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Wu J, Yang J, Yuan Z, Zhang J, Zhang Z, Qin T, Li X, Deng H, Gong L. Functional connectome gradient predicts clinical symptoms of chronic insomnia disorder. Prog Neuropsychopharmacol Biol Psychiatry 2024; 135:111120. [PMID: 39154930 DOI: 10.1016/j.pnpbp.2024.111120] [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: 05/29/2024] [Revised: 07/30/2024] [Accepted: 08/15/2024] [Indexed: 08/20/2024]
Abstract
Insomnia is the second most prevalent psychiatric disorder worldwide, but the understanding of the pathophysiology of insomnia remains fragmented. In this study, we calculated the connectome gradient in 50 chronic insomnia disorder (CID) patients and 38 healthy controls (HC) to assess changes due to insomnia and utilized these gradients in a connectome-based predictive modeling (CPM) to predict clinical symptoms associated with insomnia. The results suggested that insomnia led to significant alterations in the functional gradients of some brain areas. Specifically, the gradient scores in the middle frontal gyrus, superior anterior cingulate gyrus, and right nucleus accumbens were significantly higher in the CID patients than in the HC group, whereas the scores in the middle occipital gyrus, right fusiform gyrus, and right postcentral gyrus were significantly lower than in the HC group. Further correlation analysis revealed that the right middle frontal gyrus is positively correlated with the self-rating anxiety scale (r=0.3702). Additionally, the prediction model built with functional gradients could well predict the sleep quality (r=0.5858), anxiety (r=0.6150), and depression (r=0.4022) levels of insomnia patients. This offers an objective depiction of the clinical diagnosis of insomnia, yielding a beneficial impact on the identification of effective biomarkers and the comprehension of insomnia.
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Affiliation(s)
- Jiahui Wu
- College of Nuclear Technology and Automation Engineering, Chengdu University of Technology, Chengdu, China
| | - Jianbo Yang
- Sichuan University of Science and Engineering, Zigong, China
| | - Zhen Yuan
- Bioimaging Core, Faculty of Health Sciences, University of Macau, Macau, SAR China
| | - Jiang Zhang
- College of Electrical Engineering, Sichuan University, Chengdu, China.
| | - Zhiwei Zhang
- College of Electrical Engineering, Sichuan University, Chengdu, China
| | - Tianwei Qin
- College of Electrical Engineering, Sichuan University, Chengdu, China
| | - Xiaoxuan Li
- College of Electrical Engineering, Sichuan University, Chengdu, China
| | - Hanbin Deng
- Sichuan Institute of Computer Sciences, Chengdu, China.
| | - Liang Gong
- Department of Neurology, Chengdu Second People's Hospital, Chengdu, China.
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2
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Elsayed M, Owens MM, Balodis I, MacKillop J. Empirical examination of working memory performance and its neural correlates in relation to delay discounting in two large samples. Behav Brain Res 2024; 475:115217. [PMID: 39181217 DOI: 10.1016/j.bbr.2024.115217] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/29/2024] [Revised: 08/16/2024] [Accepted: 08/21/2024] [Indexed: 08/27/2024]
Abstract
The neurobiological basis of working memory and delay discounting are theorized to overlap, but few studies have empirically examined these relations in large samples. To address this, we investigated the association of neural activation during an fMRI N-Back working memory task with delay discounting area, as well as in- and out-of-scanner working memory measures. These analyses were conducted in two large task fMRI datasets, the Human Connectome Project and the Adolescent Brain Cognitive Development Study. Although in- and out-of-scanner working memory performance were significantly associated with N-back task brain activation regions, contrary to our hypotheses, there were no significant associations between working memory task activation and delay discounting scores. These findings call into question the extent of the neural overlap in delay discounting and working memory and highlight the need for more investigations directly interrogating overlapping and distinct brain regions across cognitive neuroscience tasks.
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Affiliation(s)
- Mahmoud Elsayed
- Department of Psychiatry and Behavioural Neurosciences, Faculty of Science, McMaster University, 1280 Main Street West, Hamilton, ON L8S 4K1, Canada; Peter Boris Centre for Addictions Research, St. Joseph's Healthcare Hamilton/McMaster University, 100 West 5th Street, Hamilton, ON L8P 3R2, Canada.
| | - Max M Owens
- Department of Psychiatry and Behavioural Neurosciences, Faculty of Science, McMaster University, 1280 Main Street West, Hamilton, ON L8S 4K1, Canada; Peter Boris Centre for Addictions Research, St. Joseph's Healthcare Hamilton/McMaster University, 100 West 5th Street, Hamilton, ON L8P 3R2, Canada
| | - Iris Balodis
- Department of Psychiatry and Behavioural Neurosciences, Faculty of Science, McMaster University, 1280 Main Street West, Hamilton, ON L8S 4K1, Canada; Peter Boris Centre for Addictions Research, St. Joseph's Healthcare Hamilton/McMaster University, 100 West 5th Street, Hamilton, ON L8P 3R2, Canada
| | - James MacKillop
- Department of Psychiatry and Behavioural Neurosciences, Faculty of Science, McMaster University, 1280 Main Street West, Hamilton, ON L8S 4K1, Canada; Peter Boris Centre for Addictions Research, St. Joseph's Healthcare Hamilton/McMaster University, 100 West 5th Street, Hamilton, ON L8P 3R2, Canada
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3
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Loosen AM, Seow TXF, Hauser TU. Consistency within change: Evaluating the psychometric properties of a widely used predictive-inference task. Behav Res Methods 2024; 56:7410-7426. [PMID: 38844601 PMCID: PMC11362202 DOI: 10.3758/s13428-024-02427-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] [Accepted: 04/12/2024] [Indexed: 08/30/2024]
Abstract
Rapid adaptation to sudden changes in the environment is a hallmark of flexible human behaviour. Many computational, neuroimaging, and even clinical investigations studying this cognitive process have relied on a behavioural paradigm known as the predictive-inference task. However, the psychometric quality of this task has never been examined, leaving unanswered whether it is indeed suited to capture behavioural variation on a within- and between-subject level. Using a large-scale test-retest design (T1: N = 330; T2: N = 219), we assessed the internal (internal consistency) and temporal (test-retest reliability) stability of the task's most used measures. We show that the main measures capturing flexible belief and behavioural adaptation yield good internal consistency and overall satisfying test-retest reliability. However, some more complex markers of flexible behaviour show lower psychometric quality. Our findings have implications for the large corpus of previous studies using this task and provide clear guidance as to which measures should and should not be used in future studies.
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Affiliation(s)
- Alisa M Loosen
- Max Planck UCL Centre for Computational Psychiatry and Ageing Research, London, UK.
- Wellcome Centre for Human Neuroimaging, University College London, University College London, London, UK.
- Center for Computational Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY, USA.
| | - Tricia X F Seow
- Max Planck UCL Centre for Computational Psychiatry and Ageing Research, London, UK
- Wellcome Centre for Human Neuroimaging, University College London, University College London, London, UK
| | - Tobias U Hauser
- Max Planck UCL Centre for Computational Psychiatry and Ageing Research, London, UK
- Wellcome Centre for Human Neuroimaging, University College London, University College London, London, UK
- Department of Psychiatry and Psychotherapy, Medical School and University Hospital, Eberhard Karls University of Tübingen, Tübingen, Germany
- German Center for Mental Health (DZPG), Tübingen, Germany
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4
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Yoshmaru D, Tsurugizawa T, Hata J, Muta K, Marusaki T, Hayashi N, Shibukawa S, Hagiya K, Okano H, Okano HJ. Similarity and characterization of structural and functional neural connections within species under isoflurane anesthesia in the common marmoset. Neuroimage 2024:120854. [PMID: 39278381 DOI: 10.1016/j.neuroimage.2024.120854] [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: 05/17/2024] [Revised: 08/19/2024] [Accepted: 09/13/2024] [Indexed: 09/18/2024] Open
Abstract
The common marmoset is an essential model for understanding social cognition and neurodegenerative diseases. This study explored the structural and functional brain connectivity in a marmoset under isoflurane anesthesia, aiming to statistically overcome the effects of high inter-individual variability and noise-related confounds such as physiological noise, ensuring robust and reliable data. Similarities and differences in individual subject data, including assessments of functional and structural brain connectivities derived from resting-state functional MRI and diffusion tensor imaging were meticulously captured. The findings highlighted the high consistency of structural neural connections within the species, indicating a stable neural architecture, while functional connectivity under anesthesia displayed considerable variability. Through independent component and dual regression analyses, several distinct brain connectivities were identified, elucidating their characteristics under anesthesia. Insights into the structural and functional features of the marmoset brain from this study affirm its value as a neuroscience research model, promising advancements in the field through fundamental and translational studies.
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Affiliation(s)
- Daisuke Yoshmaru
- Division of Regenerative Medicine, The Jikei University School of Medicine, Tokyo, Japan; Laboratory for Marmoset Neural Architecture, RIKEN Center for Brain Science, Saitama, Japan; National Institute of Advanced Industrial Science and Technology (AIST), Tsukuba, Japan; Faculty of Engineering, University of Tsukuba, Tsukuba, Ibaraki, Japan; Department of Radiology, Tokyo Medical University, Tokyo, Japan; Department of Physiology, Keio University School of Medicine, Tokyo, Japan
| | - Tomokazu Tsurugizawa
- Division of Regenerative Medicine, The Jikei University School of Medicine, Tokyo, Japan; National Institute of Advanced Industrial Science and Technology (AIST), Tsukuba, Japan; Faculty of Engineering, University of Tsukuba, Tsukuba, Ibaraki, Japan
| | - Junichi Hata
- Division of Regenerative Medicine, The Jikei University School of Medicine, Tokyo, Japan; Laboratory for Marmoset Neural Architecture, RIKEN Center for Brain Science, Saitama, Japan; Graduate School of Human Health Sciences, Tokyo Metropolitan University, Tokyo, Japan; Department of Physiology, Keio University School of Medicine, Tokyo, Japan
| | - Kanako Muta
- Laboratory for Marmoset Neural Architecture, RIKEN Center for Brain Science, Saitama, Japan; Graduate School of Human Health Sciences, Tokyo Metropolitan University, Tokyo, Japan
| | - Takuto Marusaki
- Graduate School of Human Health Sciences, Tokyo Metropolitan University, Tokyo, Japan
| | - Naoya Hayashi
- Laboratory for Marmoset Neural Architecture, RIKEN Center for Brain Science, Saitama, Japan; Graduate School of Human Health Sciences, Tokyo Metropolitan University, Tokyo, Japan; Department of Radiology, Tokyo Medical University, Tokyo, Japan
| | - Shuhei Shibukawa
- Department of Radiology, Tokyo Medical University, Tokyo, Japan; Faculty of Health Science, Department of Radiological Technology, Juntendo University, Tokyo, Japan
| | - Kei Hagiya
- Laboratory for Marmoset Neural Architecture, RIKEN Center for Brain Science, Saitama, Japan
| | - Hideyuki Okano
- Laboratory for Marmoset Neural Architecture, RIKEN Center for Brain Science, Saitama, Japan; Department of Physiology, Keio University School of Medicine, Tokyo, Japan.
| | - Hirotaka James Okano
- Division of Regenerative Medicine, The Jikei University School of Medicine, Tokyo, Japan; Laboratory for Marmoset Neural Architecture, RIKEN Center for Brain Science, Saitama, Japan.
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5
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Ekhtiari H, Zare-Bidoky M, Sangchooli A, Valyan A, Abi-Dargham A, Cannon DM, Carter CS, Garavan H, George TP, Ghobadi-Azbari P, Juchem C, Krystal JH, Nichols TE, Öngür D, Pernet CR, Poldrack RA, Thompson PM, Paulus MP. Reporting checklists in neuroimaging: promoting transparency, replicability, and reproducibility. Neuropsychopharmacology 2024:10.1038/s41386-024-01973-5. [PMID: 39242922 DOI: 10.1038/s41386-024-01973-5] [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: 04/15/2024] [Revised: 08/08/2024] [Accepted: 08/12/2024] [Indexed: 09/09/2024]
Abstract
Neuroimaging plays a crucial role in understanding brain structure and function, but the lack of transparency, reproducibility, and reliability of findings is a significant obstacle for the field. To address these challenges, there are ongoing efforts to develop reporting checklists for neuroimaging studies to improve the reporting of fundamental aspects of study design and execution. In this review, we first define what we mean by a neuroimaging reporting checklist and then discuss how a reporting checklist can be developed and implemented. We consider the core values that should inform checklist design, including transparency, repeatability, data sharing, diversity, and supporting innovations. We then share experiences with currently available neuroimaging checklists. We review the motivation for creating checklists and whether checklists achieve their intended objectives, before proposing a development cycle for neuroimaging reporting checklists and describing each implementation step. We emphasize the importance of reporting checklists in enhancing the quality of data repositories and consortia, how they can support education and best practices, and how emerging computational methods, like artificial intelligence, can help checklist development and adherence. We also highlight the role that funding agencies and global collaborations can play in supporting the adoption of neuroimaging reporting checklists. We hope this review will encourage better adherence to available checklists and promote the development of new ones, and ultimately increase the quality, transparency, and reproducibility of neuroimaging research.
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Affiliation(s)
- Hamed Ekhtiari
- Department of Psychiatry, University of Minnesota, Minneapolis, MN, USA.
- Laureate Institute for Brain Research, Tulsa, OK, USA.
| | - Mehran Zare-Bidoky
- Iranian National Center for Addiction Studies, Tehran University of Medical Sciences, Tehran, Iran
| | - Arshiya Sangchooli
- Melbourne School of Psychological Sciences, University of Melbourne, Melbourne, VIC, Australia
| | - Alireza Valyan
- Iranian National Center for Addiction Studies, Tehran University of Medical Sciences, Tehran, Iran
| | - Anissa Abi-Dargham
- Department of Psychiatry and Behavioral Health, Stony Brook University Renaissance School of Medicine, Stony Brook, NY, USA
- Department of Psychiatry, Columbia University Vagelos School of Medicine and New York State Psychiatric Institute, New York, NY, USA
| | - Dara M Cannon
- Clinical Neuroimaging Laboratory, Center for Neuroimaging, Cognition & Genomics, College of Medicine, Nursing & Health Sciences, University of Galway, Galway, Ireland
| | - Cameron S Carter
- Department of Psychiatry and Human Behavior, University of California at Irvine, Irvine, CA, USA
| | - Hugh Garavan
- Departments of Psychiatry and Psychology, University of Vermont, Burlington, VT, USA
| | - Tony P George
- Institute for Mental Health Policy and Research at CAMH, Toronto, ON, Canada
- Department of Psychiatry, Institute of Medical Sciences, University of Toronto, Toronto, ON, Canada
| | - Peyman Ghobadi-Azbari
- Iranian National Center for Addiction Studies, Tehran University of Medical Sciences, Tehran, Iran
| | - Christoph Juchem
- Department of Biomedical Engineering, Columbia University Fu Foundation, School of Engineering and Applied Science, New York, NY, USA
- Department of Radiology, Columbia University College of Physicians and Surgeons, New York, NY, USA
| | - John H Krystal
- Department of Psychiatry, Yale School of Medicine, New Haven, CT, USA
- U.S. Department of Veterans Affairs National Center for Posttraumatic Stress Disorder, Clinical Neurosciences Division, VA Connecticut Healthcare System, West Haven, CT, USA
| | - Thomas E Nichols
- Nuffield Department of Population Health, Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, University of Oxford, Oxford, UK
| | - Dost Öngür
- McLean Hospital, Belmont, MA, USA
- Department of Psychiatry, Harvard Medical School, Boston, MA, USA
| | - Cyril R Pernet
- Neurobiology Research Unit, Copenhagen University Hospital, Rigshospitalet, Copenhagen, Denmark
| | | | - Paul M Thompson
- Imaging Genetics Center, Mark & Mary Stevens Neuroimaging & Informatics Institute, Keck School of Medicine, University of Southern California, Marina del Rey, CA, USA
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6
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Ekhtiari H, Sangchooli A, Carmichael O, Moeller FG, O'Donnell P, Oquendo M, Paulus MP, Pizzagalli DA, Ramey T, Schacht J, Zare-Bidoky M, Childress AR, Brady K. Neuroimaging Biomarkers in Addiction. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2024:2024.09.02.24312084. [PMID: 39281741 PMCID: PMC11398440 DOI: 10.1101/2024.09.02.24312084] [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: 09/18/2024]
Abstract
As a neurobiological process, addiction involves pathological patterns of engagement with substances and a range of behaviors with a chronic and relapsing course. Neuroimaging technologies assess brain activity, structure, physiology, and metabolism at scales ranging from neurotransmitter receptors to large-scale brain networks, providing unique windows into the core neural processes implicated in substance use disorders. Identified aberrations in the neural substrates of reward and salience processing, response inhibition, interoception, and executive functions with neuroimaging can inform the development of pharmacological, neuromodulatory, and psychotherapeutic interventions to modulate the disordered neurobiology. Based on our systematic search, 409 protocols registered on ClinicalTrials.gov include the use of one or more neuroimaging paradigms as an outcome measure in addiction, with the majority (N=268) employing functional magnetic resonance imaging (fMRI), followed by positron emission tomography (PET) (N=71), electroencephalography (EEG) (N=50), structural magnetic resonance imaging (MRI) (N=35) and magnetic resonance spectroscopy (MRS) (N=35). Furthermore, in a PubMed systematic review, we identified 61 meta-analyses including 30 fMRI, 22 structural MRI, 8 EEG, 7 PET, and 3 MRS meta-analyses suggesting potential biomarkers in addictions. These studies can facilitate the development of a range of biomarkers that may prove useful in the arsenal of addiction treatments in the coming years. There is evidence that these markers of large-scale brain structure and activity may indicate vulnerability or separate disease subtypes, predict response to treatment, or provide objective measures of treatment response or recovery. Neuroimaging biomarkers can also suggest novel targets for interventions. Closed or open loop interventions can integrate these biomarkers with neuromodulation in real-time or offline to personalize stimulation parameters and deliver the precise intervention. This review provides an overview of neuroimaging modalities in addiction, potential neuroimaging biomarkers, and their physiologic and clinical relevance. Future directions and challenges in bringing these putative biomarkers from the bench to the bedside are also discussed.
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Affiliation(s)
- Hamed Ekhtiari
- Department of Psychiatry, University of Minnesota Medical School, Minneapolis, MN, USA (Ekhtiari); Laureate Institute for Brain Research (LIBR), Tulsa, OK, USA (Ekhtiari, Paulus); School of Psychological Sciences, Faculty of Medicine, Dentistry and Health Sciences, University of Melbourne, Melbourne, Australia (Sangchooli); Pennington Biomedical Research Center, Baton Rouge, Louisiana, USA (Carmichael); Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA (Oquendo, Childress); Institute for Drug and Alcohol Studies, Virginia Commonwealth University, Richmond, VA, USA (Moeller); Translational Medicine, Sage Therapeutics, Cambridge, MA, USA and McLean Hospital, Department of Psychiatry, Harvard Medical School, Belmont, MA, USA (O'Donnell); Division of Depression and Anxiety, McLean Hospital, Belmont, MA, USA (Pizzaggali); National Institute on Drug Abuse, Bethesda, MD, USA (Ramey); Department of Psychiatry, University of Colorado Anschutz Medical Campus, Aurora, CO, USA (Schacht); Iranian National Center for Addiction Studies (INCAS), Tehran University of Medical Sciences, Tehran, Iran (Zare-Bidoky); Department of Psychiatry and Behavioral Sciences, Medical University of South Carolina, Charleston, SC, USA (Brady)
| | - Arshiya Sangchooli
- Department of Psychiatry, University of Minnesota Medical School, Minneapolis, MN, USA (Ekhtiari); Laureate Institute for Brain Research (LIBR), Tulsa, OK, USA (Ekhtiari, Paulus); School of Psychological Sciences, Faculty of Medicine, Dentistry and Health Sciences, University of Melbourne, Melbourne, Australia (Sangchooli); Pennington Biomedical Research Center, Baton Rouge, Louisiana, USA (Carmichael); Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA (Oquendo, Childress); Institute for Drug and Alcohol Studies, Virginia Commonwealth University, Richmond, VA, USA (Moeller); Translational Medicine, Sage Therapeutics, Cambridge, MA, USA and McLean Hospital, Department of Psychiatry, Harvard Medical School, Belmont, MA, USA (O'Donnell); Division of Depression and Anxiety, McLean Hospital, Belmont, MA, USA (Pizzaggali); National Institute on Drug Abuse, Bethesda, MD, USA (Ramey); Department of Psychiatry, University of Colorado Anschutz Medical Campus, Aurora, CO, USA (Schacht); Iranian National Center for Addiction Studies (INCAS), Tehran University of Medical Sciences, Tehran, Iran (Zare-Bidoky); Department of Psychiatry and Behavioral Sciences, Medical University of South Carolina, Charleston, SC, USA (Brady)
| | - Owen Carmichael
- Department of Psychiatry, University of Minnesota Medical School, Minneapolis, MN, USA (Ekhtiari); Laureate Institute for Brain Research (LIBR), Tulsa, OK, USA (Ekhtiari, Paulus); School of Psychological Sciences, Faculty of Medicine, Dentistry and Health Sciences, University of Melbourne, Melbourne, Australia (Sangchooli); Pennington Biomedical Research Center, Baton Rouge, Louisiana, USA (Carmichael); Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA (Oquendo, Childress); Institute for Drug and Alcohol Studies, Virginia Commonwealth University, Richmond, VA, USA (Moeller); Translational Medicine, Sage Therapeutics, Cambridge, MA, USA and McLean Hospital, Department of Psychiatry, Harvard Medical School, Belmont, MA, USA (O'Donnell); Division of Depression and Anxiety, McLean Hospital, Belmont, MA, USA (Pizzaggali); National Institute on Drug Abuse, Bethesda, MD, USA (Ramey); Department of Psychiatry, University of Colorado Anschutz Medical Campus, Aurora, CO, USA (Schacht); Iranian National Center for Addiction Studies (INCAS), Tehran University of Medical Sciences, Tehran, Iran (Zare-Bidoky); Department of Psychiatry and Behavioral Sciences, Medical University of South Carolina, Charleston, SC, USA (Brady)
| | - F Gerard Moeller
- Department of Psychiatry, University of Minnesota Medical School, Minneapolis, MN, USA (Ekhtiari); Laureate Institute for Brain Research (LIBR), Tulsa, OK, USA (Ekhtiari, Paulus); School of Psychological Sciences, Faculty of Medicine, Dentistry and Health Sciences, University of Melbourne, Melbourne, Australia (Sangchooli); Pennington Biomedical Research Center, Baton Rouge, Louisiana, USA (Carmichael); Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA (Oquendo, Childress); Institute for Drug and Alcohol Studies, Virginia Commonwealth University, Richmond, VA, USA (Moeller); Translational Medicine, Sage Therapeutics, Cambridge, MA, USA and McLean Hospital, Department of Psychiatry, Harvard Medical School, Belmont, MA, USA (O'Donnell); Division of Depression and Anxiety, McLean Hospital, Belmont, MA, USA (Pizzaggali); National Institute on Drug Abuse, Bethesda, MD, USA (Ramey); Department of Psychiatry, University of Colorado Anschutz Medical Campus, Aurora, CO, USA (Schacht); Iranian National Center for Addiction Studies (INCAS), Tehran University of Medical Sciences, Tehran, Iran (Zare-Bidoky); Department of Psychiatry and Behavioral Sciences, Medical University of South Carolina, Charleston, SC, USA (Brady)
| | - Patricio O'Donnell
- Department of Psychiatry, University of Minnesota Medical School, Minneapolis, MN, USA (Ekhtiari); Laureate Institute for Brain Research (LIBR), Tulsa, OK, USA (Ekhtiari, Paulus); School of Psychological Sciences, Faculty of Medicine, Dentistry and Health Sciences, University of Melbourne, Melbourne, Australia (Sangchooli); Pennington Biomedical Research Center, Baton Rouge, Louisiana, USA (Carmichael); Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA (Oquendo, Childress); Institute for Drug and Alcohol Studies, Virginia Commonwealth University, Richmond, VA, USA (Moeller); Translational Medicine, Sage Therapeutics, Cambridge, MA, USA and McLean Hospital, Department of Psychiatry, Harvard Medical School, Belmont, MA, USA (O'Donnell); Division of Depression and Anxiety, McLean Hospital, Belmont, MA, USA (Pizzaggali); National Institute on Drug Abuse, Bethesda, MD, USA (Ramey); Department of Psychiatry, University of Colorado Anschutz Medical Campus, Aurora, CO, USA (Schacht); Iranian National Center for Addiction Studies (INCAS), Tehran University of Medical Sciences, Tehran, Iran (Zare-Bidoky); Department of Psychiatry and Behavioral Sciences, Medical University of South Carolina, Charleston, SC, USA (Brady)
| | - Maria Oquendo
- Department of Psychiatry, University of Minnesota Medical School, Minneapolis, MN, USA (Ekhtiari); Laureate Institute for Brain Research (LIBR), Tulsa, OK, USA (Ekhtiari, Paulus); School of Psychological Sciences, Faculty of Medicine, Dentistry and Health Sciences, University of Melbourne, Melbourne, Australia (Sangchooli); Pennington Biomedical Research Center, Baton Rouge, Louisiana, USA (Carmichael); Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA (Oquendo, Childress); Institute for Drug and Alcohol Studies, Virginia Commonwealth University, Richmond, VA, USA (Moeller); Translational Medicine, Sage Therapeutics, Cambridge, MA, USA and McLean Hospital, Department of Psychiatry, Harvard Medical School, Belmont, MA, USA (O'Donnell); Division of Depression and Anxiety, McLean Hospital, Belmont, MA, USA (Pizzaggali); National Institute on Drug Abuse, Bethesda, MD, USA (Ramey); Department of Psychiatry, University of Colorado Anschutz Medical Campus, Aurora, CO, USA (Schacht); Iranian National Center for Addiction Studies (INCAS), Tehran University of Medical Sciences, Tehran, Iran (Zare-Bidoky); Department of Psychiatry and Behavioral Sciences, Medical University of South Carolina, Charleston, SC, USA (Brady)
| | - Martin P Paulus
- Department of Psychiatry, University of Minnesota Medical School, Minneapolis, MN, USA (Ekhtiari); Laureate Institute for Brain Research (LIBR), Tulsa, OK, USA (Ekhtiari, Paulus); School of Psychological Sciences, Faculty of Medicine, Dentistry and Health Sciences, University of Melbourne, Melbourne, Australia (Sangchooli); Pennington Biomedical Research Center, Baton Rouge, Louisiana, USA (Carmichael); Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA (Oquendo, Childress); Institute for Drug and Alcohol Studies, Virginia Commonwealth University, Richmond, VA, USA (Moeller); Translational Medicine, Sage Therapeutics, Cambridge, MA, USA and McLean Hospital, Department of Psychiatry, Harvard Medical School, Belmont, MA, USA (O'Donnell); Division of Depression and Anxiety, McLean Hospital, Belmont, MA, USA (Pizzaggali); National Institute on Drug Abuse, Bethesda, MD, USA (Ramey); Department of Psychiatry, University of Colorado Anschutz Medical Campus, Aurora, CO, USA (Schacht); Iranian National Center for Addiction Studies (INCAS), Tehran University of Medical Sciences, Tehran, Iran (Zare-Bidoky); Department of Psychiatry and Behavioral Sciences, Medical University of South Carolina, Charleston, SC, USA (Brady)
| | - Diego A Pizzagalli
- Department of Psychiatry, University of Minnesota Medical School, Minneapolis, MN, USA (Ekhtiari); Laureate Institute for Brain Research (LIBR), Tulsa, OK, USA (Ekhtiari, Paulus); School of Psychological Sciences, Faculty of Medicine, Dentistry and Health Sciences, University of Melbourne, Melbourne, Australia (Sangchooli); Pennington Biomedical Research Center, Baton Rouge, Louisiana, USA (Carmichael); Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA (Oquendo, Childress); Institute for Drug and Alcohol Studies, Virginia Commonwealth University, Richmond, VA, USA (Moeller); Translational Medicine, Sage Therapeutics, Cambridge, MA, USA and McLean Hospital, Department of Psychiatry, Harvard Medical School, Belmont, MA, USA (O'Donnell); Division of Depression and Anxiety, McLean Hospital, Belmont, MA, USA (Pizzaggali); National Institute on Drug Abuse, Bethesda, MD, USA (Ramey); Department of Psychiatry, University of Colorado Anschutz Medical Campus, Aurora, CO, USA (Schacht); Iranian National Center for Addiction Studies (INCAS), Tehran University of Medical Sciences, Tehran, Iran (Zare-Bidoky); Department of Psychiatry and Behavioral Sciences, Medical University of South Carolina, Charleston, SC, USA (Brady)
| | - Tatiana Ramey
- Department of Psychiatry, University of Minnesota Medical School, Minneapolis, MN, USA (Ekhtiari); Laureate Institute for Brain Research (LIBR), Tulsa, OK, USA (Ekhtiari, Paulus); School of Psychological Sciences, Faculty of Medicine, Dentistry and Health Sciences, University of Melbourne, Melbourne, Australia (Sangchooli); Pennington Biomedical Research Center, Baton Rouge, Louisiana, USA (Carmichael); Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA (Oquendo, Childress); Institute for Drug and Alcohol Studies, Virginia Commonwealth University, Richmond, VA, USA (Moeller); Translational Medicine, Sage Therapeutics, Cambridge, MA, USA and McLean Hospital, Department of Psychiatry, Harvard Medical School, Belmont, MA, USA (O'Donnell); Division of Depression and Anxiety, McLean Hospital, Belmont, MA, USA (Pizzaggali); National Institute on Drug Abuse, Bethesda, MD, USA (Ramey); Department of Psychiatry, University of Colorado Anschutz Medical Campus, Aurora, CO, USA (Schacht); Iranian National Center for Addiction Studies (INCAS), Tehran University of Medical Sciences, Tehran, Iran (Zare-Bidoky); Department of Psychiatry and Behavioral Sciences, Medical University of South Carolina, Charleston, SC, USA (Brady)
| | - Joseph Schacht
- Department of Psychiatry, University of Minnesota Medical School, Minneapolis, MN, USA (Ekhtiari); Laureate Institute for Brain Research (LIBR), Tulsa, OK, USA (Ekhtiari, Paulus); School of Psychological Sciences, Faculty of Medicine, Dentistry and Health Sciences, University of Melbourne, Melbourne, Australia (Sangchooli); Pennington Biomedical Research Center, Baton Rouge, Louisiana, USA (Carmichael); Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA (Oquendo, Childress); Institute for Drug and Alcohol Studies, Virginia Commonwealth University, Richmond, VA, USA (Moeller); Translational Medicine, Sage Therapeutics, Cambridge, MA, USA and McLean Hospital, Department of Psychiatry, Harvard Medical School, Belmont, MA, USA (O'Donnell); Division of Depression and Anxiety, McLean Hospital, Belmont, MA, USA (Pizzaggali); National Institute on Drug Abuse, Bethesda, MD, USA (Ramey); Department of Psychiatry, University of Colorado Anschutz Medical Campus, Aurora, CO, USA (Schacht); Iranian National Center for Addiction Studies (INCAS), Tehran University of Medical Sciences, Tehran, Iran (Zare-Bidoky); Department of Psychiatry and Behavioral Sciences, Medical University of South Carolina, Charleston, SC, USA (Brady)
| | - Mehran Zare-Bidoky
- Department of Psychiatry, University of Minnesota Medical School, Minneapolis, MN, USA (Ekhtiari); Laureate Institute for Brain Research (LIBR), Tulsa, OK, USA (Ekhtiari, Paulus); School of Psychological Sciences, Faculty of Medicine, Dentistry and Health Sciences, University of Melbourne, Melbourne, Australia (Sangchooli); Pennington Biomedical Research Center, Baton Rouge, Louisiana, USA (Carmichael); Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA (Oquendo, Childress); Institute for Drug and Alcohol Studies, Virginia Commonwealth University, Richmond, VA, USA (Moeller); Translational Medicine, Sage Therapeutics, Cambridge, MA, USA and McLean Hospital, Department of Psychiatry, Harvard Medical School, Belmont, MA, USA (O'Donnell); Division of Depression and Anxiety, McLean Hospital, Belmont, MA, USA (Pizzaggali); National Institute on Drug Abuse, Bethesda, MD, USA (Ramey); Department of Psychiatry, University of Colorado Anschutz Medical Campus, Aurora, CO, USA (Schacht); Iranian National Center for Addiction Studies (INCAS), Tehran University of Medical Sciences, Tehran, Iran (Zare-Bidoky); Department of Psychiatry and Behavioral Sciences, Medical University of South Carolina, Charleston, SC, USA (Brady)
| | - Anna Rose Childress
- Department of Psychiatry, University of Minnesota Medical School, Minneapolis, MN, USA (Ekhtiari); Laureate Institute for Brain Research (LIBR), Tulsa, OK, USA (Ekhtiari, Paulus); School of Psychological Sciences, Faculty of Medicine, Dentistry and Health Sciences, University of Melbourne, Melbourne, Australia (Sangchooli); Pennington Biomedical Research Center, Baton Rouge, Louisiana, USA (Carmichael); Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA (Oquendo, Childress); Institute for Drug and Alcohol Studies, Virginia Commonwealth University, Richmond, VA, USA (Moeller); Translational Medicine, Sage Therapeutics, Cambridge, MA, USA and McLean Hospital, Department of Psychiatry, Harvard Medical School, Belmont, MA, USA (O'Donnell); Division of Depression and Anxiety, McLean Hospital, Belmont, MA, USA (Pizzaggali); National Institute on Drug Abuse, Bethesda, MD, USA (Ramey); Department of Psychiatry, University of Colorado Anschutz Medical Campus, Aurora, CO, USA (Schacht); Iranian National Center for Addiction Studies (INCAS), Tehran University of Medical Sciences, Tehran, Iran (Zare-Bidoky); Department of Psychiatry and Behavioral Sciences, Medical University of South Carolina, Charleston, SC, USA (Brady)
| | - Kathleen Brady
- Department of Psychiatry, University of Minnesota Medical School, Minneapolis, MN, USA (Ekhtiari); Laureate Institute for Brain Research (LIBR), Tulsa, OK, USA (Ekhtiari, Paulus); School of Psychological Sciences, Faculty of Medicine, Dentistry and Health Sciences, University of Melbourne, Melbourne, Australia (Sangchooli); Pennington Biomedical Research Center, Baton Rouge, Louisiana, USA (Carmichael); Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA (Oquendo, Childress); Institute for Drug and Alcohol Studies, Virginia Commonwealth University, Richmond, VA, USA (Moeller); Translational Medicine, Sage Therapeutics, Cambridge, MA, USA and McLean Hospital, Department of Psychiatry, Harvard Medical School, Belmont, MA, USA (O'Donnell); Division of Depression and Anxiety, McLean Hospital, Belmont, MA, USA (Pizzaggali); National Institute on Drug Abuse, Bethesda, MD, USA (Ramey); Department of Psychiatry, University of Colorado Anschutz Medical Campus, Aurora, CO, USA (Schacht); Iranian National Center for Addiction Studies (INCAS), Tehran University of Medical Sciences, Tehran, Iran (Zare-Bidoky); Department of Psychiatry and Behavioral Sciences, Medical University of South Carolina, Charleston, SC, USA (Brady)
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7
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Leve LD, Kanamori M, Humphreys KL, Jaffee SR, Nusslock R, Oro V, Hyde LW. The Promise and Challenges of Integrating Biological and Prevention Sciences: A Community-Engaged Model for the Next Generation of Translational Research. PREVENTION SCIENCE : THE OFFICIAL JOURNAL OF THE SOCIETY FOR PREVENTION RESEARCH 2024:10.1007/s11121-024-01720-8. [PMID: 39225944 DOI: 10.1007/s11121-024-01720-8] [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] [Accepted: 08/11/2024] [Indexed: 09/04/2024]
Abstract
Beginning with the successful sequencing of the human genome two decades ago, the possibility of developing personalized health interventions based on one's biology has captured the imagination of researchers, medical providers, and individuals seeking health care services. However, the application of a personalized medicine approach to emotional and behavioral health has lagged behind the development of personalized approaches for physical health conditions. There is potential value in developing improved methods for integrating biological science with prevention science to identify risk and protective mechanisms that have biological underpinnings, and then applying that knowledge to inform prevention and intervention services for emotional and behavioral health. This report represents the work of a task force appointed by the Board of the Society for Prevention Research to explore challenges and recommendations for the integration of biological and prevention sciences. We present the state of the science and barriers to progress in integrating the two approaches, followed by recommended strategies that would promote the responsible integration of biological and prevention sciences. Recommendations are grounded in Community-Based Participatory Research approaches, with the goal of centering equity in future research aimed at integrating the two disciplines to ultimately improve the well-being of those who have disproportionately experienced or are at risk for experiencing emotional and behavioral problems.
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Affiliation(s)
- Leslie D Leve
- Prevention Science Institute, University of Oregon, Eugene, USA.
- Department of Counseling Psychology and Human Services, University of Oregon, Eugene, USA.
- Cambridge Public Health, University of Cambridge, Cambridge, UK.
| | - Mariano Kanamori
- Department of Public Health Sciences, University of Miami Miller School of Medicine, Miami, USA
| | - Kathryn L Humphreys
- Department of Psychology and Human Development, Vanderbilt University, Nashville, USA
| | - Sara R Jaffee
- Department of Psychology, University of Pennsylvania, Philadelphia, USA
| | - Robin Nusslock
- Department of Psychology & Institute for Policy Research, Northwestern University, Evanston, USA
| | - Veronica Oro
- Prevention Science Institute, University of Oregon, Eugene, USA
| | - Luke W Hyde
- Department of Psychology & Survey Research Center at the Institute for Social Research, University of Michigan, Ann Arbor, USA
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8
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Gradwohl G, Snipes S, Walitza S, Huber R, Gerstenberg M. Timing and cortical region matter: theta power differences between teenagers affected by Major Depression and healthy controls. J Neural Transm (Vienna) 2024; 131:1105-1115. [PMID: 39105815 PMCID: PMC11365826 DOI: 10.1007/s00702-024-02810-1] [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: 01/22/2024] [Accepted: 07/15/2024] [Indexed: 08/07/2024]
Abstract
In adults affected by Major Depressive Disorder (MDD), most findings point to higher electroencephalographic (EEG) theta power during wake compared to healthy controls (HC) as a potential biomarker aiding the diagnostic process or subgrouping for stratified treatment. Besides these group differences, theta power is modulated by time of day, sleep/wake history, and age. Thus, we aimed at assessing if the time of recording alters theta power in teenagers affected by MDD or HC. Standardized wake EEG power was assessed with high-density EEG in 15 children and adolescents with MDD and in 15 age- and sex-matched HC in the evening and morning. Using a two-way ANOVA, group, time, and their interaction were tested. In patients, the current severity of depression was rated using the Children's Depression Rating Scale. Broadband EEG power was lower in the morning after sleep, with a significant interaction (group x time) in central regions in the 4-6 Hz range. In MDD relative to HC, theta power was decreased over occipital areas in the evening and increased over frontal areas in the morning. A higher frontal theta power was correlated with more severe depressive mood in the morning but not in the evening. This was a cross-sectional study design, including patients on antidepressant medication. In conclusion, depending on time of recording, region-specific opposite differences of theta power were found between teenagers with MDD and HC. These findings stress the importance of the time of the recording when investigating theta power's relationship to psychopathology.
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Affiliation(s)
- Gideon Gradwohl
- Lev Academic Center, Department of Computer Sciences, Jerusalem College of Technology, Jerusalem, Israel
| | - Sophia Snipes
- Child Development Center, University Children's Hospital Zurich, University of Zurich, Zurich, Switzerland
- Children's Research Center, University Children's Hospital Zurich, University of Zurich, Zurich, Switzerland
- Neural Control of Movement Lab, Department of Health Sciences and Technology, ETH Zurich, Zurich, Switzerland
| | - Susanne Walitza
- Department of Child and Adolescent Psychiatry and Psychotherapy, Psychiatric University Hospital Zurich, University of Zurich, Zurich, Switzerland
- Neuroscicence Center Zurich, University of Zurich and Swiss Federal Institute of Technology Zurich, Zurich, Switzerland
- Zurich Center for Integrative Human Physiology, University of Zurich, Zurich, Switzerland
| | - Reto Huber
- Child Development Center, University Children's Hospital Zurich, University of Zurich, Zurich, Switzerland
- Children's Research Center, University Children's Hospital Zurich, University of Zurich, Zurich, Switzerland
- Department of Child and Adolescent Psychiatry and Psychotherapy, Psychiatric University Hospital Zurich, University of Zurich, Zurich, Switzerland
- Neuroscicence Center Zurich, University of Zurich and Swiss Federal Institute of Technology Zurich, Zurich, Switzerland
| | - Miriam Gerstenberg
- Children's Research Center, University Children's Hospital Zurich, University of Zurich, Zurich, Switzerland.
- Department of Child and Adolescent Psychiatry and Psychotherapy, Psychiatric University Hospital Zurich, University of Zurich, Zurich, Switzerland.
- Department of Child and Adolescent Psychiatry and Psychotherapy, Outpatient Services Winterthur, Psychiatric University Hospital Zurich, Albanistrasse 24, Winterthur, 8400, Switzerland.
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9
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Vandewouw MM, Ye YJ, Crosbie J, Schachar RJ, Iaboni A, Georgiades S, Nicolson R, Kelley E, Ayub M, Jones J, Arnold PD, Taylor MJ, Lerch JP, Anagnostou E, Kushki A. Dataset factors associated with age-related changes in brain structure and function in neurodevelopmental conditions. Hum Brain Mapp 2024; 45:e26815. [PMID: 39254138 PMCID: PMC11386318 DOI: 10.1002/hbm.26815] [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: 09/07/2023] [Revised: 07/08/2024] [Accepted: 07/29/2024] [Indexed: 09/11/2024] Open
Abstract
With brain structure and function undergoing complex changes throughout childhood and adolescence, age is a critical consideration in neuroimaging studies, particularly for those of individuals with neurodevelopmental conditions. However, despite the increasing use of large, consortium-based datasets to examine brain structure and function in neurotypical and neurodivergent populations, it is unclear whether age-related changes are consistent between datasets and whether inconsistencies related to differences in sample characteristics, such as demographics and phenotypic features, exist. To address this, we built models of age-related changes of brain structure (regional cortical thickness and regional surface area; N = 1218) and function (resting-state functional connectivity strength; N = 1254) in two neurodiverse datasets: the Province of Ontario Neurodevelopmental Network and the Healthy Brain Network. We examined whether deviations from these models differed between the datasets, and explored whether these deviations were associated with demographic and clinical variables. We found significant differences between the two datasets for measures of cortical surface area and functional connectivity strength throughout the brain. For regional measures of cortical surface area, the patterns of differences were associated with race/ethnicity, while for functional connectivity strength, positive associations were observed with head motion. Our findings highlight that patterns of age-related changes in the brain may be influenced by demographic and phenotypic characteristics, and thus future studies should consider these when examining or controlling for age effects in analyses.
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Affiliation(s)
- Marlee M Vandewouw
- Autism Research Centre, Bloorview Research Institute, Holland Bloorview Kids Rehabilitation Hospital, Toronto, Ontario, Canada
- Institute of Biomedical Engineering, University of Toronto, Toronto, Canada
| | - Yifan Julia Ye
- Autism Research Centre, Bloorview Research Institute, Holland Bloorview Kids Rehabilitation Hospital, Toronto, Ontario, Canada
- Division of Engineering Science, University of Toronto, Toronto, Canada
| | - Jennifer Crosbie
- Department of Psychiatry, University of Toronto, Toronto, Canada
- Department of Psychiatry, The Hospital for Sick Children, Toronto, Ontario, Canada
| | - Russell J Schachar
- Department of Psychiatry, University of Toronto, Toronto, Canada
- Department of Psychiatry, The Hospital for Sick Children, Toronto, Ontario, Canada
| | - Alana Iaboni
- Autism Research Centre, Bloorview Research Institute, Holland Bloorview Kids Rehabilitation Hospital, Toronto, Ontario, Canada
| | - Stelios Georgiades
- Department of Psychiatry and Behavioural Neurosciences, McMaster University, Hamilton, Canada
| | - Robert Nicolson
- Department of Psychiatry, Western University, London, Canada
| | - Elizabeth Kelley
- Department of Psychology, Queen's University, Kingston, Canada
- Centre for Neuroscience Studies, Queen's University, Kingston, Canada
- Department of Psychiatry, Queen's University, Kingston, Canada
| | - Muhammad Ayub
- Department of Psychiatry, Queen's University, Kingston, Canada
- Division of Psychiatry, University of College London, London, UK
| | - Jessica Jones
- Department of Psychology, Queen's University, Kingston, Canada
- Centre for Neuroscience Studies, Queen's University, Kingston, Canada
- Department of Psychiatry, Queen's University, Kingston, Canada
| | - Paul D Arnold
- The Mathison Centre for Mental Health Research & Education, Cumming School of Medicine, University of Calgary, Calgary, Canada
| | - Margot J Taylor
- Department of Diagnostic and Interventional Radiology, The Hospital for Sick Children, Toronto, Canada
- Program in Neurosciences and Mental Health, The Hospital for Sick Children, Toronto, Canada
- Department of Psychology, University of Toronto, Toronto, Canada
- Department of Medical Imaging, University of Toronto, Toronto, Canada
| | - Jason P Lerch
- Program in Neurosciences and Mental Health, The Hospital for Sick Children, Toronto, Canada
- Wellcome Centre for Integrative Neuroimaging, FMRIB, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, UK
- Department of Medical Biophysics, University of Toronto, Toronto, Canada
| | - Evdokia Anagnostou
- Autism Research Centre, Bloorview Research Institute, Holland Bloorview Kids Rehabilitation Hospital, Toronto, Ontario, Canada
- Program in Neurosciences and Mental Health, The Hospital for Sick Children, Toronto, Canada
- Institute of Medical Science, University of Toronto, Toronto, Canada
| | - Azadeh Kushki
- Autism Research Centre, Bloorview Research Institute, Holland Bloorview Kids Rehabilitation Hospital, Toronto, Ontario, Canada
- Institute of Biomedical Engineering, University of Toronto, Toronto, Canada
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10
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Beatty CC, Gallardo M, Ferry RA, Feldman J, Levy A, Grieshaber A, Nelson BD. Pathological personality domains and punishment-enhanced error-related negativity. Int J Psychophysiol 2024; 203:112408. [PMID: 39097099 DOI: 10.1016/j.ijpsycho.2024.112408] [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: 05/22/2024] [Revised: 07/26/2024] [Accepted: 07/31/2024] [Indexed: 08/05/2024]
Abstract
The error-related negativity (ERN) is an event-related potential that is observed after the commission of an error and is hypothesized to index threat sensitivity. The ERN is associated with multiple psychiatric disorders, but it is unclear if similar results are due to higher-order dimensions of psychopathology. When errors are punished, the ERN is further enhanced, which might better isolate threat sensitivity. However, few studies have examined whether psychopathology is associated with punishment enhancement of the ERN. In a clinical sample of 170 adults, the present study examined the association between pathological personality domains and predictable vs. unpredictable punishment-enhanced ERN. Results indicated that the ERN was enhanced when errors were punished compared to not punished. Greater negative emotionality was associated with a greater predictable punishment-enhanced ERN, while greater disinhibition was associated with smaller predictable punishment-enhanced ERN. The study suggests that higher-order pathological personality domains demonstrate discriminate relationships with punishment-enhanced error-related brain activity.
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Affiliation(s)
- Clare C Beatty
- Department of Psychology, Stony Brook University, United States of America.
| | - Marcela Gallardo
- Department of Psychology, Stony Brook University, United States of America
| | - Rachel A Ferry
- Department of Psychology, Stony Brook University, United States of America
| | - Jacob Feldman
- Department of Psychology, Stony Brook University, United States of America
| | - Adina Levy
- Department of Psychology, Stony Brook University, United States of America
| | | | - Brady D Nelson
- Department of Psychology, Stony Brook University, United States of America
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11
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Chenot Q, Hamery C, Truninger M, Langer N, De Boissezon X, Scannella S. Investigating the relationship between resting-state EEG microstates and executive functions: A null finding. Cortex 2024; 178:1-17. [PMID: 38954985 DOI: 10.1016/j.cortex.2024.05.019] [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: 12/04/2023] [Revised: 05/21/2024] [Accepted: 05/23/2024] [Indexed: 07/04/2024]
Abstract
Recent advances in cognitive neurosciences suggest that intrinsic brain networks dynamics are associated with cognitive functioning. Despite this emerging perspective, limited research exists to validate this hypothesis. This Registered Report aimed to specifically test the relationship between intrinsic brain spatio-temporal dynamics and executive functions. Resting-state EEG microstates were used to assess brain spatio-temporal dynamics, while a comprehensive battery of nine cognitive function tasks was employed to evaluate executive functions in 140 participants. We hypothesized that microstates (class C and D) metrics would correlate with an executive functions composite score. Contrary to expectations, our hypotheses were not supported by the data. We however observed a small, non-significant trend with a negative correlation between microstate D occurrences and executive functions scores (r = -.18, 95% CI [-.33, -.01]) which however did not meet the adjusted threshold for significance. In light of the inconclusive or minor effect sizes observed, the assertion that intrinsic brain networks dynamics - as measured by resting-state EEG microstate metrics - are a reliable signature of executive functioning remains unsupported.
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Affiliation(s)
- Quentin Chenot
- Fédération ENAC ISAE-SUPAERO ONERA, Université de Toulouse, France.
| | - Caroline Hamery
- Fédération ENAC ISAE-SUPAERO ONERA, Université de Toulouse, France
| | - Moritz Truninger
- Methods of Plasticity Research, Department of Psychology, University of Zurich, Zurich, Switzerland
| | - Nicolas Langer
- Methods of Plasticity Research, Department of Psychology, University of Zurich, Zurich, Switzerland
| | - Xavier De Boissezon
- UMR 1214-Inserm, UPS-ToNIC, Toulouse NeuroImaging Center, Université de Toulouse, Hôpital Purpan, Pavillon Baudot, Toulouse, France; Department of Rehabilitation and Physical Medicine, Pôle Neurosciences, Centre Hospitalier Universitaire de Toulouse CHU, Toulouse, France
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12
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Shin S, Seok JW, Kim K, Kim J, Nam HY, Pak K. Poor sleep quality is associated with decreased regional brain glucose metabolism in healthy middle-aged adults. Neuroimage 2024; 298:120814. [PMID: 39187219 DOI: 10.1016/j.neuroimage.2024.120814] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/12/2024] [Revised: 08/19/2024] [Accepted: 08/23/2024] [Indexed: 08/28/2024] Open
Abstract
Sleep disturbance is associated with the development of neurodegenerative disease. We aimed to address the effects of sleep quality on brain glucose metabolism measured by 18F-Fl uorodeoxyglucose (18F-FDG) positron emission tomography (PET) in healthy middle-aged adults. A total of 378 healthy men (mean age: 42.8±3.6 years) were included in this study. Participants underwent brain 18F-FDG PET and completed the Korean version of the Pittsburgh Sleep Quality Index (PSQI-K). Additionally, anthropometric measurements were obtained. PETs were spatially normalized to MNI space using PET templates from SPM5 with PMOD. The Automated Anatomical Labeling 2 atlas was used to define regions of interest (ROIs). The mean uptake of each ROI was scaled to the mean of the global cortical uptake of each individual and defined as the standardized uptake value ratio (SUVR). After the logarithmic transformation of the regional SUVR, the effects of the PSQI-K on the regional SUVR were investigated using Bayesian hierarchical modeling. Brain glucose metabolism of the posterior cingulate, precuneus, and thalamus showed a negative association with total PSQI-K scores in the Bayesian model ROI-based analysis. Voxel-based analysis using statistical parametric mapping revealed a negative association between the total PSQI-K scores and brain glucose metabolism of the precuneus, postcentral gyrus, posterior cingulate, and thalamus. Poor sleep quality is negatively associated with brain glucose metabolism in the precuneus, posterior cingulate, and thalamus. Therefore, the importance of sleep should not be overlooked, even in healthy middle-aged adults.
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Affiliation(s)
- Seunghyeon Shin
- Department of Nuclear Medicine, Samsung Changwon Hospital, Sungkyunkwan University School of Medicine, Changwon, Republic of Korea.
| | - Ju Won Seok
- Department of Nuclear Medicine, Chung-Ang University College of Medicine, Seoul, Republic of Korea.
| | - Keunyoung Kim
- Department of Nuclear Medicine and Biomedical Research Institute, Pusan National University Hospital, Busan, Republic of Korea; School of Medicine, Pusan National University, Busan, Republic of Korea.
| | - Jihyun Kim
- Department of Nuclear Medicine and Biomedical Research Institute, Pusan National University Hospital, Busan, Republic of Korea.
| | - Hyun-Yeol Nam
- Department of Nuclear Medicine, Samsung Changwon Hospital, Sungkyunkwan University School of Medicine, Changwon, Republic of Korea.
| | - Kyoungjune Pak
- Department of Nuclear Medicine and Biomedical Research Institute, Pusan National University Hospital, Busan, Republic of Korea; School of Medicine, Pusan National University, Busan, Republic of Korea.
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13
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Novak L, Malinakova K, Trnka R, Mikoska P, Sverak T, Kiiski H, Tavel P, van Dijk JP. Neural bases of social deficits in ADHD: A systematic review. Does the Theory of Mind matter? Brain Res Bull 2024; 215:111011. [PMID: 38906229 DOI: 10.1016/j.brainresbull.2024.111011] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/01/2024] [Revised: 05/31/2024] [Accepted: 06/11/2024] [Indexed: 06/23/2024]
Abstract
INTRODUCTION The Attention Deficit Hyperactivity Disorder (ADHD) causes serious interpersonal problems from childhood to adulthood, one of them being problematic social functioning. This phenomenon in ADHD should be associated with impairments in the Theory of Mind (ToM). Therefore, understanding the neural correlates of the ToM could be crucial for helping individuals with ADHD with their social functioning. Thus, we aimed to review published literature concerning neuroanatomical and functional correlates of ToM deficits in children and adolescents with ADHD. METHODS We reviewed studies published between 1970 and 2023. In accordance with PRISMA guidelines, after data from three databases were collected, two authors (LN and PM) independently screened all relevant records (n=638) and consequently, both authors did the data extraction. The quality of the included studies (n=5) was measured by a modified version of The Newcastle-Ottawa Scale and by measures specific for our study. This systematic review was registered on PROSPERO (CRD42020139847). RESULTS Results indicated that impairments in performing of the ToM tasks were negatively associated with the grey matter volume in the bilateral amygdala and hippocampus in both, ADHD and control group. In EEG studies, a significantly greater electrophysiological activity during ToM tasks was observed in the, frontal, temporal, parietal and occipital lobes in participants with ADHD as compared to healthy subjects. CONCLUSION More research is needed to explore the ToM deficits in children with ADHD. Future research might focus on the neural circuits associated with attention and inhibition, which deficits seems to contribute to the ToM deficits in children and adolescents with ADHD.
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Affiliation(s)
- Lukas Novak
- Olomouc University Social Health Institute, Palacký University Olomouc, Olomouc, Czech Republic; Department of Community and Occupational Medicine, University Medical Center Groningen, University of Groningen, Groningen, the Netherlands.
| | - Klara Malinakova
- Olomouc University Social Health Institute, Palacký University Olomouc, Olomouc, Czech Republic
| | - Radek Trnka
- Olomouc University Social Health Institute, Palacký University Olomouc, Olomouc, Czech Republic; Prague College of Psychosocial Studies, Prague, Czech Republic
| | - Petr Mikoska
- Olomouc University Social Health Institute, Palacký University Olomouc, Olomouc, Czech Republic
| | - Tomas Sverak
- Department of Psychiatry, University Hospital Brno and Faculty of Medicine, Masaryk University, Brno, Czech Republic
| | - Hanni Kiiski
- Trinity Institute of Neuroscience and School of Psychology, Trinity College Dublin, Ireland
| | - Peter Tavel
- Olomouc University Social Health Institute, Palacký University Olomouc, Olomouc, Czech Republic
| | - Jitse P van Dijk
- Olomouc University Social Health Institute, Palacký University Olomouc, Olomouc, Czech Republic; Department of Community and Occupational Medicine, University Medical Center Groningen, University of Groningen, Groningen, the Netherlands; Graduate School Kosice Institute for Society and Health, P.J. Safarik University in Kosice, Kosice, Slovak Republic
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14
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Marek S, Laumann TO. Replicability and generalizability in population psychiatric neuroimaging. Neuropsychopharmacology 2024:10.1038/s41386-024-01960-w. [PMID: 39215207 DOI: 10.1038/s41386-024-01960-w] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/25/2024] [Revised: 07/19/2024] [Accepted: 07/29/2024] [Indexed: 09/04/2024]
Abstract
Studies linking mental health with brain function in cross-sectional population-based association studies have historically relied on small, underpowered samples. Given the small effect sizes typical of such brain-wide associations, studies require samples into the thousands to achieve the statistical power necessary for replicability. Here, we detail how small sample sizes have hampered replicability and provide sample size targets given established association strength benchmarks. Critically, while replicability will improve with larger samples, it is not guaranteed that observed effects will meaningfully apply to target populations of interest (i.e., be generalizable). We discuss important considerations related to generalizability in psychiatric neuroimaging and provide an example of generalizability failure due to "shortcut learning" in brain-based predictions of mental health phenotypes. Shortcut learning is a phenomenon whereby machine learning models learn an association between the brain and an unmeasured construct (the shortcut), rather than the intended target of mental health. Given the complex nature of brain-behavior interactions, the future of epidemiological approaches to brain-based studies of mental health will require large, diverse samples with comprehensive assessment.
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Affiliation(s)
- Scott Marek
- Mallinckrodt Institute of Radiology, Washington University in St. Louis School of Medicine, St. Louis, MO, USA.
- Department of Psychiatry, Washington University in St. Louis School of Medicine, St. Louis, MO, USA.
- Neuroimaging Labs Research Center, Washington University in St. Louis School of Medicine, St. Louis, MO, USA.
- AI Institute for Health, Washington University in St. Louis School of Medicine, St. Louis, MO, USA.
| | - Timothy O Laumann
- Department of Psychiatry, Washington University in St. Louis School of Medicine, St. Louis, MO, USA
- Neuroimaging Labs Research Center, Washington University in St. Louis School of Medicine, St. Louis, MO, USA
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15
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Buehler SK, Lowther M, Lukow PB, Kirk PA, Pike AC, Yamamori Y, Chavanne AV, Gormley S, Goble T, Tuominen EW, Aylward J, McCloud T, Rodriguez-Sanchez J, Robinson OJ. Independent replications reveal anterior and posterior cingulate cortex activation underlying state anxiety-attenuated face encoding. COMMUNICATIONS PSYCHOLOGY 2024; 2:80. [PMID: 39184223 PMCID: PMC11343718 DOI: 10.1038/s44271-024-00128-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 01/18/2024] [Accepted: 08/08/2024] [Indexed: 08/27/2024]
Abstract
Anxiety involves the anticipation of aversive outcomes and can impair neurocognitive processes, such as the ability to recall faces encoded during the anxious state. It is important to precisely delineate and determine the replicability of these effects using causal state anxiety inductions in the general population. This study therefore aimed to replicate prior research on the distinct impacts of threat-of-shock-induced anxiety on the encoding and recognition stage of emotional face processing, in a large asymptomatic sample (n = 92). We successfully replicated previous results demonstrating impaired recognition of faces encoded under threat-of-shock. This was supported by a mega-analysis across three independent studies using the same paradigm (n = 211). Underlying this, a whole-brain fMRI analysis revealed enhanced activation in the posterior cingulate cortex (PCC), alongside previously seen activity in the anterior cingulate cortex (ACC) when combined in a mega-analysis with the fMRI findings we aimed to replicate. We further found replications of hippocampus activation when the retrieval and encoding states were congruent. Our results support the notion that state anxiety disrupts face recognition, potentially due to attentional demands of anxious arousal competing with affective stimuli processing during encoding and suggest that regions of the cingulate cortex play pivotal roles in this.
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Affiliation(s)
| | | | | | - Peter A. Kirk
- National Institute of Mental Health, Washington, DC USA
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16
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Cornwell BR, Didier PR, Grogans SE, Anderson AS, Islam S, Kim HC, Kuhn M, Tillman RM, Hur J, Scott ZS, Fox AS, DeYoung KA, Smith JF, Shackman AJ. A shared threat-anticipation circuit is dynamically engaged at different moments by certain and uncertain threat. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.07.10.602972. [PMID: 39026814 PMCID: PMC11257510 DOI: 10.1101/2024.07.10.602972] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/20/2024]
Abstract
Temporal dynamics play a central role in models of emotion: "fear" is widely conceptualized as a phasic response to certain-and-imminent danger, whereas "anxiety" is a sustained response to uncertain-or-distal harm. Yet the underlying human neurobiology remains contentious. Leveraging an ethnoracially diverse sample, translationally relevant paradigm, and theory-driven modeling approach, we demonstrate that certain and uncertain threat recruit a shared threat-anticipation circuit. This cortico-subcortical circuit exhibits persistently elevated activation when anticipating uncertain-threat encounters and a transient burst of activation in the moments before certain encounters. For many scientists and clinicians, feelings are the defining feature of human fear and anxiety. Here we used an independently validated brain signature to covertly decode the momentary dynamics of anticipatory distress for the first time. Results mirrored the dynamics of neural activation. These observations provide fresh insights into the neurobiology of threat-elicited emotions and set the stage for more ambitious clinical and mechanistic research.
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Affiliation(s)
- Brian R. Cornwell
- Department of Psychological & Brain Sciences, George Washington University, Washington, DC 20006 USA
| | - Paige R. Didier
- Department of Psychology, University of Maryland, College Park, MD 20742 USA
| | - Shannon E. Grogans
- Department of Psychology, University of Maryland, College Park, MD 20742 USA
| | - Allegra S. Anderson
- Department of Psychiatry and Human Behavior, Brown University, Providence, RI 02912 USA
| | - Samiha Islam
- Department of Psychology, University of Pennsylvania, Philadelphia, PA USA
| | - Hyung Cho Kim
- Department of Psychology, University of Maryland, College Park, MD 20742 USA
- Department of Neuroscience and Cognitive Science Program, University of Maryland, College Park, MD 20742 USA
| | - Manuel Kuhn
- Center for Depression, Anxiety and Stress Research, McLean Hospital, Harvard Medical School, Belmont, MA 02478 USA
| | | | - Juyoen Hur
- Department of Psychology, Yonsei University, Seoul 03722, Republic of Korea
| | - Zachary S. Scott
- Department of Psychology, University of Maryland, College Park, MD 20742 USA
| | - Andrew S. Fox
- Department of Psychology, University of California, Davis, CA 95616 USA
- California National Primate Research Center, University of California, Davis, CA 95616 USA
| | - Kathryn A. DeYoung
- Department of Psychology, University of Maryland, College Park, MD 20742 USA
| | - Jason F. Smith
- Department of Psychology, University of Maryland, College Park, MD 20742 USA
| | - Alexander J. Shackman
- Department of Psychology, University of Maryland, College Park, MD 20742 USA
- Department of Neuroscience and Cognitive Science Program, University of Maryland, College Park, MD 20742 USA
- Department of Maryland Neuroimaging Center, University of Maryland, College Park, MD 20742 USA
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17
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Zabihi M, Kia SM, Wolfers T, de Boer S, Fraza C, Dinga R, Arenas AL, Bzdok D, Beckmann CF, Marquand A. Nonlinear latent representations of high-dimensional task-fMRI data: Unveiling cognitive and behavioral insights in heterogeneous spatial maps. PLoS One 2024; 19:e0308329. [PMID: 39116147 PMCID: PMC11309387 DOI: 10.1371/journal.pone.0308329] [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: 10/18/2023] [Accepted: 07/22/2024] [Indexed: 08/10/2024] Open
Abstract
Finding an interpretable and compact representation of complex neuroimaging data is extremely useful for understanding brain behavioral mapping and hence for explaining the biological underpinnings of mental disorders. However, hand-crafted representations, as well as linear transformations, may inadequately capture the considerable variability across individuals. Here, we implemented a data-driven approach using a three-dimensional autoencoder on two large-scale datasets. This approach provides a latent representation of high-dimensional task-fMRI data which can account for demographic characteristics whilst also being readily interpretable both in the latent space learned by the autoencoder and in the original voxel space. This was achieved by addressing a joint optimization problem that simultaneously reconstructs the data and predicts clinical or demographic variables. We then applied normative modeling to the latent variables to define summary statistics ('latent indices') and establish a multivariate mapping to non-imaging measures. Our model, trained with multi-task fMRI data from the Human Connectome Project (HCP) and UK biobank task-fMRI data, demonstrated high performance in age and sex predictions and successfully captured complex behavioral characteristics while preserving individual variability through a latent representation. Our model also performed competitively with respect to various baseline models including several variants of principal components analysis, independent components analysis and classical regions of interest, both in terms of reconstruction accuracy and strength of association with behavioral variables.
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Affiliation(s)
- Mariam Zabihi
- Donders Institute for Brain, Cognition and Behavior, Radboud University Nijmegen, Nijmegen, the Netherlands
- Department for Cognitive Neuroscience, Radboud University Medical Center Nijmegen, Nijmegen, the Netherlands
- MRC Unit for Lifelong Health & Ageing, University College London (UCL), London, United Kingdom
| | - Seyed Mostafa Kia
- Donders Institute for Brain, Cognition and Behavior, Radboud University Nijmegen, Nijmegen, the Netherlands
- Department for Cognitive Neuroscience, Radboud University Medical Center Nijmegen, Nijmegen, the Netherlands
- Department of Psychiatry, University Medical Center Utrecht, Utrecht, the Netherlands
| | - Thomas Wolfers
- Donders Institute for Brain, Cognition and Behavior, Radboud University Nijmegen, Nijmegen, the Netherlands
- Department for Cognitive Neuroscience, Radboud University Medical Center Nijmegen, Nijmegen, the Netherlands
- NORMENT, KG Jebsen Centre for Psychosis Research, Division of Mental Health and Addiction, 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
| | - Stijn de Boer
- Donders Institute for Brain, Cognition and Behavior, Radboud University Nijmegen, Nijmegen, the Netherlands
| | - Charlotte Fraza
- Donders Institute for Brain, Cognition and Behavior, Radboud University Nijmegen, Nijmegen, the Netherlands
- Department for Cognitive Neuroscience, Radboud University Medical Center Nijmegen, Nijmegen, the Netherlands
| | - Richard Dinga
- Donders Institute for Brain, Cognition and Behavior, Radboud University Nijmegen, Nijmegen, the Netherlands
- Department for Cognitive Neuroscience, Radboud University Medical Center Nijmegen, Nijmegen, the Netherlands
| | - Alberto Llera Arenas
- Donders Institute for Brain, Cognition and Behavior, Radboud University Nijmegen, Nijmegen, the Netherlands
| | - Danilo Bzdok
- Multimodal Imaging and Connectome Analysis Lab, McConnell Brain Imaging Centre, Montreal Neurological Institute and Hospital, McGill University, Montreal, Quebec, Canada
- Mila ‐ Quebec Artificial Intelligence Institute, Montreal, Quebec, Canada
| | - Christian F. Beckmann
- Donders Institute for Brain, Cognition and Behavior, Radboud University Nijmegen, Nijmegen, the Netherlands
- Department for Cognitive Neuroscience, Radboud University Medical Center Nijmegen, Nijmegen, the Netherlands
- Centre for Functional MRI of the Brain, University of Oxford, Oxford, United Kingdom
| | - Andre Marquand
- Donders Institute for Brain, Cognition and Behavior, Radboud University Nijmegen, Nijmegen, the Netherlands
- Department for Cognitive Neuroscience, Radboud University Medical Center Nijmegen, Nijmegen, the Netherlands
- Department of Neuroimaging, Institute of Psychiatry, Psychology, & Neuroscience, King’s College London, London, United Kingdom
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18
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Sadil P, Lindquist MA. From Maps to Models: A Survey on the Reliability of Small Studies of Task-Based fMRI. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.08.05.606611. [PMID: 39149240 PMCID: PMC11326202 DOI: 10.1101/2024.08.05.606611] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Subscribe] [Scholar Register] [Indexed: 08/17/2024]
Abstract
Task-based functional magnetic resonance imaging is a powerful tool for studying brain function, but neuroimaging research produces ongoing concerns regarding small-sample studies and how to interpret them. Although it is well understood that larger samples are preferable, many situations require researchers to make judgments from small studies, including reviewing the existing literature, analyzing pilot data, or assessing subsamples. Quantitative guidance on how to make these judgments remains scarce. To address this, we leverage the Human Connectome Project's Young Adult dataset to survey various analyses-from regional activation maps to predictive models. We find that, for some classic analyses such as detecting regional activation or cluster peak location, studies with as few as 40 subjects are adequate, although this depends crucially on effect sizes. For predictive modeling, similar sizes can be adequate for detecting whether features are predictable, but at least an order of magnitude more (at least hundreds) may be required for developing consistent predictions. These results offer valuable insights for designing and interpreting fMRI studies, emphasizing the importance of considering effect size, sample size, and analysis approach when assessing the reliability of findings. We hope that this survey serves as a reference for identifying which kinds of research questions can be reliably answered with small-scale studies.
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Affiliation(s)
- Patrick Sadil
- Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health, 615 N. Wolfe Street, Baltimore, Maryland 21205, USA
| | - Martin A Lindquist
- Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health, 615 N. Wolfe Street, Baltimore, Maryland 21205, USA
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19
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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. J Neurosci 2024; 44:e1868232024. [PMID: 39009438 PMCID: PMC11308352 DOI: 10.1523/jneurosci.1868-23.2024] [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: 09/29/2023] [Revised: 06/01/2024] [Accepted: 06/03/2024] [Indexed: 07/17/2024] Open
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 well-being. 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 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 nonfungible. 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 refining mechanistic research.
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Affiliation(s)
- Shannon E Grogans
- Department of Psychology, University of Maryland, College Park, Maryland 20742
| | - Juyoen Hur
- Department of Psychology, Yonsei University, Seoul 03722, Republic of Korea
| | | | - Allegra S Anderson
- Department of Psychological Sciences, Vanderbilt University, Nashville, Tennessee 37240
| | - Samiha Islam
- Department of Psychology, University of Pennsylvania, Philadelphia, Pennsylvania 19104
| | - Hyung Cho Kim
- Department of Psychology, University of Maryland, College Park, Maryland 20742
- Neuroscience and Cognitive Science Program, University of Maryland, College Park, Maryland 20742
| | - Manuel Kuhn
- Center for Depression, Anxiety and Stress Research, McLean Hospital, Harvard Medical School, Belmont, Massachusetts 02478
| | | | - Andrew S Fox
- Department of Psychology, University of California, Davis, California 95616
- California National Primate Research Center, University of California, Davis, California 95616
| | - Jason F Smith
- Department of Psychology, University of Maryland, College Park, Maryland 20742
| | - Kathryn A DeYoung
- Department of Psychology, University of Maryland, College Park, Maryland 20742
| | - Alexander J Shackman
- Department of Psychology, University of Maryland, College Park, Maryland 20742
- Neuroscience and Cognitive Science Program, University of Maryland, College Park, Maryland 20742
- Maryland Neuroimaging Center, University of Maryland, College Park, Maryland 20742
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20
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Cheng Y, Cai H, Liu S, Yang Y, Pan S, Zhang Y, Mo F, Yu Y, Zhu J. Brain Network Localization of Gray Matter Atrophy and Neurocognitive and Social Cognitive Dysfunction in Schizophrenia. Biol Psychiatry 2024:S0006-3223(24)01489-6. [PMID: 39103010 DOI: 10.1016/j.biopsych.2024.07.021] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/13/2024] [Revised: 07/13/2024] [Accepted: 07/29/2024] [Indexed: 08/07/2024]
Abstract
BACKGROUND Numerous studies have established the presence of gray matter atrophy and brain activation abnormalities during neurocognitive and social cognitive tasks in schizophrenia. Despite a growing consensus that diseases localize better to distributed brain networks than individual anatomical regions, relatively few studies have examined brain network localization of gray matter atrophy and neurocognitive and social cognitive dysfunction in schizophrenia. METHODS To address this gap, we initially identified brain locations of structural and functional abnormalities in schizophrenia from 301 published neuroimaging studies with 8712 individuals with schizophrenia and 9275 healthy control participants. By applying novel functional connectivity network mapping to large-scale resting-state functional magnetic resonance imaging datasets, we mapped these affected brain locations to 3 brain abnormality networks of schizophrenia. RESULTS The gray matter atrophy network of schizophrenia comprised a broadly distributed set of brain areas predominantly implicating the ventral attention, somatomotor, and default networks. The neurocognitive dysfunction network was also composed of widespread brain areas primarily involving the frontoparietal and default networks. By contrast, the social cognitive dysfunction network consisted of circumscribed brain regions mainly implicating the default, subcortical, and visual networks. CONCLUSIONS Our findings suggest shared and unique brain network substrates of gray matter atrophy and neurocognitive and social cognitive dysfunction in schizophrenia, which may not only refine the understanding of disease neuropathology from a network perspective but may also contribute to more targeted and effective treatments for impairments in different cognitive domains in schizophrenia.
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Affiliation(s)
- Yan Cheng
- Department of Radiology, the First Affiliated Hospital of Anhui Medical University, Hefei, China; Research Center of Clinical Medical Imaging, Anhui Province, Hefei, China; Anhui Provincial Institute of Translational Medicine, Hefei, China; Anhui Provincial Key Laboratory for Brain Bank Construction and Resource Utilization, Hefei, China
| | - Huanhuan Cai
- Department of Radiology, the First Affiliated Hospital of Anhui Medical University, Hefei, China; Research Center of Clinical Medical Imaging, Anhui Province, Hefei, China; Anhui Provincial Institute of Translational Medicine, Hefei, China; Anhui Provincial Key Laboratory for Brain Bank Construction and Resource Utilization, Hefei, China
| | - Siyu Liu
- Department of Radiology, the First Affiliated Hospital of Anhui Medical University, Hefei, China; Research Center of Clinical Medical Imaging, Anhui Province, Hefei, China; Anhui Provincial Institute of Translational Medicine, Hefei, China; Anhui Provincial Key Laboratory for Brain Bank Construction and Resource Utilization, Hefei, China
| | - Yang Yang
- Department of Radiology, the First Affiliated Hospital of Anhui Medical University, Hefei, China; Research Center of Clinical Medical Imaging, Anhui Province, Hefei, China; Anhui Provincial Institute of Translational Medicine, Hefei, China; Anhui Provincial Key Laboratory for Brain Bank Construction and Resource Utilization, Hefei, China
| | - Shan Pan
- Department of Radiology, the First Affiliated Hospital of Anhui Medical University, Hefei, China; Research Center of Clinical Medical Imaging, Anhui Province, Hefei, China; Anhui Provincial Institute of Translational Medicine, Hefei, China; Anhui Provincial Key Laboratory for Brain Bank Construction and Resource Utilization, Hefei, China
| | - Yongqi Zhang
- Department of Radiology, the First Affiliated Hospital of Anhui Medical University, Hefei, China; Research Center of Clinical Medical Imaging, Anhui Province, Hefei, China; Anhui Provincial Institute of Translational Medicine, Hefei, China; Anhui Provincial Key Laboratory for Brain Bank Construction and Resource Utilization, Hefei, China
| | - Fan Mo
- Department of Radiology, the First Affiliated Hospital of Anhui Medical University, Hefei, China; Research Center of Clinical Medical Imaging, Anhui Province, Hefei, China; Anhui Provincial Institute of Translational Medicine, Hefei, China; Anhui Provincial Key Laboratory for Brain Bank Construction and Resource Utilization, Hefei, China
| | - Yongqiang Yu
- Department of Radiology, the First Affiliated Hospital of Anhui Medical University, Hefei, China; Research Center of Clinical Medical Imaging, Anhui Province, Hefei, China; Anhui Provincial Institute of Translational Medicine, Hefei, China; Anhui Provincial Key Laboratory for Brain Bank Construction and Resource Utilization, Hefei, China.
| | - Jiajia Zhu
- Department of Radiology, the First Affiliated Hospital of Anhui Medical University, Hefei, China; Research Center of Clinical Medical Imaging, Anhui Province, Hefei, China; Anhui Provincial Institute of Translational Medicine, Hefei, China; Anhui Provincial Key Laboratory for Brain Bank Construction and Resource Utilization, Hefei, China.
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21
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Purg Suljič N, Kraljič A, Rahmati M, Cho YT, Slana Ozimič A, Murray JD, Anticevic A, Repovš G. Individual differences in spatial working memory strategies differentially reflected in the engagement of control and default brain networks. Cereb Cortex 2024; 34:bhae350. [PMID: 39214852 PMCID: PMC11364466 DOI: 10.1093/cercor/bhae350] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/22/2023] [Revised: 07/31/2024] [Accepted: 08/10/2024] [Indexed: 09/04/2024] Open
Abstract
Spatial locations can be encoded and maintained in working memory using different representations and strategies. Fine-grained representations provide detailed stimulus information, but are cognitively demanding and prone to inexactness. The uncertainty in fine-grained representations can be compensated by the use of coarse, but robust categorical representations. In this study, we employed an individual differences approach to identify brain activity correlates of the use of fine-grained and categorical representations in spatial working memory. We combined data from six functional magnetic resonance imaging studies, resulting in a sample of $155$ ($77$ women, $25 \pm 5$ years) healthy participants performing a spatial working memory task. Our results showed that individual differences in the use of spatial representations in working memory were associated with distinct patterns of brain activity. Higher precision of fine-grained representations was related to greater engagement of attentional and control brain systems throughout the task trial, and the stronger deactivation of the default network at the time of stimulus encoding. In contrast, the use of categorical representations was associated with lower default network activity during encoding and higher frontoparietal network activation during maintenance. These results may indicate a greater need for attentional resources and protection against interference for fine-grained compared with categorical representations.
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Affiliation(s)
- Nina Purg Suljič
- Department of Psychology, Faculty of Arts, University of Ljubljana, Aškerčeva 2, 1000 Ljubljana, Slovenia
| | - Aleksij Kraljič
- Department of Psychology, Faculty of Arts, University of Ljubljana, Aškerčeva 2, 1000 Ljubljana, Slovenia
| | - Masih Rahmati
- Department of Psychiatry, Yale University School of Medicine, 300 George Street, New Haven, CT 06511, USA
| | - Youngsun T Cho
- Department of Psychiatry, Yale University School of Medicine, 300 George Street, New Haven, CT 06511, USA
| | - Anka Slana Ozimič
- Department of Psychology, Faculty of Arts, University of Ljubljana, Aškerčeva 2, 1000 Ljubljana, Slovenia
| | - John D Murray
- Department of Psychiatry, Yale University School of Medicine, 300 George Street, New Haven, CT 06511, USA
- Department of Psychology, Yale University, 100 College Street, New Haven, CT 06510, USA
- Department of Physics, Yale University, 217 Prospect Street, New Haven, CT 06511, USA
| | - Alan Anticevic
- Department of Psychiatry, Yale University School of Medicine, 300 George Street, New Haven, CT 06511, USA
- Department of Psychology, Yale University, 100 College Street, New Haven, CT 06510, USA
| | - Grega Repovš
- Department of Psychology, Faculty of Arts, University of Ljubljana, Aškerčeva 2, 1000 Ljubljana, Slovenia
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22
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Schramm S, Börner C, Reichert M, Hoffmann G, Kaczmarz S, Griessmair M, Jung K, Berndt MT, Zimmer C, Baum T, Heinen F, Bonfert MV, Sollmann N. Perfusion imaging by arterial spin labeling in migraine: A literature review. J Cereb Blood Flow Metab 2024; 44:1253-1270. [PMID: 38483125 PMCID: PMC11342727 DOI: 10.1177/0271678x241237733] [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: 04/26/2023] [Revised: 01/19/2024] [Accepted: 01/23/2024] [Indexed: 08/15/2024]
Abstract
Arterial spin labeling (ASL) is a non-invasive magnetic resonance imaging (MRI) method for the assessment of cerebral blood flow (CBF). This review summarizes recent ASL-based investigations in adult and pediatric patients with migraine with aura, migraine without aura, and chronic migraine. A systematic search according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines was conducted within PubMed and reference sections of articles identified from April 2014 to November 2022. Out of 236 initial articles, 20 remained after filtering, encompassing data from 1155 subjects in total. Cross-sectional studies in adults showed inconsistent results, while longitudinal studies demonstrated that cerebral perfusion changes over the migraine cycle can be tracked using ASL. The most consistent findings were observed in ictal states among pediatric migraine patients, where studies showed hypoperfusion matching aura symptoms during early imaging followed by hyperperfusion. Overall, ASL is a useful but currently underutilized modality for evaluating cerebral perfusion in patients with migraine. The generalizability of results is currently limited by heterogeneities regarding study design and documentation of clinical variables (e.g., relation of attacks to scanning timepoint, migraine subtypes). Future MRI studies should consider augmenting imaging protocols with ASL to further elucidate perfusion dynamics in migraine.
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Affiliation(s)
- Severin Schramm
- Department of Diagnostic and Interventional Neuroradiology, School of Medicine, Klinikum rechts der Isar, Technical University of Munich, Munich, Germany
| | - Corinna Börner
- Department of Diagnostic and Interventional Neuroradiology, School of Medicine, Klinikum rechts der Isar, Technical University of Munich, Munich, Germany
- LMU Hospital, Dr. von Hauner Children’s Hospital, Department of Pediatric Neurology and Developmental Medicine, Munich, Germany
- LMU Center for Children with Medical Complexity – iSPZ Hauner, Ludwig Maximilian University, Munich, Germany
| | - Miriam Reichert
- Department of Diagnostic and Interventional Neuroradiology, School of Medicine, Klinikum rechts der Isar, Technical University of Munich, Munich, Germany
| | - Gabriel Hoffmann
- Department of Diagnostic and Interventional Neuroradiology, School of Medicine, Klinikum rechts der Isar, Technical University of Munich, Munich, Germany
- TUM-Neuroimaging Center, Klinikum rechts der Isar, Technical University of Munich, Munich, Germany
| | - Stephan Kaczmarz
- Department of Diagnostic and Interventional Neuroradiology, School of Medicine, Klinikum rechts der Isar, Technical University of Munich, Munich, Germany
- TUM-Neuroimaging Center, Klinikum rechts der Isar, Technical University of Munich, Munich, Germany
- Philips GmbH Market DACH, Hamburg, Germany
| | - Michael Griessmair
- Department of Diagnostic and Interventional Neuroradiology, School of Medicine, Klinikum rechts der Isar, Technical University of Munich, Munich, Germany
| | - Kirsten Jung
- Department of Diagnostic and Interventional Neuroradiology, School of Medicine, Klinikum rechts der Isar, Technical University of Munich, Munich, Germany
| | - Maria T Berndt
- Department of Diagnostic and Interventional Neuroradiology, School of Medicine, Klinikum rechts der Isar, Technical University of Munich, Munich, Germany
| | - Claus Zimmer
- Department of Diagnostic and Interventional Neuroradiology, School of Medicine, Klinikum rechts der Isar, Technical University of Munich, Munich, Germany
- TUM-Neuroimaging Center, Klinikum rechts der Isar, Technical University of Munich, Munich, Germany
| | - Thomas Baum
- Department of Diagnostic and Interventional Neuroradiology, School of Medicine, Klinikum rechts der Isar, Technical University of Munich, Munich, Germany
| | - Florian Heinen
- LMU Hospital, Dr. von Hauner Children’s Hospital, Department of Pediatric Neurology and Developmental Medicine, Munich, Germany
| | - Michaela V Bonfert
- LMU Hospital, Dr. von Hauner Children’s Hospital, Department of Pediatric Neurology and Developmental Medicine, Munich, Germany
- LMU Center for Children with Medical Complexity – iSPZ Hauner, Ludwig Maximilian University, Munich, Germany
| | - Nico Sollmann
- Department of Diagnostic and Interventional Neuroradiology, School of Medicine, Klinikum rechts der Isar, Technical University of Munich, Munich, Germany
- TUM-Neuroimaging Center, Klinikum rechts der Isar, Technical University of Munich, Munich, Germany
- Department of Diagnostic and Interventional Radiology, University Hospital Ulm, Ulm, Germany
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23
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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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24
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Purg Suljic N, Kraljic A, Rahmati M, Cho YT, Slana Ozimic A, Murray JD, Anticevic A, Repovs G. Individual differences in spatial working memory strategies differentially reflected in the engagement of control and default brain networks. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2023.07.07.548112. [PMID: 37662268 PMCID: PMC10473605 DOI: 10.1101/2023.07.07.548112] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 09/05/2023]
Abstract
Spatial locations can be encoded and maintained in working memory using different representations and strategies. Fine-grained representations provide detailed stimulus information, but are cognitively demanding and prone to inexactness. The uncertainty in fine-grained representations can be compensated by the use of coarse, but robust categorical representations. In this study, we employed an individual differences approach to identify brain activity correlates of the use of fine-grained and categorical representations in spatial working memory. We combined data from six fMRI studies, resulting in a sample of 155 (77 women, 25 ± 5 years) healthy participants performing a spatial working memory task. Our results showed that individual differences in the use of spatial representations in working memory were associated with distinct patterns of brain activity. Higher precision of fine-grained representations was related to greater engagement of attentional and control brain systems throughout the task trial, and the stronger deactivation of the default network at the time of stimulus encoding. In contrast, the use of categorical representations was associated with lower default network activity during encoding and higher frontoparietal network activation during maintenance. These results may indicate a greater need for attentional resources and protection against interference for fine-grained compared to categorical representations.
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25
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Lu J, Yan T, Yang L, Zhang X, Li J, Li D, Xiang J, Wang B. Brain fingerprinting and cognitive behavior predicting using functional connectome of high inter-subject variability. Neuroimage 2024; 295:120651. [PMID: 38788914 DOI: 10.1016/j.neuroimage.2024.120651] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/19/2024] [Revised: 05/16/2024] [Accepted: 05/22/2024] [Indexed: 05/26/2024] Open
Abstract
The functional connectivity (FC) graph of the brain has been widely recognized as a ``fingerprint'' that can be used to identify individuals from a group of subjects. Research has indicated that individual identification accuracy can be improved by eliminating the impact of shared information among individuals. However, current research extracts not only shared information of inter-subject but also individual-specific information from FC graphs, resulting in incomplete separation of shared information and fingerprint information among individuals, leading to lower individual identification accuracy across all functional magnetic resonance imaging (fMRI) states session pairs and poor cognitive behavior prediction performance. In this paper, we propose a method to enhance inter-subject variability combining conditional variational autoencoder (CVAE) network and sparse dictionary learning (SDL) module. By embedding fMRI state information in the encoding and decoding processes, the CVAE network can better capture and represent the common features among individuals and enhance inter-subject variability by residual. Our experimental results on Human Connectome Project (HCP) data show that the refined connectomes obtained by using CVAE with SDL can accurately distinguish an individual from the remaining participants. The success accuracies reached 99.7 % and 99.6 % in the session pair rest1-rest2 and reverse rest2-rest1, respectively. In the identification experiment involving task-task combinations carried out on the same day, the identification accuracies ranged from 94.2 % to 98.8 %. Furthermore, we showed the Frontoparietal and Default networks make the most significant contributions to individual identification and the edges that significantly contribute to individual identification are found within and between the Frontoparietal and Default networks. Additionally, high-level cognitive behaviors can also be better predicted with the obtained refined connectomes, suggesting that higher fingerprinting can be useful for resulting in higher behavioral associations. In summary, our proposed framework provides a promising approach to use functional connectivity networks for studying cognition and behavior, promoting a deeper understanding of brain functions.
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Affiliation(s)
- Jiayu Lu
- College of Computer Science and Technology, Taiyuan University of Technology, Taiyuan, 030024, China
| | - Tianyi Yan
- School of Life Science, Beijing Institute of Technology, 100081, China
| | - Lan Yang
- College of Computer Science and Technology, Taiyuan University of Technology, Taiyuan, 030024, China
| | - Xi Zhang
- College of Computer Science and Technology, Taiyuan University of Technology, Taiyuan, 030024, China
| | - Jiaxin Li
- College of Computer Science and Technology, Taiyuan University of Technology, Taiyuan, 030024, China
| | - Dandan Li
- College of Computer Science and Technology, Taiyuan University of Technology, Taiyuan, 030024, China
| | - Jie Xiang
- College of Computer Science and Technology, Taiyuan University of Technology, Taiyuan, 030024, China
| | - Bin Wang
- College of Computer Science and Technology, Taiyuan University of Technology, Taiyuan, 030024, China.
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26
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Demidenko MI, Mumford JA, Poldrack RA. Impact of analytic decisions on test-retest reliability of individual and group estimates in functional magnetic resonance imaging: a multiverse analysis using the monetary incentive delay task. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.03.19.585755. [PMID: 38562804 PMCID: PMC10983911 DOI: 10.1101/2024.03.19.585755] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 04/04/2024]
Abstract
Empirical studies reporting low test-retest reliability of individual blood oxygen-level dependent (BOLD) signal estimates in functional magnetic resonance imaging (fMRI) data have resurrected interest among cognitive neuroscientists in methods that may improve reliability in fMRI. Over the last decade, several individual studies have reported that modeling decisions, such as smoothing, motion correction and contrast selection, may improve estimates of test-retest reliability of BOLD signal estimates. However, it remains an empirical question whether certain analytic decisions consistently improve individual and group level reliability estimates in an fMRI task across multiple large, independent samples. This study used three independent samples (Ns: 60, 81, 119) that collected the same task (Monetary Incentive Delay task) across two runs and two sessions to evaluate the effects of analytic decisions on the individual (intraclass correlation coefficient [ICC(3,1)]) and group (Jaccard/Spearman rho) reliability estimates of BOLD activity of task fMRI data. The analytic decisions in this study vary across four categories: smoothing kernel (five options), motion correction (four options), task parameterizing (three options) and task contrasts (four options), totaling 240 different pipeline permutations. Across all 240 pipelines, the median ICC estimates are consistently low, with a maximum median ICC estimate of .43 - .55 across the three samples. The analytic decisions with the greatest impact on the median ICC and group similarity estimates are the Implicit Baseline contrast, Cue Model parameterization and a larger smoothing kernel. Using an Implicit Baseline in a contrast condition meaningfully increased group similarity and ICC estimates as compared to using the Neutral cue. This effect was largest for the Cue Model parameterization; however, improvements in reliability came at the cost of interpretability. This study illustrates that estimates of reliability in the MID task are consistently low and variable at small samples, and a higher test-retest reliability may not always improve interpretability of the estimated BOLD signal.
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27
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Tansey R, Graff K, Rai S, Merrikh D, Godfrey KJ, Vanderwal T, Bray S. Development of human visual cortical function: A scoping review of task- and naturalistic-fMRI studies through the interactive specialization and maturational frameworks. Neurosci Biobehav Rev 2024; 162:105729. [PMID: 38763178 DOI: 10.1016/j.neubiorev.2024.105729] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/07/2024] [Revised: 05/12/2024] [Accepted: 05/14/2024] [Indexed: 05/21/2024]
Abstract
Overarching theories such as the interactive specialization and maturational frameworks have been proposed to describe human functional brain development. However, these frameworks have not yet been systematically examined across the fMRI literature. Visual processing is one of the most well-studied fields in neuroimaging, and research in this area has recently expanded to include naturalistic paradigms that facilitate study in younger age ranges, allowing for an in-depth critical appraisal of these frameworks across childhood. To this end, we conducted a scoping review of 94 developmental visual fMRI studies, including both traditional experimental task and naturalistic studies, across multiple sub-domains (early visual processing, category-specific higher order processing, naturalistic visual processing). We found that across domains, many studies reported progressive development, but few studies describe regressive or emergent changes necessary to fit the maturational or interactive specialization frameworks. Our findings suggest a need for the expansion of developmental frameworks and clearer reporting of both progressive and regressive changes, along with well-powered, longitudinal studies.
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Affiliation(s)
- Ryann Tansey
- Department of Psychiatry, Cumming School of Medicine, University of Calgary, Calgary, AB, Canada; Alberta Children's Hospital Research Institute, University of Calgary, Calgary, AB, Canada; Hotchkiss Brain Institute, University of Calgary, Calgary, AB, Canada.
| | - Kirk Graff
- Alberta Children's Hospital Research Institute, University of Calgary, Calgary, AB, Canada; Hotchkiss Brain Institute, University of Calgary, Calgary, AB, Canada; Department of Psychiatry, Washington University in St. Louis, St. Louis, MO, USA
| | - Shefali Rai
- Alberta Children's Hospital Research Institute, University of Calgary, Calgary, AB, Canada; Hotchkiss Brain Institute, University of Calgary, Calgary, AB, Canada
| | - Daria Merrikh
- Alberta Children's Hospital Research Institute, University of Calgary, Calgary, AB, Canada; Hotchkiss Brain Institute, University of Calgary, Calgary, AB, Canada
| | - Kate J Godfrey
- Alberta Children's Hospital Research Institute, University of Calgary, Calgary, AB, Canada; Hotchkiss Brain Institute, University of Calgary, Calgary, AB, Canada
| | - Tamara Vanderwal
- Department of Psychiatry, University of British Columbia, Vancouver, BC, Canada; BC Children's Hospital Research Institute, Vancouver, BC, Canada
| | - Signe Bray
- Alberta Children's Hospital Research Institute, University of Calgary, Calgary, AB, Canada; Hotchkiss Brain Institute, University of Calgary, Calgary, AB, Canada; Department of Radiology, Cumming School of Medicine, University of Calgary, Calgary, AB, Canada; Department of Pediatrics, Cumming School of Medicine, University of Calgary, Calgary, AB, Canada
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28
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Constant-Varlet C, Nakai T, Prado J. Intergenerational transmission of brain structure and function in humans: a narrative review of designs, methods, and findings. Brain Struct Funct 2024; 229:1327-1348. [PMID: 38710874 DOI: 10.1007/s00429-024-02804-5] [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: 01/10/2024] [Accepted: 04/23/2024] [Indexed: 05/08/2024]
Abstract
Children often show cognitive and affective traits that are similar to their parents. Although this indicates a transmission of phenotypes from parents to children, little is known about the neural underpinnings of that transmission. Here, we provide a general overview of neuroimaging studies that explore the similarity between parents and children in terms of brain structure and function. We notably discuss the aims, designs, and methods of these so-called intergenerational neuroimaging studies, focusing on two main designs: the parent-child design and the multigenerational design. For each design, we also summarize the major findings, identify the sources of variability between studies, and highlight some limitations and future directions. We argue that the lack of consensus in defining the parent-child transmission of brain structure and function leads to measurement heterogeneity, which is a challenge for future studies. Additionally, multigenerational studies often use measures of family resemblance to estimate the proportion of variance attributed to genetic versus environmental factors, though this estimate is likely inflated given the frequent lack of control for shared environment. Nonetheless, intergenerational neuroimaging studies may still have both clinical and theoretical relevance, not because they currently inform about the etiology of neuromarkers, but rather because they may help identify neuromarkers and test hypotheses about neuromarkers coming from more standard neuroimaging designs.
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Affiliation(s)
- Charlotte Constant-Varlet
- Centre de Recherche en Neurosciences de Lyon (CRNL), INSERM U1028 - CNRS UMR5292, Université de Lyon, Bron, France.
| | - Tomoya Nakai
- Centre de Recherche en Neurosciences de Lyon (CRNL), INSERM U1028 - CNRS UMR5292, Université de Lyon, Bron, France
- Araya Inc., Tokyo, Japan
| | - Jérôme Prado
- Centre de Recherche en Neurosciences de Lyon (CRNL), INSERM U1028 - CNRS UMR5292, Université de Lyon, Bron, France.
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29
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Boch M, Huber L, Lamm C. Domestic dogs as a comparative model for social neuroscience: Advances and challenges. Neurosci Biobehav Rev 2024; 162:105700. [PMID: 38710423 PMCID: PMC7616343 DOI: 10.1016/j.neubiorev.2024.105700] [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: 10/03/2023] [Revised: 03/19/2024] [Accepted: 04/30/2024] [Indexed: 05/08/2024]
Abstract
Dogs and humans have lived together for thousands of years and share many analogous socio-cognitive skills. Dog neuroimaging now provides insight into the neural bases of these shared social abilities. Here, we summarize key findings from dog fMRI identifying neocortical brain areas implicated in visual social cognition, such as face, body, and emotion perception, as well as action observation in dogs. These findings provide converging evidence that the temporal cortex plays a significant role in visual social cognition in dogs. We further briefly review investigations into the neural base of the dog-human relationship, mainly involving limbic brain regions. We then discuss current challenges in the field, such as statistical power and lack of common template spaces, and how to overcome them. Finally, we argue that the foundation has now been built to investigate and compare the neural bases of more complex socio-cognitive phenomena shared by dogs and humans. This will strengthen and expand the role of the domestic dog as a powerful comparative model species and provide novel insights into the evolutionary roots of social cognition.
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Affiliation(s)
- Magdalena Boch
- Social, Cognitive and Affective Neuroscience Unit, Department of Cognition, Emotion, and Methods in Psychology, Faculty of Psychology, University of Vienna, Vienna 1010, Austria; Department of Cognitive Biology, Faculty of Life Sciences, University of Vienna, Vienna 1090, Austria.
| | - Ludwig Huber
- Comparative Cognition, Messerli Research Institute, University of Veterinary Medicine Vienna, Medical University of Vienna and University of Vienna, Vienna 1210, Austria
| | - Claus Lamm
- Social, Cognitive and Affective Neuroscience Unit, Department of Cognition, Emotion, and Methods in Psychology, Faculty of Psychology, University of Vienna, Vienna 1010, Austria; Vienna Cognitive Science Hub, University of Vienna, Vienna 1010, Austria
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30
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Nakua H, Yu JC, Abdi H, Hawco C, Voineskos A, Hill S, Lai MC, Wheeler AL, McIntosh AR, Ameis SH. Comparing the stability and reproducibility of brain-behavior relationships found using canonical correlation analysis and partial least squares within the ABCD sample. Netw Neurosci 2024; 8:576-596. [PMID: 38952810 PMCID: PMC11168718 DOI: 10.1162/netn_a_00363] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/08/2023] [Accepted: 01/17/2024] [Indexed: 07/03/2024] Open
Abstract
Canonical correlation analysis (CCA) and partial least squares correlation (PLS) detect linear associations between two data matrices by computing latent variables (LVs) having maximal correlation (CCA) or covariance (PLS). This study compared the similarity and generalizability of CCA- and PLS-derived brain-behavior relationships. Data were accessed from the baseline Adolescent Brain Cognitive Development (ABCD) dataset (N > 9,000, 9-11 years). The brain matrix consisted of cortical thickness estimates from the Desikan-Killiany atlas. Two phenotypic scales were examined separately as the behavioral matrix; the Child Behavioral Checklist (CBCL) subscale scores and NIH Toolbox performance scores. Resampling methods were used to assess significance and generalizability of LVs. LV1 for the CBCL brain relationships was found to be significant, yet not consistently stable or reproducible, across CCA and PLS models (singular value: CCA = .13, PLS = .39, p < .001). LV1 for the NIH brain relationships showed similar relationships between CCA and PLS and was found to be stable and reproducible (singular value: CCA = .21, PLS = .43, p < .001). The current study suggests that stability and reproducibility of brain-behavior relationships identified by CCA and PLS are influenced by the statistical characteristics of the phenotypic measure used when applied to a large population-based pediatric sample.
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Affiliation(s)
- Hajer Nakua
- Campbell Family Mental Health Research Institute, Centre for Addiction and Mental Health, Toronto, Ontario, Canada
- Institute of Medical Science, University of Toronto, Toronto, Ontario, Canada
| | - Ju-Chi Yu
- Campbell Family Mental Health Research Institute, Centre for Addiction and Mental Health, Toronto, Ontario, Canada
| | - Hervé Abdi
- The University of Texas at Dallas, Richardson, TX, USA
| | - Colin Hawco
- Campbell Family Mental Health Research Institute, Centre for Addiction and Mental Health, Toronto, Ontario, Canada
- Department of Psychiatry, Temerty Faculty of Medicine, University of Toronto, Toronto, Ontario, Canada
| | - Aristotle Voineskos
- Campbell Family Mental Health Research Institute, Centre for Addiction and Mental Health, Toronto, Ontario, Canada
- Department of Psychiatry, Temerty Faculty of Medicine, University of Toronto, Toronto, Ontario, Canada
| | - Sean Hill
- Campbell Family Mental Health Research Institute, Centre for Addiction and Mental Health, Toronto, Ontario, Canada
- Department of Psychiatry, Temerty Faculty of Medicine, University of Toronto, Toronto, Ontario, Canada
| | - Meng-Chuan Lai
- Campbell Family Mental Health Research Institute, Centre for Addiction and Mental Health, Toronto, Ontario, Canada
- Institute of Medical Science, University of Toronto, Toronto, Ontario, Canada
- Program in Neurosciences and Mental Health, The Hospital for Sick Children, Ontario, Canada
- Department of Psychiatry, Temerty Faculty of Medicine, University of Toronto, Toronto, Ontario, Canada
| | - Anne L. Wheeler
- Program in Neurosciences and Mental Health, The Hospital for Sick Children, Ontario, Canada
- Department of Psychiatry, Temerty Faculty of Medicine, University of Toronto, Toronto, Ontario, Canada
| | | | - Stephanie H. Ameis
- Campbell Family Mental Health Research Institute, Centre for Addiction and Mental Health, Toronto, Ontario, Canada
- Department of Psychiatry, Temerty Faculty of Medicine, University of Toronto, Toronto, Ontario, Canada
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31
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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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32
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Zhang X, Xu R, Ma H, Qian Y, Zhu J. Brain Structural and Functional Damage Network Localization of Suicide. Biol Psychiatry 2024; 95:1091-1099. [PMID: 38215816 DOI: 10.1016/j.biopsych.2024.01.003] [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: 11/04/2023] [Revised: 12/20/2023] [Accepted: 01/02/2024] [Indexed: 01/14/2024]
Abstract
BACKGROUND Extensive neuroimaging research on brain structural and functional correlates of suicide has produced inconsistent results. Despite increasing recognition that damage in multiple different brain locations that causes the same symptom can map to a common brain network, there is still a paucity of research investigating network localization of suicide. METHODS To clarify this issue, we initially identified brain structural and functional damage locations in relation to suicide from 63 published studies with 2135 suicidal and 2606 nonsuicidal individuals. By applying novel functional connectivity network mapping to large-scale discovery and validation resting-state functional magnetic resonance imaging datasets, we mapped these affected brain locations to 3 suicide brain damage networks corresponding to different imaging modalities. RESULTS The suicide gray matter volume damage network comprised widely distributed brain areas primarily involving the dorsal default mode, basal ganglia, and anterior salience networks. The suicide task-induced activation damage network was similar to but less extensive than the gray matter volume damage network, predominantly implicating the same canonical networks. The suicide resting-state activity damage network manifested as a localized set of brain regions encompassing the orbitofrontal cortex and middle cingulate cortex. CONCLUSIONS Our findings not only may help reconcile prior heterogeneous neuroimaging results, but also may provide insights into the neurobiological mechanisms of suicide from a network perspective, which may ultimately inform more targeted and effective strategies to prevent suicide.
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Affiliation(s)
- Xiaohan Zhang
- Department of Radiology, The First Affiliated Hospital of Anhui Medical University, Hefei, China; Research Center of Clinical Medical Imaging, Anhui Province, Hefei, China; Anhui Provincial Institute of Translational Medicine, Hefei, China; Anhui Provincial Key Laboratory for Brain Bank Construction and Resource Utilization, Hefei, China
| | - Ruoxuan Xu
- Department of Radiology, The First Affiliated Hospital of Anhui Medical University, Hefei, China; Research Center of Clinical Medical Imaging, Anhui Province, Hefei, China; Anhui Provincial Institute of Translational Medicine, Hefei, China; Anhui Provincial Key Laboratory for Brain Bank Construction and Resource Utilization, Hefei, China
| | - Haining Ma
- Department of Radiology, The First Affiliated Hospital of Anhui Medical University, Hefei, China; Research Center of Clinical Medical Imaging, Anhui Province, Hefei, China; Anhui Provincial Institute of Translational Medicine, Hefei, China; Anhui Provincial Key Laboratory for Brain Bank Construction and Resource Utilization, Hefei, China
| | - Yinfeng Qian
- Department of Radiology, The First Affiliated Hospital of Anhui Medical University, Hefei, China; Research Center of Clinical Medical Imaging, Anhui Province, Hefei, China; Anhui Provincial Institute of Translational Medicine, Hefei, China; Anhui Provincial Key Laboratory for Brain Bank Construction and Resource Utilization, Hefei, China.
| | - Jiajia Zhu
- Department of Radiology, The First Affiliated Hospital of Anhui Medical University, Hefei, China; Research Center of Clinical Medical Imaging, Anhui Province, Hefei, China; Anhui Provincial Institute of Translational Medicine, Hefei, China; Anhui Provincial Key Laboratory for Brain Bank Construction and Resource Utilization, Hefei, China.
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33
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Klein F. Optimizing spatial specificity and signal quality in fNIRS: an overview of potential challenges and possible options for improving the reliability of real-time applications. FRONTIERS IN NEUROERGONOMICS 2024; 5:1286586. [PMID: 38903906 PMCID: PMC11188482 DOI: 10.3389/fnrgo.2024.1286586] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 08/31/2023] [Accepted: 04/29/2024] [Indexed: 06/22/2024]
Abstract
The optical brain imaging method functional near-infrared spectroscopy (fNIRS) is a promising tool for real-time applications such as neurofeedback and brain-computer interfaces. Its combination of spatial specificity and mobility makes it particularly attractive for clinical use, both at the bedside and in patients' homes. Despite these advantages, optimizing fNIRS for real-time use requires careful attention to two key aspects: ensuring good spatial specificity and maintaining high signal quality. While fNIRS detects superficial cortical brain regions, consistently and reliably targeting specific regions of interest can be challenging, particularly in studies that require repeated measurements. Variations in cap placement coupled with limited anatomical information may further reduce this accuracy. Furthermore, it is important to maintain good signal quality in real-time contexts to ensure that they reflect the true underlying brain activity. However, fNIRS signals are susceptible to contamination by cerebral and extracerebral systemic noise as well as motion artifacts. Insufficient real-time preprocessing can therefore cause the system to run on noise instead of brain activity. The aim of this review article is to help advance the progress of fNIRS-based real-time applications. It highlights the potential challenges in improving spatial specificity and signal quality, discusses possible options to overcome these challenges, and addresses further considerations relevant to real-time applications. By addressing these topics, the article aims to help improve the planning and execution of future real-time studies, thereby increasing their reliability and repeatability.
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Affiliation(s)
- Franziska Klein
- Biomedical Devices and Systems Group, R&D Division Health, OFFIS - Institute for Information Technology, Oldenburg, Germany
- Department of Psychiatry, Psychotherapy and Psychosomatics, Medical School, RWTH Aachen University, Aachen, Germany
- Neurocognition and Functional Neurorehabilitation Group, Department of Psychology, University of Oldenburg, Oldenburg, Germany
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Hur J, Tillman RM, Kim HC, Didier P, Anderson AS, Islam S, Stockbridge MD, De Los Reyes A, DeYoung KA, Smith JF, Shackman AJ. Adolescent social anxiety is associated with diminished discrimination of anticipated threat and safety in the bed nucleus of the stria terminalis. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2023.10.30.564701. [PMID: 38853920 PMCID: PMC11160578 DOI: 10.1101/2023.10.30.564701] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/11/2024]
Abstract
Social anxiety-which typically emerges in adolescence-lies on a continuum and, when extreme, can be devastating. Socially anxious individuals are prone to heightened fear, anxiety, and the avoidance of contexts associated with potential social scrutiny. Yet most neuroimaging research has focused on acute social threat. Much less attention has been devoted to understanding the neural systems recruited during the uncertain anticipation of potential encounters with social threat. Here we used a novel fMRI paradigm to probe the neural circuitry engaged during the anticipation and acute presentation of threatening faces and voices in a racially diverse sample of 66 adolescents selectively recruited to encompass a range of social anxiety and enriched for clinically significant levels of distress and impairment. Results demonstrated that adolescents with more severe social anxiety symptoms experience heightened distress when anticipating encounters with social threat, and reduced discrimination of uncertain social threat and safety in the bed nucleus of the stria terminalis (BST), a key division of the central extended amygdala (EAc). Although the EAc-including the BST and central nucleus of the amygdala-was robustly engaged by the acute presentation of threatening faces and voices, the degree of EAc engagement was unrelated to the severity of social anxiety. Together, these observations provide a neurobiologically grounded framework for conceptualizing adolescent social anxiety and set the stage for the kinds of prospective-longitudinal and mechanistic research that will be necessary to determine causation and, ultimately, to develop improved interventions for this often-debilitating illness.
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35
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Sequeira SL, Silk JS, Jones NP, Forbes EE, Hanson JL, Hallion LS, Ladouceur CD. Pathways to adolescent social anxiety: Testing interactions between neural social reward function and perceived social threat in daily life. Dev Psychopathol 2024:1-16. [PMID: 38801123 DOI: 10.1017/s0954579424001068] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/29/2024]
Abstract
Recent theories suggest that for youth highly sensitive to incentives, perceiving more social threat may contribute to social anxiety (SA) symptoms. In 129 girls (ages 11-13) oversampled for shy/fearful temperament, we thus examined how interactions between neural responses to social reward (vs. neutral) cues (measured during anticipation of peer feedback) and perceived social threat in daily peer interactions (measured using ecological momentary assessment) predict SA symptoms two years later. No significant interactions emerged when neural reward function was modeled as a latent factor. Secondary analyses showed that higher perceived social threat was associated with more severe SA symptoms two years later only for girls with higher basolateral amygdala (BLA) activation to social reward cues at baseline. Interaction effects were specific to BLA activation to social reward (not threat) cues, though a main effect of BLA activation to social threat (vs. neutral) cues on SA emerged. Unexpectedly, interactions between social threat and BLA activation to social reward cues also predicted generalized anxiety and depression symptoms two years later, suggesting possible transdiagnostic risk pathways. Perceiving high social threat may be particularly detrimental for youth highly sensitive to reward incentives, potentially due to mediating reward learning processes, though this remains to be tested.
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Affiliation(s)
| | - Jennifer S Silk
- Department of Psychology, University of Pittsburgh, Pittsburgh, PA, USA
| | - Neil P Jones
- Department of Psychiatry, University of Pittsburgh, Pittsburgh, PA, USA
| | - Erika E Forbes
- Department of Psychiatry, University of Pittsburgh, Pittsburgh, PA, USA
| | - Jamie L Hanson
- Department of Psychology, University of Pittsburgh, Pittsburgh, PA, USA
| | - Lauren S Hallion
- Department of Psychology, University of Pittsburgh, Pittsburgh, PA, USA
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36
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Makeig S, Robbins K. Events in context-The HED framework for the study of brain, experience and behavior. Front Neuroinform 2024; 18:1292667. [PMID: 38846339 PMCID: PMC11153828 DOI: 10.3389/fninf.2024.1292667] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/11/2023] [Accepted: 04/30/2024] [Indexed: 06/09/2024] Open
Abstract
The brain is a complex dynamic system whose current state is inextricably coupled to awareness of past, current, and anticipated future threats and opportunities that continually affect awareness and behavioral goals and decisions. Brain activity is driven on multiple time scales by an ever-evolving flow of sensory, proprioceptive, and idiothetic experience. Neuroimaging experiments seek to isolate and focus on some aspect of these complex dynamics to better understand how human experience, cognition, behavior, and health are supported by brain activity. Here we consider an event-related data modeling approach that seeks to parse experience and behavior into a set of time-delimited events. We distinguish between event processes themselves, that unfold through time, and event markers that record the experiment timeline latencies of event onset, offset, and any other event phase transitions. Precise descriptions of experiment events (sensory, motor, or other) allow participant experience and behavior to be interpreted in the context either of the event itself or of all or any experiment events. We discuss how events in neuroimaging experiments have been, are currently, and should best be identified and represented with emphasis on the importance of modeling both events and event context for meaningful interpretation of relationships between brain dynamics, experience, and behavior. We show how text annotation of time series neuroimaging data using the system of Hierarchical Event Descriptors (HED; https://www.hedtags.org) can more adequately model the roles of both events and their ever-evolving context than current data annotation practice and can thereby facilitate data analysis, meta-analysis, and mega-analysis. Finally, we discuss ways in which the HED system must continue to expand to serve the evolving needs of neuroimaging research.
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Affiliation(s)
- Scott Makeig
- Swartz Center for Computational Neuroscience, Institute for Neural Computation, University of California San Diego, La Jolla, CA, United States
| | - Kay Robbins
- Department of Computer Science, University of Texas San Antonio, San Antonio, TX, United States
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37
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Hayashi S, Caron BA, Heinsfeld AS, Vinci-Booher S, McPherson B, Bullock DN, Bertò G, Niso G, Hanekamp S, Levitas D, Ray K, MacKenzie A, Avesani P, Kitchell L, Leong JK, Nascimento-Silva F, Koudoro S, Willis H, Jolly JK, Pisner D, Zuidema TR, Kurzawski JW, Mikellidou K, Bussalb A, Chaumon M, George N, Rorden C, Victory C, Bhatia D, Aydogan DB, Yeh FCF, Delogu F, Guaje J, Veraart J, Fischer J, Faskowitz J, Fabrega R, Hunt D, McKee S, Brown ST, Heyman S, Iacovella V, Mejia AF, Marinazzo D, Craddock RC, Olivetti E, Hanson JL, Garyfallidis E, Stanzione D, Carson J, Henschel R, Hancock DY, Stewart CA, Schnyer D, Eke DO, Poldrack RA, Bollmann S, Stewart A, Bridge H, Sani I, Freiwald WA, Puce A, Port NL, Pestilli F. brainlife.io: a decentralized and open-source cloud platform to support neuroscience research. Nat Methods 2024; 21:809-813. [PMID: 38605111 PMCID: PMC11093740 DOI: 10.1038/s41592-024-02237-2] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/10/2023] [Accepted: 03/05/2024] [Indexed: 04/13/2024]
Abstract
Neuroscience is advancing standardization and tool development to support rigor and transparency. Consequently, data pipeline complexity has increased, hindering FAIR (findable, accessible, interoperable and reusable) access. brainlife.io was developed to democratize neuroimaging research. The platform provides data standardization, management, visualization and processing and automatically tracks the provenance history of thousands of data objects. Here, brainlife.io is described and evaluated for validity, reliability, reproducibility, replicability and scientific utility using four data modalities and 3,200 participants.
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Affiliation(s)
| | - Bradley A Caron
- Indiana University, Bloomington, IN, USA
- The University of Texas, Austin, TX, USA
| | | | - Sophia Vinci-Booher
- Indiana University, Bloomington, IN, USA
- Vanderbilt University, Nashville, TN, USA
| | - Brent McPherson
- Indiana University, Bloomington, IN, USA
- McGill University, Montréal, Quebec, Canada
| | | | | | - Guiomar Niso
- Indiana University, Bloomington, IN, USA
- Cajal Institute, CSIC, Madrid, Spain
| | | | - Daniel Levitas
- Indiana University, Bloomington, IN, USA
- The University of Texas, Austin, TX, USA
| | | | | | | | - Lindsey Kitchell
- Indiana University, Bloomington, IN, USA
- Applied Physics Laboratory, Johns Hopkins University, Laurel, MD, USA
| | - Josiah K Leong
- Indiana University, Bloomington, IN, USA
- University of Arkansas, Fayetteville, AR, USA
| | | | | | | | | | | | | | | | - Kyriaki Mikellidou
- University of Limassol, Nicosia, Cyprus
- University of Cyprus, Nicosia, Cyprus
| | - Aurore Bussalb
- Institut du Cerveau, CNRS, Sorbonne Université, Paris, France
| | | | - Nathalie George
- Institut du Cerveau, CNRS, Sorbonne Université, Paris, France
| | | | | | | | - Dogu Baran Aydogan
- University of Eastern Finland, Kuopio, Finland
- Aalto University School of Science, Espoo, Finland
| | | | - Franco Delogu
- Lawrence Technological University, Southfield, MI, USA
| | | | | | | | | | | | - David Hunt
- Indiana University, Bloomington, IN, USA
| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | - Ashley Stewart
- University of Queensland, St Lucia, Queensland, Australia
| | | | - Ilaria Sani
- The Rockefeller University, New York, NY, USA
- University of Geneva, Geneva, Switzerland
| | | | - Aina Puce
- Indiana University, Bloomington, IN, USA
| | | | - Franco Pestilli
- Indiana University, Bloomington, IN, USA.
- The University of Texas, Austin, TX, USA.
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38
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Fedorenko E, Ivanova AA, Regev TI. The language network as a natural kind within the broader landscape of the human brain. Nat Rev Neurosci 2024; 25:289-312. [PMID: 38609551 DOI: 10.1038/s41583-024-00802-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 02/23/2024] [Indexed: 04/14/2024]
Abstract
Language behaviour is complex, but neuroscientific evidence disentangles it into distinct components supported by dedicated brain areas or networks. In this Review, we describe the 'core' language network, which includes left-hemisphere frontal and temporal areas, and show that it is strongly interconnected, independent of input and output modalities, causally important for language and language-selective. We discuss evidence that this language network plausibly stores language knowledge and supports core linguistic computations related to accessing words and constructions from memory and combining them to interpret (decode) or generate (encode) linguistic messages. We emphasize that the language network works closely with, but is distinct from, both lower-level - perceptual and motor - mechanisms and higher-level systems of knowledge and reasoning. The perceptual and motor mechanisms process linguistic signals, but, in contrast to the language network, are sensitive only to these signals' surface properties, not their meanings; the systems of knowledge and reasoning (such as the system that supports social reasoning) are sometimes engaged during language use but are not language-selective. This Review lays a foundation both for in-depth investigations of these different components of the language processing pipeline and for probing inter-component interactions.
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Affiliation(s)
- Evelina Fedorenko
- Brain and Cognitive Sciences Department, Massachusetts Institute of Technology, Cambridge, MA, USA.
- McGovern Institute for Brain Research, Massachusetts Institute of Technology, Cambridge, MA, USA.
- The Program in Speech and Hearing in Bioscience and Technology, Harvard University, Cambridge, MA, USA.
| | - Anna A Ivanova
- School of Psychology, Georgia Institute of Technology, Atlanta, GA, USA
| | - Tamar I Regev
- Brain and Cognitive Sciences Department, Massachusetts Institute of Technology, Cambridge, MA, USA
- McGovern Institute for Brain Research, Massachusetts Institute of Technology, Cambridge, MA, USA
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39
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Luo AC, Sydnor VJ, Pines A, Larsen B, Alexander-Bloch AF, Cieslak M, Covitz S, Chen AA, Esper NB, Feczko E, Franco AR, Gur RE, Gur RC, Houghton A, Hu F, Keller AS, Kiar G, Mehta K, Salum GA, Tapera T, Xu T, Zhao C, Salo T, Fair DA, Shinohara RT, Milham MP, Satterthwaite TD. Functional connectivity development along the sensorimotor-association axis enhances the cortical hierarchy. Nat Commun 2024; 15:3511. [PMID: 38664387 PMCID: PMC11045762 DOI: 10.1038/s41467-024-47748-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/28/2023] [Accepted: 04/10/2024] [Indexed: 04/28/2024] Open
Abstract
Human cortical maturation has been posited to be organized along the sensorimotor-association axis, a hierarchical axis of brain organization that spans from unimodal sensorimotor cortices to transmodal association cortices. Here, we investigate the hypothesis that the development of functional connectivity during childhood through adolescence conforms to the cortical hierarchy defined by the sensorimotor-association axis. We tested this pre-registered hypothesis in four large-scale, independent datasets (total n = 3355; ages 5-23 years): the Philadelphia Neurodevelopmental Cohort (n = 1207), Nathan Kline Institute-Rockland Sample (n = 397), Human Connectome Project: Development (n = 625), and Healthy Brain Network (n = 1126). Across datasets, the development of functional connectivity systematically varied along the sensorimotor-association axis. Connectivity in sensorimotor regions increased, whereas connectivity in association cortices declined, refining and reinforcing the cortical hierarchy. These consistent and generalizable results establish that the sensorimotor-association axis of cortical organization encodes the dominant pattern of functional connectivity development.
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Affiliation(s)
- Audrey C Luo
- Penn Lifespan Informatics and Neuroimaging Center (PennLINC), Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA
- Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA
- Lifespan Brain Institute, Children's Hospital of Philadelphia and Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA
| | - Valerie J Sydnor
- Penn Lifespan Informatics and Neuroimaging Center (PennLINC), Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA
- Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA
- Lifespan Brain Institute, Children's Hospital of Philadelphia and Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA
| | - Adam Pines
- Penn Lifespan Informatics and Neuroimaging Center (PennLINC), Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA
- Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA
- Lifespan Brain Institute, Children's Hospital of Philadelphia and Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA
- Department of Psychiatry and Behavioral Sciences, Stanford University, Stanford, CA, 94305, USA
| | - Bart Larsen
- Penn Lifespan Informatics and Neuroimaging Center (PennLINC), Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA
- Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA
- Lifespan Brain Institute, Children's Hospital of Philadelphia and Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA
- Masonic Institute for the Developing Brain, University of Minnesota, Minneapolis, MN, 55455, USA
- Department of Pediatrics, University of Minnesota Medical School, Minneapolis, MN, 55455, USA
| | - Aaron F Alexander-Bloch
- Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA
- Lifespan Brain Institute, Children's Hospital of Philadelphia and Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA
- Department of Child and Adolescent Psychiatry and Behavioral Science, Children's Hospital of Philadelphia, Philadelphia, PA, 19104, USA
| | - Matthew Cieslak
- Penn Lifespan Informatics and Neuroimaging Center (PennLINC), Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA
- Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA
- Lifespan Brain Institute, Children's Hospital of Philadelphia and Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA
| | - Sydney Covitz
- Penn Lifespan Informatics and Neuroimaging Center (PennLINC), Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA
- Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA
- Lifespan Brain Institute, Children's Hospital of Philadelphia and Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA
| | - Andrew A Chen
- Division of Biostatistics and Bioinformatics, Department of Public Health Sciences, Medical University of South Carolina, Charleston, SC, 29425, USA
| | | | - Eric Feczko
- Center for the Developing Brain, Child Mind Institute, New York, NY, 10022, USA
| | - Alexandre R Franco
- Center for the Developing Brain, Child Mind Institute, New York, NY, 10022, USA
- Center for Biomedical Imaging and Neuromodulation, Nathan Kline Institute for Psychiatric Research, Orangeburg, NY, 10962, USA
- Department of Psychiatry, NYU Grossman School of Medicine, New York, NY, 10016, USA
| | - Raquel E Gur
- Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA
- Lifespan Brain Institute, Children's Hospital of Philadelphia and Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA
- Department of Child and Adolescent Psychiatry and Behavioral Science, Children's Hospital of Philadelphia, Philadelphia, PA, 19104, USA
| | - Ruben C Gur
- Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA
- Lifespan Brain Institute, Children's Hospital of Philadelphia and Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA
| | - Audrey Houghton
- Masonic Institute for the Developing Brain, University of Minnesota, Minneapolis, MN, 55455, USA
| | - Fengling Hu
- Penn Statistics in Imaging and Visualization Center, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA
- Department of Biostatistics, Epidemiology and Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Arielle S Keller
- Penn Lifespan Informatics and Neuroimaging Center (PennLINC), Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA
- Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA
- Lifespan Brain Institute, Children's Hospital of Philadelphia and Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA
| | - Gregory Kiar
- Center for the Developing Brain, Child Mind Institute, New York, NY, 10022, USA
| | - Kahini Mehta
- Penn Lifespan Informatics and Neuroimaging Center (PennLINC), Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA
- Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA
- Lifespan Brain Institute, Children's Hospital of Philadelphia and Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA
| | - Giovanni A Salum
- Center for the Developing Brain, Child Mind Institute, New York, NY, 10022, USA
- Section on Negative Affect and Social Processes, Hospital de Clínicas de Porto Alegre, Universidade Federal do Rio Grande do Sul, Porto Alegre, Brazil
| | - Tinashe Tapera
- Penn Lifespan Informatics and Neuroimaging Center (PennLINC), Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA
- Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA
- Lifespan Brain Institute, Children's Hospital of Philadelphia and Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA
- Khoury College of Computer Sciences, Northeastern University, Boston, MA, 02115, USA
| | - Ting Xu
- Center for the Developing Brain, Child Mind Institute, New York, NY, 10022, USA
| | - Chenying Zhao
- Penn Lifespan Informatics and Neuroimaging Center (PennLINC), Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA
- Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA
- Lifespan Brain Institute, Children's Hospital of Philadelphia and Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA
- Department of Bioengineering, School of Engineering and Applied Science, University of Pennsylvania, Philadelphia, PA, 19104, USA
| | - Taylor Salo
- Penn Lifespan Informatics and Neuroimaging Center (PennLINC), Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA
- Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA
- Lifespan Brain Institute, Children's Hospital of Philadelphia and Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA
| | - Damien A Fair
- Masonic Institute for the Developing Brain, University of Minnesota, Minneapolis, MN, 55455, USA
- Department of Pediatrics, University of Minnesota Medical School, Minneapolis, MN, 55455, USA
- Institute of Child Development, College of Education and Human Development, University of Minnesota, Minneapolis, MN, 55455, USA
| | - Russell T Shinohara
- Penn Statistics in Imaging and Visualization Center, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA
- Department of Biostatistics, Epidemiology and Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
- Center for Biomedical Image Computing and Analytics, University of Pennsylvania, Philadelphia, PA, 19104, USA
| | - Michael P Milham
- Center for the Developing Brain, Child Mind Institute, New York, NY, 10022, USA
- Center for Biomedical Imaging and Neuromodulation, Nathan Kline Institute for Psychiatric Research, Orangeburg, NY, 10962, USA
| | - Theodore D Satterthwaite
- Penn Lifespan Informatics and Neuroimaging Center (PennLINC), Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA.
- Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA.
- Lifespan Brain Institute, Children's Hospital of Philadelphia and Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA.
- Center for Biomedical Image Computing and Analytics, University of Pennsylvania, Philadelphia, PA, 19104, USA.
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40
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Vaidya N, Marquand AF, Nees F, Siehl S, Schumann G. The impact of psychosocial adversity on brain and behaviour: an overview of existing knowledge and directions for future research. Mol Psychiatry 2024:10.1038/s41380-024-02556-y. [PMID: 38658773 DOI: 10.1038/s41380-024-02556-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: 06/27/2023] [Revised: 04/03/2024] [Accepted: 04/08/2024] [Indexed: 04/26/2024]
Abstract
Environmental experiences play a critical role in shaping the structure and function of the brain. Its plasticity in response to different external stimuli has been the focus of research efforts for decades. In this review, we explore the effects of adversity on brain's structure and function and its implications for brain development, adaptation, and the emergence of mental health disorders. We are focusing on adverse events that emerge from the immediate surroundings of an individual, i.e., microenvironment. They include childhood maltreatment, peer victimisation, social isolation, affective loss, domestic conflict, and poverty. We also take into consideration exposure to environmental toxins. Converging evidence suggests that different types of adversity may share common underlying mechanisms while also exhibiting unique pathways. However, they are often studied in isolation, limiting our understanding of their combined effects and the interconnected nature of their impact. The integration of large, deep-phenotyping datasets and collaborative efforts can provide sufficient power to analyse high dimensional environmental profiles and advance the systematic mapping of neuronal mechanisms. This review provides a background for future research, highlighting the importance of understanding the cumulative impact of various adversities, through data-driven approaches and integrative multimodal analysis techniques.
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Affiliation(s)
- Nilakshi Vaidya
- Centre for Population Neuroscience and Stratified Medicine (PONS), Department of Psychiatry and Clinical Neuroscience, Charité Universitätsmedizin Berlin, Berlin, Germany.
| | - Andre F Marquand
- Donders Institute for Brain, Cognition and Behavior, Radboud University Nijmegen, Nijmegen, The Netherlands
| | - Frauke Nees
- Institute of Medical Psychology and Medical Sociology, University Medical Center Schleswig Holstein, Kiel University, Kiel, Germany
| | - Sebastian Siehl
- Institute of Medical Psychology and Medical Sociology, University Medical Center Schleswig Holstein, Kiel University, Kiel, Germany
| | - Gunter Schumann
- Centre for Population Neuroscience and Stratified Medicine (PONS), Department of Psychiatry and Clinical Neuroscience, Charité Universitätsmedizin Berlin, Berlin, Germany
- Centre for Population Neuroscience and Stratified Medicine (PONS), Institute for Science and Technology of Brain-Inspired Intelligence (ISTBI), Fudan University, Shanghai, China
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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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Popp JL, Thiele JA, Faskowitz J, Seguin C, Sporns O, Hilger K. Structural-functional brain network coupling predicts human cognitive ability. Neuroimage 2024; 290:120563. [PMID: 38492685 DOI: 10.1016/j.neuroimage.2024.120563] [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: 08/01/2023] [Revised: 10/14/2023] [Accepted: 03/01/2024] [Indexed: 03/18/2024] Open
Abstract
Individual differences in general cognitive ability (GCA) have a biological basis within the structure and function of the human brain. Network neuroscience investigations revealed neural correlates of GCA in structural as well as in functional brain networks. However, whether the relationship between structural and functional networks, the structural-functional brain network coupling (SC-FC coupling), is related to individual differences in GCA remains an open question. We used data from 1030 adults of the Human Connectome Project, derived structural connectivity from diffusion weighted imaging, functional connectivity from resting-state fMRI, and assessed GCA as a latent g-factor from 12 cognitive tasks. Two similarity measures and six communication measures were used to model possible functional interactions arising from structural brain networks. SC-FC coupling was estimated as the degree to which these measures align with the actual functional connectivity, providing insights into different neural communication strategies. At the whole-brain level, higher GCA was associated with higher SC-FC coupling, but only when considering path transitivity as neural communication strategy. Taking region-specific variations in the SC-FC coupling strategy into account and differentiating between positive and negative associations with GCA, allows for prediction of individual cognitive ability scores in a cross-validated prediction framework (correlation between predicted and observed scores: r = 0.25, p < .001). The same model also predicts GCA scores in a completely independent sample (N = 567, r = 0.19, p < .001). Our results propose structural-functional brain network coupling as a neurobiological correlate of GCA and suggest brain region-specific coupling strategies as neural basis of efficient information processing predictive of cognitive ability.
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Affiliation(s)
- Johanna L Popp
- Department of Psychology I, Würzburg University, Marcusstr. 9-11, Würzburg D 97070, Germany.
| | - Jonas A Thiele
- Department of Psychology I, Würzburg University, Marcusstr. 9-11, Würzburg D 97070, Germany
| | - Joshua Faskowitz
- Department of Psychological and Brain Sciences, Indiana University, 1101 E. 10th St., Bloomington 47405-7007, IN, USA
| | - Caio Seguin
- Department of Psychological and Brain Sciences, Indiana University, 1101 E. 10th St., Bloomington 47405-7007, IN, USA
| | - Olaf Sporns
- Department of Psychological and Brain Sciences, Indiana University, 1101 E. 10th St., Bloomington 47405-7007, IN, USA
| | - Kirsten Hilger
- Department of Psychology I, Würzburg University, Marcusstr. 9-11, Würzburg D 97070, Germany.
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Carriedo N, Rodríguez-Villagra OA, Moguilner S, Morales-Sepulveda JP, Huepe-Artigas D, Soto V, Franco-O’Byrne D, Ibáñez A, Bekinschtein TA, Huepe D. Cognitive, emotional, and social factors promoting psychosocial adaptation: a study of latent profiles in people living in socially vulnerable contexts. Front Psychol 2024; 15:1321242. [PMID: 38680276 PMCID: PMC11050042 DOI: 10.3389/fpsyg.2024.1321242] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/13/2023] [Accepted: 03/20/2024] [Indexed: 05/01/2024] Open
Abstract
Introduction Social adaptation is a multifaceted process that encompasses cognitive, social, and affective factors. Previous research often focused on isolated variables, overlooking their interactions, especially in challenging environments. Our study addresses this by investigating how cognitive (working memory, verbal intelligence, self-regulation), social (affective empathy, family networks, loneliness), and psychological (locus of control, self-esteem, perceived stress) factors interact to influence social adaptation. Methods We analyzed data from 254 adults (55% female) aged 18 to 46 in economically vulnerable households in Santiago, Chile. We used Latent profile analysis (LPA) and machine learning to uncover distinct patters of socioadaptive features and identify the most discriminating features. Results LPA showed two distinct psychosocial adaptation profiles: one characterized by effective psychosocial adaptation and another by poor psychosocial adaptation. The adaptive profile featured individuals with strong emotional, cognitive, and behavioral self-regulation, an internal locus of control, high self-esteem, lower stress levels, reduced affective empathy, robust family support, and decreased loneliness. Conversely, the poorly adapted profile exhibited the opposite traits. Machine learning pinpointed six key differentiating factors in various adaptation pathways within the same vulnerable context: high self-esteem, cognitive and behavioral self-regulation, low stress levels, higher education, and increased social support. Discussion This research carries significant policy implications, highlighting the need to reinforce protective factors and psychological resources, such as self-esteem, self-regulation, and education, to foster effective adaptation in adversity. Additionally, we identified critical risk factors impacting social adaptation in vulnerable populations, advancing our understanding of this intricate phenomenon.
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Affiliation(s)
- Nuria Carriedo
- Departamento de Psicología Evolutiva y de la Educación, National Distance Education University (UNED), Madrid, Spain
| | - Odir A. Rodríguez-Villagra
- Institute for Psychological Research, University of Costa Rica, Sabanilla, San José, Costa Rica
- Neuroscience Research Center, University of Costa Rica, San José, Costa Rica
| | - Sebastián Moguilner
- Global Brain Health Institute, Trinity College Dublin, Dublin, Ireland
- Cognitive Neuroscience Center, Universidad de San Andrés, Buenos Aires, Argentina
- Latin American Brain Health, Universidad Adolfo Ibáñez, Santiago, Chile
| | - Juan Pablo Morales-Sepulveda
- University of Sydney Business School, Darlington, NSW, Australia
- Facultad de Educación Psicología y Familia, Universidad Finis Terrae, Santiago, Chile
| | - Daniela Huepe-Artigas
- Facultad de Educación Psicología y Familia, Universidad Finis Terrae, Santiago, Chile
- Center for Social and Cognitive Neuroscience, School of Psychology, Universidad Adolfo Ibáñez, Santiago, Chile
| | - Vicente Soto
- Center for Social and Cognitive Neuroscience, School of Psychology, Universidad Adolfo Ibáñez, Santiago, Chile
| | - Daniel Franco-O’Byrne
- Latin American Brain Health, Universidad Adolfo Ibáñez, Santiago, Chile
- Center for Social and Cognitive Neuroscience, School of Psychology, Universidad Adolfo Ibáñez, Santiago, Chile
| | - Agustín Ibáñez
- Global Brain Health Institute, Trinity College Dublin, Dublin, Ireland
- Cognitive Neuroscience Center, Universidad de San Andrés, Buenos Aires, Argentina
- Latin American Brain Health, Universidad Adolfo Ibáñez, Santiago, Chile
- National Scientific and Technical Research Council (CONICET), Buenos Aires, Argentina
| | - Tristan A. Bekinschtein
- Cambridge Consciousness and Cognition Lab, Department of Psychology, University of Cambridge, Cambridge, United Kingdom
| | - David Huepe
- Center for Social and Cognitive Neuroscience, School of Psychology, Universidad Adolfo Ibáñez, Santiago, Chile
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Hosseini EA, Schrimpf M, Zhang Y, Bowman S, Zaslavsky N, Fedorenko E. Artificial Neural Network Language Models Predict Human Brain Responses to Language Even After a Developmentally Realistic Amount of Training. NEUROBIOLOGY OF LANGUAGE (CAMBRIDGE, MASS.) 2024; 5:43-63. [PMID: 38645622 PMCID: PMC11025646 DOI: 10.1162/nol_a_00137] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 03/29/2023] [Accepted: 01/09/2024] [Indexed: 04/23/2024]
Abstract
Artificial neural networks have emerged as computationally plausible models of human language processing. A major criticism of these models is that the amount of training data they receive far exceeds that of humans during language learning. Here, we use two complementary approaches to ask how the models' ability to capture human fMRI responses to sentences is affected by the amount of training data. First, we evaluate GPT-2 models trained on 1 million, 10 million, 100 million, or 1 billion words against an fMRI benchmark. We consider the 100-million-word model to be developmentally plausible in terms of the amount of training data given that this amount is similar to what children are estimated to be exposed to during the first 10 years of life. Second, we test the performance of a GPT-2 model trained on a 9-billion-token dataset to reach state-of-the-art next-word prediction performance on the human benchmark at different stages during training. Across both approaches, we find that (i) the models trained on a developmentally plausible amount of data already achieve near-maximal performance in capturing fMRI responses to sentences. Further, (ii) lower perplexity-a measure of next-word prediction performance-is associated with stronger alignment with human data, suggesting that models that have received enough training to achieve sufficiently high next-word prediction performance also acquire representations of sentences that are predictive of human fMRI responses. In tandem, these findings establish that although some training is necessary for the models' predictive ability, a developmentally realistic amount of training (∼100 million words) may suffice.
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Affiliation(s)
- Eghbal A. Hosseini
- Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, MA, USA
- McGovern Institute for Brain Research, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Martin Schrimpf
- The MIT Quest for Intelligence Initiative, Cambridge, MA, USA
- Swiss Federal Institute of Technology, Lausanne, Switzerland
| | - Yian Zhang
- Computer Science Department, Stanford University, Stanford, CA, USA
| | - Samuel Bowman
- Center for Data Science, New York University, New York, NY, USA
- Department of Linguistics, New York University, New York, NY, USA
- Department of Computer Science, New York University, New York, NY, USA
| | - Noga Zaslavsky
- Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, MA, USA
- McGovern Institute for Brain Research, Massachusetts Institute of Technology, Cambridge, MA, USA
- K. Lisa Yang Integrative Computational Neuroscience (ICoN) Center, Massachusetts Institute of Technology, Cambridge, MA, USA
- Department of Language Science, University of California, Irvine, CA, USA
| | - Evelina Fedorenko
- Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, MA, USA
- McGovern Institute for Brain Research, Massachusetts Institute of Technology, Cambridge, MA, USA
- The MIT Quest for Intelligence Initiative, Cambridge, MA, USA
- Speech and Hearing Bioscience and Technology Program, Harvard University, Boston, MA, USA
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45
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Day TKM, Hermosillo R, Conan G, Randolph A, Perrone A, Earl E, Byington N, Hendrickson TJ, Elison JT, Fair DA, Feczko E. Multi-level fMRI analysis applied to hemispheric specialization in the language network, functional areas, and their behavioral correlations in the ABCD sample. Dev Cogn Neurosci 2024; 66:101355. [PMID: 38354531 PMCID: PMC10875197 DOI: 10.1016/j.dcn.2024.101355] [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: 02/14/2023] [Revised: 01/06/2024] [Accepted: 02/03/2024] [Indexed: 02/16/2024] Open
Abstract
Prior research suggests that the organization of the language network in the brain is left-dominant and becomes more lateralized with age and increasing language skill. The age at which specific components of the language network become adult-like varies depending on the abilities they subserve. So far, a large, developmental study has not included a language task paradigm, so we introduce a method to study resting-state laterality in the Adolescent Brain Cognitive Development (ABCD) study. Our approach mixes source timeseries between left and right homotopes of the (1) inferior frontal and (2) middle temporal gyri and (3) a region we term "Wernicke's area" near the supramarginal gyrus. Our large subset sample size of ABCD (n = 6153) allows improved reliability and validity compared to previous, smaller studies of brain-behavior associations. We show that behavioral metrics from the NIH Youth Toolbox and other resources are differentially related to tasks with a larger linguistic component over ones with less (e.g., executive function-dominant tasks). These baseline characteristics of hemispheric specialization in youth are critical for future work determining the correspondence of lateralization with language onset in earlier stages of development.
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Affiliation(s)
- Trevor K M Day
- Institute of Child Development, University of Minnesota, Minneapolis, MN, USA; Masonic Institute for the Developing Brain, University of Minnesota, Minneapolis, MN, USA.
| | - Robert Hermosillo
- Masonic Institute for the Developing Brain, University of Minnesota, Minneapolis, MN, USA; Department of Pediatrics, University of Minnesota, Minneapolis, MN, USA
| | - Gregory Conan
- Masonic Institute for the Developing Brain, University of Minnesota, Minneapolis, MN, USA
| | - Anita Randolph
- Masonic Institute for the Developing Brain, University of Minnesota, Minneapolis, MN, USA; Department of Pediatrics, University of Minnesota, Minneapolis, MN, USA
| | - Anders Perrone
- Masonic Institute for the Developing Brain, University of Minnesota, Minneapolis, MN, USA
| | - Eric Earl
- Data Science & Sharing Team, National Institute of Mental Health, Bethesda, MD, USA
| | - Nora Byington
- Masonic Institute for the Developing Brain, University of Minnesota, Minneapolis, MN, USA
| | - Timothy J Hendrickson
- Masonic Institute for the Developing Brain, University of Minnesota, Minneapolis, MN, USA; Informatics Institute, University of Minnesota, Minneapolis, MN, USA
| | - Jed T Elison
- Institute of Child Development, University of Minnesota, Minneapolis, MN, USA; Masonic Institute for the Developing Brain, University of Minnesota, Minneapolis, MN, USA; Department of Pediatrics, University of Minnesota, Minneapolis, MN, USA
| | - Damien A Fair
- Institute of Child Development, University of Minnesota, Minneapolis, MN, USA; Masonic Institute for the Developing Brain, University of Minnesota, Minneapolis, MN, USA; Department of Pediatrics, University of Minnesota, Minneapolis, MN, USA
| | - Eric Feczko
- Masonic Institute for the Developing Brain, University of Minnesota, Minneapolis, MN, USA; Department of Pediatrics, University of Minnesota, Minneapolis, MN, USA
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Bashford-Largo J, Nakua H, Blair RJR, Dominguez A, Hatch M, Blair KS, Dobbertin M, Ameis S, Bajaj S. A Shared Multivariate Brain-Behavior Relationship in a Transdiagnostic Sample of Adolescents. BIOLOGICAL PSYCHIATRY. COGNITIVE NEUROSCIENCE AND NEUROIMAGING 2024; 9:377-386. [PMID: 37572936 PMCID: PMC10858974 DOI: 10.1016/j.bpsc.2023.07.015] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/23/2023] [Revised: 07/10/2023] [Accepted: 07/27/2023] [Indexed: 08/14/2023]
Abstract
BACKGROUND Internalizing and externalizing psychopathology typically present in early childhood and can have negative implications on general functioning and quality of life. Prior work has linked increased psychopathology symptoms with altered brain structure. Multivariate analysis such as partial least squares correlation can help identify patterns of covariation between brain regions and psychopathology symptoms. This study examined the relationship between gray matter volume (GMV) and psychopathology symptoms in adolescents with various psychiatric diagnoses. METHODS Structural magnetic resonance imaging data were collected from 490 participants with various internalizing and externalizing diagnoses (197 female/293 male; age = 14.68 ± 2.35 years; IQ = 104.05 ± 13.11). Cortical and subcortical volumes were parcellated using the Desikan-Killiany atlas. Partial least squares correlation was used to identify multivariate linear relationships between GMV and the Strength and Difficulties Questionnaire difficulties domains (emotional, peer, conduct, and hyperactivity issues). Resampling approaches were used to determine significance (permutation test), stability (bootstrap resampling), and reproducibility (split-half resampling) of identified relationships. RESULTS We found a significant, stable, and largely reproducible dimension that linked lower Strength and Difficulties Questionnaire scores (less impairment) across all difficulties domains with greater widespread GMV (singular value = 1.17, accounts for 87.1% of the covariance; p < .001). This dimension emphasized the relationship between lower conduct problems and greater GMV in frontotemporal regions. CONCLUSIONS Our results indicate that the most significant and stable brain-behavior relationship in a transdiagnostic sample is a domain-general relationship, linking lower psychopathology symptom scores to greater global GMV. This finding suggests that a shared brain-behavior relationship may be present across adolescents with and without clinically significant psychopathology symptoms.
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Affiliation(s)
- Johannah Bashford-Largo
- Boys Town National Research Hospital, Boys Town, Nebraska; Center for Brain, Biology, and Behavior, University of Nebraska-Lincoln, Lincoln, Nebraska.
| | - Hajer Nakua
- Center for Addiction and Mental Health, University of Toronto, Toronto, Ontario, Canada
| | - R James R Blair
- Child and Adolescent Mental Health Centre, Mental Health Services, Capital Region of Denmark, Copenhagen, Denmark
| | - Ahria Dominguez
- Clinical Health, Emotion, and Neuroscience Laboratory, Department of Neurological Sciences, College of Medicine, University of Nebraska Medical Center, Omaha, Nebraska
| | - Melissa Hatch
- Mind and Brain Health Labs. Department of Neurological Sciences, College of Medicine, University of Nebraska Medical Center, Omaha, Nebraska
| | - Karina S Blair
- Boys Town National Research Hospital, Boys Town, Nebraska
| | - Matthew Dobbertin
- Boys Town National Research Hospital, Boys Town, Nebraska; Child and Adolescent Inpatient Psychiatric Unit, Boys Town National Research Hospital, Boys Town, Nebraska
| | - Stephanie Ameis
- Center for Addiction and Mental Health, University of Toronto, Toronto, Ontario, Canada; Department of Psychiatry, Temerty Faculty of Medicine, University of Toronto, Toronto, Ontario, Canada
| | - Sahil Bajaj
- Department of Cancer Systems Imaging, University of Texas, MD Anderson Center, Houston, Texas
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47
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Kammerer MK, Bott A, Strakeljahn F, Lincoln TM. Sleep spindle activity and psychotic experiences: Examining the mediating roles of attentional performance and perceptual distortions in a daytime nap study. Sleep Med 2024; 116:43-50. [PMID: 38422784 DOI: 10.1016/j.sleep.2024.02.023] [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: 10/28/2023] [Revised: 02/10/2024] [Accepted: 02/11/2024] [Indexed: 03/02/2024]
Abstract
Decreased sleep spindle activity in individuals with psychotic disorders is well studied, but its contribution to psychotic symptom formation is not well understood. This study explored potential underlying mechanisms explaining the association between decreased sleep spindle activity and psychotic symptoms. To this end, we analysed the links between sleep spindle activity and psychotic experiences and probed for the mediating roles of attentional performance and perceptual distortions in a community sample of young adults (N = 70; 26.33 ± 4.84 years). Polysomnography was recorded during a 90-min daytime nap and duration, amplitude, and density from slow (10-13 Hz) and fast (13-16 Hz) spindles were extracted. Attentional performance was assessed via a test battery and with an antisaccadic eye movement task. Psychotic experiences (i.e., paranoid thoughts; hallucinatory experiences) and perceptual distortions (i.e., anomalous perceptions; sensory gating deficits) were assessed via self-report questionnaires. We conducted sequential mediation analyses with spindle activity as predictor, psychotic experiences as dependent variable, and attentional performance and perceptual distortions as mediators. We found reduced right central spindle amplitude to be associated with paranoid thoughts. Increased antisaccadic error rate was associated with anomalous perceptions and perceptual distortions were associated with psychotic experiences. We did not find significant mediation effects. The findings support the notion that reduced sleep spindle activity is involved in the formation of paranoid thoughts and that decreased antisaccadic performance is indicative of perceptual distortions as potential precursors for psychotic experiences. However, further research is needed to corroborate the proposed mediation hypothesis.
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Affiliation(s)
- Mathias K Kammerer
- Clinical Psychology and Psychotherapy, Institute of Psychology, Faculty of Psychology and Movement Sciences, Universität Hamburg, Germany.
| | - Antonia Bott
- Clinical Psychology and Psychotherapy, Institute of Psychology, Faculty of Psychology and Movement Sciences, Universität Hamburg, Germany
| | - Felix Strakeljahn
- Clinical Psychology and Psychotherapy, Institute of Psychology, Faculty of Psychology and Movement Sciences, Universität Hamburg, Germany
| | - Tania M Lincoln
- Clinical Psychology and Psychotherapy, Institute of Psychology, Faculty of Psychology and Movement Sciences, Universität Hamburg, Germany
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48
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Smith DV, Wyngaarden J, Sharp CJ, Sazhin D, Zaff O, Fareri D, Jarcho J. An fMRI dataset of social and nonsocial reward processing in young adults. Data Brief 2024; 53:110197. [PMID: 38406247 PMCID: PMC10885710 DOI: 10.1016/j.dib.2024.110197] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/05/2024] [Revised: 02/06/2024] [Accepted: 02/09/2024] [Indexed: 02/27/2024] Open
Abstract
Trait reward sensitivity, risk for developing substance use, and mood disorders have each been linked with altered striatal responses to reward. Moreover, striatal response to reward is sensitive to social context, such as the presence of a peer, and drugs are often sought out and consumed in social contexts or as a result of social experiences. Thus, mood disorder symptoms, striatal responses to social context and social reward may play a role in substance use. To investigate this possibility, this dataset was collected as part of a National Institute on Drug Abuse (NIDA) grant titled "Aberrant Reward Sensitivity: Mechanisms Underlying Substance Use" (R03-DA046733). The overarching goal was to characterize the associations between neural responses to social and nonsocial rewards, trait reward sensitivity, substance use, and mood disorder symptoms. After obtaining questionnaire data quantifying reward sensitivity, substance use, and other psychosocial characteristics, young adults (N=59; 14 male, 45 female; mean age: 20.89 years ± 1.75 years) completed four fMRI tasks testing different features of social and reward processing. These included: 1) a strategic reward-based decision-making task with Ultimatum and Dictator Game conditions; 2) a task where participants shared rewards or losses with peers, strangers, or non-human partners; 3) a task in which participants received well-matched social and monetary rewards and punishment; and 4) a monetary incentive delay (MID) task in which participants tried to obtain or avoid rewards and losses of different magnitude. This dataset includes sociodemographic questionnaire data, anatomical, task-based fMRI, and corresponding behavioral task-based data. We outline several opportunities for extension and reuse, including exploration of individual differences, cross-task comparisons, and representational similarity analyses.
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Affiliation(s)
| | | | | | | | - Ori Zaff
- Temple University, United States
- University of Pennsylvania, United States
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49
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Vosberg DE. Sex and Gender in Population Neuroscience. Curr Top Behav Neurosci 2024. [PMID: 38509404 DOI: 10.1007/7854_2024_468] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/22/2024]
Abstract
To understand psychiatric and neurological disorders and the structural and functional properties of the human brain, it is essential to consider the roles of sex and gender. In this chapter, I first define sex and gender and describe studies of sex differences in non-human animals. In humans, I describe the sex differences in behavioral and clinical phenotypes and neuroimaging-derived phenotypes, including whole-brain measures, regional subcortical and cortical measures, and structural and functional connectivity. Although structural whole-brain sex differences are large, regional effects (adjusting for whole-brain volumes) are typically much smaller and often fail to replicate. Nevertheless, while an individual neuroimaging feature may have a small effect size, aggregating them in a "maleness/femaleness" score or machine learning multivariate paradigm may prove to be predictive and informative of sex- and gender-related traits. Finally, I conclude by summarizing emerging investigations of gender norms and gender identity and provide methodological recommendations to incorporate sex and gender in population neuroscience research.
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Affiliation(s)
- Daniel E Vosberg
- Centre Hospitalier Universitaire Sainte-Justine, University of Montreal, Montreal, QC, Canada.
- Department of Neuroscience, Faculty of Medicine, University of Montreal, Montreal, QC, Canada.
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50
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Fiorito AM, Blasi G, Brunelin J, Chowdury A, Diwadkar VA, Goghari VM, Gur RC, Kwon JS, Quarto T, Rolland B, Spilka MJ, Wolf DH, Yun JY, Fakra E, Sescousse G. Blunted brain responses to neutral faces in healthy first-degree relatives of patients with schizophrenia: an image-based fMRI meta-analysis. SCHIZOPHRENIA (HEIDELBERG, GERMANY) 2024; 10:38. [PMID: 38503766 PMCID: PMC10951276 DOI: 10.1038/s41537-024-00452-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/15/2023] [Accepted: 02/16/2024] [Indexed: 03/21/2024]
Abstract
Schizophrenia is characterized by the misattribution of emotional significance to neutral faces, accompanied by overactivations of the limbic system. To understand the disorder's genetic and environmental contributors, investigating healthy first-degree relatives is crucial. However, inconsistent findings exist regarding their ability to recognize neutral faces, with limited research exploring the cerebral correlates of neutral face processing in this population. Thus, we here investigated brain responses to neutral face processing in healthy first-degree relatives through an image-based meta-analysis of functional magnetic resonance imaging studies. We included unthresholded group-level T-maps from 5 studies comprising a total of 120 first-degree relatives and 150 healthy controls. In sensitivity analyses, we ran a combined image- and coordinate-based meta-analysis including 7 studies (157 first-degree relatives, 207 healthy controls) aiming at testing the robustness of the results in a larger sample of studies. Our findings revealed a pattern of decreased brain responses to neutral faces in relatives compared with healthy controls, particularly in limbic areas such as the bilateral amygdala, hippocampus, and insula. The same pattern was observed in sensitivity analyses. These results contrast with the overactivations observed in patients, potentially suggesting that this trait could serve as a protective factor in healthy relatives. However, further research is necessary to test this hypothesis.
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Affiliation(s)
- Anna M Fiorito
- Université Claude Bernard Lyon 1, CNRS, INSERM, Centre de Recherche en Neurosciences de Lyon CRNL U1028 UMR5292, PSYR2, 69500, Bron, France.
- Centre Hospitalier Le Vinatier, Bron, France.
| | - Giuseppe Blasi
- Group of Psychiatric Neuroscience, Department of Translational Biomedicine and Neuroscience, University of Bari Aldo Moro, Bari, Italy
- Azienda Ospedaliero-Universitaria Consorziale Policlinico, Bari, Italy
| | - Jérôme Brunelin
- Université Claude Bernard Lyon 1, CNRS, INSERM, Centre de Recherche en Neurosciences de Lyon CRNL U1028 UMR5292, PSYR2, 69500, Bron, France
- Centre Hospitalier Le Vinatier, Bron, France
| | - Asadur Chowdury
- Department of Psychiatry & Behavioral Neurosciences, Wayne State University, Detroit, MI, USA
| | - Vaibhav A Diwadkar
- Department of Psychiatry & Behavioral Neurosciences, Wayne State University, Detroit, MI, USA
| | - Vina M Goghari
- Department of Psychological Clinical Science, University of Toronto, Toronto, ON, Canada
| | - Ruben C Gur
- Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Jun Soo Kwon
- Department of Psychiatry, Seoul National University College of Medicine, Seoul, Korea
| | - Tiziana Quarto
- Department of Humanities, University of Foggia, Foggia, Italy
| | - Benjamin Rolland
- Université Claude Bernard Lyon 1, CNRS, INSERM, Centre de Recherche en Neurosciences de Lyon CRNL U1028 UMR5292, PSYR2, 69500, Bron, France
- Centre Hospitalier Le Vinatier, Bron, France
| | | | - Daniel H Wolf
- Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Je-Yeon Yun
- Department of Psychiatry, Seoul National University College of Medicine, Seoul, Korea
| | - Eric Fakra
- Université Claude Bernard Lyon 1, CNRS, INSERM, Centre de Recherche en Neurosciences de Lyon CRNL U1028 UMR5292, PSYR2, 69500, Bron, France
- Department of Psychiatry, University Hospital of Saint-Etienne, Saint-Etienne, France
| | - Guillaume Sescousse
- Université Claude Bernard Lyon 1, CNRS, INSERM, Centre de Recherche en Neurosciences de Lyon CRNL U1028 UMR5292, PSYR2, 69500, Bron, France
- Centre Hospitalier Le Vinatier, Bron, France
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