exponent (more excitation) was associated with a worse cognitive score in AD dementia patients. In contrast, a higher DFA in the hippocampi of MCI patients was associated with a worse cognitive score. This MEG-study showed E-I imbalance, likely due to increased excitation, in AD dementia, but not in early stage AD patients. To accurately determine the direction of shift in E-I balance, validations of the currently used markers and additional in vivo markers of E-I are required.
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Affiliation(s)
- Anne M van Nifterick
- Alzheimer Center Amsterdam, Neurology, Vrije Universiteit Amsterdam, Amsterdam UMC location VUmc, Amsterdam, The Netherlands.
- Clinical Neurophysiology and MEG Center, Neurology, Vrije Universiteit Amsterdam, Amsterdam UMC location VUmc, Amsterdam, The Netherlands.
- Amsterdam Neuroscience, Neurodegeneration, Amsterdam, The Netherlands.
- Amsterdam Neuroscience, Systems and Network Neurosciences, Amsterdam, The Netherlands.
| | - Danique Mulder
- Alzheimer Center Amsterdam, Neurology, Vrije Universiteit Amsterdam, Amsterdam UMC location VUmc, Amsterdam, The Netherlands
- Clinical Neurophysiology and MEG Center, Neurology, Vrije Universiteit Amsterdam, Amsterdam UMC location VUmc, Amsterdam, The Netherlands
| | - Denise J Duineveld
- Alzheimer Center Amsterdam, Neurology, Vrije Universiteit Amsterdam, Amsterdam UMC location VUmc, Amsterdam, The Netherlands
- Clinical Neurophysiology and MEG Center, Neurology, Vrije Universiteit Amsterdam, Amsterdam UMC location VUmc, Amsterdam, The Netherlands
| | - Marina Diachenko
- Department of Integrative Neurophysiology, Center for Neurogenomics and Cognitive Research, Vrije Universiteit Amsterdam, 1081 HV, Amsterdam, The Netherlands
- Amsterdam Neuroscience, Systems and Network Neurosciences, Amsterdam, The Netherlands
| | - Philip Scheltens
- Alzheimer Center Amsterdam, Neurology, Vrije Universiteit Amsterdam, Amsterdam UMC location VUmc, Amsterdam, The Netherlands
- Amsterdam Neuroscience, Neurodegeneration, Amsterdam, The Netherlands
| | - Cornelis J Stam
- Clinical Neurophysiology and MEG Center, Neurology, Vrije Universiteit Amsterdam, Amsterdam UMC location VUmc, Amsterdam, The Netherlands
- Amsterdam Neuroscience, Systems and Network Neurosciences, Amsterdam, The Netherlands
| | - Ronald E van Kesteren
- Amsterdam Neuroscience, Neurodegeneration, Amsterdam, The Netherlands
- Department of Molecular and Cellular Neurobiology, Center for Neurogenomics and Cognitive Research, Vrije Universiteit Amsterdam, 1081 HV, Amsterdam, The Netherlands
| | - Klaus Linkenkaer-Hansen
- Department of Integrative Neurophysiology, Center for Neurogenomics and Cognitive Research, Vrije Universiteit Amsterdam, 1081 HV, Amsterdam, The Netherlands
- Amsterdam Neuroscience, Systems and Network Neurosciences, Amsterdam, The Netherlands
| | - Arjan Hillebrand
- Clinical Neurophysiology and MEG Center, Neurology, Vrije Universiteit Amsterdam, Amsterdam UMC location VUmc, Amsterdam, The Netherlands
- Amsterdam Neuroscience, Systems and Network Neurosciences, Amsterdam, The Netherlands
| | - Alida A Gouw
- Alzheimer Center Amsterdam, Neurology, Vrije Universiteit Amsterdam, Amsterdam UMC location VUmc, Amsterdam, The Netherlands
- Clinical Neurophysiology and MEG Center, Neurology, Vrije Universiteit Amsterdam, Amsterdam UMC location VUmc, Amsterdam, The Netherlands
- Amsterdam Neuroscience, Neurodegeneration, Amsterdam, The Netherlands
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31
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Nanda A, Johnson GW, Mu Y, Ahrens MB, Chang C, Englot DJ, Breakspear M, Rubinov M. Time-resolved correlation of distributed brain activity tracks E-I balance and accounts for diverse scale-free phenomena. Cell Rep 2023; 42:112254. [PMID: 36966391 PMCID: PMC10518034 DOI: 10.1016/j.celrep.2023.112254] [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: 02/01/2022] [Revised: 12/22/2022] [Accepted: 02/28/2023] [Indexed: 03/27/2023] Open
Abstract
Much of systems neuroscience posits the functional importance of brain activity patterns that lack natural scales of sizes, durations, or frequencies. The field has developed prominent, and sometimes competing, explanations for the nature of this scale-free activity. Here, we reconcile these explanations across species and modalities. First, we link estimates of excitation-inhibition (E-I) balance with time-resolved correlation of distributed brain activity. Second, we develop an unbiased method for sampling time series constrained by this time-resolved correlation. Third, we use this method to show that estimates of E-I balance account for diverse scale-free phenomena without need to attribute additional function or importance to these phenomena. Collectively, our results simplify existing explanations of scale-free brain activity and provide stringent tests on future theories that seek to transcend these explanations.
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Affiliation(s)
- Aditya Nanda
- Department of Biomedical Engineering, Vanderbilt University, Nashville, TN 37235, USA.
| | - Graham W Johnson
- Department of Biomedical Engineering, Vanderbilt University, Nashville, TN 37235, USA
| | - Yu Mu
- Institute of Neuroscience, Center for Excellence in Brain Science and Intelligence Technology, Chinese Academy of Sciences, Shanghai 200031, China
| | - Misha B Ahrens
- Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, VA 20147, USA
| | - Catie Chang
- Department of Biomedical Engineering, Vanderbilt University, Nashville, TN 37235, USA; Department of Electrical and Computer Engineering, Vanderbilt University, Nashville, TN 37235, USA; Department of Computer Science, Vanderbilt University, Nashville, TN 37235, USA
| | - Dario J Englot
- Department of Biomedical Engineering, Vanderbilt University, Nashville, TN 37235, USA; Department of Neurological Surgery, Vanderbilt University Medical Center, Nashville, TN 37232, USA; Department of Radiology and Radiological Sciences, Vanderbilt University Medical Center, Nashville, TN 37232, USA
| | - Michael Breakspear
- School of Psychology, University of Newcastle, Callaghan, NSW 2308, Australia; School of Medicine and Public Health, University of Newcastle, Callaghan, NSW 2308, Australia
| | - Mikail Rubinov
- Department of Biomedical Engineering, Vanderbilt University, Nashville, TN 37235, USA; Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, VA 20147, USA; Department of Computer Science, Vanderbilt University, Nashville, TN 37235, USA.
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32
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Grosu GF, Hopp AV, Moca VV, Bârzan H, Ciuparu A, Ercsey-Ravasz M, Winkel M, Linde H, Mureșan RC. The fractal brain: scale-invariance in structure and dynamics. Cereb Cortex 2023; 33:4574-4605. [PMID: 36156074 PMCID: PMC10110456 DOI: 10.1093/cercor/bhac363] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/02/2022] [Revised: 08/09/2022] [Accepted: 08/10/2022] [Indexed: 11/12/2022] Open
Abstract
The past 40 years have witnessed extensive research on fractal structure and scale-free dynamics in the brain. Although considerable progress has been made, a comprehensive picture has yet to emerge, and needs further linking to a mechanistic account of brain function. Here, we review these concepts, connecting observations across different levels of organization, from both a structural and functional perspective. We argue that, paradoxically, the level of cortical circuits is the least understood from a structural point of view and perhaps the best studied from a dynamical one. We further link observations about scale-freeness and fractality with evidence that the environment provides constraints that may explain the usefulness of fractal structure and scale-free dynamics in the brain. Moreover, we discuss evidence that behavior exhibits scale-free properties, likely emerging from similarly organized brain dynamics, enabling an organism to thrive in an environment that shares the same organizational principles. Finally, we review the sparse evidence for and try to speculate on the functional consequences of fractality and scale-freeness for brain computation. These properties may endow the brain with computational capabilities that transcend current models of neural computation and could hold the key to unraveling how the brain constructs percepts and generates behavior.
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Affiliation(s)
- George F Grosu
- Department of Experimental and Theoretical Neuroscience, Transylvanian Institute of Neuroscience, Str. Ploiesti 33, 400157 Cluj-Napoca, Romania
- Faculty of Electronics, Telecommunications and Information Technology, Technical University of Cluj-Napoca, Str. Memorandumului 28, 400114 Cluj-Napoca, Romania
| | | | - Vasile V Moca
- Department of Experimental and Theoretical Neuroscience, Transylvanian Institute of Neuroscience, Str. Ploiesti 33, 400157 Cluj-Napoca, Romania
| | - Harald Bârzan
- Department of Experimental and Theoretical Neuroscience, Transylvanian Institute of Neuroscience, Str. Ploiesti 33, 400157 Cluj-Napoca, Romania
- Faculty of Electronics, Telecommunications and Information Technology, Technical University of Cluj-Napoca, Str. Memorandumului 28, 400114 Cluj-Napoca, Romania
| | - Andrei Ciuparu
- Department of Experimental and Theoretical Neuroscience, Transylvanian Institute of Neuroscience, Str. Ploiesti 33, 400157 Cluj-Napoca, Romania
- Faculty of Electronics, Telecommunications and Information Technology, Technical University of Cluj-Napoca, Str. Memorandumului 28, 400114 Cluj-Napoca, Romania
| | - Maria Ercsey-Ravasz
- Department of Experimental and Theoretical Neuroscience, Transylvanian Institute of Neuroscience, Str. Ploiesti 33, 400157 Cluj-Napoca, Romania
- Faculty of Physics, Babes-Bolyai University, Str. Mihail Kogalniceanu 1, 400084 Cluj-Napoca, Romania
| | - Mathias Winkel
- Merck KGaA, Frankfurter Straße 250, 64293 Darmstadt, Germany
| | - Helmut Linde
- Department of Experimental and Theoretical Neuroscience, Transylvanian Institute of Neuroscience, Str. Ploiesti 33, 400157 Cluj-Napoca, Romania
- Merck KGaA, Frankfurter Straße 250, 64293 Darmstadt, Germany
| | - Raul C Mureșan
- Department of Experimental and Theoretical Neuroscience, Transylvanian Institute of Neuroscience, Str. Ploiesti 33, 400157 Cluj-Napoca, Romania
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33
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Morales GB, di Santo S, Muñoz MA. Quasiuniversal scaling in mouse-brain neuronal activity stems from edge-of-instability critical dynamics. Proc Natl Acad Sci U S A 2023; 120:e2208998120. [PMID: 36827262 PMCID: PMC9992863 DOI: 10.1073/pnas.2208998120] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/06/2022] [Accepted: 12/31/2022] [Indexed: 02/25/2023] Open
Abstract
The brain is in a state of perpetual reverberant neural activity, even in the absence of specific tasks or stimuli. Shedding light on the origin and functional significance of such a dynamical state is essential to understanding how the brain transmits, processes, and stores information. An inspiring, albeit controversial, conjecture proposes that some statistical characteristics of empirically observed neuronal activity can be understood by assuming that brain networks operate in a dynamical regime with features, including the emergence of scale invariance, resembling those seen typically near phase transitions. Here, we present a data-driven analysis based on simultaneous high-throughput recordings of the activity of thousands of individual neurons in various regions of the mouse brain. To analyze these data, we construct a unified theoretical framework that synergistically combines a phenomenological renormalization group approach and techniques that infer the general dynamical state of a neural population, while designing complementary tools. This strategy allows us to uncover strong signatures of scale invariance that are "quasiuniversal" across brain regions and experiments, revealing that all the analyzed areas operate, to a greater or lesser extent, near the edge of instability.
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Affiliation(s)
- Guillermo B. Morales
- Departamento de Electromagnetismo y Física de la Materia, Instituto Carlos I de Física Teórica y Computacional Universidad de Granada, GranadaE-18071, Spain
| | - Serena di Santo
- Morton B. Zuckerman Mind Brain Behavior Institute Columbia University, New York, NY10027
| | - Miguel A. Muñoz
- Departamento de Electromagnetismo y Física de la Materia, Instituto Carlos I de Física Teórica y Computacional Universidad de Granada, GranadaE-18071, Spain
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34
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Jones SA, Barfield JH, Norman VK, Shew WL. Scale-free behavioral dynamics directly linked with scale-free cortical dynamics. eLife 2023; 12:e79950. [PMID: 36705565 PMCID: PMC9931391 DOI: 10.7554/elife.79950] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/03/2022] [Accepted: 01/06/2023] [Indexed: 01/28/2023] Open
Abstract
Naturally occurring body movements and collective neural activity both exhibit complex dynamics, often with scale-free, fractal spatiotemporal structure. Scale-free dynamics of both brain and behavior are important because each is associated with functional benefits to the organism. Despite their similarities, scale-free brain activity and scale-free behavior have been studied separately, without a unified explanation. Here, we show that scale-free dynamics of mouse behavior and neurons in the visual cortex are strongly related. Surprisingly, the scale-free neural activity is limited to specific subsets of neurons, and these scale-free subsets exhibit stochastic winner-take-all competition with other neural subsets. This observation is inconsistent with prevailing theories of scale-free dynamics in neural systems, which stem from the criticality hypothesis. We develop a computational model which incorporates known cell-type-specific circuit structure, explaining our findings with a new type of critical dynamics. Our results establish neural underpinnings of scale-free behavior and clear behavioral relevance of scale-free neural activity.
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Affiliation(s)
- Sabrina A Jones
- Department of Physics, University of Arkansas at FayettevilleFayettevilleUnited States
| | - Jacob H Barfield
- Department of Physics, University of Arkansas at FayettevilleFayettevilleUnited States
| | - V Kindler Norman
- Department of Physics, University of Arkansas at FayettevilleFayettevilleUnited States
| | - Woodrow L Shew
- Department of Physics, University of Arkansas at FayettevilleFayettevilleUnited States
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35
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Perquin MN, van Vugt MK, Hedge C, Bompas A. Temporal Structure in Sensorimotor Variability: A Stable Trait, But What For? COMPUTATIONAL BRAIN & BEHAVIOR 2023; 6:1-38. [PMID: 36618326 PMCID: PMC9810256 DOI: 10.1007/s42113-022-00162-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Accepted: 11/22/2022] [Indexed: 01/05/2023]
Abstract
Human performance shows substantial endogenous variability over time, and this variability is a robust marker of individual differences. Of growing interest to psychologists is the realisation that variability is not fully random, but often exhibits temporal dependencies. However, their measurement and interpretation come with several controversies. Furthermore, their potential benefit for studying individual differences in healthy and clinical populations remains unclear. Here, we gather new and archival datasets featuring 11 sensorimotor and cognitive tasks across 526 participants, to examine individual differences in temporal structures. We first investigate intra-individual repeatability of the most common measures of temporal structures - to test their potential for capturing stable individual differences. Secondly, we examine inter-individual differences in these measures using: (1) task performance assessed from the same data, (2) meta-cognitive ratings of on-taskness from thought probes occasionally presented throughout the task, and (3) self-assessed attention-deficit related traits. Across all datasets, autocorrelation at lag 1 and Power Spectra Density slope showed high intra-individual repeatability across sessions and correlated with task performance. The Detrended Fluctuation Analysis slope showed the same pattern, but less reliably. The long-term component (d) of the ARFIMA(1,d,1) model showed poor repeatability and no correlation to performance. Overall, these measures failed to show external validity when correlated with either mean subjective attentional state or self-assessed traits between participants. Thus, some measures of serial dependencies may be stable individual traits, but their usefulness in capturing individual differences in other constructs typically associated with variability in performance seems limited. We conclude with comprehensive recommendations for researchers. Supplementary Information The online version contains supplementary material available at 10.1007/s42113-022-00162-1.
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Affiliation(s)
- Marlou Nadine Perquin
- Biopsychology & Cognitive Neuroscience, Faculty of Psychology and Sports Science, Bielefeld University, Bielefeld, Germany
- Cognitive Neuroscience, Faculty of Biology, Bielefeld University, Bielefeld, Germany
- CUBRIC, School of Psychology, Cardiff University, Cardiff, UK
| | - Marieke K. van Vugt
- Bernoulli Institute for Mathematics, Computer Science and Artificial Intelligence, University of Groningen, Groningen, Netherlands
| | - Craig Hedge
- School of Psychology, College of Health & Life Sciences, Aston University, Aston, UK
| | - Aline Bompas
- CUBRIC, School of Psychology, Cardiff University, Cardiff, UK
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36
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Bellieni CV. Laughter: A signal of ceased alarm toward a perceived incongruity between life and stiffness. NEW IDEAS IN PSYCHOLOGY 2023. [DOI: 10.1016/j.newideapsych.2022.100977] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/27/2022]
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37
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Webb SJ, Naples AJ, Levin AR, Hellemann G, Borland H, Benton J, Carlos C, McAllister T, Santhosh M, Seow H, Atyabi A, Bernier R, Chawarska K, Dawson G, Dziura J, Faja S, Jeste S, Murias M, Nelson CA, Sabatos-DeVito M, Senturk D, Shic F, Sugar CA, McPartland JC. The Autism Biomarkers Consortium for Clinical Trials: Initial Evaluation of a Battery of Candidate EEG Biomarkers. Am J Psychiatry 2023; 180:41-49. [PMID: 36000217 PMCID: PMC10027395 DOI: 10.1176/appi.ajp.21050485] [Citation(s) in RCA: 18] [Impact Index Per Article: 18.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 02/07/2023]
Abstract
OBJECTIVE Numerous candidate EEG biomarkers have been put forward for use in clinical research on autism spectrum disorder (ASD), but biomarker development has been hindered by limited attention to the psychometric properties of derived variables, inconsistent results across small studies, and variable methodology. The authors evaluated the basic psychometric properties of a battery of EEG assays for their potential suitability as biomarkers in clinical trials. METHODS This was a large, multisite, naturalistic study in 6- to 11-year-old children who either had an ASD diagnosis (N=280) or were typically developing (N=119). The authors evaluated an EEG battery composed of well-studied assays of resting-state activity, face perception (faces task), biological motion perception, and visual evoked potentials (VEPs). Biomarker psychometrics were evaluated in terms of acquisition rates, construct performance, and 6-week stability. Preliminary evaluation of use was explored through group discrimination and phenotypic correlations. RESULTS Three assays (resting state, faces task, and VEP) show promise in terms of acquisition rates and construct performance. Six-week stability values in the ASD group were moderate (intraclass correlations ≥0.66) for the faces task latency of the P1 and N170, the VEP amplitude of N1 and P1, and resting alpha power. Group discrimination and phenotype correlations were primarily observed for the faces task P1 and N170. CONCLUSIONS In the context of a large-scale, rigorous evaluation of candidate EEG biomarkers for use in ASD clinical trials, neural response to faces emerged as a promising biomarker for continued evaluation. Resting-state activity and VEP yielded mixed results. The study's biological motion perception assay failed to display construct performance. The results provide information about EEG biomarker performance that is relevant for the next stage of biomarker development efforts focused on context of use.
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Affiliation(s)
- Sara Jane Webb
- Center for Child Health, Behavior, and Development and Seattle Children's Research Institute, Seattle (Webb, Borland, Benton, Santhosh, Shic); Department of Psychiatry and Behavioral Sciences, University of Washington School of Medicine, Seattle (Webb, Bernier); Yale Child Study Center (Naples, Carlos, McAllister, Chawarska, McPartland), Yale Center for Clinical Investigation (Seow), and Department of Emergency Medicine (Dziura), Yale University, New Haven, Conn.; Department of Neurology, Boston Children's Hospital, Boston (Levin); Department of Neurology, Harvard Medical School, Boston (Levin); Department of Psychiatry and Biobehavioral Sciences (Hellemann, Jeste, Senturk, Sugar) and Department of Biostatistics (Senturk, Sugar), University of California Los Angeles, Los Angeles; Department of Computer Science, University of Colorado, Colorado Springs (Atyabi); Duke Center for Autism and Brain Development (Dawson, Sabatos-DeVito) and Department of Psychiatry and Behavioral Sciences (Dawson), Duke University, Durham, N.C.; Department of Pediatrics, Harvard University, Boston (Faja, Nelson); Division of Developmental Medicine, Boston Children's Hospital, Boston (Faja, Nelson); Department of Medical Social Sciences, Northwestern University, Chicago (Murias); Graduate School of Education, Harvard University, Boston (Nelson); Department of Pediatrics, University of Washington, Seattle (Shic)
| | - Adam J Naples
- Center for Child Health, Behavior, and Development and Seattle Children's Research Institute, Seattle (Webb, Borland, Benton, Santhosh, Shic); Department of Psychiatry and Behavioral Sciences, University of Washington School of Medicine, Seattle (Webb, Bernier); Yale Child Study Center (Naples, Carlos, McAllister, Chawarska, McPartland), Yale Center for Clinical Investigation (Seow), and Department of Emergency Medicine (Dziura), Yale University, New Haven, Conn.; Department of Neurology, Boston Children's Hospital, Boston (Levin); Department of Neurology, Harvard Medical School, Boston (Levin); Department of Psychiatry and Biobehavioral Sciences (Hellemann, Jeste, Senturk, Sugar) and Department of Biostatistics (Senturk, Sugar), University of California Los Angeles, Los Angeles; Department of Computer Science, University of Colorado, Colorado Springs (Atyabi); Duke Center for Autism and Brain Development (Dawson, Sabatos-DeVito) and Department of Psychiatry and Behavioral Sciences (Dawson), Duke University, Durham, N.C.; Department of Pediatrics, Harvard University, Boston (Faja, Nelson); Division of Developmental Medicine, Boston Children's Hospital, Boston (Faja, Nelson); Department of Medical Social Sciences, Northwestern University, Chicago (Murias); Graduate School of Education, Harvard University, Boston (Nelson); Department of Pediatrics, University of Washington, Seattle (Shic)
| | - April R Levin
- Center for Child Health, Behavior, and Development and Seattle Children's Research Institute, Seattle (Webb, Borland, Benton, Santhosh, Shic); Department of Psychiatry and Behavioral Sciences, University of Washington School of Medicine, Seattle (Webb, Bernier); Yale Child Study Center (Naples, Carlos, McAllister, Chawarska, McPartland), Yale Center for Clinical Investigation (Seow), and Department of Emergency Medicine (Dziura), Yale University, New Haven, Conn.; Department of Neurology, Boston Children's Hospital, Boston (Levin); Department of Neurology, Harvard Medical School, Boston (Levin); Department of Psychiatry and Biobehavioral Sciences (Hellemann, Jeste, Senturk, Sugar) and Department of Biostatistics (Senturk, Sugar), University of California Los Angeles, Los Angeles; Department of Computer Science, University of Colorado, Colorado Springs (Atyabi); Duke Center for Autism and Brain Development (Dawson, Sabatos-DeVito) and Department of Psychiatry and Behavioral Sciences (Dawson), Duke University, Durham, N.C.; Department of Pediatrics, Harvard University, Boston (Faja, Nelson); Division of Developmental Medicine, Boston Children's Hospital, Boston (Faja, Nelson); Department of Medical Social Sciences, Northwestern University, Chicago (Murias); Graduate School of Education, Harvard University, Boston (Nelson); Department of Pediatrics, University of Washington, Seattle (Shic)
| | - Gerhard Hellemann
- Center for Child Health, Behavior, and Development and Seattle Children's Research Institute, Seattle (Webb, Borland, Benton, Santhosh, Shic); Department of Psychiatry and Behavioral Sciences, University of Washington School of Medicine, Seattle (Webb, Bernier); Yale Child Study Center (Naples, Carlos, McAllister, Chawarska, McPartland), Yale Center for Clinical Investigation (Seow), and Department of Emergency Medicine (Dziura), Yale University, New Haven, Conn.; Department of Neurology, Boston Children's Hospital, Boston (Levin); Department of Neurology, Harvard Medical School, Boston (Levin); Department of Psychiatry and Biobehavioral Sciences (Hellemann, Jeste, Senturk, Sugar) and Department of Biostatistics (Senturk, Sugar), University of California Los Angeles, Los Angeles; Department of Computer Science, University of Colorado, Colorado Springs (Atyabi); Duke Center for Autism and Brain Development (Dawson, Sabatos-DeVito) and Department of Psychiatry and Behavioral Sciences (Dawson), Duke University, Durham, N.C.; Department of Pediatrics, Harvard University, Boston (Faja, Nelson); Division of Developmental Medicine, Boston Children's Hospital, Boston (Faja, Nelson); Department of Medical Social Sciences, Northwestern University, Chicago (Murias); Graduate School of Education, Harvard University, Boston (Nelson); Department of Pediatrics, University of Washington, Seattle (Shic)
| | - Heather Borland
- Center for Child Health, Behavior, and Development and Seattle Children's Research Institute, Seattle (Webb, Borland, Benton, Santhosh, Shic); Department of Psychiatry and Behavioral Sciences, University of Washington School of Medicine, Seattle (Webb, Bernier); Yale Child Study Center (Naples, Carlos, McAllister, Chawarska, McPartland), Yale Center for Clinical Investigation (Seow), and Department of Emergency Medicine (Dziura), Yale University, New Haven, Conn.; Department of Neurology, Boston Children's Hospital, Boston (Levin); Department of Neurology, Harvard Medical School, Boston (Levin); Department of Psychiatry and Biobehavioral Sciences (Hellemann, Jeste, Senturk, Sugar) and Department of Biostatistics (Senturk, Sugar), University of California Los Angeles, Los Angeles; Department of Computer Science, University of Colorado, Colorado Springs (Atyabi); Duke Center for Autism and Brain Development (Dawson, Sabatos-DeVito) and Department of Psychiatry and Behavioral Sciences (Dawson), Duke University, Durham, N.C.; Department of Pediatrics, Harvard University, Boston (Faja, Nelson); Division of Developmental Medicine, Boston Children's Hospital, Boston (Faja, Nelson); Department of Medical Social Sciences, Northwestern University, Chicago (Murias); Graduate School of Education, Harvard University, Boston (Nelson); Department of Pediatrics, University of Washington, Seattle (Shic)
| | - Jessica Benton
- Center for Child Health, Behavior, and Development and Seattle Children's Research Institute, Seattle (Webb, Borland, Benton, Santhosh, Shic); Department of Psychiatry and Behavioral Sciences, University of Washington School of Medicine, Seattle (Webb, Bernier); Yale Child Study Center (Naples, Carlos, McAllister, Chawarska, McPartland), Yale Center for Clinical Investigation (Seow), and Department of Emergency Medicine (Dziura), Yale University, New Haven, Conn.; Department of Neurology, Boston Children's Hospital, Boston (Levin); Department of Neurology, Harvard Medical School, Boston (Levin); Department of Psychiatry and Biobehavioral Sciences (Hellemann, Jeste, Senturk, Sugar) and Department of Biostatistics (Senturk, Sugar), University of California Los Angeles, Los Angeles; Department of Computer Science, University of Colorado, Colorado Springs (Atyabi); Duke Center for Autism and Brain Development (Dawson, Sabatos-DeVito) and Department of Psychiatry and Behavioral Sciences (Dawson), Duke University, Durham, N.C.; Department of Pediatrics, Harvard University, Boston (Faja, Nelson); Division of Developmental Medicine, Boston Children's Hospital, Boston (Faja, Nelson); Department of Medical Social Sciences, Northwestern University, Chicago (Murias); Graduate School of Education, Harvard University, Boston (Nelson); Department of Pediatrics, University of Washington, Seattle (Shic)
| | - Carter Carlos
- Center for Child Health, Behavior, and Development and Seattle Children's Research Institute, Seattle (Webb, Borland, Benton, Santhosh, Shic); Department of Psychiatry and Behavioral Sciences, University of Washington School of Medicine, Seattle (Webb, Bernier); Yale Child Study Center (Naples, Carlos, McAllister, Chawarska, McPartland), Yale Center for Clinical Investigation (Seow), and Department of Emergency Medicine (Dziura), Yale University, New Haven, Conn.; Department of Neurology, Boston Children's Hospital, Boston (Levin); Department of Neurology, Harvard Medical School, Boston (Levin); Department of Psychiatry and Biobehavioral Sciences (Hellemann, Jeste, Senturk, Sugar) and Department of Biostatistics (Senturk, Sugar), University of California Los Angeles, Los Angeles; Department of Computer Science, University of Colorado, Colorado Springs (Atyabi); Duke Center for Autism and Brain Development (Dawson, Sabatos-DeVito) and Department of Psychiatry and Behavioral Sciences (Dawson), Duke University, Durham, N.C.; Department of Pediatrics, Harvard University, Boston (Faja, Nelson); Division of Developmental Medicine, Boston Children's Hospital, Boston (Faja, Nelson); Department of Medical Social Sciences, Northwestern University, Chicago (Murias); Graduate School of Education, Harvard University, Boston (Nelson); Department of Pediatrics, University of Washington, Seattle (Shic)
| | - Takumi McAllister
- Center for Child Health, Behavior, and Development and Seattle Children's Research Institute, Seattle (Webb, Borland, Benton, Santhosh, Shic); Department of Psychiatry and Behavioral Sciences, University of Washington School of Medicine, Seattle (Webb, Bernier); Yale Child Study Center (Naples, Carlos, McAllister, Chawarska, McPartland), Yale Center for Clinical Investigation (Seow), and Department of Emergency Medicine (Dziura), Yale University, New Haven, Conn.; Department of Neurology, Boston Children's Hospital, Boston (Levin); Department of Neurology, Harvard Medical School, Boston (Levin); Department of Psychiatry and Biobehavioral Sciences (Hellemann, Jeste, Senturk, Sugar) and Department of Biostatistics (Senturk, Sugar), University of California Los Angeles, Los Angeles; Department of Computer Science, University of Colorado, Colorado Springs (Atyabi); Duke Center for Autism and Brain Development (Dawson, Sabatos-DeVito) and Department of Psychiatry and Behavioral Sciences (Dawson), Duke University, Durham, N.C.; Department of Pediatrics, Harvard University, Boston (Faja, Nelson); Division of Developmental Medicine, Boston Children's Hospital, Boston (Faja, Nelson); Department of Medical Social Sciences, Northwestern University, Chicago (Murias); Graduate School of Education, Harvard University, Boston (Nelson); Department of Pediatrics, University of Washington, Seattle (Shic)
| | - Megha Santhosh
- Center for Child Health, Behavior, and Development and Seattle Children's Research Institute, Seattle (Webb, Borland, Benton, Santhosh, Shic); Department of Psychiatry and Behavioral Sciences, University of Washington School of Medicine, Seattle (Webb, Bernier); Yale Child Study Center (Naples, Carlos, McAllister, Chawarska, McPartland), Yale Center for Clinical Investigation (Seow), and Department of Emergency Medicine (Dziura), Yale University, New Haven, Conn.; Department of Neurology, Boston Children's Hospital, Boston (Levin); Department of Neurology, Harvard Medical School, Boston (Levin); Department of Psychiatry and Biobehavioral Sciences (Hellemann, Jeste, Senturk, Sugar) and Department of Biostatistics (Senturk, Sugar), University of California Los Angeles, Los Angeles; Department of Computer Science, University of Colorado, Colorado Springs (Atyabi); Duke Center for Autism and Brain Development (Dawson, Sabatos-DeVito) and Department of Psychiatry and Behavioral Sciences (Dawson), Duke University, Durham, N.C.; Department of Pediatrics, Harvard University, Boston (Faja, Nelson); Division of Developmental Medicine, Boston Children's Hospital, Boston (Faja, Nelson); Department of Medical Social Sciences, Northwestern University, Chicago (Murias); Graduate School of Education, Harvard University, Boston (Nelson); Department of Pediatrics, University of Washington, Seattle (Shic)
| | - Helen Seow
- Center for Child Health, Behavior, and Development and Seattle Children's Research Institute, Seattle (Webb, Borland, Benton, Santhosh, Shic); Department of Psychiatry and Behavioral Sciences, University of Washington School of Medicine, Seattle (Webb, Bernier); Yale Child Study Center (Naples, Carlos, McAllister, Chawarska, McPartland), Yale Center for Clinical Investigation (Seow), and Department of Emergency Medicine (Dziura), Yale University, New Haven, Conn.; Department of Neurology, Boston Children's Hospital, Boston (Levin); Department of Neurology, Harvard Medical School, Boston (Levin); Department of Psychiatry and Biobehavioral Sciences (Hellemann, Jeste, Senturk, Sugar) and Department of Biostatistics (Senturk, Sugar), University of California Los Angeles, Los Angeles; Department of Computer Science, University of Colorado, Colorado Springs (Atyabi); Duke Center for Autism and Brain Development (Dawson, Sabatos-DeVito) and Department of Psychiatry and Behavioral Sciences (Dawson), Duke University, Durham, N.C.; Department of Pediatrics, Harvard University, Boston (Faja, Nelson); Division of Developmental Medicine, Boston Children's Hospital, Boston (Faja, Nelson); Department of Medical Social Sciences, Northwestern University, Chicago (Murias); Graduate School of Education, Harvard University, Boston (Nelson); Department of Pediatrics, University of Washington, Seattle (Shic)
| | - Adham Atyabi
- Center for Child Health, Behavior, and Development and Seattle Children's Research Institute, Seattle (Webb, Borland, Benton, Santhosh, Shic); Department of Psychiatry and Behavioral Sciences, University of Washington School of Medicine, Seattle (Webb, Bernier); Yale Child Study Center (Naples, Carlos, McAllister, Chawarska, McPartland), Yale Center for Clinical Investigation (Seow), and Department of Emergency Medicine (Dziura), Yale University, New Haven, Conn.; Department of Neurology, Boston Children's Hospital, Boston (Levin); Department of Neurology, Harvard Medical School, Boston (Levin); Department of Psychiatry and Biobehavioral Sciences (Hellemann, Jeste, Senturk, Sugar) and Department of Biostatistics (Senturk, Sugar), University of California Los Angeles, Los Angeles; Department of Computer Science, University of Colorado, Colorado Springs (Atyabi); Duke Center for Autism and Brain Development (Dawson, Sabatos-DeVito) and Department of Psychiatry and Behavioral Sciences (Dawson), Duke University, Durham, N.C.; Department of Pediatrics, Harvard University, Boston (Faja, Nelson); Division of Developmental Medicine, Boston Children's Hospital, Boston (Faja, Nelson); Department of Medical Social Sciences, Northwestern University, Chicago (Murias); Graduate School of Education, Harvard University, Boston (Nelson); Department of Pediatrics, University of Washington, Seattle (Shic)
| | - Raphael Bernier
- Center for Child Health, Behavior, and Development and Seattle Children's Research Institute, Seattle (Webb, Borland, Benton, Santhosh, Shic); Department of Psychiatry and Behavioral Sciences, University of Washington School of Medicine, Seattle (Webb, Bernier); Yale Child Study Center (Naples, Carlos, McAllister, Chawarska, McPartland), Yale Center for Clinical Investigation (Seow), and Department of Emergency Medicine (Dziura), Yale University, New Haven, Conn.; Department of Neurology, Boston Children's Hospital, Boston (Levin); Department of Neurology, Harvard Medical School, Boston (Levin); Department of Psychiatry and Biobehavioral Sciences (Hellemann, Jeste, Senturk, Sugar) and Department of Biostatistics (Senturk, Sugar), University of California Los Angeles, Los Angeles; Department of Computer Science, University of Colorado, Colorado Springs (Atyabi); Duke Center for Autism and Brain Development (Dawson, Sabatos-DeVito) and Department of Psychiatry and Behavioral Sciences (Dawson), Duke University, Durham, N.C.; Department of Pediatrics, Harvard University, Boston (Faja, Nelson); Division of Developmental Medicine, Boston Children's Hospital, Boston (Faja, Nelson); Department of Medical Social Sciences, Northwestern University, Chicago (Murias); Graduate School of Education, Harvard University, Boston (Nelson); Department of Pediatrics, University of Washington, Seattle (Shic)
| | - Katarzyna Chawarska
- Center for Child Health, Behavior, and Development and Seattle Children's Research Institute, Seattle (Webb, Borland, Benton, Santhosh, Shic); Department of Psychiatry and Behavioral Sciences, University of Washington School of Medicine, Seattle (Webb, Bernier); Yale Child Study Center (Naples, Carlos, McAllister, Chawarska, McPartland), Yale Center for Clinical Investigation (Seow), and Department of Emergency Medicine (Dziura), Yale University, New Haven, Conn.; Department of Neurology, Boston Children's Hospital, Boston (Levin); Department of Neurology, Harvard Medical School, Boston (Levin); Department of Psychiatry and Biobehavioral Sciences (Hellemann, Jeste, Senturk, Sugar) and Department of Biostatistics (Senturk, Sugar), University of California Los Angeles, Los Angeles; Department of Computer Science, University of Colorado, Colorado Springs (Atyabi); Duke Center for Autism and Brain Development (Dawson, Sabatos-DeVito) and Department of Psychiatry and Behavioral Sciences (Dawson), Duke University, Durham, N.C.; Department of Pediatrics, Harvard University, Boston (Faja, Nelson); Division of Developmental Medicine, Boston Children's Hospital, Boston (Faja, Nelson); Department of Medical Social Sciences, Northwestern University, Chicago (Murias); Graduate School of Education, Harvard University, Boston (Nelson); Department of Pediatrics, University of Washington, Seattle (Shic)
| | - Geraldine Dawson
- Center for Child Health, Behavior, and Development and Seattle Children's Research Institute, Seattle (Webb, Borland, Benton, Santhosh, Shic); Department of Psychiatry and Behavioral Sciences, University of Washington School of Medicine, Seattle (Webb, Bernier); Yale Child Study Center (Naples, Carlos, McAllister, Chawarska, McPartland), Yale Center for Clinical Investigation (Seow), and Department of Emergency Medicine (Dziura), Yale University, New Haven, Conn.; Department of Neurology, Boston Children's Hospital, Boston (Levin); Department of Neurology, Harvard Medical School, Boston (Levin); Department of Psychiatry and Biobehavioral Sciences (Hellemann, Jeste, Senturk, Sugar) and Department of Biostatistics (Senturk, Sugar), University of California Los Angeles, Los Angeles; Department of Computer Science, University of Colorado, Colorado Springs (Atyabi); Duke Center for Autism and Brain Development (Dawson, Sabatos-DeVito) and Department of Psychiatry and Behavioral Sciences (Dawson), Duke University, Durham, N.C.; Department of Pediatrics, Harvard University, Boston (Faja, Nelson); Division of Developmental Medicine, Boston Children's Hospital, Boston (Faja, Nelson); Department of Medical Social Sciences, Northwestern University, Chicago (Murias); Graduate School of Education, Harvard University, Boston (Nelson); Department of Pediatrics, University of Washington, Seattle (Shic)
| | - James Dziura
- Center for Child Health, Behavior, and Development and Seattle Children's Research Institute, Seattle (Webb, Borland, Benton, Santhosh, Shic); Department of Psychiatry and Behavioral Sciences, University of Washington School of Medicine, Seattle (Webb, Bernier); Yale Child Study Center (Naples, Carlos, McAllister, Chawarska, McPartland), Yale Center for Clinical Investigation (Seow), and Department of Emergency Medicine (Dziura), Yale University, New Haven, Conn.; Department of Neurology, Boston Children's Hospital, Boston (Levin); Department of Neurology, Harvard Medical School, Boston (Levin); Department of Psychiatry and Biobehavioral Sciences (Hellemann, Jeste, Senturk, Sugar) and Department of Biostatistics (Senturk, Sugar), University of California Los Angeles, Los Angeles; Department of Computer Science, University of Colorado, Colorado Springs (Atyabi); Duke Center for Autism and Brain Development (Dawson, Sabatos-DeVito) and Department of Psychiatry and Behavioral Sciences (Dawson), Duke University, Durham, N.C.; Department of Pediatrics, Harvard University, Boston (Faja, Nelson); Division of Developmental Medicine, Boston Children's Hospital, Boston (Faja, Nelson); Department of Medical Social Sciences, Northwestern University, Chicago (Murias); Graduate School of Education, Harvard University, Boston (Nelson); Department of Pediatrics, University of Washington, Seattle (Shic)
| | - Susan Faja
- Center for Child Health, Behavior, and Development and Seattle Children's Research Institute, Seattle (Webb, Borland, Benton, Santhosh, Shic); Department of Psychiatry and Behavioral Sciences, University of Washington School of Medicine, Seattle (Webb, Bernier); Yale Child Study Center (Naples, Carlos, McAllister, Chawarska, McPartland), Yale Center for Clinical Investigation (Seow), and Department of Emergency Medicine (Dziura), Yale University, New Haven, Conn.; Department of Neurology, Boston Children's Hospital, Boston (Levin); Department of Neurology, Harvard Medical School, Boston (Levin); Department of Psychiatry and Biobehavioral Sciences (Hellemann, Jeste, Senturk, Sugar) and Department of Biostatistics (Senturk, Sugar), University of California Los Angeles, Los Angeles; Department of Computer Science, University of Colorado, Colorado Springs (Atyabi); Duke Center for Autism and Brain Development (Dawson, Sabatos-DeVito) and Department of Psychiatry and Behavioral Sciences (Dawson), Duke University, Durham, N.C.; Department of Pediatrics, Harvard University, Boston (Faja, Nelson); Division of Developmental Medicine, Boston Children's Hospital, Boston (Faja, Nelson); Department of Medical Social Sciences, Northwestern University, Chicago (Murias); Graduate School of Education, Harvard University, Boston (Nelson); Department of Pediatrics, University of Washington, Seattle (Shic)
| | - Shafali Jeste
- Center for Child Health, Behavior, and Development and Seattle Children's Research Institute, Seattle (Webb, Borland, Benton, Santhosh, Shic); Department of Psychiatry and Behavioral Sciences, University of Washington School of Medicine, Seattle (Webb, Bernier); Yale Child Study Center (Naples, Carlos, McAllister, Chawarska, McPartland), Yale Center for Clinical Investigation (Seow), and Department of Emergency Medicine (Dziura), Yale University, New Haven, Conn.; Department of Neurology, Boston Children's Hospital, Boston (Levin); Department of Neurology, Harvard Medical School, Boston (Levin); Department of Psychiatry and Biobehavioral Sciences (Hellemann, Jeste, Senturk, Sugar) and Department of Biostatistics (Senturk, Sugar), University of California Los Angeles, Los Angeles; Department of Computer Science, University of Colorado, Colorado Springs (Atyabi); Duke Center for Autism and Brain Development (Dawson, Sabatos-DeVito) and Department of Psychiatry and Behavioral Sciences (Dawson), Duke University, Durham, N.C.; Department of Pediatrics, Harvard University, Boston (Faja, Nelson); Division of Developmental Medicine, Boston Children's Hospital, Boston (Faja, Nelson); Department of Medical Social Sciences, Northwestern University, Chicago (Murias); Graduate School of Education, Harvard University, Boston (Nelson); Department of Pediatrics, University of Washington, Seattle (Shic)
| | - Michael Murias
- Center for Child Health, Behavior, and Development and Seattle Children's Research Institute, Seattle (Webb, Borland, Benton, Santhosh, Shic); Department of Psychiatry and Behavioral Sciences, University of Washington School of Medicine, Seattle (Webb, Bernier); Yale Child Study Center (Naples, Carlos, McAllister, Chawarska, McPartland), Yale Center for Clinical Investigation (Seow), and Department of Emergency Medicine (Dziura), Yale University, New Haven, Conn.; Department of Neurology, Boston Children's Hospital, Boston (Levin); Department of Neurology, Harvard Medical School, Boston (Levin); Department of Psychiatry and Biobehavioral Sciences (Hellemann, Jeste, Senturk, Sugar) and Department of Biostatistics (Senturk, Sugar), University of California Los Angeles, Los Angeles; Department of Computer Science, University of Colorado, Colorado Springs (Atyabi); Duke Center for Autism and Brain Development (Dawson, Sabatos-DeVito) and Department of Psychiatry and Behavioral Sciences (Dawson), Duke University, Durham, N.C.; Department of Pediatrics, Harvard University, Boston (Faja, Nelson); Division of Developmental Medicine, Boston Children's Hospital, Boston (Faja, Nelson); Department of Medical Social Sciences, Northwestern University, Chicago (Murias); Graduate School of Education, Harvard University, Boston (Nelson); Department of Pediatrics, University of Washington, Seattle (Shic)
| | - Charles A Nelson
- Center for Child Health, Behavior, and Development and Seattle Children's Research Institute, Seattle (Webb, Borland, Benton, Santhosh, Shic); Department of Psychiatry and Behavioral Sciences, University of Washington School of Medicine, Seattle (Webb, Bernier); Yale Child Study Center (Naples, Carlos, McAllister, Chawarska, McPartland), Yale Center for Clinical Investigation (Seow), and Department of Emergency Medicine (Dziura), Yale University, New Haven, Conn.; Department of Neurology, Boston Children's Hospital, Boston (Levin); Department of Neurology, Harvard Medical School, Boston (Levin); Department of Psychiatry and Biobehavioral Sciences (Hellemann, Jeste, Senturk, Sugar) and Department of Biostatistics (Senturk, Sugar), University of California Los Angeles, Los Angeles; Department of Computer Science, University of Colorado, Colorado Springs (Atyabi); Duke Center for Autism and Brain Development (Dawson, Sabatos-DeVito) and Department of Psychiatry and Behavioral Sciences (Dawson), Duke University, Durham, N.C.; Department of Pediatrics, Harvard University, Boston (Faja, Nelson); Division of Developmental Medicine, Boston Children's Hospital, Boston (Faja, Nelson); Department of Medical Social Sciences, Northwestern University, Chicago (Murias); Graduate School of Education, Harvard University, Boston (Nelson); Department of Pediatrics, University of Washington, Seattle (Shic)
| | - Maura Sabatos-DeVito
- Center for Child Health, Behavior, and Development and Seattle Children's Research Institute, Seattle (Webb, Borland, Benton, Santhosh, Shic); Department of Psychiatry and Behavioral Sciences, University of Washington School of Medicine, Seattle (Webb, Bernier); Yale Child Study Center (Naples, Carlos, McAllister, Chawarska, McPartland), Yale Center for Clinical Investigation (Seow), and Department of Emergency Medicine (Dziura), Yale University, New Haven, Conn.; Department of Neurology, Boston Children's Hospital, Boston (Levin); Department of Neurology, Harvard Medical School, Boston (Levin); Department of Psychiatry and Biobehavioral Sciences (Hellemann, Jeste, Senturk, Sugar) and Department of Biostatistics (Senturk, Sugar), University of California Los Angeles, Los Angeles; Department of Computer Science, University of Colorado, Colorado Springs (Atyabi); Duke Center for Autism and Brain Development (Dawson, Sabatos-DeVito) and Department of Psychiatry and Behavioral Sciences (Dawson), Duke University, Durham, N.C.; Department of Pediatrics, Harvard University, Boston (Faja, Nelson); Division of Developmental Medicine, Boston Children's Hospital, Boston (Faja, Nelson); Department of Medical Social Sciences, Northwestern University, Chicago (Murias); Graduate School of Education, Harvard University, Boston (Nelson); Department of Pediatrics, University of Washington, Seattle (Shic)
| | - Damla Senturk
- Center for Child Health, Behavior, and Development and Seattle Children's Research Institute, Seattle (Webb, Borland, Benton, Santhosh, Shic); Department of Psychiatry and Behavioral Sciences, University of Washington School of Medicine, Seattle (Webb, Bernier); Yale Child Study Center (Naples, Carlos, McAllister, Chawarska, McPartland), Yale Center for Clinical Investigation (Seow), and Department of Emergency Medicine (Dziura), Yale University, New Haven, Conn.; Department of Neurology, Boston Children's Hospital, Boston (Levin); Department of Neurology, Harvard Medical School, Boston (Levin); Department of Psychiatry and Biobehavioral Sciences (Hellemann, Jeste, Senturk, Sugar) and Department of Biostatistics (Senturk, Sugar), University of California Los Angeles, Los Angeles; Department of Computer Science, University of Colorado, Colorado Springs (Atyabi); Duke Center for Autism and Brain Development (Dawson, Sabatos-DeVito) and Department of Psychiatry and Behavioral Sciences (Dawson), Duke University, Durham, N.C.; Department of Pediatrics, Harvard University, Boston (Faja, Nelson); Division of Developmental Medicine, Boston Children's Hospital, Boston (Faja, Nelson); Department of Medical Social Sciences, Northwestern University, Chicago (Murias); Graduate School of Education, Harvard University, Boston (Nelson); Department of Pediatrics, University of Washington, Seattle (Shic)
| | - Frederick Shic
- Center for Child Health, Behavior, and Development and Seattle Children's Research Institute, Seattle (Webb, Borland, Benton, Santhosh, Shic); Department of Psychiatry and Behavioral Sciences, University of Washington School of Medicine, Seattle (Webb, Bernier); Yale Child Study Center (Naples, Carlos, McAllister, Chawarska, McPartland), Yale Center for Clinical Investigation (Seow), and Department of Emergency Medicine (Dziura), Yale University, New Haven, Conn.; Department of Neurology, Boston Children's Hospital, Boston (Levin); Department of Neurology, Harvard Medical School, Boston (Levin); Department of Psychiatry and Biobehavioral Sciences (Hellemann, Jeste, Senturk, Sugar) and Department of Biostatistics (Senturk, Sugar), University of California Los Angeles, Los Angeles; Department of Computer Science, University of Colorado, Colorado Springs (Atyabi); Duke Center for Autism and Brain Development (Dawson, Sabatos-DeVito) and Department of Psychiatry and Behavioral Sciences (Dawson), Duke University, Durham, N.C.; Department of Pediatrics, Harvard University, Boston (Faja, Nelson); Division of Developmental Medicine, Boston Children's Hospital, Boston (Faja, Nelson); Department of Medical Social Sciences, Northwestern University, Chicago (Murias); Graduate School of Education, Harvard University, Boston (Nelson); Department of Pediatrics, University of Washington, Seattle (Shic)
| | - Catherine A Sugar
- Center for Child Health, Behavior, and Development and Seattle Children's Research Institute, Seattle (Webb, Borland, Benton, Santhosh, Shic); Department of Psychiatry and Behavioral Sciences, University of Washington School of Medicine, Seattle (Webb, Bernier); Yale Child Study Center (Naples, Carlos, McAllister, Chawarska, McPartland), Yale Center for Clinical Investigation (Seow), and Department of Emergency Medicine (Dziura), Yale University, New Haven, Conn.; Department of Neurology, Boston Children's Hospital, Boston (Levin); Department of Neurology, Harvard Medical School, Boston (Levin); Department of Psychiatry and Biobehavioral Sciences (Hellemann, Jeste, Senturk, Sugar) and Department of Biostatistics (Senturk, Sugar), University of California Los Angeles, Los Angeles; Department of Computer Science, University of Colorado, Colorado Springs (Atyabi); Duke Center for Autism and Brain Development (Dawson, Sabatos-DeVito) and Department of Psychiatry and Behavioral Sciences (Dawson), Duke University, Durham, N.C.; Department of Pediatrics, Harvard University, Boston (Faja, Nelson); Division of Developmental Medicine, Boston Children's Hospital, Boston (Faja, Nelson); Department of Medical Social Sciences, Northwestern University, Chicago (Murias); Graduate School of Education, Harvard University, Boston (Nelson); Department of Pediatrics, University of Washington, Seattle (Shic)
| | - James C McPartland
- Center for Child Health, Behavior, and Development and Seattle Children's Research Institute, Seattle (Webb, Borland, Benton, Santhosh, Shic); Department of Psychiatry and Behavioral Sciences, University of Washington School of Medicine, Seattle (Webb, Bernier); Yale Child Study Center (Naples, Carlos, McAllister, Chawarska, McPartland), Yale Center for Clinical Investigation (Seow), and Department of Emergency Medicine (Dziura), Yale University, New Haven, Conn.; Department of Neurology, Boston Children's Hospital, Boston (Levin); Department of Neurology, Harvard Medical School, Boston (Levin); Department of Psychiatry and Biobehavioral Sciences (Hellemann, Jeste, Senturk, Sugar) and Department of Biostatistics (Senturk, Sugar), University of California Los Angeles, Los Angeles; Department of Computer Science, University of Colorado, Colorado Springs (Atyabi); Duke Center for Autism and Brain Development (Dawson, Sabatos-DeVito) and Department of Psychiatry and Behavioral Sciences (Dawson), Duke University, Durham, N.C.; Department of Pediatrics, Harvard University, Boston (Faja, Nelson); Division of Developmental Medicine, Boston Children's Hospital, Boston (Faja, Nelson); Department of Medical Social Sciences, Northwestern University, Chicago (Murias); Graduate School of Education, Harvard University, Boston (Nelson); Department of Pediatrics, University of Washington, Seattle (Shic)
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One-year-later spontaneous EEG features predict visual exploratory human phenotypes. Commun Biol 2022; 5:1361. [PMID: 36509841 PMCID: PMC9744741 DOI: 10.1038/s42003-022-04294-9] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/09/2021] [Accepted: 11/24/2022] [Indexed: 12/14/2022] Open
Abstract
During visual exploration, eye movements are controlled by multiple stimulus- and goal-driven factors. We recently showed that the dynamics of eye movements -how/when the eye move- during natural scenes' free viewing were similar across individuals and identified two viewing styles: static and dynamic, characterized respectively by longer or shorter fixations. Interestingly, these styles could be revealed at rest, in the absence of any visual stimulus. This result supports a role of intrinsic activity in eye movement dynamics. Here we hypothesize that these two viewing styles correspond to different spontaneous patterns of brain activity. One year after the behavioural experiments, static and dynamic viewers were called back to the lab to record high density EEG activity during eyes open and eyes closed. Static viewers show higher cortical inhibition, slower individual alpha frequency peak, and longer memory of alpha oscillations. The opposite holds for dynamic viewers. We conclude that some properties of spontaneous activity predict exploratory eye movement dynamics during free viewing.
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Neto JP, Spitzner FP, Priesemann V. Sampling effects and measurement overlap can bias the inference of neuronal avalanches. PLoS Comput Biol 2022; 18:e1010678. [PMID: 36445932 PMCID: PMC9733887 DOI: 10.1371/journal.pcbi.1010678] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/22/2020] [Revised: 12/09/2022] [Accepted: 10/24/2022] [Indexed: 12/02/2022] Open
Abstract
To date, it is still impossible to sample the entire mammalian brain with single-neuron precision. This forces one to either use spikes (focusing on few neurons) or to use coarse-sampled activity (averaging over many neurons, e.g. LFP). Naturally, the sampling technique impacts inference about collective properties. Here, we emulate both sampling techniques on a simple spiking model to quantify how they alter observed correlations and signatures of criticality. We describe a general effect: when the inter-electrode distance is small, electrodes sample overlapping regions in space, which increases the correlation between the signals. For coarse-sampled activity, this can produce power-law distributions even for non-critical systems. In contrast, spike recordings do not suffer this particular bias and underlying dynamics can be identified. This may resolve why coarse measures and spikes have produced contradicting results in the past.
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Affiliation(s)
- Joao Pinheiro Neto
- Max Planck Institute for Dynamics and Self-Organization, Göttingen, Germany
| | - F. Paul Spitzner
- Max Planck Institute for Dynamics and Self-Organization, Göttingen, Germany
| | - Viola Priesemann
- Max Planck Institute for Dynamics and Self-Organization, Göttingen, Germany
- Bernstein Center for Computational Neuroscience, Göttingen, Germany
- Georg-August University Göttingen, Göttingen, Germany
- * E-mail:
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40
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Shumkova VV, Sitdikova VR, Silaeva VM, Suchkov DS, Minlebaev MG. Cortical Network Activity Modulation by Breath in the Anesthetized Juvenile Rat. J EVOL BIOCHEM PHYS+ 2022. [DOI: 10.1134/s0022093022060357] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/24/2022]
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41
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Rabuffo G, Sorrentino P, Bernard C, Jirsa V. Spontaneous neuronal avalanches as a correlate of access consciousness. Front Psychol 2022; 13:1008407. [PMID: 36337573 PMCID: PMC9634647 DOI: 10.3389/fpsyg.2022.1008407] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/31/2022] [Accepted: 10/04/2022] [Indexed: 09/03/2023] Open
Abstract
Decades of research have advanced our understanding of the biophysical mechanisms underlying consciousness. However, an overarching framework bridging between models of consciousness and the large-scale organization of spontaneous brain activity is still missing. Based on the observation that spontaneous brain activity dynamically switches between epochs of segregation and large-scale integration of information, we hypothesize a brain-state dependence of conscious access, whereby the presence of either segregated or integrated states marks distinct modes of information processing. We first review influential works on the neuronal correlates of consciousness, spontaneous resting-state brain activity and dynamical system theory. Then, we propose a test experiment to validate our hypothesis that conscious access occurs in aperiodic cycles, alternating windows where new incoming information is collected but not experienced, to punctuated short-lived integration events, where conscious access to previously collected content occurs. In particular, we suggest that the integration events correspond to neuronal avalanches, which are collective bursts of neuronal activity ubiquitously observed in electrophysiological recordings. If confirmed, the proposed framework would link the physics of spontaneous cortical dynamics, to the concept of ignition within the global neuronal workspace theory, whereby conscious access manifest itself as a burst of neuronal activity.
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Affiliation(s)
- Giovanni Rabuffo
- Institut de Neurosciences des Systemes, Aix-Marseille University, Marseille, France
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42
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Tian Y, Tan Z, Hou H, Li G, Cheng A, Qiu Y, Weng K, Chen C, Sun P. Theoretical foundations of studying criticality in the brain. Netw Neurosci 2022; 6:1148-1185. [PMID: 38800464 PMCID: PMC11117095 DOI: 10.1162/netn_a_00269] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/02/2022] [Accepted: 07/12/2022] [Indexed: 05/29/2024] Open
Abstract
Criticality is hypothesized as a physical mechanism underlying efficient transitions between cortical states and remarkable information-processing capacities in the brain. While considerable evidence generally supports this hypothesis, nonnegligible controversies persist regarding the ubiquity of criticality in neural dynamics and its role in information processing. Validity issues frequently arise during identifying potential brain criticality from empirical data. Moreover, the functional benefits implied by brain criticality are frequently misconceived or unduly generalized. These problems stem from the nontriviality and immaturity of the physical theories that analytically derive brain criticality and the statistic techniques that estimate brain criticality from empirical data. To help solve these problems, we present a systematic review and reformulate the foundations of studying brain criticality, that is, ordinary criticality (OC), quasi-criticality (qC), self-organized criticality (SOC), and self-organized quasi-criticality (SOqC), using the terminology of neuroscience. We offer accessible explanations of the physical theories and statistical techniques of brain criticality, providing step-by-step derivations to characterize neural dynamics as a physical system with avalanches. We summarize error-prone details and existing limitations in brain criticality analysis and suggest possible solutions. Moreover, we present a forward-looking perspective on how optimizing the foundations of studying brain criticality can deepen our understanding of various neuroscience questions.
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Affiliation(s)
- Yang Tian
- Department of Psychology & Tsinghua Laboratory of Brain and Intelligence, Tsinghua University, Beijing, China
- Laboratory of Advanced Computing and Storage, Central Research Institute, 2012 Laboratories, Huawei Technologies Co. Ltd., Beijing, China
| | - Zeren Tan
- Institute for Interdisciplinary Information Science, Tsinghua University, Beijing, China
| | - Hedong Hou
- UFR de Mathématiques, Université de Paris, Paris, France
| | - Guoqi Li
- Institute of Automation, Chinese Academy of Science, Beijing, China
- University of Chinese Academy of Science, Beijing, China
| | - Aohua Cheng
- Tsien Excellence in Engineering Program, School of Aerospace Engineering, Tsinghua University, Beijing, China
| | - Yike Qiu
- Tsien Excellence in Engineering Program, School of Aerospace Engineering, Tsinghua University, Beijing, China
| | - Kangyu Weng
- Tsien Excellence in Engineering Program, School of Aerospace Engineering, Tsinghua University, Beijing, China
| | - Chun Chen
- Department of Psychology & Tsinghua Laboratory of Brain and Intelligence, Tsinghua University, Beijing, China
| | - Pei Sun
- Department of Psychology & Tsinghua Laboratory of Brain and Intelligence, Tsinghua University, Beijing, China
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Martinez-Saito M. Discrete scaling and criticality in a chain of adaptive excitable integrators. CHAOS, SOLITONS & FRACTALS 2022; 163:112574. [DOI: 10.1016/j.chaos.2022.112574] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 09/02/2023]
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44
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Simola J, Siebenhühner F, Myrov V, Kantojärvi K, Paunio T, Palva JM, Brattico E, Palva S. Genetic polymorphisms in COMT and BDNF influence synchronization dynamics of human neuronal oscillations. iScience 2022; 25:104985. [PMID: 36093050 PMCID: PMC9460523 DOI: 10.1016/j.isci.2022.104985] [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: 03/22/2022] [Revised: 06/15/2022] [Accepted: 08/16/2022] [Indexed: 11/01/2022] Open
Abstract
Neuronal oscillations, their inter-areal synchronization, and scale-free dynamics constitute fundamental mechanisms for cognition by regulating communication in neuronal networks. These oscillatory dynamics have large inter-individual variability that is partly heritable. We hypothesized that this variability could be partially explained by genetic polymorphisms in neuromodulatory genes. We recorded resting-state magnetoencephalography (MEG) from 82 healthy participants and investigated whether oscillation dynamics were influenced by genetic polymorphisms in catechol-O-methyltransferase (COMT) Val158Met and brain-derived neurotrophic factor (BDNF) Val66Met. Both COMT and BDNF polymorphisms influenced local oscillation amplitudes and their long-range temporal correlations (LRTCs), while only BDNF polymorphism affected the strength of large-scale synchronization. Our findings demonstrate that COMT and BDNF genetic polymorphisms contribute to inter-individual variability in neuronal oscillation dynamics. Comparison of these results to computational modeling of near-critical synchronization dynamics further suggested that COMT and BDNF polymorphisms influenced local oscillations by modulating the excitation-inhibition balance according to the brain criticality framework.
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Affiliation(s)
- Jaana Simola
- Neuroscience Center, Helsinki Institute of Life Science, University of Helsinki, Haartmaninkatu 3, 00014 Helsinki, Finland
- Helsinki Collegium for Advanced Studies (HCAS), University of Helsinki, Finland
- BioMag Laboratory, HUS Medical Imaging Centre, 00029 HUS, Finland
| | - Felix Siebenhühner
- Neuroscience Center, Helsinki Institute of Life Science, University of Helsinki, Haartmaninkatu 3, 00014 Helsinki, Finland
| | - Vladislav Myrov
- Neuroscience Center, Helsinki Institute of Life Science, University of Helsinki, Haartmaninkatu 3, 00014 Helsinki, Finland
- Department of Neuroscience and Biomedical Engineering (NBE), Aalto University, 02150 Espoo, Finland
| | - Katri Kantojärvi
- Department of Public Health and Welfare, Finnish Institute for Health and Welfare, Helsinki, 00271 Helsinki, Finland
- Department of Psychiatry and SleepWell Research Program, Faculty of Medicine, University of Helsinki and Helsinki University Central Hospital, Helsinki, 00014 Helsinki, Finland
| | - Tiina Paunio
- Department of Public Health and Welfare, Finnish Institute for Health and Welfare, Helsinki, 00271 Helsinki, Finland
- Department of Psychiatry and SleepWell Research Program, Faculty of Medicine, University of Helsinki and Helsinki University Central Hospital, Helsinki, 00014 Helsinki, Finland
| | - J. Matias Palva
- Neuroscience Center, Helsinki Institute of Life Science, University of Helsinki, Haartmaninkatu 3, 00014 Helsinki, Finland
- Department of Neuroscience and Biomedical Engineering (NBE), Aalto University, 02150 Espoo, Finland
- Centre for Cognitive Neuroimaging (CCNi), Institute of Neuroscience and Psychology, University of Glasgow, Glasgow G12 8QB, UK
| | - Elvira Brattico
- Center for Music in the Brain (MIB), Department of Clinical Medicine, Aarhus University &The Royal Academy of Music Aarhus/Aalborg, 8000 Aarhus C, Denmark
- Department of Education, Psychology, Communication, University of Bari Aldo Moro, 70121 Bari, Italy
| | - Satu Palva
- Neuroscience Center, Helsinki Institute of Life Science, University of Helsinki, Haartmaninkatu 3, 00014 Helsinki, Finland
- Centre for Cognitive Neuroimaging (CCNi), Institute of Neuroscience and Psychology, University of Glasgow, Glasgow G12 8QB, UK
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45
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O'Byrne J, Jerbi K. How critical is brain criticality? Trends Neurosci 2022; 45:820-837. [PMID: 36096888 DOI: 10.1016/j.tins.2022.08.007] [Citation(s) in RCA: 54] [Impact Index Per Article: 27.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/01/2022] [Revised: 07/27/2022] [Accepted: 08/10/2022] [Indexed: 10/31/2022]
Abstract
Criticality is the singular state of complex systems poised at the brink of a phase transition between order and randomness. Such systems display remarkable information-processing capabilities, evoking the compelling hypothesis that the brain may itself be critical. This foundational idea is now drawing renewed interest thanks to high-density data and converging cross-disciplinary knowledge. Together, these lines of inquiry have shed light on the intimate link between criticality, computation, and cognition. Here, we review these emerging trends in criticality neuroscience, highlighting new data pertaining to the edge of chaos and near-criticality, and making a case for the distance to criticality as a useful metric for probing cognitive states and mental illness. This unfolding progress in the field contributes to establishing criticality theory as a powerful mechanistic framework for studying emergent function and its efficiency in both biological and artificial neural networks.
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Affiliation(s)
- Jordan O'Byrne
- Cognitive and Computational Neuroscience Lab, Psychology Department, University of Montreal, Montreal, Quebec, Canada
| | - Karim Jerbi
- Cognitive and Computational Neuroscience Lab, Psychology Department, University of Montreal, Montreal, Quebec, Canada; MILA (Quebec Artificial Intelligence Institute), Montreal, Quebec, Canada; UNIQUE Center (Quebec Neuro-AI Research Center), Montreal, Quebec, Canada.
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46
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Papo D. Attaining the recesses of the cognitive space. Cogn Neurodyn 2022; 16:767-778. [PMID: 35847536 PMCID: PMC9279523 DOI: 10.1007/s11571-021-09755-1] [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: 06/04/2021] [Revised: 10/31/2021] [Accepted: 11/08/2021] [Indexed: 11/26/2022] Open
Abstract
Existing neuropsychological tests of executive function often manifest a difficulty pinpointing cognitive deficits when these are intermittent and come in the form of omissions. We discuss the hypothesis that two partially interrelated reasons for this failure stem from relative inability of neuropsychological tests to explore the cognitive space and to explicitly take into account strategic and opportunistic resource allocation decisions, and to address the temporal aspects of both behaviour and task-related brain function in data analysis. Criteria for tasks suitable for neuropsychological assessment of executive function, as well as appropriate ways to analyse and interpret observed behavioural data are suggested. It is proposed that experimental tasks should be devised which emphasize typical rather than optimal performance, and that analyses should quantify path-dependent fluctuations in performance levels rather than averaged behaviour. Some implications for experimental neuropsychology are illustrated for the case of planning and problem-solving abilities and with particular reference to cognitive impairment in closed-head injury.
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Affiliation(s)
- David Papo
- Department of Neuroscience and Rehabilitation, Section of Physiology, University of Ferrara, Ferrara, Italy
- Fondazione Istituto Italiano di Tecnologia, Ferrara, Italy
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47
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Rocha RP, Koçillari L, Suweis S, De Filippo De Grazia M, de Schotten MT, Zorzi M, Corbetta M. Recovery of neural dynamics criticality in personalized whole-brain models of stroke. Nat Commun 2022; 13:3683. [PMID: 35760787 PMCID: PMC9237050 DOI: 10.1038/s41467-022-30892-6] [Citation(s) in RCA: 15] [Impact Index Per Article: 7.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/28/2020] [Accepted: 05/16/2022] [Indexed: 01/13/2023] Open
Abstract
The critical brain hypothesis states that biological neuronal networks, because of their structural and functional architecture, work near phase transitions for optimal response to internal and external inputs. Criticality thus provides optimal function and behavioral capabilities. We test this hypothesis by examining the influence of brain injury (strokes) on the criticality of neural dynamics estimated at the level of single participants using directly measured individual structural connectomes and whole-brain models. Lesions engender a sub-critical state that recovers over time in parallel with behavior. The improvement of criticality is associated with the re-modeling of specific white-matter connections. We show that personalized whole-brain dynamical models poised at criticality track neural dynamics, alteration post-stroke, and behavior at the level of single participants.
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Affiliation(s)
- Rodrigo P Rocha
- Departamento de Física, Centro de Ciências Físicas e Matemáticas, Universidade Federal de Santa Catarina, 88040-900, Florianópolis, SC, Brazil.
- Department of Physics, School of Philosophy, Sciences and Letters of Ribeirão Preto, University of São Paulo, Ribeirão Preto, SP, Brazil.
- Padova Neuroscience Center, Università di Padova, Padova, Italy.
| | - Loren Koçillari
- Padova Neuroscience Center, Università di Padova, Padova, Italy
- Laboratory of Neural Computation, Istituto Italiano di Tecnologia, 38068, Rovereto, Italy
- Dipartimento di Fisica e Astronomia, Università di Padova and INFN, via Marzolo 8, I-35131, Padova, Italy
| | - Samir Suweis
- Padova Neuroscience Center, Università di Padova, Padova, Italy
- Dipartimento di Fisica e Astronomia, Università di Padova and INFN, via Marzolo 8, I-35131, Padova, Italy
| | | | - Michel Thiebaut de Schotten
- Brain Connectivity and Behaviour Laboratory, BCBlab, Sorbonne Universities, Paris, France
- Groupe d'Imagerie Neurofonctionnelle, Institut des Maladies Neurodégénératives-UMR 5293, CNRS, CEA University of Bordeaux, Bordeaux, France
| | - Marco Zorzi
- IRCCS San Camillo Hospital, Venice, Italy
- Dipartimento di Psicologia Generale, Università di Padova, Padova, Italy
| | - Maurizio Corbetta
- Padova Neuroscience Center, Università di Padova, Padova, Italy
- Dipartimento di Neuroscienze, Università di Padova, Padova, Italy
- Venetian Institute of Molecular Medicine (VIMM), Fondazione Biomedica, Padova, Italy
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48
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Arata Y, Shiga I, Ikeda Y, Jurica P, Kimura H, Kiyono K, Sako Y. Insulin signaling shapes fractal scaling of C. elegans behavior. Sci Rep 2022; 12:10481. [PMID: 35729173 PMCID: PMC9213454 DOI: 10.1038/s41598-022-13022-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/07/2022] [Accepted: 05/03/2022] [Indexed: 11/08/2022] Open
Abstract
Fractal scaling in animal behavioral activity, where similar temporal patterns appear repeatedly over a series of magnifications among time scales, governs the complex behavior of various animal species and, in humans, can be altered by neurodegenerative diseases and aging. However, the mechanism underlying fractal scaling remains unknown. Here, we cultured C. elegans in a microfluidic device for 3 days and analyzed temporal patterns of C. elegans activity by fractal analyses. The residence-time distribution of C. elegans behaviors shared a common feature with those of human and mice. Specifically, the residence-time power-law distribution of the active state changed to an exponential-like decline at a longer time scale, whereas the inactive state followed a power-law distribution. An exponential-like decline appeared with nutrient supply in wild-type animals, whereas this decline disappeared in insulin-signaling-defective daf-2 and daf-16 mutants. The absolute value of the power-law exponent of the inactive state distribution increased with nutrient supply in wild-type animals, whereas the value decreased in daf-2 and daf-16 mutants. We conclude that insulin signaling differentially affects mechanisms that determine the residence time in active and inactive states in C. elegans behavior. In humans, diabetes mellitus, which is caused by defects in insulin signaling, is associated with mood disorders that affect daily behavioral activities. We hypothesize that comorbid behavioral defects in patients with diabetes may be attributed to altered fractal scaling of human behavior.
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Affiliation(s)
- Yukinobu Arata
- Cellular Informatics Laboratory, Cluster for Pioneering Research (CPR), RIKEN, 2-1 Hirosawa, Wako, Saitama, 351-0198, Japan.
| | - Itsuki Shiga
- Graduate School of Engineering Science, Osaka University, 1-3 Machikaneyama-cho, Toyonaka, Osaka, 560-8531, Japan
| | - Yusaku Ikeda
- Cellular Informatics Laboratory, Cluster for Pioneering Research (CPR), RIKEN, 2-1 Hirosawa, Wako, Saitama, 351-0198, Japan
- Department of Mechanical Engineering, School of Engineering, Tokai University, 4-1-1 Kitakaname, Hiratsuka, Kanagawa, 259-1292, Japan
| | - Peter Jurica
- Cellular Informatics Laboratory, Cluster for Pioneering Research (CPR), RIKEN, 2-1 Hirosawa, Wako, Saitama, 351-0198, Japan
| | - Hiroshi Kimura
- Department of Mechanical Engineering, School of Engineering, Tokai University, 4-1-1 Kitakaname, Hiratsuka, Kanagawa, 259-1292, Japan
| | - Ken Kiyono
- Graduate School of Engineering Science, Osaka University, 1-3 Machikaneyama-cho, Toyonaka, Osaka, 560-8531, Japan
| | - Yasushi Sako
- Cellular Informatics Laboratory, Cluster for Pioneering Research (CPR), RIKEN, 2-1 Hirosawa, Wako, Saitama, 351-0198, Japan
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49
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Mellor NG, Graham ES, Unsworth CP. Critical Spatial-Temporal Dynamics and Prominent Shape Collapse of Calcium Waves Observed in Human hNT Astrocytes in Vitro. Front Physiol 2022; 13:808730. [PMID: 35784870 PMCID: PMC9247335 DOI: 10.3389/fphys.2022.808730] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/03/2021] [Accepted: 05/31/2022] [Indexed: 11/27/2022] Open
Abstract
Networks of neurons are typically studied in the field of Criticality. However, the study of astrocyte networks in the brain has been recently lauded to be of equal importance to that of the neural networks. To date criticality assessments have only been performed on networks astrocytes from healthy rats, and astrocytes from cultured dissociated resections of intractable epilepsy. This work, for the first time, presents studies of the critical dynamics and shape collapse of calcium waves observed in cultures of healthy human astrocyte networks in vitro, derived from the human hNT cell line. In this article, we demonstrate that avalanches of spontaneous calcium waves display strong critical dynamics, including power-laws in both the size and duration distributions. In addition, the temporal profiles of avalanches displayed self-similarity, leading to shape collapse of the temporal profiles. These findings are significant as they suggest that cultured networks of healthy human hNT astrocytes self-organize to a critical point, implying that healthy astrocytic networks operate at a critical point to process and transmit information. Furthermore, this work can serve as a point of reference to which other astrocyte criticality studies can be compared.
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Affiliation(s)
- Nicholas G. Mellor
- Department of Engineering Science, The University of Auckland, Auckland, New Zealand
- *Correspondence: Nicholas G. Mellor,
| | - E. Scott Graham
- Department of Molecular Medicine and Pathology, School of Medical Sciences, The University of Auckland, Auckland, New Zealand
- Centre for Brain Research, The University of Auckland, Auckland, New Zealand
| | - Charles P. Unsworth
- Department of Engineering Science, The University of Auckland, Auckland, New Zealand
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50
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Hancock F, Rosas FE, Mediano PAM, Luppi AI, Cabral J, Dipasquale O, Turkheimer FE. May the 4C's be with you: an overview of complexity-inspired frameworks for analysing resting-state neuroimaging data. J R Soc Interface 2022; 19:20220214. [PMID: 35765805 PMCID: PMC9240685 DOI: 10.1098/rsif.2022.0214] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/17/2022] [Accepted: 06/09/2022] [Indexed: 11/12/2022] Open
Abstract
Competing and complementary models of resting-state brain dynamics contribute to our phenomenological and mechanistic understanding of whole-brain coordination and communication, and provide potential evidence for differential brain functioning associated with normal and pathological behaviour. These neuroscientific theories stem from the perspectives of physics, engineering, mathematics and psychology and create a complicated landscape of domain-specific terminology and meaning, which, when used outside of that domain, may lead to incorrect assumptions and conclusions within the neuroscience community. Here, we review and clarify the key concepts of connectivity, computation, criticality and coherence-the 4C's-and outline a potential role for metastability as a common denominator across these propositions. We analyse and synthesize whole-brain neuroimaging research, examined through functional magnetic imaging, to demonstrate that complexity science offers a principled and integrated approach to describe, and potentially understand, macroscale spontaneous brain functioning.
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Affiliation(s)
- Fran Hancock
- Department of Neuroimaging, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK
| | - Fernando E. Rosas
- Centre for Psychedelic Research, Department of Brain Science, Imperial College London, London SW7 2DD, UK
- Data Science Institute, Imperial College London, London SW7 2AZ, UK
- Centre for Complexity Science, Imperial College London, London SW7 2AZ, UK
| | - Pedro A. M. Mediano
- Department of Psychology, University of Cambridge, Cambridge CB2 3EB, UK
- Department of Psychology, Queen Mary University of London, London E1 4NS, UK
| | - Andrea I. Luppi
- Division of Anaesthesia, School of Clinical Medicine, University of Cambridge, Cambridge, UK
- Department of Clinical Neurosciences, University of Cambridge, Cambridge, UK
- Leverhulme Centre for the Future of Intelligence, University of Cambridge, Cambridge, UK
- Alan Turing Institute, London, UK
| | - Joana Cabral
- Life and Health Sciences Research Institute (ICVS), School of Medicine, University of Minho, Braga, Portugal
- Department of Psychiatry, University of Oxford, Oxford, UK
| | - Ottavia Dipasquale
- Department of Neuroimaging, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK
| | - Federico E. Turkheimer
- Department of Neuroimaging, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK
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