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Gui A, Throm E, da Costa PF, Penza F, Aguiló Mayans M, Jordan-Barros A, Haartsen R, Leech R, Jones EJH. Neuroadaptive Bayesian optimisation to study individual differences in infants' engagement with social cues. Dev Cogn Neurosci 2024; 68:101401. [PMID: 38870603 PMCID: PMC11225696 DOI: 10.1016/j.dcn.2024.101401] [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: 02/12/2024] [Revised: 05/31/2024] [Accepted: 06/01/2024] [Indexed: 06/15/2024] Open
Abstract
Infants' motivation to engage with the social world depends on the interplay between individual brain's characteristics and previous exposure to social cues such as the parent's smile or eye contact. Different hypotheses about why specific combinations of emotional expressions and gaze direction engage children have been tested with group-level approaches rather than focusing on individual differences in the social brain development. Here, a novel Artificial Intelligence-enhanced brain-imaging approach, Neuroadaptive Bayesian Optimisation (NBO), was applied to infant electro-encephalography (EEG) to understand how selected neural signals encode social cues in individual infants. EEG data from 42 6- to 9-month-old infants looking at images of their parent's face were analysed in real-time and used by a Bayesian Optimisation algorithm to identify which combination of the parent's gaze/head direction and emotional expression produces the strongest brain activation in the child. This individualised approach supported the theory that the infant's brain is maximally engaged by communicative cues with a negative valence (angry faces with direct gaze). Infants attending preferentially to faces with direct gaze had increased positive affectivity and decreased negative affectivity. This work confirmed that infants' attentional preferences for social cues are heterogeneous and shows the NBO's potential to study diversity in neurodevelopmental trajectories.
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Affiliation(s)
- A Gui
- Centre for Brain and Cognitive Development, Department of Psychological Science, Birkbeck, University of London, Malet Street, London WC1E 7HX, United Kingdom; Department of Psychology, University of Essex, Wivenhoe Park, Colchester CO4 3SQ, United Kingdom.
| | - E Throm
- Centre for Brain and Cognitive Development, Department of Psychological Science, Birkbeck, University of London, Malet Street, London WC1E 7HX, United Kingdom
| | - P F da Costa
- Department of Neuroimaging, Institute of Psychiatry, Psychology and, Neuroscience, King's College London, de Crespigny Road, London SE5 8AB, United Kingdom
| | - F Penza
- Centre for Brain and Cognitive Development, Department of Psychological Science, Birkbeck, University of London, Malet Street, London WC1E 7HX, United Kingdom
| | - M Aguiló Mayans
- Centre for Brain and Cognitive Development, Department of Psychological Science, Birkbeck, University of London, Malet Street, London WC1E 7HX, United Kingdom
| | - A Jordan-Barros
- Centre for Brain and Cognitive Development, Department of Psychological Science, Birkbeck, University of London, Malet Street, London WC1E 7HX, United Kingdom
| | - R Haartsen
- Centre for Brain and Cognitive Development, Department of Psychological Science, Birkbeck, University of London, Malet Street, London WC1E 7HX, United Kingdom
| | - R Leech
- Department of Neuroimaging, Institute of Psychiatry, Psychology and, Neuroscience, King's College London, de Crespigny Road, London SE5 8AB, United Kingdom
| | - E J H Jones
- Centre for Brain and Cognitive Development, Department of Psychological Science, Birkbeck, University of London, Malet Street, London WC1E 7HX, United Kingdom
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2
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Botvinik-Nezer R, Wager TD. Reproducibility in Neuroimaging Analysis: Challenges and Solutions. BIOLOGICAL PSYCHIATRY. COGNITIVE NEUROSCIENCE AND NEUROIMAGING 2023; 8:780-788. [PMID: 36906444 DOI: 10.1016/j.bpsc.2022.12.006] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/15/2022] [Revised: 11/27/2022] [Accepted: 12/11/2022] [Indexed: 12/23/2022]
Abstract
Recent years have marked a renaissance in efforts to increase research reproducibility in psychology, neuroscience, and related fields. Reproducibility is the cornerstone of a solid foundation of fundamental research-one that will support new theories built on valid findings and technological innovation that works. The increased focus on reproducibility has made the barriers to it increasingly apparent, along with the development of new tools and practices to overcome these barriers. Here, we review challenges, solutions, and emerging best practices with a particular emphasis on neuroimaging studies. We distinguish 3 main types of reproducibility, discussing each in turn. Analytical reproducibility is the ability to reproduce findings using the same data and methods. Replicability is the ability to find an effect in new datasets, using the same or similar methods. Finally, robustness to analytical variability refers to the ability to identify a finding consistently across variation in methods. The incorporation of these tools and practices will result in more reproducible, replicable, and robust psychological and brain research and a stronger scientific foundation across fields of inquiry.
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Affiliation(s)
- Rotem Botvinik-Nezer
- Department of Psychological and Brain Sciences, Dartmouth College, Hanover, New Hampshire.
| | - Tor D Wager
- Department of Psychological and Brain Sciences, Dartmouth College, Hanover, New Hampshire
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3
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Gallop L, Westwood SJ, Lewis Y, Campbell IC, Schmidt U. Effects of transcranial direct current stimulation in children and young people with psychiatric disorders: a systematic review. Eur Child Adolesc Psychiatry 2023:10.1007/s00787-023-02157-0. [PMID: 36764973 DOI: 10.1007/s00787-023-02157-0] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/04/2022] [Accepted: 01/26/2023] [Indexed: 02/12/2023]
Abstract
Transcranial direct current stimulation (tDCS) has demonstrated benefits in adults with various psychiatric disorders, but its clinical utility in children and young people (CYP) remains unclear. This PRISMA systematic review used published and ongoing studies to examine the effects of tDCS on disorder-specific symptoms, mood and neurocognition in CYP with psychiatric disorders. We searched Medline via PubMed, Embase, PsychINFO via OVID, and Clinicaltrials.gov up to December 2022. Eligible studies involved multiple session (i.e., treatment) tDCS in CYP (≤ 25 years old) with psychiatric disorders. Two independent raters assessed the eligibility of studies and extracted data using a custom-built form. Of 33 eligible studies (participant N = 517), the majority (n = 27) reported an improvement in at least one outcome measure of disorder-specific symptoms. Few studies (n = 13) examined tDCS effects on mood and/or neurocognition, but findings were mainly positive. Overall, tDCS was well tolerated with minimal side effects. Of 11 eligible ongoing studies, many are sham-controlled RCTs (n = 9) with better blinding techniques and a larger estimated participant enrolment (M = 79.7; range 15-172) than published studies. Although encouraging, the evidence to date is insufficient to firmly conclude that tDCS can improve clinical symptoms, mood, or cognition in CYP with psychiatric disorders. Ongoing studies appear of improved methodological quality; however, future studies should broaden outcome measures to more comprehensively assess the effects of tDCS and develop dosage guidance (i.e., treatment regimens).
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Affiliation(s)
- Lucy Gallop
- Section of Eating Disorders, Department of Psychological Medicine, Institute of Psychiatry, Psychology & Neuroscience, King's College London, De Crespigny Park, PO Box 59, London, SE5 8AF, UK.
| | - Samuel J Westwood
- Department of Psychology, School of Social Science, University of Westminster, London, W1W 6UW, UK
- Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, SE5 8AB, UK
| | - Yael Lewis
- Section of Eating Disorders, Department of Psychological Medicine, Institute of Psychiatry, Psychology & Neuroscience, King's College London, De Crespigny Park, PO Box 59, London, SE5 8AF, UK
- Hadarim Eating Disorder Unit, Shalvata Mental Health Centre, Hod Hasharon, Israel
- Sackler Faculty of Medicine, Tel-Aviv University, Tel-Aviv, Israel
| | - Iain C Campbell
- Section of Eating Disorders, Department of Psychological Medicine, Institute of Psychiatry, Psychology & Neuroscience, King's College London, De Crespigny Park, PO Box 59, London, SE5 8AF, UK
| | - Ulrike Schmidt
- Section of Eating Disorders, Department of Psychological Medicine, Institute of Psychiatry, Psychology & Neuroscience, King's College London, De Crespigny Park, PO Box 59, London, SE5 8AF, UK
- South London and Maudsley NHS Foundation Trust, London, UK
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4
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Kolskår KK, Ulrichsen KM, Richard G, Dørum ES, de Schotten MT, Rokicki J, Monereo-Sánchez J, Engvig A, Hansen HI, Nordvik JE, Westlye LT, Alnaes D. Structural disconnectome mapping of cognitive function in poststroke patients. Brain Behav 2022; 12:e2707. [PMID: 35861657 PMCID: PMC9392540 DOI: 10.1002/brb3.2707] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/21/2022] [Revised: 06/19/2022] [Accepted: 06/25/2022] [Indexed: 11/09/2022] Open
Abstract
BACKGROUND AND PURPOSE Sequalae following stroke represents a significant challenge in current rehabilitation. The location and size of focal lesions are only moderately predictive of the diverse cognitive outcome after stroke. One explanation building on recent work on brain networks proposes that the cognitive consequences of focal lesions are caused by damages to anatomically distributed brain networks supporting cognition rather than specific lesion locations. METHODS To investigate the association between poststroke structural disconnectivity and cognitive performance, we estimated individual level whole-brain disconnectivity probability maps based on lesion maps from 102 stroke patients using normative data from healthy controls. Cognitive performance was assessed in the whole sample using Montreal Cognitive Assessment, and a more comprehensive computerized test protocol was performed on a subset (n = 82). RESULTS Multivariate analysis using Partial Least Squares on the disconnectome maps revealed that higher disconnectivity in right insular and frontal operculum, superior temporal gyrus and putamen was associated with poorer MoCA performance, indicating that lesions in regions connected with these brain regions are more likely to cause cognitive impairment. Furthermore, our results indicated that disconnectivity within these clusters was associated with poorer performance across multiple cognitive domains. CONCLUSIONS These findings demonstrate that the extent and distribution of structural disconnectivity following stroke are sensitive to cognitive deficits and may provide important clinical information predicting poststroke cognitive sequalae.
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Affiliation(s)
- Knut K Kolskår
- NORMENT, Division of Mental Health and Addiction, Oslo University Hospital & Institute of Clinical Medicine, University of Oslo, Oslo, Norway.,Department of Psychology, University of Oslo, Oslo, Norway.,Sunnaas Rehabilitation Hospital HT, Nesodden, Norway
| | - Kristine M Ulrichsen
- NORMENT, Division of Mental Health and Addiction, Oslo University Hospital & Institute of Clinical Medicine, University of Oslo, Oslo, Norway.,Department of Psychology, University of Oslo, Oslo, Norway.,Sunnaas Rehabilitation Hospital HT, Nesodden, Norway
| | - Genevieve Richard
- NORMENT, Division of Mental Health and Addiction, Oslo University Hospital & Institute of Clinical Medicine, University of Oslo, Oslo, Norway
| | - Erlend S Dørum
- NORMENT, Division of Mental Health and Addiction, Oslo University Hospital & Institute of Clinical Medicine, University of Oslo, Oslo, Norway.,Department of Psychology, University of Oslo, Oslo, Norway.,Sunnaas Rehabilitation Hospital HT, Nesodden, Norway
| | - Michel Thiebaut de Schotten
- Brain Connectivity and Behaviour Laboratory, Sorbonne Universities, Paris, France.,Groupe d'Imagerie Neurofonctionnelle, Institut des Maladies Neurodégénératives-UMR 5293, CNRS, CEA University of Bordeaux, Bordeaux, France
| | - Jaroslav Rokicki
- NORMENT, Division of Mental Health and Addiction, Oslo University Hospital & Institute of Clinical Medicine, University of Oslo, Oslo, Norway.,Department of Psychology, University of Oslo, Oslo, Norway.,Centre of Research and Education in Forensic Psychiatry, Oslo University Hospital, Oslo, Norway
| | - Jennifer Monereo-Sánchez
- NORMENT, Division of Mental Health and Addiction, Oslo University Hospital & Institute of Clinical Medicine, University of Oslo, Oslo, Norway.,Faculty of Health, Medicine and Life Sciences, Maastricht University, Maastricht, the Netherlands.,Department of Radiology and Nuclear Medicine, Maastricht University Medical Center, the Netherlands
| | - Andreas Engvig
- Department of Nephrology, Oslo University Hospital, Ullevål, Norway.,Department of Medicine, Diakonhjemmet Hospital, Oslo, Norway
| | | | - Jan Egil Nordvik
- CatoSenteret Rehabilitation Center, Son, Norway.,Faculty of Health Sciences, Oslo Metropolitan University, Oslo, Norway
| | - Lars T Westlye
- NORMENT, Division of Mental Health and Addiction, Oslo University Hospital & Institute of Clinical Medicine, University of Oslo, Oslo, Norway.,Department of Psychology, University of Oslo, Oslo, Norway.,KG Jebsen Centre for Neurodevelopmental Disorders, University of Oslo, Oslo, Norway
| | - Dag Alnaes
- NORMENT, Division of Mental Health and Addiction, Oslo University Hospital & Institute of Clinical Medicine, University of Oslo, Oslo, Norway.,Bjørknes College, Oslo, Norway
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5
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Borowska A, Gao H, Lazarus A, Husmeier D. Bayesian optimisation for efficient parameter inference in a cardiac mechanics model of the left ventricle. INTERNATIONAL JOURNAL FOR NUMERICAL METHODS IN BIOMEDICAL ENGINEERING 2022; 38:e3593. [PMID: 35302293 PMCID: PMC9285944 DOI: 10.1002/cnm.3593] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 09/17/2020] [Accepted: 03/12/2022] [Indexed: 06/14/2023]
Abstract
We consider parameter inference in cardio-mechanic models of the left ventricle, in particular the one based on the Holtzapfel-Ogden (HO) constitutive law, using clinical in vivo data. The equations underlying these models do not admit closed form solutions and hence need to be solved numerically. These numerical procedures are computationally expensive making computational run times associated with numerical optimisation or sampling excessive for the uptake of the models in the clinical practice. To address this issue, we adopt the framework of Bayesian optimisation (BO), which is an efficient statistical technique of global optimisation. BO seeks the optimum of an unknown black-box function by sequentially training a statistical surrogate-model and using it to select the next query point by leveraging the associated exploration-exploitation trade-off. To guarantee that the estimates based on the in vivo data are realistic also for high-pressures, unobservable in vivo, we include a penalty term based on a previously published empirical law developed using ex vivo data. Two case studies based on real data demonstrate that the proposed BO procedure outperforms the state-of-the-art inference algorithm for the HO constitutive law.
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Affiliation(s)
| | - Hao Gao
- School of Mathematics and StatisticsUniversity of GlasgowGlasgowUK
| | - Alan Lazarus
- School of Mathematics and StatisticsUniversity of GlasgowGlasgowUK
| | - Dirk Husmeier
- School of Mathematics and StatisticsUniversity of GlasgowGlasgowUK
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6
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Ouyang G, Dien J, Lorenz R. Handling EEG artifacts and searching individually optimal experimental parameter in real time: a system development and demonstration. J Neural Eng 2022; 19. [PMID: 34902847 DOI: 10.1088/1741-2552/ac42b6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/30/2021] [Accepted: 12/13/2021] [Indexed: 02/02/2023]
Abstract
Objective.Neuroadaptive paradigms that systematically assess event-related potential (ERP) features across many different experimental parameters have the potential to improve the generalizability of ERP findings and may help to accelerate ERP-based biomarker discovery by identifying the exact experimental conditions for which ERPs differ most for a certain clinical population. Obtaining robust and reliable ERPs online is a prerequisite for ERP-based neuroadaptive research. One of the key steps involved is to correctly isolate electroencephalography artifacts in real time because they contribute a large amount of variance that, if not removed, will greatly distort the ERP obtained. Another key factor of concern is the computational cost of the online artifact handling method. This work aims to develop and validate a cost-efficient system to support ERP-based neuroadaptive research.Approach.We developed a simple online artifact handling method, single trial PCA-based artifact removal (SPA), based on variance distribution dichotomies to distinguish between artifacts and neural activity. We then applied this method in an ERP-based neuroadaptive paradigm in which Bayesian optimization was used to search individually optimal inter-stimulus-interval (ISI) that generates ERP with the highest signal-to-noise ratio.Main results.SPA was compared to other offline and online algorithms. The results showed that SPA exhibited good performance in both computational efficiency and preservation of ERP pattern. Based on SPA, the Bayesian optimization procedure was able to quickly find individually optimal ISI.Significance.The current work presents a simple yet highly cost-efficient method that has been validated in its ability to extract ERP, preserve ERP effects, and better support ERP-based neuroadaptive paradigm.
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Affiliation(s)
- Guang Ouyang
- Faculty of Education, The University of Hong Kong, Hong Kong, People's Republic of China
| | - Joseph Dien
- Department of Human Development and Quantitative Methodology, University of Maryland, College Park, MD, United States of America
| | - Romy Lorenz
- MRC Cognition and Brain Sciences Unit, University of Cambridge, Cambridge, United Kingdom.,Department of Neurophysics, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany.,Department of Psychology, Stanford University, Stanford, CA, United States of America
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7
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von Schwanenflug N, Krohn S, Heine J, Paul F, Prüss H, Finke C. OUP accepted manuscript. Brain Commun 2022; 4:fcab298. [PMID: 35169701 PMCID: PMC8833311 DOI: 10.1093/braincomms/fcab298] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/02/2021] [Revised: 10/13/2021] [Accepted: 01/03/2022] [Indexed: 11/21/2022] Open
Abstract
Traditional static functional connectivity analyses have shown distinct functional network alterations in patients with anti-N-methyl-d-aspartate receptor encephalitis. Here, we use a dynamic functional connectivity approach that increases the temporal resolution of connectivity analyses from minutes to seconds. We hereby explore the spatiotemporal variability of large-scale brain network activity in anti-N-methyl-d-aspartate receptor encephalitis and assess the discriminatory power of functional brain states in a supervised classification approach. We included resting-state functional magnetic resonance imaging data from 57 patients and 61 controls to extract four discrete connectivity states and assess state-wise group differences in functional connectivity, dwell time, transition frequency, fraction time and occurrence rate. Additionally, for each state, logistic regression models with embedded feature selection were trained to predict group status in a leave-one-out cross-validation scheme. Compared to controls, patients exhibited diverging dynamic functional connectivity patterns in three out of four states mainly encompassing the default-mode network and frontal areas. This was accompanied by a characteristic shift in the dwell time pattern and higher volatility of state transitions in patients. Moreover, dynamic functional connectivity measures were associated with disease severity and positive and negative schizophrenia-like symptoms. Predictive power was highest in dynamic functional connectivity models and outperformed static analyses, reaching up to 78.6% classification accuracy. By applying time-resolved analyses, we disentangle state-specific functional connectivity impairments and characteristic changes in temporal dynamics not detected in static analyses, offering new perspectives on the functional reorganization underlying anti-N-methyl-d-aspartate receptor encephalitis. Finally, the correlation of dynamic functional connectivity measures with disease symptoms and severity demonstrates a clinical relevance of spatiotemporal connectivity dynamics in anti-N-methyl-d-aspartate receptor encephalitis.
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Affiliation(s)
- Nina von Schwanenflug
- Department of Neurology, Charité—Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin, Humboldt-Universität zu Berlin, and Berlin Institute of Health, Berlin, Germany
- Berlin School of Mind and Brain, Humboldt-Universität zu Berlin, Berlin, Germany
| | - Stephan Krohn
- Department of Neurology, Charité—Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin, Humboldt-Universität zu Berlin, and Berlin Institute of Health, Berlin, Germany
- Berlin School of Mind and Brain, Humboldt-Universität zu Berlin, Berlin, Germany
| | - Josephine Heine
- Department of Neurology, Charité—Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin, Humboldt-Universität zu Berlin, and Berlin Institute of Health, Berlin, Germany
| | - Friedemann Paul
- Department of Neurology, Charité—Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin, Humboldt-Universität zu Berlin, and Berlin Institute of Health, Berlin, Germany
- Experimental and Clinical Research Center, Max-Delbrück Center for Molecular Medicine and Charité—Universitätsmedizin Berlin, Berlin, Germany
- Berlin Institute of Health, Berlin, Germany
- NeuroCure Clinical Research Center, Charité—Universitätsmedizin Corporate Member of Freie Universität Berlin, Humboldt-Universität zu Berlin, and Berlin Institute of Health, Berlin, Germany
| | - Harald Prüss
- Department of Neurology, Charité—Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin, Humboldt-Universität zu Berlin, and Berlin Institute of Health, Berlin, Germany
- German Centre for Neurodegenerative Diseases, DZNE, Berlin, Germany
| | - Carsten Finke
- Correspondence to: Carsten Finke Charitéplatz 1 10117 Berlin, Germany E-mail:
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