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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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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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Gohil C, Roberts E, Timms R, Skates A, Higgins C, Quinn A, Pervaiz U, van Amersfoort J, Notin P, Gal Y, Adaszewski S, Woolrich M. Mixtures of large-scale dynamic functional brain network modes. Neuroimage 2022; 263:119595. [PMID: 36041643 DOI: 10.1016/j.neuroimage.2022.119595] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/04/2022] [Revised: 08/12/2022] [Accepted: 08/26/2022] [Indexed: 10/31/2022] Open
Abstract
Accurate temporal modelling of functional brain networks is essential in the quest for understanding how such networks facilitate cognition. Researchers are beginning to adopt time-varying analyses for electrophysiological data that capture highly dynamic processes on the order of milliseconds. Typically, these approaches, such as clustering of functional connectivity profiles and Hidden Markov Modelling (HMM), assume mutual exclusivity of networks over time. Whilst a powerful constraint, this assumption may be compromising the ability of these approaches to describe the data effectively. Here, we propose a new generative model for functional connectivity as a time-varying linear mixture of spatially distributed statistical "modes". The temporal evolution of this mixture is governed by a recurrent neural network, which enables the model to generate data with a rich temporal structure. We use a Bayesian framework known as amortised variational inference to learn model parameters from observed data. We call the approach DyNeMo (for Dynamic Network Modes), and show using simulations it outperforms the HMM when the assumption of mutual exclusivity is violated. In resting-state MEG, DyNeMo reveals a mixture of modes that activate on fast time scales of 100-150 ms, which is similar to state lifetimes found using an HMM. In task MEG data, DyNeMo finds modes with plausible, task-dependent evoked responses without any knowledge of the task timings. Overall, DyNeMo provides decompositions that are an approximate remapping of the HMM's while showing improvements in overall explanatory power. However, the magnitude of the improvements suggests that the HMM's assumption of mutual exclusivity can be reasonable in practice. Nonetheless, DyNeMo provides a flexible framework for implementing and assessing future modelling developments.
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Affiliation(s)
- Chetan Gohil
- Oxford Centre for Human Brain Activity (OHBA), Wellcome Centre for Integrative Neuroimaging, Department of Psychiatry, University of Oxford, Oxford, OX3 7JX, United Kingdom
| | - Evan Roberts
- Oxford Centre for Human Brain Activity (OHBA), Wellcome Centre for Integrative Neuroimaging, Department of Psychiatry, University of Oxford, Oxford, OX3 7JX, United Kingdom
| | - Ryan Timms
- Oxford Centre for Human Brain Activity (OHBA), Wellcome Centre for Integrative Neuroimaging, Department of Psychiatry, University of Oxford, Oxford, OX3 7JX, United Kingdom
| | - Alex Skates
- Oxford Centre for Human Brain Activity (OHBA), Wellcome Centre for Integrative Neuroimaging, Department of Psychiatry, University of Oxford, Oxford, OX3 7JX, United Kingdom
| | - Cameron Higgins
- Oxford Centre for Human Brain Activity (OHBA), Wellcome Centre for Integrative Neuroimaging, Department of Psychiatry, University of Oxford, Oxford, OX3 7JX, United Kingdom
| | - Andrew Quinn
- Oxford Centre for Human Brain Activity (OHBA), Wellcome Centre for Integrative Neuroimaging, Department of Psychiatry, University of Oxford, Oxford, OX3 7JX, United Kingdom
| | - Usama Pervaiz
- Oxford Centre for Functional MRI of the Brain (FMRIB), Wellcome Centre for Integrative Neuroimaging, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford OX3 9DU, United Kingdom
| | - Joost van Amersfoort
- Oxford Applied and Theoretical Machine Learning (OATML), Department of Computer Science, University of Oxford, Oxford, OX1 3QD, United Kingdom
| | - Pascal Notin
- Oxford Applied and Theoretical Machine Learning (OATML), Department of Computer Science, University of Oxford, Oxford, OX1 3QD, United Kingdom
| | - Yarin Gal
- Oxford Applied and Theoretical Machine Learning (OATML), Department of Computer Science, University of Oxford, Oxford, OX1 3QD, United Kingdom
| | - Stanislaw Adaszewski
- Pharma Research and Early Development Operations, Roche Innovation Center Basel, F. Hoffman - La Roche AG, Basel CH-4070, Switzerland
| | - Mark Woolrich
- Oxford Centre for Human Brain Activity (OHBA), Wellcome Centre for Integrative Neuroimaging, Department of Psychiatry, University of Oxford, Oxford, OX3 7JX, United Kingdom
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Hirano Y, Uhlhaas PJ. Current findings and perspectives on aberrant neural oscillations in schizophrenia. Psychiatry Clin Neurosci 2021; 75:358-368. [PMID: 34558155 DOI: 10.1111/pcn.13300] [Citation(s) in RCA: 45] [Impact Index Per Article: 15.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/01/2021] [Revised: 08/20/2021] [Accepted: 09/09/2021] [Indexed: 12/11/2022]
Abstract
There is now consistent evidence that neural oscillation at low- and high-frequencies constitute an important aspect of the pathophysiology of schizophrenia. Specifically, impaired rhythmic activity may underlie the deficit to generate coherent cognition and behavior, leading to the characteristic symptoms of psychosis and cognitive deficits. Importantly, the generating mechanisms of neural oscillations are relatively well-understood and thus enable the targeted search for the underlying circuit impairments and novel treatment targets. In the following review, we will summarize and assess the evidence for aberrant rhythmic activity in schizophrenia through evaluating studies that have utilized Electro/Magnetoencephalography to examine neural oscillations during sensory and cognitive tasks as well as during resting-state measurements. These data will be linked to current evidence from post-mortem, neuroimaging, genetics, and animal models that have implicated deficits in GABAergic interneurons and glutamatergic neurotransmission in oscillatory deficits in schizophrenia. Finally, we will highlight methodological and analytical challenges as well as provide recommendations for future research.
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Affiliation(s)
- Yoji Hirano
- Department of Neuropsychiatry, Graduate School of Medical Sciences, Kyushu University, Fukuoka, Japan
- Institute of Industrial Science, The University of Tokyo, Tokyo, Japan
| | - Peter J Uhlhaas
- Department of Child and Adolescent Psychiatry, Charité - Universitätsmedizin, Berlin, Germany
- Institute of Neuroscience and Psychology, University of Glasgow, Glasgow, UK
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