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Wu YH, Podvalny E, Levinson M, He BJ. Network mechanisms of ongoing brain activity's influence on conscious visual perception. Nat Commun 2024; 15:5720. [PMID: 38977709 PMCID: PMC11231278 DOI: 10.1038/s41467-024-50102-9] [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: 11/27/2023] [Accepted: 06/28/2024] [Indexed: 07/10/2024] Open
Abstract
Sensory inputs enter a constantly active brain, whose state is always changing from one moment to the next. Currently, little is known about how ongoing, spontaneous brain activity participates in online task processing. We employed 7 Tesla fMRI and a threshold-level visual perception task to probe the effects of prestimulus ongoing brain activity on perceptual decision-making and conscious recognition. Prestimulus activity originating from distributed brain regions, including visual cortices and regions of the default-mode and cingulo-opercular networks, exerted a diverse set of effects on the sensitivity and criterion of conscious recognition, and categorization performance. We further elucidate the mechanisms underlying these behavioral effects, revealing how prestimulus activity modulates multiple aspects of stimulus processing in highly specific and network-dependent manners. These findings reveal heretofore unknown network mechanisms underlying ongoing brain activity's influence on conscious perception, and may hold implications for understanding the precise roles of spontaneous activity in other brain functions.
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Affiliation(s)
- Yuan-Hao Wu
- Neuroscience Institute, New York University Grossman School of Medicine, New York, NY, 10016, USA
| | - Ella Podvalny
- Neuroscience Institute, New York University Grossman School of Medicine, New York, NY, 10016, USA
- The Nash Family Department of Neuroscience, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA
| | - Max Levinson
- Neuroscience Institute, New York University Grossman School of Medicine, New York, NY, 10016, USA
| | - Biyu J He
- Neuroscience Institute, New York University Grossman School of Medicine, New York, NY, 10016, USA.
- Department of Neurology, New York University Grossman School of Medicine, New York, NY, 10016, USA.
- Department of Neuroscience & Physiology, New York University Grossman School of Medicine, New York, NY, 10016, USA.
- Department of Radiology, New York University Grossman School of Medicine, New York, NY, 10016, USA.
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Boring MJ, Richardson RM, Ghuman AS. Interacting ventral temporal gradients of timescales and functional connectivity and their relationships to visual behavior. iScience 2024; 27:110003. [PMID: 38868193 PMCID: PMC11166696 DOI: 10.1016/j.isci.2024.110003] [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: 11/11/2022] [Revised: 04/02/2024] [Accepted: 05/14/2024] [Indexed: 06/14/2024] Open
Abstract
Cortical gradients in endogenous and stimulus-evoked neurodynamic timescales, and long-range cortical interactions, provide organizational constraints to the brain and influence neural populations' roles in cognition. It is unclear how these functional gradients interrelate and which influence behavior. Here, intracranial recordings from 4,090 electrode contacts in 35 individuals map gradients of neural timescales and functional connectivity to assess their interactions along category-selective ventral temporal cortex. Endogenous and stimulus-evoked information processing timescales were not significantly correlated with one another suggesting that local neural timescales are context dependent and may arise through distinct neurophysiological mechanisms. Endogenous neural timescales correlated with functional connectivity even after removing the effects of shared anatomical gradients. Neural timescales and functional connectivity correlated with how strongly a population's activity predicted behavior in a simple visual task. These results suggest both interrelated and distinct neurophysiological processes give rise to different functional connectivity and neural timescale gradients, which together influence behavior.
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Affiliation(s)
- Matthew J. Boring
- Center for Neuroscience at the University of Pittsburgh, University of Pittsburgh, Pittsburgh, PA, USA
- Center for the Neural Basis of Cognition, University of Pittsburgh and Carnegie Mellon University, Pittsburgh, PA, USA
- Department of Neurological Surgery, University of Pittsburgh, Pittsburgh, PA, USA
| | - R. Mark Richardson
- Department of Neurological Surgery, University of Pittsburgh, Pittsburgh, PA, USA
- Department of Neurosurgery, Massachusetts General Hospital, Boston, MA, USA
- Harvard Medical School, Boston, MA, USA
| | - Avniel Singh Ghuman
- Center for Neuroscience at the University of Pittsburgh, University of Pittsburgh, Pittsburgh, PA, USA
- Center for the Neural Basis of Cognition, University of Pittsburgh and Carnegie Mellon University, Pittsburgh, PA, USA
- Department of Neurological Surgery, University of Pittsburgh, Pittsburgh, PA, USA
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Li Y, Yang H, Gu S. Enhancing neural encoding models for naturalistic perception with a multi-level integration of deep neural networks and cortical networks. Sci Bull (Beijing) 2024; 69:1738-1747. [PMID: 38490889 DOI: 10.1016/j.scib.2024.02.035] [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: 04/01/2023] [Revised: 06/27/2023] [Accepted: 02/23/2024] [Indexed: 03/17/2024]
Abstract
Cognitive neuroscience aims to develop computational models that can accurately predict and explain neural responses to sensory inputs in the cortex. Recent studies attempt to leverage the representation power of deep neural networks (DNNs) to predict the brain response and suggest a correspondence between artificial and biological neural networks in their feature representations. However, typical voxel-wise encoding models tend to rely on specific networks designed for computer vision tasks, leading to suboptimal brain-wide correspondence during cognitive tasks. To address this challenge, this work proposes a novel approach that upgrades voxel-wise encoding models through multi-level integration of features from DNNs and information from brain networks. Our approach combines DNN feature-level ensemble learning and brain atlas-level model integration, resulting in significant improvements in predicting whole-brain neural activity during naturalistic video perception. Furthermore, this multi-level integration framework enables a deeper understanding of the brain's neural representation mechanism, accurately predicting the neural response to complex visual concepts. We demonstrate that neural encoding models can be optimized by leveraging a framework that integrates both data-driven approaches and theoretical insights into the functional structure of the cortical networks.
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Affiliation(s)
- Yuanning Li
- School of Biomedical Engineering & State Key Laboratory of Advanced Medical Materials and Devices, ShanghaiTech University, Shanghai 201210, China.
| | - Huzheng Yang
- School of Computer Science and Engineering, University of Electronic Science and Technology of China, Chengdu 611731, China; Department of Computer and Information Science, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Shi Gu
- School of Computer Science and Engineering, University of Electronic Science and Technology of China, Chengdu 611731, China; Shenzhen Institute for Advanced Study, University of Electronic Science and Technology of China, Shenzhen 518110, China.
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Sui Y, Yu H, Zhang C, Chen Y, Jiang C, Li L. Deep brain-machine interfaces: sensing and modulating the human deep brain. Natl Sci Rev 2022; 9:nwac212. [PMID: 36644311 PMCID: PMC9834907 DOI: 10.1093/nsr/nwac212] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/08/2022] [Revised: 10/02/2022] [Accepted: 10/04/2022] [Indexed: 01/18/2023] Open
Abstract
Different from conventional brain-machine interfaces that focus more on decoding the cerebral cortex, deep brain-machine interfaces enable interactions between external machines and deep brain structures. They sense and modulate deep brain neural activities, aiming at function restoration, device control and therapeutic improvements. In this article, we provide an overview of multiple deep brain recording and stimulation techniques that can serve as deep brain-machine interfaces. We highlight two widely used interface technologies, namely deep brain stimulation and stereotactic electroencephalography, for technical trends, clinical applications and brain connectivity research. We discuss the potential to develop closed-loop deep brain-machine interfaces and achieve more effective and applicable systems for the treatment of neurological and psychiatric disorders.
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Affiliation(s)
- Yanan Sui
- National Engineering Research Center of Neuromodulation, Tsinghua University, Beijing 100084, China
| | - Huiling Yu
- National Engineering Research Center of Neuromodulation, Tsinghua University, Beijing 100084, China
| | - Chen Zhang
- National Engineering Research Center of Neuromodulation, Tsinghua University, Beijing 100084, China
| | - Yue Chen
- National Engineering Research Center of Neuromodulation, Tsinghua University, Beijing 100084, China
| | - Changqing Jiang
- National Engineering Research Center of Neuromodulation, Tsinghua University, Beijing 100084, China
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Sadaghiani S, Brookes MJ, Baillet S. Connectomics of human electrophysiology. Neuroimage 2022; 247:118788. [PMID: 34906715 PMCID: PMC8943906 DOI: 10.1016/j.neuroimage.2021.118788] [Citation(s) in RCA: 48] [Impact Index Per Article: 24.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/15/2021] [Revised: 11/03/2021] [Accepted: 12/06/2021] [Indexed: 12/15/2022] Open
Abstract
We present both a scientific overview and conceptual positions concerning the challenges and assets of electrophysiological measurements in the search for the nature and functions of the human connectome. We discuss how the field has been inspired by findings and approaches from functional magnetic resonance imaging (fMRI) and informed by a small number of significant multimodal empirical studies, which show that the canonical networks that are commonplace in fMRI are in fact rooted in electrophysiological processes. This review is also an opportunity to produce a brief, up-to-date critical survey of current data modalities and analytical methods available for deriving both static and dynamic connectomes from electrophysiology. We review hurdles that challenge the significance and impact of current electrophysiology connectome research. We then encourage the field to take a leap of faith and embrace the wealth of electrophysiological signals, despite their apparent, disconcerting complexity. Our position is that electrophysiology connectomics is poised to inform testable mechanistic models of information integration in hierarchical brain networks, constructed from observable oscillatory and aperiodic signal components and their polyrhythmic interactions.
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Affiliation(s)
- Sepideh Sadaghiani
- Department of Psychology, University of Illinois, Urbana-Champaign, IL, United States; Beckman Institute for Advanced Science and Technology, University of Illinois, Urbana-Champaign, IL, United States
| | - Matthew J Brookes
- Sir Peter Mansfield Imaging Centre, School of Physics and Astronomy, University of Nottingham, University Park, Nottingham NG72RD, United Kingdom
| | - Sylvain Baillet
- McConnell Brain Imaging Centre, Montreal Neurological Institute, McGill University, Montreal, QC, Canada
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Baror S, He BJ. Spontaneous perception: a framework for task-free, self-paced perception. Neurosci Conscious 2021; 2021:niab016. [PMID: 34377535 PMCID: PMC8333690 DOI: 10.1093/nc/niab016] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/05/2021] [Revised: 05/13/2021] [Accepted: 06/15/2021] [Indexed: 11/20/2022] Open
Abstract
Flipping through social media feeds, viewing exhibitions in a museum, or walking through the botanical gardens, people consistently choose to engage with and disengage from visual content. Yet, in most laboratory settings, the visual stimuli, their presentation duration, and the task at hand are all controlled by the researcher. Such settings largely overlook the spontaneous nature of human visual experience, in which perception takes place independently from specific task constraints and its time course is determined by the observer as a self-governing agent. Currently, much remains unknown about how spontaneous perceptual experiences unfold in the brain. Are all perceptual categories extracted during spontaneous perception? Does spontaneous perception inherently involve volition? Is spontaneous perception segmented into discrete episodes? How do different neural networks interact over time during spontaneous perception? These questions are imperative to understand our conscious visual experience in daily life. In this article we propose a framework for spontaneous perception. We first define spontaneous perception as a task-free and self-paced experience. We propose that spontaneous perception is guided by four organizing principles that grant it temporal and spatial structures. These principles include coarse-to-fine processing, continuity and segmentation, agency and volition, and associative processing. We provide key suggestions illustrating how these principles may interact with one another in guiding the multifaceted experience of spontaneous perception. We point to testable predictions derived from this framework, including (but not limited to) the roles of the default-mode network and slow cortical potentials in underlying spontaneous perception. We conclude by suggesting several outstanding questions for future research, extending the relevance of this framework to consciousness and spontaneous brain activity. In conclusion, the spontaneous perception framework proposed herein integrates components in human perception and cognition, which have been traditionally studied in isolation, and opens the door to understand how visual perception unfolds in its most natural context.
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Affiliation(s)
- Shira Baror
- Neuroscience Institute, New York University School of Medicine, 435 E 30th Street, New York, NY 10016, USA
| | - Biyu J He
- Neuroscience Institute, New York University School of Medicine, 435 E 30th Street, New York, NY 10016, USA
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