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Varga L, Moca VV, Molnár B, Perez-Cervera L, Selim MK, Díaz-Parra A, Moratal D, Péntek B, Sommer WH, Mureșan RC, Canals S, Ercsey-Ravasz M. Brain dynamics supported by a hierarchy of complex correlation patterns defining a robust functional architecture. Cell Syst 2024; 15:770-786.e5. [PMID: 39142285 DOI: 10.1016/j.cels.2024.07.003] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/02/2023] [Revised: 11/01/2023] [Accepted: 07/22/2024] [Indexed: 08/16/2024]
Abstract
Functional magnetic resonance imaging (fMRI) provides insights into cognitive processes with significant clinical potential. However, delays in brain region communication and dynamic variations are often overlooked in functional network studies. We demonstrate that networks extracted from fMRI cross-correlation matrices, considering time lags between signals, show remarkable reliability when focusing on statistical distributions of network properties. This reveals a robust brain functional connectivity pattern, featuring a sparse backbone of strong 0-lag correlations and weaker links capturing coordination at various time delays. This dynamic yet stable network architecture is consistent across rats, marmosets, and humans, as well as in electroencephalogram (EEG) data, indicating potential universality in brain dynamics. Second-order properties of the dynamic functional network reveal a remarkably stable hierarchy of functional correlations in both group-level comparisons and test-retest analyses. Validation using alcohol use disorder fMRI data uncovers broader shifts in network properties than previously reported, demonstrating the potential of this method for identifying disease biomarkers.
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Affiliation(s)
- Levente Varga
- Faculty of Mathematics and Computer Science, Babeș-Bolyai University, Cluj-Napoca, Romania; Faculty of Physics, Babeș-Bolyai University, Cluj-Napoca, Romania; Transylvanian Institute of Neuroscience, Cluj-Napoca, Romania
| | - Vasile V Moca
- Transylvanian Institute of Neuroscience, Cluj-Napoca, Romania
| | - Botond Molnár
- Faculty of Mathematics and Computer Science, Babeș-Bolyai University, Cluj-Napoca, Romania; Faculty of Physics, Babeș-Bolyai University, Cluj-Napoca, Romania; Transylvanian Institute of Neuroscience, Cluj-Napoca, Romania
| | - Laura Perez-Cervera
- Instituto de Neurociencias, Consejo Superior de Investigaciones Científicas, Universidad Miguel Hernández, San Juan de Alicante, Spain
| | - Mohamed Kotb Selim
- Instituto de Neurociencias, Consejo Superior de Investigaciones Científicas, Universidad Miguel Hernández, San Juan de Alicante, Spain
| | - Antonio Díaz-Parra
- Center for Biomaterials and Tissue Engineering, Universitat Politècnica de València, Valencia, Spain
| | - David Moratal
- Center for Biomaterials and Tissue Engineering, Universitat Politècnica de València, Valencia, Spain
| | - Balázs Péntek
- Faculty of Physics, Babeș-Bolyai University, Cluj-Napoca, Romania
| | - Wolfgang H Sommer
- Institute of Psychopharmacology and Clinic for Addiction Medicine, Central Institute of Mental Health, Medical Faculty Mannheim, University of Heidelberg, Mannheim, Germany
| | - Raul C Mureșan
- Transylvanian Institute of Neuroscience, Cluj-Napoca, Romania; STAR-UBB Institute, Babeș-Bolyai University, Cluj-Napoca, Romania.
| | - Santiago Canals
- Instituto de Neurociencias, Consejo Superior de Investigaciones Científicas, Universidad Miguel Hernández, San Juan de Alicante, Spain.
| | - Maria Ercsey-Ravasz
- Faculty of Physics, Babeș-Bolyai University, Cluj-Napoca, Romania; Transylvanian Institute of Neuroscience, Cluj-Napoca, Romania.
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2
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Papadopoulos S, Szul MJ, Congedo M, Bonaiuto JJ, Mattout J. Beta bursts question the ruling power for brain-computer interfaces. J Neural Eng 2024; 21:016010. [PMID: 38167234 DOI: 10.1088/1741-2552/ad19ea] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/15/2023] [Accepted: 01/02/2024] [Indexed: 01/05/2024]
Abstract
Objective: Current efforts to build reliable brain-computer interfaces (BCI) span multiple axes from hardware, to software, to more sophisticated experimental protocols, and personalized approaches. However, despite these abundant efforts, there is still room for significant improvement. We argue that a rather overlooked direction lies in linking BCI protocols with recent advances in fundamental neuroscience.Approach: In light of these advances, and particularly the characterization of the burst-like nature of beta frequency band activity and the diversity of beta bursts, we revisit the role of beta activity in 'left vs. right hand' motor imagery (MI) tasks. Current decoding approaches for such tasks take advantage of the fact that MI generates time-locked changes in induced power in the sensorimotor cortex and rely on band-passed power changes in single or multiple channels. Although little is known about the dynamics of beta burst activity during MI, we hypothesized that beta bursts should be modulated in a way analogous to their activity during performance of real upper limb movements.Main results and Significance: We show that classification features based on patterns of beta burst modulations yield decoding results that are equivalent to or better than typically used beta power across multiple open electroencephalography datasets, thus providing insights into the specificity of these bio-markers.
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Affiliation(s)
- Sotirios Papadopoulos
- University Lyon 1, Lyon, France
- Lyon Neuroscience Research Center, CRNL, INSERM U1028, CNRS, UMR5292, Lyon, France
- Institut de Sciences Cognitives Marc Jeannerod, CNRS, UMR5229, Lyon, France
| | - Maciej J Szul
- University Lyon 1, Lyon, France
- Institut de Sciences Cognitives Marc Jeannerod, CNRS, UMR5229, Lyon, France
| | - Marco Congedo
- GIPSA-lab, University Grenoble Alpes, CNRS, Grenoble-INP, Grenoble, France
| | - James J Bonaiuto
- University Lyon 1, Lyon, France
- Institut de Sciences Cognitives Marc Jeannerod, CNRS, UMR5229, Lyon, France
| | - Jérémie Mattout
- University Lyon 1, Lyon, France
- Lyon Neuroscience Research Center, CRNL, INSERM U1028, CNRS, UMR5292, Lyon, France
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Ardelean ER, Bârzan H, Ichim AM, Mureşan RC, Moca VV. Sharp detection of oscillation packets in rich time-frequency representations of neural signals. Front Hum Neurosci 2023; 17:1112415. [PMID: 38144896 PMCID: PMC10748759 DOI: 10.3389/fnhum.2023.1112415] [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: 11/30/2022] [Accepted: 11/13/2023] [Indexed: 12/26/2023] Open
Abstract
Brain oscillations most often occur in bursts, called oscillation packets, which span a finite extent in time and frequency. Recent studies have shown that these packets portray a much more dynamic picture of synchronization and transient communication between sites than previously thought. To understand their nature and statistical properties, techniques are needed to objectively detect oscillation packets and to quantify their temporal and frequency extent, as well as their magnitude. There are various methods to detect bursts of oscillations. The simplest ones divide the signal into band limited sub-components, quantifying the strength of the resulting components. These methods cannot by themselves cope with broadband transients that look like genuine oscillations when restricted to a narrow band. The most successful detection methods rely on time-frequency representations, which can readily show broadband transients and harmonics. However, the performance of such methods is conditioned by the ability of the representation to localize packets simultaneously in time and frequency, and by the capabilities of packet detection techniques, whose current state of the art is limited to extraction of bounding boxes. Here, we focus on the second problem, introducing two detection methods that use concepts derived from clustering and topographic prominence. These methods are able to delineate the packets' precise contour in the time-frequency plane. We validate the new approaches using both synthetic and real data recorded in humans and animals and rely on a super-resolution time-frequency representation, namely the superlets, as input to the detection algorithms. In addition, we define robust tests for benchmarking and compare the new methods to previous techniques. Results indicate that the two methods we introduce shine in low signal-to-noise ratio conditions, where they only miss a fraction of packets undetected by previous methods. Finally, algorithms that delineate precisely the border of spectral features and their subcomponents offer far more valuable information than simple rectangular bounding boxes (time and frequency span) and can provide a solid foundation to investigate neural oscillations' dynamics.
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Affiliation(s)
- Eugen-Richard Ardelean
- Experimental and Theoretical Neuroscience Laboratory, Transylvanian Institute of Neuroscience, Cluj-Napoca, Romania
- Computer Science Department, Technical University of Cluj-Napoca, Cluj-Napoca, Romania
| | - Harald Bârzan
- Experimental and Theoretical Neuroscience Laboratory, Transylvanian Institute of Neuroscience, Cluj-Napoca, Romania
| | - Ana-Maria Ichim
- Experimental and Theoretical Neuroscience Laboratory, Transylvanian Institute of Neuroscience, Cluj-Napoca, Romania
| | - Raul Cristian Mureşan
- Experimental and Theoretical Neuroscience Laboratory, Transylvanian Institute of Neuroscience, Cluj-Napoca, Romania
- STAR-UBB Institute, Babeş-Bolyai University, Cluj-Napoca, Romania
| | - Vasile Vlad Moca
- Experimental and Theoretical Neuroscience Laboratory, Transylvanian Institute of Neuroscience, Cluj-Napoca, Romania
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Csaky R, van Es MWJ, Jones OP, Woolrich M. Interpretable many-class decoding for MEG. Neuroimage 2023; 282:120396. [PMID: 37805019 PMCID: PMC10938061 DOI: 10.1016/j.neuroimage.2023.120396] [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: 07/12/2023] [Revised: 09/11/2023] [Accepted: 09/27/2023] [Indexed: 10/09/2023] Open
Abstract
Multivariate pattern analysis (MVPA) of Magnetoencephalography (MEG) and Electroencephalography (EEG) data is a valuable tool for understanding how the brain represents and discriminates between different stimuli. Identifying the spatial and temporal signatures of stimuli is typically a crucial output of these analyses. Such analyses are mainly performed using linear, pairwise, sliding window decoding models. These allow for relative ease of interpretation, e.g. by estimating a time-course of decoding accuracy, but have limited decoding performance. On the other hand, full epoch multiclass decoding models, commonly used for brain-computer interface (BCI) applications, can provide better decoding performance. However interpretation methods for such models have been designed with a low number of classes in mind. In this paper, we propose an approach that combines a multiclass, full epoch decoding model with supervised dimensionality reduction, while still being able to reveal the contributions of spatiotemporal and spectral features using permutation feature importance. Crucially, we introduce a way of doing supervised dimensionality reduction of input features within a neural network optimised for the classification task, improving performance substantially. We demonstrate the approach on 3 different many-class task-MEG datasets using image presentations. Our results demonstrate that this approach consistently achieves higher accuracy than the peak accuracy of a sliding window decoder while estimating the relevant spatiotemporal features in the MEG signal.
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Affiliation(s)
- Richard Csaky
- Oxford Centre for Human Brain Activity, Department of Psychiatry, University of Oxford, OX3 7JX, Oxford, UK; Wellcome Centre for Integrative Neuroimaging, OX3 9DU, Oxford, UK; Christ Church, OX1 1DP, Oxford, UK.
| | - Mats W J van Es
- Oxford Centre for Human Brain Activity, Department of Psychiatry, University of Oxford, OX3 7JX, Oxford, UK; Wellcome Centre for Integrative Neuroimaging, OX3 9DU, Oxford, UK.
| | - Oiwi Parker Jones
- Wellcome Centre for Integrative Neuroimaging, OX3 9DU, Oxford, UK; Department of Engineering Science, University of Oxford, OX1 3PJ, Oxford, UK; Jesus College, OX1 3DW, Oxford, UK.
| | - Mark Woolrich
- Oxford Centre for Human Brain Activity, Department of Psychiatry, University of Oxford, OX3 7JX, Oxford, UK; Wellcome Centre for Integrative Neuroimaging, OX3 9DU, Oxford, UK.
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Dwiel LL, Henricks AM, Bragg E, Gui J, Doucette WT. Neural oscillations in the ventral striatum reveal differences between the encoding of palatable food and ethanol consumption. ALCOHOL, CLINICAL & EXPERIMENTAL RESEARCH 2023; 47:1327-1340. [PMID: 37166071 PMCID: PMC10601443 DOI: 10.1111/acer.15101] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/14/2022] [Revised: 04/24/2023] [Accepted: 05/03/2023] [Indexed: 05/12/2023]
Abstract
BACKGROUND Across multiple levels of investigation, there appear to be convergent neuronal processes underlying substance use and other motivated behaviors (i.e., the pursuit and consumption of rewarding substances). The consumption of alcohol and sweet, high-fat food engages many of the same brain regions, especially, the ventral striatum. In the current study, we hypothesized that ventral striatal local field potentials (LFPs) recorded during self-administration sessions could be used to detect when the consumption of 10% ethanol or sweet-fat food (SF) was occurring compared to all other behaviors, including naturalistic controls (i.e., water or house-chow). METHODS We used an intermittent limited access approach to condition Sprague-Dawley rats to consume either ethanol or SF while we recorded LFPs. We used machine learning and simple logistic regressions to determine whether LFP features could classify when consumption of each substance was occurring, and whether a general model could predict consumption of both substances. We report performance as the average area under the receiver operator characteristic curve (AUROC). RESULTS Consumption of a single substance was differentiable from all other behaviors, as evidenced by the AUROC (ethanol = 0.84 and SF = 0.83, p < 0.01). Models built from the combined dataset (general) did modestly overall (general → general = 0.68, p < 0.05), and did not detect the consumption of the two substances similarly (general → SF = 0.5 and general → ethanol = 0.63, p > 0.05). CONCLUSIONS Models successfully classified ethanol and SF consumption versus all other behavior/naturalistic controls. However, the findings highlight differences in how the ventral striatum represents the consumption of ethanol and SF and show that, although there is potential for finding biomarkers related to substance use, it may be difficult to build a model that performs well detecting multiple substances.
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Affiliation(s)
- Lucas L. Dwiel
- Department of Psychiatry, Geisel School of Medicine at Dartmouth
| | | | - Elise Bragg
- Department of Psychiatry, Geisel School of Medicine at Dartmouth
| | - Jiang Gui
- Department of Biomedical Data Science, Geisel School of Medicine at Dartmouth
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Cariani P, Baker JM. Time Is of the Essence: Neural Codes, Synchronies, Oscillations, Architectures. Front Comput Neurosci 2022; 16:898829. [PMID: 35814343 PMCID: PMC9262106 DOI: 10.3389/fncom.2022.898829] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/17/2022] [Accepted: 05/04/2022] [Indexed: 11/25/2022] Open
Abstract
Time is of the essence in how neural codes, synchronies, and oscillations might function in encoding, representation, transmission, integration, storage, and retrieval of information in brains. This Hypothesis and Theory article examines observed and possible relations between codes, synchronies, oscillations, and types of neural networks they require. Toward reverse-engineering informational functions in brains, prospective, alternative neural architectures incorporating principles from radio modulation and demodulation, active reverberant circuits, distributed content-addressable memory, signal-signal time-domain correlation and convolution operations, spike-correlation-based holography, and self-organizing, autoencoding anticipatory systems are outlined. Synchronies and oscillations are thought to subserve many possible functions: sensation, perception, action, cognition, motivation, affect, memory, attention, anticipation, and imagination. These include direct involvement in coding attributes of events and objects through phase-locking as well as characteristic patterns of spike latency and oscillatory response. They are thought to be involved in segmentation and binding, working memory, attention, gating and routing of signals, temporal reset mechanisms, inter-regional coordination, time discretization, time-warping transformations, and support for temporal wave-interference based operations. A high level, partial taxonomy of neural codes consists of channel, temporal pattern, and spike latency codes. The functional roles of synchronies and oscillations in candidate neural codes, including oscillatory phase-offset codes, are outlined. Various forms of multiplexing neural signals are considered: time-division, frequency-division, code-division, oscillatory-phase, synchronized channels, oscillatory hierarchies, polychronous ensembles. An expandable, annotative neural spike train framework for encoding low- and high-level attributes of events and objects is proposed. Coding schemes require appropriate neural architectures for their interpretation. Time-delay, oscillatory, wave-interference, synfire chain, polychronous, and neural timing networks are discussed. Some novel concepts for formulating an alternative, more time-centric theory of brain function are discussed. As in radio communication systems, brains can be regarded as networks of dynamic, adaptive transceivers that broadcast and selectively receive multiplexed temporally-patterned pulse signals. These signals enable complex signal interactions that select, reinforce, and bind common subpatterns and create emergent lower dimensional signals that propagate through spreading activation interference networks. If memory traces share the same kind of temporal pattern forms as do active neuronal representations, then distributed, holograph-like content-addressable memories are made possible via temporal pattern resonances.
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Affiliation(s)
- Peter Cariani
- Hearing Research Center, Boston University, Boston, MA, United States
- Department of Otolaryngology-Head and Neck Surgery, Harvard Medical School, Boston, MA, United States
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