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Skrzatek A, Nuic D, Cherif S, Beranger B, Gallea C, Bardinet E, Welter ML. Brain modulation after exergaming training in advanced forms of Parkinson's disease: a randomized controlled study. J Neuroeng Rehabil 2024; 21:133. [PMID: 39103924 DOI: 10.1186/s12984-024-01430-w] [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: 03/25/2024] [Accepted: 07/22/2024] [Indexed: 08/07/2024] Open
Abstract
BACKGROUND Physical activity combined with virtual reality and exergaming has emerged as a new technique to improve engagement and provide clinical benefit for gait and balance disorders in people with Parkinson's disease (PD). OBJECTIVE To investigate the effects of a training protocol using a home-based exergaming system on brain volume and resting-state functional connectivity (rs-FC) in persons with PD. METHODS A single blind randomized controlled trial was conducted in people with PD with gait and/or balance disorders. The experimental (active) group performed 18 training sessions at home by playing a custom-designed exergame with full body movements, standing in front of a RGB-D Kinect® motion sensor, while the control group played using the computer keyboard. Both groups received the same training program. Clinical scales, gait recordings, and brain MRI were performed before and after training. We assessed the effects of both training on both the grey matter volumes (GVM) and rs-FC, within and between groups. RESULTS Twenty-three patients were enrolled and randomly assigned to either the active (n = 11) or control (n = 12) training groups. Comparing pre- to post-training, the active group showed significant improvements in gait and balance disorders, with decreased rs-FC between the sensorimotor, attentional and basal ganglia networks, but with an increase between the cerebellar and basal ganglia networks. In contrast, the control group showed no significant changes, and rs-FC significantly decreased in the mesolimbic and visuospatial cerebellar and basal ganglia networks. Post-training, the rs-FC was greater in the active relative to the control group between the basal ganglia, motor cortical and cerebellar areas, and bilaterally between the insula and the inferior temporal lobe. Conversely, rs FC was lower in the active relative to the control group between the pedunculopontine nucleus and cerebellar areas, between the temporal inferior lobes and the right thalamus, between the left putamen and dorsolateral prefrontal cortex, and within the default mode network. CONCLUSIONS Full-body movement training using a customized exergame induced brain rs-FC changes within the sensorimotor, attentional and cerebellar networks in people with PD. Further research is needed to comprehensively understand the neurophysiological effects of such training approaches. Trial registration ClinicalTrials.gov NCT03560089.
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Affiliation(s)
- Anna Skrzatek
- Institut du Cerveau - Paris Brain Institute - ICM, Sorbonne Université, Inserm, CNRS, 47 bd de l'Hôpital, 75013, Paris, France
| | - Dijana Nuic
- Institut du Cerveau - Paris Brain Institute - ICM, Sorbonne Université, Inserm, CNRS, 47 bd de l'Hôpital, 75013, Paris, France
| | - Saoussen Cherif
- Institut du Cerveau - Paris Brain Institute - ICM, Sorbonne Université, Inserm, CNRS, 47 bd de l'Hôpital, 75013, Paris, France
- Genious Healthcare France, Paris, France
| | - Benoit Beranger
- Institut du Cerveau - Paris Brain Institute - ICM, Sorbonne Université, Inserm, CNRS, 47 bd de l'Hôpital, 75013, Paris, France
- CENIR core facility, Paris Brain Institute, Paris, France
| | - Cecile Gallea
- Institut du Cerveau - Paris Brain Institute - ICM, Sorbonne Université, Inserm, CNRS, 47 bd de l'Hôpital, 75013, Paris, France
- CENIR core facility, Paris Brain Institute, Paris, France
| | - Eric Bardinet
- Institut du Cerveau - Paris Brain Institute - ICM, Sorbonne Université, Inserm, CNRS, 47 bd de l'Hôpital, 75013, Paris, France
- CENIR core facility, Paris Brain Institute, Paris, France
| | - Marie-Laure Welter
- Institut du Cerveau - Paris Brain Institute - ICM, Sorbonne Université, Inserm, CNRS, 47 bd de l'Hôpital, 75013, Paris, France.
- PANAM core facility, Paris Brain Institute, Paris, France.
- Neurophysiology Department, CHU Rouen, Rouen University, Rouen, France.
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Chao ZC, Komatsu M, Matsumoto M, Iijima K, Nakagaki K, Ichinohe N. Erroneous predictive coding across brain hierarchies in a non-human primate model of autism spectrum disorder. Commun Biol 2024; 7:851. [PMID: 38992101 PMCID: PMC11239931 DOI: 10.1038/s42003-024-06545-3] [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: 02/13/2024] [Accepted: 07/03/2024] [Indexed: 07/13/2024] Open
Abstract
In autism spectrum disorder (ASD), atypical sensory experiences are often associated with irregularities in predictive coding, which proposes that the brain creates hierarchical sensory models via a bidirectional process of predictions and prediction errors. However, it remains unclear how these irregularities manifest across different functional hierarchies in the brain. To address this, we study a marmoset model of ASD induced by valproic acid (VPA) treatment. We record high-density electrocorticography (ECoG) during an auditory task with two layers of temporal control, and applied a quantitative model to quantify the integrity of predictive coding across two distinct hierarchies. Our results demonstrate a persistent pattern of sensory hypersensitivity and unstable predictions across two brain hierarchies in VPA-treated animals, and reveal the associated spatio-spectro-temporal neural signatures. Despite the regular occurrence of imprecise predictions in VPA-treated animals, we observe diverse configurations of underestimation or overestimation of sensory regularities within the hierarchies. Our results demonstrate the coexistence of the two primary Bayesian accounts of ASD: overly-precise sensory observations and weak prior beliefs, and offer a potential multi-layered biomarker for ASD, which could enhance our understanding of its diverse symptoms.
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Affiliation(s)
- Zenas C Chao
- International Research Center for Neurointelligence (WPI-IRCN), UTIAS, The University of Tokyo, 113-0033, Tokyo, Japan.
| | - Misako Komatsu
- Institute of Innovative Research, Tokyo Institute of Technology, 226-8503, Tokyo, Japan.
- RIKEN Center for Brain Science, 351-0198, Wako, Japan.
- Department of Ultrastructural Research, National Institute of Neuroscience, National Center of Neurology and Psychiatry (NCNP), 187-8502, Tokyo, Japan.
| | - Madoka Matsumoto
- Department of Preventive Intervention for Psychiatric Disorders, National Institute of Mental Health, National Center of Neurology and Psychiatry (NCNP), 187-8553, Tokyo, Japan
| | - Kazuki Iijima
- Department of Preventive Intervention for Psychiatric Disorders, National Institute of Mental Health, National Center of Neurology and Psychiatry (NCNP), 187-8553, Tokyo, Japan
| | - Keiko Nakagaki
- Department of Ultrastructural Research, National Institute of Neuroscience, National Center of Neurology and Psychiatry (NCNP), 187-8502, Tokyo, Japan
| | - Noritaka Ichinohe
- Department of Ultrastructural Research, National Institute of Neuroscience, National Center of Neurology and Psychiatry (NCNP), 187-8502, Tokyo, Japan.
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Bhandari A, Keglovits H, Badre D. Task structure tailors the geometry of neural representations in human lateral prefrontal cortex. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.03.06.583429. [PMID: 38496680 PMCID: PMC10942429 DOI: 10.1101/2024.03.06.583429] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 03/19/2024]
Abstract
How do human brains represent tasks of varying structure? The lateral prefrontal cortex (lPFC) flexibly represents task information. However, principles that shape lPFC representational geometry remain unsettled. We use fMRI and pattern analyses to reveal the structure of lPFC representational geometries as humans perform two distinct categorization tasks- one with flat, conjunctive categories and another with hierarchical, context-dependent categories. We show that lPFC encodes task-relevant information with task-tailored geometries of intermediate dimensionality. These geometries preferentially enhance the separability of task-relevant variables while encoding a subset in abstract form. Specifically, in the flat task, a global axis encodes response-relevant categories abstractly, while category-specific local geometries are high-dimensional. In the hierarchy task, a global axis abstractly encodes the higher-level context, while low-dimensional, context-specific local geometries compress irrelevant information and abstractly encode the relevant information. Comparing these task geometries exposes generalizable principles by which lPFC tailors representations to different tasks.
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Affiliation(s)
- Apoorva Bhandari
- Department of Cognitive, Linguistic and Psychological Sciences, Brown University, 190 Thayer St., Providence, RI 02912, USA
| | - Haley Keglovits
- Department of Cognitive, Linguistic and Psychological Sciences, Brown University, 190 Thayer St., Providence, RI 02912, USA
- Robert J & Nancy D Carney Institute for Brain Science, Brown University, 164 Angell St, Providence, RI 02912, USA
| | - David Badre
- Department of Cognitive, Linguistic and Psychological Sciences, Brown University, 190 Thayer St., Providence, RI 02912, USA
- Robert J & Nancy D Carney Institute for Brain Science, Brown University, 164 Angell St, Providence, RI 02912, USA
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4
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Suthaharan P, Thompson SL, Rossi-Goldthorpe RA, Rudebeck PH, Walton ME, Chakraborty S, Noonan MP, Costa VD, Murray EA, Mathys CD, Groman SM, Mitchell AS, Taylor JR, Corlett PR, Chang SWC. Lesions to the mediodorsal thalamus, but not orbitofrontal cortex, enhance volatility beliefs linked to paranoia. Cell Rep 2024; 43:114355. [PMID: 38870010 PMCID: PMC11231991 DOI: 10.1016/j.celrep.2024.114355] [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: 11/02/2023] [Revised: 04/13/2024] [Accepted: 05/29/2024] [Indexed: 06/15/2024] Open
Abstract
Beliefs-attitudes toward some state of the environment-guide action selection and should be robust to variability but sensitive to meaningful change. Beliefs about volatility (expectation of change) are associated with paranoia in humans, but the brain regions responsible for volatility beliefs remain unknown. The orbitofrontal cortex (OFC) is central to adaptive behavior, whereas the magnocellular mediodorsal thalamus (MDmc) is essential for arbitrating between perceptions and action policies. We assessed belief updating in a three-choice probabilistic reversal learning task following excitotoxic lesions of the MDmc (n = 3) or OFC (n = 3) and compared performance with that of unoperated monkeys (n = 14). Computational analyses indicated a double dissociation: MDmc, but not OFC, lesions were associated with erratic switching behavior and heightened volatility belief (as in paranoia in humans), whereas OFC, but not MDmc, lesions were associated with increased lose-stay behavior and reward learning rates. Given the consilience across species and models, these results have implications for understanding paranoia.
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Affiliation(s)
- Praveen Suthaharan
- Interdepartmental Neuroscience Program, Yale School of Medicine, New Haven, CT, USA; Kavli Institute for Neuroscience, Yale University, New Haven, CT, USA; Department of Psychiatry, Yale University, New Haven, CT, USA
| | | | - Rosa A Rossi-Goldthorpe
- Interdepartmental Neuroscience Program, Yale School of Medicine, New Haven, CT, USA; Department of Psychiatry, Yale University, New Haven, CT, USA
| | | | - Mark E Walton
- Department of Experimental Psychology, Oxford University, Oxford, UK
| | - Subhojit Chakraborty
- Department of Experimental Psychology, Oxford University, Oxford, UK; NIHR Biomedical Research Centre, Moorfields Eye Hospital NHS Foundation Trust and UCL Institute of Ophthalmology, London EC1V 9EL, UK
| | - Maryann P Noonan
- Department of Experimental Psychology, Oxford University, Oxford, UK; Department of Psychology, University of York, York, UK
| | - Vincent D Costa
- Division of Neuroscience, Oregon National Primate Research Center, Oregon Health and Science University, Beaverton, OR, USA
| | | | - Christoph D Mathys
- Interacting Minds Centre, Aarhus University, Aarhus, Denmark; Translational Neuromodeling Unit (TNU), Institute for Biomedical Engineering, University of Zurich and ETH Zurich, Zurich, Switzerland
| | - Stephanie M Groman
- Department of Psychiatry, Yale University, New Haven, CT, USA; Department of Neuroscience, University of Minnesota Medical School, Minneapolis, MN, USA; Department of Anesthesia and Critical Care, University of Chicago, Chicago, IL, USA
| | - Anna S Mitchell
- Department of Experimental Psychology, Oxford University, Oxford, UK; School of Psychology, Speech, and Hearing, University of Canterbury, Christchurch, New Zealand
| | - Jane R Taylor
- Interdepartmental Neuroscience Program, Yale School of Medicine, New Haven, CT, USA; Department of Psychiatry, Yale University, New Haven, CT, USA; Department of Psychology, Yale University, New Haven, CT, USA; Wu Tsai Institute, Yale University, New Haven, CT, USA; Department of Neuroscience, Yale University, New Haven, CT, USA
| | - Philip R Corlett
- Interdepartmental Neuroscience Program, Yale School of Medicine, New Haven, CT, USA; Kavli Institute for Neuroscience, Yale University, New Haven, CT, USA; Department of Psychiatry, Yale University, New Haven, CT, USA; Department of Psychology, Yale University, New Haven, CT, USA; Wu Tsai Institute, Yale University, New Haven, CT, USA.
| | - Steve W C Chang
- Interdepartmental Neuroscience Program, Yale School of Medicine, New Haven, CT, USA; Kavli Institute for Neuroscience, Yale University, New Haven, CT, USA; Department of Psychology, Yale University, New Haven, CT, USA; Wu Tsai Institute, Yale University, New Haven, CT, USA; Department of Neuroscience, Yale University, New Haven, CT, USA.
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5
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Le Heron C, Horne KL, MacAskill MR, Livingstone L, Melzer TR, Myall D, Pitcher T, Dalrymple-Alford J, Anderson T, Harrison S. Cross-Sectional and Longitudinal Association of Clinical and Neurocognitive Factors With Apathy in Patients With Parkinson Disease. Neurology 2024; 102:e209301. [PMID: 38830182 DOI: 10.1212/wnl.0000000000209301] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/05/2024] Open
Abstract
BACKGROUND AND OBJECTIVES A robust understanding of the natural history of apathy in Parkinson disease (PD) is foundational for developing effective clinical management tools. However, large longitudinal studies are lacking while the literature is inconsistent about even cross-sectional associations. We aimed to determine the longitudinal predictors of apathy development in a large cohort of people with PD and its cross-sectional associations and trajectories over time, using sophisticated Bayesian modeling techniques. METHODS People with PD followed up in the longitudinal New Zealand Parkinson's progression project were included. Apathy was defined using the neuropsychiatric inventory subscale ≥4, and analyses were also repeated using a less stringent cutoff of ≥1. Both MoCA and comprehensive neuropsychological testing were used as appropriate to the model. Depression was assessed using the hospital anxiety and depression scale. Cross-sectional Bayesian regressions were conducted, and a multistate predictive model was used to identify factors that predict the initial onset of apathy in nonapathetic PD, while also accounting for the competing risk of death. The relationship between apathy presence and mortality was also investigated. RESULTS Three hundred forty-six people with PD followed up for up to 14 years across a total of 1,392 sessions were included. Apathy occurrence did not vary significantly across the disease course (disease duration odds ratio [OR] = 0.55, [95% CI 0.28-1.12], affecting approximately 11% or 22% of people at any time depending on the NPI cutoff used. Its presence was associated with a significantly higher risk of death after controlling for all other factors (hazard ratio [HR] = 2.92 [1.50-5.66]). Lower cognition, higher depression levels, and greater motor severity predicted apathy development in those without motivational deficits (HR [cognition] = 0.66 [0.48-0.90], HR [depression] = 1.45 [1.04-2.02], HR [motor severity] = 1.37 [1.01-1.86]). Cognition and depression were also associated with apathy cross-sectionally, along with male sex and possibly lower dopaminergic therapy level, but apathy still occurred across the full spectrum of each variable (OR [cognition] = 0.58 [0.44-0.76], OR [depression] = 1.43 [1.04-1.97], OR [female sex] = 0.45 [0.22-0.92], and OR [levodopa equivalent dose] = 0.78 [0.59-1.04]. DISCUSSION Apathy occurs across the PD time course and is associated with higher mortality. Depressive symptoms and cognitive impairment in particular predict its future development in those with normal motivation.
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Affiliation(s)
- Campbell Le Heron
- From the Department of Medicine (C.L.H., M.R.M., T.R.M., T.P., J.D.-A., T.A., S.H.), University of Otago, Christchurch; New Zealand Brain Research Institute (C.L.H., K.-L.H., M.R.M., L.L., T.R.M., D.M., T.P., J.D.-A., T.A.), Christchurch; Department of Neurology (C.L.H., T.A.), Christchurch Hospital; and Department of Psychology (C.L.H., J.D.-A.), Speech and Hearing, University of Canterbury, Christchurch, New Zealand
| | - Kyla-Louise Horne
- From the Department of Medicine (C.L.H., M.R.M., T.R.M., T.P., J.D.-A., T.A., S.H.), University of Otago, Christchurch; New Zealand Brain Research Institute (C.L.H., K.-L.H., M.R.M., L.L., T.R.M., D.M., T.P., J.D.-A., T.A.), Christchurch; Department of Neurology (C.L.H., T.A.), Christchurch Hospital; and Department of Psychology (C.L.H., J.D.-A.), Speech and Hearing, University of Canterbury, Christchurch, New Zealand
| | - Michael R MacAskill
- From the Department of Medicine (C.L.H., M.R.M., T.R.M., T.P., J.D.-A., T.A., S.H.), University of Otago, Christchurch; New Zealand Brain Research Institute (C.L.H., K.-L.H., M.R.M., L.L., T.R.M., D.M., T.P., J.D.-A., T.A.), Christchurch; Department of Neurology (C.L.H., T.A.), Christchurch Hospital; and Department of Psychology (C.L.H., J.D.-A.), Speech and Hearing, University of Canterbury, Christchurch, New Zealand
| | - Leslie Livingstone
- From the Department of Medicine (C.L.H., M.R.M., T.R.M., T.P., J.D.-A., T.A., S.H.), University of Otago, Christchurch; New Zealand Brain Research Institute (C.L.H., K.-L.H., M.R.M., L.L., T.R.M., D.M., T.P., J.D.-A., T.A.), Christchurch; Department of Neurology (C.L.H., T.A.), Christchurch Hospital; and Department of Psychology (C.L.H., J.D.-A.), Speech and Hearing, University of Canterbury, Christchurch, New Zealand
| | - Tracy R Melzer
- From the Department of Medicine (C.L.H., M.R.M., T.R.M., T.P., J.D.-A., T.A., S.H.), University of Otago, Christchurch; New Zealand Brain Research Institute (C.L.H., K.-L.H., M.R.M., L.L., T.R.M., D.M., T.P., J.D.-A., T.A.), Christchurch; Department of Neurology (C.L.H., T.A.), Christchurch Hospital; and Department of Psychology (C.L.H., J.D.-A.), Speech and Hearing, University of Canterbury, Christchurch, New Zealand
| | - Daniel Myall
- From the Department of Medicine (C.L.H., M.R.M., T.R.M., T.P., J.D.-A., T.A., S.H.), University of Otago, Christchurch; New Zealand Brain Research Institute (C.L.H., K.-L.H., M.R.M., L.L., T.R.M., D.M., T.P., J.D.-A., T.A.), Christchurch; Department of Neurology (C.L.H., T.A.), Christchurch Hospital; and Department of Psychology (C.L.H., J.D.-A.), Speech and Hearing, University of Canterbury, Christchurch, New Zealand
| | - Toni Pitcher
- From the Department of Medicine (C.L.H., M.R.M., T.R.M., T.P., J.D.-A., T.A., S.H.), University of Otago, Christchurch; New Zealand Brain Research Institute (C.L.H., K.-L.H., M.R.M., L.L., T.R.M., D.M., T.P., J.D.-A., T.A.), Christchurch; Department of Neurology (C.L.H., T.A.), Christchurch Hospital; and Department of Psychology (C.L.H., J.D.-A.), Speech and Hearing, University of Canterbury, Christchurch, New Zealand
| | - John Dalrymple-Alford
- From the Department of Medicine (C.L.H., M.R.M., T.R.M., T.P., J.D.-A., T.A., S.H.), University of Otago, Christchurch; New Zealand Brain Research Institute (C.L.H., K.-L.H., M.R.M., L.L., T.R.M., D.M., T.P., J.D.-A., T.A.), Christchurch; Department of Neurology (C.L.H., T.A.), Christchurch Hospital; and Department of Psychology (C.L.H., J.D.-A.), Speech and Hearing, University of Canterbury, Christchurch, New Zealand
| | - Tim Anderson
- From the Department of Medicine (C.L.H., M.R.M., T.R.M., T.P., J.D.-A., T.A., S.H.), University of Otago, Christchurch; New Zealand Brain Research Institute (C.L.H., K.-L.H., M.R.M., L.L., T.R.M., D.M., T.P., J.D.-A., T.A.), Christchurch; Department of Neurology (C.L.H., T.A.), Christchurch Hospital; and Department of Psychology (C.L.H., J.D.-A.), Speech and Hearing, University of Canterbury, Christchurch, New Zealand
| | - Samuel Harrison
- From the Department of Medicine (C.L.H., M.R.M., T.R.M., T.P., J.D.-A., T.A., S.H.), University of Otago, Christchurch; New Zealand Brain Research Institute (C.L.H., K.-L.H., M.R.M., L.L., T.R.M., D.M., T.P., J.D.-A., T.A.), Christchurch; Department of Neurology (C.L.H., T.A.), Christchurch Hospital; and Department of Psychology (C.L.H., J.D.-A.), Speech and Hearing, University of Canterbury, Christchurch, New Zealand
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Fennema D, Barker GJ, O'Daly O, Duan S, Godlewska BR, Goldsmith K, Young AH, Moll J, Zahn R. Neural responses to facial emotions and subsequent clinical outcomes in difficult-to-treat depression. Psychol Med 2024:1-9. [PMID: 38757184 DOI: 10.1017/s0033291724001144] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 05/18/2024]
Abstract
BACKGROUND Amygdala and dorsal anterior cingulate cortex responses to facial emotions have shown promise in predicting treatment response in medication-free major depressive disorder (MDD). Here, we examined their role in the pathophysiology of clinical outcomes in more chronic, difficult-to-treat forms of MDD. METHODS Forty-five people with current MDD who had not responded to ⩾2 serotonergic antidepressants (n = 42, meeting pre-defined fMRI minimum quality thresholds) were enrolled and followed up over four months of standard primary care. Prior to medication review, subliminal facial emotion fMRI was used to extract blood-oxygen level-dependent effects for sad v. happy faces from two pre-registered a priori defined regions: bilateral amygdala and dorsal/pregenual anterior cingulate cortex. Clinical outcome was the percentage change on the self-reported Quick Inventory of Depressive Symptomatology (16-item). RESULTS We corroborated our pre-registered hypothesis (NCT04342299) that lower bilateral amygdala activation for sad v. happy faces predicted favorable clinical outcomes (rs[38] = 0.40, p = 0.01). In contrast, there was no effect for dorsal/pregenual anterior cingulate cortex activation (rs[38] = 0.18, p = 0.29), nor when using voxel-based whole-brain analyses (voxel-based Family-Wise Error-corrected p < 0.05). Predictive effects were mainly driven by the right amygdala whose response to happy faces was reduced in patients with higher anxiety levels. CONCLUSIONS We confirmed the prediction that a lower amygdala response to negative v. positive facial expressions might be an adaptive neural signature, which predicts subsequent symptom improvement also in difficult-to-treat MDD. Anxiety reduced adaptive amygdala responses.
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Affiliation(s)
- Diede Fennema
- Centre of Affective Disorders, Institute of Psychiatry, Psychology & Neuroscience, Centre for Affective Disorders, King's College London, London, UK
| | - Gareth J Barker
- Department of Neuroimaging, Institute of Psychiatry, Psychology & Neuroscience, King's College London, London, UK
| | - Owen O'Daly
- Department of Neuroimaging, Institute of Psychiatry, Psychology & Neuroscience, King's College London, London, UK
| | - Suqian Duan
- Centre of Affective Disorders, Institute of Psychiatry, Psychology & Neuroscience, Centre for Affective Disorders, King's College London, London, UK
| | - Beata R Godlewska
- Psychopharmacology Research Unit, University Department of Psychiatry, University of Oxford, Oxford, UK
- Oxford Health NHS Foundation Trust, Warneford Hospital, Oxford, UK
| | - Kimberley Goldsmith
- Department of Biostatistics and Health Informatics, Institute of Psychiatry, Psychology & Neuroscience, King's College London, London, UK
| | - Allan H Young
- Centre of Affective Disorders, Institute of Psychiatry, Psychology & Neuroscience, Centre for Affective Disorders, King's College London, London, UK
- National Service for Affective Disorders, South London and Maudsley NHS Foundation Trust, London, UK
| | - Jorge Moll
- Cognitive and Behavioural Neuroscience Unit, D'Or Institute for Research and Education (IDOR), Pioneer Science Program, Rio de Janeiro, Brazil
| | - Roland Zahn
- Centre of Affective Disorders, Institute of Psychiatry, Psychology & Neuroscience, Centre for Affective Disorders, King's College London, London, UK
- National Service for Affective Disorders, South London and Maudsley NHS Foundation Trust, London, UK
- Cognitive and Behavioural Neuroscience Unit, D'Or Institute for Research and Education (IDOR), Pioneer Science Program, Rio de Janeiro, Brazil
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Xie K, Royer J, Larivière S, Rodriguez-Cruces R, Frässle S, Cabalo DG, Ngo A, DeKraker J, Auer H, Tavakol S, Weng Y, Abdallah C, Arafat T, Horwood L, Frauscher B, Caciagli L, Bernasconi A, Bernasconi N, Zhang Z, Concha L, Bernhardt BC. Atypical connectome topography and signal flow in temporal lobe epilepsy. Prog Neurobiol 2024; 236:102604. [PMID: 38604584 DOI: 10.1016/j.pneurobio.2024.102604] [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: 06/26/2023] [Revised: 12/18/2023] [Accepted: 04/07/2024] [Indexed: 04/13/2024]
Abstract
Temporal lobe epilepsy (TLE) is the most common pharmaco-resistant epilepsy in adults. While primarily associated with mesiotemporal pathology, recent evidence suggests that brain alterations in TLE extend beyond the paralimbic epicenter and impact macroscale function and cognitive functions, particularly memory. Using connectome-wide manifold learning and generative models of effective connectivity, we examined functional topography and directional signal flow patterns between large-scale neural circuits in TLE at rest. Studying a multisite cohort of 95 patients with TLE and 95 healthy controls, we observed atypical functional topographies in the former group, characterized by reduced differentiation between sensory and transmodal association cortices, with most marked effects in bilateral temporo-limbic and ventromedial prefrontal cortices. These findings were consistent across all study sites, present in left and right lateralized patients, and validated in a subgroup of patients with histopathological validation of mesiotemporal sclerosis and post-surgical seizure freedom. Moreover, they were replicated in an independent cohort of 30 TLE patients and 40 healthy controls. Further analyses demonstrated that reduced differentiation related to decreased functional signal flow into and out of temporolimbic cortical systems and other brain networks. Parallel analyses of structural and diffusion-weighted MRI data revealed that topographic alterations were independent of TLE-related cortical thinning but partially mediated by white matter microstructural changes that radiated away from paralimbic circuits. Finally, we found a strong association between the degree of functional alterations and behavioral markers of memory dysfunction. Our work illustrates the complex landscape of macroscale functional imbalances in TLE, which can serve as intermediate markers bridging microstructural changes and cognitive impairment.
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Affiliation(s)
- Ke Xie
- Multimodal Imaging and Connectome Analysis Laboratory, McConnell Brain Imaging Centre, Montreal Neurological Institute and Hospital, McGill University, Montreal, QC H3A 2B4, Canada
| | - Jessica Royer
- Multimodal Imaging and Connectome Analysis Laboratory, McConnell Brain Imaging Centre, Montreal Neurological Institute and Hospital, McGill University, Montreal, QC H3A 2B4, Canada; Analytical Neurophysiology Laboratory, Montreal Neurological Institute and Hospital, McGill University, Montreal, QC H3A 2B4, Canada
| | - Sara Larivière
- Multimodal Imaging and Connectome Analysis Laboratory, McConnell Brain Imaging Centre, Montreal Neurological Institute and Hospital, McGill University, Montreal, QC H3A 2B4, Canada
| | - Raul Rodriguez-Cruces
- Multimodal Imaging and Connectome Analysis Laboratory, McConnell Brain Imaging Centre, Montreal Neurological Institute and Hospital, McGill University, Montreal, QC H3A 2B4, Canada
| | - Stefan Frässle
- Translational Neuromodeling Unit (TNU), Institute for Biomedical Engineering, University of Zurich and ETH Zurich, Zurich, Switzerland
| | - Donna Gift Cabalo
- Multimodal Imaging and Connectome Analysis Laboratory, McConnell Brain Imaging Centre, Montreal Neurological Institute and Hospital, McGill University, Montreal, QC H3A 2B4, Canada
| | - Alexander Ngo
- Multimodal Imaging and Connectome Analysis Laboratory, McConnell Brain Imaging Centre, Montreal Neurological Institute and Hospital, McGill University, Montreal, QC H3A 2B4, Canada
| | - Jordan DeKraker
- Multimodal Imaging and Connectome Analysis Laboratory, McConnell Brain Imaging Centre, Montreal Neurological Institute and Hospital, McGill University, Montreal, QC H3A 2B4, Canada
| | - Hans Auer
- Multimodal Imaging and Connectome Analysis Laboratory, McConnell Brain Imaging Centre, Montreal Neurological Institute and Hospital, McGill University, Montreal, QC H3A 2B4, Canada
| | - Shahin Tavakol
- Multimodal Imaging and Connectome Analysis Laboratory, McConnell Brain Imaging Centre, Montreal Neurological Institute and Hospital, McGill University, Montreal, QC H3A 2B4, Canada
| | - Yifei Weng
- Department of Medical Imaging, Jinling Hospital, Nanjing University School of Medicine, Nanjing, China
| | - Chifaou Abdallah
- Analytical Neurophysiology Laboratory, Montreal Neurological Institute and Hospital, McGill University, Montreal, QC H3A 2B4, Canada
| | - Thaera Arafat
- Multimodal Imaging and Connectome Analysis Laboratory, McConnell Brain Imaging Centre, Montreal Neurological Institute and Hospital, McGill University, Montreal, QC H3A 2B4, Canada
| | - Linda Horwood
- Multimodal Imaging and Connectome Analysis Laboratory, McConnell Brain Imaging Centre, Montreal Neurological Institute and Hospital, McGill University, Montreal, QC H3A 2B4, Canada; Analytical Neurophysiology Laboratory, Montreal Neurological Institute and Hospital, McGill University, Montreal, QC H3A 2B4, Canada
| | - Birgit Frauscher
- Analytical Neurophysiology Laboratory, Montreal Neurological Institute and Hospital, McGill University, Montreal, QC H3A 2B4, Canada; Department of Neurology, Duke University School of Medicine and Department of Biomedical Engineering, Duke University Pratt School of Engineering, Durham, NC 27705, USA
| | - Lorenzo Caciagli
- Department of Bioengineering, University of Pennsylvania, Philadelphia, PA 19104, USA; Department of Neurology, Inselspital, Sleep-Wake-Epilepsy-Center, Bern University Hospital, University of Bern, Bern, Switzerland; Department of Clinical and Experimental Epilepsy, UCL Queen Square Institute of Neurology, London WC1N 3 BG, United Kingdom
| | - Andrea Bernasconi
- Neuroimaging of Epilepsy Laboratory, McConnell Brain Imaging Centre, Montreal Neurological Institute and Hospital, McGill University, Montreal, QC H3A 2B4, Canada
| | - Neda Bernasconi
- Neuroimaging of Epilepsy Laboratory, McConnell Brain Imaging Centre, Montreal Neurological Institute and Hospital, McGill University, Montreal, QC H3A 2B4, Canada
| | - Zhiqiang Zhang
- Department of Medical Imaging, Jinling Hospital, Nanjing University School of Medicine, Nanjing, China
| | - Luis Concha
- Institute of Neurobiology, Universidad Nacional Autónoma de Mexico (UNAM), Queretaro, Mexico
| | - Boris C Bernhardt
- Multimodal Imaging and Connectome Analysis Laboratory, McConnell Brain Imaging Centre, Montreal Neurological Institute and Hospital, McGill University, Montreal, QC H3A 2B4, Canada.
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8
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Wang W, Zhu C, Jia T, Zu M, Tang Y, Zhou L, Tian Y, Si B, Zhou K. Reviving Bistable Perception in Patients With Depression by Decreasing the Overestimation of Prior Precision. Cogn Sci 2024; 48:e13452. [PMID: 38742272 DOI: 10.1111/cogs.13452] [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: 02/03/2023] [Revised: 04/01/2024] [Accepted: 04/18/2024] [Indexed: 05/16/2024]
Abstract
Slower perceptual alternations, a notable perceptual effect observed in psychiatric disorders, can be alleviated by antidepressant therapies that affect serotonin levels in the brain. While these phenomena have been well documented, the underlying neurocognitive mechanisms remain to be elucidated. Our study bridges this gap by employing a computational cognitive approach within a Bayesian predictive coding framework to explore these mechanisms in depression. We fitted a prediction error (PE) model to behavioral data from a binocular rivalry task, uncovering that significantly higher initial prior precision and lower PE led to a slower switch rate in patients with depression. Furthermore, serotonin-targeting antidepressant treatments significantly decreased the prior precision and increased PE, both of which were predictive of improvements in the perceptual alternation rate of depression patients. These findings indicated that the substantially slower perception switch rate in patients with depression was caused by the greater reliance on top-down priors and that serotonin treatment's efficacy was in its recalibration of these priors and enhancement of PE. Our study not only elucidates the cognitive underpinnings of depression, but also suggests computational modeling as a potent tool for integrating cognitive science with clinical psychology, advancing our understanding and treatment of cognitive impairments in depression.
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Affiliation(s)
- Wenbo Wang
- Beijing Key Laboratory of Applied Experimental Psychology, National Demonstration Center for Experimental Psychology Education (Beijing Normal University), Faculty of Psychology, Beijing Normal University
| | - Changbo Zhu
- State Key Laboratory of Robotics, Shenyang Institute of Automation, Chinese Academy of Sciences
- Institutes for Robotics and Intelligent Manufacturing, Chinese Academy of Sciences
- University of Chinese Academy of Sciences, Beijing
| | | | - Meidan Zu
- Department of Psychology and Sleep Medicine, The Second Hospital of Anhui Medical University
| | - Yandong Tang
- State Key Laboratory of Robotics, Shenyang Institute of Automation, Chinese Academy of Sciences
- Institutes for Robotics and Intelligent Manufacturing, Chinese Academy of Sciences
- University of Chinese Academy of Sciences, Beijing
| | - Liqin Zhou
- Beijing Key Laboratory of Applied Experimental Psychology, National Demonstration Center for Experimental Psychology Education (Beijing Normal University), Faculty of Psychology, Beijing Normal University
| | - Yanghua Tian
- Department of Neurology, The Second Hospital of Anhui Medical University
- Department of Neurology, The First Hospital of Anhui Medical University
| | - Bailu Si
- School of Systems Science, Beijing Normal University
| | - Ke Zhou
- Beijing Key Laboratory of Applied Experimental Psychology, National Demonstration Center for Experimental Psychology Education (Beijing Normal University), Faculty of Psychology, Beijing Normal University
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9
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Fennema D, Barker GJ, O’Daly O, Duan S, Carr E, Goldsmith K, Young AH, Moll J, Zahn R. The Role of Subgenual Resting-State Connectivity Networks in Predicting Prognosis in Major Depressive Disorder. BIOLOGICAL PSYCHIATRY GLOBAL OPEN SCIENCE 2024; 4:100308. [PMID: 38645404 PMCID: PMC11033067 DOI: 10.1016/j.bpsgos.2024.100308] [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: 08/18/2023] [Revised: 12/18/2023] [Accepted: 03/05/2024] [Indexed: 04/23/2024] Open
Abstract
Background A seminal study found higher subgenual frontal cortex resting-state connectivity with 2 left ventral frontal regions and the dorsal midbrain to predict better response to psychotherapy versus medication in individuals with treatment-naïve major depressive disorder (MDD). Here, we examined whether these subgenual networks also play a role in the pathophysiology of clinical outcomes in MDD with early treatment resistance in primary care. Methods Forty-five people with current MDD who had not responded to ≥2 serotonergic antidepressants (n = 43, meeting predefined functional magnetic resonance imaging minimum quality thresholds) were enrolled and followed over 4 months of standard care. Functional magnetic resonance imaging resting-state connectivity between the preregistered subgenual frontal cortex seed and 3 previously identified left ventromedial, ventrolateral prefrontal/insula, and dorsal midbrain regions was extracted. The clinical outcome was the percentage change on the self-reported 16-item Quick Inventory of Depressive Symptomatology. Results We observed a reversal of our preregistered hypothesis in that higher resting-state connectivity between the subgenual cortex and the a priori ventrolateral prefrontal/insula region predicted favorable rather than unfavorable clinical outcomes (rs39 = -0.43, p = .006). This generalized to the sample including participants with suboptimal functional magnetic resonance imaging quality (rs43 = -0.35, p = .02). In contrast, no effects (rs39 = 0.12, rs39 = -0.01) were found for connectivity with the other 2 preregistered regions or in a whole-brain analysis (voxel-based familywise error-corrected p < .05). Conclusions Subgenual connectivity with the ventrolateral prefrontal cortex/insula is relevant for subsequent clinical outcomes in current MDD with early treatment resistance. Its positive association with favorable outcomes could be explained primarily by psychosocial rather than the expected pharmacological changes during the follow-up period.
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Affiliation(s)
- Diede Fennema
- Centre of Affective Disorders, Institute of Psychiatry, Psychology and Neuroscience, Centre for Affective Disorders, King’s College London, London, United Kingdom
| | - Gareth J. Barker
- Department of Neuroimaging, Institute of Psychiatry, Psychology and Neuroscience, King’s College London, London, United Kingdom
| | - Owen O’Daly
- Department of Neuroimaging, Institute of Psychiatry, Psychology and Neuroscience, King’s College London, London, United Kingdom
| | - Suqian Duan
- Centre of Affective Disorders, Institute of Psychiatry, Psychology and Neuroscience, Centre for Affective Disorders, King’s College London, London, United Kingdom
| | - Ewan Carr
- Department of Biostatics and Health Informatics, Institute of Psychiatry, Psychology and Neuroscience, King’s College London, London, United Kingdom
| | - Kimberley Goldsmith
- Department of Biostatics and Health Informatics, Institute of Psychiatry, Psychology and Neuroscience, King’s College London, London, United Kingdom
| | - Allan H. Young
- Centre of Affective Disorders, Institute of Psychiatry, Psychology and Neuroscience, Centre for Affective Disorders, King’s College London, London, United Kingdom
- National Service for Affective Disorders, South London and Maudsley National Health Service Foundation Trust, London, United Kingdom
| | - Jorge Moll
- Cognitive and Behavioural Neuroscience Unit, D’Or Institute for Research and Education, Rio de Janeiro, Brazil
| | - Roland Zahn
- Centre of Affective Disorders, Institute of Psychiatry, Psychology and Neuroscience, Centre for Affective Disorders, King’s College London, London, United Kingdom
- Cognitive and Behavioural Neuroscience Unit, D’Or Institute for Research and Education, Rio de Janeiro, Brazil
- National Service for Affective Disorders, South London and Maudsley National Health Service Foundation Trust, London, United Kingdom
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10
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Brand K, Wise T, Hess AJ, Russell BR, Stephan KE, Harrison OK. Incorporating uncertainty within dynamic interoceptive learning. Front Psychol 2024; 15:1254564. [PMID: 38646115 PMCID: PMC11026658 DOI: 10.3389/fpsyg.2024.1254564] [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: 07/24/2023] [Accepted: 03/18/2024] [Indexed: 04/23/2024] Open
Abstract
Introduction Interoception, the perception of the internal state of the body, has been shown to be closely linked to emotions and mental health. Of particular interest are interoceptive learning processes that capture associations between environmental cues and body signals as a basis for making homeostatically relevant predictions about the future. One method of measuring respiratory interoceptive learning that has shown promising results is the Breathing Learning Task (BLT). While the original BLT required binary predictions regarding the presence or absence of an upcoming inspiratory resistance, here we extended this paradigm to capture continuous measures of prediction (un)certainty. Methods Sixteen healthy participants completed the continuous version of the BLT, where they were asked to predict the likelihood of breathing resistances on a continuous scale from 0.0 to 10.0. In order to explain participants' responses, a Rescorla-Wagner model of associative learning was combined with suitable observation models for continuous or binary predictions, respectively. For validation, we compared both models against corresponding null models and examined the correlation between observed and modeled predictions. The model was additionally extended to test whether learning rates differed according to stimuli valence. Finally, summary measures of prediction certainty as well as model estimates for learning rates were considered against interoceptive and mental health questionnaire measures. Results Our results demonstrated that the continuous model fits closely captured participant behavior using empirical data, and the binarised predictions showed excellent replicability compared to previously collected data. However, the model extension indicated that there were no significant differences between learning rates for negative (i.e. breathing resistance) and positive (i.e. no breathing resistance) stimuli. Finally, significant correlations were found between fatigue severity and both prediction certainty and learning rate, as well as between anxiety sensitivity and prediction certainty. Discussion These results demonstrate the utility of gathering enriched continuous prediction data in interoceptive learning tasks, and suggest that the updated BLT is a promising paradigm for future investigations into interoceptive learning and potential links to mental health.
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Affiliation(s)
- Katja Brand
- Translational Neuromodeling Unit, Institute for Biomedical Engineering, University of Zurich and ETH Zurich, Zurich, Switzerland
- Department of Psychology, University of Otago, Dunedin, New Zealand
| | - Toby Wise
- King's College London, Institute of Psychiatry, Psychology and Neuroscience, London, United Kingdom
| | - Alexander J. Hess
- Translational Neuromodeling Unit, Institute for Biomedical Engineering, University of Zurich and ETH Zurich, Zurich, Switzerland
| | | | - Klaas E. Stephan
- Translational Neuromodeling Unit, Institute for Biomedical Engineering, University of Zurich and ETH Zurich, Zurich, Switzerland
- Max Planck Institute for Metabolism Research, Cologne, Germany
| | - Olivia K. Harrison
- Translational Neuromodeling Unit, Institute for Biomedical Engineering, University of Zurich and ETH Zurich, Zurich, Switzerland
- Department of Psychology, University of Otago, Dunedin, New Zealand
- Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, United Kingdom
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11
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Medrano J, Friston K, Zeidman P. Linking fast and slow: The case for generative models. Netw Neurosci 2024; 8:24-43. [PMID: 38562283 PMCID: PMC10861163 DOI: 10.1162/netn_a_00343] [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: 08/08/2023] [Accepted: 10/11/2023] [Indexed: 04/04/2024] Open
Abstract
A pervasive challenge in neuroscience is testing whether neuronal connectivity changes over time due to specific causes, such as stimuli, events, or clinical interventions. Recent hardware innovations and falling data storage costs enable longer, more naturalistic neuronal recordings. The implicit opportunity for understanding the self-organised brain calls for new analysis methods that link temporal scales: from the order of milliseconds over which neuronal dynamics evolve, to the order of minutes, days, or even years over which experimental observations unfold. This review article demonstrates how hierarchical generative models and Bayesian inference help to characterise neuronal activity across different time scales. Crucially, these methods go beyond describing statistical associations among observations and enable inference about underlying mechanisms. We offer an overview of fundamental concepts in state-space modeling and suggest a taxonomy for these methods. Additionally, we introduce key mathematical principles that underscore a separation of temporal scales, such as the slaving principle, and review Bayesian methods that are being used to test hypotheses about the brain with multiscale data. We hope that this review will serve as a useful primer for experimental and computational neuroscientists on the state of the art and current directions of travel in the complex systems modelling literature.
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Affiliation(s)
- Johan Medrano
- The Wellcome Centre for Human Neuroimaging, UCL Queen Square Institute of Neurology, London, UK
| | - Karl Friston
- The Wellcome Centre for Human Neuroimaging, UCL Queen Square Institute of Neurology, London, UK
| | - Peter Zeidman
- The Wellcome Centre for Human Neuroimaging, UCL Queen Square Institute of Neurology, London, UK
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12
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Engel M, Mueller L, Döring A, Afzali M, Jones DK. Maximizing SNR per unit time in diffusion MRI with multiband T-Hex spirals. Magn Reson Med 2024; 91:1323-1336. [PMID: 38156527 PMCID: PMC10953427 DOI: 10.1002/mrm.29953] [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: 05/18/2023] [Revised: 10/03/2023] [Accepted: 11/14/2023] [Indexed: 12/30/2023]
Abstract
PURPOSE The characterization of tissue microstructure using diffusion MRI (dMRI) signals is rapidly evolving, with increasing sophistication of signal representations and microstructure models. However, this progress often requires signals to be acquired with very high b-values (e.g., b > 30 ms/μm2 ), along many directions, and using multiple b-values, leading to long scan times and extremely low SNR in dMRI images. The purpose of this work is to boost the SNR efficiency of dMRI by combining three particularly efficient spatial encoding techniques and utilizing a high-performance gradient system (Gmax ≤ 300 mT/m) for efficient diffusion encoding. METHODS Spiral readouts, multiband imaging, and sampling on tilted hexagonal grids (T-Hex) are combined and implemented on a 3T MRI system with ultra-strong gradients. Image reconstruction is performed through an iterative cg-SENSE algorithm incorporating static off-resonance distributions and field dynamics as measured with an NMR field camera. Additionally, T-Hex multiband is combined with a more conventional EPI-readout and compared with state-of-the-art blipped-CAIPIRINHA sampling. The advantage of the proposed approach is furthermore investigated for clinically available gradient performance and diffusion kurtosis imaging. RESULTS High fidelity in vivo images with b-values up to 40 ms/μm2 are obtained. The approach provides superior SNR efficiency over other state-of-the-art multiband diffusion readout schemes. CONCLUSION The demonstrated gains hold promise for the widespread dissemination of advanced microstructural scans, especially in clinical populations.
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Affiliation(s)
- Maria Engel
- Cardiff University Brain Research Imaging Centre (CUBRIC)Cardiff UniversityCardiffUK
| | - Lars Mueller
- Cardiff University Brain Research Imaging Centre (CUBRIC)Cardiff UniversityCardiffUK
- Leeds Institute of Cardiovascular and Metabolic MedicineUniversity of LeedsLeedsUK
| | - André Döring
- Cardiff University Brain Research Imaging Centre (CUBRIC)Cardiff UniversityCardiffUK
| | - Maryam Afzali
- Cardiff University Brain Research Imaging Centre (CUBRIC)Cardiff UniversityCardiffUK
- Leeds Institute of Cardiovascular and Metabolic MedicineUniversity of LeedsLeedsUK
| | - Derek K. Jones
- Cardiff University Brain Research Imaging Centre (CUBRIC)Cardiff UniversityCardiffUK
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13
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Araújo A, Duarte IC, Sousa T, Oliveira J, Pereira AT, Macedo A, Castelo-Branco M. Neural inhibition as implemented by an actor-critic model involves the human dorsal striatum and ventral tegmental area. Sci Rep 2024; 14:6363. [PMID: 38493169 PMCID: PMC10944470 DOI: 10.1038/s41598-024-56161-8] [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: 10/13/2023] [Accepted: 03/02/2024] [Indexed: 03/18/2024] Open
Abstract
Inhibition is implicated across virtually all human experiences. As a trade-off of being very efficient, this executive function is also prone to many errors. Rodent and computational studies show that midbrain regions play crucial roles during errors by sending dopaminergic learning signals to the basal ganglia for behavioural adjustment. However, the parallels between animal and human neural anatomy and function are not determined. We scanned human adults while they performed an fMRI inhibitory task requiring trial-and-error learning. Guided by an actor-critic model, our results implicate the dorsal striatum and the ventral tegmental area as the actor and the critic, respectively. Using a multilevel and dimensional approach, we also demonstrate a link between midbrain and striatum circuit activity, inhibitory performance, and self-reported autistic and obsessive-compulsive subclinical traits.
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Affiliation(s)
- Ana Araújo
- Coimbra Institute for Biomedical Imaging and Translational Research (CIBIT), University of Coimbra, Coimbra, Portugal
- Institute for Nuclear Sciences Applied to Health (ICNAS), University of Coimbra, Coimbra, Portugal
- Institute of Psychological Medicine, Faculty of Medicine, University of Coimbra, Coimbra, Portugal
- Faculty of Medicine, University of Coimbra, Coimbra, Portugal
- Department of Psychiatry, Coimbra Hospital and University Centre, Coimbra, Portugal
| | - Isabel Catarina Duarte
- Coimbra Institute for Biomedical Imaging and Translational Research (CIBIT), University of Coimbra, Coimbra, Portugal
- Institute for Nuclear Sciences Applied to Health (ICNAS), University of Coimbra, Coimbra, Portugal
| | - Teresa Sousa
- Coimbra Institute for Biomedical Imaging and Translational Research (CIBIT), University of Coimbra, Coimbra, Portugal
- Institute for Nuclear Sciences Applied to Health (ICNAS), University of Coimbra, Coimbra, Portugal
| | - Joana Oliveira
- Coimbra Institute for Biomedical Imaging and Translational Research (CIBIT), University of Coimbra, Coimbra, Portugal
- Institute for Nuclear Sciences Applied to Health (ICNAS), University of Coimbra, Coimbra, Portugal
| | - Ana Telma Pereira
- Coimbra Institute for Biomedical Imaging and Translational Research (CIBIT), University of Coimbra, Coimbra, Portugal
- Institute for Nuclear Sciences Applied to Health (ICNAS), University of Coimbra, Coimbra, Portugal
- Institute of Psychological Medicine, Faculty of Medicine, University of Coimbra, Coimbra, Portugal
- Faculty of Medicine, University of Coimbra, Coimbra, Portugal
| | - António Macedo
- Coimbra Institute for Biomedical Imaging and Translational Research (CIBIT), University of Coimbra, Coimbra, Portugal
- Institute for Nuclear Sciences Applied to Health (ICNAS), University of Coimbra, Coimbra, Portugal
- Institute of Psychological Medicine, Faculty of Medicine, University of Coimbra, Coimbra, Portugal
- Faculty of Medicine, University of Coimbra, Coimbra, Portugal
- Department of Psychiatry, Coimbra Hospital and University Centre, Coimbra, Portugal
| | - Miguel Castelo-Branco
- Coimbra Institute for Biomedical Imaging and Translational Research (CIBIT), University of Coimbra, Coimbra, Portugal.
- Institute for Nuclear Sciences Applied to Health (ICNAS), University of Coimbra, Coimbra, Portugal.
- Faculty of Medicine, University of Coimbra, Coimbra, Portugal.
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14
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Mukherjee A, Halassa MM. The Associative Thalamus: A Switchboard for Cortical Operations and a Promising Target for Schizophrenia. Neuroscientist 2024; 30:132-147. [PMID: 38279699 PMCID: PMC10822032 DOI: 10.1177/10738584221112861] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/28/2024]
Abstract
Schizophrenia is a brain disorder that profoundly perturbs cognitive processing. Despite the success in treating many of its symptoms, the field lacks effective methods to measure and address its impact on reasoning, inference, and decision making. Prefrontal cortical abnormalities have been well documented in schizophrenia, but additional dysfunction in the interactions between the prefrontal cortex and thalamus have recently been described. This dysfunction may be interpreted in light of parallel advances in neural circuit research based on nonhuman animals, which show critical thalamic roles in maintaining and switching prefrontal activity patterns in various cognitive tasks. Here, we review this basic literature and connect it to emerging innovations in clinical research. We highlight the value of focusing on associative thalamic structures not only to better understand the very nature of cognitive processing but also to leverage these circuits for diagnostic and therapeutic development in schizophrenia. We suggest that the time is right for building close bridges between basic thalamic research and its clinical translation, particularly in the domain of cognition and schizophrenia.
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Affiliation(s)
- Arghya Mukherjee
- Department of Brain and Cognitive Sciences, McGovern Institute for Brain Research, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Michael M Halassa
- Department of Brain and Cognitive Sciences, McGovern Institute for Brain Research, Massachusetts Institute of Technology, Cambridge, MA, USA
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15
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Schubert J, Suess N, Weisz N. Individual prediction tendencies do not generalize across modalities. Psychophysiology 2024; 61:e14435. [PMID: 37691098 PMCID: PMC10909557 DOI: 10.1111/psyp.14435] [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: 02/15/2023] [Revised: 08/04/2023] [Accepted: 08/22/2023] [Indexed: 09/12/2023]
Abstract
Predictive processing theories, which model the brain as a "prediction machine", explain a wide range of cognitive functions, including learning, perception and action. Furthermore, it is increasingly accepted that aberrant prediction tendencies play a crucial role in psychiatric disorders. Given this explanatory value for clinical psychiatry, prediction tendencies are often implicitly conceptualized as individual traits or as tendencies that generalize across situations. As this has not yet explicitly been shown, in the current study, we quantify to what extent the individual tendency to anticipate sensory features of high probability generalizes across modalities. Using magnetoencephalography (MEG), we recorded brain activity while participants were presented with a sequence of four different (either visual or auditory) stimuli, which changed according to predefined transitional probabilities of two entropy levels: ordered vs. random. Our results show that, on a group-level, under conditions of low entropy, stimulus features of high probability are preactivated in the auditory but not in the visual modality. Crucially, the magnitude of the individual tendency to predict sensory events seems not to correlate between the two modalities. Furthermore, reliability statistics indicate poor internal consistency, suggesting that the measures from the different modalities are unlikely to reflect a single, common cognitive process. In sum, our findings suggest that quantification and interpretation of individual prediction tendencies cannot be generalized across modalities.
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Affiliation(s)
- Juliane Schubert
- Centre for Cognitive Neuroscience and Department of PsychologyUniversity of SalzburgSalzburgAustria
| | - Nina Suess
- Centre for Cognitive Neuroscience and Department of PsychologyUniversity of SalzburgSalzburgAustria
| | - Nathan Weisz
- Centre for Cognitive Neuroscience and Department of PsychologyUniversity of SalzburgSalzburgAustria
- Neuroscience InstituteChristian Doppler University Hospital, Paracelsus Medical UniversitySalzburgAustria
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16
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Hauke DJ, Charlton CE, Schmidt A, Griffiths JD, Woods SW, Ford JM, Srihari VH, Roth V, Diaconescu AO, Mathalon DH. Aberrant Hierarchical Prediction Errors Are Associated With Transition to Psychosis: A Computational Single-Trial Analysis of the Mismatch Negativity. BIOLOGICAL PSYCHIATRY. COGNITIVE NEUROSCIENCE AND NEUROIMAGING 2023; 8:1176-1185. [PMID: 37536567 DOI: 10.1016/j.bpsc.2023.07.011] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/03/2023] [Revised: 07/18/2023] [Accepted: 07/20/2023] [Indexed: 08/05/2023]
Abstract
BACKGROUND Mismatch negativity reductions are among the most reliable biomarkers for schizophrenia and have been associated with increased risk for conversion to psychosis in individuals who are at clinical high risk for psychosis (CHR-P). Here, we adopted a computational approach to develop a mechanistic model of mismatch negativity reductions in CHR-P individuals and patients early in the course of schizophrenia. METHODS Electroencephalography was recorded in 38 CHR-P individuals (15 converters), 19 patients early in the course of schizophrenia (≤5 years), and 44 healthy control participants during three different auditory oddball mismatch negativity paradigms including 10% duration, frequency, or double deviants, respectively. We modeled sensory learning with the hierarchical Gaussian filter and extracted precision-weighted prediction error trajectories from the model to assess how the expression of hierarchical prediction errors modulated electroencephalography amplitudes over sensor space and time. RESULTS Both low-level sensory and high-level volatility precision-weighted prediction errors were altered in CHR-P individuals and patients early in the course of schizophrenia compared with healthy control participants. Moreover, low-level precision-weighted prediction errors were significantly different in CHR-P individuals who later converted to psychosis compared with nonconverters. CONCLUSIONS Our results implicate altered processing of hierarchical prediction errors as a computational mechanism in early psychosis consistent with predictive coding accounts of psychosis. This computational model seems to capture pathophysiological mechanisms that are relevant to early psychosis and the risk for future psychosis in CHR-P individuals and may serve as predictive biomarkers and mechanistic targets for the development of novel treatments.
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Affiliation(s)
- Daniel J Hauke
- Centre for Medical Image Computing, Department of Computer Science, University College London, London, United Kingdom.
| | - Colleen E Charlton
- Krembil Centre for Neuroinformatics, Centre for Addiction and Mental Health, Toronto, Ontario, Canada
| | - André Schmidt
- Department of Psychiatry, University of Basel, Basel, Switzerland
| | - John D Griffiths
- Krembil Centre for Neuroinformatics, Centre for Addiction and Mental Health, Toronto, Ontario, Canada; Department of Psychiatry, University of Toronto, Toronto, Ontario, Canada
| | - Scott W Woods
- Department of Psychiatry, Yale University School of Medicine, New Haven, Connecticut
| | - Judith M Ford
- Mental Health Service, Veterans Affairs San Francisco Health Care System, San Francisco, California; Department of Psychiatry and Behavioral Sciences, University of California San Francisco, San Francisco, California
| | - Vinod H Srihari
- Department of Psychiatry, Yale University School of Medicine, New Haven, Connecticut
| | - Volker Roth
- Department of Mathematics and Computer Science, University of Basel, Basel, Switzerland
| | - Andreea O Diaconescu
- Krembil Centre for Neuroinformatics, Centre for Addiction and Mental Health, Toronto, Ontario, Canada; Department of Psychiatry, University of Toronto, Toronto, Ontario, Canada; Institute of Medical Sciences, University of Toronto, Toronto, Ontario, Canada; Department of Psychology, University of Toronto, Toronto, Ontario, Canada
| | - Daniel H Mathalon
- Mental Health Service, Veterans Affairs San Francisco Health Care System, San Francisco, California; Department of Psychiatry and Behavioral Sciences, University of California San Francisco, San Francisco, California
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17
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Wang BA, Drammis S, Hummos A, Halassa MM, Pleger B. Modulation of prefrontal couplings by prior belief-related responses in ventromedial prefrontal cortex. Front Neurosci 2023; 17:1278096. [PMID: 38033544 PMCID: PMC10684683 DOI: 10.3389/fnins.2023.1278096] [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: 08/15/2023] [Accepted: 10/30/2023] [Indexed: 12/02/2023] Open
Abstract
Humans and other animals can maintain constant payoffs in an uncertain environment by steadily re-evaluating and flexibly adjusting current strategy, which largely depends on the interactions between the prefrontal cortex (PFC) and mediodorsal thalamus (MD). While the ventromedial PFC (vmPFC) represents the level of uncertainty (i.e., prior belief about external states), it remains unclear how the brain recruits the PFC-MD network to re-evaluate decision strategy based on the uncertainty. Here, we leverage non-linear dynamic causal modeling on fMRI data to test how prior belief-dependent activity in vmPFC gates the information flow in the PFC-MD network when individuals switch their decision strategy. We show that the prior belief-related responses in vmPFC had a modulatory influence on the connections from dorsolateral PFC (dlPFC) to both, lateral orbitofrontal (lOFC) and MD. Bayesian parameter averaging revealed that only the connection from the dlPFC to lOFC surpassed the significant threshold, which indicates that the weaker the prior belief, the less was the inhibitory influence of the vmPFC on the strength of effective connections from dlPFC to lOFC. These findings suggest that the vmPFC acts as a gatekeeper for the recruitment of processing resources to re-evaluate the decision strategy in situations of high uncertainty.
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Affiliation(s)
- Bin A. Wang
- Department of Neurology, BG University Hospital Bergmannsheil, Ruhr-University Bochum, Bochum, Germany
- Collaborative Research Centre 874 "Integration and Representation of Sensory Processes", Ruhr-University Bochum, Bochum, Germany
- Guangdong Key Laboratory of Mental Health and Cognitive Science, Ministry of Education Key Laboratory of Brain Cognition and Educational Science, School of Psychology, Center for Studies of Psychological Application, South China Normal University, Guangzhou, China
| | - Sabrina Drammis
- Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology, Cambridge, MA, United States
- McGovern Institute for Brain Research, Massachusetts Institute of Technology, Cambridge, MA, United States
- Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, MA, United States
| | - Ali Hummos
- McGovern Institute for Brain Research, Massachusetts Institute of Technology, Cambridge, MA, United States
- Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, MA, United States
| | - Michael M. Halassa
- Department of Neuroscience, Tufts University School of Medicine, Boston, MA, United States
| | - Burkhard Pleger
- Department of Neurology, BG University Hospital Bergmannsheil, Ruhr-University Bochum, Bochum, Germany
- Collaborative Research Centre 874 "Integration and Representation of Sensory Processes", Ruhr-University Bochum, Bochum, Germany
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18
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Romaniuk L, MacSweeney N, Atkinson K, Chan SWY, Barbu MC, Lawrie SM, Whalley HC. Striatal correlates of Bayesian beliefs in self-efficacy in adolescents and their relation to mood and autonomy: a pilot study. Cereb Cortex Commun 2023; 4:tgad020. [PMID: 38089939 PMCID: PMC10712445 DOI: 10.1093/texcom/tgad020] [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: 10/17/2023] [Accepted: 10/25/2023] [Indexed: 02/02/2024] Open
Abstract
Major depressive disorder often originates in adolescence and is associated with long-term functional impairment. Mechanistically characterizing this heterogeneous illness could provide important leads for optimizing treatment. Importantly, reward learning is known to be disrupted in depression. In this pilot fMRI study of 21 adolescents (16-20 years), we assessed how reward network disruption impacts specifically on Bayesian belief representations of self-efficacy (SE-B) and their associated uncertainty (SE-U), using a modified instrumental learning task probing activation induced by the opportunity to choose, and an optimal Hierarchical Gaussian Filter computational model. SE-U engaged caudate, nucleus accumbens (NAcc), precuneus, posterior parietal and dorsolateral prefrontal cortex (PFWE < 0.005). Sparse partial least squares analysis identified SE-U striatal activation as associating with one's sense of perceived choice and depressive symptoms, particularly anhedonia and negative feelings about oneself. As Bayesian uncertainty modulates belief flexibility and their capacity to steer future actions, this suggests that these striatal signals may be informative developmentally, longitudinally and in assessing response to treatment.
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Affiliation(s)
- Liana Romaniuk
- Division of Psychiatry, University of Edinburgh, Kennedy Tower, Royal Edinburgh Hospital, Morningside Park, Edinburgh EH10 5H, United Kingdom
| | - Niamh MacSweeney
- Division of Psychiatry, University of Edinburgh, Kennedy Tower, Royal Edinburgh Hospital, Morningside Park, Edinburgh EH10 5H, United Kingdom
| | - Kimberley Atkinson
- Division of Psychiatry, University of Edinburgh, Kennedy Tower, Royal Edinburgh Hospital, Morningside Park, Edinburgh EH10 5H, United Kingdom
| | - Stella W Y Chan
- School of Psychology & Clinical Language Sciences, University of Reading, Earley Gate, Whiteknights, Reading RG6 6ES, United Kingdom
| | - Miruna C Barbu
- Division of Psychiatry, University of Edinburgh, Kennedy Tower, Royal Edinburgh Hospital, Morningside Park, Edinburgh EH10 5H, United Kingdom
| | - Stephen M Lawrie
- Division of Psychiatry, University of Edinburgh, Kennedy Tower, Royal Edinburgh Hospital, Morningside Park, Edinburgh EH10 5H, United Kingdom
| | - Heather C Whalley
- Division of Psychiatry, University of Edinburgh, Kennedy Tower, Royal Edinburgh Hospital, Morningside Park, Edinburgh EH10 5H, United Kingdom
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19
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Saberi A, Paquola C, Wagstyl K, Hettwer MD, Bernhardt BC, Eickhoff SB, Valk SL. The regional variation of laminar thickness in the human isocortex is related to cortical hierarchy and interregional connectivity. PLoS Biol 2023; 21:e3002365. [PMID: 37943873 PMCID: PMC10684102 DOI: 10.1371/journal.pbio.3002365] [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: 03/28/2023] [Revised: 11/28/2023] [Accepted: 10/06/2023] [Indexed: 11/12/2023] Open
Abstract
The human isocortex consists of tangentially organized layers with unique cytoarchitectural properties. These layers show spatial variations in thickness and cytoarchitecture across the neocortex, which is thought to support function through enabling targeted corticocortical connections. Here, leveraging maps of the 6 cortical layers based on 3D human brain histology, we aimed to quantitatively characterize the systematic covariation of laminar structure in the cortex and its functional consequences. After correcting for the effect of cortical curvature, we identified a spatial pattern of changes in laminar thickness covariance from lateral frontal to posterior occipital regions, which differentiated the dominance of infra- versus supragranular layer thickness. Corresponding to the laminar regularities of cortical connections along cortical hierarchy, the infragranular-dominant pattern of laminar thickness was associated with higher hierarchical positions of regions, mapped based on resting-state effective connectivity in humans and tract-tracing of structural connections in macaques. Moreover, we show that regions with similar laminar thickness patterns have a higher likelihood of structural connections and strength of functional connections. In sum, here we characterize the organization of laminar thickness in the human isocortex and its association with cortico-cortical connectivity, illustrating how laminar organization may provide a foundational principle of cortical function.
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Affiliation(s)
- Amin Saberi
- Otto Hahn Research Group for Cognitive Neurogenetics, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany
- Institute of Neurosciences and Medicine (INM-7), Research Centre Jülich, Jülich, Germany
- Institute of Systems Neuroscience, Heinrich Heine University Düsseldorf, Düsseldorf, Germany
| | - Casey Paquola
- Institute of Neurosciences and Medicine (INM-7), Research Centre Jülich, Jülich, Germany
| | - Konrad Wagstyl
- Wellcome Trust Centre for Neuroimaging, University College London, London, United Kingdom
| | - Meike D. Hettwer
- Otto Hahn Research Group for Cognitive Neurogenetics, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany
- Institute of Neurosciences and Medicine (INM-7), Research Centre Jülich, Jülich, Germany
- Institute of Systems Neuroscience, Heinrich Heine University Düsseldorf, Düsseldorf, Germany
- Max Planck School of Cognition, Leipzig, Germany
| | - Boris C. Bernhardt
- Multimodal Imaging and Connectome Analysis Laboratory, McConnell Brain Imaging Centre, Montreal Neurological Institute and Hospital, McGill University, Montreal, Canada
| | - Simon B. Eickhoff
- Institute of Neurosciences and Medicine (INM-7), Research Centre Jülich, Jülich, Germany
- Institute of Systems Neuroscience, Heinrich Heine University Düsseldorf, Düsseldorf, Germany
| | - Sofie L. Valk
- Otto Hahn Research Group for Cognitive Neurogenetics, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany
- Institute of Neurosciences and Medicine (INM-7), Research Centre Jülich, Jülich, Germany
- Institute of Systems Neuroscience, Heinrich Heine University Düsseldorf, Düsseldorf, Germany
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20
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Menéndez-Granda M, Schmidt N, Orth M, Klink K, Horn S, Kliegel M, Peter J. The effect of loss incentives on prospective memory in healthy older adults: study protocol of a randomized controlled trial using ultra-high field fMRI. BMC Psychiatry 2023; 23:722. [PMID: 37803337 PMCID: PMC10557285 DOI: 10.1186/s12888-023-05229-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/15/2023] [Accepted: 09/27/2023] [Indexed: 10/08/2023] Open
Abstract
BACKGROUND Prospective memory is important for our health and independence but declines with age. Hence, interventions to enhance prospective memory, for example by providing an incentive, may promote healthy ageing. The neuroanatomical correlates of prospective memory and the processing of incentive-related prospective memory changes in older adults are not fully understood. In an fMRI study, we will therefore test whether incentives improve prospective memory in older adults and how prospective memory is processed in the brain in general, and when incentives are provided. Since goals and interests change across adulthood, avoiding losses is becoming more important for older adults than achieving gains. We therefore posit that loss-related incentives will enhance prospective memory, which will be subserved by increased prefrontal and midbrain activity. METHODS We will include n = 60 healthy older adults (60-75 years of age) in a randomized, single-blind, and parallel-group study. We will acquire 7T fMRI data in an incentive group and a control group (n = 30 each, stratified by education, age, and sex). Before and after fMRI, all participants will complete questionnaires and cognitive tests to assess possible confounders (e.g., income, personality traits, sensitivity to reward or punishment). DISCUSSION The results of this study will clarify whether loss-related incentives can enhance prospective memory and how any enhancement is processed in the brain. In addition, we will determine how prospective memory is processed in the brain in general. The results of our study will be an important step towards a better understanding of how prospective memory changes when we get older and for developing interventions to counteract cognitive decline.
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Affiliation(s)
- Marta Menéndez-Granda
- University Hospital of Old Age Psychiatry and Psychotherapy, University of Bern, Bern, Switzerland
- Graduate School for Health Sciences, University of Bern, Bern, Switzerland
- Swiss Institute for Translational and Entrepreneurial Medicine, Bern, Switzerland
| | - Nadine Schmidt
- University Hospital of Old Age Psychiatry and Psychotherapy, University of Bern, Bern, Switzerland
- Graduate School for Health Sciences, University of Bern, Bern, Switzerland
- Swiss Institute for Translational and Entrepreneurial Medicine, Bern, Switzerland
| | - Michael Orth
- University Hospital of Old Age Psychiatry and Psychotherapy, University of Bern, Bern, Switzerland
- Department of Neurology, Inselspital, Bern University Hospital, University of Bern, Bern, Switzerland
| | - Katharina Klink
- University Hospital of Old Age Psychiatry and Psychotherapy, University of Bern, Bern, Switzerland
- Swiss Institute for Translational and Entrepreneurial Medicine, Bern, Switzerland
| | - Sebastian Horn
- Department of Psychology, University of Zurich, Zurich, Switzerland
| | - Matthias Kliegel
- Faculty of Psychology and Educational Sciences, University of Geneva, Geneva, Switzerland
- Centre for the Interdisciplinary Study of Gerontology and Vulnerability, University of Geneva, Geneva, Switzerland
- Swiss Centre of Expertise in Life Course Research, LIVES Centre, Lausanne and Geneva, Switzerland
| | - Jessica Peter
- University Hospital of Old Age Psychiatry and Psychotherapy, University of Bern, Bern, Switzerland.
- Swiss Institute for Translational and Entrepreneurial Medicine, Bern, Switzerland.
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21
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Zeidman P, Friston K, Parr T. A primer on Variational Laplace (VL). Neuroimage 2023; 279:120310. [PMID: 37544417 PMCID: PMC10951963 DOI: 10.1016/j.neuroimage.2023.120310] [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: 09/30/2022] [Revised: 07/13/2023] [Accepted: 08/04/2023] [Indexed: 08/08/2023] Open
Abstract
This article details a scheme for approximate Bayesian inference, which has underpinned thousands of neuroimaging studies since its introduction 15 years ago. Variational Laplace (VL) provides a generic approach to fitting linear or non-linear models, which may be static or dynamic, returning a posterior probability density over the model parameters and an approximation of log model evidence, which enables Bayesian model comparison. VL applies variational Bayesian inference in conjunction with quadratic or Laplace approximations of the evidence lower bound (free energy). Importantly, update equations do not need to be derived for each model under consideration, providing a general method for fitting a broad class of models. This primer is intended for experimenters and modellers who may wish to fit models to data using variational Bayesian methods, without assuming previous experience of variational Bayes or machine learning. Accompanying code demonstrates how to fit different kinds of model using the reference implementation of the VL scheme in the open-source Statistical Parametric Mapping (SPM) software package. In addition, we provide a standalone software function that does not require SPM, in order to ease translation to other fields, together with detailed pseudocode. Finally, the supplementary materials provide worked derivations of the key equations.
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Affiliation(s)
- Peter Zeidman
- Wellcome Centre for Human Neuroimaging, UCL, 12 Queen Square, London WC1N 3AR, United Kingdom.
| | - Karl Friston
- Wellcome Centre for Human Neuroimaging, UCL, 12 Queen Square, London WC1N 3AR, United Kingdom
| | - Thomas Parr
- Wellcome Centre for Human Neuroimaging, UCL, 12 Queen Square, London WC1N 3AR, United Kingdom
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22
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Groot JM, Miletic S, Isherwood SJS, Tse DHY, Habli S, Håberg AK, Forstmann BU, Bazin PL, Mittner M. Echoes from Intrinsic Connectivity Networks in the Subcortex. J Neurosci 2023; 43:6609-6618. [PMID: 37562962 PMCID: PMC10538587 DOI: 10.1523/jneurosci.1020-23.2023] [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: 06/01/2023] [Revised: 07/11/2023] [Accepted: 07/28/2023] [Indexed: 08/12/2023] Open
Abstract
Decades of research have greatly improved our understanding of intrinsic human brain organization in terms of functional networks and the transmodal hubs within the cortex at which they converge. However, substrates of multinetwork integration in the human subcortex are relatively uncharted. Here, we leveraged recent advances in subcortical atlasing and ultra-high field (7 T) imaging optimized for the subcortex to investigate the functional architecture of 14 individual structures in healthy adult males and females with a fully data-driven approach. We revealed that spontaneous neural activity in subcortical regions can be decomposed into multiple independent subsignals that correlate with, or "echo," the activity in functional networks across the cortex. Distinct subregions of the thalamus, striatum, claustrum, and hippocampus showed a varied pattern of echoes from attention, control, visual, somatomotor, and default mode networks, demonstrating evidence for a heterogeneous organization supportive of functional integration. Multiple network activity furthermore converged within the globus pallidus externa, substantia nigra, and ventral tegmental area but was specific to one subregion, while the amygdala and pedunculopontine nucleus preferentially affiliated with a single network, showing a more homogeneous topography. Subregional connectivity of the globus pallidus interna, subthalamic nucleus, red nucleus, periaqueductal gray, and locus coeruleus did not resemble patterns of cortical network activity. Together, these finding describe potential mechanisms through which the subcortex participates in integrated and segregated information processing and shapes the spontaneous cognitive dynamics during rest.SIGNIFICANCE STATEMENT Despite the impact of subcortical dysfunction on brain health and cognition, large-scale functional mapping of subcortical structures severely lags behind that of the cortex. Recent developments in subcortical atlasing and imaging at ultra-high field provide new avenues for studying the intricate functional architecture of the human subcortex. With a fully data-driven analysis, we reveal subregional connectivity profiles of a large set of noncortical structures, including those rarely studied in fMRI research. The results have implications for understanding how the functional organization of the subcortex facilitates integrative processing through cross-network information convergence, paving the way for future work aimed at improving our knowledge of subcortical contributions to intrinsic brain dynamics and spontaneous cognition.
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Affiliation(s)
- Josephine M Groot
- Department of Psychology, UiT-Arctic University of Norway, Tromsø, 9037, Norway
- Integrative Model-based Cognitive Neuroscience research unit, University of Amsterdam, Amsterdam, 1001 NK, The Netherlands
| | - Steven Miletic
- Integrative Model-based Cognitive Neuroscience research unit, University of Amsterdam, Amsterdam, 1001 NK, The Netherlands
| | - Scott J S Isherwood
- Integrative Model-based Cognitive Neuroscience research unit, University of Amsterdam, Amsterdam, 1001 NK, The Netherlands
| | - Desmond H Y Tse
- Department of Neuropsychology and Psychopharmacology, Maastricht University, Maastricht, 6200 MD, The Netherlands
| | - Sarah Habli
- Department of Psychology, Norwegian University of Science and Technology, Trondheim, 8900, Norway
| | - Asta K Håberg
- Department of Neuromedicine and Movement Science, Norwegian University of Science and Technology, Trondheim, 8900, Norway
- Department of Radiology and Nuclear Medicine, St. Olavs Hospital, Trondheim, 7006, Norway
| | - Birte U Forstmann
- Integrative Model-based Cognitive Neuroscience research unit, University of Amsterdam, Amsterdam, 1001 NK, The Netherlands
| | - Pierre-Louis Bazin
- Department of Psychology, UiT-Arctic University of Norway, Tromsø, 9037, Norway
- Departments of Neurophysics and Neurology, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, 04303, Germany
| | - Matthias Mittner
- Department of Psychology, UiT-Arctic University of Norway, Tromsø, 9037, Norway
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23
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Valevicius D, Beck N, Kasper L, Boroday S, Bayer J, Rioux P, Caron B, Adalat R, Evans AC, Khalili-Mahani N. Web-based processing of physiological noise in fMRI: addition of the PhysIO toolbox to CBRAIN. Front Neuroinform 2023; 17:1251023. [PMID: 37841811 PMCID: PMC10569687 DOI: 10.3389/fninf.2023.1251023] [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: 06/30/2023] [Accepted: 09/05/2023] [Indexed: 10/17/2023] Open
Abstract
Neuroimaging research requires sophisticated tools for analyzing complex data, but efficiently leveraging these tools can be a major challenge, especially on large datasets. CBRAIN is a web-based platform designed to simplify the use and accessibility of neuroimaging research tools for large-scale, collaborative studies. In this paper, we describe how CBRAIN's unique features and infrastructure were leveraged to integrate TAPAS PhysIO, an open-source MATLAB toolbox for physiological noise modeling in fMRI data. This case study highlights three key elements of CBRAIN's infrastructure that enable streamlined, multimodal tool integration: a user-friendly GUI, a Brain Imaging Data Structure (BIDS) data-entry schema, and convenient in-browser visualization of results. By incorporating PhysIO into CBRAIN, we achieved significant improvements in the speed, ease of use, and scalability of physiological preprocessing. Researchers now have access to a uniform and intuitive interface for analyzing data, which facilitates remote and collaborative evaluation of results. With these improvements, CBRAIN aims to become an essential open-science tool for integrative neuroimaging research, supporting FAIR principles and enabling efficient workflows for complex analysis pipelines.
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Affiliation(s)
- Darius Valevicius
- McGill Centre for Integrative Neuroscience (MCIN), Ludmer Centre for Neuroinformatics and Mental Health, Montreal Neurological Institute (MNI), McGill University, Montreal, QC, Canada
| | - Natacha Beck
- McGill Centre for Integrative Neuroscience (MCIN), Ludmer Centre for Neuroinformatics and Mental Health, Montreal Neurological Institute (MNI), McGill University, Montreal, QC, Canada
| | - Lars Kasper
- BRAIN-TO Lab, Krembil Brain Institute, University Health Network, Toronto, ON, Canada
- Translational Neuromodeling Unit, Institute for Biomedical Engineering, University of Zurich and ETH Zurich, Zurich, Switzerland
| | - Sergiy Boroday
- McGill Centre for Integrative Neuroscience (MCIN), Ludmer Centre for Neuroinformatics and Mental Health, Montreal Neurological Institute (MNI), McGill University, Montreal, QC, Canada
| | - Johanna Bayer
- Center for Youth Mental Health, The University of Melbourne, Melbourne, VIC, Australia
- Orygen Youth Health, Orygen, Melbourne, VIC, Australia
| | - Pierre Rioux
- McGill Centre for Integrative Neuroscience (MCIN), Ludmer Centre for Neuroinformatics and Mental Health, Montreal Neurological Institute (MNI), McGill University, Montreal, QC, Canada
| | - Bryan Caron
- McGill Centre for Integrative Neuroscience (MCIN), Ludmer Centre for Neuroinformatics and Mental Health, Montreal Neurological Institute (MNI), McGill University, Montreal, QC, Canada
| | - Reza Adalat
- McGill Centre for Integrative Neuroscience (MCIN), Ludmer Centre for Neuroinformatics and Mental Health, Montreal Neurological Institute (MNI), McGill University, Montreal, QC, Canada
| | - Alan C. Evans
- McGill Centre for Integrative Neuroscience (MCIN), Ludmer Centre for Neuroinformatics and Mental Health, Montreal Neurological Institute (MNI), McGill University, Montreal, QC, Canada
| | - Najmeh Khalili-Mahani
- McGill Centre for Integrative Neuroscience (MCIN), Ludmer Centre for Neuroinformatics and Mental Health, Montreal Neurological Institute (MNI), McGill University, Montreal, QC, Canada
- Department of Electrical and Computer Engineering, Concordia University, Montreal, QC, Canada
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24
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Qiao L, Zhang L, Chen A. Control dilemma: Evidence of the stability-flexibility trade-off. Int J Psychophysiol 2023; 191:29-41. [PMID: 37499985 DOI: 10.1016/j.ijpsycho.2023.07.002] [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: 03/06/2023] [Revised: 07/21/2023] [Accepted: 07/24/2023] [Indexed: 07/29/2023]
Abstract
Cognitive control can be applied flexibly when task goals or environments change (i.e., cognitive flexibility), or stably to pursue a goal in the face of distraction (i.e., cognitive stability). Whether these seemingly contradictory characteristics have an inverse relationship has been controversial, as some studies have suggested a trade-off mechanism between cognitive flexibility and cognitive stability, while others have not found such reciprocal associations. This study investigated the possible antagonistic correlation between cognitive flexibility and stability using a novel version of the flexibility-stability paradigm and the classic cued task switching paradigm. In Experiment 1, we showed that cognitive flexibility was inversely correlated with cognitive stability, as increased distractor proportions were associated with decreased cognitive flexibility and greater cognitive stability. Moreover, cognitive flexibility and stability were regulated by a single control system instead of two independent control mechanisms, as the model selection results indicated that the reciprocally regulated model with one integration parameter outperformed all other models, and the model parameter was inversely linked to cognitive flexibility and stability. We found similar results using the classic cued task switching paradigm in Experiment 2. Therefore, a trade-off between cognitive flexibility and stability was observed from the paradigms used in this study.
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Affiliation(s)
- Lei Qiao
- School of Psychology, Shenzhen University, Shenzhen, China
| | - Lijie Zhang
- School of Education Science, Guangdong Polytechnic Normal University, Guangzhou 510665, China.
| | - Antao Chen
- Department of Psychology, Shanghai University of Sport, Shanghai, China
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25
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Teh I, Shelley D, Boyle JH, Zhou F, Poenar A, Sharrack N, Foster RJ, Yuldasheva NY, Parker GJM, Dall'Armellina E, Plein S, Schneider JE, Szczepankiewicz F. Cardiac q-space trajectory imaging by motion-compensated tensor-valued diffusion encoding in human heart in vivo. Magn Reson Med 2023; 90:150-165. [PMID: 36941736 PMCID: PMC10952623 DOI: 10.1002/mrm.29637] [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: 09/26/2022] [Revised: 01/25/2023] [Accepted: 02/23/2023] [Indexed: 03/23/2023]
Abstract
PURPOSE Tensor-valued diffusion encoding can probe more specific features of tissue microstructure than what is available by conventional diffusion weighting. In this work, we investigate the technical feasibility of tensor-valued diffusion encoding at high b-values with q-space trajectory imaging (QTI) analysis, in the human heart in vivo. METHODS Ten healthy volunteers were scanned on a 3T scanner. We designed time-optimal gradient waveforms for tensor-valued diffusion encoding (linear and planar) with second-order motion compensation. Data were analyzed with QTI. Normal values and repeatability were investigated for the mean diffusivity (MD), fractional anisotropy (FA), microscopic FA (μFA), isotropic, anisotropic and total mean kurtosis (MKi, MKa, and MKt), and orientation coherence (Cc ). A phantom, consisting of two fiber blocks at adjustable angles, was used to evaluate sensitivity of parameters to orientation dispersion and diffusion time. RESULTS QTI data in the left ventricular myocardium were MD = 1.62 ± 0.07 μm2 /ms, FA = 0.31 ± 0.03, μFA = 0.43 ± 0.07, MKa = 0.20 ± 0.07, MKi = 0.13 ± 0.03, MKt = 0.33 ± 0.09, and Cc = 0.56 ± 0.22 (mean ± SD across subjects). Phantom experiments showed that FA depends on orientation dispersion, whereas μFA was insensitive to this effect. CONCLUSION We demonstrated the first tensor-valued diffusion encoding and QTI analysis in the heart in vivo, along with first measurements of myocardial μFA, MKi, MKa, and Cc . The methodology is technically feasible and provides promising novel biomarkers for myocardial tissue characterization.
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Affiliation(s)
- Irvin Teh
- Leeds Institute of Cardiovascular and Metabolic MedicineUniversity of LeedsLeedsUK
| | - David Shelley
- Leeds Institute of Cardiovascular and Metabolic MedicineUniversity of LeedsLeedsUK
- Leeds Teaching Hospitals TrustLeedsUK
| | - Jordan H. Boyle
- Faculty of Industrial Design EngineeringDelft University of TechnologyDelftNetherlands
| | - Fenglei Zhou
- Center for Medical Image Computing, Department of Medical Physics & Biomedical Engineering and Department of NeuroinflammationUniversity College LondonLondonUK
- Astrea BioseparationCombertonUK
| | - Ana‐Maria Poenar
- Leeds Institute of Cardiovascular and Metabolic MedicineUniversity of LeedsLeedsUK
| | - Noor Sharrack
- Leeds Institute of Cardiovascular and Metabolic MedicineUniversity of LeedsLeedsUK
| | - Richard J. Foster
- Leeds Institute of Cardiovascular and Metabolic MedicineUniversity of LeedsLeedsUK
| | - Nadira Y. Yuldasheva
- Leeds Institute of Cardiovascular and Metabolic MedicineUniversity of LeedsLeedsUK
| | - Geoff J. M. Parker
- Center for Medical Image Computing, Department of Medical Physics & Biomedical Engineering and Department of NeuroinflammationUniversity College LondonLondonUK
- Bioxydyn LimitedManchesterUK
| | - Erica Dall'Armellina
- Leeds Institute of Cardiovascular and Metabolic MedicineUniversity of LeedsLeedsUK
| | - Sven Plein
- Leeds Institute of Cardiovascular and Metabolic MedicineUniversity of LeedsLeedsUK
| | - Jürgen E. Schneider
- Leeds Institute of Cardiovascular and Metabolic MedicineUniversity of LeedsLeedsUK
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26
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Wang BA, Veismann M, Banerjee A, Pleger B. Human orbitofrontal cortex signals decision outcomes to sensory cortex during behavioral adaptations. Nat Commun 2023; 14:3552. [PMID: 37322004 PMCID: PMC10272188 DOI: 10.1038/s41467-023-38671-7] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/09/2022] [Accepted: 05/10/2023] [Indexed: 06/17/2023] Open
Abstract
The ability to respond flexibly to an ever-changing environment relies on the orbitofrontal cortex (OFC). However, how the OFC associates sensory information with predicted outcomes to enable flexible sensory learning in humans remains elusive. Here, we combine a probabilistic tactile reversal learning task with functional magnetic resonance imaging (fMRI) to investigate how lateral OFC (lOFC) interacts with the primary somatosensory cortex (S1) to guide flexible tactile learning in humans. fMRI results reveal that lOFC and S1 exhibit distinct task-dependent engagement: while the lOFC responds transiently to unexpected outcomes immediately following reversals, S1 is persistently engaged during re-learning. Unlike the contralateral stimulus-selective S1, activity in ipsilateral S1 mirrors the outcomes of behavior during re-learning, closely related to top-down signals from lOFC. These findings suggest that lOFC contributes to teaching signals to dynamically update representations in sensory areas, which implement computations critical for adaptive behavior.
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Affiliation(s)
- Bin A Wang
- Department of Neurology, BG University Hospital Bergmannsheil, Ruhr-University Bochum, Bochum, Germany
- Collaborative Research Centre 874 "Integration and Representation of Sensory Processes", Ruhr-University Bochum, Bochum, Germany
| | - Maike Veismann
- Department of Neurology, BG University Hospital Bergmannsheil, Ruhr-University Bochum, Bochum, Germany
- Collaborative Research Centre 874 "Integration and Representation of Sensory Processes", Ruhr-University Bochum, Bochum, Germany
| | - Abhishek Banerjee
- Adaptive Decisions Lab, Biosciences Institute, Newcastle University, Newcastle upon Tyne, UK.
| | - Burkhard Pleger
- Department of Neurology, BG University Hospital Bergmannsheil, Ruhr-University Bochum, Bochum, Germany.
- Collaborative Research Centre 874 "Integration and Representation of Sensory Processes", Ruhr-University Bochum, Bochum, Germany.
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27
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Xie K, Royer J, Larivière S, Rodriguez-Cruces R, Frässle S, Cabalo DG, Ngo A, DeKraker J, Auer H, Tavakol S, Weng Y, Abdallah C, Horwood L, Frauscher B, Caciagli L, Bernasconi A, Bernasconi N, Zhang Z, Concha L, Bernhardt BC. Atypical connectome topography and signal flow in temporal lobe epilepsy. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.05.23.541934. [PMID: 37292996 PMCID: PMC10245853 DOI: 10.1101/2023.05.23.541934] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/10/2023]
Abstract
Temporal lobe epilepsy (TLE) is one of the most common pharmaco-resistant epilepsies in adults. While hippocampal pathology is the hallmark of this condition, emerging evidence indicates that brain alterations extend beyond the mesiotemporal epicenter and affect macroscale brain function and cognition. We studied macroscale functional reorganization in TLE, explored structural substrates, and examined cognitive associations. We investigated a multisite cohort of 95 patients with pharmaco-resistant TLE and 95 healthy controls using state-of-the-art multimodal 3T magnetic resonance imaging (MRI). We quantified macroscale functional topographic organization using connectome dimensionality reduction techniques and estimated directional functional flow using generative models of effective connectivity. We observed atypical functional topographies in patients with TLE relative to controls, manifesting as reduced functional differentiation between sensory/motor networks and transmodal systems such as the default mode network, with peak alterations in bilateral temporal and ventromedial prefrontal cortices. TLE-related topographic changes were consistent in all three included sites and reflected reductions in hierarchical flow patterns between cortical systems. Integration of parallel multimodal MRI data indicated that these findings were independent of TLE-related cortical grey matter atrophy, but mediated by microstructural alterations in the superficial white matter immediately beneath the cortex. The magnitude of functional perturbations was robustly associated with behavioral markers of memory function. Overall, this work provides converging evidence for macroscale functional imbalances, contributing microstructural alterations, and their associations with cognitive dysfunction in TLE.
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Affiliation(s)
- Ke Xie
- Multimodal Imaging and Connectome Analysis Laboratory, McConnell Brain Imaging Centre, Montreal Neurological Institute, McGill University, Montreal, Quebec, Canada
| | - Jessica Royer
- Multimodal Imaging and Connectome Analysis Laboratory, McConnell Brain Imaging Centre, Montreal Neurological Institute, McGill University, Montreal, Quebec, Canada
- Analytical Neurophysiology Laboratory, Montreal Neurological Institute, McGill University, Montreal, Quebec, Canada
| | - Sara Larivière
- Multimodal Imaging and Connectome Analysis Laboratory, McConnell Brain Imaging Centre, Montreal Neurological Institute, McGill University, Montreal, Quebec, Canada
| | - Raul Rodriguez-Cruces
- Multimodal Imaging and Connectome Analysis Laboratory, McConnell Brain Imaging Centre, Montreal Neurological Institute, McGill University, Montreal, Quebec, Canada
| | - Stefan Frässle
- Translational Neuromodeling Unit (TNU), Institute for Biomedical Engineering, University of Zurich and ETH Zurich, Zurich, Switzerland
| | - Donna Gift Cabalo
- Multimodal Imaging and Connectome Analysis Laboratory, McConnell Brain Imaging Centre, Montreal Neurological Institute, McGill University, Montreal, Quebec, Canada
| | - Alexander Ngo
- Multimodal Imaging and Connectome Analysis Laboratory, McConnell Brain Imaging Centre, Montreal Neurological Institute, McGill University, Montreal, Quebec, Canada
| | - Jordan DeKraker
- Multimodal Imaging and Connectome Analysis Laboratory, McConnell Brain Imaging Centre, Montreal Neurological Institute, McGill University, Montreal, Quebec, Canada
| | - Hans Auer
- Multimodal Imaging and Connectome Analysis Laboratory, McConnell Brain Imaging Centre, Montreal Neurological Institute, McGill University, Montreal, Quebec, Canada
| | - Shahin Tavakol
- Multimodal Imaging and Connectome Analysis Laboratory, McConnell Brain Imaging Centre, Montreal Neurological Institute, McGill University, Montreal, Quebec, Canada
| | - Yifei Weng
- Department of Medical Imaging, Jinling Hospital, Nanjing University School of Medicine, Nanjing, China
| | - Chifaou Abdallah
- Analytical Neurophysiology Laboratory, Montreal Neurological Institute, McGill University, Montreal, Quebec, Canada
| | - Linda Horwood
- Multimodal Imaging and Connectome Analysis Laboratory, McConnell Brain Imaging Centre, Montreal Neurological Institute, McGill University, Montreal, Quebec, Canada
- Analytical Neurophysiology Laboratory, Montreal Neurological Institute, McGill University, Montreal, Quebec, Canada
| | - Birgit Frauscher
- Analytical Neurophysiology Laboratory, Montreal Neurological Institute, McGill University, Montreal, Quebec, Canada
| | - Lorenzo Caciagli
- Department of Biomedical Engineering, University of Pennsylvania, Philadelphia, Pennsylvania 19104, USA
| | - Andrea Bernasconi
- Neuroimaging of Epilepsy Laboratory, McConnell Brain Imaging Centre, Montreal Neurological Institute, McGill University, Montreal, Quebec, Canada
| | - Neda Bernasconi
- Neuroimaging of Epilepsy Laboratory, McConnell Brain Imaging Centre, Montreal Neurological Institute, McGill University, Montreal, Quebec, Canada
| | - Zhiqiang Zhang
- Department of Medical Imaging, Jinling Hospital, Nanjing University School of Medicine, Nanjing, China
| | - Luis Concha
- Brain Connectivity Laboratory, Institute of Neurobiology, Universidad Nacional Autónoma de Mexico (UNAM), Mexico
| | - Boris C Bernhardt
- Multimodal Imaging and Connectome Analysis Laboratory, McConnell Brain Imaging Centre, Montreal Neurological Institute, McGill University, Montreal, Quebec, Canada
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28
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Suthaharan P, Corlett PR. Assumed shared belief about conspiracy theories in social networks protects paranoid individuals against distress. Sci Rep 2023; 13:6084. [PMID: 37055504 PMCID: PMC10100615 DOI: 10.1038/s41598-023-33305-w] [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: 10/07/2022] [Accepted: 04/10/2023] [Indexed: 04/15/2023] Open
Abstract
Paranoia is the belief that others intend you harm. It is related to conspiracy theories, wherein those others represent an organized faction, coordinating the harm against self and others, and violating societal norms. Current psychological studies of paranoid conspiracy theorizing focus either on the individual or their broader social network. Likewise, theories of belief formation and updating often contain individual level processes as well as broader interpersonal and organizational factors. Here we examine paranoia and conspiracy theorizing in terms of individual behavioral predictors (performance on a probabilistic reversal learning task which assays belief updating) as well as social sensing: we ask participants to report the features of their social network, including whether their friends and acquaintances share their paranoid conspiratorial beliefs. We find that people who believe paranoid conspiracy theories expect more volatility during the task. They also assume that members of their social network share their paranoid beliefs. Critically, those participants with larger social networks and greater assumed shared belief tend to harbor their conspiratorial beliefs with less emotional distress and expect less volatility in the task. This is evidence that, like political and religious beliefs, conspiracy theories may flourish under a sacred canopy of belief consensus. These data suggest that friends and acquaintances may serve as sources of credulity and moving between them may sustain conspiracy beliefs when there is detraction. This hybrid individual/social account may shed light on clinical paranoia and persecutory delusion, wherein disability is defined normatively, and social supports are fewer.
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Affiliation(s)
- Praveen Suthaharan
- Interdepartmental Neuroscience Program, Yale University, New Haven, CT, USA
- Kavli Institute for Neuroscience, Yale University, New Haven, CT, USA
- Department of Psychiatry, Connecticut Mental Health Center, Yale University, New Haven, CT, USA
| | - Philip R Corlett
- Kavli Institute for Neuroscience, Yale University, New Haven, CT, USA.
- Department of Psychiatry, Connecticut Mental Health Center, Yale University, New Haven, CT, USA.
- Department of Psychology, Yale University, New Haven, CT, USA.
- Wu Tsai Institute, Yale University, New Haven, CT, USA.
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29
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Galioulline H, Frässle S, Harrison S, Pereira I, Heinzle J, Stephan KE. Predicting Future Depressive Episodes from Resting-State fMRI with Generative Embedding. Neuroimage 2023; 273:119986. [PMID: 36958617 DOI: 10.1016/j.neuroimage.2023.119986] [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: 10/16/2022] [Revised: 02/15/2023] [Accepted: 02/25/2023] [Indexed: 03/25/2023] Open
Abstract
After a first episode of major depressive disorder (MDD), there is substantial risk for a long-term remitting-relapsing course. Prevention and early interventions are thus critically important. Various studies have examined the feasibility of detecting at-risk individuals based on out-of-sample predictions about the future occurrence of depression. However, functional magnetic resonance imaging (MRI) has received very little attention for this purpose so far. Here, we explored the utility of generative models (i.e. different dynamic causal models, DCMs) as well as functional connectivity (FC) for predicting future episodes of depression in never-depressed adults, using a large dataset (N=906) of task-free ("resting state") fMRI data from the UK Biobank. Connectivity analyses were conducted using timeseries from pre-computed spatially independent components of different dimensionalities. Over a three year period, 50% of participants showed indications of at least one depressive episode, while the other 50% did not. Using nested cross-validation for training and a held-out test set (80/20 split), we systematically examined the combination of 8 connectivity feature sets and 17 classifiers. We found that a generative embedding procedure based on combining regression DCM (rDCM) with a support vector machine (SVM) enabled the best predictions, both on the training set (0.63 accuracy, 0.66 area under the curve, AUC) and the test set (0.62 accuracy, 0.64 AUC; p<0.001). However, on the test set, rDCM was only slightly superior to predictions based on FC (0.59 accuracy, 0.61 AUC). Interpreting model predictions based on SHAP (SHapley Additive exPlanations) values suggested that the most predictive connections were widely distributed and not confined to specific networks. Overall, our analyses suggest (i) ways of improving future fMRI-based generative embedding approaches for the early detection of individuals at-risk for depression and that (ii) achieving accuracies of clinical utility may require combination of fMRI with other data modalities.
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Affiliation(s)
- Herman Galioulline
- Translational Neuromodeling Unit (TNU), Institute for Biomedical Engineering, University of Zurich & ETH Zurich, 8032 Zurich, Switzerland.
| | - Stefan Frässle
- Translational Neuromodeling Unit (TNU), Institute for Biomedical Engineering, University of Zurich & ETH Zurich, 8032 Zurich, Switzerland
| | - Sam Harrison
- Translational Neuromodeling Unit (TNU), Institute for Biomedical Engineering, University of Zurich & ETH Zurich, 8032 Zurich, Switzerland
| | - Inês Pereira
- Translational Neuromodeling Unit (TNU), Institute for Biomedical Engineering, University of Zurich & ETH Zurich, 8032 Zurich, Switzerland
| | - Jakob Heinzle
- Translational Neuromodeling Unit (TNU), Institute for Biomedical Engineering, University of Zurich & ETH Zurich, 8032 Zurich, Switzerland
| | - Klaas Enno Stephan
- Translational Neuromodeling Unit (TNU), Institute for Biomedical Engineering, University of Zurich & ETH Zurich, 8032 Zurich, Switzerland; Max Planck Institute for Metabolism Research, Cologne, Germany
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30
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Karvelis P, Paulus MP, Diaconescu AO. Individual differences in computational psychiatry: a review of current challenges. Neurosci Biobehav Rev 2023; 148:105137. [PMID: 36940888 DOI: 10.1016/j.neubiorev.2023.105137] [Citation(s) in RCA: 14] [Impact Index Per Article: 14.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/19/2023] [Revised: 03/04/2023] [Accepted: 03/14/2023] [Indexed: 03/23/2023]
Abstract
Bringing precision to the understanding and treatment of mental disorders requires instruments for studying clinically relevant individual differences. One promising approach is the development of computational assays: integrating computational models with cognitive tasks to infer latent patient-specific disease processes in brain computations. While recent years have seen many methodological advancements in computational modelling and many cross-sectional patient studies, much less attention has been paid to basic psychometric properties (reliability and construct validity) of the computational measures provided by the assays. In this review, we assess the extent of this issue by examining emerging empirical evidence. We find that many computational measures suffer from poor psychometric properties, which poses a risk of invalidating previous findings and undermining ongoing research efforts using computational assays to study individual (and even group) differences. We provide recommendations for how to address these problems and, crucially, embed them within a broader perspective on key developments that are needed for translating computational assays to clinical practice.
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Affiliation(s)
- Povilas Karvelis
- Krembil Centre for Neuroinformatics, Centre for Addiction and Mental Health (CAMH), Toronto, ON, Canada.
| | - Martin P Paulus
- Laureate Institute for Brain Research, Tulsa, OK, USA; Oxley College of Health Sciences, The University of Tulsa, Tulsa, OK, USA
| | - Andreea O Diaconescu
- Krembil Centre for Neuroinformatics, Centre for Addiction and Mental Health (CAMH), Toronto, ON, Canada; Department of Psychiatry, University of Toronto, Toronto, ON, Canada; Institute of Medical Sciences, University of Toronto, Toronto, ON, Canada; Department of Psychology, University of Toronto, Toronto, ON, Canada
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31
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Hein TP, Gong Z, Ivanova M, Fedele T, Nikulin V, Herrojo Ruiz M. Anterior cingulate and medial prefrontal cortex oscillations underlie learning alterations in trait anxiety in humans. Commun Biol 2023; 6:271. [PMID: 36922553 PMCID: PMC10017780 DOI: 10.1038/s42003-023-04628-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/25/2022] [Accepted: 02/27/2023] [Indexed: 03/18/2023] Open
Abstract
Anxiety has been linked to altered belief formation and uncertainty estimation, impacting learning. Identifying the neural processes underlying these changes is important for understanding brain pathology. Here, we show that oscillatory activity in the medial prefrontal, anterior cingulate and orbitofrontal cortex (mPFC, ACC, OFC) explains anxiety-related learning alterations. In a magnetoencephalography experiment, two groups of human participants pre-screened with high and low trait anxiety (HTA, LTA: 39) performed a probabilistic reward-based learning task. HTA undermined learning through an overestimation of volatility, leading to faster belief updating, more stochastic decisions and pronounced lose-shift tendencies. On a neural level, we observed increased gamma activity in the ACC, dmPFC, and OFC during encoding of precision-weighted prediction errors in HTA, accompanied by suppressed ACC alpha/beta activity. Our findings support the association between altered learning and belief updating in anxiety and changes in gamma and alpha/beta activity in the ACC, dmPFC, and OFC.
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Affiliation(s)
- Thomas P Hein
- Goldsmiths, University of London, Psychology Department, Whitehead Building New Cross, London, SE14 6NW, UK
| | - Zheng Gong
- Centre for Cognition and Decision making, Institute for Cognitive Neuroscience, HSE University, Moscow, Russian Federation
| | - Marina Ivanova
- Centre for Cognition and Decision making, Institute for Cognitive Neuroscience, HSE University, Moscow, Russian Federation
| | - Tommaso Fedele
- Centre for Cognition and Decision making, Institute for Cognitive Neuroscience, HSE University, Moscow, Russian Federation
| | - Vadim Nikulin
- Department of Neurology, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany
| | - Maria Herrojo Ruiz
- Goldsmiths, University of London, Psychology Department, Whitehead Building New Cross, London, SE14 6NW, UK.
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32
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Finke JB, Stalder T, Klucken T. Pupil dilation tracks divergent learning processes in aware versus unaware Pavlovian conditioning. Psychophysiology 2023:e14288. [PMID: 36906907 DOI: 10.1111/psyp.14288] [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: 03/10/2022] [Revised: 09/22/2022] [Accepted: 02/18/2023] [Indexed: 03/13/2023]
Abstract
Evidence regarding unaware differential fear conditioning in humans is mixed and even less is known about the effects of contingency awareness on appetitive conditioning. Phasic pupil dilation responses (PDR) might be more sensitive for capturing implicit learning than other measures, such as skin conductance responses (SCR). Here, we report data from two delay conditioning experiments utilizing PDR (alongside SCR and subjective assessments) to investigate the role of contingency awareness in aversive and appetitive conditioning. In both experiments, valence of unconditioned stimuli (UCS) was varied within participants by administering aversive (mild electric shocks) and appetitive UCSs (monetary rewards). Preceding visual stimuli (CSs) predicted either the reward, the shock (65% reinforcement), or neither UCS. In Exp. 1, participants were fully instructed about CS-UCS contingencies, whereas in Exp. 2, no such information was given. PDR and SCR demonstrated successful differential conditioning in Exp. 1 and in (learned) aware participants in Exp. 2. In non-instructed participants who remained fully unaware of contingencies (Exp. 2), differential modulation of early PDR (immediately after CS onset) by appetitive cues emerged. Associations with model-derived learning parameters further suggest that early PDR in unaware participants mainly reflect implicit learning of expected outcome value, whereas early PDR in aware (instructed/learned-aware) participants presumably index attentional processes (related to uncertainty/prediction error processing). Similar, but less clear results emerged for later PDR (preceding UCS onset). Our data argue in favor of a dual-process account of associative learning, suggesting that value-related processing can take place irrespective of mechanisms involved in conscious memory formation.
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Affiliation(s)
- Johannes B Finke
- Department of Clinical Psychology & Psychotherapy, University of Siegen, Siegen, Germany
| | - Tobias Stalder
- Department of Clinical Psychology & Psychotherapy, University of Siegen, Siegen, Germany
| | - Tim Klucken
- Department of Clinical Psychology & Psychotherapy, University of Siegen, Siegen, Germany
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33
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Tecilla M, Großbach M, Gentile G, Holland P, Sporn S, Antonini A, Herrojo Ruiz M. Modulation of Motor Vigor by Expectation of Reward Probability Trial-by-Trial Is Preserved in Healthy Ageing and Parkinson's Disease Patients. J Neurosci 2023; 43:1757-1777. [PMID: 36732072 PMCID: PMC10010462 DOI: 10.1523/jneurosci.1583-22.2022] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/18/2022] [Revised: 12/13/2022] [Accepted: 12/31/2022] [Indexed: 02/04/2023] Open
Abstract
Motor improvements, such as faster movement times or increased velocity, have been associated with reward magnitude in deterministic contexts. Yet whether individual inferences on reward probability influence motor vigor dynamically remains undetermined. We investigated how dynamically inferring volatile action-reward contingencies modulated motor performance trial-by-trial. We conducted three studies that coupled a reversal learning paradigm with a motor sequence task and used a validated hierarchical Bayesian model to fit trial-by-trial data. In Study 1, we tested healthy younger [HYA; 37 (24 females)] and older adults [HOA; 37 (17 females)], and medicated Parkinson's disease (PD) patients [20 (7 females)]. We showed that stronger predictions about the tendency of the action-reward contingency led to faster performance tempo, commensurate with movement time, on a trial-by-trial basis without robustly modulating reaction time (RT). Using Bayesian linear mixed models, we demonstrated a similar invigoration effect on performance tempo in HYA, HOA, and PD, despite HOA and PD being slower than HYA. In Study 2 [HYA, 39 (29 females)], we additionally showed that retrospective subjective inference about credit assignment did not contribute to differences in motor vigor effects. Last, Study 3 [HYA, 33 (27 females)] revealed that explicit beliefs about the reward tendency (confidence ratings) modulated performance tempo trial-by-trial. Our study is the first to reveal that the dynamic updating of beliefs about volatile action-reward contingencies positively biases motor performance through faster tempo. We also provide robust evidence for a preserved sensitivity of motor vigor to inferences about the action-reward mapping in aging and medicated PD.SIGNIFICANCE STATEMENT Navigating a world rich in uncertainty relies on updating beliefs about the probability that our actions lead to reward. Here, we investigated how inferring the action-reward contingencies in a volatile environment modulated motor vigor trial-by-trial in healthy younger and older adults, and in Parkinson's disease (PD) patients on medication. We found an association between trial-by-trial predictions about the tendency of the action-reward contingency and performance tempo, with stronger expectations speeding the movement. We additionally provided evidence for a similar sensitivity of performance tempo to the strength of these predictions in all groups. Thus, dynamic beliefs about the changing relationship between actions and their outcome enhanced motor vigor. This positive bias was not compromised by age or Parkinson's disease.
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Affiliation(s)
- Margherita Tecilla
- Department of Psychology, Goldsmiths, University of London, London SE146NW, United Kingdom
| | - Michael Großbach
- Institute of Music Physiology and Musicians' Medicine, Hannover University of Music Drama and Media, Hannover 30175, Germany
| | - Giovanni Gentile
- Parkinson and Movement Disorders Unit, Study Center for Neurodegeneration (CESNE), Department of Neuroscience, University of Padua, Padua 35131, Italy
| | - Peter Holland
- Department of Psychology, Goldsmiths, University of London, London SE146NW, United Kingdom
| | - Sebastian Sporn
- Department of Clinical and Movement Neuroscience, Queen Square Institute of Neurology, University College London, London WC1N3BG, United Kingdom
| | - Angelo Antonini
- Parkinson and Movement Disorders Unit, Study Center for Neurodegeneration (CESNE), Department of Neuroscience, University of Padua, Padua 35131, Italy
| | - Maria Herrojo Ruiz
- Department of Psychology, Goldsmiths, University of London, London SE146NW, United Kingdom
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34
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Schmidt SA, Shahsavarani S, Khan RA, Tai Y, Granato EC, Willson CM, Ramos P, Sherman P, Esquivel C, Sutton BP, Husain F. An examination of the reliability of seed-to-seed resting state functional connectivity in tinnitus patients. NEUROIMAGE: REPORTS 2023. [DOI: 10.1016/j.ynirp.2023.100158] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/17/2023]
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35
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Isherwood SJS, Bazin PL, Miletić S, Stevenson NR, Trutti AC, Tse DHY, Heathcote A, Matzke D, Innes RJ, Habli S, Sokołowski DR, Alkemade A, Håberg AK, Forstmann BU. Investigating Intra-Individual Networks of Response Inhibition and Interference Resolution using 7T MRI. Neuroimage 2023; 271:119988. [PMID: 36868392 DOI: 10.1016/j.neuroimage.2023.119988] [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: 10/21/2022] [Revised: 02/20/2023] [Accepted: 02/25/2023] [Indexed: 03/05/2023] Open
Abstract
Response inhibition and interference resolution are often considered subcomponents of an overarching inhibition system that utilizes the so-called cortico-basal-ganglia loop. Up until now, most previous functional magnetic resonance imaging (fMRI) literature has compared the two using between-subject designs, pooling data in the form of a meta-analysis or comparing different groups. Here, we investigate the overlap of activation patterns underlying response inhibition and interference resolution on a within-subject level, using ultra-high field MRI. In this model-based study, we furthered the functional analysis with cognitive modelling techniques to provide a more in-depth understanding of behaviour. We applied the stop-signal task and multi-source interference task to measure response inhibition and interference resolution, respectively. Our results lead us to conclude that these constructs are rooted in anatomically distinct brain areas and provide little evidence for spatial overlap. Across the two tasks, common BOLD responses were observed in the inferior frontal gyrus and anterior insula. Interference resolution relied more heavily on subcortical components, specifically nodes of the commonly referred to indirect and hyperdirect pathways, as well as the anterior cingulate cortex, and pre-supplementary motor area. Our data indicated that orbitofrontal cortex activation is specific to response inhibition. Our model-based approach provided evidence for the dissimilarity in behavioural dynamics between the two tasks. The current work exemplifies the importance of reducing inter-individual variance when comparing network patterns and the value of UHF-MRI for high resolution functional mapping.
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Affiliation(s)
- S J S Isherwood
- Integrative Model-Based Cognitive Neuroscience Research Unit, University of Amsterdam, Amsterdam, The Netherlands.
| | - P L Bazin
- Integrative Model-Based Cognitive Neuroscience Research Unit, University of Amsterdam, Amsterdam, The Netherlands; Department of Neurophysics, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany
| | - S Miletić
- Integrative Model-Based Cognitive Neuroscience Research Unit, University of Amsterdam, Amsterdam, The Netherlands
| | - N R Stevenson
- Integrative Model-Based Cognitive Neuroscience Research Unit, University of Amsterdam, Amsterdam, The Netherlands
| | - A C Trutti
- Integrative Model-Based Cognitive Neuroscience Research Unit, University of Amsterdam, Amsterdam, The Netherlands; Institute of Psychology, Leiden University, Leiden, The Netherlands
| | - D H Y Tse
- Norwegian University of Science and Technology, Trondheim, Norway
| | - A Heathcote
- Department of Psychological Methods, University of Amsterdam, Amsterdam, The Netherlands
| | - D Matzke
- Department of Psychological Methods, University of Amsterdam, Amsterdam, The Netherlands
| | - R J Innes
- Integrative Model-Based Cognitive Neuroscience Research Unit, University of Amsterdam, Amsterdam, The Netherlands
| | - S Habli
- Norwegian University of Science and Technology, Trondheim, Norway
| | - D R Sokołowski
- Norwegian University of Science and Technology, Trondheim, Norway
| | - A Alkemade
- Integrative Model-Based Cognitive Neuroscience Research Unit, University of Amsterdam, Amsterdam, The Netherlands
| | - A K Håberg
- Norwegian University of Science and Technology, Trondheim, Norway
| | - B U Forstmann
- Integrative Model-Based Cognitive Neuroscience Research Unit, University of Amsterdam, Amsterdam, The Netherlands
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36
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Drusko A, Baumeister D, McPhee Christensen M, Kold S, Fisher VL, Treede RD, Powers A, Graven-Nielsen T, Tesarz J. A novel computational approach to pain perception modelling within a Bayesian framework using quantitative sensory testing. Sci Rep 2023; 13:3196. [PMID: 36823292 PMCID: PMC9950064 DOI: 10.1038/s41598-023-29758-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/31/2022] [Accepted: 02/09/2023] [Indexed: 02/25/2023] Open
Abstract
Pain perception can be studied as an inferential process in which prior information influences the perception of nociceptive input. To date, there are no suitable psychophysical paradigms to measure this at an individual level. We developed a quantitative sensory testing paradigm allowing for quantification of the influence of prior expectations versus current nociceptive input during perception. Using a Pavlovian-learning task, we investigated the influence of prior expectations on the belief about the varying strength of association between a painful electrical cutaneous stimulus and a visual cue in healthy subjects (N = 70). The belief in cue-pain associations was examined with computational modelling using a Hierarchical Gaussian Filter (HGF). Prior weighting estimates in the HGF model were compared with the established measures of conditioned pain modulation (CPM) and temporal summation of pain (TSP) assessed by cuff algometry. Subsequent HGF-modelling and estimation of the influence of prior beliefs on perception showed that 70% of subjects had a higher reliance on nociceptive input during perception of acute pain stimuli, whereas 30% showed a stronger weighting of prior expectations over sensory evidence. There was no association between prior weighting estimates and CPM or TSP. The data demonstrates relevant individual differences in prior weighting and suggests an importance of top-down cognitive processes on pain perception. Our new psychophysical testing paradigm provides a method to identify individuals with traits suggesting greater reliance on prior expectations in pain perception, which may be a risk factor for developing chronic pain and may be differentially responsive to learning-based interventions.
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Affiliation(s)
- Armin Drusko
- Department of General Internal Medicine and Psychosomatics, University Hospital Heidelberg, Im Neuenheimer Feld 410, 69120, Heidelberg, Germany
| | - David Baumeister
- Department of General Internal Medicine and Psychosomatics, University Hospital Heidelberg, Im Neuenheimer Feld 410, 69120, Heidelberg, Germany
| | - Megan McPhee Christensen
- Center for Neuroplasticity and Pain (CNAP), Department of Health Science and Technology, Aalborg University, Aalborg, Denmark
| | - Sebastian Kold
- Center for Neuroplasticity and Pain (CNAP), Department of Health Science and Technology, Aalborg University, Aalborg, Denmark
| | - Victoria Lynn Fisher
- Department of Psychiatry, Yale University School of Medicine, New Haven, CT, USA
| | - Rolf-Detlef Treede
- Mannheim Center for Translational Neuroscience (MCTN), Heidelberg University, Heidelberg, Germany
| | - Albert Powers
- Department of Psychiatry, Yale University School of Medicine, New Haven, CT, USA
| | - Thomas Graven-Nielsen
- Center for Neuroplasticity and Pain (CNAP), Department of Health Science and Technology, Aalborg University, Aalborg, Denmark
| | - Jonas Tesarz
- Department of General Internal Medicine and Psychosomatics, University Hospital Heidelberg, Im Neuenheimer Feld 410, 69120, Heidelberg, Germany.
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Campbell MEJ, Sherwell CS, Cunnington R, Brown S, Breakspear M. Reaction Time "Mismatch Costs" Change with the Likelihood of Stimulus-Response Compatibility. Psychon Bull Rev 2023; 30:184-199. [PMID: 36008626 PMCID: PMC9971163 DOI: 10.3758/s13423-022-02161-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 07/25/2022] [Indexed: 11/08/2022]
Abstract
Dyadic interactions require dynamic correspondence between one's own movements and those of the other agent. This mapping is largely viewed as imitative, with the behavioural hallmark being a reaction-time cost for mismatched actions. Yet the complex motor patterns humans enact together extend beyond direct-matching, varying adaptively between imitation, complementary movements, and counter-imitation. Optimal behaviour requires an agent to predict not only what is likely to be observed but also how that observed action will relate to their own motor planning. In 28 healthy adults, we examined imitation and counter-imitation in a task that varied the likelihood of stimulus-response congruence from highly predictable, to moderately predictable, to unpredictable. To gain mechanistic insights into the statistical learning of stimulus-response compatibility, we compared two computational models of behaviour: (1) a classic fixed learning-rate model (Rescorla-Wagner reinforcement [RW]) and (2) a hierarchical model of perceptual-behavioural processes in which the learning rate adapts to the inferred environmental volatility (hierarchical Gaussian filter [HGF]). Though more complex and hence penalized by model selection, the HGF provided a more likely model of the participants' behaviour. Matching motor responses were only primed (faster) in the most experimentally volatile context. This bias was reversed so that mismatched actions were primed when beliefs about volatility were lower. Inferential statistics indicated that matching responses were only primed in unpredictable contexts when stimuli-response congruence was at 50:50 chance. Outside of these unpredictable blocks the classic stimulus-response compatibility effect was reversed: Incongruent responses were faster than congruent ones. We show that hierarchical Bayesian learning of environmental statistics may underlie response priming during dyadic interactions.
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Affiliation(s)
- Megan E J Campbell
- School of Psychological Sciences, University of Newcastle, Callaghan, Australia.
- Hunter Medical Research Institute, Newcastle, Lot 1 Kookaburra Circuit, New Lambton Heights, NSW, 2305, Australia.
- The Queensland Brain Institute, The University of Queensland, St Lucia, Australia.
| | - Chase S Sherwell
- School of Education, University of Queensland, St Lucia, Australia
| | - Ross Cunnington
- School of Psychology, University of Queensland, St Lucia, Australia
| | - Scott Brown
- School of Psychological Sciences, University of Newcastle, Callaghan, Australia
| | - Michael Breakspear
- School of Psychological Sciences, University of Newcastle, Callaghan, Australia
- School of Medicine, University of Newcastle, Callaghan, Australia
- Schools of Psychological Sciences & Medicine, University of Newcastle, Callaghan, Australia
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Knolle F, Sterner E, Moutoussis M, Adams RA, Griffin JD, Haarsma J, Taverne H, Goodyer IM, Fletcher PC, Murray GK. Action selection in early stages of psychosis: an active inference approach. J Psychiatry Neurosci 2023; 48:E78-E89. [PMID: 36810306 PMCID: PMC9949875 DOI: 10.1503/jpn.220141] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/09/2022] [Revised: 11/11/2022] [Accepted: 11/28/2022] [Indexed: 02/23/2023] Open
Abstract
BACKGROUND To interact successfully with their environment, humans need to build a model to make sense of noisy and ambiguous inputs. An inaccurate model, as suggested to be the case for people with psychosis, disturbs optimal action selection. Recent computational models, such as active inference, have emphasized the importance of action selection, treating it as a key part of the inferential process. Based on an active inference framework, we sought to evaluate previous knowledge and belief precision in an action-based task, given that alterations in these parameters have been linked to the development of psychotic symptoms. We further sought to determine whether task performance and modelling parameters would be suitable for classification of patients and controls. METHODS Twenty-three individuals with an at-risk mental state, 26 patients with first-episode psychosis and 31 controls completed a probabilistic task in which action choice (go/no-go) was dissociated from outcome valence (gain or loss). We evaluated group differences in performance and active inference model parameters and performed receiver operating characteristic (ROC) analyses to assess group classification. RESULTS We found reduced overall performance in patients with psychosis. Active inference modelling revealed that patients showed increased forgetting, reduced confidence in policy selection and less optimal general choice behaviour, with poorer action-state associations. Importantly, ROC analysis showed fair-to-good classification performance for all groups, when combining modelling parameters and performance measures. LIMITATIONS The sample size is moderate. CONCLUSION Active inference modelling of this task provides further explanation for dysfunctional mechanisms underlying decision-making in psychosis and may be relevant for future research on the development of biomarkers for early identification of psychosis.
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Affiliation(s)
- Franziska Knolle
- From the Department of Diagnostic and Interventional Neuroradiology, School of Medicine, Technical University of Munich, Munich, Germany (Knolle, Sterner); the Department of Psychiatry, University of Cambridge, Cambridge, UK (Knolle, Griffin, Taverne, Goodyer, Fletcher, Murray); the Max Planck-UCL Centre for Computational Psychiatry and Ageing Research, London, UK (Moutoussis, Adams); the Centre for Medical Image Computing, Department of Computer Science, University College London, London, UK (Adams); the Wellcome Centre for Human Neuroimaging, University College London, London, UK (Haarsma); the University of Amsterdam, Amsterdam, NL (Taverne); Wellcome Trust MRC Institute of Metabolic Science, University of Cambridge, Cambridge Biomedical Campus, Cambridge, UK (Goodyer, Fletcher); Cambridgeshire and Peterborough NHS Foundation Trust, Cambridge, UK (Murray)
| | - Elisabeth Sterner
- From the Department of Diagnostic and Interventional Neuroradiology, School of Medicine, Technical University of Munich, Munich, Germany (Knolle, Sterner); the Department of Psychiatry, University of Cambridge, Cambridge, UK (Knolle, Griffin, Taverne, Goodyer, Fletcher, Murray); the Max Planck-UCL Centre for Computational Psychiatry and Ageing Research, London, UK (Moutoussis, Adams); the Centre for Medical Image Computing, Department of Computer Science, University College London, London, UK (Adams); the Wellcome Centre for Human Neuroimaging, University College London, London, UK (Haarsma); the University of Amsterdam, Amsterdam, NL (Taverne); Wellcome Trust MRC Institute of Metabolic Science, University of Cambridge, Cambridge Biomedical Campus, Cambridge, UK (Goodyer, Fletcher); Cambridgeshire and Peterborough NHS Foundation Trust, Cambridge, UK (Murray)
| | - Michael Moutoussis
- From the Department of Diagnostic and Interventional Neuroradiology, School of Medicine, Technical University of Munich, Munich, Germany (Knolle, Sterner); the Department of Psychiatry, University of Cambridge, Cambridge, UK (Knolle, Griffin, Taverne, Goodyer, Fletcher, Murray); the Max Planck-UCL Centre for Computational Psychiatry and Ageing Research, London, UK (Moutoussis, Adams); the Centre for Medical Image Computing, Department of Computer Science, University College London, London, UK (Adams); the Wellcome Centre for Human Neuroimaging, University College London, London, UK (Haarsma); the University of Amsterdam, Amsterdam, NL (Taverne); Wellcome Trust MRC Institute of Metabolic Science, University of Cambridge, Cambridge Biomedical Campus, Cambridge, UK (Goodyer, Fletcher); Cambridgeshire and Peterborough NHS Foundation Trust, Cambridge, UK (Murray)
| | - Rick A Adams
- From the Department of Diagnostic and Interventional Neuroradiology, School of Medicine, Technical University of Munich, Munich, Germany (Knolle, Sterner); the Department of Psychiatry, University of Cambridge, Cambridge, UK (Knolle, Griffin, Taverne, Goodyer, Fletcher, Murray); the Max Planck-UCL Centre for Computational Psychiatry and Ageing Research, London, UK (Moutoussis, Adams); the Centre for Medical Image Computing, Department of Computer Science, University College London, London, UK (Adams); the Wellcome Centre for Human Neuroimaging, University College London, London, UK (Haarsma); the University of Amsterdam, Amsterdam, NL (Taverne); Wellcome Trust MRC Institute of Metabolic Science, University of Cambridge, Cambridge Biomedical Campus, Cambridge, UK (Goodyer, Fletcher); Cambridgeshire and Peterborough NHS Foundation Trust, Cambridge, UK (Murray)
| | - Juliet D Griffin
- From the Department of Diagnostic and Interventional Neuroradiology, School of Medicine, Technical University of Munich, Munich, Germany (Knolle, Sterner); the Department of Psychiatry, University of Cambridge, Cambridge, UK (Knolle, Griffin, Taverne, Goodyer, Fletcher, Murray); the Max Planck-UCL Centre for Computational Psychiatry and Ageing Research, London, UK (Moutoussis, Adams); the Centre for Medical Image Computing, Department of Computer Science, University College London, London, UK (Adams); the Wellcome Centre for Human Neuroimaging, University College London, London, UK (Haarsma); the University of Amsterdam, Amsterdam, NL (Taverne); Wellcome Trust MRC Institute of Metabolic Science, University of Cambridge, Cambridge Biomedical Campus, Cambridge, UK (Goodyer, Fletcher); Cambridgeshire and Peterborough NHS Foundation Trust, Cambridge, UK (Murray)
| | - Joost Haarsma
- From the Department of Diagnostic and Interventional Neuroradiology, School of Medicine, Technical University of Munich, Munich, Germany (Knolle, Sterner); the Department of Psychiatry, University of Cambridge, Cambridge, UK (Knolle, Griffin, Taverne, Goodyer, Fletcher, Murray); the Max Planck-UCL Centre for Computational Psychiatry and Ageing Research, London, UK (Moutoussis, Adams); the Centre for Medical Image Computing, Department of Computer Science, University College London, London, UK (Adams); the Wellcome Centre for Human Neuroimaging, University College London, London, UK (Haarsma); the University of Amsterdam, Amsterdam, NL (Taverne); Wellcome Trust MRC Institute of Metabolic Science, University of Cambridge, Cambridge Biomedical Campus, Cambridge, UK (Goodyer, Fletcher); Cambridgeshire and Peterborough NHS Foundation Trust, Cambridge, UK (Murray)
| | - Hilde Taverne
- From the Department of Diagnostic and Interventional Neuroradiology, School of Medicine, Technical University of Munich, Munich, Germany (Knolle, Sterner); the Department of Psychiatry, University of Cambridge, Cambridge, UK (Knolle, Griffin, Taverne, Goodyer, Fletcher, Murray); the Max Planck-UCL Centre for Computational Psychiatry and Ageing Research, London, UK (Moutoussis, Adams); the Centre for Medical Image Computing, Department of Computer Science, University College London, London, UK (Adams); the Wellcome Centre for Human Neuroimaging, University College London, London, UK (Haarsma); the University of Amsterdam, Amsterdam, NL (Taverne); Wellcome Trust MRC Institute of Metabolic Science, University of Cambridge, Cambridge Biomedical Campus, Cambridge, UK (Goodyer, Fletcher); Cambridgeshire and Peterborough NHS Foundation Trust, Cambridge, UK (Murray)
| | - Ian M Goodyer
- From the Department of Diagnostic and Interventional Neuroradiology, School of Medicine, Technical University of Munich, Munich, Germany (Knolle, Sterner); the Department of Psychiatry, University of Cambridge, Cambridge, UK (Knolle, Griffin, Taverne, Goodyer, Fletcher, Murray); the Max Planck-UCL Centre for Computational Psychiatry and Ageing Research, London, UK (Moutoussis, Adams); the Centre for Medical Image Computing, Department of Computer Science, University College London, London, UK (Adams); the Wellcome Centre for Human Neuroimaging, University College London, London, UK (Haarsma); the University of Amsterdam, Amsterdam, NL (Taverne); Wellcome Trust MRC Institute of Metabolic Science, University of Cambridge, Cambridge Biomedical Campus, Cambridge, UK (Goodyer, Fletcher); Cambridgeshire and Peterborough NHS Foundation Trust, Cambridge, UK (Murray)
| | - Paul C Fletcher
- From the Department of Diagnostic and Interventional Neuroradiology, School of Medicine, Technical University of Munich, Munich, Germany (Knolle, Sterner); the Department of Psychiatry, University of Cambridge, Cambridge, UK (Knolle, Griffin, Taverne, Goodyer, Fletcher, Murray); the Max Planck-UCL Centre for Computational Psychiatry and Ageing Research, London, UK (Moutoussis, Adams); the Centre for Medical Image Computing, Department of Computer Science, University College London, London, UK (Adams); the Wellcome Centre for Human Neuroimaging, University College London, London, UK (Haarsma); the University of Amsterdam, Amsterdam, NL (Taverne); Wellcome Trust MRC Institute of Metabolic Science, University of Cambridge, Cambridge Biomedical Campus, Cambridge, UK (Goodyer, Fletcher); Cambridgeshire and Peterborough NHS Foundation Trust, Cambridge, UK (Murray)
| | - Graham K Murray
- From the Department of Diagnostic and Interventional Neuroradiology, School of Medicine, Technical University of Munich, Munich, Germany (Knolle, Sterner); the Department of Psychiatry, University of Cambridge, Cambridge, UK (Knolle, Griffin, Taverne, Goodyer, Fletcher, Murray); the Max Planck-UCL Centre for Computational Psychiatry and Ageing Research, London, UK (Moutoussis, Adams); the Centre for Medical Image Computing, Department of Computer Science, University College London, London, UK (Adams); the Wellcome Centre for Human Neuroimaging, University College London, London, UK (Haarsma); the University of Amsterdam, Amsterdam, NL (Taverne); Wellcome Trust MRC Institute of Metabolic Science, University of Cambridge, Cambridge Biomedical Campus, Cambridge, UK (Goodyer, Fletcher); Cambridgeshire and Peterborough NHS Foundation Trust, Cambridge, UK (Murray)
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Task-evoked pupillary responses track precision-weighted prediction errors and learning rate during interceptive visuomotor actions. Sci Rep 2022; 12:22098. [PMID: 36543845 PMCID: PMC9772236 DOI: 10.1038/s41598-022-26544-w] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/12/2022] [Accepted: 12/15/2022] [Indexed: 12/24/2022] Open
Abstract
In this study, we examined the relationship between physiological encoding of surprise and the learning of anticipatory eye movements. Active inference portrays perception and action as interconnected inference processes, driven by the imperative to minimise the surprise of sensory observations. To examine this characterisation of oculomotor learning during a hand-eye coordination task, we tested whether anticipatory eye movements were updated in accordance with Bayesian principles and whether trial-by-trial learning rates tracked pupil dilation as a marker of 'surprise'. Forty-four participants completed an interception task in immersive virtual reality that required them to hit bouncing balls that had either expected or unexpected bounce profiles. We recorded anticipatory eye movements known to index participants' beliefs about likely ball bounce trajectories. By fitting a hierarchical Bayesian inference model to the trial-wise trajectories of these predictive eye movements, we were able to estimate each individual's expectations about bounce trajectories, rates of belief updating, and precision-weighted prediction errors. We found that the task-evoked pupil response tracked prediction errors and learning rates but not beliefs about ball bounciness or environmental volatility. These findings are partially consistent with active inference accounts and shed light on how encoding of surprise may shape the control of action.
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40
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Kafadar E, Fisher VL, Quagan B, Hammer A, Jaeger H, Mourgues C, Thomas R, Chen L, Imtiaz A, Sibarium E, Negreira AM, Sarisik E, Polisetty V, Benrimoh D, Sheldon AD, Lim C, Mathys C, Powers AR. Conditioned Hallucinations and Prior Overweighting Are State-Sensitive Markers of Hallucination Susceptibility. Biol Psychiatry 2022; 92:772-780. [PMID: 35843743 PMCID: PMC10575690 DOI: 10.1016/j.biopsych.2022.05.007] [Citation(s) in RCA: 15] [Impact Index Per Article: 7.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/27/2021] [Revised: 04/08/2022] [Accepted: 05/02/2022] [Indexed: 11/18/2022]
Abstract
BACKGROUND Recent advances in computational psychiatry have identified latent cognitive and perceptual states that predispose to psychotic symptoms. Behavioral data fit to Bayesian models have demonstrated an overreliance on priors (i.e., prior overweighting) during perception in select samples of individuals with hallucinations, corresponding to increased precision of prior expectations over incoming sensory evidence. However, the clinical utility of this observation depends on the extent to which it reflects static symptom risk or current symptom state. METHODS To determine whether task performance and estimated prior weighting relate to specific elements of symptom expression, a large, heterogeneous, and deeply phenotyped sample of hallucinators (n = 249) and nonhallucinators (n = 209) performed the conditioned hallucination (CH) task. RESULTS We found that CH rates predicted stable measures of hallucination status (i.e., peak frequency). However, CH rates were more sensitive to hallucination state (i.e., recent frequency), significantly correlating with recent hallucination severity and driven by heightened reliance on past experiences (priors). To further test the sensitivity of CH rate and prior weighting to symptom severity, a subset of participants with hallucinations (n = 40) performed a repeated-measures version of the CH task. Changes in both CH frequency and prior weighting varied with changes in auditory hallucination frequency on follow-up. CONCLUSIONS These results indicate that CH rate and prior overweighting are state markers of hallucination status, potentially useful in tracking disease development and treatment response.
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Affiliation(s)
- Eren Kafadar
- Yale University School of Medicine and the Connecticut Mental Health Center, New Haven, Connecticut
| | - Victoria L Fisher
- Yale University School of Medicine and the Connecticut Mental Health Center, New Haven, Connecticut
| | - Brittany Quagan
- Yale University School of Medicine and the Connecticut Mental Health Center, New Haven, Connecticut
| | - Allison Hammer
- Yale University School of Medicine and the Connecticut Mental Health Center, New Haven, Connecticut
| | - Hale Jaeger
- Yale University School of Medicine and the Connecticut Mental Health Center, New Haven, Connecticut
| | - Catalina Mourgues
- Yale University School of Medicine and the Connecticut Mental Health Center, New Haven, Connecticut
| | - Rigi Thomas
- School of Naturopathic Medicine, Southwest College of Naturopathic Medicine and Health Sciences, Tempe, Arizona
| | - Linda Chen
- Faculty of Science, University of British Columbia, Vancouver, British Columbia, Canada
| | - Ayyub Imtiaz
- Department of Psychiatry, St Elizabeth's Hospital, Washington, DC
| | - Ely Sibarium
- Yale University School of Medicine and the Connecticut Mental Health Center, New Haven, Connecticut
| | | | - Elif Sarisik
- Istanbul Faculty of Medicine, Istanbul University, Istanbul, Turkey; Max Planck Institute for Psychiatry, Munich, Germany
| | - Vasishta Polisetty
- Department of Psychiatry, All India Institute of Medical Sciences, New Delhi, India
| | - David Benrimoh
- McGill University School of Medicine, Montreal, Quebec, Canada
| | - Andrew D Sheldon
- Yale University School of Medicine and the Connecticut Mental Health Center, New Haven, Connecticut
| | - Chris Lim
- Yale University School of Medicine and the Connecticut Mental Health Center, New Haven, Connecticut
| | - Christoph Mathys
- Interacting Minds Centre, Aarhus University, Aarhus C, Denmark; Translational Neuromodeling Unit, Institute for Biomedical Engineering, University of Zürich and ETH Zürich, Zurich, Switzerland; Neuroscience Area, Scuola Internazionale Superiore di Studi Avanzati, Trieste, Italy
| | - Albert R Powers
- Yale University School of Medicine and the Connecticut Mental Health Center, New Haven, Connecticut.
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An optimized protocol for assessing changes in mouse whole-brain activity using opto-fMRI. STAR Protoc 2022; 3:101761. [PMID: 36240060 PMCID: PMC9568887 DOI: 10.1016/j.xpro.2022.101761] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/29/2022] [Revised: 08/10/2022] [Accepted: 09/15/2022] [Indexed: 11/07/2022] Open
Abstract
Functional magnetic resonance imaging (fMRI) in mouse brain, paired with spatially and temporally defined manipulations, offers a powerful tool to causally explain the effect of specific neuronal activity on brain network dynamics. Here, we present an optimized protocol to measure cell-type-specific contributions to changes in whole-brain dynamics in mice using optogenetics (opto)-fMRI. This protocol details the injection of ChR2-expressing AAV, the implantation of optical fiber, the steps to perform opto-BOLD (blood-oxygenation-level-dependent) fMRI recording, and data analysis. For complete details on the use and execution of this protocol, please refer to Grimm et al. (2021).
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Chao ZC, Huang YT, Wu CT. A quantitative model reveals a frequency ordering of prediction and prediction-error signals in the human brain. Commun Biol 2022; 5:1076. [PMID: 36216885 PMCID: PMC9550773 DOI: 10.1038/s42003-022-04049-6] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/26/2022] [Accepted: 09/29/2022] [Indexed: 11/29/2022] Open
Abstract
The human brain is proposed to harbor a hierarchical predictive coding neuronal network underlying perception, cognition, and action. In support of this theory, feedforward signals for prediction error have been reported. However, the identification of feedback prediction signals has been elusive due to their causal entanglement with prediction-error signals. Here, we use a quantitative model to decompose these signals in electroencephalography during an auditory task, and identify their spatio-spectral-temporal signatures across two functional hierarchies. Two prediction signals are identified in the period prior to the sensory input: a low-level signal representing the tone-to-tone transition in the high beta frequency band, and a high-level signal for the multi-tone sequence structure in the low beta band. Subsequently, prediction-error signals dependent on the prior predictions are found in the gamma band. Our findings reveal a frequency ordering of prediction signals and their hierarchical interactions with prediction-error signals supporting predictive coding theory. A computational framework can extract spatio-spectro-temporal neural signatures corresponding to hierarchical prediction and prediction errors in a local-global auditory task.
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Affiliation(s)
- Zenas C Chao
- International Research Center for Neurointelligence (WPI-IRCN), UTIAS, The University of Tokyo, Tokyo, Japan.
| | - Yiyuan Teresa Huang
- International Research Center for Neurointelligence (WPI-IRCN), UTIAS, The University of Tokyo, Tokyo, Japan.,School of Occupational Therapy, College of Medicine, National Taiwan University, Taipei, Taiwan
| | - Chien-Te Wu
- International Research Center for Neurointelligence (WPI-IRCN), UTIAS, The University of Tokyo, Tokyo, Japan.,School of Occupational Therapy, College of Medicine, National Taiwan University, Taipei, Taiwan
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De Castro Martins C, Chaminade T, Cavazza M. Causal Analysis of Activity in Social Brain Areas During Human-Agent Conversation. FRONTIERS IN NEUROERGONOMICS 2022; 3:843005. [PMID: 38235459 PMCID: PMC10790851 DOI: 10.3389/fnrgo.2022.843005] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 12/24/2021] [Accepted: 04/11/2022] [Indexed: 01/19/2024]
Abstract
This article investigates the differences in cognitive and neural mechanisms between human-human and human-virtual agent interaction using a dataset recorded in an ecologically realistic environment. We use Convergent Cross Mapping (CCM) to investigate functional connectivity between pairs of regions involved in the framework of social cognitive neuroscience, namely the fusiform gyrus, superior temporal sulcus (STS), temporoparietal junction (TPJ), and the dorsolateral prefrontal cortex (DLPFC)-taken as prefrontal asymmetry. Our approach is a compromise between investigating local activation in specific regions and investigating connectivity networks that may form part of larger networks. In addition to concording with previous studies, our results suggest that the right TPJ is one of the most reliable areas for assessing processes occurring during human-virtual agent interactions, both in a static and dynamic sense.
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Affiliation(s)
| | - Thierry Chaminade
- Institut de Neurosciences de la Timone (INT, UMR7289), Aix-Marseille University-CNRS, Marseille, France
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Bollmann S, Mattern H, Bernier M, Robinson SD, Park DJ, Speck O, Polimeni JR. Imaging of the pial arterial vasculature of the human brain in vivo using high-resolution 7T time-of-flight angiography. eLife 2022; 11:71186. [PMID: 35486089 PMCID: PMC9150892 DOI: 10.7554/elife.71186] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/11/2021] [Accepted: 04/28/2022] [Indexed: 11/30/2022] Open
Abstract
The pial arterial vasculature of the human brain is the only blood supply to the neocortex, but quantitative data on the morphology and topology of these mesoscopic arteries (diameter 50–300 µm) remains scarce. Because it is commonly assumed that blood flow velocities in these vessels are prohibitively slow, non-invasive time-of-flight magnetic resonance angiography (TOF-MRA)—which is well suited to high 3D imaging resolutions—has not been applied to imaging the pial arteries. Here, we provide a theoretical framework that outlines how TOF-MRA can visualize small pial arteries in vivo, by employing extremely small voxels at the size of individual vessels. We then provide evidence for this theory by imaging the pial arteries at 140 µm isotropic resolution using a 7 Tesla (T) magnetic resonance imaging (MRI) scanner and prospective motion correction, and show that pial arteries one voxel width in diameter can be detected. We conclude that imaging pial arteries is not limited by slow blood flow, but instead by achievable image resolution. This study represents the first targeted, comprehensive account of imaging pial arteries in vivo in the human brain. This ultra-high-resolution angiography will enable the characterization of pial vascular anatomy across the brain to investigate patterns of blood supply and relationships between vascular and functional architecture.
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Affiliation(s)
- Saskia Bollmann
- Centre for Advanced Imaging, The University of Queensland, Brisbane, Australia
| | - Hendrik Mattern
- Department of Biomedical Magnetic Resonance, Otto-von-Guericke University Magdeburg, Magdeburg, Germany
| | - Michaël Bernier
- Athinoula A Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, United States
| | - Simon D Robinson
- Centre for Advanced Imaging, The University of Queensland, Brisbane, Australia
| | - Daniel J Park
- Athinoula A Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, United States
| | - Oliver Speck
- German Center for Neurodegenerative Diseases, Magdeburg, Germany
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Sorokin MY, Palchikova EI, Kibitov AA, Kasyanov ED, Khobeysh MA, Zubova EY. Mental State of Inpatients With COVID-19: A Computational Psychiatry Approach. Front Psychiatry 2022; 13:801135. [PMID: 35463517 PMCID: PMC9021726 DOI: 10.3389/fpsyt.2022.801135] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/24/2021] [Accepted: 02/15/2022] [Indexed: 12/25/2022] Open
Abstract
BACKGROUND The overload of healthcare systems around the world and the danger of infection have limited the ability of researchers to obtain sufficient and reliable data on psychopathology in hospitalized patients with coronavirus disease 2019 (COVID-19). The relationship between severe acute respiratory syndrome with the coronavirus 2 (SARS-CoV-2) infection and specific mental disturbances remains poorly understood. AIM To reveal the possibility of identifying the typology and frequency of psychiatric syndromes associated with acute COVID-19 using cluster analysis of discrete psychopathological phenomena. MATERIALS AND METHODS Descriptive data on the mental state of 55 inpatients with COVID-19 were obtained by young-career physicians. Classification of observed clinical phenomena was performed with k-means cluster analysis of variables coded from the main psychopathological symptoms. Dispersion analysis with p level 0.05 was used to reveal the clusters differences in demography, parameters of inflammation, and respiration function collected on the basis of the original medical records. RESULTS Three resulting clusters of patients were identified: (1) persons with anxiety; disorders of fluency and tempo of thinking, mood, attention, and motor-volitional sphere; reduced insight; and pessimistic plans for the future (n = 11); (2) persons without psychopathology (n = 37); and (3) persons with disorientation; disorders of memory, attention, fluency, and tempo of thinking; and reduced insight (n = 7). The development of a certain type of impaired mental state was specifically associated with the following: age, lung lesions according to computed tomography, saturation, respiratory rate, C-reactive protein level, and platelet count. CONCLUSION Anxiety and/or mood disturbances with psychomotor retardation as well as symptoms of impaired consciousness, memory, and insight may be considered as neuropsychiatric manifestations of COVID-19 and should be used for clinical risk assessment.
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Affiliation(s)
- Mikhail Yu Sorokin
- The Integrative Pharmaco-Psychotherapy of Patients With Mental Disorders Department, V.M. Bekhterev National Medical Research Center for Psychiatry and Neurology, Saint Petersburg, Russia
| | - Ekaterina I Palchikova
- The Geriatric Psychiatry Department, V.M. Bekhterev National Medical Research Center for Psychiatry and Neurology, Saint Petersburg, Russia
| | - Andrey A Kibitov
- The Educational Department, V.M. Bekhterev National Medical Research Center for Psychiatry and Neurology, Saint Petersburg, Russia
| | - Evgeny D Kasyanov
- The Translational Psychiatry Department, V.M. Bekhterev National Medical Research Center for Psychiatry and Neurology, Saint Petersburg, Russia
| | - Maria A Khobeysh
- The Integrative Pharmaco-Psychotherapy of Patients With Mental Disorders Department, V.M. Bekhterev National Medical Research Center for Psychiatry and Neurology, Saint Petersburg, Russia
| | - Elena Yu Zubova
- The Educational Department, V.M. Bekhterev National Medical Research Center for Psychiatry and Neurology, Saint Petersburg, Russia
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Grimm C, Frässle S, Steger C, von Ziegler L, Sturman O, Shemesh N, Peleg-Raibstein D, Burdakov D, Bohacek J, Stephan KE, Razansky D, Wenderoth N, Zerbi V. Optogenetic activation of striatal D1R and D2R cells differentially engages downstream connected areas beyond the basal ganglia. Cell Rep 2021; 37:110161. [PMID: 34965430 DOI: 10.1016/j.celrep.2021.110161] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/08/2021] [Revised: 10/20/2021] [Accepted: 12/01/2021] [Indexed: 11/16/2022] Open
Abstract
The basal ganglia (BG) are a group of subcortical nuclei responsible for motor and executive function. Central to BG function are striatal cells expressing D1 (D1R) and D2 (D2R) dopamine receptors. D1R and D2R cells are considered functional antagonists that facilitate voluntary movements and inhibit competing motor patterns, respectively. However, whether they maintain a uniform function across the striatum and what influence they exert outside the BG is unclear. Here, we address these questions by combining optogenetic activation of D1R and D2R cells in the mouse ventrolateral caudoputamen with fMRI. Striatal D1R/D2R stimulation evokes distinct activity within the BG-thalamocortical network and differentially engages cerebellar and prefrontal regions. Computational modeling of effective connectivity confirms that changes in D1R/D2R output drive functional relationships between these regions. Our results suggest a complex functional organization of striatal D1R/D2R cells and hint toward an interconnected fronto-BG-cerebellar network modulated by striatal D1R and D2R cells.
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Affiliation(s)
- Christina Grimm
- Neural Control of Movement Lab, Department of Health Sciences and Technology, ETH Zürich, Zürich, Switzerland; Neuroscience Center Zurich, ETH Zürich and University of Zurich, Zürich, Switzerland
| | - Stefan Frässle
- Translational Neuromodeling Unit (TNU), Institute for Biomedical Engineering, University of Zürich and ETH Zürich, Zürich, Switzerland
| | - Céline Steger
- Neuroscience Center Zurich, ETH Zürich and University of Zurich, Zürich, Switzerland; Center for MR Research, University Children's Hospital Zurich, Zürich, Switzerland
| | - Lukas von Ziegler
- Laboratory of Molecular and Behavioral Neuroscience, Institute for Neuroscience, Department of Health Sciences and Technology, ETH Zürich, Zürich, Switzerland; Neuroscience Center Zurich, ETH Zürich and University of Zurich, Zürich, Switzerland
| | - Oliver Sturman
- Laboratory of Molecular and Behavioral Neuroscience, Institute for Neuroscience, Department of Health Sciences and Technology, ETH Zürich, Zürich, Switzerland; Neuroscience Center Zurich, ETH Zürich and University of Zurich, Zürich, Switzerland
| | - Noam Shemesh
- Champalimaud Research, Champalimaud Centre for the Unknown, Lisbon, Portugal
| | - Daria Peleg-Raibstein
- Laboratory of Neurobehavioral Dynamics, Department of Health Sciences and Technology, Institute for Neuroscience, ETH Zürich, Zürich, Switzerland; Institute for Biomedical Engineering and Institute of Pharmacology and Toxicology, Faculty of Medicine, University of Zurich, Zürich, Switzerland
| | - Denis Burdakov
- Laboratory of Neurobehavioral Dynamics, Department of Health Sciences and Technology, Institute for Neuroscience, ETH Zürich, Zürich, Switzerland; Institute for Biomedical Engineering and Institute of Pharmacology and Toxicology, Faculty of Medicine, University of Zurich, Zürich, Switzerland; Neuroscience Center Zurich, ETH Zürich and University of Zurich, Zürich, Switzerland
| | - Johannes Bohacek
- Laboratory of Molecular and Behavioral Neuroscience, Institute for Neuroscience, Department of Health Sciences and Technology, ETH Zürich, Zürich, Switzerland; Neuroscience Center Zurich, ETH Zürich and University of Zurich, Zürich, Switzerland
| | - Klaas Enno Stephan
- Translational Neuromodeling Unit (TNU), Institute for Biomedical Engineering, University of Zürich and ETH Zürich, Zürich, Switzerland
| | - Daniel Razansky
- Institute for Biomedical Engineering, Department of Information Technology and Electrical Engineering, ETH Zürich, Zürich, Switzerland; Institute of Biological and Medical Imaging (IBMI), Technical University of Munich and Helmholtz Center Munich, Munich, Germany; Neuroscience Center Zurich, ETH Zürich and University of Zurich, Zürich, Switzerland
| | - Nicole Wenderoth
- Neural Control of Movement Lab, Department of Health Sciences and Technology, ETH Zürich, Zürich, Switzerland; Neuroscience Center Zurich, ETH Zürich and University of Zurich, Zürich, Switzerland
| | - Valerio Zerbi
- Neural Control of Movement Lab, Department of Health Sciences and Technology, ETH Zürich, Zürich, Switzerland; Neuroscience Center Zurich, ETH Zürich and University of Zurich, Zürich, Switzerland.
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47
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Harrison OK, Köchli L, Marino S, Luechinger R, Hennel F, Brand K, Hess AJ, Frässle S, Iglesias S, Vinckier F, Petzschner FH, Harrison SJ, Stephan KE. Interoception of breathing and its relationship with anxiety. Neuron 2021; 109:4080-4093.e8. [PMID: 34672986 PMCID: PMC8691949 DOI: 10.1016/j.neuron.2021.09.045] [Citation(s) in RCA: 35] [Impact Index Per Article: 11.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/13/2021] [Revised: 09/01/2021] [Accepted: 09/23/2021] [Indexed: 01/22/2023]
Abstract
Interoception, the perception of internal bodily states, is thought to be inextricably linked to affective qualities such as anxiety. Although interoception spans sensory to metacognitive processing, it is not clear whether anxiety is differentially related to these processing levels. Here we investigated this question in the domain of breathing, using computational modeling and high-field (7 T) fMRI to assess brain activity relating to dynamic changes in inspiratory resistance of varying predictability. Notably, the anterior insula was associated with both breathing-related prediction certainty and prediction errors, suggesting an important role in representing and updating models of the body. Individuals with low versus moderate anxiety traits showed differential anterior insula activity for prediction certainty. Multi-modal analyses of data from fMRI, computational assessments of breathing-related metacognition, and questionnaires demonstrated that anxiety-interoception links span all levels from perceptual sensitivity to metacognition, with strong effects seen at higher levels of interoceptive processes.
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Affiliation(s)
- Olivia K Harrison
- Translational Neuromodeling Unit, Institute for Biomedical Engineering, University of Zurich and ETH Zurich, Zurich, Switzerland; Department of Psychology, University of Otago, Dunedin, New Zealand; Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, UK.
| | - Laura Köchli
- Translational Neuromodeling Unit, Institute for Biomedical Engineering, University of Zurich and ETH Zurich, Zurich, Switzerland
| | - Stephanie Marino
- Translational Neuromodeling Unit, Institute for Biomedical Engineering, University of Zurich and ETH Zurich, Zurich, Switzerland
| | - Roger Luechinger
- Institute for Biomedical Engineering, University of Zurich and ETH Zurich, Zurich, Switzerland
| | - Franciszek Hennel
- Institute for Biomedical Engineering, University of Zurich and ETH Zurich, Zurich, Switzerland
| | - Katja Brand
- Department of Psychology, University of Otago, Dunedin, New Zealand
| | - Alexander J Hess
- Translational Neuromodeling Unit, Institute for Biomedical Engineering, University of Zurich and ETH Zurich, Zurich, Switzerland
| | - Stefan Frässle
- Translational Neuromodeling Unit, Institute for Biomedical Engineering, University of Zurich and ETH Zurich, Zurich, Switzerland
| | - Sandra Iglesias
- Translational Neuromodeling Unit, Institute for Biomedical Engineering, University of Zurich and ETH Zurich, Zurich, Switzerland
| | - Fabien Vinckier
- Translational Neuromodeling Unit, Institute for Biomedical Engineering, University of Zurich and ETH Zurich, Zurich, Switzerland; Université de Paris, Paris, France; Department of Psychiatry, Service Hospitalo-Universitaire, GHU Paris Psychiatrie & Neurosciences, Paris, France
| | - Frederike H Petzschner
- Translational Neuromodeling Unit, Institute for Biomedical Engineering, University of Zurich and ETH Zurich, Zurich, Switzerland
| | - Samuel J Harrison
- Translational Neuromodeling Unit, Institute for Biomedical Engineering, University of Zurich and ETH Zurich, Zurich, Switzerland; Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, UK
| | - Klaas E Stephan
- Translational Neuromodeling Unit, Institute for Biomedical Engineering, University of Zurich and ETH Zurich, Zurich, Switzerland; Max Planck Institute for Metabolism Research, Cologne, Germany
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48
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Riva G, Serino S, Di Lernia D, Pagnini F. Regenerative Virtual Therapy: The Use of Multisensory Technologies and Mindful Attention for Updating the Altered Representations of the Bodily Self. Front Syst Neurosci 2021; 15:749268. [PMID: 34803617 PMCID: PMC8595209 DOI: 10.3389/fnsys.2021.749268] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/29/2021] [Accepted: 10/04/2021] [Indexed: 12/25/2022] Open
Abstract
The term “regenerative medicine” (RM) indicates an emerging trend in biomedical sciences that aims at replacing, engineering, or regenerating human cells, tissues, or organs to restore or establish normal function. So far, the focus of RM has been the physical body. Neuroscience, however, is now suggesting that mental disorders can be broadly characterized by a dysfunction in the way the brain computes and integrates the representations of the inner and outer body across time [bodily self-consciousness (BSC)]. In this perspective, we proposed a new kind of clinical intervention, i.e., “Regenerative Virtual Therapy” (RVT), which integrates knowledge from different disciplines, from neuroscience to computational psychiatry, to regenerate a distorted or faulty BSC. The main goal of RVT was to use technology-based somatic modification techniques to restructure the maladaptive bodily representations behind a pathological condition. Specifically, starting from a Bayesian model of our BSC (i.e., body matrix), we suggested the use of mindful attention, cognitive reappraisal, and brain stimulation techniques merged with high-rewarding and novel synthetic multisensory bodily experience (i.e., a virtual reality full-body illusion in sync with a low predictabIlity interoceptive modulation) to rewrite a faulty experience of the body and to regenerate the wellbeing of an individual. The use of RVT will also offer an unprecedented experimental overview of the dynamics of our bodily representations, allowing the reverse-engineering of their functioning for hacking them using advanced technologies.
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Affiliation(s)
- Giuseppe Riva
- Applied Technology for Neuro-Psychology Laboratory, Istituto Auxologico Italiano, Milan, Italy.,Humane Technology Laboratory, Università Cattolica del Sacro Cuore, Milan, Italy.,Department of Psychology, Università Cattolica del Sacro Cuore, Milan, Italy
| | - Silvia Serino
- Humane Technology Laboratory, Università Cattolica del Sacro Cuore, Milan, Italy.,Department of Psychology, Università Cattolica del Sacro Cuore, Milan, Italy
| | - Daniele Di Lernia
- Humane Technology Laboratory, Università Cattolica del Sacro Cuore, Milan, Italy.,Department of Psychology, Università Cattolica del Sacro Cuore, Milan, Italy
| | - Francesco Pagnini
- Department of Psychology, Università Cattolica del Sacro Cuore, Milan, Italy.,Department of Psychology, Harvard University, Cambridge, MA, United States
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49
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Advances in spiral fMRI: A high-resolution study with single-shot acquisition. Neuroimage 2021; 246:118738. [PMID: 34800666 DOI: 10.1016/j.neuroimage.2021.118738] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/19/2020] [Revised: 10/23/2021] [Accepted: 11/15/2021] [Indexed: 01/15/2023] Open
Abstract
Spiral fMRI has been put forward as a viable alternative to rectilinear echo-planar imaging, in particular due to its enhanced average k-space speed and thus high acquisition efficiency. This renders spirals attractive for contemporary fMRI applications that require high spatiotemporal resolution, such as laminar or columnar fMRI. However, in practice, spiral fMRI is typically hampered by its reduced robustness and ensuing blurring artifacts, which arise from imperfections in both static and dynamic magnetic fields. Recently, these limitations have been overcome by the concerted application of an expanded signal model that accounts for such field imperfections, and its inversion by iterative image reconstruction. In the challenging ultra-high field environment of 7 Tesla, where field inhomogeneity effects are aggravated, both multi-shot and single-shot 2D spiral imaging at sub-millimeter resolution was demonstrated with high depiction quality and anatomical congruency. In this work, we further these advances towards a time series application of spiral readouts, namely, single-shot spiral BOLD fMRI at 0.8 mm in-plane resolution. We demonstrate that high-resolution spiral fMRI at 7 T is not only feasible, but delivers both excellent image quality, BOLD sensitivity, and spatial specificity of the activation maps, with little artifactual blurring. Furthermore, we show the versatility of the approach with a combined in/out spiral readout at a more typical resolution (1.5 mm), where the high acquisition efficiency allows to acquire two images per shot for improved sensitivity by echo combination.
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50
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Piray P, Daw ND. A model for learning based on the joint estimation of stochasticity and volatility. Nat Commun 2021; 12:6587. [PMID: 34782597 PMCID: PMC8592992 DOI: 10.1038/s41467-021-26731-9] [Citation(s) in RCA: 31] [Impact Index Per Article: 10.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/23/2021] [Accepted: 10/08/2021] [Indexed: 02/08/2023] Open
Abstract
Previous research has stressed the importance of uncertainty for controlling the speed of learning, and how such control depends on the learner inferring the noise properties of the environment, especially volatility: the speed of change. However, learning rates are jointly determined by the comparison between volatility and a second factor, moment-to-moment stochasticity. Yet much previous research has focused on simplified cases corresponding to estimation of either factor alone. Here, we introduce a learning model, in which both factors are learned simultaneously from experience, and use the model to simulate human and animal data across many seemingly disparate neuroscientific and behavioral phenomena. By considering the full problem of joint estimation, we highlight a set of previously unappreciated issues, arising from the mutual interdependence of inference about volatility and stochasticity. This interdependence complicates and enriches the interpretation of previous results, such as pathological learning in individuals with anxiety and following amygdala damage.
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Affiliation(s)
- Payam Piray
- Princeton Neuroscience Institute and Department of Psychology, Princeton University, Princeton, NJ, USA.
| | - Nathaniel D Daw
- Princeton Neuroscience Institute and Department of Psychology, Princeton University, Princeton, NJ, USA
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