1
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Knyazev GG, Savostyanov AN, Bocharov AV, Rudych PD, Saprigyn AE. Multivariate pattern analysis of cooperation and competition in constructive action. Neuropsychologia 2024; 202:108956. [PMID: 39002772 DOI: 10.1016/j.neuropsychologia.2024.108956] [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: 07/11/2023] [Revised: 06/22/2024] [Accepted: 07/10/2024] [Indexed: 07/15/2024]
Abstract
The neural underpinning of cooperative and competitive constructive activity has been investigated using mass-univariate approaches. In this study, we sought to compare the results of these approaches with the results of multivariate pattern analysis (MVPA). In particular, we wanted to test whether MVPA supports the claim made in previous studies that cooperation is associated with the activity of reward-related brain circuits. Participants were required to construct a pattern on the screen either individually or in cooperation or competition with another person during an fMRI scan. Both the MVPA classification methods and the representational similarity analysis indicated the involvement of orbitofrontal and ventromedial prefrontal areas in processes that distinguish between cooperation and competition, and activation analysis showed that these areas are more active during cooperation than during competition. However, a single trial analysis showed that the effect was reversed when only winning trials were considered. In these trials, activation of reward-related areas was higher during competition than during cooperation. Moreover, the contrast between won and lost trials in terms of reward circuits involvement was sharper under competition than under cooperation. Thus, although cooperation can be generally more rewarding than competition, it is associated with smaller difference between trials lost and trials won in terms of reward circuits activation. One may speculate that in cooperation, victory and defeat are shared with the partner and, contrary to competition, are not experienced as personal achievement or failure.
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Affiliation(s)
- G G Knyazev
- Institute of Neurosciences and Medicine, Novosibirsk, Russia.
| | - A N Savostyanov
- Institute of Neurosciences and Medicine, Novosibirsk, Russia; Institute of Cytology and Genetics SB RAS, Novosibirsk, Russia
| | - A V Bocharov
- Institute of Neurosciences and Medicine, Novosibirsk, Russia
| | - P D Rudych
- Institute of Neurosciences and Medicine, Novosibirsk, Russia
| | - A E Saprigyn
- Institute of Neurosciences and Medicine, Novosibirsk, Russia
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2
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Coraci D, Douven I, Cevolani G. Inference to the best neuroscientific explanation. STUDIES IN HISTORY AND PHILOSOPHY OF SCIENCE 2024; 107:33-42. [PMID: 39128362 DOI: 10.1016/j.shpsa.2024.06.009] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/22/2023] [Revised: 05/30/2024] [Accepted: 06/25/2024] [Indexed: 08/13/2024]
Abstract
Neuroscientists routinely use reverse inference (RI) to draw conclusions about cognitive processes from neural activation data. However, despite its widespread use, the methodological status of RI is a matter of ongoing controversy, with some critics arguing that it should be rejected wholesale on the grounds that it instantiates a deductively invalid argument form. In response to these critiques, some have proposed to conceive of RI as a form of abduction or inference to the best explanation (IBE). We side with this response but at the same time argue that a defense of RI requires more than identifying it as a form of IBE. In this paper, we give an analysis of what determines the quality of an RI conceived as an IBE and on that basis argue that whether an RI is warranted needs to be decided on a case-by-case basis. Support for our argument will come from a detailed methodological discussion of RI in cognitive neuroscience in light of what the recent literature on IBE has identified as the main quality indicators for IBEs.
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Affiliation(s)
| | - Igor Douven
- CNRS/Panthéon-Sorbonne University, IHPST, France.
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3
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Vigotsky AD, Iannetti GD, Apkarian AV. Mental state decoders: game-changers or wishful thinking? Trends Cogn Sci 2024:S1364-6613(24)00162-1. [PMID: 38991876 DOI: 10.1016/j.tics.2024.06.004] [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: 01/17/2024] [Revised: 06/12/2024] [Accepted: 06/13/2024] [Indexed: 07/13/2024]
Abstract
Decoding mental and perceptual states using fMRI has become increasingly popular over the past two decades, with numerous highly-cited studies published in high-profile journals. Nevertheless, what have we learned from these decoders? In this opinion, we argue that fMRI-based decoders are not neurophysiologically informative and are not, and likely cannot be, applicable to real-world decision-making. The former point stems from the fact that decoding models cannot disentangle neural mechanisms from their epiphenomena. The latter point stems from both logical and ethical constraints. Constructing decoders requires precious time and resources that should instead be directed toward scientific endeavors more likely to yield meaningful scientific progress.
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Affiliation(s)
| | - Gian Domenico Iannetti
- Italian Institute of Technology (IIT), Rome, Italy; University College London (UCL), London, UK
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4
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Huang S, De Brigard F, Cabeza R, Davis SW. Connectivity analyses for task-based fMRI. Phys Life Rev 2024; 49:139-156. [PMID: 38728902 PMCID: PMC11116041 DOI: 10.1016/j.plrev.2024.04.012] [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: 04/25/2024] [Accepted: 04/29/2024] [Indexed: 05/12/2024]
Abstract
Functional connectivity is conventionally defined by measuring the similarity between brain signals from two regions. The technique has become widely adopted in the analysis of functional magnetic resonance imaging (fMRI) data, where it has provided cognitive neuroscientists with abundant information on how brain regions interact to support complex cognition. However, in the past decade the notion of "connectivity" has expanded in both the complexity and heterogeneity of its application to cognitive neuroscience, resulting in greater difficulty of interpretation, replication, and cross-study comparisons. In this paper, we begin with the canonical notions of functional connectivity and then introduce recent methodological developments that either estimate some alternative form of connectivity or extend the analytical framework, with the hope of bringing better clarity for cognitive neuroscience researchers.
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Affiliation(s)
- Shenyang Huang
- Department of Psychology and Neuroscience, Duke University, Durham, NC 27708, United States; Center for Cognitive Neuroscience, Duke University, Durham, NC 27708, United States.
| | - Felipe De Brigard
- Department of Psychology and Neuroscience, Duke University, Durham, NC 27708, United States; Center for Cognitive Neuroscience, Duke University, Durham, NC 27708, United States; Department of Philosophy, Duke University, Durham, NC 27708, United States
| | - Roberto Cabeza
- Department of Psychology and Neuroscience, Duke University, Durham, NC 27708, United States; Center for Cognitive Neuroscience, Duke University, Durham, NC 27708, United States
| | - Simon W Davis
- Department of Psychology and Neuroscience, Duke University, Durham, NC 27708, United States; Department of Philosophy, Duke University, Durham, NC 27708, United States; Department of Neurology, Duke University School of Medicine, Durham, NC 27708, United States
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5
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Read ML, Berry SC, Graham KS, Voets NL, Zhang J, Aggleton JP, Lawrence AD, Hodgetts CJ. Scene-selectivity in CA1/subicular complex: Multivoxel pattern analysis at 7T. Neuropsychologia 2024; 194:108783. [PMID: 38161052 DOI: 10.1016/j.neuropsychologia.2023.108783] [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: 09/30/2023] [Revised: 12/21/2023] [Accepted: 12/27/2023] [Indexed: 01/03/2024]
Abstract
Prior univariate functional magnetic resonance imaging (fMRI) studies in humans suggest that the anteromedial subicular complex of the hippocampus is a hub for scene-based cognition. However, it is possible that univariate approaches were not sufficiently sensitive to detect scene-related activity in other subfields that have been implicated in spatial processing (e.g., CA1). Further, as connectivity-based functional gradients in the hippocampus do not respect classical subfield boundary definitions, category selectivity may be distributed across anatomical subfields. Region-of-interest approaches, therefore, may limit our ability to observe category selectivity across discrete subfield boundaries. To address these issues, we applied searchlight multivariate pattern analysis to 7T fMRI data of healthy adults who undertook a simultaneous visual odd-one-out discrimination task for scene and non-scene (including face) visual stimuli, hypothesising that scene classification would be possible in multiple hippocampal regions within, but not constrained to, anteromedial subicular complex and CA1. Indeed, we found that the scene-selective searchlight map overlapped not only with anteromedial subicular complex (distal subiculum, pre/para subiculum), but also inferior CA1, alongside posteromedial (including retrosplenial) and parahippocampal cortices. Probabilistic overlap maps revealed gradients of scene category selectivity, with the strongest overlap located in the medial hippocampus, converging with searchlight findings. This was contrasted with gradients of face category selectivity, which had stronger overlap in more lateral hippocampus, supporting ideas of parallel processing streams for these two categories. Our work helps to map the scene, in contrast to, face processing networks within, and connected to, the human hippocampus.
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Affiliation(s)
- Marie-Lucie Read
- Cardiff University Brain Research Imaging Centre, School of Psychology, Cardiff University, Maindy Road, Cardiff, CF24 4HQ, UK
| | - Samuel C Berry
- Cardiff University Brain Research Imaging Centre, School of Psychology, Cardiff University, Maindy Road, Cardiff, CF24 4HQ, UK; Department of Psychology, Royal Holloway, University of London, Egham, Surrey, TW20 0EX, UK
| | - Kim S Graham
- School of Philosophy, Psychology and Language Sciences, Dugald Stewart Building, University of Edinburgh, 3 Charles Street, Edinburgh, EH8 9AD, UK
| | - Natalie L Voets
- Wellcome Centre for Integrative Neuroimaging, FMRIB Building, John Radcliffe Hospital, Oxford, OX3 9DU2, UK
| | - Jiaxiang Zhang
- Cardiff University Brain Research Imaging Centre, School of Psychology, Cardiff University, Maindy Road, Cardiff, CF24 4HQ, UK; School of Mathematics and Computer Science, Swansea University, Swansea SA1 8DD, UK
| | - John P Aggleton
- Cardiff University Brain Research Imaging Centre, School of Psychology, Cardiff University, Maindy Road, Cardiff, CF24 4HQ, UK
| | - Andrew D Lawrence
- Cardiff University Brain Research Imaging Centre, School of Psychology, Cardiff University, Maindy Road, Cardiff, CF24 4HQ, UK; School of Philosophy, Psychology and Language Sciences, Dugald Stewart Building, University of Edinburgh, 3 Charles Street, Edinburgh, EH8 9AD, UK
| | - Carl J Hodgetts
- Cardiff University Brain Research Imaging Centre, School of Psychology, Cardiff University, Maindy Road, Cardiff, CF24 4HQ, UK; Department of Psychology, Royal Holloway, University of London, Egham, Surrey, TW20 0EX, UK.
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6
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Kuhnke P, Kiefer M, Hartwigsen G. Conceptual representations in the default, control and attention networks are task-dependent and cross-modal. BRAIN AND LANGUAGE 2023; 244:105313. [PMID: 37595340 DOI: 10.1016/j.bandl.2023.105313] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/30/2023] [Revised: 07/03/2023] [Accepted: 08/10/2023] [Indexed: 08/20/2023]
Abstract
Conceptual knowledge is central to human cognition. Neuroimaging studies suggest that conceptual processing involves modality-specific and multimodal brain regions in a task-dependent fashion. However, it remains unclear (1) to what extent conceptual feature representations are also modulated by the task, (2) whether conceptual representations in multimodal regions are indeed cross-modal, and (3) how the conceptual system relates to the large-scale functional brain networks. To address these issues, we conducted multivariate pattern analyses on fMRI data. 40 participants performed three tasks-lexical decision, sound judgment, and action judgment-on written words. We found that (1) conceptual feature representations are strongly modulated by the task, (2) conceptual representations in several multimodal regions are cross-modal, and (3) conceptual feature retrieval involves the default, frontoparietal control, and dorsal attention networks. Conceptual representations in these large-scale networks are task-dependent and cross-modal. Our findings support theories that assume conceptual processing to rely on a flexible, multi-level architecture.
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Affiliation(s)
- Philipp Kuhnke
- Wilhelm Wundt Institute for Psychology, Leipzig University, Germany; Lise Meitner Research Group Cognition and Plasticity, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany.
| | | | - Gesa Hartwigsen
- Wilhelm Wundt Institute for Psychology, Leipzig University, Germany; Lise Meitner Research Group Cognition and Plasticity, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany
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7
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Peelen MV, Downing PE. Testing cognitive theories with multivariate pattern analysis of neuroimaging data. Nat Hum Behav 2023; 7:1430-1441. [PMID: 37591984 PMCID: PMC7616245 DOI: 10.1038/s41562-023-01680-z] [Citation(s) in RCA: 8] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/01/2022] [Accepted: 07/12/2023] [Indexed: 08/19/2023]
Abstract
Multivariate pattern analysis (MVPA) has emerged as a powerful method for the analysis of functional magnetic resonance imaging, electroencephalography and magnetoencephalography data. The new approaches to experimental design and hypothesis testing afforded by MVPA have made it possible to address theories that describe cognition at the functional level. Here we review a selection of studies that have used MVPA to test cognitive theories from a range of domains, including perception, attention, memory, navigation, emotion, social cognition and motor control. This broad view reveals properties of MVPA that make it suitable for understanding the 'how' of human cognition, such as the ability to test predictions expressed at the item or event level. It also reveals limitations and points to future directions.
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Affiliation(s)
- Marius V Peelen
- Donders Institute for Brain, Cognition and Behaviour, Radboud University, Nijmegen, the Netherlands.
| | - Paul E Downing
- Cognitive Neuroscience Institute, Department of Psychology, Bangor University, Bangor, UK.
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8
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Wei J, Yao Z, Huang G, Li L, Liang Z, Zhang L, Zhang Z. Frontal-occipital phase synchronization predicts occipital alpha power in perceptual decision-making. Cogn Neurodyn 2023; 17:815-827. [PMID: 37522043 PMCID: PMC10374503 DOI: 10.1007/s11571-022-09862-7] [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: 02/03/2022] [Revised: 06/19/2022] [Accepted: 07/21/2022] [Indexed: 11/03/2022] Open
Abstract
Numerous studies of perceptual decision-making have shown that lower prestimulus alpha power leads to a higher hit rate in visual detection, which is believed to correlate with the top-down control. However, whether frontal-occipital phase synchronization underlying the top-down control could impact the occipital alpha power that directly affects the perceptual performance remains unclear. In this study, we used analyses of the general linear mixed model (GLMM) and event-related potentials (ERPs) to show that the prestimulus alpha power over the occipital area directly affected visual perception. Using both the univariate and multivariate methods, we found that low-frequency (4-30 Hz) frontal-occipital phase synchronization predicted the prestimulus alpha power over the occipital area. Overall, our results suggested that frontal-occipital phase synchronization could predict occipital alpha power that directly affects perceptual decision-making. Supplementary Information The online version contains supplementary material available at 10.1007/s11571-022-09862-7.
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Affiliation(s)
- Jinwen Wei
- School of Biomedical Engineering, Health Science Center, Shenzhen University, Shenzhen, China
- Guangdong Provincial Key Laboratory of Biomedical Measurements and Ultrasound Imaging, Shenzhen University, Shenzhen, China
| | - Ziqing Yao
- Department of Psychology, The University of Hong Kong, Hong Kong S.A.R, China
| | - Gan Huang
- School of Biomedical Engineering, Health Science Center, Shenzhen University, Shenzhen, China
- Guangdong Provincial Key Laboratory of Biomedical Measurements and Ultrasound Imaging, Shenzhen University, Shenzhen, China
| | - Linling Li
- School of Biomedical Engineering, Health Science Center, Shenzhen University, Shenzhen, China
- Guangdong Provincial Key Laboratory of Biomedical Measurements and Ultrasound Imaging, Shenzhen University, Shenzhen, China
| | - Zhen Liang
- School of Biomedical Engineering, Health Science Center, Shenzhen University, Shenzhen, China
- Guangdong Provincial Key Laboratory of Biomedical Measurements and Ultrasound Imaging, Shenzhen University, Shenzhen, China
| | - Li Zhang
- School of Biomedical Engineering, Health Science Center, Shenzhen University, Shenzhen, China
- Guangdong Provincial Key Laboratory of Biomedical Measurements and Ultrasound Imaging, Shenzhen University, Shenzhen, China
| | - Zhiguo Zhang
- Institute of Computing and Intelligence, Harbin Institute of Technology, Shenzhen, China
- Peng Cheng Laboratory, Shenzhen, China
- Marshall Laboratory of Biomedical Engineering, Shenzhen University, Shenzhen, China
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9
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Gu L, Li A, Yang R, Yang J, Pang Y, Qu J, Mei L. Category-specific and category-general neural codes of recognition memory in the ventral visual pathway. Cortex 2023; 164:77-89. [PMID: 37207411 DOI: 10.1016/j.cortex.2023.04.004] [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: 11/17/2022] [Revised: 03/09/2023] [Accepted: 04/03/2023] [Indexed: 05/21/2023]
Abstract
Researchers have identified category-specific brain regions, such as the fusiform face area (FFA) and parahippocampal place area (PPA) in the ventral visual pathway, which respond preferentially to one particular category of visual objects. In addition to their category-specific role in visual object identification and categorization, regions in the ventral visual pathway play critical roles in recognition memory. Nevertheless, it is not clear whether the contributions of those brain regions to recognition memory are category-specific or category-general. To address this question, the present study adopted a subsequent memory paradigm and multivariate pattern analysis (MVPA) to explore category-specific and category-general neural codes of recognition memory in the visual pathway. The results revealed that the right FFA and the bilateral PPA showed category-specific neural patterns supporting recognition memory of faces and scenes, respectively. In contrast, the lateral occipital cortex seemed to carry category-general neural codes of recognition memory. These results provide neuroimaging evidence for category-specific and category-general neural mechanisms of recognition memory in the ventral visual pathway.
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Affiliation(s)
- Lala Gu
- Philosophy and Social Science Laboratory of Reading and Development in Children and Adolescents (South China Normal University), Ministry of Education, China; School of Psychology, South China Normal University, Guangzhou, China; Guangdong Key Laboratory of Mental Health and Cognitive Science, South China Normal University, Guangzhou, China
| | - Aqian Li
- Philosophy and Social Science Laboratory of Reading and Development in Children and Adolescents (South China Normal University), Ministry of Education, China; School of Psychology, South China Normal University, Guangzhou, China; Guangdong Key Laboratory of Mental Health and Cognitive Science, South China Normal University, Guangzhou, China
| | - Rui Yang
- Philosophy and Social Science Laboratory of Reading and Development in Children and Adolescents (South China Normal University), Ministry of Education, China; School of Psychology, South China Normal University, Guangzhou, China; Guangdong Key Laboratory of Mental Health and Cognitive Science, South China Normal University, Guangzhou, China
| | - Jiayi Yang
- Philosophy and Social Science Laboratory of Reading and Development in Children and Adolescents (South China Normal University), Ministry of Education, China; School of Psychology, South China Normal University, Guangzhou, China; Guangdong Key Laboratory of Mental Health and Cognitive Science, South China Normal University, Guangzhou, China
| | - Yingdan Pang
- Philosophy and Social Science Laboratory of Reading and Development in Children and Adolescents (South China Normal University), Ministry of Education, China; School of Psychology, South China Normal University, Guangzhou, China; Guangdong Key Laboratory of Mental Health and Cognitive Science, South China Normal University, Guangzhou, China
| | - Jing Qu
- Philosophy and Social Science Laboratory of Reading and Development in Children and Adolescents (South China Normal University), Ministry of Education, China; School of Psychology, South China Normal University, Guangzhou, China; Guangdong Key Laboratory of Mental Health and Cognitive Science, South China Normal University, Guangzhou, China
| | - Leilei Mei
- Philosophy and Social Science Laboratory of Reading and Development in Children and Adolescents (South China Normal University), Ministry of Education, China; School of Psychology, South China Normal University, Guangzhou, China; Guangdong Key Laboratory of Mental Health and Cognitive Science, South China Normal University, Guangzhou, China.
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10
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Chauhan T, Jakovljev I, Thompson LN, Wuerger SM, Martinovic J. Decoding of EEG signals reveals non-uniformities in the neural geometry of colour. Neuroimage 2023; 268:119884. [PMID: 36657691 DOI: 10.1016/j.neuroimage.2023.119884] [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: 04/24/2022] [Revised: 11/04/2022] [Accepted: 01/15/2023] [Indexed: 01/19/2023] Open
Abstract
The idea of colour opponency maintains that colour vision arises through the comparison of two chromatic mechanisms, red versus green and yellow versus blue. The four unique hues, red, green, blue, and yellow, are assumed to appear at the null points of these the two chromatic systems. Here we hypothesise that, if unique hues represent a tractable cortical state, they should elicit more robust activity compared to other, non-unique hues. We use a spatiotemporal decoding approach to report that electroencephalographic (EEG) responses carry robust information about the tested isoluminant unique hues within a 100-350 ms window from stimulus onset. Decoding is possible in both passive and active viewing tasks, but is compromised when concurrent high luminance contrast is added to the colour signals. For large hue-differences, the efficiency of hue decoding can be predicted by mutual distance in a nominally uniform perceptual colour space. However, for small perceptual neighbourhoods around unique hues, the encoding space shows pivotal non-uniformities which suggest that anisotropies in neurometric hue-spaces may reflect perceptual unique hues.
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Affiliation(s)
- Tushar Chauhan
- The Picower Institute for Learning and Memory, Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, 02139 Cambridge MA, USA.
| | - Ivana Jakovljev
- Department of Psychology. Faculty of Philosophy, University of Novi Sad, Serbia
| | | | - Sophie M Wuerger
- Department of Psychology, University of Liverpool, Liverpool, L697ZA, UK
| | - Jasna Martinovic
- School of Psychology, University of Aberdeen, Aberdeen, AB24 3FX, UK; Department of Psychology, School of Philosophy, Psychology and Language Sciences, University of Edinburgh, Edinburgh, EH8 9JZ, UK.
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11
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Palenciano AF, Senoussi M, Formica S, González-García C. Canonical template tracking: Measuring the activation state of specific neural representations. FRONTIERS IN NEUROIMAGING 2023; 1:974927. [PMID: 37555182 PMCID: PMC10406196 DOI: 10.3389/fnimg.2022.974927] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 06/21/2022] [Accepted: 12/13/2022] [Indexed: 08/10/2023]
Abstract
Multivariate analyses of neural data have become increasingly influential in cognitive neuroscience since they allow to address questions about the representational signatures of neurocognitive phenomena. Here, we describe Canonical Template Tracking: a multivariate approach that employs independent localizer tasks to assess the activation state of specific representations during the execution of cognitive paradigms. We illustrate the benefits of this methodology in characterizing the particular content and format of task-induced representations, comparing it with standard (cross-)decoding and representational similarity analyses. Then, we discuss relevant design decisions for experiments using this analysis approach, focusing on the nature of the localizer tasks from which the canonical templates are derived. We further provide a step-by-step tutorial of this method, stressing the relevant analysis choices for functional magnetic resonance imaging and magneto/electroencephalography data. Importantly, we point out the potential pitfalls linked to canonical template tracking implementation and interpretation of the results, together with recommendations to mitigate them. To conclude, we provide some examples from previous literature that highlight the potential of this analysis to address relevant theoretical questions in cognitive neuroscience.
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Affiliation(s)
- Ana F. Palenciano
- Mind, Brain, and Behavior Research Center, University of Granada, Granada, Spain
| | - Mehdi Senoussi
- CLLE Lab, CNRS UMR 5263, University of Toulouse, Toulouse, France
- Department of Experimental Psychology, Ghent University, Ghent, Belgium
| | - Silvia Formica
- Department of Psychology, Berlin School of Mind and Brain, Humboldt Universität zu Berlin, Berlin, Germany
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12
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Three aspects of representation in neuroscience. Trends Cogn Sci 2022; 26:942-958. [PMID: 36175303 DOI: 10.1016/j.tics.2022.08.014] [Citation(s) in RCA: 12] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/04/2022] [Revised: 08/18/2022] [Accepted: 08/25/2022] [Indexed: 01/12/2023]
Abstract
Neuroscientists often describe neural activity as a representation of something, or claim to have found evidence for a neural representation, but there is considerable ambiguity about what such claims entail. Here we develop a thorough account of what 'representation' does and should do for neuroscientists in terms of three key aspects of representation. (i) Correlation: a neural representation correlates to its represented content; (ii) causal role: the representation has a characteristic effect on behavior; and (iii) teleology: a goal or purpose served by the behavior and thus the representation. We draw broadly on literature in both neuroscience and philosophy to show how these three aspects are rooted in common approaches to understanding the brain and mind. We first describe different contexts that 'representation' has been closely linked to in neuroscience, then discuss each of the three aspects in detail.
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13
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Mercier MR, Dubarry AS, Tadel F, Avanzini P, Axmacher N, Cellier D, Vecchio MD, Hamilton LS, Hermes D, Kahana MJ, Knight RT, Llorens A, Megevand P, Melloni L, Miller KJ, Piai V, Puce A, Ramsey NF, Schwiedrzik CM, Smith SE, Stolk A, Swann NC, Vansteensel MJ, Voytek B, Wang L, Lachaux JP, Oostenveld R. Advances in human intracranial electroencephalography research, guidelines and good practices. Neuroimage 2022; 260:119438. [PMID: 35792291 DOI: 10.1016/j.neuroimage.2022.119438] [Citation(s) in RCA: 47] [Impact Index Per Article: 23.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/15/2021] [Revised: 05/23/2022] [Accepted: 06/30/2022] [Indexed: 12/11/2022] Open
Abstract
Since the second-half of the twentieth century, intracranial electroencephalography (iEEG), including both electrocorticography (ECoG) and stereo-electroencephalography (sEEG), has provided an intimate view into the human brain. At the interface between fundamental research and the clinic, iEEG provides both high temporal resolution and high spatial specificity but comes with constraints, such as the individual's tailored sparsity of electrode sampling. Over the years, researchers in neuroscience developed their practices to make the most of the iEEG approach. Here we offer a critical review of iEEG research practices in a didactic framework for newcomers, as well addressing issues encountered by proficient researchers. The scope is threefold: (i) review common practices in iEEG research, (ii) suggest potential guidelines for working with iEEG data and answer frequently asked questions based on the most widespread practices, and (iii) based on current neurophysiological knowledge and methodologies, pave the way to good practice standards in iEEG research. The organization of this paper follows the steps of iEEG data processing. The first section contextualizes iEEG data collection. The second section focuses on localization of intracranial electrodes. The third section highlights the main pre-processing steps. The fourth section presents iEEG signal analysis methods. The fifth section discusses statistical approaches. The sixth section draws some unique perspectives on iEEG research. Finally, to ensure a consistent nomenclature throughout the manuscript and to align with other guidelines, e.g., Brain Imaging Data Structure (BIDS) and the OHBM Committee on Best Practices in Data Analysis and Sharing (COBIDAS), we provide a glossary to disambiguate terms related to iEEG research.
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14
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Mills-Finnerty C, Frangos E, Allen K, Komisaruk B, Wise N. Functional Magnetic Resonance Imaging Studies in Sexual Medicine: A Primer. J Sex Med 2022; 19:1073-1089. [DOI: 10.1016/j.jsxm.2022.03.217] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/04/2021] [Revised: 01/27/2022] [Accepted: 03/04/2022] [Indexed: 11/17/2022]
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15
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Moshel ML, Robinson AK, Carlson TA, Grootswagers T. Are you for real? Decoding realistic AI-generated faces from neural activity. Vision Res 2022; 199:108079. [PMID: 35749833 DOI: 10.1016/j.visres.2022.108079] [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: 03/02/2021] [Revised: 05/30/2022] [Accepted: 06/06/2022] [Indexed: 11/17/2022]
Abstract
Can we trust our eyes? Until recently, we rarely had to question whether what we see is indeed what exists, but this is changing. Artificial neural networks can now generate realistic images that challenge our perception of what is real. This new reality can have significant implications for cybersecurity, counterfeiting, fake news, and border security. We investigated how the human brain encodes and interprets realistic artificially generated images using behaviour and brain imaging. We found that we could reliably decode AI generated faces using people's neural activity. However, while at a group level people performed near chance classifying real and realistic fakes, participants tended to interchange the labels, classifying real faces as realistic fakes and vice versa. Understanding this difference between brain and behavioural responses may be key in determining the 'real' in our new reality. Stimuli, code, and data for this study can be found at https://osf.io/n2z73/.
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Affiliation(s)
- Michoel L Moshel
- School of Psychology, University of Sydney, NSW, Australia; School of Psychology, Macquarie University, NSW, Australia.
| | - Amanda K Robinson
- School of Psychology, University of Sydney, NSW, Australia; Queensland Brain Institute, The University of Queensland, QLD, Australia
| | | | - Tijl Grootswagers
- School of Psychology, University of Sydney, NSW, Australia; The MARCS Institute for Brain, Behaviour and Development, Western Sydney University, NSW, Australia
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16
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Vaccaro AG, Heydari P, Christov-Moore L, Damasio A, Kaplan JT. Perspective-taking is associated with increased discriminability of affective states in the ventromedial prefrontal cortex. Soc Cogn Affect Neurosci 2022; 17:1082-1090. [PMID: 35579186 PMCID: PMC9714424 DOI: 10.1093/scan/nsac035] [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: 03/04/2021] [Revised: 04/05/2022] [Accepted: 05/16/2022] [Indexed: 01/12/2023] Open
Abstract
Recent work using multivariate-pattern analysis (MVPA) on functional magnetic resonance imaging (fMRI) data has found that distinct affective states produce correspondingly distinct patterns of neural activity in the cerebral cortex. However, it is unclear whether individual differences in the distinctiveness of neural patterns evoked by affective stimuli underlie empathic abilities such as perspective-taking (PT). Accordingly, we examined whether we could predict PT tendency from the classification of blood-oxygen-level-dependent (BOLD) fMRI activation patterns while participants (n = 57) imagined themselves in affectively charged scenarios. We used an MVPA searchlight analysis to map where in the brain activity patterns permitted the classification of four affective states: happiness, sadness, fear and disgust. Classification accuracy was significantly above chance levels in most of the prefrontal cortex and in the posterior medial cortices. Furthermore, participants' self-reported PT was positively associated with classification accuracy in the ventromedial prefrontal cortex and insula. This finding has implications for understanding affective processing in the prefrontal cortex and for interpreting the cognitive significance of classifiable affective brain states. Our multivariate approach suggests that PT ability may rely on the grain of internally simulated affective representations rather than simply the global strength.
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Affiliation(s)
- Anthony G Vaccaro
- Jon Brain and Creativity Institute, Department of Psychology, University of Southern California, Los Angeles, CA 90089-0001, USA
| | - Panthea Heydari
- Jon Brain and Creativity Institute, Department of Psychology, University of Southern California, Los Angeles, CA 90089-0001, USA
| | - Leonardo Christov-Moore
- Jon Brain and Creativity Institute, Department of Psychology, University of Southern California, Los Angeles, CA 90089-0001, USA
| | - Antonio Damasio
- Jon Brain and Creativity Institute, Department of Psychology, University of Southern California, Los Angeles, CA 90089-0001, USA
| | - Jonas T Kaplan
- Correspondence should be addressed to Jonas T. Kaplan, Brain and Creativity Institute, 3620A McClintock Ave, Los Angeles, CA 90089, USA. E-mail:
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17
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Kaniuth P, Hebart MN. Feature-reweighted representational similarity analysis: A method for improving the fit between computational models, brains, and behavior. Neuroimage 2022; 257:119294. [PMID: 35580810 DOI: 10.1016/j.neuroimage.2022.119294] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/05/2021] [Revised: 04/10/2022] [Accepted: 05/09/2022] [Indexed: 11/26/2022] Open
Abstract
Representational Similarity Analysis (RSA) has emerged as a popular method for relating representational spaces from human brain activity, behavioral data, and computational models. RSA is based on the comparison of representational (dis-)similarity matrices (RDM or RSM), which characterize the pairwise (dis-)similarities of all conditions across all features (e.g. fMRI voxels or units of a model). However, classical RSA treats each feature as equally important. This 'equal weights' assumption contrasts with the flexibility of multivariate decoding, which reweights individual features for predicting a target variable. As a consequence, classical RSA may lead researchers to underestimate the correspondence between a model and a brain region and, in case of model comparison, may lead them to select an inferior model. The aim of this work is twofold: First, we sought to broadly test feature-reweighted RSA (FR-RSA) applied to computational models and reveal the extent to which reweighting model features improves RSM correspondence and affects model selection. Previous work suggested that reweighting can improve model selection in RSA but it has remained unclear to what extent these results generalize across datasets and data modalities. To draw more general conclusions, we utilized a range of publicly available datasets and three popular deep neural networks (DNNs). Second, we propose voxel-reweighted RSA, a novel use case of FR-RSA that reweights fMRI voxels, mirroring the rationale of multivariate decoding of optimally combining voxel activity patterns. We found that reweighting individual model units markedly improved the fit between model RSMs and target RSMs derived from several fMRI and behavioral datasets and affected model selection, highlighting the importance of considering FR-RSA. For voxel-reweighted RSA, improvements in RSM correspondence were even more pronounced, demonstrating the utility of this novel approach. We additionally show that classical noise ceilings can be exceeded when FR-RSA is applied and propose an updated approach for their computation. Taken together, our results broadly validate the use of FR-RSA for improving the fit between computational models, brain, and behavioral data, possibly allowing us to better adjudicate between competing computational models. Further, our results suggest that FR-RSA applied to brain measurement channels could become an important new method to assess the correspondence between representational spaces.
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Affiliation(s)
- Philipp Kaniuth
- Vision and Computational Cognition Group, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany.
| | - Martin N Hebart
- Vision and Computational Cognition Group, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany.
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18
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Categorical bias as a crucial parameter in visual working memory: the effect of memory load and retention interval. Cortex 2022; 154:311-321. [DOI: 10.1016/j.cortex.2022.05.007] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/14/2022] [Revised: 04/21/2022] [Accepted: 05/09/2022] [Indexed: 11/24/2022]
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19
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Scientific Exploration and Explainable Artificial Intelligence. Minds Mach (Dordr) 2022. [DOI: 10.1007/s11023-021-09583-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/18/2022]
Abstract
AbstractModels developed using machine learning are increasingly prevalent in scientific research. At the same time, these models are notoriously opaque. Explainable AI aims to mitigate the impact of opacity by rendering opaque models transparent. More than being just the solution to a problem, however, Explainable AI can also play an invaluable role in scientific exploration. This paper describes how post-hoc analytic techniques from Explainable AI can be used to refine target phenomena in medical science, to identify starting points for future investigations of (potentially) causal relationships, and to generate possible explanations of target phenomena in cognitive science. In this way, this paper describes how Explainable AI—over and above machine learning itself—contributes to the efficiency and scope of data-driven scientific research.
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20
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Rathkopf C, Heinrichs JH, Heinrichs B. Can we read minds by imaging brains? PHILOSOPHICAL PSYCHOLOGY 2022. [DOI: 10.1080/09515089.2022.2041590] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 10/19/2022]
Affiliation(s)
- Charles Rathkopf
- Institute for Neuroscience and Medicine, Jülich Research Center, Germany
| | | | - Bert Heinrichs
- Institute for Neuroscience and Medicine, Jülich Research Center, Germany
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21
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Pakravan M, Abbaszadeh M, Ghazizadeh A. Coordinated multivoxel coding beyond univariate effects is not likely to be observable in fMRI data. Neuroimage 2021; 247:118825. [PMID: 34942362 DOI: 10.1016/j.neuroimage.2021.118825] [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: 06/14/2021] [Revised: 12/10/2021] [Accepted: 12/14/2021] [Indexed: 10/19/2022] Open
Abstract
Simultaneous recording of activity across brain regions can contain additional information compared to regional recordings done in isolation. In particular, multivariate pattern analysis (MVPA) across voxels has been interpreted as evidence for distributed coding of cognitive or sensorimotor processes beyond what can be gleaned from a collection of univariate effects (UVE) using functional magnetic resonance imaging (fMRI). Here, we argue that regardless of patterns revealed, conventional MVPA is merely a decoding tool with increased sensitivity arising from considering a large number of 'weak classifiers' (i.e., single voxels) in higher dimensions. We propose instead that 'real' multivoxel coding should result in changes in higher-order statistics across voxels between conditions such as second-order multivariate effects (sMVE). Surprisingly, analysis of conditions with robust multivariate effects (MVE) revealed by MVPA failed to show significant sMVE in two species (humans and macaques). Further analysis showed that while both MVE and sMVE can be readily observed in the spiking activity of neuronal populations, the slow and nonlinear hemodynamic coupling and low spatial resolution of fMRI activations make the observation of higher-order statistics between voxels highly unlikely. These results reveal inherent limitations of fMRI signals for studying coordinated coding across voxels. Together, these findings suggest that care should be taken in interpreting significant MVPA results as representing anything beyond a collection of univariate effects.
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Affiliation(s)
- Mansooreh Pakravan
- Electrical and Computer Engineering Department, Tarbiat Modares University, Tehran, Iran.
| | - Mojtaba Abbaszadeh
- Bio-intelligence Research Unit, Electrical Engineering Department, Sharif University of Technology, Tehran, Iran; School of Cognitive Sciences, Institute for Research in Fundamental Sciences, IPM, Tehran, Iran
| | - Ali Ghazizadeh
- Bio-intelligence Research Unit, Electrical Engineering Department, Sharif University of Technology, Tehran, Iran; School of Cognitive Sciences, Institute for Research in Fundamental Sciences, IPM, Tehran, Iran.
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22
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Cohen MX. A tutorial on generalized eigendecomposition for denoising, contrast enhancement, and dimension reduction in multichannel electrophysiology. Neuroimage 2021; 247:118809. [PMID: 34906717 DOI: 10.1016/j.neuroimage.2021.118809] [Citation(s) in RCA: 18] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/26/2021] [Revised: 11/20/2021] [Accepted: 12/10/2021] [Indexed: 10/19/2022] Open
Abstract
The goal of this paper is to present a theoretical and practical introduction to generalized eigendecomposition (GED), which is a robust and flexible framework used for dimension reduction and source separation in multichannel signal processing. In cognitive electrophysiology, GED is used to create spatial filters that maximize a researcher-specified contrast. For example, one may wish to exploit an assumption that different sources have different frequency content, or that sources vary in magnitude across experimental conditions. GED is fast and easy to compute, performs well in simulated and real data, and is easily adaptable to a variety of specific research goals. This paper introduces GED in a way that ties together myriad individual publications and applications of GED in electrophysiology, and provides sample MATLAB and Python code that can be tested and adapted. Practical considerations and issues that often arise in applications are discussed.
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Affiliation(s)
- Michael X Cohen
- Donders Centre for Medical Neuroscience, Radboud University Medical Center, the Netherlands.
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23
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Brooks JA, Stolier RM, Freeman JB. Computational approaches to the neuroscience of social perception. Soc Cogn Affect Neurosci 2021; 16:827-837. [PMID: 32986115 PMCID: PMC8343569 DOI: 10.1093/scan/nsaa127] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/13/2019] [Revised: 07/23/2020] [Accepted: 09/09/2020] [Indexed: 11/14/2022] Open
Abstract
Across multiple domains of social perception-including social categorization, emotion perception, impression formation and mentalizing-multivariate pattern analysis (MVPA) of functional magnetic resonance imaging (fMRI) data has permitted a more detailed understanding of how social information is processed and represented in the brain. As in other neuroimaging fields, the neuroscientific study of social perception initially relied on broad structure-function associations derived from univariate fMRI analysis to map neural regions involved in these processes. In this review, we trace the ways that social neuroscience studies using MVPA have built on these neuroanatomical associations to better characterize the computational relevance of different brain regions, and discuss how MVPA allows explicit tests of the correspondence between psychological models and the neural representation of social information. We also describe current and future advances in methodological approaches to multivariate fMRI data and their theoretical value for the neuroscience of social perception.
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Affiliation(s)
- Jeffrey A Brooks
- Department of Psychology, New York University, New York, NY, USA
| | - Ryan M Stolier
- Columbia University, 1190 Amsterdam Ave., New York, NY 10027, USA
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24
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Okazawa G, Hatch CE, Mancoo A, Machens CK, Kiani R. Representational geometry of perceptual decisions in the monkey parietal cortex. Cell 2021; 184:3748-3761.e18. [PMID: 34171308 PMCID: PMC8273140 DOI: 10.1016/j.cell.2021.05.022] [Citation(s) in RCA: 31] [Impact Index Per Article: 10.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/08/2020] [Revised: 12/23/2020] [Accepted: 05/17/2021] [Indexed: 11/22/2022]
Abstract
Lateral intraparietal (LIP) neurons represent formation of perceptual decisions involving eye movements. In circuit models for these decisions, neural ensembles that encode actions compete to form decisions. Consequently, representation and readout of the decision variables (DVs) are implemented similarly for decisions with identical competing actions, irrespective of input and task context differences. Further, DVs are encoded as partially potentiated action plans through balance of activity of action-selective ensembles. Here, we test those core principles. We show that in a novel face-discrimination task, LIP firing rates decrease with supporting evidence, contrary to conventional motion-discrimination tasks. These opposite response patterns arise from similar mechanisms in which decisions form along curved population-response manifolds misaligned with action representations. These manifolds rotate in state space based on context, indicating distinct optimal readouts for different tasks. We show similar manifolds in lateral and medial prefrontal cortices, suggesting similar representational geometry across decision-making circuits.
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Affiliation(s)
- Gouki Okazawa
- Center for Neural Science, New York University, New York, NY 10003, USA
| | - Christina E Hatch
- Center for Neural Science, New York University, New York, NY 10003, USA
| | - Allan Mancoo
- Champalimaud Research, Champalimaud Centre for the Unknown, 1400-038 Lisbon, Portugal
| | - Christian K Machens
- Champalimaud Research, Champalimaud Centre for the Unknown, 1400-038 Lisbon, Portugal
| | - Roozbeh Kiani
- Center for Neural Science, New York University, New York, NY 10003, USA; Neuroscience Institute, NYU Langone Medical Center, New York, NY 10016, USA; Department of Psychology, New York University, New York, NY 10003, USA.
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25
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Large-Scale and Multiscale Networks in the Rodent Brain during Novelty Exploration. eNeuro 2021; 8:ENEURO.0494-20.2021. [PMID: 33757983 PMCID: PMC8121262 DOI: 10.1523/eneuro.0494-20.2021] [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: 11/20/2020] [Revised: 01/27/2021] [Accepted: 02/10/2021] [Indexed: 11/21/2022] Open
Abstract
Neural activity is coordinated across multiple spatial and temporal scales, and these patterns of coordination are implicated in both healthy and impaired cognitive operations. However, empirical cross-scale investigations are relatively infrequent, because of limited data availability and to the difficulty of analyzing rich multivariate datasets. Here, we applied frequency-resolved multivariate source-separation analyses to characterize a large-scale dataset comprising spiking and local field potential (LFP) activity recorded simultaneously in three brain regions (prefrontal cortex, parietal cortex, hippocampus) in freely-moving mice. We identified a constellation of multidimensional, inter-regional networks across a range of frequencies (2-200 Hz). These networks were reproducible within animals across different recording sessions, but varied across different animals, suggesting individual variability in network architecture. The theta band (∼4-10 Hz) networks had several prominent features, including roughly equal contribution from all regions and strong inter-network synchronization. Overall, these findings demonstrate a multidimensional landscape of large-scale functional activations of cortical networks operating across multiple spatial, spectral, and temporal scales during open-field exploration.
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26
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Wang Q, Wang Y, Wang P, Peng M, Zhang M, Zhu Y, Wei S, Chen C, Chen X, Luo S, Bai X. Neural representations of the amount and the delay time of reward in intertemporal decision making. Hum Brain Mapp 2021; 42:3450-3469. [PMID: 33934449 PMCID: PMC8249888 DOI: 10.1002/hbm.25445] [Citation(s) in RCA: 19] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/12/2021] [Revised: 02/28/2021] [Accepted: 03/30/2021] [Indexed: 11/08/2022] Open
Abstract
Numerous studies have examined the neural substrates of intertemporal decision-making, but few have systematically investigated separate neural representations of the two attributes of future rewards (i.e., the amount of the reward and the delay time). More importantly, no study has used the novel analytical method of representational connectivity analysis (RCA) to map the two dimensions' functional brain networks at the level of multivariate neural representations. This study independently manipulated the amount and delay time of rewards during an intertemporal decision task. Both univariate and multivariate pattern analyses showed that brain activity in the dorsomedial prefrontal cortex (DMPFC) and lateral frontal pole cortex (LFPC) was modulated by the amount of rewards, whereas brain activity in the DMPFC and dorsolateral prefrontal cortex (DLPFC) was modulated by the length of delay. Moreover, representational similarity analysis (RSA) revealed that even for the regions of the DMPFC that overlapped between the two dimensions, they manifested distinct neural activity patterns. In terms of individual differences, those with large delay discounting rates (k) showed greater DMPFC and LFPC activity as the amount of rewards increased but showed lower DMPFC and DLPFC activity as the delay time increased. Lastly, RCA suggested that the topological metrics (i.e., global and local efficiency) of the functional connectome subserving the delay time dimension inversely predicted individual discounting rate. These findings provide novel insights into neural representations of the two attributes in intertemporal decisions, and offer a new approach to construct task-based functional brain networks whose topological properties are related to impulsivity.
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Affiliation(s)
- Qiang Wang
- Key Research Base of Humanities and Social Sciences of the Ministry of Education, Academy of Psychology and Behavior, Tianjin Normal University, Tianjin, China.,Faculty of Psychology, Tianjin Normal University, Tianjin, China.,Tianjin Social Science Laboratory of Students' Mental Development and Learning, Tianjin, China
| | - Yajie Wang
- Faculty of Psychology, Tianjin Normal University, Tianjin, China
| | - Pinchun Wang
- Faculty of Education, Tianjin Normal University, Tianjin, China
| | - Maomiao Peng
- Department of Psychology, University of Arizona, Tucson, Arizona, USA
| | - Manman Zhang
- Key Research Base of Humanities and Social Sciences of the Ministry of Education, Academy of Psychology and Behavior, Tianjin Normal University, Tianjin, China.,Faculty of Psychology, Tianjin Normal University, Tianjin, China.,Tianjin Social Science Laboratory of Students' Mental Development and Learning, Tianjin, China
| | - Yuxuan Zhu
- Faculty of Psychology, Tianjin Normal University, Tianjin, China
| | - Shiyu Wei
- Faculty of Psychology, Tianjin Normal University, Tianjin, China
| | - Chuansheng Chen
- Department of Psychological Science, University of California, Irvine, California, USA
| | - Xiongying Chen
- The National Clinical Research Center for Mental Disorders & Beijing Key Laboratory of Mental Disorders, Beijing Anding Hospital, Capital Medical University, Beijing, China.,Advanced Innovation Center for Human Brain Protection, Capital Medical University, Beijing, China
| | - Shan Luo
- Department of Internal Medicine, Division of Endocrinology, Keck School of Medicine of the University of Southern California, Los Angeles, California, USA.,Department of Psychology, University of Southern California, Los Angeles, California, USA
| | - Xuejun Bai
- Key Research Base of Humanities and Social Sciences of the Ministry of Education, Academy of Psychology and Behavior, Tianjin Normal University, Tianjin, China.,Faculty of Psychology, Tianjin Normal University, Tianjin, China.,Tianjin Social Science Laboratory of Students' Mental Development and Learning, Tianjin, China
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27
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Arbula S, Pisanu E, Rumiati RI. Representation of social content in dorsomedial prefrontal cortex underlies individual differences in agreeableness trait. Neuroimage 2021; 235:118049. [PMID: 33848626 DOI: 10.1016/j.neuroimage.2021.118049] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/18/2020] [Revised: 04/01/2021] [Accepted: 04/02/2021] [Indexed: 01/31/2023] Open
Abstract
Personality traits reflect key aspects of individual variability in different psychological domains. Understanding the mechanisms that give rise to these differences requires an exhaustive investigation of the behaviors associated with such traits, and their underlying neural sources. Here we investigated the mechanisms underlying agreeableness, one of the five major dimensions of personality, which has been linked mainly to socio-cognitive functions. In particular, we examined whether individual differences in the neural representations of social information are related to differences in agreeableness of individuals. To this end, we adopted a multivariate representational similarity approach that captured within single individuals the activation pattern similarity of social and non-social content, and tested its relation to the agreeableness trait in a hypothesis-driven manner. The main result confirmed our prediction: processing social and non-social content led to similar patterns of activation in individuals with low agreeableness, while in more agreeable individuals these patterns were more dissimilar. Critically, this association between agreeableness and encoding similarity of social and random content was significant only in the dorsomedial prefrontal cortex, a brain region consistently involved during attributions of mental states. The present finding reveals the link between neural mechanisms underlying social information processing and agreeableness, a personality trait highly related to socio-cognitive abilities, thereby providing a step forward in characterizing its neural determinants. Furthermore, it emphasizes the advantage of multivariate pattern analysis approaches in capturing and understanding the neural sources of individual variations.
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Affiliation(s)
- Sandra Arbula
- Neuroscience Area, International School for Advanced Studies (SISSA), via Bonomea 265, Trieste 34136, Italy.
| | - Elisabetta Pisanu
- Neuroscience Area, International School for Advanced Studies (SISSA), via Bonomea 265, Trieste 34136, Italy
| | - Raffaella I Rumiati
- Neuroscience Area, International School for Advanced Studies (SISSA), via Bonomea 265, Trieste 34136, Italy; Scuola superiore di studi avanzati Sapienza (SSAS), Rome, Italy
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28
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Matheson HE, Garcea FE, Buxbaum LJ. Scene context shapes category representational geometry during processing of tools. Cortex 2021; 141:1-15. [PMID: 34020166 DOI: 10.1016/j.cortex.2021.03.021] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/28/2020] [Revised: 02/05/2021] [Accepted: 03/12/2021] [Indexed: 10/21/2022]
Abstract
Tools are ubiquitous in human environments and to think about them we use concepts. Increasingly, conceptual representation is thought to be dynamic and sensitive to the goals of the observer. Indeed, observer goals can reshape representational geometry within cortical networks supporting concepts. In the present study, we investigated the novel hypothesis that task-irrelevant scene context may implicitly alter the representational geometry of regions within the tool network. Participants performed conceptual judgments on images of tools embedded in scenes that either suggested their use (i.e., a kitchen timer sitting on a kitchen counter with vegetables in a frying pan) or that they would simply be moved (i.e., a kitchen timer sitting in an open drawer with other miscellaneous kitchen items around). We investigated whether representations in the tool network reflect category, grip, and shape information using a representational similarity analysis (RSA). We show that a) a number of regions of the tool network reflect category information about tools and b) category information predicts patterns in supramarginal gyrus more strongly in use contexts than in move contexts. Together, these results show that information about tool category is distributed across different regions of the tool network and that scene context helps shape the representational geometry of the tool network.
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Affiliation(s)
- Heath E Matheson
- University of Northern British Columbia, Prince George, BC, Canada.
| | - Frank E Garcea
- Moss Rehabilitation Research Institute, Elkins Park, PA, USA; Department of Neurosurgery, University of Rochester Medical Center, New York, USA
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29
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Liu C, Kang Y, Zhang L, Zhang J. Rapidly Decoding Image Categories From MEG Data Using a Multivariate Short-Time FC Pattern Analysis Approach. IEEE J Biomed Health Inform 2021; 25:1139-1150. [PMID: 32750957 DOI: 10.1109/jbhi.2020.3008731] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Abstract
Recent advances in the development of multivariate analysis methods have led to the application of multivariate pattern analysis (MVPA) to investigate the interactions between brain regions using graph theory (functional connectivity, FC) and decode visual categories from functional magnetic resonance imaging (fMRI) data from a continuous multicategory paradigm. To estimate stable FC patterns from fMRI data, previous studies required long periods in the order of several minutes, in comparison to the human brain that categories visual stimuli within hundreds of milliseconds. Constructing short-time dynamic FC patterns in the order of milliseconds and decoding visual categories is a relatively novel concept. In this study, we developed a multivariate decoding algorithm based on FC patterns and applied it to magnetoencephalography (MEG) data. MEG data were recorded from participants presented with image stimuli in four categories (faces, scenes, animals and tools). MEG data from 17 participants demonstrate that short-time dynamic FC patterns yield brain activity patterns that can be used to decode visual categories with high accuracy. Our results show that FC patterns change over the time window, and FC patterns extracted in the time window of 0∼200 ms after the stimulus onset were most stable. Further, the categorizing accuracy peaked (the mean binary accuracy is above 78.6% at individual level) in the FC patterns estimated within the 0∼200 ms interval. These findings elucidate the underlying connectivity information during visual category processing on a relatively smaller time scale and demonstrate that the contribution of FC patterns to categorization fluctuates over time.
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30
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Reeders PC, Hamm AG, Allen TA, Mattfeld AT. Medial prefrontal cortex and hippocampal activity differentially contribute to ordinal and temporal context retrieval during sequence memory. ACTA ACUST UNITED AC 2021; 28:134-147. [PMID: 33723033 PMCID: PMC7970742 DOI: 10.1101/lm.052365.120] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/07/2020] [Accepted: 01/20/2021] [Indexed: 11/24/2022]
Abstract
Remembering sequences of events defines episodic memory, but retrieval can be driven by both ordinality and temporal contexts. Whether these modes of retrieval operate at the same time or not remains unclear. Theoretically, medial prefrontal cortex (mPFC) confers ordinality, while the hippocampus (HC) associates events in gradually changing temporal contexts. Here, we looked for evidence of each with BOLD fMRI in a sequence task that taxes both retrieval modes. To test ordinal modes, items were transferred between sequences but retained their position (e.g., AB3). Ordinal modes activated mPFC, but not HC. To test temporal contexts, we examined items that skipped ahead across lag distances (e.g., ABD). HC, but not mPFC, tracked temporal contexts. There was a mPFC and HC by retrieval mode interaction. These current results suggest that the mPFC and HC are concurrently engaged in different retrieval modes in support of remembering when an event occurred.
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Affiliation(s)
- Puck C Reeders
- Cognitive Neuroscience Program, Department of Psychology, Florida International University, Miami, Florida 33199, USA
| | - Amanda G Hamm
- Cognitive Neuroscience Program, Department of Psychology, Florida International University, Miami, Florida 33199, USA
| | - Timothy A Allen
- Cognitive Neuroscience Program, Department of Psychology, Florida International University, Miami, Florida 33199, USA.,Department of Environmental Health Sciences, Florida International University, Miami, Florida 33199, USA.,Center for Children and Families, Florida International University, Miami, Florida 33199, USA
| | - Aaron T Mattfeld
- Cognitive Neuroscience Program, Department of Psychology, Florida International University, Miami, Florida 33199, USA.,Center for Children and Families, Florida International University, Miami, Florida 33199, USA
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Wang Q, Wei S, Im H, Zhang M, Wang P, Zhu Y, Wang Y, Bai X. Neuroanatomical and functional substrates of the greed personality trait. Brain Struct Funct 2021; 226:1269-1280. [PMID: 33683479 DOI: 10.1007/s00429-021-02240-9] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/23/2020] [Accepted: 02/22/2021] [Indexed: 12/13/2022]
Abstract
Greedy individuals often exhibit more impulsive decision-making and short-sighted behaviors. It has been assumed that altered reward circuitry and prospection network is associated with greed personality trait (GPT). In this study, we first explored the morphological characteristics (i.e., gray matter volume; GMV) of GPT combined with univariate and multivariate pattern analysis (MVPA) approaches. Second, we adopted a revised version of inter-temporal choice task and independently manipulated the amount and delay time of future rewards. Using brain-imaging design, reward- and prospection-related brain activations were assessed and their associations with GPT were further examined. The MVPA results showed that GPT was associated with the GMVs in the right lateral frontal pole cortex, left ventromedial prefrontal cortex, right lateral occipital cortex, and right occipital pole. Additionally, we observed that the amount-relevant brain activations (responding to reward circuitry) in the lateral orbitofrontal cortex were negatively associated with individual's variability in GPT scores, whereas the delay time-relevant brain activations (responding to prospection network system) in the dorsolateral prefrontal cortex, dorsomedial prefrontal cortex, superior parietal lobule, and anterior cingulate cortex were positively associated with individual's variability in GPT scores. These findings not only provide novel insights into the neuroanatomical substrates underlying the human dispositional greed, but also suggest the critical roles of reward and prospection processing on the greed.
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Affiliation(s)
- Qiang Wang
- Key Research Base of Humanities and Social Sciences of the Ministry of Education, Academy of Psychology and Behavior, Tianjin Normal University, Tianjin, 300387, China
- Faculty of Psychology, Tianjin Normal University, Tianjin, 300387, China
- Tianjin Social Science Laboratory of Students' Mental Development and Learning, Tianjin, 300387, China
| | - Shiyu Wei
- Key Research Base of Humanities and Social Sciences of the Ministry of Education, Academy of Psychology and Behavior, Tianjin Normal University, Tianjin, 300387, China
| | - Hohjin Im
- Department of Psychological Science, University of California, Irvine, CA, 92697-7085, USA
| | - Manman Zhang
- Key Research Base of Humanities and Social Sciences of the Ministry of Education, Academy of Psychology and Behavior, Tianjin Normal University, Tianjin, 300387, China
- Faculty of Psychology, Tianjin Normal University, Tianjin, 300387, China
- Tianjin Social Science Laboratory of Students' Mental Development and Learning, Tianjin, 300387, China
| | - Pinchun Wang
- Faculty of Psychology, Tianjin Normal University, Tianjin, 300387, China
| | - Yuxuan Zhu
- Faculty of Psychology, Tianjin Normal University, Tianjin, 300387, China
| | - Yajie Wang
- Faculty of Psychology, Tianjin Normal University, Tianjin, 300387, China
| | - Xuejun Bai
- Key Research Base of Humanities and Social Sciences of the Ministry of Education, Academy of Psychology and Behavior, Tianjin Normal University, Tianjin, 300387, China.
- Faculty of Psychology, Tianjin Normal University, Tianjin, 300387, China.
- Tianjin Social Science Laboratory of Students' Mental Development and Learning, Tianjin, 300387, China.
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32
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Bae H, Kim SJ, Kim CE. Lessons From Deep Neural Networks for Studying the Coding Principles of Biological Neural Networks. Front Syst Neurosci 2021; 14:615129. [PMID: 33519390 PMCID: PMC7843526 DOI: 10.3389/fnsys.2020.615129] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/08/2020] [Accepted: 12/14/2020] [Indexed: 12/26/2022] Open
Abstract
One of the central goals in systems neuroscience is to understand how information is encoded in the brain, and the standard approach is to identify the relation between a stimulus and a neural response. However, the feature of a stimulus is typically defined by the researcher's hypothesis, which may cause biases in the research conclusion. To demonstrate potential biases, we simulate four likely scenarios using deep neural networks trained on the image classification dataset CIFAR-10 and demonstrate the possibility of selecting suboptimal/irrelevant features or overestimating the network feature representation/noise correlation. Additionally, we present studies investigating neural coding principles in biological neural networks to which our points can be applied. This study aims to not only highlight the importance of careful assumptions and interpretations regarding the neural response to stimulus features but also suggest that the comparative study between deep and biological neural networks from the perspective of machine learning can be an effective strategy for understanding the coding principles of the brain.
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Affiliation(s)
- Hyojin Bae
- Department of Physiology, Gachon University College of Korean Medicine, Seongnam, South Korea
| | - Sang Jeong Kim
- Laboratory of Neurophysiology, Department of Physiology, Seoul National University College of Medicine, Seoul, South Korea
| | - Chang-Eop Kim
- Department of Physiology, Gachon University College of Korean Medicine, Seongnam, South Korea
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33
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Fang Y, Meng L, Prominski A, Schaumann EN, Seebald M, Tian B. Recent advances in bioelectronics chemistry. Chem Soc Rev 2020. [PMID: 32672777 DOI: 10.1039/d1030cs00333f] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/15/2023]
Abstract
Research in bioelectronics is highly interdisciplinary, with many new developments being based on techniques from across the physical and life sciences. Advances in our understanding of the fundamental chemistry underlying the materials used in bioelectronic applications have been a crucial component of many recent discoveries. In this review, we highlight ways in which a chemistry-oriented perspective may facilitate novel and deep insights into both the fundamental scientific understanding and the design of materials, which can in turn tune the functionality and biocompatibility of bioelectronic devices. We provide an in-depth examination of several developments in the field, organized by the chemical properties of the materials. We conclude by surveying how some of the latest major topics of chemical research may be further integrated with bioelectronics.
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Affiliation(s)
- Yin Fang
- The James Franck Institute, University of Chicago, Chicago, IL 60637, USA.
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34
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Fang Y, Meng L, Prominski A, Schaumann E, Seebald M, Tian B. Recent advances in bioelectronics chemistry. Chem Soc Rev 2020; 49:7978-8035. [PMID: 32672777 PMCID: PMC7674226 DOI: 10.1039/d0cs00333f] [Citation(s) in RCA: 35] [Impact Index Per Article: 8.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/13/2022]
Abstract
Research in bioelectronics is highly interdisciplinary, with many new developments being based on techniques from across the physical and life sciences. Advances in our understanding of the fundamental chemistry underlying the materials used in bioelectronic applications have been a crucial component of many recent discoveries. In this review, we highlight ways in which a chemistry-oriented perspective may facilitate novel and deep insights into both the fundamental scientific understanding and the design of materials, which can in turn tune the functionality and biocompatibility of bioelectronic devices. We provide an in-depth examination of several developments in the field, organized by the chemical properties of the materials. We conclude by surveying how some of the latest major topics of chemical research may be further integrated with bioelectronics.
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Affiliation(s)
- Yin Fang
- The James Franck Institute, University of Chicago, Chicago, IL 60637, USA
| | - Lingyuan Meng
- Pritzker School of Molecular Engineering, University of Chicago, Chicago, IL 60637, USA
| | | | - Erik Schaumann
- Department of Chemistry, University of Chicago, Chicago, IL 60637, USA
| | - Matthew Seebald
- Department of Chemistry, University of Chicago, Chicago, IL 60637, USA
| | - Bozhi Tian
- The James Franck Institute, University of Chicago, Chicago, IL 60637, USA
- Department of Chemistry, University of Chicago, Chicago, IL 60637, USA
- The Institute for Biophysical Dynamics, University of Chicago, Chicago, IL 60637, USA
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35
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Workman CI, Yoder KJ, Decety J. The Dark Side of Morality - Neural Mechanisms Underpinning Moral Convictions and Support for Violence. AJOB Neurosci 2020; 11:269-284. [PMID: 33196349 PMCID: PMC7939028 DOI: 10.1080/21507740.2020.1811798] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/23/2022]
Abstract
People are motivated by shared social values that, when held with moral conviction, can serve as compelling mandates capable of facilitating support for ideological violence. The current study examined this dark side of morality by identifying specific cognitive and neural mechanisms associated with beliefs about the appropriateness of sociopolitical violence, and determining the extent to which the engagement of these mechanisms was predicted by moral convictions. Participants reported their moral convictions about a variety of sociopolitical issues prior to undergoing functional MRI scanning. During scanning, they were asked to evaluate the appropriateness of violent protests that were ostensibly congruent or incongruent with their views about sociopolitical issues. Complementary univariate and multivariate analytical strategies comparing neural responses to congruent and incongruent violence identified neural mechanisms implicated in processing salience and in the encoding of subjective value. As predicted, neuro-hemodynamic response was modulated parametrically by individuals' beliefs about the appropriateness of congruent relative to incongruent sociopolitical violence in ventromedial prefrontal cortex, and by moral conviction in ventral striatum. Overall moral conviction was predicted by neural response to congruent relative to incongruent violence in amygdala. Together, these findings indicate that moral conviction about sociopolitical issues serves to increase their subjective value, overriding natural aversion to interpersonal harm.
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Affiliation(s)
- Clifford I. Workman
- Department of Psychology, University of Chicago, Chicago, IL, 60637, USA
- Department of Neurology, University of Pennsylvania, Pennsylvania, PA, 19104, USA
| | - Keith J. Yoder
- Department of Psychology, University of Chicago, Chicago, IL, 60637, USA
| | - Jean Decety
- Department of Psychology, University of Chicago, Chicago, IL, 60637, USA
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36
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Keitel A, Gross J, Kayser C. Shared and modality-specific brain regions that mediate auditory and visual word comprehension. eLife 2020; 9:e56972. [PMID: 32831168 PMCID: PMC7470824 DOI: 10.7554/elife.56972] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/16/2020] [Accepted: 08/18/2020] [Indexed: 12/22/2022] Open
Abstract
Visual speech carried by lip movements is an integral part of communication. Yet, it remains unclear in how far visual and acoustic speech comprehension are mediated by the same brain regions. Using multivariate classification of full-brain MEG data, we first probed where the brain represents acoustically and visually conveyed word identities. We then tested where these sensory-driven representations are predictive of participants' trial-wise comprehension. The comprehension-relevant representations of auditory and visual speech converged only in anterior angular and inferior frontal regions and were spatially dissociated from those representations that best reflected the sensory-driven word identity. These results provide a neural explanation for the behavioural dissociation of acoustic and visual speech comprehension and suggest that cerebral representations encoding word identities may be more modality-specific than often upheld.
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Affiliation(s)
- Anne Keitel
- Psychology, University of DundeeDundeeUnited Kingdom
- Institute of Neuroscience and Psychology, University of GlasgowGlasgowUnited Kingdom
| | - Joachim Gross
- Institute of Neuroscience and Psychology, University of GlasgowGlasgowUnited Kingdom
- Institute for Biomagnetism and Biosignalanalysis, University of MünsterMünsterGermany
| | - Christoph Kayser
- Department for Cognitive Neuroscience, Faculty of Biology, Bielefeld UniversityBielefeldGermany
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37
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Koenig-Robert R, Pearson J. Decoding Nonconscious Thought Representations during Successful Thought Suppression. J Cogn Neurosci 2020; 32:2272-2284. [PMID: 32762524 DOI: 10.1162/jocn_a_01617] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/13/2022]
Abstract
Controlling our thoughts is central to mental well-being, and its failure is at the crux of a number of mental disorders. Paradoxically, behavioral evidence shows that thought suppression often fails. Despite the broad importance of understanding the mechanisms of thought control, little is known about the fate of neural representations of suppressed thoughts. Using fMRI, we investigated the brain areas involved in controlling visual thoughts and tracked suppressed thought representations using multivoxel pattern analysis. Participants were asked to either visualize a vegetable/fruit or suppress any visual thoughts about those objects. Surprisingly, the content (object identity) of successfully suppressed thoughts was still decodable in visual areas with algorithms trained on imagery. This suggests that visual representations of suppressed thoughts are still present despite reports that they are not. Thought generation was associated with the left hemisphere, and thought suppression was associated with right hemisphere engagement. Furthermore, general linear model analyses showed that subjective success in thought suppression was correlated with engagement of executive areas, whereas thought-suppression failure was associated with engagement of visual and memory-related areas. These results suggest that the content of suppressed thoughts exists hidden from awareness, seemingly without an individual's knowledge, providing a compelling reason why thought suppression is so ineffective. These data inform models of unconscious thought production and could be used to develop new treatment approaches to disorders involving maladaptive thoughts.
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38
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Cornblath EJ, Ashourvan A, Kim JZ, Betzel RF, Ciric R, Adebimpe A, Baum GL, He X, Ruparel K, Moore TM, Gur RC, Gur RE, Shinohara RT, Roalf DR, Satterthwaite TD, Bassett DS. Temporal sequences of brain activity at rest are constrained by white matter structure and modulated by cognitive demands. Commun Biol 2020; 3:261. [PMID: 32444827 PMCID: PMC7244753 DOI: 10.1038/s42003-020-0961-x] [Citation(s) in RCA: 64] [Impact Index Per Article: 16.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/24/2020] [Accepted: 04/02/2020] [Indexed: 01/01/2023] Open
Abstract
A diverse set of white matter connections supports seamless transitions between cognitive states. However, it remains unclear how these connections guide the temporal progression of large-scale brain activity patterns in different cognitive states. Here, we analyze the brain's trajectories across a set of single time point activity patterns from functional magnetic resonance imaging data acquired during the resting state and an n-back working memory task. We find that specific temporal sequences of brain activity are modulated by cognitive load, associated with age, and related to task performance. Using diffusion-weighted imaging acquired from the same subjects, we apply tools from network control theory to show that linear spread of activity along white matter connections constrains the probabilities of these sequences at rest, while stimulus-driven visual inputs explain the sequences observed during the n-back task. Overall, these results elucidate the structural underpinnings of cognitively and developmentally relevant spatiotemporal brain dynamics.
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Affiliation(s)
- Eli J Cornblath
- Department of Neuroscience, Perelman School of Medicine, Philadelphia, PA, 19104, USA
- Department of Bioengineering, School of Engineering & Applied Science, Philadelphia, PA, 19104, USA
| | - Arian Ashourvan
- Department of Bioengineering, School of Engineering & Applied Science, Philadelphia, PA, 19104, USA
| | - Jason Z Kim
- Department of Bioengineering, School of Engineering & Applied Science, Philadelphia, PA, 19104, USA
| | - Richard F Betzel
- Department of Bioengineering, School of Engineering & Applied Science, Philadelphia, PA, 19104, USA
| | - Rastko Ciric
- Department of Psychiatry, Perelman School of Medicine, Philadelphia, PA, 19104, USA
| | - Azeez Adebimpe
- Department of Psychiatry, Perelman School of Medicine, Philadelphia, PA, 19104, USA
| | - Graham L Baum
- Department of Neuroscience, Perelman School of Medicine, Philadelphia, PA, 19104, USA
- Department of Bioengineering, School of Engineering & Applied Science, Philadelphia, PA, 19104, USA
- Department of Psychiatry, Perelman School of Medicine, Philadelphia, PA, 19104, USA
| | - Xiaosong He
- Department of Bioengineering, School of Engineering & Applied Science, Philadelphia, PA, 19104, USA
| | - Kosha Ruparel
- Department of Psychiatry, Perelman School of Medicine, Philadelphia, PA, 19104, USA
| | - Tyler M Moore
- Department of Psychiatry, Perelman School of Medicine, Philadelphia, PA, 19104, USA
| | - Ruben C Gur
- Department of Psychiatry, Perelman School of Medicine, Philadelphia, PA, 19104, USA
- Department of Neurology, Perelman School of Medicine, Philadelphia, PA, 19104, USA
- Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA
| | - Raquel E Gur
- Department of Psychiatry, Perelman School of Medicine, Philadelphia, PA, 19104, USA
- Department of Neurology, Perelman School of Medicine, Philadelphia, PA, 19104, USA
- Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA
| | - Russell T Shinohara
- Department of Biostatistics, Epidemiology, & Informatics, Perelman School of Medicine, Philadelphia, PA, 19104, USA
| | - David R Roalf
- Department of Psychiatry, Perelman School of Medicine, Philadelphia, PA, 19104, USA
| | | | - Danielle S Bassett
- Department of Bioengineering, School of Engineering & Applied Science, Philadelphia, PA, 19104, USA.
- Department of Psychiatry, Perelman School of Medicine, Philadelphia, PA, 19104, USA.
- Department of Physics & Astronomy, College of Arts & Sciences, Philadelphia, PA, 19104, USA.
- Department of Neurology, Perelman School of Medicine, Philadelphia, PA, 19104, USA.
- Department of Electrical & Systems Engineering, School of Engineering & Applied Science, Philadelphia, PA, 19104, USA.
- Santa Fe Institute, Santa Fe, NM, 87501, USA.
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39
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Wang X, Liang X, Jiang Z, Nguchu BA, Zhou Y, Wang Y, Wang H, Li Y, Zhu Y, Wu F, Gao J, Qiu B. Decoding and mapping task states of the human brain via deep learning. Hum Brain Mapp 2020; 41:1505-1519. [PMID: 31816152 PMCID: PMC7267978 DOI: 10.1002/hbm.24891] [Citation(s) in RCA: 31] [Impact Index Per Article: 7.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/18/2019] [Revised: 11/20/2019] [Accepted: 11/27/2019] [Indexed: 02/06/2023] Open
Abstract
Support vector machine (SVM)-based multivariate pattern analysis (MVPA) has delivered promising performance in decoding specific task states based on functional magnetic resonance imaging (fMRI) of the human brain. Conventionally, the SVM-MVPA requires careful feature selection/extraction according to expert knowledge. In this study, we propose a deep neural network (DNN) for directly decoding multiple brain task states from fMRI signals of the brain without any burden for feature handcrafts. We trained and tested the DNN classifier using task fMRI data from the Human Connectome Project's S1200 dataset (N = 1,034). In tests to verify its performance, the proposed classification method identified seven tasks with an average accuracy of 93.7%. We also showed the general applicability of the DNN for transfer learning to small datasets (N = 43), a situation encountered in typical neuroscience research. The proposed method achieved an average accuracy of 89.0 and 94.7% on a working memory task and a motor classification task, respectively, higher than the accuracy of 69.2 and 68.6% obtained by the SVM-MVPA. A network visualization analysis showed that the DNN automatically detected features from areas of the brain related to each task. Without incurring the burden of handcrafting the features, the proposed deep decoding method can classify brain task states highly accurately, and is a powerful tool for fMRI researchers.
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Affiliation(s)
- Xiaoxiao Wang
- Centers for Biomedical EngineeringUniversity of Science and Technology of ChinaHefeiChina
| | - Xiao Liang
- Centers for Biomedical EngineeringUniversity of Science and Technology of ChinaHefeiChina
| | - Zhoufan Jiang
- Centers for Biomedical EngineeringUniversity of Science and Technology of ChinaHefeiChina
| | - Benedictor A. Nguchu
- Centers for Biomedical EngineeringUniversity of Science and Technology of ChinaHefeiChina
| | - Yawen Zhou
- Centers for Biomedical EngineeringUniversity of Science and Technology of ChinaHefeiChina
| | - Yanming Wang
- Centers for Biomedical EngineeringUniversity of Science and Technology of ChinaHefeiChina
| | - Huijuan Wang
- Centers for Biomedical EngineeringUniversity of Science and Technology of ChinaHefeiChina
| | - Yu Li
- Centers for Biomedical EngineeringUniversity of Science and Technology of ChinaHefeiChina
| | - Yuying Zhu
- Centers for Biomedical EngineeringUniversity of Science and Technology of ChinaHefeiChina
| | - Feng Wu
- Centers for Biomedical EngineeringUniversity of Science and Technology of ChinaHefeiChina
| | - Jia‐Hong Gao
- MRI Research Center and Beijing City Key Lab for Medical Physics and EngineeringPeking UniversityBeijingChina
- McGovern Institute for Brain Research, Peking UniversityBeijingChina
| | - Bensheng Qiu
- Centers for Biomedical EngineeringUniversity of Science and Technology of ChinaHefeiChina
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40
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Leopold DA, Park SH. Studying the visual brain in its natural rhythm. Neuroimage 2020; 216:116790. [PMID: 32278093 DOI: 10.1016/j.neuroimage.2020.116790] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/08/2019] [Revised: 03/26/2020] [Accepted: 03/30/2020] [Indexed: 12/27/2022] Open
Abstract
How the brain fluidly orchestrates visual behavior is a central question in cognitive neuroscience. Researchers studying neural responses in humans and nonhuman primates have mapped out visual response profiles and cognitive modulation in a large number of brain areas, most often using pared down stimuli and highly controlled behavioral paradigms. The historical emphasis on reductionism has placed most studies at one pole of an inherent trade-off between strictly controlled experimental variables and open designs that monitor the brain during its natural modes of operation. This bias toward simplified experiments has strongly shaped the field of visual neuroscience, with little guarantee that the principles and concepts established within that framework will apply more generally. In recent years, a growing number of studies have begun to relax strict experimental control with the aim of understanding how the brain responds under more naturalistic conditions. In this article, we survey research that has explicitly embraced the complexity and rhythm of natural vision. We focus on those studies most pertinent to understanding high-level visual specializations in brains of humans and nonhuman primates. We conclude that representationalist concepts borne from conventional visual experiments fall short in their ability to capture the real-life visual operations undertaken by the brain. More naturalistic approaches, though fraught with experimental and analytic challenges, provide fertile ground for neuroscientists seeking new inroads to investigate how the brain supports core aspects of our daily visual experience.
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Affiliation(s)
- David A Leopold
- Section on Cognitive Neurophysiology and Imaging, Laboratory of Neuropsychology, National Institute of Mental Health, National Institutes of Health, Bethesda, MD, 20892, USA; Neurophysiology Imaging Facility, National Institute of Mental Health, National Institute of Neurological Disorders and Stroke, National Eye Institute, National Institutes of Health, Bethesda, MD, 20892, USA.
| | - Soo Hyun Park
- Section on Cognitive Neurophysiology and Imaging, Laboratory of Neuropsychology, National Institute of Mental Health, National Institutes of Health, Bethesda, MD, 20892, USA
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41
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Divergence in cortical representations of threat generalization in affective versus perceptual circuitry in childhood: Relations with anxiety. Neuropsychologia 2020; 142:107416. [PMID: 32173623 DOI: 10.1016/j.neuropsychologia.2020.107416] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/17/2019] [Revised: 01/22/2020] [Accepted: 02/29/2020] [Indexed: 01/16/2023]
Abstract
Children at risk for anxiety display elevated threat sensitivity and may inaccurately classify safe stimuli as threatening, a process known as overgeneralization. Little is known about whether such overgeneralization might stem from altered sensory representations of stimuli resembling threat, especially in youth. Here we implement representational similarity analysis of fMRI data to examine the similarity of neural representations of threat versus ambiguous or safe stimuli in threat and perceptual neurocircuitry among children at varying levels of anxiety traits. Three weeks after completing threat conditioning and extinction, children underwent an fMRI extinction recall task, during which they viewed the extinguished threat cue (CS+), safety cue (CS-) and generalization stimuli (GS) consisting of CS-/CS+ blends. Multivoxel BOLD signal patterns were measured in seven regions of interest: four affective areas (ventromedial prefrontal cortex (vmPFC), anterior insular cortex (AIC), dorsomedial prefrontal cortex (dmPFC), and amygdala) and three perceptual areas (inferior temporal cortex (ITC) and visual areas V1 and V4). Compared to low anxious children, children with high trait anxiety evidenced less neural pattern differentiation between the CS+ and similar GS, particularly in the vmPFC. Together, these results demonstrate the utility of multivariate neuroimaging approaches in arbitrating the relative contributions of perceptual versus affective sources to threat generalization.
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Abstract
The dynamical evolution of a system of interacting elements can be predicted in terms of its elementary constituents and their interactions, or in terms of the system’s global state transitions. For this reason, systems with equivalent global dynamics are often taken to be equivalent for all relevant purposes. Nevertheless, such systems may still vary in their causal composition—the way mechanisms within the system specify causes and effects over different subsets of system elements. We demonstrate this point based on a set of small discrete dynamical systems with reversible dynamics that cycle through all their possible states. Our analysis elucidates the role of composition within the formal framework of integrated information theory. We show that the global dynamical and information-theoretic capacities of reversible systems can be maximal even though they may differ, quantitatively and qualitatively, in the information that their various subsets specify about each other (intrinsic information). This can be the case even for a system and its time-reversed equivalent. Due to differences in their causal composition, two systems with equivalent global dynamics may still differ in their capacity for autonomy, agency, and phenomenology.
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43
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Ritchie JB, de Beeck HO. Using neural distance to predict reaction time for categorizing the animacy, shape, and abstract properties of objects. Sci Rep 2019; 9:13201. [PMID: 31519992 PMCID: PMC6744425 DOI: 10.1038/s41598-019-49732-7] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/14/2018] [Accepted: 08/23/2019] [Indexed: 11/29/2022] Open
Abstract
A large number of neuroimaging studies have shown that information about object category can be decoded from regions of the ventral visual pathway. One question is how this information might be functionally exploited in the brain. In an attempt to help answer this question, some studies have adopted a neural distance-to-bound approach, and shown that distance to a classifier decision boundary through neural activation space can be used to predict reaction times (RT) on animacy categorization tasks. However, these experiments have not controlled for possible visual confounds, such as shape, in their stimulus design. In the present study we sought to determine whether, when animacy and shape properties are orthogonal, neural distance in low- and high-level visual cortex would predict categorization RTs, and whether a combination of animacy and shape distance might predict RTs when categories crisscrossed the two stimulus dimensions, and so were not linearly separable. In line with previous results, we found that RTs correlated with neural distance, but only for animate stimuli, with similar, though weaker, asymmetric effects for the shape and crisscrossing tasks. Taken together, these results suggest there is potential to expand the neural distance-to-bound approach to other divisions beyond animacy and object category.
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Affiliation(s)
- J Brendan Ritchie
- Laboratory of Biological Psychology, Department of Brain and Cognition, KU Leuven, 3000, Leuven, Belgium.
| | - Hans Op de Beeck
- Laboratory of Biological Psychology, Department of Brain and Cognition, KU Leuven, 3000, Leuven, Belgium
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44
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Affiliation(s)
- J. Brendan Ritchie
- Laboratory of Biological Psychology, Brain and Cognition Unit, KU Leuven, Leuven, Belgium
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45
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Povich M. Model-based cognitive neuroscience: Multifield mechanistic integration in practice. THEORY & PSYCHOLOGY 2019. [DOI: 10.1177/0959354319863880] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
Abstract
Autonomist accounts of cognitive science suggest that cognitive model building and theory construction (can or should) proceed independently of findings in neuroscience. Common functionalist justifications of autonomy rely on there being relatively few constraints between neural structure and cognitive function. In contrast, an integrative mechanistic perspective stresses the mutual constraining of structure and function. In this article, I show how Model-Based Cognitive Neuroscience (MBCN) epitomizes the integrative mechanistic perspective and concentrates the most revolutionary elements of the cognitive neuroscience revolution. I also show how the prominent subset account of functional realization supports the integrative mechanistic perspective I take on MBCN and use it to clarify the intralevel and interlevel components of integration.
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Bigenwald A, Chambon V. Criminal Responsibility and Neuroscience: No Revolution Yet. Front Psychol 2019; 10:1406. [PMID: 31316418 PMCID: PMC6610327 DOI: 10.3389/fpsyg.2019.01406] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/15/2019] [Accepted: 05/31/2019] [Indexed: 11/25/2022] Open
Abstract
Since the 1990's, neurolaw is on the rise. At the heart of heated debates lies the recurrent theme of a neuro-revolution of criminal responsibility. However, caution should be observed: the alleged foundations of criminal responsibility (amongst which free will) are often inaccurate and the relative imperviousness of its real foundations to scientific facts often underestimated. Neuroscientific findings may impact on social institutions, but only insofar as they also engage in a political justification of the changes being called for, convince populations, and take into consideration the ensuing consequences. Moreover, the many limits of neuroscientific tools call for increased vigilance when, if ever, using neuroscientific evidence in a courtroom. In this article, we aim at setting the basis for future sound debates on the contribution of neuroscience to criminal law, and in particular to the assessment of criminal responsibility. As such, we provide analytical tools to grasp the political and normative nature of criminal responsibility and review the current or projected use of neuroscience in the law, all the while bearing in mind the highly publicized question: can neuroscience revolutionize criminal responsibility? Answering this question implicitly requires answering a second question: should neuroscience revolutionize the institution of criminal responsibility? Answering both, in turn, requires drawing the line between science and normativity, revolution and dialogue, fantasies and legitimate hopes.
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Affiliation(s)
- Ariane Bigenwald
- Département de Philosophie, Université Paris I Panthéon Sorbonne, Paris, France
- Institut Jean Nicod (ENS – EHESS – CNRS), Département d’Etudes Cognitives, Ecole Normale Supérieure, PSL University, Paris, France
| | - Valerian Chambon
- Institut Jean Nicod (ENS – EHESS – CNRS), Département d’Etudes Cognitives, Ecole Normale Supérieure, PSL University, Paris, France
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Kriegeskorte N, Douglas PK. Interpreting encoding and decoding models. Curr Opin Neurobiol 2019; 55:167-179. [PMID: 31039527 DOI: 10.1016/j.conb.2019.04.002] [Citation(s) in RCA: 81] [Impact Index Per Article: 16.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/13/2018] [Revised: 04/08/2019] [Accepted: 04/10/2019] [Indexed: 11/18/2022]
Abstract
Encoding and decoding models are widely used in systems, cognitive, and computational neuroscience to make sense of brain-activity data. However, the interpretation of their results requires care. Decoding models can help reveal whether particular information is present in a brain region in a format the decoder can exploit. Encoding models make comprehensive predictions about representational spaces. In the context of sensory experiments, where stimuli are experimentally controlled, encoding models enable us to test and compare brain-computational theories. Encoding and decoding models typically include fitted linear-model components. Sometimes the weights of the fitted linear combinations are interpreted as reflecting, in an encoding model, the contribution of different sensory features to the representation or, in a decoding model, the contribution of different measured brain responses to a decoded feature. Such interpretations can be problematic when the predictor variables or their noise components are correlated and when priors (or penalties) are used to regularize the fit. Encoding and decoding models are evaluated in terms of their generalization performance. The correct interpretation depends on the level of generalization a model achieves (e.g. to new response measurements for the same stimuli, to new stimuli from the same population, or to stimuli from a different population). Significant decoding or encoding performance of a single model (at whatever level of generality) does not provide strong constraints for theory. Many models must be tested and inferentially compared for analyses to drive theoretical progress.
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Affiliation(s)
- Nikolaus Kriegeskorte
- Department of Psychology, Department of Neuroscience, Department of Electrical Engineering, Zuckerman Mind Brain Behavior Institute, Columbia University, New York, NY, United States.
| | - Pamela K Douglas
- Center for Cognitive Neuroscience, University of California, Los Angeles, CA, United States
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Wajnerman Paz A. Using neural response properties to draw the distinction between modal and amodal representations. PHILOSOPHICAL PSYCHOLOGY 2019. [DOI: 10.1080/09515089.2018.1563677] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 10/27/2022]
Affiliation(s)
- Abel Wajnerman Paz
- Department of Philosophy, Universidad Alberto Hurtado, Santiago de Chile, Chile
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Snoek L, Miletić S, Scholte HS. How to control for confounds in decoding analyses of neuroimaging data. Neuroimage 2019; 184:741-760. [DOI: 10.1016/j.neuroimage.2018.09.074] [Citation(s) in RCA: 31] [Impact Index Per Article: 6.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/30/2018] [Revised: 09/04/2018] [Accepted: 09/25/2018] [Indexed: 12/31/2022] Open
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Ritchie JB, Op de Beeck H. A Varying Role for Abstraction in Models of Category Learning Constructed from Neural Representations in Early Visual Cortex. J Cogn Neurosci 2019; 31:155-173. [DOI: 10.1162/jocn_a_01339] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/04/2022]
Abstract
The human capacity for visual categorization is core to how we make sense of the visible world. Although a substantive body of research in cognitive neuroscience has localized this capacity to regions of human visual cortex, relatively few studies have investigated the role of abstraction in how representations for novel object categories are constructed from the neural representation of stimulus dimensions. Using human fMRI coupled with formal modeling of observer behavior, we assess a wide range of categorization models that vary in their level of abstraction from collections of subprototypes to representations of individual exemplars. The category learning tasks range from simple linear and unidimensional category rules to complex crisscross rules that require a nonlinear combination of multiple dimensions. We show that models based on neural responses in primary visual cortex favor a variable, but often limited, extent of abstraction in the construction of representations for novel categories, which differ in degree across tasks and individuals.
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