1
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Mori K, Zatorre R. State-dependent connectivity in auditory-reward networks predicts peak pleasure experiences to music. PLoS Biol 2024; 22:e3002732. [PMID: 39133721 PMCID: PMC11318860 DOI: 10.1371/journal.pbio.3002732] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/30/2024] [Accepted: 07/03/2024] [Indexed: 08/16/2024] Open
Abstract
Music can evoke pleasurable and rewarding experiences. Past studies that examined task-related brain activity revealed individual differences in musical reward sensitivity traits and linked them to interactions between the auditory and reward systems. However, state-dependent fluctuations in spontaneous neural activity in relation to music-driven rewarding experiences have not been studied. Here, we used functional MRI to examine whether the coupling of auditory-reward networks during a silent period immediately before music listening can predict the degree of musical rewarding experience of human participants (N = 49). We used machine learning models and showed that the functional connectivity between auditory and reward networks, but not others, could robustly predict subjective, physiological, and neurobiological aspects of the strong musical reward of chills. Specifically, the right auditory cortex-striatum/orbitofrontal connections predicted the reported duration of chills and the activation level of nucleus accumbens and insula, whereas the auditory-amygdala connection was associated with psychophysiological arousal. Furthermore, the predictive model derived from the first sample of individuals was generalized in an independent dataset using different music samples. The generalization was successful only for state-like, pre-listening functional connectivity but not for stable, intrinsic functional connectivity. The current study reveals the critical role of sensory-reward connectivity in pre-task brain state in modulating subsequent rewarding experience.
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Affiliation(s)
- Kazuma Mori
- Institute for Quantum Life Science, National Institutes for Quantum Science and Technology, Chiba, Japan
- Center for Information and Neural Networks (CiNet), National Institute of Information and Communications Technology (NICT), Osaka, Japan
| | - Robert Zatorre
- Montréal Neurological Institute, McGill University, Montréal, Canada
- International Laboratory for Brain, Music and Sound Research, Montréal, Canada
- Centre for Research in Brain, Language and Music, Montréal, Canada
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2
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Botvinik-Nezer R, Petre B, Ceko M, Lindquist MA, Friedman NP, Wager TD. Placebo treatment affects brain systems related to affective and cognitive processes, but not nociceptive pain. Nat Commun 2024; 15:6017. [PMID: 39019888 PMCID: PMC11255344 DOI: 10.1038/s41467-024-50103-8] [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: 01/29/2024] [Accepted: 06/28/2024] [Indexed: 07/19/2024] Open
Abstract
Drug treatments for pain often do not outperform placebo, and a better understanding of placebo mechanisms is needed to improve treatment development and clinical practice. In a large-scale fMRI study (N = 392) with pre-registered analyses, we tested whether placebo analgesic treatment modulates nociceptive processes, and whether its effects generalize from conditioned to unconditioned pain modalities. Placebo treatment caused robust analgesia in conditioned thermal pain that generalized to unconditioned mechanical pain. However, placebo did not decrease pain-related fMRI activity in brain measures linked to nociceptive pain, including the Neurologic Pain Signature (NPS) and spinothalamic pathway regions, with strong support for null effects in Bayes Factor analyses. In addition, surprisingly, placebo increased activity in some spinothalamic regions for unconditioned mechanical pain. In contrast, placebo reduced activity in a neuromarker associated with higher-level contributions to pain, the Stimulus Intensity Independent Pain Signature (SIIPS), and affected activity in brain regions related to motivation and value, in both pain modalities. Individual differences in behavioral analgesia were correlated with neural changes in both modalities. Our results indicate that cognitive and affective processes primarily drive placebo analgesia, and show the potential of neuromarkers for separating treatment influences on nociception from influences on evaluative processes.
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Affiliation(s)
- Rotem Botvinik-Nezer
- Department of Psychology, The Hebrew University of Jerusalem, Jerusalem, Israel.
- Department of Psychological and Brain Sciences, Dartmouth College, Hanover, NH, USA.
| | - Bogdan Petre
- Department of Psychological and Brain Sciences, Dartmouth College, Hanover, NH, USA
| | - Marta Ceko
- Institute of Cognitive Science, University of Colorado Boulder, Boulder, CO, USA
| | - Martin A Lindquist
- Department of Biostatistics, Johns Hopkins University, Baltimore, MD, USA
| | - Naomi P Friedman
- Institute for Behavioral Genetics, University of Colorado Boulder, Boulder, CO, USA
- Department of Psychology and Neuroscience, University of Colorado Boulder, Boulder, CO, USA
| | - Tor D Wager
- Department of Psychological and Brain Sciences, Dartmouth College, Hanover, NH, USA.
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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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Gan X, Zhou F, Xu T, Liu X, Zhang R, Zheng Z, Yang X, Zhou X, Yu F, Li J, Cui R, Wang L, Yuan J, Yao D, Becker B. A neurofunctional signature of subjective disgust generalizes to oral distaste and socio-moral contexts. Nat Hum Behav 2024; 8:1383-1402. [PMID: 38641635 DOI: 10.1038/s41562-024-01868-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/29/2023] [Accepted: 03/19/2024] [Indexed: 04/21/2024]
Abstract
While disgust originates in the hard-wired mammalian distaste response, the conscious experience of disgust in humans strongly depends on subjective appraisal and may even extend to socio-moral contexts. Here, in a series of studies, we combined functional magnetic resonance imaging with machine-learning-based predictive modelling to establish a comprehensive neurobiological model of subjective disgust. The developed neurofunctional signature accurately predicted momentary self-reported subjective disgust across discovery (n = 78) and pre-registered validation (n = 30) cohorts and generalized across core disgust (n = 34 and n = 26), gustatory distaste (n = 30) and socio-moral (unfair offers; n = 43) contexts. Disgust experience was encoded in distributed cortical and subcortical systems, and exhibited distinct and shared neural representations with subjective fear or negative affect in interoceptive-emotional awareness and conscious appraisal systems, while the signatures most accurately predicted the respective target experience. We provide an accurate functional magnetic resonance imaging signature for disgust with a high potential to resolve ongoing evolutionary debates.
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Affiliation(s)
- Xianyang Gan
- The Center of Psychosomatic Medicine, Sichuan Provincial Center for Mental Health, Sichuan Provincial People's Hospital, University of Electronic Science and Technology of China, Chengdu, China
- Clinical Hospital of Chengdu Brain Science Institute, MOE Key Laboratory for Neuroinformation, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, China
| | - Feng Zhou
- Faculty of Psychology, Southwest University, Chongqing, China
| | - Ting Xu
- The Center of Psychosomatic Medicine, Sichuan Provincial Center for Mental Health, Sichuan Provincial People's Hospital, University of Electronic Science and Technology of China, Chengdu, China
- Clinical Hospital of Chengdu Brain Science Institute, MOE Key Laboratory for Neuroinformation, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, China
| | - Xiaobo Liu
- McConnell Brain Imaging Centre, Montreal Neurological Institute, McGill University, Montreal, Quebec, Canada
| | - Ran Zhang
- The Center of Psychosomatic Medicine, Sichuan Provincial Center for Mental Health, Sichuan Provincial People's Hospital, University of Electronic Science and Technology of China, Chengdu, China
- Clinical Hospital of Chengdu Brain Science Institute, MOE Key Laboratory for Neuroinformation, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, China
| | - Zihao Zheng
- The Center of Psychosomatic Medicine, Sichuan Provincial Center for Mental Health, Sichuan Provincial People's Hospital, University of Electronic Science and Technology of China, Chengdu, China
- Clinical Hospital of Chengdu Brain Science Institute, MOE Key Laboratory for Neuroinformation, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, China
| | - Xi Yang
- Faculty of Health, Medicine and Life Sciences, Maastricht University, Maastricht, the Netherlands
| | - Xinqi Zhou
- Sichuan Key Laboratory of Psychology and Behavior of Discipline Inspection and Supervision, Institute of Brain and Psychological Sciences, Sichuan Normal University, Chengdu, China
| | - Fangwen Yu
- The Center of Psychosomatic Medicine, Sichuan Provincial Center for Mental Health, Sichuan Provincial People's Hospital, University of Electronic Science and Technology of China, Chengdu, China
- Clinical Hospital of Chengdu Brain Science Institute, MOE Key Laboratory for Neuroinformation, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, China
| | - Jialin Li
- Max Planck School of Cognition, Leipzig, Germany
| | - Ruifang Cui
- The Center of Psychosomatic Medicine, Sichuan Provincial Center for Mental Health, Sichuan Provincial People's Hospital, University of Electronic Science and Technology of China, Chengdu, China
- Clinical Hospital of Chengdu Brain Science Institute, MOE Key Laboratory for Neuroinformation, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, China
| | - Lan Wang
- The Center of Psychosomatic Medicine, Sichuan Provincial Center for Mental Health, Sichuan Provincial People's Hospital, University of Electronic Science and Technology of China, Chengdu, China
- Clinical Hospital of Chengdu Brain Science Institute, MOE Key Laboratory for Neuroinformation, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, China
| | - Jiajin Yuan
- Sichuan Key Laboratory of Psychology and Behavior of Discipline Inspection and Supervision, Institute of Brain and Psychological Sciences, Sichuan Normal University, Chengdu, China
| | - Dezhong Yao
- The Center of Psychosomatic Medicine, Sichuan Provincial Center for Mental Health, Sichuan Provincial People's Hospital, University of Electronic Science and Technology of China, Chengdu, China
- Clinical Hospital of Chengdu Brain Science Institute, MOE Key Laboratory for Neuroinformation, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, China
| | - Benjamin Becker
- The Center of Psychosomatic Medicine, Sichuan Provincial Center for Mental Health, Sichuan Provincial People's Hospital, University of Electronic Science and Technology of China, Chengdu, China.
- Clinical Hospital of Chengdu Brain Science Institute, MOE Key Laboratory for Neuroinformation, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, China.
- State Key Laboratory for Brain and Cognitive Sciences, The University of Hong Kong, Hong Kong, China.
- Department of Psychology, The University of Hong Kong, Hong Kong, China.
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5
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Park Y, Zhang Y, Schwartz F, Iuculano T, Chang H, Menon V. Integrated number sense tutoring remediates aberrant neural representations in children with mathematical disabilities. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.04.09.587577. [PMID: 38645139 PMCID: PMC11030345 DOI: 10.1101/2024.04.09.587577] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 04/23/2024]
Abstract
Number sense is essential for early mathematical development but it is compromised in children with mathematical disabilities (MD). Here we investigate the impact of a personalized 4-week Integrated Number Sense (INS) tutoring program aimed at improving the connection between nonsymbolic (sets of objects) and symbolic (Arabic numerals) representations in children with MD. Utilizing neural pattern analysis, we found that INS tutoring not only improved cross-format mapping but also significantly boosted arithmetic fluency in children with MD. Critically, the tutoring normalized previously low levels of cross-format neural representations in these children to pre-tutoring levels observed in typically developing, especially in key brain regions associated with numerical cognition. Moreover, we identified distinct, 'inverted U-shaped' neurodevelopmental changes in the MD group, suggesting unique neural plasticity during mathematical skill development. Our findings highlight the effectiveness of targeted INS tutoring for remediating numerical deficits in MD, and offer a foundation for developing evidence-based educational interventions.
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Affiliation(s)
- Yunji Park
- Department of Psychiatry & Behavioral Sciences, Stanford University, Stanford, CA, 94305
| | - Yuan Zhang
- Department of Psychiatry & Behavioral Sciences, Stanford University, Stanford, CA, 94305
| | - Flora Schwartz
- Department of Psychiatry & Behavioral Sciences, Stanford University, Stanford, CA, 94305
| | - Teresa Iuculano
- Centre National de la Recherche Scientifique & Université Paris Sorbonne, Paris 75016, France
| | - Hyesang Chang
- Department of Psychiatry & Behavioral Sciences, Stanford University, Stanford, CA, 94305
| | - Vinod Menon
- Department of Psychiatry & Behavioral Sciences, Stanford University, Stanford, CA, 94305
- Department of Neurology and Neurological Sciences, Stanford University, Stanford, CA, 94305
- Stanford Neuroscience Institute, Stanford University, Stanford, California, CA, 94305
- Symbolic Systems Program, Stanford University, Stanford, California, CA, 94305
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6
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Heinen R, Bierbrauer A, Wolf OT, Axmacher N. Representational formats of human memory traces. Brain Struct Funct 2024; 229:513-529. [PMID: 37022435 PMCID: PMC10978732 DOI: 10.1007/s00429-023-02636-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/06/2022] [Accepted: 03/28/2023] [Indexed: 04/07/2023]
Abstract
Neural representations are internal brain states that constitute the brain's model of the external world or some of its features. In the presence of sensory input, a representation may reflect various properties of this input. When perceptual information is no longer available, the brain can still activate representations of previously experienced episodes due to the formation of memory traces. In this review, we aim at characterizing the nature of neural memory representations and how they can be assessed with cognitive neuroscience methods, mainly focusing on neuroimaging. We discuss how multivariate analysis techniques such as representational similarity analysis (RSA) and deep neural networks (DNNs) can be leveraged to gain insights into the structure of neural representations and their different representational formats. We provide several examples of recent studies which demonstrate that we are able to not only measure memory representations using RSA but are also able to investigate their multiple formats using DNNs. We demonstrate that in addition to slow generalization during consolidation, memory representations are subject to semantization already during short-term memory, by revealing a shift from visual to semantic format. In addition to perceptual and conceptual formats, we describe the impact of affective evaluations as an additional dimension of episodic memories. Overall, these studies illustrate how the analysis of neural representations may help us gain a deeper understanding of the nature of human memory.
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Affiliation(s)
- Rebekka Heinen
- Department of Neuropsychology, Institute of Cognitive Neuroscience, Faculty of Psychology, Ruhr University Bochum, Universitätsstraße 150, 44801, Bochum, Germany.
| | - Anne Bierbrauer
- Department of Neuropsychology, Institute of Cognitive Neuroscience, Faculty of Psychology, Ruhr University Bochum, Universitätsstraße 150, 44801, Bochum, Germany
- Institute for Systems Neuroscience, Medical Center Hamburg-Eppendorf, Martinistraße 52, 20251, Hamburg, Germany
| | - Oliver T Wolf
- Department of Cognitive Psychology, Institute of Cognitive Neuroscience, Faculty of Psychology, Ruhr University Bochum, Universitätsstraße 150, 44801, Bochum, Germany
| | - Nikolai Axmacher
- Department of Neuropsychology, Institute of Cognitive Neuroscience, Faculty of Psychology, Ruhr University Bochum, Universitätsstraße 150, 44801, Bochum, Germany
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7
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Chen J, Yuan D, Dong R, Cai J, Ai Z, Zhou S. Artificial intelligence significantly facilitates development in the mental health of college students: a bibliometric analysis. Front Psychol 2024; 15:1375294. [PMID: 38515973 PMCID: PMC10955080 DOI: 10.3389/fpsyg.2024.1375294] [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: 01/23/2024] [Accepted: 02/26/2024] [Indexed: 03/23/2024] Open
Abstract
Objective College students are currently grappling with severe mental health challenges, and research on artificial intelligence (AI) related to college students mental health, as a crucial catalyst for promoting psychological well-being, is rapidly advancing. Employing bibliometric methods, this study aim to analyze and discuss the research on AI in college student mental health. Methods Publications pertaining to AI and college student mental health were retrieved from the Web of Science core database. The distribution of publications were analyzed to gage the predominant productivity. Data on countries, authors, journal, and keywords were analyzed using VOSViewer, exploring collaboration patterns, disciplinary composition, research hotspots and trends. Results Spanning 2003 to 2023, the study encompassed 1722 publications, revealing notable insights: (1) a gradual rise in annual publications, reaching its zenith in 2022; (2) Journal of Affective Disorders and Psychiatry Research emerged were the most productive and influential sources in this field, with significant contributions from China, the United States, and their affiliated higher education institutions; (3) the primary mental health issues were depression and anxiety, with machine learning and AI having the widest range of applications; (4) an imperative for enhanced international and interdisciplinary collaboration; (5) research hotspots exploring factors influencing college student mental health and AI applications. Conclusion This study provides a succinct yet comprehensive overview of this field, facilitating a nuanced understanding of prospective applications of AI in college student mental health. Professionals can leverage this research to discern the advantages, risks, and potential impacts of AI in this critical field.
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Affiliation(s)
- Jing Chen
- Wuhan University China Institute of Boundary and Ocean Studies, Wuhan, China
| | - Dongfeng Yuan
- Faculty of Pharmacy, Hubei University of Chinese Medicine, Wuhan, China
| | - Ruotong Dong
- Faculty of Pharmacy, Hubei University of Chinese Medicine, Wuhan, China
| | - Jingyi Cai
- Faculty of Pharmacy, Hubei University of Chinese Medicine, Wuhan, China
| | - Zhongzhu Ai
- Faculty of Pharmacy, Hubei University of Chinese Medicine, Wuhan, China
- Hubei Shizhen Laboratory, Wuhan, China
| | - Shanshan Zhou
- Hubei Shizhen Laboratory, Wuhan, China
- The First Clinical Medical School, Hubei University of Chinese Medicine, Wuhan, China
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8
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Liu X, Jiao G, Zhou F, Kendrick KM, Yao D, Gong Q, Xiang S, Jia T, Zhang XY, Zhang J, Feng J, Becker B. A neural signature for the subjective experience of threat anticipation under uncertainty. Nat Commun 2024; 15:1544. [PMID: 38378947 PMCID: PMC10879105 DOI: 10.1038/s41467-024-45433-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/29/2023] [Accepted: 01/22/2024] [Indexed: 02/22/2024] Open
Abstract
Uncertainty about potential future threats and the associated anxious anticipation represents a key feature of anxiety. However, the neural systems that underlie the subjective experience of threat anticipation under uncertainty remain unclear. Combining an uncertainty-variation threat anticipation paradigm that allows precise modulation of the level of momentary anxious arousal during functional magnetic resonance imaging (fMRI) with multivariate predictive modeling, we train a brain model that accurately predicts subjective anxious arousal intensity during anticipation and test it across 9 samples (total n = 572, both gender). Using publicly available datasets, we demonstrate that the whole-brain signature specifically predicts anxious anticipation and is not sensitive in predicting pain, general anticipation or unspecific emotional and autonomic arousal. The signature is also functionally and spatially distinguishable from representations of subjective fear or negative affect. We develop a sensitive, generalizable, and specific neuroimaging marker for the subjective experience of uncertain threat anticipation that can facilitate model development.
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Affiliation(s)
- Xiqin Liu
- Huaxi MR Research Center (HMRRC), Department of Radiology, West China Hospital of Sichuan University, Chengdu, Sichuan, China
- MOE Key Laboratory for Neuroinformation, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, Sichuan, China
- The Center of Psychosomatic Medicine, Sichuan Provincial Center for Mental Health, Sichuan Provincial People's Hospital, University of Electronic Science and Technology of China, Chengdu, Sichuan, China
| | - Guojuan Jiao
- MOE Key Laboratory for Neuroinformation, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, Sichuan, China
- The Second Xiangya Hospital of Central South University, Changsha, Hunan, China
| | - Feng Zhou
- Faculty of Psychology, Southwest University, Chongqing, China
- MOE Key Laboratory of Cognition and Personality, Chongqing, China
| | - Keith M Kendrick
- MOE Key Laboratory for Neuroinformation, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, Sichuan, China
| | - Dezhong Yao
- MOE Key Laboratory for Neuroinformation, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, Sichuan, China
| | - Qiyong Gong
- Huaxi MR Research Center (HMRRC), Department of Radiology, West China Hospital of Sichuan University, Chengdu, Sichuan, China
- Department of Radiology, West China Xiamen Hospital of Sichuan University, Xiamen, Fujian, China
| | - Shitong Xiang
- Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, China
- Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence, (Fudan University), Ministry of Education, Shanghai, China
| | - Tianye Jia
- Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, China
- Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence, (Fudan University), Ministry of Education, Shanghai, China
- The Centre for Population Neuroscience and Stratified Medicine (PONS), ISTBI, Fudan University, Shanghai, China
- SGDP Centre, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK
| | - Xiao-Yong Zhang
- Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, China
- Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence, (Fudan University), Ministry of Education, Shanghai, China
| | - Jie Zhang
- Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, China
- Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence, (Fudan University), Ministry of Education, Shanghai, China
| | - Jianfeng Feng
- Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, China
- Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence, (Fudan University), Ministry of Education, Shanghai, China
- MOE Frontiers Center for Brain Science, Fudan University, Shanghai, China
- Zhangjiang Fudan International Innovation Center, Shanghai, China
| | - Benjamin Becker
- State Key Laboratory of Brain and Cognitive Sciences, The University of Hong Kong, Hong Kong, China.
- Department of Psychology, The University of Hong Kong, Hong Kong, China.
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9
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Vidaurre D. A generative model of electrophysiological brain responses to stimulation. eLife 2024; 12:RP87729. [PMID: 38231034 PMCID: PMC10945576 DOI: 10.7554/elife.87729] [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] [Indexed: 01/18/2024] Open
Abstract
Each brain response to a stimulus is, to a large extent, unique. However this variability, our perceptual experience feels stable. Standard decoding models, which utilise information across several areas to tap into stimuli representation and processing, are fundamentally based on averages. Therefore, they can focus precisely on the features that are most stable across stimulus presentations. But which are these features exactly is difficult to address in the absence of a generative model of the signal. Here, I introduce genephys, a generative model of brain responses to stimulation publicly available as a Python package that, when confronted with a decoding algorithm, can reproduce the structured patterns of decoding accuracy that we observe in real data. Using this approach, I characterise how these patterns may be brought about by the different aspects of the signal, which in turn may translate into distinct putative neural mechanisms. In particular, the model shows that the features in the data that support successful decoding-and, therefore, likely reflect stable mechanisms of stimulus representation-have an oscillatory component that spans multiple channels, frequencies, and latencies of response; and an additive, slower response with a specific (cross-frequency) relation to the phase of the oscillatory component. At the individual trial level, still, responses are found to be highly variable, which can be due to various factors including phase noise and probabilistic activations.
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Affiliation(s)
- Diego Vidaurre
- Center for Functionally Integrative Neuroscience, Department of Clinical Medicine, Aarhus UniversityAarhusDenmark
- Department of Psychiatry, Oxford UniversityOxfordUnited Kingdom
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10
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Xiang S, Jia T, Xie C, Zhu Z, Cheng W, Schumann G, Robbins TW, Feng J. Fractionation of neural reward processing into independent components by novel decoding principle. Neuroimage 2023; 284:120463. [PMID: 37989457 DOI: 10.1016/j.neuroimage.2023.120463] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/16/2023] [Revised: 11/12/2023] [Accepted: 11/17/2023] [Indexed: 11/23/2023] Open
Abstract
How to retrieve latent neurobehavioural processes from complex neurobiological signals is an important yet unresolved challenge. Here, we develop a novel approach, orthogonal-Decoding multi-Cognitive Processes (DeCoP), to reveal underlying latent neurobehavioural processing and show that its performance is superior to traditional non-orthogonal decoding in terms of both false inference and robustness. Processing value and salience information are two fundamental but mutually confounded pathways of reward reinforcement essential for decision making. During reward/punishment anticipation, we applied DeCoP to decode brain-wide responses into spatially overlapping, yet functionally independent, evaluation and readiness processes, which are modulated differentially by meso‑limbic vs nigro-striatal dopamine systems. Using DeCoP, we further demonstrated that most brain regions only encoded abstract information but not the exact input, except for dorsal anterior cingulate cortex and insula. Furthermore, we anticipate our novel analytical principle to be applied generally in decoding multiple latent neurobehavioral processes and thus advance both the design and hypothesis testing for cognitive tasks.
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Affiliation(s)
- Shitong Xiang
- Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, China; Ministry of Education, Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence (Fudan University), China
| | - Tianye Jia
- Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, China; Ministry of Education, Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence (Fudan University), China; Centre for Population Neuroscience and Precision Medicine (PONS), Institute of Psychiatry, Psychology & Neuroscience, SGDP Centre, King's College London, SE5 8AF, United Kingdom; Centre for Population Neuroscience and Precision Medicine (PONS), Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, China
| | - Chao Xie
- Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, China; Ministry of Education, Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence (Fudan University), China
| | - Zhichao Zhu
- Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, China; Ministry of Education, Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence (Fudan University), China
| | - Wei Cheng
- Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, China; Ministry of Education, Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence (Fudan University), China
| | - Gunter Schumann
- Centre for Population Neuroscience and Precision Medicine (PONS), Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, China; Department of Psychiatry and Psychotherapy, Centre for Population Neuroscience and Precision Medicine (PONS), CCM, Charite Universitaetsmedizin, Berlin, Germany
| | - Trevor W Robbins
- Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, China; Ministry of Education, Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence (Fudan University), China; Department of Psychology and Behavioural and Clinical Neuroscience Institute, University of Cambridge, Cambridge, United Kingdom
| | - Jianfeng Feng
- Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, China; Ministry of Education, Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence (Fudan University), China; Department of Computer Science, University of Warwick, Coventry, United Kingdom; School of Mathematical Sciences and Centre for Computational Systems Biology, Fudan University, Shanghai, China.
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11
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Zhang JX, Dixon ML, Goldin PR, Spiegel D, Gross JJ. The Neural Separability of Emotion Reactivity and Regulation. AFFECTIVE SCIENCE 2023; 4:617-629. [PMID: 38156247 PMCID: PMC10751283 DOI: 10.1007/s42761-023-00227-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/01/2023] [Accepted: 11/03/2023] [Indexed: 12/30/2023]
Abstract
One foundational distinction in affective science is between emotion reactivity and regulation. This conceptual distinction has long been assumed to be instantiated in spatially separable brain systems (a typical example: amygdala/insula for reactivity and frontoparietal areas for regulation). In this research, we begin by reviewing previous findings that support and contradict the neural separability hypothesis concerning emotional reactivity and regulation. Further, we conduct a direct test of this hypothesis with empirical data. In five studies involving healthy and clinical samples (total n = 336), we assessed neural responses using fMRI while participants were asked to either react naturally or regulate their emotions (using reappraisal) while viewing emotionally evocative stimuli. Across five studies, we failed to find support for the neural separability hypothesis. In univariate analyses, both presumptive "reactivity" and "regulation" brain regions demonstrated equal or greater activation for the reactivity contrast than for the regulation contrast. In multivariate pattern analyses (MVPA), classifiers decoded reactivity (vs. neutral) trials more accurately than regulation (vs. reactivity) trials using multivoxel data in both presumptive "reactivity" and "regulation" regions. These findings suggest that emotion reactivity and regulation-as measured via fMRI-may not be as spatially separable in the brain as previously assumed. Our secondary whole-brain analyses revealed largely consistent results. We discuss the two theoretical possibilities regarding the neural separability hypothesis and offer thoughts for future research. Supplementary Information The online version contains supplementary material available at 10.1007/s42761-023-00227-9.
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Affiliation(s)
- Jin-Xiao Zhang
- Department of Psychology, Stanford University, Stanford, CA USA
| | - Matt L. Dixon
- Department of Psychology, Stanford University, Stanford, CA USA
| | - Philippe R. Goldin
- Betty Irene Moore School of Nursing, University of California Davis, Davis, CA USA
| | - David Spiegel
- Department of Psychiatry and Behavioral Sciences, Stanford University, Stanford, CA USA
| | - James J. Gross
- Department of Psychology, Stanford University, Stanford, CA USA
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12
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Bruera A, Tao Y, Anderson A, Çokal D, Haber J, Poesio M. Modeling Brain Representations of Words' Concreteness in Context Using GPT-2 and Human Ratings. Cogn Sci 2023; 47:e13388. [PMID: 38103208 DOI: 10.1111/cogs.13388] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/19/2023] [Revised: 09/12/2023] [Accepted: 10/27/2023] [Indexed: 12/18/2023]
Abstract
The meaning of most words in language depends on their context. Understanding how the human brain extracts contextualized meaning, and identifying where in the brain this takes place, remain important scientific challenges. But technological and computational advances in neuroscience and artificial intelligence now provide unprecedented opportunities to study the human brain in action as language is read and understood. Recent contextualized language models seem to be able to capture homonymic meaning variation ("bat", in a baseball vs. a vampire context), as well as more nuanced differences of meaning-for example, polysemous words such as "book", which can be interpreted in distinct but related senses ("explain a book", information, vs. "open a book", object) whose differences are fine-grained. We study these subtle differences in lexical meaning along the concrete/abstract dimension, as they are triggered by verb-noun semantic composition. We analyze functional magnetic resonance imaging (fMRI) activations elicited by Italian verb phrases containing nouns whose interpretation is affected by the verb to different degrees. By using a contextualized language model and human concreteness ratings, we shed light on where in the brain such fine-grained meaning variation takes place and how it is coded. Our results show that phrase concreteness judgments and the contextualized model can predict BOLD activation associated with semantic composition within the language network. Importantly, representations derived from a complex, nonlinear composition process consistently outperform simpler composition approaches. This is compatible with a holistic view of semantic composition in the brain, where semantic representations are modified by the process of composition itself. When looking at individual brain areas, we find that encoding performance is statistically significant, although with differing patterns of results, suggesting differential involvement, in the posterior superior temporal sulcus, inferior frontal gyrus and anterior temporal lobe, and in motor areas previously associated with processing of concreteness/abstractness.
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Affiliation(s)
- Andrea Bruera
- School of Electronic Engineering and Computer Science, Cognitive Science Research Group, Queen Mary University of London
- Lise Meitner Research Group Cognition and Plasticity, Max Planck Institute for Human Cognitive and Brain Sciences
| | - Yuan Tao
- Department of Cognitive Science, Johns Hopkins University
| | | | - Derya Çokal
- Department of German Language and Literature I-Linguistics, University of Cologne
| | - Janosch Haber
- School of Electronic Engineering and Computer Science, Cognitive Science Research Group, Queen Mary University of London
- Chattermill, London
| | - Massimo Poesio
- School of Electronic Engineering and Computer Science, Cognitive Science Research Group, Queen Mary University of London
- Department of Information and Computing Sciences, University of Utrecht
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13
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Morgenroth E, Vilaclara L, Muszynski M, Gaviria J, Vuilleumier P, Van De Ville D. Probing neurodynamics of experienced emotions-a Hitchhiker's guide to film fMRI. Soc Cogn Affect Neurosci 2023; 18:nsad063. [PMID: 37930850 PMCID: PMC10656947 DOI: 10.1093/scan/nsad063] [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: 03/16/2023] [Revised: 08/04/2023] [Accepted: 11/01/2023] [Indexed: 11/08/2023] Open
Abstract
Film functional magnetic resonance imaging (fMRI) has gained tremendous popularity in many areas of neuroscience. However, affective neuroscience remains somewhat behind in embracing this approach, even though films lend themselves to study how brain function gives rise to complex, dynamic and multivariate emotions. Here, we discuss the unique capabilities of film fMRI for emotion research, while providing a general guide of conducting such research. We first give a brief overview of emotion theories as these inform important design choices. Next, we discuss films as experimental paradigms for emotion elicitation and address the process of annotating them. We then situate film fMRI in the context of other fMRI approaches, and present an overview of results from extant studies so far with regard to advantages of film fMRI. We also give an overview of state-of-the-art analysis techniques including methods that probe neurodynamics. Finally, we convey limitations of using film fMRI to study emotion. In sum, this review offers a practitioners' guide to the emerging field of film fMRI and underscores how it can advance affective neuroscience.
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Affiliation(s)
- Elenor Morgenroth
- Neuro-X Institute, École Polytechnique Fédérale de Lausanne, Geneva 1202, Switzerland
- Department of Radiology and Medical Informatics, University of Geneva, Geneva 1202, Switzerland
- Swiss Center for Affective Sciences, University of Geneva, Geneva 1202, Switzerland
| | - Laura Vilaclara
- Neuro-X Institute, École Polytechnique Fédérale de Lausanne, Geneva 1202, Switzerland
- Department of Radiology and Medical Informatics, University of Geneva, Geneva 1202, Switzerland
| | - Michal Muszynski
- Department of Basic Neurosciences, University of Geneva, Geneva 1202, Switzerland
| | - Julian Gaviria
- Swiss Center for Affective Sciences, University of Geneva, Geneva 1202, Switzerland
- Department of Basic Neurosciences, University of Geneva, Geneva 1202, Switzerland
- Department of Psychiatry, University of Geneva, Geneva 1202, Switzerland
| | - Patrik Vuilleumier
- Swiss Center for Affective Sciences, University of Geneva, Geneva 1202, Switzerland
- Department of Basic Neurosciences, University of Geneva, Geneva 1202, Switzerland
- CIBM Center for Biomedical Imaging, Geneva 1202, Switzerland
| | - Dimitri Van De Ville
- Neuro-X Institute, École Polytechnique Fédérale de Lausanne, Geneva 1202, Switzerland
- Department of Radiology and Medical Informatics, University of Geneva, Geneva 1202, Switzerland
- CIBM Center for Biomedical Imaging, Geneva 1202, Switzerland
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14
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Stendel MS, Chavez RS. Beyond the brain localization of complex traits: Distributed white matter markers of personality. J Pers 2023; 91:1140-1151. [PMID: 36273276 DOI: 10.1111/jopy.12788] [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: 03/08/2022] [Revised: 09/09/2022] [Accepted: 10/14/2022] [Indexed: 11/30/2022]
Abstract
OBJECTIVE Extensive work in personality neuroscience has shown mixed results in the ability to localize reliable relationships between personality traits and neuroimaging measures. However, recent work in translational neuroimaging has recognized that multifaceted psychological dispositions are not represented in discrete, highly localized brain areas. As such, standard univariate neuroimaging analyses may not be well-suited for capturing broad personality traits supported by distributed networks. METHOD The present study uses an out-of-sample predictive modeling approach to identify multivariate signatures of Big Five personality traits within the structural integrity of white matter pathways using diffusion magnetic resonance imaging. In Study 1 (N = 491), we trained a ridge regression model to predict each of the Big Five traits and tested these models in an independent hold-out subsample. RESULTS We found that models for both Neuroticism and Openness were significantly related to predictive accuracy in the hold-out sample. Study 2 (N = 108) applied Study 1's predictive models to an independent set of data collected on a different scanner and using a different Big Five scale. Here, we found that the model for Neuroticism remained a significant predictor of individual difference. CONCLUSION Our findings provide evidence that this white matter signature of Neuroticism generalizes across differences in measurement and samples.
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Affiliation(s)
- Moriah S Stendel
- Department of Psychology, University of Oregon, Eugene, Oregon, USA
| | - Robert S Chavez
- Department of Psychology, University of Oregon, Eugene, Oregon, USA
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15
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Bouhassira D, Attal N. Personalized treatment of neuropathic pain: Where are we now? Eur J Pain 2023; 27:1084-1098. [PMID: 37114461 DOI: 10.1002/ejp.2120] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/16/2023] [Revised: 04/07/2023] [Accepted: 04/15/2023] [Indexed: 04/29/2023]
Abstract
BACKGROUND The treatment of neuropathic pain remains a major unmet need that the development of personalized and refined treatment strategies may contribute to address. DATABASE In this narrative review, we summarize the various approaches based on objective biomarkers or clinical markers that could be used. RESULTS In principle, the validation of objective biomarkers would be the most robust approach. However, although promising results have been reported demonstrating a potential value of genomics, anatomical or functional markers, the clinical validation of these markers has only just begun. Thus, most of the strategies documented to date have been based on the development of clinical markers. In particular, many studies have suggested that the identification of specific subgroups of patients presenting with specific combinations of symptoms and signs would be a relevant approach. Two main approaches have been used to identify relevant sensory profiles: quantitative sensory testing and specific patients reported outcomes based on description of pain qualities. CONCLUSION We discuss here the advantages and limitations of these approaches, which are not mutually exclusive. SIGNIFICANCE Recent data indicate that various new treatment strategies based on predictive biological and/or clinical markers could be helpful to better personalized and therefore improve the management of neuropathic pain.
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Affiliation(s)
- Didier Bouhassira
- Inserm U987, UVSQ-Paris-Saclay University, Ambroise Pare Hospital, Boulogne-Billancourt, France
| | - Nadine Attal
- Inserm U987, UVSQ-Paris-Saclay University, Ambroise Pare Hospital, Boulogne-Billancourt, France
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16
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Botvinik-Nezer R, Petre B, Ceko M, Lindquist MA, Friedman NP, Wager TD. Placebo treatment affects brain systems related to affective and cognitive processes, but not nociceptive pain. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.09.21.558825. [PMID: 37790543 PMCID: PMC10543005 DOI: 10.1101/2023.09.21.558825] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/05/2023]
Abstract
Placebo analgesia is a replicable and well-studied phenomenon, yet it remains unclear to what degree it includes modulation of nociceptive processes. Some studies find effects consistent with nociceptive effects, but meta-analyses show that these effects are often small. We analyzed placebo analgesia in a large fMRI study (N = 392), including placebo effects on brain responses to noxious stimuli. Placebo treatment caused robust analgesia in both conditioned thermal and unconditioned mechanical pain. Placebo did not decrease fMRI activity in nociceptive pain regions, including the Neurologic Pain Signature (NPS) and pre-registered spinothalamic pathway regions, with strong support from Bayes Factor analyses. However, placebo treatment affected activity in pre-registered analyses of a second neuromarker, the Stimulus Intensity Independent Pain Signature (SIIPS), and several associated a priori brain regions related to motivation and value, in both thermal and mechanical pain. Individual differences in behavioral analgesia were correlated with neural changes in both thermal and mechanical pain. Our results indicate that processes related to affective and cognitive aspects of pain primarily drive placebo analgesia.
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17
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Robinson AK, Quek GL, Carlson TA. Visual Representations: Insights from Neural Decoding. Annu Rev Vis Sci 2023; 9:313-335. [PMID: 36889254 DOI: 10.1146/annurev-vision-100120-025301] [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] [Indexed: 03/10/2023]
Abstract
Patterns of brain activity contain meaningful information about the perceived world. Recent decades have welcomed a new era in neural analyses, with computational techniques from machine learning applied to neural data to decode information represented in the brain. In this article, we review how decoding approaches have advanced our understanding of visual representations and discuss efforts to characterize both the complexity and the behavioral relevance of these representations. We outline the current consensus regarding the spatiotemporal structure of visual representations and review recent findings that suggest that visual representations are at once robust to perturbations, yet sensitive to different mental states. Beyond representations of the physical world, recent decoding work has shone a light on how the brain instantiates internally generated states, for example, during imagery and prediction. Going forward, decoding has remarkable potential to assess the functional relevance of visual representations for human behavior, reveal how representations change across development and during aging, and uncover their presentation in various mental disorders.
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Affiliation(s)
- Amanda K Robinson
- Queensland Brain Institute, The University of Queensland, Brisbane, Australia;
| | - Genevieve L Quek
- The MARCS Institute for Brain, Behaviour and Development, Western Sydney University, Sydney, Australia;
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18
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Zhang Y, Rennig J, Magnotti JF, Beauchamp MS. Multivariate fMRI responses in superior temporal cortex predict visual contributions to, and individual differences in, the intelligibility of noisy speech. Neuroimage 2023; 278:120271. [PMID: 37442310 PMCID: PMC10460966 DOI: 10.1016/j.neuroimage.2023.120271] [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: 03/07/2023] [Revised: 06/20/2023] [Accepted: 07/06/2023] [Indexed: 07/15/2023] Open
Abstract
Humans have the unique ability to decode the rapid stream of language elements that constitute speech, even when it is contaminated by noise. Two reliable observations about noisy speech perception are that seeing the face of the talker improves intelligibility and the existence of individual differences in the ability to perceive noisy speech. We introduce a multivariate BOLD fMRI measure that explains both observations. In two independent fMRI studies, clear and noisy speech was presented in visual, auditory and audiovisual formats to thirty-seven participants who rated intelligibility. An event-related design was used to sort noisy speech trials by their intelligibility. Individual-differences multidimensional scaling was applied to fMRI response patterns in superior temporal cortex and the dissimilarity between responses to clear speech and noisy (but intelligible) speech was measured. Neural dissimilarity was less for audiovisual speech than auditory-only speech, corresponding to the greater intelligibility of noisy audiovisual speech. Dissimilarity was less in participants with better noisy speech perception, corresponding to individual differences. These relationships held for both single word and entire sentence stimuli, suggesting that they were driven by intelligibility rather than the specific stimuli tested. A neural measure of perceptual intelligibility may aid in the development of strategies for helping those with impaired speech perception.
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Affiliation(s)
- Yue Zhang
- Department of Neurosurgery, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, United States; Department of Neurosurgery, Baylor College of Medicine, Houston, TX, United States
| | - Johannes Rennig
- Division of Neuropsychology, Center of Neurology, Hertie-Institute for Clinical Brain Research, University of Tübingen, Tübingen, Germany
| | - John F Magnotti
- Department of Neurosurgery, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, United States
| | - Michael S Beauchamp
- Department of Neurosurgery, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, United States.
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19
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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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20
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Veillette JP, Ho L, Nusbaum HC. Permutation-based group sequential analyses for cognitive neuroscience. Neuroimage 2023; 277:120232. [PMID: 37348624 DOI: 10.1016/j.neuroimage.2023.120232] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/15/2023] [Revised: 06/13/2023] [Accepted: 06/15/2023] [Indexed: 06/24/2023] Open
Abstract
Cognitive neuroscientists have been grappling with two related experimental design problems. First, the complexity of neuroimaging data (e.g. often hundreds of thousands of correlated measurements) and analysis pipelines demands bespoke, non-parametric statistical tests for valid inference, and these tests often lack an agreed-upon method for performing a priori power analyses. Thus, sample size determination for neuroimaging studies is often arbitrary or inferred from other putatively but questionably similar studies, which can result in underpowered designs - undermining the efficacy of neuroimaging research. Second, when meta-analyses estimate the sample sizes required to obtain reasonable statistical power, estimated sample sizes can be prohibitively large given the resource constraints of many labs. We propose the use of sequential analyses to partially address both of these problems. Sequential study designs - in which the data is analyzed at interim points during data collection and data collection can be stopped if the planned test statistic satisfies a stopping rule specified a priori - are common in the clinical trial literature, due to the efficiency gains they afford over fixed-sample designs. However, the corrections used to control false positive rates in existing approaches to sequential testing rely on parametric assumptions that are often violated in neuroimaging settings. We introduce a general permutation scheme that allows sequential designs to be used with arbitrary test statistics. By simulation, we show that this scheme controls the false positive rate across multiple interim analyses. Then, performing power analyses for seven evoked response effects seen in the EEG literature, we show that this sequential analysis approach can substantially outperform fixed-sample approaches (i.e. require fewer subjects, on average, to detect a true effect) when study designs are sufficiently well-powered. To facilitate the adoption of this methodology, we provide a Python package "niseq" with sequential implementations of common tests used for neuroimaging: cluster-based permutation tests, threshold-free cluster enhancement, t-max, F-max, and the network-based statistic with tutorial examples using EEG and fMRI data.
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Affiliation(s)
| | - Letitia Ho
- Department of Psychology, University of Chicago, United States
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21
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Rosenberg AM, Saggar M, Monzel AS, Devine J, Rogu P, Limoges A, Junker A, Sandi C, Mosharov EV, Dumitriu D, Anacker C, Picard M. Brain mitochondrial diversity and network organization predict anxiety-like behavior in male mice. Nat Commun 2023; 14:4726. [PMID: 37563104 PMCID: PMC10415311 DOI: 10.1038/s41467-023-39941-0] [Citation(s) in RCA: 11] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/23/2021] [Accepted: 07/04/2023] [Indexed: 08/12/2023] Open
Abstract
The brain and behavior are under energetic constraints, limited by mitochondrial energy transformation capacity. However, the mitochondria-behavior relationship has not been systematically studied at a brain-wide scale. Here we examined the association between multiple features of mitochondrial respiratory chain capacity and stress-related behaviors in male mice with diverse behavioral phenotypes. Miniaturized assays of mitochondrial respiratory chain enzyme activities and mitochondrial DNA (mtDNA) content were deployed on 571 samples across 17 brain areas, defining specific patterns of mito-behavior associations. By applying multi-slice network analysis to our brain-wide mitochondrial dataset, we identified three large-scale networks of brain areas with shared mitochondrial signatures. A major network composed of cortico-striatal areas exhibited the strongest mitochondria-behavior correlations, accounting for up to 50% of animal-to-animal behavioral differences, suggesting that this mito-based network is functionally significant. The mito-based brain networks also overlapped with regional gene expression and structural connectivity, and exhibited distinct molecular mitochondrial phenotype signatures. This work provides convergent multimodal evidence anchored in enzyme activities, gene expression, and animal behavior that distinct, behaviorally-relevant mitochondrial phenotypes exist across the male mouse brain.
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Affiliation(s)
- Ayelet M Rosenberg
- Division of Behavioral Medicine, Department of Psychiatry, Columbia University Irving Medical Center, New York, NY, USA
| | - Manish Saggar
- Department of Psychiatry and Behavioral Sciences, Stanford University, Stanford, CA, USA
| | - Anna S Monzel
- Division of Behavioral Medicine, Department of Psychiatry, Columbia University Irving Medical Center, New York, NY, USA
| | - Jack Devine
- Division of Behavioral Medicine, Department of Psychiatry, Columbia University Irving Medical Center, New York, NY, USA
| | - Peter Rogu
- Columbia University Institute for Developmental Sciences, Department of Psychiatry, Columbia University Irving Medical Center, New York, NY, USA
| | - Aaron Limoges
- Department of Biological Sciences, Columbia University, New York, NY, USA
- Division of Systems Neuroscience, Department of Psychiatry, Columbia University Irving Medical Center, New York, NY, USA
| | - Alex Junker
- Division of Behavioral Medicine, Department of Psychiatry, Columbia University Irving Medical Center, New York, NY, USA
| | - Carmen Sandi
- Brain Mind Institute, Ecole Polytechnique Federal de Lausanne (EPFL), Lausanne, Switzerland
| | - Eugene V Mosharov
- Division of Molecular Therapeutics, Department of Psychiatry, Columbia University Irving Medical Center, New York, NY, USA
- New York State Psychiatric Institute, New York, NY, USA
| | - Dani Dumitriu
- Columbia University Institute for Developmental Sciences, Department of Psychiatry, Columbia University Irving Medical Center, New York, NY, USA
- Department of Pediatrics, Columbia University Irving Medical Center, New York, NY, USA
- Division of Developmental Neuroscience, Department of Psychiatry, Columbia University Irving Medical Center, New York, NY, USA
| | - Christoph Anacker
- Columbia University Institute for Developmental Sciences, Department of Psychiatry, Columbia University Irving Medical Center, New York, NY, USA
- Division of Systems Neuroscience, Department of Psychiatry, Columbia University Irving Medical Center, New York, NY, USA
- New York State Psychiatric Institute, New York, NY, USA
| | - Martin Picard
- Division of Behavioral Medicine, Department of Psychiatry, Columbia University Irving Medical Center, New York, NY, USA.
- New York State Psychiatric Institute, New York, NY, USA.
- Department of Neurology, H. Houston Merritt Center, Columbia Translational Neuroscience Initiative, Columbia University Irving Medical Center, New York, NY, USA.
- Robert N Butler Columbia Aging Center, Columbia University Mailman School of Public Health, New York, NY, USA.
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22
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Nebe S, Reutter M, Baker DH, Bölte J, Domes G, Gamer M, Gärtner A, Gießing C, Gurr C, Hilger K, Jawinski P, Kulke L, Lischke A, Markett S, Meier M, Merz CJ, Popov T, Puhlmann LMC, Quintana DS, Schäfer T, Schubert AL, Sperl MFJ, Vehlen A, Lonsdorf TB, Feld GB. Enhancing precision in human neuroscience. eLife 2023; 12:e85980. [PMID: 37555830 PMCID: PMC10411974 DOI: 10.7554/elife.85980] [Citation(s) in RCA: 8] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/20/2023] [Accepted: 07/23/2023] [Indexed: 08/10/2023] Open
Abstract
Human neuroscience has always been pushing the boundary of what is measurable. During the last decade, concerns about statistical power and replicability - in science in general, but also specifically in human neuroscience - have fueled an extensive debate. One important insight from this discourse is the need for larger samples, which naturally increases statistical power. An alternative is to increase the precision of measurements, which is the focus of this review. This option is often overlooked, even though statistical power benefits from increasing precision as much as from increasing sample size. Nonetheless, precision has always been at the heart of good scientific practice in human neuroscience, with researchers relying on lab traditions or rules of thumb to ensure sufficient precision for their studies. In this review, we encourage a more systematic approach to precision. We start by introducing measurement precision and its importance for well-powered studies in human neuroscience. Then, determinants for precision in a range of neuroscientific methods (MRI, M/EEG, EDA, Eye-Tracking, and Endocrinology) are elaborated. We end by discussing how a more systematic evaluation of precision and the application of respective insights can lead to an increase in reproducibility in human neuroscience.
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Affiliation(s)
- Stephan Nebe
- Zurich Center for Neuroeconomics, Department of Economics, University of ZurichZurichSwitzerland
| | - Mario Reutter
- Department of Psychology, Julius-Maximilians-UniversityWürzburgGermany
| | - Daniel H Baker
- Department of Psychology and York Biomedical Research Institute, University of YorkYorkUnited Kingdom
| | - Jens Bölte
- Institute for Psychology, University of Münster, Otto-Creuzfeldt Center for Cognitive and Behavioral NeuroscienceMünsterGermany
| | - Gregor Domes
- Department of Biological and Clinical Psychology, University of TrierTrierGermany
- Institute for Cognitive and Affective NeuroscienceTrierGermany
| | - Matthias Gamer
- Department of Psychology, Julius-Maximilians-UniversityWürzburgGermany
| | - Anne Gärtner
- Faculty of Psychology, Technische Universität DresdenDresdenGermany
| | - Carsten Gießing
- Biological Psychology, Department of Psychology, School of Medicine and Health Sciences, Carl von Ossietzky University of OldenburgOldenburgGermany
| | - Caroline Gurr
- Department of Child and Adolescent Psychiatry, Psychosomatics and Psychotherapy, University Hospital, Goethe UniversityFrankfurtGermany
- Brain Imaging Center, Goethe UniversityFrankfurtGermany
| | - Kirsten Hilger
- Department of Psychology, Julius-Maximilians-UniversityWürzburgGermany
- Department of Psychology, Psychological Diagnostics and Intervention, Catholic University of Eichstätt-IngolstadtEichstättGermany
| | - Philippe Jawinski
- Department of Psychology, Humboldt-Universität zu BerlinBerlinGermany
| | - Louisa Kulke
- Department of Developmental with Educational Psychology, University of BremenBremenGermany
| | - Alexander Lischke
- Department of Psychology, Medical School HamburgHamburgGermany
- Institute of Clinical Psychology and Psychotherapy, Medical School HamburgHamburgGermany
| | - Sebastian Markett
- Department of Psychology, Humboldt-Universität zu BerlinBerlinGermany
| | - Maria Meier
- Department of Psychology, University of KonstanzKonstanzGermany
- University Psychiatric Hospitals, Child and Adolescent Psychiatric Research Department (UPKKJ), University of BaselBaselSwitzerland
| | - Christian J Merz
- Department of Cognitive Psychology, Institute of Cognitive Neuroscience, Faculty of Psychology, Ruhr University BochumBochumGermany
| | - Tzvetan Popov
- Department of Psychology, Methods of Plasticity Research, University of ZurichZurichSwitzerland
| | - Lara MC Puhlmann
- Leibniz Institute for Resilience ResearchMainzGermany
- Max Planck Institute for Human Cognitive and Brain SciencesLeipzigGermany
| | - Daniel S Quintana
- Max Planck Institute for Human Cognitive and Brain SciencesLeipzigGermany
- NevSom, Department of Rare Disorders & Disabilities, Oslo University HospitalOsloNorway
- KG Jebsen Centre for Neurodevelopmental Disorders, University of OsloOsloNorway
- Norwegian Centre for Mental Disorders Research (NORMENT), University of OsloOsloNorway
| | - Tim Schäfer
- Department of Child and Adolescent Psychiatry, Psychosomatics and Psychotherapy, University Hospital, Goethe UniversityFrankfurtGermany
- Brain Imaging Center, Goethe UniversityFrankfurtGermany
| | | | - Matthias FJ Sperl
- Department of Clinical Psychology and Psychotherapy, University of GiessenGiessenGermany
- Center for Mind, Brain and Behavior, Universities of Marburg and GiessenGiessenGermany
| | - Antonia Vehlen
- Department of Biological and Clinical Psychology, University of TrierTrierGermany
| | - Tina B Lonsdorf
- Department of Systems Neuroscience, University Medical Center Hamburg-EppendorfHamburgGermany
- Department of Psychology, Biological Psychology and Cognitive Neuroscience, University of BielefeldBielefeldGermany
| | - Gordon B Feld
- Department of Clinical Psychology, Central Institute of Mental Health, Medical Faculty Mannheim, Heidelberg UniversityMannheimGermany
- Department of Psychology, Heidelberg UniversityHeidelbergGermany
- Department of Addiction Behavior and Addiction Medicine, Central Institute of Mental Health, Medical Faculty Mannheim, Heidelberg UniversityMannheimGermany
- Department of Psychiatry and Psychotherapy, Central Institute of Mental Health, Medical Faculty Mannheim, Heidelberg UniversityMannheimGermany
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23
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Liu J, Chang H, Abrams DA, Kang JB, Chen L, Rosenberg-Lee M, Menon V. Atypical cognitive training-induced learning and brain plasticity and their relation to insistence on sameness in children with autism. eLife 2023; 12:e86035. [PMID: 37534879 PMCID: PMC10550286 DOI: 10.7554/elife.86035] [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: 01/08/2023] [Accepted: 08/02/2023] [Indexed: 08/04/2023] Open
Abstract
Children with autism spectrum disorders (ASDs) often display atypical learning styles; however, little is known regarding learning-related brain plasticity and its relation to clinical phenotypic features. Here, we investigate cognitive learning and neural plasticity using functional brain imaging and a novel numerical problem-solving training protocol. Children with ASD showed comparable learning relative to typically developing children but were less likely to shift from rule-based to memory-based strategy. While learning gains in typically developing children were associated with greater plasticity of neural representations in the medial temporal lobe and intraparietal sulcus, learning in children with ASD was associated with more stable neural representations. Crucially, the relation between learning and plasticity of neural representations was moderated by insistence on sameness, a core phenotypic feature of ASD. Our study uncovers atypical cognitive and neural mechanisms underlying learning in children with ASD, and informs pedagogical strategies for nurturing cognitive abilities in childhood autism.
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Affiliation(s)
- Jin Liu
- Department of Psychiatry & Behavioral Sciences, Stanford University School of Medicine, Stanford, United States
| | - Hyesang Chang
- Department of Psychiatry & Behavioral Sciences, Stanford University School of Medicine, Stanford, United States
| | - Daniel A Abrams
- Department of Psychiatry & Behavioral Sciences, Stanford University School of Medicine, Stanford, United States
| | - Julia Boram Kang
- Department of Psychiatry & Behavioral Sciences, Stanford University School of Medicine, Stanford, United States
| | - Lang Chen
- Department of Psychiatry & Behavioral Sciences, Stanford University School of Medicine, Stanford, United States
- Department of Psychology, Santa Clara University, Santa Clara, United States
| | - Miriam Rosenberg-Lee
- Department of Psychiatry & Behavioral Sciences, Stanford University School of Medicine, Stanford, United States
- Department of Psychology, Rutgers University, Newark, United States
| | - Vinod Menon
- Department of Psychiatry & Behavioral Sciences, Stanford University School of Medicine, Stanford, United States
- Department of Neurology & Neurological Sciences, Stanford Neurosciences Institute, Stanford, United States
- Stanford Neurosciences Institute, Stanford University School of Medicine, Stanford, United States
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24
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Grogans SE, Bliss-Moreau E, Buss KA, Clark LA, Fox AS, Keltner D, Cowen AS, Kim JJ, Kragel PA, MacLeod C, Mobbs D, Naragon-Gainey K, Fullana MA, Shackman AJ. The nature and neurobiology of fear and anxiety: State of the science and opportunities for accelerating discovery. Neurosci Biobehav Rev 2023; 151:105237. [PMID: 37209932 PMCID: PMC10330657 DOI: 10.1016/j.neubiorev.2023.105237] [Citation(s) in RCA: 14] [Impact Index Per Article: 14.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/27/2023] [Revised: 05/11/2023] [Accepted: 05/13/2023] [Indexed: 05/22/2023]
Abstract
Fear and anxiety play a central role in mammalian life, and there is considerable interest in clarifying their nature, identifying their biological underpinnings, and determining their consequences for health and disease. Here we provide a roundtable discussion on the nature and biological bases of fear- and anxiety-related states, traits, and disorders. The discussants include scientists familiar with a wide variety of populations and a broad spectrum of techniques. The goal of the roundtable was to take stock of the state of the science and provide a roadmap to the next generation of fear and anxiety research. Much of the discussion centered on the key challenges facing the field, the most fruitful avenues for future research, and emerging opportunities for accelerating discovery, with implications for scientists, funders, and other stakeholders. Understanding fear and anxiety is a matter of practical importance. Anxiety disorders are a leading burden on public health and existing treatments are far from curative, underscoring the urgency of developing a deeper understanding of the factors governing threat-related emotions.
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Affiliation(s)
- Shannon E Grogans
- Department of Psychology, University of Maryland, College Park, MD 20742, USA
| | - Eliza Bliss-Moreau
- Department of Psychology, University of California, Davis, CA 95616, USA; California National Primate Research Center, University of California, Davis, CA 95616, USA
| | - Kristin A Buss
- Department of Psychology, The Pennsylvania State University, University Park, PA 16802 USA
| | - Lee Anna Clark
- Department of Psychology, University of Notre Dame, Notre Dame, IN 46556, USA
| | - Andrew S Fox
- Department of Psychology, University of California, Davis, CA 95616, USA; California National Primate Research Center, University of California, Davis, CA 95616, USA
| | - Dacher Keltner
- Department of Psychology, University of California, Berkeley, Berkeley, CA 94720, USA
| | | | - Jeansok J Kim
- Department of Psychology, University of Washington, Seattle, WA 98195, USA
| | - Philip A Kragel
- Department of Psychology, Emory University, Atlanta, GA 30322, USA
| | - Colin MacLeod
- Centre for the Advancement of Research on Emotion, School of Psychological Science, The University of Western Australia, Perth, WA 6009, Australia
| | - Dean Mobbs
- Department of Humanities and Social Sciences, California Institute of Technology, Pasadena, California 91125, USA; Computation and Neural Systems Program, California Institute of Technology, Pasadena, CA 91125, USA
| | - Kristin Naragon-Gainey
- School of Psychological Science, University of Western Australia, Perth, WA 6009, Australia
| | - Miquel A Fullana
- Adult Psychiatry and Psychology Department, Institute of Neurosciences, Hospital Clinic, Barcelona, Spain; Imaging of Mood, and Anxiety-Related Disorders Group, Institut d'Investigacions Biomèdiques August Pi i Sunyer, CIBERSAM, University of Barcelona, Barcelona, Spain
| | - Alexander J Shackman
- Department of Psychology, University of Maryland, College Park, MD 20742, USA; Neuroscience and Cognitive Science Program, University of Maryland, College Park, MD 20742, USA; Maryland Neuroimaging Center, University of Maryland, College Park, MD 20742, USA.
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25
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Rockholt MM, Kenefati G, Doan LV, Chen ZS, Wang J. In search of a composite biomarker for chronic pain by way of EEG and machine learning: where do we currently stand? Front Neurosci 2023; 17:1186418. [PMID: 37389362 PMCID: PMC10301750 DOI: 10.3389/fnins.2023.1186418] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/14/2023] [Accepted: 05/12/2023] [Indexed: 07/01/2023] Open
Abstract
Machine learning is becoming an increasingly common component of routine data analyses in clinical research. The past decade in pain research has witnessed great advances in human neuroimaging and machine learning. With each finding, the pain research community takes one step closer to uncovering fundamental mechanisms underlying chronic pain and at the same time proposing neurophysiological biomarkers. However, it remains challenging to fully understand chronic pain due to its multidimensional representations within the brain. By utilizing cost-effective and non-invasive imaging techniques such as electroencephalography (EEG) and analyzing the resulting data with advanced analytic methods, we have the opportunity to better understand and identify specific neural mechanisms associated with the processing and perception of chronic pain. This narrative literature review summarizes studies from the last decade describing the utility of EEG as a potential biomarker for chronic pain by synergizing clinical and computational perspectives.
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Affiliation(s)
- Mika M. Rockholt
- Department of Anesthesiology, Perioperative Care and Pain Management, New York University Grossman School of Medicine, New York, NY, United States
| | - George Kenefati
- Department of Anesthesiology, Perioperative Care and Pain Management, New York University Grossman School of Medicine, New York, NY, United States
| | - Lisa V. Doan
- Department of Anesthesiology, Perioperative Care and Pain Management, New York University Grossman School of Medicine, New York, NY, United States
| | - Zhe Sage Chen
- Department of Psychiatry, New York University Grossman School of Medicine, New York, NY, United States
- Department of Neuroscience & Physiology, Neuroscience Institute, New York University Grossman School of Medicine, New York, NY, United States
- Department of Biomedical Engineering, New York University Tandon School of Engineering, Brooklyn, NY, United States
| | - Jing Wang
- Department of Anesthesiology, Perioperative Care and Pain Management, New York University Grossman School of Medicine, New York, NY, United States
- Department of Neuroscience & Physiology, Neuroscience Institute, New York University Grossman School of Medicine, New York, NY, United States
- Department of Biomedical Engineering, New York University Tandon School of Engineering, Brooklyn, NY, United States
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26
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G-Guzmán E, Perl YS, Vohryzek J, Escrichs A, Manasova D, Türker B, Tagliazucchi E, Kringelbach M, Sitt JD, Deco G. The lack of temporal brain dynamics asymmetry as a signature of impaired consciousness states. Interface Focus 2023; 13:20220086. [PMID: 37065259 PMCID: PMC10102727 DOI: 10.1098/rsfs.2022.0086] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/15/2022] [Accepted: 03/17/2023] [Indexed: 04/18/2023] Open
Abstract
Life is a constant battle against equilibrium. From the cellular level to the macroscopic scale, living organisms as dissipative systems require the violation of their detailed balance, i.e. metabolic enzymatic reactions, in order to survive. We present a framework based on temporal asymmetry as a measure of non-equilibrium. By means of statistical physics, it was discovered that temporal asymmetries establish an arrow of time useful for assessing the reversibility in human brain time series. Previous studies in human and non-human primates have shown that decreased consciousness states such as sleep and anaesthesia result in brain dynamics closer to the equilibrium. Furthermore, there is growing interest in the analysis of brain symmetry based on neuroimaging recordings and since it is a non-invasive technique, it can be extended to different brain imaging modalities and applied at different temporo-spatial scales. In the present study, we provide a detailed description of our methodological approach, paying special attention to the theories that motivated this work. We test, for the first time, the reversibility analysis in human functional magnetic resonance imaging data in patients suffering from disorder of consciousness. We verify that the tendency of a decrease in the asymmetry of the brain signal together with the decrease in non-stationarity are key characteristics of impaired consciousness states. We expect that this work will open the way for assessing biomarkers for patients' improvement and classification, as well as motivating further research on the mechanistic understanding underlying states of impaired consciousness.
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Affiliation(s)
- Elvira G-Guzmán
- Department of Information and Communication Technologies, Centre for Brain and Cognition, Computational Neuroscience Group, Universitat Pompeu Fabra, Barcelona, Spain
| | - Yonatan Sanz Perl
- Department of Information and Communication Technologies, Centre for Brain and Cognition, Computational Neuroscience Group, Universitat Pompeu Fabra, Barcelona, Spain
- Sorbonne Université, Institut du Cerveau - Paris Brain Institute - ICM, Inserm Physiological Investigation of Clinically Normal and Impaired Cognition Team, CNRS, 75013, Paris, France
| | - Jakub Vohryzek
- Department of Information and Communication Technologies, Centre for Brain and Cognition, Computational Neuroscience Group, Universitat Pompeu Fabra, Barcelona, Spain
- Centre for Eudaimonia and Human Flourishing, Linacre College, University of Oxford, Oxford, UK
| | - Anira Escrichs
- Department of Information and Communication Technologies, Centre for Brain and Cognition, Computational Neuroscience Group, Universitat Pompeu Fabra, Barcelona, Spain
| | - Dragana Manasova
- Sorbonne Université, Institut du Cerveau - Paris Brain Institute - ICM, Inserm Physiological Investigation of Clinically Normal and Impaired Cognition Team, CNRS, 75013, Paris, France
- Université Paris Cité, Paris, France
| | - Başak Türker
- Sorbonne Université, Institut du Cerveau - Paris Brain Institute - ICM, Inserm Physiological Investigation of Clinically Normal and Impaired Cognition Team, CNRS, 75013, Paris, France
| | - Enzo Tagliazucchi
- Buenos Aires Physics Institute and Physics Department, University of Buenos Aires, Buenos Aires, Argentina
- Latin American Brain Health (BrainLat), Universidad Adolfo Ibáñez, Santiago, Chile
| | - Morten Kringelbach
- Centre for Eudaimonia and Human Flourishing, Linacre College, University of Oxford, Oxford, UK
- Department of Clinical Medicine, Center for Music in the Brain, Aarhus University, Jutland, Denmark
| | - Jacobo D. Sitt
- Sorbonne Université, Institut du Cerveau - Paris Brain Institute - ICM, Inserm Physiological Investigation of Clinically Normal and Impaired Cognition Team, CNRS, 75013, Paris, France
| | - Gustavo Deco
- Department of Information and Communication Technologies, Centre for Brain and Cognition, Computational Neuroscience Group, Universitat Pompeu Fabra, Barcelona, Spain
- Institució Catalana de la Recerca i Estudis Avançats (ICREA), Barcelona, Catalonia, Spain
- Department of Neuropsychology, Max Planck Institute for human Cognitive and Brain Sciences, Leipzig, Germany
- School of Psychological Sciences, Monash University, Melbourne, Australia
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27
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Cheng P, Li Y, Wang G, Dong H, Liu H, Shen W, Zhou W. Aberrant topology of white matter networks in patients with methamphetamine dependence and its application in support vector machine-based classification. Sci Rep 2023; 13:6958. [PMID: 37117256 PMCID: PMC10147725 DOI: 10.1038/s41598-023-33199-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/23/2022] [Accepted: 04/08/2023] [Indexed: 04/30/2023] Open
Abstract
Brain white matter (WM) networks have been widely studied in neuropsychiatric disorders. However, few studies have evaluated alterations in WM network topological organization in patients with methamphetamine (MA) dependence. Therefore, using machine learning classification methods to analyze WM network topological attributes may give new insights into patients with MA dependence. In the study, diffusion tensor imaging-based probabilistic tractography was used to map the weighted WM networks in 46 MA-dependent patients and 46 control subjects. Using graph-theoretical analyses, the global and regional topological attributes of WM networks for both groups were calculated and compared to determine inter-group differences using a permutation-based general linear model. In addition, the study used a support vector machine (SVM) learning approach to construct a classifier for discriminating subjects with MA dependence from control subjects. Relative to the control group, the MA-dependent group exhibited abnormal topological organization, as evidenced by decreased small-worldness and modularity, and increased nodal efficiency in the right medial superior temporal gyrus, right pallidum, and right ventromedial putamen; the MA-dependent group had the higher hubness scores in 25 regions, which were mainly located in the default mode network. An SVM trained with topological attributes achieved classification accuracy, sensitivity, specificity, and kappa values of 98.09% ± 2.59%, 98.24% ± 4.00%, 97.94% ± 4.26%, and 96.18% ± 5.19% for patients with MA dependence. Our results may suggest altered global WM structural networks in MA-dependent patients. Furthermore, the abnormal WM network topological attributes may provide promising features for the construction of high-efficacy classification models.
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Affiliation(s)
- Ping Cheng
- Department of Radiology, Ningbo Medical Treatment Center Lihuili Hospital, Ningbo University, 57# Xing Ning Road, Ningbo, Zhejiang, China
| | - Yadi Li
- Department of Radiology, Ningbo Medical Treatment Center Lihuili Hospital, Ningbo University, 57# Xing Ning Road, Ningbo, Zhejiang, China.
| | - Gaoyan Wang
- Department of Radiology, Ningbo Medical Treatment Center Lihuili Hospital, Ningbo University, 57# Xing Ning Road, Ningbo, Zhejiang, China
| | - Haibo Dong
- Department of Radiology, Ningbo Medical Treatment Center Lihuili Hospital, Ningbo University, 57# Xing Ning Road, Ningbo, Zhejiang, China
| | - Huifen Liu
- Department of Psychiatry, Ningbo Kangning Hospital, Ningbo University, 1# Zhuangyu South Road, Ningbo, Zhejiang, China
| | - Wenwen Shen
- Department of Psychiatry, Ningbo Kangning Hospital, Ningbo University, 1# Zhuangyu South Road, Ningbo, Zhejiang, China
| | - Wenhua Zhou
- Department of Psychiatry, Ningbo Kangning Hospital, Ningbo University, 1# Zhuangyu South Road, Ningbo, Zhejiang, China.
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28
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Abstract
Frameworks of emotional development have tended to focus on how environmental factors shape children's emotion understanding. However, individual experiences of emotion represent a complex interplay between both external environmental inputs and internal somatovisceral signaling. Here, we discuss the importance of afferent signals and coordination between central and peripheral mechanisms in affective response processing. We propose that incorporating somatovisceral theories of emotions into frameworks of emotional development can inform how children understand emotions in themselves and others. We highlight promising directions for future research on emotional development incorporating this perspective, namely afferent cardiac processing and interoception, immune activation, physiological synchrony, and social touch.
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Affiliation(s)
- Kelly E Faig
- Department of Psychology, Hamilton College, 198 College Hill Road, Clinton, NY 13502
| | - Karen E Smith
- Department of Psychology, the University of Wisconsin, 1500 Highland Blvd, Madison, WI, 53705
| | - Stephanie J Dimitroff
- Department of Psychology, Universität Konstanz, Universitätsstraße 10, 78464 Konstanz, Germany
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29
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Speer SPH, Keysers C, Barrios JC, Teurlings CJS, Smidts A, Boksem MAS, Wager TD, Gazzola V. A multivariate brain signature for reward. Neuroimage 2023; 271:119990. [PMID: 36878456 DOI: 10.1016/j.neuroimage.2023.119990] [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: 07/15/2022] [Revised: 02/20/2023] [Accepted: 02/25/2023] [Indexed: 03/07/2023] Open
Abstract
The processing of reinforcers and punishers is crucial to adapt to an ever changing environment and its dysregulation is prevalent in mental health and substance use disorders. While many human brain measures related to reward have been based on activity in individual brain regions, recent studies indicate that many affective and motivational processes are encoded in distributed systems that span multiple regions. Consequently, decoding these processes using individual regions yields small effect sizes and limited reliability, whereas predictive models based on distributed patterns yield larger effect sizes and excellent reliability. To create such a predictive model for the processes of rewards and losses, termed the Brain Reward Signature (BRS), we trained a model to predict the signed magnitude of monetary rewards on the Monetary Incentive Delay task (MID; N = 39) and achieved a highly significant decoding performance (92% for decoding rewards versus losses). We subsequently demonstrate the generalizability of our signature on another version of the MID in a different sample (92% decoding accuracy; N = 12) and on a gambling task from a large sample (73% decoding accuracy, N = 1084). We further provided preliminary data to characterize the specificity of the signature by illustrating that the signature map generates estimates that significantly differ between rewarding and negative feedback (92% decoding accuracy) but do not differ for conditions that differ in disgust rather than reward in a novel Disgust-Delay Task (N = 39). Finally, we show that passively viewing positive and negatively valenced facial expressions loads positively on our signature, in line with previous studies on morbid curiosity. We thus created a BRS that can accurately predict brain responses to rewards and losses in active decision making tasks, and that possibly relates to information seeking in passive observational tasks.
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Affiliation(s)
- Sebastian P H Speer
- Social Brain Lab, Netherlands Institute for Neuroscience, Amsterdam, The Netherlands; Princeton Neuroscience Institute, Princeton University, Princeton, NJ 08544, USA
| | - Christian Keysers
- Social Brain Lab, Netherlands Institute for Neuroscience, Amsterdam, The Netherlands; Brain and Cognition, Department of Psychology, University of Amsterdam, The Netherlands
| | | | - Cas J S Teurlings
- Social Brain Lab, Netherlands Institute for Neuroscience, Amsterdam, The Netherlands
| | - Ale Smidts
- Rotterdam School of Management, Erasmus University, 3062 PA Rotterdam, The Netherlands
| | - Maarten A S Boksem
- Rotterdam School of Management, Erasmus University, 3062 PA Rotterdam, The Netherlands
| | - Tor D Wager
- Department of Psychological and Brain Sciences, Dartmouth College, Hanover, NH 03755, USA
| | - Valeria Gazzola
- Social Brain Lab, Netherlands Institute for Neuroscience, Amsterdam, The Netherlands.
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30
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Koban L, Lee S, Schelski DS, Simon MC, Lerman C, Weber B, Kable JW, Plassmann H. An fMRI-Based Brain Marker of Individual Differences in Delay Discounting. J Neurosci 2023; 43:1600-1613. [PMID: 36657973 PMCID: PMC10008056 DOI: 10.1523/jneurosci.1343-22.2022] [Citation(s) in RCA: 9] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/08/2022] [Revised: 12/02/2022] [Accepted: 12/07/2022] [Indexed: 01/20/2023] Open
Abstract
Individual differences in delay discounting-how much we discount future compared to immediate rewards-are associated with general life outcomes, psychopathology, and obesity. Here, we use machine learning on fMRI activity during an intertemporal choice task to develop a functional brain marker of these individual differences in human adults. Training and cross-validating the marker in one dataset (Study 1, N = 110 male adults) resulted in a significant prediction-outcome correlation (r = 0.49), generalized to predict individual differences in a completely independent dataset (Study 2: N = 145 male and female adults, r = 0.45), and predicted discounting several weeks later. Out-of-sample responses of the functional brain marker, but not discounting behavior itself, differed significantly between overweight and lean individuals in both studies, and predicted fasting-state blood levels of insulin, c-peptide, and leptin in Study 1. Significant predictive weights of the marker were found in cingulate, insula, and frontoparietal areas, among others, suggesting an interplay among regions associated with valuation, conflict processing, and cognitive control. This new functional brain marker is a step toward a generalizable brain model of individual differences in delay discounting. Future studies can evaluate it as a potential transdiagnostic marker of altered decision-making in different clinical and developmental populations.SIGNIFICANCE STATEMENT People differ substantially in how much they prefer smaller sooner rewards or larger later rewards such as spending money now versus saving it for retirement. These individual differences are generally stable over time and have been related to differences in mental and bodily health. What is their neurobiological basis? We applied machine learning to brain-imaging data to identify a novel brain activity pattern that accurately predicts how much people prefer sooner versus later rewards, and which can be used as a new brain-based measure of intertemporal decision-making in future studies. The resulting functional brain marker also predicts overweight and metabolism-related blood markers, providing new insight into the possible links between metabolism and the cognitive and brain processes involved in intertemporal decision-making.
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Affiliation(s)
- Leonie Koban
- Marketing Area, INSEAD, F-77300 Fontainebleau, France
- Control-Interoception-Attention Team, Paris Brain Institute (ICM), INSERM U1127, CNRS UMR7225, Sorbonne University, 75013 Paris, France
- CNRS, INSERM, Centre de Recherche en Neurosciences de Lyon CRNL U1028 UMR5292, Université Claude Bernard Lyon 1, 69500 Bron, France
| | - Sangil Lee
- Department of Psychology, University of Pennsylvania, Philadelphia, Pennsylvania 19104-6018
| | - Daniela S Schelski
- Center for Economics and Neuroscience, University of Bonn, 53113 Bonn, Germany
- Institute of Experimental Epileptology and Cognition Research, University of Bonn Medical Center, 53113 Bonn, Germany
| | - Marie-Christine Simon
- Institute for Nutrition and Food Science, Nutrition and Microbiota, University of Bonn, 53113 Bonn, Germany
| | - Caryn Lerman
- Norris Comprehensive Cancer Center, University of Southern California, Los Angeles, California 90033
| | - Bernd Weber
- Center for Economics and Neuroscience, University of Bonn, 53113 Bonn, Germany
- Institute of Experimental Epileptology and Cognition Research, University of Bonn Medical Center, 53113 Bonn, Germany
| | - Joseph W Kable
- Department of Psychology, University of Pennsylvania, Philadelphia, Pennsylvania 19104-6018
| | - Hilke Plassmann
- Marketing Area, INSEAD, F-77300 Fontainebleau, France
- Control-Interoception-Attention Team, Paris Brain Institute (ICM), INSERM U1127, CNRS UMR7225, Sorbonne University, 75013 Paris, France
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31
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Zhang R, Zhao W, Qi Z, Xu T, Zhou F, Becker B. Angiotensin II Regulates the Neural Expression of Subjective Fear in Humans: A Precision Pharmaco-Neuroimaging Approach. BIOLOGICAL PSYCHIATRY. COGNITIVE NEUROSCIENCE AND NEUROIMAGING 2023; 8:262-270. [PMID: 36174930 DOI: 10.1016/j.bpsc.2022.09.008] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/30/2022] [Revised: 08/23/2022] [Accepted: 09/19/2022] [Indexed: 02/05/2023]
Abstract
BACKGROUND Rodent models and pharmacological neuroimaging studies in humans have been used to test novel pharmacological agents to reduce fear. However, these strategies are limited with respect to determining process-specific effects on the actual subjective experience of fear, which represents the key symptom that motivates patients to seek treatment. In this study, we used a novel precision pharmacological functional magnetic resonance imaging approach based on process-specific neuroaffective signatures to determine effects of the selective angiotensin II type 1 receptor (AT1R) antagonist losartan on the subjective experience of fear. METHODS In a double-blind, placebo-controlled, randomized pharmacological functional magnetic resonance imaging design, healthy participants (N = 87) were administered 50 mg losartan or placebo before they underwent an oddball paradigm that included neutral, novel, and fear oddballs. Effects of losartan on brain activity and connectivity as well as on process-specific multivariate neural signatures were examined. RESULTS AT1R blockade selectively reduced neurofunctional reactivity to fear-inducing visual oddballs in terms of attenuating dorsolateral prefrontal activity and amygdala-ventral anterior cingulate communication. Neurofunctional decoding further demonstrated fear-specific effects in that AT1R blockade reduced the neural expression of subjective fear but not of threat or nonspecific negative affect and did not influence reactivity to novel oddballs. CONCLUSIONS These results show a specific role of the AT1R in regulating the subjective fear experience and demonstrate the feasibility of a precision pharmacological functional magnetic resonance imaging approach to the affective characterization of novel receptor targets for fear in humans.
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Affiliation(s)
- Ran Zhang
- Center of Psychosomatic Medicine, Sichuan Provincial Center for Mental Health, Sichuan Provincial People's Hospital, University of Electronic Science and Technology of China, Chengdu, China; Ministry of Education, Key Laboratory for Neuroinformation, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, China
| | - Weihua Zhao
- Ministry of Education, Key Laboratory for Neuroinformation, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, China
| | - Ziyu Qi
- Center of Psychosomatic Medicine, Sichuan Provincial Center for Mental Health, Sichuan Provincial People's Hospital, University of Electronic Science and Technology of China, Chengdu, China; Ministry of Education, Key Laboratory for Neuroinformation, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, China
| | - Ting Xu
- Center of Psychosomatic Medicine, Sichuan Provincial Center for Mental Health, Sichuan Provincial People's Hospital, University of Electronic Science and Technology of China, Chengdu, China; Ministry of Education, Key Laboratory for Neuroinformation, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, China
| | - Feng Zhou
- Faculty of Psychology, Southwest University, ChongQing, China; Key Laboratory of Cognition and Personality, Ministry of Education, ChongQing, China.
| | - Benjamin Becker
- Center of Psychosomatic Medicine, Sichuan Provincial Center for Mental Health, Sichuan Provincial People's Hospital, University of Electronic Science and Technology of China, Chengdu, China; Ministry of Education, Key Laboratory for Neuroinformation, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, China.
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32
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de la Fuente LA, Zamberlan F, Bocaccio H, Kringelbach M, Deco G, Perl YS, Pallavicini C, Tagliazucchi E. Temporal irreversibility of neural dynamics as a signature of consciousness. Cereb Cortex 2023; 33:1856-1865. [PMID: 35512291 DOI: 10.1093/cercor/bhac177] [Citation(s) in RCA: 9] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/19/2022] [Revised: 04/16/2022] [Accepted: 04/18/2022] [Indexed: 11/14/2022] Open
Abstract
Dissipative systems evolve in the preferred temporal direction indicated by the thermodynamic arrow of time. The fundamental nature of this temporal asymmetry led us to hypothesize its presence in the neural activity evoked by conscious perception of the physical world, and thus its covariance with the level of conscious awareness. We implemented a data-driven deep learning framework to decode the temporal inversion of electrocorticography signals acquired from non-human primates. Brain activity time series recorded during conscious wakefulness could be distinguished from their inverted counterparts with high accuracy, both using frequency and phase information. However, classification accuracy was reduced for data acquired during deep sleep and under ketamine-induced anesthesia; moreover, the predictions obtained from multiple independent neural networks were less consistent for sleep and anesthesia than for conscious wakefulness. Finally, the analysis of feature importance scores highlighted transitions between slow ($\approx$20 Hz) and fast frequencies (>40 Hz) as the main contributors to the temporal asymmetry observed during conscious wakefulness. Our results show that a preferred temporal direction is manifest in the neural activity evoked by conscious mentation and in the phenomenology of the passage of time, establishing common ground to tackle the relationship between brain and subjective experience.
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Affiliation(s)
- Laura Alethia de la Fuente
- Department of Physics, University of Buenos Aires 1428, Argentina.,Institute of Cognitive and Translational Neuroscience, INECO Foundation, Favaloro University, Buenos Aires 1058, Argentina.,National Scientific and Technical Research Council, Buenos Aires 1425, Argentina
| | - Federico Zamberlan
- Department of Physics, University of Buenos Aires 1428, Argentina.,National Scientific and Technical Research Council, Buenos Aires 1425, Argentina.,Cognitive Science and Artificial Intelligence Department, Tilburg University, Tilburg 5000, The Netherlands
| | - Hernán Bocaccio
- Department of Physics, University of Buenos Aires 1428, Argentina.,National Scientific and Technical Research Council, Buenos Aires 1425, Argentina
| | - Morten Kringelbach
- Centre for Eudaimonia and Human Flourishing, Linacre College, University of Oxford OX1, UK.,Department of Psychiatry, University of Oxford, Oxford OX3, UK.,Center for Music in the Brain, Department of Clinical Medicine, Aarhus University 8000, DK
| | - Gustavo Deco
- Center for Brain and Cognition, Computational Neuroscience Group, Department of Information and Communication Technologies, Universitat Pompeu Fabra, Barcelona 08018, Spain.,Institució Catalana de la Recerca i Estudis Avançats (ICREA), Barcelona 08010, Spain.,Department of Neuropsychology, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig 04103, Germany.,School of Psychological Sciences, Monash University, Melbourne, Clayton VIC 3800, Australia
| | - Yonatan Sanz Perl
- Department of Physics, University of Buenos Aires 1428, Argentina.,Center for Brain and Cognition, Computational Neuroscience Group, Department of Information and Communication Technologies, Universitat Pompeu Fabra, Barcelona 08018, Spain
| | - Carla Pallavicini
- Department of Physics, University of Buenos Aires 1428, Argentina.,National Scientific and Technical Research Council, Buenos Aires 1425, Argentina
| | - Enzo Tagliazucchi
- Department of Physics, University of Buenos Aires 1428, Argentina.,National Scientific and Technical Research Council, Buenos Aires 1425, Argentina.,Latin American Brain Health Institute (BrainLat), Universidad Adolfo Ibanez, Santiago 7910000, Chile
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33
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Perszyk EE, Davis XS, Djordjevic J, Jones-Gotman M, Trinh J, Hutelin Z, Veldhuizen MG, Koban L, Wager TD, Kober H, Small DM. Odor imagery but not perception drives risk for food cue reactivity and increased adiposity. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.02.06.527292. [PMID: 36798231 PMCID: PMC9934556 DOI: 10.1101/2023.02.06.527292] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 02/10/2023]
Abstract
Mental imagery has been proposed to play a critical role in the amplification of cravings. Here we tested whether olfactory imagery drives food cue reactivity strength to promote adiposity in 45 healthy individuals. We measured odor perception, odor imagery ability, and food cue reactivity using self-report, perceptual testing, and neuroimaging. Adiposity was assessed at baseline and one year later. Brain responses to real and imagined odors were analyzed with univariate and multivariate decoding methods to identify pattern-based olfactory codes. We found that the accuracy of decoding imagined, but not real, odor quality correlated with a perceptual measure of odor imagery ability and with greater adiposity changes. This latter relationship was mediated by cue-potentiated craving and intake. Collectively, these findings establish odor imagery ability as a risk factor for weight gain and more specifically as a mechanism by which exposure to food cues promotes craving and overeating.
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Affiliation(s)
- Emily E. Perszyk
- Modern Diet and Physiology Research Center, New Haven, CT 06510, USA
- Department of Psychiatry, Yale University School of Medicine, New Haven, CT 06511, USA
| | - Xue S. Davis
- Modern Diet and Physiology Research Center, New Haven, CT 06510, USA
- Department of Psychiatry, Yale University School of Medicine, New Haven, CT 06511, USA
| | - Jelena Djordjevic
- Department of Neurology and Neurosurgery, Montreal Neurological Institute and Hospital, McGill University, Montréal, QC H3A 2B4, Canada
| | - Marilyn Jones-Gotman
- Department of Neurology and Neurosurgery, Montreal Neurological Institute and Hospital, McGill University, Montréal, QC H3A 2B4, Canada
| | - Jessica Trinh
- Modern Diet and Physiology Research Center, New Haven, CT 06510, USA
- Department of Psychiatry, Yale University School of Medicine, New Haven, CT 06511, USA
| | - Zach Hutelin
- Modern Diet and Physiology Research Center, New Haven, CT 06510, USA
- Department of Psychiatry, Yale University School of Medicine, New Haven, CT 06511, USA
| | - Maria G. Veldhuizen
- Department of Anatomy, Faculty of Medicine, Mersin University, Ciftlikkoy Campus, Mersin 33343, Turkey
| | - Leonie Koban
- Lyon Neuroscience Research Center (CRNL), CNRS, INSERM, University Claude Bernard Lyon 1, France
| | - Tor D. Wager
- Department of Psychological and Brain Sciences, Dartmouth College, Hanover, NH 03755, USA
| | - Hedy Kober
- Department of Psychiatry, Yale University School of Medicine, New Haven, CT 06511, USA
- Department of Psychology, Yale University, New Haven, CT 06511, USA
| | - Dana M. Small
- Modern Diet and Physiology Research Center, New Haven, CT 06510, USA
- Department of Psychiatry, Yale University School of Medicine, New Haven, CT 06511, USA
- Department of Psychology, Yale University, New Haven, CT 06511, USA
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34
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Abstract
Craving is a core characteristic of drug addiction and eating disorders. A new study identifies an fMRI-based neural signature of craving that is common to both food and drugs, predicts self-reported craving, distinguishes drug users from non-users, and tracks the efficacy of a cognitive therapy technique to reduce craving.
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Affiliation(s)
- Greg Kronberg
- Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Department of Neuroscience, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Rita Z Goldstein
- Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY, USA.
- Department of Neuroscience, Icahn School of Medicine at Mount Sinai, New York, NY, USA.
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35
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Perovnik M, Rus T, Schindlbeck KA, Eidelberg D. Functional brain networks in the evaluation of patients with neurodegenerative disorders. Nat Rev Neurol 2023; 19:73-90. [PMID: 36539533 DOI: 10.1038/s41582-022-00753-3] [Citation(s) in RCA: 17] [Impact Index Per Article: 17.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 11/21/2022] [Indexed: 12/24/2022]
Abstract
Network analytical tools are increasingly being applied to brain imaging maps of resting metabolic activity (PET) or blood oxygenation-dependent signals (functional MRI) to characterize the abnormal neural circuitry that underlies brain diseases. This approach is particularly valuable for the study of neurodegenerative disorders, which are characterized by stereotyped spread of pathology along discrete neural pathways. Identification and validation of disease-specific brain networks facilitate the quantitative assessment of pathway changes over time and during the course of treatment. Network abnormalities can often be identified before symptom onset and can be used to track disease progression even in the preclinical period. Likewise, network activity can be modulated by treatment and might therefore be used as a marker of efficacy in clinical trials. Finally, early differential diagnosis can be achieved by simultaneously measuring the activity levels of multiple disease networks in an individual patient's scans. Although these techniques were originally developed for PET, over the past several years analogous methods have been introduced for functional MRI, a more accessible non-invasive imaging modality. This advance is expected to broaden the application of network tools to large and diverse patient populations.
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Affiliation(s)
- Matej Perovnik
- Department of Neurology, University Medical Center Ljubljana, Ljubljana, Slovenia.,Medical Faculty, University of Ljubljana, Ljubljana, Slovenia
| | - Tomaž Rus
- Department of Neurology, University Medical Center Ljubljana, Ljubljana, Slovenia.,Medical Faculty, University of Ljubljana, Ljubljana, Slovenia
| | | | - David Eidelberg
- Center for Neurosciences, The Feinstein Institutes for Medical Research, Manhasset, NY, USA.
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36
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Koban L, Wager TD, Kober H. A neuromarker for drug and food craving distinguishes drug users from non-users. Nat Neurosci 2023; 26:316-325. [PMID: 36536243 DOI: 10.1038/s41593-022-01228-w] [Citation(s) in RCA: 35] [Impact Index Per Article: 35.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/26/2021] [Accepted: 11/01/2022] [Indexed: 12/24/2022]
Abstract
Craving is a core feature of substance use disorders. It is a strong predictor of substance use and relapse and is linked to overeating, gambling, and other maladaptive behaviors. Craving is measured via self-report, which is limited by introspective access and sociocultural contexts. Neurobiological markers of craving are both needed and lacking, and it remains unclear whether craving for drugs and food involve similar mechanisms. Across three functional magnetic resonance imaging studies (n = 99), we used machine learning to identify a cross-validated neuromarker that predicts self-reported intensity of cue-induced drug and food craving (P < 0.0002). This pattern, which we term the Neurobiological Craving Signature (NCS), includes ventromedial prefrontal and cingulate cortices, ventral striatum, temporal/parietal association areas, mediodorsal thalamus and cerebellum. Importantly, NCS responses to drug versus food cues discriminate drug users versus non-users with 82% accuracy. The NCS is also modulated by a self-regulation strategy. Transfer between separate neuromarkers for drug and food craving suggests shared neurobiological mechanisms. Future studies can assess the discriminant and convergent validity of the NCS and test whether it responds to clinical interventions and predicts long-term clinical outcomes.
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Affiliation(s)
- Leonie Koban
- Paris Brain Institute (ICM), Inserm, CNRS, Sorbonne Université, Paris, France.
- Centre de Recherche en Neurosciences de Lyon (CRNL), CNRS, INSERM, Université Claude Bernard Lyon 1, Bron, France.
| | - Tor D Wager
- Department of Psychological and Brain Sciences, Dartmouth College, Hanover, NH, USA.
| | - Hedy Kober
- Department of Psychiatry and Psychology, Yale University, New Haven, CT, USA.
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37
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Liu J, Chang H, Abrams DA, Kang JB, Chen L, Rosenberg-Lee M, Menon V. Atypical cognitive training-induced learning and brain plasticity and their relation to insistence on sameness in children with autism. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.01.25.525594. [PMID: 36747659 PMCID: PMC9900852 DOI: 10.1101/2023.01.25.525594] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 06/18/2023]
Abstract
Children with autism spectrum disorders (ASD) often display atypical learning styles, however little is known regarding learning-related brain plasticity and its relation to clinical phenotypic features. Here, we investigate cognitive learning and neural plasticity using functional brain imaging and a novel numerical problem-solving training protocol. Children with ASD showed comparable learning relative to typically developing children but were less likely to shift from rule-based to memory-based strategy. Critically, while learning gains in typically developing children were associated with greater plasticity of neural representations in the medial temporal lobe and intraparietal sulcus, learning in children with ASD was associated with more stable neural representations. Crucially, the relation between learning and plasticity of neural representations was moderated by insistence on sameness, a core phenotypic feature of ASD. Our study uncovers atypical cognitive and neural mechanisms underlying learning in children with ASD, and informs pedagogical strategies for nurturing cognitive abilities in childhood autism.
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Affiliation(s)
- Jin Liu
- Department of Psychiatry & Behavioral Sciences, Stanford University School of Medicine, Stanford, CA 94305
| | - Hyesang Chang
- Department of Psychiatry & Behavioral Sciences, Stanford University School of Medicine, Stanford, CA 94305
| | - Daniel A. Abrams
- Department of Psychiatry & Behavioral Sciences, Stanford University School of Medicine, Stanford, CA 94305
| | - Julia Boram Kang
- Department of Psychiatry & Behavioral Sciences, Stanford University School of Medicine, Stanford, CA 94305
| | - Lang Chen
- Department of Psychiatry & Behavioral Sciences, Stanford University School of Medicine, Stanford, CA 94305
- Department of Psychology, Santa Clara University, Santa Clara, CA 95053
| | - Miriam Rosenberg-Lee
- Department of Psychiatry & Behavioral Sciences, Stanford University School of Medicine, Stanford, CA 94305
- Department of Psychology, Rutgers University, Newark, NJ 07102
| | - Vinod Menon
- Department of Psychiatry & Behavioral Sciences, Stanford University School of Medicine, Stanford, CA 94305
- Department of Neurology & Neurological Sciences, Stanford University School of Medicine, Stanford, CA 94305
- Stanford Neurosciences Institute, Stanford University School of Medicine, Stanford, CA 94305
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38
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Herzog R, Rosas FE, Whelan R, Fittipaldi S, Santamaria-Garcia H, Cruzat J, Birba A, Moguilner S, Tagliazucchi E, Prado P, Ibanez A. Genuine high-order interactions in brain networks and neurodegeneration. Neurobiol Dis 2022; 175:105918. [PMID: 36375407 PMCID: PMC11195446 DOI: 10.1016/j.nbd.2022.105918] [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: 08/13/2022] [Revised: 10/18/2022] [Accepted: 11/03/2022] [Indexed: 11/13/2022] Open
Abstract
Brain functional networks have been traditionally studied considering only interactions between pairs of regions, neglecting the richer information encoded in higher orders of interactions. In consequence, most of the connectivity studies in neurodegeneration and dementia use standard pairwise metrics. Here, we developed a genuine high-order functional connectivity (HOFC) approach that captures interactions between 3 or more regions across spatiotemporal scales, delivering a more biologically plausible characterization of the pathophysiology of neurodegeneration. We applied HOFC to multimodal (electroencephalography [EEG], and functional magnetic resonance imaging [fMRI]) data from patients diagnosed with behavioral variant of frontotemporal dementia (bvFTD), Alzheimer's disease (AD), and healthy controls. HOFC revealed large effect sizes, which, in comparison to standard pairwise metrics, provided a more accurate and parsimonious characterization of neurodegeneration. The multimodal characterization of neurodegeneration revealed hypo and hyperconnectivity on medium to large-scale brain networks, with a larger contribution of the former. Regions as the amygdala, the insula, and frontal gyrus were associated with both effects, suggesting potential compensatory processes in hub regions. fMRI revealed hypoconnectivity in AD between regions of the default mode, salience, visual, and auditory networks, while in bvFTD between regions of the default mode, salience, and somatomotor networks. EEG revealed hypoconnectivity in the γ band between frontal, limbic, and sensory regions in AD, and in the δ band between frontal, temporal, parietal and posterior areas in bvFTD, suggesting additional pathophysiological processes that fMRI alone can not capture. Classification accuracy was comparable with standard biomarkers and robust against confounders such as sample size, age, education, and motor artifacts (from fMRI and EEG). We conclude that high-order interactions provide a detailed, EEG- and fMRI compatible, biologically plausible, and psychopathological-specific characterization of different neurodegenerative conditions.
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Affiliation(s)
- Rubén Herzog
- Latin American Brain Health (BrainLat), Universidad Adolfo Ibáñez, Santiago, Chile; Fundación para el Estudio de la Conciencia Humana (EcoH), Chile
| | - Fernando E Rosas
- Fundación para el Estudio de la Conciencia Humana (EcoH), Chile; Centre for Psychedelic Research, Department of Brain Sciences, Imperial College London, UK; Data Science Institute, Imperial College London, UK; Centre for Complexity Science, Imperial College London, UK; Department of Informatics, University of Sussex, Brighton, UK
| | - Robert Whelan
- Global Brain Health Institute (GBHI), Trinity College Dublin, Dublin 2, Ireland
| | - Sol Fittipaldi
- Latin American Brain Health (BrainLat), Universidad Adolfo Ibáñez, Santiago, Chile; Global Brain Health Institute (GBHI), Trinity College Dublin, Dublin 2, Ireland; Cognitive Neuroscience Center (CNC), Universidad de San Andrés & CONICET, Buenos Aires, Argentina
| | | | - Josephine Cruzat
- Latin American Brain Health (BrainLat), Universidad Adolfo Ibáñez, Santiago, Chile; Fundación para el Estudio de la Conciencia Humana (EcoH), Chile
| | - Agustina Birba
- Cognitive Neuroscience Center (CNC), Universidad de San Andrés & CONICET, Buenos Aires, Argentina
| | - Sebastian Moguilner
- Latin American Brain Health (BrainLat), Universidad Adolfo Ibáñez, Santiago, Chile
| | - Enzo Tagliazucchi
- Latin American Brain Health (BrainLat), Universidad Adolfo Ibáñez, Santiago, Chile; Buenos Aires Physics Institute and Physics Department, University of Buenos Aires, Buenos Aires, Argentina
| | - Pavel Prado
- Latin American Brain Health (BrainLat), Universidad Adolfo Ibáñez, Santiago, Chile.
| | - Agustin Ibanez
- Latin American Brain Health (BrainLat), Universidad Adolfo Ibáñez, Santiago, Chile; Global Brain Health Institute (GBHI), Trinity College Dublin, Dublin 2, Ireland; Cognitive Neuroscience Center (CNC), Universidad de San Andrés & CONICET, Buenos Aires, Argentina; Global Brain Health Institute (GBHI), University of California San Francisco (UCSF), CA, USA.
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39
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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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40
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Klein S, Kruse O, Tapia León I, Van Oudenhove L, van 't Hof SR, Klucken T, Wager TD, Stark R. Cross-paradigm integration shows a common neural basis for aversive and appetitive conditioning. Neuroimage 2022; 263:119594. [PMID: 36041642 DOI: 10.1016/j.neuroimage.2022.119594] [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: 03/03/2022] [Revised: 07/22/2022] [Accepted: 08/25/2022] [Indexed: 10/31/2022] Open
Abstract
Sharing imaging data and comparing them across different psychological tasks is becoming increasingly possible as the open science movement advances. Such cross-paradigm integration has the potential to identify commonalities in findings that neighboring areas of study thought to be paradigm-specific. However, even the integration of research from closely related paradigms, such as aversive and appetitive classical conditioning is rare - even though qualitative comparisons already hint at how similar the 'fear network' and 'reward network' may be. We aimed to validate these theories by taking a multivariate approach to assess commonalities across paradigms empirically. Specifically, we quantified the similarity of an aversive conditioning pattern derived from meta-analysis to appetitive conditioning fMRI data. We tested pattern expression in three independent appetitive conditioning studies with 29, 76 and 38 participants each. During fMRI scanning, participants in each cohorts performed an appetitive conditioning task in which a CS+ was repeatedly rewarded with money and a CS- was never rewarded. The aversive pattern was highly similar to appetitive CS+ > CS- contrast maps across samples and variations of the appetitive conditioning paradigms. Moreover, the pattern distinguished the CS+ from the CS- with above-chance accuracy in every sample. These findings provide robust empirical evidence for an underlying neural system common to appetitive and aversive learning. We believe that this approach provides a way to empirically integrate the steadily growing body of fMRI findings across paradigms.
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Affiliation(s)
- Sanja Klein
- Department of Psychotherapy and Systems Neuroscience, Justus Liebig University, Giessen 35394, Germany; Bender Institute for Neuroimaging (BION), Justus Liebig University, Giessen 35394, Germany; Center of Mind, Brain and Behavior, Universities of Marburg and Giessen, Marburg 35032, Germany.
| | - Onno Kruse
- Department of Psychotherapy and Systems Neuroscience, Justus Liebig University, Giessen 35394, Germany; Bender Institute for Neuroimaging (BION), Justus Liebig University, Giessen 35394, Germany
| | - Isabell Tapia León
- Bender Institute for Neuroimaging (BION), Justus Liebig University, Giessen 35394, Germany; Clinical Psychology and Psychotherapy, University Siegen, Siegen 57076, Germany
| | - Lukas Van Oudenhove
- Department of Chronic Diseases and Metabolism (CHROMETA), Laboratory for Brain-Gut Axis Studies (LaBGAS), Translational Research Centre for Gastrointestinal Disorders TARGID, KU Leuven, Leuven, Belgium; Leuven Brain Institute, KU Leuven, Leuven, Belgium; Department of Psychological and Brain Sciences, Cognitive and Affective Neuroscience Lab, Dartmouth College, Hanover, NH, USA
| | - Sophie R van 't Hof
- Department of Psychiatry, Amsterdam University Medical Centers, Amsterdam 1105 AZ, The Netherlands
| | - Tim Klucken
- Clinical Psychology and Psychotherapy, University Siegen, Siegen 57076, Germany
| | - Tor D Wager
- Department of Psychological and Brain Sciences, Cognitive and Affective Neuroscience Lab, Dartmouth College, Hanover, NH, USA
| | - Rudolf Stark
- Department of Psychotherapy and Systems Neuroscience, Justus Liebig University, Giessen 35394, Germany; Bender Institute for Neuroimaging (BION), Justus Liebig University, Giessen 35394, Germany; Center of Mind, Brain and Behavior, Universities of Marburg and Giessen, Marburg 35032, Germany
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41
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Jiang R, Woo CW, Qi S, Wu J, Sui J. Interpreting Brain Biomarkers: Challenges and solutions in interpreting machine learning-based predictive neuroimaging. IEEE SIGNAL PROCESSING MAGAZINE 2022; 39:107-118. [PMID: 36712588 PMCID: PMC9880880 DOI: 10.1109/msp.2022.3155951] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/18/2023]
Abstract
Predictive modeling of neuroimaging data (predictive neuroimaging) for evaluating individual differences in various behavioral phenotypes and clinical outcomes is of growing interest. However, the field is experiencing challenges regarding the interpretability of the results. Approaches to defining the specific contribution of functional connections, regions, or networks in prediction models are urgently needed, which may help explore the underlying mechanisms. In this article, we systematically review the methods and applications for interpreting brain signatures derived from predictive neuroimaging based on a survey of 326 research articles. Strengths, limitations, and the suitable conditions for major interpretation strategies are also deliberated. In-depth discussion of common issues in existing literature and the corresponding recommendations to address these pitfalls are provided. We highly recommend exhaustive validation on the reliability and interpretability of the biomarkers across multiple datasets and contexts, which thereby could translate technical advances in neuroimaging into concrete improvements in precision medicine.
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Affiliation(s)
- Rongtao Jiang
- Department of Radiology and Biomedical Imaging, Yale School of Medicine, New Haven, CT, USA, 06520
| | - Choong-Wan Woo
- Center for Neuroscience Imaging Research, Institute for Basic Science, Suwon, South Korea, 16419
- Department of Biomedical Engineering, Sungkyunkwan University, Suwon, South Korea, 16419
- Department of Intelligent Precision Healthcare Convergence, Sungkyunkwan University, Suwon, Korea, 16419
| | - Shile Qi
- College of Computer Science and Technology, Nanjing University of Aeronautics and Astronautics, Nanjing, China, 211106
| | - Jing Wu
- Department of Medical Oncology, Beijing You-An Hospital, Capital Medical University, Beijing, China, 100069
| | - Jing Sui
- State Key Laboratory of Brain Cognition and Learning, Beijing Normal University, Beijing, China, 100875
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42
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Chae Y, Park HJ, Lee IS. Pain modalities in the body and brain: Current knowledge and future perspectives. Neurosci Biobehav Rev 2022; 139:104744. [PMID: 35716877 DOI: 10.1016/j.neubiorev.2022.104744] [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: 03/18/2022] [Revised: 05/29/2022] [Accepted: 06/11/2022] [Indexed: 11/16/2022]
Abstract
Development and validation of pain biomarkers has become a major issue in pain research. Recent advances in multimodal data acquisition have allowed researchers to gather multivariate and multilevel whole-body measurements in patients with pain conditions, and data analysis techniques such as machine learning have led to novel findings in neural biomarkers for pain. Most studies have focused on the development of a biomarker to predict the severity of pain with high precision and high specificity, however, a similar approach to discriminate different modalities of pain is lacking. Identification of more accurate and specific pain biomarkers will require an in-depth understanding of the modality specificity of pain. In this review, we summarize early and recent findings on the modality specificity of pain in the brain, with a focus on distinct neural activity patterns between chronic clinical and acute experimental pain, direct, social, and vicarious pain, and somatic and visceral pain. We also suggest future directions to improve our current strategy of pain management using our knowledge of modality-specific aspects of pain.
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Affiliation(s)
- Younbyoung Chae
- College of Korean Medicine, Kyung Hee University, Seoul, the Republic of Korea; Acupuncture & Meridian Science Research Center, Kyung Hee University, Seoul, the Republic of Korea
| | - Hi-Joon Park
- College of Korean Medicine, Kyung Hee University, Seoul, the Republic of Korea; Acupuncture & Meridian Science Research Center, Kyung Hee University, Seoul, the Republic of Korea
| | - In-Seon Lee
- College of Korean Medicine, Kyung Hee University, Seoul, the Republic of Korea; Acupuncture & Meridian Science Research Center, Kyung Hee University, Seoul, the Republic of Korea.
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43
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Kohoutová L, Atlas LY, Büchel C, Buhle JT, Geuter S, Jepma M, Koban L, Krishnan A, Lee DH, Lee S, Roy M, Schafer SM, Schmidt L, Wager TD, Woo CW. Individual variability in brain representations of pain. Nat Neurosci 2022; 25:749-759. [PMID: 35637368 PMCID: PMC9435464 DOI: 10.1038/s41593-022-01081-x] [Citation(s) in RCA: 17] [Impact Index Per Article: 8.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/27/2021] [Accepted: 04/22/2022] [Indexed: 01/22/2023]
Abstract
Characterizing cerebral contributions to individual variability in pain processing is crucial for personalized pain medicine, but has yet to be done. In the present study, we address this problem by identifying brain regions with high versus low interindividual variability in their relationship with pain. We trained idiographic pain-predictive models with 13 single-trial functional MRI datasets (n = 404, discovery set) and quantified voxel-level importance for individualized pain prediction. With 21 regions identified as important pain predictors, we examined the interindividual variability of local pain-predictive weights in these regions. Higher-order transmodal regions, such as ventromedial and ventrolateral prefrontal cortices, showed larger individual variability, whereas unimodal regions, such as somatomotor cortices, showed more stable pain representations across individuals. We replicated this result in an independent dataset (n = 124). Overall, our study identifies cerebral sources of individual differences in pain processing, providing potential targets for personalized assessment and treatment of pain.
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Affiliation(s)
- Lada Kohoutová
- Center for Neuroscience Imaging Research, Institute for Basic Science, Suwon, South Korea
- Department of Biomedical Engineering, Sungkyunkwan University, Suwon, South Korea
- Department of Intelligent Precision Healthcare Convergence, Sungkyunkwan University, Suwon, South Korea
| | - Lauren Y Atlas
- National Center for Complementary and Integrative Health, National Institutes of Health, Bethesda, MD, USA
- National Institute on Drug Abuse, National Institutes of Health, Bethesda, MD, USA
- National Institute of Mental Health, National Institutes of Health, Bethesda, MD, USA
| | - Christian Büchel
- Department of Systems Neuroscience, University Medical Centre Hamburg-Eppendorf, Hamburg, Germany
| | - Jason T Buhle
- Department of Psychology, University of Southern California, Los Angeles, CA, USA
| | - Stephan Geuter
- Department of Biostatistics, Johns Hopkins University, Baltimore, MD, USA
- Institute of Cognitive Science, University of Colorado Boulder, Boulder, CO, USA
| | - Marieke Jepma
- Department of Psychology, University of Amsterdam, Amsterdam, The Netherlands
| | - Leonie Koban
- Control-Interoception-Attention Team, Paris Brain Institute (ICM), INSERM, CNRS, Sorbonne University, Paris, France
| | - Anjali Krishnan
- Department of Psychology, Brooklyn College of the City University of New York, New York, NY, USA
| | - Dong Hee Lee
- Center for Neuroscience Imaging Research, Institute for Basic Science, Suwon, South Korea
- Department of Biomedical Engineering, Sungkyunkwan University, Suwon, South Korea
- Department of Intelligent Precision Healthcare Convergence, Sungkyunkwan University, Suwon, South Korea
| | - Sungwoo Lee
- Center for Neuroscience Imaging Research, Institute for Basic Science, Suwon, South Korea
- Department of Biomedical Engineering, Sungkyunkwan University, Suwon, South Korea
- Department of Intelligent Precision Healthcare Convergence, Sungkyunkwan University, Suwon, South Korea
| | - Mathieu Roy
- Department of Psychology, McGill University, Montreal, Quebec, Canada
| | - Scott M Schafer
- Department of Psychology and Neuroscience, University of Colorado Boulder, Boulder, CO, USA
| | - Liane Schmidt
- Control-Interoception-Attention Team, Paris Brain Institute (ICM), INSERM, CNRS, Sorbonne University, Paris, France
| | - Tor D Wager
- Department of Psychological and Brain Sciences, Dartmouth College, Hanover, NH, USA
| | - Choong-Wan Woo
- Center for Neuroscience Imaging Research, Institute for Basic Science, Suwon, South Korea.
- Department of Biomedical Engineering, Sungkyunkwan University, Suwon, South Korea.
- Department of Intelligent Precision Healthcare Convergence, Sungkyunkwan University, Suwon, South Korea.
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Spatiotemporal dynamics of odor representations in the human brain revealed by EEG decoding. Proc Natl Acad Sci U S A 2022; 119:e2114966119. [PMID: 35584113 PMCID: PMC9173780 DOI: 10.1073/pnas.2114966119] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/05/2022] Open
Abstract
To elucidate when and where in the brain different aspects of odor perception emerge, we decoded odors from an electroencephalogram and associated the results with perception and source activities. The odor information was decoded 100 ms after odor onset at the earliest, with its signal sources estimated in and around the olfactory areas. The neural representation of odor unpleasantness emerged 300 ms after odor onset, followed by pleasantness and perceived quality at 500 ms. During this time, brain regions representing odor information spread rapidly from the olfactory areas to regions associated with emotional, semantic, and memory processing. The results suggested that odor perception emerges through computations in these areas, with different perceptual aspects having different spatiotemporal dynamics. How the human brain translates olfactory inputs into diverse perceptions, from pleasurable floral smells to sickening smells of decay, is one of the fundamental questions in olfaction. To examine how different aspects of olfactory perception emerge in space and time in the human brain, we performed time-resolved multivariate pattern analysis of scalp-recorded electroencephalogram responses to 10 perceptually diverse odors and associated the resulting decoding accuracies with perception and source activities. Mean decoding accuracies of odors exceeded the chance level 100 ms after odor onset and reached maxima at 350 ms. The result suggests that the neural representations of individual odors were maximally separated at 350 ms. Perceptual representations emerged following the decoding peak: unipolar unpleasantness (neutral to unpleasant) from 300 ms, and pleasantness (neutral to pleasant) and perceptual quality (applicability to verbal descriptors such as “fruity” or “flowery”) from 500 ms after odor onset, with all these perceptual representations reaching their maxima after 600 ms. A source estimation showed that the areas representing the odor information, estimated based on the decoding accuracies, were localized in and around the primary and secondary olfactory areas at 100 to 350 ms after odor onset. Odor representations then expanded into larger areas associated with emotional, semantic, and memory processing, with the activities of these later areas being significantly associated with perception. These results suggest that initial odor information coded in the olfactory areas (<350 ms) evolves into their perceptual realizations (300 to >600 ms) through computations in widely distributed cortical regions, with different perceptual aspects having different spatiotemporal dynamics.
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Petre B, Kragel P, Atlas LY, Geuter S, Jepma M, Koban L, Krishnan A, Lopez-Sola M, Losin EAR, Roy M, Woo CW, Wager TD. A multistudy analysis reveals that evoked pain intensity representation is distributed across brain systems. PLoS Biol 2022; 20:e3001620. [PMID: 35500023 PMCID: PMC9098029 DOI: 10.1371/journal.pbio.3001620] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/23/2021] [Revised: 05/12/2022] [Accepted: 04/07/2022] [Indexed: 01/22/2023] Open
Abstract
Information is coded in the brain at multiple anatomical scales: locally, distributed across regions and networks, and globally. For pain, the scale of representation has not been formally tested, and quantitative comparisons of pain representations across regions and networks are lacking. In this multistudy analysis of 376 participants across 11 studies, we compared multivariate predictive models to investigate the spatial scale and location of evoked heat pain intensity representation. We compared models based on (a) a single most pain-predictive region or resting-state network; (b) pain-associated cortical-subcortical systems developed from prior literature ("multisystem models"); and (c) a model spanning the full brain. We estimated model accuracy using leave-one-study-out cross-validation (CV; 7 studies) and subsequently validated in 4 independent holdout studies. All spatial scales conveyed information about pain intensity, but distributed, multisystem models predicted pain 20% more accurately than any individual region or network and were more generalizable to multimodal pain (thermal, visceral, and mechanical) and specific to pain. Full brain models showed no predictive advantage over multisystem models. These findings show that multiple cortical and subcortical systems are needed to decode pain intensity, especially heat pain, and that representation of pain experience may not be circumscribed by any elementary region or canonical network. Finally, the learner generalization methods we employ provide a blueprint for evaluating the spatial scale of information in other domains.
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Affiliation(s)
- Bogdan Petre
- Dartmouth College, Hanover, New Hampshire, United States of America
| | - Philip Kragel
- University of Colorado Boulder, Colorado, United States of America
| | - Lauren Y. Atlas
- National Center for Complementary and Integrative Health, National Institutes of Health, Bethesda, Maryland, United States of America
- National Institute of Mental Health, National Institutes of Health, Bethesda, Maryland, United States of America
- National Institute on Drug Abuse, National Institutes of Health, Baltimore, Maryland, United States of America
| | - Stephan Geuter
- Johns Hopkins University, Baltimore, Maryland, United States of America
| | | | | | - Anjali Krishnan
- Brooklyn College of the City University of New York, Brooklyn, New York, United States of America
| | - Marina Lopez-Sola
- Department of Medicine, School of Medicine and Health Sciences, University of Barcelona, Barcelona, Spain
| | | | | | - Choong-Wan Woo
- Center for Neuroscience Imaging Research, Institute for Basic Science, Suwon, Gyeonggi-do, Republic of Korea
| | - Tor D. Wager
- Dartmouth College, Hanover, New Hampshire, United States of America
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46
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Dennison JB, Sazhin D, Smith DV. Decision neuroscience and neuroeconomics: Recent progress and ongoing challenges. WILEY INTERDISCIPLINARY REVIEWS. COGNITIVE SCIENCE 2022; 13:e1589. [PMID: 35137549 PMCID: PMC9124684 DOI: 10.1002/wcs.1589] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/05/2020] [Revised: 11/28/2021] [Accepted: 12/21/2021] [Indexed: 01/10/2023]
Abstract
In the past decade, decision neuroscience and neuroeconomics have developed many new insights in the study of decision making. This review provides an overarching update on how the field has advanced in this time period. Although our initial review a decade ago outlined several theoretical, conceptual, methodological, empirical, and practical challenges, there has only been limited progress in resolving these challenges. We summarize significant trends in decision neuroscience through the lens of the challenges outlined for the field and review examples where the field has had significant, direct, and applicable impacts across economics and psychology. First, we review progress on topics including reward learning, explore-exploit decisions, risk and ambiguity, intertemporal choice, and valuation. Next, we assess the impacts of emotion, social rewards, and social context on decision making. Then, we follow up with how individual differences impact choices and new exciting developments in the prediction and neuroforecasting of future decisions. Finally, we consider how trends in decision-neuroscience research reflect progress toward resolving past challenges, discuss new and exciting applications of recent research, and identify new challenges for the field. This article is categorized under: Psychology > Reasoning and Decision Making Psychology > Emotion and Motivation.
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Affiliation(s)
- Jeffrey B Dennison
- Department of Psychology, Temple University, Philadelphia, Pennsylvania, USA
| | - Daniel Sazhin
- Department of Psychology, Temple University, Philadelphia, Pennsylvania, USA
| | - David V Smith
- Department of Psychology, Temple University, Philadelphia, Pennsylvania, USA
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Vachon-Presseau E, Abdullah TB, Berger SE, Huang L, Griffith JW, Schnitzer TJ, Apkarian AV. Validating a biosignature-predicting placebo pill response in chronic pain in the settings of a randomized controlled trial. Pain 2022; 163:910-922. [PMID: 34433773 PMCID: PMC8863986 DOI: 10.1097/j.pain.0000000000002450] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/15/2021] [Accepted: 08/08/2021] [Indexed: 11/25/2022]
Abstract
ABSTRACT The objective of this study is to validate a placebo pill response predictive model-a biosignature-that classifies chronic pain patients into placebo responders (predicted-PTxResp) and nonresponders (predicted-PTxNonR) and test whether it can dissociate placebo and active treatment responses. The model, based on psychological and brain functional connectivity, was derived in our previous study and blindly applied to current trial participants. Ninety-four chronic low back pain (CLBP) patients were classified into predicted-PTxResp or predicted-PTxNonR and randomized into no treatment, placebo treatment, or naproxen treatment. To monitor analgesia, back pain intensity was collected twice a day: 3 weeks baseline, 6 weeks of treatment, and 3 weeks of washout. Eighty-nine CLBP patients were included in the intent-to-treat analyses and 77 CLBP patients in the per-protocol analyses. Both analyses showed similar results. At the group level, the predictive model performed remarkably well, dissociating the separate effect sizes of pure placebo response and pure active treatment response and demonstrating that these effects interacted additively. Pain relief was about 15% stronger in the predicted-PTxResp compared with the predicted-PTxNonR receiving either placebo or naproxen, and the predicted-PTxNonR successfully isolated the active drug effect. At a single subject level, the biosignature better predicted placebo nonresponders, with poor accuracy. One component of the biosignature (dorsolateral prefrontal cortex-precentral gyrus functional connectivity) could be generalized across 3 placebo studies and in 2 different cohorts-CLBP and osteoarthritis pain patients. This study shows that a biosignature can predict placebo response at a group level in the setting of a randomized controlled trial.
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Affiliation(s)
- Etienne Vachon-Presseau
- Faculty of Dentistry, McGill University, Montreal, QC, Canada
- Department of Anesthesia, Faculty of Medicine, McGill University, Montreal, QC, Canada
- Alan Edwards Centre for Research on Pain (AECRP), McGill University, Montreal, QC, Canada
| | - Taha B. Abdullah
- Department of Physiology, Northwestern University Feinberg School of Medicine, 710 N Lake Shore Drive, Room 1020, Chicago, IL 60611, USA
| | - Sara E. Berger
- Healthcare and Life Sciences Department, IBM Watson Research Center, 1101 Kitchawan Rd, Yorktown Heights, NY 10598, USA
| | - Lejian Huang
- Department of Physiology, Northwestern University Feinberg School of Medicine, 710 N Lake Shore Drive, Room 1020, Chicago, IL 60611, USA
| | - James W. Griffith
- Department of Medical Social Sciences, Northwestern University Feinberg School of Medicine, 710 N Lake Shore Drive, Room 1020, Chicago, IL 60611, USA
| | - Thomas J. Schnitzer
- Departments of Internal Medicine and Rheumatology, Northwestern University Feinberg School of Medicine, 710 N Lake Shore Drive, Room 1020, Chicago, IL 60611, USA
- Department of Physical Medicine and Rehabilitation, Northwestern University Feinberg School of Medicine, 710 N Lake Shore Drive, Room 1020, Chicago, IL 60611, USA
| | - A. Vania Apkarian
- Department of Physiology, Northwestern University Feinberg School of Medicine, 710 N Lake Shore Drive, Room 1020, Chicago, IL 60611, USA
- Department of Physical Medicine and Rehabilitation, Northwestern University Feinberg School of Medicine, 710 N Lake Shore Drive, Room 1020, Chicago, IL 60611, USA
- Department of Anesthesia, Northwestern University Feinberg School of Medicine, 710 N Lake Shore Drive, Room 1020, Chicago, IL 60611, USA
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48
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Jabakhanji R, Vigotsky AD, Bielefeld J, Huang L, Baliki MN, Iannetti G, Apkarian AV. Limits of decoding mental states with fMRI. Cortex 2022; 149:101-122. [PMID: 35219121 PMCID: PMC9238276 DOI: 10.1016/j.cortex.2021.12.015] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/23/2021] [Revised: 11/22/2021] [Accepted: 12/13/2021] [Indexed: 12/15/2022]
Abstract
A growing number of studies claim to decode mental states using multi-voxel decoders of brain activity. It has been proposed that the fixed, fine-grained, multi-voxel patterns in these decoders are necessary for discriminating between and identifying mental states. Here, we present evidence that the efficacy of these decoders might be overstated. Across various tasks, decoder patterns were spatially imprecise, as decoder performance was unaffected by spatial smoothing; 90% redundant, as selecting a random 10% of a decoder's constituent voxels recovered full decoder performance; and performed similarly to brain activity maps used as decoders. We distinguish decoder performance in discriminating between mental states from performance in identifying a given mental state, and show that even when discrimination performance is adequate, identification can be poor. Finally, we demonstrate that simple and intuitive similarity metrics explain 91% and 62% of discrimination performance within- and across-subjects, respectively. These findings indicate that currently used across-subject decoders of mental states are superfluous and inappropriate for decision-making.
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Affiliation(s)
- Rami Jabakhanji
- Department of Neuroscience, Feinberg School of Medicine, Northwestern University, Chicago, USA; Center for Translational Pain Research, Feinberg School of Medicine, Northwestern University, Chicago, USA
| | - Andrew D Vigotsky
- Departments of Biomedical Engineering and Statistics, Northwestern University, Evanston, USA; Center for Translational Pain Research, Feinberg School of Medicine, Northwestern University, Chicago, USA
| | - Jannis Bielefeld
- Department of Neuroscience, Feinberg School of Medicine, Northwestern University, Chicago, USA; Center for Translational Pain Research, Feinberg School of Medicine, Northwestern University, Chicago, USA
| | - Lejian Huang
- Department of Neuroscience, Feinberg School of Medicine, Northwestern University, Chicago, USA; Center for Translational Pain Research, Feinberg School of Medicine, Northwestern University, Chicago, USA
| | - Marwan N Baliki
- Department of Physical Medicine and Rehabilitation, Feinberg School of Medicine, Northwestern University, Chicago, USA; Shirley Ryan AbilityLab, Chicago, USA; Center for Translational Pain Research, Feinberg School of Medicine, Northwestern University, Chicago, USA
| | - Giandomenico Iannetti
- Division of Biosciences, University College London, London, UK; Neuroscience and Behaviour Laboratory, Italian Institute of Technology, Rome, Italy
| | - A Vania Apkarian
- Department of Neuroscience, Feinberg School of Medicine, Northwestern University, Chicago, USA; Department of Physical Medicine and Rehabilitation, Feinberg School of Medicine, Northwestern University, Chicago, USA; Department of Anesthesiology, Feinberg School of Medicine, Northwestern University, Chicago, USA; Center for Translational Pain Research, Feinberg School of Medicine, Northwestern University, Chicago, USA.
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49
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Kragel PA, Hariri AR, LaBar KS. The Temporal Dynamics of Spontaneous Emotional Brain States and Their Implications for Mental Health. J Cogn Neurosci 2022; 34:715-728. [PMID: 34705046 PMCID: PMC9026845 DOI: 10.1162/jocn_a_01787] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/04/2022]
Abstract
Temporal processes play an important role in elaborating and regulating emotional responding during routine mind wandering. However, it is unknown whether the human brain reliably transitions among multiple emotional states at rest and how psychopathology alters these affect dynamics. Here, we combined pattern classification and stochastic process modeling to investigate the chronometry of spontaneous brain activity indicative of six emotions (anger, contentment, fear, happiness, sadness, and surprise) and a neutral state. We modeled the dynamic emergence of these brain states during resting-state fMRI and validated the results across two population cohorts-the Duke Neurogenetics Study and the Nathan Kline Institute Rockland Sample. Our findings indicate that intrinsic emotional brain dynamics are effectively characterized as a discrete-time Markov process, with affective states organized around a neutral hub. The centrality of this network hub is disrupted in individuals with psychopathology, whose brain state transitions exhibit greater inertia and less frequent resetting from emotional to neutral states. These results yield novel insights into how the brain signals spontaneous emotions and how alterations in their temporal dynamics contribute to compromised mental health.
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50
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Rybář M, Daly I. Neural decoding of semantic concepts: A systematic literature review. J Neural Eng 2022; 19. [PMID: 35344941 DOI: 10.1088/1741-2552/ac619a] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/15/2021] [Accepted: 03/27/2022] [Indexed: 11/12/2022]
Abstract
Objective Semantic concepts are coherent entities within our minds. They underpin our thought processes and are a part of the basis for our understanding of the world. Modern neuroscience research is increasingly exploring how individual semantic concepts are encoded within our brains and a number of studies are beginning to reveal key patterns of neural activity that underpin specific concepts. Building upon this basic understanding of the process of semantic neural encoding, neural engineers are beginning to explore tools and methods for semantic decoding: identifying which semantic concepts an individual is focused on at a given moment in time from recordings of their neural activity. In this paper we review the current literature on semantic neural decoding. Approach We conducted this review according to the Preferred Reporting Items for Systematic reviews and Meta-Analysis (PRISMA) guidelines. Specifically, we assess the eligibility of published peer-reviewed reports via a search of PubMed and Google Scholar. We identify a total of 74 studies in which semantic neural decoding is used to attempt to identify individual semantic concepts from neural activity. Results Our review reveals how modern neuroscientific tools have been developed to allow decoding of individual concepts from a range of neuroimaging modalities. We discuss specific neuroimaging methods, experimental designs, and machine learning pipelines that are employed to aid the decoding of semantic concepts. We quantify the efficacy of semantic decoders by measuring information transfer rates. We also discuss current challenges presented by this research area and present some possible solutions. Finally, we discuss some possible emerging and speculative future directions for this research area. Significance Semantic decoding is a rapidly growing area of research. However, despite its increasingly widespread popularity and use in neuroscientific research this is the first literature review focusing on this topic across neuroimaging modalities and with a focus on quantifying the efficacy of semantic decoders.
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Affiliation(s)
- Milan Rybář
- School of Computer Science and Electronic Engineering, University of Essex, Wivenhoe Park, Colchester, Essex, CO4 3SQ, UNITED KINGDOM OF GREAT BRITAIN AND NORTHERN IRELAND
| | - Ian Daly
- University of Essex, School of Computer Science and Electronic Engineering, Wivenhoe Park, Colchester, Colchester, Essex, CO4 3SQ, UNITED KINGDOM OF GREAT BRITAIN AND NORTHERN IRELAND
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