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Ren J, An N, Zhang Y, Wang D, Sun Z, Lin C, Cui W, Wang W, Zhou Y, Zhang W, Hu Q, Zhang P, Hu D, Wang D, Liu H. SUGAR: Spherical ultrafast graph attention framework for cortical surface registration. Med Image Anal 2024; 94:103122. [PMID: 38428270 DOI: 10.1016/j.media.2024.103122] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/04/2023] [Revised: 01/25/2024] [Accepted: 02/22/2024] [Indexed: 03/03/2024]
Abstract
Cortical surface registration plays a crucial role in aligning cortical functional and anatomical features across individuals. However, conventional registration algorithms are computationally inefficient. Recently, learning-based registration algorithms have emerged as a promising solution, significantly improving processing efficiency. Nonetheless, there remains a gap in the development of a learning-based method that exceeds the state-of-the-art conventional methods simultaneously in computational efficiency, registration accuracy, and distortion control, despite the theoretically greater representational capabilities of deep learning approaches. To address the challenge, we present SUGAR, a unified unsupervised deep-learning framework for both rigid and non-rigid registration. SUGAR incorporates a U-Net-based spherical graph attention network and leverages the Euler angle representation for deformation. In addition to the similarity loss, we introduce fold and multiple distortion losses to preserve topology and minimize various types of distortions. Furthermore, we propose a data augmentation strategy specifically tailored for spherical surface registration to enhance the registration performance. Through extensive evaluation involving over 10,000 scans from 7 diverse datasets, we showed that our framework exhibits comparable or superior registration performance in accuracy, distortion, and test-retest reliability compared to conventional and learning-based methods. Additionally, SUGAR achieves remarkable sub-second processing times, offering a notable speed-up of approximately 12,000 times in registering 9,000 subjects from the UK Biobank dataset in just 32 min. This combination of high registration performance and accelerated processing time may greatly benefit large-scale neuroimaging studies.
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Affiliation(s)
| | - Ning An
- Changping Laboratory, Beijing, China
| | | | | | | | - Cong Lin
- Changping Laboratory, Beijing, China
| | - Weigang Cui
- School of Engineering Medicine, Beihang University, Beijing, China
| | | | - Ying Zhou
- Changping Laboratory, Beijing, China
| | - Wei Zhang
- Changping Laboratory, Beijing, China; Academy for Advanced Interdisciplinary Studies, Peking University, Beijing, China
| | - Qingyu Hu
- Changping Laboratory, Beijing, China
| | | | - Dan Hu
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Charlestown, MA, USA
| | - Danhong Wang
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Charlestown, MA, USA
| | - Hesheng Liu
- Changping Laboratory, Beijing, China; Biomedical Pioneering Innovation Center (BIOPIC), Peking University, Beijing, China.
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2
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Voineskos AN, Hawco C, Neufeld NH, Turner JA, Ameis SH, Anticevic A, Buchanan RW, Cadenhead K, Dazzan P, Dickie EW, Gallucci J, Lahti AC, Malhotra AK, Öngür D, Lencz T, Sarpal DK, Oliver LD. Functional magnetic resonance imaging in schizophrenia: current evidence, methodological advances, limitations and future directions. World Psychiatry 2024; 23:26-51. [PMID: 38214624 PMCID: PMC10786022 DOI: 10.1002/wps.21159] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 01/13/2024] Open
Abstract
Functional neuroimaging emerged with great promise and has provided fundamental insights into the neurobiology of schizophrenia. However, it has faced challenges and criticisms, most notably a lack of clinical translation. This paper provides a comprehensive review and critical summary of the literature on functional neuroimaging, in particular functional magnetic resonance imaging (fMRI), in schizophrenia. We begin by reviewing research on fMRI biomarkers in schizophrenia and the clinical high risk phase through a historical lens, moving from case-control regional brain activation to global connectivity and advanced analytical approaches, and more recent machine learning algorithms to identify predictive neuroimaging features. Findings from fMRI studies of negative symptoms as well as of neurocognitive and social cognitive deficits are then reviewed. Functional neural markers of these symptoms and deficits may represent promising treatment targets in schizophrenia. Next, we summarize fMRI research related to antipsychotic medication, psychotherapy and psychosocial interventions, and neurostimulation, including treatment response and resistance, therapeutic mechanisms, and treatment targeting. We also review the utility of fMRI and data-driven approaches to dissect the heterogeneity of schizophrenia, moving beyond case-control comparisons, as well as methodological considerations and advances, including consortia and precision fMRI. Lastly, limitations and future directions of research in the field are discussed. Our comprehensive review suggests that, in order for fMRI to be clinically useful in the care of patients with schizophrenia, research should address potentially actionable clinical decisions that are routine in schizophrenia treatment, such as which antipsychotic should be prescribed or whether a given patient is likely to have persistent functional impairment. The potential clinical utility of fMRI is influenced by and must be weighed against cost and accessibility factors. Future evaluations of the utility of fMRI in prognostic and treatment response studies may consider including a health economics analysis.
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Affiliation(s)
- Aristotle N Voineskos
- Campbell Family Mental Health Research Institute and Brain Health Imaging Centre, Centre for Addiction and Mental Health, Toronto, ON, Canada
- Department of Psychiatry, Temerty Faculty of Medicine, University of Toronto, Toronto, ON, Canada
| | - Colin Hawco
- Campbell Family Mental Health Research Institute and Brain Health Imaging Centre, Centre for Addiction and Mental Health, Toronto, ON, Canada
- Department of Psychiatry, Temerty Faculty of Medicine, University of Toronto, Toronto, ON, Canada
| | - Nicholas H Neufeld
- Campbell Family Mental Health Research Institute and Brain Health Imaging Centre, Centre for Addiction and Mental Health, Toronto, ON, Canada
- Department of Psychiatry, Temerty Faculty of Medicine, University of Toronto, Toronto, ON, Canada
| | - Jessica A Turner
- Department of Psychiatry and Behavioral Health, Wexner Medical Center, Ohio State University, Columbus, OH, USA
| | - Stephanie H Ameis
- Campbell Family Mental Health Research Institute and Brain Health Imaging Centre, Centre for Addiction and Mental Health, Toronto, ON, Canada
- Department of Psychiatry, Temerty Faculty of Medicine, University of Toronto, Toronto, ON, Canada
- Cundill Centre for Child and Youth Depression and McCain Centre for Child, Youth and Family Mental Health, Centre for Addiction and Mental Health, Toronto, ON, Canada
| | - Alan Anticevic
- Interdepartmental Neuroscience Program, Yale University, New Haven, CT, USA
- Department of Psychiatry, Yale University, New Haven, CT, USA
| | - Robert W Buchanan
- Maryland Psychiatric Research Center, Department of Psychiatry, University of Maryland School of Medicine, Baltimore, MD, USA
| | - Kristin Cadenhead
- Department of Psychiatry, University of California San Diego, La Jolla, CA, USA
| | - Paola Dazzan
- Department of Psychological Medicine, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK
| | - Erin W Dickie
- Campbell Family Mental Health Research Institute and Brain Health Imaging Centre, Centre for Addiction and Mental Health, Toronto, ON, Canada
- Department of Psychiatry, Temerty Faculty of Medicine, University of Toronto, Toronto, ON, Canada
| | - Julia Gallucci
- Campbell Family Mental Health Research Institute and Brain Health Imaging Centre, Centre for Addiction and Mental Health, Toronto, ON, Canada
- Institute of Medical Science, University of Toronto, Toronto, ON, Canada
| | - Adrienne C Lahti
- Department of Psychiatry and Behavioral Neurobiology, University of Alabama at Birmingham, Birmingham, AL, USA
| | - Anil K Malhotra
- Institute for Behavioral Science, Feinstein Institutes for Medical Research, Manhasset, NY, USA
- Department of Psychiatry, Zucker School of Medicine at Hofstra/Northwell, Hempstead, NY, USA
- Department of Molecular Medicine, Zucker School of Medicine at Hofstra/Northwell, Hempstead, NY, USA
- Department of Psychiatry, Zucker Hillside Hospital Division of Northwell Health, Glen Oaks, NY, USA
| | - Dost Öngür
- McLean Hospital/Harvard Medical School, Belmont, MA, USA
| | - Todd Lencz
- Institute for Behavioral Science, Feinstein Institutes for Medical Research, Manhasset, NY, USA
- Department of Psychiatry, Zucker School of Medicine at Hofstra/Northwell, Hempstead, NY, USA
- Department of Molecular Medicine, Zucker School of Medicine at Hofstra/Northwell, Hempstead, NY, USA
- Department of Psychiatry, Zucker Hillside Hospital Division of Northwell Health, Glen Oaks, NY, USA
| | - Deepak K Sarpal
- Department of Psychiatry, School of Medicine, University of Pittsburgh, Pittsburgh, PA, USA
| | - Lindsay D Oliver
- Campbell Family Mental Health Research Institute and Brain Health Imaging Centre, Centre for Addiction and Mental Health, Toronto, ON, Canada
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3
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Xing XX, Gao X, Jiang C. Individual Variability of Human Cortical Spontaneous Activity by 3T/7T fMRI. Neuroscience 2023; 528:117-128. [PMID: 37544577 DOI: 10.1016/j.neuroscience.2023.07.032] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/10/2022] [Revised: 07/29/2023] [Accepted: 07/31/2023] [Indexed: 08/08/2023]
Abstract
Mapping variability in cortical spontaneous activity (CSA) is an essential goal of understanding various sources of dark brain energy in human neuroscience. CSA was traditionally characterized using resting-state functional MRI (rfMRI) at 1.5T or 3T magnets while recently with 7T-rfMRI. However, the utility and interpretability of 7T-rfMRI must first be established for its variability. By leveraging rfMRI data from the Human Connectome Project (HCP), we derived CSA metrics with 3T-rfMRI and 7T-rfMRI for the same 84 healthy participants (52 females). The 7T-rfMRI produces different CSA metrics at multiple spatial-scales and their variability from the 3T-rfMRI. These differences were spatially dependent and varied according to specific cortical organization. For the amplitude metric, 7T-rfMRI enhanced its spatial contrasts in the anterior cortex but weakened it in the posterior cortex. An opposite pattern was observed for the connectivity metrics. The reliability changes of these metrics were scale dependent, indicating enhanced reliability for connectivity but weakened reliability for amplitude by 7T-rfMRI. These effects were primarily located in the high-order associate cortex, parsing the corresponding changes in individual differences with respect to 7T-rfMRI: (1) higher connectivity variability between participants and the lower connectivity variability within individual participants, and (2) lower amplitude variability between participants and higher amplitude variability within participants. Our work, for the first time, demonstrated the variability of the human CSA across space, rfMRI settings/platforms, and individuals. We discussed the statistical implications of our findings on CSA-based experimental designs and reproducible neuroscience as well as their translational value for personalized applications.
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Affiliation(s)
- Xiu-Xia Xing
- Department of Applied Mathematics, College of Mathematics, Faculty of Science, Beijing University of Technology, Beijing 100124, China.
| | - Xiao Gao
- School of Psychology, Capital Normal University, Beijing 100048, China
| | - Chao Jiang
- Faculty of Psychology, Southwest University, Chongqing 400715, China
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Yang H, Yao X, Zhang H, Meng C, Biswal B. Estimating dynamic individual coactivation patterns based on densely sampled resting-state fMRI data and utilizing it for better subject identification. Brain Struct Funct 2023; 228:1755-1769. [PMID: 37572108 DOI: 10.1007/s00429-023-02689-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/04/2023] [Accepted: 07/16/2023] [Indexed: 08/14/2023]
Abstract
As a complex dynamic system, the brain exhibits spatially organized recurring patterns of activity over time. Coactivation patterns (CAPs), which analyzes data from each single frame, have been utilized to detect transient brain activity states recently. However, previous CAP analyses have been conducted at the group level, which might neglect meaningful individual differences. Here, we estimated individual CAP states at both subject- and scan-level based on a densely sampled dataset: Midnight Scan Club. We used differential identifiability, which measures the gap between intra- and inter-subject similarity, to evaluate individual differences. We found individual CAPs at the subject-level achieved the best fingerprinting ability by maintaining high intra-subject similarity and enlarging inter-subject differences, and brain regions of association networks mainly contributed to the identifiability. On the other hand, scan-level CAP states were unstable across scans for the same participant. Expectedly, we found subject-specific CAPs became more reliable and discriminative with more data (i.e., longer duration). As the acquisition time of each participant is limited in practice, our results recommend a data collection strategy that collects more scans with appropriate duration (e.g., 12 ~ 15 min/scan) to obtain more reliable subject-specific CAPs, when total acquisition time is fixed (e.g., 150 min). In summary, this work has constructed reliable subject-specific CAP states with meaningful individual differences, and recommended an appropriate data collection strategy, which can guide subsequent investigations into individualized brain dynamics.
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Affiliation(s)
- Hang Yang
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Laboratory for Neuroinformation, Center for Information in Medicine, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, 611731, China.
- Chinese Institute for Brain Research, Beijing, 102206, China.
| | - Xing Yao
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Laboratory for Neuroinformation, Center for Information in Medicine, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, 611731, China
| | - Hong Zhang
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Laboratory for Neuroinformation, Center for Information in Medicine, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, 611731, China
| | - Chun Meng
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Laboratory for Neuroinformation, Center for Information in Medicine, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, 611731, China
| | - Bharat Biswal
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Laboratory for Neuroinformation, Center for Information in Medicine, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, 611731, China.
- Department of Biomedical Engineering, New Jersey Institute of Technology, University Heights, 607 Fenster Hall, Newark, NJ, 07102, USA.
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5
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Nebe S, Reutter M, Baker DH, Bölte J, Domes G, Gamer M, Gärtner A, Gießing C, Gurr C, Hilger K, Jawinski P, Kulke L, Lischke A, Markett S, Meier M, Merz CJ, Popov T, Puhlmann LMC, Quintana DS, Schäfer T, Schubert AL, Sperl MFJ, Vehlen A, Lonsdorf TB, Feld GB. Enhancing precision in human neuroscience. eLife 2023; 12:e85980. [PMID: 37555830 PMCID: PMC10411974 DOI: 10.7554/elife.85980] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/20/2023] [Accepted: 07/23/2023] [Indexed: 08/10/2023] Open
Abstract
Human neuroscience has always been pushing the boundary of what is measurable. During the last decade, concerns about statistical power and replicability - in science in general, but also specifically in human neuroscience - have fueled an extensive debate. One important insight from this discourse is the need for larger samples, which naturally increases statistical power. An alternative is to increase the precision of measurements, which is the focus of this review. This option is often overlooked, even though statistical power benefits from increasing precision as much as from increasing sample size. Nonetheless, precision has always been at the heart of good scientific practice in human neuroscience, with researchers relying on lab traditions or rules of thumb to ensure sufficient precision for their studies. In this review, we encourage a more systematic approach to precision. We start by introducing measurement precision and its importance for well-powered studies in human neuroscience. Then, determinants for precision in a range of neuroscientific methods (MRI, M/EEG, EDA, Eye-Tracking, and Endocrinology) are elaborated. We end by discussing how a more systematic evaluation of precision and the application of respective insights can lead to an increase in reproducibility in human neuroscience.
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Affiliation(s)
- Stephan Nebe
- Zurich Center for Neuroeconomics, Department of Economics, University of ZurichZurichSwitzerland
| | - Mario Reutter
- Department of Psychology, Julius-Maximilians-UniversityWürzburgGermany
| | - Daniel H Baker
- Department of Psychology and York Biomedical Research Institute, University of YorkYorkUnited Kingdom
| | - Jens Bölte
- Institute for Psychology, University of Münster, Otto-Creuzfeldt Center for Cognitive and Behavioral NeuroscienceMünsterGermany
| | - Gregor Domes
- Department of Biological and Clinical Psychology, University of TrierTrierGermany
- Institute for Cognitive and Affective NeuroscienceTrierGermany
| | - Matthias Gamer
- Department of Psychology, Julius-Maximilians-UniversityWürzburgGermany
| | - Anne Gärtner
- Faculty of Psychology, Technische Universität DresdenDresdenGermany
| | - Carsten Gießing
- Biological Psychology, Department of Psychology, School of Medicine and Health Sciences, Carl von Ossietzky University of OldenburgOldenburgGermany
| | - Caroline Gurr
- Department of Child and Adolescent Psychiatry, Psychosomatics and Psychotherapy, University Hospital, Goethe UniversityFrankfurtGermany
- Brain Imaging Center, Goethe UniversityFrankfurtGermany
| | - Kirsten Hilger
- Department of Psychology, Julius-Maximilians-UniversityWürzburgGermany
- Department of Psychology, Psychological Diagnostics and Intervention, Catholic University of Eichstätt-IngolstadtEichstättGermany
| | - Philippe Jawinski
- Department of Psychology, Humboldt-Universität zu BerlinBerlinGermany
| | - Louisa Kulke
- Department of Developmental with Educational Psychology, University of BremenBremenGermany
| | - Alexander Lischke
- Department of Psychology, Medical School HamburgHamburgGermany
- Institute of Clinical Psychology and Psychotherapy, Medical School HamburgHamburgGermany
| | - Sebastian Markett
- Department of Psychology, Humboldt-Universität zu BerlinBerlinGermany
| | - Maria Meier
- Department of Psychology, University of KonstanzKonstanzGermany
- University Psychiatric Hospitals, Child and Adolescent Psychiatric Research Department (UPKKJ), University of BaselBaselSwitzerland
| | - Christian J Merz
- Department of Cognitive Psychology, Institute of Cognitive Neuroscience, Faculty of Psychology, Ruhr University BochumBochumGermany
| | - Tzvetan Popov
- Department of Psychology, Methods of Plasticity Research, University of ZurichZurichSwitzerland
| | - Lara MC Puhlmann
- Leibniz Institute for Resilience ResearchMainzGermany
- Max Planck Institute for Human Cognitive and Brain SciencesLeipzigGermany
| | - Daniel S Quintana
- Max Planck Institute for Human Cognitive and Brain SciencesLeipzigGermany
- NevSom, Department of Rare Disorders & Disabilities, Oslo University HospitalOsloNorway
- KG Jebsen Centre for Neurodevelopmental Disorders, University of OsloOsloNorway
- Norwegian Centre for Mental Disorders Research (NORMENT), University of OsloOsloNorway
| | - Tim Schäfer
- Department of Child and Adolescent Psychiatry, Psychosomatics and Psychotherapy, University Hospital, Goethe UniversityFrankfurtGermany
- Brain Imaging Center, Goethe UniversityFrankfurtGermany
| | | | - Matthias FJ Sperl
- Department of Clinical Psychology and Psychotherapy, University of GiessenGiessenGermany
- Center for Mind, Brain and Behavior, Universities of Marburg and GiessenGiessenGermany
| | - Antonia Vehlen
- Department of Biological and Clinical Psychology, University of TrierTrierGermany
| | - Tina B Lonsdorf
- Department of Systems Neuroscience, University Medical Center Hamburg-EppendorfHamburgGermany
- Department of Psychology, Biological Psychology and Cognitive Neuroscience, University of BielefeldBielefeldGermany
| | - Gordon B Feld
- Department of Clinical Psychology, Central Institute of Mental Health, Medical Faculty Mannheim, Heidelberg UniversityMannheimGermany
- Department of Psychology, Heidelberg UniversityHeidelbergGermany
- Department of Addiction Behavior and Addiction Medicine, Central Institute of Mental Health, Medical Faculty Mannheim, Heidelberg UniversityMannheimGermany
- Department of Psychiatry and Psychotherapy, Central Institute of Mental Health, Medical Faculty Mannheim, Heidelberg UniversityMannheimGermany
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