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Foster M, Scheinost D. Brain states as wave-like motifs. Trends Cogn Sci 2024; 28:492-503. [PMID: 38582654 DOI: 10.1016/j.tics.2024.03.004] [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: 05/27/2023] [Revised: 02/29/2024] [Accepted: 03/11/2024] [Indexed: 04/08/2024]
Abstract
There is ample evidence of wave-like activity in the brain at multiple scales and levels. This emerging literature supports the broader adoption of a wave perspective of brain activity. Specifically, a brain state can be described as a set of recurring, sequential patterns of propagating brain activity, namely a wave. We examine a collective body of experimental work investigating wave-like properties. Based on these works, we consider brain states as waves using a scale-agnostic framework across time and space. Emphasis is placed on the sequentiality and periodicity associated with brain activity. We conclude by discussing the implications, prospects, and experimental opportunities of this framework.
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Affiliation(s)
- Maya Foster
- Department of Biomedical Engineering, Yale University, New Haven, CT, USA.
| | - Dustin Scheinost
- Department of Biomedical Engineering, Yale University, New Haven, CT, USA; Department of Radiology and Biomedical Engineering, Yale School of Medicine, New Haven, CT, USA
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Yaremkevich DD, Scherbakov AV, De Clerk L, Kukhtaruk SM, Nadzeyka A, Campion R, Rushforth AW, Savel'ev S, Balanov AG, Bayer M. On-chip phonon-magnon reservoir for neuromorphic computing. Nat Commun 2023; 14:8296. [PMID: 38097654 PMCID: PMC10721880 DOI: 10.1038/s41467-023-43891-y] [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: 04/21/2023] [Accepted: 11/22/2023] [Indexed: 12/17/2023] Open
Abstract
Reservoir computing is a concept involving mapping signals onto a high-dimensional phase space of a dynamical system called "reservoir" for subsequent recognition by an artificial neural network. We implement this concept in a nanodevice consisting of a sandwich of a semiconductor phonon waveguide and a patterned ferromagnetic layer. A pulsed write-laser encodes input signals into propagating phonon wavepackets, interacting with ferromagnetic magnons. The second laser reads the output signal reflecting a phase-sensitive mix of phonon and magnon modes, whose content is highly sensitive to the write- and read-laser positions. The reservoir efficiently separates the visual shapes drawn by the write-laser beam on the nanodevice surface in an area with a size comparable to a single pixel of a modern digital camera. Our finding suggests the phonon-magnon interaction as a promising hardware basis for realizing on-chip reservoir computing in future neuromorphic architectures.
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Affiliation(s)
- Dmytro D Yaremkevich
- Experimentelle Physik 2, Technische Universität Dortmund, D-44227, Dortmund, Germany
| | - Alexey V Scherbakov
- Experimentelle Physik 2, Technische Universität Dortmund, D-44227, Dortmund, Germany.
| | - Luke De Clerk
- Department of Physics, Loughborough University, Loughborough, LE11 3TU, UK
- Machine Learning Development, SS&C Technologies, 128 Queen Victoria Street, London, EC4V 4BJ, UK
| | - Serhii M Kukhtaruk
- Department of Theoretical Physics, V. E. Lashkaryov Institute of Semiconductor Physics, 03028, Kyiv, Ukraine
| | | | - Richard Campion
- School of Physics and Astronomy, University of Nottingham, Nottingham, NG7 2RD, UK
| | - Andrew W Rushforth
- School of Physics and Astronomy, University of Nottingham, Nottingham, NG7 2RD, UK
| | - Sergey Savel'ev
- Department of Physics, Loughborough University, Loughborough, LE11 3TU, UK
| | | | - Manfred Bayer
- Experimentelle Physik 2, Technische Universität Dortmund, D-44227, Dortmund, Germany
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Guan S, Jiang R, Chen DY, Michael A, Meng C, Biswal B. Multifractal long-range dependence pattern of functional magnetic resonance imaging in the human brain at rest. Cereb Cortex 2023; 33:11594-11608. [PMID: 37851793 DOI: 10.1093/cercor/bhad393] [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: 09/12/2023] [Revised: 10/01/2023] [Accepted: 10/02/2023] [Indexed: 10/20/2023] Open
Abstract
Long-range dependence is a prevalent phenomenon in various biological systems that characterizes the long-memory effect of temporal fluctuations. While recent research suggests that functional magnetic resonance imaging signal has fractal property, it remains unknown about the multifractal long-range dependence pattern of resting-state functional magnetic resonance imaging signals. The current study adopted the multifractal detrended fluctuation analysis on highly sampled resting-state functional magnetic resonance imaging scans to investigate long-range dependence profile associated with the whole-brain voxels as specific functional networks. Our findings revealed the long-range dependence's multifractal properties. Moreover, long-term persistent fluctuations are found for all stations with stronger persistency in whole-brain regions. Subsets with large fluctuations contribute more to the multifractal spectrum in the whole brain. Additionally, we found that the preprocessing with band-pass filtering provided significantly higher reliability for estimating long-range dependence. Our validation analysis confirmed that the optimal pipeline of long-range dependence analysis should include band-pass filtering and removal of daily temporal dependence. Furthermore, multifractal long-range dependence characteristics in healthy control and schizophrenia are different significantly. This work has provided an analytical pipeline for the multifractal long-range dependence in the resting-state functional magnetic resonance imaging signal. The findings suggest differential long-memory effects in the intrinsic functional networks, which may offer a neural marker finding for understanding brain function and pathology.
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Affiliation(s)
- Sihai Guan
- College of Electronic and Information, Southwest Minzu University, Chengdu 610041, China
- Key Laboratory of Electronic and Information Engineering, State Ethnic Affairs Commission, Chengdu 610041, China
| | - Runzhou Jiang
- School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu 611731, China
- Medical Equipment Department, Xiangyang No.1 People's Hospital, Xiangyang 441000, China
| | - Donna Y Chen
- Department of Biomedical Engineering, New Jersey Institute of Technology, Newark, NJ 07102, United States
| | - Andrew Michael
- Duke Institute for Brain Sciences, Duke University, Durham, NC 27708, United States
| | - Chun Meng
- School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu 611731, China
| | - Bharat Biswal
- School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu 611731, China
- Department of Biomedical Engineering, New Jersey Institute of Technology, Newark, NJ 07102, United States
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Wang Q, Zhao S, Liu T, Han J, Liu C. Temporal fingerprints of cortical gyrification in marmosets and humans. Cereb Cortex 2023; 33:9802-9814. [PMID: 37434368 PMCID: PMC10656951 DOI: 10.1093/cercor/bhad245] [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: 02/18/2023] [Revised: 06/01/2023] [Accepted: 06/02/2023] [Indexed: 07/13/2023] Open
Abstract
Recent neuroimaging studies in humans have reported distinct temporal dynamics of gyri and sulci, which may be associated with putative functions of cortical gyrification. However, the complex folding patterns of the human cortex make it difficult to explain temporal patterns of gyrification. In this study, we used the common marmoset as a simplified model to examine the temporal characteristics and compare them with the complex gyrification of humans. Using a brain-inspired deep neural network, we obtained reliable temporal-frequency fingerprints of gyri and sulci from the awake rs-fMRI data of marmosets and humans. Notably, the temporal fingerprints of one region successfully classified the gyrus/sulcus of another region in both marmosets and humans. Additionally, the temporal-frequency fingerprints were remarkably similar in both species. We then analyzed the resulting fingerprints in several domains and adopted the Wavelet Transform Coherence approach to characterize the gyro-sulcal coupling patterns. In both humans and marmosets, sulci exhibited higher frequency bands than gyri, and the two were temporally coupled within the same range of phase angles. This study supports the notion that gyri and sulci possess unique and evolutionarily conserved features that are consistent across functional areas, and advances our understanding of the functional role of cortical gyrification.
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Affiliation(s)
- Qiyu Wang
- School of Automation, Northwestern Polytechnical University, Xi'an 710072, China
- CAS Center for Excellence in Brain Science and Intelligence Technology, Institute of Neuroscience, Chinese Academy of Sciences, Shanghai 200031, China
| | - Shijie Zhao
- School of Automation, Northwestern Polytechnical University, Xi'an 710072, China
- Research & Development Institute of Northwestern Polytechnical University in Shenzhen, Shenzhen 518063, China
| | - Tianming Liu
- Cortical Architecture Imaging and Discovery Lab, Department of Computer Science and Bioimaging Research Center, The University of Georgia, Athens, GA 30602, United States
| | - Junwei Han
- School of Automation, Northwestern Polytechnical University, Xi'an 710072, China
| | - Cirong Liu
- CAS Center for Excellence in Brain Science and Intelligence Technology, Institute of Neuroscience, Chinese Academy of Sciences, Shanghai 200031, China
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Signorelli CM, Boils JD, Tagliazucchi E, Jarraya B, Deco G. From Brain-Body Function to Conscious Interactions. Neurosci Biobehav Rev 2022; 141:104833. [PMID: 36037978 DOI: 10.1016/j.neubiorev.2022.104833] [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: 05/24/2022] [Revised: 08/06/2022] [Accepted: 08/18/2022] [Indexed: 11/15/2022]
Abstract
In this review, we discuss empirical results inspiring the introduction of a formal mathematical multilayer model for the biological neuroscience of conscious experience. First, we motivate the discussion through evidence regarding the dynamic brain. Second, we review different brain-body couplings associated with conscious experience and its potential role in driving brain dynamics. Third, we introduce the machinery of multilayer networks to account for several types of interactions in brain-body systems. Then, a multilayer structure consists of two main generalizations: a formal semantic to study biological systems, and an integrative account for several signatures and models of consciousness. Finally, under this framework, we define composition of layers to account for entangled features of brain-body systems related to conscious experience. As such, a multilayer mathematical framework is highly integrative and thus may be more complete than other models. In this short review, we discuss a variety of empirical results inspiring the introduction of a formal mathematical multilayer model for the biological neuroscience of conscious experience.
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Affiliation(s)
- Camilo Miguel Signorelli
- Department of Computer Science, University of Oxford, Oxford, 7 Parks Rd, OxfordOX1 3QG, United Kingdom; Physiology of Cognition, GIGA-CRC In Vivo Imaging, Allée du 6 Août, 8 (B30), 4000 Sart Tilman, University of Liège, Belgium; Cognitive Neuroimaging Unit, INSERM, CEA, CNRS, Université Paris-Saclay, NeuroSpin center, 91191 Gif/Yvette, France; Computational Neuroscience Group, Center for Brain and Cognition, Universitat Pompeu Fabra, Barcelona, Spain.
| | - Joaquín Díaz Boils
- Universidad Internacional de La Rioja, Avda La Paz, 137, Logroño, La Rioja, Spain
| | - Enzo Tagliazucchi
- Physics Department, University of Buenos Aires, Buenos Aires, Argentina
| | - Bechir Jarraya
- Cognitive Neuroimaging Unit, INSERM, CEA, CNRS, Université Paris-Saclay, NeuroSpin center, 91191 Gif/Yvette, France
| | - Gustavo Deco
- Computational Neuroscience Group, Center for Brain and Cognition, Universitat Pompeu Fabra, Barcelona, Spain
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