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Gilmore AW, Audrain S, Snow J, Gollomp E, Wilson JM, Agron AM, Hammoud DA, Butman JA, Martin A. Long-term retention of real-world experiences in a patient with profound amnesia. Neuropsychologia 2024:109010. [PMID: 39389294 DOI: 10.1016/j.neuropsychologia.2024.109010] [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: 02/27/2024] [Revised: 08/31/2024] [Accepted: 10/06/2024] [Indexed: 10/12/2024]
Abstract
The medial temporal lobe (MTL) is known to be critical for healthy memory function, but patients with MTL damage can, under certain circumstances, demonstrate successful learning of novel information learned outside the laboratory. Here, we describe a patient, D.C., with extensive but focal bilateral MTL damage centering primarily on his hippocampus, whose memory for real-world experiences was assessed. Tests of remote memory indicated at least some capacity to retrieve specific details. To test his anterograde memory, he was taken on a tour of the NIH Clinical Center, with unique events occurring at each of ten specific locations. His memory for these events was tested after one hour, and again after fifteen months. Initially, D.C. could not recall having participated in the tour, even when cued with photographs of specific places he had visited. However, he achieved 90% accuracy on a forced choice recognition test of old and new objects he encountered on the tour, and his recognition of these objects remained intact over a year later when he was tested once again. Subsequent recognition memory tests using novel picture stimuli in a standard laboratory-style computer task resulted in chance-level performance across multiple test formats and stimulus categories. These findings suggest a potentially privileged role for natural learning for long-term retention in a patient with severely damaged medial temporal lobes.
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Affiliation(s)
- Adrian W Gilmore
- Section on Cognitive Neuropsychology, Laboratory of Brain and Cognition, National Institute of Mental Health, Bethesda, MD, USA.
| | - Sam Audrain
- Section on Cognitive Neuropsychology, Laboratory of Brain and Cognition, National Institute of Mental Health, Bethesda, MD, USA
| | - Joseph Snow
- Office of the Clinical Director, National Institute of Mental Health, Bethesda, MD, USA
| | - Elyse Gollomp
- Office of the Clinical Director, National Institute of Mental Health, Bethesda, MD, USA
| | - Jenna M Wilson
- Section on Cognitive Neuropsychology, Laboratory of Brain and Cognition, National Institute of Mental Health, Bethesda, MD, USA
| | - Anna M Agron
- Section on Cognitive Neuropsychology, Laboratory of Brain and Cognition, National Institute of Mental Health, Bethesda, MD, USA
| | - Dima A Hammoud
- Center for Infectious Disease Imaging, NIH Clinical Center, Bethesda, MD, USA; Radiology, and Imaging Services, NIH Clinical Center, Bethesda, MD, USA
| | - John A Butman
- Radiology, and Imaging Services, NIH Clinical Center, Bethesda, MD, USA; Human Imaging and Image Processing Core, Center for Neuroscience and Regenerative Medicine, Bethesda, MD, USA
| | - Alex Martin
- Section on Cognitive Neuropsychology, Laboratory of Brain and Cognition, National Institute of Mental Health, Bethesda, MD, USA
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2
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Teng J, McKenna MR, Gbadeyan O, Prakash RS. Linking the neural signature of response time variability to Alzheimer's disease pathology and cognitive functioning. Netw Neurosci 2024; 8:697-713. [PMID: 39355446 PMCID: PMC11340992 DOI: 10.1162/netn_a_00373] [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: 10/26/2023] [Accepted: 04/01/2024] [Indexed: 10/03/2024] Open
Abstract
Promising evidence has suggested potential links between mind-wandering and Alzheimer's disease (AD). Yet, older adults with diagnosable neurocognitive disorders show reduced meta-awareness, thus questioning the validity of probe-assessed mind-wandering in older adults. In prior work, we employed response time variability as an objective, albeit indirect, marker of mind-wandering to identify patterns of functional connectivity that predicted mind-wandering. In the current study, we evaluated the association of this connectome-based, mind-wandering model with cerebral spinal fluid (CSF) p-tau/Aβ 42 ratio in 289 older adults from the Alzheimer's Disease NeuroImaging Initiative (ADNI). Moreover, we examined if this model was similarly associated with individual differences in composite measures of global cognition, episodic memory, and executive functioning. Edges from the high response time variability model were significantly associated with CSF p-tau/Aβ ratio. Furthermore, connectivity strength within edges associated with high response time variability was negatively associated with global cognition and episodic memory functioning. This study provides the first empirical support for a link between an objective neuromarker of mind-wandering and AD pathophysiology. Given the observed association between mind-wandering and cognitive functioning in older adults, interventions targeted at reducing mind-wandering, particularly before the onset of AD pathogenesis, may make a significant contribution to the prevention of AD-related cognitive decline.
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Affiliation(s)
- James Teng
- Department of Psychology, The Ohio State University, Columbus, OH, USA
- Center for Cognitive and Behavioral Brain Imaging, The Ohio State University, Columbus, OH, USA
| | - Michael R McKenna
- Department of Psychology, The Ohio State University, Columbus, OH, USA
| | - Oyetunde Gbadeyan
- National Centre for Healthy Ageing, Peninsula Clinical School, Faculty of Medicine, Monash University, Melbourne, Australia
| | - Ruchika S Prakash
- Department of Psychology, The Ohio State University, Columbus, OH, USA
- Center for Cognitive and Behavioral Brain Imaging, The Ohio State University, Columbus, OH, USA
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3
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Olfati M, Samea F, Faghihroohi S, Balajoo SM, Küppers V, Genon S, Patil K, Eickhoff SB, Tahmasian M. Prediction of depressive symptoms severity based on sleep quality, anxiety, and gray matter volume: a generalizable machine learning approach across three datasets. EBioMedicine 2024; 108:105313. [PMID: 39255547 PMCID: PMC11414575 DOI: 10.1016/j.ebiom.2024.105313] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/04/2024] [Revised: 08/02/2024] [Accepted: 08/14/2024] [Indexed: 09/12/2024] Open
Abstract
BACKGROUND Depressive symptoms are rising in the general population, but their associated factors are unclear. Although the link between sleep disturbances and depressive symptoms severity (DSS) is reported, the predictive role of sleep on DSS and the impact of anxiety and the brain on their relationship remained obscure. METHODS Using three population-based datasets (N = 1813), we trained the machine learning models in the primary dataset (N = 1101) to assess the predictive role of sleep quality, anxiety problems, and brain structural (and functional) measurements on DSS, then we tested our models' performance in two independent datasets (N = 378, N = 334) to test the generalizability of our findings. Furthermore, we applied our model to a smaller longitudinal subsample (N = 66). In addition, we performed a mediation analysis to identify the role of anxiety and brain measurements on the sleep quality and DSS association. FINDINGS Sleep quality could predict individual DSS (r = 0.43, R2 = 0.18, rMSE = 2.73), and adding anxiety, contrary to brain measurements, strengthened its prediction performance (r = 0.67, R2 = 0.45, rMSE = 2.25). Importantly, out-of-cohort validations in other cross-sectional datasets and a longitudinal subsample provided robust similar results. Furthermore, anxiety scores, contrary to brain measurements, mediated the association between sleep quality and DSS. INTERPRETATION Poor sleep quality could predict DSS at the individual subject level across three datasets. Anxiety scores not only increased the predictive model's performance but also mediated the link between sleep quality and DSS. FUNDING The study is supported by Helmholtz Imaging Platform grant (NimRLS, ZTI-PF-4-010), the Deutsche Forschungsgemeinschaft (DFG, GE 2835/2-1, GE 2835/4-1), the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation)-Project-ID 431549029-SFB 1451, the programme "Profilbildung 2020" (grant no. PROFILNRW-2020-107-A), an initiative of the Ministry of Culture and Science of the State of Northrhine Westphalia.
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Affiliation(s)
- Mahnaz Olfati
- Institute of Medical Science and Technology, Shahid Beheshti University, Tehran, Iran
| | - Fateme Samea
- Institute of Systems Neuroscience, Medical Faculty, Heinrich Heine University Düsseldorf, Düsseldorf, Germany; Institute of Neuroscience and Medicine, Brain & Behaviour (INM-7), Research Centre Jülich, Jülich, Germany
| | - Shahrooz Faghihroohi
- Institute of Medical Science and Technology, Shahid Beheshti University, Tehran, Iran
| | - Somayeh Maleki Balajoo
- Institute of Systems Neuroscience, Medical Faculty, Heinrich Heine University Düsseldorf, Düsseldorf, Germany; Institute of Neuroscience and Medicine, Brain & Behaviour (INM-7), Research Centre Jülich, Jülich, Germany
| | - Vincent Küppers
- Institute of Systems Neuroscience, Medical Faculty, Heinrich Heine University Düsseldorf, Düsseldorf, Germany; Institute of Neuroscience and Medicine, Brain & Behaviour (INM-7), Research Centre Jülich, Jülich, Germany; Department of Nuclear Medicine, University Hospital and Medical Faculty, University of Cologne, Cologne, Germany
| | - Sarah Genon
- Institute of Systems Neuroscience, Medical Faculty, Heinrich Heine University Düsseldorf, Düsseldorf, Germany; Institute of Neuroscience and Medicine, Brain & Behaviour (INM-7), Research Centre Jülich, Jülich, Germany
| | - Kaustubh Patil
- Institute of Systems Neuroscience, Medical Faculty, Heinrich Heine University Düsseldorf, Düsseldorf, Germany; Institute of Neuroscience and Medicine, Brain & Behaviour (INM-7), Research Centre Jülich, Jülich, Germany
| | - Simon B Eickhoff
- Institute of Systems Neuroscience, Medical Faculty, Heinrich Heine University Düsseldorf, Düsseldorf, Germany; Institute of Neuroscience and Medicine, Brain & Behaviour (INM-7), Research Centre Jülich, Jülich, Germany
| | - Masoud Tahmasian
- Institute of Systems Neuroscience, Medical Faculty, Heinrich Heine University Düsseldorf, Düsseldorf, Germany; Institute of Neuroscience and Medicine, Brain & Behaviour (INM-7), Research Centre Jülich, Jülich, Germany; Department of Nuclear Medicine, University Hospital and Medical Faculty, University of Cologne, Cologne, Germany.
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4
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Fotiadis P, Parkes L, Davis KA, Satterthwaite TD, Shinohara RT, Bassett DS. Structure-function coupling in macroscale human brain networks. Nat Rev Neurosci 2024; 25:688-704. [PMID: 39103609 DOI: 10.1038/s41583-024-00846-6] [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: 07/03/2024] [Indexed: 08/07/2024]
Abstract
Precisely how the anatomical structure of the brain gives rise to a repertoire of complex functions remains incompletely understood. A promising manifestation of this mapping from structure to function is the dependency of the functional activity of a brain region on the underlying white matter architecture. Here, we review the literature examining the macroscale coupling between structural and functional connectivity, and we establish how this structure-function coupling (SFC) can provide more information about the underlying workings of the brain than either feature alone. We begin by defining SFC and describing the computational methods used to quantify it. We then review empirical studies that examine the heterogeneous expression of SFC across different brain regions, among individuals, in the context of the cognitive task being performed, and over time, as well as its role in fostering flexible cognition. Last, we investigate how the coupling between structure and function is affected in neurological and psychiatric conditions, and we report how aberrant SFC is associated with disease duration and disease-specific cognitive impairment. By elucidating how the dynamic relationship between the structure and function of the brain is altered in the presence of neurological and psychiatric conditions, we aim to not only further our understanding of their aetiology but also establish SFC as a new and sensitive marker of disease symptomatology and cognitive performance. Overall, this Review collates the current knowledge regarding the regional interdependency between the macroscale structure and function of the human brain in both neurotypical and neuroatypical individuals.
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Affiliation(s)
- Panagiotis Fotiadis
- Department of Neuroscience, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA.
- Department of Bioengineering, University of Pennsylvania, Philadelphia, PA, USA.
- Department of Anaesthesiology, University of Michigan, Ann Arbor, MI, USA.
| | - Linden Parkes
- Department of Bioengineering, University of Pennsylvania, Philadelphia, PA, USA
| | - Kathryn A Davis
- Department of Neurology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Theodore D Satterthwaite
- Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
- Penn Statistics in Imaging and Visualization Center, Department of Biostatistics, Epidemiology, and Informatics, University of Pennsylvania, Philadelphia, PA, USA
- Center for Biomedical Image Computing & Analytics, University of Pennsylvania, Philadelphia, PA, USA
- Penn Lifespan Informatics and Neuroimaging Center, University of Pennsylvania, Philadelphia, PA, USA
| | - Russell T Shinohara
- Penn Statistics in Imaging and Visualization Center, Department of Biostatistics, Epidemiology, and Informatics, University of Pennsylvania, Philadelphia, PA, USA
- Center for Biomedical Image Computing & Analytics, University of Pennsylvania, Philadelphia, PA, USA
| | - Dani S Bassett
- Department of Bioengineering, University of Pennsylvania, Philadelphia, PA, USA.
- Department of Neurology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA.
- Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA.
- Department of Electrical & Systems Engineering, University of Pennsylvania, Philadelphia, PA, USA.
- Department of Physics & Astronomy, University of Pennsylvania, Philadelphia, PA, USA.
- Santa Fe Institute, Santa Fe, NM, USA.
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5
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Dean DC, Tisdall MD, Wisnowski JL, Feczko E, Gagoski B, Alexander AL, Edden RAE, Gao W, Hendrickson TJ, Howell BR, Huang H, Humphreys KL, Riggins T, Sylvester CM, Weldon KB, Yacoub E, Ahtam B, Beck N, Banerjee S, Boroday S, Caprihan A, Caron B, Carpenter S, Chang Y, Chung AW, Cieslak M, Clarke WT, Dale A, Das S, Davies-Jenkins CW, Dufford AJ, Evans AC, Fesselier L, Ganji SK, Gilbert G, Graham AM, Gudmundson AT, Macgregor-Hannah M, Harms MP, Hilbert T, Hui SCN, Irfanoglu MO, Kecskemeti S, Kober T, Kuperman JM, Lamichhane B, Landman BA, Lecour-Bourcher X, Lee EG, Li X, MacIntyre L, Madjar C, Manhard MK, Mayer AR, Mehta K, Moore LA, Murali-Manohar S, Navarro C, Nebel MB, Newman SD, Newton AT, Noeske R, Norton ES, Oeltzschner G, Ongaro-Carcy R, Ou X, Ouyang M, Parrish TB, Pekar JJ, Pengo T, Pierpaoli C, Poldrack RA, Rajagopalan V, Rettmann DW, Rioux P, Rosenberg JT, Salo T, Satterthwaite TD, Scott LS, Shin E, Simegn G, Simmons WK, Song Y, Tikalsky BJ, Tkach J, van Zijl PCM, Vannest J, Versluis M, Zhao Y, Zöllner HJ, Fair DA, Smyser CD, Elison JT. Quantifying brain development in the HEALthy Brain and Child Development (HBCD) Study: The magnetic resonance imaging and spectroscopy protocol. Dev Cogn Neurosci 2024; 70:101452. [PMID: 39341120 PMCID: PMC11466640 DOI: 10.1016/j.dcn.2024.101452] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/06/2024] [Revised: 08/29/2024] [Accepted: 09/13/2024] [Indexed: 09/30/2024] Open
Abstract
The HEALthy Brain and Child Development (HBCD) Study, a multi-site prospective longitudinal cohort study, will examine human brain, cognitive, behavioral, social, and emotional development beginning prenatally and planned through early childhood. The acquisition of multimodal magnetic resonance-based brain development data is central to the study's core protocol. However, application of Magnetic Resonance Imaging (MRI) methods in this population is complicated by technical challenges and difficulties of imaging in early life. Overcoming these challenges requires an innovative and harmonized approach, combining age-appropriate acquisition protocols together with specialized pediatric neuroimaging strategies. The HBCD MRI Working Group aimed to establish a core acquisition protocol for all 27 HBCD Study recruitment sites to measure brain structure, function, microstructure, and metabolites. Acquisition parameters of individual modalities have been matched across MRI scanner platforms for harmonized acquisitions and state-of-the-art technologies are employed to enable faster and motion-robust imaging. Here, we provide an overview of the HBCD MRI protocol, including decisions of individual modalities and preliminary data. The result will be an unparalleled resource for examining early neurodevelopment which enables the larger scientific community to assess normative trajectories from birth through childhood and to examine the genetic, biological, and environmental factors that help shape the developing brain.
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Affiliation(s)
- Douglas C Dean
- Department of Pediatrics, University of Wisconsin-Madison, Madison, WI, USA; Department of Medical Physics, University of Wisconsin-Madison, Madison, WI, USA; Waisman Center, University of Wisconsin-Madison, Madison, WI, USA.
| | - M Dylan Tisdall
- Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Jessica L Wisnowski
- Department of Pediatrics, Children's Hospital Los Angeles, University of Southern California Keck School of Medicine, Los Angeles, CA, USA; Department of Radiology, Children's Hospital Los Angeles, University of Southern California Keck School of Medicine, Los Angeles, CA, USA
| | - Eric Feczko
- Department of Pediatrics, University of Minnesota, Minneapolis, MN, USA; Masonic Institute for the Developing Brain, University of Minnesota, Minneapolis, MN, USA
| | - Borjan Gagoski
- Department of Radiology, Harvard Medical School, Boston, MA, USA; Fetal-Neonatal Neuroimaging & Developmental Science Center, Boston Children's Hospital, Boston, MA, USA
| | - Andrew L Alexander
- Department of Medical Physics, University of Wisconsin-Madison, Madison, WI, USA; Waisman Center, University of Wisconsin-Madison, Madison, WI, USA; Department of Psychiatry, University of Wisconsin-Madison, Madison, WI, USA
| | - Richard A E Edden
- Russell H. Morgan Department of Radiology and Radiological Science, The Johns Hopkins University School of Medicine, Baltimore, MD, USA; F. M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Institute, Baltimore, MD, USA
| | - Wei Gao
- Biomedical Imaging Research Institute, Cedars-Sinai Medical Center, Los Angeles, CA, USA; Department of Biomedical Sciences and Imaging, Cedars-Sinai Medical Center, Los Angeles, CA, USA
| | - Timothy J Hendrickson
- Masonic Institute for the Developing Brain, University of Minnesota, Minneapolis, MN, USA; Minnesota Supercomputing Institute, University of Minnesota, Minneapolis, MN, USA; Bioinformatics and Computational Biology Program, University of Minnesota, Minneapolis, MN, USA
| | - Brittany R Howell
- Fralin Biomedical Research Institute at VTC, Virginia Tech, Roanoke, VA, USA; Department of Human Development and Family Science, Virginia Tech, Blacksburg, VA, USA
| | - Hao Huang
- Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA; Department of Radiology, Children's Hospital of Philadelphia, Philadelphia, PA, USA
| | - Kathryn L Humphreys
- Department of Psychology and Human Development, Vanderbilt University, Nashville, TN, USA
| | - Tracy Riggins
- Department of Psychology, University of Maryland, College Park, MD, USA
| | - Chad M Sylvester
- Department of Psychiatry, Washington University in St. Louis, St. Louis, MO, USA; Department of Radiology, Washington University in St. Louis, St. Louis, MO, USA; Taylor Family Institute for Innovative Psychiatric Research, Washington University in St. Louis, St. Louis, MO, USA
| | - Kimberly B Weldon
- Masonic Institute for the Developing Brain, University of Minnesota, Minneapolis, MN, USA
| | - Essa Yacoub
- Center for Magnetic Resonance Research, University of Minnesota, Minneapolis, MN, USA
| | - Banu Ahtam
- Fetal-Neonatal Neuroimaging & Developmental Science Center, Boston Children's Hospital, Boston, MA, USA; Department of Pediatrics, Harvard Medical School, Boston, MA, USA
| | - Natacha Beck
- McGill Centre for Integrative Neuroscience, McGill University, Montréal, Québec, Canada; Montréal Neurological Institute-Hospital, Montréal, Québec, Canada; McConnell Brain Imaging Centre, McGill University, Montréal, Québec, Canada
| | | | - Sergiy Boroday
- McGill Centre for Integrative Neuroscience, McGill University, Montréal, Québec, Canada; Montréal Neurological Institute-Hospital, Montréal, Québec, Canada; McConnell Brain Imaging Centre, McGill University, Montréal, Québec, Canada
| | | | - Bryan Caron
- McGill Centre for Integrative Neuroscience, McGill University, Montréal, Québec, Canada; Montréal Neurological Institute-Hospital, Montréal, Québec, Canada; McConnell Brain Imaging Centre, McGill University, Montréal, Québec, Canada
| | - Samuel Carpenter
- Department of Psychiatry, Oregon Health & Science University, Portland, OR, USA
| | | | - Ai Wern Chung
- Fetal-Neonatal Neuroimaging & Developmental Science Center, Boston Children's Hospital, Boston, MA, USA; Department of Pediatrics, Harvard Medical School, Boston, MA, USA
| | - Matthew Cieslak
- Penn Lifespan Informatics and Neuroimaging Center (PennLINC), Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - William T Clarke
- Wellcome Centre for Integrative Neuroimaging, FMRIB, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, UK
| | - Anders Dale
- Department of Radiology, University of California San Diego, La Jolla, CA, USA; Multimodal Imaging Laboratory, University of California San Diego, La Jolla, CA, USA; Department of Psychiatry, University of California San Diego, La Jolla, CA, USA; Department of Neurosciences, University of California San Diego, La Jolla, CA, USA
| | - Samir Das
- McGill Centre for Integrative Neuroscience, McGill University, Montréal, Québec, Canada; Montréal Neurological Institute-Hospital, Montréal, Québec, Canada; McConnell Brain Imaging Centre, McGill University, Montréal, Québec, Canada
| | - Christopher W Davies-Jenkins
- Russell H. Morgan Department of Radiology and Radiological Science, The Johns Hopkins University School of Medicine, Baltimore, MD, USA; F. M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Institute, Baltimore, MD, USA
| | - Alexander J Dufford
- Department of Psychiatry and Center for Mental Health Innovation, Oregon Health & Science University, Portland, OR, USA
| | - Alan C Evans
- McGill Centre for Integrative Neuroscience, McGill University, Montréal, Québec, Canada; Montréal Neurological Institute-Hospital, Montréal, Québec, Canada; McConnell Brain Imaging Centre, McGill University, Montréal, Québec, Canada
| | - Laetitia Fesselier
- McGill Centre for Integrative Neuroscience, McGill University, Montréal, Québec, Canada; Montréal Neurological Institute-Hospital, Montréal, Québec, Canada; McConnell Brain Imaging Centre, McGill University, Montréal, Québec, Canada
| | - Sandeep K Ganji
- MR Clinical Science, Philips Healthcare, Best, the Netherlands
| | - Guillaume Gilbert
- MR Clinical Science, Philips Healthcare, Mississauga, Ontario, Canada
| | - Alice M Graham
- Department of Psychiatry, Oregon Health & Science University, Portland, OR, USA
| | - Aaron T Gudmundson
- Russell H. Morgan Department of Radiology and Radiological Science, The Johns Hopkins University School of Medicine, Baltimore, MD, USA; F. M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Institute, Baltimore, MD, USA
| | - Maren Macgregor-Hannah
- Masonic Institute for the Developing Brain, University of Minnesota, Minneapolis, MN, USA; Minnesota Supercomputing Institute, University of Minnesota, Minneapolis, MN, USA
| | - Michael P Harms
- Department of Psychiatry, Washington University in St. Louis, St. Louis, MO, USA
| | - Tom Hilbert
- Advanced Clinical Imaging Technology, Siemens Healthineers International AG, Lausanne, Switzerland,; Department of Radiology, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland,; LTS5, École Polytechnique Fédérale de Lausanne, Lausanne, Switzerland
| | - Steve C N Hui
- Developing Brain Institute, Children's National Hospital, Washington, DC, USA; Department of Radiology, The George Washington University School of Medicine and Health Sciences, Washington, DC, USA; Department of Pediatrics, The George Washington University School of Medicine and Health Sciences, Washington, DC, USA
| | - M Okan Irfanoglu
- Quantitative Medical Imaging Laboratory, National Institute of Biomedical Imaging and Bioengineering, National Institutes of Health, Bethesda, MD, USA
| | | | - Tobias Kober
- Advanced Clinical Imaging Technology, Siemens Healthineers International AG, Lausanne, Switzerland,; Department of Radiology, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland,; LTS5, École Polytechnique Fédérale de Lausanne, Lausanne, Switzerland
| | - Joshua M Kuperman
- Department of Radiology, University of California San Diego, La Jolla, CA, USA
| | - Bidhan Lamichhane
- Center for Health Sciences, Oklahoma State University, Tulsa, OK, USA
| | - Bennett A Landman
- Department of Electrical and Computer Engineering, Vanderbilt University, Nashville, TN, USA
| | - Xavier Lecour-Bourcher
- McGill Centre for Integrative Neuroscience, McGill University, Montréal, Québec, Canada; Montréal Neurological Institute-Hospital, Montréal, Québec, Canada; McConnell Brain Imaging Centre, McGill University, Montréal, Québec, Canada
| | - Erik G Lee
- Masonic Institute for the Developing Brain, University of Minnesota, Minneapolis, MN, USA; Minnesota Supercomputing Institute, University of Minnesota, Minneapolis, MN, USA; Bioinformatics and Computational Biology Program, University of Minnesota, Minneapolis, MN, USA
| | - Xu Li
- Russell H. Morgan Department of Radiology and Radiological Science, The Johns Hopkins University School of Medicine, Baltimore, MD, USA; F. M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Institute, Baltimore, MD, USA
| | - Leigh MacIntyre
- McGill Centre for Integrative Neuroscience, McGill University, Montréal, Québec, Canada; Montréal Neurological Institute-Hospital, Montréal, Québec, Canada; Lasso Informatics, Canada
| | - Cecile Madjar
- McGill Centre for Integrative Neuroscience, McGill University, Montréal, Québec, Canada; Montréal Neurological Institute-Hospital, Montréal, Québec, Canada; McConnell Brain Imaging Centre, McGill University, Montréal, Québec, Canada
| | - Mary Kate Manhard
- Department of Radiology, Cincinnati Children's Hospital Medical Center, Cincinnati, OH, USA; Department of Radiology, University of Cincinnati College of Medicine, Cincinnati, OH, USA
| | | | - Kahini Mehta
- Penn Lifespan Informatics and Neuroimaging Center (PennLINC), Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Lucille A Moore
- Masonic Institute for the Developing Brain, University of Minnesota, Minneapolis, MN, USA
| | - Saipavitra Murali-Manohar
- Russell H. Morgan Department of Radiology and Radiological Science, The Johns Hopkins University School of Medicine, Baltimore, MD, USA; F. M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Institute, Baltimore, MD, USA
| | - Cristian Navarro
- Center for Neurodevelopmental and Imaging Research, Kennedy Krieger Institute, Baltimore, MD, USA
| | - Mary Beth Nebel
- Center for Neurodevelopmental and Imaging Research, Kennedy Krieger Institute, Baltimore, MD, USA; Department of Neurology, The Johns Hopkins School of Medicine, Baltimore, MD, USA
| | - Sharlene D Newman
- Alabama Life Research Institute, University of Alabama, Tuscaloosa, AL, USA; Department of Psychology, University of Alabama, Tuscaloosa, AL, USA
| | - Allen T Newton
- Department of Radiology and Radiological Sciences, Vanderbilt University Medical Center, Nashville, TN, USA; Vanderbilt University Institute of Imaging Science, Vanderbilt University Medical Center, Nashville, TN, USA; Monroe Carell Jr. Children's Hospital at Vandebrilt, Vanderbilt University Medical Center, Nashville, TN, USA
| | | | - Elizabeth S Norton
- Department of Communication Sciences and Disorders, School of Communication, Northwestern University, Evanston, IL, USA; Department of Medical Social Sciences, Feinberg School of Medicine, Chicago, IL, USA
| | - Georg Oeltzschner
- Russell H. Morgan Department of Radiology and Radiological Science, The Johns Hopkins University School of Medicine, Baltimore, MD, USA; F. M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Institute, Baltimore, MD, USA
| | - Regis Ongaro-Carcy
- McGill Centre for Integrative Neuroscience, McGill University, Montréal, Québec, Canada; Montréal Neurological Institute-Hospital, Montréal, Québec, Canada; McConnell Brain Imaging Centre, McGill University, Montréal, Québec, Canada
| | - Xiawei Ou
- Department of Radiology, University of Arkansas for Medical Sciences, Little Rock, AR, USA; Arkansas Children's Research Institute, University of Arkansas for Medical Sciences, Little Rock, AR, USA
| | - Minhui Ouyang
- Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA; Department of Radiology, Children's Hospital of Philadelphia, Philadelphia, PA, USA
| | - Todd B Parrish
- Department of Radiology, Feinberg School of Medicine, Chicago, IL, USA; Department of Biomedical Engineering, Northwestern University, Evanston, IL, USA
| | - James J Pekar
- Russell H. Morgan Department of Radiology and Radiological Science, The Johns Hopkins University School of Medicine, Baltimore, MD, USA; F. M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Institute, Baltimore, MD, USA
| | - Thomas Pengo
- Masonic Institute for the Developing Brain, University of Minnesota, Minneapolis, MN, USA; Minnesota Supercomputing Institute, University of Minnesota, Minneapolis, MN, USA
| | - Carlo Pierpaoli
- Quantitative Medical Imaging Laboratory, National Institute of Biomedical Imaging and Bioengineering, National Institutes of Health, Bethesda, MD, USA
| | | | - Vidya Rajagopalan
- Department of Pediatrics, Children's Hospital Los Angeles, University of Southern California Keck School of Medicine, Los Angeles, CA, USA; Department of Radiology, Children's Hospital Los Angeles, University of Southern California Keck School of Medicine, Los Angeles, CA, USA
| | | | - Pierre Rioux
- McGill Centre for Integrative Neuroscience, McGill University, Montréal, Québec, Canada; Montréal Neurological Institute-Hospital, Montréal, Québec, Canada; McConnell Brain Imaging Centre, McGill University, Montréal, Québec, Canada
| | - Jens T Rosenberg
- Advanced Magnetic Resonance Imaging and Spectroscopy Facility, McKnight Brain Institute, University of Florida, Gainesville, FL, USA
| | - Taylor Salo
- Penn Lifespan Informatics and Neuroimaging Center (PennLINC), Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Theodore D Satterthwaite
- Penn Lifespan Informatics and Neuroimaging Center (PennLINC), Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Lisa S Scott
- Department of Psychology, University of Florida, Gainesville, FL, USA
| | - Eunkyung Shin
- Department of Psychology, Pennsylvania State University, University Park, PA, USA
| | - Gizeaddis Simegn
- Russell H. Morgan Department of Radiology and Radiological Science, The Johns Hopkins University School of Medicine, Baltimore, MD, USA; F. M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Institute, Baltimore, MD, USA
| | - W Kyle Simmons
- Department of Pharmacology and Physiology, Oklahoma State University Center for Health Sciences, Tulsa, OK, USA; OSU Biomedical Imaging Center, Oklahoma State University Center for Health Sciences, Tulsa, OK, USA
| | - Yulu Song
- Russell H. Morgan Department of Radiology and Radiological Science, The Johns Hopkins University School of Medicine, Baltimore, MD, USA; F. M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Institute, Baltimore, MD, USA
| | - Barry J Tikalsky
- Masonic Institute for the Developing Brain, University of Minnesota, Minneapolis, MN, USA
| | - Jean Tkach
- Department of Radiology, Cincinnati Children's Hospital Medical Center, Cincinnati, OH, USA; Department of Radiology, University of Cincinnati College of Medicine, Cincinnati, OH, USA
| | - Peter C M van Zijl
- Russell H. Morgan Department of Radiology and Radiological Science, The Johns Hopkins University School of Medicine, Baltimore, MD, USA; F. M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Institute, Baltimore, MD, USA
| | - Jennifer Vannest
- Department of Communication Sciences and Disorders, University of Cincinnati, Cincinnati, OH, USA; Communication Sciences Research Center, Cincinnati Children's Hospital Medical Center, Cincinnati, OH, USA
| | | | - Yansong Zhao
- MR Clinical Science, Philips Healthcare, Cleveland, OH, USA
| | - Helge J Zöllner
- Russell H. Morgan Department of Radiology and Radiological Science, The Johns Hopkins University School of Medicine, Baltimore, MD, USA; F. M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Institute, Baltimore, MD, USA
| | - Damien A Fair
- Department of Pediatrics, University of Minnesota, Minneapolis, MN, USA; Masonic Institute for the Developing Brain, University of Minnesota, Minneapolis, MN, USA; Institute of Child Development, University of Minnesota, Minneapolis, MN, USA.
| | - Christopher D Smyser
- Department of Neurology, Washington University in St. Louis, St. Louis, MO, USA; Department of Pediatrics, Washington University in St. Louis, St. Louis, MO, USA; Mallinckrodt Institute of Radiology, Washington University in St. Louis, St. Louis, MO, USA.
| | - Jed T Elison
- Department of Pediatrics, University of Minnesota, Minneapolis, MN, USA; Masonic Institute for the Developing Brain, University of Minnesota, Minneapolis, MN, USA; Institute of Child Development, University of Minnesota, Minneapolis, MN, USA.
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Casamitjana A, Mancini M, Robinson E, Peter L, Annunziata R, Althonayan J, Crampsie S, Blackburn E, Billot B, Atzeni A, Puonti O, Balbastre Y, Schmidt P, Hughes J, Augustinack JC, Edlow BL, Zöllei L, Thomas DL, Kliemann D, Bocchetta M, Strand C, Holton JL, Jaunmuktane Z, Iglesias JE. A next-generation, histological atlas of the human brain and its application to automated brain MRI segmentation. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.02.05.579016. [PMID: 39282320 PMCID: PMC11398399 DOI: 10.1101/2024.02.05.579016] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Indexed: 09/21/2024]
Abstract
Magnetic resonance imaging (MRI) is the standard tool to image the human brain in vivo. In this domain, digital brain atlases are essential for subject-specific segmentation of anatomical regions of interest (ROIs) and spatial comparison of neuroanatomy from different subjects in a common coordinate frame. High-resolution, digital atlases derived from histology (e.g., Allen atlas [7], BigBrain [13], Julich [15]), are currently the state of the art and provide exquisite 3D cytoarchitectural maps, but lack probabilistic labels throughout the whole brain. Here we present NextBrain, a next-generation probabilistic atlas of human brain anatomy built from serial 3D histology and corresponding highly granular delineations of five whole brain hemispheres. We developed AI techniques to align and reconstruct ~10,000 histological sections into coherent 3D volumes with joint geometric constraints (no overlap or gaps between sections), as well as to semi-automatically trace the boundaries of 333 distinct anatomical ROIs on all these sections. Comprehensive delineation on multiple cases enabled us to build the first probabilistic histological atlas of the whole human brain. Further, we created a companion Bayesian tool for automated segmentation of the 333 ROIs in any in vivo or ex vivo brain MRI scan using the NextBrain atlas. We showcase two applications of the atlas: automated segmentation of ultra-high-resolution ex vivo MRI and volumetric analysis of Alzheimer's disease and healthy brain ageing based on ~4,000 publicly available in vivo MRI scans. We publicly release: the raw and aligned data (including an online visualisation tool); the probabilistic atlas; the segmentation tool; and ground truth delineations for a 100 μm isotropic ex vivo hemisphere (that we use for quantitative evaluation of our segmentation method in this paper). By enabling researchers worldwide to analyse brain MRI scans at a superior level of granularity without manual effort or highly specific neuroanatomical knowledge, NextBrain holds promise to increase the specificity of MRI findings and ultimately accelerate our quest to understand the human brain in health and disease.
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Affiliation(s)
- Adrià Casamitjana
- Department of Medical Physics and Biomedical Engineering, University College London, London, United Kingdom
- Research Institute of Computer Vision and Robotics, University of Girona, Girona, Spain
| | - Matteo Mancini
- Department of Medical Physics and Biomedical Engineering, University College London, London, United Kingdom
- Department of Cardiovascular, Endocrine-Metabolic Diseases and Aging, Italian National Institute of Health, Rome, Italy
- Cardiff University Brain Research Imaging Centre, Cardiff University, Cardiff, United Kingdom
| | - Eleanor Robinson
- Department of Medical Physics and Biomedical Engineering, University College London, London, United Kingdom
| | - Loïc Peter
- Department of Medical Physics and Biomedical Engineering, University College London, London, United Kingdom
| | - Roberto Annunziata
- Department of Medical Physics and Biomedical Engineering, University College London, London, United Kingdom
| | - Juri Althonayan
- Department of Medical Physics and Biomedical Engineering, University College London, London, United Kingdom
| | - Shauna Crampsie
- Department of Medical Physics and Biomedical Engineering, University College London, London, United Kingdom
| | - Emily Blackburn
- Department of Medical Physics and Biomedical Engineering, University College London, London, United Kingdom
| | - Benjamin Billot
- Department of Medical Physics and Biomedical Engineering, University College London, London, United Kingdom
- Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology, Cambridge, MA, United States
| | - Alessia Atzeni
- Department of Medical Physics and Biomedical Engineering, University College London, London, United Kingdom
| | - Oula Puonti
- Danish Research Centre for Magnetic Resonance, Centre for Functional and Diagnostic Imaging and Research, Copenhagen University Hospital - Amager and Hvidovre, Copenhagen, Denmark
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital and Harvard Medical School, Boston, MA, United States
| | - Yaël Balbastre
- Department of Medical Physics and Biomedical Engineering, University College London, London, United Kingdom
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital and Harvard Medical School, Boston, MA, United States
| | - Peter Schmidt
- Advanced Research Computing Centre, University College London, London, United Kingdom
| | - James Hughes
- Advanced Research Computing Centre, University College London, London, United Kingdom
| | - Jean C Augustinack
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital and Harvard Medical School, Boston, MA, United States
| | - Brian L Edlow
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital and Harvard Medical School, Boston, MA, United States
- Center for Neurotechnology and Neurorecovery, Department of Neurology, Massachusetts General Hospital and Harvard Medical School, Boston, MA, United States
| | - Lilla Zöllei
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital and Harvard Medical School, Boston, MA, United States
| | - David L Thomas
- Dementia Research Centre, Department of Neurodegenerative Disease, UCL Queen Square Institute of Neurology, University College London, London, United Kingdom
- Neuroradiological Academic Unit, Department of Brain Repair and Rehabilitation, UCL Queen Square Institute of Neurology, University College London, London, United Kingdom
| | - Dorit Kliemann
- Department of Psychological and Brain Sciences, University of Iowa, Iowa City, IA, United States
| | - Martina Bocchetta
- Dementia Research Centre, Department of Neurodegenerative Disease, UCL Queen Square Institute of Neurology, University College London, London, United Kingdom
- Centre for Cognitive and Clinical Neuroscience, Division of Psychology, Department of Life Sciences, College of Health, Medicine and Life Sciences, Brunel University London, United Kingdom
| | - Catherine Strand
- Queen Square Brain Bank for Neurological Disorders, Department of Clinical and Movement Neurosciences, UCL Queen Square Institute of Neurology, University College London, London, United Kingdom
| | - Janice L Holton
- Queen Square Brain Bank for Neurological Disorders, Department of Clinical and Movement Neurosciences, UCL Queen Square Institute of Neurology, University College London, London, United Kingdom
| | - Zane Jaunmuktane
- Queen Square Brain Bank for Neurological Disorders, Department of Clinical and Movement Neurosciences, UCL Queen Square Institute of Neurology, University College London, London, United Kingdom
| | - Juan Eugenio Iglesias
- Department of Medical Physics and Biomedical Engineering, University College London, London, United Kingdom
- Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology, Cambridge, MA, United States
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital and Harvard Medical School, Boston, MA, United States
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7
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Menardi A, Spoa M, Vallesi A. Brain topology underlying executive functions across the lifespan: focus on the default mode network. Front Psychol 2024; 15:1441584. [PMID: 39295768 PMCID: PMC11408365 DOI: 10.3389/fpsyg.2024.1441584] [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: 05/31/2024] [Accepted: 08/19/2024] [Indexed: 09/21/2024] Open
Abstract
Introduction While traditional neuroimaging approaches to the study of executive functions (EFs) have typically employed task-evoked paradigms, resting state studies are gaining popularity as a tool for investigating inter-individual variability in the functional connectome and its relationship to cognitive performance outside of the scanner. Method Using resting state functional magnetic resonance imaging data from the Human Connectome Project Lifespan database, the present study capitalized on graph theory to chart cross-sectional variations in the intrinsic functional organization of the frontoparietal (FPN) and the default mode (DMN) networks in 500 healthy individuals (from 10 to 100 years of age), to investigate the neural underpinnings of EFs across the lifespan. Results Topological properties of both the FPN and DMN were associated with EF performance but not with a control task of picture naming, providing specificity in support for a tight link between neuro-functional and cognitive-behavioral efficiency within the EF domain. The topological organization of the DMN, however, appeared more sensitive to age-related changes relative to that of the FPN. Discussion The DMN matures earlier in life than the FPN and it ıs more susceptible to neurodegenerative changes. Because its activity is stronger in conditions of resting state, the DMN might be easier to measure in noncompliant populations and in those at the extremes of the life-span curve, namely very young or elder participants. Here, we argue that the study of its functional architecture in relation to higher order cognition across the lifespan might, thus, be of greater interest compared with what has been traditionally thought.
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Affiliation(s)
- A Menardi
- Department of Neuroscience, University of Padova, Padova, Italy
- Padova Neuroscience Center, University of Padova, Padova, Italy
| | - M Spoa
- Department of General Psychology, University of Padova, Padova, Italy
| | - A Vallesi
- Department of Neuroscience, University of Padova, Padova, Italy
- Padova Neuroscience Center, University of Padova, Padova, Italy
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Huang AS, Kang K, Vandekar S, Rogers BP, Heckers S, Woodward ND. Lifespan development of thalamic nuclei and characterizing thalamic nuclei abnormalities in schizophrenia using normative modeling. Neuropsychopharmacology 2024; 49:1518-1527. [PMID: 38480909 PMCID: PMC11319674 DOI: 10.1038/s41386-024-01837-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] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/04/2023] [Revised: 02/13/2024] [Accepted: 02/21/2024] [Indexed: 03/18/2024]
Abstract
Thalamic abnormalities have been repeatedly implicated in the pathophysiology of schizophrenia and other neurodevelopmental disorders. Uncovering the etiology of thalamic abnormalities and how they may contribute to illness phenotypes faces at least two obstacles. First, the typical developmental trajectories of thalamic nuclei and their association with cognition across the lifespan are largely unknown. Second, modest effect sizes indicate marked individual differences and pose a significant challenge to personalized medicine. To address these knowledge gaps, we characterized the development of thalamic nuclei volumes using normative models generated from the Human Connectome Project Lifespan datasets (5-100+ years), then applied them to an independent clinical cohort to determine the frequency of thalamic volume deviations in people with schizophrenia (17-61 years). Normative models revealed diverse non-linear age effects across the lifespan. Association nuclei exhibited negative age effects during youth but stabilized in adulthood until turning negative again with older age. Sensorimotor nuclei volumes remained relatively stable through youth and adulthood until also turning negative with older age. Up to 18% of individuals with schizophrenia exhibited abnormally small (i.e., below the 5th centile) mediodorsal and pulvinar volumes, and the degree of deviation, but not raw volumes, correlated with the severity of cognitive impairment. While case-control differences are robust, only a minority of patients demonstrate unusually small thalamic nuclei volumes. Normative modeling enables the identification of these individuals, which is a necessary step toward precision medicine.
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Affiliation(s)
- Anna S Huang
- Department of Psychiatry and Behavioral Sciences, Vanderbilt University Medical Center, Nashville, TN, USA.
| | - Kaidi Kang
- Department of Biostatistics, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Simon Vandekar
- Department of Biostatistics, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Baxter P Rogers
- Vanderbilt University Institute of Imaging Sciences, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Stephan Heckers
- Department of Psychiatry and Behavioral Sciences, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Neil D Woodward
- Department of Psychiatry and Behavioral Sciences, Vanderbilt University Medical Center, Nashville, TN, USA
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Mendez Colmenares A, Thomas ML, Anderson C, Arciniegas DB, Calhoun V, Choi IY, Kramer AF, Li K, Lee J, Lee P, Burzynska AZ. Testing the structural disconnection hypothesis: Myelin content correlates with memory in healthy aging. Neurobiol Aging 2024; 141:21-33. [PMID: 38810596 PMCID: PMC11290458 DOI: 10.1016/j.neurobiolaging.2024.05.013] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/12/2024] [Revised: 05/07/2024] [Accepted: 05/21/2024] [Indexed: 05/31/2024]
Abstract
INTRODUCTION The "structural disconnection" hypothesis of cognitive aging suggests that deterioration of white matter (WM), especially myelin, results in cognitive decline, yet in vivo evidence is inconclusive. METHODS We examined age differences in WM microstructure using Myelin Water Imaging and Diffusion Tensor Imaging in 141 healthy participants (age 20-79). We used the Virginia Cognitive Aging Project and the NIH Toolbox® to generate composites for memory, processing speed, and executive function. RESULTS Voxel-wise analyses showed that lower myelin water fraction (MWF), predominantly in prefrontal WM, genu of the corpus callosum, and posterior limb of the internal capsule was associated with reduced memory performance after controlling for age, sex, and education. In structural equation modeling, MWF in the prefrontal white matter and genu of the corpus callosum significantly mediated the effect of age on memory, whereas fractional anisotropy (FA) did not. DISCUSSION Our findings support the disconnection hypothesis, showing that myelin decline contributes to age-related memory loss and opens avenues for interventions targeting myelin health.
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Affiliation(s)
- Andrea Mendez Colmenares
- The BRAiN lab, Department of Human Development and Family Studies/Molecular, Cellular and Integrative Neurosciences, Colorado State University, Behavioral Sciences Building, 303, 410 W Pitkin St, Fort Collins, CO 80523, USA
| | - Michael L Thomas
- Department of Psychology, Colorado State University, Behavioral Sciences Building, 303, 410 W Pitkin, St, Fort Collins, CO 80523, USA
| | - Charles Anderson
- Department of Computer Science, Colorado State University, 456 University Ave #444, Fort Collins, CO 80521, USA
| | - David B Arciniegas
- Marcus Institute for Brain Health, University of Colorado Anschutz Medical Campus, 12348 E Montview Blvd, Aurora, CO 80045, USA
| | - Vince Calhoun
- Tri-institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State, Georgia Tech, Emory, 55 Park Pl NE, Atlanta, GA 30303, USA
| | - In-Young Choi
- Department of Neurology, Department of Radiology, Hoglund Biomedical Imaging Center, University of Kansas Medical Center, 3805 Eaton St, Kansas City, KS 66103, USA
| | - Arthur F Kramer
- Beckman Institute for Advanced Science and Technology at the University of Illinois, 405 N Mathews Ave, Urbana, IL 61801, USA; Center for Cognitive & Brain Health, Northeastern University, Address: 360 Huntington Ave, Boston, MA 02115, USA
| | - Kaigang Li
- Department of Health and Exercise Science, Colorado State University, 951 W Plum St, Fort Collins, CO 80521, USA
| | - Jongho Lee
- Department of Electrical and Computer Engineering, Seoul National University, 232 Gongneung-ro, Nowon-gu, Seoul 01811, South Korea
| | - Phil Lee
- Department of Radiology, Hoglund Biomedical Imaging Center, University of Kansas Medical Center, 3805 Eaton St, Kansas City, KS 66103, USA
| | - Agnieszka Z Burzynska
- The BRAiN lab, Department of Human Development and Family Studies/Molecular, Cellular and Integrative Neurosciences, Colorado State University, Behavioral Sciences Building, 303, 410 W Pitkin St, Fort Collins, CO 80523, USA.
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Callow DD, Spira AP, Bakker A, Smith JC. Sleep Quality Moderates the Associations between Cardiorespiratory Fitness and Hippocampal and Entorhinal Volume in Middle-Aged and Older Adults. Med Sci Sports Exerc 2024; 56:1740-1746. [PMID: 38742864 PMCID: PMC11326995 DOI: 10.1249/mss.0000000000003454] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/16/2024]
Abstract
INTRODUCTION/PURPOSE As individuals age, the entorhinal cortex (ERC) and hippocampus-crucial structures for memory-tend to atrophy, with related cognitive decline. Simultaneously, lifestyle factors that can be modified, such as exercise and sleep, have been separately linked to slowing of brain atrophy and functional decline. However, the synergistic impact of fitness and sleep on susceptible brain structures in aging adults remains uncertain. METHODS We examined both independent and interactive associations of fitness and subjective sleep quality with regard to ERC thickness and hippocampal volume in 598 middle-aged and older adults from the Human Connectome Lifespan Aging Project. Cardiorespiratory fitness was assessed using the 2-min walk test, whereas subjective sleep quality was measured with the continuous Pittsburgh Sleep Quality Index global score. High-resolution structural magnetic resonance imaging was used to examine mean ERC thickness and bilateral hippocampal volume. Through multiple linear regression analyses, we investigated the moderating effects of subjective sleep quality on the association between fitness and brain structure, accounting for age, sex, education, body mass index, gait speed, and subjective physical activity. RESULTS We found that greater cardiorespiratory fitness, but not subjective sleep quality, was positively associated with bilateral hippocampal volume and ERC thickness. Notably, significant interaction effects suggest that poor subjective sleep quality was associated with a weaker association between fitness and both hippocampal volume and ERC thickness. CONCLUSIONS Findings suggest the potential importance of both cardiorespiratory fitness and subjective sleep quality in preserving critical, age-vulnerable brain structures. Interventions targeting brain health should consider potential combined effects of sleep and fitness on brain health.
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Affiliation(s)
- Daniel D Callow
- Department of Psychiatry and Behavioral Sciences, Johns Hopkins University School of Medicine, Baltimore, MD
| | | | | | - J Carson Smith
- Department of Kinesiology, University of Maryland, College Park, MD
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11
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Hoang-Dang B, Halavi SE, Rotstein NM, Spivak NM, Dang NH, Cvijanovic L, Hiller SH, Vallejo-Martelo M, Rosenberg BM, Swenson A, Becerra S, Sun M, Revett ME, Kronemyer D, Berlow R, Craske MG, Suthana N, Monti MM, Zbozinek TD, Bookheimer SY, Kuhn TP. Transcranial Focused Ultrasound Targeting the Amygdala May Increase Psychophysiological and Subjective Negative Emotional Reactivity in Healthy Older Adults. BIOLOGICAL PSYCHIATRY GLOBAL OPEN SCIENCE 2024; 4:100342. [PMID: 39092138 PMCID: PMC11293512 DOI: 10.1016/j.bpsgos.2024.100342] [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: 01/18/2024] [Revised: 05/22/2024] [Accepted: 05/27/2024] [Indexed: 08/04/2024] Open
Abstract
Background The amygdala is highly implicated in an array of psychiatric disorders but is not accessible using currently available noninvasive neuromodulatory techniques. Low-intensity transcranial focused ultrasound (TFUS) is a neuromodulatory technique that has the capability of reaching subcortical regions noninvasively. Methods We studied healthy older adult participants (N = 21, ages 48-79 years) who received TFUS targeting the right amygdala and left entorhinal cortex (active control region) using a 2-visit within-participant crossover design. Before and after TFUS, behavioral measures were collected via the State-Trait Anxiety Inventory and an emotional reactivity and regulation task utilizing neutral and negatively valenced images from the International Affective Picture System. Heart rate and self-reported emotional valence and arousal were measured during the emotional reactivity and regulation task to investigate subjective and physiological responses to the task. Results Significant increases in both self-reported arousal in response to negative images and heart rate during emotional reactivity and regulation task intertrial intervals were observed when TFUS targeted the amygdala; these changes were not evident when the entorhinal cortex was targeted. No significant changes were found for state anxiety, self-reported valence to the negative images, cardiac response to the negative images, or emotion regulation. Conclusions The results of this study provide preliminary evidence that a single session of TFUS targeting the amygdala may alter psychophysiological and subjective emotional responses, indicating some potential for future neuropsychiatric applications. However, more work on TFUS parameters and targeting optimization is necessary to determine how to elicit changes in a more clinically advantageous way.
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Affiliation(s)
- Bianca Hoang-Dang
- Department of Psychiatry and Behavioral Sciences, University of California, Los Angeles, Los Angeles, California
| | - Sabrina E. Halavi
- Department of Psychiatry and Behavioral Sciences, University of California, Los Angeles, Los Angeles, California
| | - Natalie M. Rotstein
- Department of Psychiatry and Behavioral Sciences, University of California, Los Angeles, Los Angeles, California
| | - Norman M. Spivak
- Department of Psychiatry and Behavioral Sciences, University of California, Los Angeles, Los Angeles, California
- Department of Neurosurgery, University of California, Los Angeles, Los Angeles, California
- UCLA David Geffen School of Medicine Medical Scientist Training Program, University of California, Los Angeles, Los Angeles, California
| | - Nolan H. Dang
- Department of Psychiatry and Behavioral Sciences, University of California, Los Angeles, Los Angeles, California
- Department of Radiology, University of Colorado School of Medicine, Aurora, Colorado
| | - Luka Cvijanovic
- Neuroscience Interdepartmental Program, University of California, Los Angeles, Los Angeles, California
| | - Sonja H. Hiller
- Department of Psychiatry and Behavioral Sciences, University of California, Los Angeles, Los Angeles, California
| | - Mauricio Vallejo-Martelo
- Department of Psychiatry and Behavioral Sciences, University of California, Los Angeles, Los Angeles, California
- Department of Neurosurgery, University of California, Los Angeles, Los Angeles, California
- Department of Bioengineering, University of California, Los Angeles, Los Angeles, California
| | - Benjamin M. Rosenberg
- Department of Psychology, University of California, Los Angeles, Los Angeles, California
| | - Andrew Swenson
- Neuroscience Interdepartmental Program, University of California, Los Angeles, Los Angeles, California
| | - Sergio Becerra
- Department of Psychiatry and Behavioral Sciences, University of California, Los Angeles, Los Angeles, California
| | - Michael Sun
- Department of Psychology, University of California, Los Angeles, Los Angeles, California
- Department of Psychological and Brain Sciences, Dartmouth College, Hanover, New Hampshire
| | - Malina E. Revett
- Department of Psychiatry and Behavioral Sciences, University of California, Los Angeles, Los Angeles, California
| | - David Kronemyer
- Department of Psychiatry and Behavioral Sciences, University of California, Los Angeles, Los Angeles, California
| | - Rustin Berlow
- American Brain Stimulation Clinic, Del Mar, California
| | - Michelle G. Craske
- Department of Psychology, University of California, Los Angeles, Los Angeles, California
| | - Nanthia Suthana
- Department of Psychiatry and Behavioral Sciences, University of California, Los Angeles, Los Angeles, California
- Department of Neurosurgery, University of California, Los Angeles, Los Angeles, California
- Department of Bioengineering, University of California, Los Angeles, Los Angeles, California
- Department of Psychology, University of California, Los Angeles, Los Angeles, California
| | - Martin M. Monti
- Department of Psychiatry and Behavioral Sciences, University of California, Los Angeles, Los Angeles, California
- Department of Neurosurgery, University of California, Los Angeles, Los Angeles, California
- Department of Psychology, University of California, Los Angeles, Los Angeles, California
| | - Tomislav D. Zbozinek
- Department of Psychiatry and Behavioral Sciences, University of California, Los Angeles, Los Angeles, California
- Department of Psychology, University of California, Los Angeles, Los Angeles, California
| | - Susan Y. Bookheimer
- Department of Psychiatry and Behavioral Sciences, University of California, Los Angeles, Los Angeles, California
| | - Taylor P. Kuhn
- Department of Psychiatry and Behavioral Sciences, University of California, Los Angeles, Los Angeles, California
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Feng Y, Villalón-Reina JE, Nir TM, Chandio BQ, Jahanshad N, Thompson PM. BundleAGE: Predicting White Matter Age using Along-Tract Microstructural Profiles from Diffusion MRI. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.08.16.608347. [PMID: 39229061 PMCID: PMC11370403 DOI: 10.1101/2024.08.16.608347] [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: 09/05/2024]
Abstract
Brain Age Gap Estimation (BrainAGE) is an estimate of the gap between a person's chronological age (CA) and a measure of their brain's 'biological age' (BA). This metric is often used as a marker of accelerated aging, albeit with some caveats. Age prediction models trained on brain structural and functional MRI have been employed to derive BrainAGE biomarkers, for predicting the risk of neurodegeneration. While voxel-based and along-tract microstructural maps from diffusion MRI have been used to study brain aging, no studies have evaluated along-tract microstructure for computing BrainAGE. In this study, we train machine learning models to predict a person's age using along-tract microstructural profiles from diffusion tensor imaging. We were able to demonstrate differential aging patterns across different white matter bundles and microstructural measures. The novel Bundle Age Gap Estimation (BundleAGE) biomarker shows potential in quantifying risk factors for neurodegenerative diseases and aging, while incorporating finer scale information throughout white matter bundles.
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Affiliation(s)
- Yixue Feng
- Imaging Genetics Center, Mark and Mary Stevens Institute for Neuroimaging and Informatics, Keck School of Medicine, University of Southern California, Marina del Rey, CA, United States
| | - Julio E Villalón-Reina
- Imaging Genetics Center, Mark and Mary Stevens Institute for Neuroimaging and Informatics, Keck School of Medicine, University of Southern California, Marina del Rey, CA, United States
| | - Talia M Nir
- Imaging Genetics Center, Mark and Mary Stevens Institute for Neuroimaging and Informatics, Keck School of Medicine, University of Southern California, Marina del Rey, CA, United States
| | - Bramsh Q Chandio
- Imaging Genetics Center, Mark and Mary Stevens Institute for Neuroimaging and Informatics, Keck School of Medicine, University of Southern California, Marina del Rey, CA, United States
| | - Neda Jahanshad
- Imaging Genetics Center, Mark and Mary Stevens Institute for Neuroimaging and Informatics, Keck School of Medicine, University of Southern California, Marina del Rey, CA, United States
| | - Paul M Thompson
- Imaging Genetics Center, Mark and Mary Stevens Institute for Neuroimaging and Informatics, Keck School of Medicine, University of Southern California, Marina del Rey, CA, United States
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Taghvaei M, Jones CK, Luna LP, Gujar SK, Sair HI. Asymmetry of the Frontal Aslant Tract Depends on Handedness. AJNR Am J Neuroradiol 2024; 45:1090-1097. [PMID: 38964863 PMCID: PMC11383403 DOI: 10.3174/ajnr.a8270] [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: 12/22/2023] [Accepted: 02/28/2024] [Indexed: 07/06/2024]
Abstract
BACKGROUND AND PURPOSE The human brain displays structural and functional disparities between its hemispheres, with such asymmetry extending to the frontal aslant tract. This plays a role in a variety of cognitive functions, including speech production, language processing, and executive functions. However, the factors influencing the laterality of the frontal aslant tract remain incompletely understood. Handedness is hypothesized to impact frontal aslant tract laterality, given its involvement in both language and motor control. In this study, we aimed to investigate the relationship between handedness and frontal aslant tract lateralization, providing insight into this aspect of brain organization. MATERIALS AND METHODS The Automated Tractography Pipeline was used to generate the frontal aslant tract for both right and left hemispheres in a cohort of 720 subjects sourced from the publicly available Human Connectome Project in Aging database. Subsequently, macrostructural and microstructural parameters of the right and left frontal aslant tract were extracted for each individual in the study population. The Edinburgh Handedness Inventory scores were used for the classification of handedness, and a comparative analysis across various handedness groups was performed. RESULTS An age-related decline in both macrostructural parameters and microstructural integrity was noted within the studied population. The frontal aslant tract demonstrated a greater volume and larger diameter in male subjects compared with female participants. Additionally, a left-side laterality of the frontal aslant tract was observed within the general population. In the right-handed group, the volume (P < .001), length (P < .001), and diameter (P = .004) of the left frontal aslant tract were found to be higher than those of the right frontal aslant tract. Conversely, in the left-handed group, the volume (P = .040) and diameter (P = .032) of the left frontal aslant tract were lower than those of the right frontal aslant tract. Furthermore, in the right-handed group, the volume and diameter of the frontal aslant tract showed left-sided lateralization, while in the left-handed group, a right-sided lateralization was evident. CONCLUSIONS The laterality of the frontal aslant tract appears to differ with handedness. This finding highlights the complex interaction between brain lateralization and handedness, emphasizing the importance of considering handedness as a factor in evaluating brain structure and function.
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Affiliation(s)
- Mohammad Taghvaei
- From the Department of Neurology (M.T.), University of Pennsylvania, Philadelphia, Pennsylvania
| | - Craig K Jones
- Department of Computer Science (C.K.J.), The Whiting School of Engineering, Johns Hopkins University, Baltimore, Maryland
- The Malone Center for Engineering in Healthcare (C.K.J., H.I.S.), The Whiting School of Engineering, Johns Hopkins University, Baltimore, Maryland
- The Russell. H. Morgan Department of Radiology and Radiological Science (C.K.J., L.P.L., S.K.G., H.I.S.), Johns Hopkins University School of Medicine, Baltimore, Maryland
| | - Licia P Luna
- The Russell. H. Morgan Department of Radiology and Radiological Science (C.K.J., L.P.L., S.K.G., H.I.S.), Johns Hopkins University School of Medicine, Baltimore, Maryland
| | - Sachin K Gujar
- The Russell. H. Morgan Department of Radiology and Radiological Science (C.K.J., L.P.L., S.K.G., H.I.S.), Johns Hopkins University School of Medicine, Baltimore, Maryland
| | - Haris I Sair
- The Malone Center for Engineering in Healthcare (C.K.J., H.I.S.), The Whiting School of Engineering, Johns Hopkins University, Baltimore, Maryland
- The Russell. H. Morgan Department of Radiology and Radiological Science (C.K.J., L.P.L., S.K.G., H.I.S.), Johns Hopkins University School of Medicine, Baltimore, Maryland
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14
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Rubbert C, Wolf L, Vach M, Ivan VL, Hedderich DM, Gaser C, Dahnke R, Caspers J. Normal cohorts in automated brain atrophy estimation: how many healthy subjects to include? Eur Radiol 2024; 34:5276-5286. [PMID: 38189981 PMCID: PMC11255074 DOI: 10.1007/s00330-023-10522-5] [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: 04/03/2023] [Revised: 11/17/2023] [Accepted: 11/25/2023] [Indexed: 01/09/2024]
Abstract
OBJECTIVES This study investigates the influence of normal cohort (NC) size and the impact of different NCs on automated MRI-based brain atrophy estimation. METHODS A pooled NC of 3945 subjects (NCpool) was retrospectively created from five publicly available cohorts. Voxel-wise gray matter volume atrophy maps were calculated for 48 Alzheimer's disease (AD) patients (55-82 years) using veganbagel and dynamic normal templates with an increasing number of healthy subjects randomly drawn from NCpool (initially three, and finally 100 subjects). Over 100 repeats of the process, the mean over a voxel-wise standard deviation of gray matter z-scores was established and plotted against the number of subjects in the templates. The knee point of these curves was defined as the minimum number of subjects required for consistent brain atrophy estimation. Atrophy maps were calculated using each NC for AD patients and matched healthy controls (HC). Two readers rated the extent of mesiotemporal atrophy to discriminate AD/HC. RESULTS The maximum knee point was at 15 subjects. For 21 AD/21 HC, a sufficient number of subjects were available in each NC for validation. Readers agreed on the AD diagnosis in all cases (Kappa for the extent of atrophy, 0.98). No differences in diagnoses between NCs were observed (intraclass correlation coefficient, 0.91; Cochran's Q, p = 0.19). CONCLUSION At least 15 subjects should be included in age- and sex-specific normal templates for consistent brain atrophy estimation. In the study's context, qualitative interpretation of regional atrophy allows reliable AD diagnosis with a high inter-reader agreement, irrespective of the NC used. CLINICAL RELEVANCE STATEMENT The influence of normal cohorts (NCs) on automated brain atrophy estimation, typically comparing individual scans to NCs, remains largely unexplored. Our study establishes the minimum number of NC-subjects needed and demonstrates minimal impact of different NCs on regional atrophy estimation. KEY POINTS • Software-based brain atrophy estimation often relies on normal cohorts for comparisons. • At least 15 subjects must be included in an age- and sex-specific normal cohort. • Using different normal cohorts does not influence regional atrophy estimation.
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Affiliation(s)
- Christian Rubbert
- Department of Diagnostic and Interventional Radiology, Medical Faculty and University Hospital Düsseldorf, Heinrich-Heine-University, Düsseldorf, Germany.
| | - Luisa Wolf
- Department of Diagnostic and Interventional Radiology, Medical Faculty and University Hospital Düsseldorf, Heinrich-Heine-University, Düsseldorf, Germany
| | - Marius Vach
- Department of Diagnostic and Interventional Radiology, Medical Faculty and University Hospital Düsseldorf, Heinrich-Heine-University, Düsseldorf, Germany
| | - Vivien L Ivan
- Department of Diagnostic and Interventional Radiology, Medical Faculty and University Hospital Düsseldorf, Heinrich-Heine-University, Düsseldorf, Germany
| | - Dennis M Hedderich
- Department of Diagnostic and Interventional Neuroradiology, School of Medicine, Technical University of Munich, D-81675, Munich, Germany
| | - Christian Gaser
- Department of Psychiatry and Psychotherapy, Jena University Hospital, D-07745, Jena, Germany
- Department of Neurology, Jena University Hospital, D-07745, Jena, Germany
- German Center for Mental Health (DZPG), Jena, Germany
| | - Robert Dahnke
- Department of Psychiatry and Psychotherapy, Jena University Hospital, D-07745, Jena, Germany
- Department of Neurology, Jena University Hospital, D-07745, Jena, Germany
- German Center for Mental Health (DZPG), Jena, Germany
- Center of Functionally Integrative Neuroscience, Aarhus University, 8000, Aarhus, Denmark
| | - Julian Caspers
- Department of Diagnostic and Interventional Radiology, Medical Faculty and University Hospital Düsseldorf, Heinrich-Heine-University, Düsseldorf, Germany
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Wenzel ES, Van Doorn JL, Schroeder RA, Ances B, Bookheimer S, Terpstra M, Woods RP, Maki PM. Mental health during the COVID-19 pandemic: the importance of social support in midlife women. Climacteric 2024; 27:373-381. [PMID: 38695574 PMCID: PMC11362980 DOI: 10.1080/13697137.2024.2340476] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/09/2023] [Revised: 03/26/2024] [Accepted: 04/03/2024] [Indexed: 05/16/2024]
Abstract
OBJECTIVE This study aimed to examine sex differences in factors associated with mood and anxiety in midlife men and women during the COVID-19 pandemic. METHODS During a remote visit, 312 adults aged 40-60 years (167 female; 23.6% perimenopausal) from the Human Connectome Project in Aging completed PROMIS measures of depression, anxiety and anger/irritability; perceived stress; and questions about social support, financial stress and menopause stage. Multivariate linear regression models assessed sex differences in mental health and the association of social support, financial stress and menopause stage with mental health. RESULTS Anxiety was higher in women than in men (b = 2.39, p = 0.02). For women only, decreased social support was associated with increased anxiety (b = -2.26, p = 0.002), anger/irritability (b = -1.89, p = 0.02) and stress (b = -1.67, p = 0.002). For women only, not having close family was associated with increased depressive symptoms (b = -6.60, p = 0.01) and stress (b = -7.03, p < 0.001). For both sexes, having children was associated with lower depressive symptoms (b = -3.08, p = 0.002), anxiety (b = -1.93, p = 0.07), anger/irritability (b = -2.73, p = 0.02) and stress (b = -1.44, p = 0.07). Menopause stage was unrelated to mental health. CONCLUSION Social support, but not financial stress, influenced mental health during the COVID-19 pandemic at midlife, particularly for women.
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Affiliation(s)
| | - Jacob L. Van Doorn
- Department of Psychiatry, University of Illinois at Chicago, Chicago, IL, USA
| | | | - Beau Ances
- Department of Neurology, WA University in St. Louis, St. Louis, MO, USA
| | - Susan Bookheimer
- Department of Psychiatry and Biobehavioral Sciences, David Geffen School of Medicine at UCLA, University of CA, Los Angeles, CA, USA
| | | | - Roger P. Woods
- Department of Psychiatry and Biobehavioral Sciences, David Geffen School of Medicine at UCLA, University of CA, Los Angeles, CA, USA
- Department of Neurology, David Geffen School of Medicine at UCLA, University of California, Los Angeles, CA, USA
| | - Pauline M. Maki
- Department of Psychology, University of IL at Chicago, Chicago, IL, USA
- Department of Psychiatry, University of Illinois at Chicago, Chicago, IL, USA
- Department of Obstetrics & Gynecology, University of Illinois at Chicago, Chicago, IL, USA
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16
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Geraets AFJ, Schram MT, Jansen JFA, Köhler S, van Boxtel MPJ, Eussen SJPM, Koster A, Stehouwer CDA, Bosma H, Leist AK. The associations of socioeconomic position with structural brain damage and connectivity and cognitive functioning: The Maastricht Study. Soc Sci Med 2024; 355:117111. [PMID: 39018997 DOI: 10.1016/j.socscimed.2024.117111] [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: 01/16/2024] [Revised: 07/05/2024] [Accepted: 07/06/2024] [Indexed: 07/19/2024]
Abstract
BACKGROUND Socioeconomic inequalities in cognitive impairment may partly act through structural brain damage and reduced connectivity. This study investigated the extent to which the association of early-life socioeconomic position (SEP) with later-life cognitive functioning is mediated by later-life SEP, and whether the associations of SEP with later-life cognitive functioning can be explained by structural brain damage and connectivity. METHODS We used cross-sectional data from the Dutch population-based Maastricht Study (n = 4,839; mean age 59.2 ± 8.7 years, 49.8% women). Early-life SEP was assessed by self-reported poverty during childhood and parental education. Later-life SEP included education, occupation, and current household income. Participants underwent cognitive testing and 3-T magnetic resonance imaging to measure volumes of white matter hyperintensities, grey matter, white matter, cerebrospinal fluid, and structural connectivity. Multiple linear regression analyses tested the associations between SEP, markers of structural brain damage and connectivity, and cognitive functioning. Mediation was tested using structural equation modeling. RESULTS Although there were direct associations between both indicators of SEP and later-life cognitive functioning, a large part of the association between early-life SEP and later-life cognitive functioning was explained by later-life SEP (72.2%). The extent to which structural brain damage or connectivity acted as mediators between SEP and cognitive functioning was small (up to 5.9%). CONCLUSIONS We observed substantial SEP differences in later-life cognitive functioning. Associations of structural brain damage and connectivity with cognitive functioning were relatively small, and only marginally explained the SEP gradients in cognitive functioning.
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Affiliation(s)
- Anouk F J Geraets
- Department of Social Sciences, University of Luxembourg, Esch-Sur-Alzette, Luxembourg.
| | - Miranda T Schram
- Department of Psychiatry and Neuropsychology, Maastricht, The Netherlands; Department of Internal Medicine, Maastricht, The Netherlands; Heart and Vascular Centre, Maastricht, The Netherlands; School for Mental Health and Neuroscience (MHeNs), Maastricht, The Netherlands; School for Cardiovascular Diseases (CARIM), Maastricht, The Netherlands
| | - Jacobus F A Jansen
- School for Mental Health and Neuroscience (MHeNs), Maastricht, The Netherlands; Department of Radiology, Maastricht, The Netherlands
| | - Sebastian Köhler
- Department of Psychiatry and Neuropsychology, Maastricht, The Netherlands; School for Mental Health and Neuroscience (MHeNs), Maastricht, The Netherlands; Alzheimer Centrum Limburg, Maastricht, The Netherlands
| | - Martin P J van Boxtel
- Department of Psychiatry and Neuropsychology, Maastricht, The Netherlands; School for Mental Health and Neuroscience (MHeNs), Maastricht, The Netherlands; Alzheimer Centrum Limburg, Maastricht, The Netherlands
| | - Simone J P M Eussen
- School for Cardiovascular Diseases (CARIM), Maastricht, The Netherlands; Department of Epidemiology, Maastricht, The Netherlands; Care and Public Health Research Institute (CAPHRI), Maastricht, The Netherlands
| | - Annemarie Koster
- Care and Public Health Research Institute (CAPHRI), Maastricht, The Netherlands; Department of Social Medicine, Maastricht University Medical Centre+ (MUMC+), Maastricht, the Netherlands
| | - Coen D A Stehouwer
- Department of Internal Medicine, Maastricht, The Netherlands; School for Cardiovascular Diseases (CARIM), Maastricht, The Netherlands
| | - Hans Bosma
- Care and Public Health Research Institute (CAPHRI), Maastricht, The Netherlands; Department of Social Medicine, Maastricht University Medical Centre+ (MUMC+), Maastricht, the Netherlands
| | - Anja K Leist
- Department of Social Sciences, University of Luxembourg, Esch-Sur-Alzette, Luxembourg
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17
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Ge YJ, Fu Y, Gong W, Cheng W, Yu JT. Genetic architecture of brain morphology and overlap with neuropsychiatric traits. Trends Genet 2024; 40:706-717. [PMID: 38702264 DOI: 10.1016/j.tig.2024.04.005] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/12/2024] [Revised: 04/05/2024] [Accepted: 04/12/2024] [Indexed: 05/06/2024]
Abstract
Uncovering the genetic architectures of brain morphology offers valuable insights into brain development and disease. Genetic association studies of brain morphological phenotypes have discovered thousands of loci. However, interpretation of these loci presents a significant challenge. One potential solution is exploring the genetic overlap between brain morphology and disorders, which can improve our understanding of their complex relationships, ultimately aiding in clinical applications. In this review, we examine current evidence on the genetic associations between brain morphology and neuropsychiatric traits. We discuss the impact of these associations on the diagnosis, prediction, and treatment of neuropsychiatric diseases, along with suggestions for future research directions.
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Affiliation(s)
- Yi-Jun Ge
- Department of Neurology and National Center for Neurological Disorders, Huashan Hospital, State Key Laboratory of Medical Neurobiology and MOE Frontiers Center for Brain Science, Shanghai Medical College, Fudan University, Shanghai, China
| | - Yan Fu
- Department of Neurology, Qingdao Municipal Hospital, Qingdao University, Qingdao, 266071, China
| | - Weikang Gong
- School of Data Science, Fudan University, Shanghai, China; Centre for Functional MRI of the Brain (FMRIB), Nuffield Department of Clinical Neurosciences, Wellcome Centre for Integrative Neuroimaging, University of Oxford, Oxford, OX3 9DU, UK
| | - Wei Cheng
- Department of Neurology and National Center for Neurological Disorders, Huashan Hospital, State Key Laboratory of Medical Neurobiology and MOE Frontiers Center for Brain Science, Shanghai Medical College, Fudan University, Shanghai, China; Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, China; Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence, Fudan University, Ministry of Education, Shanghai, China.
| | - Jin-Tai Yu
- Department of Neurology and National Center for Neurological Disorders, Huashan Hospital, State Key Laboratory of Medical Neurobiology and MOE Frontiers Center for Brain Science, Shanghai Medical College, Fudan University, Shanghai, China.
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18
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Singh K, Barsoum S, Schilling KG, An Y, Ferrucci L, Benjamini D. Neuronal microstructural changes in the human brain are associated with neurocognitive aging. Aging Cell 2024; 23:e14166. [PMID: 38659245 PMCID: PMC11258428 DOI: 10.1111/acel.14166] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/18/2023] [Revised: 02/22/2024] [Accepted: 03/19/2024] [Indexed: 04/26/2024] Open
Abstract
Gray matter (GM) alterations play a role in aging-related disorders like Alzheimer's disease and related dementias, yet MRI studies mainly focus on macroscopic changes. Although reliable indicators of atrophy, morphological metrics like cortical thickness lack the sensitivity to detect early changes preceding visible atrophy. Our study aimed at exploring the potential of diffusion MRI in unveiling sensitive markers of cortical and subcortical age-related microstructural changes and assessing their associations with cognitive and behavioral deficits. We leveraged the Human Connectome Project-Aging cohort that included 707 participants (394 female; median age = 58, range = 36-90 years) and applied the powerful mean apparent diffusion propagator model to measure microstructural parameters, along with comprehensive behavioral and cognitive test scores. Both macro- and microstructural GM characteristics were strongly associated with age, with widespread significant microstructural correlations reflective of cellular morphological changes, reduced cellular density, increased extracellular volume, and increased membrane permeability. Importantly, when correlating MRI and cognitive test scores, our findings revealed no link between macrostructural volumetric changes and neurobehavioral performance. However, we found that cellular and extracellular alterations in cortical and subcortical GM regions were associated with neurobehavioral performance. Based on these findings, it is hypothesized that increased microstructural heterogeneity and decreased neurite orientation dispersion precede macrostructural changes, and that they play an important role in subsequent cognitive decline. These alterations are suggested to be early markers of neurocognitive performance that may distinctly aid in identifying the mechanisms underlying phenotypic aging and subsequent age-related functional decline.
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Affiliation(s)
- Kavita Singh
- Multiscale Imaging and Integrative Biophysics UnitNational Institute on Aging, NIHBaltimoreMarylandUSA
| | - Stephanie Barsoum
- Multiscale Imaging and Integrative Biophysics UnitNational Institute on Aging, NIHBaltimoreMarylandUSA
| | - Kurt G. Schilling
- Department of Radiology and Radiological SciencesVanderbilt University Medical CenterNashvilleTennesseeUSA
| | - Yang An
- Brain Aging and Behavior SectionNational Institute on Aging, NIHBaltimoreMarylandUSA
| | - Luigi Ferrucci
- Translational Gerontology BranchNational Institute on Aging, NIHBaltimoreMarylandUSA
| | - Dan Benjamini
- Multiscale Imaging and Integrative Biophysics UnitNational Institute on Aging, NIHBaltimoreMarylandUSA
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19
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Kennedy E, Liebel SW, Lindsey HM, Vadlamani S, Lei PW, Adamson MM, Alda M, Alonso-Lana S, Anderson TJ, Arango C, Asarnow RF, Avram M, Ayesa-Arriola R, Babikian T, Banaj N, Bird LJ, Borgwardt S, Brodtmann A, Brosch K, Caeyenberghs K, Calhoun VD, Chiaravalloti ND, Cifu DX, Crespo-Facorro B, Dalrymple-Alford JC, Dams-O’Connor K, Dannlowski U, Darby D, Davenport N, DeLuca J, Diaz-Caneja CM, Disner SG, Dobryakova E, Ehrlich S, Esopenko C, Ferrarelli F, Frank LE, Franz CE, Fuentes-Claramonte P, Genova H, Giza CC, Goltermann J, Grotegerd D, Gruber M, Gutierrez-Zotes A, Ha M, Haavik J, Hinkin C, Hoskinson KR, Hubl D, Irimia A, Jansen A, Kaess M, Kang X, Kenney K, Keřková B, Khlif MS, Kim M, Kindler J, Kircher T, Knížková K, Kolskår KK, Krch D, Kremen WS, Kuhn T, Kumari V, Kwon J, Langella R, Laskowitz S, Lee J, Lengenfelder J, Liou-Johnson V, Lippa SM, Løvstad M, Lundervold AJ, Marotta C, Marquardt CA, Mattos P, Mayeli A, McDonald CR, Meinert S, Melzer TR, Merchán-Naranjo J, Michel C, Morey RA, Mwangi B, Myall DJ, Nenadić I, Newsome MR, Nunes A, O’Brien T, Oertel V, Ollinger J, Olsen A, Ortiz García de la Foz V, Ozmen M, Pardoe H, Parent M, Piras F, Piras F, Pomarol-Clotet E, Repple J, Richard G, Rodriguez J, Rodriguez M, Rootes-Murdy K, Rowland J, Ryan NP, Salvador R, Sanders AM, Schmidt A, Soares JC, Spalleta G, Španiel F, Sponheim SR, Stasenko A, Stein F, Straube B, Thames A, Thomas-Odenthal F, Thomopoulos SI, Tone EB, Torres I, Troyanskaya M, Turner JA, Ulrichsen KM, Umpierrez G, Vecchio D, Vilella E, Vivash L, Walker WC, Werden E, Westlye LT, Wild K, Wroblewski A, Wu MJ, Wylie GR, Yatham LN, Zunta-Soares GB, Thompson PM, Pugh MJ, Tate DF, Hillary FG, Wilde EA, Dennis EL. Verbal Learning and Memory Deficits across Neurological and Neuropsychiatric Disorders: Insights from an ENIGMA Mega Analysis. Brain Sci 2024; 14:669. [PMID: 39061410 PMCID: PMC11274572 DOI: 10.3390/brainsci14070669] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/07/2024] [Revised: 06/20/2024] [Accepted: 06/26/2024] [Indexed: 07/28/2024] Open
Abstract
Deficits in memory performance have been linked to a wide range of neurological and neuropsychiatric conditions. While many studies have assessed the memory impacts of individual conditions, this study considers a broader perspective by evaluating how memory recall is differentially associated with nine common neuropsychiatric conditions using data drawn from 55 international studies, aggregating 15,883 unique participants aged 15-90. The effects of dementia, mild cognitive impairment, Parkinson's disease, traumatic brain injury, stroke, depression, attention-deficit/hyperactivity disorder (ADHD), schizophrenia, and bipolar disorder on immediate, short-, and long-delay verbal learning and memory (VLM) scores were estimated relative to matched healthy individuals. Random forest models identified age, years of education, and site as important VLM covariates. A Bayesian harmonization approach was used to isolate and remove site effects. Regression estimated the adjusted association of each clinical group with VLM scores. Memory deficits were strongly associated with dementia and schizophrenia (p < 0.001), while neither depression nor ADHD showed consistent associations with VLM scores (p > 0.05). Differences associated with clinical conditions were larger for longer delayed recall duration items. By comparing VLM across clinical conditions, this study provides a foundation for enhanced diagnostic precision and offers new insights into disease management of comorbid disorders.
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Affiliation(s)
- Eamonn Kennedy
- Department of Neurology, University of Utah School of Medicine, Salt Lake City, UT 84132, USA; (E.K.); (S.W.L.); (H.M.L.); (S.V.); (M.R.N.); (M.J.P.); (D.F.T.); (E.A.W.)
- Division of Epidemiology, University of Utah, Salt Lake City, UT 84108, USA;
- George E Wahlen Veterans Affairs Medical Center, Salt Lake City, UT 84148, USA
| | - Spencer W. Liebel
- Department of Neurology, University of Utah School of Medicine, Salt Lake City, UT 84132, USA; (E.K.); (S.W.L.); (H.M.L.); (S.V.); (M.R.N.); (M.J.P.); (D.F.T.); (E.A.W.)
- George E Wahlen Veterans Affairs Medical Center, Salt Lake City, UT 84148, USA
| | - Hannah M. Lindsey
- Department of Neurology, University of Utah School of Medicine, Salt Lake City, UT 84132, USA; (E.K.); (S.W.L.); (H.M.L.); (S.V.); (M.R.N.); (M.J.P.); (D.F.T.); (E.A.W.)
- George E Wahlen Veterans Affairs Medical Center, Salt Lake City, UT 84148, USA
| | - Shashank Vadlamani
- Department of Neurology, University of Utah School of Medicine, Salt Lake City, UT 84132, USA; (E.K.); (S.W.L.); (H.M.L.); (S.V.); (M.R.N.); (M.J.P.); (D.F.T.); (E.A.W.)
| | - Pui-Wa Lei
- Department of Educational Psychology, Counseling, and Special Education, Pennsylvania State University, University Park, PA 16802, USA;
| | - Maheen M. Adamson
- WRIISC-WOMEN & Rehabilitation Department, VA Palo Alto, Palo Alto, CA 94304, USA (X.K.); (V.L.-J.)
- Neurosurgery, Stanford School of Medicine, Stanford, CA 94305, USA
| | - Martin Alda
- Department of Psychiatry, Dalhousie University, Halifax, NS B3H 4R2, Canada; (M.A.); (A.N.)
| | - Silvia Alonso-Lana
- FIDMAG Research Foundation, 08025 Barcelona, Spain; (S.A.-L.); (P.F.-C.); (E.P.-C.); (R.S.)
- Centro Investigación Biomédica en Red Salud Mental (CIBERSAM), 28029 Madrid, Spain; (C.A.); (R.A.-A.); (B.C.-F.); (A.G.-Z.); (E.V.)
- Ace Alzheimer Center Barcelona, Universitat Internacional de Catalunya, 08022 Barcelona, Spain
| | - Tim J. Anderson
- Department of Medicine, University of Otago, Christchurch 8011, New Zealand; (T.J.A.); (J.C.D.-A.); (T.R.M.)
- New Zealand Brain Research Institute, Christchurch 8011, New Zealand;
- Department of Neurology, Te Whatu Ora–Health New Zealand Waitaha Canterbury, Christchurch 8011, New Zealand
| | - Celso Arango
- Centro Investigación Biomédica en Red Salud Mental (CIBERSAM), 28029 Madrid, Spain; (C.A.); (R.A.-A.); (B.C.-F.); (A.G.-Z.); (E.V.)
- Department of Child and Adolescent Psychiatry, Institute of Psychiatry and Mental Health, Hospital General Universitario Gregorio Marañón, Instituto de Investigación Sanitaria Gregorio Marañón (IiSGM), School of Medicine, Universidad Complutense, 28040 Madrid, Spain; (C.M.D.-C.); (J.M.-N.)
| | - Robert F. Asarnow
- Department of Psychiatry and Biobehavioral Sciences, Semel Institute for Neuroscience and Human Behavior, University of California Los Angeles, Los Angeles, CA 90095, USA; (R.F.A.); (T.B.); (C.H.); (T.K.); (A.T.)
- Brain Research Institute, University of California Los Angeles, Los Angeles, CA 90095, USA
- Department of Psychology, University of California Los Angeles, Los Angeles, CA 90095, USA
| | - Mihai Avram
- Translational Psychiatry, Department of Psychiatry and Psychotherapy, University of Lübeck, 23562 Lübeck, Germany; (M.A.); (S.B.)
| | - Rosa Ayesa-Arriola
- Centro Investigación Biomédica en Red Salud Mental (CIBERSAM), 28029 Madrid, Spain; (C.A.); (R.A.-A.); (B.C.-F.); (A.G.-Z.); (E.V.)
- Department of Psychiatry, Marqués de Valdecilla University Hospital, Instituto de Investigación Sanitaria Valdecilla (IDIVAL), School of Medicine, University of Cantabria, 39008 Santander, Spain;
| | - Talin Babikian
- Department of Psychiatry and Biobehavioral Sciences, Semel Institute for Neuroscience and Human Behavior, University of California Los Angeles, Los Angeles, CA 90095, USA; (R.F.A.); (T.B.); (C.H.); (T.K.); (A.T.)
- UCLA Steve Tisch BrainSPORT Program, University of California Los Angeles, Los Angeles, CA 90095, USA;
| | - Nerisa Banaj
- Laboratory of Neuropsychiatry, Santa Lucia Foundation IRCCS, 00179 Rome, Italy; (N.B.); (R.L.); (F.P.); (F.P.); (G.S.); (D.V.)
| | - Laura J. Bird
- School of Clinical Sciences, Monash University, Clayton, VIC 3800, Australia;
| | - Stefan Borgwardt
- Translational Psychiatry, Department of Psychiatry and Psychotherapy, University of Lübeck, 23562 Lübeck, Germany; (M.A.); (S.B.)
- Center of Brain, Behaviour and Metabolism (CBBM), University of Lübeck, 23562 Lübeck, Germany
| | - Amy Brodtmann
- Cognitive Health Initiative, School of Translational Medicine, Monash University, Melbourne, VIC 3800, Australia;
- Department of Medicine, Royal Melbourne Hospital, Melbourne, VIC 3050, Australia;
| | - Katharina Brosch
- Department of Psychiatry and Psychotherapy, University of Marburg, 35032 Marburg, Germany; (K.B.); (A.J.); (T.K.); (I.N.); (F.S.); (B.S.); (F.T.-O.); (A.W.)
- Institute of Behavioral Science, Feinstein Institutes for Medical Research, Manhasset, NY 11030, USA
| | - Karen Caeyenberghs
- Cognitive Neuroscience Unit, School of Psychology, Deakin University, Burwood, VIC 3125, Australia;
| | - Vince D. Calhoun
- Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State, Georgia Tech, Emory University, Atlanta, GA 30322, USA; (V.D.C.); (K.R.-M.)
| | - Nancy D. Chiaravalloti
- Centers for Neuropsychology, Neuroscience & Traumatic Brain Injury Research, Kessler Foundation, East Hanover, NJ 07936, USA;
- Department of Physical Medicine & Rehabilitation, Rutgers, New Jersey Medical School, Newark, NJ 07103, USA; (J.D.); (E.D.); (H.G.); (D.K.); (J.L.); (G.R.W.)
| | - David X. Cifu
- Rehabilitation Medicine Department, National Institutes of Health Clinical Center, Bethesda, MD 20892, USA;
| | - Benedicto Crespo-Facorro
- Centro Investigación Biomédica en Red Salud Mental (CIBERSAM), 28029 Madrid, Spain; (C.A.); (R.A.-A.); (B.C.-F.); (A.G.-Z.); (E.V.)
- Department of Psychiatry, Virgen del Rocio University Hospital, School of Medicine, University of Seville, IBIS, 41013 Seville, Spain
| | - John C. Dalrymple-Alford
- Department of Medicine, University of Otago, Christchurch 8011, New Zealand; (T.J.A.); (J.C.D.-A.); (T.R.M.)
- New Zealand Brain Research Institute, Christchurch 8011, New Zealand;
- School of Psychology, Speech and Hearing, University of Canterbury, Christchurch 8041, New Zealand
| | - Kristen Dams-O’Connor
- Department of Rehabilitation and Human Performance, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA (C.E.)
- Department of Neurology, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
| | - Udo Dannlowski
- Institute for Translational Psychiatry, University of Münster, 48149 Münster, Germany; (U.D.); (J.G.); (D.G.); (M.G.); (S.M.); (J.R.)
| | - David Darby
- Department of Neuroscience, Monash University, Melbourne, VIC 3800, Australia; (D.D.); (C.M.); (L.V.)
- Department of Neurology, Alfred Health, Melbourne, VIC 3004, Australia
- The Florey Institute of Neuroscience and Mental Health, Melbourne, VIC 3052, Australia; (H.P.); (E.W.)
| | - Nicholas Davenport
- Department of Psychiatry and Behavioral Sciences, University of Minnesota Medical School, Minneapolis, MN 55455, USA; (N.D.); (S.G.D.); (C.A.M.); (S.R.S.)
- Minneapolis VA Health Care System, Minneapolis, MN 55417, USA
| | - John DeLuca
- Department of Physical Medicine & Rehabilitation, Rutgers, New Jersey Medical School, Newark, NJ 07103, USA; (J.D.); (E.D.); (H.G.); (D.K.); (J.L.); (G.R.W.)
- Kessler Foundation, East Hanover, NJ 07936, USA
| | - Covadonga M. Diaz-Caneja
- Department of Child and Adolescent Psychiatry, Institute of Psychiatry and Mental Health, Hospital General Universitario Gregorio Marañón, Instituto de Investigación Sanitaria Gregorio Marañón (IiSGM), School of Medicine, Universidad Complutense, 28040 Madrid, Spain; (C.M.D.-C.); (J.M.-N.)
| | - Seth G. Disner
- Department of Psychiatry and Behavioral Sciences, University of Minnesota Medical School, Minneapolis, MN 55455, USA; (N.D.); (S.G.D.); (C.A.M.); (S.R.S.)
- Minneapolis VA Health Care System, Minneapolis, MN 55417, USA
| | - Ekaterina Dobryakova
- Department of Physical Medicine & Rehabilitation, Rutgers, New Jersey Medical School, Newark, NJ 07103, USA; (J.D.); (E.D.); (H.G.); (D.K.); (J.L.); (G.R.W.)
- Center for Traumatic Brain Injury, Kessler Foundation, East Hanover, NJ 07936, USA
| | - Stefan Ehrlich
- Translational Developmental Neuroscience Section, Division of Psychological and Social Medicine and Developmental Neurosciences, Faculty of Medicine, Technische Universität Dresden, 01307 Dresden, Germany;
- Eating Disorders Research and Treatment Center, Department of Child and Adolescent Psychiatry, Faculty of Medicine, Technische Universität Dresden, 01307 Dresden, Germany
| | - Carrie Esopenko
- Department of Rehabilitation and Human Performance, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA (C.E.)
| | - Fabio Ferrarelli
- Department of Psychiatry, University of Pittsburgh, Pittsburgh, PA 15213, USA; (F.F.); (A.M.)
| | - Lea E. Frank
- Department of Psychology, University of Oregon, Eugene, OR 97403, USA
| | - Carol E. Franz
- Department of Psychiatry, University of California San Diego, La Jolla, CA 92093, USA; (C.E.F.); (W.S.K.); (J.R.); (A.S.)
- Center for Behavior Genetics of Aging, University of California San Diego, La Jolla, CA 92093, USA
| | - Paola Fuentes-Claramonte
- FIDMAG Research Foundation, 08025 Barcelona, Spain; (S.A.-L.); (P.F.-C.); (E.P.-C.); (R.S.)
- Centro Investigación Biomédica en Red Salud Mental (CIBERSAM), 28029 Madrid, Spain; (C.A.); (R.A.-A.); (B.C.-F.); (A.G.-Z.); (E.V.)
| | - Helen Genova
- Department of Physical Medicine & Rehabilitation, Rutgers, New Jersey Medical School, Newark, NJ 07103, USA; (J.D.); (E.D.); (H.G.); (D.K.); (J.L.); (G.R.W.)
- Center for Autism Research, Kessler Foundation, East Hanover, NJ 07936, USA
| | - Christopher C. Giza
- UCLA Steve Tisch BrainSPORT Program, University of California Los Angeles, Los Angeles, CA 90095, USA;
- Department of Pediatrics, Division of Neurology, UCLA Mattel Children’s Hospital, Los Angeles, CA 90095, USA
- Department of Neurosurgery, David Geffen School of Medicine at UCLA, Los Angeles, CA 90095, USA
| | - Janik Goltermann
- Institute for Translational Psychiatry, University of Münster, 48149 Münster, Germany; (U.D.); (J.G.); (D.G.); (M.G.); (S.M.); (J.R.)
| | - Dominik Grotegerd
- Institute for Translational Psychiatry, University of Münster, 48149 Münster, Germany; (U.D.); (J.G.); (D.G.); (M.G.); (S.M.); (J.R.)
| | - Marius Gruber
- Institute for Translational Psychiatry, University of Münster, 48149 Münster, Germany; (U.D.); (J.G.); (D.G.); (M.G.); (S.M.); (J.R.)
- Department of Psychiatry, Psychosomatic Medicine and Psychotherapy, University Hospital Frankfurt, Goethe University, 60590 Frankfurt, Germany
| | - Alfonso Gutierrez-Zotes
- Centro Investigación Biomédica en Red Salud Mental (CIBERSAM), 28029 Madrid, Spain; (C.A.); (R.A.-A.); (B.C.-F.); (A.G.-Z.); (E.V.)
- Hospital Universitari Institut Pere Mata, 43007 Tarragona, Spain
- Institut d’Investiació Sanitària Pere Virgili-CERCA, Universitat Rovira i Virgili, 43007 Tarragona, Spain
| | - Minji Ha
- Department of Brain and Cognitive Sciences, Seoul National University College of Natural Sciences, Seoul 08826, Republic of Korea; (M.H.); (J.K.); (J.L.)
| | - Jan Haavik
- Department of Biomedicine, University of Bergen, 5007 Bergen, Norway;
- Division of Psychiatry, Haukeland University Hospital, 5021 Bergen, Norway
| | - Charles Hinkin
- Department of Psychiatry and Biobehavioral Sciences, Semel Institute for Neuroscience and Human Behavior, University of California Los Angeles, Los Angeles, CA 90095, USA; (R.F.A.); (T.B.); (C.H.); (T.K.); (A.T.)
| | - Kristen R. Hoskinson
- Center for Biobehavioral Health, The Abigail Wexner Research Institute at Nationwide Children’s Hospital, Columbus, OH 43205, USA;
- Section of Pediatrics, The Ohio State University College of Medicine, Columbus, OH 43210, USA
| | - Daniela Hubl
- Translational Research Centre, University Hospital of Psychiatry and Psychotherapy, University of Bern, 3000 Bern, Switzerland;
| | - Andrei Irimia
- Ethel Percy Andrus Gerontology Center, Leonard Davis School of Gerontology, University of Southern California, Los Angeles, CA 90089, USA;
- Department of Biomedical Engineering, Viterbi School of Engineering, University of Southern California, Los Angeles, CA 90089, USA
- Department of Quantitative & Computational Biology, Dornsife College of Arts & Sciences, University of Southern California, Los Angeles, CA 90089, USA
| | - Andreas Jansen
- Department of Psychiatry and Psychotherapy, University of Marburg, 35032 Marburg, Germany; (K.B.); (A.J.); (T.K.); (I.N.); (F.S.); (B.S.); (F.T.-O.); (A.W.)
| | - Michael Kaess
- University Hospital of Child and Adolescent Psychiatry and Psychotherapy, University of Bern, 3000 Bern, Switzerland; (M.K.); (J.K.); (C.M.)
- Clinic of Child and Adolescent Psychiatry, Centre of Psychosocial Medicine, University of Heidelberg, 69120 Heidelberg, Germany
| | - Xiaojian Kang
- WRIISC-WOMEN & Rehabilitation Department, VA Palo Alto, Palo Alto, CA 94304, USA (X.K.); (V.L.-J.)
| | - Kimbra Kenney
- Department of Neurology, Uniformed Services University of the Health Sciences, Bethesda, MD 20814, USA;
| | - Barbora Keřková
- National Institute of Mental Health, 250 67 Klecany, Czech Republic; (B.K.); (K.K.); (M.R.); (F.Š.)
| | - Mohamed Salah Khlif
- Cognitive Health Initiative, Central Clinical School, Monash University, Melbourne, VIC 3800, Australia;
| | - Minah Kim
- Department of Neuropsychiatry, Seoul National University Hospital, Seoul 03080, Republic of Korea;
- Department of Psychiatry, Seoul National University College of Medicine, Seoul 03080, Republic of Korea
| | - Jochen Kindler
- University Hospital of Child and Adolescent Psychiatry and Psychotherapy, University of Bern, 3000 Bern, Switzerland; (M.K.); (J.K.); (C.M.)
| | - Tilo Kircher
- Department of Psychiatry and Psychotherapy, University of Marburg, 35032 Marburg, Germany; (K.B.); (A.J.); (T.K.); (I.N.); (F.S.); (B.S.); (F.T.-O.); (A.W.)
| | - Karolina Knížková
- National Institute of Mental Health, 250 67 Klecany, Czech Republic; (B.K.); (K.K.); (M.R.); (F.Š.)
- Department of Psychiatry, First Faculty of Medicine, Charles University and General University Hospital, 128 00 Prague, Czech Republic
| | - Knut K. Kolskår
- Norwegian Centre for Mental Disorders Research (NORMENT), Division of Mental Health and Addiction, Oslo University Hospital, 0424 Oslo, Norway; (K.K.K.); (G.R.); (A.-M.S.); (K.M.U.); (L.T.W.)
- Department of Psychology, University of Oslo, 0373 Oslo, Norway;
- Department of Research, Sunnaas Rehabilitation Hospital, 1450 Nesodden, Norway
| | - Denise Krch
- Department of Physical Medicine & Rehabilitation, Rutgers, New Jersey Medical School, Newark, NJ 07103, USA; (J.D.); (E.D.); (H.G.); (D.K.); (J.L.); (G.R.W.)
- Center for Traumatic Brain Injury, Kessler Foundation, East Hanover, NJ 07936, USA
| | - William S. Kremen
- Department of Psychiatry, University of California San Diego, La Jolla, CA 92093, USA; (C.E.F.); (W.S.K.); (J.R.); (A.S.)
- Center for Behavior Genetics of Aging, University of California San Diego, La Jolla, CA 92093, USA
| | - Taylor Kuhn
- Department of Psychiatry and Biobehavioral Sciences, Semel Institute for Neuroscience and Human Behavior, University of California Los Angeles, Los Angeles, CA 90095, USA; (R.F.A.); (T.B.); (C.H.); (T.K.); (A.T.)
| | - Veena Kumari
- Department of Life Sciences, College of Health, Medicine and Life Sciences, Brunel University London, Uxbridge UB8 3PH, UK;
| | - Junsoo Kwon
- Department of Brain and Cognitive Sciences, Seoul National University College of Natural Sciences, Seoul 08826, Republic of Korea; (M.H.); (J.K.); (J.L.)
- Department of Neuropsychiatry, Seoul National University Hospital, Seoul 03080, Republic of Korea;
- Department of Psychiatry, Seoul National University College of Medicine, Seoul 03080, Republic of Korea
| | - Roberto Langella
- Laboratory of Neuropsychiatry, Santa Lucia Foundation IRCCS, 00179 Rome, Italy; (N.B.); (R.L.); (F.P.); (F.P.); (G.S.); (D.V.)
| | - Sarah Laskowitz
- Brain Imaging and Analysis Center, Duke University, Durham, NC 27710, USA; (S.L.); (R.A.M.)
| | - Jungha Lee
- Department of Brain and Cognitive Sciences, Seoul National University College of Natural Sciences, Seoul 08826, Republic of Korea; (M.H.); (J.K.); (J.L.)
| | - Jean Lengenfelder
- Department of Physical Medicine & Rehabilitation, Rutgers, New Jersey Medical School, Newark, NJ 07103, USA; (J.D.); (E.D.); (H.G.); (D.K.); (J.L.); (G.R.W.)
- Center for Traumatic Brain Injury, Kessler Foundation, East Hanover, NJ 07936, USA
| | - Victoria Liou-Johnson
- WRIISC-WOMEN & Rehabilitation Department, VA Palo Alto, Palo Alto, CA 94304, USA (X.K.); (V.L.-J.)
| | - Sara M. Lippa
- National Intrepid Center of Excellence, Walter Reed National Military Medical Center, Bethesda, MD 20814, USA; (S.M.L.); (J.O.)
- Department of Neuroscience, Uniformed Services University of the Health Sciences, Bethesda, MD 20814, USA
| | - Marianne Løvstad
- Department of Psychology, University of Oslo, 0373 Oslo, Norway;
- Department of Research, Sunnaas Rehabilitation Hospital, 1450 Nesodden, Norway
| | - Astri J. Lundervold
- Department of Biological and Medical Psychology, University of Bergen, 5007 Bergen, Norway;
| | - Cassandra Marotta
- Department of Neuroscience, Monash University, Melbourne, VIC 3800, Australia; (D.D.); (C.M.); (L.V.)
- Department of Neurology, Alfred Health, Melbourne, VIC 3004, Australia
| | - Craig A. Marquardt
- Department of Psychiatry and Behavioral Sciences, University of Minnesota Medical School, Minneapolis, MN 55455, USA; (N.D.); (S.G.D.); (C.A.M.); (S.R.S.)
- Minneapolis VA Health Care System, Minneapolis, MN 55417, USA
| | - Paulo Mattos
- Institute D’Or for Research and Education (IDOR), São Paulo 04501-000, Brazil;
| | - Ahmad Mayeli
- Department of Psychiatry, University of Pittsburgh, Pittsburgh, PA 15213, USA; (F.F.); (A.M.)
| | - Carrie R. McDonald
- Department of Radiation Medicine and Applied Sciences and Psychiatry, University of California San Diego, La Jolla, CA 92093, USA;
- Center for Multimodal Imaging and Genetics, University of California San Diego, La Jolla, CA 92093, USA
| | - Susanne Meinert
- Institute for Translational Psychiatry, University of Münster, 48149 Münster, Germany; (U.D.); (J.G.); (D.G.); (M.G.); (S.M.); (J.R.)
- Institute for Translational Neuroscience, University of Münster, 48149 Münster, Germany
| | - Tracy R. Melzer
- Department of Medicine, University of Otago, Christchurch 8011, New Zealand; (T.J.A.); (J.C.D.-A.); (T.R.M.)
- New Zealand Brain Research Institute, Christchurch 8011, New Zealand;
- School of Psychology, Speech and Hearing, University of Canterbury, Christchurch 8041, New Zealand
| | - Jessica Merchán-Naranjo
- Department of Child and Adolescent Psychiatry, Institute of Psychiatry and Mental Health, Hospital General Universitario Gregorio Marañón, Instituto de Investigación Sanitaria Gregorio Marañón (IiSGM), School of Medicine, Universidad Complutense, 28040 Madrid, Spain; (C.M.D.-C.); (J.M.-N.)
| | - Chantal Michel
- University Hospital of Child and Adolescent Psychiatry and Psychotherapy, University of Bern, 3000 Bern, Switzerland; (M.K.); (J.K.); (C.M.)
| | - Rajendra A. Morey
- Brain Imaging and Analysis Center, Duke University, Durham, NC 27710, USA; (S.L.); (R.A.M.)
- VISN 6 MIRECC, Durham VA, Durham, NC 27705, USA
| | - Benson Mwangi
- Center of Excellence on Mood Disorders, Louis A Faillace, MD Department of Psychiatry and Behavioral Sciences, McGovern Medical School, The University of Texas Health Science Center at Houston, Houston, TX 77030, USA; (B.M.); (J.C.S.); (M.-J.W.); (G.B.Z.-S.)
| | - Daniel J. Myall
- New Zealand Brain Research Institute, Christchurch 8011, New Zealand;
| | - Igor Nenadić
- Department of Psychiatry and Psychotherapy, University of Marburg, 35032 Marburg, Germany; (K.B.); (A.J.); (T.K.); (I.N.); (F.S.); (B.S.); (F.T.-O.); (A.W.)
| | - Mary R. Newsome
- Department of Neurology, University of Utah School of Medicine, Salt Lake City, UT 84132, USA; (E.K.); (S.W.L.); (H.M.L.); (S.V.); (M.R.N.); (M.J.P.); (D.F.T.); (E.A.W.)
- George E Wahlen Veterans Affairs Medical Center, Salt Lake City, UT 84148, USA
| | - Abraham Nunes
- Department of Psychiatry, Dalhousie University, Halifax, NS B3H 4R2, Canada; (M.A.); (A.N.)
- Faculty of Computer Science, Dalhousie University, Halifax, NS B3H 4R2, Canada
| | - Terence O’Brien
- Department of Medicine, Royal Melbourne Hospital, Melbourne, VIC 3050, Australia;
- Department of Neuroscience, The School of Translational Medicine, Alfred Health, Monash University, Melbourne VIC 3004, Australia
| | - Viola Oertel
- Department of Psychiatry, Psychosomatic Medicine and Psychotherapy, Frankfurt University, 60590 Frankfurt, Germany;
| | - John Ollinger
- National Intrepid Center of Excellence, Walter Reed National Military Medical Center, Bethesda, MD 20814, USA; (S.M.L.); (J.O.)
| | - Alexander Olsen
- Department of Psychology, Norwegian University of Science and Technology, 7491 Trondheim, Norway;
- Department of Physical Medicine and Rehabilitation, St Olavs Hospital, Trondheim University Hospital, 7006 Trondheim, Norway
- NorHEAD—Norwegian Centre for Headache Research, 7491 Trondheim, Norway
| | - Victor Ortiz García de la Foz
- Department of Psychiatry, Marqués de Valdecilla University Hospital, Instituto de Investigación Sanitaria Valdecilla (IDIVAL), School of Medicine, University of Cantabria, 39008 Santander, Spain;
| | - Mustafa Ozmen
- Division of Epidemiology, University of Utah, Salt Lake City, UT 84108, USA;
- Department of Electrical and Electronics Engineering, Antalya Bilim University, 07190 Antalya, Turkey
| | - Heath Pardoe
- The Florey Institute of Neuroscience and Mental Health, Melbourne, VIC 3052, Australia; (H.P.); (E.W.)
| | - Marise Parent
- Neuroscience Institute & Department of Psychology, Georgia State University, Atlanta, GA 30303, USA;
| | - Fabrizio Piras
- Laboratory of Neuropsychiatry, Santa Lucia Foundation IRCCS, 00179 Rome, Italy; (N.B.); (R.L.); (F.P.); (F.P.); (G.S.); (D.V.)
| | - Federica Piras
- Laboratory of Neuropsychiatry, Santa Lucia Foundation IRCCS, 00179 Rome, Italy; (N.B.); (R.L.); (F.P.); (F.P.); (G.S.); (D.V.)
| | - Edith Pomarol-Clotet
- FIDMAG Research Foundation, 08025 Barcelona, Spain; (S.A.-L.); (P.F.-C.); (E.P.-C.); (R.S.)
- Centro Investigación Biomédica en Red Salud Mental (CIBERSAM), 28029 Madrid, Spain; (C.A.); (R.A.-A.); (B.C.-F.); (A.G.-Z.); (E.V.)
| | - Jonathan Repple
- Institute for Translational Psychiatry, University of Münster, 48149 Münster, Germany; (U.D.); (J.G.); (D.G.); (M.G.); (S.M.); (J.R.)
- Department of Psychiatry, Psychosomatic Medicine and Psychotherapy, University Hospital Frankfurt, Goethe University, 60590 Frankfurt, Germany
| | - Geneviève Richard
- Norwegian Centre for Mental Disorders Research (NORMENT), Division of Mental Health and Addiction, Oslo University Hospital, 0424 Oslo, Norway; (K.K.K.); (G.R.); (A.-M.S.); (K.M.U.); (L.T.W.)
| | - Jonathan Rodriguez
- Department of Psychiatry, University of California San Diego, La Jolla, CA 92093, USA; (C.E.F.); (W.S.K.); (J.R.); (A.S.)
| | - Mabel Rodriguez
- National Institute of Mental Health, 250 67 Klecany, Czech Republic; (B.K.); (K.K.); (M.R.); (F.Š.)
| | - Kelly Rootes-Murdy
- Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State, Georgia Tech, Emory University, Atlanta, GA 30322, USA; (V.D.C.); (K.R.-M.)
| | - Jared Rowland
- WG (Bill) Hefner VA Medical Center, Salisbury, NC 28144, USA;
- Department of Neurobiology & Anatomy, Wake Forest School of Medicine, Winston-Salem, NC 27157, USA
- VA Mid-Atlantic Mental Illness Research Education and Clinical Center (MA-MIRECC), Durham, NC 27705, USA
| | - Nicholas P. Ryan
- Cognitive Neuroscience Unit, School of Psychology, Deakin University, Geelong, VIC 3220, Australia;
- Department of Paediatrics, The University of Melbourne, Parkville, VIC 3052, Australia
| | - Raymond Salvador
- FIDMAG Research Foundation, 08025 Barcelona, Spain; (S.A.-L.); (P.F.-C.); (E.P.-C.); (R.S.)
- Centro Investigación Biomédica en Red Salud Mental (CIBERSAM), 28029 Madrid, Spain; (C.A.); (R.A.-A.); (B.C.-F.); (A.G.-Z.); (E.V.)
| | - Anne-Marthe Sanders
- Norwegian Centre for Mental Disorders Research (NORMENT), Division of Mental Health and Addiction, Oslo University Hospital, 0424 Oslo, Norway; (K.K.K.); (G.R.); (A.-M.S.); (K.M.U.); (L.T.W.)
- Department of Psychology, University of Oslo, 0373 Oslo, Norway;
- Department of Research, Sunnaas Rehabilitation Hospital, 1450 Nesodden, Norway
| | - Andre Schmidt
- Department of Psychiatry (UPK), University of Basel, 4002 Basel, Switzerland;
| | - Jair C. Soares
- Center of Excellence on Mood Disorders, Louis A Faillace, MD Department of Psychiatry and Behavioral Sciences, McGovern Medical School, The University of Texas Health Science Center at Houston, Houston, TX 77030, USA; (B.M.); (J.C.S.); (M.-J.W.); (G.B.Z.-S.)
| | - Gianfranco Spalleta
- Laboratory of Neuropsychiatry, Santa Lucia Foundation IRCCS, 00179 Rome, Italy; (N.B.); (R.L.); (F.P.); (F.P.); (G.S.); (D.V.)
| | - Filip Španiel
- National Institute of Mental Health, 250 67 Klecany, Czech Republic; (B.K.); (K.K.); (M.R.); (F.Š.)
- 3rd Faculty of Medicine, Charles University, 100 00 Prague, Czech Republic
| | - Scott R. Sponheim
- Department of Psychiatry and Behavioral Sciences, University of Minnesota Medical School, Minneapolis, MN 55455, USA; (N.D.); (S.G.D.); (C.A.M.); (S.R.S.)
- Minneapolis VA Health Care System, Minneapolis, MN 55417, USA
| | - Alena Stasenko
- Department of Psychiatry, University of California San Diego, La Jolla, CA 92093, USA; (C.E.F.); (W.S.K.); (J.R.); (A.S.)
- Center for Multimodal Imaging and Genetics, University of California San Diego, La Jolla, CA 92093, USA
| | - Frederike Stein
- Department of Psychiatry and Psychotherapy, University of Marburg, 35032 Marburg, Germany; (K.B.); (A.J.); (T.K.); (I.N.); (F.S.); (B.S.); (F.T.-O.); (A.W.)
| | - Benjamin Straube
- Department of Psychiatry and Psychotherapy, University of Marburg, 35032 Marburg, Germany; (K.B.); (A.J.); (T.K.); (I.N.); (F.S.); (B.S.); (F.T.-O.); (A.W.)
| | - April Thames
- Department of Psychiatry and Biobehavioral Sciences, Semel Institute for Neuroscience and Human Behavior, University of California Los Angeles, Los Angeles, CA 90095, USA; (R.F.A.); (T.B.); (C.H.); (T.K.); (A.T.)
| | - Florian Thomas-Odenthal
- Department of Psychiatry and Psychotherapy, University of Marburg, 35032 Marburg, Germany; (K.B.); (A.J.); (T.K.); (I.N.); (F.S.); (B.S.); (F.T.-O.); (A.W.)
| | - Sophia I. Thomopoulos
- Imaging Genetics Center, Stevens Neuroimaging & Informatics Institute, Keck School of Medicine of the University of Southern California, Marina del Rey, CA 90292, USA; (S.I.T.); (P.M.T.)
| | - Erin B. Tone
- Department of Psychology, Georgia State University, Atlanta, GA 30303, USA;
| | - Ivan Torres
- Department of Psychiatry, University of British Columbia, Vancouver, BC V6T 1Z4, Canada; (I.T.); (L.N.Y.)
- British Columbia Mental Health and Substance Use Services Research Institute, Vancouver, BC V5Z 1M9, Canada
| | - Maya Troyanskaya
- Michael E DeBakey Veterans Affairs Medical Center, Houston, TX 77030, USA;
- H Ben Taub Department of Physical Medicine and Rehabilitation, Baylor College of Medicine, Houston, TX 77030, USA
| | - Jessica A. Turner
- Psychiatry and Behavioral Health, Ohio State Wexner Medical Center, Columbus, OH 43210, USA;
| | - Kristine M. Ulrichsen
- Norwegian Centre for Mental Disorders Research (NORMENT), Division of Mental Health and Addiction, Oslo University Hospital, 0424 Oslo, Norway; (K.K.K.); (G.R.); (A.-M.S.); (K.M.U.); (L.T.W.)
- Department of Psychology, University of Oslo, 0373 Oslo, Norway;
- Department of Research, Sunnaas Rehabilitation Hospital, 1450 Nesodden, Norway
| | - Guillermo Umpierrez
- Division of Endocrinology, Emory University School of Medicine, Atlanta, GA 30322, USA;
| | - Daniela Vecchio
- Laboratory of Neuropsychiatry, Santa Lucia Foundation IRCCS, 00179 Rome, Italy; (N.B.); (R.L.); (F.P.); (F.P.); (G.S.); (D.V.)
| | - Elisabet Vilella
- Centro Investigación Biomédica en Red Salud Mental (CIBERSAM), 28029 Madrid, Spain; (C.A.); (R.A.-A.); (B.C.-F.); (A.G.-Z.); (E.V.)
- Hospital Universitari Institut Pere Mata, 43007 Tarragona, Spain
- Institut d’Investiació Sanitària Pere Virgili-CERCA, Universitat Rovira i Virgili, 43007 Tarragona, Spain
| | - Lucy Vivash
- Department of Neuroscience, Monash University, Melbourne, VIC 3800, Australia; (D.D.); (C.M.); (L.V.)
- Department of Neurology, Alfred Health, Melbourne, VIC 3004, Australia
| | - William C. Walker
- Department of Physical Medicine and Rehabilitation, Virginia Commonwealth University, Richmond, VA 23298, USA;
- Richmond Veterans Affairs (VA) Medical Center, Central Virginia VA Health Care System, Richmond, VA 23249, USA
| | - Emilio Werden
- The Florey Institute of Neuroscience and Mental Health, Melbourne, VIC 3052, Australia; (H.P.); (E.W.)
| | - Lars T. Westlye
- Norwegian Centre for Mental Disorders Research (NORMENT), Division of Mental Health and Addiction, Oslo University Hospital, 0424 Oslo, Norway; (K.K.K.); (G.R.); (A.-M.S.); (K.M.U.); (L.T.W.)
- Department of Psychology, University of Oslo, 0373 Oslo, Norway;
- KG Jebsen Center for Neurodevelopmental Disorders, University of Oslo, 0372 Oslo, Norway
| | - Krista Wild
- Department of Psychology, Phoenix VA Health Care System, Phoenix, AZ 85012, USA;
| | - Adrian Wroblewski
- Department of Psychiatry and Psychotherapy, University of Marburg, 35032 Marburg, Germany; (K.B.); (A.J.); (T.K.); (I.N.); (F.S.); (B.S.); (F.T.-O.); (A.W.)
| | - Mon-Ju Wu
- Center of Excellence on Mood Disorders, Louis A Faillace, MD Department of Psychiatry and Behavioral Sciences, McGovern Medical School, The University of Texas Health Science Center at Houston, Houston, TX 77030, USA; (B.M.); (J.C.S.); (M.-J.W.); (G.B.Z.-S.)
| | - Glenn R. Wylie
- Department of Physical Medicine & Rehabilitation, Rutgers, New Jersey Medical School, Newark, NJ 07103, USA; (J.D.); (E.D.); (H.G.); (D.K.); (J.L.); (G.R.W.)
- Rocco Ortenzio Neuroimaging Center, Kessler Foundation, East Hanover, NJ 07936, USA
| | - Lakshmi N. Yatham
- Department of Psychiatry, University of British Columbia, Vancouver, BC V6T 1Z4, Canada; (I.T.); (L.N.Y.)
| | - Giovana B. Zunta-Soares
- Center of Excellence on Mood Disorders, Louis A Faillace, MD Department of Psychiatry and Behavioral Sciences, McGovern Medical School, The University of Texas Health Science Center at Houston, Houston, TX 77030, USA; (B.M.); (J.C.S.); (M.-J.W.); (G.B.Z.-S.)
| | - Paul M. Thompson
- Imaging Genetics Center, Stevens Neuroimaging & Informatics Institute, Keck School of Medicine of the University of Southern California, Marina del Rey, CA 90292, USA; (S.I.T.); (P.M.T.)
- Departments of Neurology, Pediatrics, Psychiatry, Radiology, Engineering, and Ophthalmology, University of Southern California, Los Angeles, CA 90089, USA
| | - Mary Jo Pugh
- Department of Neurology, University of Utah School of Medicine, Salt Lake City, UT 84132, USA; (E.K.); (S.W.L.); (H.M.L.); (S.V.); (M.R.N.); (M.J.P.); (D.F.T.); (E.A.W.)
- Division of Epidemiology, University of Utah, Salt Lake City, UT 84108, USA;
| | - David F. Tate
- Department of Neurology, University of Utah School of Medicine, Salt Lake City, UT 84132, USA; (E.K.); (S.W.L.); (H.M.L.); (S.V.); (M.R.N.); (M.J.P.); (D.F.T.); (E.A.W.)
- George E Wahlen Veterans Affairs Medical Center, Salt Lake City, UT 84148, USA
| | - Frank G. Hillary
- Department of Psychology, Penn State University, State College, PA 16801, USA;
- Department of Neurology, Hershey Medical Center, State College, PA 16801, USA
- Social Life and Engineering Science Imaging Center, Penn State University, State College, PA 16801, USA
| | - Elisabeth A. Wilde
- Department of Neurology, University of Utah School of Medicine, Salt Lake City, UT 84132, USA; (E.K.); (S.W.L.); (H.M.L.); (S.V.); (M.R.N.); (M.J.P.); (D.F.T.); (E.A.W.)
- George E Wahlen Veterans Affairs Medical Center, Salt Lake City, UT 84148, USA
| | - Emily L. Dennis
- Department of Neurology, University of Utah School of Medicine, Salt Lake City, UT 84132, USA; (E.K.); (S.W.L.); (H.M.L.); (S.V.); (M.R.N.); (M.J.P.); (D.F.T.); (E.A.W.)
- George E Wahlen Veterans Affairs Medical Center, Salt Lake City, UT 84148, USA
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20
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Jansen MG, Zwiers MP, Marques JP, Chan KS, Amelink JS, Altgassen M, Oosterman JM, Norris DG. The Advanced BRain Imaging on ageing and Memory (ABRIM) data collection: Study design, data processing, and rationale. PLoS One 2024; 19:e0306006. [PMID: 38905233 PMCID: PMC11192316 DOI: 10.1371/journal.pone.0306006] [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: 01/16/2024] [Accepted: 06/07/2024] [Indexed: 06/23/2024] Open
Abstract
To understand the neurocognitive mechanisms that underlie heterogeneity in cognitive ageing, recent scientific efforts have led to a growing public availability of imaging cohort data. The Advanced BRain Imaging on ageing and Memory (ABRIM) project aims to add to these existing datasets by taking an adult lifespan approach to provide a cross-sectional, normative database with a particular focus on connectivity, myelinization and iron content of the brain in concurrence with cognitive functioning, mechanisms of reserve, and sleep-wake rhythms. ABRIM freely shares MRI and behavioural data from 295 participants between 18-80 years, stratified by age decade and sex (median age 52, IQR 36-66, 53.20% females). The ABRIM MRI collection consists of both the raw and pre-processed structural and functional MRI data to facilitate data usage among both expert and non-expert users. The ABRIM behavioural collection includes measures of cognitive functioning (i.e., global cognition, processing speed, executive functions, and memory), proxy measures of cognitive reserve (e.g., educational attainment, verbal intelligence, and occupational complexity), and various self-reported questionnaires (e.g., on depressive symptoms, pain, and the use of memory strategies in daily life and during a memory task). In a sub-sample (n = 120), we recorded sleep-wake rhythms using an actigraphy device (Actiwatch 2, Philips Respironics) for a period of 7 consecutive days. Here, we provide an in-depth description of our study protocol, pre-processing pipelines, and data availability. ABRIM provides a cross-sectional database on healthy participants throughout the adult lifespan, including numerous parameters relevant to improve our understanding of cognitive ageing. Therefore, ABRIM enables researchers to model the advanced imaging parameters and cognitive topologies as a function of age, identify the normal range of values of such parameters, and to further investigate the diverse mechanisms of reserve and resilience.
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Affiliation(s)
- Michelle G. Jansen
- Donders Centre for Cognition, Donders Institute for Brain, Cognition and Behaviour, Radboud University, Nijmegen, the Netherlands
| | - Marcel P. Zwiers
- Donders Centre for Cognitive Neuroimaging, Donders Institute for Brain, Cognition and Behaviour, Radboud University, Nijmegen, the Netherlands
| | - Jose P. Marques
- Donders Centre for Cognitive Neuroimaging, Donders Institute for Brain, Cognition and Behaviour, Radboud University, Nijmegen, the Netherlands
| | - Kwok-Shing Chan
- Donders Centre for Cognitive Neuroimaging, Donders Institute for Brain, Cognition and Behaviour, Radboud University, Nijmegen, the Netherlands
| | - Jitse S. Amelink
- Donders Centre for Cognitive Neuroimaging, Donders Institute for Brain, Cognition and Behaviour, Radboud University, Nijmegen, the Netherlands
- Language and Genetics Department, Max Planck Institute for Psycholinguistics, Radboud University, Nijmegen, the Netherlands
| | - Mareike Altgassen
- Department of Psychology, Johannes Gutenberg-University Mainz, Mainz, Germany
| | - Joukje M. Oosterman
- Donders Centre for Cognition, Donders Institute for Brain, Cognition and Behaviour, Radboud University, Nijmegen, the Netherlands
| | - David G. Norris
- Donders Centre for Cognitive Neuroimaging, Donders Institute for Brain, Cognition and Behaviour, Radboud University, Nijmegen, the Netherlands
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21
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Shankar A, Tanner JC, Mao T, Betzel RF, Prakash RS. Edge-Community Entropy Is a Novel Neural Correlate of Aging and Moderator of Fluid Cognition. J Neurosci 2024; 44:e1701232024. [PMID: 38719449 PMCID: PMC11209649 DOI: 10.1523/jneurosci.1701-23.2024] [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: 09/10/2023] [Revised: 02/28/2024] [Accepted: 03/27/2024] [Indexed: 06/21/2024] Open
Abstract
Decreased neuronal specificity of the brain in response to cognitive demands (i.e., neural dedifferentiation) has been implicated in age-related cognitive decline. Investigations into functional connectivity analogs of these processes have focused primarily on measuring segregation of nonoverlapping networks at rest. Here, we used an edge-centric network approach to derive entropy, a measure of specialization, from spatially overlapping communities during cognitive task fMRI. Using Human Connectome Project Lifespan data (713 participants, 36-100 years old, 55.7% female), we characterized a pattern of nodal despecialization differentially affecting the medial temporal lobe and limbic, visual, and subcortical systems. At the whole-brain level, global entropy moderated declines in fluid cognition across the lifespan and uniquely covaried with age when controlling for the network segregation metric modularity. Importantly, relationships between both metrics (entropy and modularity) and fluid cognition were age dependent, although entropy's relationship with cognition was specific to older adults. These results suggest entropy is a potentially important metric for examining how neurological processes in aging affect functional specialization at the nodal, network, and whole-brain level.
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Affiliation(s)
- Anita Shankar
- Department of Psychology, The Ohio State University, Columbus, Ohio 43210
| | - Jacob C Tanner
- Cognitive Science Program, Indiana University, Bloomington, Indiana 47401
- School of Informatics, Computing, and Engineering, Indiana University, Bloomington, Indiana 47401
| | - Tianrui Mao
- Department of Psychology, The Ohio State University, Columbus, Ohio 43210
| | - Richard F Betzel
- Cognitive Science Program, Indiana University, Bloomington, Indiana 47401
- Department of Psychological and Brain Sciences, Indiana University, Bloomington, Indiana 47401
- Program in Neuroscience, Indiana University, Bloomington, Indiana 47401
- Network Science Institute, Indiana University, Bloomington, Indiana 47401
| | - Ruchika S Prakash
- Department of Psychology, The Ohio State University, Columbus, Ohio 43210
- Center for Cognitive and Behavioral Brain Imaging, The Ohio State University, Columbus, Ohio 43210
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22
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Tetereva A, Pat N. Brain age has limited utility as a biomarker for capturing fluid cognition in older individuals. eLife 2024; 12:RP87297. [PMID: 38869938 PMCID: PMC11175613 DOI: 10.7554/elife.87297] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/14/2024] Open
Abstract
One well-known biomarker candidate that supposedly helps capture fluid cognition is Brain Age, or a predicted value based on machine-learning models built to predict chronological age from brain MRI. To formally evaluate the utility of Brain Age for capturing fluid cognition, we built 26 age-prediction models for Brain Age based on different combinations of MRI modalities, using the Human Connectome Project in Aging (n=504, 36-100 years old). First, based on commonality analyses, we found a large overlap between Brain Age and chronological age: Brain Age could uniquely add only around 1.6% in explaining variation in fluid cognition over and above chronological age. Second, the age-prediction models that performed better at predicting chronological age did NOT necessarily create better Brain Age for capturing fluid cognition over and above chronological age. Instead, better-performing age-prediction models created Brain Age that overlapped larger with chronological age, up to around 29% out of 32%, in explaining fluid cognition. Third, Brain Age missed around 11% of the total variation in fluid cognition that could have been explained by the brain variation. That is, directly predicting fluid cognition from brain MRI data (instead of relying on Brain Age and chronological age) could lead to around a 1/3-time improvement of the total variation explained. Accordingly, we demonstrated the limited utility of Brain Age as a biomarker for fluid cognition and made some suggestions to ensure the utility of Brain Age in explaining fluid cognition and other phenotypes of interest.
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Affiliation(s)
- Alina Tetereva
- Department of Psychology, University of OtagoDunedinNew Zealand
| | - Narun Pat
- Department of Psychology, University of OtagoDunedinNew Zealand
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23
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Kruper J, Hagen MP, Rheault F, Crane I, Gilmore A, Narayan M, Motwani K, Lila E, Rorden C, Yeatman JD, Rokem A. Tractometry of the Human Connectome Project: resources and insights. Front Neurosci 2024; 18:1389680. [PMID: 38933816 PMCID: PMC11199395 DOI: 10.3389/fnins.2024.1389680] [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: 02/22/2024] [Accepted: 05/15/2024] [Indexed: 06/28/2024] Open
Abstract
Introduction The Human Connectome Project (HCP) has become a keystone dataset in human neuroscience, with a plethora of important applications in advancing brain imaging methods and an understanding of the human brain. We focused on tractometry of HCP diffusion-weighted MRI (dMRI) data. Methods We used an open-source software library (pyAFQ; https://yeatmanlab.github.io/pyAFQ) to perform probabilistic tractography and delineate the major white matter pathways in the HCP subjects that have a complete dMRI acquisition (n = 1,041). We used diffusion kurtosis imaging (DKI) to model white matter microstructure in each voxel of the white matter, and extracted tract profiles of DKI-derived tissue properties along the length of the tracts. We explored the empirical properties of the data: first, we assessed the heritability of DKI tissue properties using the known genetic linkage of the large number of twin pairs sampled in HCP. Second, we tested the ability of tractometry to serve as the basis for predictive models of individual characteristics (e.g., age, crystallized/fluid intelligence, reading ability, etc.), compared to local connectome features. To facilitate the exploration of the dataset we created a new web-based visualization tool and use this tool to visualize the data in the HCP tractometry dataset. Finally, we used the HCP dataset as a test-bed for a new technological innovation: the TRX file-format for representation of dMRI-based streamlines. Results We released the processing outputs and tract profiles as a publicly available data resource through the AWS Open Data program's Open Neurodata repository. We found heritability as high as 0.9 for DKI-based metrics in some brain pathways. We also found that tractometry extracts as much useful information about individual differences as the local connectome method. We released a new web-based visualization tool for tractometry-"Tractoscope" (https://nrdg.github.io/tractoscope). We found that the TRX files require considerably less disk space-a crucial attribute for large datasets like HCP. In addition, TRX incorporates a specification for grouping streamlines, further simplifying tractometry analysis.
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Affiliation(s)
- John Kruper
- Department of Psychology, University of Washington, Seattle, WA, United States
| | - McKenzie P. Hagen
- Department of Psychology, University of Washington, Seattle, WA, United States
| | - François Rheault
- Department of Computer Science, Universitè de Sherbrooke, Sherbrooke, QC, Canada
| | - Isaac Crane
- Department of Psychology, University of Chicago, Chicago, IL, United States
| | - Asa Gilmore
- Department of Psychology, University of Washington, Seattle, WA, United States
| | - Manjari Narayan
- Graduate School of Education, Stanford University, Stanford, CA, United States
| | - Keshav Motwani
- Department of Biostatistics, University of Washington, Seattle, WA, United States
| | - Eardi Lila
- Department of Biostatistics, University of Washington, Seattle, WA, United States
| | - Chris Rorden
- Department of Psychology, University of South Carolina, Columbia, SC, United States
| | - Jason D. Yeatman
- Graduate School of Education, Stanford University, Stanford, CA, United States
| | - Ariel Rokem
- Department of Psychology, University of Washington, Seattle, WA, United States
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24
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Küppers V, Bi H, Nicolaisen-Sobesky E, Hoffstaedter F, Yeo BT, Drzezga A, Eickhoff SB, Tahmasian M. Lower motor performance is linked with poor sleep quality, depressive symptoms, and grey matter volume alterations. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.06.07.597666. [PMID: 38895316 PMCID: PMC11185664 DOI: 10.1101/2024.06.07.597666] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/21/2024]
Abstract
Motor performance (MP) is essential for functional independence and well-being, particularly in later life. However, the relationship between behavioural aspects such as sleep quality and depressive symptoms, which contribute to MP, and the underlying structural brain substrates of their interplay remains unclear. This study used three population-based cohorts of younger and older adults (n=1,950) from the Human Connectome Project-Young Adult (HCP-YA), HCP-Aging (HCP-A), and enhanced Nathan Kline Institute-Rockland sample (eNKI-RS). Several canonical correlation analyses were computed within a machine learning framework to assess the associations between each of the three domains (sleep quality, depressive symptoms, grey matter volume (GMV)) and MP. The HCP-YA analyses showed progressively stronger associations between MP and each domain: depressive symptoms (unexpectedly positive, r=0.13, SD=0.06), sleep quality (r=0.17, SD=0.05), and GMV (r=0.19, SD=0.06). Combining sleep and depressive symptoms significantly improved the canonical correlations (r=0.25, SD=0.05), while the addition of GMV exhibited no further increase (r=0.23, SD=0.06). In young adults, better sleep quality, mild depressive symptoms, and GMV of several brain regions were associated with better MP. This was conceptually replicated in young adults from the eNKI-RS cohort. In HCP-Aging, better sleep quality, fewer depressive symptoms, and increased GMV were associated with MP. Robust multivariate associations were observed between sleep quality, depressive symptoms and GMV with MP, as well as age-related variations in these factors. Future studies should further explore these associations and consider interventions targeting sleep and mental health to test the potential effects on MP across the lifespan.
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Affiliation(s)
- Vincent Küppers
- Department of Nuclear Medicine, University Hospital and Medical Faculty, University of Cologne, Cologne, Germany
- Institute of Neuroscience and Medicine, Brain and Behaviour (INM-7), Research Center Jülich, Jülich, Germany
- Institute of Systems Neuroscience, Medical Faculty and University Hospital Düsseldorf, Heinrich Heine University, Düsseldorf, Germany
| | - Hanwen Bi
- Institute of Neuroscience and Medicine, Brain and Behaviour (INM-7), Research Center Jülich, Jülich, Germany
- Institute of Systems Neuroscience, Medical Faculty and University Hospital Düsseldorf, Heinrich Heine University, Düsseldorf, Germany
| | - Eliana Nicolaisen-Sobesky
- Institute of Neuroscience and Medicine, Brain and Behaviour (INM-7), Research Center Jülich, Jülich, Germany
- Institute of Systems Neuroscience, Medical Faculty and University Hospital Düsseldorf, Heinrich Heine University, Düsseldorf, Germany
| | - Felix Hoffstaedter
- Institute of Neuroscience and Medicine, Brain and Behaviour (INM-7), Research Center Jülich, Jülich, Germany
- Institute of Systems Neuroscience, Medical Faculty and University Hospital Düsseldorf, Heinrich Heine University, Düsseldorf, Germany
| | - B.T. Thomas Yeo
- Centre for Sleep and Cognition & Centre for Translational MR Research, Yong Loo Lin School of Medicine, National University of Singapore, Singapore
- Department of Electrical and Computer Engineering, National University of Singapore, Singapore
- N.1 Institute for Health, National University of Singapore, Singapore
- Integrative Sciences and Engineering Programme (ISEP), National University of Singapore, Singapore
- Department of Medicine, Human Potential Translational Research Programme & Institute for Digital Medicine (WisDM), Yong Loo Lin School of Medicine, National University of Singapore, Singapore
- Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, MA, USA
| | - Alexander Drzezga
- Department of Nuclear Medicine, University Hospital and Medical Faculty, University of Cologne, Cologne, Germany
- German Center for Neurodegenerative Diseases (DZNE), Bonn-Cologne, Germany
- Institute of Neuroscience and Medicine, Molecular Organization of the Brain (INM-2), Research Centre Jülich, Jülich, Germany
| | - Simon B. Eickhoff
- Institute of Neuroscience and Medicine, Brain and Behaviour (INM-7), Research Center Jülich, Jülich, Germany
- Institute of Systems Neuroscience, Medical Faculty and University Hospital Düsseldorf, Heinrich Heine University, Düsseldorf, Germany
| | - Masoud Tahmasian
- Department of Nuclear Medicine, University Hospital and Medical Faculty, University of Cologne, Cologne, Germany
- Institute of Neuroscience and Medicine, Brain and Behaviour (INM-7), Research Center Jülich, Jülich, Germany
- Institute of Systems Neuroscience, Medical Faculty and University Hospital Düsseldorf, Heinrich Heine University, Düsseldorf, Germany
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25
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Yu J. Age-related decline in thickness and surface area in the cortical surface and hippocampus: lifespan trajectories and decade-by-decade analyses. GeroScience 2024:10.1007/s11357-024-01220-1. [PMID: 38831181 DOI: 10.1007/s11357-024-01220-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/23/2024] [Accepted: 05/21/2024] [Indexed: 06/05/2024] Open
Abstract
Previous studies on age-related changes in cortical and hippocampal morphology were not designed or able to reveal the complex spatial patterns of changes across the lifespan. To this end, the current study examined these changes in a decade-by-decade manner by comparing consecutive age decades at the vertex-wise level. Additionally, the lifespan trajectories of cortical/hippocampal mean thickness and total surface area were modeled and plotted out to provide an overview of their age-related changes. Using two lifespan datasets (Ntotal = 1378; 18 ≤ age ≤ 100), vertex-wise thickness and surface area measurements were extracted from the cortical and unfolded hippocampal surfaces and analyzed using whole-brain/hippocampus vertex-wise analyses. Lifespan trajectories of cortical/hippocampal mean thickness and total surface area were modeled with generalized additive models for location, scale, and shape. These models revealed fairly linear declines in both cortical measures and inverted U-shaped trajectories for both hippocampal measures. Across the different age decades, the sizes and locations of cortical thinning clusters were highly variable across the age decades. No significant clusters of cortical surface area changes were observed across the age decades. Significant clusters of hippocampal surface area and thickness reduction were not observed until the 70s. Generally, the agreement between datasets on the hippocampal findings was much higher than those of the cortical surface. These findings revealed important nuances in the age-related changes of cortical and hippocampal morphology and cautioned against using lifespan trajectories to infer decade-by-decade changes in the cortical surface and the hippocampus.
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Affiliation(s)
- Junhong Yu
- Psychology, School of Social Sciences, Nanyang Technological University, 48 Nanyang Avenue, Singapore, 639798, Singapore.
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26
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Li J, Zhang C, Meng Y, Yang S, Xia J, Chen H, Liao W. Morphometric brain organization across the human lifespan reveals increased dispersion linked to cognitive performance. PLoS Biol 2024; 22:e3002647. [PMID: 38900742 PMCID: PMC11189252 DOI: 10.1371/journal.pbio.3002647] [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: 09/25/2023] [Accepted: 04/26/2024] [Indexed: 06/22/2024] Open
Abstract
The human brain is organized as segregation and integration units and follows complex developmental trajectories throughout life. The cortical manifold provides a new means of studying the brain's organization in a multidimensional connectivity gradient space. However, how the brain's morphometric organization changes across the human lifespan remains unclear. Here, leveraging structural magnetic resonance imaging scans from 1,790 healthy individuals aged 8 to 89 years, we investigated age-related global, within- and between-network dispersions to reveal the segregation and integration of brain networks from 3D manifolds based on morphometric similarity network (MSN), combining multiple features conceptualized as a "fingerprint" of an individual's brain. Developmental trajectories of global dispersion unfolded along patterns of molecular brain organization, such as acetylcholine receptor. Communities were increasingly dispersed with age, reflecting more disassortative morphometric similarity profiles within a community. Increasing within-network dispersion of primary motor and association cortices mediated the influence of age on the cognitive flexibility of executive functions. We also found that the secondary sensory cortices were decreasingly dispersed with the rest of the cortices during aging, possibly indicating a shift of secondary sensory cortices across the human lifespan from an extreme to a more central position in 3D manifolds. Together, our results reveal the age-related segregation and integration of MSN from the perspective of a multidimensional gradient space, providing new insights into lifespan changes in multiple morphometric features of the brain, as well as the influence of such changes on cognitive performance.
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Affiliation(s)
- Jiao Li
- The Clinical Hospital of Chengdu Brain Science Institute, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, China
- MOE Key Lab for Neuroinformation, High-Field Magnetic Resonance Brain Imaging Key Laboratory of Sichuan Province, University of Electronic Science and Technology of China, Chengdu, China
| | - Chao Zhang
- The Clinical Hospital of Chengdu Brain Science Institute, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, China
- MOE Key Lab for Neuroinformation, High-Field Magnetic Resonance Brain Imaging Key Laboratory of Sichuan Province, University of Electronic Science and Technology of China, Chengdu, China
| | - Yao Meng
- The Clinical Hospital of Chengdu Brain Science Institute, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, China
- MOE Key Lab for Neuroinformation, High-Field Magnetic Resonance Brain Imaging Key Laboratory of Sichuan Province, University of Electronic Science and Technology of China, Chengdu, China
| | - Siqi Yang
- The Clinical Hospital of Chengdu Brain Science Institute, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, China
- MOE Key Lab for Neuroinformation, High-Field Magnetic Resonance Brain Imaging Key Laboratory of Sichuan Province, University of Electronic Science and Technology of China, Chengdu, China
| | - Jie Xia
- The Clinical Hospital of Chengdu Brain Science Institute, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, China
- MOE Key Lab for Neuroinformation, High-Field Magnetic Resonance Brain Imaging Key Laboratory of Sichuan Province, University of Electronic Science and Technology of China, Chengdu, China
| | - Huafu Chen
- The Clinical Hospital of Chengdu Brain Science Institute, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, China
- MOE Key Lab for Neuroinformation, High-Field Magnetic Resonance Brain Imaging Key Laboratory of Sichuan Province, University of Electronic Science and Technology of China, Chengdu, China
| | - Wei Liao
- The Clinical Hospital of Chengdu Brain Science Institute, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, China
- MOE Key Lab for Neuroinformation, High-Field Magnetic Resonance Brain Imaging Key Laboratory of Sichuan Province, University of Electronic Science and Technology of China, Chengdu, China
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27
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Bernstein-Eliav M, Tavor I. The Prediction of Brain Activity from Connectivity: Advances and Applications. Neuroscientist 2024; 30:367-377. [PMID: 36250457 PMCID: PMC11107130 DOI: 10.1177/10738584221130974] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
The human brain is composed of multiple, discrete, functionally specialized regions that are interconnected to form large-scale distributed networks. Using advanced brain-imaging methods and machine-learning analytical approaches, recent studies have demonstrated that regional brain activity during the performance of various cognitive tasks can be accurately predicted from patterns of task-independent brain connectivity. In this review article, we first present evidence for the predictability of brain activity from structural connectivity (i.e., white matter connections) and functional connectivity (i.e., temporally synchronized task-free activations). We then discuss the implications of such predictions to clinical populations, such as patients diagnosed with psychiatric disorders or neurologic diseases, and to the study of brain-behavior associations. We conclude that connectivity may serve as an infrastructure that dictates brain activity, and we pinpoint several open questions and directions for future research.
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Affiliation(s)
| | - Ido Tavor
- Sackler Faculty of Medicine, Tel Aviv University, Tel Aviv, Israel
- Sagol School of Neuroscience, Tel Aviv University, Tel Aviv, Israel
- Strauss Center for Computational Neuroimaging, Tel Aviv University, Tel Aviv, Israel
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28
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Morys F, Tremblay C, Rahayel S, Hansen JY, Dai A, Misic B, Dagher A. Neural correlates of obesity across the lifespan. Commun Biol 2024; 7:656. [PMID: 38806652 PMCID: PMC11133431 DOI: 10.1038/s42003-024-06361-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/04/2023] [Accepted: 05/20/2024] [Indexed: 05/30/2024] Open
Abstract
Associations between brain and obesity are bidirectional: changes in brain structure and function underpin over-eating, while chronic adiposity leads to brain atrophy. Investigating brain-obesity interactions across the lifespan can help better understand these relationships. This study explores the interaction between obesity and cortical morphometry in children, young adults, adults, and older adults. We also investigate the genetic, neurochemical, and cognitive correlates of the brain-obesity associations. Our findings reveal a pattern of lower cortical thickness in fronto-temporal brain regions associated with obesity across all age cohorts and varying age-dependent patterns in the remaining brain regions. In adults and older adults, obesity correlates with neurochemical changes and expression of inflammatory and mitochondrial genes. In children and older adults, adiposity is associated with modifications in brain regions involved in emotional and attentional processes. Thus, obesity might originate from cognitive changes during early adolescence, leading to neurodegeneration in later life through mitochondrial and inflammatory mechanisms.
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Affiliation(s)
- Filip Morys
- Montreal Neurological Institute, McGill University, H3A 2B4, Montreal, QC, Canada.
| | - Christina Tremblay
- Montreal Neurological Institute, McGill University, H3A 2B4, Montreal, QC, Canada
| | - Shady Rahayel
- Department of Medicine and Medical Specialties, University of Montreal, Montreal, QC, Canada
- Center for Advanced Research in Sleep Medicine, Hopital du Sacre-Coeur de Montreal, Montreal, QC, Canada
| | - Justine Y Hansen
- Montreal Neurological Institute, McGill University, H3A 2B4, Montreal, QC, Canada
| | - Alyssa Dai
- Montreal Neurological Institute, McGill University, H3A 2B4, Montreal, QC, Canada
| | - Bratislav Misic
- Montreal Neurological Institute, McGill University, H3A 2B4, Montreal, QC, Canada
| | - Alain Dagher
- Montreal Neurological Institute, McGill University, H3A 2B4, Montreal, QC, Canada
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Conte S, Zimmerman D, Richards JE. White matter trajectories over the lifespan. PLoS One 2024; 19:e0301520. [PMID: 38758830 PMCID: PMC11101104 DOI: 10.1371/journal.pone.0301520] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/28/2023] [Accepted: 03/14/2024] [Indexed: 05/19/2024] Open
Abstract
White matter (WM) changes occur throughout the lifespan at a different rate for each developmental period. We aggregated 10879 structural MRIs and 6186 diffusion-weighted MRIs from participants between 2 weeks to 100 years of age. Age-related changes in gray matter and WM partial volumes and microstructural WM properties, both brain-wide and on 29 reconstructed tracts, were investigated as a function of biological sex and hemisphere, when appropriate. We investigated the curve fit that would best explain age-related differences by fitting linear, cubic, quadratic, and exponential models to macro and microstructural WM properties. Following the first steep increase in WM volume during infancy and childhood, the rate of development slows down in adulthood and decreases with aging. Similarly, microstructural properties of WM, particularly fractional anisotropy (FA) and mean diffusivity (MD), follow independent rates of change across the lifespan. The overall increase in FA and decrease in MD are modulated by demographic factors, such as the participant's age, and show different hemispheric asymmetries in some association tracts reconstructed via probabilistic tractography. All changes in WM macro and microstructure seem to follow nonlinear trajectories, which also differ based on the considered metric. Exponential changes occurred for the WM volume and FA and MD values in the first five years of life. Collectively, these results provide novel insight into how changes in different metrics of WM occur when a lifespan approach is considered.
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Affiliation(s)
- Stefania Conte
- Department of Psychology, State University of New York at Binghamton, Vestal, NY, United States of America
| | - Dabriel Zimmerman
- Department of Biomedical Engineering, Boston University, Boston, MA, United States of America
| | - John E. Richards
- Department of Psychology, University of South Carolina, Columbia, SC, United States of America
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Pan Y, Bi C, Kochunov P, Shardell M, Smith JC, McCoy RG, Ye Z, Yu J, Lu T, Yang Y, Lee H, Liu S, Gao S, Ma Y, Li Y, Chen C, Ma T, Wang Z, Nichols T, Hong LE, Chen S. Brain-wide functional connectome analysis of 40,000 individuals reveals brain networks that show aging effects in older adults. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.05.17.594743. [PMID: 38798606 PMCID: PMC11118564 DOI: 10.1101/2024.05.17.594743] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/29/2024]
Abstract
The functional connectome changes with aging. We systematically evaluated aging related alterations in the functional connectome using a whole-brain connectome network analysis in 39,675 participants in UK Biobank project. We used adaptive dense network discovery tools to identify networks directly associated with aging from resting-state fMRI data. We replicated our findings in 499 participants from the Lifespan Human Connectome Project in Aging study. The results consistently revealed two motor-related subnetworks (both permutation test p-values <0.001) that showed a decline in resting-state functional connectivity (rsFC) with increasing age. The first network primarily comprises sensorimotor and dorsal/ventral attention regions from precentral gyrus, postcentral gyrus, superior temporal gyrus, and insular gyrus, while the second network is exclusively composed of basal ganglia regions, namely the caudate, putamen, and globus pallidus. Path analysis indicates that white matter fractional anisotropy mediates 19.6% (p<0.001, 95% CI [7.6% 36.0%]) and 11.5% (p<0.001, 95% CI [6.3% 17.0%]) of the age-related decrease in both networks, respectively. The total volume of white matter hyperintensity mediates 32.1% (p<0.001, 95% CI [16.8% 53.0%]) of the aging-related effect on rsFC in the first subnetwork.
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Affiliation(s)
- Yezhi Pan
- Maryland Psychiatric Research Center, Department of Psychiatry, School of Medicine, University of Maryland, Baltimore, Maryland, United States of America
| | - Chuan Bi
- Maryland Psychiatric Research Center, Department of Psychiatry, School of Medicine, University of Maryland, Baltimore, Maryland, United States of America
| | - Peter Kochunov
- Department of Psychiatry and Behavioral Science, University of Texas Health Science Center Houston, Houston, United States of America
| | - Michelle Shardell
- Department of Epidemiology and Public Health and Institute for Genome Sciences, University of Maryland School of Medicine, Baltimore, Maryland, United States of America
| | - J. Carson Smith
- Department of Kinesiology, University of Maryland, College Park, Maryland, United States of America
| | - Rozalina G. McCoy
- Division of Endocrinology, Diabetes, & Nutrition, Department of Medicine, School of Medicine, University of Maryland, Baltimore, Maryland, United States of America
| | - Zhenyao Ye
- Maryland Psychiatric Research Center, Department of Psychiatry, School of Medicine, University of Maryland, Baltimore, Maryland, United States of America
- Division of Biostatistics and Bioinformatics, Department of Epidemiology and Public Health, School of Medicine, University of Maryland, Baltimore, Maryland, United States of America
| | - Jiaao Yu
- Department of Mathematics, University of Maryland, College Park, Maryland, United States of America
| | - Tong Lu
- Department of Mathematics, University of Maryland, College Park, Maryland, United States of America
| | - Yifan Yang
- Department of Mathematics, University of Maryland, College Park, Maryland, United States of America
| | - Hwiyoung Lee
- Maryland Psychiatric Research Center, Department of Psychiatry, School of Medicine, University of Maryland, Baltimore, Maryland, United States of America
| | - Song Liu
- School of Computer Science and Technology, Qilu University of Technology (Shandong Academy of Sciences), Jinan, Shandong, China
| | - Si Gao
- Maryland Psychiatric Research Center, Department of Psychiatry, School of Medicine, University of Maryland, Baltimore, Maryland, United States of America
| | - Yizhou Ma
- Maryland Psychiatric Research Center, Department of Psychiatry, School of Medicine, University of Maryland, Baltimore, Maryland, United States of America
| | - Yiran Li
- Department of Diagnostic Radiology & Nuclear Medicine, University of Maryland School of Medicine, Baltimore, Maryland, United States of America
| | - Chixiang Chen
- Division of Biostatistics and Bioinformatics, Department of Epidemiology and Public Health, School of Medicine, University of Maryland, Baltimore, Maryland, United States of America
| | - Tianzhou Ma
- Department of Epidemiology and Biostatistics, School of Public Health, University of Maryland, College Park, Maryland, United States of America
| | - Ze Wang
- Department of Diagnostic Radiology & Nuclear Medicine, University of Maryland School of Medicine, Baltimore, Maryland, United States of America
| | - Thomas Nichols
- Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, Nuffield Department of Population Health, University of Oxford, Oxford, United Kingdom
| | - L. Elliot Hong
- Department of Psychiatry and Behavioral Science, University of Texas Health Science Center Houston, Houston, United States of America
| | - Shuo Chen
- Maryland Psychiatric Research Center, Department of Psychiatry, School of Medicine, University of Maryland, Baltimore, Maryland, United States of America
- Division of Biostatistics and Bioinformatics, Department of Epidemiology and Public Health, School of Medicine, University of Maryland, Baltimore, Maryland, United States of America
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Del Mauro G, Sevel LS, Boissoneault J, Wang Z. Divergent association between pain intensity and resting-state fMRI-based brain entropy in different age groups. J Neurosci Res 2024; 102:e25341. [PMID: 38751218 PMCID: PMC11154588 DOI: 10.1002/jnr.25341] [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: 04/24/2024] [Accepted: 04/27/2024] [Indexed: 06/11/2024]
Abstract
Pain is a multidimensional subjective experience sustained by multiple brain regions involved in different aspects of pain experience. We used brain entropy (BEN) estimated from resting-state fMRI (rsfMRI) data to investigate the neural correlates of pain experience. BEN was estimated from rs-fMRI data provided by two datasets with different age range: the Human Connectome Project-Young Adult (HCP-YA) and the Human Connectome project-Aging (HCP-A) datasets. Retrospective assessment of experienced pain intensity was retrieved from both datasets. No main effect of pain intensity was observed. The interaction between pain and age, however, was related to increased BEN in several pain-related brain regions, reflecting greater variability of spontaneous brain activity. Dividing the sample into a young adult group (YG) and a middle age-aging group (MAG) resulted in two divergent patterns of pain-BEN association: In the YG, pain intensity was related to reduced BEN in brain regions involved in the sensory processing of pain; in the MAG, pain was associated with increased BEN in areas related to both sensory and cognitive aspects of pain experience.
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Affiliation(s)
- Gianpaolo Del Mauro
- Department of Diagnostic Radiology and Nuclear Medicine, University of Maryland School of Medicine, Baltimore, Maryland, USA
| | - Landrew Samuel Sevel
- Department of Anesthesiology, University of Minnesota, Minneapolis, Minnesota, USA
| | - Jeff Boissoneault
- Department of Anesthesiology, University of Minnesota, Minneapolis, Minnesota, USA
| | - Ze Wang
- Department of Diagnostic Radiology and Nuclear Medicine, University of Maryland School of Medicine, Baltimore, Maryland, USA
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32
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Miron G, Müller PM, Hohmann L, Oltmanns F, Holtkamp M, Meisel C, Chien C. Cortical Thickness Patterns of Cognitive Impairment Phenotypes in Drug-Resistant Temporal Lobe Epilepsy. Ann Neurol 2024; 95:984-997. [PMID: 38391006 DOI: 10.1002/ana.26893] [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: 09/22/2023] [Revised: 01/26/2024] [Accepted: 02/02/2024] [Indexed: 02/24/2024]
Abstract
OBJECTIVE In temporal lobe epilepsy (TLE), a taxonomy classifying patients into 3 cognitive phenotypes has been adopted: minimally, focally, or multidomain cognitively impaired (CI). We examined gray matter (GM) thickness patterns of cognitive phenotypes in drug-resistant TLE and assessed potential use for predicting postsurgical cognitive outcomes. METHODS TLE patients undergoing presurgical evaluation were categorized into cognitive phenotypes. Network edge weights and distances were calculated using type III analysis of variance F-statistics from comparisons of GM regions within each TLE cognitive phenotype and age- and sex-matched healthy participants. In resected patients, logistic regression models (LRMs) based on network analysis results were used for prediction of postsurgical cognitive outcome. RESULTS A total of 124 patients (63 females, mean age ± standard deviation [SD] = 36.0 ± 12.0 years) and 117 healthy controls (63 females, mean age ± SD = 36.1 ± 12.0 years) were analyzed. In the multidomain CI group (n = 66, 53.2%), 28 GM regions were significantly thinner compared to healthy controls. Focally impaired patients (n = 37, 29.8%) showed 13 regions, whereas minimally impaired patients (n = 21, 16.9%) had 2 significantly thinner GM regions. Regions affected in both multidomain and focally impaired patients included the anterior cingulate cortex, medial prefrontal cortex, medial temporal, and lateral temporal regions. In 69 (35 females, mean age ± SD = 33.6 ± 18.0 years) patients who underwent surgery, LRMs based on network-identified GM regions predicted postsurgical verbal memory worsening with a receiver operating curve area under the curve of 0.70 ± 0.15. INTERPRETATION A differential pattern of GM thickness can be found across different cognitive phenotypes in TLE. Including magnetic resonance imaging with clinical measures associated with cognitive profiles has potential in predicting postsurgical cognitive outcomes in drug-resistant TLE. ANN NEUROL 2024;95:984-997.
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Affiliation(s)
- Gadi Miron
- Computational Neurology, Department of Neurology, Charité-Universitätsmedizin Berlin, Berlin, Germany
- Epilepsy Center Berlin-Brandenburg, Department of Neurology, Charité-Universitätsmedizin Berlin, Berlin, Germany
- Epilepsy Center Berlin-Brandenburg, Institute for Diagnostics of Epilepsy, Berlin, Germany
- Berlin Institute of Health, Berlin, Germany
| | - Paul Manuel Müller
- Computational Neurology, Department of Neurology, Charité-Universitätsmedizin Berlin, Berlin, Germany
- Berlin Institute of Health, Berlin, Germany
- NeuroCure Cluster of Excellence, Charité-Universitätsmedizin Berlin, Berlin, Germany
| | - Louisa Hohmann
- Epilepsy Center Berlin-Brandenburg, Department of Neurology, Charité-Universitätsmedizin Berlin, Berlin, Germany
- Epilepsy Center Berlin-Brandenburg, Institute for Diagnostics of Epilepsy, Berlin, Germany
| | - Frank Oltmanns
- Epilepsy Center Berlin-Brandenburg, Institute for Diagnostics of Epilepsy, Berlin, Germany
| | - Martin Holtkamp
- Epilepsy Center Berlin-Brandenburg, Department of Neurology, Charité-Universitätsmedizin Berlin, Berlin, Germany
- Epilepsy Center Berlin-Brandenburg, Institute for Diagnostics of Epilepsy, Berlin, Germany
| | - Christian Meisel
- Computational Neurology, Department of Neurology, Charité-Universitätsmedizin Berlin, Berlin, Germany
- Berlin Institute of Health, Berlin, Germany
- NeuroCure Cluster of Excellence, Charité-Universitätsmedizin Berlin, Berlin, Germany
- Bernstein Center for Computational Neuroscience, Berlin, Germany
- Center for Stroke Research Berlin, Berlin, Germany
| | - Claudia Chien
- Experimental Clinical and Research Center, Charité-Universitätsmedizin Berlin, Berlin, Germany
- Neuroscience Clinical Research Center, Charité-Universitätsmedizin Berlin, Berlin, Germany
- Department of Psychiatry and Neuroscience, Charité-Universitätsmedizin Berlin, Berlin, Germany
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Jamison KW, Gu Z, Wang Q, Sabuncu MR, Kuceyeski A. Release the Krakencoder: A unified brain connectome translation and fusion tool. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.04.12.589274. [PMID: 38659856 PMCID: PMC11042193 DOI: 10.1101/2024.04.12.589274] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 04/26/2024]
Abstract
Brain connectivity can be estimated in many ways, depending on modality and processing strategy. Here we present the Krakencoder, a joint connectome mapping tool that simultaneously, bidirectionally translates between structural (SC) and functional connectivity (FC), and across different atlases and processing choices via a common latent representation. These mappings demonstrate unprecedented accuracy and individual-level identifiability; the mapping between SC and FC has identifiability 42-54% higher than existing models. The Krakencoder combines all connectome flavors via a shared low-dimensional latent space. This "fusion" representation i) better reflects familial relatedness, ii) preserves age- and sex-relevant information and iii) enhances cognition-relevant information. The Krakencoder can be applied without retraining to new, out-of-age-distribution data while still preserving inter-individual differences in the connectome predictions and familial relationships in the latent representations. The Krakencoder is a significant leap forward in capturing the relationship between multi-modal brain connectomes in an individualized, behaviorally- and demographically-relevant way.
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Affiliation(s)
- Keith W Jamison
- Department of Computational Biology, Cornell University, Ithaca, NY, USA
- Department of Radiology, Weill Cornell Medicine, New York, New York, USA
| | - Zijin Gu
- School of Electrical and Computer Engineering, Cornell University and Cornell Tech, New York, NY, USA
| | - Qinxin Wang
- Department of Biomedical Engineering, Tsinghua University, Beijing, 100084, China
| | - Mert R Sabuncu
- Department of Radiology, Weill Cornell Medicine, New York, New York, USA
- School of Electrical and Computer Engineering, Cornell University and Cornell Tech, New York, NY, USA
| | - Amy Kuceyeski
- Department of Computational Biology, Cornell University, Ithaca, NY, USA
- Department of Radiology, Weill Cornell Medicine, New York, New York, USA
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34
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Bätz LR, Ye S, Lan X, Ziaei M. Increased functional integration of emotional control network in late adulthood. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.04.10.588823. [PMID: 38659752 PMCID: PMC11040603 DOI: 10.1101/2024.04.10.588823] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 04/26/2024]
Abstract
Across the adult lifespan, there are changes in how emotions are perceived and regulated. As individuals age, there is an observed improvement in emotion regulation and overall quicker recovery from negative emotions. While previous studies have shown differences in emotion processing in late adulthood, the corresponding differences in large-scale brain networks remain largely underexplored. By utilizing large-scale datasets such as the Human Connectome Project (HCP-Aging, N = 621 ) and Cambridge Centre for Ageing and Neuroscience (Cam-CAN, N = 333 ), we were able to investigate how emotion regulation networks' functional topography differs across the entire adult lifespan. Based on previous meta-analytic work that identified four large-scale functional brain networks involved in emotion generation and regulation, we found an increase in the functional integration of the emotional control network among older adults. Additionally, confirming through the nonlinear model, individuals around the age of 70 showed a steadier decline in integration of a network mediating emotion generation and regulation via interoception. Furthermore, the analyses revealed a negative association between age and perceived stress and loneliness that could be attributed to differences in large-scale emotion regulation networks. Our study highlights the importance of identifying topological changes in the functional emotion network architecture across the lifespan, as it allows for a better understanding of emotional aging and psychological well-being in late adulthood.
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Affiliation(s)
- Leona Rahel Bätz
- Kavli Institute for Systems Neuroscience, Norwegian University of Science and Technology, Trondheim, Norway
| | - Shuer Ye
- Kavli Institute for Systems Neuroscience, Norwegian University of Science and Technology, Trondheim, Norway
| | - Xiaqing Lan
- Kavli Institute for Systems Neuroscience, Norwegian University of Science and Technology, Trondheim, Norway
| | - Maryam Ziaei
- Kavli Institute for Systems Neuroscience, Norwegian University of Science and Technology, Trondheim, Norway
- Queensland Brain Institute, University of Queensland, Brisbane, Australia
- K.G. Jebsen Centre for Alzheimer’s disease, Norwegian University of Science and Technology, Trondheim, Norway
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Aiskovich M, Castro E, Reinen JM, Fadnavis S, Mehta A, Li H, Dhurandhar A, Cecchi GA, Polosecki P. Fusion of biomedical imaging studies for increased sample size and diversity: a case study of brain MRI. FRONTIERS IN RADIOLOGY 2024; 4:1283392. [PMID: 38645773 PMCID: PMC11026619 DOI: 10.3389/fradi.2024.1283392] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 08/25/2023] [Accepted: 03/11/2024] [Indexed: 04/23/2024]
Abstract
Data collection, curation, and cleaning constitute a crucial phase in Machine Learning (ML) projects. In biomedical ML, it is often desirable to leverage multiple datasets to increase sample size and diversity, but this poses unique challenges, which arise from heterogeneity in study design, data descriptors, file system organization, and metadata. In this study, we present an approach to the integration of multiple brain MRI datasets with a focus on homogenization of their organization and preprocessing for ML. We use our own fusion example (approximately 84,000 images from 54,000 subjects, 12 studies, and 88 individual scanners) to illustrate and discuss the issues faced by study fusion efforts, and we examine key decisions necessary during dataset homogenization, presenting in detail a database structure flexible enough to accommodate multiple observational MRI datasets. We believe our approach can provide a basis for future similarly-minded biomedical ML projects.
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Affiliation(s)
| | - Eduardo Castro
- IBM T. J. Watson Research Center, Yorktown Heights, New York, NY, United States
| | - Jenna M. Reinen
- IBM T. J. Watson Research Center, Yorktown Heights, New York, NY, United States
| | - Shreyas Fadnavis
- IBM T. J. Watson Research Center, Yorktown Heights, New York, NY, United States
| | - Anushree Mehta
- IBM T. J. Watson Research Center, Yorktown Heights, New York, NY, United States
| | - Hongyang Li
- IBM T. J. Watson Research Center, Yorktown Heights, New York, NY, United States
| | - Amit Dhurandhar
- IBM T. J. Watson Research Center, Yorktown Heights, New York, NY, United States
| | - Guillermo A. Cecchi
- IBM T. J. Watson Research Center, Yorktown Heights, New York, NY, United States
| | - Pablo Polosecki
- IBM T. J. Watson Research Center, Yorktown Heights, New York, NY, United States
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Jang I, Li B, Rashid B, Jacoby J, Huang SY, Dickerson BC, Salat DH. Brain structural indicators of β-amyloid neuropathology. Neurobiol Aging 2024; 136:157-170. [PMID: 38382159 PMCID: PMC10938906 DOI: 10.1016/j.neurobiolaging.2024.01.005] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/19/2023] [Revised: 01/10/2024] [Accepted: 01/13/2024] [Indexed: 02/23/2024]
Abstract
Recent efforts demonstrated the efficacy of identifying early-stage neuropathology of Alzheimer's disease (AD) through lumbar puncture cerebrospinal fluid assessment and positron emission tomography (PET) radiotracer imaging. These methods are effective yet are invasive, expensive, and not widely accessible. We extend and improve the multiscale structural mapping (MSSM) procedure to develop structural indicators of β-amyloid neuropathology in preclinical AD, by capturing both macrostructural and microstructural properties throughout the cerebral cortex using a structural MRI. We find that the MSSM signal is regionally altered in clear positive and negative cases of preclinical amyloid pathology (N = 220) when cortical thickness alone or hippocampal volume is not. It exhibits widespread effects of amyloid positivity across the posterior temporal, parietal, and medial prefrontal cortex, surprisingly consistent with the typical pattern of amyloid deposition. The MSSM signal is significantly correlated with amyloid PET in almost half of the cortex, much of which overlaps with regions where beta-amyloid accumulates, suggesting it could provide a regional brain 'map' that is not available from systemic markers such as plasma markers.
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Affiliation(s)
- Ikbeom Jang
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, MA, USA; Department of Radiology, Harvard Medical School, Boston, MA, USA; Division of Computer Engineering, Hankuk University of Foreign Studies, Yongin, South Korea.
| | - Binyin Li
- Department of Neurology, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Barnaly Rashid
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, MA, USA; Department of Neurology, Harvard Medical School, Boston, MA, USA
| | - John Jacoby
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, MA, USA; Department of Radiology, Harvard Medical School, Boston, MA, USA
| | - Susie Y Huang
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, MA, USA; Department of Radiology, Harvard Medical School, Boston, MA, USA; Harvard-MIT Division of Health Sciences and Technology, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Bradford C Dickerson
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, MA, USA; Department of Neurology, Harvard Medical School, Boston, MA, USA; Department of Psychiatry, Harvard Medical School, Boston, MA, USA
| | - David H Salat
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, MA, USA; Department of Radiology, Harvard Medical School, Boston, MA, USA; Neuroimaging Research for Veterans Center, VA Boston Healthcare System, Boston, MA, USA
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37
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Kopetzky SJ, Li Y, Kaiser M, Butz-Ostendorf M. Predictability of intelligence and age from structural connectomes. PLoS One 2024; 19:e0301599. [PMID: 38557681 PMCID: PMC10984540 DOI: 10.1371/journal.pone.0301599] [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: 04/06/2023] [Accepted: 03/19/2024] [Indexed: 04/04/2024] Open
Abstract
In this study, structural images of 1048 healthy subjects from the Human Connectome Project Young Adult study and 94 from ADNI-3 study were processed by an in-house tractography pipeline and analyzed together with pre-processed data of the same subjects from braingraph.org. Whole brain structural connectome features were used to build a simple correlation-based regression machine learning model to predict intelligence and age of healthy subjects. Our results showed that different forms of intelligence as well as age are predictable to a certain degree from diffusion tensor imaging detecting anatomical fiber tracts in the living human brain. Though we did not identify significant differences in the prediction capability for the investigated features depending on the imaging feature extraction method, we did find that crystallized intelligence was consistently better predictable than fluid intelligence from structural connectivity data through all datasets. Our findings suggest a practical and scalable processing and analysis framework to explore broader research topics employing brain MR imaging.
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Affiliation(s)
- Sebastian J. Kopetzky
- Labvantage—Biomax GmbH, Planegg, Germany
- School of Life Sciences, Technical University of Munich, Freising, Germany
| | - Yong Li
- Labvantage—Biomax GmbH, Planegg, Germany
| | - Marcus Kaiser
- Precision Imaging Beacon, School of Medicine, University of Nottingham, Nottingham, United Kingdom
- Department of Functional Neurosurgery, Rui Jin Hospital, Shanghai Jiao Tong University, Shanghai, China
| | - Markus Butz-Ostendorf
- Labvantage—Biomax GmbH, Planegg, Germany
- Laboratory for Parallel Programming, Department of Computer Science, Technical University of Darmstadt, Darmstadt, Germany
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38
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Christova P, James LM, Georgopoulos AP. Negative association between neurovascular coupling and cortical gray matter volume during the lifespan. J Neurophysiol 2024; 131:778-784. [PMID: 38478986 PMCID: PMC11305651 DOI: 10.1152/jn.00005.2024] [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: 01/05/2024] [Revised: 02/20/2024] [Accepted: 03/13/2024] [Indexed: 04/16/2024] Open
Abstract
Recent studies have established the moment-to-moment turnover of the blood-oxygen-level-dependent signal (TBOLD) at resting state as a key measure of local cortical brain function. Here, we sought to extend that line of research by evaluating TBOLD in 70 cortical areas with respect to corresponding brain volume, age, and sex across the lifespan in 1,344 healthy participants including 633 from the Human Connectome Project (HCP)-Development cohort (294 males and 339 females, age range 8-21 yr) and 711 healthy participants from HCP-Aging cohort (316 males and 395 females, 36-90 yr old). In both groups, we found that 1) TBOLD increased with age, 2) volume decreased with age, and 3) TBOLD and volume were highly significantly negatively correlated, independent of age. The inverse association between TBOLD and volume was documented in nearly all 70 brain areas and for both sexes, with slightly stronger associations documented for males. The strong correspondence between TBOLD and volume across age and sex suggests a common influence such as chronic neuroinflammation contributing to reduced cortical volume and increased TBOLD across the lifespan.NEW & NOTEWORTHY We report a significant negative association between resting functional magnetic resonance imaging (fMRI) blood-oxygen-level-dependent (BOLD) signal turnover (TBOLD) and cortical gray matter volume across the lifespan, such that TBOLD increased whereas volume decreased. We attribute this association to a hypothesized chronic, low-grade neuroinflammation, probably induced by various neurotropic pathogens, including human herpes viruses known to be dormant in the brain in a latent state and reactivated by stress, fever, and various environmental exposures, such as ultraviolet light.
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Affiliation(s)
- Peka Christova
- Department of Veterans Affairs Health Care System, The Neuroimaging Research Group, Brain Sciences Center, Minneapolis, Minnesota, United States
- Department of Neuroscience, University of Minnesota Medical School, Minneapolis, Minnesota, United States
- Cognitive Sciences Center, University of Minnesota, Minneapolis, Minnesota, United States
| | - Lisa M James
- Department of Veterans Affairs Health Care System, The Neuroimaging Research Group, Brain Sciences Center, Minneapolis, Minnesota, United States
- Department of Neuroscience, University of Minnesota Medical School, Minneapolis, Minnesota, United States
- Cognitive Sciences Center, University of Minnesota, Minneapolis, Minnesota, United States
- Department of Psychiatry, University of Minnesota Medical School, Minneapolis, Minnesota, United States
| | - Apostolos P Georgopoulos
- Department of Veterans Affairs Health Care System, The Neuroimaging Research Group, Brain Sciences Center, Minneapolis, Minnesota, United States
- Department of Neuroscience, University of Minnesota Medical School, Minneapolis, Minnesota, United States
- Cognitive Sciences Center, University of Minnesota, Minneapolis, Minnesota, United States
- Department of Psychiatry, University of Minnesota Medical School, Minneapolis, Minnesota, United States
- Department of Neurology, University of Minnesota Medical School, Minneapolis, Minnesota, United States
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Epihova G, Astle DE. What is developmental about developmental prosopagnosia? Cortex 2024; 173:333-338. [PMID: 38460488 DOI: 10.1016/j.cortex.2024.02.006] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/04/2023] [Revised: 12/21/2023] [Accepted: 02/19/2024] [Indexed: 03/11/2024]
Abstract
Developmental prosopagnosia (DP) is characterised by difficulties recognising face identities and is associated with diverse co-occurring object recognition difficulties. The high co-occurrence rate and heterogeneity of associated difficulties in DP is an intrinsic feature of developmental conditions, where co-occurrence of difficulties is the rule, rather than the exception. However, despite its name, cognitive and neural theories of DP rarely consider the developmental context in which these difficulties occur. This leaves a large gap in our understanding of how DP emerges in light of the developmental trajectory of face recognition. Here, we argue that progress in the field requires re-considering the developmental origins of differences in face recognition abilities, rather than studying the end-state alone. In practice, considering development in DP necessitates a re-evaluation of current approaches in recruitment, design, and analyses.
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Affiliation(s)
- Gabriela Epihova
- MRC Cognition and Brain Sciences Unit, University of Cambridge, Cambridge, UK.
| | - Duncan E Astle
- MRC Cognition and Brain Sciences Unit, University of Cambridge, Cambridge, UK; Department of Psychiatry, University of Cambridge, Cambridge, UK
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40
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Young T, Kumar VJ, Saranathan M. Normative modeling of thalamic nuclear volumes. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2024:2024.03.06.24303871. [PMID: 38496426 PMCID: PMC10942522 DOI: 10.1101/2024.03.06.24303871] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 03/19/2024]
Abstract
Thalamic nuclei have been implicated in neurodegenerative and neuropsychiatric disorders. Normative models for thalamic nuclear volumes have not been proposed thus far. The aim of this work was to establish normative models of thalamic nuclear volumes and subsequently investigate changes in thalamic nuclei in cognitive and psychiatric disorders. Volumes of the bilateral thalami and 12 nuclear regions were generated from T1 MRI data using a novel segmentation method (HIPS-THOMAS) in healthy control subjects (n=2374) and non-control subjects (n=695) with early and late mild cognitive impairment (EMCI, LMCI), Alzheimer's disease (AD), Early psychosis and Schizophrenia, Bipolar disorder, and Attention deficit hyperactivity disorder. Three different normative modelling methods were evaluated while controlling for sex, intracranial volume, and site. Z-scores and extreme z-score deviations were calculated and compared across phenotypes. GAMLSS models performed the best. Statistically significant shifts in z-score distributions consistent with atrophy were observed for most phenotypes. Shifts of progressively increasing magnitude were observed bilaterally from EMCI to AD with larger shifts in the left thalamic regions. Heterogeneous shifts were observed in psychiatric diagnoses with a predilection for the right thalamic regions. Here we present the first normative models of thalamic nuclear volumes and highlight their utility in evaluating heterogenous disorders such as Schizophrenia.
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Affiliation(s)
- Taylor Young
- Department of Psychiatry, University of Massachusetts Chan Medical School, Worcester, MA
- Department of Neurology, University of Massachusetts Chan Medical School, Worcester, MA
| | | | - Manojkumar Saranathan
- Department of Radiology, University of Massachusetts Chan Medical School, Worcester, MA
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41
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Li M, Schilling KG, Gao F, Xu L, Choi S, Gao Y, Zu Z, Anderson AW, Ding Z, Landman BA, Gore JC. Quantification of mediation effects of white matter functional characteristics on cognitive decline in aging. Cereb Cortex 2024; 34:bhae114. [PMID: 38517178 PMCID: PMC10958767 DOI: 10.1093/cercor/bhae114] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/12/2023] [Revised: 02/29/2024] [Accepted: 03/03/2024] [Indexed: 03/23/2024] Open
Abstract
Cognitive decline with aging involves multifactorial processes, including changes in brain structure and function. This study focuses on the role of white matter functional characteristics, as reflected in blood oxygenation level-dependent signals, in age-related cognitive deterioration. Building on previous research confirming the reproducibility and age-dependence of blood oxygenation level-dependent signals acquired via functional magnetic resonance imaging, we here employ mediation analysis to test if aging affects cognition through white matter blood oxygenation level-dependent signal changes, impacting various cognitive domains and specific white matter regions. We used independent component analysis of resting-state blood oxygenation level-dependent signals to segment white matter into coherent hubs, offering a data-driven view of white matter's functional architecture. Through correlation analysis, we constructed a graph network and derived metrics to quantitatively assess regional functional properties based on resting-state blood oxygenation level-dependent fluctuations. Our analysis identified significant mediators in the age-cognition relationship, indicating that aging differentially influences cognitive functions by altering the functional characteristics of distinct white matter regions. These findings enhance our understanding of the neurobiological basis of cognitive aging, highlighting the critical role of white matter in maintaining cognitive integrity and proposing new approaches to assess interventions targeting cognitive decline in older populations.
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Affiliation(s)
- Muwei Li
- Vanderbilt University Institute of Imaging Science, Vanderbilt University Medical Center, Nashville, TN 37232, United States
- Department of Radiology and Radiological Sciences, Vanderbilt University Medical Center, Nashville, TN 37232, United States
| | - Kurt G Schilling
- Vanderbilt University Institute of Imaging Science, Vanderbilt University Medical Center, Nashville, TN 37232, United States
- Department of Radiology and Radiological Sciences, Vanderbilt University Medical Center, Nashville, TN 37232, United States
| | - Fei Gao
- Department of Radiology, Shandong Provincial Hospital Affiliated to Shandong First Medical University, Jinan 250021, China
| | - Lyuan Xu
- Vanderbilt University Institute of Imaging Science, Vanderbilt University Medical Center, Nashville, TN 37232, United States
- Department of Electrical and Computer Engineering, Vanderbilt University, Nashville, TN 37235, United States
| | - Soyoung Choi
- Vanderbilt University Institute of Imaging Science, Vanderbilt University Medical Center, Nashville, TN 37232, United States
- Department of Radiology and Radiological Sciences, Vanderbilt University Medical Center, Nashville, TN 37232, United States
| | - Yurui Gao
- Vanderbilt University Institute of Imaging Science, Vanderbilt University Medical Center, Nashville, TN 37232, United States
- Department of Biomedical Engineering, Vanderbilt University, Nashville, TN 37240, United States
| | - Zhongliang Zu
- Vanderbilt University Institute of Imaging Science, Vanderbilt University Medical Center, Nashville, TN 37232, United States
- Department of Radiology and Radiological Sciences, Vanderbilt University Medical Center, Nashville, TN 37232, United States
| | - Adam W Anderson
- Vanderbilt University Institute of Imaging Science, Vanderbilt University Medical Center, Nashville, TN 37232, United States
- Department of Biomedical Engineering, Vanderbilt University, Nashville, TN 37240, United States
| | - Zhaohua Ding
- Vanderbilt University Institute of Imaging Science, Vanderbilt University Medical Center, Nashville, TN 37232, United States
- Department of Electrical and Computer Engineering, Vanderbilt University, Nashville, TN 37235, United States
- Department of Biomedical Engineering, Vanderbilt University, Nashville, TN 37240, United States
- Department of Computer Science, Vanderbilt University, Nashville, TN 37240, United States
| | - Bennett A Landman
- Vanderbilt University Institute of Imaging Science, Vanderbilt University Medical Center, Nashville, TN 37232, United States
- Department of Radiology and Radiological Sciences, Vanderbilt University Medical Center, Nashville, TN 37232, United States
- Department of Electrical and Computer Engineering, Vanderbilt University, Nashville, TN 37235, United States
- Department of Biomedical Engineering, Vanderbilt University, Nashville, TN 37240, United States
- Department of Computer Science, Vanderbilt University, Nashville, TN 37240, United States
| | - John C Gore
- Vanderbilt University Institute of Imaging Science, Vanderbilt University Medical Center, Nashville, TN 37232, United States
- Department of Radiology and Radiological Sciences, Vanderbilt University Medical Center, Nashville, TN 37232, United States
- Department of Biomedical Engineering, Vanderbilt University, Nashville, TN 37240, United States
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Taghvaei M, Mechanic-Hamilton DJ, Sadaghiani S, Shakibajahromi B, Dolui S, Das S, Brown C, Tackett W, Khandelwal P, Cook P, Shinohara RT, Yushkevich P, Bassett DS, Wolk DA, Detre JA. Impact of white matter hyperintensities on structural connectivity and cognition in cognitively intact ADNI participants. Neurobiol Aging 2024; 135:79-90. [PMID: 38262221 PMCID: PMC10872454 DOI: 10.1016/j.neurobiolaging.2023.10.012] [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: 04/24/2023] [Revised: 10/19/2023] [Accepted: 10/22/2023] [Indexed: 01/25/2024]
Abstract
We used indirect brain mapping with virtual lesion tractography to test the hypothesis that the extent of white matter tract disconnection due to white matter hyperintensities (WMH) is associated with corresponding tract-specific cognitive performance decrements. To estimate tract disconnection, WMH masks were extracted from FLAIR MRI data of 481 cognitively intact participants in the Alzheimer's Disease Neuroimaging Initiative (ADNI) and used as regions of avoidance for fiber tracking in diffusion MRI data from 50 healthy young participants from the Human Connectome Project. Estimated tract disconnection in the right inferior fronto-occipital fasciculus, right frontal aslant tract, and right superior longitudinal fasciculus mediated the effects of WMH volume on executive function. Estimated tract disconnection in the left uncinate fasciculus mediated the effects of WMH volume on memory and in the right frontal aslant tract on language. In a subset of ADNI control participants with amyloid data, positive status increased the probability of periventricular WMH and moderated the relationship between WMH burden and tract disconnection in executive function performance.
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Affiliation(s)
- Mohammad Taghvaei
- Department of Neurology, University of Pennsylvania, Philadelphia, PA, USA
| | | | | | | | - Sudipto Dolui
- Department of Radiology, University of Pennsylvania, Philadelphia, PA, USA
| | - Sandhitsu Das
- Department of Neurology, University of Pennsylvania, Philadelphia, PA, USA
| | - Christopher Brown
- Department of Neurology, University of Pennsylvania, Philadelphia, PA, USA
| | - William Tackett
- Department of Neurology, University of Pennsylvania, Philadelphia, PA, USA
| | - Pulkit Khandelwal
- Department of Radiology, University of Pennsylvania, Philadelphia, PA, USA
| | - Philip Cook
- Department of Radiology, University of Pennsylvania, Philadelphia, PA, USA
| | - Russell T Shinohara
- Penn Statistics in Imaging and Visualization Center, Department of Biostatistics, Epidemiology, and Informatics, University of Pennsylvania, Philadelphia, PA, USA
| | - Paul Yushkevich
- Department of Radiology, University of Pennsylvania, Philadelphia, PA, USA
| | - Danielle S Bassett
- Department of Bioengineering, University of Pennsylvania, Philadelphia, PA, USA
| | - David A Wolk
- Department of Neurology, University of Pennsylvania, Philadelphia, PA, USA
| | - John A Detre
- Department of Neurology, University of Pennsylvania, Philadelphia, PA, USA.
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Hett K, McKnight CD, Leguizamon M, Lindsey JS, Eisma JJ, Elenberger J, Stark AJ, Song AK, Aumann M, Considine CM, Claassen DO, Donahue MJ. Deep learning segmentation of peri-sinus structures from structural magnetic resonance imaging: validation and normative ranges across the adult lifespan. Fluids Barriers CNS 2024; 21:15. [PMID: 38350930 PMCID: PMC10865560 DOI: 10.1186/s12987-024-00516-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: 09/22/2023] [Accepted: 01/26/2024] [Indexed: 02/15/2024] Open
Abstract
BACKGROUND Peri-sinus structures such as arachnoid granulations (AG) and the parasagittal dural (PSD) space have gained much recent attention as sites of cerebral spinal fluid (CSF) egress and neuroimmune surveillance. Neurofluid circulation dysfunction may manifest as morphological changes in these structures, however, automated quantification of these structures is not possible and rather characterization often requires exogenous contrast agents and manual delineation. METHODS We propose a deep learning architecture to automatically delineate the peri-sinus space (e.g., PSD and intravenous AG structures) using two cascaded 3D fully convolutional neural networks applied to submillimeter 3D T2-weighted non-contrasted MRI images, which can be routinely acquired on all major MRI scanner vendors. The method was evaluated through comparison with gold-standard manual tracing from a neuroradiologist (n = 80; age range = 11-83 years) and subsequently applied in healthy participants (n = 1,872; age range = 5-100 years), using data from the Human Connectome Project, to provide exemplar metrics across the lifespan. Dice-Sørensen and a generalized linear model was used to assess PSD and AG changes across the human lifespan using quadratic restricted splines, incorporating age and sex as covariates. RESULTS Findings demonstrate that the PSD and AG volumes can be segmented using T2-weighted MRI with a Dice-Sørensen coefficient and accuracy of 80.7 and 74.6, respectively. Across the lifespan, we observed that total PSD volume increases with age with a linear interaction of gender and age equal to 0.9 cm3 per year (p < 0.001). Similar trends were observed in the frontal and parietal, but not occipital, PSD. An increase in AG volume was observed in the third to sixth decades of life, with a linear effect of age equal to 0.64 mm3 per year (p < 0.001) for total AG volume and 0.54 mm3 (p < 0.001) for maximum AG volume. CONCLUSIONS A tool that can be applied to quantify PSD and AG volumes from commonly acquired T2-weighted MRI scans is reported and exemplar volumetric ranges of these structures are provided, which should provide an exemplar for studies of neurofluid circulation dysfunction. Software and training data are made freely available online ( https://github.com/hettk/spesis ).
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Affiliation(s)
- Kilian Hett
- Dept. of Neurology, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Colin D McKnight
- Dept. of Radiology and Radiological Sciences, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Melanie Leguizamon
- Dept. of Neurology, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Jennifer S Lindsey
- Dept. of Radiology and Radiological Sciences, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Jarrod J Eisma
- Dept. of Neurology, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Jason Elenberger
- Dept. of Neurology, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Adam J Stark
- Dept. of Neurology, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Alexander K Song
- Dept. of Neurology, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Megan Aumann
- Dept. of Neurology, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Ciaran M Considine
- Dept. of Psychiatry and Behavioral Sciences, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Daniel O Claassen
- Dept. of Neurology, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Manus J Donahue
- Dept. of Neurology, Vanderbilt University Medical Center, Nashville, TN, USA.
- Dept. of Psychiatry and Behavioral Sciences, Vanderbilt University Medical Center, Nashville, TN, USA.
- Dept. of Electrical and Computer Engineering, Vanderbilt University, Nashville, TN, USA.
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44
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Huang S, Zhong L, Shi Y. Automated Mapping of Residual Distortion Severity in Diffusion MRI. COMPUTATIONAL DIFFUSION MRI : MICCAI WORKSHOP 2024; 14328:58-69. [PMID: 38500569 PMCID: PMC10948104 DOI: 10.1007/978-3-031-47292-3_6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 03/20/2024]
Abstract
Susceptibility-induced distortion is a common artifact in diffusion MRI (dMRI), which deforms the dMRI locally and poses significant challenges in connectivity analysis. While various methods were proposed to correct the distortion, residual distortions often persist at varying degrees across brain regions and subjects. Generating a voxel-level residual distortion severity map can thus be a valuable tool to better inform downstream connectivity analysis. To fill this current gap in dMRI analysis, we propose a supervised deep-learning network to predict a severity map of residual distortion. The training process is supervised using the structural similarity index measure (SSIM) of the fiber orientation distribution (FOD) in two opposite phase encoding (PE) directions. Only b0 images and related outputs from the distortion correction methods are needed as inputs in the testing process. The proposed method is applicable in large-scale datasets such as the UK Biobank, Adolescent Brain Cognitive Development (ABCD), and other emerging studies that only have complete dMRI data in one PE direction but acquires b0 images in both PEs. In our experiments, we trained the proposed model using the Lifespan Human Connectome Project Aging (HCP-Aging) dataset ( n = 662 ) and apply the trained model to data ( n = 1330 ) from UK Biobank. Our results show low training, validation, and test errors, and the severity map correlates excellently with an FOD integrity measure in both HCP-Aging and UK Biobank data. The proposed method is also highly efficient and can generate the severity map in around 1 second for each subject.
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Affiliation(s)
- Shuo Huang
- Stevens Neuroimaging and Informatics Institute, Keck School of Medicine, University of Southern California (USC), Los Angeles, CA 90033, USA
- Alfred E. Mann Department of Biomedical Engineering, Viterbi School of Engineering, University of Southern California (USC), Los Angeles, CA 90089, USA
| | - Lujia Zhong
- Stevens Neuroimaging and Informatics Institute, Keck School of Medicine, University of Southern California (USC), Los Angeles, CA 90033, USA
- Ming Hsieh Department of Electrical and Computer Engineering, Viterbi School of Engineering, University of Southern California (USC), Los Angeles, CA 90089, USA
| | - Yonggang Shi
- Stevens Neuroimaging and Informatics Institute, Keck School of Medicine, University of Southern California (USC), Los Angeles, CA 90033, USA
- Alfred E. Mann Department of Biomedical Engineering, Viterbi School of Engineering, University of Southern California (USC), Los Angeles, CA 90089, USA
- Ming Hsieh Department of Electrical and Computer Engineering, Viterbi School of Engineering, University of Southern California (USC), Los Angeles, CA 90089, USA
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45
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Devignes Q, Ren B, Clancy KJ, Howell K, Pollmann Y, Martinez-Sanchez L, Beard C, Kumar P, Rosso IM. Trauma-related intrusive memories and anterior hippocampus structural covariance: an ecological momentary assessment study in posttraumatic stress disorder. Transl Psychiatry 2024; 14:74. [PMID: 38307849 PMCID: PMC10837434 DOI: 10.1038/s41398-024-02795-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/04/2023] [Revised: 01/09/2024] [Accepted: 01/17/2024] [Indexed: 02/04/2024] Open
Abstract
Trauma-related intrusive memories (TR-IMs) are hallmark symptoms of posttraumatic stress disorder (PTSD), but their neural correlates remain partly unknown. Given its role in autobiographical memory, the hippocampus may play a critical role in TR-IM neurophysiology. The anterior and posterior hippocampi are known to have partially distinct functions, including during retrieval of autobiographical memories. This study aimed to investigate the relationship between TR-IM frequency and the anterior and posterior hippocampi morphology in PTSD. Ninety-three trauma-exposed adults completed daily ecological momentary assessments for fourteen days to capture their TR-IM frequency. Participants then underwent anatomical magnetic resonance imaging to obtain measures of anterior and posterior hippocampal volumes. Partial least squares analysis was applied to identify a structural covariance network that differentiated the anterior and posterior hippocampi. Poisson regression models examined the relationship of TR-IM frequency with anterior and posterior hippocampal volumes and the resulting structural covariance network. Results revealed no significant relationship of TR-IM frequency with hippocampal volumes. However, TR-IM frequency was significantly negatively correlated with the expression of a structural covariance pattern specifically associated with the anterior hippocampus volume. This association remained significant after accounting for the severity of PTSD symptoms other than intrusion symptoms. The network included the bilateral inferior temporal gyri, superior frontal gyri, precuneus, and fusiform gyri. These novel findings indicate that higher TR-IM frequency in individuals with PTSD is associated with lower structural covariance between the anterior hippocampus and other brain regions involved in autobiographical memory, shedding light on the neural correlates underlying this core symptom of PTSD.
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Affiliation(s)
- Quentin Devignes
- Center for Depression, Anxiety and Stress Disorders, McLean Hospital, Belmont, MA, USA.
- Department of Psychiatry, Harvard Medical School, Boston, MA, USA.
| | - Boyu Ren
- Department of Psychiatry, Harvard Medical School, Boston, MA, USA
- Psychiatric Biostatistics Laboratory, McLean Hospital, Belmont, MA, USA
| | - Kevin J Clancy
- Center for Depression, Anxiety and Stress Disorders, McLean Hospital, Belmont, MA, USA
- Department of Psychiatry, Harvard Medical School, Boston, MA, USA
| | - Kristin Howell
- Center for Depression, Anxiety and Stress Disorders, McLean Hospital, Belmont, MA, USA
| | - Yara Pollmann
- Center for Depression, Anxiety and Stress Disorders, McLean Hospital, Belmont, MA, USA
| | | | - Courtney Beard
- Department of Psychiatry, Harvard Medical School, Boston, MA, USA
- Division of Depression and Anxiety Disorders, McLean Hospital, Belmont, MA, USA
| | - Poornima Kumar
- Center for Depression, Anxiety and Stress Disorders, McLean Hospital, Belmont, MA, USA
- Department of Psychiatry, Harvard Medical School, Boston, MA, USA
| | - Isabelle M Rosso
- Center for Depression, Anxiety and Stress Disorders, McLean Hospital, Belmont, MA, USA.
- Department of Psychiatry, Harvard Medical School, Boston, MA, USA.
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46
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Yu J. Age-related decrease in inter-subject similarity of cortical morphology and task and resting-state functional connectivity. GeroScience 2024; 46:697-711. [PMID: 38006514 PMCID: PMC10828367 DOI: 10.1007/s11357-023-01008-9] [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: 08/07/2023] [Accepted: 11/02/2023] [Indexed: 11/27/2023] Open
Abstract
"All old people are the same" is an unfortunate characterization of the perceived homogeneity in the older age group. This study attempts to debunk this myth in the context of the structural and functional brain. Within older relative to younger age groups, individuals are hypothesized to be more dissimilar to their similar-aged peers-thus demonstrating an age-related divergence. This study analyzed functional connectivity (FC) during multiple fMRI paradigms (2 rest + 5 tasks) and cortical thickness (CT) data from two lifespan datasets (Ntotal = 1161). On average, between-subject FC/CT correlations became weaker in the older age groups. Further analyses ruled out the possibility that more rapid age-related changes in older brains have increased the dissimilarity in these older age groups. Brain-wide analyses revealed significant effects of age-related divergence across most of the brain. Finally, CT similarity between a dyad significantly predicted their FC similarity across multiple fMRI task paradigms-demonstrating a close relationship between brain structure and function even at the between-dyad level. Contrary to the myth that "all old people are the same," these findings suggest young people are more similar to each other. This study presents major implications in the study of neural fingerprinting and brain-behavior associations.
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Affiliation(s)
- Junhong Yu
- Psychology, School of Social Sciences, Nanyang Technological University, 48 Nanyang Avenue, Singapore, 639798, Singapore.
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47
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Han L, Chan MY, Agres PF, Winter-Nelson E, Zhang Z, Wig GS. Measures of resting-state brain network segregation and integration vary in relation to data quantity: implications for within and between subject comparisons of functional brain network organization. Cereb Cortex 2024; 34:bhad506. [PMID: 38385891 PMCID: PMC10883417 DOI: 10.1093/cercor/bhad506] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/06/2023] [Revised: 12/05/2023] [Accepted: 12/16/2023] [Indexed: 02/23/2024] Open
Abstract
Measures of functional brain network segregation and integration vary with an individual's age, cognitive ability, and health status. Based on these relationships, these measures are frequently examined to study and quantify large-scale patterns of network organization in both basic and applied research settings. However, there is limited information on the stability and reliability of the network measures as applied to functional time-series; these measurement properties are critical to understand if the measures are to be used for individualized characterization of brain networks. We examine measurement reliability using several human datasets (Midnight Scan Club and Human Connectome Project [both Young Adult and Aging]). These datasets include participants with multiple scanning sessions, and collectively include individuals spanning a broad age range of the adult lifespan. The measurement and reliability of measures of resting-state network segregation and integration vary in relation to data quantity for a given participant's scan session; notably, both properties asymptote when estimated using adequate amounts of clean data. We demonstrate how this source of variability can systematically bias interpretation of differences and changes in brain network organization if appropriate safeguards are not included. These observations have important implications for cross-sectional, longitudinal, and interventional comparisons of functional brain network organization.
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Affiliation(s)
- Liang Han
- Center for Vital Longevity and School of Behavioral and Brain Sciences, University of Texas at Dallas, Dallas, TX 75235, United States
| | - Micaela Y Chan
- Center for Vital Longevity and School of Behavioral and Brain Sciences, University of Texas at Dallas, Dallas, TX 75235, United States
| | - Phillip F Agres
- Center for Vital Longevity and School of Behavioral and Brain Sciences, University of Texas at Dallas, Dallas, TX 75235, United States
| | - Ezra Winter-Nelson
- Center for Vital Longevity and School of Behavioral and Brain Sciences, University of Texas at Dallas, Dallas, TX 75235, United States
| | - Ziwei Zhang
- Center for Vital Longevity and School of Behavioral and Brain Sciences, University of Texas at Dallas, Dallas, TX 75235, United States
| | - Gagan S Wig
- Center for Vital Longevity and School of Behavioral and Brain Sciences, University of Texas at Dallas, Dallas, TX 75235, United States
- Department of Psychiatry, University of Texas Southwestern Medical Center, Dallas, TX 75390, United States
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48
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Yang C, Coalson TS, Smith SM, Elam JS, Van Essen DC, Glasser MF. Automating the Human Connectome Project's Temporal ICA Pipeline. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.01.15.574667. [PMID: 38293188 PMCID: PMC10827070 DOI: 10.1101/2024.01.15.574667] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/01/2024]
Abstract
Functional magnetic resonance imaging (fMRI) data are dominated by noise and artifacts, with only a small fraction of the variance relating to neural activity. Temporal independent component analysis (tICA) is a recently developed method that enables selective denoising of fMRI artifacts related to physiology such as respiration. However, an automated and easy to use pipeline for tICA has not previously been available; instead, two manual steps have been necessary: 1) setting the group spatial ICA dimensionality after MELODIC's Incremental Group-PCA (MIGP) and 2) labeling tICA components as artifacts versus signals. Moreover, guidance has been lacking as to how many subjects and timepoints are needed to adequately re-estimate the temporal ICA decomposition and what alternatives are available for smaller groups or even individual subjects. Here, we introduce a nine-step fully automated tICA pipeline which removes global artifacts from fMRI dense timeseries after sICA+FIX cleaning and MSMAll alignment driven by functionally relevant areal features. Additionally, we have developed an automated "reclean" Pipeline for improved spatial ICA (sICA) artifact removal. Two major automated components of the pipeline are 1) an automatic group spatial ICA (sICA) dimensionality selection for MIGP data enabled by fitting multiple Wishart distributions; 2) a hierarchical classifier to distinguish group tICA signal components from artifactual components, equipped with a combination of handcrafted features from domain expert knowledge and latent features obtained via self-supervised learning on spatial maps. We demonstrate that the dimensionality estimated for the MIGP data from HCP Young Adult 3T and 7T datasets is comparable to previous manual tICA estimates, and that the group sICA decomposition is highly reproducible. We also show that the tICA classifier achieved over 0.98 Precision-Recall Area Under Curve (PR-AUC) and that the correctly classified components account for over 95% of the tICA-represented variance on multiple held-out evaluation datasets including the HCP-Young Adult, HCP-Aging and HCP-Development datasets under various settings. Our automated tICA pipeline is now available as part of the HCP pipelines, providing a powerful and user-friendly tool for the neuroimaging community.
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Burzynska AZ, Anderson C, Arciniegas DB, Calhoun V, Choi IY, Mendez Colmenares A, Kramer AF, Li K, Lee J, Lee P, Thomas ML. Correlates of axonal content in healthy adult span: Age, sex, myelin, and metabolic health. CEREBRAL CIRCULATION - COGNITION AND BEHAVIOR 2024; 6:100203. [PMID: 38292016 PMCID: PMC10827486 DOI: 10.1016/j.cccb.2024.100203] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 10/23/2023] [Revised: 01/10/2024] [Accepted: 01/11/2024] [Indexed: 02/01/2024]
Abstract
As the emerging treatments that target grey matter pathology in Alzheimer's Disease have limited effectiveness, there is a critical need to identify new neural targets for treatments. White matter's (WM) metabolic vulnerability makes it a promising candidate for new interventions. This study examined the age and sex differences in estimates of axonal content, as well the associations of with highly prevalent modifiable health risk factors such as metabolic syndrome and adiposity. We estimated intra-axonal volume fraction (ICVF) using the Neurite Orientation Dispersion and Density Imaging (NODDI) in a sample of 89 cognitively and neurologically healthy adults (20-79 years). We showed that ICVF correlated positively with age and estimates of myelin content. The ICVF was also lower in women than men, across all ages, which difference was accounted for by intracranial volume. Finally, we found no association of metabolic risk or adiposity scores with the current estimates of ICVF. In addition, the previously observed adiposity-myelin associations (Burzynska et al., 2023) were independent of ICVF. Although our findings confirm the vulnerability of axons to aging, they suggest that metabolic dysfunction may selectively affect myelin content, at least in cognitively and neurologically healthy adults with low metabolic risk, and when using the specific MRI techniques. Future studies need to revisit our findings using larger samples and different MRI approaches, and identify modifiable factors that accelerate axonal deterioration as well as mechanisms linking peripheral metabolism with the health of myelin.
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Affiliation(s)
- Agnieszka Z Burzynska
- The BRAiN lab, Department of Human Development and Family Studies/Molecular, Cellular and Integrative Neurosciences, Colorado State University, Fort Collins, CO, USA
| | - Charles Anderson
- Department of Computer Science, Colorado State University, Fort Collins, CO, USA
| | - David B. Arciniegas
- Marcus Institute for Brain Health, University of Colorado Anschutz Medical Campus, Aurora, CO, USA
| | - Vince Calhoun
- Tri-institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State, Georgia Tech, Emory, Atlanta, GA, USA
| | - In-Young Choi
- Department of Neurology, Department of Radiology, Hoglund Biomedical Imaging Center, University of Kansas Medical Center, Kansas City, KS, USA
| | - Andrea Mendez Colmenares
- The BRAiN lab, Department of Human Development and Family Studies/Molecular, Cellular and Integrative Neurosciences, Colorado State University, Fort Collins, CO, USA
| | - Arthur F Kramer
- Beckman Institute for Advanced Science and Technology at the University of Illinois, IL, USA
- Center for Cognitive & Brain Health, Northeastern University, Boston, MA, USA
| | - Kaigang Li
- Department of Health and Exercise Science, Colorado State University, Fort Collins, CO, USA
| | - Jongho Lee
- Department of Electrical and Computer Engineering, Seoul National University, Seoul, Republic of Korea
| | - Phil Lee
- Department of Radiology, Hoglund Biomedical Imaging Center, University of Kansas Medical Center, Kansas City, KS, USA
| | - Michael L Thomas
- Department of Psychology, Colorado State University, Fort Collins, CO, USA
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Singh K, Barsoum S, Schilling KG, An Y, Ferrucci L, Benjamini D. Neuronal microstructural changes in the human brain are associated with neurocognitive aging. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.01.11.575206. [PMID: 38260525 PMCID: PMC10802615 DOI: 10.1101/2024.01.11.575206] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/24/2024]
Abstract
Gray matter (GM) alterations play a role in aging-related disorders like Alzheimer's disease and related dementias, yet MRI studies mainly focus on macroscopic changes. Although reliable indicators of atrophy, morphological metrics like cortical thickness lack the sensitivity to detect early changes preceding visible atrophy. Our study aimed at exploring the potential of diffusion MRI in unveiling sensitive markers of cortical and subcortical age-related microstructural changes and assessing their associations with cognitive and behavioral deficits. We leveraged the Human Connectome Project-Aging cohort that included 707 unimpaired participants (394 female; median age = 58, range = 36-90 years) and applied the powerful mean apparent diffusion propagator model to measure microstructural parameters, along with comprehensive behavioral and cognitive test scores. Both macro- and microstructural GM characteristics were strongly associated with age, with widespread significant microstructural correlations reflective of cellular morphological changes, reduced cellular density, increased extracellular volume, and increased membrane permeability. Importantly, when correlating MRI and cognitive test scores, our findings revealed no link between macrostructural volumetric changes and neurobehavioral performance. However, we found that cellular and extracellular alterations in cortical and subcortical GM regions were associated with neurobehavioral performance. Based on these findings, it is hypothesized that increased microstructural heterogeneity and decreased neurite orientation dispersion precede macrostructural changes, and that they play an important role in subsequent cognitive decline. These alterations are suggested to be early markers of neurocognitive performance that may distinctly aid in identifying the mechanisms underlying phenotypic aging and subsequent age-related functional decline.
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Affiliation(s)
- Kavita Singh
- Multiscale Imaging and Integrative Biophysics Unit, National Institute on Aging, NIH, Baltimore, MD, USA
| | - Stephanie Barsoum
- Multiscale Imaging and Integrative Biophysics Unit, National Institute on Aging, NIH, Baltimore, MD, USA
| | - Kurt G Schilling
- Department of Radiology and Radiological Sciences, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Yang An
- Brain Aging and Behavior Section, National Institute on Aging, NIH, Baltimore, MD, USA
| | - Luigi Ferrucci
- Translational Gerontology Branch, National Institute on Aging, NIH, Baltimore, MD, USA
| | - Dan Benjamini
- Multiscale Imaging and Integrative Biophysics Unit, National Institute on Aging, NIH, Baltimore, MD, USA
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