1
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Hickling AL, Clark IA, Wu YI, Maguire EA. Automated protocols for delineating human hippocampal subfields from 3 Tesla and 7 Tesla magnetic resonance imaging data. Hippocampus 2024; 34:302-308. [PMID: 38593279 DOI: 10.1002/hipo.23606] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/16/2023] [Revised: 03/11/2024] [Accepted: 03/25/2024] [Indexed: 04/11/2024]
Abstract
Researchers who study the human hippocampus are naturally interested in how its subfields function. However, many researchers are precluded from examining subfields because their manual delineation from magnetic resonance imaging (MRI) scans (still the gold standard approach) is time consuming and requires significant expertise. To help ameliorate this issue, we present here two protocols, one for 3T MRI and the other for 7T MRI, that permit automated hippocampus segmentation into six subregions, namely dentate gyrus/cornu ammonis (CA)4, CA2/3, CA1, subiculum, pre/parasubiculum, and uncus along the entire length of the hippocampus. These protocols are particularly notable relative to existing resources in that they were trained and tested using large numbers of healthy young adults (n = 140 at 3T, n = 40 at 7T) whose hippocampi were manually segmented by experts from MRI scans. Using inter-rater reliability analyses, we showed that the quality of automated segmentations produced by these protocols was high and comparable to expert manual segmenters. We provide full open access to the automated protocols, and anticipate they will save hippocampus researchers a significant amount of time. They could also help to catalyze subfield research, which is essential for gaining a full understanding of how the hippocampus functions.
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Affiliation(s)
- Alice L Hickling
- Wellcome Centre for Human Neuroimaging, Department of Imaging Neuroscience, UCL Queen Square Institute of Neurology, University College London, London, UK
| | - Ian A Clark
- Wellcome Centre for Human Neuroimaging, Department of Imaging Neuroscience, UCL Queen Square Institute of Neurology, University College London, London, UK
| | - Yan I Wu
- Wellcome Centre for Human Neuroimaging, Department of Imaging Neuroscience, UCL Queen Square Institute of Neurology, University College London, London, UK
| | - Eleanor A Maguire
- Wellcome Centre for Human Neuroimaging, Department of Imaging Neuroscience, UCL Queen Square Institute of Neurology, University College London, London, UK
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2
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Wuestefeld A, Baumeister H, Adams JN, de Flores R, Hodgetts CJ, Mazloum-Farzaghi N, Olsen RK, Puliyadi V, Tran TT, Bakker A, Canada KL, Dalton MA, Daugherty AM, La Joie R, Wang L, Bedard ML, Buendia E, Chung E, Denning A, Del Mar Arroyo-Jiménez M, Artacho-Pérula E, Irwin DJ, Ittyerah R, Lee EB, Lim S, Del Pilar Marcos-Rabal M, Iñiguez de Onzoño Martin MM, Lopez MM, de la Rosa Prieto C, Schuck T, Trotman W, Vela A, Yushkevich P, Amunts K, Augustinack JC, Ding SL, Insausti R, Kedo O, Berron D, Wisse LEM. Comparison of histological delineations of medial temporal lobe cortices by four independent neuroanatomy laboratories. Hippocampus 2024; 34:241-260. [PMID: 38415962 PMCID: PMC11039382 DOI: 10.1002/hipo.23602] [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: 01/25/2024] [Accepted: 02/04/2024] [Indexed: 02/29/2024]
Abstract
The medial temporal lobe (MTL) cortex, located adjacent to the hippocampus, is crucial for memory and prone to the accumulation of certain neuropathologies such as Alzheimer's disease neurofibrillary tau tangles. The MTL cortex is composed of several subregions which differ in their functional and cytoarchitectonic features. As neuroanatomical schools rely on different cytoarchitectonic definitions of these subregions, it is unclear to what extent their delineations of MTL cortex subregions overlap. Here, we provide an overview of cytoarchitectonic definitions of the entorhinal and parahippocampal cortices as well as Brodmann areas (BA) 35 and 36, as provided by four neuroanatomists from different laboratories, aiming to identify the rationale for overlapping and diverging delineations. Nissl-stained series were acquired from the temporal lobes of three human specimens (two right and one left hemisphere). Slices (50 μm thick) were prepared perpendicular to the long axis of the hippocampus spanning the entire longitudinal extent of the MTL cortex. Four neuroanatomists annotated MTL cortex subregions on digitized slices spaced 5 mm apart (pixel size 0.4 μm at 20× magnification). Parcellations, terminology, and border placement were compared among neuroanatomists. Cytoarchitectonic features of each subregion are described in detail. Qualitative analysis of the annotations showed higher agreement in the definitions of the entorhinal cortex and BA35, while the definitions of BA36 and the parahippocampal cortex exhibited less overlap among neuroanatomists. The degree of overlap of cytoarchitectonic definitions was partially reflected in the neuroanatomists' agreement on the respective delineations. Lower agreement in annotations was observed in transitional zones between structures where seminal cytoarchitectonic features are expressed less saliently. The results highlight that definitions and parcellations of the MTL cortex differ among neuroanatomical schools and thereby increase understanding of why these differences may arise. This work sets a crucial foundation to further advance anatomically-informed neuroimaging research on the human MTL cortex.
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Affiliation(s)
- Anika Wuestefeld
- Clinical Memory Research Unit, Department of Clinical Sciences Malmö, Lund University, Lund, Sweden
| | - Hannah Baumeister
- German Center for Neurodegenerative Diseases (DZNE), Magdeburg, Germany
| | - Jenna N Adams
- Department of Neurobiology and Behavior, University of California, Irvine, Irvine, California, USA
| | - Robin de Flores
- INSERM UMR-S U1237, PhIND "Physiopathology and Imaging of Neurological Disorders", Institut Blood and Brain @ Caen-Normandie, Caen-Normandie University, GIP Cyceron, France
| | | | - Negar Mazloum-Farzaghi
- University of Toronto, Toronto, Ontario, Canada
- Rotman Research Institute, Toronto, Ontario, Canada
| | - Rosanna K Olsen
- University of Toronto, Toronto, Ontario, Canada
- Rotman Research Institute, Toronto, Ontario, Canada
| | - Vyash Puliyadi
- Department of Psychological and Brain Sciences, Johns Hopkins University, Baltimore, Maryland, USA
| | - Tammy T Tran
- Department of Psychology, Stanford University, Stanford, California, USA
| | - Arnold Bakker
- Department of Psychiatry and Behavioral Sciences, Johns Hopkins University, Baltimore, Maryland, USA
| | - Kelsey L Canada
- Institute of Gerontology, Wayne State University, Detroit, Michigan, USA
| | | | - Ana M Daugherty
- Institute of Gerontology, Wayne State University, Detroit, Michigan, USA
- Department of Psychology, Wayne State University, Detroit, Michigan, USA
| | - Renaud La Joie
- Memory and Aging Center, Department of Neurology, Weill Institute for Neurosciences, University of California, San Francisco, California, USA
| | - Lei Wang
- The Ohio State University, Columbus, Ohio, USA
| | - Madigan L Bedard
- Department of Pharmacology, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, USA
| | - Esther Buendia
- Human Neuroanatomy Laboratory, University of Castilla-La Mancha, Albacete, Spain
| | - Eunice Chung
- University of Pennsylvania, Philadelphia, Pennsylvania, USA
| | - Amanda Denning
- University of Pennsylvania, Philadelphia, Pennsylvania, USA
| | | | | | - David J Irwin
- University of Pennsylvania, Philadelphia, Pennsylvania, USA
| | | | - Edward B Lee
- University of Pennsylvania, Philadelphia, Pennsylvania, USA
| | - Sydney Lim
- University of Pennsylvania, Philadelphia, Pennsylvania, USA
| | | | | | - Monica Munoz Lopez
- Human Neuroanatomy Laboratory, University of Castilla-La Mancha, Albacete, Spain
| | | | - Theresa Schuck
- University of Pennsylvania, Philadelphia, Pennsylvania, USA
| | | | - Alicia Vela
- Human Neuroanatomy Laboratory, University of Castilla-La Mancha, Albacete, Spain
| | | | - Katrin Amunts
- Institute of Neuroscience and Medicine (INM-1), Research Center Jülich, Jülich, Germany
- C. & O. Vogt Institute for Brain Research, Medical Faculty, University Hospital Düsseldorf, Heinrich-Heine-University, Düsseldorf, Germany
| | | | - Song-Lin Ding
- Allen Institute for Brain Science, Seattle, Washington, USA
| | - Ricardo Insausti
- Human Neuroanatomy Laboratory, University of Castilla-La Mancha, Albacete, Spain
| | - Olga Kedo
- Institute of Neuroscience and Medicine (INM-1), Research Center Jülich, Jülich, Germany
| | - David Berron
- German Center for Neurodegenerative Diseases (DZNE), Magdeburg, Germany
| | - Laura E M Wisse
- Department of Diagnostic Radiology, Lund University, Lund, Sweden
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3
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Haast RAM, Kashyap S, Ivanov D, Yousif MD, DeKraker J, Poser BA, Khan AR. Insights into hippocampal perfusion using high-resolution, multi-modal 7T MRI. Proc Natl Acad Sci U S A 2024; 121:e2310044121. [PMID: 38446857 PMCID: PMC10945835 DOI: 10.1073/pnas.2310044121] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/19/2023] [Accepted: 12/26/2023] [Indexed: 03/08/2024] Open
Abstract
We present a comprehensive study on the non-invasive measurement of hippocampal perfusion. Using high-resolution 7 tesla arterial spin labeling (ASL) data, we generated robust perfusion maps and observed significant variations in perfusion among hippocampal subfields, with CA1 exhibiting the lowest perfusion levels. Notably, these perfusion differences were robust and already detectable with 50 perfusion-weighted images per subject, acquired in 5 min. To understand the underlying factors, we examined the influence of image quality metrics, various tissue microstructure and morphometric properties, macrovasculature, and cytoarchitecture. We observed higher perfusion in regions located closer to arteries, demonstrating the influence of vascular proximity on hippocampal perfusion. Moreover, ex vivo cytoarchitectonic features based on neuronal density differences appeared to correlate stronger with hippocampal perfusion than morphometric measures like gray matter thickness. These findings emphasize the interplay between microvasculature, macrovasculature, and metabolic demand in shaping hippocampal perfusion. Our study expands the current understanding of hippocampal physiology and its relevance to neurological disorders. By providing in vivo evidence of perfusion differences between hippocampal subfields, our findings have implications for diagnosis and potential therapeutic interventions. In conclusion, our study provides a valuable resource for extensively characterizing hippocampal perfusion.
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Affiliation(s)
- Roy A. M. Haast
- Centre of Functional and Metabolic Mapping, Robarts Research Institute, Western University, London, ONN6A 3K7, Canada
| | - Sriranga Kashyap
- Department of Cognitive Neuroscience, Faculty of Psychology and Neuroscience, Maastricht University, Maastricht6200, The Netherlands
- Krembil Brain Institute, University Health Network, Toronto, ONM5G 2C4, Canada
| | - Dimo Ivanov
- Department of Cognitive Neuroscience, Faculty of Psychology and Neuroscience, Maastricht University, Maastricht6200, The Netherlands
| | - Mohamed D. Yousif
- Centre of Functional and Metabolic Mapping, Robarts Research Institute, Western University, London, ONN6A 3K7, Canada
| | - Jordan DeKraker
- Montreal Neurological Institute and Hospital, McGill University, Montreal, QCH3A 0G4, Canada
| | - Benedikt A. Poser
- Department of Cognitive Neuroscience, Faculty of Psychology and Neuroscience, Maastricht University, Maastricht6200, The Netherlands
| | - Ali R. Khan
- Centre of Functional and Metabolic Mapping, Robarts Research Institute, Western University, London, ONN6A 3K7, Canada
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4
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Bein O, Davachi L. Event Integration and Temporal Differentiation: How Hierarchical Knowledge Emerges in Hippocampal Subfields through Learning. J Neurosci 2024; 44:e0627232023. [PMID: 38129134 PMCID: PMC10919070 DOI: 10.1523/jneurosci.0627-23.2023] [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: 04/05/2023] [Revised: 11/10/2023] [Accepted: 11/16/2023] [Indexed: 12/23/2023] Open
Abstract
Everyday life is composed of events organized by changes in contexts, with each event containing an unfolding sequence of occurrences. A major challenge facing our memory systems is how to integrate sequential occurrences within events while also maintaining their details and avoiding over-integration across different contexts. We asked if and how distinct hippocampal subfields come to hierarchically and, in parallel, represent both event context and subevent occurrences with learning. Female and male human participants viewed sequential events defined as sequences of objects superimposed on shared color frames while undergoing high-resolution fMRI. Importantly, these events were repeated to induce learning. Event segmentation, as indexed by increased reaction times at event boundaries, was observed in all repetitions. Temporal memory decisions were quicker for items from the same event compared to across different events, indicating that events shaped memory. With learning, hippocampal CA3 multivoxel activation patterns clustered to reflect the event context, with more clustering correlated with behavioral facilitation during event transitions. In contrast, in the dentate gyrus (DG), temporally proximal items that belonged to the same event became associated with more differentiated neural patterns. A computational model explained these results by dynamic inhibition in the DG. Additional similarity measures support the notion that CA3 clustered representations reflect shared voxel populations, while DG's distinct item representations reflect different voxel populations. These findings suggest an interplay between temporal differentiation in the DG and attractor dynamics in CA3. They advance our understanding of how knowledge is structured through integration and separation across time and context.
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Affiliation(s)
- Oded Bein
- Princeton Neuroscience Institute, Princeton University, Princeton, New Jersey 08540
| | - Lila Davachi
- Department of Psychology, Columbia University, New York, New York 10027
- Center for Clinical Research, The Nathan S. Kline Institute for Psychiatric Research, Orangeburg, New York 10962
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5
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González-Arnay E, Pérez-Santos I, Jiménez-Sánchez L, Cid E, Gal B, de la Prida LM, Cavada C. Immunohistochemical field parcellation of the human hippocampus along its antero-posterior axis. Brain Struct Funct 2024; 229:359-385. [PMID: 38180568 PMCID: PMC10917878 DOI: 10.1007/s00429-023-02725-9] [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: 04/15/2023] [Accepted: 10/15/2023] [Indexed: 01/06/2024]
Abstract
The primate hippocampus includes the dentate gyrus, cornu ammonis (CA), and subiculum. CA is subdivided into four fields (CA1-CA3, plus CA3h/hilus of the dentate gyrus) with specific pyramidal cell morphology and connections. Work in non-human mammals has shown that hippocampal connectivity is precisely patterned both in the laminar and longitudinal axes. One of the main handicaps in the study of neuropathological semiology in the human hippocampus is the lack of clear laminar and longitudinal borders. The aim of this study was to explore a histochemical segmentation of the adult human hippocampus, integrating field (medio-lateral), laminar, and anteroposterior longitudinal patterning. We provide criteria for head-body-tail field and subfield parcellation of the human hippocampus based on immunodetection of Rabphilin3a (Rph3a), Purkinje-cell protein 4 (PCP4), Chromogranin A and Regulation of G protein signaling-14 (RGS-14). Notably, Rph3a and PCP4 allow to identify the border between CA3 and CA2, while Chromogranin A and RGS-14 give specific staining of CA2. We also provide novel histological data about the composition of human-specific regions of the anterior and posterior hippocampus. The data are given with stereotaxic coordinates along the longitudinal axis. This study provides novel insights for a detailed region-specific parcellation of the human hippocampus useful for human brain imaging and neuropathology.
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Affiliation(s)
- Emilio González-Arnay
- Department of Anatomy, Histology and Neuroscience, Universidad Autónoma de Madrid, Madrid, Spain
- Department of Basic Medical Science-Division of Human Anatomy, Universidad de La Laguna, Santa Cruz de Tenerife, Canary Islands, Spain
| | - Isabel Pérez-Santos
- Department of Anatomy, Histology and Neuroscience, Universidad Autónoma de Madrid, Madrid, Spain
| | - Lorena Jiménez-Sánchez
- Department of Anatomy, Histology and Neuroscience, Universidad Autónoma de Madrid, Madrid, Spain
- Centre for Clinical Brain Sciences, University of Edinburgh, Edinburgh, UK
| | - Elena Cid
- Instituto Cajal, CSIC, Madrid, Spain
| | - Beatriz Gal
- Instituto Cajal, CSIC, Madrid, Spain
- Universidad CEU-San Pablo, Madrid, Spain
| | | | - Carmen Cavada
- Department of Anatomy, Histology and Neuroscience, Universidad Autónoma de Madrid, Madrid, Spain.
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6
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Rosenblum EW, Williams EM, Champion SN, Frosch MP, Augustinack JC. The prosubiculum in the human hippocampus: A rostrocaudal, feature-driven, and systematic approach. J Comp Neurol 2024; 532:e25604. [PMID: 38477395 PMCID: PMC11060218 DOI: 10.1002/cne.25604] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/28/2023] [Revised: 01/12/2024] [Accepted: 02/27/2024] [Indexed: 03/14/2024]
Abstract
The hippocampal subfield prosubiculum (ProS), is a conserved neuroanatomic region in mouse, monkey, and human. This area lies between CA1 and subiculum (Sub) and particularly lacks consensus on its boundaries; reports have varied on the description of its features and location. In this report, we review, refine, and evaluate four cytoarchitectural features that differentiate ProS from its neighboring subfields: (1) small neurons, (2) lightly stained neurons, (3) superficial clustered neurons, and (4) a cell sparse zone. ProS was delineated in all cases (n = 10). ProS was examined for its cytoarchitectonic features and location rostrocaudally, from the anterior head through the body in the hippocampus. The most common feature was small pyramidal neurons, which were intermingled with larger pyramidal neurons in ProS. We quantitatively measured ProS pyramidal neurons, which showed (average, width at pyramidal base = 14.31 µm, n = 400 per subfield). CA1 neurons averaged 15.57 µm and Sub neurons averaged 15.63 µm, both were significantly different than ProS (Kruskal-Wallis test, p < .0001). The other three features observed were lightly stained neurons, clustered neurons, and a cell sparse zone. Taken together, these findings suggest that ProS is an independent subfield, likely with distinct functional contributions to the broader interconnected hippocampal network. Our results suggest that ProS is a cytoarchitecturally varied subfield, both for features and among individuals. This diverse architecture in features and individuals for ProS could explain the long-standing complexity regarding the identification of this subfield.
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Affiliation(s)
- Emma W Rosenblum
- Department of Radiology, Athinoula A. Martinos Center, Massachusetts General Hospital, Charlestown, Massachusetts, USA
| | - Emily M Williams
- Department of Radiology, Athinoula A. Martinos Center, Massachusetts General Hospital, Charlestown, Massachusetts, USA
| | - Samantha N Champion
- C.S. Kubik Laboratory for Neuropathology, Massachusetts General Hospital and Harvard Medical School, Boston, Massachusetts, USA
| | - Matthew P Frosch
- C.S. Kubik Laboratory for Neuropathology, Massachusetts General Hospital and Harvard Medical School, Boston, Massachusetts, USA
| | - Jean C Augustinack
- Department of Radiology, Athinoula A. Martinos Center, Massachusetts General Hospital, Charlestown, Massachusetts, USA
- Harvard Medical School, Boston, Massachusetts, USA
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7
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Wuestefeld A, Baumeister H, Adams JN, de Flores R, Hodgetts C, Mazloum-Farzaghi N, Olsen RK, Puliyadi V, Tran TT, Bakker A, Canada KL, Dalton MA, Daugherty AM, Joie RL, Wang L, Bedard M, Buendia E, Chung E, Denning A, Arroyo-Jiménez MDM, Artacho-Pérula E, Irwin DJ, Ittyerah R, Lee EB, Lim S, Marcos-Rabal MDP, Martin MMIDO, Lopez MM, Prieto CDLR, Schuck T, Trotman W, Vela A, Yushkevich P, Amunts K, Augustinack JC, Ding SL, Insausti R, Kedo O, Berron D, Wisse LEM. Comparison of histological delineations of medial temporal lobe cortices by four independent neuroanatomy laboratories. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2023.05.24.542054. [PMID: 37292729 PMCID: PMC10245880 DOI: 10.1101/2023.05.24.542054] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/10/2023]
Abstract
The medial temporal lobe (MTL) cortex, located adjacent to the hippocampus, is crucial for memory and prone to the accumulation of certain neuropathologies such as Alzheimer's disease neurofibrillary tau tangles. The MTL cortex is composed of several subregions which differ in their functional and cytoarchitectonic features. As neuroanatomical schools rely on different cytoarchitectonic definitions of these subregions, it is unclear to what extent their delineations of MTL cortex subregions overlap. Here, we provide an overview of cytoarchitectonic definitions of the cortices that make up the parahippocampal gyrus (entorhinal and parahippocampal cortices) and the adjacent Brodmann areas (BA) 35 and 36, as provided by four neuroanatomists from different laboratories, aiming to identify the rationale for overlapping and diverging delineations. Nissl-stained series were acquired from the temporal lobes of three human specimens (two right and one left hemisphere). Slices (50 µm thick) were prepared perpendicular to the long axis of the hippocampus spanning the entire longitudinal extent of the MTL cortex. Four neuroanatomists annotated MTL cortex subregions on digitized (20X resolution) slices with 5 mm spacing. Parcellations, terminology, and border placement were compared among neuroanatomists. Cytoarchitectonic features of each subregion are described in detail. Qualitative analysis of the annotations showed higher agreement in the definitions of the entorhinal cortex and BA35, while definitions of BA36 and the parahippocampal cortex exhibited less overlap among neuroanatomists. The degree of overlap of cytoarchitectonic definitions was partially reflected in the neuroanatomists' agreement on the respective delineations. Lower agreement in annotations was observed in transitional zones between structures where seminal cytoarchitectonic features are expressed more gradually. The results highlight that definitions and parcellations of the MTL cortex differ among neuroanatomical schools and thereby increase understanding of why these differences may arise. This work sets a crucial foundation to further advance anatomically-informed human neuroimaging research on the MTL cortex.
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Affiliation(s)
- Anika Wuestefeld
- Clinical Memory Research Unit, Department of Clinical Sciences, Malmö, Lund University, Sweden
| | - Hannah Baumeister
- German Center for Neurodegenerative Diseases (DZNE), Magdeburg, Germany
| | - Jenna N Adams
- Department of Neurobiology and Behavior, University of California, Irvine, Irvine, CA, USA
| | - Robin de Flores
- INSERM UMR-S U1237, PhIND "Physiopathology and Imaging of Neurological Disorders", Institut Blood and Brain, Caen-Normandie University, Caen-Normandie, France
| | | | - Negar Mazloum-Farzaghi
- University of Toronto, Toronto, ON, Canada
- Rotman Research Institute, North York, ON, Canada
| | - Rosanna K Olsen
- University of Toronto, Toronto, ON, Canada
- Rotman Research Institute, North York, ON, Canada
| | - Vyash Puliyadi
- Department of Psychological and Brain Sciences, Johns Hopkins University, Baltimore, MD, USA
| | - Tammy T Tran
- Department of Psychology, Stanford University, Stanford, CA, USA
| | - Arnold Bakker
- Department of Psychological and Brain Sciences, Johns Hopkins University, Baltimore, MD, USA
| | - Kelsey L Canada
- Institute of Gerontology, Wayne State University, Detroit, MI, USA
| | | | - Ana M Daugherty
- Institute of Gerontology, Wayne State University, Detroit, MI, USA
- Department of Psychology, Wayne State University, Detroit, MI, USA
| | - Renaud La Joie
- Memory and Aging Center, Department of Neurology, Weill Institute for Neurosciences, University of California, San Francisco USA
| | - Lei Wang
- The Ohio State University, Columbus, OH, USA
| | - Madigan Bedard
- Department of Pharmacology, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | | | - Eunice Chung
- University of Pennsylvania, Philadelphia, PA, USA
| | | | | | | | | | | | - Edward B Lee
- University of Pennsylvania, Philadelphia, PA, USA
| | - Sydney Lim
- University of Pennsylvania, Philadelphia, PA, USA
| | | | | | | | | | | | | | - Alicia Vela
- University of Castilla-La Mancha, Albacete, Spain
| | | | - Katrin Amunts
- Institute of Neuroscience and Medicine (INM-1), Research Center Jülich, Jülich, Germany
- C. & O. Vogt Institute for Brain Research, Medical Faculty, University Hospital Düsseldorf, Heinrich-Heine-University, Düsseldorf, Germany
| | | | | | | | - Olga Kedo
- Institute of Neuroscience and Medicine (INM-1), Research Center Jülich, Jülich, Germany
| | - David Berron
- German Center for Neurodegenerative Diseases (DZNE), Magdeburg, Germany
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8
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Canada K, Mazloum-Farzaghi N, Rådman G, Adams J, Bakker A, Baumeister H, Berron D, Bocchetta M, Carr V, Dalton M, de Flores R, Keresztes A, La Joie R, Mueller S, Raz N, Santini T, Shaw T, Stark C, Tran T, Wang L, Wisse L, Wuestefeld A, Yushkevich P, Olsen R, Daugherty A. A (Sub)field Guide to Quality Control in Hippocampal Subfield Segmentation on Highresolution T 2-weighted MRI. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.11.29.568895. [PMID: 38076964 PMCID: PMC10705396 DOI: 10.1101/2023.11.29.568895] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 12/21/2023]
Abstract
Inquiries into properties of brain structure and function have progressed due to developments in magnetic resonance imaging (MRI). To sustain progress in investigating and quantifying neuroanatomical details in vivo, the reliability and validity of brain measurements are paramount. Quality control (QC) is a set of procedures for mitigating errors and ensuring the validity and reliability of brain measurements. Despite its importance, there is little guidance on best QC practices and reporting procedures. The study of hippocampal subfields in vivo is a critical case for QC because of their small size, inter-dependent boundary definitions, and common artifacts in the MRI data used for subfield measurements. We addressed this gap by surveying the broader scientific community studying hippocampal subfields on their views and approaches to QC. We received responses from 37 investigators spanning 10 countries, covering different career stages, and studying both healthy and pathological development and aging. In this sample, 81% of researchers considered QC to be very important or important, and 19% viewed it as fairly important. Despite this, only 46% of researchers reported on their QC processes in prior publications. In many instances, lack of reporting appeared due to ambiguous guidance on relevant details and guidance for reporting, rather than absence of QC. Here, we provide recommendations for correcting errors to maximize reliability and minimize bias. We also summarize threats to segmentation accuracy, review common QC methods, and make recommendations for best practices and reporting in publications. Implementing the recommended QC practices will collectively improve inferences to the larger population, as well as have implications for clinical practice and public health.
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Affiliation(s)
- K.L. Canada
- Institute of Gerontology, Wayne State University, Detroit, MI 48202
| | - N. Mazloum-Farzaghi
- Department of Psychology, University of Toronto, Toronto, Ontario, Canada
- Rotman Research Institute, Baycrest Health Sciences, Toronto, Ontario, Canada
| | - G. Rådman
- Department of Clinical Sciences Lund, Lund University, Lund, Sweden
| | - J.N. Adams
- Department of Neurobiology and Behavior, University of California, Irvine, CA 92697
| | - A. Bakker
- Department of Psychiatry and Behavioral Sciences, Johns Hopkins University School of Medicine, Baltimore, MD 21287
| | - H. Baumeister
- German Center for Neurodegenerative Diseases (DZNE), Magdeburg, Germany
| | - D. Berron
- German Center for Neurodegenerative Diseases (DZNE), Magdeburg, Germany
| | - M. Bocchetta
- Dementia Research Centre, Department of Neurodegenerative Disease, UCL Queen Square Institute of Neurology, University College London, London, UK
- Centre for Cognitive and Clinical Neuroscience, Division of Psychology, Department of Life Sciences, College of Health, Medicine and Life Sciences, Brunel University London, London, UK
| | - V. Carr
- Department of Psychology, San Jose State University, San Jose, CA 95192
| | - M.A. Dalton
- School of Psychology, University of Sydney, Sydney, Australia
| | - R. de Flores
- INSERM UMR-S U1237, Physiopathology and Imaging of Neurological Disorders (PhIND), Institut Blood and Brain @ Caen-Normandie, Caen-Normandie University, GIP Cyceron, France
| | - A. Keresztes
- Brain Imaging Centre, Research Centre for Natural Sciences, Eötvös Loránd Research Network (ELKH), Budapest, Hungary
- Institute of Psychology, ELTE Eötvös Loránd University, Budapest, Hungary
- Center for Lifespan Psychology, Max Planck Institute for Human Development, Berlin, Germany
| | - R. La Joie
- Memory and Aging Center, Department of Neurology, Weill Institute for Neurosciences, University of California, San Francisco, CA 94158
| | - S.G. Mueller
- Department of Radiology, University of California, San Francisco, CA 94143
- Center for Imaging of Neurodegenerative Diseases, San Francisco VA Medical Center, San Francisco, California 94121
| | - N. Raz
- Center for Lifespan Psychology, Max Planck Institute for Human Development, Berlin, Germany
- Department of Psychology, Stony Brook University, Stony Brook, NY 11794
| | - T. Santini
- Department of Bioengineering, University of Pittsburgh, Pittsburgh, PA 15213
| | - T. Shaw
- School of Electrical Engineering and Computer Science, The University of Queensland, Brisbane, Australia
| | - C.E.L. Stark
- Department of Neurobiology and Behavior, University of California, Irvine, CA 92697
| | - T.T. Tran
- Department of Psychology, Stanford University, Stanford, CA 94305
| | - L. Wang
- Department of Psychiatry and Behavioral Health, The Ohio State University Wexner Medical Center, Columbus, OH 43210
| | - L.E.M. Wisse
- Department of Clinical Sciences Lund, Lund University, Lund, Sweden
| | - A. Wuestefeld
- Clinical Memory Research Unit, Department of Clinical Sciences, Malmö, Lund University, Sweden
| | - P.A. Yushkevich
- Penn Image, Computing and Science Laboratory, Department of Radiology, University of Pennsylvania, Philadelphia, PA 19104
| | - R.K. Olsen
- Department of Psychology, University of Toronto, Toronto, Ontario, Canada
- Rotman Research Institute, Baycrest Health Sciences, Toronto, Ontario, Canada
| | - A.M. Daugherty
- Institute of Gerontology, Wayne State University, Detroit, MI 48202
- Department of Psychology, Wayne State University, Detroit, MI 48202
- Michigan Alzheimer’s Disease Research Center, Ann Arbor, MI 48105
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9
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DeKraker J, Palomero-Gallagher N, Kedo O, Ladbon-Bernasconi N, Muenzing SEA, Axer M, Amunts K, Khan AR, Bernhardt BC, Evans AC. Evaluation of surface-based hippocampal registration using ground-truth subfield definitions. eLife 2023; 12:RP88404. [PMID: 37956092 PMCID: PMC10642966 DOI: 10.7554/elife.88404] [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: 11/15/2023] Open
Abstract
The hippocampus is an archicortical structure, consisting of subfields with unique circuits. Understanding its microstructure, as proxied by these subfields, can improve our mechanistic understanding of learning and memory and has clinical potential for several neurological disorders. One prominent issue is how to parcellate, register, or retrieve homologous points between two hippocampi with grossly different morphologies. Here, we present a surface-based registration method that solves this issue in a contrast-agnostic, topology-preserving manner. Specifically, the entire hippocampus is first analytically unfolded, and then samples are registered in 2D unfolded space based on thickness, curvature, and gyrification. We demonstrate this method in seven 3D histology samples and show superior alignment with respect to subfields using this method over more conventional registration approaches.
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Affiliation(s)
- Jordan DeKraker
- Montreal Neurological Institute and Hospital, McGill UniversityMontrealCanada
| | - Nicola Palomero-Gallagher
- Institute of Neuroscience and Medicine INM-1, Research Centre JülichJülichGermany
- C. & O. Vogt Institute for Brain Research, University Hospital Düsseldorf, Heinrich-Heine-UniversityDüsseldorfGermany
| | - Olga Kedo
- Institute of Neuroscience and Medicine INM-1, Research Centre JülichJülichGermany
| | | | - Sascha EA Muenzing
- Institute of Neuroscience and Medicine INM-1, Research Centre JülichJülichGermany
| | - Markus Axer
- Institute of Neuroscience and Medicine INM-1, Research Centre JülichJülichGermany
| | - Katrin Amunts
- Institute of Neuroscience and Medicine INM-1, Research Centre JülichJülichGermany
- C. & O. Vogt Institute for Brain Research, University Hospital Düsseldorf, Heinrich-Heine-UniversityDüsseldorfGermany
| | - Ali R Khan
- Robarts Research Institute, University of Western OntarioLondonCanada
| | - Boris C Bernhardt
- Montreal Neurological Institute and Hospital, McGill UniversityMontrealCanada
| | - Alan C Evans
- Montreal Neurological Institute and Hospital, McGill UniversityMontrealCanada
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10
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Diers K, Baumeister H, Jessen F, Düzel E, Berron D, Reuter M. An automated, geometry-based method for hippocampal shape and thickness analysis. Neuroimage 2023; 276:120182. [PMID: 37230208 DOI: 10.1016/j.neuroimage.2023.120182] [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: 02/01/2023] [Revised: 04/29/2023] [Accepted: 05/22/2023] [Indexed: 05/27/2023] Open
Abstract
The hippocampus is one of the most studied neuroanatomical structures due to its involvement in attention, learning, and memory as well as its atrophy in ageing, neurological, and psychiatric diseases. Hippocampal shape changes, however, are complex and cannot be fully characterized by a single summary metric such as hippocampal volume as determined from MR images. In this work, we propose an automated, geometry-based approach for the unfolding, point-wise correspondence, and local analysis of hippocampal shape features such as thickness and curvature. Starting from an automated segmentation of hippocampal subfields, we create a 3D tetrahedral mesh model as well as a 3D intrinsic coordinate system of the hippocampal body. From this coordinate system, we derive local curvature and thickness estimates as well as a 2D sheet for hippocampal unfolding. We evaluate the performance of our algorithm with a series of experiments to quantify neurodegenerative changes in Mild Cognitive Impairment and Alzheimer's disease dementia. We find that hippocampal thickness estimates detect known differences between clinical groups and can determine the location of these effects on the hippocampal sheet. Further, thickness estimates improve classification of clinical groups and cognitively unimpaired controls when added as an additional predictor. Comparable results are obtained with different datasets and segmentation algorithms. Taken together, we replicate canonical findings on hippocampal volume/shape changes in dementia, extend them by gaining insight into their spatial localization on the hippocampal sheet, and provide additional, complementary information beyond traditional measures. We provide a new set of sensitive processing and analysis tools for the analysis of hippocampal geometry that allows comparisons across studies without relying on image registration or requiring manual intervention.
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Affiliation(s)
- Kersten Diers
- AI in Medical Imaging, German Center for Neurodegenerative Diseases (DZNE), Bonn, Germany
| | - Hannah Baumeister
- Clinical Cognitive Neuroscience Group, German Center for Neurodegenerative Diseases (DZNE), Magdeburg, Germany
| | - Frank Jessen
- Clinical Alzheimer's Disease Research, German Center for Neurodegenerative Diseases (DZNE), Bonn, Germany; Department of Psychiatry, Medical Faculty, University of Cologne, Cologne, Germany; Excellence Cluster on Cellular Stress Responses in Aging-Associated Diseases (CECAD), University of Cologne, Cologne, Germany
| | - Emrah Düzel
- Clinical Neurophysiology and Memory Group, German Center for Neurodegenerative Diseases (DZNE), Magdeburg, Germany; Institute of Cognitive Neurology and Dementia Research, Otto-von-Guericke University, Magdeburg, Germany; Institute of Cognitive Neuroscience, University College London, London, United Kingdom
| | - David Berron
- Clinical Cognitive Neuroscience Group, German Center for Neurodegenerative Diseases (DZNE), Magdeburg, Germany; Clinical Memory Research Unit, Department of Clinical Sciences Malmö, Lund University, Lund, Sweden
| | - Martin Reuter
- AI in Medical Imaging, German Center for Neurodegenerative Diseases (DZNE), Bonn, Germany; A.A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Boston MA, USA; Department of Radiology, Harvard Medical School, Boston MA, USA.
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11
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Haast RAM, Kashyap S, Ivanov D, Yousif MD, DeKraker J, Poser BA, Khan AR. Novel insights into hippocampal perfusion using high-resolution, multi-modal 7T MRI. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.07.19.549533. [PMID: 37503042 PMCID: PMC10370151 DOI: 10.1101/2023.07.19.549533] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/29/2023]
Abstract
We present a comprehensive study on the non-invasive measurement of hippocampal perfusion. Using high-resolution 7 Tesla arterial spin labelling data, we generated robust perfusion maps and observed significant variations in perfusion among hippocampal subfields, with CA1 exhibiting the lowest perfusion levels. Notably, these perfusion differences were robust and detectable even within five minutes and just fifty perfusion-weighted images per subject. To understand the underlying factors, we examined the influence of image quality metrics, various tissue microstructure and morphometry properties, macrovasculature and cytoarchitecture. We observed higher perfusion in regions located closer to arteries, demonstrating the influence of vascular proximity on hippocampal perfusion. Moreover, ex vivo cytoarchitectonic features based on neuronal density differences appeared to correlate stronger with hippocampal perfusion than morphometric measures like gray matter thickness. These findings emphasize the interplay between microvasculature, macrovasculature, and metabolic demand in shaping hippocampal perfusion. Our study expands the current understanding of hippocampal physiology and its relevance to neurological disorders. By providing in vivo evidence of perfusion differences between hippocampal subfields, our findings have implications for diagnosis and potential therapeutic interventions. In conclusion, our study provides a valuable resource for extensively characterising hippocampal perfusion.
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Affiliation(s)
- Roy A M Haast
- Centre of Functional and Metabolic Mapping, Western University, London, Ontario, Canada
| | - Sriranga Kashyap
- Department of Cognitive Neuroscience, Faculty of Psychology and Neuroscience, Maastricht University, Maastricht, Netherlands
- Krembil Brain Institute, University Health Network, Toronto, Ontario, Canada
| | - Dimo Ivanov
- Department of Cognitive Neuroscience, Faculty of Psychology and Neuroscience, Maastricht University, Maastricht, Netherlands
| | - Mohamed D Yousif
- Centre of Functional and Metabolic Mapping, Western University, London, Ontario, Canada
| | - Jordan DeKraker
- Montreal Neurological Institute and Hospital, McGill University, Montreal, Quebec, Canada
| | - Benedikt A Poser
- Department of Cognitive Neuroscience, Faculty of Psychology and Neuroscience, Maastricht University, Maastricht, Netherlands
| | - Ali R Khan
- Centre of Functional and Metabolic Mapping, Western University, London, Ontario, Canada
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12
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Tregidgo HFJ, Soskic S, Althonayan J, Maffei C, Van Leemput K, Golland P, Insausti R, Lerma-Usabiaga G, Caballero-Gaudes C, Paz-Alonso PM, Yendiki A, Alexander DC, Bocchetta M, Rohrer JD, Iglesias JE. Accurate Bayesian segmentation of thalamic nuclei using diffusion MRI and an improved histological atlas. Neuroimage 2023; 274:120129. [PMID: 37088323 PMCID: PMC10636587 DOI: 10.1016/j.neuroimage.2023.120129] [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: 09/20/2022] [Revised: 03/30/2023] [Accepted: 04/20/2023] [Indexed: 04/25/2023] Open
Abstract
The human thalamus is a highly connected brain structure, which is key for the control of numerous functions and is involved in several neurological disorders. Recently, neuroimaging studies have increasingly focused on the volume and connectivity of the specific nuclei comprising this structure, rather than looking at the thalamus as a whole. However, accurate identification of cytoarchitectonically designed histological nuclei on standard in vivo structural MRI is hampered by the lack of image contrast that can be used to distinguish nuclei from each other and from surrounding white matter tracts. While diffusion MRI may offer such contrast, it has lower resolution and lacks some boundaries visible in structural imaging. In this work, we present a Bayesian segmentation algorithm for the thalamus. This algorithm combines prior information from a probabilistic atlas with likelihood models for both structural and diffusion MRI, allowing segmentation of 25 thalamic labels per hemisphere informed by both modalities. We present an improved probabilistic atlas, incorporating thalamic nuclei identified from histology and 45 white matter tracts surrounding the thalamus identified in ultra-high gradient strength diffusion imaging. We present a family of likelihood models for diffusion tensor imaging, ensuring compatibility with the vast majority of neuroimaging datasets that include diffusion MRI data. The use of these diffusion likelihood models greatly improves identification of nuclear groups versus segmentation based solely on structural MRI. Dice comparison of 5 manually identifiable groups of nuclei to ground truth segmentations show improvements of up to 10 percentage points. Additionally, our chosen model shows a high degree of reliability, with median test-retest Dice scores above 0.85 for four out of five nuclei groups, whilst also offering improved detection of differential thalamic involvement in Alzheimer's disease (AUROC 81.98%). The probabilistic atlas and segmentation tool will be made publicly available as part of the neuroimaging package FreeSurfer (https://freesurfer.net/fswiki/ThalamicNucleiDTI).
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Affiliation(s)
- Henry F J Tregidgo
- Centre for Medical Image Computing, Department of Medical Physics and Biomedical Engineering, University College London, UK.
| | - Sonja Soskic
- Centre for Medical Image Computing, Department of Medical Physics and Biomedical Engineering, University College London, UK
| | - Juri Althonayan
- Centre for Medical Image Computing, Department of Medical Physics and Biomedical Engineering, University College London, UK
| | - Chiara Maffei
- Martinos Center for Biomedical Imaging, Massachusetts General Hospital and Harvard Medical School, USA
| | - Koen Van Leemput
- Martinos Center for Biomedical Imaging, Massachusetts General Hospital and Harvard Medical School, USA; Department of Health Technology, Technical University of Denmark, Denmark
| | - Polina Golland
- Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology, USA
| | - Ricardo Insausti
- Human Neuroanatomy Laboratory, University of Castilla-La Mancha, Spain
| | - Garikoitz Lerma-Usabiaga
- BCBL. Basque Center on Cognition, Brain and Language, Spain; Ikerbasque, Basque Foundation for Science, Bilbao, Spain
| | | | - Pedro M Paz-Alonso
- BCBL. Basque Center on Cognition, Brain and Language, Spain; Ikerbasque, Basque Foundation for Science, Bilbao, Spain
| | - Anastasia Yendiki
- Martinos Center for Biomedical Imaging, Massachusetts General Hospital and Harvard Medical School, USA
| | - Daniel C Alexander
- Centre for Medical Image Computing, Department of Computer Science, University College London, UK
| | - Martina Bocchetta
- Dementia Research Centre, Department of Neurodegenerative Disease, UCL Queen Square Institute of Neurology, University College London, UK; Centre for Cognitive and Clinical Neuroscience, Department of Life Sciences, College of Health, Medicine and Life Sciences, Brunel University London, UK
| | - Jonathan D Rohrer
- Dementia Research Centre, Department of Neurodegenerative Disease, UCL Queen Square Institute of Neurology, University College London, UK
| | - Juan Eugenio Iglesias
- Centre for Medical Image Computing, Department of Medical Physics and Biomedical Engineering, University College London, UK; Martinos Center for Biomedical Imaging, Massachusetts General Hospital and Harvard Medical School, USA; Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology, USA
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13
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Poiret C, Bouyeure A, Patil S, Grigis A, Duchesnay E, Faillot M, Bottlaender M, Lemaitre F, Noulhiane M. A fast and robust hippocampal subfields segmentation: HSF revealing lifespan volumetric dynamics. Front Neuroinform 2023; 17:1130845. [PMID: 37396459 PMCID: PMC10308024 DOI: 10.3389/fninf.2023.1130845] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/23/2022] [Accepted: 05/22/2023] [Indexed: 07/04/2023] Open
Abstract
The hippocampal subfields, pivotal to episodic memory, are distinct both in terms of cyto- and myeloarchitectony. Studying the structure of hippocampal subfields in vivo is crucial to understand volumetric trajectories across the lifespan, from the emergence of episodic memory during early childhood to memory impairments found in older adults. However, segmenting hippocampal subfields on conventional MRI sequences is challenging because of their small size. Furthermore, there is to date no unified segmentation protocol for the hippocampal subfields, which limits comparisons between studies. Therefore, we introduced a novel segmentation tool called HSF short for hippocampal segmentation factory, which leverages an end-to-end deep learning pipeline. First, we validated HSF against currently used tools (ASHS, HIPS, and HippUnfold). Then, we used HSF on 3,750 subjects from the HCP development, young adults, and aging datasets to study the effect of age and sex on hippocampal subfields volumes. Firstly, we showed HSF to be closer to manual segmentation than other currently used tools (p < 0.001), regarding the Dice Coefficient, Hausdorff Distance, and Volumetric Similarity. Then, we showed differential maturation and aging across subfields, with the dentate gyrus being the most affected by age. We also found faster growth and decay in men than in women for most hippocampal subfields. Thus, while we introduced a new, fast and robust end-to-end segmentation tool, our neuroanatomical results concerning the lifespan trajectories of the hippocampal subfields reconcile previous conflicting results.
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Affiliation(s)
- Clement Poiret
- UNIACT, NeuroSpin, CEA Paris-Saclay, Frederic Joliot Institute, Gif-sur-Yvette, France
- NeuroSpin, CEA Paris-Saclay, Frederic Joliot Institute, Gif-sur-Yvette, France
- InDEV, NeuroDiderot, Université Paris Cité, Inserm, Paris, France
| | - Antoine Bouyeure
- UNIACT, NeuroSpin, CEA Paris-Saclay, Frederic Joliot Institute, Gif-sur-Yvette, France
- NeuroSpin, CEA Paris-Saclay, Frederic Joliot Institute, Gif-sur-Yvette, France
- InDEV, NeuroDiderot, Université Paris Cité, Inserm, Paris, France
| | - Sandesh Patil
- UNIACT, NeuroSpin, CEA Paris-Saclay, Frederic Joliot Institute, Gif-sur-Yvette, France
- NeuroSpin, CEA Paris-Saclay, Frederic Joliot Institute, Gif-sur-Yvette, France
- InDEV, NeuroDiderot, Université Paris Cité, Inserm, Paris, France
| | - Antoine Grigis
- NeuroSpin, CEA Paris-Saclay, Frederic Joliot Institute, Gif-sur-Yvette, France
- InDEV, NeuroDiderot, Université Paris Cité, Inserm, Paris, France
| | - Edouard Duchesnay
- NeuroSpin, CEA Paris-Saclay, Frederic Joliot Institute, Gif-sur-Yvette, France
- InDEV, NeuroDiderot, Université Paris Cité, Inserm, Paris, France
| | - Matthieu Faillot
- NeuroSpin, CEA Paris-Saclay, Frederic Joliot Institute, Gif-sur-Yvette, France
- BioMaps, Service Hospitalier Frédéric Joliot, CNRS, Inserm, Université Paris-Saclay, Orsay, France
| | - Michel Bottlaender
- NeuroSpin, CEA Paris-Saclay, Frederic Joliot Institute, Gif-sur-Yvette, France
- BioMaps, Service Hospitalier Frédéric Joliot, CNRS, Inserm, Université Paris-Saclay, Orsay, France
| | - Frederic Lemaitre
- CETAPS EA 3832, Université de Rouen, Rouen, France
- CRIOBE, UAR 3278, CNRS-EPHE-UPVD, Mooréa, France
| | - Marion Noulhiane
- UNIACT, NeuroSpin, CEA Paris-Saclay, Frederic Joliot Institute, Gif-sur-Yvette, France
- NeuroSpin, CEA Paris-Saclay, Frederic Joliot Institute, Gif-sur-Yvette, France
- InDEV, NeuroDiderot, Université Paris Cité, Inserm, Paris, France
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14
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Kahhale I, Buser NJ, Madan CR, Hanson JL. Quantifying numerical and spatial reliability of hippocampal and amygdala subdivisions in FreeSurfer. Brain Inform 2023; 10:9. [PMID: 37029203 PMCID: PMC10082143 DOI: 10.1186/s40708-023-00189-5] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/09/2022] [Accepted: 03/24/2023] [Indexed: 04/09/2023] Open
Abstract
On-going, large-scale neuroimaging initiatives can aid in uncovering neurobiological causes and correlates of poor mental health, disease pathology, and many other important conditions. As projects grow in scale with hundreds, even thousands, of individual participants and scans collected, quantification of brain structures by automated algorithms is becoming the only truly tractable approach. Here, we assessed the spatial and numerical reliability for newly deployed automated segmentation of hippocampal subfields and amygdala nuclei in FreeSurfer 7. In a sample of participants with repeated structural imaging scans (N = 928), we found numerical reliability (as assessed by intraclass correlations, ICCs) was reasonable. Approximately 95% of hippocampal subfields had "excellent" numerical reliability (ICCs ≥ 0.90), while only 67% of amygdala subnuclei met this same threshold. In terms of spatial reliability, 58% of hippocampal subfields and 44% of amygdala subnuclei had Dice coefficients ≥ 0.70. Notably, multiple regions had poor numerical and/or spatial reliability. We also examined correlations between spatial reliability and person-level factors (e.g., participant age; T1 image quality). Both sex and image scan quality were related to variations in spatial reliability metrics. Examined collectively, our work suggests caution should be exercised for a few hippocampal subfields and amygdala nuclei with more variable reliability.
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15
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Insausti R, Insausti AM, Muñoz López M, Medina Lorenzo I, Arroyo-Jiménez MDM, Marcos Rabal MP, de la Rosa-Prieto C, Delgado-González JC, Montón Etxeberria J, Cebada-Sánchez S, Raspeño-García JF, Iñiguez de Onzoño MM, Molina Romero FJ, Benavides-Piccione R, Tapia-González S, Wisse LEM, Ravikumar S, Wolk DA, DeFelipe J, Yushkevich P, Artacho-Pérula E. Ex vivo, in situ perfusion protocol for human brain fixation compatible with microscopy, MRI techniques, and anatomical studies. Front Neuroanat 2023; 17:1149674. [PMID: 37034833 PMCID: PMC10076536 DOI: 10.3389/fnana.2023.1149674] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/22/2023] [Accepted: 02/28/2023] [Indexed: 04/11/2023] Open
Abstract
We present a method for human brain fixation based on simultaneous perfusion of 4% paraformaldehyde through carotids after a flush with saline. The left carotid cannula is used to perfuse the body with 10% formalin, to allow further use of the body for anatomical research or teaching. The aim of our method is to develop a vascular fixation protocol for the human brain, by adapting protocols that are commonly used in experimental animal studies. We show that a variety of histological procedures can be carried out (cyto- and myeloarchitectonics, histochemistry, immunohistochemistry, intracellular cell injection, and electron microscopy). In addition, ex vivo, ex situ high-resolution MRI (9.4T) can be obtained in the same specimens. This procedure resulted in similar morphological features to those obtained by intravascular perfusion in experimental animals, provided that the postmortem interval was under 10 h for several of the techniques used and under 4 h in the case of intracellular injections and electron microscopy. The use of intravascular fixation of the brain inside the skull provides a fixed whole human brain, perfectly fitted to the skull, with negligible deformation compared to conventional techniques. Given this characteristic of ex vivo, in situ fixation, this procedure can probably be considered the most suitable one available for ex vivo MRI scans of the brain. We describe the compatibility of the method proposed for intravascular fixation of the human brain and fixation of the donor's body for anatomical purposes. Thus, body donor programs can provide human brain tissue, while the remainder of the body can also be fixed for anatomical studies. Therefore, this method of human brain fixation through the carotid system optimizes the procurement of human brain tissue, allowing a greater understanding of human neurological diseases, while benefiting anatomy departments by making the remainder of the body available for teaching purposes.
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Affiliation(s)
- Ricardo Insausti
- Human Neuroanatomy Laboratory, Neuromax CSIC Associated Unit, Medical Sciences Department, School of Medicine and CRIB, University of Castilla La Mancha, Albacete, Spain
| | - Ana María Insausti
- Department of Health, School of Medicine, Public University of Navarra, Pamplona, Spain
| | - Mónica Muñoz López
- Human Neuroanatomy Laboratory, Neuromax CSIC Associated Unit, Medical Sciences Department, School of Medicine and CRIB, University of Castilla La Mancha, Albacete, Spain
| | - Isidro Medina Lorenzo
- Human Neuroanatomy Laboratory, Neuromax CSIC Associated Unit, Medical Sciences Department, School of Medicine and CRIB, University of Castilla La Mancha, Albacete, Spain
| | - Maria del Mar Arroyo-Jiménez
- Human Neuroanatomy Laboratory, Neuromax CSIC Associated Unit, Medical Sciences Department, School of Medicine and CRIB, University of Castilla La Mancha, Albacete, Spain
| | - María Pilar Marcos Rabal
- Human Neuroanatomy Laboratory, Neuromax CSIC Associated Unit, Medical Sciences Department, School of Medicine and CRIB, University of Castilla La Mancha, Albacete, Spain
| | - Carlos de la Rosa-Prieto
- Human Neuroanatomy Laboratory, Neuromax CSIC Associated Unit, Medical Sciences Department, School of Medicine and CRIB, University of Castilla La Mancha, Albacete, Spain
| | - José Carlos Delgado-González
- Human Neuroanatomy Laboratory, Neuromax CSIC Associated Unit, Medical Sciences Department, School of Medicine and CRIB, University of Castilla La Mancha, Albacete, Spain
| | - Javier Montón Etxeberria
- Human Neuroanatomy Laboratory, Neuromax CSIC Associated Unit, Medical Sciences Department, School of Medicine and CRIB, University of Castilla La Mancha, Albacete, Spain
| | - Sandra Cebada-Sánchez
- Human Neuroanatomy Laboratory, Neuromax CSIC Associated Unit, Medical Sciences Department, School of Medicine and CRIB, University of Castilla La Mancha, Albacete, Spain
| | - Juan Francisco Raspeño-García
- Human Neuroanatomy Laboratory, Neuromax CSIC Associated Unit, Medical Sciences Department, School of Medicine and CRIB, University of Castilla La Mancha, Albacete, Spain
| | - María Mercedes Iñiguez de Onzoño
- Human Neuroanatomy Laboratory, Neuromax CSIC Associated Unit, Medical Sciences Department, School of Medicine and CRIB, University of Castilla La Mancha, Albacete, Spain
| | - Francisco Javier Molina Romero
- Human Neuroanatomy Laboratory, Neuromax CSIC Associated Unit, Medical Sciences Department, School of Medicine and CRIB, University of Castilla La Mancha, Albacete, Spain
| | - Ruth Benavides-Piccione
- Laboratorio Cajal de Circuitos Corticales, Centro de Tecnología Biomédica, Universidad Politécnica de Madrid, and Instituto Cajal, CSIC, Madrid, Spain
| | - Silvia Tapia-González
- Laboratorio Cajal de Circuitos Corticales, Centro de Tecnología Biomédica, Universidad Politécnica de Madrid, and Instituto Cajal, CSIC, Madrid, Spain
| | | | - Sadhana Ravikumar
- Department of Radiology, University of Pennsylvania, Philadelphia, PA, United States
| | - David A. Wolk
- Department of Neurology, University of Pennsylvania, Philadelphia, PA, United States
| | - Javier DeFelipe
- Laboratorio Cajal de Circuitos Corticales, Centro de Tecnología Biomédica, Universidad Politécnica de Madrid, and Instituto Cajal, CSIC, Madrid, Spain
| | - Paul Yushkevich
- Department of Radiology, University of Pennsylvania, Philadelphia, PA, United States
| | - Emilio Artacho-Pérula
- Human Neuroanatomy Laboratory, Neuromax CSIC Associated Unit, Medical Sciences Department, School of Medicine and CRIB, University of Castilla La Mancha, Albacete, Spain
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16
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Williams EM, Rosenblum EW, Pihlstrom N, Llamas-Rodríguez J, Champion S, Frosch MP, Augustinack JC. Pentad: A reproducible cytoarchitectonic protocol and its application to parcellation of the human hippocampus. Front Neuroanat 2023; 17:1114757. [PMID: 36843959 PMCID: PMC9947247 DOI: 10.3389/fnana.2023.1114757] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/02/2022] [Accepted: 01/13/2023] [Indexed: 02/11/2023] Open
Abstract
Introduction The hippocampus is integral for learning and memory and is targeted by multiple diseases. Neuroimaging approaches frequently use hippocampal subfield volumes as a standard measure of neurodegeneration, thus making them an essential biomarker to study. Collectively, histologic parcellation studies contain various disagreements, discrepancies, and omissions. The present study aimed to advance the hippocampal subfield segmentation field by establishing the first histology based parcellation protocol, applied to n = 22 human hippocampal samples. Methods The protocol focuses on five cellular traits observed in the pyramidal layer of the human hippocampus. We coin this approach the pentad protocol. The traits were: chromophilia, neuron size, packing density, clustering, and collinearity. Subfields included were CA1, CA2, CA3, CA4, prosubiculum, subiculum, presubiculum, parasubiculum, as well as the medial (uncal) subfields Subu, CA1u, CA2u, CA3u, and CA4u. We also establish nine distinct anterior-posterior levels of the hippocampus in the coronal plane to document rostrocaudal differences. Results Applying the pentad protocol, we parcellated 13 subfields at nine levels in 22 samples. We found that CA1 had the smallest neurons, CA2 showed high neuronal clustering, and CA3 displayed the most collinear neurons of the CA fields. The border between presubiculum and subiculum was staircase shaped, and parasubiculum had larger neurons than presubiculum. We also demonstrate cytoarchitectural evidence that CA4 and prosubiculum exist as individual subfields. Discussion This protocol is comprehensive, regimented and supplies a high number of samples, hippocampal subfields, and anterior-posterior coronal levels. The pentad protocol utilizes the gold standard approach for the human hippocampus subfield parcellation.
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Affiliation(s)
- Emily M. Williams
- Department of Radiology, Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Boston, MA, United States
| | - Emma W. Rosenblum
- Department of Radiology, Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Boston, MA, United States
| | - Nicole Pihlstrom
- Department of Radiology, Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Boston, MA, United States
| | - Josué Llamas-Rodríguez
- Department of Radiology, Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Boston, MA, United States
| | - Samantha Champion
- Department of Neuropathology, Massachusetts General Hospital, Boston, MA, United States
| | - Matthew P. Frosch
- Department of Neuropathology, Massachusetts General Hospital, Boston, MA, United States
| | - Jean C. Augustinack
- Department of Radiology, Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Boston, MA, United States
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17
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Gregory S, Pullen H, Ritchie CW, Shannon OM, Stevenson EJ, Muniz-Terrera G. Mediterranean diet and structural neuroimaging biomarkers of Alzheimer's and cerebrovascular disease: A systematic review. Exp Gerontol 2023; 172:112065. [PMID: 36529364 DOI: 10.1016/j.exger.2022.112065] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/09/2022] [Revised: 11/23/2022] [Accepted: 12/13/2022] [Indexed: 12/23/2022]
Abstract
Previous studies have demonstrated an association between adherence to the Mediterranean diet (MedDiet) and better cognitive performance, lower incidence of dementia and lower Alzheimer's disease biomarker burden. The aim of this systematic review was to evaluate the evidence base for MedDiet associations with hippocampal volume and white matter hyperintensity volume (WMHV). We searched systematically for studies reporting on MedDiet and hippocampal volume or WMHV in MedLine, EMBASE, CINAHL and PsycInfo. Searches were initially carried out on 21st July 2021 with final searches run on 23rd November 2022. Risk of bias was assessed using the NIH Quality Assessment Tool for Observational Cohort and Cross-Sectional Studies. Of an initial 112 papers identified, seven papers were eligible for inclusion in the review reporting on 21,933 participants. Four studies reported on hippocampal volume, with inconclusive or no associations seen with MedDiet adherence. Two studies found a significant association between higher MedDiet adherence and lower WMHV, while two other studies found no significant associations. Overall these results highlight a gap in our knowledge about the associations between the MedDiet and AD and cerebrovascular related structural neuroimaging findings.
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Affiliation(s)
- Sarah Gregory
- Edinburgh Dementia Prevention, Centre for Clinical Brain Sciences, University of Edinburgh, Edinburgh, UK.
| | - Hannah Pullen
- Edinburgh Dementia Prevention, Centre for Clinical Brain Sciences, University of Edinburgh, Edinburgh, UK.
| | - Craig W Ritchie
- Edinburgh Dementia Prevention, Centre for Clinical Brain Sciences, University of Edinburgh, Edinburgh, UK; Brain Health Scotland, UK.
| | - Oliver M Shannon
- Faculty of Medical Sciences, Human Nutrition Research Centre, Newcastle University, Newcastle Upon Tyne, UK.
| | - Emma J Stevenson
- Faculty of Medical Sciences, Human Nutrition Research Centre, Newcastle University, Newcastle Upon Tyne, UK.
| | - Graciela Muniz-Terrera
- Edinburgh Dementia Prevention, Centre for Clinical Brain Sciences, University of Edinburgh, Edinburgh, UK; Social Medicine, Ohio University, OH, USA.
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18
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Watanabe K, Okamoto N, Ueda I, Tesen H, Fujii R, Ikenouchi A, Yoshimura R, Kakeda S. Disturbed hippocampal intra-network in first-episode of drug-naïve major depressive disorder. Brain Commun 2023; 5:fcac323. [PMID: 36601619 PMCID: PMC9798279 DOI: 10.1093/braincomms/fcac323] [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: 03/14/2022] [Revised: 09/27/2022] [Accepted: 12/06/2022] [Indexed: 12/13/2022] Open
Abstract
Complex networks inside the hippocampus could provide new insights into hippocampal abnormalities in various psychiatric disorders and dementia. However, evaluating intra-networks in the hippocampus using MRI is challenging. Here, we employed a high spatial resolution of conventional structural imaging and independent component analysis to investigate intra-networks structural covariance in the hippocampus. We extracted the intra-networks based on the intrinsic connectivity of each 0.9 mm isotropic voxel to every other voxel using a data-driven approach. With a total volume of 3 cc, the hippocampus contains 4115 voxels for a 0.9 mm isotropic voxel size or 375 voxels for a 2 mm isotropic voxel of high-resolution functional or diffusion tensor imaging. Therefore, the novel method presented in the current study could evaluate the hippocampal intra-networks in detail. Furthermore, we investigated the abnormality of the intra-networks in major depressive disorders. A total of 77 patients with first-episode drug-naïve major depressive disorder and 79 healthy subjects were recruited. The independent component analysis extracted seven intra-networks from hippocampal structural images, which were divided into four bilateral networks and three networks along the longitudinal axis. A significant difference was observed in the bilateral hippocampal tail network between patients with major depressive disorder and healthy subjects. In the logistic regression analysis, two bilateral networks were significant predictors of major depressive disorder, with an accuracy of 78.1%. In conclusion, we present a novel method for evaluating intra-networks in the hippocampus. One advantage of this method is that a detailed network can be estimated using conventional structural imaging. In addition, we found novel bilateral networks in the hippocampus that were disturbed in patients with major depressive disorders, and these bilateral networks could predict major depressive disorders.
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Affiliation(s)
- Keita Watanabe
- Open Innovation Institute, Kyoto University, Kyoto 6068501, Japan
| | - Naomichi Okamoto
- Department of Psychiatry, University of Occupational and Environmental Health, Kitakyushu 8078555, Japan
| | - Issei Ueda
- Department of Radiology, Graduate School of Medicine, Hirosaki University, Hirosaki 0368502, Japan
| | - Hirofumi Tesen
- Department of Psychiatry, University of Occupational and Environmental Health, Kitakyushu 8078555, Japan
| | - Rintaro Fujii
- Department of Psychiatry, University of Occupational and Environmental Health, Kitakyushu 8078555, Japan
| | - Atsuko Ikenouchi
- Department of Psychiatry, University of Occupational and Environmental Health, Kitakyushu 8078555, Japan
| | - Reiji Yoshimura
- Department of Psychiatry, University of Occupational and Environmental Health, Kitakyushu 8078555, Japan
| | - Shingo Kakeda
- Department of Radiology, Graduate School of Medicine, Hirosaki University, Hirosaki 0368502, Japan
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19
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Distinct multivariate structural brain profiles are related to variations in short- and long-delay memory consolidation across children and young adults. Dev Cogn Neurosci 2022; 59:101192. [PMID: 36566622 PMCID: PMC9803921 DOI: 10.1016/j.dcn.2022.101192] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/06/2022] [Revised: 12/12/2022] [Accepted: 12/21/2022] [Indexed: 12/24/2022] Open
Abstract
From early to middle childhood, brain regions that underlie memory consolidation undergo profound maturational changes. However, there is little empirical investigation that directly relates age-related differences in brain structural measures to memory consolidation processes. The present study examined memory consolidation of intentionally studied object-location associations after one night of sleep (short delay) and after two weeks (long delay) in normally developing 5-to-7-year-old children (n = 50) and young adults (n = 39). Behavioural differences in memory retention rate were related to structural brain measures. Our results showed that children, in comparison to young adults, retained correctly learnt object-location associations less robustly over short and long delay. Moreover, using partial least squares correlation method, a unique multivariate profile comprised of specific neocortical (prefrontal, parietal, and occipital), cerebellar, and hippocampal head and subfield structures in the body was found to be associated with variation in short-delay memory retention. A different multivariate profile comprised of a reduced set of brain structures, mainly consisting of neocortical (prefrontal, parietal, and occipital), hippocampal head, and selective hippocampal subfield structures (CA1-2 and subiculum) was associated with variation in long-delay memory retention. Taken together, the results suggest that multivariate structural pattern of unique sets of brain regions are related to variations in short- and long-delay memory consolidation across children and young adults.
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20
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Dick AS, Ralph Y, Farrant K, Reeb-Sutherland B, Pruden S, Mattfeld AT. Volumetric development of hippocampal subfields and hippocampal white matter connectivity: Relationship with episodic memory. Dev Psychobiol 2022; 64:e22333. [PMID: 36426794 DOI: 10.1002/dev.22333] [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: 02/02/2022] [Revised: 08/22/2022] [Accepted: 09/02/2022] [Indexed: 01/27/2023]
Abstract
The hippocampus is a complex structure composed of distinct subfields. It has been central to understanding neural foundations of episodic memory. In the current cross-sectional study, using a large sample of 830, 3- to 21-year-olds from a unique, publicly available dataset we examined the following questions: (1) Is there elevated grey matter volume of the hippocampus and subfields in late compared to early development? (2) How does hippocampal volume compare with the rest of the cerebral cortex at different developmental stages? and (3) What is the relation between hippocampal volume and connectivity with episodic memory performance? We found hippocampal subfield volumes exhibited a nonlinear relation with age and showed a lag in volumetric change with age when compared to adjacent cortical regions (e.g., entorhinal cortex). We also observed a significant reduction in cortical volume across older cohorts, while hippocampal volume showed the opposite pattern. In addition to age-related differences in gray matter volume, dentate gyrus/cornu ammonis 3 volume was significantly related to episodic memory. We did not, however, find any associations with episodic memory performance and connectivity through the uncinate fasciculus, fornix, or cingulum. The results are discussed in the context of current research and theories of hippocampal development and its relation to episodic memory.
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Affiliation(s)
- Anthony Steven Dick
- Department of Psychology, Florida International University, Miami, Florida, USA
| | - Yvonne Ralph
- Department of Psychology, Florida International University, Miami, Florida, USA
| | - Kristafor Farrant
- Department of Psychology, Florida International University, Miami, Florida, USA
| | | | - Shannon Pruden
- Department of Psychology, Florida International University, Miami, Florida, USA
| | - Aaron T Mattfeld
- Department of Psychology, Florida International University, Miami, Florida, USA
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21
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Gregory S, Saunders S, Ritchie CW. Science disconnected: the translational gap between basic science, clinical trials, and patient care in Alzheimer's disease. THE LANCET. HEALTHY LONGEVITY 2022; 3:e797-e803. [PMID: 36356629 DOI: 10.1016/s2666-7568(22)00219-7] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/17/2022] [Revised: 08/22/2022] [Accepted: 09/08/2022] [Indexed: 11/09/2022] Open
Abstract
Both research and clinical practice have traditionally centred on the dementia syndrome of Alzheimer's disease rather than its preclinical and prodromal stages. However, there is a strong scientific and ethical impetus to shift focus to earlier disease stages to improve brain health outcomes and help to keep affected individuals symptom-free (dementia-free) for as long as possible. We provide an overview of recent advancements in early detection, drug development, and trial methodology that should be utilised in the development of new therapies for use in brain health clinics. We propose a triad approach to Alzheimer's disease clinical trials, encompassing (1) experimental medicine studies to gather greater knowledge of disease mechanisms, (2) a more comprehensive platform of phase 2 learning trials to inform phase 3 confirmatory trials, and (3) precision medicine involving smaller subgroups of patients with shared characteristics. This triad would ensure that treatment targets are identified accurately, trial methodology focuses on at-risk populations, and sensitive outcome measures capture potential treatment effects. Clinical services around the world must embrace the brain health clinic model so that neurodegenerative diseases can be detected in their earliest phase to quicken drug development pipelines and potentially improve prognosis.
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Affiliation(s)
- Sarah Gregory
- Edinburgh Dementia Prevention, Centre for Clinical Brain Sciences, Outpatient Department 2, Western General Hospital, University of Edinburgh, Edinburgh, UK.
| | - Stina Saunders
- Edinburgh Dementia Prevention, Centre for Clinical Brain Sciences, Outpatient Department 2, Western General Hospital, University of Edinburgh, Edinburgh, UK
| | - Craig W Ritchie
- Edinburgh Dementia Prevention, Centre for Clinical Brain Sciences, Outpatient Department 2, Western General Hospital, University of Edinburgh, Edinburgh, UK; Brain Health Scotland, Edinburgh, UK
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22
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Sedwick VM, Autry AE. Anatomical and molecular features of the amygdalohippocampal transition area and its role in social and emotional behavior processes. Neurosci Biobehav Rev 2022; 142:104893. [PMID: 36179917 PMCID: PMC11106034 DOI: 10.1016/j.neubiorev.2022.104893] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/02/2022] [Revised: 09/21/2022] [Accepted: 09/24/2022] [Indexed: 02/04/2023]
Abstract
The amygdalohippocampal transition area (AHi) has emerged as a critical nucleus of sociosexual behaviors such as mating, parenting, and aggression. The AHi has been overlooked in rodent and human amygdala studies until recently. The AHi is hypothesized to play a role in metabolic and cognitive functions as well as social behaviors based on its connectivity and molecular composition. The AHi is small nucleus rich in neuropeptide and hormone receptors and is contiguous with the ventral subiculum of the hippocampus-hence its designation as a "transition area". Literature focused on the AHi can be difficult to interpret because of changing nomenclature and conflation with neighboring nuclei. Here we summarize what is currently known about AHi structure and development, connections throughout the brain, molecular composition, and functional significance. We aim to delineate current knowledge regarding the AHi, identify potential functions with supporting evidence, and ultimately make clear the importance of the AHi in sociosexual function.
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Affiliation(s)
- Victoria M Sedwick
- Dominick P. Purpura Department of Neuroscience, Albert Einstein College of Medicine, Bronx, NY, USA
| | - Anita E Autry
- Dominick P. Purpura Department of Neuroscience, Albert Einstein College of Medicine, Bronx, NY, USA; Department of Psychiatry and Behavioral Sciences, Albert Einstein College of Medicine, Bronx, NY, USA.
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23
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Casamitjana A, Iglesias JE. High-resolution atlasing and segmentation of the subcortex: Review and perspective on challenges and opportunities created by machine learning. Neuroimage 2022; 263:119616. [PMID: 36084858 DOI: 10.1016/j.neuroimage.2022.119616] [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: 03/29/2022] [Revised: 08/30/2022] [Accepted: 09/05/2022] [Indexed: 11/17/2022] Open
Abstract
This paper reviews almost three decades of work on atlasing and segmentation methods for subcortical structures in human brain MRI. In writing this survey, we have three distinct aims. First, to document the evolution of digital subcortical atlases of the human brain, from the early MRI templates published in the nineties, to the complex multi-modal atlases at the subregion level that are available today. Second, to provide a detailed record of related efforts in the automated segmentation front, from earlier atlas-based methods to modern machine learning approaches. And third, to present a perspective on the future of high-resolution atlasing and segmentation of subcortical structures in in vivo human brain MRI, including open challenges and opportunities created by recent developments in machine learning.
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Affiliation(s)
- Adrià Casamitjana
- Centre for Medical Image Computing, Department of Medical Physics and Biomedical Engineering, University College London, UK.
| | - Juan Eugenio Iglesias
- Centre for Medical Image Computing, Department of Medical Physics and Biomedical Engineering, University College London, UK; Martinos Center for Biomedical Imaging, Massachusetts General Hospital and Harvard Medical School, Boston, USA; Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology, Boston, USA
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24
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Seidler RD, Stern C, Basner M, Stahn AC, Wuyts FL, zu Eulenburg P. Future research directions to identify risks and mitigation strategies for neurostructural, ocular, and behavioral changes induced by human spaceflight: A NASA-ESA expert group consensus report. Front Neural Circuits 2022; 16:876789. [PMID: 35991346 PMCID: PMC9387435 DOI: 10.3389/fncir.2022.876789] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/15/2022] [Accepted: 06/27/2022] [Indexed: 11/13/2022] Open
Abstract
A team of experts on the effects of the spaceflight environment on the brain and eye (SANS: Spaceflight-Associated Neuro-ocular Syndrome) was convened by NASA and ESA to (1) review spaceflight-associated structural and functional changes of the human brain and eye, and any interactions between the two; and (2) identify critical future research directions in this area to help characterize the risk and identify possible countermeasures and strategies to mitigate the spaceflight-induced brain and eye alterations. The experts identified 14 critical future research directions that would substantially advance our knowledge of the effects of spending prolonged periods of time in the spaceflight environment on SANS, as well as brain structure and function. They used a paired comparison approach to rank the relative importance of these 14 recommendations, which are discussed in detail in the main report and are summarized briefly below.
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Affiliation(s)
- Rachael D. Seidler
- Department of Applied Physiology & Kinesiology, Health and Human Performance, University of Florida, Gainesville, FL, United States
| | - Claudia Stern
- Department of Clinical Aerospace Medicine, German Aerospace Center (DLR) and ISS Operations and Astronauts Group, European Astronaut Centre, European Space Agency (ESA), Cologne, Germany
- *Correspondence: Claudia Stern,
| | - Mathias Basner
- Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, United States
| | - Alexander C. Stahn
- Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, United States
| | - Floris L. Wuyts
- Department of Physics, University of Antwerp, Antwerp, Belgium
- Laboratory for Equilibrium Investigations and Aerospace (LEIA), Antwerp, Belgium
| | - Peter zu Eulenburg
- German Vertigo and Balance Center, University Hospital, Ludwig-Maximilians-Universität München (LMU), Munich, Germany
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25
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Hari E, Kurt E, Bayram A, Kizilates-Evin G, Acar B, Demiralp T, Gurvit H. Volumetric changes within hippocampal subfields in Alzheimer’s disease continuum. Neurol Sci 2022; 43:4175-4183. [DOI: 10.1007/s10072-022-05890-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/11/2021] [Accepted: 01/09/2022] [Indexed: 10/19/2022]
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26
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Wittayer M, Hoyer C, Roßmanith C, Platten M, Gass A, Szabo K. Hippocampal subfield involvement in patients with transient global amnesia. J Neuroimaging 2022; 32:264-267. [PMID: 35106877 DOI: 10.1111/jon.12973] [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: 12/21/2021] [Revised: 01/12/2022] [Accepted: 01/12/2022] [Indexed: 11/29/2022] Open
Abstract
BACKGROUND AND PURPOSE Transient global amnesia (TGA) is a rare neurological disorder causing a transient disturbance of episodic long-term memory. Its etiology remains yet to be identified; the only consistently reported findings in patients with TGA are small hyperintense lesions in the hippocampus on diffusion-weighted magnetic resonance imaging (DWI). The aim of this study was to define whether these lesions are subfield specific, as suggested previously. METHODS High-resolution multiplanar reformation T1 and DWI of the hippocampus were acquired in 25 patients after TGA with a total of 43 hippocampal lesions. Hippocampal subfields were determined using the FreeSurfer software and the location of the DWI lesions was transformed to the T1 images after data co-registration. Additionally, hippocampal subfield volumes in each patient were calculated and compared with that of 20 healthy controls. RESULTS Hippocampal lesions were most frequently detected in the cornu ammonis area 1 (CA1) subfield (30.2%), the hippocampal tail (28.0%), and the subiculum (21.0%); however, lesions were also found in other subfields. There was no significant difference between patients and controls concerning the volumes of the hippocampal subfields. CONCLUSIONS Contrasting previous assumptions, we found DWI hyperintense lesions not to be restricted to the CA1 subfield. The visualization of focal hippocampal lesions on diffusion imaging located to several different hippocampal subfields suggests a potential pathophysiology of TGA independent of microstructural hippocampal anatomy and subfield-specific vulnerability.
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Affiliation(s)
- Matthias Wittayer
- Department of Neurology, Medical Faculty Mannheim, Heidelberg University, Mannheim, Germany.,Mannheim Center for Translational Neurosciences (MCTN), Heidelberg University, Mannheim, Germany
| | - Carolin Hoyer
- Department of Neurology, Medical Faculty Mannheim, Heidelberg University, Mannheim, Germany.,Mannheim Center for Translational Neurosciences (MCTN), Heidelberg University, Mannheim, Germany
| | - Christina Roßmanith
- Department of Neurology, Medical Faculty Mannheim, Heidelberg University, Mannheim, Germany.,Mannheim Center for Translational Neurosciences (MCTN), Heidelberg University, Mannheim, Germany
| | - Michael Platten
- Department of Neurology, Medical Faculty Mannheim, Heidelberg University, Mannheim, Germany.,Mannheim Center for Translational Neurosciences (MCTN), Heidelberg University, Mannheim, Germany
| | - Achim Gass
- Department of Neurology, Medical Faculty Mannheim, Heidelberg University, Mannheim, Germany.,Mannheim Center for Translational Neurosciences (MCTN), Heidelberg University, Mannheim, Germany
| | - Kristina Szabo
- Department of Neurology, Medical Faculty Mannheim, Heidelberg University, Mannheim, Germany.,Mannheim Center for Translational Neurosciences (MCTN), Heidelberg University, Mannheim, Germany
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27
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Zajner C, Spreng RN, Bzdok D. Lacking Social Support is Associated With Structural Divergences in Hippocampus-Default Network Co-Variation Patterns. Soc Cogn Affect Neurosci 2022; 17:802-818. [PMID: 35086149 PMCID: PMC9433851 DOI: 10.1093/scan/nsac006] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/19/2021] [Revised: 11/17/2021] [Accepted: 01/25/2022] [Indexed: 11/22/2022] Open
Abstract
Elaborate social interaction is a pivotal asset of the human species. The complexity of people’s social lives may constitute the dominating factor in the vibrancy of many individuals’ environment. The neural substrates linked to social cognition thus appear especially susceptible when people endure periods of social isolation: here, we zoom in on the systematic inter-relationships between two such neural substrates, the allocortical hippocampus (HC) and the neocortical default network (DN). Previous human social neuroscience studies have focused on the DN, while HC subfields have been studied in most detail in rodents and monkeys. To bring into contact these two separate research streams, we directly quantified how DN subregions are coherently co-expressed with specific HC subfields in the context of social isolation. A two-pronged decomposition of structural brain scans from ∼40 000 UK Biobank participants linked lack of social support to mostly lateral subregions in the DN patterns. This lateral DN association co-occurred with HC patterns that implicated especially subiculum, presubiculum, CA2, CA3 and dentate gyrus. Overall, the subregion divergences within spatially overlapping signatures of HC–DN co-variation followed a clear segregation into the left and right brain hemispheres. Separable regimes of structural HC–DN co-variation also showed distinct associations with the genetic predisposition for lacking social support at the population level.
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Affiliation(s)
- Chris Zajner
- McConnell Brain Imaging Centre (BIC), Montreal Neurological Institute (MNI), Faculty of Medicine, McGill University, Montreal H3A2B4, Canada
| | - R Nathan Spreng
- McConnell Brain Imaging Centre (BIC), Montreal Neurological Institute (MNI), Faculty of Medicine, McGill University, Montreal H3A2B4, Canada
| | - Danilo Bzdok
- Correspondence should be addressed to Danilo Bzdok, McConnell Brain Imaging Centre (BIC), Montreal Neurological Institute (MNI), Faculty of Medicine, McGill University, Montreal H3A2B4, Canada. E-mail:
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28
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DeKraker J, Haast RAM, Yousif MD, Karat B, Lau JC, Köhler S, Khan AR. Automated hippocampal unfolding for morphometry and subfield segmentation with HippUnfold. eLife 2022; 11:77945. [PMID: 36519725 PMCID: PMC9831605 DOI: 10.7554/elife.77945] [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: 02/17/2022] [Accepted: 12/13/2022] [Indexed: 12/16/2022] Open
Abstract
Like neocortical structures, the archicortical hippocampus differs in its folding patterns across individuals. Here, we present an automated and robust BIDS-App, HippUnfold, for defining and indexing individual-specific hippocampal folding in MRI, analogous to popular tools used in neocortical reconstruction. Such tailoring is critical for inter-individual alignment, with topology serving as the basis for homology. This topological framework enables qualitatively new analyses of morphological and laminar structure in the hippocampus or its subfields. It is critical for refining current neuroimaging analyses at a meso- as well as micro-scale. HippUnfold uses state-of-the-art deep learning combined with previously developed topological constraints to generate uniquely folded surfaces to fit a given subject's hippocampal conformation. It is designed to work with commonly employed sub-millimetric MRI acquisitions, with possible extension to microscopic resolution. In this paper, we describe the power of HippUnfold in feature extraction, and highlight its unique value compared to several extant hippocampal subfield analysis methods.
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Affiliation(s)
- Jordan DeKraker
- Robarts Research Institute, Schulich School of Medicine and Dentistry, The University of Western OntarioLondonCanada,Western Institute for Neuroscience, The University of Western OntarioLondonCanada
| | - Roy AM Haast
- Robarts Research Institute, Schulich School of Medicine and Dentistry, The University of Western OntarioLondonCanada
| | - Mohamed D Yousif
- Robarts Research Institute, Schulich School of Medicine and Dentistry, The University of Western OntarioLondonCanada
| | - Bradley Karat
- Robarts Research Institute, Schulich School of Medicine and Dentistry, The University of Western OntarioLondonCanada
| | - Jonathan C Lau
- Robarts Research Institute, Schulich School of Medicine and Dentistry, The University of Western OntarioLondonCanada,Department of Clinical Neurological Sciences, Division of Neurosurgery, Schulich School of Medicine & Dentistry, The University of Western OntarioLondonCanada,School of Biomedical Engineering, The University of Western OntarioLondonCanada
| | - Stefan Köhler
- Western Institute for Neuroscience, The University of Western OntarioLondonCanada,Department of Psychology, Faculty of Social Science, The University of Western OntarioLondonCanada
| | - Ali R Khan
- Robarts Research Institute, Schulich School of Medicine and Dentistry, The University of Western OntarioLondonCanada,Western Institute for Neuroscience, The University of Western OntarioLondonCanada,School of Biomedical Engineering, The University of Western OntarioLondonCanada,Department of Medical Biophysics, Schulich School of Medicine and Dentistry, The University of Western OntarioLondonCanada
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29
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Zajner C, Spreng RN, Bzdok D. Loneliness is linked to specific subregional alterations in hippocampus-default network covariation. J Neurophysiol 2021; 126:2138-2157. [PMID: 34817294 PMCID: PMC8715056 DOI: 10.1152/jn.00339.2021] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/22/2022] Open
Abstract
Social interaction complexity makes humans unique. But in times of social deprivation, this strength risks exposure of important vulnerabilities. Human social neuroscience studies have placed a premium on the default network (DN). In contrast, hippocampus (HC) subfields have been intensely studied in rodents and monkeys. To bridge these two literatures, we here quantified how DN subregions systematically covary with specific HC subfields in the context of subjective social isolation (i.e., loneliness). By codecomposition using structural brain scans of ∼40,000 UK Biobank participants, loneliness was specially linked to midline subregions in the uncovered DN patterns. These association cortex patterns coincided with concomitant HC patterns implicating especially CA1 and molecular layer. These patterns also showed a strong affiliation with the fornix white matter tract and the nucleus accumbens. In addition, separable signatures of structural HC-DN covariation had distinct associations with the genetic predisposition for loneliness at the population level. NEW & NOTEWORTHY The hippocampus and default network have been implicated in rich social interaction. Yet, these allocortical and neocortical neural systems have been interrogated in mostly separate literatures. Here, we conjointly investigate the hippocampus and default network at a subregion level, by capitalizing structural brain scans from ∼40,000 participants. We thus reveal unique insights on the nature of the “lonely brain” by estimating the regimes of covariation between the hippocampus and default network at population scale.
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Affiliation(s)
- Chris Zajner
- McConnell Brain Imaging Centre (BIC), Montreal Neurological Institute (MNI), Faculty of Medicine, McGill University, Montreal, Quebec, Canada
| | - R Nathan Spreng
- McConnell Brain Imaging Centre (BIC), Montreal Neurological Institute (MNI), Faculty of Medicine, McGill University, Montreal, Quebec, Canada.,Department of Neurology and Neurosurgery, McGill University, Montreal, Quebec, Canada.,Departments of Psychiatry and Psychology, McGill University, Montreal, QC, Canada.,Douglas Mental Health University Institute, Verdun, Quebec, Canada
| | - Danilo Bzdok
- McConnell Brain Imaging Centre (BIC), Montreal Neurological Institute (MNI), Faculty of Medicine, McGill University, Montreal, Quebec, Canada.,Department of Biomedical Engineering, Faculty of Medicine, McGill University, Montreal, Quebec, Canada.,Mila-Quebec Artificial Intelligence Institute, Montreal, Quebec, Canada
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30
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Genon S, Bernhardt BC, La Joie R, Amunts K, Eickhoff SB. The many dimensions of human hippocampal organization and (dys)function. Trends Neurosci 2021; 44:977-989. [PMID: 34756460 DOI: 10.1016/j.tins.2021.10.003] [Citation(s) in RCA: 38] [Impact Index Per Article: 12.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/23/2021] [Revised: 09/06/2021] [Accepted: 10/05/2021] [Indexed: 11/19/2022]
Abstract
The internal organization of hippocampal formation has been studied for more than a century. Although early accounts emphasized its subfields along the medial-lateral axis, findings in recent decades have highlighted also the anterior-to-posterior (i.e., longitudinal) axis as a key contributor to this brain region's functional organization. Hence, understanding of hippocampal function likely demands characterizing both medial-to-lateral and anterior-to-posterior axes, an approach that has been concretized by recent advances in in vivo parcellation and gradient mapping techniques. Following a short historical overview, we review the evidence provided by these approaches in brain-mapping studies, as well as the perspectives they open for addressing the behavioral relevance of the interacting organizational axes in healthy and clinical populations.
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Affiliation(s)
- Sarah Genon
- Institute of Neuroscience and Medicine, Brain and Behaviour (INM-7), Research Centre Jülich, Jülich, Germany; Institute of Systems Neuroscience, Medical Faculty, Heinrich-Heine-University Düsseldorf, Düsseldorf, Germany.
| | | | - Renaud La Joie
- Memory and Aging Center, Weill Institute for Neuroscience, University of California, San Francisco, San Francisco, CA, USA
| | - Katrin Amunts
- Institute of Neuroscience and Medicine, Structural and Functional Organisation of the Brain (INM-1), Research Centre Jülich, Jülich, Germany; C. & O. Vogt Institute for Brain Research, Medical Faculty, University Hospital Düsseldorf, Heinrich Heine University, Düsseldorf, Germany
| | - Simon B Eickhoff
- Institute of Neuroscience and Medicine, Brain and Behaviour (INM-7), Research Centre Jülich, Jülich, Germany; Institute of Systems Neuroscience, Medical Faculty, Heinrich-Heine-University Düsseldorf, Düsseldorf, Germany
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31
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Bocchetta M, Malpetti M, Todd EG, Rowe JB, Rohrer JD. Looking beneath the surface: the importance of subcortical structures in frontotemporal dementia. Brain Commun 2021; 3:fcab158. [PMID: 34458729 PMCID: PMC8390477 DOI: 10.1093/braincomms/fcab158] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 05/21/2021] [Indexed: 12/15/2022] Open
Abstract
Whilst initial anatomical studies of frontotemporal dementia focussed on cortical involvement, the relevance of subcortical structures to the pathophysiology of frontotemporal dementia has been increasingly recognized over recent years. Key structures affected include the caudate, putamen, nucleus accumbens, and globus pallidus within the basal ganglia, the hippocampus and amygdala within the medial temporal lobe, the basal forebrain, and the diencephalon structures of the thalamus, hypothalamus and habenula. At the most posterior aspect of the brain, focal involvement of brainstem and cerebellum has recently also been shown in certain subtypes of frontotemporal dementia. Many of the neuroimaging studies on subcortical structures in frontotemporal dementia have been performed in clinically defined sporadic cases. However, investigations of genetically- and pathologically-confirmed forms of frontotemporal dementia are increasingly common and provide molecular specificity to the changes observed. Furthermore, detailed analyses of sub-nuclei and subregions within each subcortical structure are being added to the literature, allowing refinement of the patterns of subcortical involvement. This review focuses on the existing literature on structural imaging and neuropathological studies of subcortical anatomy across the spectrum of frontotemporal dementia, along with investigations of brain–behaviour correlates that examine the cognitive sequelae of specific subcortical involvement: it aims to ‘look beneath the surface’ and summarize the patterns of subcortical involvement have been described in frontotemporal dementia.
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Affiliation(s)
- Martina Bocchetta
- Dementia Research Centre, Department of Neurodegenerative Disease, UCL Queen Square Institute of Neurology, University College London, London, UK
| | - Maura Malpetti
- Department of Clinical Neurosciences and Cambridge University Hospitals NHS Trust, University of Cambridge, Cambridge, UK
| | - Emily G Todd
- Dementia Research Centre, Department of Neurodegenerative Disease, UCL Queen Square Institute of Neurology, University College London, London, UK
| | - James B Rowe
- Department of Clinical Neurosciences and Cambridge University Hospitals NHS Trust, University of Cambridge, Cambridge, UK.,Medical Research Council Cognition and Brain Sciences Unit, University of Cambridge, Cambridge, UK
| | - Jonathan D Rohrer
- Dementia Research Centre, Department of Neurodegenerative Disease, UCL Queen Square Institute of Neurology, University College London, London, UK
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32
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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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33
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Paquola C, Royer J, Lewis LB, Lepage C, Glatard T, Wagstyl K, DeKraker J, Toussaint PJ, Valk SL, Collins L, Khan AR, Amunts K, Evans AC, Dickscheid T, Bernhardt B. The BigBrainWarp toolbox for integration of BigBrain 3D histology with multimodal neuroimaging. eLife 2021; 10:e70119. [PMID: 34431476 PMCID: PMC8445620 DOI: 10.7554/elife.70119] [Citation(s) in RCA: 20] [Impact Index Per Article: 6.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/06/2021] [Accepted: 08/23/2021] [Indexed: 01/03/2023] Open
Abstract
Neuroimaging stands to benefit from emerging ultrahigh-resolution 3D histological atlases of the human brain; the first of which is 'BigBrain'. Here, we review recent methodological advances for the integration of BigBrain with multi-modal neuroimaging and introduce a toolbox, 'BigBrainWarp', that combines these developments. The aim of BigBrainWarp is to simplify workflows and support the adoption of best practices. This is accomplished with a simple wrapper function that allows users to easily map data between BigBrain and standard MRI spaces. The function automatically pulls specialised transformation procedures, based on ongoing research from a wide collaborative network of researchers. Additionally, the toolbox improves accessibility of histological information through dissemination of ready-to-use cytoarchitectural features. Finally, we demonstrate the utility of BigBrainWarp with three tutorials and discuss the potential of the toolbox to support multi-scale investigations of brain organisation.
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Affiliation(s)
- Casey Paquola
- McConnell Brain Imaging Centre, Montreal Neurological Institute and Hospital, McGill UniversityMontréalCanada
- Institute of Neuroscience and Medicine (INM-1), Forschungszentrum JülichJülichGermany
| | - Jessica Royer
- McConnell Brain Imaging Centre, Montreal Neurological Institute and Hospital, McGill UniversityMontréalCanada
| | - Lindsay B Lewis
- McConnell Brain Imaging Centre, Montreal Neurological Institute and Hospital, McGill UniversityMontréalCanada
| | - Claude Lepage
- McConnell Brain Imaging Centre, Montreal Neurological Institute and Hospital, McGill UniversityMontréalCanada
| | - Tristan Glatard
- Department of Computer Science and Software Engineering, Concordia UniversityMontrealCanada
| | - Konrad Wagstyl
- Wellcome Trust Centre for Neuroimaging, University College LondonLondonUnited Kingdom
| | - Jordan DeKraker
- McConnell Brain Imaging Centre, Montreal Neurological Institute and Hospital, McGill UniversityMontréalCanada
- Brain and Mind Institute, University of Western OntarioOntarioCanada
| | - Paule-J Toussaint
- McConnell Brain Imaging Centre, Montreal Neurological Institute and Hospital, McGill UniversityMontréalCanada
| | - Sofie L Valk
- Otto Hahn Group Cognitive Neurogenetics, Max Planck Institute for Human Cognitive and Brain SciencesLeipzigGermany
- Institute of Neuroscience and Medicine (INM-7), Forschungszentrum JülichJülichGermany
| | - Louis Collins
- McConnell Brain Imaging Centre, Montreal Neurological Institute and Hospital, McGill UniversityMontréalCanada
| | - Ali R Khan
- Department of Medical Biophysics, Schulich School of Medicine & Dentistry, University of Western OntarioLondonCanada
| | - Katrin Amunts
- Institute of Neuroscience and Medicine (INM-1), Forschungszentrum JülichJülichGermany
| | - Alan C Evans
- McConnell Brain Imaging Centre, Montreal Neurological Institute and Hospital, McGill UniversityMontréalCanada
| | - Timo Dickscheid
- Institute of Neuroscience and Medicine (INM-1), Forschungszentrum JülichJülichGermany
| | - Boris Bernhardt
- McConnell Brain Imaging Centre, Montreal Neurological Institute and Hospital, McGill UniversityMontréalCanada
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34
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Bender AR, Brandmaier AM, Düzel S, Keresztes A, Pasternak O, Lindenberger U, Kühn S. Hippocampal Subfields and Limbic White Matter Jointly Predict Learning Rate in Older Adults. Cereb Cortex 2021; 30:2465-2477. [PMID: 31800016 DOI: 10.1093/cercor/bhz252] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/09/2019] [Revised: 08/20/2019] [Accepted: 10/01/2019] [Indexed: 12/21/2022] Open
Abstract
Age-related memory impairments have been linked to differences in structural brain parameters, including cerebral white matter (WM) microstructure and hippocampal (HC) volume, but their combined influences are rarely investigated. In a population-based sample of 337 older participants aged 61-82 years (Mage = 69.66, SDage = 3.92 years), we modeled the independent and joint effects of limbic WM microstructure and HC subfield volumes on verbal learning. Participants completed a verbal learning task of recall over five repeated trials and underwent magnetic resonance imaging (MRI), including structural and diffusion scans. We segmented three HC subregions on high-resolution MRI data and sampled mean fractional anisotropy (FA) from bilateral limbic WM tracts identified via deterministic fiber tractography. Using structural equation modeling, we evaluated the associations between learning rate and latent factors representing FA sampled from limbic WM tracts, and HC subfield volumes, and their latent interaction. Results showed limbic WM and the interaction of HC and WM-but not HC volume alone-predicted verbal learning rates. Model decomposition revealed HC volume is only positively associated with learning rate in individuals with higher WM anisotropy. We conclude that the structural characteristics of limbic WM regions and HC volume jointly contribute to verbal learning in older adults.
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Affiliation(s)
- Andrew R Bender
- Departments of Epidemiology and Biostatistics, Neurology and Ophthalmology, College of Human Medicine, Michigan State University, East Lansing, MI 48824, USA.,Center for Lifespan Psychology, Max Planck Institute for Human Development, D-14195 Berlin, Germany
| | - Andreas M Brandmaier
- Center for Lifespan Psychology, Max Planck Institute for Human Development, D-14195 Berlin, Germany.,Max Planck UCL Centre for Computational Psychiatry and Ageing Research, D-14195 Berlin, Germany and London, UK WC1B 5EH
| | - Sandra Düzel
- Center for Lifespan Psychology, Max Planck Institute for Human Development, D-14195 Berlin, Germany
| | - Attila Keresztes
- Center for Lifespan Psychology, Max Planck Institute for Human Development, D-14195 Berlin, Germany.,Research Centre for Natural Sciences, Hungarian Academy of Sciences, H-1117 Budapest, Hungary.,Faculty of Education and Psychology, Eötvös Loránd University, H-1053 Budapest, Hungary
| | - Ofer Pasternak
- Departments of Psychiatry and Radiology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA 02115, USA
| | - Ulman Lindenberger
- Center for Lifespan Psychology, Max Planck Institute for Human Development, D-14195 Berlin, Germany.,Max Planck UCL Centre for Computational Psychiatry and Ageing Research, D-14195 Berlin, Germany and London, UK WC1B 5EH.,European University Institute, I-50014. San Domenico di Fiesole, Italy
| | - Simone Kühn
- Center for Lifespan Psychology, Max Planck Institute for Human Development, D-14195 Berlin, Germany.,Department of Psychiatry and Psychotherapy, University Clinic Hamburg-Eppendorf, 20246 Hamburg, Germany
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35
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DeKraker J, Köhler S, Khan AR. Surface-based hippocampal subfield segmentation. Trends Neurosci 2021; 44:856-863. [PMID: 34304910 DOI: 10.1016/j.tins.2021.06.005] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/07/2020] [Revised: 05/25/2021] [Accepted: 06/15/2021] [Indexed: 10/20/2022]
Abstract
Though it is often termed 'subcortical,' the hippocampus is composed of a folded 'archicortical' sheet contiguous with the neocortex. The human hippocampus varies considerably in its internal folding configuration, creating major challenges in interindividual alignment and parcellation into subfields. In this opinion article, we discuss surface-based methods that aim to explicitly model hippocampal folding, similar to methods used in the neocortex, allowing interindividual alignment in an unfolded or flat-mapped 2D space. Such an approach enables detailed morphological characterization, constrains the problem of subfield segmentation, and provides a way to visualize data without occlusions. We argue that, when applied to magnetic resonance imaging (MRI) data, such methods overcome pitfalls of more conventional manual or registration-based subfield segmentation approaches.
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Affiliation(s)
- Jordan DeKraker
- Brain and Mind Institute, University of Western Ontario, London, ON, Canada; Robarts Research Institute, Schulich School of Medicine and Dentistry, University of Western Ontario, London, ON, Canada.
| | - Stefan Köhler
- Brain and Mind Institute, University of Western Ontario, London, ON, Canada; Department of Psychology, University of Western Ontario, London, ON, Canada.
| | - Ali R Khan
- Brain and Mind Institute, University of Western Ontario, London, ON, Canada; Robarts Research Institute, Schulich School of Medicine and Dentistry, University of Western Ontario, London, ON, Canada; School of Biomedical Engineering, University of Western Ontario, London, ON, Canada; Department of Medical Biophysics, University of Western Ontario, London, ON, Canada.
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36
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Hippocampal subfield volumes across the healthy lifespan and the effects of MR sequence on estimates. Neuroimage 2021; 233:117931. [DOI: 10.1016/j.neuroimage.2021.117931] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/08/2021] [Accepted: 02/28/2021] [Indexed: 01/18/2023] Open
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37
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Vilor-Tejedor N, Evans TE, Adams HH, González-de-Echávarri JM, Molinuevo JL, Guigo R, Gispert JD, Operto G. Genetic Influences on Hippocampal Subfields: An Emerging Area of Neuroscience Research. NEUROLOGY-GENETICS 2021; 7:e591. [PMID: 34124350 PMCID: PMC8192059 DOI: 10.1212/nxg.0000000000000591] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 08/26/2020] [Accepted: 03/03/2021] [Indexed: 11/15/2022]
Abstract
There is clear evidence that hippocampal subfield volumes have partly distinct genetic determinants associated with specific biological processes. The identification of genetic correlates of hippocampal subfield volumes may help to elucidate the mechanisms of neurologic diseases, as well as aging and neurodegenerative processes. However, despite the emerging interest in this area of research, the current knowledge of the genetic architecture of hippocampal subfields has not yet been consolidated. We aimed to provide a review of the current evidence from genetic studies of hippocampal subfields, highlighting current priorities and upcoming challenges. The limited number of studies investigating the influential genetic effects on hippocampal subfields, a lack of replicated results and longitudinal designs, and modest sample sizes combined with insufficient standardization of protocols are identified as the most pressing challenges in this emerging area of research.
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Affiliation(s)
- Natalia Vilor-Tejedor
- Barcelonaβeta Brain Research Center (BBRC) (N.V.-T., J.M.G.-d-E., J.L.M., J.D.G., G.O.), Pasqual Maragall Foundation; Centre for Genomic Regulation (CRG) (N.V.-T., R.G.), the Barcelona Institute for Science and Technology, Spain; Department of Clinical Genetics (N.V.-T., T.E.E., H.H.A.), Erasmus Medical Center, Rotterdam, the Netherlands; Universitat Pompeu Fabra (N.V.-T., J.M.G.--E., J.L.M., R.G., J.D.G.), Barcelona, Spain; Department of Radiology and Nuclear Medicine (H.H.A.), Erasmus Medical Center, Rotterdam, the Netherlands; IMIM (Hospital del Mar Medical Research Institute) (J.L.M., J.D.G., G.O.), Barcelona, Spain; Centro de Investigación Biomédica en Red de Fragilidad y Envejecimiento Saludable (CIBERFES) (J.L.M., G.O.); and Centro de Investigación Biomédica en Red Bioingeniería (J.D.G.), Biomateriales y Nanomedicina, Madrid, Spain
| | - Tavia E Evans
- Barcelonaβeta Brain Research Center (BBRC) (N.V.-T., J.M.G.-d-E., J.L.M., J.D.G., G.O.), Pasqual Maragall Foundation; Centre for Genomic Regulation (CRG) (N.V.-T., R.G.), the Barcelona Institute for Science and Technology, Spain; Department of Clinical Genetics (N.V.-T., T.E.E., H.H.A.), Erasmus Medical Center, Rotterdam, the Netherlands; Universitat Pompeu Fabra (N.V.-T., J.M.G.--E., J.L.M., R.G., J.D.G.), Barcelona, Spain; Department of Radiology and Nuclear Medicine (H.H.A.), Erasmus Medical Center, Rotterdam, the Netherlands; IMIM (Hospital del Mar Medical Research Institute) (J.L.M., J.D.G., G.O.), Barcelona, Spain; Centro de Investigación Biomédica en Red de Fragilidad y Envejecimiento Saludable (CIBERFES) (J.L.M., G.O.); and Centro de Investigación Biomédica en Red Bioingeniería (J.D.G.), Biomateriales y Nanomedicina, Madrid, Spain
| | - Hieab H Adams
- Barcelonaβeta Brain Research Center (BBRC) (N.V.-T., J.M.G.-d-E., J.L.M., J.D.G., G.O.), Pasqual Maragall Foundation; Centre for Genomic Regulation (CRG) (N.V.-T., R.G.), the Barcelona Institute for Science and Technology, Spain; Department of Clinical Genetics (N.V.-T., T.E.E., H.H.A.), Erasmus Medical Center, Rotterdam, the Netherlands; Universitat Pompeu Fabra (N.V.-T., J.M.G.--E., J.L.M., R.G., J.D.G.), Barcelona, Spain; Department of Radiology and Nuclear Medicine (H.H.A.), Erasmus Medical Center, Rotterdam, the Netherlands; IMIM (Hospital del Mar Medical Research Institute) (J.L.M., J.D.G., G.O.), Barcelona, Spain; Centro de Investigación Biomédica en Red de Fragilidad y Envejecimiento Saludable (CIBERFES) (J.L.M., G.O.); and Centro de Investigación Biomédica en Red Bioingeniería (J.D.G.), Biomateriales y Nanomedicina, Madrid, Spain
| | - José María González-de-Echávarri
- Barcelonaβeta Brain Research Center (BBRC) (N.V.-T., J.M.G.-d-E., J.L.M., J.D.G., G.O.), Pasqual Maragall Foundation; Centre for Genomic Regulation (CRG) (N.V.-T., R.G.), the Barcelona Institute for Science and Technology, Spain; Department of Clinical Genetics (N.V.-T., T.E.E., H.H.A.), Erasmus Medical Center, Rotterdam, the Netherlands; Universitat Pompeu Fabra (N.V.-T., J.M.G.--E., J.L.M., R.G., J.D.G.), Barcelona, Spain; Department of Radiology and Nuclear Medicine (H.H.A.), Erasmus Medical Center, Rotterdam, the Netherlands; IMIM (Hospital del Mar Medical Research Institute) (J.L.M., J.D.G., G.O.), Barcelona, Spain; Centro de Investigación Biomédica en Red de Fragilidad y Envejecimiento Saludable (CIBERFES) (J.L.M., G.O.); and Centro de Investigación Biomédica en Red Bioingeniería (J.D.G.), Biomateriales y Nanomedicina, Madrid, Spain
| | - José Luis Molinuevo
- Barcelonaβeta Brain Research Center (BBRC) (N.V.-T., J.M.G.-d-E., J.L.M., J.D.G., G.O.), Pasqual Maragall Foundation; Centre for Genomic Regulation (CRG) (N.V.-T., R.G.), the Barcelona Institute for Science and Technology, Spain; Department of Clinical Genetics (N.V.-T., T.E.E., H.H.A.), Erasmus Medical Center, Rotterdam, the Netherlands; Universitat Pompeu Fabra (N.V.-T., J.M.G.--E., J.L.M., R.G., J.D.G.), Barcelona, Spain; Department of Radiology and Nuclear Medicine (H.H.A.), Erasmus Medical Center, Rotterdam, the Netherlands; IMIM (Hospital del Mar Medical Research Institute) (J.L.M., J.D.G., G.O.), Barcelona, Spain; Centro de Investigación Biomédica en Red de Fragilidad y Envejecimiento Saludable (CIBERFES) (J.L.M., G.O.); and Centro de Investigación Biomédica en Red Bioingeniería (J.D.G.), Biomateriales y Nanomedicina, Madrid, Spain
| | - Roderic Guigo
- Barcelonaβeta Brain Research Center (BBRC) (N.V.-T., J.M.G.-d-E., J.L.M., J.D.G., G.O.), Pasqual Maragall Foundation; Centre for Genomic Regulation (CRG) (N.V.-T., R.G.), the Barcelona Institute for Science and Technology, Spain; Department of Clinical Genetics (N.V.-T., T.E.E., H.H.A.), Erasmus Medical Center, Rotterdam, the Netherlands; Universitat Pompeu Fabra (N.V.-T., J.M.G.--E., J.L.M., R.G., J.D.G.), Barcelona, Spain; Department of Radiology and Nuclear Medicine (H.H.A.), Erasmus Medical Center, Rotterdam, the Netherlands; IMIM (Hospital del Mar Medical Research Institute) (J.L.M., J.D.G., G.O.), Barcelona, Spain; Centro de Investigación Biomédica en Red de Fragilidad y Envejecimiento Saludable (CIBERFES) (J.L.M., G.O.); and Centro de Investigación Biomédica en Red Bioingeniería (J.D.G.), Biomateriales y Nanomedicina, Madrid, Spain
| | - Juan Domingo Gispert
- Barcelonaβeta Brain Research Center (BBRC) (N.V.-T., J.M.G.-d-E., J.L.M., J.D.G., G.O.), Pasqual Maragall Foundation; Centre for Genomic Regulation (CRG) (N.V.-T., R.G.), the Barcelona Institute for Science and Technology, Spain; Department of Clinical Genetics (N.V.-T., T.E.E., H.H.A.), Erasmus Medical Center, Rotterdam, the Netherlands; Universitat Pompeu Fabra (N.V.-T., J.M.G.--E., J.L.M., R.G., J.D.G.), Barcelona, Spain; Department of Radiology and Nuclear Medicine (H.H.A.), Erasmus Medical Center, Rotterdam, the Netherlands; IMIM (Hospital del Mar Medical Research Institute) (J.L.M., J.D.G., G.O.), Barcelona, Spain; Centro de Investigación Biomédica en Red de Fragilidad y Envejecimiento Saludable (CIBERFES) (J.L.M., G.O.); and Centro de Investigación Biomédica en Red Bioingeniería (J.D.G.), Biomateriales y Nanomedicina, Madrid, Spain
| | - Grégory Operto
- Barcelonaβeta Brain Research Center (BBRC) (N.V.-T., J.M.G.-d-E., J.L.M., J.D.G., G.O.), Pasqual Maragall Foundation; Centre for Genomic Regulation (CRG) (N.V.-T., R.G.), the Barcelona Institute for Science and Technology, Spain; Department of Clinical Genetics (N.V.-T., T.E.E., H.H.A.), Erasmus Medical Center, Rotterdam, the Netherlands; Universitat Pompeu Fabra (N.V.-T., J.M.G.--E., J.L.M., R.G., J.D.G.), Barcelona, Spain; Department of Radiology and Nuclear Medicine (H.H.A.), Erasmus Medical Center, Rotterdam, the Netherlands; IMIM (Hospital del Mar Medical Research Institute) (J.L.M., J.D.G., G.O.), Barcelona, Spain; Centro de Investigación Biomédica en Red de Fragilidad y Envejecimiento Saludable (CIBERFES) (J.L.M., G.O.); and Centro de Investigación Biomédica en Red Bioingeniería (J.D.G.), Biomateriales y Nanomedicina, Madrid, Spain
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38
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Bussy A, Patel R, Plitman E, Tullo S, Salaciak A, Bedford SA, Farzin S, Béland ML, Valiquette V, Kazazian C, Tardif CL, Devenyi GA, Chakravarty MM. Hippocampal shape across the healthy lifespan and its relationship with cognition. Neurobiol Aging 2021; 106:153-168. [PMID: 34280848 DOI: 10.1016/j.neurobiolaging.2021.03.018] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/30/2020] [Revised: 03/02/2021] [Accepted: 03/29/2021] [Indexed: 01/18/2023]
Abstract
The study of the hippocampus across the healthy adult lifespan has rendered inconsistent findings. While volumetric measurements have often been a popular technique for analysis, more advanced morphometric techniques have demonstrated compelling results that highlight the importance and improved specificity of shape-based measures. Here, the MAGeT Brain algorithm was applied on 134 healthy individuals aged 18-81 years old to extract hippocampal subfield volumes and hippocampal shape measurements, namely: local surface area (SA) and displacement. We used linear-, second- or third-order natural splines to examine the relationships between hippocampal measures and age. In addition, partial least squares analyses were performed to relate volume and shape measurements with cognitive and demographic information. Volumetric results indicated a relative preservation of the right cornus ammonis 1 with age and a global volume reduction linked with older age, female sex, lower levels of education and cognitive performance. Vertex-wise analysis demonstrated an SA preservation in the anterior hippocampus with a peak during the sixth decade, while the posterior hippocampal SA gradually decreased across lifespan. Overall, SA decrease was linked to older age, female sex and, to a lesser extent lower levels of education and cognitive performance. Outward displacement in the lateral hippocampus and inward displacement in the medial hippocampus were enlarged with older age, lower levels of cognition and education, indicating an accentuation of the hippocampal "C" shape with age. Taken together, our findings suggest that vertex-wise analyses have higher spatial specifity and that sex, education, and cognition are implicated in the differential impact of age on hippocampal subregions throughout its anteroposterior and medial-lateral axes. This article is part of the Virtual Special Issue titled COGNITIVE NEU- ROSCIENCE OF HEALTHY AND PATHOLOGICAL AGING. The full issue can be found on ScienceDirect at https://www.sciencedirect.com/journal/neurobiology-of-aging/special-issue/105379XPWJP.
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Affiliation(s)
- Aurélie Bussy
- Computional Brain Anatomy (CoBrA) Laboratory, Cerebral Imaging Centre, Douglas Mental Health University Institute, Montreal, Quebec, Canada; Integrated Program in Neuroscience, McGill University, Montreal, Quebec, Canada.
| | - Raihaan Patel
- Computional Brain Anatomy (CoBrA) Laboratory, Cerebral Imaging Centre, Douglas Mental Health University Institute, Montreal, Quebec, Canada; Department of Biomedical Engineering, McGill University, Montreal, Quebec, Canada
| | - Eric Plitman
- Computional Brain Anatomy (CoBrA) Laboratory, Cerebral Imaging Centre, Douglas Mental Health University Institute, Montreal, Quebec, Canada; Department of Psychiatry, McGill University, Montreal, Quebec, Canada
| | - Stephanie Tullo
- Computional Brain Anatomy (CoBrA) Laboratory, Cerebral Imaging Centre, Douglas Mental Health University Institute, Montreal, Quebec, Canada; Integrated Program in Neuroscience, McGill University, Montreal, Quebec, Canada
| | - Alyssa Salaciak
- Computional Brain Anatomy (CoBrA) Laboratory, Cerebral Imaging Centre, Douglas Mental Health University Institute, Montreal, Quebec, Canada
| | - Saashi A Bedford
- Computional Brain Anatomy (CoBrA) Laboratory, Cerebral Imaging Centre, Douglas Mental Health University Institute, Montreal, Quebec, Canada; Department of Psychiatry, McGill University, Montreal, Quebec, Canada
| | - Sarah Farzin
- Computional Brain Anatomy (CoBrA) Laboratory, Cerebral Imaging Centre, Douglas Mental Health University Institute, Montreal, Quebec, Canada
| | - Marie-Lise Béland
- Computional Brain Anatomy (CoBrA) Laboratory, Cerebral Imaging Centre, Douglas Mental Health University Institute, Montreal, Quebec, Canada
| | - Vanessa Valiquette
- Computional Brain Anatomy (CoBrA) Laboratory, Cerebral Imaging Centre, Douglas Mental Health University Institute, Montreal, Quebec, Canada; Integrated Program in Neuroscience, McGill University, Montreal, Quebec, Canada
| | - Christina Kazazian
- Computional Brain Anatomy (CoBrA) Laboratory, Cerebral Imaging Centre, Douglas Mental Health University Institute, Montreal, Quebec, Canada
| | - Christine L Tardif
- Department of Biomedical Engineering, McGill University, Montreal, Quebec, Canada; McConnell Brain Imaging Centre, Montreal Neurological Institute, Montreal, Quebec, Canada; Department of Neurology and Neurosurgery, McGill University, Montreal, Quebec, Canada
| | - Gabriel A Devenyi
- Computional Brain Anatomy (CoBrA) Laboratory, Cerebral Imaging Centre, Douglas Mental Health University Institute, Montreal, Quebec, Canada; Department of Psychiatry, McGill University, Montreal, Quebec, Canada
| | - M Mallar Chakravarty
- Computional Brain Anatomy (CoBrA) Laboratory, Cerebral Imaging Centre, Douglas Mental Health University Institute, Montreal, Quebec, Canada; Integrated Program in Neuroscience, McGill University, Montreal, Quebec, Canada; Department of Biomedical Engineering, McGill University, Montreal, Quebec, Canada; Department of Psychiatry, McGill University, Montreal, Quebec, Canada
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Canada KL, Hancock GR, Riggins T. Modeling longitudinal changes in hippocampal subfields and relations with memory from early- to mid-childhood. Dev Cogn Neurosci 2021; 48:100947. [PMID: 33774332 PMCID: PMC8039550 DOI: 10.1016/j.dcn.2021.100947] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/18/2020] [Revised: 03/17/2021] [Accepted: 03/21/2021] [Indexed: 01/25/2023] Open
Abstract
The hippocampus has been suggested to show protracted postnatal developmental growth across childhood. Most previous studies during this developmental period have been cross-sectional in nature and have focused on age-related differences in either hippocampal subregions or subfields, but not both, potentially missing localized changes. This study capitalized on a latent structural equation modeling approach to examine the longitudinal development of hippocampal subfields (cornu ammonis (CA) 2-4/dentate gyrus (DG), CA1, subiculum) in both the head and the body of the hippocampus, separately, in 165 typically developing 4- to 8-year-old children. Our findings document differential development of subfields within hippocampal head and body. Specifically, within hippocampal head, CA1 volume increased between 4-5 years and within hippocampal body, CA2-4/DG and subiculum volume increased between 5-6 years. Additionally, changes in CA1 volume in the head and changes in subiculum in the body between 4-5 years related to improvements in memory between 4-5 years. These findings demonstrate the protracted development of subfields in vivo during early- to mid-childhood, illustrate the importance of considering subfields separately in the head and body of the hippocampus, document co-occurring development of brain and behavior, and highlight the strength of longitudinal data and latent modeling when examining brain development.
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Affiliation(s)
- Kelsey L Canada
- Department of Psychology, University of Maryland, College Park, United States.
| | - Gregory R Hancock
- Department of Human Development and Quantitative Methodology, University of Maryland, College Park, United States
| | - Tracy Riggins
- Department of Psychology, University of Maryland, College Park, United States
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40
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Bienkowski MS, Sepehrband F, Kurniawan ND, Stanis J, Korobkova L, Khanjani N, Clark K, Hintiryan H, Miller CA, Dong HW. Homologous laminar organization of the mouse and human subiculum. Sci Rep 2021; 11:3729. [PMID: 33580088 PMCID: PMC7881248 DOI: 10.1038/s41598-021-81362-w] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/23/2020] [Accepted: 08/10/2020] [Indexed: 11/09/2022] Open
Abstract
The subiculum is the major output component of the hippocampal formation and one of the major brain structures most affected by Alzheimer's disease. Our previous work revealed a hidden laminar architecture within the mouse subiculum. However, the rotation of the hippocampal longitudinal axis across species makes it unclear how the laminar organization is represented in human subiculum. Using in situ hybridization data from the Allen Human Brain Atlas, we demonstrate that the human subiculum also contains complementary laminar gene expression patterns similar to the mouse. In addition, we provide evidence that the molecular domain boundaries in human subiculum correspond to microstructural differences observed in high resolution MRI and fiber density imaging. Finally, we show both similarities and differences in the gene expression profile of subiculum pyramidal cells within homologous lamina. Overall, we present a new 3D model of the anatomical organization of human subiculum and its evolution from the mouse.
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Affiliation(s)
- Michael S Bienkowski
- USC Stevens Neuroimaging and Informatics Institute, Laboratory of Neuro Imaging (LONI), Keck School of Medicine of University of Southern California, Los Angeles, CA, 90033, USA. .,Zilkha Neurogenetic Institute, Keck School of Medicine of University of Southern California, Los Angeles, CA, 90033, USA.
| | - Farshid Sepehrband
- USC Stevens Neuroimaging and Informatics Institute, Laboratory of Neuro Imaging (LONI), Keck School of Medicine of University of Southern California, Los Angeles, CA, 90033, USA.,Department of Neurology, Keck School of Medicine of University of Southern California, Los Angeles, CA, 90033, USA
| | - Nyoman D Kurniawan
- Center for Advanced Imaging, The University of Queensland, Brisbane, Australia
| | - Jim Stanis
- USC Stevens Neuroimaging and Informatics Institute, Laboratory of Neuro Imaging (LONI), Keck School of Medicine of University of Southern California, Los Angeles, CA, 90033, USA
| | - Laura Korobkova
- USC Stevens Neuroimaging and Informatics Institute, Laboratory of Neuro Imaging (LONI), Keck School of Medicine of University of Southern California, Los Angeles, CA, 90033, USA
| | - Neda Khanjani
- USC Stevens Neuroimaging and Informatics Institute, Laboratory of Neuro Imaging (LONI), Keck School of Medicine of University of Southern California, Los Angeles, CA, 90033, USA
| | - Kristi Clark
- Department of Neurology, Keck School of Medicine of University of Southern California, Los Angeles, CA, 90033, USA
| | - Houri Hintiryan
- USC Stevens Neuroimaging and Informatics Institute, Laboratory of Neuro Imaging (LONI), Keck School of Medicine of University of Southern California, Los Angeles, CA, 90033, USA.,Department of Neurobiology, David Geffen School of Medicine at UCLA, Los Angeles, CA, 90095, USA
| | - Carol A Miller
- Department of Pathology, Keck School of Medicine of University of Southern California, Los Angeles, CA, 90033, USA
| | - Hong-Wei Dong
- USC Stevens Neuroimaging and Informatics Institute, Laboratory of Neuro Imaging (LONI), Keck School of Medicine of University of Southern California, Los Angeles, CA, 90033, USA. .,Zilkha Neurogenetic Institute, Keck School of Medicine of University of Southern California, Los Angeles, CA, 90033, USA. .,Department of Neurology, Keck School of Medicine of University of Southern California, Los Angeles, CA, 90033, USA. .,Department of Neurobiology, David Geffen School of Medicine at UCLA, Los Angeles, CA, 90095, USA.
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41
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Guyot A, Fouquier ABG, Gerardin E, Chupin M, Glaunes JA, Marrakchi-Kacem L, Germain J, Boutet C, Cury C, Hertz-Pannier L, Vignaud A, Durrleman S, Henry TR, van de Moortele PF, Trouve A, Colliot O. A Diffeomorphic Vector Field Approach to Analyze the Thickness of the Hippocampus From 7 T MRI. IEEE Trans Biomed Eng 2021; 68:393-403. [PMID: 32746019 DOI: 10.1109/tbme.2020.2999941] [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] [Indexed: 11/07/2022]
Abstract
OBJECTIVE 7-Tesla MRI of the hippocampus enhances the visualization of its internal substructures. Among these substructures, the cornu Ammonis and subiculum form a contiguous folded ribbon of gray matter. Here, we propose a method to analyze local thickness measurements of this ribbon. METHODS We introduce an original approach based upon the estimation of a diffeomorphic vector field that traverses the ribbon. The method is designed to handle specificities of the hippocampus and corresponding 7-Tesla acquisitions: highly convoluted surface, non-closed ribbon, incompletely defined inner/outer boundaries, anisotropic acquisitions. We furthermore propose to conduct group comparisons using a population template built from the central surfaces of individual subjects. RESULTS We first assessed the robustness of our approach to anisotropy, as well as to inter-rater variability, on a post-mortem scan and on in vivo acquisitions respectively. We then conducted a group study on a dataset of in vivo MRI from temporal lobe epilepsy (TLE) patients and healthy controls. The method detected local thinning patterns in patients, predominantly ipsilaterally to the seizure focus, which is consistent with medical knowledge. CONCLUSION This new technique allows measuring the thickness of the hippocampus from 7-Tesla MRI. It shows good robustness with respect to anisotropy and inter-rater variability and has the potential to detect local atrophy in patients. SIGNIFICANCE As 7-Tesla MRI is increasingly available, this new method may become a useful tool to study local alterations of the hippocampus in brain disorders. It is made freely available to the community (code: https://github.com/aramis-lab/hiplay7-thickness, postmortem segmentation: https://doi.org/10.5281/zenodo.3533264).
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42
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Stark SM, Frithsen A, Stark CE. Age-related alterations in functional connectivity along the longitudinal axis of the hippocampus and its subfields. Hippocampus 2021; 31:11-27. [PMID: 32918772 PMCID: PMC8354549 DOI: 10.1002/hipo.23259] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/29/2019] [Revised: 07/31/2020] [Accepted: 08/07/2020] [Indexed: 12/13/2022]
Abstract
Hippocampal circuit alterations that differentially affect hippocampal subfields are associated with age-related memory decline. Additionally, functional organization along the longitudinal axis of the hippocampus has revealed distinctions between anterior and posterior (A-P) connectivity. Here, we examined the functional connectivity (FC) differences between young and older adults at high-resolution within the medial temporal lobe network (entorhinal, perirhinal, and parahippocampal cortices), allowing us to explore how hippocampal subfield connectivity across the longitudinal axis of the hippocampus changes with age. Overall, we found reliably greater connectivity for younger adults than older adults between the hippocampus and parahippocampal cortex (PHC) and perirhinal cortex (PRC). This drop in functional connectivity was more pronounced in the anterior regions of the hippocampus than the posterior ones, consistent for each of the hippocampal subfields. Further, intra-hippocampal connectivity also reflected an age-related decrease in functional connectivity within the anterior hippocampus in older adults that was offset by an increase in posterior hippocampal functional connectivity. Interestingly, the anterior-posterior dysfunction in older adults between hippocampus and PHC was predictive of lure discrimination performance on the Mnemonic similarity task (MST), suggesting a role in memory performance. While age-related dysfunction within the hippocampal subfields has been well-documented, these results suggest that the age-related dysfunction in hippocampal connectivity across the longitudinal axis may also contribute significantly to memory decline in older adults.
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Affiliation(s)
- Shauna M. Stark
- Department of Neurobiology and Behavior, University of California Irvine
| | - Amy Frithsen
- Department of Neurobiology and Behavior, University of California Irvine
| | - Craig E.L. Stark
- Department of Neurobiology and Behavior, University of California Irvine
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43
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Hu N, Luo C, Zhang W, Yang X, Xiao Y, Sweeney JA, Lui S, Gong Q. Hippocampal subfield alterations in schizophrenia: A selective review of structural MRI studies. Biomark Neuropsychiatry 2020. [DOI: 10.1016/j.bionps.2020.100026] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/28/2022] Open
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44
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Automatic multispectral MRI segmentation of human hippocampal subfields: an evaluation of multicentric test-retest reproducibility. Brain Struct Funct 2020; 226:137-150. [PMID: 33231744 PMCID: PMC7817563 DOI: 10.1007/s00429-020-02172-w] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/21/2019] [Accepted: 11/09/2020] [Indexed: 12/18/2022]
Abstract
Accurate and reproducible automated segmentation of human hippocampal subfields is of interest to study their roles in cognitive functions and disease processes. Multispectral structural MRI methods have been proposed to improve automated hippocampal subfield segmentation accuracy, but the reproducibility in a multicentric setting is, to date, not well characterized. Here, we assessed test-retest reproducibility of FreeSurfer 6.0 hippocampal subfield segmentations using multispectral MRI analysis pipelines (22 healthy subjects scanned twice, a week apart, at four 3T MRI sites). The harmonized MRI protocol included two 3D-T1, a 3D-FLAIR, and a high-resolution 2D-T2. After within-session T1 averaging, subfield volumes were segmented using three pipelines with different multispectral data: two longitudinal ("long_T1s" and "long_T1s_FLAIR") and one cross-sectional ("long_T1s_FLAIR_crossT2"). Volume reproducibility was quantified in magnitude (reproducibility error-RE) and space (DICE coefficient). RE was lower in all hippocampal subfields, except for hippocampal fissure, using the longitudinal pipelines compared to long_T1s_FLAIR_crossT2 (average RE reduction of 0.4-3.6%). Similarly, the longitudinal pipelines showed a higher spatial reproducibility (1.1-7.8% of DICE improvement) in all hippocampal structures compared to long_T1s_FLAIR_crossT2. Moreover, long_T1s_FLAIR provided a small but significant RE improvement in comparison to long_T1s (p = 0.015), whereas no significant DICE differences were found. In addition, structures with volumes larger than 200 mm3 had better RE (1-2%) and DICE (0.7-0.95) than smaller structures. In summary, our study suggests that the most reproducible hippocampal subfield FreeSurfer segmentations are derived from a longitudinal pipeline using 3D-T1s and 3D-FLAIR. Adapting a longitudinal pipeline to include high-resolution 2D-T2 may lead to further improvements.
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45
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Paquola C, Benkarim O, DeKraker J, Larivière S, Frässle S, Royer J, Tavakol S, Valk S, Bernasconi A, Bernasconi N, Khan A, Evans AC, Razi A, Smallwood J, Bernhardt BC. Convergence of cortical types and functional motifs in the human mesiotemporal lobe. eLife 2020; 9:e60673. [PMID: 33146610 PMCID: PMC7671688 DOI: 10.7554/elife.60673] [Citation(s) in RCA: 28] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/03/2020] [Accepted: 11/03/2020] [Indexed: 01/24/2023] Open
Abstract
The mesiotemporal lobe (MTL) is implicated in many cognitive processes, is compromised in numerous brain disorders, and exhibits a gradual cytoarchitectural transition from six-layered parahippocampal isocortex to three-layered hippocampal allocortex. Leveraging an ultra-high-resolution histological reconstruction of a human brain, our study showed that the dominant axis of MTL cytoarchitectural differentiation follows the iso-to-allocortical transition and depth-specific variations in neuronal density. Projecting the histology-derived MTL model to in-vivo functional MRI, we furthermore determined how its cytoarchitecture underpins its intrinsic effective connectivity and association to large-scale networks. Here, the cytoarchitectural gradient was found to underpin intrinsic effective connectivity of the MTL, but patterns differed along the anterior-posterior axis. Moreover, while the iso-to-allocortical gradient parametrically represented the multiple-demand relative to task-negative networks, anterior-posterior gradients represented transmodal versus unimodal networks. Our findings establish that the combination of micro- and macrostructural features allow the MTL to represent dominant motifs of whole-brain functional organisation.
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Affiliation(s)
- Casey Paquola
- Multimodal Imaging and Connectome Analysis Lab, McConnell Brain Imaging Centre, Montreal Neurological Institute and Hospital, McGill UniversityMontrealCanada
| | - Oualid Benkarim
- Multimodal Imaging and Connectome Analysis Lab, McConnell Brain Imaging Centre, Montreal Neurological Institute and Hospital, McGill UniversityMontrealCanada
| | - Jordan DeKraker
- Brain and Mind Institute, University of Western OntarioLondonCanada
| | - Sara Larivière
- Multimodal Imaging and Connectome Analysis Lab, McConnell Brain Imaging Centre, Montreal Neurological Institute and Hospital, McGill UniversityMontrealCanada
| | - Stefan Frässle
- Translational Neuromodeling Unit, Institute for Biomedical Engineering, University of Zurich & ETH ZurichZurichSwitzerland
| | - Jessica Royer
- Multimodal Imaging and Connectome Analysis Lab, McConnell Brain Imaging Centre, Montreal Neurological Institute and Hospital, McGill UniversityMontrealCanada
| | - Shahin Tavakol
- Multimodal Imaging and Connectome Analysis Lab, McConnell Brain Imaging Centre, Montreal Neurological Institute and Hospital, McGill UniversityMontrealCanada
| | - Sofie Valk
- Institute of Neuroscience and Medicine (INM-7: Brain and Behaviour), Research Centre JülichJülichGermany
- Institute of Systems Neuroscience, Heinrich Heine University DüsseldorfDüsseldorfGermany
| | - Andrea Bernasconi
- Neuroimaging Of Epilepsy Laboratory, McConnell Brain Imaging Centre, Montreal Neurological Institute and Hospital, McGill UniversityMontrealCanada
| | - Neda Bernasconi
- Neuroimaging Of Epilepsy Laboratory, McConnell Brain Imaging Centre, Montreal Neurological Institute and Hospital, McGill UniversityMontrealCanada
| | - Ali Khan
- Brain and Mind Institute, University of Western OntarioLondonCanada
| | - Alan C Evans
- McConnell Brain Imaging Centre, Montreal Neurological Institute and Hospital, McGill UniversityMontrealCanada
- McGill Centre for Integrative Neuroscience, McGill UniversityMontrealCanada
| | | | | | - Boris C Bernhardt
- Multimodal Imaging and Connectome Analysis Lab, McConnell Brain Imaging Centre, Montreal Neurological Institute and Hospital, McGill UniversityMontrealCanada
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46
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Foster CM, Kennedy KM, Daugherty AM, Rodrigue KM. Contribution of iron and Aβ to age differences in entorhinal and hippocampal subfield volume. Neurology 2020; 95:e2586-e2594. [PMID: 32938781 PMCID: PMC7682827 DOI: 10.1212/wnl.0000000000010868] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/29/2019] [Accepted: 06/26/2020] [Indexed: 12/17/2022] Open
Abstract
OBJECTIVE To test the hypothesis that the combination of elevated global β-AMYLOID (Aβ) burden and greater striatal iron content would be associated with smaller entorhinal cortex (ERC) volume, but not hippocampal subfield volumes, we measured volume and iron content using high-resolution MRI and Aβ using PET imaging in a cross-sectional sample of 70 cognitively normal older adults. METHODS Participants were scanned with florbetapir 18F PET to obtain Aβ standardized uptake value ratios. Susceptibility-weighted MRI was collected and processed to yield R2* images, and striatal regions of interest (ROIs) were manually placed to obtain a measure of striatal iron burden. Ultra-high resolution T2/PD-weighted MRIs were segmented to measure medial temporal lobe (MTL) volumes. Analyses were conducted using mixed-effects models with MTL ROI as a within-participant factor; age, iron content, and Aβ as between-participant factors; and MTL volumes (ERC and 3 hippocampal subfield regions) as the dependent variable. RESULTS The model indicated a significant 4-way interaction among age, iron, Aβ, and MTL region. Post hoc analyses indicated that the 3-way interaction among age, Aβ, and iron content was selective to the ERC (β = -3.34, standard error = 1.33, 95% confidence interval -5.95 to -0.72), whereas a significant negative association between age and ERC volume was present only in individuals with both elevated iron content and Aβ. CONCLUSIONS These findings highlight the importance of studying Aβ in the context of other, potentially synergistic age-related brain factors such as iron accumulation and the potential role for iron as an important contributor to the earliest, preclinical stages of pathologic aging.
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Affiliation(s)
- Chris M Foster
- From the School of Behavioral and Brain Sciences (C.M.F., K.M.K., K.M.R.), Center for Vital Longevity, University of Texas at Dallas; and Department of Psychology (A.M.D.) and Department of Psychiatry and Behavioral Neurosciences, Institute of Gerontology, Wayne State University, Detroit, MI
| | - Kristen M Kennedy
- From the School of Behavioral and Brain Sciences (C.M.F., K.M.K., K.M.R.), Center for Vital Longevity, University of Texas at Dallas; and Department of Psychology (A.M.D.) and Department of Psychiatry and Behavioral Neurosciences, Institute of Gerontology, Wayne State University, Detroit, MI
| | - Ana M Daugherty
- From the School of Behavioral and Brain Sciences (C.M.F., K.M.K., K.M.R.), Center for Vital Longevity, University of Texas at Dallas; and Department of Psychology (A.M.D.) and Department of Psychiatry and Behavioral Neurosciences, Institute of Gerontology, Wayne State University, Detroit, MI
| | - Karen M Rodrigue
- From the School of Behavioral and Brain Sciences (C.M.F., K.M.K., K.M.R.), Center for Vital Longevity, University of Texas at Dallas; and Department of Psychology (A.M.D.) and Department of Psychiatry and Behavioral Neurosciences, Institute of Gerontology, Wayne State University, Detroit, MI.
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47
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Integration and differentiation of hippocampal memory traces. Neurosci Biobehav Rev 2020; 118:196-208. [DOI: 10.1016/j.neubiorev.2020.07.024] [Citation(s) in RCA: 22] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/19/2020] [Revised: 07/11/2020] [Accepted: 07/20/2020] [Indexed: 11/23/2022]
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48
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Coughlin DG, Ittyerah R, Peterson C, Phillips JS, Miller S, Rascovsky K, Weintraub D, Siderowf AD, Duda JE, Hurtig HI, Wolk DA, McMillan CT, Yushkevich PA, Grossman M, Lee EB, Trojanowski JQ, Irwin DJ. Hippocampal subfield pathologic burden in Lewy body diseases vs. Alzheimer's disease. Neuropathol Appl Neurobiol 2020; 46:707-721. [PMID: 32892355 DOI: 10.1111/nan.12659] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/21/2020] [Revised: 07/21/2020] [Accepted: 08/22/2020] [Indexed: 12/16/2022]
Abstract
AIMS Lewy body diseases (LBD) are characterized by alpha-synuclein (SYN) pathology, but comorbid Alzheimer's disease (AD) pathology is common and the relationship between these pathologies in microanatomic hippocampal subfields is understudied. Here we use digital histological methods to test the association between hippocampal SYN pathology and the distribution of tau and amyloid-beta (Aβ) pathology in LBD and contrast with AD subjects. We also correlate pathologic burden with antemortem episodic memory testing. METHODS Hippocampal sections from 49 autopsy-confirmed LBD cases, 30 with no/low AD copathology (LBD - AD) and 19 with moderate/severe AD copathology (LBD + AD), and 30 AD patients were stained for SYN, tau, and Aβ. Sections underwent digital histological analysis of subfield pathological burden which was correlated with antemortem memory testing. RESULTS LBD - AD and LBD + AD had similar severity and distribution of SYN pathology (P > 0.05), CA2/3 being the most affected subfield (P < 0.02). In LBD, SYN correlated with tau across subfields (R = 0.49, P < 0.001). Tau burden was higher in AD than LBD + AD (P < 0.001), CA1/subiculum and entorhinal cortex (ERC) being most affected regions (P = 0.04 to <0.01). However, tau pathology in LBD - AD was greatest in CA2/3, which was equivalent to LBD + AD. Aβ severity and distribution was similar between LBD + AD and AD. Total hippocampal tau and CA2/3 tau was inversely correlated with memory performance in LBD (R = -0.52, -0.69, P = 0.04, 0.009). CONCLUSIONS Our findings suggest that tau burden in hippocampal subfields may map closely with the distribution of SYN pathology in subfield CA2/3 in LBD diverging from traditional AD and contribute to episodic memory dysfunction in LBD.
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Affiliation(s)
- D G Coughlin
- Penn Digital Neuropathology Laboratory at the University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA.,Department of Neurology at the University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA.,Department of Neurosciences, University California San Diego, San Diego, CA, USA
| | - R Ittyerah
- Department of Radiology at the University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA
| | - C Peterson
- Penn Digital Neuropathology Laboratory at the University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA.,Department of Neurology at the University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA
| | - J S Phillips
- Penn Digital Neuropathology Laboratory at the University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA.,Department of Neurology at the University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA.,Frontotemporal Dementia Center at the University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA
| | - S Miller
- Penn Digital Neuropathology Laboratory at the University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA
| | - K Rascovsky
- Department of Neurology at the University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA.,Frontotemporal Dementia Center at the University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA
| | - D Weintraub
- Department of Neurology at the University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA.,LBDA Research Center of Excellence at the University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA.,Michael J. Crescenz VA Medical Center, Parkinson's Disease Research, Education, and Clinical Center, Philadelphia, PA, USA
| | - A D Siderowf
- Department of Neurology at the University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA.,LBDA Research Center of Excellence at the University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA
| | - J E Duda
- Department of Neurology at the University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA.,LBDA Research Center of Excellence at the University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA.,Michael J. Crescenz VA Medical Center, Parkinson's Disease Research, Education, and Clinical Center, Philadelphia, PA, USA
| | - H I Hurtig
- Department of Neurology at the University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA
| | - D A Wolk
- Department of Neurology at the University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA.,Alzheimer's disease Research Center at the University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA
| | - C T McMillan
- Department of Neurology at the University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA.,Frontotemporal Dementia Center at the University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA
| | - P A Yushkevich
- Department of Radiology at the University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA
| | - M Grossman
- Department of Neurology at the University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA.,Frontotemporal Dementia Center at the University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA
| | - E B Lee
- Department of Pathology at the University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA.,Center for Neurodegenerative Disease Research at the University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA
| | - J Q Trojanowski
- Department of Pathology at the University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA.,Center for Neurodegenerative Disease Research at the University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA
| | - D J Irwin
- Penn Digital Neuropathology Laboratory at the University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA.,Department of Neurology at the University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA.,Frontotemporal Dementia Center at the University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA.,LBDA Research Center of Excellence at the University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA
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49
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Bein O, Duncan K, Davachi L. Mnemonic prediction errors bias hippocampal states. Nat Commun 2020; 11:3451. [PMID: 32651370 PMCID: PMC7351776 DOI: 10.1038/s41467-020-17287-1] [Citation(s) in RCA: 32] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/17/2019] [Accepted: 06/16/2020] [Indexed: 11/10/2022] Open
Abstract
When our experience violates our predictions, it is adaptive to upregulate encoding of novel information, while down-weighting retrieval of erroneous memory predictions to promote an updated representation of the world. We asked whether mnemonic prediction errors promote hippocampal encoding versus retrieval states, as marked by distinct network connectivity between hippocampal subfields. During fMRI scanning, participants were cued to internally retrieve well-learned complex room-images and were then presented with either an identical or a modified image (0-4 changes). In the left hemisphere, we find that CA1-entorhinal connectivity increases, and CA1-CA3 connectivity decreases, with the number of changes. Further, in the left CA1, the similarity between activity patterns during cued-retrieval of the learned room and during the image is lower when the image includes changes, consistent with a prediction error signal in CA1. Our findings provide a mechanism by which mnemonic prediction errors may drive memory updating—by biasing hippocampal states. When our expectations are violated, it is adaptive to update our internal models to improve predictions in the future. Here, the authors show that during mnemonic violations, hippocampal networks are biased towards an encoding state and away from a retrieval state to potentially update these predictions.
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Affiliation(s)
- Oded Bein
- Department of Psychology, New York University, New York, NY, 10003, USA.
| | - Katherine Duncan
- Department of Psychology, University of Toronto, Toronto, ON, M5S 3G3, Canada
| | - Lila Davachi
- Department of Psychology, Columbia University, New York, NY, 10027, USA. .,Center for Biomedical Imaging and Neuromodulation, The Nathan S. Kline Institute for Psychiatric Research, Orangeburg, NY, 10962, USA.
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50
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Bennett IJ, Stark SM, Stark CEL. Recognition Memory Dysfunction Relates to Hippocampal Subfield Volume: A Study of Cognitively Normal and Mildly Impaired Older Adults. J Gerontol B Psychol Sci Soc Sci 2020; 74:1132-1141. [PMID: 29401233 DOI: 10.1093/geronb/gbx181] [Citation(s) in RCA: 25] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/28/2017] [Accepted: 01/02/2018] [Indexed: 12/14/2022] Open
Abstract
OBJECTIVES The current study examined recognition memory dysfunction and its neuroanatomical substrates in cognitively normal older adults and those diagnosed with mild cognitive impairment (MCI). METHODS Participants completed the Mnemonic Similarity Task, which provides simultaneous measures of recognition memory and mnemonic discrimination. They also underwent structural neuroimaging to assess volume of medial temporal cortex and hippocampal subfields. RESULTS As expected, individuals diagnosed with MCI had significantly worse recognition memory performance and reduced volume across medial temporal cortex and hippocampal subfields relative to cognitively normal older adults. After controlling for diagnostic group differences, however, recognition memory was significantly related to whole hippocampus volume, and to volume of the dentate gyrus/CA3 subfield in particular. Recognition memory was also related to mnemonic discrimination, a fundamental component of episodic memory that has previously been linked to dentate gyrus/CA3 structure and function. DISCUSSION Results reveal that hippocampal subfield volume is sensitive to individual differences in recognition memory in older adults independent of clinical diagnosis. This supports the notion that episodic memory declines along a continuum within this age group, not just between diagnostic groups.
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Affiliation(s)
- Ilana J Bennett
- Department of Psychology, University of California, Riverside
| | - Shauna M Stark
- Department of Neurobiology and Behavior, University of California, Irvine
| | - Craig E L Stark
- Department of Neurobiology and Behavior, University of California, Irvine.,Center for the Neurobiology of Learning and Memory, University of California, Irvine
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