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Kucikova L, Xiong X, Reinecke P, Madden J, Jackson E, Tappin O, Huang W, Dounavi ME, Su L. The Effects of APOEe4 Allele on Cerebral Structure, Function, and Related Interactions with Cognition in Young Adults. Ageing Res Rev 2024; 101:102510. [PMID: 39326705 DOI: 10.1016/j.arr.2024.102510] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/09/2024] [Revised: 09/11/2024] [Accepted: 09/16/2024] [Indexed: 09/28/2024]
Abstract
In the last decade, extensive research has emerged into understanding the impact of risk factors for Alzheimer's Disease (AD) on brain in pre-symptomatic stages. We investigated the neuroimaging correlates of the APOEe4 genetic risk factor for (AD) in young adulthood, its relationship with cognition, and potential effects of other variables on the findings. While conventional volumetric analyses revealed no consistent differences, more sophisticated analyses identified subtle structural differences between APOEe4-carriers and non-carriers. Findings from diffusion studies were limited, but functional studies demonstrated consistent alterations in connectivity and activity. The complex relationship between APOE genotype, neuroimaging variables, and cognition revealed no consensus on the directionality of findings. Methodological choices, including analytical approaches, sample size, and the influence of other genes, gender, and ethnicity, varied across studies, impacting comparability and generalizability. Recommendations for future research include multimodal and longitudinal imaging, standardisation of pipelines, advanced analytical techniques, and collaborative data pooling.
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Affiliation(s)
- Ludmila Kucikova
- Neuroscience Institute, School of Medicine and Population Health, University of Sheffield, Sheffield, United Kingdom; Insigneo Institute for In Silico Medicine, University of Sheffield, Sheffield, United Kingdom
| | - Xiong Xiong
- Neuroscience Institute, School of Medicine and Population Health, University of Sheffield, Sheffield, United Kingdom; School of Information and Communication Engineering, Beijing University of Posts and Telecommunications, Beijing, China
| | - Patricia Reinecke
- Academic Unit of Medical Education, Medical School, University of Sheffield, Sheffield, United Kingdom
| | - Jessica Madden
- Academic Unit of Medical Education, Medical School, University of Sheffield, Sheffield, United Kingdom
| | - Elizabeth Jackson
- Academic Unit of Medical Education, Medical School, University of Sheffield, Sheffield, United Kingdom
| | - Oliver Tappin
- Academic Unit of Medical Education, Medical School, University of Sheffield, Sheffield, United Kingdom
| | - Weijie Huang
- Neuroscience Institute, School of Medicine and Population Health, University of Sheffield, Sheffield, United Kingdom; State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, China; Department of Psychiatry, School of Clinical Medicine, University of Cambridge, Cambridge, United Kingdom
| | - Maria-Eleni Dounavi
- Department of Psychiatry, School of Clinical Medicine, University of Cambridge, Cambridge, United Kingdom
| | - Li Su
- Neuroscience Institute, School of Medicine and Population Health, University of Sheffield, Sheffield, United Kingdom; Insigneo Institute for In Silico Medicine, University of Sheffield, Sheffield, United Kingdom; Department of Psychiatry, School of Clinical Medicine, University of Cambridge, Cambridge, United Kingdom.
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2
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Le Grand Q, Tsuchida A, Koch A, Imtiaz MA, Aziz NA, Vigneron C, Zago L, Lathrop M, Dubrac A, Couffinhal T, Crivello F, Matthews PM, Mishra A, Breteler MMB, Tzourio C, Debette S. Diffusion imaging genomics provides novel insight into early mechanisms of cerebral small vessel disease. Mol Psychiatry 2024:10.1038/s41380-024-02604-7. [PMID: 38811690 DOI: 10.1038/s41380-024-02604-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/31/2023] [Revised: 05/06/2024] [Accepted: 05/09/2024] [Indexed: 05/31/2024]
Abstract
Cerebral small vessel disease (cSVD) is a leading cause of stroke and dementia. Genetic risk loci for white matter hyperintensities (WMH), the most common MRI-marker of cSVD in older age, were recently shown to be significantly associated with white matter (WM) microstructure on diffusion tensor imaging (signal-based) in young adults. To provide new insights into these early changes in WM microstructure and their relation with cSVD, we sought to explore the genetic underpinnings of cutting-edge tissue-based diffusion imaging markers across the adult lifespan. We conducted a genome-wide association study of neurite orientation dispersion and density imaging (NODDI) markers in young adults (i-Share study: N = 1 758, (mean[range]) 22.1[18-35] years), with follow-up in young middle-aged (Rhineland Study: N = 714, 35.2[30-40] years) and late middle-aged to older individuals (UK Biobank: N = 33 224, 64.3[45-82] years). We identified 21 loci associated with NODDI markers across brain regions in young adults. The most robust association, replicated in both follow-up cohorts, was with Neurite Density Index (NDI) at chr5q14.3, a known WMH locus in VCAN. Two additional loci were replicated in UK Biobank, at chr17q21.2 with NDI, and chr19q13.12 with Orientation Dispersion Index (ODI). Transcriptome-wide association studies showed associations of STAT3 expression in arterial and adipose tissue (chr17q21.2) with NDI, and of several genes at chr19q13.12 with ODI. Genetic susceptibility to larger WMH volume, but not to vascular risk factors, was significantly associated with decreased NDI in young adults, especially in regions known to harbor WMH in older age. Individually, seven of 25 known WMH risk loci were associated with NDI in young adults. In conclusion, we identified multiple novel genetic risk loci associated with NODDI markers, particularly NDI, in early adulthood. These point to possible early-life mechanisms underlying cSVD and to processes involving remyelination, neurodevelopment and neurodegeneration, with a potential for novel approaches to prevention.
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Affiliation(s)
- Quentin Le Grand
- University of Bordeaux, INSERM, Bordeaux Population Health research center, UMR1219, F-33000, Bordeaux, France
- Population Health Sciences, German Center for Neurodegenerative Diseases (DZNE), Bonn, Germany
| | - Ami Tsuchida
- University of Bordeaux, INSERM, Bordeaux Population Health research center, UMR1219, F-33000, Bordeaux, France
- University of Bordeaux, Institute of Neurodegenerative Diseases, UMR5293, Neurofunctional Imaging Group, F-33000, Bordeaux, France
- CNRS, Institute of Neurodegenerative Diseases, UMR5293, Neurofunctional Imaging Group, F-33000, Bordeaux, France
- CEA, Institute of Neurodegenerative Diseases, UMR5293, Neurofunctional Imaging Group, F-33000, Bordeaux, France
| | - Alexandra Koch
- Population Health Sciences, German Center for Neurodegenerative Diseases (DZNE), Bonn, Germany
| | - Mohammed-Aslam Imtiaz
- Population Health Sciences, German Center for Neurodegenerative Diseases (DZNE), Bonn, Germany
| | - N Ahmad Aziz
- Population Health Sciences, German Center for Neurodegenerative Diseases (DZNE), Bonn, Germany
- Department of Neurology, Faculty of Medicine, University of Bonn, Bonn, Germany
| | - Chloé Vigneron
- University of Bordeaux, INSERM, Bordeaux Population Health research center, UMR1219, F-33000, Bordeaux, France
| | - Laure Zago
- University of Bordeaux, Institute of Neurodegenerative Diseases, UMR5293, Neurofunctional Imaging Group, F-33000, Bordeaux, France
- CNRS, Institute of Neurodegenerative Diseases, UMR5293, Neurofunctional Imaging Group, F-33000, Bordeaux, France
- CEA, Institute of Neurodegenerative Diseases, UMR5293, Neurofunctional Imaging Group, F-33000, Bordeaux, France
| | - Mark Lathrop
- Department of Human Genetics, McGill University, Montreal, Quebec, Canada; Victor Phillip Dahdaleh Institute of Genomic Medicine at McGill University, Montreal, QC, H3A 0G1, Canada
| | - Alexandre Dubrac
- Centre de Recherche, CHU Sainte-Justine, Montréal, QC, Canada
- Département de Pathologie et Biologie Cellulaire, Université de Montréal, Montréal, QC, Canada
- Département d'Ophtalmologie, Université de Montréal, Montréal, QC, Canada
| | - Thierry Couffinhal
- University of Bordeaux, INSERM, Biologie des maladies cardiovasculaires, U1034, F-33600, Pessac, France
| | - Fabrice Crivello
- University of Bordeaux, Institute of Neurodegenerative Diseases, UMR5293, Neurofunctional Imaging Group, F-33000, Bordeaux, France
- CNRS, Institute of Neurodegenerative Diseases, UMR5293, Neurofunctional Imaging Group, F-33000, Bordeaux, France
- CEA, Institute of Neurodegenerative Diseases, UMR5293, Neurofunctional Imaging Group, F-33000, Bordeaux, France
| | - Paul M Matthews
- UK Dementia Research Institute and Department of Brain Sciences, Imperial College, London, UK
| | - Aniket Mishra
- University of Bordeaux, INSERM, Bordeaux Population Health research center, UMR1219, F-33000, Bordeaux, France
| | - Monique M B Breteler
- Population Health Sciences, German Center for Neurodegenerative Diseases (DZNE), Bonn, Germany
- Institute for Medical Biometry, Informatics and Epidemiology (IMBIE), Faculty of Medicine, University of Bonn, Bonn, Germany
| | - Christophe Tzourio
- University of Bordeaux, INSERM, Bordeaux Population Health research center, UMR1219, F-33000, Bordeaux, France
- Bordeaux University Hospital, Department of Medical Informatics, F-33000, Bordeaux, France
| | - Stéphanie Debette
- University of Bordeaux, INSERM, Bordeaux Population Health research center, UMR1219, F-33000, Bordeaux, France.
- Bordeaux University Hospital, Department of Neurology, Institute for Neurodegenerative Diseases, F-33000, Bordeaux, France.
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3
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Joliot M, Cremona S, Tzourio C, Etard O. Modulate the impact of the drowsiness on the resting state functional connectivity. Sci Rep 2024; 14:8652. [PMID: 38622265 PMCID: PMC11018752 DOI: 10.1038/s41598-024-59476-8] [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: 09/01/2023] [Accepted: 04/11/2024] [Indexed: 04/17/2024] Open
Abstract
This research explores different methodologies to modulate the effects of drowsiness on functional connectivity (FC) during resting-state functional magnetic resonance imaging (RS-fMRI). The study utilized a cohort of students (MRi-Share) and classified individuals into drowsy, alert, and mixed/undetermined states based on observed respiratory oscillations. We analyzed the FC group difference between drowsy and alert individuals after five different processing methods: the reference method, two based on physiological and a global signal regression of the BOLD time series signal, and two based on Gaussian standardizations of the FC distribution. According to the reference method, drowsy individuals exhibit higher cortico-cortical FC than alert individuals. First, we demonstrated that each method reduced the differences between drowsy and alert states. The second result is that the global signal regression was quantitively the most effective, minimizing significant FC differences to only 3.3% of the total FCs. However, one should consider the risks of overcorrection often associated with this methodology. Therefore, choosing a less aggressive form of regression, such as the physiological method or Gaussian-based approaches, might be a more cautious approach. Third and last, using the Gaussian-based methods, cortico-subcortical and intra-default mode network (DMN) FCs were significantly greater in alert than drowsy subjects. These findings bear resemblance to the anticipated patterns during the onset of sleep, where the cortex isolates itself to assist in transitioning into deeper slow wave sleep phases, simultaneously disconnecting the DMN.
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Affiliation(s)
- Marc Joliot
- GIN, IMN UMR5293, CEA, CNRS, Université de Bordeaux, Bordeaux, France.
| | - Sandrine Cremona
- GIN, IMN UMR5293, CEA, CNRS, Université de Bordeaux, Bordeaux, France
| | | | - Olivier Etard
- Normandie Université, UNICAEN, INSERM, COMETE U1075, CYCERON, CHU Caen, 14000, Caen, France
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4
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Mosharov EV, Rosenberg AM, Monzel AS, Osto CA, Stiles L, Rosoklija GB, Dwork AJ, Bindra S, Zhang Y, Fujita M, Mariani MB, Bakalian M, Sulzer D, De Jager PL, Menon V, Shirihai OS, Mann JJ, Underwood M, Boldrini M, de Schotten MT, Picard M. A Human Brain Map of Mitochondrial Respiratory Capacity and Diversity. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.03.05.583623. [PMID: 38496679 PMCID: PMC10942385 DOI: 10.1101/2024.03.05.583623] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 03/19/2024]
Abstract
Mitochondrial oxidative phosphorylation (OxPhos) powers brain activity1,2, and mitochondrial defects are linked to neurodegenerative and neuropsychiatric disorders3,4, underscoring the need to define the brain's molecular energetic landscape5-10. To bridge the cognitive neuroscience and cell biology scale gap, we developed a physical voxelization approach to partition a frozen human coronal hemisphere section into 703 voxels comparable to neuroimaging resolution (3×3×3 mm). In each cortical and subcortical brain voxel, we profiled mitochondrial phenotypes including OxPhos enzyme activities, mitochondrial DNA and volume density, and mitochondria-specific respiratory capacity. We show that the human brain contains a diversity of mitochondrial phenotypes driven by both topology and cell types. Compared to white matter, grey matter contains >50% more mitochondria. We show that the more abundant grey matter mitochondria also are biochemically optimized for energy transformation, particularly among recently evolved cortical brain regions. Scaling these data to the whole brain, we created a backward linear regression model integrating several neuroimaging modalities11, thereby generating a brain-wide map of mitochondrial distribution and specialization that predicts mitochondrial characteristics in an independent brain region of the same donor brain. This new approach and the resulting MitoBrainMap of mitochondrial phenotypes provide a foundation for exploring the molecular energetic landscape that enables normal brain functions, relating it to neuroimaging data, and defining the subcellular basis for regionalized brain processes relevant to neuropsychiatric and neurodegenerative disorders.
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Affiliation(s)
- Eugene V Mosharov
- Department of Psychiatry, Divisions of Molecular Therapeutics and Behavioral Medicine, Columbia University Irving Medical Center, New York, NY, USA
- New York State Psychiatric Institute, New York, NY, USA
| | - Ayelet M Rosenberg
- Department of Psychiatry, Divisions of Molecular Therapeutics and Behavioral Medicine, Columbia University Irving Medical Center, New York, NY, USA
| | - Anna S Monzel
- Department of Psychiatry, Divisions of Molecular Therapeutics and Behavioral Medicine, Columbia University Irving Medical Center, New York, NY, USA
| | - Corey A Osto
- Department of Medicine, Endocrinology, and Department of Molecular and Medical Pharmacology, David Geffen School of Medicine, University of California, Los Angeles, CA
| | - Linsey Stiles
- Department of Medicine, Endocrinology, and Department of Molecular and Medical Pharmacology, David Geffen School of Medicine, University of California, Los Angeles, CA
| | - Gorazd B Rosoklija
- New York State Psychiatric Institute, New York, NY, USA
- Department of Psychiatry, Division of Molecular Imaging and Neuropathology, Columbia University Irving Medical Center, New York, NY, USA
| | - Andrew J Dwork
- New York State Psychiatric Institute, New York, NY, USA
- Department of Psychiatry, Division of Molecular Imaging and Neuropathology, Columbia University Irving Medical Center, New York, NY, USA
- Department of Pathology and Cell Biology, Columbia University, New York, NY, USA
| | - Snehal Bindra
- Department of Psychiatry, Divisions of Molecular Therapeutics and Behavioral Medicine, Columbia University Irving Medical Center, New York, NY, USA
| | - Ya Zhang
- Center for Translational & Computational Neuroimmunology, Neuroimmunology Division, Department of Neurology and the Taub Institute for Research on Alzheimer's Disease and the Aging Brain, Columbia University Irving Medical Center, New York, NY, USA
| | - Masashi Fujita
- Center for Translational & Computational Neuroimmunology, Neuroimmunology Division, Department of Neurology and the Taub Institute for Research on Alzheimer's Disease and the Aging Brain, Columbia University Irving Medical Center, New York, NY, USA
| | - Madeline B Mariani
- New York State Psychiatric Institute, New York, NY, USA
- Department of Psychiatry, Division of Molecular Imaging and Neuropathology, Columbia University Irving Medical Center, New York, NY, USA
| | - Mihran Bakalian
- New York State Psychiatric Institute, New York, NY, USA
- Department of Psychiatry, Division of Molecular Imaging and Neuropathology, Columbia University Irving Medical Center, New York, NY, USA
| | - David Sulzer
- Department of Psychiatry, Divisions of Molecular Therapeutics and Behavioral Medicine, Columbia University Irving Medical Center, New York, NY, USA
- New York State Psychiatric Institute, New York, NY, USA
- Departments of Neurology and Pharmacology, Columbia University Irving Medical Center, New York, NY, USA; Aligning Science Across Parkinson's (ASAP) Collaborative Research Network, Chevy Chase, MD, USA
| | - Philip L De Jager
- Center for Translational & Computational Neuroimmunology, Neuroimmunology Division, Department of Neurology and the Taub Institute for Research on Alzheimer's Disease and the Aging Brain, Columbia University Irving Medical Center, New York, NY, USA
| | - Vilas Menon
- Center for Translational & Computational Neuroimmunology, Neuroimmunology Division, Department of Neurology and the Taub Institute for Research on Alzheimer's Disease and the Aging Brain, Columbia University Irving Medical Center, New York, NY, USA
| | - Orian S Shirihai
- Department of Medicine, Endocrinology, and Department of Molecular and Medical Pharmacology, David Geffen School of Medicine, University of California, Los Angeles, CA
| | - J John Mann
- New York State Psychiatric Institute, New York, NY, USA
- Department of Psychiatry, Division of Molecular Imaging and Neuropathology, Columbia University Irving Medical Center, New York, NY, USA
| | - Mark Underwood
- New York State Psychiatric Institute, New York, NY, USA
- Department of Psychiatry, Division of Molecular Imaging and Neuropathology, Columbia University Irving Medical Center, New York, NY, USA
| | - Maura Boldrini
- New York State Psychiatric Institute, New York, NY, USA
- Department of Psychiatry, Division of Molecular Imaging and Neuropathology, Columbia University Irving Medical Center, New York, NY, USA
| | - Michel Thiebaut de Schotten
- Brain Connectivity and Behavior Laboratory, Paris, France; Groupe d'Imagerie Neurofonctionnelle, Institut des Maladies Neurodégénératives-UMR 5293, CNRS, CEA University of Bordeaux, France
| | - Martin Picard
- Department of Psychiatry, Divisions of Molecular Therapeutics and Behavioral Medicine, Columbia University Irving Medical Center, New York, NY, USA
- New York State Psychiatric Institute, New York, NY, USA
- Department of Neurology, H. Houston Merritt Center, Columbia Translational Neuroscience Initiative, Columbia University Irving Medical Center, New York, NY, USA
- Robert N Butler Columbia Aging Center, Columbia University Mailman School of Public Health, New York, NY, USA
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Dishner KA, McRae-Posani B, Bhowmik A, Jochelson MS, Holodny A, Pinker K, Eskreis-Winkler S, Stember JN. A Survey of Publicly Available MRI Datasets for Potential Use in Artificial Intelligence Research. J Magn Reson Imaging 2024; 59:450-480. [PMID: 37888298 PMCID: PMC10873125 DOI: 10.1002/jmri.29101] [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: 07/31/2023] [Revised: 10/15/2023] [Accepted: 10/16/2023] [Indexed: 10/28/2023] Open
Abstract
Artificial intelligence (AI) has the potential to bring transformative improvements to the field of radiology; yet, there are barriers to widespread clinical adoption. One of the most important barriers has been access to large, well-annotated, widely representative medical image datasets, which can be used to accurately train AI programs. Creating such datasets requires time and expertise and runs into constraints around data security and interoperability, patient privacy, and appropriate data use. Recognizing these challenges, several institutions have started curating and providing publicly available, high-quality datasets that can be accessed by researchers to advance AI models. The purpose of this work was to review the publicly available MRI datasets that can be used for AI research in radiology. Despite being an emerging field, a simple internet search for open MRI datasets presents an overwhelming number of results. Therefore, we decided to create a survey of the major publicly accessible MRI datasets in different subfields of radiology (brain, body, and musculoskeletal), and list the most important features of value to the AI researcher. To complete this review, we searched for publicly available MRI datasets and assessed them based on several parameters (number of subjects, demographics, area of interest, technical features, and annotations). We reviewed 110 datasets across sub-fields with 1,686,245 subjects in 12 different areas of interest ranging from spine to cardiac. This review is meant to serve as a reference for researchers to help spur advancements in the field of AI for radiology. LEVEL OF EVIDENCE: Level 4 TECHNICAL EFFICACY: Stage 6.
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Affiliation(s)
- Katharine A. Dishner
- Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, NY 10065
- SUNY Downstate College of Medicine, Brooklyn, NY 11203
| | - Bala McRae-Posani
- Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, NY 10065
- Weill Cornell Medicine, New York, NY 10065
| | - Arka Bhowmik
- Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, NY 10065
| | - Maxine S. Jochelson
- Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, NY 10065
| | - Andrei Holodny
- Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, NY 10065
- Department of Radiology, Weill Cornell Medicine, New York, NY 10065
- Department of Neuroscience, Weill Cornell Graduate School of the Medical Sciences, New York, NY 10065
| | - Katja Pinker
- Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, NY 10065
| | | | - Joseph N. Stember
- Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, NY 10065
- Department of Radiology, Weill Cornell Medicine, New York, NY 10065
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Tsuchida A, Boutinaud P, Verrecchia V, Tzourio C, Debette S, Joliot M. Early detection of white matter hyperintensities using SHIVA-WMH detector. Hum Brain Mapp 2024; 45:e26548. [PMID: 38050769 PMCID: PMC10789222 DOI: 10.1002/hbm.26548] [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: 05/03/2023] [Revised: 10/06/2023] [Accepted: 11/16/2023] [Indexed: 12/06/2023] Open
Abstract
White matter hyperintensities (WMHs) are well-established markers of cerebral small vessel disease, and are associated with an increased risk of stroke, dementia, and mortality. Although their prevalence increases with age, small and punctate WMHs have been reported with surprisingly high frequency even in young, neurologically asymptomatic adults. However, most automated methods to segment WMH published to date are not optimized for detecting small and sparse WMH. Here we present the SHIVA-WMH tool, a deep-learning (DL)-based automatic WMH segmentation tool that has been trained with manual segmentations of WMH in a wide range of WMH severity. We show that it is able to detect WMH with high efficiency in subjects with only small punctate WMH as well as in subjects with large WMHs (i.e., with confluency) in evaluation datasets from three distinct databases: magnetic resonance imaging-Share consisting of young university students, MICCAI 2017 WMH challenge dataset consisting of older patients from memory clinics, and UK Biobank with community-dwelling middle-aged and older adults. Across these three cohorts with a wide-ranging WMH load, our tool achieved voxel-level and individual lesion cluster-level Dice scores of 0.66 and 0.71, respectively, which were higher than for three reference tools tested: the lesion prediction algorithm implemented in the lesion segmentation toolbox (LPA: Schmidt), PGS tool, a DL-based algorithm and the current winner of the MICCAI 2017 WMH challenge (Park et al.), and HyperMapper tool (Mojiri Forooshani et al.), another DL-based method with high reported performance in subjects with mild WMH burden. Our tool is publicly and openly available to the research community to facilitate investigations of WMH across a wide range of severity in other cohorts, and to contribute to our understanding of the emergence and progression of WMH.
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Affiliation(s)
- Ami Tsuchida
- GIN, IMN‐UMR5293Université de Bordeaux, CEA, CNRSBordeauxFrance
- BPH‐U1219, INSERMUniversité de BordeauxBordeauxFrance
| | | | - Violaine Verrecchia
- GIN, IMN‐UMR5293Université de Bordeaux, CEA, CNRSBordeauxFrance
- BPH‐U1219, INSERMUniversité de BordeauxBordeauxFrance
| | | | | | - Marc Joliot
- GIN, IMN‐UMR5293Université de Bordeaux, CEA, CNRSBordeauxFrance
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7
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Lima Rebouças SC, Crivello F, Tsuchida A, Tzourio C, Schweitzer C, Korobelnik J, Delcourt C, Helmer C. Association of retinal nerve layers thickness and brain imaging in healthy young subjects from the i-Share-Bordeaux study. Hum Brain Mapp 2023; 44:4722-4737. [PMID: 37401639 PMCID: PMC10400793 DOI: 10.1002/hbm.26412] [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: 07/05/2022] [Revised: 05/18/2023] [Accepted: 06/13/2023] [Indexed: 07/05/2023] Open
Abstract
Given the anatomical and functional similarities between the retina and the brain, the retina could be a "window" for viewing brain structures. We investigated the association between retinal nerve fiber layers (peripapillary retinal nerve fiber layer, ppRNFL; macular ganglion cell-inner plexiform layer, GC-IPL; and macular ganglion cell complex, GCC), and brain magnetic resonance imaging (MRI) parameters in young health adults. We included 857 students (mean age: 23.3 years, 71.3% women) from the i-Share study. We used multivariate linear models to study the cross-sectional association of each retinal nerve layer thickness assessed by spectral-domain optical coherence tomography (SD-OCT) with structural (volumes and cortical thickness), and microstructural brain markers, assessed on MRI globally and regionally. Microstructural MRI parameters included diffusion tensor imaging (DTI) and Neurite Orientation Dispersion and Density Imaging (NODDI). On global brain analysis, thicker ppRNFL, GC-IPL and GCC were all significantly associated with patterns of diffusion metrics consistent with higher WM microstructural integrity. In regional analyses, after multiple testing corrections, our results suggested significant associations of some retinal nerve layers with brain regional gray matter occipital volumes and with diffusion MRI parameters in a region involved in the visual pathway and in regions containing associative tracts. No associations were found with global volumes or with global or regional cortical thicknesses. Results of this study suggest that some retinal nerve layers may reflect brain structures. Further studies are needed to confirm these results in young subjects.
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Affiliation(s)
| | | | - Ami Tsuchida
- University of Bordeaux, CNRS, CEA, IMN, GINBordeauxFrance
| | | | - Cédric Schweitzer
- Department of OphthalmologyBordeaux University HospitalBordeauxFrance
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8
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Cremona S, Joliot M, Mellet E. Cluster-based characterization of consistencies in individuals' thought profiles at rest in a cohort of 1779 French university students. CURRENT PSYCHOLOGY 2022. [DOI: 10.1007/s12144-022-04185-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/28/2022]
Abstract
AbstractIs ongoing conscious thought spontaneous and situation-related, or is it recurrent and dependent on psychological dispositions? The answer is critical for resting-state functional connectivity (RSFC) paradigms that seek to correlate neuroanatomical states with conscious mental states. The goal of the present study was to characterize individual resting state thought profiles (RSTPs) and identify the recurrent ones, i.e., that could both be predicted by personality traits and predict subsequent negative affective states. The 1779 participants had a mean age of 22.1 years, 71.8% were females, and 71.8% were undergraduates. We collected the form and content of their thoughts during a 15-min RSFC session with a computerized retrospective self-questionnaire (ReSQ 2.0). Subsamples of participants also completed online autoquestionnaires assessing their psychological maturity and trait negative affectivity (with a four-day gap on average, N = 1270) and subsequent depressive and anxious states (1.4 years later on average, N = 922). Based on the multiple correspondence and clustering analyses of the ReSQ 2.0 responses, we identified six RSTPs distinctive by their content scope, temporal orientation, empathetic concern, and emotional valence. Multivariate analyses revealed that the probability of experiencing five of the six RSTPs was predicted by trait negative affectivity interacting with psychological maturity. Among them, a negatively valenced RSTP also increased the likelihood of subsequent negative affective states, suggesting its stable and recurrent nature. Identifying recurrent RSTPs is helpful for the future understanding of RSTPs’ contribution to RSFC. Additionally, it will be relevant to test whether acting on psychological maturity can alter the relationship between ongoing conscious thought and negative affectivity.
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9
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Relationship of neurite architecture to brain activity during task-based fMRI. Neuroimage 2022; 262:119575. [PMID: 35987489 DOI: 10.1016/j.neuroimage.2022.119575] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/22/2022] [Revised: 08/13/2022] [Accepted: 08/16/2022] [Indexed: 11/21/2022] Open
Abstract
Functional MRI (fMRI) has been widely used to examine changes in neuronal activity during cognitive tasks. Commonly used measures of gray matter macrostructure (e.g., cortical thickness, surface area, volume) do not consistently appear to serve as structural correlates of brain function. In contrast, gray matter microstructure, measured using neurite orientation dispersion and density imaging (NODDI), enables the estimation of indices of neurite density (neurite density index; NDI) and organization (orientation dispersion index; ODI) in gray matter. Our study explored the relationship among neurite architecture, BOLD (blood-oxygen-level-dependent) fMRI, and cognition, using a large sample (n = 750) of young adults of the human connectome project (HCP) and two tasks that index more cortical (working memory) and more subcortical (emotion processing) targeting of brain functions. Using NODDI, fMRI, structural MRI and task performance data, hierarchical regression analyses revealed that higher working memory- and emotion processing-evoked BOLD activity was related to lower ODI in the right DLPFC, and lower ODI and NDI values in the right and left amygdala, respectively. Common measures of brain macrostructure (i.e., DLPFC thickness/surface area and amygdala volume) did not explain any additional variance (beyond neurite architecture) in BOLD activity. A moderating effect of neurite architecture on the relationship between emotion processing task-evoked BOLD response and performance was observed. Our findings provide evidence that neuro-/social-affective cognition-related BOLD activity is partially driven by the local neurite organization and density with direct impact on emotion processing. In vivo gray matter microstructure represents a new target of investigation providing strong potential for clinical translation.
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Trotier AJ, Dilharreguy B, Anandra S, Corbin N, Lefrançois W, Ozenne V, Miraux S, Ribot EJ. The Compressed Sensing MP2RAGE as a Surrogate to the MPRAGE for Neuroimaging at 3 T. Invest Radiol 2022; 57:366-378. [PMID: 35030106 PMCID: PMC9390231 DOI: 10.1097/rli.0000000000000849] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/03/2021] [Accepted: 11/08/2021] [Indexed: 11/26/2022]
Abstract
OBJECTIVES The magnetization-prepared 2 rapid acquisition gradient echo (MP2RAGE) sequence provides quantitative T1 maps in addition to high-contrast morphological images. Advanced acceleration techniques such as compressed sensing (CS) allow its acquisition time to be compatible with clinical applications. To consider its routine use in future neuroimaging protocols, the repeatability of the segmented brain structures was evaluated and compared with the standard morphological sequence (magnetization-prepared rapid gradient echo [MPRAGE]). The repeatability of the T1 measurements was also assessed. MATERIALS AND METHODS Thirteen healthy volunteers were scanned either 3 or 4 times at several days of interval, on a 3 T clinical scanner, with the 2 sequences (CS-MP2RAGE and MPRAGE), set with the same spatial resolution (0.8-mm isotropic) and scan duration (6 minutes 21 seconds). The reconstruction time of the CS-MP2RAGE outputs (including the 2 echo images, the MP2RAGE image, and the T1 map) was 3 minutes 33 seconds, using an open-source in-house algorithm implemented in the Gadgetron framework.Both precision and variability of volume measurements obtained from CAT12 and VolBrain were assessed. The T1 accuracy and repeatability were measured on phantoms and on humans and were compared with literature.Volumes obtained from the CS-MP2RAGE and the MPRAGE images were compared using Student t tests (P < 0.05 was considered significant). RESULTS The CS-MP2RAGE acquisition provided morphological images of the same quality and higher contrasts than the standard MPRAGE images. Similar intravolunteer variabilities were obtained with the CS-MP2RAGE and the MPRAGE segmentations. In addition, high-resolution T1 maps were obtained from the CS-MP2RAGE. T1 times of white and gray matters and several deep gray nuclei are consistent with the literature and show very low variability (<1%). CONCLUSIONS The CS-MP2RAGE can be used in future protocols to rapidly obtain morphological images and quantitative T1 maps in 3-dimensions while maintaining high repeatability in volumetry and relaxation times.
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Affiliation(s)
- Aurélien J. Trotier
- From the Centre de Résonance Magnétique des Systèmes Biologiques, UMR5536, CNRS/Université de Bordeaux
| | - Bixente Dilharreguy
- Biomedical Imaging Facility (pIBIO), UMS3767, CNRS/Université de Bordeaux, Bordeaux, France
| | - Serge Anandra
- Biomedical Imaging Facility (pIBIO), UMS3767, CNRS/Université de Bordeaux, Bordeaux, France
| | - Nadège Corbin
- From the Centre de Résonance Magnétique des Systèmes Biologiques, UMR5536, CNRS/Université de Bordeaux
- UCL Queen Square Institute of Neurology, Wellcome Centre for Human Neuroimaging, University College of London, London, United Kingdom
| | - William Lefrançois
- From the Centre de Résonance Magnétique des Systèmes Biologiques, UMR5536, CNRS/Université de Bordeaux
| | - Valery Ozenne
- From the Centre de Résonance Magnétique des Systèmes Biologiques, UMR5536, CNRS/Université de Bordeaux
| | - Sylvain Miraux
- From the Centre de Résonance Magnétique des Systèmes Biologiques, UMR5536, CNRS/Université de Bordeaux
| | - Emeline J. Ribot
- From the Centre de Résonance Magnétique des Systèmes Biologiques, UMR5536, CNRS/Université de Bordeaux
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11
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Cremona S, Zago L, Mellet E, Petit L, Laurent A, Pepe A, Tsuchida A, Beguedou N, Joliot M, Tzourio C, Mazoyer B, Crivello F. Novel characterization of the relationship between verbal list-learning outcomes and hippocampal subfields in healthy adults. Hum Brain Mapp 2021; 42:5264-5277. [PMID: 34453474 PMCID: PMC8519870 DOI: 10.1002/hbm.25614] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/11/2021] [Revised: 06/29/2021] [Accepted: 07/20/2021] [Indexed: 11/10/2022] Open
Abstract
The relationship between hippocampal subfield volumetry and verbal list‐learning test outcomes have mostly been studied in clinical and elderly populations, and remain controversial. For the first time, we characterized a relationship between verbal list‐learning test outcomes and hippocampal subfield volumetry on two large separate datasets of 447 and 1,442 healthy young and middle‐aged adults, and explored the processes that could explain this relationship. We observed a replicable positive linear correlation between verbal list‐learning test free recall scores and CA1 volume, specific to verbal list learning as demonstrated by the hippocampal subfield volumetry independence from verbal intelligence. Learning meaningless items was also positively correlated with CA1 volume, pointing to the role of the test design rather than word meaning. Accordingly, we found that association‐based mnemonics mediated the relationship between verbal list‐learning test outcomes and CA1 volume. This mediation suggests that integrating items into associative representations during verbal list‐learning tests explains CA1 volume variations: this new explanation is consistent with the associative functions of the human CA1.
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Affiliation(s)
- Sandrine Cremona
- Université de Bordeaux - Neurocampus, CEA, CNRS, IMN UMR 5293, Bordeaux, France
| | - Laure Zago
- Université de Bordeaux - Neurocampus, CEA, CNRS, IMN UMR 5293, Bordeaux, France
| | - Emmanuel Mellet
- Université de Bordeaux - Neurocampus, CEA, CNRS, IMN UMR 5293, Bordeaux, France
| | - Laurent Petit
- Université de Bordeaux - Neurocampus, CEA, CNRS, IMN UMR 5293, Bordeaux, France
| | - Alexandre Laurent
- Université de Bordeaux - Neurocampus, CEA, CNRS, IMN UMR 5293, Bordeaux, France
| | - Antonietta Pepe
- Université de Bordeaux - Neurocampus, CEA, CNRS, IMN UMR 5293, Bordeaux, France
| | - Ami Tsuchida
- Université de Bordeaux - Neurocampus, CEA, CNRS, IMN UMR 5293, Bordeaux, France
| | - Naka Beguedou
- Université de Bordeaux - Neurocampus, CEA, CNRS, IMN UMR 5293, Bordeaux, France
| | - Marc Joliot
- Université de Bordeaux - Neurocampus, CEA, CNRS, IMN UMR 5293, Bordeaux, France
| | - Christophe Tzourio
- Université de Bordeaux - Département Santé publique, INSERM, BPH U 1219, Bordeaux, France
| | - Bernard Mazoyer
- Université de Bordeaux - Neurocampus, CEA, CNRS, IMN UMR 5293, Bordeaux, France.,Institut des maladies neurodégénératives clinique, CHU de Bordeaux, Bordeaux, France
| | - Fabrice Crivello
- Université de Bordeaux - Neurocampus, CEA, CNRS, IMN UMR 5293, Bordeaux, France
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12
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Tsuchida A, Laurent A, Crivello F, Petit L, Pepe A, Beguedou N, Debette S, Tzourio C, Mazoyer B. Age-Related Variations in Regional White Matter Volumetry and Microstructure During the Post-adolescence Period: A Cross-Sectional Study of a Cohort of 1,713 University Students. Front Syst Neurosci 2021; 15:692152. [PMID: 34413727 PMCID: PMC8369154 DOI: 10.3389/fnsys.2021.692152] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/07/2021] [Accepted: 07/05/2021] [Indexed: 12/30/2022] Open
Abstract
Human brain white matter undergoes a protracted maturation that continues well into adulthood. Recent advances in diffusion-weighted imaging (DWI) methods allow detailed characterizations of the microstructural architecture of white matter, and they are increasingly utilized to study white matter changes during development and aging. However, relatively little is known about the late maturational changes in the microstructural architecture of white matter during post-adolescence. Here we report on regional changes in white matter volume and microstructure in young adults undergoing university-level education. As part of the MRi-Share multi-modal brain MRI database, multi-shell, high angular resolution DWI data were acquired in a unique sample of 1,713 university students aged 18-26. We assessed the age and sex dependence of diffusion metrics derived from diffusion tensor imaging (DTI) and neurite orientation dispersion and density imaging (NODDI) in the white matter regions as defined in the John Hopkins University (JHU) white matter labels atlas. We demonstrate that while regional white matter volume is relatively stable over the age range of our sample, the white matter microstructural properties show clear age-related variations. Globally, it is characterized by a robust increase in neurite density index (NDI), and to a lesser extent, orientation dispersion index (ODI). These changes are accompanied by a decrease in diffusivity. In contrast, there is minimal age-related variation in fractional anisotropy. There are regional variations in these microstructural changes: some tracts, most notably cingulum bundles, show a strong age-related increase in NDI coupled with decreases in radial and mean diffusivity, while others, mainly cortico-spinal projection tracts, primarily show an ODI increase and axial diffusivity decrease. These age-related variations are not different between males and females, but males show higher NDI and ODI and lower diffusivity than females across many tracts. These findings emphasize the complexity of changes in white matter structure occurring in this critical period of late maturation in early adulthood.
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Affiliation(s)
- Ami Tsuchida
- Groupe d'Imagerie Neurofonctionnelle, Institut des Maladies Neurodégénératives, UMR 5293, Université de Bordeaux, Bordeaux, France.,Groupe d'Imagerie Neurofonctionnelle, Institut des Maladies Neurodégénératives, UMR 5293, CNRS, Bordeaux, France.,Groupe d'Imagerie Neurofonctionnelle, Institut des Maladies Neurodégénératives, UMR 5293, CEA, Bordeaux, France
| | - Alexandre Laurent
- Groupe d'Imagerie Neurofonctionnelle, Institut des Maladies Neurodégénératives, UMR 5293, Université de Bordeaux, Bordeaux, France.,Groupe d'Imagerie Neurofonctionnelle, Institut des Maladies Neurodégénératives, UMR 5293, CNRS, Bordeaux, France.,Groupe d'Imagerie Neurofonctionnelle, Institut des Maladies Neurodégénératives, UMR 5293, CEA, Bordeaux, France
| | - Fabrice Crivello
- Groupe d'Imagerie Neurofonctionnelle, Institut des Maladies Neurodégénératives, UMR 5293, Université de Bordeaux, Bordeaux, France.,Groupe d'Imagerie Neurofonctionnelle, Institut des Maladies Neurodégénératives, UMR 5293, CNRS, Bordeaux, France.,Groupe d'Imagerie Neurofonctionnelle, Institut des Maladies Neurodégénératives, UMR 5293, CEA, Bordeaux, France
| | - Laurent Petit
- Groupe d'Imagerie Neurofonctionnelle, Institut des Maladies Neurodégénératives, UMR 5293, Université de Bordeaux, Bordeaux, France.,Groupe d'Imagerie Neurofonctionnelle, Institut des Maladies Neurodégénératives, UMR 5293, CNRS, Bordeaux, France.,Groupe d'Imagerie Neurofonctionnelle, Institut des Maladies Neurodégénératives, UMR 5293, CEA, Bordeaux, France
| | - Antonietta Pepe
- Groupe d'Imagerie Neurofonctionnelle, Institut des Maladies Neurodégénératives, UMR 5293, Université de Bordeaux, Bordeaux, France.,Groupe d'Imagerie Neurofonctionnelle, Institut des Maladies Neurodégénératives, UMR 5293, CNRS, Bordeaux, France.,Groupe d'Imagerie Neurofonctionnelle, Institut des Maladies Neurodégénératives, UMR 5293, CEA, Bordeaux, France
| | - Naka Beguedou
- Groupe d'Imagerie Neurofonctionnelle, Institut des Maladies Neurodégénératives, UMR 5293, Université de Bordeaux, Bordeaux, France.,Groupe d'Imagerie Neurofonctionnelle, Institut des Maladies Neurodégénératives, UMR 5293, CNRS, Bordeaux, France.,Groupe d'Imagerie Neurofonctionnelle, Institut des Maladies Neurodégénératives, UMR 5293, CEA, Bordeaux, France
| | - Stephanie Debette
- Université de Bordeaux, Inserm, Bordeaux Population Health Research Center, U1219, CHU Bordeaux, Bordeaux, France.,Centre Hospitalier Universitaire, Bordeaux, France
| | - Christophe Tzourio
- Université de Bordeaux, Inserm, Bordeaux Population Health Research Center, U1219, CHU Bordeaux, Bordeaux, France.,Centre Hospitalier Universitaire, Bordeaux, France
| | - Bernard Mazoyer
- Groupe d'Imagerie Neurofonctionnelle, Institut des Maladies Neurodégénératives, UMR 5293, Université de Bordeaux, Bordeaux, France.,Groupe d'Imagerie Neurofonctionnelle, Institut des Maladies Neurodégénératives, UMR 5293, CNRS, Bordeaux, France.,Groupe d'Imagerie Neurofonctionnelle, Institut des Maladies Neurodégénératives, UMR 5293, CEA, Bordeaux, France.,Université de Bordeaux, Inserm, Bordeaux Population Health Research Center, U1219, CHU Bordeaux, Bordeaux, France.,Centre Hospitalier Universitaire, Bordeaux, France
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