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Sarisik E, Popovic D, Keeser D, Khuntia A, Schiltz K, Falkai P, Pogarell O, Koutsouleris N. EEG-based Signatures of Schizophrenia, Depression, and Aberrant Aging: A Supervised Machine Learning Investigation. Schizophr Bull 2024:sbae150. [PMID: 39248267 DOI: 10.1093/schbul/sbae150] [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: 09/10/2024]
Abstract
BACKGROUND Electroencephalography (EEG) is a noninvasive, cost-effective, and robust tool, which directly measures in vivo neuronal mass activity with high temporal resolution. Combined with state-of-the-art machine learning (ML) techniques, EEG recordings could potentially yield in silico biomarkers of severe mental disorders. HYPOTHESIS Pathological and physiological aging processes influence the electrophysiological signatures of schizophrenia (SCZ) and major depressive disorder (MDD). STUDY DESIGN From a single-center cohort (N = 735, 51.6% male) comprising healthy control individuals (HC, N = 245) and inpatients suffering from SCZ (N = 250) or MDD (N = 240), we acquired resting-state 19 channel-EEG recordings. Using repeated nested cross-validation, support vector machine models were trained to (1) classify patients with SCZ or MDD and HC individuals and (2) predict age in HC individuals. The age model was applied to patient groups to calculate Electrophysiological Age Gap Estimation (EphysAGE) as the difference between predicted and chronological age. The links between EphysAGE, diagnosis, and medication were then further explored. STUDY RESULTS The classification models robustly discriminated SCZ from HC (balanced accuracy, BAC = 72.7%, P < .001), MDD from HC (BAC = 67.0%, P < .001), and SCZ from MDD individuals (BAC = 63.2%, P < .001). Notably, central alpha (8-11 Hz) power decrease was the most consistently predictive feature for SCZ and MDD. Higher EphysAGE was associated with an increased likelihood of being misclassified as SCZ in HC and MDD (ρHC = 0.23, P < .001; ρMDD = 0.17, P = .01). CONCLUSIONS ML models can extract electrophysiological signatures of MDD and SCZ for potential clinical use. However, the impact of aging processes on diagnostic separability calls for timely application of such models, possibly in early recognition settings.
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Affiliation(s)
- Elif Sarisik
- Max Planck Fellow Group Precision Psychiatry, Max Planck Institute of Psychiatry, Munich, Germany
- Department of Psychiatry and Psychotherapy, LMU University Hospital, LMU Munich, Munich, Germany
- International Max Planck Research School for Translational Psychiatry (IMPRS-TP), Munich, Germany
| | - David Popovic
- Max Planck Fellow Group Precision Psychiatry, Max Planck Institute of Psychiatry, Munich, Germany
- Department of Psychiatry and Psychotherapy, LMU University Hospital, LMU Munich, Munich, Germany
- International Max Planck Research School for Translational Psychiatry (IMPRS-TP), Munich, Germany
- German Center for Mental Health (DZPG), Partner Site Munich, Munich, Germany
| | - Daniel Keeser
- Department of Psychiatry and Psychotherapy, LMU University Hospital, LMU Munich, Munich, Germany
- German Center for Mental Health (DZPG), Partner Site Munich, Munich, Germany
- NeuroImaging Core Unit Munich (NICUM), LMU University Hospital, LMU Munich, Munich, Germany
- Munich Center for Neurosciences, LMU Munich, Munich, Germany
| | - Adyasha Khuntia
- Department of Psychiatry and Psychotherapy, LMU University Hospital, LMU Munich, Munich, Germany
- International Max Planck Research School for Translational Psychiatry (IMPRS-TP), Munich, Germany
| | - Kolja Schiltz
- Max Planck Fellow Group Precision Psychiatry, Max Planck Institute of Psychiatry, Munich, Germany
| | - Peter Falkai
- Max Planck Fellow Group Precision Psychiatry, Max Planck Institute of Psychiatry, Munich, Germany
- Department of Psychiatry and Psychotherapy, LMU University Hospital, LMU Munich, Munich, Germany
- German Center for Mental Health (DZPG), Partner Site Munich, Munich, Germany
| | - Oliver Pogarell
- Max Planck Fellow Group Precision Psychiatry, Max Planck Institute of Psychiatry, Munich, Germany
| | - Nikolaos Koutsouleris
- Max Planck Fellow Group Precision Psychiatry, Max Planck Institute of Psychiatry, Munich, Germany
- Department of Psychiatry and Psychotherapy, LMU University Hospital, LMU Munich, Munich, Germany
- German Center for Mental Health (DZPG), Partner Site Munich, Munich, Germany
- Munich Center for Neurosciences, LMU Munich, Munich, Germany
- Institute of Psychiatry, Psychology and Neuroscience, King's College, London, UK
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2
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Loosen AM, Kato A, Gu X. Revisiting the role of computational neuroimaging in the era of integrative neuroscience. Neuropsychopharmacology 2024:10.1038/s41386-024-01946-8. [PMID: 39242921 DOI: 10.1038/s41386-024-01946-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/05/2024] [Revised: 07/12/2024] [Accepted: 07/17/2024] [Indexed: 09/09/2024]
Abstract
Computational models have become integral to human neuroimaging research, providing both mechanistic insights and predictive tools for human cognition and behavior. However, concerns persist regarding the ecological validity of lab-based neuroimaging studies and whether their spatiotemporal resolution is not sufficient for capturing neural dynamics. This review aims to re-examine the utility of computational neuroimaging, particularly in light of the growing prominence of alternative neuroscientific methods and the growing emphasis on more naturalistic behaviors and paradigms. Specifically, we will explore how computational modeling can both enhance the analysis of high-dimensional imaging datasets and, conversely, how neuroimaging, in conjunction with other data modalities, can inform computational models through the lens of neurobiological plausibility. Collectively, this evidence suggests that neuroimaging remains critical for human neuroscience research, and when enhanced by computational models, imaging can serve an important role in bridging levels of analysis and understanding. We conclude by proposing key directions for future research, emphasizing the development of standardized paradigms and the integrative use of computational modeling across neuroimaging techniques.
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Affiliation(s)
- Alisa M Loosen
- Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY, USA.
- Center for Computational Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY, USA.
- Nash Family Department of Neuroscience, Icahn School of Medicine at Mount Sinai, New York, NY, USA.
| | - Ayaka Kato
- Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY, USA.
- Center for Computational Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY, USA.
- Nash Family Department of Neuroscience, Icahn School of Medicine at Mount Sinai, New York, NY, USA.
| | - Xiaosi Gu
- Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Center for Computational Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Nash Family Department of Neuroscience, Icahn School of Medicine at Mount Sinai, New York, NY, USA
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3
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DeRosa J, Friedman NP, Calhoun V, Banich MT. Neurodevelopmental subtypes of functional brain organization in the ABCD study using a rigorous analytic framework. Neuroimage 2024; 299:120827. [PMID: 39245397 DOI: 10.1016/j.neuroimage.2024.120827] [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: 03/20/2024] [Revised: 08/02/2024] [Accepted: 08/31/2024] [Indexed: 09/10/2024] Open
Abstract
The current study demonstrates that an individual's resting-state functional connectivity (RSFC) is a dependable biomarker for identifying differential patterns of cognitive and emotional functioning during late childhood. Using baseline RSFC data from the Adolescent Brain Cognitive Development (ABCD) study, which includes children aged 9-11, we identified four distinct RSFC subtypes. We introduce an integrated methodological pipeline for testing the reliability and importance of these subtypes. In the Identification phase, Leiden Community Detection defined RSFC subtypes, with their reproducibility confirmed through a split-sample technique in the Validation stage. The Evaluation phase showed that distinct cognitive and mental health profiles are associated with each subtype, with the Predictive phase indicating that subtypes better predict various cognitive and mental health characteristics than individual RSFC connections. The Replication stage employed bootstrapping and down-sampling methods to substantiate the reproducibility of these subtypes further. This work allows future explorations of developmental trajectories of these RSFC subtypes.
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Affiliation(s)
- Jacob DeRosa
- Department of Psychology and Neuroscience, University of Colorado Boulder, United States; Institute of Cognitive Science, University of Colorado Boulder, United States.
| | - Naomi P Friedman
- Department of Psychology and Neuroscience, University of Colorado Boulder, United States; Institute for Behavioral Genetics, University of Colorado Boulder, United States
| | - Vince Calhoun
- Tri-institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State University, Georgia Institute of Technology, Emory University, United States
| | - Marie T Banich
- Department of Psychology and Neuroscience, University of Colorado Boulder, United States; Institute of Cognitive Science, University of Colorado Boulder, United States
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4
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Liloia D, Zamfira DA, Tanaka M, Manuello J, Crocetta A, Keller R, Cozzolino M, Duca S, Cauda F, Costa T. Disentangling the role of gray matter volume and concentration in autism spectrum disorder: A meta-analytic investigation of 25 years of voxel-based morphometry research. Neurosci Biobehav Rev 2024; 164:105791. [PMID: 38960075 DOI: 10.1016/j.neubiorev.2024.105791] [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: 10/26/2023] [Revised: 05/22/2024] [Accepted: 06/27/2024] [Indexed: 07/05/2024]
Abstract
Despite over two decades of neuroimaging research, a unanimous definition of the pattern of structural variation associated with autism spectrum disorder (ASD) has yet to be found. One potential impeding issue could be the sometimes ambiguous use of measurements of variations in gray matter volume (GMV) or gray matter concentration (GMC). In fact, while both can be calculated using voxel-based morphometry analysis, these may reflect different underlying pathological mechanisms. We conducted a coordinate-based meta-analysis, keeping apart GMV and GMC studies of subjects with ASD. Results showed distinct and non-overlapping patterns for the two measures. GMV decreases were evident in the cerebellum, while GMC decreases were mainly found in the temporal and frontal regions. GMV increases were found in the parietal, temporal, and frontal brain regions, while GMC increases were observed in the anterior cingulate cortex and middle frontal gyrus. Age-stratified analyses suggested that such variations are dynamic across the ASD lifespan. The present findings emphasize the importance of considering GMV and GMC as distinct yet synergistic indices in autism research.
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Affiliation(s)
- Donato Liloia
- GCS-fMRI, Koelliker Hospital and Department of Psychology, University of Turin, Turin, Italy; Functional Neuroimaging and Complex Neural Systems (FOCUS) Laboratory, Department of Psychology, University of Turin, Turin, Italy
| | - Denisa Adina Zamfira
- School of Psychology, Vita-Salute San Raffaele University, Milan, Italy; Division of Neuroscience, IRCCS San Raffaele Scientific Institute, Milan, Italy
| | - Masaru Tanaka
- HUN-REN-SZTE Neuroscience Research Group, Hungarian Research Network, University of Szeged (HUN-REN-SZTE), Danube Neuroscience Research Laboratory, Szeged, Hungary
| | - Jordi Manuello
- GCS-fMRI, Koelliker Hospital and Department of Psychology, University of Turin, Turin, Italy; Functional Neuroimaging and Complex Neural Systems (FOCUS) Laboratory, Department of Psychology, University of Turin, Turin, Italy.
| | - Annachiara Crocetta
- GCS-fMRI, Koelliker Hospital and Department of Psychology, University of Turin, Turin, Italy; Functional Neuroimaging and Complex Neural Systems (FOCUS) Laboratory, Department of Psychology, University of Turin, Turin, Italy
| | - Roberto Keller
- Adult Autism Center, DSM Local Health Unit, ASL TO, Turin, Italy
| | - Mauro Cozzolino
- Department of Humanities, Philosophical and Educational Sciences, University of Salerno, Fisciano, Italy
| | - Sergio Duca
- GCS-fMRI, Koelliker Hospital and Department of Psychology, University of Turin, Turin, Italy; Functional Neuroimaging and Complex Neural Systems (FOCUS) Laboratory, Department of Psychology, University of Turin, Turin, Italy
| | - Franco Cauda
- GCS-fMRI, Koelliker Hospital and Department of Psychology, University of Turin, Turin, Italy; Functional Neuroimaging and Complex Neural Systems (FOCUS) Laboratory, Department of Psychology, University of Turin, Turin, Italy; Neuroscience Institute of Turin (NIT), Turin, Italy
| | - Tommaso Costa
- GCS-fMRI, Koelliker Hospital and Department of Psychology, University of Turin, Turin, Italy; Functional Neuroimaging and Complex Neural Systems (FOCUS) Laboratory, Department of Psychology, University of Turin, Turin, Italy; Neuroscience Institute of Turin (NIT), Turin, Italy
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Iqbal MS, Belal Bin Heyat M, Parveen S, Ammar Bin Hayat M, Roshanzamir M, Alizadehsani R, Akhtar F, Sayeed E, Hussain S, Hussein HS, Sawan M. Progress and trends in neurological disorders research based on deep learning. Comput Med Imaging Graph 2024; 116:102400. [PMID: 38851079 DOI: 10.1016/j.compmedimag.2024.102400] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/02/2024] [Revised: 05/07/2024] [Accepted: 05/13/2024] [Indexed: 06/10/2024]
Abstract
In recent years, deep learning (DL) has emerged as a powerful tool in clinical imaging, offering unprecedented opportunities for the diagnosis and treatment of neurological disorders (NDs). This comprehensive review explores the multifaceted role of DL techniques in leveraging vast datasets to advance our understanding of NDs and improve clinical outcomes. Beginning with a systematic literature review, we delve into the utilization of DL, particularly focusing on multimodal neuroimaging data analysis-a domain that has witnessed rapid progress and garnered significant scientific interest. Our study categorizes and critically analyses numerous DL models, including Convolutional Neural Networks (CNNs), LSTM-CNN, GAN, and VGG, to understand their performance across different types of Neurology Diseases. Through particular analysis, we identify key benchmarks and datasets utilized in training and testing DL models, shedding light on the challenges and opportunities in clinical neuroimaging research. Moreover, we discuss the effectiveness of DL in real-world clinical scenarios, emphasizing its potential to revolutionize ND diagnosis and therapy. By synthesizing existing literature and describing future directions, this review not only provides insights into the current state of DL applications in ND analysis but also covers the way for the development of more efficient and accessible DL techniques. Finally, our findings underscore the transformative impact of DL in reshaping the landscape of clinical neuroimaging, offering hope for enhanced patient care and groundbreaking discoveries in the field of neurology. This review paper is beneficial for neuropathologists and new researchers in this field.
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Affiliation(s)
- Muhammad Shahid Iqbal
- Department of Computer Science and Information Technology, Women University of Azad Jammu & Kashmir, Bagh, Pakistan.
| | - Md Belal Bin Heyat
- CenBRAIN Neurotech Center of Excellence, School of Engineering, Westlake University, Hangzhou, Zhejiang, China.
| | - Saba Parveen
- College of Electronics and Information Engineering, Shenzhen University, Shenzhen 518060, China.
| | | | - Mohamad Roshanzamir
- Department of Computer Engineering, Faculty of Engineering, Fasa University, Fasa, Iran.
| | - Roohallah Alizadehsani
- Institute for Intelligent Systems Research and Innovation, Deakin University, VIC 3216, Australia.
| | - Faijan Akhtar
- School of Computer Science and Engineering, University of Electronic Science and Technology of China, Chengdu, China.
| | - Eram Sayeed
- Kisan Inter College, Dhaurahara, Kushinagar, India.
| | - Sadiq Hussain
- Department of Examination, Dibrugarh University, Assam 786004, India.
| | - Hany S Hussein
- Electrical Engineering Department, Faculty of Engineering, King Khalid University, Abha 61411, Saudi Arabia; Electrical Engineering Department, Faculty of Engineering, Aswan University, Aswan 81528, Egypt.
| | - Mohamad Sawan
- CenBRAIN Neurotech Center of Excellence, School of Engineering, Westlake University, Hangzhou, Zhejiang, China.
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Wen X, Yang W, Du Z, Zhao J, Li Y, Yu D, Zhang J, Liu J, Yuan K. Multimodal frontal neuroimaging markers predict longitudinal craving reduction in abstinent individuals with heroin use disorder. J Psychiatr Res 2024; 177:1-10. [PMID: 38964089 DOI: 10.1016/j.jpsychires.2024.06.035] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/18/2024] [Revised: 06/02/2024] [Accepted: 06/24/2024] [Indexed: 07/06/2024]
Abstract
The variation in improvement among individuals with addiction after abstinence is a critical issue. Here, we aimed to identify robust multimodal markers associated with high response to 8-month abstinence in the individuals with heroin use disorder (HUD) and explore whether the identified markers could be generalized to the individuals with methamphetamine use disorder (MUD). According to the median of craving changes, 53 individuals with HUD with 8-month abstinence were divided into two groups: higher craving reduction and lower craving reduction. At baseline, clinical variables, cortical thickness and subcortical volume, fractional anisotropy (FA) of fibers and resting-state functional connectivity (RSFC) were extracted. Different strategies (single metric, multimodal neuroimaging fusion and multimodal neuroimaging-clinical data fusion) were used to identify reliable features for discriminating the individuals with HUD with higher craving reduction from those with lower reduction. The generalization ability of the identified features was validated in the 21 individuals with MUD. Multimodal neuroimaging-clinical fusion features with best performance was achieved an 87.1 ± 3.89% average accuracy in individuals with HUD, with a moderate accuracy of 66.7% when generalizing to individuals with MUD. The multimodal neuroimaging features, primarily converging in frontal regions (e.g., the left superior frontal (LSF) thickness, FA of the LSF-occipital tract, and RSFC of left middle frontal-right superior temporal lobe), collectively contributed to prediction alongside dosage and attention impulsiveness. In this study, we identified the validated multimodal frontal neuroimaging markers associated with higher response to long-term abstinence and revealed insights for the neural mechanisms of addiction abstinence, contributing to clinical strategies and treatment for addiction.
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Affiliation(s)
- Xinwen Wen
- School of Life Science and Technology, Xidian University, Xi'an, Shaanxi, 710126, China
| | - Wenhan Yang
- Department of Radiology, The Second Xiangya Hospital of Central South University, Changsha, China.
| | - Zhe Du
- School of Life Science and Technology, Xidian University, Xi'an, Shaanxi, 710126, China
| | - Jiahao Zhao
- School of Life Science and Technology, Xidian University, Xi'an, Shaanxi, 710126, China
| | - Yangding Li
- Hunan Provincial Key Laboratory of Intelligent Computing and Language Information Processing, Hunan Normal University, Changsha, China
| | - Dahua Yu
- Inner Mongolia Key Laboratory of Pattern Recognition and Intelligent Image Processing, School of Information Engineering, Inner Mongolia University of Science and Technology, Baotou, Inner Mongolia, 014010, China
| | - Jun Zhang
- Hunan Judicial Police Academy, Changsha, China
| | - Jun Liu
- Department of Radiology, The Second Xiangya Hospital of Central South University, Changsha, China.
| | - Kai Yuan
- School of Life Science and Technology, Xidian University, Xi'an, Shaanxi, 710126, China; Inner Mongolia Key Laboratory of Pattern Recognition and Intelligent Image Processing, School of Information Engineering, Inner Mongolia University of Science and Technology, Baotou, Inner Mongolia, 014010, China; Engineering Research Center of Molecular and Neuro Imaging Ministry of Education, Xi'an, Shaanxi, China; Xi'an Key Laboratory of Intelligent Sensing and Regulation of Trans-Scale Life Information, School of Life Science and Technology, Xidian University, Xi'an, Shaanxi, 710126, China.
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Zhao T, Wang B, Liang W, Cheng S, Wang B, Cui M, Shou J. Accuracy of 18F-FDG PET Imaging in Differentiating Parkinson's Disease from Atypical Parkinsonian Syndromes: A Systematic Review and Meta-Analysis. Acad Radiol 2024:S1076-6332(24)00579-8. [PMID: 39183130 DOI: 10.1016/j.acra.2024.08.016] [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: 07/03/2024] [Revised: 07/26/2024] [Accepted: 08/09/2024] [Indexed: 08/27/2024]
Abstract
RATIONALE AND OBJECTIVE To quantitatively assess the accuracy of 18F-FDG PET in differentiating Parkinson's Disease (PD) from Atypical Parkinsonian Syndromes (APSs). METHODS PubMed, Embase, and Web of Science databases were searched to identify studies published from the inception of the databases up to June 2024 that used 18F-FDG PET imaging for the differential diagnosis of PD and APSs. The risk of bias in the included studies was assessed using the QUADAS-2 or QUADAS-AI tool. Bivariate random-effects models were used to calculate the pooled sensitivity, specificity, and the area under the curves (AUC) of summary receiver operating characteristic (SROC). RESULTS 24 studies met the inclusion criteria, involving a total of 1508 PD patients and 1370 APSs patients. 12 studies relied on visual interpretation by radiologists, of which the pooled sensitivity, specificity, and SROC-AUC for direct visual interpretation in diagnosing PD were 96% (95%CI: 91%, 98%), 90% (95%CI: 83%, 95%), and 0.98 (95%CI: 0.96, 0.99), respectively; the pooled sensitivity, specificity, and SROC-AUC for visual interpretation supported by univariate algorithms in diagnosing PD were 93% (95%CI: 90%, 95%), 90% (95%CI: 85%, 94%), and 0.96 (95%CI: 0.94, 0.97), respectively. 12 studies relied on artificial intelligence (AI) to analyze 18F-FDG PET imaging data. The pooled sensitivity, specificity, and SROC-AUC of machine learning (ML) for diagnosing PD were 87% (95%CI: 82%, 91%), 91% (95%CI: 86%, 94%), and 0.95 (95%CI: 0.93, 0.96), respectively. The pooled sensitivity, specificity, and SROC-AUC of deep learning (DL) for diagnosing PD were 97% (95%CI: 95%, 98%), 95% (95%CI: 89%, 98%), and 0.98 (95%CI: 0.96, 0.99), respectively. CONCLUSION 18F-FDG PET has a high accuracy in differentiating PD from APS, among which AI-assisted automatic classification performs well, with a diagnostic accuracy comparable to that of radiologists, and is expected to become an important auxiliary means of clinical diagnosis in the future.
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Affiliation(s)
- Tailiang Zhao
- Department of Neurosurgery, The Fifth Affiliated Hospital of Zhengzhou University, Zhengzhou 450052, Henan, China
| | - Bingbing Wang
- Department of Neurosurgery, The Fifth Affiliated Hospital of Zhengzhou University, Zhengzhou 450052, Henan, China
| | - Wei Liang
- Department of Neurosurgery, The Fifth Affiliated Hospital of Zhengzhou University, Zhengzhou 450052, Henan, China
| | - Sen Cheng
- Department of Neurosurgery, The Fifth Affiliated Hospital of Zhengzhou University, Zhengzhou 450052, Henan, China
| | - Bin Wang
- Department of Cardiology, Beijing Tsinghua Changgung Hospital, School of Clinical Medicine, Tsinghua University, Beijing 100000, China
| | - Ming Cui
- Department of Neurology, The Fifth Affiliated Hospital of Zhengzhou University, Zhengzhou 450052, Henan, China
| | - Jixin Shou
- Department of Neurosurgery, The Fifth Affiliated Hospital of Zhengzhou University, Zhengzhou 450052, Henan, China.
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Bautin P, Fortier MA, Sean M, Little G, Martel M, Descoteaux M, Léonard G, Tétreault P. What has brain diffusion magnetic resonance imaging taught us about chronic primary pain: a narrative review. Pain 2024:00006396-990000000-00689. [PMID: 39172945 DOI: 10.1097/j.pain.0000000000003345] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/16/2024] [Accepted: 06/13/2024] [Indexed: 08/24/2024]
Abstract
ABSTRACT Chronic pain is a pervasive and debilitating condition with increasing implications for public health, affecting millions of individuals worldwide. Despite its high prevalence, the underlying neural mechanisms and pathophysiology remain only partly understood. Since its introduction 35 years ago, brain diffusion magnetic resonance imaging (MRI) has emerged as a powerful tool to investigate changes in white matter microstructure and connectivity associated with chronic pain. This review synthesizes findings from 58 articles that constitute the current research landscape, covering methods and key discoveries. We discuss the evidence supporting the role of altered white matter microstructure and connectivity in chronic primary pain conditions, highlighting the importance of studying multiple chronic pain syndromes to identify common neurobiological pathways. We also explore the prospective clinical utility of diffusion MRI, such as its role in identifying diagnostic, prognostic, and therapeutic biomarkers. Furthermore, we address shortcomings and challenges associated with brain diffusion MRI in chronic primary pain studies, emphasizing the need for the harmonization of data acquisition and analysis methods. We conclude by highlighting emerging approaches and prospective avenues in the field that may provide new insights into the pathophysiology of chronic pain and potential new therapeutic targets. Because of the limited current body of research and unidentified targeted therapeutic strategies, we are forced to conclude that further research is required. However, we believe that brain diffusion MRI presents a promising opportunity for enhancing our understanding of chronic pain and improving clinical outcomes.
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Affiliation(s)
- Paul Bautin
- Department of Anesthesiology, Faculty of Medicine and Health Sciences, Université de Sherbrooke, Sherbrooke, QC, Canada
- Centre de recherche du Centre hospitalier universitaire de Sherbrooke, Sherbrooke, QC, Canada
| | - Marc-Antoine Fortier
- Department of Anesthesiology, Faculty of Medicine and Health Sciences, Université de Sherbrooke, Sherbrooke, QC, Canada
- Centre de recherche du Centre hospitalier universitaire de Sherbrooke, Sherbrooke, QC, Canada
| | - Monica Sean
- Department of Anesthesiology, Faculty of Medicine and Health Sciences, Université de Sherbrooke, Sherbrooke, QC, Canada
- Centre de recherche du Centre hospitalier universitaire de Sherbrooke, Sherbrooke, QC, Canada
| | - Graham Little
- Sherbrooke Connectivity Imaging Lab (SCIL), Computer Science Department, Université de Sherbrooke, Sherbrooke, QC, Canada
| | - Marylie Martel
- Centre de recherche du Centre hospitalier universitaire de Sherbrooke, Sherbrooke, QC, Canada
| | - Maxime Descoteaux
- Sherbrooke Connectivity Imaging Lab (SCIL), Computer Science Department, Université de Sherbrooke, Sherbrooke, QC, Canada
| | - Guillaume Léonard
- School of Rehabilitation, Faculty of Medicine and Health Sciences, Université de Sherbrooke, Sherbrooke, QC, Canada
- Research Centre on Aging du Centre intégré universitaire de santé et de services sociaux de l'Estrie-Centre hospitalier universitaire de Sherbrooke, Sherbrooke, QC, Canada
| | - Pascal Tétreault
- Department of Anesthesiology, Faculty of Medicine and Health Sciences, Université de Sherbrooke, Sherbrooke, QC, Canada
- Centre de recherche du Centre hospitalier universitaire de Sherbrooke, Sherbrooke, QC, Canada
- Department of Medical Imaging and Radiation Sciences, Faculty of Medicine and Health Sciences, Université de Sherbrooke, Sherbrooke, QC, Canada
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Rudroff T, Rainio O, Klén R. Leveraging Artificial Intelligence to Optimize Transcranial Direct Current Stimulation for Long COVID Management: A Forward-Looking Perspective. Brain Sci 2024; 14:831. [PMID: 39199522 PMCID: PMC11353063 DOI: 10.3390/brainsci14080831] [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: 07/23/2024] [Revised: 08/12/2024] [Accepted: 08/18/2024] [Indexed: 09/01/2024] Open
Abstract
Long COVID (Coronavirus disease), affecting millions globally, presents unprecedented challenges to healthcare systems due to its complex, multifaceted nature and the lack of effective treatments. This perspective review explores the potential of artificial intelligence (AI)-guided transcranial direct current stimulation (tDCS) as an innovative approach to address the urgent need for effective Long COVID management. The authors examine how AI could optimize tDCS protocols, enhance clinical trial design, and facilitate personalized treatment for the heterogeneous manifestations of Long COVID. Key areas discussed include AI-driven personalization of tDCS parameters based on individual patient characteristics and real-time symptom fluctuations, the use of machine learning for patient stratification, and the development of more sensitive outcome measures in clinical trials. This perspective addresses ethical considerations surrounding data privacy, algorithmic bias, and equitable access to AI-enhanced treatments. It also explores challenges and opportunities for implementing AI-guided tDCS across diverse healthcare settings globally. Future research directions are outlined, including the need for large-scale validation studies and investigations of long-term efficacy and safety. The authors argue that while AI-guided tDCS shows promise for addressing the complex nature of Long COVID, significant technical, ethical, and practical challenges remain. They emphasize the importance of interdisciplinary collaboration, patient-centered approaches, and a commitment to global health equity in realizing the potential of this technology. This perspective article provides a roadmap for researchers, clinicians, and policymakers involved in developing and implementing AI-guided neuromodulation therapies for Long COVID and potentially other neurological and psychiatric conditions.
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Affiliation(s)
- Thorsten Rudroff
- Turku PET Centre, University of Turku, Turku University Hospital, 20520 Turku, Finland; (O.R.); (R.K.)
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Ramduny J, Kelly C. Connectome-based fingerprinting: reproducibility, precision, and behavioral prediction. Neuropsychopharmacology 2024:10.1038/s41386-024-01962-8. [PMID: 39147868 DOI: 10.1038/s41386-024-01962-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/29/2024] [Revised: 08/02/2024] [Accepted: 08/05/2024] [Indexed: 08/17/2024]
Abstract
Functional magnetic resonance imaging-based functional connectivity enables the non-invasive mapping of individual differences in brain functional organization to individual differences in a vast array of behavioral phenotypes. This flexibility has renewed the search for neuroimaging-based biomarkers that exhibit reproducibility, prediction, and precision. Functional connectivity-based measures that share these three characteristics are key to achieving this goal. Here, we review the functional connectome fingerprinting approach and discuss its value, not only as a simple and intuitive conceptualization of the "functional connectome" that provides new insights into how the connectome is altered in association with psychiatric symptoms, but also as a straightforward and interpretable method for indexing the reproducibility of functional connectivity-based measures. We discuss how these advantages provide new avenues for strengthening reproducibility, precision, and behavioral prediction for functional connectomics and we consider new directions toward discovering better biomarkers for neuropsychiatric conditions.
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Affiliation(s)
- Jivesh Ramduny
- Department of Psychology, Yale University, New Haven, CT, USA.
- Kavli Institute for Neuroscience, Yale University, New Haven, CT, USA.
| | - Clare Kelly
- School of Psychology, Trinity College Dublin, Dublin, Ireland.
- Department of Psychiatry, School of Medicine, Trinity College Dublin, Dublin, Ireland.
- Trinity College Institute of Neuroscience, Trinity College Dublin, Dublin, Ireland.
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11
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Huang H, Chen Z, Fan B, Huang D, Qiu Z, Luo C, Zheng J. Abnormal global and local connectivity in patients with anti-N-methyl-D-aspartate receptor encephalitis: A resting-state functional MRI study. Brain Res 2024; 1837:148985. [PMID: 38714228 DOI: 10.1016/j.brainres.2024.148985] [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: 11/07/2023] [Revised: 03/27/2024] [Accepted: 05/04/2024] [Indexed: 05/09/2024]
Abstract
OBJECTIVE We decided to investigate the changes of global and local connectivity in anti-N-methyl-D-aspartate receptor (anti-NMDAR) encephalitis patients based on eigenvector centrality (EC) and regional homogeneity (ReHo). We sought new biomarkers to identify the patients based on multivariate pattern analysis (MVPA). METHODS Functional MRI (fMRI) was performed on all participants. EC, ReHo and MVPA were used to analyze the fMRI images. The correlation between the global or local connectivity and neuropsychology tests was detected. RESULTS The MoCA scores of the patients were lower than those of the healthy controls (HCs), while the HAMD24 and HAMA scores of the patients were higher than those of the HCs. Increased EC values in the right calcarine (CAL.R) and decreased EC values in the right putamen (PUT.R) distinguished these subjects with anti-NMDAR encephalitis from HCs. The higher ReHo values in the left postcentral gyrus (PoCG.L) were detected in the patients. The correlation analysis showed that the EC values in the PUT.R were negatively correlated with HAMD24 and HAMA scores, while the ReHo values in the PoCG.L were negatively correlated with MoCA scores. Better classification performance was reached in the EC-based classifier (AUC = 0.80), while weaker classification performance was achieved in the ReHo-based classifier (AUC = 0.74) or the classifier based on EC and ReHo (AUC = 0.74). The brain areas with large weights were located in the frontal lobe, parietal lobe, cerebellum and basal ganglia. CONCLUSION Our findings suggest that abnormal global and local connectivity may play an important part in the pathophysiological mechanism of neuropsychiatric symptoms in the anti-NMDAR encephalitis patients. The EC-based classifier may be better than the ReHo-based classifier in identifying anti-NMDAR encephalitis patients.
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Affiliation(s)
- Huachun Huang
- Department of Neurology, the First Affiliated Hospital of Guangxi Medical University, Nanning, China
| | - Zexiang Chen
- Department of Neurology, the First Affiliated Hospital of Guangxi Medical University, Nanning, China
| | - Binglin Fan
- Department of Neurology, the First Affiliated Hospital of Guangxi Medical University, Nanning, China
| | - Dongying Huang
- Department of Neurology, the First Affiliated Hospital of Guangxi Medical University, Nanning, China
| | - Zhuoyan Qiu
- Department of Neurology, the First Affiliated Hospital of Guangxi Medical University, Nanning, China
| | - Cuimi Luo
- Department of Neurology, the First Affiliated Hospital of Guangxi Medical University, Nanning, China
| | - Jinou Zheng
- Department of Neurology, the First Affiliated Hospital of Guangxi Medical University, Nanning, China.
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12
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Gell M, Noble S, Laumann TO, Nelson SM, Tervo-Clemmens B. Psychiatric neuroimaging designs for individualised, cohort, and population studies. Neuropsychopharmacology 2024:10.1038/s41386-024-01918-y. [PMID: 39143320 DOI: 10.1038/s41386-024-01918-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/01/2024] [Revised: 05/30/2024] [Accepted: 06/11/2024] [Indexed: 08/16/2024]
Abstract
Psychiatric neuroimaging faces challenges to rigour and reproducibility that prompt reconsideration of the relative strengths and limitations of study designs. Owing to high resource demands and varying inferential goals, current designs differentially emphasise sample size, measurement breadth, and longitudinal assessments. In this overview and perspective, we provide a guide to the current landscape of psychiatric neuroimaging study designs with respect to this balance of scientific goals and resource constraints. Through a heuristic data cube contrasting key design features, we discuss a resulting trade-off among small sample, precision longitudinal studies (e.g., individualised studies and cohorts) and large sample, minimally longitudinal, population studies. Precision studies support tests of within-person mechanisms, via intervention and tracking of longitudinal course. Population studies support tests of generalisation across multifaceted individual differences. A proposed reciprocal validation model (RVM) aims to recursively leverage these complementary designs in sequence to accumulate evidence, optimise relative strengths, and build towards improved long-term clinical utility.
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Affiliation(s)
- Martin Gell
- Department of Psychiatry, Psychotherapy and Psychosomatics, Faculty of Medicine, RWTH Aachen University, Aachen, Germany.
- Institute of Neuroscience and Medicine (INM-7: Brain & Behaviour), Research Centre Jülich, Jülich, Germany.
- Masonic Institute for the Developing Brain, University of Minnesota, Minneapolis, MN, USA.
| | - Stephanie Noble
- Psychology Department, Northeastern University, Boston, MA, USA
- Bioengineering Department, Northeastern University, Boston, MA, USA
- Center for Cognitive and Brain Health, Northeastern University, Boston, MA, USA
| | - Timothy O Laumann
- Department of Psychiatry, Washington University School of Medicine, St. Louis, MO, USA
| | - Steven M Nelson
- Masonic Institute for the Developing Brain, University of Minnesota, Minneapolis, MN, USA
- Department of Pediatrics, University of Minnesota, Minneapolis, MN, USA
| | - Brenden Tervo-Clemmens
- Masonic Institute for the Developing Brain, University of Minnesota, Minneapolis, MN, USA.
- Department of Psychiatry and Behavioral Sciences, University of Minnesota, Minneapolis, MN, USA.
- Institute for Translational Neuroscience, University of Minnesota, Minneapolis, MN, USA.
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13
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Garnaik UC, Chandra A, Goel VK, Gulyás B, Padmanabhan P, Agarwal S. Development of SERS Active Nanoprobe for Selective Adsorption and Detection of Alzheimer's Disease Biomarkers Based on Molecular Docking. Int J Nanomedicine 2024; 19:8271-8284. [PMID: 39161360 PMCID: PMC11330857 DOI: 10.2147/ijn.s446212] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/04/2023] [Accepted: 04/08/2024] [Indexed: 08/21/2024] Open
Abstract
Purpose Development of SERS-based Raman nanoprobes can detect the misfolding of Amyloid beta (Aβ) 42 peptides, making them a viable diagnostic technique for Alzheimer's disease (AD). The detection and imaging of amyloid peptides and fibrils are expected to help in the early identification of AD. Methods Here, we propose a fast, easy-to-use, and simple scheme based on the selective adsorption of Aβ42 molecules on SERS active gold nanoprobe (RB-AuNPs) of diameter 29 ± 3 nm for Detection of Alzheimer's Disease Biomarkers. Binding with the peptides results in a spectrum shift, which correlates with the target peptide. We also demonstrated the possibility of using silver nanoparticles (AgNPs) as precursors for the preparation of a SERS active nanoprobe with carbocyanine (CC) dye and AgNPs known as silver nanoprobe (CC-AgNPs) of diameter 25 ± 4 nm. Results RB-AuNPs probe binding with the peptides results in a spectrum shift, which correlates with the target peptide. Arginine peak appears after the conjugation confirms the binding of Aβ 42 with the nanoprobe. Tyrosine peaks appear after conjugated Aβ42 with CC-AgNPs providing binding of the peptide with the probe. The nanoprobe produced a strong, stable SERS signal. Further molecular docking was utilized to analyse the interaction and propose a structural hypothesis for the process of binding the nanoprobe to Aβ42 and Tau protein. Conclusion This peptide-probe interaction provides a general enhancement factor and the molecular structure of the misfolded peptides. Secondary structural information may be obtained at the molecular level for specific residues owing to isotope shifts in the Raman spectra. Conjugation of the nanoprobe with Aβ42 selectively detected AD in bodily fluids. The proposed nanoprobes can be easily applied to the detection of Aβ plaques in blood, saliva, and sweat samples.
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Affiliation(s)
| | - Anshuman Chandra
- School of Physical Sciences, Jawaharlal Nehru University, New Delhi, India
| | - Vijay Kumar Goel
- School of Physical Sciences, Jawaharlal Nehru University, New Delhi, India
| | - Balázs Gulyás
- Cognitive Neuroimaging Centre, Nanyang Technological University (NTU), Singapore, Singapore
| | | | - Shilpi Agarwal
- School of Physical Sciences, Jawaharlal Nehru University, New Delhi, India
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14
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Saltoun K, Yeo BTT, Paul L, Diedrichsen J, Bzdok D. Longitudinal changes in brain asymmetry track lifestyle and disease. RESEARCH SQUARE 2024:rs.3.rs-4798448. [PMID: 39149493 PMCID: PMC11326383 DOI: 10.21203/rs.3.rs-4798448/v1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 08/17/2024]
Abstract
Human beings may have evolved the largest asymmetries of brain organization in the animal kingdom. Hemispheric left-vs-right specialization is especially pronounced in our species-unique capacities. Yet, brain asymmetry features appear to be strongly shaped by non-genetic influences. We hence charted the largest longitudinal brain-imaging adult resource, yielding evidence that brain asymmetry changes continuously in a manner suggestive of neural plasticity. In the UK Biobank population cohort, we demonstrate that asymmetry changes show robust associations across 959 distinct phenotypic variables spanning 11 categories. We also find that changes in brain asymmetry over years co-occur with changes among specific lifestyle markers. Finally, we reveal relevance of brain asymmetry changes to major disease categories across thousands of medical diagnoses. Our results challenge the tacit assumption that asymmetrical neural systems are highly conserved throughout adulthood.
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Affiliation(s)
- Karin Saltoun
- The Neuro - Montreal Neurological Institute (MNI), McConnell Brain Imaging Centre, Department of Biomedical Engineering, Faculty of Medicine, School of Computer Science, McGill University, Montreal, Canada
- Mila - Quebec Artificial Intelligence Institute, Montreal, QC, Canada
| | - B T Thomas Yeo
- Centre for Sleep and Cognition & Centre for Translational MR Research, Yong Loo Lin School of Medicine, National University of Singapore, Singapore
- Department of Medicine, Human Potential Translational Research Programme & Institute for Digital Medicine (WisDM), Yong Loo Lin School of Medicine, National University of Singapore, Singapore
- Department of Electrical and Computer Engineering, National University of Singapore, Singapore
- N.1 Institute for Health, National University of Singapore, Singapore
- Integrative Sciences and Engineering Programme (ISEP), National University of Singapore
- Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, MA, USA
| | - Lynn Paul
- Division of the Humanities and Social Sciences, California Institute of Technology, Pasadena, CA, 91125, USA
| | - Jorn Diedrichsen
- Western Institute of Neuroscience, Western University, London, Ontario, Canada
| | - Danilo Bzdok
- The Neuro - Montreal Neurological Institute (MNI), McConnell Brain Imaging Centre, Department of Biomedical Engineering, Faculty of Medicine, School of Computer Science, McGill University, Montreal, Canada
- Mila - Quebec Artificial Intelligence Institute, Montreal, QC, Canada
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15
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Wang Q, Wang W, Fang Y, Yap PT, Zhu H, Li HJ, Qiao L, Liu M. Leveraging Brain Modularity Prior for Interpretable Representation Learning of fMRI. IEEE Trans Biomed Eng 2024; 71:2391-2401. [PMID: 38412079 PMCID: PMC11257815 DOI: 10.1109/tbme.2024.3370415] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/29/2024]
Abstract
Resting-state functional magnetic resonance imaging (rs-fMRI) can reflect spontaneous neural activities in the brain and is widely used for brain disorder analysis. Previous studies focus on extracting fMRI representations using machine/deep learning methods, but these features typically lack biological interpretability. The human brain exhibits a remarkable modular structure in spontaneous brain functional networks, with each module comprised of functionally interconnected brain regions-of-interest (ROIs). However, existing learning-based methods cannot adequately utilize such brain modularity prior. In this paper, we propose a brain modularity-constrained dynamic representation learning framework for interpretable fMRI analysis, consisting of dynamic graph construction, dynamic graph learning via a novel modularity-constrained graph neural network (MGNN), and prediction and biomarker detection. The designed MGNN is constrained by three core neurocognitive modules (i.e., salience network, central executive network, and default mode network), encouraging ROIs within the same module to share similar representations. To further enhance discriminative ability of learned features, we encourage the MGNN to preserve network topology of input graphs via a graph topology reconstruction constraint. Experimental results on 534 subjects with rs-fMRI scans from two datasets validate the effectiveness of the proposed method. The identified discriminative brain ROIs and functional connectivities can be regarded as potential fMRI biomarkers to aid in clinical diagnosis.
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16
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Rosenblatt M, Tejavibulya L, Sun H, Camp CC, Khaitova M, Adkinson BD, Jiang R, Westwater ML, Noble S, Scheinost D. Power and reproducibility in the external validation of brain-phenotype predictions. Nat Hum Behav 2024:10.1038/s41562-024-01931-7. [PMID: 39085406 DOI: 10.1038/s41562-024-01931-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/30/2023] [Accepted: 06/18/2024] [Indexed: 08/02/2024]
Abstract
Brain-phenotype predictive models seek to identify reproducible and generalizable brain-phenotype associations. External validation, or the evaluation of a model in external datasets, is the gold standard in evaluating the generalizability of models in neuroimaging. Unlike typical studies, external validation involves two sample sizes: the training and the external sample sizes. Thus, traditional power calculations may not be appropriate. Here we ran over 900 million resampling-based simulations in functional and structural connectivity data to investigate the relationship between training sample size, external sample size, phenotype effect size, theoretical power and simulated power. Our analysis included a wide range of datasets: the Healthy Brain Network, the Adolescent Brain Cognitive Development Study, the Human Connectome Project (Development and Young Adult), the Philadelphia Neurodevelopmental Cohort, the Queensland Twin Adolescent Brain Project, and the Chinese Human Connectome Project; and phenotypes: age, body mass index, matrix reasoning, working memory, attention problems, anxiety/depression symptoms and relational processing. High effect size predictions achieved adequate power with training and external sample sizes of a few hundred individuals, whereas low and medium effect size predictions required hundreds to thousands of training and external samples. In addition, most previous external validation studies used sample sizes prone to low power, and theoretical power curves should be adjusted for the training sample size. Furthermore, model performance in internal validation often informed subsequent external validation performance (Pearson's r difference <0.2), particularly for well-harmonized datasets. These results could help decide how to power future external validation studies.
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Affiliation(s)
- Matthew Rosenblatt
- Department of Biomedical Engineering, Yale University, New Haven, CT, USA.
| | - Link Tejavibulya
- Interdepartmental Neuroscience Program, Yale University, New Haven, CT, USA
| | - Huili Sun
- Department of Biomedical Engineering, Yale University, New Haven, CT, USA
| | - Chris C Camp
- Interdepartmental Neuroscience Program, Yale University, New Haven, CT, USA
| | - Milana Khaitova
- Department of Radiology and Biomedical Imaging, Yale School of Medicine, New Haven, CT, USA
| | - Brendan D Adkinson
- Interdepartmental Neuroscience Program, Yale University, New Haven, CT, USA
| | - Rongtao Jiang
- Department of Radiology and Biomedical Imaging, Yale School of Medicine, New Haven, CT, USA
| | - Margaret L Westwater
- Department of Radiology and Biomedical Imaging, Yale School of Medicine, New Haven, CT, USA
| | - Stephanie Noble
- Department of Radiology and Biomedical Imaging, Yale School of Medicine, New Haven, CT, USA
- Department of Bioengineering, Northeastern University, Boston, MA, USA
- Department of Psychology, Northeastern University, Boston, MA, USA
| | - Dustin Scheinost
- Department of Biomedical Engineering, Yale University, New Haven, CT, USA
- Interdepartmental Neuroscience Program, Yale University, New Haven, CT, USA
- Department of Radiology and Biomedical Imaging, Yale School of Medicine, New Haven, CT, USA
- Child Study Center, Yale School of Medicine, New Haven, CT, USA
- Department of Statistics and Data Science, Yale University, New Haven, CT, USA
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17
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Botvinik-Nezer R, Petre B, Ceko M, Lindquist MA, Friedman NP, Wager TD. Placebo treatment affects brain systems related to affective and cognitive processes, but not nociceptive pain. Nat Commun 2024; 15:6017. [PMID: 39019888 PMCID: PMC11255344 DOI: 10.1038/s41467-024-50103-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/29/2024] [Accepted: 06/28/2024] [Indexed: 07/19/2024] Open
Abstract
Drug treatments for pain often do not outperform placebo, and a better understanding of placebo mechanisms is needed to improve treatment development and clinical practice. In a large-scale fMRI study (N = 392) with pre-registered analyses, we tested whether placebo analgesic treatment modulates nociceptive processes, and whether its effects generalize from conditioned to unconditioned pain modalities. Placebo treatment caused robust analgesia in conditioned thermal pain that generalized to unconditioned mechanical pain. However, placebo did not decrease pain-related fMRI activity in brain measures linked to nociceptive pain, including the Neurologic Pain Signature (NPS) and spinothalamic pathway regions, with strong support for null effects in Bayes Factor analyses. In addition, surprisingly, placebo increased activity in some spinothalamic regions for unconditioned mechanical pain. In contrast, placebo reduced activity in a neuromarker associated with higher-level contributions to pain, the Stimulus Intensity Independent Pain Signature (SIIPS), and affected activity in brain regions related to motivation and value, in both pain modalities. Individual differences in behavioral analgesia were correlated with neural changes in both modalities. Our results indicate that cognitive and affective processes primarily drive placebo analgesia, and show the potential of neuromarkers for separating treatment influences on nociception from influences on evaluative processes.
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Affiliation(s)
- Rotem Botvinik-Nezer
- Department of Psychology, The Hebrew University of Jerusalem, Jerusalem, Israel.
- Department of Psychological and Brain Sciences, Dartmouth College, Hanover, NH, USA.
| | - Bogdan Petre
- Department of Psychological and Brain Sciences, Dartmouth College, Hanover, NH, USA
| | - Marta Ceko
- Institute of Cognitive Science, University of Colorado Boulder, Boulder, CO, USA
| | - Martin A Lindquist
- Department of Biostatistics, Johns Hopkins University, Baltimore, MD, USA
| | - Naomi P Friedman
- Institute for Behavioral Genetics, University of Colorado Boulder, Boulder, CO, USA
- Department of Psychology and Neuroscience, University of Colorado Boulder, Boulder, CO, USA
| | - Tor D Wager
- Department of Psychological and Brain Sciences, Dartmouth College, Hanover, NH, USA.
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18
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Kincses B, Forkmann K, Schlitt F, Jan Pawlik R, Schmidt K, Timmann D, Elsenbruch S, Wiech K, Bingel U, Spisak T. An externally validated resting-state brain connectivity signature of pain-related learning. Commun Biol 2024; 7:875. [PMID: 39020002 PMCID: PMC11255216 DOI: 10.1038/s42003-024-06574-y] [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: 10/09/2023] [Accepted: 07/10/2024] [Indexed: 07/19/2024] Open
Abstract
Pain can be conceptualized as a precision signal for reinforcement learning in the brain and alterations in these processes are a hallmark of chronic pain conditions. Investigating individual differences in pain-related learning therefore holds important clinical and translational relevance. Here, we developed and externally validated a novel resting-state brain connectivity-based predictive model of pain-related learning. The pre-registered external validation indicates that the proposed model explains 8-12% of the inter-individual variance in pain-related learning. Model predictions are driven by connections of the amygdala, posterior insula, sensorimotor, frontoparietal, and cerebellar regions, outlining a network commonly described in aversive learning and pain. We propose the resulting model as a robust and highly accessible biomarker candidate for clinical and translational pain research, with promising implications for personalized treatment approaches and with a high potential to advance our understanding of the neural mechanisms of pain-related learning.
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Affiliation(s)
- Balint Kincses
- Department of Neurology, Center for Translational Neuro- and Behavioral Sciences, University Medicine Essen, Essen, Germany.
- Institute for Diagnostic and Interventional Radiology and Neuroradiology, University Medicine Essen, Essen, Germany.
| | - Katarina Forkmann
- Department of Neurology, Center for Translational Neuro- and Behavioral Sciences, University Medicine Essen, Essen, Germany
| | - Frederik Schlitt
- Department of Neurology, Center for Translational Neuro- and Behavioral Sciences, University Medicine Essen, Essen, Germany
| | - Robert Jan Pawlik
- Department of Medical Psychology and Medical Sociology, Faculty of Medicine, Ruhr University Bochum, Bochum, Germany
| | - Katharina Schmidt
- Department of Neurology, Center for Translational Neuro- and Behavioral Sciences, University Medicine Essen, Essen, Germany
| | - Dagmar Timmann
- Department of Neurology, Center for Translational Neuro- and Behavioral Sciences, University Medicine Essen, Essen, Germany
| | - Sigrid Elsenbruch
- Department of Neurology, Center for Translational Neuro- and Behavioral Sciences, University Medicine Essen, Essen, Germany
- Department of Medical Psychology and Medical Sociology, Faculty of Medicine, Ruhr University Bochum, Bochum, Germany
| | - Katja Wiech
- Wellcome Centre for Integrative Neuroimaging, FMRIB, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, UK
| | - Ulrike Bingel
- Department of Neurology, Center for Translational Neuro- and Behavioral Sciences, University Medicine Essen, Essen, Germany
| | - Tamas Spisak
- Department of Neurology, Center for Translational Neuro- and Behavioral Sciences, University Medicine Essen, Essen, Germany
- Institute for Diagnostic and Interventional Radiology and Neuroradiology, University Medicine Essen, Essen, Germany
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19
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Vigotsky AD, Iannetti GD, Apkarian AV. Mental state decoders: game-changers or wishful thinking? Trends Cogn Sci 2024:S1364-6613(24)00162-1. [PMID: 38991876 DOI: 10.1016/j.tics.2024.06.004] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/17/2024] [Revised: 06/12/2024] [Accepted: 06/13/2024] [Indexed: 07/13/2024]
Abstract
Decoding mental and perceptual states using fMRI has become increasingly popular over the past two decades, with numerous highly-cited studies published in high-profile journals. Nevertheless, what have we learned from these decoders? In this opinion, we argue that fMRI-based decoders are not neurophysiologically informative and are not, and likely cannot be, applicable to real-world decision-making. The former point stems from the fact that decoding models cannot disentangle neural mechanisms from their epiphenomena. The latter point stems from both logical and ethical constraints. Constructing decoders requires precious time and resources that should instead be directed toward scientific endeavors more likely to yield meaningful scientific progress.
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Affiliation(s)
| | - Gian Domenico Iannetti
- Italian Institute of Technology (IIT), Rome, Italy; University College London (UCL), London, UK
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20
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Jin MX, Qin PP, Xia AWL, Kan RLD, Zhang BBB, Tang AHP, Li ASM, Lin TTZ, Giron CG, Pei JJ, Kranz GS. Neurophysiological and neuroimaging markers of repetitive transcranial magnetic stimulation treatment response in major depressive disorder: A systematic review and meta-analysis of predictive modeling studies. Neurosci Biobehav Rev 2024; 162:105695. [PMID: 38710424 DOI: 10.1016/j.neubiorev.2024.105695] [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: 10/26/2023] [Revised: 04/10/2024] [Accepted: 04/26/2024] [Indexed: 05/08/2024]
Abstract
Predicting repetitive transcranial magnetic stimulation (rTMS) treatment outcomes in major depressive disorder (MDD) could reduce the financial and psychological risks of treatment failure. We systematically reviewed and meta-analyzed studies that leveraged neurophysiological and neuroimaging markers to predict rTMS response in MDD. Five databases were searched from inception to May 25, 2023. The primary meta-analytic outcome was predictive accuracy pooled from classification models. Regression models were summarized qualitatively. A promising marker was identified if it showed a sensitivity and specificity of 80% or higher in at least two independent studies. Searching yielded 36 studies. Twenty-two classification modeling studies produced an estimated area under the summary receiver operating characteristic curve of 0.87 (95% CI = 0.83-0.92), with 86.8% sensitivity (95% CI = 80.6-91.2%) and 81.9% specificity (95% CI = 76.1-86.4%). Frontal theta cordance measured by electroencephalography is closest to proof of concept. Predicting rTMS response using neurophysiological and neuroimaging markers is promising for clinical decision-making. However, replications by different research groups are needed to establish rigorous markers.
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Affiliation(s)
- Min Xia Jin
- Department of Rehabilitation Sciences, The Hong Kong Polytechnic University, Hong Kong, Special Administrative Region of China; Shanghai YangZhi Rehabilitation Hospital (Shanghai Sunshine Rehabilitation Center), School of Medicine, Tongji University, Shanghai, China
| | - Penny Ping Qin
- Department of Rehabilitation Sciences, The Hong Kong Polytechnic University, Hong Kong, Special Administrative Region of China
| | - Adam Wei Li Xia
- Department of Rehabilitation Sciences, The Hong Kong Polytechnic University, Hong Kong, Special Administrative Region of China
| | - Rebecca Lai Di Kan
- Department of Rehabilitation Sciences, The Hong Kong Polytechnic University, Hong Kong, Special Administrative Region of China
| | - Bella Bing Bing Zhang
- Department of Rehabilitation Sciences, The Hong Kong Polytechnic University, Hong Kong, Special Administrative Region of China
| | - Alvin Hong Pui Tang
- Department of Rehabilitation Sciences, The Hong Kong Polytechnic University, Hong Kong, Special Administrative Region of China
| | - Ami Sin Man Li
- Department of Rehabilitation Sciences, The Hong Kong Polytechnic University, Hong Kong, Special Administrative Region of China
| | - Tim Tian Ze Lin
- Department of Rehabilitation Sciences, The Hong Kong Polytechnic University, Hong Kong, Special Administrative Region of China
| | - Cristian G Giron
- Department of Rehabilitation Sciences, The Hong Kong Polytechnic University, Hong Kong, Special Administrative Region of China
| | - Jun Jie Pei
- Department of Rehabilitation Medicine, Sir Run Run Shaw Hospital, School of Medicine, Zhejiang University, Zhejiang, China
| | - Georg S Kranz
- Department of Rehabilitation Sciences, The Hong Kong Polytechnic University, Hong Kong, Special Administrative Region of China; Mental Health Research Center, The Hong Kong Polytechnic University, Hong Kong, Special Administrative Region of China; Department of Psychiatry and Psychotherapy, Comprehensive Center for Clinical Neurosciences and Mental Health, Medical University of Vienna, Austria.
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21
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Huang H, Huang D, Luo C, Qiu Z, Zheng J. Abnormalities of regional brain activity and executive function in patients with temporal lobe epilepsy: A cross-sectional and longitudinal resting-state functional MRI study. Neuroradiology 2024; 66:1093-1104. [PMID: 38668803 DOI: 10.1007/s00234-024-03368-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/15/2024] [Accepted: 04/22/2024] [Indexed: 06/05/2024]
Abstract
PURPOSE We decided to track changes in regional brain activity and executive function in temporal lobe epilepsy (TLE) patients based on cross-sectional and longitudinal designs and sought potential imaging features for follow-up observation. METHODS Thirty-two TLE patients and thirty-three healthy controls (HCs) were recruited to detect changes in fractional amplitude of low-frequency fluctuation (fALFF) and regional homogeneity (ReHo) and to evaluate executive function both at baseline and at two-year (23.3 ± 8.3 months) follow-up. Moreover, multivariate pattern analysis (MVPA) was used for follow-up observation. RESULTS TLE patients displayed lower fALFF values in the right superior frontal gyrus (SFG) and higher ReHo values in the left putamen (PUT) relative to the HCs. Longitudinal analysis revealed that TLE patients at follow-up exhibited higher fALFF values in the left postcentral gyrus (PoCG), higher ReHo values in the left PoCG and the right middle frontal gyrus (MFG), lower ReHo values in the bilateral PUT and the right fusiform gyrus (FFG) compared with these patients at baseline. The executive function was impaired in TLE patients but didn't deteriorate over time. No correlations were discovered between regional brain activity and executive function. The MVPA based on ReHo performed well in differentiating the follow-up group from the baseline group. CONCLUSION We revealed the abnormalities in regional brain activity and executive function as well as their longitudinal trends in TLE patients. The ReHo might be a good imaging feature for follow-up observation.
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Affiliation(s)
- Huachun Huang
- Department of Neurology, the First Affiliated Hospital of Guangxi Medical University, Nanning, China
| | - Dongying Huang
- Department of Neurology, the First Affiliated Hospital of Guangxi Medical University, Nanning, China
| | - Cuimi Luo
- Department of Neurology, the First Affiliated Hospital of Guangxi Medical University, Nanning, China
| | - Zhuoyan Qiu
- Department of Neurology, the First Affiliated Hospital of Guangxi Medical University, Nanning, China
| | - Jinou Zheng
- Department of Neurology, the First Affiliated Hospital of Guangxi Medical University, Nanning, China.
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22
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Gan X, Zhou F, Xu T, Liu X, Zhang R, Zheng Z, Yang X, Zhou X, Yu F, Li J, Cui R, Wang L, Yuan J, Yao D, Becker B. A neurofunctional signature of subjective disgust generalizes to oral distaste and socio-moral contexts. Nat Hum Behav 2024; 8:1383-1402. [PMID: 38641635 DOI: 10.1038/s41562-024-01868-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/29/2023] [Accepted: 03/19/2024] [Indexed: 04/21/2024]
Abstract
While disgust originates in the hard-wired mammalian distaste response, the conscious experience of disgust in humans strongly depends on subjective appraisal and may even extend to socio-moral contexts. Here, in a series of studies, we combined functional magnetic resonance imaging with machine-learning-based predictive modelling to establish a comprehensive neurobiological model of subjective disgust. The developed neurofunctional signature accurately predicted momentary self-reported subjective disgust across discovery (n = 78) and pre-registered validation (n = 30) cohorts and generalized across core disgust (n = 34 and n = 26), gustatory distaste (n = 30) and socio-moral (unfair offers; n = 43) contexts. Disgust experience was encoded in distributed cortical and subcortical systems, and exhibited distinct and shared neural representations with subjective fear or negative affect in interoceptive-emotional awareness and conscious appraisal systems, while the signatures most accurately predicted the respective target experience. We provide an accurate functional magnetic resonance imaging signature for disgust with a high potential to resolve ongoing evolutionary debates.
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Affiliation(s)
- Xianyang Gan
- The Center of Psychosomatic Medicine, Sichuan Provincial Center for Mental Health, Sichuan Provincial People's Hospital, University of Electronic Science and Technology of China, Chengdu, China
- Clinical Hospital of Chengdu Brain Science Institute, MOE Key Laboratory for Neuroinformation, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, China
| | - Feng Zhou
- Faculty of Psychology, Southwest University, Chongqing, China
| | - Ting Xu
- The Center of Psychosomatic Medicine, Sichuan Provincial Center for Mental Health, Sichuan Provincial People's Hospital, University of Electronic Science and Technology of China, Chengdu, China
- Clinical Hospital of Chengdu Brain Science Institute, MOE Key Laboratory for Neuroinformation, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, China
| | - Xiaobo Liu
- McConnell Brain Imaging Centre, Montreal Neurological Institute, McGill University, Montreal, Quebec, Canada
| | - Ran Zhang
- The Center of Psychosomatic Medicine, Sichuan Provincial Center for Mental Health, Sichuan Provincial People's Hospital, University of Electronic Science and Technology of China, Chengdu, China
- Clinical Hospital of Chengdu Brain Science Institute, MOE Key Laboratory for Neuroinformation, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, China
| | - Zihao Zheng
- The Center of Psychosomatic Medicine, Sichuan Provincial Center for Mental Health, Sichuan Provincial People's Hospital, University of Electronic Science and Technology of China, Chengdu, China
- Clinical Hospital of Chengdu Brain Science Institute, MOE Key Laboratory for Neuroinformation, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, China
| | - Xi Yang
- Faculty of Health, Medicine and Life Sciences, Maastricht University, Maastricht, the Netherlands
| | - Xinqi Zhou
- Sichuan Key Laboratory of Psychology and Behavior of Discipline Inspection and Supervision, Institute of Brain and Psychological Sciences, Sichuan Normal University, Chengdu, China
| | - Fangwen Yu
- The Center of Psychosomatic Medicine, Sichuan Provincial Center for Mental Health, Sichuan Provincial People's Hospital, University of Electronic Science and Technology of China, Chengdu, China
- Clinical Hospital of Chengdu Brain Science Institute, MOE Key Laboratory for Neuroinformation, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, China
| | - Jialin Li
- Max Planck School of Cognition, Leipzig, Germany
| | - Ruifang Cui
- The Center of Psychosomatic Medicine, Sichuan Provincial Center for Mental Health, Sichuan Provincial People's Hospital, University of Electronic Science and Technology of China, Chengdu, China
- Clinical Hospital of Chengdu Brain Science Institute, MOE Key Laboratory for Neuroinformation, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, China
| | - Lan Wang
- The Center of Psychosomatic Medicine, Sichuan Provincial Center for Mental Health, Sichuan Provincial People's Hospital, University of Electronic Science and Technology of China, Chengdu, China
- Clinical Hospital of Chengdu Brain Science Institute, MOE Key Laboratory for Neuroinformation, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, China
| | - Jiajin Yuan
- Sichuan Key Laboratory of Psychology and Behavior of Discipline Inspection and Supervision, Institute of Brain and Psychological Sciences, Sichuan Normal University, Chengdu, China
| | - Dezhong Yao
- The Center of Psychosomatic Medicine, Sichuan Provincial Center for Mental Health, Sichuan Provincial People's Hospital, University of Electronic Science and Technology of China, Chengdu, China
- Clinical Hospital of Chengdu Brain Science Institute, MOE Key Laboratory for Neuroinformation, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, China
| | - Benjamin Becker
- The Center of Psychosomatic Medicine, Sichuan Provincial Center for Mental Health, Sichuan Provincial People's Hospital, University of Electronic Science and Technology of China, Chengdu, China.
- Clinical Hospital of Chengdu Brain Science Institute, MOE Key Laboratory for Neuroinformation, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, China.
- State Key Laboratory for Brain and Cognitive Sciences, The University of Hong Kong, Hong Kong, China.
- Department of Psychology, The University of Hong Kong, Hong Kong, China.
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Chhade F, Tabbal J, Paban V, Auffret M, Hassan M, Vérin M. Predicting creative behavior using resting-state electroencephalography. Commun Biol 2024; 7:790. [PMID: 38951602 PMCID: PMC11217288 DOI: 10.1038/s42003-024-06461-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/03/2023] [Accepted: 06/14/2024] [Indexed: 07/03/2024] Open
Abstract
Neuroscience research has shown that specific brain patterns can relate to creativity during multiple tasks but also at rest. Nevertheless, the electrophysiological correlates of a highly creative brain remain largely unexplored. This study aims to uncover resting-state networks related to creative behavior using high-density electroencephalography (HD-EEG) and to test whether the strength of functional connectivity within these networks could predict individual creativity in novel subjects. We acquired resting state HD-EEG data from 90 healthy participants who completed a creative behavior inventory. We then employed connectome-based predictive modeling; a machine-learning technique that predicts behavioral measures from brain connectivity features. Using a support vector regression, our results reveal functional connectivity patterns related to high and low creativity, in the gamma frequency band (30-45 Hz). In leave-one-out cross-validation, the combined model of high and low networks predicts individual creativity with very good accuracy (r = 0.36, p = 0.00045). Furthermore, the model's predictive power is established through external validation on an independent dataset (N = 41), showing a statistically significant correlation between observed and predicted creativity scores (r = 0.35, p = 0.02). These findings reveal large-scale networks that could predict creative behavior at rest, providing a crucial foundation for developing HD-EEG-network-based markers of creativity.
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Affiliation(s)
- Fatima Chhade
- CIC-IT INSERM 1414, Université de Rennes, Rennes, France.
| | - Judie Tabbal
- Institute of Clinical Neurosciences of Rennes (INCR), Rennes, France
- MINDIG, Rennes, France
| | - Véronique Paban
- CRPN, CNRS-UMR 7077, Aix Marseille Université, Marseille, France
| | - Manon Auffret
- CIC-IT INSERM 1414, Université de Rennes, Rennes, France
- France Développement Électronique, Monswiller, France
| | - Mahmoud Hassan
- MINDIG, Rennes, France
- School of Science and Engineering, Reykjavik University, Reykjavik, Iceland
| | - Marc Vérin
- CIC-IT INSERM 1414, Université de Rennes, Rennes, France
- B-CLINE, Laboratoire Interdisciplinaire pour l'Innovation et la Recherche en Santé d'Orléans (LI²RSO), Université d'Orléans, Orléans, France
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24
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Lee SA, Lee JJ, Han J, Choi M, Wager TD, Woo CW. Brain representations of affective valence and intensity in sustained pleasure and pain. Proc Natl Acad Sci U S A 2024; 121:e2310433121. [PMID: 38857402 PMCID: PMC11194486 DOI: 10.1073/pnas.2310433121] [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: 06/21/2023] [Accepted: 04/18/2024] [Indexed: 06/12/2024] Open
Abstract
Pleasure and pain are two fundamental, intertwined aspects of human emotions. Pleasurable sensations can reduce subjective feelings of pain and vice versa, and we often perceive the termination of pain as pleasant and the absence of pleasure as unpleasant. This implies the existence of brain systems that integrate them into modality-general representations of affective experiences. Here, we examined representations of affective valence and intensity in an functional MRI (fMRI) study (n = 58) of sustained pleasure and pain. We found that the distinct subpopulations of voxels within the ventromedial and lateral prefrontal cortices, the orbitofrontal cortex, the anterior insula, and the amygdala were involved in decoding affective valence versus intensity. Affective valence and intensity predictive models showed significant decoding performance in an independent test dataset (n = 62). These models were differentially connected to distinct large-scale brain networks-the intensity model to the ventral attention network and the valence model to the limbic and default mode networks. Overall, this study identified the brain representations of affective valence and intensity across pleasure and pain, promoting a systems-level understanding of human affective experiences.
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Affiliation(s)
- Soo Ahn Lee
- Center for Neuroscience Imaging Research, Institute for Basic Science, Suwon16419, Republic of Korea
- Department of Biomedical Engineering, Sungkyunkwan University, Suwon16419, Republic of Korea
- Department of Intelligent Precision Healthcare Convergence, Sungkyunkwan University, Suwon16419, Republic of Korea
| | - Jae-Joong Lee
- Center for Neuroscience Imaging Research, Institute for Basic Science, Suwon16419, Republic of Korea
| | - Jisoo Han
- Korea Brain Research Institute, Daegu41062, Republic of Korea
| | - Myunghwan Choi
- Center for Neuroscience Imaging Research, Institute for Basic Science, Suwon16419, Republic of Korea
- School of Biological Sciences, Seoul National University, Seoul08826, Republic of Korea
| | - Tor D. Wager
- Department of Psychological and Brain Sciences, Dartmouth College, Hanover, NH03755
| | - Choong-Wan Woo
- Center for Neuroscience Imaging Research, Institute for Basic Science, Suwon16419, Republic of Korea
- Department of Biomedical Engineering, Sungkyunkwan University, Suwon16419, Republic of Korea
- Department of Intelligent Precision Healthcare Convergence, Sungkyunkwan University, Suwon16419, Republic of Korea
- Life-inspired Neural Network for Prediction and Optimization Research Group, Suwon16419, Republic of Korea
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25
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Rudroff T, Rainio O, Klén R. AI for the prediction of early stages of Alzheimer's disease from neuroimaging biomarkers - A narrative review of a growing field. Neurol Sci 2024:10.1007/s10072-024-07649-8. [PMID: 38866971 DOI: 10.1007/s10072-024-07649-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/24/2024] [Accepted: 06/10/2024] [Indexed: 06/14/2024]
Abstract
OBJECTIVES The objectives of this narrative review are to summarize the current state of AI applications in neuroimaging for early Alzheimer's disease (AD) prediction and to highlight the potential of AI techniques in improving early AD diagnosis, prognosis, and management. METHODS We conducted a narrative review of studies using AI techniques applied to neuroimaging data for early AD prediction. We examined single-modality studies using structural MRI and PET imaging, as well as multi-modality studies integrating multiple neuroimaging techniques and biomarkers. Furthermore, they reviewed longitudinal studies that model AD progression and identify individuals at risk of rapid decline. RESULTS Single-modality studies using structural MRI and PET imaging have demonstrated high accuracy in classifying AD and predicting progression from mild cognitive impairment (MCI) to AD. Multi-modality studies, integrating multiple neuroimaging techniques and biomarkers, have shown improved performance and robustness compared to single-modality approaches. Longitudinal studies have highlighted the value of AI in modeling AD progression and identifying individuals at risk of rapid decline. However, challenges remain in data standardization, model interpretability, generalizability, clinical integration, and ethical considerations. CONCLUSION AI techniques applied to neuroimaging data have the potential to improve early AD diagnosis, prognosis, and management. Addressing challenges related to data standardization, model interpretability, generalizability, clinical integration, and ethical considerations is crucial for realizing the full potential of AI in AD research and clinical practice. Collaborative efforts among researchers, clinicians, and regulatory agencies are needed to develop reliable, robust, and ethical AI tools that can benefit AD patients and society.
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Affiliation(s)
- Thorsten Rudroff
- Department of Health and Human Physiology, University of Iowa, Iowa City, IA, 52242, USA.
- Department of Neurology, University of Iowa Hospitals and Clinics, Iowa City, IA, 52242, USA.
| | - Oona Rainio
- Turku PET Centre, University of Turku and Turku University Hospital, Turku, Finland
| | - Riku Klén
- Turku PET Centre, University of Turku and Turku University Hospital, Turku, Finland
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26
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Bryant AG, Aquino K, Parkes L, Fornito A, Fulcher BD. Extracting interpretable signatures of whole-brain dynamics through systematic comparison. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.01.10.573372. [PMID: 38915560 PMCID: PMC11195072 DOI: 10.1101/2024.01.10.573372] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/26/2024]
Abstract
The brain's complex distributed dynamics are typically quantified using a limited set of manually selected statistical properties, leaving the possibility that alternative dynamical properties may outperform those reported for a given application. Here, we address this limitation by systematically comparing diverse, interpretable features of both intra-regional activity and inter-regional functional coupling from resting-state functional magnetic resonance imaging (rs-fMRI) data, demonstrating our method using case-control comparisons of four neuropsychiatric disorders. Our findings generally support the use of linear time-series analysis techniques for rs-fMRI case-control analyses, while also identifying new ways to quantify informative dynamical fMRI structures. While simple statistical representations of fMRI dynamics performed surprisingly well (e.g., properties within a single brain region), combining intra-regional properties with inter-regional coupling generally improved performance, underscoring the distributed, multifaceted changes to fMRI dynamics in neuropsychiatric disorders. The comprehensive, data-driven method introduced here enables systematic identification and interpretation of quantitative dynamical signatures of multivariate time-series data, with applicability beyond neuroimaging to diverse scientific problems involving complex time-varying systems.
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Affiliation(s)
- Annie G. Bryant
- School of Physics, The University of Sydney, Camperdown, NSW, Australia
| | - Kevin Aquino
- School of Physics, The University of Sydney, Camperdown, NSW, Australia
- Brain Key Incorporated, San Francisco, CA, USA
| | - Linden Parkes
- Department of Psychiatry, Brain Health Institute, Rutgers University, Piscataway, NJ, USA
- Turner Institute for Brain & Mental Health, Monash University, VIC, Australia
| | - Alex Fornito
- Turner Institute for Brain & Mental Health, Monash University, VIC, Australia
| | - Ben D. Fulcher
- School of Physics, The University of Sydney, Camperdown, NSW, Australia
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27
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Wang X, Chen Q, Zhuang K, Zhang J, Cortes RA, Holzman DD, Fan L, Liu C, Sun J, Li X, Li Y, Feng Q, Chen H, Feng T, Lei X, He Q, Green AE, Qiu J. Semantic associative abilities and executive control functions predict novelty and appropriateness of idea generation. Commun Biol 2024; 7:703. [PMID: 38849461 PMCID: PMC11161622 DOI: 10.1038/s42003-024-06405-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/01/2023] [Accepted: 05/31/2024] [Indexed: 06/09/2024] Open
Abstract
Novelty and appropriateness are two fundamental components of creativity. However, the way in which novelty and appropriateness are separated at behavioral and neural levels remains poorly understood. In the present study, we aim to distinguish behavioral and neural bases of novelty and appropriateness of creative idea generation. In alignment with two established theories of creative thinking, which respectively, emphasize semantic association and executive control, behavioral results indicate that novelty relies more on associative abilities, while appropriateness relies more on executive functions. Next, employing a connectome predictive modeling (CPM) approach in resting-state fMRI data, we define two functional network-based models-dominated by interactions within the default network and by interactions within the limbic network-that respectively, predict novelty and appropriateness (i.e., cross-brain prediction). Furthermore, the generalizability and specificity of the two functional connectivity patterns are verified in additional resting-state fMRI and task fMRI. Finally, the two functional connectivity patterns, respectively mediate the relationship between semantic association/executive control and novelty/appropriateness. These findings provide global and predictive distinctions between novelty and appropriateness in creative idea generation.
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Affiliation(s)
- Xueyang Wang
- Key Laboratory of Cognition and Personality (SWU), Ministry of Education, Chongqing, China
- Faculty of Psychology, Southwest University, Chongqing, China
| | - Qunlin Chen
- Key Laboratory of Cognition and Personality (SWU), Ministry of Education, Chongqing, China
- Faculty of Psychology, Southwest University, Chongqing, China
| | - Kaixiang Zhuang
- Key Laboratory of Cognition and Personality (SWU), Ministry of Education, Chongqing, China
- Faculty of Psychology, Southwest University, Chongqing, China
| | - Jingyi Zhang
- Key Laboratory of Cognition and Personality (SWU), Ministry of Education, Chongqing, China
- Faculty of Psychology, Southwest University, Chongqing, China
| | - Robert A Cortes
- Department of Psychology, Georgetown University, Washington, DC, USA
| | - Daniel D Holzman
- Department of Psychology, Georgetown University, Washington, DC, USA
| | - Li Fan
- Key Laboratory of Cognition and Personality (SWU), Ministry of Education, Chongqing, China
- Faculty of Psychology, Southwest University, Chongqing, China
| | - Cheng Liu
- Key Laboratory of Cognition and Personality (SWU), Ministry of Education, Chongqing, China
- Faculty of Psychology, Southwest University, Chongqing, China
| | - Jiangzhou Sun
- Key Laboratory of Cognition and Personality (SWU), Ministry of Education, Chongqing, China
- Faculty of Psychology, Southwest University, Chongqing, China
| | - Xianrui Li
- Key Laboratory of Cognition and Personality (SWU), Ministry of Education, Chongqing, China
- Faculty of Psychology, Southwest University, Chongqing, China
| | - Yu Li
- Key Laboratory of Cognition and Personality (SWU), Ministry of Education, Chongqing, China
- Faculty of Psychology, Southwest University, Chongqing, China
| | - Qiuyang Feng
- Key Laboratory of Cognition and Personality (SWU), Ministry of Education, Chongqing, China
- Faculty of Psychology, Southwest University, Chongqing, China
| | - Hong Chen
- Key Laboratory of Cognition and Personality (SWU), Ministry of Education, Chongqing, China
- Faculty of Psychology, Southwest University, Chongqing, China
| | - Tingyong Feng
- Key Laboratory of Cognition and Personality (SWU), Ministry of Education, Chongqing, China
- Faculty of Psychology, Southwest University, Chongqing, China
| | - Xu Lei
- Key Laboratory of Cognition and Personality (SWU), Ministry of Education, Chongqing, China
- Faculty of Psychology, Southwest University, Chongqing, China
| | - Qinghua He
- Key Laboratory of Cognition and Personality (SWU), Ministry of Education, Chongqing, China
- Faculty of Psychology, Southwest University, Chongqing, China
| | - Adam E Green
- Department of Psychology, Georgetown University, Washington, DC, USA.
| | - Jiang Qiu
- Key Laboratory of Cognition and Personality (SWU), Ministry of Education, Chongqing, China.
- Faculty of Psychology, Southwest University, Chongqing, China.
- Southwest University Branch, Collaborative Innovation Center of Assessment Toward Basic Education Quality, Beijing Normal University, Chongqing, China.
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28
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Luppi AI, Gellersen HM, Liu ZQ, Peattie ARD, Manktelow AE, Adapa R, Owen AM, Naci L, Menon DK, Dimitriadis SI, Stamatakis EA. Systematic evaluation of fMRI data-processing pipelines for consistent functional connectomics. Nat Commun 2024; 15:4745. [PMID: 38834553 PMCID: PMC11150439 DOI: 10.1038/s41467-024-48781-5] [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: 10/04/2022] [Accepted: 05/10/2024] [Indexed: 06/06/2024] Open
Abstract
Functional interactions between brain regions can be viewed as a network, enabling neuroscientists to investigate brain function through network science. Here, we systematically evaluate 768 data-processing pipelines for network reconstruction from resting-state functional MRI, evaluating the effect of brain parcellation, connectivity definition, and global signal regression. Our criteria seek pipelines that minimise motion confounds and spurious test-retest discrepancies of network topology, while being sensitive to both inter-subject differences and experimental effects of interest. We reveal vast and systematic variability across pipelines' suitability for functional connectomics. Inappropriate choice of data-processing pipeline can produce results that are not only misleading, but systematically so, with the majority of pipelines failing at least one criterion. However, a set of optimal pipelines consistently satisfy all criteria across different datasets, spanning minutes, weeks, and months. We provide a full breakdown of each pipeline's performance across criteria and datasets, to inform future best practices in functional connectomics.
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Affiliation(s)
- Andrea I Luppi
- Division of Anaesthesia, University of Cambridge, Cambridge, UK.
- Department of Clinical Neurosciences, University of Cambridge, Cambridge, UK.
- St John's College, University of Cambridge, Cambridge, UK.
- Montreal Neurological Institute, McGill University, Montreal, Canada.
| | - Helena M Gellersen
- German Center for Neurodegenerative Diseases, Magdeburg, Germany
- Department of Psychology, University of Cambridge, Cambridge, UK
| | - Zhen-Qi Liu
- Montreal Neurological Institute, McGill University, Montreal, Canada
| | - Alexander R D Peattie
- Division of Anaesthesia, University of Cambridge, Cambridge, UK
- Department of Clinical Neurosciences, University of Cambridge, Cambridge, UK
| | - Anne E Manktelow
- Division of Anaesthesia, University of Cambridge, Cambridge, UK
- Department of Clinical Neurosciences, University of Cambridge, Cambridge, UK
| | - Ram Adapa
- Division of Anaesthesia, University of Cambridge, Cambridge, UK
- Department of Clinical Neurosciences, University of Cambridge, Cambridge, UK
| | - Adrian M Owen
- Department of Psychology, Western Institute for Neuroscience (WIN), Western University, London, ON, Canada
- Department of Physiology and Pharmacology, Western Institute for Neuroscience (WIN), Western University, London, ON, Canada
| | - Lorina Naci
- Trinity College Institute of Neuroscience, School of Psychology, Trinity College Dublin, Dublin, Ireland
| | - David K Menon
- Division of Anaesthesia, University of Cambridge, Cambridge, UK
| | - Stavros I Dimitriadis
- Department of Clinical Psychology and Psychobiology, University of Barcelona, Barcelona, Spain
- Institut de Neurociències, University of Barcelona, Barcelona, Spain
- Neuroinformatics Group, Cardiff University Brain Research Imaging Centre (CUBRIC), School of Psychology, College of Biomedical and Life Sciences, Cardiff, Wales, UK
- Division of Psychological Medicine and Clinical Neurosciences, School of Medicine, College of Biomedical and Life Sciences, Cardiff University, Cardiff, Wales, UK
- Neuroscience and Mental Health Research Institute, School of Medicine, College of Biomedical and Life Sciences, Cardiff University, Cardiff, Wales, UK
- MRC Centre for Neuropsychiatric Genetics and Genomics, School of Medicine, College of Biomedical and Life Sciences, Cardiff University, Cardiff, Wales, UK
- Integrative Neuroimaging Lab, Thessaloniki, Greece
| | - Emmanuel A Stamatakis
- Division of Anaesthesia, University of Cambridge, Cambridge, UK
- Department of Clinical Neurosciences, University of Cambridge, Cambridge, UK
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29
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Batta I, Abrol A, Calhoun VD. Multimodal active subspace analysis for computing assessment oriented subspaces from neuroimaging data. J Neurosci Methods 2024; 406:110109. [PMID: 38494061 PMCID: PMC11100582 DOI: 10.1016/j.jneumeth.2024.110109] [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: 03/09/2023] [Revised: 02/12/2024] [Accepted: 03/12/2024] [Indexed: 03/19/2024]
Abstract
BACKGROUND For successful biomarker discovery, it is essential to develop computational frameworks that summarize high-dimensional neuroimaging data in terms of involved sub-systems of the brain, while also revealing underlying heterogeneous functional and structural changes covarying with specific cognitive and biological traits. However, unsupervised decompositions do not inculcate clinical assessment information, while supervised approaches extract only individual feature importance, thereby impeding qualitative interpretation at the level of subspaces. NEW METHOD We present a novel framework to extract robust multimodal brain subspaces associated with changes in a given cognitive or biological trait. Our approach involves active subspace learning on the gradients of a trained machine learning model followed by clustering to extract and summarize the most salient and consistent subspaces associated with the target variable. RESULTS Through a rigorous cross-validation procedure on an Alzheimer's disease (AD) dataset, our framework successfully extracts multimodal subspaces specific to a given clinical assessment (e.g., memory and other cognitive skills), and also retains predictive performance in standard machine learning algorithms. We also show that the salient active subspace directions occur consistently across randomly sub-sampled repetitions of the analysis. COMPARISON WITH EXISTING METHOD(S) Compared to existing unsupervised decompositions based on principle component analysis, the subspace components in our framework retain higher predictive information. CONCLUSIONS As an important step towards biomarker discovery, our framework not only uncovers AD-related brain regions in the associated brain subspaces, but also enables automated identification of multiple underlying structural and functional sub-systems of the brain that collectively characterize changes in memory and proficiency in cognitive skills related to brain disorders like AD.
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Affiliation(s)
- Ishaan Batta
- Center for Translational Research in Neuroimaging and Data Science (TReNDS): Georgia State University, Georgia Institute of Technology, and Emory University, Atlanta, USA; Department of Electrical and Computer Engineering, Georgia Institute of Technology, Atlanta, USA.
| | - Anees Abrol
- Center for Translational Research in Neuroimaging and Data Science (TReNDS): Georgia State University, Georgia Institute of Technology, and Emory University, Atlanta, USA
| | - Vince D Calhoun
- Center for Translational Research in Neuroimaging and Data Science (TReNDS): Georgia State University, Georgia Institute of Technology, and Emory University, Atlanta, USA; Department of Electrical and Computer Engineering, Georgia Institute of Technology, Atlanta, USA
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30
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Crone EA, Bol T, Braams BR, de Rooij M, Franke B, Franken I, Gazzola V, Güroğlu B, Huizenga H, Hulshoff Pol H, Keijsers L, Keysers C, Krabbendam L, Jansen L, Popma A, Stulp G, van Atteveldt N, van Duijvenvoorde A, Veenstra R. Growing Up Together in Society (GUTS): A team science effort to predict societal trajectories in adolescence and young adulthood. Dev Cogn Neurosci 2024; 67:101403. [PMID: 38852381 PMCID: PMC11214182 DOI: 10.1016/j.dcn.2024.101403] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/01/2024] [Revised: 05/09/2024] [Accepted: 06/04/2024] [Indexed: 06/11/2024] Open
Abstract
Our society faces a great diversity of opportunities for youth. The 10-year Growing Up Together in Society (GUTS) program has the long-term goal to understand which combination of measures best predict societal trajectories, such as school success, mental health, well-being, and developing a sense of belonging in society. Our leading hypothesis is that self-regulation is key to how adolescents successfully navigate the demands of contemporary society. We aim to test these questions using socio-economic, questionnaire (including experience sampling methods), behavioral, brain (fMRI, sMRI, EEG), hormonal, and genetic measures in four large cohorts including adolescents and young adults. Two cohorts are designed as test and replication cohorts to test the developmental trajectory of self-regulation, including adolescents of different socioeconomic status thereby bridging individual, family, and societal perspectives. The third cohort consists of an entire social network to examine how neural and self-regulatory development influences and is influenced by whom adolescents and young adults choose to interact with. The fourth cohort includes youth with early signs of antisocial and delinquent behavior to understand patterns of societal development in individuals at the extreme ends of self-regulation and societal participation, and examines pathways into and out of delinquency. We will complement the newly collected cohorts with data from existing large-scale population-based and case-control cohorts. The study is embedded in a transdisciplinary approach that engages stakeholders throughout the design stage, with a strong focus on citizen science and youth participation in study design, data collection, and interpretation of results, to ensure optimal translation to youth in society.
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Affiliation(s)
- Eveline A Crone
- Erasmus School of Social and Behavioral Sciences, Erasmus University Rotterdam, the Netherlands; Leiden University, Institute of Psychology, the Netherlands.
| | - Thijs Bol
- Department of Sociology, University of Amsterdam, the Netherlands
| | - Barbara R Braams
- Department of Clinical, Neuro, and Developmental Psychology, Faculty of Behavioral and Movement Sciences, Vrije Universiteit Amsterdam, the Netherlands
| | - Mark de Rooij
- Leiden University, Institute of Psychology, the Netherlands
| | - Barbara Franke
- Radboud University Medical Center, Donders Institute for Brain, Cognition and Behaviour, Departments of Cognitive Neuroscience and Human Genetics, Nijmegen, the Netherlands
| | - Ingmar Franken
- Erasmus School of Social and Behavioral Sciences, Erasmus University Rotterdam, the Netherlands
| | - Valeria Gazzola
- Social Brain Lab, Netherlands Institute for Neuroscience (KNAW) and University of Amsterdam, Amsterdam, the Netherlands
| | - Berna Güroğlu
- Leiden University, Institute of Psychology, the Netherlands
| | - Hilde Huizenga
- Department of Psychology, University of Amsterdam, the Netherlands
| | | | - Loes Keijsers
- Erasmus School of Social and Behavioral Sciences, Erasmus University Rotterdam, the Netherlands
| | - Christian Keysers
- Social Brain Lab, Netherlands Institute for Neuroscience (KNAW) and University of Amsterdam, Amsterdam, the Netherlands
| | - Lydia Krabbendam
- Department of Clinical, Neuro, and Developmental Psychology, Faculty of Behavioral and Movement Sciences, Vrije Universiteit Amsterdam, the Netherlands
| | - Lucres Jansen
- Department of Child and Adolescent Psychiatry & Psychosocial Care, AmsterdamUMC and Research Institute Amsterdam Public Health, Amsterdam, the Netherlands
| | - Arne Popma
- Department of Child and Adolescent Psychiatry & Psychosocial Care, AmsterdamUMC and Research Institute Amsterdam Public Health, Amsterdam, the Netherlands
| | - Gert Stulp
- University of Groningen, Department of Sociology / Inter-University Center for Social Science Theory and Methodology, Groningen, the Netherlands
| | - Nienke van Atteveldt
- Department of Clinical, Neuro, and Developmental Psychology, Faculty of Behavioral and Movement Sciences, Vrije Universiteit Amsterdam, the Netherlands
| | | | - René Veenstra
- University of Groningen, Department of Sociology / Inter-University Center for Social Science Theory and Methodology, Groningen, the Netherlands
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Ysbæk-Nielsen AT. Connectome-based predictive modelling estimates individual cognitive status in Parkinson's disease. Parkinsonism Relat Disord 2024; 123:106020. [PMID: 38579439 DOI: 10.1016/j.parkreldis.2024.106020] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/19/2023] [Revised: 01/30/2024] [Accepted: 01/30/2024] [Indexed: 04/07/2024]
Abstract
INTRODUCTION The progressive nature of Parkinson's disease (PD) affords emphasis on accurate early-stage individual-level assessment of risk and intervention appropriateness. In PD, cognitive impairment (CI) may follow or precede motor symptoms but are generally underdetected. In addition to impeding daily functioning and quality of life, CIs increase the risk for later conversion to dementia, providing a pressing need to develop novel tools to detect and interpret them. Connectome-based predictive modelling (CPM) is an emerging machine-learning approach to individual prediction that holds translational promise due to its noninvasiveness and simple implementation. The aim of this study was to investigate CPM's potential to predict and understand CIs in PD. METHODS Resting-state functional connectivity from 58 patients with PD of varying cognitive status was used to train a CPM-model to predict a global cognitive composite (GCC) score. The model was validated using cross-validation, permutation testing, and internal stability analyses. The combined predictive strength of two brain connectivity networks, positive and negative, directly and inversely correlated with GCC, respectively, was assessed. RESULTS The model significantly predicted individual GCC scores, r = 0.63, pperm < .05. Separately, the positive and negative networks were similar in performance, rs ≥ .58, ps < .05, but varied in anatomical distribution. CONCLUSIONS This study identified a connectome predictive of cognitive scores in PD, with features overlapping with established and emerging evidence on aberrant connectivity in PD-related CIs. Overall, CPM appears promising for clinical translation in this population, but longitudinal studies with out-of-sample validation are needed.
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32
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McWhinney SR, Hlinka J, Bakstein E, Dietze LMF, Corkum ELV, Abé C, Alda M, Alexander N, Benedetti F, Berk M, Bøen E, Bonnekoh LM, Boye B, Brosch K, Canales‐Rodríguez EJ, Cannon DM, Dannlowski U, Demro C, Diaz‐Zuluaga A, Elvsåshagen T, Eyler LT, Fortea L, Fullerton JM, Goltermann J, Gotlib IH, Grotegerd D, Haarman B, Hahn T, Howells FM, Jamalabadi H, Jansen A, Kircher T, Klahn AL, Kuplicki R, Lahud E, Landén M, Leehr EJ, Lopez‐Jaramillo C, Mackey S, Malt U, Martyn F, Mazza E, McDonald C, McPhilemy G, Meier S, Meinert S, Melloni E, Mitchell PB, Nabulsi L, Nenadić I, Nitsch R, Opel N, Ophoff RA, Ortuño M, Overs BJ, Pineda‐Zapata J, Pomarol‐Clotet E, Radua J, Repple J, Roberts G, Rodriguez‐Cano E, Sacchet MD, Salvador R, Savitz J, Scheffler F, Schofield PR, Schürmeyer N, Shen C, Sim K, Sponheim SR, Stein DJ, Stein F, Straube B, Suo C, Temmingh H, Teutenberg L, Thomas‐Odenthal F, Thomopoulos SI, Urosevic S, Usemann P, van Haren NEM, Vargas C, Vieta E, Vilajosana E, Vreeker A, Winter NR, Yatham LN, Thompson PM, Andreassen OA, Ching CRK, Hajek T. Principal component analysis as an efficient method for capturing multivariate brain signatures of complex disorders-ENIGMA study in people with bipolar disorders and obesity. Hum Brain Mapp 2024; 45:e26682. [PMID: 38825977 PMCID: PMC11144951 DOI: 10.1002/hbm.26682] [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: 02/09/2024] [Revised: 03/21/2024] [Accepted: 03/28/2024] [Indexed: 06/04/2024] Open
Abstract
Multivariate techniques better fit the anatomy of complex neuropsychiatric disorders which are characterized not by alterations in a single region, but rather by variations across distributed brain networks. Here, we used principal component analysis (PCA) to identify patterns of covariance across brain regions and relate them to clinical and demographic variables in a large generalizable dataset of individuals with bipolar disorders and controls. We then compared performance of PCA and clustering on identical sample to identify which methodology was better in capturing links between brain and clinical measures. Using data from the ENIGMA-BD working group, we investigated T1-weighted structural MRI data from 2436 participants with BD and healthy controls, and applied PCA to cortical thickness and surface area measures. We then studied the association of principal components with clinical and demographic variables using mixed regression models. We compared the PCA model with our prior clustering analyses of the same data and also tested it in a replication sample of 327 participants with BD or schizophrenia and healthy controls. The first principal component, which indexed a greater cortical thickness across all 68 cortical regions, was negatively associated with BD, BMI, antipsychotic medications, and age and was positively associated with Li treatment. PCA demonstrated superior goodness of fit to clustering when predicting diagnosis and BMI. Moreover, applying the PCA model to the replication sample yielded significant differences in cortical thickness between healthy controls and individuals with BD or schizophrenia. Cortical thickness in the same widespread regional network as determined by PCA was negatively associated with different clinical and demographic variables, including diagnosis, age, BMI, and treatment with antipsychotic medications or lithium. PCA outperformed clustering and provided an easy-to-use and interpret method to study multivariate associations between brain structure and system-level variables. PRACTITIONER POINTS: In this study of 2770 Individuals, we confirmed that cortical thickness in widespread regional networks as determined by principal component analysis (PCA) was negatively associated with relevant clinical and demographic variables, including diagnosis, age, BMI, and treatment with antipsychotic medications or lithium. Significant associations of many different system-level variables with the same brain network suggest a lack of one-to-one mapping of individual clinical and demographic factors to specific patterns of brain changes. PCA outperformed clustering analysis in the same data set when predicting group or BMI, providing a superior method for studying multivariate associations between brain structure and system-level variables.
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Affiliation(s)
- Sean R. McWhinney
- Department of PsychiatryDalhousie UniversityHalifaxNova ScotiaCanada
| | - Jaroslav Hlinka
- Department of Complex SystemsInstitute of Computer Science, Czech Academy of SciencesPragueCzech Republic
- National Institute of Mental HealthKlecanyCzech Republic
| | - Eduard Bakstein
- National Institute of Mental HealthKlecanyCzech Republic
- Department of CyberneticsCzech Technical UniversityPragueCzech Republic
| | - Lorielle M. F. Dietze
- Department of PsychiatryDalhousie UniversityHalifaxNova ScotiaCanada
- Department of Medical NeuroscienceDalhousie UniversityHalifaxNova ScotiaCanada
| | | | - Christoph Abé
- Department of Clinical NeuroscienceKarolinska InstitutetStockholmSweden
| | - Martin Alda
- Department of PsychiatryDalhousie UniversityHalifaxNova ScotiaCanada
- National Institute of Mental HealthKlecanyCzech Republic
| | - Nina Alexander
- Department of Psychiatry and PsychotherapyPhilipps‐University MarburgMarburgGermany
| | - Francesco Benedetti
- Vita‐Salute San Raffaele UniversityMilanItaly
- Division of Neuroscience, Psychiatry and Psychobiology UnitIRCCS San Raffaele Scientific InstituteMilanItaly
| | - Michael Berk
- Institute for Mental and Physical Health and Clinical Translation, School of Medicine, Barwon HealthDeakin UniversityGeelongVictoriaAustralia
| | - Erlend Bøen
- Unit for Psychosomatics/CL Outpatient Clinic for Adults, Division of Mental Health and AddictionOslo University HospitalOsloNorway
| | - Linda M. Bonnekoh
- Institute for Translational PsychiatryUniversity of MünsterMünsterGermany
- Department of Child Adolescent Psychiatry and PsychotherapyUniversity of MünsterMünsterGermany
| | - Birgitte Boye
- Unit for Psychosomatics and C‐L Psychiatry for AdultsOslo University HospitalOsloNorway
- Department of Behavioural MedicineInstitute of Basic Medical Sciences, University of OsloOsloNorway
| | - Katharina Brosch
- Department of Psychiatry and PsychotherapyPhilipps‐University MarburgMarburgGermany
- Institute of Behavioral ScienceFeinstein Institutes for Medical ResearchManhassetNew YorkUSA
| | - Erick J. Canales‐Rodríguez
- FIDMAG Germanes Hospitalàries Research FoundationBarcelonaSpain
- CIBERSAM, Instituto de Salud Carlos IIIBarcelonaSpain
| | - Dara M. Cannon
- Clinical Neuroimaging Laboratory, Galway Neuroscience CentreCollege of Medicine Nursing and Health Sciences, University of GalwayGalwayIreland
| | - Udo Dannlowski
- Institute for Translational PsychiatryUniversity of MünsterMünsterGermany
| | - Caroline Demro
- Department of Psychiatry & Behavioral SciencesUniversity of MinnesotaMinneapolisMinnesotaUSA
| | - Ana Diaz‐Zuluaga
- Research Group in Psychiatry GIPSI, Department of Psychiatry, Faculty of MedicineUniversidad de AntioquiaMedellinColombia
| | - Torbjørn Elvsåshagen
- Department of Behavioural MedicineInstitute of Basic Medical Sciences, University of OsloOsloNorway
- Institute of Clinical Medicine, Norwegian Centre for Mental Disorders Research (NORMENT)University of Oslo and Division of Mental Health and Addiction, Oslo University HospitalOsloNorway
- Department of Neurology, Division of Clinical NeuroscienceOslo University HospitalOsloNorway
| | - Lisa T. Eyler
- Department of PsychiatryUniversity of California San DiegoLa JollaCaliforniaUSA
- Desert‐Pacific MIRECC, VA San Diego HealthcareSan DiegoCaliforniaUSA
| | - Lydia Fortea
- Institut d'Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS), CIBERSAM, Instituto de Salud Carlos IIIUniversity of BarcelonaBarcelonaSpain
| | - Janice M. Fullerton
- Neuroscience Research AustraliaRandwickNew South WalesAustralia
- School of Biomedical Sciences, Faculty of Medicine & HealthUniversity of New South WalesSydneyNew South WalesAustralia
| | - Janik Goltermann
- Institute for Translational PsychiatryUniversity of MünsterMünsterGermany
| | - Ian H. Gotlib
- Department of PsychologyStanford UniversityStanfordCaliforniaUSA
| | - Dominik Grotegerd
- Institute for Translational PsychiatryUniversity of MünsterMünsterGermany
| | - Bartholomeus Haarman
- Department of PsychiatryUniversity Medical Center Groningen, University of GroningenGroningenThe Netherlands
| | - Tim Hahn
- Institute for Translational PsychiatryUniversity of MünsterMünsterGermany
| | - Fleur M. Howells
- Neuroscience InstituteUniversity of Cape TownCape TownSouth Africa
- Department of Psychiatry and Mental HealthUniversity of Cape TownCape TownSouth Africa
| | - Hamidreza Jamalabadi
- Department of Psychiatry and PsychotherapyPhilipps‐University MarburgMarburgGermany
| | - Andreas Jansen
- Department of Psychiatry and PsychotherapyPhilipps‐University MarburgMarburgGermany
- Core‐Facility Brainimaging, Faculty of MedicineUniversity of MarburgGermany
| | - Tilo Kircher
- Department of Psychiatry and PsychotherapyPhilipps‐University MarburgMarburgGermany
| | - Anna Luisa Klahn
- Institute of Neuroscience and PhysiologySahlgrenska Academy at Gothenburg UniversityGothenburgSweden
| | | | - Elijah Lahud
- Department of Psychiatry & Behavioral SciencesUniversity of MinnesotaMinneapolisMinnesotaUSA
| | - Mikael Landén
- Institute of Neuroscience and PhysiologySahlgrenska Academy at Gothenburg UniversityGothenburgSweden
- Department of Medical Epidemiology and BiostatisticsKarolinska InstitutetStockholmSweden
| | - Elisabeth J. Leehr
- Institute for Translational PsychiatryUniversity of MünsterMünsterGermany
| | - Carlos Lopez‐Jaramillo
- Research Group in Psychiatry GIPSI, Department of Psychiatry, Faculty of MedicineUniversidad de AntioquiaMedellinColombia
| | - Scott Mackey
- Department of PsychiatryUniversity of Vermont College of MedicineBurlingtonVermontUSA
| | - Ulrik Malt
- Unit for Psychosomatics/CL Outpatient Clinic for Adults, Division of Mental Health and AddictionOslo University HospitalOsloNorway
- Institute of Clinical Medicine, Department of NeurologyUniversity of OsloOsloNorway
| | - Fiona Martyn
- Clinical Neuroimaging Laboratory, Galway Neuroscience CentreCollege of Medicine Nursing and Health Sciences, University of GalwayGalwayIreland
| | - Elena Mazza
- Vita‐Salute San Raffaele UniversityMilanItaly
- Division of Neuroscience, Psychiatry and Psychobiology UnitIRCCS San Raffaele Scientific InstituteMilanItaly
| | - Colm McDonald
- Clinical Neuroimaging Laboratory, Galway Neuroscience CentreCollege of Medicine Nursing and Health Sciences, University of GalwayGalwayIreland
| | - Genevieve McPhilemy
- Clinical Neuroimaging Laboratory, Galway Neuroscience CentreCollege of Medicine Nursing and Health Sciences, University of GalwayGalwayIreland
| | - Sandra Meier
- Department of PsychiatryDalhousie UniversityHalifaxNova ScotiaCanada
| | - Susanne Meinert
- Institute for Translational PsychiatryUniversity of MünsterMünsterGermany
- Institute for Translational NeuroscienceUniversity of MünsterMünsterGermany
| | - Elisa Melloni
- Vita‐Salute San Raffaele UniversityMilanItaly
- Division of Neuroscience, Psychiatry and Psychobiology UnitIRCCS San Raffaele Scientific InstituteMilanItaly
| | - Philip B. Mitchell
- Discipline of Psychiatry and Mental Health, School of Clinical Medicine, Faculty of Medicine & HealthUniversity of New South WalesSydneyNew South WalesAustralia
| | - Leila Nabulsi
- Clinical Neuroimaging Laboratory, Galway Neuroscience CentreCollege of Medicine Nursing and Health Sciences, University of GalwayGalwayIreland
| | - Igor Nenadić
- Department of Psychiatry and PsychotherapyPhilipps‐University MarburgMarburgGermany
| | - Robert Nitsch
- Institute for Translational NeuroscienceUniversity of MünsterMünsterGermany
| | - Nils Opel
- Institute for Translational PsychiatryUniversity of MünsterMünsterGermany
- Department of Psychiatry and PsychotherapyJena University HospitalJenaGermany
- German Center for Mental Health (DZPG), Site Jena‐Magdeburg‐HalleGermany
| | - Roel A. Ophoff
- UCLA Center for Neurobehavioral GeneticsLos AngelesCaliforniaUSA
| | - Maria Ortuño
- Institut d'Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS)BarcelonaSpain
| | | | - Julian Pineda‐Zapata
- Research GroupInstituto de Alta Tecnología Médica, Ayudas diagnósticas SURAMedellinColombia
| | - Edith Pomarol‐Clotet
- FIDMAG Germanes Hospitalàries Research FoundationBarcelonaSpain
- CIBERSAM, Instituto de Salud Carlos IIIBarcelonaSpain
| | - Joaquim Radua
- Institut d'Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS), CIBERSAM, Instituto de Salud Carlos IIIUniversity of BarcelonaBarcelonaSpain
| | - Jonathan Repple
- Institute for Translational PsychiatryUniversity of MünsterMünsterGermany
- Department of Psychiatry, Psychosomatic Medicine and PsychotherapyGoethe University Frankfurt, University HospitalFrankfurtGermany
| | - Gloria Roberts
- Discipline of Psychiatry and Mental Health, School of Clinical Medicine, Faculty of Medicine & HealthUniversity of New South WalesSydneyNew South WalesAustralia
| | - Elena Rodriguez‐Cano
- FIDMAG Germanes Hospitalàries Research FoundationBarcelonaSpain
- CIBERSAM, Instituto de Salud Carlos IIIBarcelonaSpain
| | - Matthew D. Sacchet
- Meditation Research Program, Department of PsychiatryMassachusetts General Hospital, Harvard Medical SchoolBostonMassachusettsUSA
| | - Raymond Salvador
- FIDMAG Germanes Hospitalàries Research FoundationBarcelonaSpain
- CIBERSAM, Instituto de Salud Carlos IIIBarcelonaSpain
| | - Jonathan Savitz
- Laureate Institute for Brain ResearchTulsaOklahomaUSA
- Oxley College of Health SciencesThe University of TulsaTulsaOklahomaUSA
| | - Freda Scheffler
- Neuroscience InstituteUniversity of Cape TownCape TownSouth Africa
- Department of Psychiatry and Mental HealthUniversity of Cape TownCape TownSouth Africa
| | - Peter R. Schofield
- Neuroscience Research AustraliaRandwickNew South WalesAustralia
- School of Biomedical Sciences, Faculty of Medicine & HealthUniversity of New South WalesSydneyNew South WalesAustralia
| | - Navid Schürmeyer
- Institute for Translational PsychiatryUniversity of MünsterMünsterGermany
| | - Chen Shen
- Department of PsychologyUniversity of MinnesotaMinneapolisMinnesotaUSA
| | - Kang Sim
- West Region, Institute of Mental HealthSingaporeSingapore
- Yong Loo Lin School of MedicineNational University of SingaporeSingaporeSingapore
| | - Scott R. Sponheim
- Department of Psychiatry & Behavioral SciencesUniversity of MinnesotaMinneapolisMinnesotaUSA
- Minneapolis VA Health Care SystemMinneapolisMinnesotaUSA
| | - Dan J. Stein
- Neuroscience InstituteUniversity of Cape TownCape TownSouth Africa
- Department of Psychiatry and Mental HealthUniversity of Cape TownCape TownSouth Africa
- South African MRC Unit on Risk & Resilience in Mental DisordersUniversity of Cape TownCape TownSouth Africa
| | - Frederike Stein
- Department of Psychiatry and PsychotherapyPhilipps‐University MarburgMarburgGermany
| | - Benjamin Straube
- Department of Psychiatry and PsychotherapyPhilipps‐University MarburgMarburgGermany
| | - Chao Suo
- Turner Institute for Brain and Mental Health, School of Psychological Sciences and Monash Biomedical ImagingMonash UniversityMelbourneVictoriaAustralia
| | - Henk Temmingh
- Department of Psychiatry and Mental HealthUniversity of Cape TownCape TownSouth Africa
| | - Lea Teutenberg
- Department of Psychiatry and PsychotherapyPhilipps‐University MarburgMarburgGermany
| | | | - Sophia I. Thomopoulos
- Imaging Genetics Center, Mark and Mary Stevens Neuroimaging and Informatics Institute, Keck School of MedicineUniversity of Southern CaliforniaMarina del ReyCaliforniaUSA
| | - Snezana Urosevic
- Department of Psychiatry & Behavioral SciencesUniversity of MinnesotaMinneapolisMinnesotaUSA
- Minneapolis VA Health Care SystemMinneapolisMinnesotaUSA
| | - Paula Usemann
- Department of Psychiatry and PsychotherapyPhilipps‐University MarburgMarburgGermany
| | - Neeltje E. M. van Haren
- Department of Child and Adolescent Psychiatry/PsychologyErasmus University Medical CenterRotterdamThe Netherlands
- Department of PsychiatryUniversity Medical Center UtrechtUtrechtThe Netherlands
| | - Cristian Vargas
- Research Group in Psychiatry GIPSI, Department of Psychiatry, Faculty of MedicineUniversidad de AntioquiaMedellinColombia
| | - Eduard Vieta
- Institut d'Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS), CIBERSAM, Instituto de Salud Carlos III, Institute of NeuroscienceUniversity of Barcelona, Hospital ClínicBarcelonaSpain
| | - Enric Vilajosana
- Institut d'Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS)BarcelonaSpain
| | - Annabel Vreeker
- Department of Child and Adolescent Psychiatry/PsychologyErasmus University Medical CenterRotterdamThe Netherlands
- Department of Psychology, Education and Child StudiesErasmus University RotterdamRotterdamThe Netherlands
| | - Nils R. Winter
- Institute for Translational PsychiatryUniversity of MünsterMünsterGermany
| | | | - Paul M. Thompson
- Imaging Genetics Center, Mark and Mary Stevens Neuroimaging and Informatics Institute, Keck School of MedicineUniversity of Southern CaliforniaMarina del ReyCaliforniaUSA
| | - Ole A. Andreassen
- Institute of Clinical Medicine, Norwegian Centre for Mental Disorders Research (NORMENT)University of Oslo and Division of Mental Health and Addiction, Oslo University HospitalOsloNorway
| | - Christopher R. K. Ching
- Imaging Genetics Center, Mark and Mary Stevens Neuroimaging and Informatics Institute, Keck School of MedicineUniversity of Southern CaliforniaMarina del ReyCaliforniaUSA
| | - Tomas Hajek
- Department of PsychiatryDalhousie UniversityHalifaxNova ScotiaCanada
- National Institute of Mental HealthKlecanyCzech Republic
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Grogans SE, Hur J, Barstead MG, Anderson AS, Islam S, Kim HC, Kuhn M, Tillman RM, Fox AS, Smith JF, DeYoung KA, Shackman AJ. Neuroticism/negative emotionality is associated with increased reactivity to uncertain threat in the bed nucleus of the stria terminalis, not the amygdala. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2023.02.09.527767. [PMID: 36798350 PMCID: PMC9934698 DOI: 10.1101/2023.02.09.527767] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 02/12/2023]
Abstract
Neuroticism/Negative Emotionality (N/NE)-the tendency to experience anxiety, fear, and other negative emotions-is a fundamental dimension of temperament with profound consequences for health, wealth, and wellbeing. Elevated N/NE is associated with a panoply of adverse outcomes, from reduced socioeconomic attainment to psychiatric illness. Animal research suggests that N/NE reflects heightened reactivity to uncertain threat in the bed nucleus of the stria terminalis (BST) and central nucleus of the amygdala (Ce), but the relevance of these discoveries to humans has remained unclear. Here we used a novel combination of psychometric, psychophysiological, and neuroimaging approaches to rigorously test this hypothesis in an ethnoracially diverse, sex-balanced sample of 220 emerging adults selectively recruited to encompass a broad spectrum of N/NE. Cross-validated robust-regression analyses demonstrated that N/NE is preferentially associated with heightened BST activation during the uncertain anticipation of a genuinely distressing threat (aversive multimodal stimulation), whereas N/NE was unrelated to BST activation during certain-threat anticipation, Ce activation during either type of threat anticipation, or BST/Ce reactivity to threat-related faces. It is often assumed that different threat paradigms are interchangeable assays of individual differences in brain function, yet this has rarely been tested. Our results revealed negligible associations between BST/Ce reactivity to the anticipation of threat and the presentation of threat-related faces, indicating that the two tasks are non-fungible. These observations provide a framework for conceptualizing emotional traits and disorders; for guiding the design and interpretation of biobank and other neuroimaging studies of psychiatric risk, disease, and treatment; and for informing mechanistic research.
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Affiliation(s)
- Shannon E. Grogans
- Department of Psychology, University of Maryland, College Park, MD 20742 USA
| | - Juyoen Hur
- Department of Psychology, Yonsei University, Seoul 03722, Republic of Korea
| | | | - Allegra S. Anderson
- Department of Psychological Sciences, Vanderbilt University, Nashville, TN 37240 USA
| | - Samiha Islam
- Department of Psychology, University of Pennsylvania, Philadelphia, PA USA
| | - Hyung Cho Kim
- Department of Psychology, University of Maryland, College Park, MD 20742 USA
- Department of Neuroscience and Cognitive Science Program, University of Maryland, College Park, MD 20742 USA
| | - Manuel Kuhn
- Center for Depression, Anxiety and Stress Research, McLean Hospital, Harvard Medical School, Belmont, MA 02478 USA
| | | | - Andrew S. Fox
- Department of Psychology, University of California, Davis, CA 95616 USA
- California National Primate Research Center, University of California, Davis, CA 95616 USA
| | - Jason F. Smith
- Department of Psychology, University of Maryland, College Park, MD 20742 USA
| | - Kathryn A. DeYoung
- Department of Psychology, University of Maryland, College Park, MD 20742 USA
| | - Alexander J. Shackman
- Department of Psychology, University of Maryland, College Park, MD 20742 USA
- Department of Neuroscience and Cognitive Science Program, University of Maryland, College Park, MD 20742 USA
- Department of Maryland Neuroimaging Center, University of Maryland, College Park, MD 20742 USA
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Taubert M, Ziegler G, Lehmann N. Higher surface folding of the human premotor cortex is associated with better long-term learning capability. Commun Biol 2024; 7:635. [PMID: 38796622 PMCID: PMC11127997 DOI: 10.1038/s42003-024-06309-z] [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: 09/21/2023] [Accepted: 05/08/2024] [Indexed: 05/28/2024] Open
Abstract
The capacity to learn enabled the human species to adapt to various challenging environmental conditions and pass important achievements on to the next generation. A growing body of research suggests links between neocortical folding properties and numerous aspects of human behavior, but their impact on enhanced human learning capacity remains unexplored. Here we leverage three training cohorts to demonstrate that higher levels of premotor cortical folding reliably predict individual long-term learning gains in a challenging new motor task, above and beyond initial performance differences. Individual folding-related predisposition to motor learning was found to be independent of cortical thickness and intracortical microstructure, but dependent on larger cortical surface area in premotor regions. We further show that learning-relevant features of cortical folding occurred in close spatial proximity to practice-induced structural brain plasticity. Our results suggest a link between neocortical surface folding and human behavioral adaptability.
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Affiliation(s)
- Marco Taubert
- Department of Sport Science, Institute III, Faculty of Humanities, Otto von Guericke University, Zschokkestraße 32, 39104, Magdeburg, Germany.
- Center for Behavioral and Brain Science (CBBS), Otto von Guericke University, Universitätsplatz 2, 39106, Magdeburg, Germany.
- Collaborative Research Center 1436 Neural Resources of Cognition, Otto von Guericke University, Leipziger Str. 44, 39120, Magdeburg, Germany.
| | - Gabriel Ziegler
- Collaborative Research Center 1436 Neural Resources of Cognition, Otto von Guericke University, Leipziger Str. 44, 39120, Magdeburg, Germany
- Germany German Center for Neurodegenerative Diseases (DZNE), Leipziger Straße 44, 39120, Magdeburg, Germany
- Institute of Cognitive Neurology and Dementia Research, Otto von Guericke University, Leipziger Str. 44, 39120, Magdeburg, Germany
| | - Nico Lehmann
- Department of Sport Science, Institute III, Faculty of Humanities, Otto von Guericke University, Zschokkestraße 32, 39104, Magdeburg, Germany
- Collaborative Research Center 1436 Neural Resources of Cognition, Otto von Guericke University, Leipziger Str. 44, 39120, Magdeburg, Germany
- Department of Neurology, Max Planck Institute for Human Cognitive and Brain Sciences, Stephanstraße 1a, 04103, Leipzig, Germany
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Chen Y, Xu J, Wu J, Chen H, Kang Y, Yang Y, Gong Z, Huang Y, Wang H, Wang B, Zhan S, Tan W. Aberrant concordance among dynamics of spontaneous brain activity in patients with migraine without aura: A multivariate pattern analysis study. Heliyon 2024; 10:e30008. [PMID: 38737279 PMCID: PMC11088259 DOI: 10.1016/j.heliyon.2024.e30008] [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: 10/05/2023] [Revised: 04/08/2024] [Accepted: 04/18/2024] [Indexed: 05/14/2024] Open
Abstract
Background Alterations in the static and dynamic characteristics of spontaneous brain activity have been extensively studied to investigate functional brain changes in migraine without aura (MwoA). However, alterations in concordance among the dynamics of spontaneous brain activity in MwoA remain largely unknown. This study aimed to determine the possibilities of diagnosis based on the concordance indices. Methods Resting-state functional MRI scans were performed on 32 patients with MwoA and 33 matched healthy controls (HCs) in the first cohort, as well as 36 patients with MwoA and 32 HCs in the validation cohort. The dynamic indices including fractional amplitude of low-frequency fluctuation, regional homogeneity, voxel-mirrored homotopic connectivity, degree centrality and global signal connectivity were analyzed. We calculated the concordance of grey matter volume-wise (across voxels) and voxel-wise (across time windows) to quantify the degree of integration among different functional levels represented by these dynamic indices. Subsequently, the voxel-wise concordance alterations were analyzed as features for multi-voxel pattern analysis (MVPA) utilizing the support vector machine. Results Compared with that of HCs, patients with MwoA had lower whole-grey matter volume-wise concordance, and the mean value of volume-wise concordance was negatively correlated with the frequency of migraine attacks. The MVPA results revealed that the most discriminative brain regions were the right thalamus, right cerebellar Crus II, left insula, left precentral gyrus, right cuneus, and left inferior occipital gyrus. Conclusions Concordance alterations in the dynamics of spontaneous brain activity in brain regions could be an important feature in the identification of patients with MwoA.
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Affiliation(s)
- Yilei Chen
- Department of Radiology, Shuguang Hospital Affiliated to Shanghai University of Traditional Chinese Medicine, Shanghai, China
| | - Jun Xu
- Pharmacy Department, Shanghai Municipal Hospital of Traditional Chinese Medicine, Shanghai University of Traditional Chinese Medicine, Shanghai, China
| | - Jiazhen Wu
- Department of Radiology, Shanghai Municipal Hospital of Traditional Chinese Medicine, Shanghai University of Traditional Chinese Medicine, Shanghai, China
| | - Hui Chen
- Department of Radiology, Shuguang Hospital Affiliated to Shanghai University of Traditional Chinese Medicine, Shanghai, China
| | - Yingjie Kang
- Department of Radiology, Shuguang Hospital Affiliated to Shanghai University of Traditional Chinese Medicine, Shanghai, China
| | - Yuchan Yang
- Department of Radiology, Shuguang Hospital Affiliated to Shanghai University of Traditional Chinese Medicine, Shanghai, China
| | - Zhigang Gong
- Department of Radiology, Shuguang Hospital Affiliated to Shanghai University of Traditional Chinese Medicine, Shanghai, China
| | - Yanwen Huang
- Department of Radiology, Shuguang Hospital Affiliated to Shanghai University of Traditional Chinese Medicine, Shanghai, China
| | - Hui Wang
- Department of Radiology, Shuguang Hospital Affiliated to Shanghai University of Traditional Chinese Medicine, Shanghai, China
| | - Bo Wang
- Department of Acupuncture and Moxibustion, Shuguang Hospital Affiliated to Shanghai University of Traditional Chinese Medicine, Shanghai, China
| | - Songhua Zhan
- Department of Radiology, Shuguang Hospital Affiliated to Shanghai University of Traditional Chinese Medicine, Shanghai, China
| | - Wenli Tan
- Department of Radiology, Shuguang Hospital Affiliated to Shanghai University of Traditional Chinese Medicine, Shanghai, China
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Tetereva A, Knodt AR, Melzer TR, van der Vliet W, Gibson B, Hariri AR, Whitman ET, Li J, Deng J, Ireland D, Ramrakha S, Pat N. Improving Predictability, Test-Retest Reliability and Generalisability of Brain-Wide Associations for Cognitive Abilities via Multimodal Stacking. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.05.03.589404. [PMID: 38746222 PMCID: PMC11092590 DOI: 10.1101/2024.05.03.589404] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/16/2024]
Abstract
Brain-wide association studies (BWASs) have attempted to relate cognitive abilities with brain phenotypes, but have been challenged by issues such as predictability, test-retest reliability, and cross-cohort generalisability. To tackle these challenges, we proposed "stacking" that combines brain magnetic resonance imaging of different modalities, from task-fMRI contrasts and functional connectivity during tasks and rest to structural measures, into one prediction model. We benchmarked the benefits of stacking, using the Human Connectome Projects: Young Adults and Aging and the Dunedin Multidisciplinary Health and Development Study. For predictability, stacked models led to out-of-sample r ∼.5-.6 when predicting cognitive abilities at the time of scanning and 36 years earlier. For test-retest reliability, stacked models reached an excellent level of reliability (ICC>.75), even when we stacked only task-fMRI contrasts together. For generalisability, a stacked model with non-task MRI built from one dataset significantly predicted cognitive abilities in other datasets. Altogether, stacking is a viable approach to undertake the three challenges of BWAS for cognitive abilities.
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Leonardsen EH, Persson K, Grødem E, Dinsdale N, Schellhorn T, Roe JM, Vidal-Piñeiro D, Sørensen Ø, Kaufmann T, Westman E, Marquand A, Selbæk G, Andreassen OA, Wolfers T, Westlye LT, Wang Y. Constructing personalized characterizations of structural brain aberrations in patients with dementia using explainable artificial intelligence. NPJ Digit Med 2024; 7:110. [PMID: 38698139 PMCID: PMC11066104 DOI: 10.1038/s41746-024-01123-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/10/2023] [Accepted: 04/23/2024] [Indexed: 05/05/2024] Open
Abstract
Deep learning approaches for clinical predictions based on magnetic resonance imaging data have shown great promise as a translational technology for diagnosis and prognosis in neurological disorders, but its clinical impact has been limited. This is partially attributed to the opaqueness of deep learning models, causing insufficient understanding of what underlies their decisions. To overcome this, we trained convolutional neural networks on structural brain scans to differentiate dementia patients from healthy controls, and applied layerwise relevance propagation to procure individual-level explanations of the model predictions. Through extensive validations we demonstrate that deviations recognized by the model corroborate existing knowledge of structural brain aberrations in dementia. By employing the explainable dementia classifier in a longitudinal dataset of patients with mild cognitive impairment, we show that the spatially rich explanations complement the model prediction when forecasting transition to dementia and help characterize the biological manifestation of disease in the individual brain. Overall, our work exemplifies the clinical potential of explainable artificial intelligence in precision medicine.
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Affiliation(s)
- Esten H Leonardsen
- Department of Psychology, University of Oslo, Oslo, Norway.
- Norwegian Centre for Mental Disorders Research (NORMENT), Oslo University Hospital & Institute of Clinical Medicine, University of Oslo, Oslo, Norway.
| | - Karin Persson
- The Norwegian National Centre for Ageing and Health, Vestfold Hospital Trust, Tønsberg, Norway
- Department of Geriatric Medicine, Oslo University Hospital, Oslo, Norway
| | - Edvard Grødem
- Department of Psychology, University of Oslo, Oslo, Norway
- Computational Radiology & Artificial Intelligence (CRAI) Unit, Division of Radiology and Nuclear Medicine, Oslo University Hospital, Oslo, Norway
| | - Nicola Dinsdale
- Oxford Machine Learning in NeuroImaging (OMNI) Lab, University of Oxford, Oxford, UK
| | - Till Schellhorn
- Institute of Clinical Medicine, University of Oslo, Oslo, Norway
- Division of Radiology and Nuclear Medicine, Oslo University Hospital, Oslo, Norway
| | - James M Roe
- Department of Psychology, University of Oslo, Oslo, Norway
| | | | | | - Tobias Kaufmann
- Norwegian Centre for Mental Disorders Research (NORMENT), Oslo University Hospital & Institute of Clinical Medicine, University of Oslo, Oslo, Norway
- Department of Psychiatry and Psychotherapy, Tübingen Center for Mental Health, University of Tübingen, Tübingen, Germany
- German Center for Mental Health (DZPG), Munich, Germany
| | - Eric Westman
- Division of Clinical Geriatrics, Department of Neurobiology, Care Sciences, and Society, Karolinska Institutet, Stockholm, Sweden
| | - Andre Marquand
- Donders Institute for Brain, Cognition and Behaviour, Radboud University Medical Centre, Nijmegen, the Netherlands
| | - Geir Selbæk
- The Norwegian National Centre for Ageing and Health, Vestfold Hospital Trust, Tønsberg, Norway
- Department of Geriatric Medicine, Oslo University Hospital, Oslo, Norway
| | - Ole A Andreassen
- Norwegian Centre for Mental Disorders Research (NORMENT), Oslo University Hospital & Institute of Clinical Medicine, University of Oslo, Oslo, Norway
- KG Jebsen Center for Neurodevelopmental Disorders, University of Oslo, Oslo, Norway
| | - Thomas Wolfers
- Department of Psychology, University of Oslo, Oslo, Norway
- Norwegian Centre for Mental Disorders Research (NORMENT), Oslo University Hospital & Institute of Clinical Medicine, University of Oslo, Oslo, Norway
- Department of Psychiatry and Psychotherapy, Tübingen Center for Mental Health, University of Tübingen, Tübingen, Germany
- German Center for Mental Health (DZPG), Munich, Germany
| | - Lars T Westlye
- Department of Psychology, University of Oslo, Oslo, Norway
- Norwegian Centre for Mental Disorders Research (NORMENT), Oslo University Hospital & Institute of Clinical Medicine, University of Oslo, Oslo, Norway
- KG Jebsen Center for Neurodevelopmental Disorders, University of Oslo, Oslo, Norway
| | - Yunpeng Wang
- Department of Psychology, University of Oslo, Oslo, Norway
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Harp NR, Wager TD, Kober H. Neuromarkers in addiction: definitions, development strategies, and recent advances. J Neural Transm (Vienna) 2024; 131:509-523. [PMID: 38630190 DOI: 10.1007/s00702-024-02766-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/21/2023] [Accepted: 03/12/2024] [Indexed: 04/28/2024]
Abstract
Substance use disorders (SUDs) are the most costly and prevalent psychiatric conditions. Recent calls emphasize a need for biomarkers-measurable, stable indicators of normal and abnormal processes and response to treatment or environmental agents-and, in particular, brain-based neuromarkers that will advance understanding of the neurobiological basis of SUDs and clinical practice. To develop neuromarkers, researchers must be grounded in evidence that a putative marker (i) is sensitive and specific to the psychological phenomenon of interest, (ii) constitutes a predictive model, and (iii) generalizes to novel observations (e.g., through internal cross-validation and external application to novel data). These neuromarkers may be used to index risk of developing SUDs (susceptibility), classify individuals with SUDs (diagnostic), assess risk for progression to more severe pathology (prognostic) or index current severity of pathology (monitoring), detect response to treatment (response), and predict individualized treatment outcomes (predictive). Here, we outline guidelines for developing and assessing neuromarkers, we then review recent advances toward neuromarkers in addiction neuroscience centering our discussion around neuromarkers of craving-a core feature of SUDs. In doing so, we specifically focus on the Neurobiological Craving Signature (NCS), which show great promise for meeting the demand of neuromarkers.
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Affiliation(s)
- Nicholas R Harp
- Department of Psychiatry, Yale University, New Haven, CT, USA
| | - Tor D Wager
- Department of Psychological & Brain Sciences, Dartmouth College, Hanover, NH, USA
| | - Hedy Kober
- Department of Psychiatry, Yale University, New Haven, CT, USA.
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Zhi D, Jiang R, Pearlson G, Fu Z, Qi S, Yan W, Feng A, Xu M, Calhoun V, Sui J. Triple Interactions Between the Environment, Brain, and Behavior in Children: An ABCD Study. Biol Psychiatry 2024; 95:828-838. [PMID: 38151182 PMCID: PMC11006588 DOI: 10.1016/j.biopsych.2023.12.019] [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: 07/27/2023] [Revised: 12/14/2023] [Accepted: 12/18/2023] [Indexed: 12/29/2023]
Abstract
BACKGROUND Environmental exposures play a crucial role in shaping children's behavioral development. However, the mechanisms by which these exposures interact with brain functional connectivity and influence behavior remain unexplored. METHODS We investigated the comprehensive environment-brain-behavior triple interactions through rigorous association, prediction, and mediation analyses, while adjusting for multiple confounders. Particularly, we examined the predictive power of brain functional network connectivity (FNC) and 41 environmental exposures for 23 behaviors related to cognitive ability and mental health in 7655 children selected from the Adolescent Brain Cognitive Development (ABCD) Study at both baseline and follow-up. RESULTS FNC demonstrated more predictability for cognitive abilities than for mental health, with cross-validation from the UK Biobank study (N = 20,852), highlighting the importance of thalamus and hippocampus in longitudinal prediction, while FNC+environment demonstrated more predictive power than FNC in both cross-sectional and longitudinal prediction of all behaviors, especially for mental health (r = 0.32-0.63). We found that family and neighborhood exposures were common critical environmental influencers on cognitive ability and mental health, which can be mediated by FNC significantly. Healthy perinatal development was a unique protective factor for higher cognitive ability, whereas sleep problems, family conflicts, and adverse school environments specifically increased risk of poor mental health. CONCLUSIONS This work revealed comprehensive environment-brain-behavior triple interactions based on the ABCD Study, identified cognitive control and default mode networks as the most predictive functional networks for a wide repertoire of behaviors, and underscored the long-lasting impact of critical environmental exposures on childhood development, in which sleep problems were the most prominent factors affecting mental health.
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Affiliation(s)
- Dongmei Zhi
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, China
| | - Rongtao Jiang
- Department of Radiology and Biomedical Imaging, Yale School of Medicine, New Haven, Connecticut
| | - Godfrey Pearlson
- Department of Psychiatry and Neurobiology, Yale School of Medicine, New Haven, Connecticut
| | - Zening Fu
- Tri-institutional Center for Translational Research in Neuroimaging and Data Science, Georgia Institute of Technology, Emory University, and Georgia State University, Atlanta, Georgia
| | - Shile Qi
- College of Computer Science and Technology, Nanjing University of Aeronautics and Astronautics, Nanjing, China
| | - Weizheng Yan
- National Institute on Alcohol Abuse and Alcoholism, Lab of Neuroimaging, National Institutes of Health, Bethesda, Maryland
| | - Aichen Feng
- Brainnetome Center and National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing, China; School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing, China
| | - Ming Xu
- Brainnetome Center and National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing, China; School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing, China
| | - Vince Calhoun
- Tri-institutional Center for Translational Research in Neuroimaging and Data Science, Georgia Institute of Technology, Emory University, and Georgia State University, Atlanta, Georgia.
| | - Jing Sui
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, China; Tri-institutional Center for Translational Research in Neuroimaging and Data Science, Georgia Institute of Technology, Emory University, and Georgia State University, Atlanta, Georgia.
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Emish M, Young SD. Remote Wearable Neuroimaging Devices for Health Monitoring and Neurophenotyping: A Scoping Review. Biomimetics (Basel) 2024; 9:237. [PMID: 38667247 PMCID: PMC11048695 DOI: 10.3390/biomimetics9040237] [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: 03/04/2024] [Revised: 04/05/2024] [Accepted: 04/11/2024] [Indexed: 04/28/2024] Open
Abstract
Digital health tracking is a source of valuable insights for public health research and consumer health technology. The brain is the most complex organ, containing information about psychophysical and physiological biomarkers that correlate with health. Specifically, recent developments in electroencephalogram (EEG), functional near-infra-red spectroscopy (fNIRS), and photoplethysmography (PPG) technologies have allowed the development of devices that can remotely monitor changes in brain activity. The inclusion criteria for the papers in this review encompassed studies on self-applied, remote, non-invasive neuroimaging techniques (EEG, fNIRS, or PPG) within healthcare applications. A total of 23 papers were reviewed, comprising 17 on using EEGs for remote monitoring and 6 on neurofeedback interventions, while no papers were found related to fNIRS and PPG. This review reveals that previous studies have leveraged mobile EEG devices for remote monitoring across the mental health, neurological, and sleep domains, as well as for delivering neurofeedback interventions. With headsets and ear-EEG devices being the most common, studies found mobile devices feasible for implementation in study protocols while providing reliable signal quality. Moderate to substantial agreement overall between remote and clinical-grade EEGs was found using statistical tests. The results highlight the promise of portable brain-imaging devices with regard to continuously evaluating patients in natural settings, though further validation and usability enhancements are needed as this technology develops.
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Affiliation(s)
- Mohamed Emish
- Department of Informatics, University of California, Irvine, CA 92697-3100, USA;
| | - Sean D. Young
- Department of Informatics, University of California, Irvine, CA 92697-3100, USA;
- Department of Emergency Medicine, University of California, Irvine, CA 92697-3100, USA
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41
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Fu Z, Batta I, Wu L, Abrol A, Agcaoglu O, Salman MS, Du Y, Iraji A, Shultz S, Sui J, Calhoun VD. Searching Reproducible Brain Features using NeuroMark: Templates for Different Age Populations and Imaging Modalities. Neuroimage 2024; 292:120617. [PMID: 38636639 DOI: 10.1016/j.neuroimage.2024.120617] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/08/2024] [Revised: 04/03/2024] [Accepted: 04/15/2024] [Indexed: 04/20/2024] Open
Abstract
A primary challenge to the data-driven analysis is the balance between poor generalizability of population-based research and characterizing more subject-, study- and population-specific variability. We previously introduced a fully automated spatially constrained independent component analysis (ICA) framework called NeuroMark and its functional MRI (fMRI) template. NeuroMark has been successfully applied in numerous studies, identifying brain markers reproducible across datasets and disorders. The first NeuroMark template was constructed based on young adult cohorts. We recently expanded on this initiative by creating a standardized normative multi-spatial-scale functional template using over 100,000 subjects, aiming to improve generalizability and comparability across studies involving diverse cohorts. While a unified template across the lifespan is desirable, a comprehensive investigation of the similarities and differences between components from different age populations might help systematically transform our understanding of the human brain by revealing the most well-replicated and variable network features throughout the lifespan. In this work, we introduced two significant expansions of NeuroMark templates first by generating replicable fMRI templates for infants, adolescents, and aging cohorts, and second by incorporating structural MRI (sMRI) and diffusion MRI (dMRI) modalities. Specifically, we built spatiotemporal fMRI templates based on 6,000 resting-state scans from four datasets. This is the first attempt to create robust ICA templates covering dynamic brain development across the lifespan. For the sMRI and dMRI data, we used two large publicly available datasets including more than 30,000 scans to build reliable templates. We employed a spatial similarity analysis to identify replicable templates and investigate the degree to which unique and similar patterns are reflective in different age populations. Our results suggest remarkably high similarity of the resulting adapted components, even across extreme age differences. With the new templates, the NeuroMark framework allows us to perform age-specific adaptations and to capture features adaptable to each modality, therefore facilitating biomarker identification across brain disorders. In sum, the present work demonstrates the generalizability of NeuroMark templates and suggests the potential of new templates to boost accuracy in mental health research and advance our understanding of lifespan and cross-modal alterations.
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Affiliation(s)
- Zening Fu
- Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State University, Georgia Institute of Technology, Emory University, Atlanta, Georgia, United States.
| | - Ishaan Batta
- Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State University, Georgia Institute of Technology, Emory University, Atlanta, Georgia, United States
| | - Lei Wu
- Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State University, Georgia Institute of Technology, Emory University, Atlanta, Georgia, United States
| | - Anees Abrol
- Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State University, Georgia Institute of Technology, Emory University, Atlanta, Georgia, United States
| | - Oktay Agcaoglu
- Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State University, Georgia Institute of Technology, Emory University, Atlanta, Georgia, United States
| | - Mustafa S Salman
- Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State University, Georgia Institute of Technology, Emory University, Atlanta, Georgia, United States
| | - Yuhui Du
- Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State University, Georgia Institute of Technology, Emory University, Atlanta, Georgia, United States
| | - Armin Iraji
- Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State University, Georgia Institute of Technology, Emory University, Atlanta, Georgia, United States
| | - Sarah Shultz
- Department of Pediatrics, Emory University School of Medicine, Atlanta, Georgia, United States
| | - Jing Sui
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, China
| | - Vince D Calhoun
- Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State University, Georgia Institute of Technology, Emory University, Atlanta, Georgia, United States
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Zhou Y, Chen X, Gu R, Xiang YT, Hajcak G, Wang G. Personalized identification and intervention of depression in adolescents: A tertiary-level framework. Sci Bull (Beijing) 2024; 69:867-871. [PMID: 38302329 DOI: 10.1016/j.scib.2024.01.024] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/03/2024]
Affiliation(s)
- Yuan Zhou
- Beijing Key Laboratory of Mental Disorders, National Clinical Research Center for Mental Disorders & National Center for Mental Disorders, Beijing Anding Hospital, Capital Medical University, Beijing 100088, China; CAS Key Laboratory of Behavioral Science, Institute of Psychology, Beijing 100101, China; Department of Psychology, University of Chinese Academy of Sciences, Beijing 100049, China
| | - Xu Chen
- Beijing Key Laboratory of Mental Disorders, National Clinical Research Center for Mental Disorders & National Center for Mental Disorders, Beijing Anding Hospital, Capital Medical University, Beijing 100088, China
| | - Ruolei Gu
- CAS Key Laboratory of Behavioral Science, Institute of Psychology, Beijing 100101, China; Department of Psychology, University of Chinese Academy of Sciences, Beijing 100049, China
| | - Yu-Tao Xiang
- Unit of Psychiatry, Department of Public Health and Medicinal Administration, & Institute of Translational Medicine, Faculty of Health Sciences, University of Macau, Macao 999078, China; Centre for Cognitive and Brain Sciences, University of Macau, Macao 999078, China
| | - Greg Hajcak
- School of Education and Counseling Psychology, Santa Clara University, Santa Clara CA 95053, USA
| | - Gang Wang
- Beijing Key Laboratory of Mental Disorders, National Clinical Research Center for Mental Disorders & National Center for Mental Disorders, Beijing Anding Hospital, Capital Medical University, Beijing 100088, China; Advanced Innovation Center for Human Brain Protection, Capital Medical University, Beijing 100069, China.
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Tong X, Xie H, Wu W, Keller CJ, Fonzo GA, Chidharom M, Carlisle NB, Etkin A, Zhang Y. Individual deviations from normative electroencephalographic connectivity predict antidepressant response. J Affect Disord 2024; 351:220-230. [PMID: 38281595 PMCID: PMC10923099 DOI: 10.1016/j.jad.2024.01.177] [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: 08/23/2023] [Revised: 01/15/2024] [Accepted: 01/18/2024] [Indexed: 01/30/2024]
Abstract
BACKGROUND Antidepressant medications yield unsatisfactory treatment outcomes in patients with major depressive disorder (MDD) with modest advantages over the placebo, partly due to the elusive mechanisms of antidepressant responses and unexplained heterogeneity in patient's response to treatment. Here we develop a novel normative modeling framework to quantify individual deviations in psychopathological dimensions that offers a promising avenue for the personalized treatment for psychiatric disorders. METHODS We built a normative model with resting-state electroencephalography (EEG) connectivity data from healthy controls of three independent cohorts. We characterized the individual deviation of MDD patients from the healthy norms, based on which we trained sparse predictive models for treatment responses of MDD patients (102 sertraline-medicated and 119 placebo-medicated). Hamilton depression rating scale (HAMD-17) was assessed at both baseline and after the eight-week antidepressant treatment. RESULTS We successfully predicted treatment outcomes for patients receiving sertraline (r = 0.43, p < 0.001) and placebo (r = 0.33, p < 0.001). We also showed that the normative modeling framework successfully distinguished subclinical and diagnostic variabilities among subjects. From the predictive models, we identified key connectivity signatures in resting-state EEG for antidepressant treatment, suggesting differences in neural circuit involvement between sertraline and placebo responses. CONCLUSIONS Our findings and highly generalizable framework advance the neurobiological understanding in the potential pathways of antidepressant responses, enabling more targeted and effective personalized MDD treatment. TRIAL REGISTRATION Establishing Moderators and Biosignatures of Antidepressant Response for Clinical Care for Depression (EMBARC), NCT#01407094.
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Affiliation(s)
- Xiaoyu Tong
- Department of Bioengineering, Lehigh University, Bethlehem, PA, USA
| | - Hua Xie
- Center for Neuroscience Research, Children's National Hospital, Washington, DC, USA; George Washington University School of Medicine, Washington, DC, USA
| | - Wei Wu
- Alto Neuroscience, Inc., Los Altos, CA, USA
| | - Corey J Keller
- Department of Psychiatry and Behavioral Sciences, Stanford University, CA, USA; Veterans Affairs Palo Alto Healthcare System, Sierra Pacific Mental Illness, Research, Education, and Clinical Center (MIRECC), Palo Alto, CA, USA
| | - Gregory A Fonzo
- Center for Psychedelic Research and Therapy, Department of Psychiatry and Behavioral Sciences, Dell Medical School, The University of Texas at Austin, TX, USA
| | | | | | - Amit Etkin
- Alto Neuroscience, Inc., Los Altos, CA, USA
| | - Yu Zhang
- Department of Bioengineering, Lehigh University, Bethlehem, PA, USA; Department of Electrical and Computer Engineering, Lehigh University, Bethlehem, PA, USA.
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Li M, Racey C, Rae CL, Strawson W, Critchley HD, Ward J. Can the neural representation of physical pain predict empathy for pain in others? Soc Cogn Affect Neurosci 2024; 19:nsae023. [PMID: 38481007 PMCID: PMC11008503 DOI: 10.1093/scan/nsae023] [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: 07/25/2023] [Revised: 01/16/2024] [Accepted: 03/19/2024] [Indexed: 04/12/2024] Open
Abstract
The question of whether physical pain and vicarious pain have some shared neural substrates is unresolved. Recent research has argued that physical and vicarious pain are represented by dissociable multivariate brain patterns by creating biomarkers for physical pain (Neurologic Pain Signature, NPS) and vicarious pain (Vicarious Pain Signature, VPS), respectively. In the current research, the NPS and two versions of the VPS were applied to three fMRI datasets (one new, two published) relating to vicarious pain which focused on between-subject differences in vicarious pain (Datasets 1 and 3) and within-subject manipulations of perspective taking (Dataset 2). Results show that (i) NPS can distinguish brain responses to images of pain vs no-pain and to a greater extent in vicarious pain responders who report experiencing pain when observing pain and (ii) neither version of the VPS mapped on to individual differences in vicarious pain and the two versions differed in their success in predicting vicarious pain overall. This study suggests that the NPS (created to detect physical pain) is, under some circumstances, sensitive to vicarious pain and there is significant variability in VPS measures (created to detect vicarious pain) to act as generalizable biomarkers of vicarious pain.
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Affiliation(s)
- M Li
- School of Psychology, University of Sussex, Brighton BN1 9QH, UK
| | - C Racey
- School of Psychology, University of Sussex, Brighton BN1 9QH, UK
| | - C L Rae
- School of Psychology, University of Sussex, Brighton BN1 9QH, UK
| | - W Strawson
- Brighton and Sussex Medical School, University of Sussex, Brighton BN1 9PX, UK
| | - H D Critchley
- Brighton and Sussex Medical School, University of Sussex, Brighton BN1 9PX, UK
| | - J Ward
- School of Psychology, University of Sussex, Brighton BN1 9QH, UK
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Chou TL, Shih CH, Chou PC, Lai JH, Huang TW. Use of a wearable device to compare subjective and objective fatigue in lung cancer patients and cancer-free controls. Eur J Oncol Nurs 2024; 70:102587. [PMID: 38652934 DOI: 10.1016/j.ejon.2024.102587] [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: 10/24/2023] [Revised: 03/26/2024] [Accepted: 03/31/2024] [Indexed: 04/25/2024]
Abstract
PURPOSE The study evaluates the use of heart rate variability (HRV), a measure of autonomic nervous system (ANS) modulation via wearable smart bands, to objectively assess cancer-related fatigue (CRF) levels. It aims to enhance understanding of fatigue by distinguishing between LF/HF ratios and LF/HF disorder ratios through HRV and photoplethysmography (PPG), identifying them as potential biomarkers. METHODS Seventy-one lung cancer patients and 75 non-cancer controls wore smart bands for one week. Fatigue was assessed using Brief Fatigue Inventory, alongside sleep quality and daily interference. HRV parameters were analyzed to compare groups. RESULTS Cancer patients showed higher fatigue and interference levels than controls (64.8% vs. 54.7%). Those with mild fatigue had elevated LF/HF disorder ratios during sleep (40% vs. 20%, P = 0.01), similar to those with moderate to severe fatigue (50% vs. 20%, P = 0.01), indicating more significant autonomic dysregulation. Notably, mild fatigue patients had higher mean LF/HF ratios than controls (1.9 ± 1.34 vs. 1.2 ± 0.6, P = 0.01), underscoring the potential of disorder ratios in signaling fatigue severity. CONCLUSIONS Utilizing wearable smart bands for HRV-based analysis is feasible for objectively assess CRF levels in cancer patients, especially during sleep. By distinguishing between LF/HF ratios and LF/HF disorder ratios, our findings suggest that wearable technology and detailed HRV analysis offer promising avenues for real-time fatigue monitoring. This approach has the potential to significantly improve cancer care by providing new methods for managing and intervening in CRF, particularly with a focus on autonomic dysregulation as a crucial factor.
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Affiliation(s)
- Ting-Ling Chou
- School of Nursing, College of Nursing, Taipei Medical University, Taipei, Taiwan
| | - Chi-Huang Shih
- Department of Computer Science and Information Engineering, National Chin-Yi University of Technology, Taichung, Taiwan
| | - Pai-Chien Chou
- Department of Internal Medicine, Taipei Medical University Hospital, Taipei, Taiwan; Division of Thoracic Medicine, Department of Internal Medicine, School of Medicine, Taipei Medical University, Taipei, Taiwan
| | - Jun-Hung Lai
- Division of Gastroenterology & Hepatology, Department of Internal Medicine, Erlin Christian Hospital, Changhua, Taiwan
| | - Tsai-Wei Huang
- School of Nursing, College of Nursing, Taipei Medical University, Taipei, Taiwan; Cochrane Taiwan, Taipei Medical University, Taipei, Taiwan; Research Center in Nursing Clinical Application, Wan Fang Hospital, Taipei Medical University, Taipei, Taiwan; Department of Nursing, Wan Fang Hospital, Taipei Medical University, Taipei, Taiwan.
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van der Horn HJ, Vakhtin AA, Julio K, Nitschke S, Shaff N, Dodd AB, Erhardt E, Phillips JP, Pirio Richardson S, Deligtisch A, Stewart M, Suarez Cedeno G, Meles SK, Mayer AR, Ryman SG. Parkinson's disease cerebrovascular reactivity pattern: A feasibility study. J Cereb Blood Flow Metab 2024:271678X241241895. [PMID: 38578669 DOI: 10.1177/0271678x241241895] [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: 04/06/2024]
Abstract
A mounting body of research points to cerebrovascular dysfunction as a fundamental element in the pathophysiology of Parkinson's disease (PD). In the current feasibility study, blood-oxygen-level-dependent (BOLD) MRI was used to measure cerebrovascular reactivity (CVR) in response to hypercapnia in 26 PD patients and 16 healthy controls (HC), and aimed to find a multivariate pattern specific to PD. Whole-brain maps of CVR amplitude (i.e., magnitude of response to CO2) and latency (i.e., time to reach maximum amplitude) were computed, which were further analyzed using scaled sub-profile model principal component analysis (SSM-PCA) with leave-one-out cross-validation. A meaningful pattern based on CVR latency was identified, which was named the PD CVR pattern (PD-CVRP). This pattern was characterized by relatively increased latency in basal ganglia, sensorimotor cortex, supplementary motor area, thalamus and visual cortex, as well as decreased latency in the cerebral white matter, relative to HC. There were no significant associations with clinical measures, though sample size may have limited our ability to detect significant associations. In summary, the PD-CVRP highlights the importance of cerebrovascular dysfunction in PD, and may be a potential biomarker for future clinical research and practice.
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Affiliation(s)
- Harm Jan van der Horn
- Department of Translational Neuroscience, The Mind Research Network, Albuquerque, NM, USA
| | - Andrei A Vakhtin
- Department of Translational Neuroscience, The Mind Research Network, Albuquerque, NM, USA
| | - Kayla Julio
- Department of Translational Neuroscience, The Mind Research Network, Albuquerque, NM, USA
| | - Stephanie Nitschke
- Department of Translational Neuroscience, The Mind Research Network, Albuquerque, NM, USA
| | - Nicholas Shaff
- Department of Translational Neuroscience, The Mind Research Network, Albuquerque, NM, USA
| | - Andrew B Dodd
- Department of Translational Neuroscience, The Mind Research Network, Albuquerque, NM, USA
| | - Erik Erhardt
- Department of Mathematics and Statistics, University of New Mexico, Albuquerque, NM, USA
| | - John P Phillips
- Department of Translational Neuroscience, The Mind Research Network, Albuquerque, NM, USA
| | - Sarah Pirio Richardson
- Nene and Jamie Koch Comprehensive Movement Disorder Center, Department of Neurology, University of New Mexico, Albuquerque, NM, USA
- New Mexico VA Health Care System, Albuquerque, NM, USA
| | - Amanda Deligtisch
- Nene and Jamie Koch Comprehensive Movement Disorder Center, Department of Neurology, University of New Mexico, Albuquerque, NM, USA
| | - Melanie Stewart
- Nene and Jamie Koch Comprehensive Movement Disorder Center, Department of Neurology, University of New Mexico, Albuquerque, NM, USA
| | - Gerson Suarez Cedeno
- Nene and Jamie Koch Comprehensive Movement Disorder Center, Department of Neurology, University of New Mexico, Albuquerque, NM, USA
| | - Sanne K Meles
- Department of Neurology, University of Groningen, University Medical Center Groningen, Groningen, The Netherlands
| | - Andrew R Mayer
- Department of Translational Neuroscience, The Mind Research Network, Albuquerque, NM, USA
| | - Sephira G Ryman
- Department of Translational Neuroscience, The Mind Research Network, Albuquerque, NM, USA
- Nene and Jamie Koch Comprehensive Movement Disorder Center, Department of Neurology, University of New Mexico, Albuquerque, NM, USA
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Meng J, Zhang T, Hao T, Xie X, Zhang M, Zhang L, Wan X, Zhu C, Li Q, Wang K. Functional and Structural Abnormalities in the Pain Network of Generalized Anxiety Disorder Patients with Pain Symptoms. Neuroscience 2024; 543:28-36. [PMID: 38382693 DOI: 10.1016/j.neuroscience.2024.02.006] [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: 10/23/2023] [Revised: 01/16/2024] [Accepted: 02/08/2024] [Indexed: 02/23/2024]
Abstract
Pain symptoms significantly impact the well-being and work capacity of individuals with generalized anxiety disorder (GAD), and hinder treatment and recovery. Despite existing literature focusing on the neural substrate of pain and anxiety separately, further exploration is needed to understand the possible neuroimaging mechanisms of the pain symptoms in GAD patients. We recruited 73 GAD patients and 75 matched healthy controls (HC) for clinical assessments, as well as resting-state functional and structural magnetic resonance imaging scans. We defined a pain-related network through a published meta-analysis, including the insula, thalamus, periaqueductal gray, prefrontal cortex, anterior cingulate cortex, amygdala, and hippocampus. Subsequently, we conducted the regional homogeneity (ReHo) and the gray matter volume (GMV) within the pain-related network. Correlation analysis was then employed to explore associations between abnormal regions and self-reported outcomes, assessed using the Patient Health Questionnaire-15 (PHQ-15) and pain scores. We observed significantly increased ReHo in the bilateral insula but decreased GMV in the bilateral thalamus of GAD compared to HC. Further correlation analysis revealed a positive correlation between ReHo of the left anterior insula and pain scores in GAD patients, while a respective negative correlation between GMV of the bilateral thalamus and PHQ-15 scores. In summary, GAD patients exhibit structural and functional abnormalities in pain-related networks. The enhanced ReHo in the left anterior insula is correlated with pain symptoms, which might be a crucial brain region of pain symptoms in GAD.
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Affiliation(s)
- Jie Meng
- School of Mental Health and Psychological Sciences, Anhui Medical University, Hefei, Anhui Province, China; Collaborative Innovation Center of Neuropsychiatric Disorders and Mental Health, Hefei, Anhui Province, China; Anhui Province Key Laboratory of Cognition and Neuropsychiatric Disorders, Hefei, Anhui Province, China
| | - Ting Zhang
- Department of Psychiatry, The First Affiliated Hospital of Anhui Medical University, Hefei, Anhui Province, China; Collaborative Innovation Center of Neuropsychiatric Disorders and Mental Health, Hefei, Anhui Province, China; Anhui Province Key Laboratory of Cognition and Neuropsychiatric Disorders, Hefei, Anhui Province, China
| | - Tong Hao
- School of Mental Health and Psychological Sciences, Anhui Medical University, Hefei, Anhui Province, China; Collaborative Innovation Center of Neuropsychiatric Disorders and Mental Health, Hefei, Anhui Province, China; Anhui Province Key Laboratory of Cognition and Neuropsychiatric Disorders, Hefei, Anhui Province, China
| | - Xiaohui Xie
- Department of Neurology, The Second Affiliated Hospital of Anhui Medical University, Hefei, Anhui Province, China; Collaborative Innovation Center of Neuropsychiatric Disorders and Mental Health, Hefei, Anhui Province, China; Anhui Province Key Laboratory of Cognition and Neuropsychiatric Disorders, Hefei, Anhui Province, China
| | - Mengdan Zhang
- School of Mental Health and Psychological Sciences, Anhui Medical University, Hefei, Anhui Province, China; Collaborative Innovation Center of Neuropsychiatric Disorders and Mental Health, Hefei, Anhui Province, China; Anhui Province Key Laboratory of Cognition and Neuropsychiatric Disorders, Hefei, Anhui Province, China
| | - Lei Zhang
- School of Mental Health and Psychological Sciences, Anhui Medical University, Hefei, Anhui Province, China; Collaborative Innovation Center of Neuropsychiatric Disorders and Mental Health, Hefei, Anhui Province, China; Anhui Province Key Laboratory of Cognition and Neuropsychiatric Disorders, Hefei, Anhui Province, China
| | - Xingsong Wan
- Affiliated Psychological Hospital of Anhui Medical University, Hefei, Anhui Province, China; Anhui Mental Health Center, Hefei, Anhui Province, China
| | - Chunyan Zhu
- School of Mental Health and Psychological Sciences, Anhui Medical University, Hefei, Anhui Province, China; Collaborative Innovation Center of Neuropsychiatric Disorders and Mental Health, Hefei, Anhui Province, China; Anhui Province Key Laboratory of Cognition and Neuropsychiatric Disorders, Hefei, Anhui Province, China; Institute of Artificial Intelligence, University of Science and Technology of China, Hefei, Anhui Province, China
| | - Qianqian Li
- Department of Psychology and Sleep Medicine, The Second Affiliated Hospital of Anhui Medical University, Hefei, Anhui Province, China; Collaborative Innovation Center of Neuropsychiatric Disorders and Mental Health, Hefei, Anhui Province, China; Anhui Province Key Laboratory of Cognition and Neuropsychiatric Disorders, Hefei, Anhui Province, China.
| | - Kai Wang
- School of Mental Health and Psychological Sciences, Anhui Medical University, Hefei, Anhui Province, China; Department of Neurology, The First Affiliated Hospital of Anhui Medical University, Hefei, Anhui Province, China; Collaborative Innovation Center of Neuropsychiatric Disorders and Mental Health, Hefei, Anhui Province, China; Anhui Province Key Laboratory of Cognition and Neuropsychiatric Disorders, Hefei, Anhui Province, China; Institute of Artificial Intelligence, University of Science and Technology of China, Hefei, Anhui Province, China.
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Mittal P, Sao AK, Biswal B, Di X, Dileep AD. Network-wise analysis of movie-specific information in dynamic functional connectivity using COBE. Cereb Cortex 2024; 34:bhae170. [PMID: 38679477 DOI: 10.1093/cercor/bhae170] [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: 12/18/2023] [Revised: 03/29/2024] [Accepted: 04/01/2024] [Indexed: 05/01/2024] Open
Abstract
Movie watching during functional magnetic resonance imaging has emerged as a promising tool to measure the complex behavior of the brain in response to a stimulus similar to real-life situations. It has been observed that presenting a movie (sequence of events) as a stimulus will lead to a unique time course of dynamic functional connectivity related to movie stimuli that can be compared across the participants. We assume that the observed dynamic functional connectivity across subjects can be divided into following 2 components: (i) specific to a movie stimulus (depicting group-level behavior in functional connectivity) and (ii) individual-specific behavior (not necessarily common across the subjects). In this work, using the dynamic time warping distance measure, we have shown the extent of similarity between the temporal sequences of functional connectivity while the underlying movie stimuli were same and different. Further, the temporal sequence of functional connectivity patterns related to a movie is enhanced by suppressing the subject-specific components of dynamic functional connectivity using common and orthogonal basis extraction. Quantitative analysis using the F-ratio measure reveals significant differences in dynamic functional connectivity within the somatomotor network and default mode network, as well as between the occipital network and somatomotor networks.
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Affiliation(s)
- Priyanka Mittal
- School of Computing and Electrical Engineering, Indian Institute of Technology Mandi 175005, Himachal Pradesh, India
| | - Anil K Sao
- Electrical Engineering and Computer Science, Indian Institute of Technology Bhilai, Raipur 492015, Chhattisgarh, India
| | - Bharat Biswal
- Department of Biomedical Engineering, New Jersey Institute of Technology, Newark 07102, NJ, United States
| | - Xin Di
- Department of Biomedical Engineering, New Jersey Institute of Technology, Newark 07102, NJ, United States
| | - Aroor Dinesh Dileep
- School of Computing and Electrical Engineering, Indian Institute of Technology Mandi 175005, Himachal Pradesh, India
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49
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Jockwitz C, Krämer C, Dellani P, Caspers S. Differential predictability of cognitive profiles from brain structure in older males and females. GeroScience 2024; 46:1713-1730. [PMID: 37730943 PMCID: PMC10828131 DOI: 10.1007/s11357-023-00934-y] [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: 07/27/2023] [Accepted: 09/04/2023] [Indexed: 09/22/2023] Open
Abstract
Structural brain imaging parameters may successfully predict cognitive performance in neurodegenerative diseases but mostly fail to predict cognitive abilities in healthy older adults. One important aspect contributing to this might be sex differences. Behaviorally, older males and females have been found to differ in terms of cognitive profiles, which cannot be captured by examining them as one homogenous group. In the current study, we examined whether the prediction of cognitive performance from brain structure, i.e. region-wise grey matter volume (GMV), would benefit from the investigation of sex-specific cognitive profiles in a large sample of older adults (1000BRAINS; N = 634; age range 55-85 years). Prediction performance was assessed using a machine learning (ML) approach. Targets represented a) a whole-sample cognitive component solution extracted from males and females, and b) sex-specific cognitive components. Results revealed a generally low predictability of cognitive profiles from region-wise GMV. In males, low predictability was observed across both, the whole sample as well as sex-specific cognitive components. In females, however, predictability differences across sex-specific cognitive components were observed, i.e. visual working memory (WM) and executive functions showed higher predictability than fluency and verbal WM. Hence, results accentuated that addressing sex-specific cognitive profiles allowed a more fine-grained investigation of predictability differences, which may not be observable in the prediction of the whole-sample solution. The current findings not only emphasize the need to further investigate the predictive power of each cognitive component, but they also emphasize the importance of sex-specific analyses in older adults.
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Affiliation(s)
- Christiane Jockwitz
- Institute of Neuroscience and Medicine (INM-1), Research Centre Jülich, Jülich, Germany.
- Institute for Anatomy I, Medical Faculty & University Hospital Düsseldorf, Heinrich Heine University Düsseldorf, Düsseldorf, Germany.
| | - Camilla Krämer
- Institute of Neuroscience and Medicine (INM-1), Research Centre Jülich, Jülich, Germany
- Institute for Anatomy I, Medical Faculty & University Hospital Düsseldorf, Heinrich Heine University Düsseldorf, Düsseldorf, Germany
| | - Paulo Dellani
- Institute of Neuroscience and Medicine (INM-1), Research Centre Jülich, Jülich, Germany
- Institute for Anatomy I, Medical Faculty & University Hospital Düsseldorf, Heinrich Heine University Düsseldorf, Düsseldorf, Germany
| | - Svenja Caspers
- Institute of Neuroscience and Medicine (INM-1), Research Centre Jülich, Jülich, Germany
- Institute for Anatomy I, Medical Faculty & University Hospital Düsseldorf, Heinrich Heine University Düsseldorf, Düsseldorf, Germany
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50
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DeRosa J, Friedman NP, Calhoun V, Banich MT. Neurodevelopmental Subtypes of Functional Brain Organization in the ABCD Study Using a Rigorous Analytic Framework. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.03.16.585343. [PMID: 38559171 PMCID: PMC10979961 DOI: 10.1101/2024.03.16.585343] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 04/04/2024]
Abstract
The current study demonstrates that an individual's resting-state functional connectivity (RSFC) is a dependable biomarker for identifying differential patterns of cognitive and emotional functioning during late childhood. Using baseline RSFC data from the Adolescent Brain Cognitive Development (ABCD) study, which includes children aged 9-11, we identified four distinct RSFC subtypes We introduce an integrated methodological pipeline for testing the reliability and importance of these subtypes. In the Identification phase, Leiden Community Detection defined RSFC subtypes, with their reproducibility confirmed through a split-sample technique in the Validation stage. The Evaluation phase showed that distinct cognitive and mental health profiles are associated with each subtype, with the Predictive phase indicating that subtypes better predict various cognitive and mental health characteristics than individual RSFC connections. The Replication stage employed bootstrapping and down-sampling methods to substantiate the reproducibility of these subtypes further. This work allows future explorations of developmental trajectories of these RSFC subtypes.
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Affiliation(s)
- Jacob DeRosa
- Department of Psychology and Neuroscience, University of Colorado Boulder
- Institute of Cognitive Science, University of Colorado Boulder
| | - Naomi P. Friedman
- Department of Psychology and Neuroscience, University of Colorado Boulder
- Institute for Behavioral Genetics, University of Colorado Boulder
| | - Vince Calhoun
- Tri-institutional Center for Translational Research in Neuroimaging and Data Science, (TReNDS), Georgia State University, Georgia Institute of Technology, Emory University
| | - Marie T. Banich
- Department of Psychology and Neuroscience, University of Colorado Boulder
- Institute of Cognitive Science, University of Colorado Boulder
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