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Marx GA, Kauffman J, McKenzie AT, Koenigsberg DG, McMillan CT, Morgello S, Karlovich E, Insausti R, Richardson TE, Walker JM, White CL, Babrowicz BM, Shen L, McKee AC, Stein TD, Farrell K, Crary JF. Histopathologic brain age estimation via multiple instance learning. Acta Neuropathol 2023; 146:785-802. [PMID: 37815677 PMCID: PMC10627911 DOI: 10.1007/s00401-023-02636-3] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/03/2023] [Revised: 09/14/2023] [Accepted: 09/18/2023] [Indexed: 10/11/2023]
Abstract
Understanding age acceleration, the discordance between biological and chronological age, in the brain can reveal mechanistic insights into normal physiology as well as elucidate pathological determinants of age-related functional decline and identify early disease changes in the context of Alzheimer's and other disorders. Histopathological whole slide images provide a wealth of pathologic data on the cellular level that can be leveraged to build deep learning models to assess age acceleration. Here, we used a collection of digitized human post-mortem hippocampal sections to develop a histological brain age estimation model. Our model predicted brain age within a mean absolute error of 5.45 ± 0.22 years, with attention weights corresponding to neuroanatomical regions vulnerable to age-related changes. We found that histopathologic brain age acceleration had significant associations with clinical and pathologic outcomes that were not found with epigenetic based measures. Our results indicate that histopathologic brain age is a powerful, independent metric for understanding factors that contribute to brain aging.
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Affiliation(s)
- Gabriel A Marx
- Department of Pathology, Icahn School of Medicine at Mount Sinai, Friedman Brain Institute, 1 Gustave L. Levy Place, Box 1194, New York, NY, 10029, USA
- Department of Artificial Intelligence and Human Health, Nash Family Department of Neuroscience, Ronald M. Loeb Center for Alzheimer's Disease, Friedman Brain Institute, Neuropathology Brain Bank and Research CoRE, Icahn School of Medicine at Mount Sinai, 1 Gustave L. Levy Place, Box 1194, New York, NY, 10029, USA
| | - Justin Kauffman
- Department of Pathology, Icahn School of Medicine at Mount Sinai, Friedman Brain Institute, 1 Gustave L. Levy Place, Box 1194, New York, NY, 10029, USA
- Department of Artificial Intelligence and Human Health, Nash Family Department of Neuroscience, Ronald M. Loeb Center for Alzheimer's Disease, Friedman Brain Institute, Neuropathology Brain Bank and Research CoRE, Icahn School of Medicine at Mount Sinai, 1 Gustave L. Levy Place, Box 1194, New York, NY, 10029, USA
| | - Andrew T McKenzie
- Department of Pathology, Icahn School of Medicine at Mount Sinai, Friedman Brain Institute, 1 Gustave L. Levy Place, Box 1194, New York, NY, 10029, USA
- Department of Artificial Intelligence and Human Health, Nash Family Department of Neuroscience, Ronald M. Loeb Center for Alzheimer's Disease, Friedman Brain Institute, Neuropathology Brain Bank and Research CoRE, Icahn School of Medicine at Mount Sinai, 1 Gustave L. Levy Place, Box 1194, New York, NY, 10029, USA
- Department of Neurology, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Daniel G Koenigsberg
- Department of Pathology, Icahn School of Medicine at Mount Sinai, Friedman Brain Institute, 1 Gustave L. Levy Place, Box 1194, New York, NY, 10029, USA
- Department of Artificial Intelligence and Human Health, Nash Family Department of Neuroscience, Ronald M. Loeb Center for Alzheimer's Disease, Friedman Brain Institute, Neuropathology Brain Bank and Research CoRE, Icahn School of Medicine at Mount Sinai, 1 Gustave L. Levy Place, Box 1194, New York, NY, 10029, USA
| | - Cory T McMillan
- Frontotemporal Degeneration Center, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Susan Morgello
- Department of Pathology, Icahn School of Medicine at Mount Sinai, Friedman Brain Institute, 1 Gustave L. Levy Place, Box 1194, New York, NY, 10029, USA
- Department of Neurology, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Nash Family Department of Neuroscience, Icahn School of Medicine at Mount Sinai, Friedman Brain Institute, New York, NY, USA
| | - Esma Karlovich
- Department of Pathology, Icahn School of Medicine at Mount Sinai, Friedman Brain Institute, 1 Gustave L. Levy Place, Box 1194, New York, NY, 10029, USA
| | - Ricardo Insausti
- Human Neuroanatomy Laboratory, School of Medicine, University of Castilla-La Mancha, Albacete, Spain
| | - Timothy E Richardson
- Department of Pathology, Icahn School of Medicine at Mount Sinai, Friedman Brain Institute, 1 Gustave L. Levy Place, Box 1194, New York, NY, 10029, USA
| | - Jamie M Walker
- Department of Pathology, Icahn School of Medicine at Mount Sinai, Friedman Brain Institute, 1 Gustave L. Levy Place, Box 1194, New York, NY, 10029, USA
| | - Charles L White
- Department of Pathology, University of Texas Southwestern Medical Center, Dallas, TX, USA
| | - Bergan M Babrowicz
- Department of Pathology, Icahn School of Medicine at Mount Sinai, Friedman Brain Institute, 1 Gustave L. Levy Place, Box 1194, New York, NY, 10029, USA
| | - Li Shen
- Nash Family Department of Neuroscience, Icahn School of Medicine at Mount Sinai, Friedman Brain Institute, New York, NY, USA
| | - Ann C McKee
- Department of Pathology, Alzheimer's Disease and CTE Center, Boston University School of Medicine, Boston, MA, USA
- Department of Veterans Affairs Medical Center, Bedford, MA, USA
- VA Boston Healthcare System, Boston, MA, USA
| | - Thor D Stein
- Department of Pathology, Alzheimer's Disease and CTE Center, Boston University School of Medicine, Boston, MA, USA
- Department of Veterans Affairs Medical Center, Bedford, MA, USA
- VA Boston Healthcare System, Boston, MA, USA
| | - Kurt Farrell
- Department of Pathology, Icahn School of Medicine at Mount Sinai, Friedman Brain Institute, 1 Gustave L. Levy Place, Box 1194, New York, NY, 10029, USA.
- Department of Artificial Intelligence and Human Health, Nash Family Department of Neuroscience, Ronald M. Loeb Center for Alzheimer's Disease, Friedman Brain Institute, Neuropathology Brain Bank and Research CoRE, Icahn School of Medicine at Mount Sinai, 1 Gustave L. Levy Place, Box 1194, New York, NY, 10029, USA.
| | - John F Crary
- Department of Pathology, Icahn School of Medicine at Mount Sinai, Friedman Brain Institute, 1 Gustave L. Levy Place, Box 1194, New York, NY, 10029, USA.
- Department of Artificial Intelligence and Human Health, Nash Family Department of Neuroscience, Ronald M. Loeb Center for Alzheimer's Disease, Friedman Brain Institute, Neuropathology Brain Bank and Research CoRE, Icahn School of Medicine at Mount Sinai, 1 Gustave L. Levy Place, Box 1194, New York, NY, 10029, USA.
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Siestrup S, Schubotz RI. Minor Changes Change Memories: Functional Magnetic Resonance Imaging and Behavioral Reflections of Episodic Prediction Errors. J Cogn Neurosci 2023; 35:1823-1845. [PMID: 37677059 DOI: 10.1162/jocn_a_02047] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 09/09/2023]
Abstract
Episodic memories can be modified, a process that is potentially driven by mnemonic prediction errors. In the present study, we used modified cues to induce prediction errors of different episodic relevance. Participants encoded episodes in the form of short toy stories and then returned for an fMRI session on the subsequent day. Here, participants were presented either original episodes or slightly modified versions thereof. Modifications consisted of replacing a single object within the episode and either challenged the gist of an episode (gist modifications) or left it intact (surface modifications). On the next day, participants completed a post-fMRI memory test that probed memories for originally encoded episodes. Both types of modifications triggered brain activation in regions we previously found to be involved in the processing of content-based mnemonic prediction errors (i.e., the exchange of an object). Specifically, these were ventrolateral pFC, intraparietal cortex, and lateral occipitotemporal cortex. In addition, gist modifications triggered pronounced brain responses, whereas those for surface modification were only significant in the right inferior frontal sulcus. Processing of gist modifications also involved the posterior temporal cortex and the precuneus. Interestingly, our findings confirmed the posterior hippocampal role of detail processing in episodic memory, as evidenced by increased posterior hippocampal activity for surface modifications compared with gist modifications. In the post-fMRI memory test, previous experience with surface modified, but not gist-modified episodes, increased erroneous acceptance of the same modified versions as originally encoded. Whereas surface-level prediction errors might increase uncertainty and facilitate confusion of alternative episode representations, gist-level prediction errors seem to trigger the clear distinction of independent episodes.
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Affiliation(s)
- Sophie Siestrup
- University of Münster, Germany
- Otto Creutzfeldt Center for Cognitive and Behavioral Neuroscience, University of Münster, Germany
| | - Ricarda I Schubotz
- University of Münster, Germany
- Otto Creutzfeldt Center for Cognitive and Behavioral Neuroscience, University of Münster, Germany
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Balajoo SM, Eickhoff SB, Masouleh SK, Plachti A, Waite L, Saberi A, Bahri MA, Bastin C, Salmon E, Hoffstaedter F, Palomero-Gallagher N, Genon S. Hippocampal metabolic subregions and networks: Behavioral, molecular, and pathological aging profiles. Alzheimers Dement 2023; 19:4787-4804. [PMID: 37014937 PMCID: PMC10698199 DOI: 10.1002/alz.13056] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/28/2023] [Accepted: 03/01/2023] [Indexed: 04/06/2023]
Abstract
INTRODUCTION Hippocampal local and network dysfunction is the hallmark of Alzheimer's disease (AD). METHODS We characterized the spatial patterns of hippocampus differentiation based on brain co-metabolism in healthy elderly participants and demonstrated their relevance to study local metabolic changes and associated dysfunction in pathological aging. RESULTS The hippocampus can be differentiated into anterior/posterior and dorsal cornu ammonis (CA)/ventral (subiculum) subregions. While anterior/posterior CA show co-metabolism with different regions of the subcortical limbic networks, the anterior/posterior subiculum are parts of cortical networks supporting object-centered memory and higher cognitive demands, respectively. Both networks show relationships with the spatial patterns of gene expression pertaining to cell energy metabolism and AD's process. Finally, while local metabolism is generally lower in posterior regions, the anterior-posterior imbalance is maximal in late mild cognitive impairment with the anterior subiculum being relatively preserved. DISCUSSION Future studies should consider bidimensional hippocampal differentiation and in particular the posterior subicular region to better understand pathological aging.
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Affiliation(s)
- Somayeh Maleki Balajoo
- Institute of Systems Neuroscience, Heinrich Heine University Duesseldorf, Duesseldorf, Germany
- Institute of Neuroscience and Medicine (INM-7), Research Centre Juelich, Juelich, Germany
| | - Simon B. Eickhoff
- Institute of Systems Neuroscience, Heinrich Heine University Duesseldorf, Duesseldorf, Germany
- Institute of Neuroscience and Medicine (INM-7), Research Centre Juelich, Juelich, Germany
| | - Shahrzad Kharabian Masouleh
- Institute of Systems Neuroscience, Heinrich Heine University Duesseldorf, Duesseldorf, Germany
- Institute of Neuroscience and Medicine (INM-7), Research Centre Juelich, Juelich, Germany
| | - Anna Plachti
- Institute of Systems Neuroscience, Heinrich Heine University Duesseldorf, Duesseldorf, Germany
- Institute of Neuroscience and Medicine (INM-7), Research Centre Juelich, Juelich, Germany
- Danish Research Centre for Magnetic Resonance, Centre for Functional and Diagnostic Imaging and Research, Copenhagen University Hospital Hvidovre, Hvidovre, Denmark
| | - Laura Waite
- Institute of Neuroscience and Medicine (INM-7), Research Centre Juelich, Juelich, Germany
| | - Amin Saberi
- Institute of Systems Neuroscience, Heinrich Heine University Duesseldorf, Duesseldorf, Germany
- Institute of Neuroscience and Medicine (INM-7), Research Centre Juelich, Juelich, Germany
- Otto Hahn Research Group for Cognitive Neurogenetics, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany
| | - Mohamed Ali Bahri
- GIGA-Cyclotron Research Centre-In Vivo Imaging, University of Liège, Liège, Belgium
| | - Christine Bastin
- GIGA-Cyclotron Research Centre-In Vivo Imaging, University of Liège, Liège, Belgium
- Psychology and Cognitive Neuroscience Research Unit, University of Liège, Liège, Belgium
| | - Eric Salmon
- GIGA-Cyclotron Research Centre-In Vivo Imaging, University of Liège, Liège, Belgium
- Psychology and Cognitive Neuroscience Research Unit, University of Liège, Liège, Belgium
- Department of Neurology, University Hospital of Liège, Liège, Belgium
| | - Felix Hoffstaedter
- Institute of Neuroscience and Medicine (INM-7), Research Centre Juelich, Juelich, Germany
| | - Nicola Palomero-Gallagher
- Institute of Neuroscience and Medicine (INM‑1), Research Centre Juelich, Juelich, Germany
- Department of Psychiatry, Psychotherapy and Psychosomatics, Medical Faculty, RWTH Aachen University, Aachen, Germany
- Cécile and Oskar Vogt Institute for Brain Research, Heinrich Heine University Duesseldorf, Duesseldorf, Germany
| | - Sarah Genon
- Institute of Systems Neuroscience, Heinrich Heine University Duesseldorf, Duesseldorf, Germany
- Institute of Neuroscience and Medicine (INM-7), Research Centre Juelich, Juelich, Germany
- GIGA-Cyclotron Research Centre-In Vivo Imaging, University of Liège, Liège, Belgium
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Prasad KM, Muldoon B, Theis N, Iyengar S, Keshavan MS. Multipronged investigation of morphometry and connectivity of hippocampal network in relation to risk for psychosis using ultrahigh field MRI. Schizophr Res 2023; 256:88-97. [PMID: 37196534 PMCID: PMC10363272 DOI: 10.1016/j.schres.2023.05.002] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/09/2022] [Revised: 04/10/2023] [Accepted: 05/02/2023] [Indexed: 05/19/2023]
Abstract
Hippocampal abnormalities are associated with psychosis-risk states. Given the complexity of hippocampal anatomy, we conducted a multipronged examination of morphometry of regions connected with hippocampus, and structural covariance network (SCN) and diffusion-weighted circuitry among 27 familial high-risk (FHR) individuals who were past the highest risk for conversion to psychoses and 41 healthy controls using ultrahigh-field high-resolution 7 Tesla (7T) structural and diffusion MRI data. We obtained fractional anisotropy and diffusion streams of white matter connections and examined correspondence of diffusion streams with SCN edges. Nearly 89 % of the FHR group had an axis-I disorder including 5 with schizophrenia. Therefore, we compared the entire FHR group regardless of the diagnosis (All_FHR = 27) and FHR-without-schizophrenia (n = 22) with 41 controls in this integrative multimodal analysis. We found striking volume loss in bilateral hippocampus, particularly the head, bilateral thalamus, caudate, and prefrontal regions. All_FHR and FHR-without-SZ SCNs showed significantly lower assortativity and transitivity but higher diameter compared to controls, but FHR-without-SZ SCN differed on every graph metric compared to All_FHR suggesting disarrayed network with no hippocampal hubs. Fractional anisotropy and diffusion streams were lower in FHR suggesting white matter network impairment. White matter edges showed significantly higher correspondence with SCN edges in FHR compared to controls. These differences correlated with psychopathology and cognitive measures. Our data suggest that hippocampus may be a "neural hub" contributing to psychosis risk. Higher correspondence of white matter tracts with SCN edges suggest that shared volume loss may be more coordinated among regions within the hippocampal white matter circuitry.
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Affiliation(s)
- Konasale M Prasad
- Department of Psychiatry, University of Pittsburgh School of Medicine, Pittsburgh, PA, United States of America; Department of Bioengineering, University of Pittsburgh Swanson School of Engineering, Pittsburgh, PA, United States of America; VA Pittsburgh Healthcare System, Pittsburgh, PA, United States of America.
| | - Brendan Muldoon
- Department of Psychiatry, University of Pittsburgh School of Medicine, Pittsburgh, PA, United States of America
| | - Nicholas Theis
- Department of Psychiatry, University of Pittsburgh School of Medicine, Pittsburgh, PA, United States of America
| | - Satish Iyengar
- Department of Statistics, University of Pittsburgh, Pittsburgh, PA, United States of America
| | - Matcheri S Keshavan
- Department of Psychiatry, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA, United States of America
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5
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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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de Flores R, Das SR, Xie L, Wisse LEM, Lyu X, Shah P, Yushkevich PA, Wolk DA. Medial Temporal Lobe Networks in Alzheimer's Disease: Structural and Molecular Vulnerabilities. J Neurosci 2022; 42:2131-2141. [PMID: 35086906 PMCID: PMC8916768 DOI: 10.1523/jneurosci.0949-21.2021] [Citation(s) in RCA: 20] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/03/2021] [Revised: 11/30/2021] [Accepted: 12/04/2021] [Indexed: 11/21/2022] Open
Abstract
The medial temporal lobe (MTL) is connected to the rest of the brain through two main networks: the anterior-temporal (AT) and the posterior-medial (PM) systems. Given the crucial role of the MTL and networks in the physiopathology of Alzheimer's disease (AD), the present study aimed at (1) investigating whether MTL atrophy propagates specifically within the AT and PM networks, and (2) evaluating the vulnerability of these networks to AD proteinopathies. To do that, we used neuroimaging data acquired in human male and female in three distinct cohorts: (1) resting-state functional MRI (rs-fMRI) from the aging brain cohort (ABC) to define the AT and PM networks (n = 68); (2) longitudinal structural MRI from Alzheimer's disease neuroimaging initiative (ADNI)GO/2 to highlight structural covariance patterns (n = 349); and (3) positron emission tomography (PET) data from ADNI3 to evaluate the networks' vulnerability to amyloid and tau (n = 186). Our results suggest that the atrophy of distinct MTL subregions propagates within the AT and PM networks in a dissociable manner. Brodmann area (BA)35 structurally covaried within the AT network while the parahippocampal cortex (PHC) covaried within the PM network. In addition, these networks are differentially associated with relative tau and amyloid burden, with higher tau levels in AT than in PM and higher amyloid levels in PM than in AT. Our results also suggest differences in the relative burden of tau species. The current results provide further support for the notion that two distinct MTL networks display differential alterations in the context of AD. These findings have important implications for disease spread and the cognitive manifestations of AD.SIGNIFICANCE STATEMENT The current study provides further support for the notion that two distinct medial temporal lobe (MTL) networks, i.e., anterior-temporal (AT) and the posterior-medial (PM), display differential alterations in the context of Alzheimer's disease (AD). Importantly, neurodegeneration appears to occur within these networks in a dissociable manner marked by their covariance patterns. In addition, the AT and PM networks are also differentially associated with relative tau and amyloid burden, and perhaps differences in the relative burden of tau species [e.g., neurofibriliary tangles (NFTs) vs tau in neuritic plaques]. These findings, in the context of a growing literature consistent with the present results, have important implications for disease spread and the cognitive manifestations of AD in light of the differential cognitive processes ascribed to them.
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Affiliation(s)
- Robin de Flores
- Department of Neurology, University of Pennsylvania, Philadelphia 19104, Pennsylvania
- Université de Caen Normandie, Institut National de la Santé et de la Recherche Médicale Unité Mixte de Recherche Scientifique (UMRS) Unité 1237, Caen 14000, France
| | - Sandhitsu R Das
- Penn Image Computing and Science Laboratory (PICSL), University of Pennsylvania, Philadelphia 19104, Pennsylvania
| | - Long Xie
- Penn Image Computing and Science Laboratory (PICSL), University of Pennsylvania, Philadelphia 19104, Pennsylvania
- Department of Radiology, University of Pennsylvania, Philadelphia 19104, Pennsylvania
| | - Laura E M Wisse
- Penn Image Computing and Science Laboratory (PICSL), University of Pennsylvania, Philadelphia 19104, Pennsylvania
- Department of Diagnostic Radiology, Lund University, Lund 22185, Sweden
| | - Xueying Lyu
- Department of Bioengineering, University of Pennsylvania, Philadelphia 19104, Pennsylvania
| | - Preya Shah
- Department of Bioengineering, University of Pennsylvania, Philadelphia 19104, Pennsylvania
| | - Paul A Yushkevich
- Penn Image Computing and Science Laboratory (PICSL), University of Pennsylvania, Philadelphia 19104, Pennsylvania
| | - David A Wolk
- Department of Neurology, University of Pennsylvania, Philadelphia 19104, Pennsylvania
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7
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Aznárez-Sanado M, Eudave L, Martínez M, Luis EO, Villagra F, Loayza FR, Fernández-Seara MA, Pastor MA. Brain Activity and Functional Connectivity Patterns Associated With Fast and Slow Motor Sequence Learning in Late Middle Adulthood. Front Aging Neurosci 2022; 13:778201. [PMID: 35095468 PMCID: PMC8792532 DOI: 10.3389/fnagi.2021.778201] [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: 09/16/2021] [Accepted: 12/22/2021] [Indexed: 11/13/2022] Open
Abstract
The human brain undergoes structural and functional changes across the lifespan. The study of motor sequence learning in elderly subjects is of particularly interest since previous findings in young adults might not replicate during later stages of adulthood. The present functional magnetic resonance imaging (fMRI) study assessed the performance, brain activity and functional connectivity patterns associated with motor sequence learning in late middle adulthood. For this purpose, a total of 25 subjects were evaluated during early stages of learning [i.e., fast learning (FL)]. A subset of these subjects (n = 11) was evaluated after extensive practice of a motor sequence [i.e., slow learning (SL) phase]. As expected, late middle adults improved motor performance from FL to SL. Learning-related brain activity patterns replicated most of the findings reported previously in young subjects except for the lack of hippocampal activity during FL and the involvement of cerebellum during SL. Regarding functional connectivity, precuneus and sensorimotor lobule VI of the cerebellum showed a central role during improvement of novel motor performance. In the sample of subjects evaluated, connectivity between the posterior putamen and parietal and frontal regions was significantly decreased with aging during SL. This age-related connectivity pattern may reflect losses in network efficiency when approaching late adulthood. Altogether, these results may have important applications, for instance, in motor rehabilitation programs.
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Affiliation(s)
- Maite Aznárez-Sanado
- School of Education and Psychology, University of Navarra, Pamplona, Spain
- Neuroimaging Laboratory, Center for Applied Medical Research (CIMA), University of Navarra, Pamplona, Spain
| | - Luis Eudave
- School of Education and Psychology, University of Navarra, Pamplona, Spain
- Neuroimaging Laboratory, Center for Applied Medical Research (CIMA), University of Navarra, Pamplona, Spain
| | - Martín Martínez
- School of Education and Psychology, University of Navarra, Pamplona, Spain
- Neuroimaging Laboratory, Center for Applied Medical Research (CIMA), University of Navarra, Pamplona, Spain
| | - Elkin O. Luis
- School of Education and Psychology, University of Navarra, Pamplona, Spain
- Neuroimaging Laboratory, Center for Applied Medical Research (CIMA), University of Navarra, Pamplona, Spain
| | - Federico Villagra
- Neuroimaging Laboratory, Center for Applied Medical Research (CIMA), University of Navarra, Pamplona, Spain
- Institute of Biological, Environmental and Rural Sciences (IBERS), Aberystwyth University, Aberystwyth, United Kingdom
| | - Francis R. Loayza
- Neuroimaging Laboratory, Center for Applied Medical Research (CIMA), University of Navarra, Pamplona, Spain
- Faculty of Mechanical Engineering and Production Sciences (FIMCP), Escuela Superior Politecnica del Litoral (ESPOL), Guayaquil, Ecuador
| | - María A. Fernández-Seara
- Instituto de Investigación Sanitaria de Navarra (IdiSNA), Pamplona, Spain
- Department of Radiology, Clínica Universidad de Navarra, Pamplona, Spain
| | - María A. Pastor
- Neuroimaging Laboratory, Center for Applied Medical Research (CIMA), University of Navarra, Pamplona, Spain
- Instituto de Investigación Sanitaria de Navarra (IdiSNA), Pamplona, Spain
- School of Medicine, University of Navarra, Pamplona, Spain
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8
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Prasad K, Rubin J, Mitra A, Lewis M, Theis N, Muldoon B, Iyengar S, Cape J. Structural covariance networks in schizophrenia: A systematic review Part II. Schizophr Res 2022; 239:176-191. [PMID: 34902650 PMCID: PMC8785680 DOI: 10.1016/j.schres.2021.11.036] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/15/2021] [Revised: 09/02/2021] [Accepted: 11/23/2021] [Indexed: 01/10/2023]
Abstract
BACKGROUND Examination of structural covariance network (SCN) is gaining prominence among the strategies to delineate dysconnectivity that case-control morphometric comparisons cannot address. Part II of this review extends on the part I of the review that included SCN studies using statistical approaches by examining SCN studies applying graph theoretic approaches to elucidate network properties in schizophrenia. This review also includes SCN studies using graph theoretic or statistical approaches on persons at-risk for schizophrenia. METHODS A systematic literature search was conducted for peer-reviewed publications using different keywords and keyword combinations for schizophrenia and risk for schizophrenia. Thirteen studies on schizophrenia and five on persons at risk for schizophrenia met the criteria. RESULTS A variety of findings from over the last 1½ decades showing qualitative and quantitative differences in the global and local structural connectome in schizophrenia are described. These observations include altered hub patterns, disrupted network topology and hierarchical organization of the brain, and impaired connections that may be localized to default mode, executive control, and dorsal attention networks. Some of these connectomic alterations were observed in persons at-risk for schizophrenia before the onset of the illness. CONCLUSIONS Observed disruptions may reduce network efficiency and capacity to integrate information. Further, global connectomic changes were not schizophrenia-specific but local network changes were. Existing studies have used different atlases for brain parcellation, examined different morphometric features, and patients at different stages of illness making it difficult to conduct meta-analysis. Future studies should harmonize such methodological differences to facilitate meta-analysis and also elucidate causal underpinnings of dysconnectivity.
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Affiliation(s)
- Konasale Prasad
- University of Pittsburgh School of Medicine, Western Psychiatric Institute and Clinic, 3811 O'Hara St, Pittsburgh, PA 15213, United States of America; University of Pittsburgh Swanson School of Engineering, 3700 O'Hara St, Pittsburgh, PA 15213, United States of America; VA Pittsburgh Healthcare System, University Dr C, Pittsburgh, PA 15240, United States of America.
| | - Jonathan Rubin
- Department of Mathematics, University of Pittsburgh, 917 Cathedral of Learning, Pittsburgh, PA 15260, United States of America
| | - Anirban Mitra
- Department of Statistics, University of Pittsburgh, 230 South Bouquet Street, Pittsburgh, PA 15260, United States of America
| | - Madison Lewis
- University of Pittsburgh Swanson School of Engineering, 3700 O'Hara St, Pittsburgh, PA 15213, United States of America
| | - Nicholas Theis
- University of Pittsburgh School of Medicine, Western Psychiatric Institute and Clinic, 3811 O'Hara St, Pittsburgh, PA 15213, United States of America
| | - Brendan Muldoon
- University of Pittsburgh School of Medicine, Western Psychiatric Institute and Clinic, 3811 O'Hara St, Pittsburgh, PA 15213, United States of America
| | - Satish Iyengar
- Department of Statistics, University of Pittsburgh, 230 South Bouquet Street, Pittsburgh, PA 15260, United States of America
| | - Joshua Cape
- Department of Statistics, University of Pittsburgh, 230 South Bouquet Street, Pittsburgh, PA 15260, United States of America
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9
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Chauveau L, Kuhn E, Palix C, Felisatti F, Ourry V, de La Sayette V, Chételat G, de Flores R. Medial Temporal Lobe Subregional Atrophy in Aging and Alzheimer's Disease: A Longitudinal Study. Front Aging Neurosci 2021; 13:750154. [PMID: 34720998 PMCID: PMC8554299 DOI: 10.3389/fnagi.2021.750154] [Citation(s) in RCA: 14] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/30/2021] [Accepted: 09/13/2021] [Indexed: 11/13/2022] Open
Abstract
Medial temporal lobe (MTL) atrophy is a key feature of Alzheimer's disease (AD), however, it also occurs in typical aging. To enhance the clinical utility of this biomarker, we need to better understand the differential effects of age and AD by encompassing the full AD-continuum from cognitively unimpaired (CU) to dementia, including all MTL subregions with up-to-date approaches and using longitudinal designs to assess atrophy more sensitively. Age-related trajectories were estimated using the best-fitted polynomials in 209 CU adults (aged 19–85). Changes related to AD were investigated among amyloid-negative (Aβ−) (n = 46) and amyloid-positive (Aβ+) (n = 14) CU, Aβ+ patients with mild cognitive impairment (MCI) (n = 33) and AD (n = 31). Nineteen MCI-to-AD converters were also compared with 34 non-converters. Relationships with cognitive functioning were evaluated in 63 Aβ+ MCI and AD patients. All participants were followed up to 47 months. MTL subregions, namely, the anterior and posterior hippocampus (aHPC/pHPC), entorhinal cortex (ERC), Brodmann areas (BA) 35 and 36 [as perirhinal cortex (PRC) substructures], and parahippocampal cortex (PHC), were segmented from a T1-weighted MRI using a new longitudinal pipeline (LASHiS). Statistical analyses were performed using mixed models. Adult lifespan models highlighted both linear (PRC, BA35, BA36, PHC) and nonlinear (HPC, aHPC, pHPC, ERC) trajectories. Group comparisons showed reduced baseline volumes and steeper volume declines over time for most of the MTL subregions in Aβ+ MCI and AD patients compared to Aβ− CU, but no differences between Aβ− and Aβ+ CU or between Aβ+ MCI and AD patients (except in ERC). Over time, MCI-to-AD converters exhibited a greater volume decline than non-converters in HPC, aHPC, and pHPC. Most of the MTL subregions were related to episodic memory performances but not to executive functioning or speed processing. Overall, these results emphasize the benefits of studying MTL subregions to distinguish age-related changes from AD. Interestingly, MTL subregions are unequally vulnerable to aging, and those displaying non-linear age-trajectories, while not damaged in preclinical AD (Aβ+ CU), were particularly affected from the prodromal stage (Aβ+ MCI). This volume decline in hippocampal substructures might also provide information regarding the conversion from MCI to AD-dementia. All together, these findings provide new insights into MTL alterations, which are crucial for AD-biomarkers definition.
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Affiliation(s)
- Léa Chauveau
- U1237 PhIND, Inserm, Caen-Normandie University, GIP Cyceron, Caen, France
| | - Elizabeth Kuhn
- U1237 PhIND, Inserm, Caen-Normandie University, GIP Cyceron, Caen, France
| | - Cassandre Palix
- U1237 PhIND, Inserm, Caen-Normandie University, GIP Cyceron, Caen, France
| | | | - Valentin Ourry
- U1237 PhIND, Inserm, Caen-Normandie University, GIP Cyceron, Caen, France.,U1077 NIMH, Inserm, Caen-Normandie University, École Pratique des Hautes Études, Caen, France
| | - Vincent de La Sayette
- U1077 NIMH, Inserm, Caen-Normandie University, École Pratique des Hautes Études, Caen, France
| | - Gaël Chételat
- U1237 PhIND, Inserm, Caen-Normandie University, GIP Cyceron, Caen, France
| | - Robin de Flores
- U1237 PhIND, Inserm, Caen-Normandie University, GIP Cyceron, Caen, France
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10
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Fu Z, Zhao M, He Y, Wang X, Lu J, Li S, Li X, Kang G, Han Y, Li S. Divergent Connectivity Changes in Gray Matter Structural Covariance Networks in Subjective Cognitive Decline, Amnestic Mild Cognitive Impairment, and Alzheimer's Disease. Front Aging Neurosci 2021; 13:686598. [PMID: 34483878 PMCID: PMC8415752 DOI: 10.3389/fnagi.2021.686598] [Citation(s) in RCA: 15] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/27/2021] [Accepted: 07/19/2021] [Indexed: 01/18/2023] Open
Abstract
Alzheimer’s disease (AD) has a long preclinical stage that can last for decades prior to progressing toward amnestic mild cognitive impairment (aMCI) and/or dementia. Subjective cognitive decline (SCD) is characterized by self-experienced memory decline without any evidence of objective cognitive decline and is regarded as the later stage of preclinical AD. It has been reported that the changes in structural covariance patterns are affected by AD pathology in the patients with AD and aMCI within the specific large-scale brain networks. However, the changes in structural covariance patterns including normal control (NC), SCD, aMCI, and AD are still poorly understood. In this study, we recruited 42 NCs, 35 individuals with SCD, 43 patients with aMCI, and 41 patients with AD. Gray matter (GM) volumes were extracted from 10 readily identifiable regions of interest involved in high-order cognitive function and AD-related dysfunctional structures. The volume values were used to predict the regional densities in the whole brain by using voxel-based statistical and multiple linear regression models. Decreased structural covariance and weakened connectivity strength were observed in individuals with SCD compared with NCs. Structural covariance networks (SCNs) seeding from the default mode network (DMN), salience network, subfields of the hippocampus, and cholinergic basal forebrain showed increased structural covariance at the early stage of AD (referring to aMCI) and decreased structural covariance at the dementia stage (referring to AD). Moreover, the SCN seeding from the executive control network (ECN) showed a linearly increased extent of the structural covariance during the early and dementia stages. The results suggest that changes in structural covariance patterns as the order of NC-SCD-aMCI-AD are divergent and dynamic, and support the structural disconnection hypothesis in individuals with SCD.
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Affiliation(s)
- Zhenrong Fu
- School of Biological Science and Medical Engineering, Beijing Advanced Innovation Center for Biomedical Engineering, Beihang University, Beijing, China
| | - Mingyan Zhao
- Department of Neurology, Tangshan Gongren Hospital, Tangshan, China.,Department of Neurology, Xuanwu Hospital of Capital Medical University, Beijing, China
| | - Yirong He
- School of Biological Science and Medical Engineering, Beijing Advanced Innovation Center for Biomedical Engineering, Beihang University, Beijing, China
| | - Xuetong Wang
- School of Biological Science and Medical Engineering, Beijing Advanced Innovation Center for Biomedical Engineering, Beihang University, Beijing, China
| | - Jiadong Lu
- School of Biological Science and Medical Engineering, Beijing Advanced Innovation Center for Biomedical Engineering, Beihang University, Beijing, China
| | - Shaoxian Li
- School of Biological Science and Medical Engineering, Beijing Advanced Innovation Center for Biomedical Engineering, Beihang University, Beijing, China
| | - Xin Li
- School of Electrical Engineering, Yanshan University, Qinhuangdao, China.,Measurement Technology and Instrumentation Key Laboratory of Hebei Province, Qinhuangdao, China
| | - Guixia Kang
- School of Information and Communication Engineering, Beijing University of Posts and Telecommunications, Beijing, China
| | - Ying Han
- Department of Neurology, Xuanwu Hospital of Capital Medical University, Beijing, China.,Biomedical Engineering Institute, Hainan University, Haikou, China.,Center of Alzheimer's Disease, Beijing Institute for Brain Disorders, Beijing, China.,National Clinical Research Center for Geriatric Disorders, Beijing, China
| | - Shuyu Li
- School of Biological Science and Medical Engineering, Beijing Advanced Innovation Center for Biomedical Engineering, Beihang University, Beijing, China
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11
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Du C, Chen Y, Chen K, Zhang Z. Disrupted anterior and posterior hippocampal structural networks correlate impaired verbal memory and spatial memory in different subtypes of mild cognitive impairment. Eur J Neurol 2021; 28:3955-3964. [PMID: 34310802 DOI: 10.1111/ene.15036] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/06/2021] [Accepted: 07/21/2021] [Indexed: 11/27/2022]
Abstract
BACKGROUND AND PURPOSE The anterior and posterior hippocampal networks represent verbal and spatial memory, respectively, and may play different roles in the pathological mechanism of amnestic mild cognitive impairment (aMCI) and non-amnestic MCI (naMCI), which has not been explored. METHODS A total of 990 older adults with 791 normal controls (NCs) (65 ± 6 years, 502 women), 140 aMCI (66 ± 7 years, 84 women) and 59 naMCI (66 ± 7 years, 38 women) were included. A multivariate method, partial least squares, was used to assess the structural covariance networks of the anterior hippocampus (aHC) and posterior hippocampus (pHC), and their relationships with verbal memory and spatial memory in the three groups. RESULTS Three aHC and pHC structural covariance network patterns emerged: (1) the age pattern; (2) the specific aMCI pattern; and (3) the spatial memory pattern. Furthermore, aMCI patients had more extensive and severe damage in the three patterns, and correlated with greater decline in verbal memory, which was mainly characterized by the aHC network. CONCLUSIONS The aMCI and naMCI showed different patterns and damage in the structural covariance networks, and functional segregation of the aHC and pHC networks still exists in the process of pathological aging. A potential neural explanation is provided for the conversion of aMCI and naMCI into different types of dementia in the future.
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Affiliation(s)
- Chao Du
- State Key Laboratory of Cognitive Neuroscience and Learning, and IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, China.,Beijing Aging Brain Rejuvenation Initiative Centre, Beijing Normal University, Beijing, China
| | - Yaojing Chen
- State Key Laboratory of Cognitive Neuroscience and Learning, and IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, China.,Beijing Aging Brain Rejuvenation Initiative Centre, Beijing Normal University, Beijing, China
| | - Kewei Chen
- Banner Alzheimer's Institute, Phoenix, AZ, USA.,Shanghai Green Valley Pharmaceutical Company, Ltd., Shanghai, China
| | - Zhanjun Zhang
- State Key Laboratory of Cognitive Neuroscience and Learning, and IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, China.,Beijing Aging Brain Rejuvenation Initiative Centre, Beijing Normal University, Beijing, China
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12
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Plachti A, Kharabian S, Eickhoff SB, Maleki Balajoo S, Hoffstaedter F, Varikuti DP, Jockwitz C, Caspers S, Amunts K, Genon S. Hippocampus co-atrophy pattern in dementia deviates from covariance patterns across the lifespan. Brain 2021; 143:2788-2802. [PMID: 32851402 DOI: 10.1093/brain/awaa222] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/06/2020] [Revised: 04/29/2020] [Accepted: 05/21/2020] [Indexed: 12/22/2022] Open
Abstract
The hippocampus is a plastic region and highly susceptible to ageing and dementia. Previous studies explicitly imposed a priori models of hippocampus when investigating ageing and dementia-specific atrophy but led to inconsistent results. Consequently, the basic question of whether macrostructural changes follow a cytoarchitectonic or functional organization across the adult lifespan and in age-related neurodegenerative disease remained open. The aim of this cross-sectional study was to identify the spatial pattern of hippocampus differentiation based on structural covariance with a data-driven approach across structural MRI data of large cohorts (n = 2594). We examined the pattern of structural covariance of hippocampus voxels in young, middle-aged, elderly, mild cognitive impairment and dementia disease samples by applying a clustering algorithm revealing differentiation in structural covariance within the hippocampus. In all the healthy and in the mild cognitive impaired participants, the hippocampus was robustly divided into anterior, lateral and medial subregions reminiscent of cytoarchitectonic division. In contrast, in dementia patients, the pattern of subdivision was closer to known functional differentiation into an anterior, body and tail subregions. These results not only contribute to a better understanding of co-plasticity and co-atrophy in the hippocampus across the lifespan and in dementia, but also provide robust data-driven spatial representations (i.e. maps) for structural studies.
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Affiliation(s)
- Anna Plachti
- Institute of Systems Neuroscience, Heinrich Heine University Düsseldorf, Düsseldorf 40225, Germany.,Institute of Neuroscience and Medicine (INM-1, INM-7), Research Centre Jülich, Jülich, Germany
| | - Shahrzad Kharabian
- Institute of Systems Neuroscience, Heinrich Heine University Düsseldorf, Düsseldorf 40225, Germany.,Institute of Neuroscience and Medicine (INM-1, INM-7), Research Centre Jülich, Jülich, Germany
| | - Simon B Eickhoff
- Institute of Systems Neuroscience, Heinrich Heine University Düsseldorf, Düsseldorf 40225, Germany.,Institute of Neuroscience and Medicine (INM-1, INM-7), Research Centre Jülich, Jülich, Germany
| | - Somayeh Maleki Balajoo
- Institute of Neuroscience and Medicine (INM-1, INM-7), Research Centre Jülich, Jülich, Germany
| | - Felix Hoffstaedter
- Institute of Neuroscience and Medicine (INM-1, INM-7), Research Centre Jülich, Jülich, Germany
| | - Deepthi P Varikuti
- Institute of Neuroscience and Medicine (INM-1, INM-7), Research Centre Jülich, Jülich, Germany
| | - Christiane Jockwitz
- Institute of Neuroscience and Medicine (INM-1, INM-7), Research Centre Jülich, Jülich, Germany.,Department of Psychiatry, Psychotherapy and Psychosomatics, Medical Faculty, RWTH Aachen University, Aachen, Germany
| | - Svenja Caspers
- Institute of Neuroscience and Medicine (INM-1, INM-7), Research Centre Jülich, Jülich, Germany.,JARA-BRAIN, Jülich-Aachen Research Alliance, Jülich, Germany.,Institute for Anatomy I, Medical Faculty, Heinrich Heine University, Düsseldorf, Germany
| | - Katrin Amunts
- Institute of Neuroscience and Medicine (INM-1, INM-7), Research Centre Jülich, Jülich, Germany.,JARA-BRAIN, Jülich-Aachen Research Alliance, Jülich, Germany.,C. & O. Vogt Institute for Brain Research, Heinrich Heine University, Düsseldorf, Germany
| | - Sarah Genon
- Institute of Neuroscience and Medicine (INM-1, INM-7), Research Centre Jülich, Jülich, Germany.,GIGA-CRC In vivo Imaging, University of Liege, Liege, Belgium
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13
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The corticolimbic structural covariance network as an early predictive biosignature for cognitive impairment in Parkinson's disease. Sci Rep 2021; 11:862. [PMID: 33441662 PMCID: PMC7806769 DOI: 10.1038/s41598-020-79403-x] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/23/2020] [Accepted: 12/02/2020] [Indexed: 01/01/2023] Open
Abstract
Structural covariance assesses similarities in gray matter between brain regions and can be applied to study networks of the brain. In this study, we explored correlations between structural covariance networks (SCNs) and cognitive impairment in Parkinson’s disease patients. 101 PD patients and 58 age- and sex-matched healthy controls were enrolled in the study. For each participant, comprehensive neuropsychological testing using the Wechsler Adult Intelligence Scale-III and Cognitive Ability Screening Instrument were conducted. Structural brain MR images were acquired using a 3.0T whole body GE Signa MRI system. T1 structural images were preprocessed and analyzed using Statistical Parametric Mapping software (SPM12) running on Matlab R2016a for voxel-based morphometric analysis and SCN analysis. PD patients with normal cognition received follow-up neuropsychological testing at 1-year interval. Cognitive impairment in PD is associated with degeneration of the amygdala/hippocampus SCN. PD patients with dementia exhibited increased covariance over the prefrontal cortex compared to PD patients with normal cognition (PDN). PDN patients who had developed cognitive impairment at follow-up exhibited decreased gray matter volume of the amygdala/hippocampus SCN in the initial MRI. Our results support a neural network-based mechanism for cognitive impairment in PD patients. SCN analysis may reveal vulnerable networks that can be used to early predict cognitive decline in PD patients.
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14
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Kuo CY, Lee PL, Hung SC, Liu LK, Lee WJ, Chung CP, Yang AC, Tsai SJ, Wang PN, Chen LK, Chou KH, Lin CP. Large-Scale Structural Covariance Networks Predict Age in Middle-to-Late Adulthood: A Novel Brain Aging Biomarker. Cereb Cortex 2020; 30:5844-5862. [PMID: 32572452 DOI: 10.1093/cercor/bhaa161] [Citation(s) in RCA: 27] [Impact Index Per Article: 6.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/20/2020] [Revised: 05/05/2020] [Accepted: 05/21/2020] [Indexed: 12/31/2022] Open
Abstract
The aging process is accompanied by changes in the brain's cortex at many levels. There is growing interest in summarizing these complex brain-aging profiles into a single, quantitative index that could serve as a biomarker both for characterizing individual brain health and for identifying neurodegenerative and neuropsychiatric diseases. Using a large-scale structural covariance network (SCN)-based framework with machine learning algorithms, we demonstrate this framework's ability to predict individual brain age in a large sample of middle-to-late age adults, and highlight its clinical specificity for several disease populations from a network perspective. A proposed estimator with 40 SCNs could predict individual brain age, balancing between model complexity and prediction accuracy. Notably, we found that the most significant SCN for predicting brain age included the caudate nucleus, putamen, hippocampus, amygdala, and cerebellar regions. Furthermore, our data indicate a larger brain age disparity in patients with schizophrenia and Alzheimer's disease than in healthy controls, while this metric did not differ significantly in patients with major depressive disorder. These findings provide empirical evidence supporting the estimation of brain age from a brain network perspective, and demonstrate the clinical feasibility of evaluating neurological diseases hypothesized to be associated with accelerated brain aging.
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Affiliation(s)
- Chen-Yuan Kuo
- Department of Biomedical Imaging and Radiological Sciences, National Yang Ming University, Taipei 11221, Taiwan
| | - Pei-Lin Lee
- Institute of Neuroscience, National Yang Ming University, Taipei 11221, Taiwan
| | - Sheng-Che Hung
- Department of Radiology, University of North Carolina, Chapel Hill, NC 27514, USA
| | - Li-Kuo Liu
- Aging and Health Research Center, National Yang Ming University, Taipei 11221, Taiwan.,Center for Geriatrics and Gerontology, Taipei Veterans General Hospital, Taipei 11217, Taiwan
| | - Wei-Ju Lee
- Aging and Health Research Center, National Yang Ming University, Taipei 11221, Taiwan.,Department of Family Medicine, Yuanshan Branch, Taipei Veterans General Hospital, Yi-Lan 264, Taiwan
| | - Chih-Ping Chung
- Department of Neurology, School of Medicine, National Yang Ming University, Taipei 11221, Taiwan.,Department of Neurology, Neurological Institute, Taipei Veterans General Hospital, Taipei 11217, Taiwan
| | - Albert C Yang
- Department of Psychiatry, Taipei Veterans General Hospital, Taipei 11217, Taiwan
| | - Shih-Jen Tsai
- Department of Psychiatry, Taipei Veterans General Hospital, Taipei 11217, Taiwan
| | - Pei-Ning Wang
- Department of Neurology, School of Medicine, National Yang Ming University, Taipei 11221, Taiwan.,Department of Neurology, Neurological Institute, Taipei Veterans General Hospital, Taipei 11217, Taiwan.,Brain Research Center, National Yang Ming University, Taipei 11221, Taiwan
| | - Liang-Kung Chen
- Aging and Health Research Center, National Yang Ming University, Taipei 11221, Taiwan.,Center for Geriatrics and Gerontology, Taipei Veterans General Hospital, Taipei 11217, Taiwan
| | - Kun-Hsien Chou
- Institute of Neuroscience, National Yang Ming University, Taipei 11221, Taiwan.,Brain Research Center, National Yang Ming University, Taipei 11221, Taiwan
| | - Ching-Po Lin
- Department of Biomedical Imaging and Radiological Sciences, National Yang Ming University, Taipei 11221, Taiwan.,Institute of Neuroscience, National Yang Ming University, Taipei 11221, Taiwan.,Aging and Health Research Center, National Yang Ming University, Taipei 11221, Taiwan.,Brain Research Center, National Yang Ming University, Taipei 11221, Taiwan
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