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Liao H, Cheng J, Pan D, Deng Z, Liu Y, Jiang J, Cai J, He B, Lei M, Li H, Li Y, Xu Y, Tang Y. Association of earlier age at menopause with risk of incident dementia, brain structural indices and the potential mediators: a prospective community-based cohort study. EClinicalMedicine 2023; 60:102033. [PMID: 37396803 PMCID: PMC10314163 DOI: 10.1016/j.eclinm.2023.102033] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/11/2023] [Revised: 04/26/2023] [Accepted: 05/17/2023] [Indexed: 07/04/2023] Open
Abstract
Background To date, there is no homogeneous evidence of whether earlier age at menopause is associated with incident dementia. In addition, the underlying mechanism and driven mediators are largely unknown. We aimed to fill these knowledge gaps. Methods This community-based cohort study included 154,549 postmenopausal women without dementia at enrolment (between 2006 and 2010) from the UK Biobank who were followed up until June 2021. We followed up until June 2021. Age at menopause was entered as a categorical variable (<40, 40-49, and ≥50 years) with ≥50 years taken as a reference. The primary outcome was all-cause dementia in a time-to-event analysis and the secondary outcomes included Alzheimer's disease, vascular dementia, and other types of dementia. In addition, we investigated the association between magnetic resonance (MR) brain structure indices with earlier menopause, and explored the potential underlying driven mediators on the relationship between earlier menopause and dementia. Findings 2266 (1.47%) dementia cases were observed over a median follow-up period of 12.3 years. After adjusting for confounders, women with earlier menopause showed a higher risk of all-cause dementia compared with those ≥50 years (adjusted-HRs [95% CIs]: 1.21 [1.09-1.34] and 1.71 [1.38-2.11] in the 40-49 years and <40 years groups, respectively; P for trend <0.001). No significant interactions between earlier menopause and polygenic risk score, cardiometabolic factors, type of menopause, or hormone-replacement therapy strata were found. Earlier menopause was negatively associated with brain MR global and regional grey matter indices, and positively associated with white matter hyperintensity. The relationship between earlier menopause and dementia was partially mediated by menopause-related comorbidities including sleep disturbance, mental health disorder, frailty, chronic pain, and metabolic syndrome, with the proportion (95% CI) of mediation effect being 3.35% (2.18-5.40), 1.38% (1.05-3.20), 5.23% (3.12-7.83), 3.64% (2.88-5.62) and 3.01% (2.29-4.40), respectively. Multiple mediator analysis showed a combined effect being 13.21% (11.11-18.20). Interpretation Earlier age at menopause was associated with risk of incident dementia and deteriorating brain health. Further studies are warranted to clarify the underlying mechanisms by which earlier age at menopause is linked to an increased risk of dementia, and to determine public health strategies to attenuate this association. Funding National Natural Science Foundation of China, the Science and Technology Program of Guangzhou, the Key Area Research and Development Program of Guangdong Province, the China Postdoctoral Science Foundation, and the Guangdong Basic and Applied Basic Research Foundation.
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Affiliation(s)
- Huanquan Liao
- Department of Neurology, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, Guangzhou, China
- Department of Neurology, the Seventh Affiliated Hospital, Sun Yat-sen University, Shenzhen, China
| | - Jinping Cheng
- Department of Neurology, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, Guangzhou, China
| | - Dong Pan
- Department of Neurology, the Eighth Affiliated Hospital, Sun Yat-sen University, Shenzhen, China
| | - Zhenhong Deng
- Department of Neurology, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, Guangzhou, China
| | - Ying Liu
- Department of Neurology, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, Guangzhou, China
| | - Jingru Jiang
- Department of Neurology, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, Guangzhou, China
| | - Jinhua Cai
- Department of Neurology, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, Guangzhou, China
| | - Baixuan He
- Department of Neurology, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, Guangzhou, China
| | - Ming Lei
- Department of Neurology, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, Guangzhou, China
| | - Honghong Li
- Department of Neurology, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, Guangzhou, China
| | - Yi Li
- Department of Neurology, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, Guangzhou, China
| | - Yongteng Xu
- Department of Neurology, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, Guangzhou, China
| | - Yamei Tang
- Department of Neurology, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, Guangzhou, China
- Guangdong Provincial Key Laboratory of Malignant Tumor Epigenetics and Gene Regulation, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, Guangzhou, China
- Guangdong Province Key Laboratory of Brain Function and Disease, Zhongshan School of Medicine, Sun Yat-Sen University, Guangzhou, China
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Steinbart D, Yaakub SN, Steinbrenner M, Guldin LS, Holtkamp M, Keller SS, Weber B, Rüber T, Heckemann RA, Ilyas-Feldmann M, Hammers A. Automatic and manual segmentation of the piriform cortex: Method development and validation in patients with temporal lobe epilepsy and Alzheimer's disease. Hum Brain Mapp 2023; 44:3196-3209. [PMID: 37052063 PMCID: PMC10171523 DOI: 10.1002/hbm.26274] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/16/2022] [Revised: 02/10/2023] [Accepted: 02/24/2023] [Indexed: 04/14/2023] Open
Abstract
The piriform cortex (PC) is located at the junction of the temporal and frontal lobes. It is involved physiologically in olfaction as well as memory and plays an important role in epilepsy. Its study at scale is held back by the absence of automatic segmentation methods on MRI. We devised a manual segmentation protocol for PC volumes, integrated those manually derived images into the Hammers Atlas Database (n = 30) and used an extensively validated method (multi-atlas propagation with enhanced registration, MAPER) for automatic PC segmentation. We applied automated PC volumetry to patients with unilateral temporal lobe epilepsy with hippocampal sclerosis (TLE; n = 174 including n = 58 controls) and to the Alzheimer's Disease Neuroimaging Initiative cohort (ADNI; n = 151, of whom with mild cognitive impairment (MCI), n = 71; Alzheimer's disease (AD), n = 33; controls, n = 47). In controls, mean PC volume was 485 mm3 on the right and 461 mm3 on the left. Automatic and manual segmentations overlapped with a Jaccard coefficient (intersection/union) of ~0.5 and a mean absolute volume difference of ~22 mm3 in healthy controls, ~0.40/ ~28 mm3 in patients with TLE, and ~ 0.34/~29 mm3 in patients with AD. In patients with TLE, PC atrophy lateralised to the side of hippocampal sclerosis (p < .001). In patients with MCI and AD, PC volumes were lower than those of controls bilaterally (p < .001). Overall, we have validated automatic PC volumetry in healthy controls and two types of pathology. The novel finding of early atrophy of PC at the stage of MCI possibly adds a novel biomarker. PC volumetry can now be applied at scale.
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Affiliation(s)
- David Steinbart
- Charité - Universitätsmedizin Berlin, Freie Universität and Humboldt-Universität zu Berlin, Department of Neurology, Epilepsy-Center Berlin-Brandenburg, Berlin, Germany
- King's College London & Guy's and St Thomas' PET Centre, School of Biomedical Engineering and Imaging Sciences, St Thomas' Hospital, London, UK
| | - Siti N Yaakub
- King's College London & Guy's and St Thomas' PET Centre, School of Biomedical Engineering and Imaging Sciences, St Thomas' Hospital, London, UK
- School of Psychology, Faculty of Health, University of Plymouth, Plymouth, UK
| | - Mirja Steinbrenner
- Charité - Universitätsmedizin Berlin, Freie Universität and Humboldt-Universität zu Berlin, Department of Neurology, Epilepsy-Center Berlin-Brandenburg, Berlin, Germany
| | - Lynn S Guldin
- King's College London & Guy's and St Thomas' PET Centre, School of Biomedical Engineering and Imaging Sciences, St Thomas' Hospital, London, UK
| | - Martin Holtkamp
- Charité - Universitätsmedizin Berlin, Freie Universität and Humboldt-Universität zu Berlin, Department of Neurology, Epilepsy-Center Berlin-Brandenburg, Berlin, Germany
| | - Simon S Keller
- Department of Pharmacology and Therapeutics, Institute of Systems, Molecular and Integrative Biology, University of Liverpool, Liverpool, UK
- Department of Neuroradiology, The Walton Centre NHS Foundation Trust, Liverpool, UK
| | - Bernd Weber
- Center for Economics and Neuroscience, University of Bonn, Bonn, Germany
- Institute of Experimental Epileptology and Cognition Research, University Hospital Bonn, Bonn, Germany
| | - Theodor Rüber
- Institute of Experimental Epileptology and Cognition Research, University Hospital Bonn, Bonn, Germany
| | - Rolf A Heckemann
- Department of Medical Radiation Sciences, University of Gothenburg, Gothenburg, Sweden
| | - Maria Ilyas-Feldmann
- Charité - Universitätsmedizin Berlin, Freie Universität and Humboldt-Universität zu Berlin, Department of Neurology, Epilepsy-Center Berlin-Brandenburg, Berlin, Germany
| | - Alexander Hammers
- King's College London & Guy's and St Thomas' PET Centre, School of Biomedical Engineering and Imaging Sciences, St Thomas' Hospital, London, UK
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Tafuri B, Filardi M, Urso D, Gnoni V, De Blasi R, Nigro S, Logroscino G. Asymmetry of radiomics features in the white matter of patients with primary progressive aphasia. Front Aging Neurosci 2023; 15:1120935. [PMID: 37213534 PMCID: PMC10196268 DOI: 10.3389/fnagi.2023.1120935] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/10/2022] [Accepted: 04/17/2023] [Indexed: 05/23/2023] Open
Abstract
Introduction Primary Progressive Aphasia (PPA) is a neurological disease characterized by linguistic deficits. Semantic (svPPA) and non-fluent/agrammatic (nfvPPA) variants are the two main clinical subtypes. We applied a novel analytical framework, based on radiomic analysis, to investigate White Matter (WM) asymmetry and to examine whether asymmetry is associated with verbal fluency performance. Methods Analyses were performed on T1-weighted images including 56 patients with PPA (31 svPPA and 25 nfvPPA) and 53 age- and sex-matched controls. Asymmetry Index (AI) was computed for 86 radiomics features in 34 white matter regions. The relationships between AI, verbal fluency performance (semantic and phonemic) and Boston Naming Test score (BNT) were explored through Spearman correlation analysis. Results Relative to controls, WM asymmetry in svPPA patients involved regions adjacent to middle temporal cortex as part of the inferior longitudinal (ILF), fronto-occipital (IFOF) and superior longitudinal fasciculi. Conversely, nfvPPA patients showed an asymmetry of WM in lateral occipital regions (ILF/IFOF). A higher lateralization involving IFOF, cingulum and forceps minor was found in nfvPPA compared to svPPA patients. In nfvPPA patients, semantic fluency was positively correlated to asymmetry in ILF/IFOF tracts. Performances at BNT were associated with AI values of the middle temporal (ILF/SLF) and parahippocampal (ILF/IFOF) gyri in svPPA patients. Discussion Radiomics features depicted distinct pathways of asymmetry in svPPA and nfvPPA involving damage of principal fiber tracts associated with speech and language. Assessing asymmetry of radiomics in PPA allows achieving a deeper insight into the neuroanatomical damage and may represent a candidate severity marker for language impairments in PPA patients.
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Affiliation(s)
- Benedetta Tafuri
- Center for Neurodegenerative Diseases and the Aging Brain, University of Bari Aldo Moro, “Pia Fondazione Cardinale G. Panico”, Lecce, Italy
- Department of Translational Biomedicine and Neuroscience (DiBraiN), University of Bari Aldo Moro, Bari, Italy
- *Correspondence: Benedetta Tafuri,
| | - Marco Filardi
- Center for Neurodegenerative Diseases and the Aging Brain, University of Bari Aldo Moro, “Pia Fondazione Cardinale G. Panico”, Lecce, Italy
- Department of Translational Biomedicine and Neuroscience (DiBraiN), University of Bari Aldo Moro, Bari, Italy
| | - Daniele Urso
- Center for Neurodegenerative Diseases and the Aging Brain, University of Bari Aldo Moro, “Pia Fondazione Cardinale G. Panico”, Lecce, Italy
- Department of Neurosciences, King’s College London, Institute of Psychiatry, Psychology and Neuroscience, London, United Kingdom
| | - Valentina Gnoni
- Center for Neurodegenerative Diseases and the Aging Brain, University of Bari Aldo Moro, “Pia Fondazione Cardinale G. Panico”, Lecce, Italy
- Department of Neurosciences, King’s College London, Institute of Psychiatry, Psychology and Neuroscience, London, United Kingdom
- Sleep and Brain Plasticity Centre, CNS, IoPPN, King’s College London, London, United Kingdom
| | - Roberto De Blasi
- Center for Neurodegenerative Diseases and the Aging Brain, University of Bari Aldo Moro, “Pia Fondazione Cardinale G. Panico”, Lecce, Italy
- Department of Diagnostic Imaging, Pia Fondazione di Culto e Religione “Card. G. Panico”, Tricase, Italy
| | - Salvatore Nigro
- Center for Neurodegenerative Diseases and the Aging Brain, University of Bari Aldo Moro, “Pia Fondazione Cardinale G. Panico”, Lecce, Italy
| | - Giancarlo Logroscino
- Center for Neurodegenerative Diseases and the Aging Brain, University of Bari Aldo Moro, “Pia Fondazione Cardinale G. Panico”, Lecce, Italy
- Department of Translational Biomedicine and Neuroscience (DiBraiN), University of Bari Aldo Moro, Bari, Italy
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Qubad M, Barnes-Scheufler CV, Schaum M, Raspor E, Rösler L, Peters B, Schiweck C, Goebel R, Reif A, Bittner RA. Improved correspondence of fMRI visual field localizer data after cortex-based macroanatomical alignment. Sci Rep 2022; 12:14310. [PMID: 35995943 PMCID: PMC9395433 DOI: 10.1038/s41598-022-17909-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/01/2021] [Accepted: 08/02/2022] [Indexed: 11/30/2022] Open
Abstract
Studying the visual system with fMRI often requires using localizer paradigms to define regions of interest (ROIs). However, the considerable interindividual variability of the cerebral cortex represents a crucial confound for group-level analyses. Cortex-based alignment (CBA) techniques reliably reduce interindividual macroanatomical variability. Yet, their utility has not been assessed for visual field localizer paradigms, which map specific parts of the visual field within retinotopically organized visual areas. We evaluated CBA for an attention-enhanced visual field localizer, mapping homologous parts of each visual quadrant in 50 participants. We compared CBA with volume-based alignment and a surface-based analysis, which did not include macroanatomical alignment. CBA led to the strongest increase in the probability of activation overlap (up to 86%). At the group level, CBA led to the most consistent increase in ROI size while preserving vertical ROI symmetry. Overall, our results indicate that in addition to the increased signal-to-noise ratio of a surface-based analysis, macroanatomical alignment considerably improves statistical power. These findings confirm and extend the utility of CBA for the study of the visual system in the context of group analyses. CBA should be particularly relevant when studying neuropsychiatric disorders with abnormally increased interindividual macroanatomical variability.
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Affiliation(s)
- Mishal Qubad
- Department of Psychiatry, Psychosomatic Medicine and Psychotherapy and Brain Imaging Center, University Hospital Frankfurt, Goethe University, Frankfurt am Main, Germany
| | - Catherine V Barnes-Scheufler
- Department of Psychiatry, Psychosomatic Medicine and Psychotherapy and Brain Imaging Center, University Hospital Frankfurt, Goethe University, Frankfurt am Main, Germany
| | - Michael Schaum
- Leibniz Institute for Resilience Research, Mainz, Germany
| | - Eva Raspor
- Department of Psychiatry, Psychosomatic Medicine and Psychotherapy and Brain Imaging Center, University Hospital Frankfurt, Goethe University, Frankfurt am Main, Germany
| | - Lara Rösler
- Department of Psychiatry, Psychosomatic Medicine and Psychotherapy and Brain Imaging Center, University Hospital Frankfurt, Goethe University, Frankfurt am Main, Germany.,Netherlands Institute for Neuroscience, Amsterdam, The Netherlands
| | - Benjamin Peters
- Institute of Medical Psychology, University Hospital Frankfurt, Goethe University, Frankfurt am Main, Germany.,Zuckerman Mind Brain Behavior Institute, Columbia University, New York, NY, USA
| | - Carmen Schiweck
- Department of Psychiatry, Psychosomatic Medicine and Psychotherapy and Brain Imaging Center, University Hospital Frankfurt, Goethe University, Frankfurt am Main, Germany
| | - Rainer Goebel
- Netherlands Institute for Neuroscience, Amsterdam, The Netherlands.,Department of Cognitive Neuroscience, Faculty of Psychology and Neuroscience, Maastricht University, Maastricht, The Netherlands
| | - Andreas Reif
- Department of Psychiatry, Psychosomatic Medicine and Psychotherapy and Brain Imaging Center, University Hospital Frankfurt, Goethe University, Frankfurt am Main, Germany
| | - Robert A Bittner
- Department of Psychiatry, Psychosomatic Medicine and Psychotherapy and Brain Imaging Center, University Hospital Frankfurt, Goethe University, Frankfurt am Main, Germany. .,Ernst Strüngmann Institute for Neuroscience (ESI) in Cooperation With Max Planck Society, Frankfurt am Main, Germany.
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Deshpande G, Zhao X, Robinson J. Functional Parcellation of the Hippocampus based on its Layer-specific Connectivity with Default Mode and Dorsal Attention Networks. Neuroimage 2022; 254:119078. [PMID: 35276366 DOI: 10.1016/j.neuroimage.2022.119078] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/04/2019] [Revised: 01/29/2022] [Accepted: 03/07/2022] [Indexed: 12/25/2022] Open
Abstract
Recent neuroimaging evidence suggests that there might be an anterior-posterior functional differentiation of the hippocampus along the long-axis. The HERNET (hippocampal encoding/retrieval and network) model proposed an encoding/retrieval dichotomy with the anterior hippocampus more connected to the dorsal attention network (DAN) during memory encoding, and the posterior portions more connected to the default mode network (DMN) during retrieval. Evidence both for and against the HERNET model has been reported. In this study, we test the validity of the HERNET model non-invasively in humans by computing functional connectivity (FC) in layer-specific cortico-hippocampal microcircuits. This was achieved by acquiring sub-millimeter functional magnetic resonance imaging (fMRI) data during encoding/retrieval tasks at 7T. Specifically, FC between infra-granular output layers of DAN with hippocampus during encoding and FC between supra-granular input layers of DMN with hippocampus during retrieval were computed to test the predictions of the HERNET model. Our results support some predictions of the HERNET model including anterior-posterior gradient along the long axis of the hippocampus. While preferential relationships between the entire hippocampus and DAN/DMN during encoding/retrieval, respectively, were observed as predicted, anterior-posterior specificity in these network relationships could not be confirmed. The strength and clarity of evidence for/against the HERNET model were superior with layer-specific data compared to conventional volume data.
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Affiliation(s)
- Gopikrishna Deshpande
- AU MRI Research Center, Department of Electrical and Computer Engineering, Auburn University, 560 Devall Dr, Suite 266D, Auburn, AL 36849, USA; Department of Psychological Sciences, Auburn University, Auburn, AL, USA; Alabama Advanced Imaging Consortium, Birmingham, AL, USA; Center for Neuroscience, Auburn University, Auburn, AL, USA; Key Laboratory for Learning and Cognition, School of Psychology, Capital Normal University, Beijing, China; Department of Psychiatry, National Institute of Mental Health and Neurosciences, Bangalore, India; Centre for Brain Research, Indian Institute of Science, Bangalore, India.
| | - Xinyu Zhao
- AU MRI Research Center, Department of Electrical and Computer Engineering, Auburn University, 560 Devall Dr, Suite 266D, Auburn, AL 36849, USA; Quora Inc., Mountain View, CA, USA
| | - Jennifer Robinson
- AU MRI Research Center, Department of Electrical and Computer Engineering, Auburn University, 560 Devall Dr, Suite 266D, Auburn, AL 36849, USA; Department of Psychological Sciences, Auburn University, Auburn, AL, USA; Alabama Advanced Imaging Consortium, Birmingham, AL, USA; Center for Neuroscience, Auburn University, Auburn, AL, USA
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Vega-Torres JD, Ontiveros-Angel P, Terrones E, Stuffle EC, Solak S, Tyner E, Oropeza M, dela Peña I, Obenaus A, Ford BD, Figueroa JD. Short-term exposure to an obesogenic diet during adolescence elicits anxiety-related behavior and neuroinflammation: modulatory effects of exogenous neuregulin-1. Transl Psychiatry 2022; 12:83. [PMID: 35220393 PMCID: PMC8882169 DOI: 10.1038/s41398-022-01788-2] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/09/2021] [Revised: 12/27/2021] [Accepted: 01/07/2022] [Indexed: 11/21/2022] Open
Abstract
Childhood obesity leads to hippocampal atrophy and altered cognition. However, the molecular mechanisms underlying these impairments are poorly understood. The neurotrophic factor neuregulin-1 (NRG1) and its cognate ErbB4 receptor play critical roles in hippocampal maturation and function. This study aimed to determine whether exogenous NRG1 administration reduces hippocampal abnormalities and neuroinflammation in rats exposed to an obesogenic Western-like diet (WD). Lewis rats were randomly divided into four groups (12 rats/group): (1) control diet+vehicle (CDV); (2) CD + NRG1 (CDN) (daily intraperitoneal injections: 5 μg/kg/day; between postnatal day, PND 21-PND 41); (3) WD + VEH (WDV); (4) WD + NRG1 (WDN). Neurobehavioral assessments were performed at PND 43-49. Brains were harvested for MRI and molecular analyses at PND 49. We found that NRG1 administration reduced hippocampal volume (7%) and attenuated hippocampal-dependent cued fear conditioning in CD rats (56%). NRG1 administration reduced PSD-95 protein expression (30%) and selectively reduced hippocampal cytokine levels (IL-33, GM-CSF, CCL-2, IFN-γ) while significantly impacting microglia morphology (increased span ratio and reduced circularity). WD rats exhibited reduced right hippocampal volume (7%), altered microglia morphology (reduced density and increased lacunarity), and increased levels of cytokines implicated in neuroinflammation (IL-1α, TNF-α, IL-6). Notably, NRG1 synergized with the WD to increase hippocampal ErbB4 phosphorylation and the tumor necrosis alpha converting enzyme (TACE/ADAM17) protein levels. Although the results did not provide sufficient evidence to conclude that exogenous NRG1 administration is beneficial to alleviate obesity-related outcomes in adolescent rats, we identified a potential novel interaction between obesogenic diet exposure and TACE/ADAM17-NRG1-ErbB4 signaling during hippocampal maturation. Our results indicate that supraoptimal ErbB4 activities may contribute to the abnormal hippocampal structure and cognitive vulnerabilities observed in obese individuals.
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Affiliation(s)
- Julio David Vega-Torres
- grid.43582.380000 0000 9852 649XCenter for Health Disparities and Molecular Medicine and Department of Basic Sciences, Physiology Division, Department of Basic Sciences, Loma Linda University Health School of Medicine, Loma Linda, CA USA
| | - Perla Ontiveros-Angel
- grid.43582.380000 0000 9852 649XCenter for Health Disparities and Molecular Medicine and Department of Basic Sciences, Physiology Division, Department of Basic Sciences, Loma Linda University Health School of Medicine, Loma Linda, CA USA
| | - Esmeralda Terrones
- grid.43582.380000 0000 9852 649XCenter for Health Disparities and Molecular Medicine and Department of Basic Sciences, Physiology Division, Department of Basic Sciences, Loma Linda University Health School of Medicine, Loma Linda, CA USA
| | - Erwin C. Stuffle
- grid.43582.380000 0000 9852 649XCenter for Health Disparities and Molecular Medicine and Department of Basic Sciences, Physiology Division, Department of Basic Sciences, Loma Linda University Health School of Medicine, Loma Linda, CA USA
| | - Sara Solak
- grid.43582.380000 0000 9852 649XDepartment of Pharmaceutical and Administrative Sciences, Loma Linda University Health School of Pharmacy, Loma Linda, CA USA
| | - Emma Tyner
- grid.43582.380000 0000 9852 649XDepartment of Pharmaceutical and Administrative Sciences, Loma Linda University Health School of Pharmacy, Loma Linda, CA USA
| | - Marie Oropeza
- grid.43582.380000 0000 9852 649XDepartment of Pharmaceutical and Administrative Sciences, Loma Linda University Health School of Pharmacy, Loma Linda, CA USA
| | - Ike dela Peña
- grid.43582.380000 0000 9852 649XDepartment of Pharmaceutical and Administrative Sciences, Loma Linda University Health School of Pharmacy, Loma Linda, CA USA
| | - Andre Obenaus
- grid.266093.80000 0001 0668 7243Department of Pediatrics, University of California-Irvine, Irvine, CA USA
| | - Byron D. Ford
- grid.266097.c0000 0001 2222 1582Division of Biomedical Sciences, University of California-Riverside School of Medicine, Riverside, CA USA
| | - Johnny D. Figueroa
- grid.43582.380000 0000 9852 649XCenter for Health Disparities and Molecular Medicine and Department of Basic Sciences, Physiology Division, Department of Basic Sciences, Loma Linda University Health School of Medicine, Loma Linda, CA USA
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OUP accepted manuscript. Arch Clin Neuropsychol 2022; 37:1502-1514. [DOI: 10.1093/arclin/acac018] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 03/17/2022] [Indexed: 11/13/2022] Open
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Zeng Q, Li K, Luo X, Wang S, Xu X, Li Z, Zhang T, Liu X, Fu Y, Xu X, Wang C, Wang T, Zhou J, Liu Z, Chen Y, Huang P, Zhang M. Distinct Atrophy Pattern of Hippocampal Subfields in Patients with Progressive and Stable Mild Cognitive Impairment: A Longitudinal MRI Study. J Alzheimers Dis 2021; 79:237-247. [PMID: 33252076 DOI: 10.3233/jad-200775] [Citation(s) in RCA: 20] [Impact Index Per Article: 6.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/26/2023]
Abstract
BACKGROUND Predicting the prognosis of mild cognitive impairment (MCI) has outstanding clinical value, and the hippocampal volume is a reliable imaging biomarker of AD diagnosis. OBJECTIVE We aimed to longitudinally assess hippocampal sub-regional difference (volume and asymmetry) among progressive MCI (pMCI), stable MCI (sMCI) patients, and normal elderly. METHODS We identified 29 pMCI, 52 sMCI, and 102 normal controls (NC) from the ADNI database. All participants underwent neuropsychological assessment and 3T MRI scans three times. The time interval between consecutive MRI sessions was about 1 year. Volumes of hippocampal subfield were measured by Freesurfer. Based on the analysis of variance, repeated measures analyses, and receiver operating characteristic curves, we compared cross-sectional and longitudinal alteration sub-regional volume and asymmetry index. RESULTS Compared to NC, both MCI groups showed significant atrophy in all subfields. At baseline, pMCI have a smaller volume than sMCI in the bilateral subiculum, molecular layer (ML), the molecular and granule cell layers of the dentate gyrus, cornu ammonis 4, and right tail. Furthermore, repeated measures analyses revealed that pMCI patients showed a faster volume loss than sMCI in bilateral subiculum and ML. After controlling for age, gender, and education, most results remained unchanged. However, none of the hippocampal sub-regional volumes performed better than the whole hippocampus in ROC analyses, and no asymmetric difference between pMCI and sMCI was found. CONCLUSION The faster volume loss in subiculum and ML suggest a higher risk of disease progression in MCI patients. The hippocampal asymmetry may have smaller value in predicting the MCI prognosis.
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Affiliation(s)
- Qingze Zeng
- Department of Radiology, 2nd Affiliated Hospital of Zhejiang University School of Medicine, China
| | - Kaicheng Li
- Department of Radiology, 2nd Affiliated Hospital of Zhejiang University School of Medicine, China
| | - Xiao Luo
- Department of Radiology, 2nd Affiliated Hospital of Zhejiang University School of Medicine, China
| | - Shuyue Wang
- Department of Radiology, 2nd Affiliated Hospital of Zhejiang University School of Medicine, China
| | - Xiaopei Xu
- Department of Radiology, 2nd Affiliated Hospital of Zhejiang University School of Medicine, China
| | - Zheyu Li
- Department of Neurology, 2nd Affiliated Hospital of Zhejiang University School of Medicine, China
| | - Tianyi Zhang
- Department of Neurology, 2nd Affiliated Hospital of Zhejiang University School of Medicine, China
| | - Xiaocao Liu
- Department of Radiology, 2nd Affiliated Hospital of Zhejiang University School of Medicine, China
| | - Yanv Fu
- Department of Neurology, 2nd Affiliated Hospital of Zhejiang University School of Medicine, China
| | - Xiaojun Xu
- Department of Radiology, 2nd Affiliated Hospital of Zhejiang University School of Medicine, China
| | - Chao Wang
- Department of Radiology, 2nd Affiliated Hospital of Zhejiang University School of Medicine, China
| | - Tao Wang
- Department of Radiology, 2nd Affiliated Hospital of Zhejiang University School of Medicine, China
| | - Jiong Zhou
- Department of Neurology, 2nd Affiliated Hospital of Zhejiang University School of Medicine, China
| | - Zhirong Liu
- Department of Neurology, 2nd Affiliated Hospital of Zhejiang University School of Medicine, China
| | - Yanxing Chen
- Department of Neurology, 2nd Affiliated Hospital of Zhejiang University School of Medicine, China
| | - Peiyu Huang
- Department of Radiology, 2nd Affiliated Hospital of Zhejiang University School of Medicine, China
| | - Minming Zhang
- Department of Radiology, 2nd Affiliated Hospital of Zhejiang University School of Medicine, China
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9
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Garcia-Dias R, Scarpazza C, Baecker L, Vieira S, Pinaya WHL, Corvin A, Redolfi A, Nelson B, Crespo-Facorro B, McDonald C, Tordesillas-Gutiérrez D, Cannon D, Mothersill D, Hernaus D, Morris D, Setien-Suero E, Donohoe G, Frisoni G, Tronchin G, Sato J, Marcelis M, Kempton M, van Haren NEM, Gruber O, McGorry P, Amminger P, McGuire P, Gong Q, Kahn RS, Ayesa-Arriola R, van Amelsvoort T, Ortiz-García de la Foz V, Calhoun V, Cahn W, Mechelli A. Neuroharmony: A new tool for harmonizing volumetric MRI data from unseen scanners. Neuroimage 2020; 220:117127. [PMID: 32634595 PMCID: PMC7573655 DOI: 10.1016/j.neuroimage.2020.117127] [Citation(s) in RCA: 40] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/22/2020] [Revised: 06/08/2020] [Accepted: 06/30/2020] [Indexed: 02/05/2023] Open
Abstract
•We present Neuroharmony, a harmonization tool for images from unseen scanners. •We developed Neuroharmony using a total of 15,026 sMRI images. •The tool was able to reduce scanner-related bias from unseen scans. •Neuroharmony represents a significant step towards imaging-based clinical tools. •Neuroharmony is available at https://github.com/garciadias/Neuroharmony .
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Affiliation(s)
- Rafael Garcia-Dias
- Department of Psychosis Studies, Institute of Psychiatry, Psychology & Neuroscience, King's College London, 16 De Crespigny Park, SE5 8AF, United Kingdom.
| | - Cristina Scarpazza
- Department of Psychosis Studies, Institute of Psychiatry, Psychology & Neuroscience, King's College London, 16 De Crespigny Park, SE5 8AF, United Kingdom; Department of General Psychology, University of Padova, Via Venezia 8, Padova, Italy
| | - Lea Baecker
- Department of Psychosis Studies, Institute of Psychiatry, Psychology & Neuroscience, King's College London, 16 De Crespigny Park, SE5 8AF, United Kingdom
| | - Sandra Vieira
- Department of Psychosis Studies, Institute of Psychiatry, Psychology & Neuroscience, King's College London, 16 De Crespigny Park, SE5 8AF, United Kingdom
| | - Walter H L Pinaya
- Department of Psychosis Studies, Institute of Psychiatry, Psychology & Neuroscience, King's College London, 16 De Crespigny Park, SE5 8AF, United Kingdom; Center of Mathematics, Computing, and Cognition, Universidade Federal do ABC, Santo André, Brazil
| | - Aiden Corvin
- Department of Psychiatry, School of Medicine, Trinity College Dublin, Dublin, Ireland
| | - Alberto Redolfi
- Laboratory of Neuroinformatics, IRCCS Istituto Centro San Giovanni di Dio Fatebenefratelli, Brescia, Italy
| | - Barnaby Nelson
- Orygen, The National Centre of Excellence in Youth Mental Health, University of Melbourne, Melbourne, Victoria, Australia; Centre for Youth Mental Health, University of Melbourne, Melbourne, Victoria, Australia
| | - Benedicto Crespo-Facorro
- Centro Investigación Biomédica en Red de Salud Mental (CIBERSAM), Spain; Departamento de Psiquiatria, Universidad de Sevilla, Instituto de Biomedicina de Sevilla (IBIS), Spain; Hospital Universitario Virgen del Rocío, Sevilla, Spain; Department of Psychiatry, Marqués de Valdecilla University Hospital, IDIVAL, School of Medicine, University of Cantabria, Santander, Spain
| | - Colm McDonald
- Clinical Neuroimaging Laboratory, School of Medicine & Center for Neuroimaging and Cognitive Genomics, NUI Galway University, Galway, Ireland
| | - Diana Tordesillas-Gutiérrez
- Centro Investigación Biomédica en Red de Salud Mental (CIBERSAM), Spain; Neuroimaging Unit, Technological Facilities, Valdecilla Biomedical Research Institute IDIVAL, Spain
| | - Dara Cannon
- Clinical Neuroimaging Laboratory, School of Medicine & Center for Neuroimaging and Cognitive Genomics, NUI Galway University, Galway, Ireland
| | - David Mothersill
- School of Psychology & Center for Neuroimaging and Cognitive Genomics, NUI Galway University, Galway, Ireland
| | - Dennis Hernaus
- Department of Psychiatry and Neuropsychology, School of Mental Health and Neuroscience, Maastricht, the Netherlands
| | - Derek Morris
- Discipline of Biochemistry & Center for Neuroimaging and Cognitive Genomics, NUI Galway University, Galway, Ireland
| | - Esther Setien-Suero
- Centro Investigación Biomédica en Red de Salud Mental (CIBERSAM), Spain; Department of Psychiatry, Marqués de Valdecilla University Hospital, IDIVAL, School of Medicine, University of Cantabria, Santander, Spain
| | - Gary Donohoe
- School of Psychology & Center for Neuroimaging and Cognitive Genomics, NUI Galway University, Galway, Ireland
| | - Giovanni Frisoni
- Memory Clinic and LANVIE-Laboratory of Neuroimaging of Ageing, University Hospitals and University of Geneva, Geneva, Switzerland; Laboratory of Alzheimer's Neuroimaging and Epidemiology - LANE, IRCCS Istituto Centro San Giovanni di Dio Fatebenefratelli, Brescia, Italy
| | - Giulia Tronchin
- Clinical Neuroimaging Laboratory, School of Medicine & Center for Neuroimaging and Cognitive Genomics, NUI Galway University, Galway, Ireland
| | - João Sato
- Center of Mathematics, Computing, and Cognition, Universidade Federal do ABC, Santo André, Brazil
| | - Machteld Marcelis
- Department of Psychiatry and Neuropsychology, School of Mental Health and Neuroscience, Maastricht, the Netherlands
| | - Matthew Kempton
- Department of Psychosis Studies, Institute of Psychiatry, Psychology & Neuroscience, King's College London, 16 De Crespigny Park, SE5 8AF, United Kingdom
| | - Neeltje E M van Haren
- Department of Child and Adolescent Psychiatry/Psychology, Erasmus Medical Centre - Sophia Children's Hospital, Rotterdam, Netherlands
| | - Oliver Gruber
- Section for Experimental Psychopathology and Neuroimaging, Department of General Psychiatry, Heidelberg University, Germany; Center for Translational Research in Systems Neuroscience and Psychiatry, Department of Psychiatry and Psychotherapy, University Medical Center Göttingen, Germany
| | - Patrick McGorry
- Orygen, The National Centre of Excellence in Youth Mental Health, University of Melbourne, Melbourne, Victoria, Australia; Centre for Youth Mental Health, University of Melbourne, Melbourne, Victoria, Australia
| | - Paul Amminger
- Orygen, The National Centre of Excellence in Youth Mental Health, University of Melbourne, Melbourne, Victoria, Australia; Centre for Youth Mental Health, University of Melbourne, Melbourne, Victoria, Australia
| | - Philip McGuire
- Department of Psychosis Studies, Institute of Psychiatry, Psychology & Neuroscience, King's College London, 16 De Crespigny Park, SE5 8AF, United Kingdom
| | - Qiyong Gong
- Huaxi MR Research Center (HMRRC), Department of Radiology, West China Hospital of Sichuan University, Chengdu, China; Psychoradiology Research Unit of Chinese Academy of Medical Sciences, West China Hospital of Sichuan University, Chengdu, Sichuan, China; Department of Radiology, Shengjing Hospital of China Medical University, Shenyang, Liaoning, China
| | - René S Kahn
- Department of Psychiatry, University Medical Center Utrecht Brain Center, Utrecht, the Netherlands; Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Rosa Ayesa-Arriola
- Centro Investigación Biomédica en Red de Salud Mental (CIBERSAM), Spain; Department of Psychiatry, Marqués de Valdecilla University Hospital, IDIVAL, School of Medicine, University of Cantabria, Santander, Spain
| | - Therese van Amelsvoort
- Department of Psychiatry and Neuropsychology, School of Mental Health and Neuroscience, Maastricht, the Netherlands
| | - Victor Ortiz-García de la Foz
- Centro Investigación Biomédica en Red de Salud Mental (CIBERSAM), Spain; Department of Psychiatry, Marqués de Valdecilla University Hospital, IDIVAL, School of Medicine, University of Cantabria, Santander, Spain
| | - Vince Calhoun
- Tri-institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia; State University, Georgia Institute of Technology, Emory University, Atlanta, GA, USA
| | - Wiepke Cahn
- Department of Psychiatry, University Medical Center Utrecht Brain Center, Utrecht, the Netherlands
| | - Andrea Mechelli
- Department of Psychosis Studies, Institute of Psychiatry, Psychology & Neuroscience, King's College London, 16 De Crespigny Park, SE5 8AF, United Kingdom
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10
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Utsumi T, Kodaka F, Maikusa N, Yamazaki R, Shigeta M. Inter-method reliability between automatic region of interest analytic application with multi-atlas segmentation and FreeSurfer. Psychogeriatrics 2020; 20:699-705. [PMID: 32510746 DOI: 10.1111/psyg.12567] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/23/2019] [Revised: 04/29/2020] [Accepted: 05/04/2020] [Indexed: 12/01/2022]
Abstract
AIM Alzheimer's disease (AD) is a progressive neurodegenerative disease characterized by the aggregation of amyloid-β and phosphorylated tau proteins. Magnetic resonance imaging (MRI) is a useful means of detecting hippocampal atrophy. However, instead of visual inspection, objective and time-saving tools for automated region of interest (ROI) analysis are needed. Advances in MRI segmentation techniques have enabled a multi-atlas approach with fewer errors than a conventional single-atlas approach. To support the clinical application of multi-atlas segmentation, an automated ROI analytic application consisting of multi-atlas segmentation with joint label fusion and corrective learning was developed: T-Proto. In the present study, we evaluated the inter-method reliability between T-Proto and a reference ROI analytic software, FreeSurfer. METHODS This was a database study. MRI data from 30 patients with AD were selected, and the inter-method reliability was assessed in terms of the intra-class correlation coefficient (ICC). A post-hoc comparison according to the severity of AD was also performed. RESULTS Almost all the regional volumes estimated with T-Proto were smaller than those estimated with FreeSurfer. The regional ICC values between the two methods showed moderate to excellent reliability. A post-hoc comparison revealed a similar t-value and effect size between both methods for the hippocampus. CONCLUSION In the present study, we showed that automated regional analysis using T-Proto was reliable in the hippocampus in terms of ICC, compared with FreeSurfer.
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Affiliation(s)
- Tomohiro Utsumi
- Department of Psychiatry, The Jikei University School of Medicine, Tokyo, Japan
| | - Fumitoshi Kodaka
- Department of Psychiatry, The Jikei University School of Medicine, Tokyo, Japan
| | - Norihide Maikusa
- Department of Psychiatry, The Jikei University School of Medicine, Tokyo, Japan
| | - Ryuichi Yamazaki
- Department of Psychiatry, The Jikei University School of Medicine, Tokyo, Japan
| | - Masahiro Shigeta
- Department of Psychiatry, The Jikei University School of Medicine, Tokyo, Japan
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11
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Östberg A, Ledig C, Katila A, Maanpää HR, Posti JP, Takala R, Tallus J, Glocker B, Rueckert D, Tenovuo O. Volume Change in Frontal Cholinergic Structures After Traumatic Brain Injury and Cognitive Outcome. Front Neurol 2020; 11:832. [PMID: 32903569 PMCID: PMC7438550 DOI: 10.3389/fneur.2020.00832] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/27/2020] [Accepted: 07/03/2020] [Indexed: 01/02/2023] Open
Abstract
The cholinergic nuclei in the basal forebrain innervate frontal cortical structures regulating attention. Our aim was to investigate if cognitive test results measuring attention relate to the longitudinal volume change of cholinergically innervated structures following traumatic brain injury (TBI). During the prospective, observational TBIcare project patients with all severities of TBI (n = 114) and controls with acute orthopedic injuries (n = 17) were recruited. Head MRI was obtained in both acute (mean 2 weeks post-injury) and late (mean 8 months) time points. T1-weighted 3D MR images were analyzed with an automatic segmentation method to evaluate longitudinal, structural brain volume change. The cognitive outcome was assessed with the Cambridge Neuropsychological Test Automated Battery (CANTAB). Analyses included 16 frontal cortical structures, of which four showed a significant correlation between post-traumatic volume change and the CANTAB test results. The strongest correlation was found between the volume loss of the supplementary motor cortex and motor screening task results (R-sq 0.16, p < 0.0001), where poorer test results correlated with greater atrophy. Of the measured sum structures, greater cortical gray matter atrophy rate showed a significant correlation with the poorer CANTAB test results. TBI caused volume loss of frontal cortical structures that are heavily innervated by cholinergic neurons is associated with neuropsychological test results measuring attention.
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Affiliation(s)
- Anna Östberg
- Division of Clinical Neurosciences, Turku Brain Injury Centre, Turku University Hospital, Turku, Finland.,Department of Neurology, Institute of Clinical Medicine, University of Turku, Turku, Finland.,Department of Neurosurgery, Neurocenter, Turku University Hospital, Turku, Finland
| | - Christian Ledig
- Department of Computing, Imperial College London, London, United Kingdom
| | - Ari Katila
- Department of Perioperative Services, Intensive Care and Pain Medicine, Turku University Hospital, Turku, Finland
| | - Henna-Riikka Maanpää
- Department of Neurology, Institute of Clinical Medicine, University of Turku, Turku, Finland.,Department of Neurosurgery, Neurocenter, Turku University Hospital, Turku, Finland
| | - Jussi P Posti
- Division of Clinical Neurosciences, Turku Brain Injury Centre, Turku University Hospital, Turku, Finland.,Department of Neurology, Institute of Clinical Medicine, University of Turku, Turku, Finland.,Department of Neurosurgery, Neurocenter, Turku University Hospital, Turku, Finland
| | - Riikka Takala
- Department of Perioperative Services, Intensive Care and Pain Medicine, Turku University Hospital, Turku, Finland
| | - Jussi Tallus
- Department of Neurology, Institute of Clinical Medicine, University of Turku, Turku, Finland
| | - Ben Glocker
- Department of Computing, Imperial College London, London, United Kingdom
| | - Daniel Rueckert
- Department of Computing, Imperial College London, London, United Kingdom
| | - Olli Tenovuo
- Division of Clinical Neurosciences, Turku Brain Injury Centre, Turku University Hospital, Turku, Finland.,Department of Neurology, Institute of Clinical Medicine, University of Turku, Turku, Finland
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12
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Baudat C, Maréchal B, Corredor-Jerez R, Kober T, Meuli R, Hagmann P, Michel P, Maeder P, Dunet V. Automated MRI-based volumetry of basal ganglia and thalamus at the chronic phase of cortical stroke. Neuroradiology 2020; 62:1371-1380. [PMID: 32556424 PMCID: PMC7568697 DOI: 10.1007/s00234-020-02477-x] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/21/2020] [Accepted: 06/10/2020] [Indexed: 12/11/2022]
Abstract
Purpose We aimed at assessing the potential of automated MR morphometry to assess individual basal ganglia and thalamus volumetric changes at the chronic phase after cortical stroke. Methods Ninety-six patients (mean age: 65 ± 18 years, male 55) with cortical stroke at the chronic phase were retrospectively included. Patients were scanned at 1.5 T or 3 T using a T1-MPRAGE sequence. Resulting 3D images were processed with the MorphoBox prototype software to automatically segment basal ganglia and thalamus structures, and to obtain Z scores considering the confounding effects of age and sex. Stroke volume was estimated by manual delineation on T2-SE imaging. Z scores were compared between ipsi- and contralateral stroke side and according to the vascular territory. Potential relationship between Z scores and stroke volume was assessed using the Spearman correlation coefficient. Results Basal ganglia and thalamus volume Z scores were lower ipsilaterally to MCA territory stroke (p values < 0.034) while they were not different between ipsi- and contralateral stroke sides in non-MCA territory stroke (p values > 0.37). In MCA territory stroke, ipsilateral caudate nucleus (rho = − 0.34, p = 0.007), putamen (rho = − 0.50, p < 0.001), pallidum (rho = − 0.44, p < 0.001), and thalamus (rho = − 0.48, p < 0.001) volume Z scores negatively correlated with the cortical stroke volume. This relation was not influenced by cardiovascular risk factors or time since stroke. Conclusion Automated MR morphometry demonstrated atrophy of ipsilateral basal ganglia and thalamus at the chronic phase after cortical stroke in the MCA territory. The atrophy was related to stroke volume. These results confirm the potential role for automated MRI morphometry to assess remote changes after stroke.
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Affiliation(s)
- Cindy Baudat
- Department of Diagnostic and Interventional Radiology, Lausanne University Hospital and University of Lausanne, Rue du Bugnon 46, CH-1011, Lausanne, Switzerland
| | - Bénédicte Maréchal
- Department of Diagnostic and Interventional Radiology, Lausanne University Hospital and University of Lausanne, Rue du Bugnon 46, CH-1011, Lausanne, Switzerland.,Advanced Clinical Imaging Technology, Siemens Healthcare AG, Lausanne, Switzerland
| | - Ricardo Corredor-Jerez
- Department of Diagnostic and Interventional Radiology, Lausanne University Hospital and University of Lausanne, Rue du Bugnon 46, CH-1011, Lausanne, Switzerland.,Advanced Clinical Imaging Technology, Siemens Healthcare AG, Lausanne, Switzerland
| | - Tobias Kober
- Department of Diagnostic and Interventional Radiology, Lausanne University Hospital and University of Lausanne, Rue du Bugnon 46, CH-1011, Lausanne, Switzerland.,Advanced Clinical Imaging Technology, Siemens Healthcare AG, Lausanne, Switzerland
| | - Reto Meuli
- Department of Diagnostic and Interventional Radiology, Lausanne University Hospital and University of Lausanne, Rue du Bugnon 46, CH-1011, Lausanne, Switzerland
| | - Patric Hagmann
- Department of Diagnostic and Interventional Radiology, Lausanne University Hospital and University of Lausanne, Rue du Bugnon 46, CH-1011, Lausanne, Switzerland
| | - Patrik Michel
- Stroke Center, Neurology Service, Department of Clinical Neurosciences, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland
| | - Philippe Maeder
- Department of Diagnostic and Interventional Radiology, Lausanne University Hospital and University of Lausanne, Rue du Bugnon 46, CH-1011, Lausanne, Switzerland
| | - Vincent Dunet
- Department of Diagnostic and Interventional Radiology, Lausanne University Hospital and University of Lausanne, Rue du Bugnon 46, CH-1011, Lausanne, Switzerland.
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13
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Yaakub SN, Heckemann RA, Keller SS, McGinnity CJ, Weber B, Hammers A. On brain atlas choice and automatic segmentation methods: a comparison of MAPER & FreeSurfer using three atlas databases. Sci Rep 2020; 10:2837. [PMID: 32071355 PMCID: PMC7028906 DOI: 10.1038/s41598-020-57951-6] [Citation(s) in RCA: 18] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/03/2019] [Accepted: 11/27/2019] [Indexed: 11/09/2022] Open
Abstract
Several automatic image segmentation methods and few atlas databases exist for analysing structural T1-weighted magnetic resonance brain images. The impact of choosing a combination has not hitherto been described but may bias comparisons across studies. We evaluated two segmentation methods (MAPER and FreeSurfer), using three publicly available atlas databases (Hammers_mith, Desikan-Killiany-Tourville, and MICCAI 2012 Grand Challenge). For each combination of atlas and method, we conducted a leave-one-out cross-comparison to estimate the segmentation accuracy of FreeSurfer and MAPER. We also used each possible combination to segment two datasets of patients with known structural abnormalities (Alzheimer's disease (AD) and mesial temporal lobe epilepsy with hippocampal sclerosis (HS)) and their matched healthy controls. MAPER was better than FreeSurfer at modelling manual segmentations in the healthy control leave-one-out analyses in two of the three atlas databases, and the Hammers_mith atlas database transferred to new datasets best regardless of segmentation method. Both segmentation methods reliably identified known abnormalities in each patient group. Better separation was seen for FreeSurfer in the AD and left-HS datasets, and for MAPER in the right-HS dataset. We provide detailed quantitative comparisons for multiple anatomical regions, thus enabling researchers to make evidence-based decisions on their choice of atlas and segmentation method.
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Affiliation(s)
- Siti Nurbaya Yaakub
- King's College London & Guy's and St Thomas' PET Centre, School of Biomedical Engineering & Imaging Sciences, King's College London, London, United Kingdom
| | - Rolf A Heckemann
- MedTech West at Sahlgrenska University Hospital Gothenburg, Gothenburg, Sweden
- Department of Radiation Physics, Institute of Clinical Sciences, Gothenburg University, Gothenburg, Sweden
- Division of Brain Sciences, Imperial College London, London, United Kingdom
| | - Simon S Keller
- Department of Molecular and Clinical Pharmacology, Institute of Translational Medicine, University of Liverpool, Liverpool, United Kingdom
- Department of Neuroradiology, The Walton Centre NHS Foundation Trust, Liverpool, United Kingdom
| | - Colm J McGinnity
- King's College London & Guy's and St Thomas' PET Centre, School of Biomedical Engineering & Imaging Sciences, King's College London, London, United Kingdom
| | - Bernd Weber
- Center for Economics and Neuroscience, University of Bonn, Bonn, Germany
- Institute of Experimental Epileptology and Cognition Research, University Hospital Bonn, Bonn, Germany
| | - Alexander Hammers
- King's College London & Guy's and St Thomas' PET Centre, School of Biomedical Engineering & Imaging Sciences, King's College London, London, United Kingdom.
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14
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Hofer C, Kwitt R, Höller Y, Trinka E, Uhl A. An empirical assessment of appearance descriptors applied to MRI for automated diagnosis of TLE and MCI. Comput Biol Med 2019; 117:103592. [PMID: 32072961 DOI: 10.1016/j.compbiomed.2019.103592] [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: 02/19/2019] [Revised: 12/19/2019] [Accepted: 12/19/2019] [Indexed: 11/12/2022]
Abstract
OBJECTIVE Differential diagnosis of mild cognitive impairment MCI and temporal lobe epilepsy TLE is a debated issue, specifically because these conditions may coincide in the elderly population. We evaluate automated differential diagnosis based on characteristics derived from structural brain MRI of different brain regions. METHODS In 22 healthy controls, 19 patients with MCI, and 17 patients with TLE we used scale invariant feature transform (SIFT), local binary patterns (LBP), and wavelet-based features and investigate their predictive performance for MCI and TLE. RESULTS The classification based on SIFT features resulted in an accuracy of 81% of MCI vs. TLE and reasonable generalizability. Local binary patterns yielded satisfactory diagnostic performance with up to 94.74% sensitivity and 88.24% specificity in the right Thalamus for the distinction of MCI vs. TLE, but with limited generalizable. Wavelet features yielded similar results as LPB with 94.74% sensitivity and 82.35% specificity but generalize better. SIGNIFICANCE Features beyond volume analysis are a valid approach when applied to specific regions of the brain. Most significant information could be extracted from the thalamus, frontal gyri, and temporal regions, among others. These results suggest that analysis of changes of the central nervous system should not be limited to the most typical regions of interest such as the hippocampus and parahippocampal areas. Region-independent approaches can add considerable information for diagnosis. We emphasize the need to characterize generalizability in future studies, as our results demonstrate that not doing so can lead to overestimation of classification results. LIMITATIONS The data used within this study allows for separation of MCI and TLE subjects using a simple age threshold. While we present a strong indication that the presented method is age-invariant and therefore agnostic to this situation, new data would be needed for a rigorous empirical assessment of this findings.
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Affiliation(s)
- Christoph Hofer
- Department of Computer Science, University of Salzburg, Austria.
| | - Roland Kwitt
- Department of Computer Science, University of Salzburg, Austria.
| | - Yvonne Höller
- Department of Neurology, Christian Doppler Medical Centre, Paracelsus Medical University, Salzburg, Austria; Centre for Cognitive Neuroscience, Paracelsus Medical University, Salzburg, Austria.
| | - Eugen Trinka
- Spinal Cord Injury & Tissue Regeneration Centre Salzburg, Paracelsus Medical University, Salzburg, Austria; Department of Neurology, Christian Doppler Medical Centre, Paracelsus Medical University, Salzburg, Austria; Centre for Cognitive Neuroscience, Paracelsus Medical University, Salzburg, Austria.
| | - Andreas Uhl
- Department of Computer Science, University of Salzburg, Austria.
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15
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Low A, Mak E, Malpetti M, Chouliaras L, Nicastro N, Su L, Holland N, Rittman T, Rodríguez PV, Passamonti L, Bevan-Jones WR, Jones PS, Rowe JB, O'Brien JT. Asymmetrical atrophy of thalamic subnuclei in Alzheimer's disease and amyloid-positive mild cognitive impairment is associated with key clinical features. ALZHEIMER'S & DEMENTIA: DIAGNOSIS, ASSESSMENT & DISEASE MONITORING 2019; 11:690-699. [PMID: 31667328 PMCID: PMC6811895 DOI: 10.1016/j.dadm.2019.08.001] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 12/23/2022]
Abstract
Introduction Although widespread cortical asymmetries have been identified in Alzheimer's disease (AD), thalamic asymmetries and their relevance to clinical severity in AD remain unclear. Methods Lateralization indices were computed for individual thalamic subnuclei of 65 participants (33 healthy controls, 14 amyloid-positive patients with mild cognitive impairment, and 18 patients with AD dementia). We compared lateralization indices across diagnostic groups and correlated them with clinical measures. Results Although overall asymmetry of the thalamus did not differ between groups, greater leftward lateralization of atrophy in the ventral nuclei was demonstrated in AD, compared with controls and amyloid-positive mild cognitive impairment. Increased posterior ventrolateral and ventromedial nuclei asymmetry were associated with worse cognitive dysfunction, informant-reported neuropsychiatric symptoms, and functional ability. Discussion Leftward ventral thalamic atrophy was associated with disease severity in AD. Our findings suggest the clinically relevant involvement of thalamic nuclei in the pathophysiology of AD.
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Affiliation(s)
- Audrey Low
- Department of Psychiatry, University of Cambridge, Cambridge, United Kingdom
| | - Elijah Mak
- Department of Psychiatry, University of Cambridge, Cambridge, United Kingdom
| | - Maura Malpetti
- Department of Clinical Neurosciences, University of Cambridge, Cambridge, United Kingdom
| | - Leonidas Chouliaras
- Department of Psychiatry, University of Cambridge, Cambridge, United Kingdom
| | - Nicolas Nicastro
- Department of Psychiatry, University of Cambridge, Cambridge, United Kingdom
| | - Li Su
- Department of Psychiatry, University of Cambridge, Cambridge, United Kingdom
| | - Negin Holland
- Department of Clinical Neurosciences, University of Cambridge, Cambridge, United Kingdom
| | - Timothy Rittman
- Department of Clinical Neurosciences, University of Cambridge, Cambridge, United Kingdom
| | | | - Luca Passamonti
- Department of Clinical Neurosciences, University of Cambridge, Cambridge, United Kingdom
| | - W Richard Bevan-Jones
- Department of Clinical Neurosciences, University of Cambridge, Cambridge, United Kingdom
| | - Pp Simon Jones
- Department of Clinical Neurosciences, University of Cambridge, Cambridge, United Kingdom
| | - James B Rowe
- Department of Clinical Neurosciences, University of Cambridge, Cambridge, United Kingdom
| | - John T O'Brien
- Department of Psychiatry, University of Cambridge, Cambridge, United Kingdom
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16
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Ma D, Popuri K, Bhalla M, Sangha O, Lu D, Cao J, Jacova C, Wang L, Beg MF. Quantitative assessment of field strength, total intracranial volume, sex, and age effects on the goodness of harmonization for volumetric analysis on the ADNI database. Hum Brain Mapp 2019; 40:1507-1527. [PMID: 30431208 PMCID: PMC6449147 DOI: 10.1002/hbm.24463] [Citation(s) in RCA: 23] [Impact Index Per Article: 4.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/07/2018] [Revised: 10/25/2018] [Accepted: 10/26/2018] [Indexed: 12/29/2022] Open
Abstract
When analyzing large multicenter databases, the effects of multiple confounding covariates increase the variability in the data and may reduce the ability to detect changes due to the actual effect of interest, for example, changes due to disease. Efficient ways to evaluate the effect of covariates toward the data harmonization are therefore important. In this article, we showcase techniques to assess the "goodness of harmonization" of covariates. We analyze 7,656 MR images in the multisite, multiscanner Alzheimer's Disease Neuroimaging Initiative (ADNI) database. We present a comparison of three methods for estimating total intracranial volume to assess their robustness and correct the brain structure volumes using the residual method and the proportional (normalization by division) method. We then evaluated the distribution of brain structure volumes over the entire ADNI database before and after accounting for multiple covariates such as total intracranial volume, scanner field strength, sex, and age using two techniques: (a) Zscapes, a panoramic visualization technique to analyze the entire database and (b) empirical cumulative distributions functions. The results from this study highlight the importance of assessing the goodness of data harmonization as a necessary preprocessing step when pooling large data set with multiple covariates, prior to further statistical data analysis.
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Affiliation(s)
- Da Ma
- School of Engineering ScienceSimon Fraser UniversityBurnabyBritish ColumbiaCanada
| | - Karteek Popuri
- School of Engineering ScienceSimon Fraser UniversityBurnabyBritish ColumbiaCanada
| | - Mahadev Bhalla
- School of Engineering ScienceSimon Fraser UniversityBurnabyBritish ColumbiaCanada
- Faculty of MedicineUniversity of British ColumbiaVancouverBritish ColumbiaCanada
| | - Oshin Sangha
- School of Engineering ScienceSimon Fraser UniversityBurnabyBritish ColumbiaCanada
| | - Donghuan Lu
- School of Engineering ScienceSimon Fraser UniversityBurnabyBritish ColumbiaCanada
| | - Jiguo Cao
- Department of Statistics and Actuarial ScienceSimon Fraser UniversityBurnabyBritish ColumbiaCanada
| | - Claudia Jacova
- Department of Medicine, Division of NeurologyUniversity of British ColumbiaVancouverBritish ColumbiaCanada
| | - Lei Wang
- Feinberg School of Medicine, Northwestern UniversityChicagoIllinois
| | - Mirza Faisal Beg
- School of Engineering ScienceSimon Fraser UniversityBurnabyBritish ColumbiaCanada
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17
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Hurtz S, Chow N, Watson AE, Somme JH, Goukasian N, Hwang KS, Morra J, Elashoff D, Gao S, Petersen RC, Aisen PS, Thompson PM, Apostolova LG. Automated and manual hippocampal segmentation techniques: Comparison of results, reproducibility and clinical applicability. Neuroimage Clin 2018; 21:101574. [PMID: 30553759 PMCID: PMC6413347 DOI: 10.1016/j.nicl.2018.10.012] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/25/2017] [Revised: 10/08/2018] [Accepted: 10/13/2018] [Indexed: 01/21/2023]
Abstract
BACKGROUND Imaging techniques used to measure hippocampal atrophy are key to understanding the clinical progression of Alzheimer's disease (AD). Various semi-automated hippocampal segmentation techniques are available and require human expert input to learn how to accurately segment new data. Our goal was to compare 1) the performance of our automated hippocampal segmentation technique relative to manual segmentations, and 2) the performance of our automated technique when provided with a training set from two different raters. We also explored the ability of hippocampal volumes obtained using manual and automated hippocampal segmentations to predict conversion from MCI to AD. METHODS We analyzed 161 1.5 T T1-weighted brain magnetic resonance images (MRI) from the ADCS Donepezil/Vitamin E clinical study. All subjects carried a diagnosis of mild cognitive impairment (MCI). Three different segmentation outputs (one produced by manual tracing and two produced by a semi-automated algorithm trained with training sets developed by two raters) were compared using single measure intraclass correlation statistics (smICC). The radial distance method was used to assess each segmentation technique's ability to detect hippocampal atrophy in 3D. We then compared how well each segmentation method detected baseline hippocampal differences between MCI subjects who remained stable (MCInc) and those who converted to AD (MCIc) during the trial. Our statistical maps were corrected for multiple comparisons using permutation-based statistics with a threshold of p < .01. RESULTS Our smICC analyses showed significant agreement between the manual and automated hippocampal segmentations from rater 1 [right smICC = 0.78 (95%CI 0.72-0.84); left smICC = 0.79 (95%CI 0.72-0.85)], the manual segmentations from rater 1 versus the automated segmentations from rater 2 [right smICC = 0.78 (95%CI 0.7-0.84); left smICC = 0.78 (95%CI 0.71-0.84)], and the automated segmentations of rater 1 versus rater 2 [right smICC = 0.97 (95%CI 0.96-0.98); left smICC = 0.97 (95%CI 0.96-0.98)]. All three segmentation methods detected significant CA1 and subicular atrophy in MCIc compared to MCInc at baseline (manual: right pcorrected = 0.0112, left pcorrected = 0.0006; automated rater 1: right pcorrected = 0.0318, left pcorrected = 0.0302; automated rater 2: right pcorrected = 0.0029, left pcorrected = 0.0166). CONCLUSIONS The hippocampal volumes obtained with a fast semi-automated segmentation method were highly comparable to the ones obtained with the labor-intensive manual segmentation method. The AdaBoost automated hippocampal segmentation technique is highly reliable allowing the efficient analysis of large data sets.
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Affiliation(s)
- Sona Hurtz
- Drexel University College of Medicine, Philadelphia, PA, USA
| | - Nicole Chow
- School of Medicine, University of California Irvine, Irvine, CA, USA
| | - Amity E Watson
- Monash Alfred Psychiatry Research Centre, Central Clinical School, The Alfred Hospital and Monash University, Melbourne, Australia
| | - Johanne H Somme
- Department of Neurology, Alava University Hospital, Alava, Spain
| | - Naira Goukasian
- University of Vermont College of Medicine, Burlington, VT, USA
| | - Kristy S Hwang
- Department of Neurology, Emory University School of Medicine, Atlanta, GA, USA
| | | | - David Elashoff
- Medicine Statistics Core, David Geffen School of Medicine, UCLA, Los Angeles, CA, USA
| | - Sujuan Gao
- Department of Biostatistics, Indiana University School of Medicine, Indianapolis, IN, USA
| | | | - Paul S Aisen
- Department of Neurology, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
| | - Paul M Thompson
- Department of Ophthalmology, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
| | - Liana G Apostolova
- Department of Neurology, Indiana University, Indianapolis, IN, USA; Department of Radiological Sciences, Indiana University, Indianapolis, IN, USA; Department of Medical and Molecular Genetics, Indiana University, Indianapolis, IN, USA.
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18
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Sogaard I, Ni R. Mediating Age-related Cognitive Decline through Lifestyle Activities: A Brief Review of the Effects of Physical Exercise and Sports-playing on Older Adult Cognition. ACTA PSYCHOPATHOLOGICA 2018; 4:22. [PMID: 35308579 PMCID: PMC8932955 DOI: 10.4172/2469-6676.100178] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/14/2023]
Abstract
Normal aging is associated with variable declines in perception and cognition, which may be mediated through active engagement in certain lifestyle activities. The aim of this review was to discuss the relationship between cognitive functioning in older adulthood and participation in various types of physical exercise and sports-playing activities. Most studies have focused on the beneficial effects of relatively nonspecific forms of aerobic physical activity, although some emerging evidence has suggested that more specific forms of sports-playing activities may confer greater cognitive benefit in specific areas of cognitive functioning. The evidence reviewed suggests the potential for simple lifestyle-related behaviors to mediate the cognitive decline often found in older adults, and to enhance the aging brain's cognitive reserve. However, more work is needed in order to ascertain the variable outcomes of exercise type, duration, and frequency, and the cognitive effects of various sports activities.
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Affiliation(s)
| | - Rui Ni
- Corresponding author: Rui Ni, , Associate Professor, Department of Psychology, Wichita State University, Wichita, KS, USA, Tel: (316) 978-3886, Fax: (316) 978-3086
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19
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Sarica A, Vasta R, Novellino F, Vaccaro MG, Cerasa A, Quattrone A. MRI Asymmetry Index of Hippocampal Subfields Increases Through the Continuum From the Mild Cognitive Impairment to the Alzheimer's Disease. Front Neurosci 2018; 12:576. [PMID: 30186103 PMCID: PMC6111896 DOI: 10.3389/fnins.2018.00576] [Citation(s) in RCA: 56] [Impact Index Per Article: 9.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/30/2018] [Accepted: 07/30/2018] [Indexed: 12/14/2022] Open
Abstract
Objective: It is well-known that the hippocampus presents significant asymmetry in Alzheimer's disease (AD) and that difference in volumes between left and right exists and varies with disease progression. However, few works investigated whether the asymmetry degree of subfields of hippocampus changes through the continuum from Mild Cognitive Impairment (MCI) to AD. Thus, aim of the present work was to evaluate the Asymmetry Index (AI) of hippocampal substructures as possible MRI biomarkers of Dementia. Moreover, we aimed to assess whether the subfields presented peculiar differences between left and right hemispheres. We also investigated the relationship between the asymmetry magnitude in hippocampal subfields and the decline of verbal memory as assessed by Rey's auditory verbal learning test (RAVLT). Methods: Four-hundred subjects were selected from ADNI, equally divided into healthy controls (HC), AD, stable MCI (sMCI), and progressive MCI (pMCI). The structural baseline T1s were processed with FreeSurfer 6.0 and volumes of whole hippocampus (WH) and 12 subfields were extracted. The AI was calculated as: (|Left-Right|/(Left+Right))*100. ANCOVA was used for evaluating AI differences between diagnoses, while paired t-test was applied for assessing changes between left and right volumes, separately for each group. Partial correlation was performed for exploring relationship between RAVLT summary scores (Immediate, Learning, Forgetting, Percent Forgetting) and hippocampal substructures AI. The statistical threshold was Bonferroni corrected p < 0.05/13 = 0.0038. Results: We found a general trend of increased degree of asymmetry with increasing severity of diagnosis. Indeed, AD presented the higher magnitude of asymmetry compared with HC, sMCI and pMCI, in the WH (AI mean 5.13 ± 4.29 SD) and in each of its twelve subfields. Moreover, we found in AD a significant negative correlation (r = -0.33, p = 0.00065) between the AI of parasubiculum (mean 12.70 ± 9.59 SD) and the RAVLT Learning score (mean 1.70 ± 1.62 SD). Conclusions: Our findings showed that hippocampal subfields AI varies differently among the four groups HC, sMCI, pMCI, and AD. Moreover, we found-for the first time-that hippocampal substructures had different sub-patterns of lateralization compared with the whole hippocampus. Importantly, the severity in learning rate was correlated with pathological high degree of asymmetry in parasubiculum of AD patients.
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Affiliation(s)
- Alessia Sarica
- Neuroscience Centre, Magna Graecia University, Catanzaro, Italy
| | - Roberta Vasta
- Neuroscience Centre, Magna Graecia University, Catanzaro, Italy
| | - Fabiana Novellino
- Neuroimaging Research Unit, Institute of Molecular Bioimaging and Physiology, National Research Council, Catanzaro, Italy
| | | | - Antonio Cerasa
- Neuroimaging Research Unit, Institute of Molecular Bioimaging and Physiology, National Research Council, Catanzaro, Italy
- S. Anna Institute and Research in Advanced Neurorehabilitation, Crotone, Italy
| | - Aldo Quattrone
- Neuroscience Centre, Magna Graecia University, Catanzaro, Italy
- Neuroimaging Research Unit, Institute of Molecular Bioimaging and Physiology, National Research Council, Catanzaro, Italy
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20
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Ledig C, Schuh A, Guerrero R, Heckemann RA, Rueckert D. Structural brain imaging in Alzheimer's disease and mild cognitive impairment: biomarker analysis and shared morphometry database. Sci Rep 2018; 8:11258. [PMID: 30050078 PMCID: PMC6062561 DOI: 10.1038/s41598-018-29295-9] [Citation(s) in RCA: 66] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/02/2018] [Accepted: 07/06/2018] [Indexed: 12/22/2022] Open
Abstract
Magnetic resonance (MR) imaging is a powerful technique for non-invasive in-vivo imaging of the human brain. We employed a recently validated method for robust cross-sectional and longitudinal segmentation of MR brain images from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) cohort. Specifically, we segmented 5074 MR brain images into 138 anatomical regions and extracted time-point specific structural volumes and volume change during follow-up intervals of 12 or 24 months. We assessed the extracted biomarkers by determining their power to predict diagnostic classification and by comparing atrophy rates to published meta-studies. The approach enables comprehensive analysis of structural changes within the whole brain. The discriminative power of individual biomarkers (volumes/atrophy rates) is on par with results published by other groups. We publish all quality-checked brain masks, structural segmentations, and extracted biomarkers along with this article. We further share the methodology for brain extraction (pincram) and segmentation (MALPEM, MALPEM4D) as open source projects with the community. The identified biomarkers hold great potential for deeper analysis, and the validated methodology can readily be applied to other imaging cohorts.
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Affiliation(s)
- Christian Ledig
- Imperial College London, Department of Computing, London, SW7 2AZ, UK.
| | - Andreas Schuh
- Imperial College London, Department of Computing, London, SW7 2AZ, UK
| | - Ricardo Guerrero
- Imperial College London, Department of Computing, London, SW7 2AZ, UK
| | - Rolf A Heckemann
- MedTech West, Sahlgrenska University Hospital, 413 45, Gothenburg, Sweden.,Department of Radiation Therapy, Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden.,Division of Brain Sciences, Imperial College London, London, UK
| | - Daniel Rueckert
- Imperial College London, Department of Computing, London, SW7 2AZ, UK
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21
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Tullo S, Devenyi GA, Patel R, Park MTM, Collins DL, Chakravarty MM. Warping an atlas derived from serial histology to 5 high-resolution MRIs. Sci Data 2018; 5:180107. [PMID: 29917012 PMCID: PMC6007088 DOI: 10.1038/sdata.2018.107] [Citation(s) in RCA: 27] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/30/2017] [Accepted: 04/06/2018] [Indexed: 11/09/2022] Open
Abstract
Previous work from our group demonstrated the use of multiple input atlases to a modified multi-atlas framework (MAGeT-Brain) to improve subject-based segmentation accuracy. Currently, segmentation of the striatum, globus pallidus and thalamus are generated from a single high-resolution and -contrast MRI atlas derived from annotated serial histological sections. Here, we warp this atlas to five high-resolution MRI templates to create five de novo atlases. The overall goal of this work is to use these newly warped atlases as input to MAGeT-Brain in an effort to consolidate and improve the workflow presented in previous manuscripts from our group, allowing for simultaneous multi-structure segmentation. The work presented details the methodology used for the creation of the atlases using a technique previously proposed, where atlas labels are modified to mimic the intensity and contrast profile of MRI to facilitate atlas-to-template nonlinear transformation estimation. Dice's Kappa metric was used to demonstrate high quality registration and segmentation accuracy of the atlases. The final atlases are available at https://github.com/CobraLab/atlases/tree/master/5-atlas-subcortical.
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Affiliation(s)
- Stephanie Tullo
- Integrated Program in Neuroscience, McGill University, Montreal, Canada.,Computational Brain Anatomy Laboratory, Cerebral Imaging Centre, Douglas Mental Health University Institute, Montreal, Canada
| | - Gabriel A Devenyi
- Computational Brain Anatomy Laboratory, Cerebral Imaging Centre, Douglas Mental Health University Institute, Montreal, Canada.,Department of Psychiatry, McGill University, Montreal, Canada
| | - Raihaan Patel
- Computational Brain Anatomy Laboratory, Cerebral Imaging Centre, Douglas Mental Health University Institute, Montreal, Canada.,Department of Biological and Biomedical Engineering, McGill University, Montreal, Canada
| | - Min Tae M Park
- Computational Brain Anatomy Laboratory, Cerebral Imaging Centre, Douglas Mental Health University Institute, Montreal, Canada.,Schulich School of Medicine and Dentistry, Western University, London, ON, Canada
| | - D Louis Collins
- Department of Biological and Biomedical Engineering, McGill University, Montreal, Canada.,McConnell Brain Imaging Centre, Montreal Neurological Institute, Montreal, Canada
| | - M Mallar Chakravarty
- Computational Brain Anatomy Laboratory, Cerebral Imaging Centre, Douglas Mental Health University Institute, Montreal, Canada.,Department of Psychiatry, McGill University, Montreal, Canada.,Department of Biological and Biomedical Engineering, McGill University, Montreal, Canada
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22
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Erus G, Doshi J, An Y, Verganelakis D, Resnick SM, Davatzikos C. Longitudinally and inter-site consistent multi-atlas based parcellation of brain anatomy using harmonized atlases. Neuroimage 2018; 166:71-78. [PMID: 29107121 PMCID: PMC5748021 DOI: 10.1016/j.neuroimage.2017.10.026] [Citation(s) in RCA: 36] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/01/2017] [Revised: 09/29/2017] [Accepted: 10/13/2017] [Indexed: 11/17/2022] Open
Abstract
As longitudinal and multi-site studies become increasingly frequent in neuroimaging, maintaining longitudinal and inter-scanner consistency of brain parcellation has become a major challenge due to variation in scanner models and/or image acquisition protocols across scanners and sites. We present a new automated segmentation method specifically designed to achieve a consistent parcellation of anatomical brain structures in such heterogeneous datasets. Our method combines a site-specific atlas creation strategy with a state-of-the-art multi-atlas anatomical label fusion framework. Site-specific atlases are computed such that they preserve image intensity characteristics of each site's scanner and acquisition protocol, while atlas pairs share anatomical labels in a way consistent with inter-scanner acquisition variations. This harmonization of atlases improves inter-study and longitudinal consistency of segmentations in the subsequent consensus labeling step. We tested this approach on a large sample of older adults from the Baltimore Longitudinal Study of Aging (BLSA) who had longitudinal scans acquired using two scanners that vary with respect to vendor and image acquisition protocol. We compared the proposed method to standard multi-atlas segmentation for both cross-sectional and longitudinal analyses. The harmonization significantly reduced scanner-related differences in the age trends of ROI volumes, improved longitudinal consistency of segmentations, and resulted in higher across-scanner intra-class correlations, particularly in the white matter.
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Affiliation(s)
- Guray Erus
- Center for Biomedical Image Computing and Analytics, University of Pennsylvania, Philadelphia, PA, USA.
| | - Jimit Doshi
- Center for Biomedical Image Computing and Analytics, University of Pennsylvania, Philadelphia, PA, USA
| | - Yang An
- Laboratory of Behavioral Neuroscience, National Institute on Aging, Baltimore, MD, USA
| | | | - Susan M Resnick
- Laboratory of Behavioral Neuroscience, National Institute on Aging, Baltimore, MD, USA
| | - Christos Davatzikos
- Center for Biomedical Image Computing and Analytics, University of Pennsylvania, Philadelphia, PA, USA
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23
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Leandrou S, Petroudi S, Kyriacou PA, Reyes-Aldasoro CC, Pattichis CS. Quantitative MRI Brain Studies in Mild Cognitive Impairment and Alzheimer's Disease: A Methodological Review. IEEE Rev Biomed Eng 2018; 11:97-111. [PMID: 29994606 DOI: 10.1109/rbme.2018.2796598] [Citation(s) in RCA: 52] [Impact Index Per Article: 8.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/11/2022]
Abstract
Classifying and predicting Alzheimer's disease (AD) in individuals with memory disorders through clinical and psychometric assessment is challenging, especially in mild cognitive impairment (MCI) subjects. Quantitative structural magnetic resonance imaging acquisition methods in combination with computer-aided diagnosis are currently being used for the assessment of AD. These acquisitions methods include voxel-based morphometry, volumetric measurements in specific regions of interest (ROIs), cortical thickness measurements, shape analysis, and texture analysis. This review evaluates the aforementioned methods in the classification of cases into one of the following three groups: normal controls, MCI, and AD subjects. Furthermore, the performance of the methods is assessed on the prediction of conversion from MCI to AD. In parallel, it is also assessed which ROIs are preferred in both classification and prognosis through the different states of the disease. Structural changes in the early stages of the disease are more pronounced in the medial temporal lobe, especially in the entorhinal cortex, whereas with disease progression, both entorhinal cortex and hippocampus offer similar discriminative power. However, for the conversion from MCI subjects to AD, entorhinal cortex provides better predictive accuracies rather than other structures, such as the hippocampus.
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24
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Ledig C, Kamnitsas K, Koikkalainen J, Posti JP, Takala RSK, Katila A, Frantzén J, Ala-Seppälä H, Kyllönen A, Maanpää HR, Tallus J, Lötjönen J, Glocker B, Tenovuo O, Rueckert D. Regional brain morphometry in patients with traumatic brain injury based on acute- and chronic-phase magnetic resonance imaging. PLoS One 2017; 12:e0188152. [PMID: 29182625 PMCID: PMC5705131 DOI: 10.1371/journal.pone.0188152] [Citation(s) in RCA: 22] [Impact Index Per Article: 3.1] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/02/2017] [Accepted: 11/01/2017] [Indexed: 02/02/2023] Open
Abstract
Traumatic brain injury (TBI) is caused by a sudden external force and can be very heterogeneous in its manifestation. In this work, we analyse T1-weighted magnetic resonance (MR) brain images that were prospectively acquired from patients who sustained mild to severe TBI. We investigate the potential of a recently proposed automatic segmentation method to support the outcome prediction of TBI. Specifically, we extract meaningful cross-sectional and longitudinal measurements from acute- and chronic-phase MR images. We calculate regional volume and asymmetry features at the acute/subacute stage of the injury (median: 19 days after injury), to predict the disability outcome of 67 patients at the chronic disease stage (median: 229 days after injury). Our results indicate that small structural volumes in the acute stage (e.g. of the hippocampus, accumbens, amygdala) can be strong predictors for unfavourable disease outcome. Further, group differences in atrophy are investigated. We find that patients with unfavourable outcome show increased atrophy. Among patients with severe disability outcome we observed a significantly higher mean reduction of cerebral white matter (3.1%) as compared to patients with low disability outcome (0.7%).
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Affiliation(s)
- Christian Ledig
- Imperial College London, Department of Computing, London, United Kingdom
- * E-mail:
| | | | - Juha Koikkalainen
- Combinostics, Tampere, Finland
- VTT Technical Research Centre of Finland, Tampere, Finland
| | - Jussi P. Posti
- Department of Clinical Medicine, University of Turku, Turku, Finland
- Division of Clinical Neurosciences, Turku Brain Injury Centre, Turku University Hospital, Turku, Finland
- Division of Clinical Neurosciences, Department of Neurosurgery, Turku University Hospital, Turku, Finland
| | - Riikka S. K. Takala
- Perioperative Services, Intensive Care Medicine and Pain Management, Turku University Hospital and University of Turku, Turku, Finland
| | - Ari Katila
- Perioperative Services, Intensive Care Medicine and Pain Management, Turku University Hospital and University of Turku, Turku, Finland
| | - Janek Frantzén
- Department of Clinical Medicine, University of Turku, Turku, Finland
- Division of Clinical Neurosciences, Turku Brain Injury Centre, Turku University Hospital, Turku, Finland
- Division of Clinical Neurosciences, Department of Neurosurgery, Turku University Hospital, Turku, Finland
| | - Henna Ala-Seppälä
- Department of Clinical Medicine, University of Turku, Turku, Finland
| | - Anna Kyllönen
- Department of Clinical Medicine, University of Turku, Turku, Finland
| | | | - Jussi Tallus
- Department of Clinical Medicine, University of Turku, Turku, Finland
| | - Jyrki Lötjönen
- Combinostics, Tampere, Finland
- VTT Technical Research Centre of Finland, Tampere, Finland
| | - Ben Glocker
- Imperial College London, Department of Computing, London, United Kingdom
| | - Olli Tenovuo
- Department of Clinical Medicine, University of Turku, Turku, Finland
- Division of Clinical Neurosciences, Turku Brain Injury Centre, Turku University Hospital, Turku, Finland
| | - Daniel Rueckert
- Imperial College London, Department of Computing, London, United Kingdom
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25
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Wild HM, Heckemann RA, Studholme C, Hammers A. Gyri of the human parietal lobe: Volumes, spatial extents, automatic labelling, and probabilistic atlases. PLoS One 2017; 12:e0180866. [PMID: 28846692 PMCID: PMC5573296 DOI: 10.1371/journal.pone.0180866] [Citation(s) in RCA: 16] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/11/2016] [Accepted: 06/22/2017] [Indexed: 01/16/2023] Open
Abstract
Accurately describing the anatomy of individual brains enables interlaboratory communication of functional and developmental studies and is crucial for possible surgical interventions. The human parietal lobe participates in multimodal sensory integration including language processing and also contains the primary somatosensory area. We describe detailed protocols to subdivide the parietal lobe, analyze morphological and volumetric characteristics, and create probabilistic atlases in MNI152 stereotaxic space. The parietal lobe was manually delineated on 3D T1 MR images of 30 healthy subjects and divided into four regions: supramarginal gyrus (SMG), angular gyrus (AG), superior parietal lobe (supPL) and postcentral gyrus (postCG). There was the expected correlation of male gender with larger brain and intracranial volume. We examined a wide range of anatomical features of the gyri and the sulci separating them. At least a rudimentary primary intermediate sulcus of Jensen (PISJ) separating SMG and AG was identified in nearly all (59/60) hemispheres. Presence of additional gyri in SMG and AG was related to sulcal features and volumetric characteristics. The parietal lobe was slightly (2%) larger on the left, driven by leftward asymmetries of the postCG and SMG. Intersubject variability was highest for SMG and AG, and lowest for postCG. Overall the morphological characteristics tended to be symmetrical, and volumes also tended to covary between hemispheres. This may reflect developmental as well as maturation factors. To assess the accuracy with which the labels can be used to segment newly acquired (unlabelled) T1-weighted brain images, we applied multi-atlas label propagation software (MAPER) in a leave-one-out experiment and compared the resulting automatic labels with the manually prepared ones. The results showed strong agreement (mean Jaccard index 0.69, corresponding to a mean Dice index of 0.82, average mean volume error of 0.6%). Stereotaxic probabilistic atlases of each subregion were obtained. They illustrate the physiological brain torque, with structures in the right hemisphere positioned more anteriorly than in the left, and right/left positional differences of up to 10 mm. They also allow an assessment of sulcal variability, e.g. low variability for parietooccipital fissure and cingulate sulcus. Illustrated protocols, individual label sets, probabilistic atlases, and a maximum-probability atlas which takes into account surrounding structures are available for free download under academic licences.
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Affiliation(s)
- Heather M. Wild
- Neurodis Foundation, Lyon, France
- Univ Lyon, Université Claude Bernard Lyon 1, Inserm, Stem Cell and Brain Research Institute U1208, Bron, France
| | - Rolf A. Heckemann
- Neurodis Foundation, Lyon, France
- MedTech West at Sahlgrenska University Hospital, University of Gothenburg, Gothenburg, Sweden
| | - Colin Studholme
- Department of Pediatrics, Division of Neonatology, University of Washington, Seattle, Washington, United States of America
| | - Alexander Hammers
- Neurodis Foundation, Lyon, France
- Division of Imaging Sciences and Biomedical Engineering, King’s College London, London, United Kingdom
- * E-mail:
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26
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Faillenot I, Heckemann RA, Frot M, Hammers A. Macroanatomy and 3D probabilistic atlas of the human insula. Neuroimage 2017; 150:88-98. [DOI: 10.1016/j.neuroimage.2017.01.073] [Citation(s) in RCA: 70] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/06/2015] [Revised: 12/16/2016] [Accepted: 01/30/2017] [Indexed: 11/28/2022] Open
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Sexual divergence in microtubule function: the novel intranasal microtubule targeting SKIP normalizes axonal transport and enhances memory. Mol Psychiatry 2016; 21:1467-76. [PMID: 26782054 DOI: 10.1038/mp.2015.208] [Citation(s) in RCA: 66] [Impact Index Per Article: 8.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/14/2015] [Revised: 11/17/2015] [Accepted: 11/24/2015] [Indexed: 01/21/2023]
Abstract
Activity-dependent neuroprotective protein (ADNP), essential for brain formation, is a frequent autism spectrum disorder (ASD)-mutated gene. ADNP associates with microtubule end-binding proteins (EBs) through its SxIP motif, to regulate dendritic spine formation and brain plasticity. Here, we reveal SKIP, a novel four-amino-acid peptide representing an EB-binding site, as a replacement therapy in an outbred Adnp-deficient mouse model. We discovered, for the first time, axonal transport deficits in Adnp(+/-) mice (measured by manganese-enhanced magnetic resonance imaging), with significant male-female differences. RNA sequencing evaluations showed major age, sex and genotype differences. Function enrichment and focus on major gene expression changes further implicated channel/transporter function and the cytoskeleton. In particular, a significant maturation change (1 month-five months) was observed in beta1 tubulin (Tubb1) mRNA, only in Adnp(+/+) males, and sex-dependent increase in calcium channel mRNA (Cacna1e) in Adnp(+/+) males compared with females. At the protein level, the Adnp(+/-) mice exhibited impaired hippocampal expression of the calcium channel (voltage-dependent calcium channel, Cacnb1) as well as other key ASD-linked genes including the serotonin transporter (Slc6a4), and the autophagy regulator, BECN1 (Beclin1), in a sex-dependent manner. Intranasal SKIP treatment normalized social memory in 8- to 9-month-old Adnp(+/-)-treated mice to placebo-control levels, while protecting axonal transport and ameliorating changes in ASD-like gene expression. The control, all d-amino analog D-SKIP, did not mimic SKIP activity. SKIP presents a novel prototype for potential ASD drug development, a prevalent unmet medical need.
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28
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Sargolzaei S, Sargolzaei A, Cabrerizo M, Chen G, Goryawala M, Pinzon-Ardila A, Gonzalez-Arias SM, Adjouadi M. Estimating Intracranial Volume in Brain Research: An Evaluation of Methods. Neuroinformatics 2016; 13:427-41. [PMID: 25822811 DOI: 10.1007/s12021-015-9266-5] [Citation(s) in RCA: 27] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/18/2023]
Abstract
Intracranial volume (ICV) is a standard measure often used in morphometric analyses to correct for head size in brain studies. Inaccurate ICV estimation could introduce bias in the outcome. The current study provides a decision aid in defining protocols for ICV estimation across different subject groups in terms of sampling frequencies that can be optimally used on the volumetric MRI data, and type of software most suitable for use in estimating the ICV measure. Four groups of 53 subjects are considered, including adult controls (AC, adults with Alzheimer's disease (AD), pediatric controls (PC) and group of pediatric epilepsy subjects (PE). Reference measurements were calculated for each subject by manually tracing intracranial cavity without sub-sampling. The reliability of reference measurements were assured through intra- and inter- variation analyses. Three publicly well-known software packages (FreeSurfer Ver. 5.3.0, FSL Ver. 5.0, SPM8 and SPM12) were examined in their ability to automatically estimate ICV across the groups. Results on sub-sampling studies with a 95 % confidence showed that in order to keep the accuracy of the inter-leaved slice sampling protocol above 99 %, sampling period cannot exceed 20 mm for AC, 25 mm for PC, 15 mm for AD and 17 mm for the PE groups. The study assumes a priori knowledge about the population under study into the automated ICV estimation. Tuning of the parameters in FSL and the use of proper atlas in SPM showed significant reduction in the systematic bias and the error in ICV estimation via these automated tools. SPM12 with the use of pediatric template is found to be a more suitable candidate for PE group. SPM12 and FSL subjected to tuning are the more appropriate tools for the PC group. The random error is minimized for FS in AD group and SPM8 showed less systematic bias. Across the AC group, both SPM12 and FS performed well but SPM12 reported lesser amount of systematic bias.
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Affiliation(s)
- Saman Sargolzaei
- Department of Electrical and Computer Engineering, Florida International University, Miami, FL, USA
| | - Arman Sargolzaei
- Department of Electrical and Computer Engineering, Florida International University, Miami, FL, USA
| | - Mercedes Cabrerizo
- Department of Electrical and Computer Engineering, Florida International University, Miami, FL, USA
| | - Gang Chen
- Scientific and Statistical Computing Core, NIMH/NIH/HHS, Bethesda, MD, USA
| | - Mohammed Goryawala
- Department of Electrical and Computer Engineering, Florida International University, Miami, FL, USA
| | | | - Sergio M Gonzalez-Arias
- Baptist Health Neuroscience Center, Baptist Hospital, Miami, FL, USA.,Department of Neuroscience, Herbert Wertheim College of Medicine, Florida International University, Miami, FL, USA
| | - Malek Adjouadi
- Department of Electrical and Computer Engineering, Florida International University, Miami, FL, USA. .,Department of Biomedical Engineering, Florida International University, Miami, FL, USA. .,, 10555W. Flagler St, ECE 2220, Miami, FL, 33174, USA.
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Bhagwat N, Pipitone J, Winterburn JL, Guo T, Duerden EG, Voineskos AN, Lepage M, Miller SP, Pruessner JC, Chakravarty MM. Manual-Protocol Inspired Technique for Improving Automated MR Image Segmentation during Label Fusion. Front Neurosci 2016; 10:325. [PMID: 27486386 PMCID: PMC4949270 DOI: 10.3389/fnins.2016.00325] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/21/2015] [Accepted: 06/28/2016] [Indexed: 01/08/2023] Open
Abstract
Recent advances in multi-atlas based algorithms address many of the previous limitations in model-based and probabilistic segmentation methods. However, at the label fusion stage, a majority of algorithms focus primarily on optimizing weight-maps associated with the atlas library based on a theoretical objective function that approximates the segmentation error. In contrast, we propose a novel method—Autocorrecting Walks over Localized Markov Random Fields (AWoL-MRF)—that aims at mimicking the sequential process of manual segmentation, which is the gold-standard for virtually all the segmentation methods. AWoL-MRF begins with a set of candidate labels generated by a multi-atlas segmentation pipeline as an initial label distribution and refines low confidence regions based on a localized Markov random field (L-MRF) model using a novel sequential inference process (walks). We show that AWoL-MRF produces state-of-the-art results with superior accuracy and robustness with a small atlas library compared to existing methods. We validate the proposed approach by performing hippocampal segmentations on three independent datasets: (1) Alzheimer's Disease Neuroimaging Database (ADNI); (2) First Episode Psychosis patient cohort; and (3) A cohort of preterm neonates scanned early in life and at term-equivalent age. We assess the improvement in the performance qualitatively as well as quantitatively by comparing AWoL-MRF with majority vote, STAPLE, and Joint Label Fusion methods. AWoL-MRF reaches a maximum accuracy of 0.881 (dataset 1), 0.897 (dataset 2), and 0.807 (dataset 3) based on Dice similarity coefficient metric, offering significant performance improvements with a smaller atlas library (< 10) over compared methods. We also evaluate the diagnostic utility of AWoL-MRF by analyzing the volume differences per disease category in the ADNI1: Complete Screening dataset. We have made the source code for AWoL-MRF public at: https://github.com/CobraLab/AWoL-MRF.
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Affiliation(s)
- Nikhil Bhagwat
- Institute of Biomaterials and Biomedical Engineering, University of TorontoToronto, ON, Canada; Cerebral Imaging Centre, Douglas Mental Health University InstituteVerdun, QC, Canada; Kimel Family Translational Imaging-Genetics Research Lab, Research Imaging Centre, Campbell Family Mental Health Research Institute, Centre for Addiction and Mental HealthToronto, ON, Canada
| | - Jon Pipitone
- Kimel Family Translational Imaging-Genetics Research Lab, Research Imaging Centre, Campbell Family Mental Health Research Institute, Centre for Addiction and Mental Health Toronto, ON, Canada
| | - Julie L Winterburn
- Institute of Biomaterials and Biomedical Engineering, University of TorontoToronto, ON, Canada; Cerebral Imaging Centre, Douglas Mental Health University InstituteVerdun, QC, Canada; Kimel Family Translational Imaging-Genetics Research Lab, Research Imaging Centre, Campbell Family Mental Health Research Institute, Centre for Addiction and Mental HealthToronto, ON, Canada
| | - Ting Guo
- Neurosciences and Mental Health, The Hospital for Sick Children Research InstituteToronto, ON, Canada; Department of Paediatrics, The Hospital for Sick Children and the University of TorontoToronto, ON, Canada
| | - Emma G Duerden
- Neurosciences and Mental Health, The Hospital for Sick Children Research InstituteToronto, ON, Canada; Department of Paediatrics, The Hospital for Sick Children and the University of TorontoToronto, ON, Canada
| | - Aristotle N Voineskos
- Kimel Family Translational Imaging-Genetics Research Lab, Research Imaging Centre, Campbell Family Mental Health Research Institute, Centre for Addiction and Mental HealthToronto, ON, Canada; Department of Psychiatry, University of TorontoToronto, ON, Canada
| | - Martin Lepage
- Cerebral Imaging Centre, Douglas Mental Health University InstituteVerdun, QC, Canada; Department of Psychiatry, McGill UniversityMontreal, QC, Canada
| | - Steven P Miller
- Neurosciences and Mental Health, The Hospital for Sick Children Research InstituteToronto, ON, Canada; Department of Paediatrics, The Hospital for Sick Children and the University of TorontoToronto, ON, Canada
| | - Jens C Pruessner
- Cerebral Imaging Centre, Douglas Mental Health University InstituteVerdun, QC, Canada; McGill Centre for Studies in AgingMontreal, QC, Canada
| | - M Mallar Chakravarty
- Institute of Biomaterials and Biomedical Engineering, University of TorontoToronto, ON, Canada; Cerebral Imaging Centre, Douglas Mental Health University InstituteVerdun, QC, Canada; Department of Psychiatry, McGill UniversityMontreal, QC, Canada; Biological and Biomedical Engineering, McGill UniversityMontreal, QC, Canada
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Takács P, Vitál Z, Ferincz Á, Staszny Á. Repeatability, Reproducibility, Separative Power and Subjectivity of Different Fish Morphometric Analysis Methods. PLoS One 2016; 11:e0157890. [PMID: 27327896 PMCID: PMC4915670 DOI: 10.1371/journal.pone.0157890] [Citation(s) in RCA: 23] [Impact Index Per Article: 2.9] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/16/2015] [Accepted: 06/05/2016] [Indexed: 11/19/2022] Open
Abstract
We compared the repeatability, reproducibility (intra- and inter-measurer similarity), separative power and subjectivity (measurer effect on results) of four morphometric methods frequently used in ichthyological research, the "traditional" caliper-based (TRA) and truss-network (TRU) distance methods and two geometric methods that compare landmark coordinates on the body (GMB) and scales (GMS). In each case, measurements were performed three times by three measurers on the same specimen of three common cyprinid species (roach Rutilus rutilus (Linnaeus, 1758), bleak Alburnus alburnus (Linnaeus, 1758) and Prussian carp Carassius gibelio (Bloch, 1782)) collected from three closely-situated sites in the Lake Balaton catchment (Hungary) in 2014. TRA measurements were made on conserved specimens using a digital caliper, while TRU, GMB and GMS measurements were undertaken on digital images of the bodies and scales. In most cases, intra-measurer repeatability was similar. While all four methods were able to differentiate the source populations, significant differences were observed in their repeatability, reproducibility and subjectivity. GMB displayed highest overall repeatability and reproducibility and was least burdened by measurer effect. While GMS showed similar repeatability to GMB when fish scales had a characteristic shape, it showed significantly lower reproducability (compared with its repeatability) for each species than the other methods. TRU showed similar repeatability as the GMS. TRA was the least applicable method as measurements were obtained from the fish itself, resulting in poor repeatability and reproducibility. Although all four methods showed some degree of subjectivity, TRA was the only method where population-level detachment was entirely overwritten by measurer effect. Based on these results, we recommend a) avoidance of aggregating different measurer's datasets when using TRA and GMS methods; and b) use of image-based methods for morphometric surveys. Automation of the morphometric workflow would also reduce any measurer effect and eliminate measurement and data-input errors.
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Affiliation(s)
- Péter Takács
- MTA, Centre for Ecological Research, Balaton Limnological Institute, Tihany, Hungary
- * E-mail:
| | - Zoltán Vitál
- MTA, Centre for Ecological Research, Balaton Limnological Institute, Tihany, Hungary
| | - Árpád Ferincz
- Szent István University, Department of Aquaculture, Gödöllő, Hungary
| | - Ádám Staszny
- Szent István University, Department of Aquaculture, Gödöllő, Hungary
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31
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Knight MJ, McCann B, Kauppinen RA, Coulthard EJ. Magnetic Resonance Imaging to Detect Early Molecular and Cellular Changes in Alzheimer's Disease. Front Aging Neurosci 2016; 8:139. [PMID: 27378911 PMCID: PMC4909770 DOI: 10.3389/fnagi.2016.00139] [Citation(s) in RCA: 23] [Impact Index Per Article: 2.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/08/2015] [Accepted: 05/27/2016] [Indexed: 11/13/2022] Open
Abstract
Recent pharmaceutical trials have demonstrated that slowing or reversing pathology in Alzheimer's disease is likely to be possible only in the earliest stages of disease, perhaps even before significant symptoms develop. Pathology in Alzheimer's disease accumulates for well over a decade before symptoms are detected giving a large potential window of opportunity for intervention. It is therefore important that imaging techniques detect subtle changes in brain tissue before significant macroscopic brain atrophy. Current diagnostic techniques often do not permit early diagnosis or are too expensive for routine clinical use. Magnetic Resonance Imaging (MRI) is the most versatile, affordable, and powerful imaging modality currently available, being able to deliver detailed analyses of anatomy, tissue volumes, and tissue state. In this mini-review, we consider how MRI might detect patients at risk of future dementia in the early stages of pathological change when symptoms are mild. We consider the contributions made by the various modalities of MRI (structural, diffusion, perfusion, relaxometry) in identifying not just atrophy (a late-stage AD symptom) but more subtle changes reflective of early dementia pathology. The sensitivity of MRI not just to gross anatomy but to the underlying "health" at the cellular (and even molecular) scales, makes it very well suited to this task.
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Affiliation(s)
- Michael J Knight
- School of Experimental Psychology, University of Bristol Bristol, UK
| | - Bryony McCann
- School of Experimental Psychology, University of Bristol Bristol, UK
| | - Risto A Kauppinen
- School of Experimental Psychology, University of BristolBristol, UK; Clinical Research and Imaging Centre, University of BristolBristol, UK
| | - Elizabeth J Coulthard
- Research into Memory the Brain and Dementia Group, Institute of Clinical Neuroscience, University of BristolBristol, UK; North Bristol NHS TrustBristol, UK
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Liu S, Cai W, Pujol S, Kikinis R, Feng DD. Cross-View Neuroimage Pattern Analysis in Alzheimer's Disease Staging. Front Aging Neurosci 2016; 8:23. [PMID: 26941639 PMCID: PMC4763344 DOI: 10.3389/fnagi.2016.00023] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/08/2015] [Accepted: 02/01/2016] [Indexed: 01/24/2023] Open
Abstract
The research on staging of pre-symptomatic and prodromal phase of neurological disorders, e.g., Alzheimer's disease (AD), is essential for prevention of dementia. New strategies for AD staging with a focus on early detection, are demanded to optimize potential efficacy of disease-modifying therapies that can halt or slow the disease progression. Recently, neuroimaging are increasingly used as additional research-based markers to detect AD onset and predict conversion of MCI and normal control (NC) to AD. Researchers have proposed a variety of neuroimaging biomarkers to characterize the patterns of the pathology of AD and MCI, and suggested that multi-view neuroimaging biomarkers could lead to better performance than single-view biomarkers in AD staging. However, it is still unclear what leads to such synergy and how to preserve or maximize. In an attempt to answer these questions, we proposed a cross-view pattern analysis framework for investigating the synergy between different neuroimaging biomarkers. We quantitatively analyzed nine types of biomarkers derived from FDG-PET and T1-MRI, and evaluated their performance in a task of classifying AD, MCI, and NC subjects obtained from the ADNI baseline cohort. The experiment results showed that these biomarkers could depict the pathology of AD from different perspectives, and output distinct patterns that are significantly associated with the disease progression. Most importantly, we found that these features could be separated into clusters, each depicting a particular aspect; and the inter-cluster features could always achieve better performance than the intra-cluster features in AD staging.
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Affiliation(s)
- Sidong Liu
- The Biomedical and Multimedia Information Technology Research Group, School of Information Technologies, The University of SydneySydney, NSW, Australia
| | - Weidong Cai
- The Biomedical and Multimedia Information Technology Research Group, School of Information Technologies, The University of SydneySydney, NSW, Australia
| | - Sonia Pujol
- The Surgical Planning Laboratory, Harvard Medical School, Brigham and Women's HospitalBoston, MA, USA
| | - Ron Kikinis
- The Surgical Planning Laboratory, Harvard Medical School, Brigham and Women's HospitalBoston, MA, USA
| | - Dagan D. Feng
- The Biomedical and Multimedia Information Technology Research Group, School of Information Technologies, The University of SydneySydney, NSW, Australia
- The Med-X Research Institute, Shanghai Jiao Tong UniversityShanghai, China
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Garali I, Adel M, Bourennane S, Ceccaldi M, Guedj E. Brain region of interest selection for 18FDG positrons emission tomography computer-aided image classification. Ing Rech Biomed 2016. [DOI: 10.1016/j.irbm.2015.10.002] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
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34
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Sapey-Triomphe LA, Heckemann RA, Boublay N, Dorey JM, Hénaff MA, Rouch I, Padovan C, Hammers A, Krolak-Salmon P. Neuroanatomical Correlates of Recognizing Face Expressions in Mild Stages of Alzheimer's Disease. PLoS One 2015; 10:e0143586. [PMID: 26673928 PMCID: PMC4684414 DOI: 10.1371/journal.pone.0143586] [Citation(s) in RCA: 29] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/02/2015] [Accepted: 11/07/2015] [Indexed: 11/19/2022] Open
Abstract
Early Alzheimer's disease can involve social disinvestment, possibly as a consequence of impairment of nonverbal communication skills. This study explores whether patients with Alzheimer's disease at the mild cognitive impairment or mild dementia stage have impaired recognition of emotions in facial expressions, and describes neuroanatomical correlates of emotion processing impairment. As part of the ongoing PACO study (personality, Alzheimer's disease and behaviour), 39 patients with Alzheimer's disease at the mild cognitive impairment or mild dementia stage and 39 matched controls completed tests involving discrimination of four basic emotions-happiness, fear, anger, and disgust-on photographs of faces. In patients, automatic volumetry of 83 brain regions was performed on structural magnetic resonance images using MAPER (multi-atlas propagation with enhanced registration). From the literature, we identified for each of the four basic emotions one brain region thought to be primarily associated with the function of recognizing that emotion. We hypothesized that the volume of each of these regions would be correlated with subjects' performance in recognizing the associated emotion. Patients showed deficits of basic emotion recognition, and these impairments were correlated with the volumes of the expected regions of interest. Unexpectedly, most of these correlations were negative: better emotional facial recognition was associated with lower brain volume. In particular, recognition of fear was negatively correlated with the volume of amygdala, disgust with pallidum, and happiness with fusiform gyrus. Recognition impairment in mild stages of Alzheimer's disease for a given emotion was thus associated with less visible atrophy of functionally responsible brain structures within the patient group. Possible explanations for this counterintuitive result include neuroinflammation, regional β-amyloid deposition, or transient overcompensation during early stages of Alzheimer's disease.
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Affiliation(s)
- Laurie-Anne Sapey-Triomphe
- The Neurodis Foundation, CERMEP Imagerie du Vivant, Lyon, France
- Brain Dynamics and Cognition, Lyon Neuroscience Research Center, INSERM U1028, CNRS UMR 5292, Lyon, France
- Ecole Normale Supérieure de Lyon, Lyon, France
| | - Rolf A. Heckemann
- The Neurodis Foundation, CERMEP Imagerie du Vivant, Lyon, France
- MedTech West at Sahlgrenska University Hospital, University of Gothenburg, Gothenburg, Sweden
- Division of Brain Sciences, Imperial College London, London, United Kingdom
| | - Nawele Boublay
- Clinical and Research Memory Center of Lyon, Hôpital des Charpennes, Hospices Civils de Lyon, Lyon, France
- Department of Medical Information and Research Evaluation, Hospices Civils de Lyon, Lyon, France
- University Lyon 1, F-69000, Lyon, France
| | - Jean-Michel Dorey
- Clinical and Research Memory Center of Lyon, Hôpital des Charpennes, Hospices Civils de Lyon, Lyon, France
- Centre Hospitalier Le Vinatier, Pôle Est, Bron, France
| | - Marie-Anne Hénaff
- Brain Dynamics and Cognition, Lyon Neuroscience Research Center, INSERM U1028, CNRS UMR 5292, Lyon, France
| | - Isabelle Rouch
- Clinical and Research Memory Center of Lyon, Hôpital des Charpennes, Hospices Civils de Lyon, Lyon, France
| | - Catherine Padovan
- Brain Dynamics and Cognition, Lyon Neuroscience Research Center, INSERM U1028, CNRS UMR 5292, Lyon, France
- Centre Hospitalier Le Vinatier, Pôle Est, Bron, France
| | - Alexander Hammers
- The Neurodis Foundation, CERMEP Imagerie du Vivant, Lyon, France
- Division of Imaging Sciences and Biomedical Engineering, King’s College London, London, United Kingdom
| | - Pierre Krolak-Salmon
- Brain Dynamics and Cognition, Lyon Neuroscience Research Center, INSERM U1028, CNRS UMR 5292, Lyon, France
- Clinical and Research Memory Center of Lyon, Hôpital des Charpennes, Hospices Civils de Lyon, Lyon, France
- University Lyon 1, F-69000, Lyon, France
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Abstract
Content-based medical image retrieval (CBMIR) is an active research area for disease diagnosis and treatment but it can be problematic given the small visual variations between anatomical structures. We propose a retrieval method based on a bag-of-visual-words (BoVW) to identify discriminative characteristics between different medical images with Pruned Dictionary based on Latent Semantic Topic description. We refer to this as the PD-LST retrieval. Our method has two main components. First, we calculate a topic-word significance value for each visual word given a certain latent topic to evaluate how the word is connected to this latent topic. The latent topics are learnt, based on the relationship between the images and words, and are employed to bridge the gap between low-level visual features and high-level semantics. These latent topics describe the images and words semantically and can thus facilitate more meaningful comparisons between the words. Second, we compute an overall-word significance value to evaluate the significance of a visual word within the entire dictionary. We designed an iterative ranking method to measure overall-word significance by considering the relationship between all latent topics and words. The words with higher values are considered meaningful with more significant discriminative power in differentiating medical images. We evaluated our method on two public medical imaging datasets and it showed improved retrieval accuracy and efficiency.
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Zhang F, Song Y, Cai W, Liu S, Liu S, Pujol S, Kikinis R, Xia Y, Fulham MJ, Feng DD, Alzheimers Disease Neuroimaging Initiative. Pairwise Latent Semantic Association for Similarity Computation in Medical Imaging. IEEE Trans Biomed Eng 2015; 63:1058-1069. [PMID: 26372117 DOI: 10.1109/tbme.2015.2478028] [Citation(s) in RCA: 17] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Abstract
Retrieving medical images that present similar diseases is an active research area for diagnostics and therapy. However, it can be problematic given the visual variations between anatomical structures. In this paper, we propose a new feature extraction method for similarity computation in medical imaging. Instead of the low-level visual appearance, we design a CCA-PairLDA feature representation method to capture the similarity between images with high-level semantics. First, we extract the PairLDA topics to represent an image as a mixture of latent semantic topics in an image pair context. Second, we generate a CCA-correlation model to represent the semantic association between an image pair for similarity computation. While PairLDA adjusts the latent topics for all image pairs, CCA-correlation helps to associate an individual image pair. In this way, the semantic descriptions of an image pair are closely correlated, and naturally correspond to similarity computation between images. We evaluated our method on two public medical imaging datasets for image retrieval and showed improved performance.
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Affiliation(s)
- Fan Zhang
- Biomedical and Multimedia Information Technology Research Group, School of Information Technologies, University of Sydney, Sydney, N.S.W., Australia
| | - Yang Song
- Biomedical and BMIT Research Group, School of Information Technologies, University of Sydney
| | - Weidong Cai
- Biomedical and Multimedia Information Technology Research Group, School of Information Technologies, University of Sydney
| | - Sidong Liu
- Biomedical and BMIT Research Group, School of Information Technologies, University of Sydney
| | - Siqi Liu
- Biomedical and Multimedia Information Technology Research Group, School of Information Technologies, University of Sydney
| | - Sonia Pujol
- Surgical Planning Lab, Brigham & Women's Hospital, Harvard Medical School
| | - Ron Kikinis
- Surgical Planning Lab, Brigham & Women's Hospital, Harvard Medical School
| | - Yong Xia
- Shaanxi Key Lab of Speech and Image Information Processing, School of Computer Science and Technology, Northwestern Polytechnical University
| | - Michael J Fulham
- Department of PET and Nuclear Medicine, Royal Prince Alfred Hospital
| | - David Dagan Feng
- BMIT Research Group, School of Information Technologies, University of Sydney
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Liu S, Cai W, Liu S, Zhang F, Fulham M, Feng D, Pujol S, Kikinis R. Multimodal neuroimaging computing: the workflows, methods, and platforms. Brain Inform 2015; 2:181-195. [PMID: 27747508 PMCID: PMC4737665 DOI: 10.1007/s40708-015-0020-4] [Citation(s) in RCA: 14] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/29/2015] [Accepted: 08/20/2015] [Indexed: 12/20/2022] Open
Abstract
The last two decades have witnessed the explosive growth in the development and use of noninvasive neuroimaging technologies that advance the research on human brain under normal and pathological conditions. Multimodal neuroimaging has become a major driver of current neuroimaging research due to the recognition of the clinical benefits of multimodal data, and the better access to hybrid devices. Multimodal neuroimaging computing is very challenging, and requires sophisticated computing to address the variations in spatiotemporal resolution and merge the biophysical/biochemical information. We review the current workflows and methods for multimodal neuroimaging computing, and also demonstrate how to conduct research using the established neuroimaging computing packages and platforms.
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Affiliation(s)
- Sidong Liu
- School of IT, The University of Sydney, Sydney, Australia.
| | - Weidong Cai
- School of IT, The University of Sydney, Sydney, Australia
| | - Siqi Liu
- School of IT, The University of Sydney, Sydney, Australia
| | - Fan Zhang
- School of IT, The University of Sydney, Sydney, Australia
- Surgical Planning Laboratory, Harvard Medical School, Boston, USA
| | - Michael Fulham
- Department of PET and Nuclear Medicine, Royal Prince Alfred Hospital, Sydney Medical School, The University of Sydney, Sydney, Australia
| | - Dagan Feng
- School of IT, The University of Sydney, Sydney, Australia
- Med-X Research Institute, Shanghai Jiao Tong University, Shanghai, China
| | - Sonia Pujol
- Surgical Planning Laboratory, Harvard Medical School, Boston, USA
| | - Ron Kikinis
- Surgical Planning Laboratory, Harvard Medical School, Boston, USA
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38
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Amygdalar and hippocampal volume: A comparison between manual segmentation, Freesurfer and VBM. J Neurosci Methods 2015; 253:254-61. [DOI: 10.1016/j.jneumeth.2015.05.024] [Citation(s) in RCA: 70] [Impact Index Per Article: 7.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/26/2015] [Revised: 04/24/2015] [Accepted: 05/26/2015] [Indexed: 12/16/2022]
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Iglesias JE, Sabuncu MR. Multi-atlas segmentation of biomedical images: A survey. Med Image Anal 2015; 24:205-219. [PMID: 26201875 PMCID: PMC4532640 DOI: 10.1016/j.media.2015.06.012] [Citation(s) in RCA: 358] [Impact Index Per Article: 39.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/01/2014] [Revised: 06/12/2015] [Accepted: 06/15/2015] [Indexed: 10/23/2022]
Abstract
Multi-atlas segmentation (MAS), first introduced and popularized by the pioneering work of Rohlfing, et al. (2004), Klein, et al. (2005), and Heckemann, et al. (2006), is becoming one of the most widely-used and successful image segmentation techniques in biomedical applications. By manipulating and utilizing the entire dataset of "atlases" (training images that have been previously labeled, e.g., manually by an expert), rather than some model-based average representation, MAS has the flexibility to better capture anatomical variation, thus offering superior segmentation accuracy. This benefit, however, typically comes at a high computational cost. Recent advancements in computer hardware and image processing software have been instrumental in addressing this challenge and facilitated the wide adoption of MAS. Today, MAS has come a long way and the approach includes a wide array of sophisticated algorithms that employ ideas from machine learning, probabilistic modeling, optimization, and computer vision, among other fields. This paper presents a survey of published MAS algorithms and studies that have applied these methods to various biomedical problems. In writing this survey, we have three distinct aims. Our primary goal is to document how MAS was originally conceived, later evolved, and now relates to alternative methods. Second, this paper is intended to be a detailed reference of past research activity in MAS, which now spans over a decade (2003-2014) and entails novel methodological developments and application-specific solutions. Finally, our goal is to also present a perspective on the future of MAS, which, we believe, will be one of the dominant approaches in biomedical image segmentation.
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Affiliation(s)
| | - Mert R Sabuncu
- A.A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Harvard Medical School, USA.
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40
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Weiner MW, Veitch DP, Aisen PS, Beckett LA, Cairns NJ, Cedarbaum J, Green RC, Harvey D, Jack CR, Jagust W, Luthman J, Morris JC, Petersen RC, Saykin AJ, Shaw L, Shen L, Schwarz A, Toga AW, Trojanowski JQ. 2014 Update of the Alzheimer's Disease Neuroimaging Initiative: A review of papers published since its inception. Alzheimers Dement 2015; 11:e1-120. [PMID: 26073027 PMCID: PMC5469297 DOI: 10.1016/j.jalz.2014.11.001] [Citation(s) in RCA: 203] [Impact Index Per Article: 22.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Revised: 04/18/2013] [Indexed: 01/18/2023]
Abstract
The Alzheimer's Disease Neuroimaging Initiative (ADNI) is an ongoing, longitudinal, multicenter study designed to develop clinical, imaging, genetic, and biochemical biomarkers for the early detection and tracking of Alzheimer's disease (AD). The initial study, ADNI-1, enrolled 400 subjects with early mild cognitive impairment (MCI), 200 with early AD, and 200 cognitively normal elderly controls. ADNI-1 was extended by a 2-year Grand Opportunities grant in 2009 and by a competitive renewal, ADNI-2, which enrolled an additional 550 participants and will run until 2015. This article reviews all papers published since the inception of the initiative and summarizes the results to the end of 2013. The major accomplishments of ADNI have been as follows: (1) the development of standardized methods for clinical tests, magnetic resonance imaging (MRI), positron emission tomography (PET), and cerebrospinal fluid (CSF) biomarkers in a multicenter setting; (2) elucidation of the patterns and rates of change of imaging and CSF biomarker measurements in control subjects, MCI patients, and AD patients. CSF biomarkers are largely consistent with disease trajectories predicted by β-amyloid cascade (Hardy, J Alzheimer's Dis 2006;9(Suppl 3):151-3) and tau-mediated neurodegeneration hypotheses for AD, whereas brain atrophy and hypometabolism levels show predicted patterns but exhibit differing rates of change depending on region and disease severity; (3) the assessment of alternative methods of diagnostic categorization. Currently, the best classifiers select and combine optimum features from multiple modalities, including MRI, [(18)F]-fluorodeoxyglucose-PET, amyloid PET, CSF biomarkers, and clinical tests; (4) the development of blood biomarkers for AD as potentially noninvasive and low-cost alternatives to CSF biomarkers for AD diagnosis and the assessment of α-syn as an additional biomarker; (5) the development of methods for the early detection of AD. CSF biomarkers, β-amyloid 42 and tau, as well as amyloid PET may reflect the earliest steps in AD pathology in mildly symptomatic or even nonsymptomatic subjects and are leading candidates for the detection of AD in its preclinical stages; (6) the improvement of clinical trial efficiency through the identification of subjects most likely to undergo imminent future clinical decline and the use of more sensitive outcome measures to reduce sample sizes. Multimodal methods incorporating APOE status and longitudinal MRI proved most highly predictive of future decline. Refinements of clinical tests used as outcome measures such as clinical dementia rating-sum of boxes further reduced sample sizes; (7) the pioneering of genome-wide association studies that leverage quantitative imaging and biomarker phenotypes, including longitudinal data, to confirm recently identified loci, CR1, CLU, and PICALM and to identify novel AD risk loci; (8) worldwide impact through the establishment of ADNI-like programs in Japan, Australia, Argentina, Taiwan, China, Korea, Europe, and Italy; (9) understanding the biology and pathobiology of normal aging, MCI, and AD through integration of ADNI biomarker and clinical data to stimulate research that will resolve controversies about competing hypotheses on the etiopathogenesis of AD, thereby advancing efforts to find disease-modifying drugs for AD; and (10) the establishment of infrastructure to allow sharing of all raw and processed data without embargo to interested scientific investigators throughout the world.
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Affiliation(s)
- Michael W Weiner
- Department of Veterans Affairs Medical Center, Center for Imaging of Neurodegenerative Diseases, San Francisco, CA, USA; Department of Radiology, University of California, San Francisco, CA, USA; Department of Medicine, University of California, San Francisco, CA, USA; Department of Psychiatry, University of California, San Francisco, CA, USA; Department of Neurology, University of California, San Francisco, CA, USA.
| | - Dallas P Veitch
- Department of Veterans Affairs Medical Center, Center for Imaging of Neurodegenerative Diseases, San Francisco, CA, USA
| | - Paul S Aisen
- Department of Neurosciences, University of California, San Diego, La Jolla, CA, USA
| | - Laurel A Beckett
- Division of Biostatistics, Department of Public Health Sciences, University of California, Davis, CA, USA
| | - Nigel J Cairns
- Knight Alzheimer's Disease Research Center, Washington University School of Medicine, Saint Louis, MO, USA; Department of Neurology, Washington University School of Medicine, Saint Louis, MO, USA
| | - Jesse Cedarbaum
- Neurology Early Clinical Development, Biogen Idec, Cambridge, MA, USA
| | - Robert C Green
- Division of Genetics, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA
| | - Danielle Harvey
- Division of Biostatistics, Department of Public Health Sciences, University of California, Davis, CA, USA
| | | | - William Jagust
- Helen Wills Neuroscience Institute, University of California Berkeley, Berkeley, CA, USA
| | - Johan Luthman
- Neuroscience Clinical Development, Neuroscience & General Medicine Product Creation Unit, Eisai Inc., Philadelphia, PA, USA
| | - John C Morris
- Department of Neurosciences, University of California, San Diego, La Jolla, CA, USA
| | | | - Andrew J Saykin
- Department of Radiology and Imaging Sciences, Indiana University School of Medicine, Indianapolis, IN, USA; Department of Medical and Molecular Genetics, Indiana University School of Medicine, Indianapolis, IN, USA
| | - Leslie Shaw
- Department of Pathology and Laboratory Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Li Shen
- Department of Radiology and Imaging Sciences, Indiana University School of Medicine, Indianapolis, IN, USA
| | - Adam Schwarz
- Tailored Therapeutics, Eli Lilly and Company, Indianapolis, IN, USA
| | - Arthur W Toga
- Laboratory of Neuroimaging, Institute of Neuroimaging and Informatics, Keck School of Medicine of University of Southern California, Los Angeles, CA, USA
| | - John Q Trojanowski
- Institute on Aging, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA; Alzheimer's Disease Core Center, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA; Udall Parkinson's Research Center, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA; Department of Pathology and Laboratory Medicine, Center for Neurodegenerative Research, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
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Sargolzaei S, Sargolzaei A, Cabrerizo M, Chen G, Goryawala M, Noei S, Zhou Q, Duara R, Barker W, Adjouadi M. A practical guideline for intracranial volume estimation in patients with Alzheimer's disease. BMC Bioinformatics 2015; 16 Suppl 7:S8. [PMID: 25953026 PMCID: PMC4423585 DOI: 10.1186/1471-2105-16-s7-s8] [Citation(s) in RCA: 25] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/04/2022] Open
Abstract
Background Intracranial volume (ICV) is an important normalization measure used in morphometric analyses to correct for head size in studies of Alzheimer Disease (AD). Inaccurate ICV estimation could introduce bias in the outcome. The current study provides a decision aid in defining protocols for ICV estimation in patients with Alzheimer disease in terms of sampling frequencies that can be optimally used on the volumetric MRI data, and the type of software most suitable for use in estimating the ICV measure. Methods Two groups of 22 subjects are considered, including adult controls (AC) and patients with Alzheimer Disease (AD). Reference measurements were calculated for each subject by manually tracing intracranial cavity by the means of visual inspection. The reliability of reference measurements were assured through intra- and inter- variation analyses. Three publicly well-known software packages (Freesurfer, FSL, and SPM) were examined in their ability to automatically estimate ICV across the groups. Results Analysis of the results supported the significant effect of estimation method, gender, cognitive condition of the subject and the interaction among method and cognitive condition factors in the measured ICV. Results on sub-sampling studies with a 95% confidence showed that in order to keep the accuracy of the interleaved slice sampling protocol above 99%, the sampling period cannot exceed 20 millimeters for AC and 15 millimeters for AD. Freesurfer showed promising estimates for both adult groups. However SPM showed more consistency in its ICV estimation over the different phases of the study. Conclusions This study emphasized the importance in selecting the appropriate protocol, the choice of the sampling period in the manual estimation of ICV and selection of suitable software for the automated estimation of ICV. The current study serves as an initial framework for establishing an appropriate protocol in both manual and automatic ICV estimations with different subject populations.
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42
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Hammers A. Diagnostica per immagini cerebrale tramite tomografia a emissione di positroni in neurologia: dalla misurazione del flusso sanguigno e del metabolismo all’esplorazione della neurotrasmissione. Neurologia 2015. [DOI: 10.1016/s1634-7072(15)70502-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/24/2022] Open
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Tong T, Gray K, Gao Q, Chen L, Rueckert D. Nonlinear Graph Fusion for Multi-modal Classification of Alzheimer’s Disease. MACHINE LEARNING IN MEDICAL IMAGING 2015. [DOI: 10.1007/978-3-319-24888-2_10] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/09/2023]
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44
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Robust whole-brain segmentation: application to traumatic brain injury. Med Image Anal 2014; 21:40-58. [PMID: 25596765 DOI: 10.1016/j.media.2014.12.003] [Citation(s) in RCA: 100] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/08/2014] [Revised: 12/14/2014] [Accepted: 12/15/2014] [Indexed: 11/23/2022]
Abstract
We propose a framework for the robust and fully-automatic segmentation of magnetic resonance (MR) brain images called "Multi-Atlas Label Propagation with Expectation-Maximisation based refinement" (MALP-EM). The presented approach is based on a robust registration approach (MAPER), highly performant label fusion (joint label fusion) and intensity-based label refinement using EM. We further adapt this framework to be applicable for the segmentation of brain images with gross changes in anatomy. We propose to account for consistent registration errors by relaxing anatomical priors obtained by multi-atlas propagation and a weighting scheme to locally combine anatomical atlas priors and intensity-refined posterior probabilities. The method is evaluated on a benchmark dataset used in a recent MICCAI segmentation challenge. In this context we show that MALP-EM is competitive for the segmentation of MR brain scans of healthy adults when compared to state-of-the-art automatic labelling techniques. To demonstrate the versatility of the proposed approach, we employed MALP-EM to segment 125 MR brain images into 134 regions from subjects who had sustained traumatic brain injury (TBI). We employ a protocol to assess segmentation quality if no manual reference labels are available. Based on this protocol, three independent, blinded raters confirmed on 13 MR brain scans with pathology that MALP-EM is superior to established label fusion techniques. We visually confirm the robustness of our segmentation approach on the full cohort and investigate the potential of derived symmetry-based imaging biomarkers that correlate with and predict clinically relevant variables in TBI such as the Marshall Classification (MC) or Glasgow Outcome Score (GOS). Specifically, we show that we are able to stratify TBI patients with favourable outcomes from non-favourable outcomes with 64.7% accuracy using acute-phase MR images and 66.8% accuracy using follow-up MR images. Furthermore, we are able to differentiate subjects with the presence of a mass lesion or midline shift from those with diffuse brain injury with 76.0% accuracy. The thalamus, putamen, pallidum and hippocampus are particularly affected. Their involvement predicts TBI disease progression.
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45
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Liu S, Liu S, Cai W, Che H, Pujol S, Kikinis R, Feng D, Fulham MJ. Multimodal neuroimaging feature learning for multiclass diagnosis of Alzheimer's disease. IEEE Trans Biomed Eng 2014; 62:1132-40. [PMID: 25423647 DOI: 10.1109/tbme.2014.2372011] [Citation(s) in RCA: 224] [Impact Index Per Article: 22.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/04/2023]
Abstract
The accurate diagnosis of Alzheimer's disease (AD) is essential for patient care and will be increasingly important as disease modifying agents become available, early in the course of the disease. Although studies have applied machine learning methods for the computer-aided diagnosis of AD, a bottleneck in the diagnostic performance was shown in previous methods, due to the lacking of efficient strategies for representing neuroimaging biomarkers. In this study, we designed a novel diagnostic framework with deep learning architecture to aid the diagnosis of AD. This framework uses a zero-masking strategy for data fusion to extract complementary information from multiple data modalities. Compared to the previous state-of-the-art workflows, our method is capable of fusing multimodal neuroimaging features in one setting and has the potential to require less labeled data. A performance gain was achieved in both binary classification and multiclass classification of AD. The advantages and limitations of the proposed framework are discussed.
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46
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Pipitone J, Park MTM, Winterburn J, Lett TA, Lerch JP, Pruessner JC, Lepage M, Voineskos AN, Chakravarty MM. Multi-atlas segmentation of the whole hippocampus and subfields using multiple automatically generated templates. Neuroimage 2014; 101:494-512. [DOI: 10.1016/j.neuroimage.2014.04.054] [Citation(s) in RCA: 268] [Impact Index Per Article: 26.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/04/2013] [Revised: 04/15/2014] [Accepted: 04/19/2014] [Indexed: 11/16/2022] Open
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47
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Perez-Gonzalez JL, Yanez-Suarez O, Bribiesca E, Cosío FA, Jiménez JR, Medina-Bañuelos V. Description and classification of normal and pathological aging processes based on brain magnetic resonance imaging morphology measures. J Med Imaging (Bellingham) 2014; 1:034002. [PMID: 26158061 PMCID: PMC4478725 DOI: 10.1117/1.jmi.1.3.034002] [Citation(s) in RCA: 8] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/09/2014] [Revised: 07/15/2014] [Accepted: 09/15/2014] [Indexed: 11/14/2022] Open
Abstract
We present a discrete compactness (DC) index, together with a classification scheme, based both on the size and shape features extracted from brain volumes, to determine different aging stages: healthy controls (HC), mild cognitive impairment (MCI), and Alzheimer's disease (AD). A set of 30 brain magnetic resonance imaging (MRI) volumes for each group was segmented and two indices were measured for several structures: three-dimensional DC and normalized volumes (NVs). The discrimination power of these indices was determined by means of the area under the curve (AUC) of the receiver operating characteristic, where the proposed compactness index showed an average AUC of 0.7 for HC versus MCI comparison, 0.9 for HC versus AD separation, and 0.75 for MCI versus AD groups. In all cases, this index outperformed the discrimination capability of the NV. Using selected features from the set of DC and NV measures, three support vector machines were optimized and validated for the pairwise separation of the three classes. Our analysis shows classification rates of up to 98.3% between HC and AD, 85% between HC and MCI, and 93.3% for MCI and AD separation. These results outperform those reported in the literature and demonstrate the viability of the proposed morphological indices to classify different aging stages.
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Affiliation(s)
- Jorge Luis Perez-Gonzalez
- Universidad Autónoma Metropolitana, Neuroimaging Laboratory, Department of Electrical Engineering, Iztapalapa, México D.F. 09340, Mexico
| | - Oscar Yanez-Suarez
- Universidad Autónoma Metropolitana, Neuroimaging Laboratory, Department of Electrical Engineering, Iztapalapa, México D.F. 09340, Mexico
| | - Ernesto Bribiesca
- Universidad Nacional Autónoma de México, IIMAS, Department of Computer Science, México D.F. 04510, Mexico
| | - Fernando Arámbula Cosío
- Universidad Nacional Autónoma de México, Biomedical Imaging Lab. Centro de Ciencias Aplicadas y Desarrollo Tecnológico, México D.F. 04510, Mexico
| | - Juan Ramón Jiménez
- Universidad Autónoma Metropolitana, Neuroimaging Laboratory, Department of Electrical Engineering, Iztapalapa, México D.F. 09340, Mexico
| | - Veronica Medina-Bañuelos
- Universidad Autónoma Metropolitana, Neuroimaging Laboratory, Department of Electrical Engineering, Iztapalapa, México D.F. 09340, Mexico
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Martinez-Torteya A, Rodriguez-Rojas J, Celaya-Padilla JM, Galván-Tejada JI, Treviño V, Tamez-Peña J. Magnetization-prepared rapid acquisition with gradient echo magnetic resonance imaging signal and texture features for the prediction of mild cognitive impairment to Alzheimer's disease progression. J Med Imaging (Bellingham) 2014; 1:031005. [PMID: 26158047 DOI: 10.1117/1.jmi.1.3.031005] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/01/2014] [Revised: 07/27/2014] [Accepted: 08/22/2014] [Indexed: 01/31/2023] Open
Abstract
Early diagnoses of Alzheimer's disease (AD) would confer many benefits. Several biomarkers have been proposed to achieve such a task, where features extracted from magnetic resonance imaging (MRI) have played an important role. However, studies have focused exclusively on morphological characteristics. This study aims to determine whether features relating to the signal and texture of the image could predict mild cognitive impairment (MCI) to AD progression. Clinical, biological, and positron emission tomography information and MRI images of 62 subjects from the AD neuroimaging initiative were used in this study, extracting 4150 features from each MRI. Within this multimodal database, a feature selection algorithm was used to obtain an accurate and small logistic regression model, generated by a methodology that yielded a mean blind test accuracy of 0.79. This model included six features, five of them obtained from the MRI images, and one obtained from genotyping. A risk analysis divided the subjects into low-risk and high-risk groups according to a prognostic index. The groups were statistically different ([Formula: see text]). These results demonstrated that MRI features related to both signal and texture add MCI to AD predictive power, and supported the ongoing notion that multimodal biomarkers outperform single-modality ones.
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Affiliation(s)
- Antonio Martinez-Torteya
- Tecnológico de Monterrey , Cátedra de Bioinformática, Escuela de Ingeniería, Departamento de Ciencias Computacionales, Monterrey 64849, Mexico
| | - Juan Rodriguez-Rojas
- Tecnológico de Monterrey , Cátedra de Bioinformática, Escuela de Ingeniería, Departamento de Ciencias Computacionales, Monterrey 64849, Mexico
| | - José M Celaya-Padilla
- Tecnológico de Monterrey , Cátedra de Bioinformática, Escuela de Ingeniería, Departamento de Ciencias Computacionales, Monterrey 64849, Mexico
| | - Jorge I Galván-Tejada
- Tecnológico de Monterrey , Cátedra de Bioinformática, Escuela de Ingeniería, Departamento de Ciencias Computacionales, Monterrey 64849, Mexico
| | - Victor Treviño
- Tecnológico de Monterrey , Cátedra de Bioinformática, Escuela de Ingeniería, Departamento de Ciencias Computacionales, Monterrey 64849, Mexico ; Tecnológico de Monterrey , Cátedra de Bioinformática, Escuela de Medicina, Departamento de Investigación e Innovación, Monterrey 64710, Mexico
| | - Jose Tamez-Peña
- Tecnológico de Monterrey , Cátedra de Bioinformática, Escuela de Ingeniería, Departamento de Ciencias Computacionales, Monterrey 64849, Mexico ; Tecnológico de Monterrey , Cátedra de Bioinformática, Escuela de Medicina, Departamento de Investigación e Innovación, Monterrey 64710, Mexico
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Liu S, Cai W, Wen L, Feng DD, Pujol S, Kikinis R, Fulham MJ, Eberl S. Multi-Channel neurodegenerative pattern analysis and its application in Alzheimer's disease characterization. Comput Med Imaging Graph 2014; 38:436-44. [PMID: 24933011 PMCID: PMC4135007 DOI: 10.1016/j.compmedimag.2014.05.003] [Citation(s) in RCA: 25] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/12/2012] [Revised: 04/10/2014] [Accepted: 05/02/2014] [Indexed: 11/25/2022]
Abstract
Neuroimaging has played an important role in non-invasive diagnosis and differentiation of neurodegenerative disorders, such as Alzheimer's disease and Mild Cognitive Impairment. Various features have been extracted from the neuroimaging data to characterize the disorders, and these features can be roughly divided into global and local features. Recent studies show a tendency of using local features in disease characterization, since they are capable of identifying the subtle disease-specific patterns associated with the effects of the disease on human brain. However, problems arise if the neuroimaging database involved multiple disorders or progressive disorders, as disorders of different types or at different progressive stages might exhibit different degenerative patterns. It is difficult for the researchers to reach consensus on what brain regions could effectively distinguish multiple disorders or multiple progression stages. In this study we proposed a Multi-Channel pattern analysis approach to identify the most discriminative local brain metabolism features for neurodegenerative disorder characterization. We compared our method to global methods and other pattern analysis methods based on clinical expertise or statistics tests. The preliminary results suggested that the proposed Multi-Channel pattern analysis method outperformed other approaches in Alzheimer's disease characterization, and meanwhile provided important insights into the underlying pathology of Alzheimer's disease and Mild Cognitive Impairment.
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Affiliation(s)
- Sidong Liu
- Biomedical and Multimedia Information Technology (BMIT) Research Group, School of Information Technologies, University of Sydney, Australia; Surgical Planning Laboratory (SPL), Brigham and Women's Hospital, Harvard Medical School, United States.
| | - Weidong Cai
- Biomedical and Multimedia Information Technology (BMIT) Research Group, School of Information Technologies, University of Sydney, Australia
| | - Lingfeng Wen
- Biomedical and Multimedia Information Technology (BMIT) Research Group, School of Information Technologies, University of Sydney, Australia; Department of PET and Nuclear Medicine, Royal Prince Alfred Hospital, Sydney, Australia
| | - David Dagan Feng
- Biomedical and Multimedia Information Technology (BMIT) Research Group, School of Information Technologies, University of Sydney, Australia; Med-X Research Institute, Shanghai Jiao Tong University, China
| | - Sonia Pujol
- Surgical Planning Laboratory (SPL), Brigham and Women's Hospital, Harvard Medical School, United States
| | - Ron Kikinis
- Surgical Planning Laboratory (SPL), Brigham and Women's Hospital, Harvard Medical School, United States
| | - Michael J Fulham
- Biomedical and Multimedia Information Technology (BMIT) Research Group, School of Information Technologies, University of Sydney, Australia; Department of PET and Nuclear Medicine, Royal Prince Alfred Hospital, Sydney, Australia; Sydney Medical School, University of Sydney, Australia
| | - Stefan Eberl
- Biomedical and Multimedia Information Technology (BMIT) Research Group, School of Information Technologies, University of Sydney, Australia; Department of PET and Nuclear Medicine, Royal Prince Alfred Hospital, Sydney, Australia
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Li M, Oishi K, He X, Qin Y, Gao F, Mori S. An efficient approach for differentiating Alzheimer's disease from normal elderly based on multicenter MRI using gray-level invariant features. PLoS One 2014; 9:e105563. [PMID: 25140532 PMCID: PMC4139346 DOI: 10.1371/journal.pone.0105563] [Citation(s) in RCA: 17] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/06/2014] [Accepted: 07/22/2014] [Indexed: 11/19/2022] Open
Abstract
Machine learning techniques, along with imaging markers extracted from structural magnetic resonance images, have been shown to increase the accuracy to differentiate patients with Alzheimer's disease (AD) from normal elderly controls. Several forms of anatomical features, such as cortical volume, shape, and thickness, have demonstrated discriminative capability. These approaches rely on accurate non-linear image transformation, which could invite several nuisance factors, such as dependency on transformation parameters and the degree of anatomical abnormality, and an unpredictable influence of residual registration errors. In this study, we tested a simple method to extract disease-related anatomical features, which is suitable for initial stratification of the heterogeneous patient populations often encountered in clinical data. The method employed gray-level invariant features, which were extracted from linearly transformed images, to characterize AD-specific anatomical features. The intensity information from a disease-specific spatial masking, which was linearly registered to each patient, was used to capture the anatomical features. We implemented a two-step feature selection for anatomic recognition. First, a statistic-based feature selection was implemented to extract AD-related anatomical features while excluding non-significant features. Then, seven knowledge-based ROIs were used to capture the local discriminative powers of selected voxels within areas that were sensitive to AD or mild cognitive impairment (MCI). The discriminative capability of the proposed feature was measured by its performance in differentiating AD or MCI from normal elderly controls (NC) using a support vector machine. The statistic-based feature selection, together with the knowledge-based masks, provided a promising solution for capturing anatomical features of the brain efficiently. For the analysis of clinical populations, which are inherently heterogeneous, this approach could stratify the large amount of data rapidly and could be combined with more detailed subsequent analyses based on non-linear transformation.
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Affiliation(s)
- Muwei Li
- College of Electronics and Information Engineering, Sichuan University, Chengdu, China
| | - Kenichi Oishi
- The Russell H. Morgan Department of Radiology and Radiological Science, The Johns Hopkins University School of Medicine, Baltimore, Maryland, United States of America
| | - Xiaohai He
- College of Electronics and Information Engineering, Sichuan University, Chengdu, China
- * E-mail: (XH); (SM)
| | - Yuanyuan Qin
- Department of Radiology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Fei Gao
- Shandong Medical Imaging Research Institute, Shandong University, Jinan, China
| | - Susumu Mori
- The Russell H. Morgan Department of Radiology and Radiological Science, The Johns Hopkins University School of Medicine, Baltimore, Maryland, United States of America
- * E-mail: (XH); (SM)
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