1
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Marsicano G, Bertini C, Ronconi L. Decoding cognition in neurodevelopmental, psychiatric and neurological conditions with multivariate pattern analysis of EEG data. Neurosci Biobehav Rev 2024; 164:105795. [PMID: 38977116 DOI: 10.1016/j.neubiorev.2024.105795] [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: 04/30/2024] [Revised: 06/21/2024] [Accepted: 07/03/2024] [Indexed: 07/10/2024]
Abstract
Multivariate pattern analysis (MVPA) of electroencephalographic (EEG) data represents a revolutionary approach to investigate how the brain encodes information. By considering complex interactions among spatio-temporal features at the individual level, MVPA overcomes the limitations of univariate techniques, which often fail to account for the significant inter- and intra-individual neural variability. This is particularly relevant when studying clinical populations, and therefore MVPA of EEG data has recently started to be employed as a tool to study cognition in brain disorders. Here, we review the insights offered by this methodology in the study of anomalous patterns of neural activity in conditions such as autism, ADHD, schizophrenia, dyslexia, neurological and neurodegenerative disorders, within different cognitive domains (perception, attention, memory, consciousness). Despite potential drawbacks that should be attentively addressed, these studies reveal a peculiar sensitivity of MVPA in unveiling dysfunctional and compensatory neurocognitive dynamics of information processing, which often remain blind to traditional univariate approaches. Such higher sensitivity in characterizing individual neurocognitive profiles can provide unique opportunities to optimise assessment and promote personalised interventions.
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Affiliation(s)
- Gianluca Marsicano
- Department of Psychology, University of Bologna, Viale Berti Pichat 5, Bologna 40121, Italy; Centre for Studies and Research in Cognitive Neuroscience, University of Bologna, Via Rasi e Spinelli 176, Cesena 47023, Italy.
| | - Caterina Bertini
- Department of Psychology, University of Bologna, Viale Berti Pichat 5, Bologna 40121, Italy; Centre for Studies and Research in Cognitive Neuroscience, University of Bologna, Via Rasi e Spinelli 176, Cesena 47023, Italy.
| | - Luca Ronconi
- School of Psychology, Vita-Salute San Raffaele University, Milan, Italy; Division of Neuroscience, IRCCS San Raffaele Scientific Institute, Milan, Italy.
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2
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Cornwell BR, Didier PR, Grogans SE, Anderson AS, Islam S, Kim HC, Kuhn M, Tillman RM, Hur J, Scott ZS, Fox AS, DeYoung KA, Smith JF, Shackman AJ. A shared threat-anticipation circuit is dynamically engaged at different moments by certain and uncertain threat. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.07.10.602972. [PMID: 39026814 PMCID: PMC11257510 DOI: 10.1101/2024.07.10.602972] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/20/2024]
Abstract
Temporal dynamics play a central role in models of emotion: "fear" is widely conceptualized as a phasic response to certain-and-imminent danger, whereas "anxiety" is a sustained response to uncertain-or-distal harm. Yet the underlying human neurobiology remains contentious. Leveraging an ethnoracially diverse sample, translationally relevant paradigm, and theory-driven modeling approach, we demonstrate that certain and uncertain threat recruit a shared threat-anticipation circuit. This cortico-subcortical circuit exhibits persistently elevated activation when anticipating uncertain-threat encounters and a transient burst of activation in the moments before certain encounters. For many scientists and clinicians, feelings are the defining feature of human fear and anxiety. Here we used an independently validated brain signature to covertly decode the momentary dynamics of anticipatory distress for the first time. Results mirrored the dynamics of neural activation. These observations provide fresh insights into the neurobiology of threat-elicited emotions and set the stage for more ambitious clinical and mechanistic research.
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Affiliation(s)
- Brian R. Cornwell
- Department of Psychological & Brain Sciences, George Washington University, Washington, DC 20006 USA
| | - Paige R. Didier
- Department of Psychology, University of Maryland, College Park, MD 20742 USA
| | - Shannon E. Grogans
- Department of Psychology, University of Maryland, College Park, MD 20742 USA
| | - Allegra S. Anderson
- Department of Psychiatry and Human Behavior, Brown University, Providence, RI 02912 USA
| | - Samiha Islam
- Department of Psychology, University of Pennsylvania, Philadelphia, PA USA
| | - Hyung Cho Kim
- Department of Psychology, University of Maryland, College Park, MD 20742 USA
- Department of Neuroscience and Cognitive Science Program, University of Maryland, College Park, MD 20742 USA
| | - Manuel Kuhn
- Center for Depression, Anxiety and Stress Research, McLean Hospital, Harvard Medical School, Belmont, MA 02478 USA
| | | | - Juyoen Hur
- Department of Psychology, Yonsei University, Seoul 03722, Republic of Korea
| | - Zachary S. Scott
- Department of Psychology, University of Maryland, College Park, MD 20742 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
| | - Kathryn A. DeYoung
- Department of Psychology, University of Maryland, College Park, MD 20742 USA
| | - Jason F. Smith
- Department of Psychology, University of Maryland, College Park, MD 20742 USA
| | - Alexander J. Shackman
- Department of Psychology, University of Maryland, College Park, MD 20742 USA
- Department of Neuroscience and Cognitive Science Program, University of Maryland, College Park, MD 20742 USA
- Department of Maryland Neuroimaging Center, University of Maryland, College Park, MD 20742 USA
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3
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Bezsudnova Y, Quinn AJ, Wynn SC, Jensen O. Spatiotemporal Properties of Common Semantic Categories for Words and Pictures. J Cogn Neurosci 2024; 36:1760-1769. [PMID: 38739567 DOI: 10.1162/jocn_a_02182] [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] [Indexed: 05/16/2024]
Abstract
The timing of semantic processing during object recognition in the brain is a topic of ongoing discussion. One way of addressing this question is by applying multivariate pattern analysis to human electrophysiological responses to object images of different semantic categories. However, although multivariate pattern analysis can reveal whether neuronal activity patterns are distinct for different stimulus categories, concerns remain on whether low-level visual features also contribute to the classification results. To circumvent this issue, we applied a cross-decoding approach to magnetoencephalography data from stimuli from two different modalities: images and their corresponding written words. We employed items from three categories and presented them in a randomized order. We show that if the classifier is trained on words, pictures are classified between 150 and 430 msec after stimulus onset, and when training on pictures, words are classified between 225 and 430 msec. The topographical map, identified using a searchlight approach for cross-modal activation in both directions, showed left lateralization, confirming the involvement of linguistic representations. These results point to semantic activation of pictorial stimuli occurring at ∼150 msec, whereas for words, the semantic activation occurs at ∼230 msec.
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Affiliation(s)
| | | | - Syanah C Wynn
- University of Birmingham
- Gutenberg University Medical Center Mainz
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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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Thomas ER, Haarsma J, Nicholson J, Yon D, Kok P, Press C. Predictions and errors are distinctly represented across V1 layers. Curr Biol 2024; 34:2265-2271.e4. [PMID: 38697110 DOI: 10.1016/j.cub.2024.04.036] [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: 01/22/2024] [Revised: 04/09/2024] [Accepted: 04/13/2024] [Indexed: 05/04/2024]
Abstract
Popular accounts of mind and brain propose that the brain continuously forms predictions about future sensory inputs and combines predictions with inputs to determine what we perceive.1,2,3,4,5,6 Under "predictive processing" schemes, such integration is supported by the hierarchical organization of the cortex, whereby feedback connections communicate predictions from higher-level deep layers to agranular (superficial and deep) lower-level layers.7,8,9,10 Predictions are compared with input to compute the "prediction error," which is transmitted up the hierarchy from superficial layers of lower cortical regions to the middle layers of higher areas, to update higher-level predictions until errors are reconciled.11,12,13,14,15 In the primary visual cortex (V1), predictions have thereby been proposed to influence representations in deep layers while error signals may be computed in superficial layers. Despite the framework's popularity, there is little evidence for these functional distinctions because, to our knowledge, unexpected sensory events have not previously been presented in human laminar paradigms to contrast against expected events. To this end, this 7T fMRI study contrasted V1 responses to expected (75% likely) and unexpected (25%) Gabor orientations. Multivariate decoding analyses revealed an interaction between expectation and layer, such that expected events could be decoded with comparable accuracy across layers, while unexpected events could only be decoded in superficial laminae. Although these results are in line with these accounts that have been popular for decades, such distinctions have not previously been demonstrated in humans. We discuss how both prediction and error processes may operate together to shape our unitary perceptual experiences.
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Affiliation(s)
- Emily R Thomas
- Neuroscience Institute, New York University Medical Center, 435 East 30(th) Street, New York 10016, USA; Department of Psychological Sciences, Birkbeck, University of London, Malet Street, London WC1E 7HX, UK.
| | - Joost Haarsma
- Wellcome Centre for Human Neuroimaging, Institute of Neurology, University College London, 12 Queen Square, London WC1N 3AR, UK
| | - Jessica Nicholson
- Department of Psychological Sciences, Birkbeck, University of London, Malet Street, London WC1E 7HX, UK
| | - Daniel Yon
- Department of Psychological Sciences, Birkbeck, University of London, Malet Street, London WC1E 7HX, UK
| | - Peter Kok
- Wellcome Centre for Human Neuroimaging, Institute of Neurology, University College London, 12 Queen Square, London WC1N 3AR, UK
| | - Clare Press
- Department of Psychological Sciences, Birkbeck, University of London, Malet Street, London WC1E 7HX, UK; Wellcome Centre for Human Neuroimaging, Institute of Neurology, University College London, 12 Queen Square, London WC1N 3AR, UK; Department of Experimental Psychology, University College London, 26 Bedford Way, London WC1H 0AP, UK.
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6
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Rocca P, Brasso C, Montemagni C, Del Favero E, Bellino S, Bozzatello P, Giordano GM, Caporusso E, Fazio L, Pergola G, Blasi G, Amore M, Calcagno P, Rossi R, Rossi A, Bertolino A, Galderisi S, Maj M. The relationship between the resting state functional connectivity and social cognition in schizophrenia: Results from the Italian Network for Research on Psychoses. Schizophr Res 2024; 267:330-340. [PMID: 38613864 DOI: 10.1016/j.schres.2024.04.009] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/07/2023] [Revised: 03/24/2024] [Accepted: 04/04/2024] [Indexed: 04/15/2024]
Abstract
Deficits in social cognition (SC) interfere with recovery in schizophrenia (SZ) and may be related to resting state brain connectivity. This study aimed at assessing the alterations in the relationship between resting state functional connectivity and the social-cognitive abilities of patients with SZ compared to healthy subjects. We divided the brain into 246 regions of interest (ROI) following the Human Healthy Volunteers Brainnetome Atlas. For each participant, we calculated the resting-state functional connectivity (rsFC) in terms of degree centrality (DC), which evaluates the total strength of the most powerful coactivations of every ROI with all other ROIs during rest. The rs-DC of the ROIs was correlated with five measures of SC assessing emotion processing and mentalizing in 45 healthy volunteers (HVs) chosen as a normative sample. Then, controlling for symptoms severity, we verified whether these significant associations were altered, i.e., absent or of opposite sign, in 55 patients with SZ. We found five significant differences between SZ patients and HVs: in the patients' group, the correlations between emotion recognition tasks and rsFC of the right entorhinal cortex (R-EC), left superior parietal lobule (L-SPL), right caudal hippocampus (R-c-Hipp), and the right caudal (R-c) and left rostral (L-r) middle temporal gyri (MTG) were lost. An altered resting state functional connectivity of the L-SPL, R-EC, R-c-Hipp, and bilateral MTG in patients with SZ may be associated with impaired emotion recognition. If confirmed, these results may enhance the development of non-invasive brain stimulation interventions targeting those cerebral regions to reduce SC deficit in SZ.
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Affiliation(s)
- Paola Rocca
- Department of Neuroscience 'Rita Levi Montalcini', University of Turin, Via Cherasco, 15, 10126 Turin, Italy
| | - Claudio Brasso
- Department of Neuroscience 'Rita Levi Montalcini', University of Turin, Via Cherasco, 15, 10126 Turin, Italy.
| | - Cristiana Montemagni
- Department of Neuroscience 'Rita Levi Montalcini', University of Turin, Via Cherasco, 15, 10126 Turin, Italy
| | - Elisa Del Favero
- Department of Neuroscience 'Rita Levi Montalcini', University of Turin, Via Cherasco, 15, 10126 Turin, Italy
| | - Silvio Bellino
- Department of Neuroscience 'Rita Levi Montalcini', University of Turin, Via Cherasco, 15, 10126 Turin, Italy
| | - Paola Bozzatello
- Department of Neuroscience 'Rita Levi Montalcini', University of Turin, Via Cherasco, 15, 10126 Turin, Italy
| | - Giulia Maria Giordano
- Department of Psychiatry, University of Campania 'Luigi Vanvitelli', Largo Madonna Delle Grazie, 1, 80138 Naples, Italy
| | - Edoardo Caporusso
- Department of Psychiatry, University of Campania 'Luigi Vanvitelli', Largo Madonna Delle Grazie, 1, 80138 Naples, Italy
| | - Leonardo Fazio
- Department of Basic Medical Science, Neuroscience and Sense Organs, University of Bari 'Aldo Moro', Policlinico, Piazza G. Cesare, 11, 70124 Bari, Italy; Department of Medicine and Surgery, LUM University, Strada Statale 100, 70010 Casamassima (BA), Italy
| | - Giulio Pergola
- Department of Basic Medical Science, Neuroscience and Sense Organs, University of Bari 'Aldo Moro', Policlinico, Piazza G. Cesare, 11, 70124 Bari, Italy
| | - Giuseppe Blasi
- Department of Basic Medical Science, Neuroscience and Sense Organs, University of Bari 'Aldo Moro', Policlinico, Piazza G. Cesare, 11, 70124 Bari, Italy
| | - Mario Amore
- Department of Neurosciences, Rehabilitation, Ophthalmology, Genetics and Maternal and Child Health, Section of Psychiatry, University of Genoa, Largo Paolo Daneo, 3, 16132 Genoa, Italy
| | - Pietro Calcagno
- Department of Neurosciences, Rehabilitation, Ophthalmology, Genetics and Maternal and Child Health, Section of Psychiatry, University of Genoa, Largo Paolo Daneo, 3, 16132 Genoa, Italy
| | - Rodolfo Rossi
- Section of Psychiatry, Department of Biotechnological and Applied Clinical Sciences, University of L'Aquila, via Vetoio - Coppito, 67100 L'Aquila, Italy; Policlinico Tor Vergata, Viale Oxford, 81, 00133 Rome, Italy
| | - Alessandro Rossi
- Section of Psychiatry, Department of Biotechnological and Applied Clinical Sciences, University of L'Aquila, via Vetoio - Coppito, 67100 L'Aquila, Italy
| | - Alessandro Bertolino
- Department of Basic Medical Science, Neuroscience and Sense Organs, University of Bari 'Aldo Moro', Policlinico, Piazza G. Cesare, 11, 70124 Bari, Italy
| | - Silvana Galderisi
- Department of Psychiatry, University of Campania 'Luigi Vanvitelli', Largo Madonna Delle Grazie, 1, 80138 Naples, Italy
| | - Mario Maj
- Department of Psychiatry, University of Campania 'Luigi Vanvitelli', Largo Madonna Delle Grazie, 1, 80138 Naples, Italy
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7
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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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8
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Read ML, Berry SC, Graham KS, Voets NL, Zhang J, Aggleton JP, Lawrence AD, Hodgetts CJ. Scene-selectivity in CA1/subicular complex: Multivoxel pattern analysis at 7T. Neuropsychologia 2024; 194:108783. [PMID: 38161052 DOI: 10.1016/j.neuropsychologia.2023.108783] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/30/2023] [Revised: 12/21/2023] [Accepted: 12/27/2023] [Indexed: 01/03/2024]
Abstract
Prior univariate functional magnetic resonance imaging (fMRI) studies in humans suggest that the anteromedial subicular complex of the hippocampus is a hub for scene-based cognition. However, it is possible that univariate approaches were not sufficiently sensitive to detect scene-related activity in other subfields that have been implicated in spatial processing (e.g., CA1). Further, as connectivity-based functional gradients in the hippocampus do not respect classical subfield boundary definitions, category selectivity may be distributed across anatomical subfields. Region-of-interest approaches, therefore, may limit our ability to observe category selectivity across discrete subfield boundaries. To address these issues, we applied searchlight multivariate pattern analysis to 7T fMRI data of healthy adults who undertook a simultaneous visual odd-one-out discrimination task for scene and non-scene (including face) visual stimuli, hypothesising that scene classification would be possible in multiple hippocampal regions within, but not constrained to, anteromedial subicular complex and CA1. Indeed, we found that the scene-selective searchlight map overlapped not only with anteromedial subicular complex (distal subiculum, pre/para subiculum), but also inferior CA1, alongside posteromedial (including retrosplenial) and parahippocampal cortices. Probabilistic overlap maps revealed gradients of scene category selectivity, with the strongest overlap located in the medial hippocampus, converging with searchlight findings. This was contrasted with gradients of face category selectivity, which had stronger overlap in more lateral hippocampus, supporting ideas of parallel processing streams for these two categories. Our work helps to map the scene, in contrast to, face processing networks within, and connected to, the human hippocampus.
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Affiliation(s)
- Marie-Lucie Read
- Cardiff University Brain Research Imaging Centre, School of Psychology, Cardiff University, Maindy Road, Cardiff, CF24 4HQ, UK
| | - Samuel C Berry
- Cardiff University Brain Research Imaging Centre, School of Psychology, Cardiff University, Maindy Road, Cardiff, CF24 4HQ, UK; Department of Psychology, Royal Holloway, University of London, Egham, Surrey, TW20 0EX, UK
| | - Kim S Graham
- School of Philosophy, Psychology and Language Sciences, Dugald Stewart Building, University of Edinburgh, 3 Charles Street, Edinburgh, EH8 9AD, UK
| | - Natalie L Voets
- Wellcome Centre for Integrative Neuroimaging, FMRIB Building, John Radcliffe Hospital, Oxford, OX3 9DU2, UK
| | - Jiaxiang Zhang
- Cardiff University Brain Research Imaging Centre, School of Psychology, Cardiff University, Maindy Road, Cardiff, CF24 4HQ, UK; School of Mathematics and Computer Science, Swansea University, Swansea SA1 8DD, UK
| | - John P Aggleton
- Cardiff University Brain Research Imaging Centre, School of Psychology, Cardiff University, Maindy Road, Cardiff, CF24 4HQ, UK
| | - Andrew D Lawrence
- Cardiff University Brain Research Imaging Centre, School of Psychology, Cardiff University, Maindy Road, Cardiff, CF24 4HQ, UK; School of Philosophy, Psychology and Language Sciences, Dugald Stewart Building, University of Edinburgh, 3 Charles Street, Edinburgh, EH8 9AD, UK
| | - Carl J Hodgetts
- Cardiff University Brain Research Imaging Centre, School of Psychology, Cardiff University, Maindy Road, Cardiff, CF24 4HQ, UK; Department of Psychology, Royal Holloway, University of London, Egham, Surrey, TW20 0EX, UK.
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