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Kang X, Wang D, Lin J, Yao H, Zhao K, Song C, Chen P, Qu Y, Yang H, Zhang Z, Zhou B, Han T, Liao Z, Chen Y, Lu J, Yu C, Wang P, Zhang X, Li M, Zhang X, Jiang T, Zhou Y, Liu B, Han Y, Liu Y. Convergent Neuroimaging and Molecular Signatures in Mild Cognitive Impairment and Alzheimer's Disease: A Data-Driven Meta-Analysis with N = 3,118. Neurosci Bull 2024:10.1007/s12264-024-01218-x. [PMID: 38824231 DOI: 10.1007/s12264-024-01218-x] [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: 08/26/2023] [Accepted: 11/24/2023] [Indexed: 06/03/2024] Open
Abstract
The current study aimed to evaluate the susceptibility to regional brain atrophy and its biological mechanism in Alzheimer's disease (AD). We conducted data-driven meta-analyses to combine 3,118 structural magnetic resonance images from three datasets to obtain robust atrophy patterns. Then we introduced a set of radiogenomic analyses to investigate the biological basis of the atrophy patterns in AD. Our results showed that the hippocampus and amygdala exhibit the most severe atrophy, followed by the temporal, frontal, and occipital lobes in mild cognitive impairment (MCI) and AD. The extent of atrophy in MCI was less severe than that in AD. A series of biological processes related to the glutamate signaling pathway, cellular stress response, and synapse structure and function were investigated through gene set enrichment analysis. Our study contributes to understanding the manifestations of atrophy and a deeper understanding of the pathophysiological processes that contribute to atrophy, providing new insight for further clinical research on AD.
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Affiliation(s)
- Xiaopeng Kang
- School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing, 100049, China
- Brainnetome Center and National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing, 100190, China
| | - Dawei Wang
- Department of Radiology, Qilu Hospital of Shandong University, Ji'nan, 250063, China
| | - Jiaji Lin
- Department of Neurology, the Second Affiliated Hospital of Air Force Medical University, Xi'an, 710032, China
- Department of Radiology, Chinese PLA General Hospital, Beijing, 100853, China
| | - Hongxiang Yao
- Department of Radiology, the Second Medical Centre, National Clinical Research Centre for Geriatric Diseases, Chinese PLA General Hospital, Beijing, 100853, China
| | - Kun Zhao
- School of Artificial Intelligence, Beijing University of Posts and Telecommunications, Beijing, 100191, China
| | - Chengyuan Song
- Department of Neurology, Qilu Hospital of Shandong University, Ji'nan, 250063, China
| | - Pindong Chen
- School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing, 100049, China
- Brainnetome Center and National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing, 100190, China
| | - Yida Qu
- School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing, 100049, China
- Brainnetome Center and National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing, 100190, China
| | - Hongwei Yang
- Department of Radiology, Xuanwu Hospital of Capital Medical University, Beijing, 100053, China
| | - Zengqiang Zhang
- Branch of Chinese, PLA General Hospital, Sanya, 572013, China
| | - Bo Zhou
- Department of Neurology, the Second Medical Centre, National Clinical Research Centre for Geriatric Diseases, Chinese PLA General Hospital, Beijing, 100853, China
| | - Tong Han
- Department of Radiology, Tianjin Huanhu Hospital, Tianjin, 300222, China
| | - Zhengluan Liao
- Department of Psychiatry, People's Hospital of Hangzhou Medical College, Zhejiang Provincial People's Hospital, Hangzhou, 310014, China
| | - Yan Chen
- Department of Psychiatry, People's Hospital of Hangzhou Medical College, Zhejiang Provincial People's Hospital, Hangzhou, 310014, China
| | - Jie Lu
- Department of Radiology, Xuanwu Hospital of Capital Medical University, Beijing, 100053, China
| | - Chunshui Yu
- Department of Radiology, Tianjin Medical University General Hospital, Tianjin, 300070, China
| | - Pan Wang
- Department of Neurology, Tianjin Huanhu Hospital, Tianjin, 300222, China
| | - Xinqing Zhang
- Department of Neurology, Xuanwu Hospital of Capital Medical University, Beijing, 100053, China
| | - Ming Li
- Key Laboratory of Animal Models and Human Disease Mechanisms of the Chinese Academy of Sciences & Yunnan Province, Kunming Institute of Zoology, Chinese Academy of Sciences, Kunming, 650201, Yunnan, China
| | - Xi Zhang
- Department of Neurology, the Second Medical Centre, National Clinical Research Centre for Geriatric Diseases, Chinese PLA General Hospital, Beijing, 100853, China
| | - Tianzi Jiang
- School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing, 100049, China
- Brainnetome Center and National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing, 100190, China
| | - Yuying Zhou
- Department of Neurology, Tianjin Huanhu Hospital, Tianjin, 300222, China
| | - Bing Liu
- School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing, 100049, China
- Brainnetome Center and National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing, 100190, China
- State Key Lab of Cognition Neuroscience & Learning, Beijing Normal University, Beijing, 100875, China
| | - Ying Han
- Department of Neurology, Xuanwu Hospital of Capital Medical University, Beijing, 100053, China.
- National Clinical Research Center for Geriatric Disorders, Beijing, 100053, China.
- Center of Alzheimer's Disease, Beijing Institute for Brain Disorders, Beijing, 100053, China.
| | - Yong Liu
- School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing, 100049, China.
- Brainnetome Center and National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing, 100190, China.
- School of Artificial Intelligence, Beijing University of Posts and Telecommunications, Beijing, 100191, China.
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Mieling M, Meier H, Bunzeck N. Structural degeneration of the nucleus basalis of Meynert in mild cognitive impairment and Alzheimer's disease - Evidence from an MRI-based meta-analysis. Neurosci Biobehav Rev 2023; 154:105393. [PMID: 37717861 DOI: 10.1016/j.neubiorev.2023.105393] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/03/2023] [Revised: 07/17/2023] [Accepted: 09/14/2023] [Indexed: 09/19/2023]
Abstract
Recent models of Alzheimer's disease (AD) suggest that neuropathological changes of the medial temporal lobe, especially entorhinal cortex, are preceded by degenerations of the cholinergic Nucleus basalis of Meynert (NbM). Evidence from imaging studies in humans, however, is limited. Therefore, we performed an activation-likelihood estimation meta-analysis on whole brain voxel-based morphometry (VBM) MRI data from 54 experiments and 2581 subjects in total. It revealed, compared to healthy older controls, reduced gray matter in the bilateral NbM in AD, but only limited evidence for such an effect in patients with mild cognitive impairment (MCI), which typically precedes AD. Both patient groups showed less gray matter in the amygdala and hippocampus, with hints towards more pronounced amygdala effects in AD. We discuss our findings in the context of studies that highlight the importance of the cholinergic basal forebrain in learning and memory throughout the lifespan, and conclude that they are partly compatible with pathological staging models suggesting initial and pronounced structural degenerations within the NbM in the progression of AD.
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Affiliation(s)
- Marthe Mieling
- Department of Psychology, University of Lübeck, Ratzeburger Allee 160, 23562 Lübeck, Germany
| | - Hannah Meier
- Department of Psychology, University of Lübeck, Ratzeburger Allee 160, 23562 Lübeck, Germany
| | - Nico Bunzeck
- Department of Psychology, University of Lübeck, Ratzeburger Allee 160, 23562 Lübeck, Germany; Center of Brain, Behavior and Metabolism, University of Lübeck, Ratzeburger Allee 160, 23562 Lübeck, Germany.
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3
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Episodic Memory in Amnestic Mild Cognitive Impairment (aMCI) and Alzheimer’s Disease Dementia (ADD): Using the “Doors and People” Tool to Differentiate between Early aMCI—Late aMCI—Mild ADD Diagnostic Groups. Diagnostics (Basel) 2022; 12:diagnostics12071768. [PMID: 35885671 PMCID: PMC9324962 DOI: 10.3390/diagnostics12071768] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/09/2022] [Revised: 07/15/2022] [Accepted: 07/19/2022] [Indexed: 11/28/2022] Open
Abstract
Episodic memory is the type of memory that allows the recollection of personal experiences containing information on what has happened and, also, where and when it happened. Because of its sensitivity to neurodegenerative diseases and the aging of the brain, it is considered a hallmark of Alzheimer’s disease dementia (ADD). The objective of the present study was to examine episodic memory in amnestic mild cognitive impairment (aMCI) and ADD. Patients with the diagnosis of early aMCI, late aMCI, and mild ADD were evaluated using the Doors and People tool which consists of four subtests examining different aspects of episodic memory. The statistical analysis with receiver operating characteristic curves (ROC) showed the discriminant potential and the cutoffs of every subtest. Overall, the evaluation of episodic memory with the Doors and People tool can discriminate with great sensitivity between the different groups of people with AD and, especially, early aMCI, late aMCI, and mild ADD patients.
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Zhang L, Fu Y, Zhao Z, Cong Z, Zheng W, Zhang Q, Yao Z, Hu B. Analysis of Hippocampus Evolution Patterns and Prediction of Conversion in Mild Cognitive Impairment Using Multivariate Morphometry Statistics. J Alzheimers Dis 2022; 86:1695-1710. [DOI: 10.3233/jad-215568] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
Abstract
Background: Mild cognitive impairment (MCI), which is generally regarded as the prodromal stage of Alzheimer’s disease (AD), is associated with morphological changes in brain structures, particularly the hippocampus. However, the indicators for characterizing the deformation of hippocampus in conventional methods are not precise enough and ignore the evolution information with the course of disease. Objective: The purpose of this study was to investigate the temporal evolution pattern of MCI and predict the conversion of MCI to AD by using the multivariate morphometry statistics (MMS) as fine features. Methods: First, we extracted MMS features from MRI scans of 64 MCI converters (MCIc), 81 MCI patients who remained stable (MCIs), and 90 healthy controls (HC). To make full use of the time information, the dynamic MMS (DMMS) features were defined. Then, the areas with significant differences between pairs of the three groups were analyzed using statistical methods and the atrophy/expansion were identified by comparing the metrics. In parallel, patch selection, sparse coding, dictionary learning and maximum pooling were used for the dimensionality reduction and the ensemble classifier GentleBoost was used to classify MCIc and MCIs. Results: The longitudinal analysis revealed that the atrophy of both MCIc and MCIs mainly distributed in dorsal CA1, then spread to subiculum and other regions gradually, while the atrophy area of MCIc was larger and more significant. And the introduction of longitudinal information promoted the accuracy to 91.76% for conversion prediction. Conclusion: The dynamic information of hippocampus holds a huge potential for understanding the pathology of MCI.
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Affiliation(s)
- Lingyu Zhang
- Gansu Provincial Key Laboratory of Wearable Computing, School of Information Science and Engineering, Lanzhou University, Lanzhou, China
| | - Yu Fu
- College of Information Science & Electronic Engineering, Zhejiang University, Hangzhou, Zhejiang, China
| | - Ziyang Zhao
- Gansu Provincial Key Laboratory of Wearable Computing, School of Information Science and Engineering, Lanzhou University, Lanzhou, China
| | - Zhaoyang Cong
- Gansu Provincial Key Laboratory of Wearable Computing, School of Information Science and Engineering, Lanzhou University, Lanzhou, China
| | - Weihao Zheng
- Gansu Provincial Key Laboratory of Wearable Computing, School of Information Science and Engineering, Lanzhou University, Lanzhou, China
| | - Qin Zhang
- Gansu Provincial Key Laboratory of Wearable Computing, School of Information Science and Engineering, Lanzhou University, Lanzhou, China
| | - Zhijun Yao
- Gansu Provincial Key Laboratory of Wearable Computing, School of Information Science and Engineering, Lanzhou University, Lanzhou, China
| | - Bin Hu
- Gansu Provincial Key Laboratory of Wearable Computing, School of Information Science and Engineering, Lanzhou University, Lanzhou, China
- College of Information Science & Electronic Engineering, Zhejiang University, Hangzhou, Zhejiang, China
- Joint Research Center for Cognitive Neurosensor Technology of Lanzhou University & Institute of Semiconductors, Chinese Academy of Sciences, Beijing, China
- Engineering Research Center of Open Source Software and Real-Time System (Lanzhou University), Ministry of Education, Lanzhou, China
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Eldridge RC, Uppal K, Shokouhi M, Smith MR, Hu X, Qin ZS, Jones DP, Hajjar I. Multiomics Analysis of Structural Magnetic Resonance Imaging of the Brain and Cerebrospinal Fluid Metabolomics in Cognitively Normal and Impaired Adults. Front Aging Neurosci 2022; 13:796067. [PMID: 35145393 PMCID: PMC8822333 DOI: 10.3389/fnagi.2021.796067] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/15/2021] [Accepted: 12/27/2021] [Indexed: 12/22/2022] Open
Abstract
INTRODUCTION Integrating brain imaging with large scale omics data may identify novel mechanisms of mild cognitive impairment (MCI) and early Alzheimer's disease (AD). We integrated and analyzed brain magnetic resonance imaging (MRI) with cerebrospinal fluid (CSF) metabolomics to elucidate metabolic mechanisms and create a "metabolic map" of the brain in prodromal AD. METHODS In 145 subjects (85 cognitively normal controls and 60 with MCI), we derived voxel-wise gray matter volume via whole-brain structural MRI and conducted high-resolution untargeted metabolomics on CSF. Using a data-driven approach consisting of partial least squares discriminant analysis, a multiomics network clustering algorithm, and metabolic pathway analysis, we described dysregulated metabolic pathways in CSF mapped to brain regions associated with MCI in our cohort. RESULTS The multiomics network algorithm clustered metabolites with contiguous imaging voxels into seven distinct communities corresponding to the following brain regions: hippocampus/parahippocampal gyrus (three distinct clusters), thalamus, posterior thalamus, parietal cortex, and occipital lobe. Metabolic pathway analysis indicated dysregulated metabolic activity in the urea cycle, and many amino acids (arginine, histidine, lysine, glycine, tryptophan, methionine, valine, glutamate, beta-alanine, and purine) was significantly associated with those regions (P < 0.05). CONCLUSION By integrating CSF metabolomics data with structural MRI data, we linked specific AD-susceptible brain regions to disrupted metabolic pathways involving nitrogen excretion and amino acid metabolism critical for cognitive function. Our findings and analytical approach may extend drug and biomarker research toward more multiomics approaches.
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Affiliation(s)
- Ronald C. Eldridge
- Nell Hodgson Woodruff School of Nursing, Emory University, Atlanta, GA, United States
| | - Karan Uppal
- Division of Pulmonary, Allergy, Critical Care, and Sleep Medicine, School of Medicine, Emory University, Atlanta, GA, United States
| | - Mahsa Shokouhi
- Department of Neurology, School of Medicine, Emory University, Atlanta, GA, United States
| | - M. Ryan Smith
- Division of Pulmonary, Allergy, Critical Care, and Sleep Medicine, School of Medicine, Emory University, Atlanta, GA, United States
| | - Xin Hu
- Division of Pulmonary, Allergy, Critical Care, and Sleep Medicine, School of Medicine, Emory University, Atlanta, GA, United States
| | - Zhaohui S. Qin
- Department of Biostatistics and Bioinformatics, Rollins School of Public Health, Emory University, Atlanta, GA, United States
| | - Dean P. Jones
- Division of Pulmonary, Allergy, Critical Care, and Sleep Medicine, School of Medicine, Emory University, Atlanta, GA, United States
| | - Ihab Hajjar
- Department of Neurology, School of Medicine, Emory University, Atlanta, GA, United States
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Andersen E, Casteigne B, Chapman WD, Creed A, Foster F, Lapins A, Shatz R, Sawyer RP. Diagnostic biomarkers in Alzheimer’s disease. Biomark Neuropsychiatry 2021. [DOI: 10.1016/j.bionps.2021.100041] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 01/14/2023] Open
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D’Atri A, Gorgoni M, Scarpelli S, Cordone S, Alfonsi V, Marra C, Ferrara M, Rossini PM, De Gennaro L. Relationship between Cortical Thickness and EEG Alterations during Sleep in the Alzheimer's Disease. Brain Sci 2021; 11:brainsci11091174. [PMID: 34573195 PMCID: PMC8468220 DOI: 10.3390/brainsci11091174] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/30/2021] [Revised: 08/31/2021] [Accepted: 09/01/2021] [Indexed: 02/05/2023] Open
Abstract
Recent evidence showed that EEG activity alterations that occur during sleep are associated with structural, age-related, changes in healthy aging brains, and predict age-related decline in memory performance. Alzheimer's disease (AD) patients show specific EEG alterations during sleep associated with cognitive decline, including reduced sleep spindles during NREM sleep and EEG slowing during REM sleep. We investigated the relationship between these EEG sleep alterations and brain structure changes in a study of 23 AD patients who underwent polysomnographic recording of their undisturbed sleep and 1.5T MRI scans. Cortical thickness measures were correlated with EEG power in the sigma band during NREM sleep and with delta- and beta-power during REM sleep. Thinning in the right precuneus correlated with all the EEG indexes considered in this study. Frontal-central NREM sigma power showed an inverse correlation with thinning of the left entorhinal cortex. Increased delta activity at the frontopolar and temporal regions was significantly associated with atrophy in some temporal, parietal, and frontal cortices, and with mean thickness of the right hemisphere. Our findings revealed an association between sleep EEG alterations and the changes to AD patients' brain structures. Findings also highlight possible compensatory processes involving the sources of frontal-central sleep spindles.
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Affiliation(s)
- Aurora D’Atri
- Department of Biotechnological and Applied Clinical Sciences, University of L’Aquila, 67100 L’Aquila, Italy; (A.D.); (M.F.)
| | - Maurizio Gorgoni
- Department of Psychology, University of Rome “Sapienza”, 00185 Rome, Italy; (M.G.); (S.S.); (V.A.)
| | - Serena Scarpelli
- Department of Psychology, University of Rome “Sapienza”, 00185 Rome, Italy; (M.G.); (S.S.); (V.A.)
| | - Susanna Cordone
- UniCamillus, Saint Camillus International University of Health Sciences, 00131 Rome, Italy;
| | - Valentina Alfonsi
- Department of Psychology, University of Rome “Sapienza”, 00185 Rome, Italy; (M.G.); (S.S.); (V.A.)
| | - Camillo Marra
- Memory Clinic-Department of Aging, Neuroscience, Orthopaedic and Head-Neck, IRCCS Foundation Policlinico Universitario Agostino Gemelli, 00168 Rome, Italy;
| | - Michele Ferrara
- Department of Biotechnological and Applied Clinical Sciences, University of L’Aquila, 67100 L’Aquila, Italy; (A.D.); (M.F.)
| | - Paolo Maria Rossini
- Department of Neuroscience & Neurorehabil., IRCCS San Raffaele-Pisana, 00163 Rome, Italy;
| | - Luigi De Gennaro
- Department of Psychology, University of Rome “Sapienza”, 00185 Rome, Italy; (M.G.); (S.S.); (V.A.)
- Correspondence: ; Tel.: +39-0649917647
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Serra L, D'Amelio M, Esposito S, Di Domenico C, Koch G, Marra C, Mercuri NB, Caltagirone C, Artusi CA, Lopiano L, Cercignani M, Bozzali M. Ventral Tegmental Area Disconnection Contributes Two Years Early to Correctly Classify Patients Converted to Alzheimer's Disease: Implications for Treatment. J Alzheimers Dis 2021; 82:985-1000. [PMID: 34120905 DOI: 10.3233/jad-210171] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
Abstract
BACKGROUND Recent cross-sectional studies highlighted the loss of dopaminergic neurons in the ventral tegmental area (VTA) as an early pathophysiological event in Alzheimer's disease (AD). OBJECTIVE In this study, we longitudinally investigated by resting-state fMRI (rs-fMRI) a cohort of patients with mild cognitive impairment (MCI) due to AD to evaluate the impact of VTA disconnection in predicting the conversion to AD. METHODS A cohort of 35 patients with MCI due to AD were recruited and followed-up for 24 months. They underwent cognitive evaluation and rs-fMRI to assess VTA connectivity at baseline and at follow-up. RESULTS At 24-month follow-up, 16 out of 35 patients converted to AD. Although converters and non-converters to AD did not differ in demographic and behavioral characteristics at baseline, the first group showed a significant reduction of VTA-driven connectivity in the posterior cingulate and precentral cortex. This pattern of additional disconnection in MCI-Converters compared to non-converters remained substantially unchanged at 24-month follow-up. CONCLUSION This study reinforces the hypothesis of an early contribution of dopaminergic dysfunction to AD evolution by targeting the default-mode network. These results have potential implications for AD staging and prognosis and support new opportunities for therapeutic interventions to slow down disease progression.
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Affiliation(s)
- Laura Serra
- Neuroimaging Laboratory, Fondazione Santa Lucia, IRCCS, Rome, Italy
| | - Marcello D'Amelio
- Laboratory Molecular Neurosciences, Fondazione Santa Lucia, IRCCS, Rome, Italy.,Unit of Molecular Neurosciences, Department of Medicine, University Campus-Biomedico, Rome, Italy
| | - Sharon Esposito
- Neuroimaging Laboratory, Fondazione Santa Lucia, IRCCS, Rome, Italy
| | | | - Giacomo Koch
- Non Invasive Brain Stimulation Unit/Department of Behavioral and Clinical Neurology, Fondazione Santa Lucia, IRCCS, Rome, Italy.,Section of Human Physiology, University of Ferrara, Ferrara, Italy
| | - Camillo Marra
- Institute of Neurology, Catholic University, Rome, Italy
| | - Nicola Biagio Mercuri
- Laboratory of Experimental Neurology, Fondazione Santa Lucia, IRCCS, Rome, Italy.,Department of Systems Medicine, University of Rome 'Tor Vergata', Rome, Italy
| | - Carlo Caltagirone
- Department of Clinical and Behavioural Neurology, Fondazione Santa Lucia, IRCCS, Rome, Italy
| | - Carlo Alberto Artusi
- 'Rita Levi Montalcini' Department of Neuroscience University of Torino, Turin, Italy
| | - Leonardo Lopiano
- 'Rita Levi Montalcini' Department of Neuroscience University of Torino, Turin, Italy
| | - Mara Cercignani
- Neuroimaging Laboratory, Fondazione Santa Lucia, IRCCS, Rome, Italy.,Cardiff University Brain Imaging Centre, School of Psychology, Cardiff University, Cardiff, Wales, United Kingdom
| | - Marco Bozzali
- 'Rita Levi Montalcini' Department of Neuroscience University of Torino, Turin, Italy.,Department of Neuroscience, Brighton & Sussex Medical School, University of Sussex, Brighton, East Sussex, United Kingdom
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Chen S, Xu W, Xue C, Hu G, Ma W, Qi W, Dong L, Lin X, Chen J. Voxelwise Meta-Analysis of Gray Matter Abnormalities in Mild Cognitive Impairment and Subjective Cognitive Decline Using Activation Likelihood Estimation. J Alzheimers Dis 2020; 77:1495-1512. [PMID: 32925061 DOI: 10.3233/jad-200659] [Citation(s) in RCA: 17] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/15/2023]
Abstract
Background: Voxel-based morphometry studies have not yielded consistent results among patients with mild cognitive impairment (MCI) and subjective cognitive decline (SCD). Objective: Therefore, we aimed to conduct a meta-analysis of gray matter (GM) abnormalities acquired from these studies to determine their respective neuroanatomical changes. Methods: We systematically searched for voxel-based whole-brain morphometry studies that compared MCI or SCD subjects with healthy controls in PubMed, Web of Science, and EMBASE databases. We used the coordinate-based method of activation likelihood estimation to determine GM changes in SCD, MCI, and MCI sub-groups (amnestic MCI and non-amnestic MCI). Results: A total of 45 studies were included in our meta-analysis. In the MCI group, we found structural atrophy of the bilateral hippocampus, parahippocampal gyrus (PHG), amygdala, right lateral globus pallidus, right insula, and left middle temporal gyrus. The aMCI group exhibited GM atrophy in the bilateral hippocampus, PHG, and amygdala. The naMCI group presented with structural atrophy in the right putamen, right insula, right precentral gyrus, left medial/superior frontal gyrus, and left anterior cingulate. The right lingual gyrus, right cuneus, and left medial frontal gyrus were atrophic GM regions in the SCD group. Conclusion: Our meta-analysis identified unique patterns of neuroanatomical alternations in both the MCI and SCD group. Structural changes in SCD patients provide new evidence for the notion that subtle impairment of visual function, perception, and cognition may be related to early signs of cognitive impairment. In addition, our findings provide a foundation for future targeted interventions at different stages of preclinical Alzheimer’s disease.
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Affiliation(s)
- Shanshan Chen
- Department of Neurology, the Affiliated Brain Hospital of Nanjing Medical University, Nanjing, Jiangsu, China
| | - Wenwen Xu
- Department of Neurology, the Affiliated Brain Hospital of Nanjing Medical University, Nanjing, Jiangsu, China
| | - Chen Xue
- Department of Radiology, the Affiliated Brain Hospital of Nanjing Medical University, Nanjing, Jiangsu, China
| | - Guanjie Hu
- Institute of Neuropsychiatry, the Affiliated Brain Hospital of Nanjing Medical University, Nanjing, Jiangsu, China
- Institute of Brain Functional Imaging, Nanjing Medical University, Nanjing, Jiangsu, China
| | - Wenying Ma
- Department of Neurology, the Affiliated Brain Hospital of Nanjing Medical University, Nanjing, Jiangsu, China
| | - Wenzhang Qi
- Department of Radiology, the Affiliated Brain Hospital of Nanjing Medical University, Nanjing, Jiangsu, China
| | - Lin Dong
- Department of Neurology, the Affiliated Brain Hospital of Nanjing Medical University, Nanjing, Jiangsu, China
| | - Xingjian Lin
- Department of Neurology, the Affiliated Brain Hospital of Nanjing Medical University, Nanjing, Jiangsu, China
| | - Jiu Chen
- Institute of Neuropsychiatry, the Affiliated Brain Hospital of Nanjing Medical University, Nanjing, Jiangsu, China
- Institute of Brain Functional Imaging, Nanjing Medical University, Nanjing, Jiangsu, China
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10
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Wang Z, Zhang X, Liu R, Wang Y, Qing Z, Lu J, Obeso I, Zhang B, Li Y. Altered sulcogyral patterns of orbitofrontal cortex in patients with mild cognitive impairment. Psychiatry Res Neuroimaging 2020; 302:111108. [PMID: 32464534 DOI: 10.1016/j.pscychresns.2020.111108] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/11/2019] [Revised: 03/16/2020] [Accepted: 04/17/2020] [Indexed: 11/24/2022]
Abstract
Mild cognitive impairment (MCI) is increasingly recognized as a risk factor for Alzheimer's disease (AD). Neuroimaging studies have revealed structural abnormalities in the orbitofrontal cortex (OFC) in MCI patients, while other findings fail to report anatomical alterations. Accordingly, structural changes in this brain region amongst MCI patients has not been well characterized. Given that OFC sulcogyral organization has increasingly been demonstrated as a reliable pre-morbid marker of pathological conditions in several neuropsychiatric disorders, we examined the distribution of OFC sulcogyral patterns (classified into Type I, II and III) based on structural brain data from 68 MCI patients and 55 healthy controls. Our results, supported by both Frequentist and Bayesian statistics, showed that MCI patients exhibited an increased prevalence of Type II pattern compared with healthy controls, particularly in the right hemisphere. Meanwhile, MCI patients showed a decreased prevalence of Type I pattern compared with healthy controls. Taken together, our results reveal a skewed distribution of OFC sulcogyral in MCI patients, possibly reflecting a potential neurodevelopmental risk marker of MCI.
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Affiliation(s)
- Zixiang Wang
- Reward, Competition and Social Neuroscience Lab, Department of Psychology, School of Social and Behavioral Sciences, Nanjing University, Nanjing, China; Institute for Brain Sciences, Nanjing University, Nanjing, China
| | - Xin Zhang
- Department of Radiology, The Affiliated Drum Tower Hospital of Nanjing University Medical School, Nanjing, China
| | - Renyuan Liu
- Department of Radiology, The Affiliated Drum Tower Hospital of Nanjing University Medical School, Nanjing, China
| | - Yang Wang
- Department of Radiology, The Affiliated Drum Tower Hospital of Nanjing University Medical School, Nanjing, China
| | - Zhao Qing
- Department of Radiology, The Affiliated Drum Tower Hospital of Nanjing University Medical School, Nanjing, China; Institute for Brain Sciences, Nanjing University, Nanjing, China
| | - Jiaming Lu
- Department of Radiology, The Affiliated Drum Tower Hospital of Nanjing University Medical School, Nanjing, China
| | - Ignacio Obeso
- HM Hospitales - HM CINAC, Móstoles, Universidad CEU-San Pablo, Madrid, Spain
| | - Bing Zhang
- Department of Radiology, The Affiliated Drum Tower Hospital of Nanjing University Medical School, Nanjing, China; Institute for Brain Sciences, Nanjing University, Nanjing, China.
| | - Yansong Li
- Reward, Competition and Social Neuroscience Lab, Department of Psychology, School of Social and Behavioral Sciences, Nanjing University, Nanjing, China; Institute for Brain Sciences, Nanjing University, Nanjing, China.
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11
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Bergamino M, Nespodzany A, Baxter LC, Burke A, Caselli RJ, Sabbagh MN, Walsh RR, Stokes AM. Preliminary Assessment of Intravoxel Incoherent Motion
Diffusion‐Weighted MRI
(
IVIM‐DWI
) Metrics in Alzheimer's Disease. J Magn Reson Imaging 2020; 52:1811-1826. [DOI: 10.1002/jmri.27272] [Citation(s) in RCA: 20] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/06/2020] [Revised: 06/15/2020] [Accepted: 06/16/2020] [Indexed: 01/25/2023] Open
Affiliation(s)
- Maurizio Bergamino
- Division of Neuroimaging Research Barrow Neurological Institute Phoenix Arizona USA
| | - Ashley Nespodzany
- Division of Neuroimaging Research Barrow Neurological Institute Phoenix Arizona USA
| | - Leslie C. Baxter
- Division of Neuroimaging Research Barrow Neurological Institute Phoenix Arizona USA
- Department of Neurology Mayo Clinic Arizona Phoenix Arizona USA
| | - Anna Burke
- Division of Neurology Barrow Neurological Institute Phoenix Arizona USA
| | | | - Marwan N. Sabbagh
- Lou Ruvo Center for Brain Health, Cleveland Clinic Las Vegas Nevada USA
| | - Ryan R. Walsh
- Division of Neurology Barrow Neurological Institute Phoenix Arizona USA
| | - Ashley M. Stokes
- Division of Neuroimaging Research Barrow Neurological Institute Phoenix Arizona USA
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12
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Yamanakkanavar N, Choi JY, Lee B. MRI Segmentation and Classification of Human Brain Using Deep Learning for Diagnosis of Alzheimer's Disease: A Survey. SENSORS (BASEL, SWITZERLAND) 2020; 20:E3243. [PMID: 32517304 PMCID: PMC7313699 DOI: 10.3390/s20113243] [Citation(s) in RCA: 54] [Impact Index Per Article: 13.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 04/16/2020] [Revised: 05/25/2020] [Accepted: 06/03/2020] [Indexed: 02/07/2023]
Abstract
Many neurological diseases and delineating pathological regions have been analyzed, and the anatomical structure of the brain researched with the aid of magnetic resonance imaging (MRI). It is important to identify patients with Alzheimer's disease (AD) early so that preventative measures can be taken. A detailed analysis of the tissue structures from segmented MRI leads to a more accurate classification of specific brain disorders. Several segmentation methods to diagnose AD have been proposed with varying complexity. Segmentation of the brain structure and classification of AD using deep learning approaches has gained attention as it can provide effective results over a large set of data. Hence, deep learning methods are now preferred over state-of-the-art machine learning methods. We aim to provide an outline of current deep learning-based segmentation approaches for the quantitative analysis of brain MRI for the diagnosis of AD. Here, we report how convolutional neural network architectures are used to analyze the anatomical brain structure and diagnose AD, discuss how brain MRI segmentation improves AD classification, describe the state-of-the-art approaches, and summarize their results using publicly available datasets. Finally, we provide insight into current issues and discuss possible future research directions in building a computer-aided diagnostic system for AD.
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Affiliation(s)
- Nagaraj Yamanakkanavar
- Department of Information and Communications Engineering, Chosun University, Gwangju 61452, Korea;
| | - Jae Young Choi
- Division of Computer & Electronic Systems Engineering, Hankuk University of Foreign Studies, Yongin 17035, Korea;
| | - Bumshik Lee
- Department of Information and Communications Engineering, Chosun University, Gwangju 61452, Korea;
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13
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Early diagnosis of Alzheimer’s disease: the role of biomarkers including advanced EEG signal analysis. Report from the IFCN-sponsored panel of experts. Clin Neurophysiol 2020; 131:1287-1310. [DOI: 10.1016/j.clinph.2020.03.003] [Citation(s) in RCA: 81] [Impact Index Per Article: 20.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/28/2018] [Revised: 03/01/2020] [Accepted: 03/02/2020] [Indexed: 02/06/2023]
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14
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Cacciaglia R, Molinuevo JL, Falcón C, Arenaza-Urquijo EM, Sánchez-Benavides G, Brugulat-Serrat A, Blennow K, Zetterberg H, Gispert JD. APOE-ε4 Shapes the Cerebral Organization in Cognitively Intact Individuals as Reflected by Structural Gray Matter Networks. Cereb Cortex 2020; 30:4110-4120. [PMID: 32163130 PMCID: PMC7264689 DOI: 10.1093/cercor/bhaa034] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/17/2019] [Revised: 01/28/2020] [Accepted: 01/30/2020] [Indexed: 11/19/2022] Open
Abstract
Gray matter networks (GMn) provide essential information on the intrinsic organization of the brain and appear to be disrupted in Alzheimer’s disease (AD). Apolipoprotein E (APOE)-ε4 represents the major genetic risk factor for AD, yet the association between APOE-ε4 and GMn has remained unexplored. Here, we determine the impact of APOE-ε4 on GMn in a large sample of cognitively unimpaired individuals, which was enriched for the genetic risk of AD. We used independent component analysis to retrieve sources of structural covariance and analyzed APOE group differences within and between networks. Analyses were repeated in a subsample of amyloid-negative subjects. Compared with noncarriers and heterozygotes, APOE-ε4 homozygotes showed increased covariance in one network including primarily right-lateralized, parietal, inferior frontal, as well as inferior and middle temporal regions, which mirrored the formerly described AD-signature. This result was confirmed in a subsample of amyloid-negative individuals. APOE-ε4 carriers showed reduced covariance between two networks encompassing frontal and temporal regions, which constitute preferential target of amyloid deposition. Our data indicate that, in asymptomatic individuals, APOE-ε4 shapes the cerebral organization in a way that recapitulates focal morphometric alterations observed in AD patients, even in absence of amyloid pathology. This suggests that structural vulnerability in neuronal networks associated with APOE-ε4 may be an early event in AD pathogenesis, possibly upstream of amyloid deposition.
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Affiliation(s)
- Raffaele Cacciaglia
- Barcelonaβeta Brain Research Center (BBRC), Pasqual Maragall Foundation, 08005 Barcelona, Spain.,Hospital del Mar Medical Research Institute (IMIM), 08005 Barcelona, Spain.,Centro de Investigación Biomédica en Red de Fragilidad y Envejecimiento Saludable (CIBERFES), 28089 Madrid, Spain
| | - José Luis Molinuevo
- Barcelonaβeta Brain Research Center (BBRC), Pasqual Maragall Foundation, 08005 Barcelona, Spain.,Hospital del Mar Medical Research Institute (IMIM), 08005 Barcelona, Spain.,Centro de Investigación Biomédica en Red de Fragilidad y Envejecimiento Saludable (CIBERFES), 28089 Madrid, Spain.,Universitat Pompeu Fabra, 08002 Barcelona, Spain
| | - Carles Falcón
- Barcelonaβeta Brain Research Center (BBRC), Pasqual Maragall Foundation, 08005 Barcelona, Spain.,Hospital del Mar Medical Research Institute (IMIM), 08005 Barcelona, Spain.,Centro de Investigación Biomédica en Red de Bioingeniería, Biomateriales y Nanomedicina (CIBERBBN), 28089 Madrid, Spain
| | - Eider M Arenaza-Urquijo
- Barcelonaβeta Brain Research Center (BBRC), Pasqual Maragall Foundation, 08005 Barcelona, Spain.,Hospital del Mar Medical Research Institute (IMIM), 08005 Barcelona, Spain.,Centro de Investigación Biomédica en Red de Fragilidad y Envejecimiento Saludable (CIBERFES), 28089 Madrid, Spain
| | - Gonzalo Sánchez-Benavides
- Barcelonaβeta Brain Research Center (BBRC), Pasqual Maragall Foundation, 08005 Barcelona, Spain.,Hospital del Mar Medical Research Institute (IMIM), 08005 Barcelona, Spain.,Centro de Investigación Biomédica en Red de Fragilidad y Envejecimiento Saludable (CIBERFES), 28089 Madrid, Spain
| | - Anna Brugulat-Serrat
- Barcelonaβeta Brain Research Center (BBRC), Pasqual Maragall Foundation, 08005 Barcelona, Spain.,Hospital del Mar Medical Research Institute (IMIM), 08005 Barcelona, Spain.,Centro de Investigación Biomédica en Red de Fragilidad y Envejecimiento Saludable (CIBERFES), 28089 Madrid, Spain.,Global Brain Health Institute, University of California San Francisco, San Francisco, CA 94115, USA
| | - Kaj Blennow
- Department of Psychiatry and Neurochemistry, Institute of Neuroscience and Physiology, The Sahlgrenska Academy at the University of Gothenburg, 41390 Mölndal, Sweden.,Clinical Neurochemistry Laboratory, Sahlgrenska University Hospital, 41390 Mölndal, Sweden
| | - Henrik Zetterberg
- Department of Psychiatry and Neurochemistry, Institute of Neuroscience and Physiology, The Sahlgrenska Academy at the University of Gothenburg, 41390 Mölndal, Sweden.,Clinical Neurochemistry Laboratory, Sahlgrenska University Hospital, 41390 Mölndal, Sweden.,UK Dementia Research Institute at UCL, WC1E 6BT London, UK.,Department of Neurodegenerative Disease, UCL Institute of Neurology, WC1N 3BG London, UK
| | - Juan Domingo Gispert
- Barcelonaβeta Brain Research Center (BBRC), Pasqual Maragall Foundation, 08005 Barcelona, Spain.,Hospital del Mar Medical Research Institute (IMIM), 08005 Barcelona, Spain.,Universitat Pompeu Fabra, 08002 Barcelona, Spain.,Centro de Investigación Biomédica en Red de Bioingeniería, Biomateriales y Nanomedicina (CIBERBBN), 28089 Madrid, Spain
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15
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Shukla D, Mandal PK, Tripathi M, Vishwakarma G, Mishra R, Sandal K. Quantitation of in vivo brain glutathione conformers in cingulate cortex among age-matched control, MCI, and AD patients using MEGA-PRESS. Hum Brain Mapp 2019; 41:194-217. [PMID: 31584232 PMCID: PMC7268069 DOI: 10.1002/hbm.24799] [Citation(s) in RCA: 41] [Impact Index Per Article: 8.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/24/2019] [Revised: 07/22/2019] [Accepted: 08/26/2019] [Indexed: 12/18/2022] Open
Abstract
Oxidative stress (OS) plays an important role in Alzheimer's disease (AD) and glutathione (GSH) mitigates this effect by maintaining redox-imbalance and free-radical neutralization. Quantified brain GSH concentration provides distinct information about OS among age-matched normal control (NC), mild cognitive impairment (MCI) and AD patients. We report alterations of in vivo GSH conformers, along with the choline, creatine, and N-acetylaspartate levels in the cingulate cortex (CC) containing anterior (ACC) and posterior (PCC) regions of 64 (27 NC, 19 MCI, and 18 AD) participants using MEscher-GArwood-Point-RESolved spectroscopy sequence. Result indicated, tissue corrected GSH depletion in PCC among MCI (p = .001) and AD (p = .028) and in ACC among MCI (p = .194) and AD (p = .025) as compared to NC. Effects of the group, region, and group × region on GSH with age and gender as covariates were analyzed using a generalized linear model with Bonferroni correction for multiple comparisons. A significant effect of group with GSH depletion in AD and MCI was observed as compared to NC. Receiver operator characteristic (ROC) analysis of GSH level in CC differentiated between MCI and NC groups with an accuracy of 82.8% and 73.5% between AD and NC groups. Multivariate ROC analysis for the combined effect of the GSH alteration in both ACC and PCC regions provided improved diagnostic accuracy of 86.6% for NC to MCI conversion and 76.4% for NC to AD conversion. We conclude that only closed GSH conformer depletion in the ACC and PCC regions is critical and constitute a potential biomarker for AD.
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Affiliation(s)
- Deepika Shukla
- Neuroimaging and Neurospectroscopy (NINS) Laboratory, National Brain Research Centre, Gurgaon, India
| | - Pravat Kumar Mandal
- Neuroimaging and Neurospectroscopy (NINS) Laboratory, National Brain Research Centre, Gurgaon, India.,Florey Institute of Neuroscience and Mental Health, Melbourne School of Medicine Campus, Melbourne, Australia
| | - Manjari Tripathi
- Department of Neurology, All India Institute of Medical Sciences, New Delhi, India
| | - Gayatri Vishwakarma
- Department of Biostatistics, Indian Spinal Injuries Centre, New Delhi, India
| | - Ritwick Mishra
- Neuroimaging and Neurospectroscopy (NINS) Laboratory, National Brain Research Centre, Gurgaon, India
| | - Kanika Sandal
- Neuroimaging and Neurospectroscopy (NINS) Laboratory, National Brain Research Centre, Gurgaon, India
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16
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Kang DW, Lim HK, Joo SH, Lee NR, Lee CU. Differential Associations Between Volumes of Atrophic Cortical Brain Regions and Memory Performances in Early and Late Mild Cognitive Impairment. Front Aging Neurosci 2019; 11:245. [PMID: 31551759 PMCID: PMC6738351 DOI: 10.3389/fnagi.2019.00245] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/27/2018] [Accepted: 08/20/2019] [Indexed: 11/13/2022] Open
Abstract
Background Early and late mild cognitive impairment (MCI) patients have been reported to have a distinctive prognosis of converting to Alzheimer’s disease. Objective To evaluate the difference in gray matter volume and assess the association between cognitive function evaluated by comprehensive cognitive function test, and cortical thickness across healthy controls (HCs) (n = 37), early (n = 30), and late MCI patients (n = 35). Methods Differences in gray matter volume were evaluated by whole brain voxel-based morphometry across the groups. Multiple regression analysis was used to analyze group by memory performance interactions for the normalized gray matter volume. Results The early MCI group showed reduced gray matter volume in the right middle temporal gyrus in comparison to the HC group. The late MCI group displayed atrophy in the left parahippocampal gyrus in comparison to the HC group. Late MCI patients exhibited a decreased gray matter volume in the left fusiform gyrus in comparison to patients in the early MCI group (Monte Carlo simulation corrected p < 0.01, Tukey post hoc tests). Furthermore, there was a significant group (HC vs. early MCI) by memory performance interaction for the normalized cortical volume of the right middle temporal gyrus. Additionally, a significant group (early MCI vs. late MCI) by memory performance interaction was found for the normalized gray matter volume of the left fusiform gyrus (p < 0.001). Conclusion Early and late MCI patients showed distinctive associations of gray matter volumes in compensatory brain regions with memory performances. The findings can contribute to a better understanding of the structural changes in compensatory brain regions to elucidate memory decline in the trajectory of the subdivided prodromal stages of the Alzheimer’s disease (AD).
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Affiliation(s)
- Dong Woo Kang
- Department of Psychiatry, Seoul St. Mary's Hospital, College of Medicine, The Catholic University of Korea, Seoul, South Korea
| | - Hyun Kook Lim
- Department of Psychiatry, Yeouido St. Mary's Hospital, College of Medicine, The Catholic University of Korea, Seoul, South Korea
| | - Soo-Hyun Joo
- Department of Psychiatry, Seoul St. Mary's Hospital, College of Medicine, The Catholic University of Korea, Seoul, South Korea
| | - Na Rae Lee
- Department of Psychiatry, Seoul St. Mary's Hospital, College of Medicine, The Catholic University of Korea, Seoul, South Korea
| | - Chang Uk Lee
- Department of Psychiatry, Seoul St. Mary's Hospital, College of Medicine, The Catholic University of Korea, Seoul, South Korea
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17
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Makovac E, Serra L, Di Domenico C, Marra C, Caltagirone C, Cercignani M, Bozzali M. Quantitative Magnetization Transfer of White Matter Tracts Correlates with Diffusion Tensor Imaging Indices in Predicting the Conversion from Mild Cognitive Impairment to Alzheimer's Disease. J Alzheimers Dis 2019; 63:561-575. [PMID: 29689722 DOI: 10.3233/jad-170995] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/31/2022]
Abstract
Patients with amnestic mild cognitive impairment (aMCI) have higher probability to develop Alzheimer's disease (AD) than elderly controls. The detection of subtle changes in brain structure associated with disease progression and the development of tools to identify patients at high risk for dementia in a short time is crucial. Here, we used probabilistic white matter (WM) tractography to explore microstructural alterations within the main association, limbic, and commissural pathways in aMCI patients who converted to AD after 1 year follow-up (MCIconverters) and those who remained stable (MCIstable). Both diffusion tensor imaging (DTI) and quantitative magnetization transfer (qMT) parameters have been considered for a comprehensive pathophysiological characterization of the WM damage. Overall, tract-specific parameters derived from qMT and DTI at baseline were able to differentiate aMCI patients who converted to AD from those who remained stable in time. In particular, the qMT exchange rate, RMB0, of the right uncinate fasciculus was significantly decreased in MCIconverters, whereas fractional anisotropy was significantly decreased in the bilateral superior cingulum in MCIconverters compared to MCIstable. These results confirm the involvement of WM and particularly of association fibers in the progression of AD, highlighting disconnection as a potential mechanism.
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Affiliation(s)
- Elena Makovac
- Neuroimaging Laboratory, IRCCS Santa Lucia Foundation, Rome
| | - Laura Serra
- Neuroimaging Laboratory, IRCCS Santa Lucia Foundation, Rome
| | | | | | - Carlo Caltagirone
- Department of Clinical and Behavioural Neurology, IRCCS Santa Lucia Foundation, Rome.,Department of Systems Medicine, University of Rome 'Tor Vergata', Rome
| | - Mara Cercignani
- Neuroimaging Laboratory, IRCCS Santa Lucia Foundation, Rome.,Department of Neuroscience, Brighton and Sussex Medical School, University of Sussex, Brighton, UK
| | - Marco Bozzali
- Neuroimaging Laboratory, IRCCS Santa Lucia Foundation, Rome.,Department of Neuroscience, Brighton and Sussex Medical School, University of Sussex, Brighton, UK
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18
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Kim YJ, Cho SK, Kim HJ, Lee JS, Lee J, Jang YK, Vogel JW, Na DL, Kim C, Seo SW. Data-driven prognostic features of cognitive trajectories in patients with amnestic mild cognitive impairments. ALZHEIMERS RESEARCH & THERAPY 2019; 11:10. [PMID: 30670089 PMCID: PMC6343354 DOI: 10.1186/s13195-018-0462-z] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 08/08/2018] [Accepted: 12/19/2018] [Indexed: 11/10/2022]
Abstract
BACKGROUND Although amnestic mild cognitive impairment (aMCI) is generally considered to be a prodromal stage of Alzheimer's disease, patients with aMCI show heterogeneous patterns of progression. Moreover, there are few studies investigating data-driven cognitive trajectory in aMCI. We therefore classified patients with aMCI based on their cognitive trajectory, measured by clinical dementia rating sum of boxes (CDR-SOB). Then, we compared the clinical and neuroimaging features among groups classified by cognitive trajectory. METHODS We retrospectively recruited 278 patients with aMCI who underwent three or more timepoints of neuropsychological testing. They also had magnetic resonance imaging (MRI) including structured three-dimensional volume images. Cortical thickness was measured using surface-based methods. We performed trajectory analyses to classify our aMCI patients according to their progression and investigate their cognitive trajectory using CDR-SOB. RESULTS Trajectory analyses showed that patients with aMCI were divided into three groups: stable (61.8%), slow decliner (31.7%), and fast decliner (6.5%). Changes throughout a mean follow-up duration of 3.7 years in the CDR-SOB for the subgroups of stable/slow/fast decliners were 1.3-, 6.4-, and 12-point increases, respectively. Decliners were older and carried apolipoprotein E4 (APOE4) genotypes more frequently than stable patients. Compared with the stable group, decliners showed a higher frequency of aMCI patients with both visual and verbal memory dysfunction, late stage aMCI, and multiple domain dysfunction. In addition, compared with the stable group, the slow decliners showed cortical thinning predominantly in bilateral parietotemporal areas, while the fast decliners showed cortical thinning predominantly in bilateral frontotemporal areas. Both decliner groups showed worse cognitive function in attention, language, visuospatial, memory, and frontal/executive domains than the stable group. CONCLUSIONS Our data-driven trajectory analysis provides new insights into heterogeneous cognitive trajectories of aMCI and further suggests that baseline clinical and neuroimaging profiles might predict aMCI patients with poor prognosis.
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Affiliation(s)
- Yeo Jin Kim
- Department of Neurology, Chuncheon Sacred Heart Hospital, Hallym University College of Medicine, Chuncheon, Korea.,Department of Neurology, Samsung Medical Center, Sungkyunkwan University School of Medicine, 50 Ilwon-dong, Kangnam-ku, Seoul, 135-710, Republic of Korea.,Neuroscience Center, Samsung Medical Center, Seoul, Korea
| | - Seong-Kyoung Cho
- Institute of Human Complexity and Systems Science, Yonsei University, Seoul, Korea
| | - Hee Jin Kim
- Department of Neurology, Samsung Medical Center, Sungkyunkwan University School of Medicine, 50 Ilwon-dong, Kangnam-ku, Seoul, 135-710, Republic of Korea.,Neuroscience Center, Samsung Medical Center, Seoul, Korea
| | - Jin San Lee
- Department of Neurology, Kyung Hee University Hospital, Seoul, Korea
| | - Juyoun Lee
- Department of Neurology, Chungnam National University Hospital, Daejeon, Korea
| | - Young Kyoung Jang
- Department of Neurology, Samsung Medical Center, Sungkyunkwan University School of Medicine, 50 Ilwon-dong, Kangnam-ku, Seoul, 135-710, Republic of Korea.,Neuroscience Center, Samsung Medical Center, Seoul, Korea
| | - Jacob W Vogel
- Montreal Neurological Institute, McGill University, Montrèal, Quebec, Canada
| | - Duk L Na
- Department of Neurology, Samsung Medical Center, Sungkyunkwan University School of Medicine, 50 Ilwon-dong, Kangnam-ku, Seoul, 135-710, Republic of Korea.,Neuroscience Center, Samsung Medical Center, Seoul, Korea.,Department of Health Sciences and Technology, SAIHST, Sungkyunkwan University, Seoul, Korea
| | - Changsoo Kim
- Institute of Human Complexity and Systems Science, Yonsei University, Seoul, Korea. .,Department of Preventive Medicine, Yonsei University College of Medicine, 50 Yonsei-ro, Seodaemun-gu, Seoul, 120-752, Republic of Korea.
| | - Sang Won Seo
- Department of Neurology, Samsung Medical Center, Sungkyunkwan University School of Medicine, 50 Ilwon-dong, Kangnam-ku, Seoul, 135-710, Republic of Korea. .,Neuroscience Center, Samsung Medical Center, Seoul, Korea. .,Department of Clinical Research Design and Evaluation, SAIHST, Sungkyunkwan University, Seoul, Korea.
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19
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Basaia S, Agosta F, Wagner L, Canu E, Magnani G, Santangelo R, Filippi M. Automated classification of Alzheimer's disease and mild cognitive impairment using a single MRI and deep neural networks. Neuroimage Clin 2018; 21:101645. [PMID: 30584016 PMCID: PMC6413333 DOI: 10.1016/j.nicl.2018.101645] [Citation(s) in RCA: 177] [Impact Index Per Article: 29.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/17/2018] [Revised: 11/21/2018] [Accepted: 12/15/2018] [Indexed: 10/27/2022]
Abstract
We built and validated a deep learning algorithm predicting the individual diagnosis of Alzheimer's disease (AD) and mild cognitive impairment who will convert to AD (c-MCI) based on a single cross-sectional brain structural MRI scan. Convolutional neural networks (CNNs) were applied on 3D T1-weighted images from ADNI and subjects recruited at our Institute (407 healthy controls [HC], 418 AD, 280 c-MCI, 533 stable MCI [s-MCI]). CNN performance was tested in distinguishing AD, c-MCI and s-MCI. High levels of accuracy were achieved in all the classifications, with the highest rates achieved in the AD vs HC classification tests using both the ADNI dataset only (99%) and the combined ADNI + non-ADNI dataset (98%). CNNs discriminated c-MCI from s-MCI patients with an accuracy up to 75% and no difference between ADNI and non-ADNI images. CNNs provide a powerful tool for the automatic individual patient diagnosis along the AD continuum. Our method performed well without any prior feature engineering and regardless the variability of imaging protocols and scanners, demonstrating that it is exploitable by not-trained operators and likely to be generalizable to unseen patient data. CNNs may accelerate the adoption of structural MRI in routine practice to help assessment and management of patients.
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Affiliation(s)
- Silvia Basaia
- Neuroimaging Research Unit, Institute of Experimental Neurology, Division of Neuroscience, San Raffaele Scientific Institute, Vita-Salute San Raffaele University, Milan, Italy
| | - Federica Agosta
- Neuroimaging Research Unit, Institute of Experimental Neurology, Division of Neuroscience, San Raffaele Scientific Institute, Vita-Salute San Raffaele University, Milan, Italy
| | | | - Elisa Canu
- Neuroimaging Research Unit, Institute of Experimental Neurology, Division of Neuroscience, San Raffaele Scientific Institute, Vita-Salute San Raffaele University, Milan, Italy
| | - Giuseppe Magnani
- Department of Neurology, Institute of Experimental Neurology, Division of Neuroscience, San Raffaele Scientific Institute, Vita-Salute San Raffaele University, Milan, Italy
| | - Roberto Santangelo
- Department of Neurology, Institute of Experimental Neurology, Division of Neuroscience, San Raffaele Scientific Institute, Vita-Salute San Raffaele University, Milan, Italy
| | - Massimo Filippi
- Neuroimaging Research Unit, Institute of Experimental Neurology, Division of Neuroscience, San Raffaele Scientific Institute, Vita-Salute San Raffaele University, Milan, Italy; Department of Neurology, Institute of Experimental Neurology, Division of Neuroscience, San Raffaele Scientific Institute, Vita-Salute San Raffaele University, Milan, Italy.
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20
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Femminella GD, Thayanandan T, Calsolaro V, Komici K, Rengo G, Corbi G, Ferrara N. Imaging and Molecular Mechanisms of Alzheimer's Disease: A Review. Int J Mol Sci 2018; 19:E3702. [PMID: 30469491 PMCID: PMC6321449 DOI: 10.3390/ijms19123702] [Citation(s) in RCA: 33] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/05/2018] [Revised: 11/13/2018] [Accepted: 11/14/2018] [Indexed: 02/07/2023] Open
Abstract
Alzheimer's disease is the most common form of dementia and is a significant burden for affected patients, carers, and health systems. Great advances have been made in understanding its pathophysiology, to a point that we are moving from a purely clinical diagnosis to a biological one based on the use of biomarkers. Among those, imaging biomarkers are invaluable in Alzheimer's, as they provide an in vivo window to the pathological processes occurring in Alzheimer's brain. While some imaging techniques are still under evaluation in the research setting, some have reached widespread clinical use. In this review, we provide an overview of the most commonly used imaging biomarkers in Alzheimer's disease, from molecular PET imaging to structural MRI, emphasising the concept that multimodal imaging would likely prove to be the optimal tool in the future of Alzheimer's research and clinical practice.
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Affiliation(s)
| | - Tony Thayanandan
- Imperial Memory Unit, Charing Cross Hospital, Imperial College London, London W6 8RF, UK.
| | - Valeria Calsolaro
- Neurology Imaging Unit, Imperial College London, London W12 0NN, UK.
| | - Klara Komici
- Department of Medicine and Health Sciences, University of Molise, 86100 Campobasso, Italy.
| | - Giuseppe Rengo
- Department of Translational Medical Sciences, Federico II University of Naples, 80131 Naples, Italy.
- Istituti Clinici Scientifici Maugeri SPA-Società Benefit, IRCCS, 82037 Telese Terme, Italy.
| | - Graziamaria Corbi
- Department of Medicine and Health Sciences, University of Molise, 86100 Campobasso, Italy.
| | - Nicola Ferrara
- Department of Translational Medical Sciences, Federico II University of Naples, 80131 Naples, Italy.
- Istituti Clinici Scientifici Maugeri SPA-Società Benefit, IRCCS, 82037 Telese Terme, Italy.
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21
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Multimodal hyper-connectivity of functional networks using functionally-weighted LASSO for MCI classification. Med Image Anal 2018; 52:80-96. [PMID: 30472348 DOI: 10.1016/j.media.2018.11.006] [Citation(s) in RCA: 36] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/23/2018] [Revised: 09/30/2018] [Accepted: 11/12/2018] [Indexed: 01/05/2023]
Abstract
Recent works have shown that hyper-networks derived from blood-oxygen-level-dependent (BOLD) fMRI, where an edge (called hyper-edge) can be connected to more than two nodes, are effective biomarkers for MCI classification. Although BOLD fMRI is a high temporal resolution fMRI approach to assess alterations in brain networks, it cannot pinpoint to a single correlation of neuronal activity since BOLD signals are composite. In contrast, arterial spin labeling (ASL) is a lower temporal resolution fMRI technique for measuring cerebral blood flow (CBF) that can provide quantitative, direct brain network physiology measurements. This paper proposes a novel sparse regression algorithm for inference of the integrated hyper-connectivity networks from BOLD fMRI and ASL fMRI. Specifically, a least absolution shrinkage and selection operator (LASSO) algorithm, which is constrained by the functional connectivity derived from ASL fMRI, is employed to estimate hyper-connectivity for characterizing BOLD-fMRI-based functional interaction among multiple regions. An ASL-derived functional connectivity is constructed by using an Ultra-GroupLASSO-UOLS algorithm, where the combination of ultra-least squares (ULS) criterion with a group LASSO (GroupLASSO) algorithm is applied to detect the topology of ASL-based functional connectivity networks, and then an ultra-orthogonal least squares (UOLS) algorithm is used to estimate the connectivity strength. By combining the complementary characterization conveyed by rs-fMRI and ASL fMRI, our multimodal hyper-networks demonstrated much better discriminative characteristics than either the conventional pairwise connectivity networks or the unimodal hyper-connectivity networks. Experimental results on publicly available ADNI dataset demonstrate that the proposed method outperforms the existing single modality based sparse functional connectivity inference methods.
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Tuokkola T, Koikkalainen J, Parkkola R, Karrasch M, Lötjönen J, Rinne JO. Longitudinal changes in the brain in mild cognitive impairment: a magnetic resonance imaging study using the visual rating method and tensor-based morphometry. Acta Radiol 2018; 59:973-979. [PMID: 28952780 DOI: 10.1177/0284185117734418] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/30/2022]
Abstract
Background Brain atrophy is associated with mild cognitive impairment (MCI), and by using volumetric and visual analyzing methods, it is possible to differentiate between individuals with progressive MCI (MCIp) and stable MCI (MCIs). Automated analysis methods detect degenerative changes in the brain earlier and more reliably than visual methods. Purpose To detect and evaluate structural brain changes between and within the MCIs, MCIp, and control groups during a two-year follow-up period. Material and Methods Brain magnetic resonance imaging (MRI) scans of 11 participants with MCIs, 18 participants with MCIp, and 84 controls were analyzed by the visual rating method (VRM) and tensor-based morphometry (TBM). Results At baseline, both VRM and TBM differentiated the whole MCI group (combined MCIs and MCIp) and the MCIp group from the control group, but they did not differentiate the MCIs group from the control group. At follow-up, both methods differentiated the MCIp group from the control group, but minor differences between the MCIs and control groups were only seen by TBM. Neuropsychological tests did not find differences between the MCIs and control groups at follow-up. Neither method revealed relevant signs of brain atrophy progression within or between MCI subgroups during the follow-up time. Conclusion Both methods are equally good in the evaluation of structural brain changes in MCI if the groups are sufficiently large and the disease progresses to AD. Only TBM disclosed minor atrophic changes in the MCIs group compared to controls at follow-up. The results need to be confirmed with a large patient group and longer follow-up time.
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Affiliation(s)
- Terhi Tuokkola
- Turku PET Centre, Turku University Hospital, Turku, Finland
| | - Juha Koikkalainen
- University of Eastern Finland, Faculty of Health Sciences, Kuopio, Finland
| | - Riitta Parkkola
- Department of Radiology, University Hospital of Turku and Turku University Hospital, Turku, Finland
| | - Mira Karrasch
- Department of Psychology, Abo Akademi University, Turku, Finland
| | - Jyrki Lötjönen
- Aalto University, Department of Neuroscience and Biomedical Engineering, Helsinki, Finland VTT Technical Research Centre of Finland, Tampere, Finland
| | - Juha O Rinne
- Turku PET Centre, Turku University Hospital, Turku, Finland
- Finland Division of Clinical Neurosciences, Turku University Hospital, Turku, Finland
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Morphological Biomarker Differentiating MCI Converters from Nonconverters: Longitudinal Evidence Based on Hemispheric Asymmetry. Behav Neurol 2018; 2018:3954101. [PMID: 29755611 PMCID: PMC5884406 DOI: 10.1155/2018/3954101] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/09/2017] [Revised: 12/11/2017] [Accepted: 01/03/2018] [Indexed: 11/17/2022] Open
Abstract
Identifying subjects with mild cognitive impairment (MCI) who may probably progress to Alzheimer's disease (AD) is important for better understanding the disease mechanisms and facilitating early treatments. In addition to the direct volumetric and thickness measurement based on high-resolution magnetic resonance imaging (MRI), hemispheric asymmetry could be a potential index to detect morphological variations in MCI patients with a high risk of conversion to AD. The present study collected a set of longitudinal MRI data from 53 MCI converters and nonconverters and investigated the asymmetry differences between groups. Asymmetry variation was observed in the medial temporal lobe, especially in the entorhinal cortex, between converters and nonconverters 3 years before the former developed AD. The proposed asymmetry analysis was observed to be sensitive to detect morphological changes between groups as compared to the methods of voxel-based morphometry (VBM) and thickness measurement. Hemispheric asymmetry in specific brain regions as a neuroimaging biomarker can provide helpful information for prediction of MCI conversion.
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Manuello J, Nani A, Premi E, Borroni B, Costa T, Tatu K, Liloia D, Duca S, Cauda F. The Pathoconnectivity Profile of Alzheimer's Disease: A Morphometric Coalteration Network Analysis. Front Neurol 2018; 8:739. [PMID: 29472885 PMCID: PMC5810291 DOI: 10.3389/fneur.2017.00739] [Citation(s) in RCA: 19] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/28/2017] [Accepted: 12/21/2017] [Indexed: 01/18/2023] Open
Abstract
Gray matter alterations are typical features of brain disorders. However, they do not impact on the brain randomly. Indeed, it has been suggested that neuropathological processes can selectively affect certain assemblies of neurons, which typically are at the center of crucial functional networks. Because of their topological centrality, these areas form a core set that is more likely to be affected by neuropathological processes. In order to identify and study the pattern formed by brain alterations in patients’ with Alzheimer’s disease (AD), we devised an innovative meta-analytic method for analyzing voxel-based morphometry data. This methodology enabled us to discover that in AD gray matter alterations do not occur randomly across the brain but, on the contrary, follow identifiable patterns of distribution. This alteration pattern exhibits a network-like structure composed of coaltered areas that can be defined as coatrophy network. Within the coatrophy network of AD, we were able to further identify a core subnetwork of coaltered areas that includes the left hippocampus, left and right amygdalae, right parahippocampal gyrus, and right temporal inferior gyrus. In virtue of their network centrality, these brain areas can be thought of as pathoconnectivity hubs.
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Affiliation(s)
- Jordi Manuello
- GCS-fMRI, Department of Psychology, Koelliker Hospital, University of Turin, Turin, Italy.,FOCUS Laboratory, Department of Psychology, University of Turin, Turin, Italy
| | - Andrea Nani
- GCS-fMRI, Department of Psychology, Koelliker Hospital, University of Turin, Turin, Italy.,FOCUS Laboratory, Department of Psychology, University of Turin, Turin, Italy.,Michael Trimble Neuropsychiatry Research Group, Birmingham and Solihull Mental Health NHS Foundation Trust, Birmingham, United Kingdom
| | - Enrico Premi
- Neurology Unit, Department of Clinical and Experimental Sciences, University of Brescia, Brescia, Italy
| | - Barbara Borroni
- Neurology Unit, Department of Clinical and Experimental Sciences, University of Brescia, Brescia, Italy
| | - Tommaso Costa
- GCS-fMRI, Department of Psychology, Koelliker Hospital, University of Turin, Turin, Italy.,FOCUS Laboratory, Department of Psychology, University of Turin, Turin, Italy
| | - Karina Tatu
- GCS-fMRI, Department of Psychology, Koelliker Hospital, University of Turin, Turin, Italy.,FOCUS Laboratory, Department of Psychology, University of Turin, Turin, Italy
| | - Donato Liloia
- FOCUS Laboratory, Department of Psychology, University of Turin, Turin, Italy
| | - Sergio Duca
- GCS-fMRI, Department of Psychology, Koelliker Hospital, University of Turin, Turin, Italy
| | - Franco Cauda
- GCS-fMRI, Department of Psychology, Koelliker Hospital, University of Turin, Turin, Italy.,FOCUS Laboratory, Department of Psychology, University of Turin, Turin, Italy
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25
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Matsuoka T, Imai A, Fujimoto H, Kato Y, Shibata K, Nakamura K, Yokota H, Yamada K, Narumoto J. Reduced Pineal Volume in Alzheimer Disease: A Retrospective Cross-sectional MR Imaging Study. Radiology 2018; 286:239-248. [DOI: 10.1148/radiol.2017170188] [Citation(s) in RCA: 28] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/11/2022]
Affiliation(s)
- Teruyuki Matsuoka
- From the Departments of Psychiatry (T.M., A.I., H.F., Y.K., K.S., K.N., J.N.) and Radiology (H.Y., K.Y.), Graduate School of Medical Science, Kyoto Prefectural University of Medicine, 465 Kajii-cho, Kawaramachi-Hirokoji, Kamigyo-ku, Kyoto 602-8566, Japan; and Department of Diagnostic Radiology and Radiation Oncology, Graduate School of Medicine, Chiba University, Chiba, Japan (H.Y.)
| | - Ayu Imai
- From the Departments of Psychiatry (T.M., A.I., H.F., Y.K., K.S., K.N., J.N.) and Radiology (H.Y., K.Y.), Graduate School of Medical Science, Kyoto Prefectural University of Medicine, 465 Kajii-cho, Kawaramachi-Hirokoji, Kamigyo-ku, Kyoto 602-8566, Japan; and Department of Diagnostic Radiology and Radiation Oncology, Graduate School of Medicine, Chiba University, Chiba, Japan (H.Y.)
| | - Hiroshi Fujimoto
- From the Departments of Psychiatry (T.M., A.I., H.F., Y.K., K.S., K.N., J.N.) and Radiology (H.Y., K.Y.), Graduate School of Medical Science, Kyoto Prefectural University of Medicine, 465 Kajii-cho, Kawaramachi-Hirokoji, Kamigyo-ku, Kyoto 602-8566, Japan; and Department of Diagnostic Radiology and Radiation Oncology, Graduate School of Medicine, Chiba University, Chiba, Japan (H.Y.)
| | - Yuka Kato
- From the Departments of Psychiatry (T.M., A.I., H.F., Y.K., K.S., K.N., J.N.) and Radiology (H.Y., K.Y.), Graduate School of Medical Science, Kyoto Prefectural University of Medicine, 465 Kajii-cho, Kawaramachi-Hirokoji, Kamigyo-ku, Kyoto 602-8566, Japan; and Department of Diagnostic Radiology and Radiation Oncology, Graduate School of Medicine, Chiba University, Chiba, Japan (H.Y.)
| | - Keisuke Shibata
- From the Departments of Psychiatry (T.M., A.I., H.F., Y.K., K.S., K.N., J.N.) and Radiology (H.Y., K.Y.), Graduate School of Medical Science, Kyoto Prefectural University of Medicine, 465 Kajii-cho, Kawaramachi-Hirokoji, Kamigyo-ku, Kyoto 602-8566, Japan; and Department of Diagnostic Radiology and Radiation Oncology, Graduate School of Medicine, Chiba University, Chiba, Japan (H.Y.)
| | - Kaeko Nakamura
- From the Departments of Psychiatry (T.M., A.I., H.F., Y.K., K.S., K.N., J.N.) and Radiology (H.Y., K.Y.), Graduate School of Medical Science, Kyoto Prefectural University of Medicine, 465 Kajii-cho, Kawaramachi-Hirokoji, Kamigyo-ku, Kyoto 602-8566, Japan; and Department of Diagnostic Radiology and Radiation Oncology, Graduate School of Medicine, Chiba University, Chiba, Japan (H.Y.)
| | - Hajime Yokota
- From the Departments of Psychiatry (T.M., A.I., H.F., Y.K., K.S., K.N., J.N.) and Radiology (H.Y., K.Y.), Graduate School of Medical Science, Kyoto Prefectural University of Medicine, 465 Kajii-cho, Kawaramachi-Hirokoji, Kamigyo-ku, Kyoto 602-8566, Japan; and Department of Diagnostic Radiology and Radiation Oncology, Graduate School of Medicine, Chiba University, Chiba, Japan (H.Y.)
| | - Kei Yamada
- From the Departments of Psychiatry (T.M., A.I., H.F., Y.K., K.S., K.N., J.N.) and Radiology (H.Y., K.Y.), Graduate School of Medical Science, Kyoto Prefectural University of Medicine, 465 Kajii-cho, Kawaramachi-Hirokoji, Kamigyo-ku, Kyoto 602-8566, Japan; and Department of Diagnostic Radiology and Radiation Oncology, Graduate School of Medicine, Chiba University, Chiba, Japan (H.Y.)
| | - Jin Narumoto
- From the Departments of Psychiatry (T.M., A.I., H.F., Y.K., K.S., K.N., J.N.) and Radiology (H.Y., K.Y.), Graduate School of Medical Science, Kyoto Prefectural University of Medicine, 465 Kajii-cho, Kawaramachi-Hirokoji, Kamigyo-ku, Kyoto 602-8566, Japan; and Department of Diagnostic Radiology and Radiation Oncology, Graduate School of Medicine, Chiba University, Chiba, Japan (H.Y.)
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Biomarkers for Alzheimer’s Disease and Frontotemporal Lobar Degeneration: Imaging. NEURODEGENER DIS 2018. [DOI: 10.1007/978-3-319-72938-1_12] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/26/2022] Open
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27
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Sha Z, Xia M, Lin Q, Cao M, Tang Y, Xu K, Song H, Wang Z, Wang F, Fox PT, Evans AC, He Y. Meta-Connectomic Analysis Reveals Commonly Disrupted Functional Architectures in Network Modules and Connectors across Brain Disorders. Cereb Cortex 2017; 28:4179-4194. [PMID: 29136110 DOI: 10.1093/cercor/bhx273] [Citation(s) in RCA: 47] [Impact Index Per Article: 6.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/17/2022] Open
Affiliation(s)
- Zhiqiang Sha
- National Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, China
- Beijing Key Laboratory of Brain Imaging and Connectomics, Beijing Normal University, Beijing, China
- IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, China
| | - Mingrui Xia
- National Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, China
- Beijing Key Laboratory of Brain Imaging and Connectomics, Beijing Normal University, Beijing, China
- IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, China
| | - Qixiang Lin
- National Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, China
- Beijing Key Laboratory of Brain Imaging and Connectomics, Beijing Normal University, Beijing, China
- IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, China
| | - Miao Cao
- National Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, China
- Beijing Key Laboratory of Brain Imaging and Connectomics, Beijing Normal University, Beijing, China
- IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, China
| | - Yanqing Tang
- Department of Psychiatry, The First Affiliated Hospital of China Medical University, Shenyang, Liaoning, China
- Brain Function Research Section, The First Affiliated Hospital of China Medical University, Shenyang, Liaoning, China
| | - Ke Xu
- Department of Radiology, The First Affiliated Hospital of China Medical University, Shenyang, Liaoning, China
| | - Haiqing Song
- Department of Neurology, Xuanwu Hospital of Capital Medical University, Beijing, China
| | - Zhiqun Wang
- Department of Radiology, Xuanwu Hospital of Capital Medical University, Beijing, China
- Department of Radiology, Dongfang Hospital, Beijing University of Chinese Medicine, Beijing, China
| | - Fei Wang
- Department of Psychiatry, The First Affiliated Hospital of China Medical University, Shenyang, Liaoning, China
- Brain Function Research Section, The First Affiliated Hospital of China Medical University, Shenyang, Liaoning, China
- Department of Radiology, The First Affiliated Hospital of China Medical University, Shenyang, Liaoning, China
| | - Peter T Fox
- Research Imaging Institute, University of Texas Health Science Center at San Antonio, TX, USA
- Department of Radiology, University of Texas Health Science Center at San Antonio, TX, USA
- South Texas Veterans Health Care System at San Antonio, TX, USA
- Shenzhen University School of Medicine, Shenzhen, China
| | - Alan C Evans
- McConnell Brain Imaging Center, Montreal Neurological Institute, Montreal, QC, Canada
| | - Yong He
- National Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, China
- Beijing Key Laboratory of Brain Imaging and Connectomics, Beijing Normal University, Beijing, China
- IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, China
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28
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Double inversion recovery imaging improves the evaluation of gray matter volume losses in patients with Alzheimer's disease and mild cognitive impairment. Brain Imaging Behav 2017; 10:1015-1028. [PMID: 26497891 DOI: 10.1007/s11682-015-9469-2] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/22/2022]
Abstract
Our goal was to investigate whether three-dimensional (3D) double inversion recovery (DIR) images can show alterations of gray matter volume (GMV) between Alzheimer's disease (AD) patients and nondemented controls and to compare alterations of GMV between groups using DIR images and those using 3D T1-weighted (T1W) images. We included 25 subjects with mild or probable AD, 25 subjects with amnestic mild cognitive impairment (MCI), and 25 elderly cognitively normal (CN) subjects. Group differences in GMV among CN, MCI, and AD patients were tested by voxel-wise, one-way ANOVA. Additional region-of-interest-based comparisons of GMV differences among the three groups for DIR and T1WI were performed using ANCOVA. Finally, ROC curve analysis was performed. In the AD group compared with the CN and MCI groups, GMV was decreased in both DIR and T1W images. However, the areas showing GMV loss were larger in DIR images compared to those in T1W images. Amygdala had the highest area under curve value for both DIR and T1W images. DIR images were sensitive for identifying GMV loss in patients with AD compared with MCI and CN subjects and areas showing GMV loss identified with DIR were extended to more brain areas than those identified with T1W. With DIR, amygdala GMV is the most sensitive in differentiating between subject groups.
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Insular atrophy at the prodromal stage of dementia with Lewy bodies: a VBM DARTEL study. Sci Rep 2017; 7:9437. [PMID: 28842567 PMCID: PMC5573371 DOI: 10.1038/s41598-017-08667-7] [Citation(s) in RCA: 34] [Impact Index Per Article: 4.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/16/2016] [Accepted: 07/12/2017] [Indexed: 11/25/2022] Open
Abstract
Diffuse atrophy including the insula was previously demonstrated in dementia with Lewy bodies (DLB) patients but little is known about the prodromal stage of DLB (pro-DLB). In this prospective study, we used SPM8-DARTEL to measure gray matter (GM) and white matter (WM) atrophy in pro-DLB patients (n = 54), prodromal Alzheimer’s disease (pro-AD) patients (n = 16), DLB patients at the stage of dementia (mild-DLB) (n = 15), and Alzheimer’s disease patients at the stage of dementia (mild-AD) (n = 28), and compared them with healthy elderly controls (HC, n = 22). Diminished GM volumes were found in bilateral insula in pro-DLB patients, a trend to significance in right hippocampus and parahippocampal gyrus in pro-AD patients, in left insula in mild-DLB patients, and in medial temporal lobes and insula in mild-AD patients. The comparison between prodromal groups did not showed any differences. The comparison between groups with dementia revealed atrophy around the left middle temporal gyrus in mild-AD patients. Reduced WM volume was observed in mild-DLB in the pons. The insula seems to be a key region in DLB as early as the prodromal stage. MRI studies looking at perfusion, and functional and anatomical connectivity are now needed to better understand the role of this region in DLB.
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Prediction and classification of Alzheimer disease based on quantification of MRI deformation. PLoS One 2017; 12:e0173372. [PMID: 28264071 PMCID: PMC5338815 DOI: 10.1371/journal.pone.0173372] [Citation(s) in RCA: 68] [Impact Index Per Article: 9.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/08/2016] [Accepted: 02/20/2017] [Indexed: 12/17/2022] Open
Abstract
Detecting early morphological changes in the brain and making early diagnosis are important for Alzheimer's disease (AD). High resolution magnetic resonance imaging can be used to help diagnosis and prediction of the disease. In this paper, we proposed a machine learning method to discriminate patients with AD or mild cognitive impairment (MCI) from healthy elderly and to predict the AD conversion in MCI patients by computing and analyzing the regional morphological differences of brain between groups. Distance between each pair of subjects was quantified from a symmetric diffeomorphic registration, followed by an embedding algorithm and a learning approach for classification. The proposed method obtained accuracy of 96.5% in differentiating mild AD from healthy elderly with the whole-brain gray matter or temporal lobe as region of interest (ROI), 91.74% in differentiating progressive MCI from healthy elderly and 88.99% in classifying progressive MCI versus stable MCI with amygdala or hippocampus as ROI. This deformation-based method has made full use of the pair-wise macroscopic shape difference between groups and consequently increased the power for discrimination.
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Moon CM, Shin IS, Jeong GW. Alterations in white matter volume and its correlation with neuropsychological scales in patients with Alzheimer's disease: a DARTEL-based voxel-based morphometry study. Acta Radiol 2017; 58:204-210. [PMID: 27081089 DOI: 10.1177/0284185116640162] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
Background Non-invasive imaging markers can be used to diagnose Alzheimer's disease (AD) in its early stages, but an optimized quantification analysis to measure the brain integrity has been less studied. Purpose To evaluate white matter volume change and its correlation with neuropsychological scales in patients with AD using a diffeomorphic anatomical registration through exponentiated lie algebra (DARTEL)-based voxel-based morphometry (VBM). Material and Methods The 21 participants comprised 11 patients with AD and 10 age-matched healthy controls. High-resolution magnetic resonance imaging (MRI) data were processed by VBM analysis based on DARTEL algorithm. Results The patients showed significant white matter volume reductions in the posterior limb of the internal capsule, cerebral peduncle of the midbrain, and parahippocampal gyrus compared to healthy controls. In correlation analysis, the parahippocampal volume was positively correlated with the Korean-mini mental state examination score in AD. Conclusion This study provides an evidence for localized white matter volume deficits in conjunction with cognitive dysfunction in AD. These findings would be helpful to understand the neuroanatomical mechanisms in AD and to robust the diagnostic accuracy for AD.
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Affiliation(s)
- Chung-Man Moon
- Research Institute for Medical Imaging, Chonnam National University Medical School, Gwangju, Republic of Korea
| | - Il-Seon Shin
- Department of Psychiatry, Chonnam National University Hospital, Gwangju, Republic of Korea
| | - Gwang-Woo Jeong
- Research Institute for Medical Imaging, Chonnam National University Medical School, Gwangju, Republic of Korea
- Department of Radiology, Chonnam National University Hospital, Chonnam Natioanl University Medical School, Gwangju, Republic of Korea
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Chapleau M, Aldebert J, Montembeault M, Brambati SM. Atrophy in Alzheimer’s Disease and Semantic Dementia: An ALE Meta-Analysis of Voxel-Based Morphometry Studies. J Alzheimers Dis 2016; 54:941-955. [DOI: 10.3233/jad-160382] [Citation(s) in RCA: 44] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/18/2023]
Affiliation(s)
- Marianne Chapleau
- Département de Psychologie, Université de Montréal, Montréal, Québec, Canada
- Centre de Recherche de l’Institut Universitaire de Gériatrie de Montréal, Montréal, Québec, Canada
| | - Joséphine Aldebert
- Département de Psychologie, Université de Montréal, Montréal, Québec, Canada
| | - Maxime Montembeault
- Département de Psychologie, Université de Montréal, Montréal, Québec, Canada
- Centre de Recherche de l’Institut Universitaire de Gériatrie de Montréal, Montréal, Québec, Canada
| | - Simona M. Brambati
- Département de Psychologie, Université de Montréal, Montréal, Québec, Canada
- Centre de Recherche de l’Institut Universitaire de Gériatrie de Montréal, Montréal, Québec, Canada
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Porat S, Goukasian N, Hwang KS, Zanto T, Do T, Pierce J, Joshi S, Woo E, Apostolova LG. Dance Experience and Associations with Cortical Gray Matter Thickness in the Aging Population. Dement Geriatr Cogn Dis Extra 2016; 6:508-517. [PMID: 27920794 PMCID: PMC5123027 DOI: 10.1159/000449130] [Citation(s) in RCA: 22] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/19/2022] Open
Abstract
INTRODUCTION We investigated the effect dance experience may have on cortical gray matter thickness and cognitive performance in elderly participants with and without mild cognitive impairment (MCI). METHODS 39 cognitively normal and 48 MCI elderly participants completed a questionnaire regarding their lifetime experience with music, dance, and song. Participants identified themselves as either dancers or nondancers. All participants received structural 1.5-tesla MRI scans and detailed clinical and neuropsychological evaluations. An advanced 3D cortical mapping technique was then applied to calculate cortical thickness. RESULTS Despite having a trend-level significantly thinner cortex, dancers performed better in cognitive tasks involving learning and memory, such as the California Verbal Learning Test-II (CVLT-II) short delay free recall (p = 0.004), the CVLT-II long delay free recall (p = 0.003), and the CVLT-II learning over trials 1-5 (p = 0.001). DISCUSSION Together, these results suggest that dance may result in an enhancement of cognitive reserve in aging, which may help avert or delay MCI.
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Affiliation(s)
- Shai Porat
- Department of Neurology, David Geffen School of Medicine at UCLA, Los Angeles, Calif, USA
- Mary S. Easton Center for Alzheimer's Disease Research, Los Angeles, Calif, USA
| | - Naira Goukasian
- Department of Neurology, David Geffen School of Medicine at UCLA, Los Angeles, Calif, USA
- Mary S. Easton Center for Alzheimer's Disease Research, Los Angeles, Calif, USA
| | - Kristy S. Hwang
- Department of Neurology, David Geffen School of Medicine at UCLA, Los Angeles, Calif, USA
- Mary S. Easton Center for Alzheimer's Disease Research, Los Angeles, Calif, USA
- Oakland University William Beaumont School of Medicine, Rochester, Mich., USA
| | - Theodore Zanto
- Department of Neurology, UCSF, San Francisco, Calif, USA
| | - Triet Do
- Department of Neurology, David Geffen School of Medicine at UCLA, Los Angeles, Calif, USA
- Mary S. Easton Center for Alzheimer's Disease Research, Los Angeles, Calif, USA
| | - Jonathan Pierce
- Department of Neurology, David Geffen School of Medicine at UCLA, Los Angeles, Calif, USA
- Mary S. Easton Center for Alzheimer's Disease Research, Los Angeles, Calif, USA
| | - Shantanu Joshi
- Department of Neurology, David Geffen School of Medicine at UCLA, Los Angeles, Calif, USA
| | - Ellen Woo
- Department of Neurology, David Geffen School of Medicine at UCLA, Los Angeles, Calif, USA
- Mary S. Easton Center for Alzheimer's Disease Research, Los Angeles, Calif, USA
| | - Liana G. Apostolova
- Department of Neurology, David Geffen School of Medicine at UCLA, Los Angeles, Calif, USA
- Mary S. Easton Center for Alzheimer's Disease Research, Los Angeles, Calif, USA
- Department of Neurology, Indiana University, Indianapolis, Ind, USA
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A fast approach for hippocampal segmentation from T1-MRI for predicting progression in Alzheimer's disease from elderly controls. J Neurosci Methods 2016; 270:61-75. [DOI: 10.1016/j.jneumeth.2016.06.013] [Citation(s) in RCA: 19] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/02/2016] [Revised: 06/14/2016] [Accepted: 06/15/2016] [Indexed: 01/08/2023]
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35
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Liu M, Zhang D, Adeli-Mosabbeb E, Shen D. Inherent Structure-Based Multiview Learning With Multitemplate Feature Representation for Alzheimer's Disease Diagnosis. IEEE Trans Biomed Eng 2016; 63:1473-82. [PMID: 26540666 PMCID: PMC4851920 DOI: 10.1109/tbme.2015.2496233] [Citation(s) in RCA: 71] [Impact Index Per Article: 8.9] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/16/2022]
Abstract
Multitemplate-based brain morphometric pattern analysis using magnetic resonance imaging has been recently proposed for automatic diagnosis of Alzheimer's disease (AD) and its prodromal stage (i.e., mild cognitive impairment or MCI). In such methods, multiview morphological patterns generated from multiple templates are used as feature representation for brain images. However, existing multitemplate-based methods often simply assume that each class is represented by a specific type of data distribution (i.e., a single cluster), while in reality, the underlying data distribution is actually not preknown. In this paper, we propose an inherent structure-based multiview leaning method using multiple templates for AD/MCI classification. Specifically, we first extract multiview feature representations for subjects using multiple selected templates and then cluster subjects within a specific class into several subclasses (i.e., clusters) in each view space. Then, we encode those subclasses with unique codes by considering both their original class information and their own distribution information, followed by a multitask feature selection model. Finally, we learn an ensemble of view-specific support vector machine classifiers based on their, respectively, selected features in each view and fuse their results to draw the final decision. Experimental results on the Alzheimer's Disease Neuroimaging Initiative database demonstrate that our method achieves promising results for AD/MCI classification, compared to the state-of-the-art multitemplate-based methods.
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Affiliation(s)
- Mingxia Liu
- Department of Radiology and Biomedical Research Imaging Center, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
| | - Daoqiang Zhang
- School of Computer Science and Technology, Nanjing University of Aeronautics and Astronautics, Nanjing 210016, China
| | - Ehsan Adeli-Mosabbeb
- Department of Radiology and Biomedical Research Imaging Center, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
| | - Dinggang Shen
- Department of Radiology and Biomedical Research Imaging Center, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA, and also with the Department of Brain and Cognitive Engineering, Korea University, Seoul 02841, Republic of Korea
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Ribeiro LG, Busatto G. Voxel-based morphometry in Alzheimers disease and mild cognitive impairment: Systematic review of studies addressing the frontal lobe. Dement Neuropsychol 2016; 10:104-112. [PMID: 29213441 PMCID: PMC5642401 DOI: 10.1590/s1980-5764-2016dn1002006] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/22/2022] Open
Abstract
Voxel-based morphometry (VBM) is a useful approach for investigating neurostructural brain changes in dementia. We systematically reviewed VBM studies of Alzheimer's disease (AD) and mild cognitive impairment (MCI), specifically focusing on grey matter (GM) atrophy in the frontal lobe. Methods Two searches were performed on the Pubmed database. A set of exclusion criteria was applied to ensure the selection of only VBM studies that directly investigated GM volume abnormalities in AD and/or MCI patients compared to cognitively normal controls. Results From a total of 46 selected articles, 35 VBM studies reported GM volume reductions in the frontal lobe. The frontal subregions, where most of the volume reductions were reported, included the inferior, superior and middle frontal gyri, as well as the anterior cingulate gyrus. We also found studies in which reduced frontal GM was detected in MCI patients who converted to AD. In a minority of studies, correlations between frontal GM volumes and behavioural changes or cognitive deficits in AD patients were investigated, with variable findings. Conclusion Results of VBM studies indicate that the frontal lobe should be regarded as an important brain area when investigating GM volume deficits in association with AD. Frontal GM loss might not be a feature specific to late AD only. Future VBM studies involving large AD samples are warranted to further investigate correlations between frontal volume deficits and both cognitive impairment and neuropsychiatric symptoms.
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Affiliation(s)
- Luís Gustavo Ribeiro
- BSc, Molecular Sciences Program, Universidade de São Paulo, São Paulo SP, Brazil
| | - Geraldo Busatto
- PhD, Department of Psychiatry, Faculdade de Medicina da Universidade de São Paulo, São Paulo SP, Brazil
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Serra L, Cercignani M, Mastropasqua C, Torso M, Spanò B, Makovac E, Viola V, Giulietti G, Marra C, Caltagirone C, Bozzali M. Longitudinal Changes in Functional Brain Connectivity Predicts Conversion to Alzheimer’s Disease. J Alzheimers Dis 2016; 51:377-89. [DOI: 10.3233/jad-150961] [Citation(s) in RCA: 47] [Impact Index Per Article: 5.9] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
Affiliation(s)
- Laura Serra
- Neuroimaging Laboratory, IRCCS Santa Lucia Foundation, Rome, Italy
| | - Mara Cercignani
- Neuroimaging Laboratory, IRCCS Santa Lucia Foundation, Rome, Italy
- Brighton & Sussex Medical School, CISC, University of Sussex, Brighton, Falmer East Sussex, UK
| | | | - Mario Torso
- Neuroimaging Laboratory, IRCCS Santa Lucia Foundation, Rome, Italy
| | - Barbara Spanò
- Neuroimaging Laboratory, IRCCS Santa Lucia Foundation, Rome, Italy
| | - Elena Makovac
- Neuroimaging Laboratory, IRCCS Santa Lucia Foundation, Rome, Italy
| | - Vanda Viola
- Neuroimaging Laboratory, IRCCS Santa Lucia Foundation, Rome, Italy
| | | | - Camillo Marra
- Institute of Neurology, Catholic University, Rome, Italy
| | - Carlo Caltagirone
- Department of Neuroscience, University of Rome ‘Tor Vergata’, Rome, Italy
- Department of Clinical and Behavioural Neurology, IRCCS Santa Lucia Foundation, Rome, Italy
| | - Marco Bozzali
- Neuroimaging Laboratory, IRCCS Santa Lucia Foundation, Rome, Italy
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Larvie M, Fischl B. Volumetric and fiber-tracing MRI methods for gray and white matter. HANDBOOK OF CLINICAL NEUROLOGY 2016; 135:39-60. [PMID: 27432659 DOI: 10.1016/b978-0-444-53485-9.00003-9] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/14/2022]
Abstract
Magnetic resonance imaging (MRI) is capable of generating high-resolution brain images with fine anatomic detail and unique tissue contrasts that reveal structures that are not visible to the eye. Sharply defined gray- and white-matter interfaces allow for quantitative anatomic analysis that can be accurately performed with largely automated segmentation methods. In an analogous fashion, diffusion MRI in the brain provides structural information based on contrasts derived from the diffusivity of water in brain tissue, which can highlight the orientation of neuronal axons. Also using largely automated methods, diffusion MRI can be used to generate models of white-matter tracts throughout the brain, a method known as tractography, as well as characterize the microstructural integrity of neuronal axons. Tractographic analysis has helped to define connectivity in the brain that powerfully informs understanding of brain function, and, together with other diffusion metrics, is useful in evaluation of the normal and diseased brain. The quantitative methods of brain segmentation, tractography, and diffusion MRI extend MRI into a realm beyond visual inspection and provide otherwise unachievable sensitivity and specificity in the analysis of brain structure and function.
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Affiliation(s)
- Mykol Larvie
- Divisions of Neuroradiology and Nuclear Medicine and Molecular Imaging, Massachusetts General Hospital, Boston, MA, USA.
| | - Bruce Fischl
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, MA, USA
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40
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Cai S, Chong T, Zhang Y, Li J, von Deneen KM, Ren J, Dong M, Huang L. Altered Functional Connectivity of Fusiform Gyrus in Subjects with Amnestic Mild Cognitive Impairment: A Resting-State fMRI Study. Front Hum Neurosci 2015; 9:471. [PMID: 26379534 PMCID: PMC4550786 DOI: 10.3389/fnhum.2015.00471] [Citation(s) in RCA: 34] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/11/2015] [Accepted: 08/12/2015] [Indexed: 11/30/2022] Open
Abstract
Visual cognition such as face recognition requests a high degree of functional integration between distributed brain areas of a network. It has been reported that the fusiform gyrus (FG) is an important brain area involved in facial cognition; altered connectivity of FG to some other regions may lead to a deficit in visual cognition especially face recognition. However, whether functional connectivity between the FG and other brain areas changes remains unclear in the resting state in amnestic mild cognitive impairment (aMCI) subjects. Here, we employed a resting-state functional MRI (fMRI) to examine alterations in functional connectivity of left/right FG comparing aMCI patients with age-matched control subjects. Forty-eight aMCI and 38 control subjects from the Alzheimer’s disease Neuroimaging Initiative were analyzed. We concentrated on the correlation between low frequency fMRI time courses in the FG and those in all other brain regions. Relative to the control group, we found some discrepant regions in the aMCI group which presented increased or decreased connectivity with the left/right FG including the left precuneus, left lingual gyrus, right thalamus, supramarginal gyrus, left supplementary motor area, left inferior temporal gyrus, and left parahippocampus. More importantly, we also obtained that both left and right FG have increased functional connections with the left middle occipital gyrus (MOG) and right anterior cingulate gyrus (ACC) in aMCI patients. That was not a coincidence and might imply that the MOG and ACC also play a critical role in visual cognition, especially face recognition. These findings in a large part supported our hypothesis and provided a new insight in understanding the important subtype of MCI.
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Affiliation(s)
- Suping Cai
- School of Life Science and Technology, Xidian University , Xi'an , China
| | - Tao Chong
- School of Life Science and Technology, Xidian University , Xi'an , China
| | - Yun Zhang
- School of Life Science and Technology, Xidian University , Xi'an , China
| | - Jun Li
- School of Life Science and Technology, Xidian University , Xi'an , China
| | - Karen M von Deneen
- School of Life Science and Technology, Xidian University , Xi'an , China
| | - Junchan Ren
- School of Life Science and Technology, Xidian University , Xi'an , China
| | - Minghao Dong
- School of Life Science and Technology, Xidian University , Xi'an , China
| | - Liyu Huang
- School of Life Science and Technology, Xidian University , Xi'an , China
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Merkel B, Steward C, Vivash L, Malpas CB, Phal P, Moffat BA, Cox KL, Ellis KA, Ames DJ, Cyarto EV, Lai MMY, Sharman MJ, Szoeke C, Masters CL, Lautenschlager NT, Desmond P. Semi-automated hippocampal segmentation in people with cognitive impairment using an age appropriate template for registration. J Magn Reson Imaging 2015; 42:1631-8. [PMID: 26140584 DOI: 10.1002/jmri.24966] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/08/2015] [Accepted: 05/07/2015] [Indexed: 11/06/2022] Open
Abstract
BACKGROUND To evaluate a new semi-automated segmentation method for calculating hippocampal volumes and to compare results with standard software tools in a cohort of people with subjective memory complaints (SMC) and mild cognitive impairment (MCI). METHODS Data from 58 participants, 39 with SMC (17 male, 22 female, mean age 72.6) and 19 with MCI (6 male, 13 female, mean age 74.3), were analyzed. For each participant, T1-weighted images were acquired using an MPRAGE sequence on a 3 Tesla MRI system. Hippocampal volumes (left, right, and total) were calculated with a new, age appropriate registration template, based on older people and using the advanced software tool ANTs (Advanced Normalization Tools). The results were compared with manual tracing (seen as the reference standard) and two widely accepted automated software tools (FSL, FreeSurfer). RESULTS The hippocampal volumes, calculated by using the age appropriate registration template were significantly (P < 0.05) more accurate (mean volume accuracy more than 90%) than those obtained with FreeSurfer and FSL (both less than 70%). Dice coefficients for the hippocampal segmentations with the new template method (75.3%) were slightly, but significantly (P < 0.05) higher than those from FreeSurfer (72.4%). CONCLUSION These results suggest that an age appropriate registration template might be a more accurate alternative to calculate hippocampal volumes when manual segmentation is not feasible.
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Affiliation(s)
- Bernd Merkel
- Department of Radiology, The University of Melbourne, Melbourne, Victoria.,Department of Radiology, The Royal Melbourne Hospital, Melbourne, Victoria
| | - Christopher Steward
- Department of Radiology, The University of Melbourne, Melbourne, Victoria.,Department of Radiology, The Royal Melbourne Hospital, Melbourne, Victoria
| | - Lucy Vivash
- Department of Medicine, The University of Melbourne, Melbourne, Victoria
| | - Charles B Malpas
- Melbourne School of Psychological Sciences, The University of Melbourne, Melbourne, Victoria
| | - Pramit Phal
- Department of Radiology, The University of Melbourne, Melbourne, Victoria.,Department of Radiology, The Royal Melbourne Hospital, Melbourne, Victoria
| | - Bradford A Moffat
- Department of Anatomy and Neuroscience, The University of Melbourne, Melbourne, Victoria
| | - Kay L Cox
- School of Medicine and Pharmacology, University of Western Australia, Perth, Western Australia
| | - Kathryn A Ellis
- Academic Unit for Psychiatry of Old Age, Department of Psychiatry, St. Vincent's Health, The University of Melbourne, Kew, Victoria
| | - David J Ames
- Academic Unit for Psychiatry of Old Age, Department of Psychiatry, St. Vincent's Health, The University of Melbourne, Kew, Victoria.,National Ageing Research Institute, Parkville, Victoria
| | | | | | | | - Cassandra Szoeke
- Department of Medicine, The University of Melbourne, Melbourne, Victoria
| | - Colin L Masters
- The Florey Institute, The University of Melbourne, Melbourne, Victoria
| | - Nicola T Lautenschlager
- Academic Unit for Psychiatry of Old Age, Department of Psychiatry, St. Vincent's Health, The University of Melbourne, Kew, Victoria.,School of Psychiatry and Clinical Neurosciences & WA Centre for Health and Ageing, The University of Western Australia, Perth, Western Australia
| | - Patricia Desmond
- Department of Radiology, The University of Melbourne, Melbourne, Victoria.,Department of Radiology, The Royal Melbourne Hospital, Melbourne, Victoria
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Sohn BK, Yi D, Seo EH, Choe YM, Kim JW, Kim SG, Choi HJ, Byun MS, Jhoo JH, Woo JI, Lee DY. Comparison of regional gray matter atrophy, white matter alteration, and glucose metabolism as a predictor of the conversion to Alzheimer's disease in mild cognitive impairment. J Korean Med Sci 2015; 30:779-87. [PMID: 26028932 PMCID: PMC4444480 DOI: 10.3346/jkms.2015.30.6.779] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/31/2014] [Accepted: 01/28/2015] [Indexed: 12/13/2022] Open
Abstract
We compared the predictive ability of the various neuroimaging tools and determined the most cost-effective, non-invasive Alzheimer's disease (AD) prediction model in mild cognitive impairment (MCI) individuals. Thirty-two MCI subjects were evaluated at baseline with [(18)F]-fluorodeoxyglucose positron emission tomography (FDG-PET), MRI, diffusion tensor imaging (DTI), and neuropsychological tests, and then followed up for 2 yr. After a follow up period, 12 MCI subjects converted to AD (MCIc) and 20 did not (MCInc). Of the voxel-based statistical comparisons of baseline neuroimaging data, the MCIc showed reduced cerebral glucose metabolism (CMgl) in the temporo-parietal, posterior cingulate, precuneus, and frontal regions, and gray matter (GM) density in multiple cortical areas including the frontal, temporal and parietal regions compared to the MCInc, whereas regional fractional anisotropy derived from DTI were not significantly different between the two groups. The MCIc also had lower Mini-Mental State Examination (MMSE) score than the MCInc. Through a series of model selection steps, the MMSE combined with CMgl model was selected as a final model (classification accuracy 93.8%). In conclusion, the combination of MMSE with regional CMgl measurement based on FDG-PET is probably the most efficient, non-invasive method to predict AD in MCI individuals after a two-year follow-up period.
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Affiliation(s)
- Bo Kyung Sohn
- Department of Psychiatry, SMG-SNU Boramae Medical Center, Seoul, Korea
| | - Dahyun Yi
- Department of Neuropsychiatry, Seoul National University Hospital, Seoul, Korea
| | - Eun Hyun Seo
- The Division of Natural Medical Science, College of Health Service, Chosun University, Gwangju, Korea
| | - Young Min Choe
- Department of Neuropsychiatry, Seoul National University Hospital, Seoul, Korea
| | - Jee Wook Kim
- Department of Neuropsychiatry, College of Medicine, Hallym University Dongtan Sacred Heart Hospital, Hwaseong, Korea
| | - Shin Gyeom Kim
- Department of Neuropsychiatry, College of Medicine, Soonchunhyang University Bucheon Hospital, Bucheon, Korea
| | - Hyo Jung Choi
- Department of Neuropsychiatry, Seoul National University Hospital, Seoul, Korea
| | - Min Soo Byun
- Department of Neuropsychiatry, Seoul National University Hospital, Seoul, Korea
| | - Jin Hyeong Jhoo
- Department of Psychiatry, Kangwon National University School of Medicine, Chuncheon, Korea
| | - Jong Inn Woo
- Department of Neuropsychiatry, Seoul National University Hospital, Seoul, Korea
- Interdisciplinary Program of Cognitive Science, Seoul National University, Seoul, Korea
- Neuroscience Research Institute of the Medical Research Center, Seoul National University, Seoul, Korea
| | - Dong Young Lee
- Department of Neuropsychiatry, Seoul National University Hospital, Seoul, Korea
- Interdisciplinary Program of Cognitive Science, Seoul National University, Seoul, Korea
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43
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Wang WY, Yu JT, Liu Y, Yin RH, Wang HF, Wang J, Tan L, Radua J, Tan L. Voxel-based meta-analysis of grey matter changes in Alzheimer's disease. Transl Neurodegener 2015; 4:6. [PMID: 25834730 PMCID: PMC4381413 DOI: 10.1186/s40035-015-0027-z] [Citation(s) in RCA: 70] [Impact Index Per Article: 7.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/08/2014] [Accepted: 03/18/2015] [Indexed: 01/18/2023] Open
Abstract
Background Voxel-based morphometry (VBM) using structural brain MRI has been widely used for the assessment of impairment in Alzheimer’s disease (AD), but previous studies in VBM studies on AD remain inconsistent. Objective We conducted meta-analyses to integrate the reported studies to determine the consistent grey matter alterations in AD based on VBM method. Methods The PubMed, ISI Web of Science, EMBASE and Medline database were searched for articles between 1995 and June 2014. Manual searches were also conducted, and authors of studies were contacted for additional data. Coordinates were extracted from clusters with significant grey matter difference between AD patients and healthy controls (HC). Meta-analysis was performed using a new improved voxel-based meta-analytic method, Effect Size Signed Differential Mapping (ES-SDM). Results Thirty data-sets comprising 960 subjects with AD and 1195 HC met inclusion criteria. Grey matter volume (GMV) reduction at 334 coordinates in AD and no GMV increase were found in the current meta-analysis. Significant reductions in GMV were robustly localized in the limbic regions (left parahippocampl gyrus and left posterior cingulate gyrus). In addition, there were GM decreases in right fusiform gyrus and right superior frontal gyrus. The findings remain largely unchanged in the jackknife sensitivity analyses. Conclusions Our meta-analysis clearly identified GMV atrophy in AD. These findings confirm that the most prominent and replicable structural abnormalities in AD are in the limbic regions and contributes to the understanding of pathophysiology underlying AD.
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Affiliation(s)
- Wen-Ying Wang
- Department of Neurology, Qingdao Municipal Hospital, School of Medicine, Qingdao University, No.5 Donghai Middle Road, Qingdao, Shandong Province 266071 China
| | - Jin-Tai Yu
- Department of Neurology, Qingdao Municipal Hospital, School of Medicine, Qingdao University, No.5 Donghai Middle Road, Qingdao, Shandong Province 266071 China ; College of Medicine and Pharmaceutics, Ocean University of China, Qingdao, 266011 China ; Department of Neurology, Qingdao Municipal Hospital, Nanjing Medical University, Nanjing, 266071 China
| | - Yong Liu
- Brainnetome Center, Institute of Automation, Chinese Academy of Sciences, Beijing, 100190 China ; National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing, 100190 China
| | - Rui-Hua Yin
- Department of Neurology, Qingdao Municipal Hospital, School of Medicine, Qingdao University, No.5 Donghai Middle Road, Qingdao, Shandong Province 266071 China
| | - Hui-Fu Wang
- Department of Neurology, Qingdao Municipal Hospital, Nanjing Medical University, Nanjing, 266071 China
| | - Jun Wang
- Department of Neurology, Qingdao Municipal Hospital, School of Medicine, Qingdao University, No.5 Donghai Middle Road, Qingdao, Shandong Province 266071 China
| | - Lin Tan
- College of Medicine and Pharmaceutics, Ocean University of China, Qingdao, 266011 China
| | - Joaquim Radua
- Department of Psychosis Studies, Institute of Psychiatry, King's College London, London, UK ; Research Unit, FIDMAG Germanes Hospitala'ries-CIBERSAM, Sant Boi de Llobregat, Barcelona, Spain
| | - Lan Tan
- Department of Neurology, Qingdao Municipal Hospital, School of Medicine, Qingdao University, No.5 Donghai Middle Road, Qingdao, Shandong Province 266071 China ; College of Medicine and Pharmaceutics, Ocean University of China, Qingdao, 266011 China ; Department of Neurology, Qingdao Municipal Hospital, Nanjing Medical University, Nanjing, 266071 China
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44
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Challis E, Hurley P, Serra L, Bozzali M, Oliver S, Cercignani M. Gaussian process classification of Alzheimer's disease and mild cognitive impairment from resting-state fMRI. Neuroimage 2015; 112:232-243. [PMID: 25731993 DOI: 10.1016/j.neuroimage.2015.02.037] [Citation(s) in RCA: 125] [Impact Index Per Article: 13.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/30/2014] [Revised: 12/22/2014] [Accepted: 02/17/2015] [Indexed: 11/29/2022] Open
Abstract
Multivariate pattern analysis and statistical machine learning techniques are attracting increasing interest from the neuroimaging community. Researchers and clinicians are also increasingly interested in the study of functional-connectivity patterns of brains at rest and how these relations might change in conditions like Alzheimer's disease or clinical depression. In this study we investigate the efficacy of a specific multivariate statistical machine learning technique to perform patient stratification from functional-connectivity patterns of brains at rest. Whilst the majority of previous approaches to this problem have employed support vector machines (SVMs) we investigate the performance of Bayesian Gaussian process logistic regression (GP-LR) models with linear and non-linear covariance functions. GP-LR models can be interpreted as a Bayesian probabilistic analogue to kernel SVM classifiers. However, GP-LR methods confer a number of benefits over kernel SVMs. Whilst SVMs only return a binary class label prediction, GP-LR, being a probabilistic model, provides a principled estimate of the probability of class membership. Class probability estimates are a measure of the confidence the model has in its predictions, such a confidence score may be extremely useful in the clinical setting. Additionally, if miss-classification costs are not symmetric, thresholds can be set to achieve either strong specificity or sensitivity scores. Since GP-LR models are Bayesian, computationally expensive cross-validation hyper-parameter grid-search methods can be avoided. We apply these methods to a sample of 77 subjects; 27 with a diagnosis of probable AD, 50 with a diagnosis of a-MCI and a control sample of 39. All subjects underwent a MRI examination at 3T to obtain a 7minute and 20second resting state scan. Our results support the hypothesis that GP-LR models can be effective at performing patient stratification: the implemented model achieves 75% accuracy disambiguating healthy subjects from subjects with amnesic mild cognitive impairment and 97% accuracy disambiguating amnesic mild cognitive impairment subjects from those with Alzheimer's disease, accuracies are estimated using a held-out test set. Both results are significant at the 1% level.
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Affiliation(s)
- Edward Challis
- Department of Physics and Astronomy, University of Sussex, Falmer, East Sussex BN1 9QH, UK
| | - Peter Hurley
- Department of Physics and Astronomy, University of Sussex, Falmer, East Sussex BN1 9QH, UK
| | - Laura Serra
- Neuroimaging Laboratory, Santa Lucia Foundation, Via Ardeatina 306, Roma, Italy
| | - Marco Bozzali
- Neuroimaging Laboratory, Santa Lucia Foundation, Via Ardeatina 306, Roma, Italy
| | - Seb Oliver
- Department of Physics and Astronomy, University of Sussex, Falmer, East Sussex BN1 9QH, UK
| | - Mara Cercignani
- Clinical Imaging Sciences Centre, Brighton and Sussex Medical School, University of Sussex, Falmer, East Sussex BN1 9PR, UK.
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45
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Liu M, Zhang D, Shen D. View-centralized multi-atlas classification for Alzheimer's disease diagnosis. Hum Brain Mapp 2015; 36:1847-65. [PMID: 25624081 DOI: 10.1002/hbm.22741] [Citation(s) in RCA: 80] [Impact Index Per Article: 8.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/05/2014] [Revised: 12/23/2014] [Accepted: 01/12/2015] [Indexed: 01/29/2023] Open
Abstract
Multi-atlas based methods have been recently used for classification of Alzheimer's disease (AD) and its prodromal stage, that is, mild cognitive impairment (MCI). Compared with traditional single-atlas based methods, multiatlas based methods adopt multiple predefined atlases and thus are less biased by a certain atlas. However, most existing multiatlas based methods simply average or concatenate the features from multiple atlases, which may ignore the potentially important diagnosis information related to the anatomical differences among different atlases. In this paper, we propose a novel view (i.e., atlas) centralized multi-atlas classification method, which can better exploit useful information in multiple feature representations from different atlases. Specifically, all brain images are registered onto multiple atlases individually, to extract feature representations in each atlas space. Then, the proposed view-centralized multi-atlas feature selection method is used to select the most discriminative features from each atlas with extra guidance from other atlases. Next, we design a support vector machine (SVM) classifier using the selected features in each atlas space. Finally, we combine multiple SVM classifiers for multiple atlases through a classifier ensemble strategy for making a final decision. We have evaluated our method on 459 subjects [including 97 AD, 117 progressive MCI (p-MCI), 117 stable MCI (s-MCI), and 128 normal controls (NC)] from the Alzheimer's Disease Neuroimaging Initiative database, and achieved an accuracy of 92.51% for AD versus NC classification and an accuracy of 78.88% for p-MCI versus s-MCI classification. These results demonstrate that the proposed method can significantly outperform the previous multi-atlas based classification methods.
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Affiliation(s)
- Mingxia Liu
- School of Computer Science and Technology, Nanjing University of Aeronautics & Astronautics, Nanjing, China; Department of Radiology and BRIC, University of North Carolina at Chapel Hill, North Carolina, USA; School of Information Science and Technology, Taishan University, Taian, China
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46
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Kehoe EG, McNulty JP, Mullins PG, Bokde ALW. Advances in MRI biomarkers for the diagnosis of Alzheimer's disease. Biomark Med 2014; 8:1151-69. [DOI: 10.2217/bmm.14.42] [Citation(s) in RCA: 36] [Impact Index Per Article: 3.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/27/2023] Open
Abstract
With the prevalence of Alzheimer's disease (AD) predicted to increase substantially over the coming decades, the development of effective biomarkers for the early detection of the disease is paramount. In this short review, the main neuroimaging techniques which have shown potential as biomarkers for AD are introduced, with a focus on MRI. Structural MRI measures of the hippocampus and medial temporal lobe are still the most clinically validated biomarkers for AD, but newer techniques such as functional MRI and diffusion tensor imaging offer great scope in tracking changes in the brain, particularly in functional and structural connectivity, which may precede gray matter atrophy. These new advances in neuroimaging methods require further development and crucially, standardization; however, before they are used as biomarkers to aid in the diagnosis of AD.
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Affiliation(s)
- Elizabeth G Kehoe
- The Trinity College Institute of Neuroscience, Trinity College Dublin, Dublin 2, Ireland
- Cognitive Systems Group, Discipline of Psychiatry, School of Medicine, Trinity College Dublin, Dublin, Ireland
| | - Jonathan P McNulty
- School of Medicine & Medical Science, University College Dublin, Dublin, Ireland
| | | | - Arun L W Bokde
- The Trinity College Institute of Neuroscience, Trinity College Dublin, Dublin 2, Ireland
- Cognitive Systems Group, Discipline of Psychiatry, School of Medicine, Trinity College Dublin, Dublin, Ireland
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47
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Abstract
Mild cognitive impairment is the term applied to the cognitive state that lies between normal aging and dementia. There has been significant controversy around describing, defining and characterizing mild cognitive impairment. This review will cover current understanding of the condition and discuss clinical features, research strategies and future directions.
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Affiliation(s)
- Craig Gordon
- ST5 Old Age Psychiatry, NHS Greater Glasgow and Clyde, Glasgow, UK University of Glasgow, MHW, 1055 Great Western Road, Glasgow, UK
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Min R, Wu G, Cheng J, Wang Q, Shen D. Multi-atlas based representations for Alzheimer's disease diagnosis. Hum Brain Mapp 2014; 35:5052-70. [PMID: 24753060 DOI: 10.1002/hbm.22531] [Citation(s) in RCA: 53] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/04/2013] [Revised: 03/12/2014] [Accepted: 04/02/2014] [Indexed: 11/12/2022] Open
Abstract
Brain morphometry based classification from magnetic resonance (MR) acquisitions has been widely investigated in the diagnosis of Alzheimer's disease (AD) and its prodromal stage, i.e., mild cognitive impairment (MCI). In the literature, a morphometric representation of brain structures is obtained by spatial normalization of each image into a common space (i.e., a pre-defined atlas) via non-linear registration, thus the corresponding regions in different brains can be compared. However, representations generated from one single atlas may not be sufficient to reveal the underlying anatomical differences between the groups of disease-affected patients and normal controls (NC). In this article, we propose a different methodology, namely the multi-atlas based morphometry, which measures morphometric representations of the same image in different spaces of multiple atlases. Representations generated from different atlases can thus provide the complementary information to discriminate different groups, and also reduce the negative impacts from registration errors. Specifically, each studied subject is registered to multiple atlases, where adaptive regional features are extracted. Then, all features from different atlases are jointly selected by a correlation and relevance based scheme, followed by final classification with the support vector machine (SVM). We have evaluated the proposed method on 459 subjects (97 AD, 117 progressive-MCI (p-MCI), 117 stable-MCI (s-MCI), and 128 NC) from the Alzheimer's Disease Neuroimaging Initiative (ADNI) database, and achieved 91.64% for AD/NC classification and 72.41% for p-MCI/s-MCI classification. Our results clearly demonstrate that the proposed multi-atlas based method can significantly outperform the previous single-atlas based methods.
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Affiliation(s)
- Rui Min
- Department of Radiology and BRIC, University of North Carolina at Chapel Hill, North Carolina
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Selective changes of resting-state brain oscillations in aMCI: an fMRI study using ALFF. BIOMED RESEARCH INTERNATIONAL 2014; 2014:920902. [PMID: 24822220 PMCID: PMC4005061 DOI: 10.1155/2014/920902] [Citation(s) in RCA: 35] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 01/28/2014] [Accepted: 03/11/2014] [Indexed: 11/17/2022]
Abstract
Mild cognitive impairment (MCI) refers to a transitional state between normal aging and dementia and is a syndrome with cognitive decline greater than expected for an individual's age and educational level. As a subtype of MCI, amnestic mild cognitive impairment (aMCI) most often leads to Alzheimer's disease. This study aims to elucidate the altered brain activation in patients with aMCI using resting-state functional magnetic resonance. We observed Frequency-dependent changes in the amplitude of low-frequency fluctuations in aMCI patients (n = 20), and normal subjects (n = 18). At the same time, we took gray matter volume as a covariate. We found that aMCI patients had decreased amplitude of low-frequency fluctuation signal in left superior temporal gyrus, right middle temporal gyrus, right inferior parietal lobe, and right postcentral gyrus compared to the control group. Specially, aMCI patients showed increased signal in left superior and middle frontal gyrus. Our results suggested that increased activation in frontal lobe of aMCI patients may indicate effective recruitment of compensatory brain resources. This finding and interpretation may lead to the better understanding of cognitive changes of aMCI.
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Busatto GF, Diniz BS, Zanetti MV. Voxel-based morphometry in Alzheimer’s disease. Expert Rev Neurother 2014; 8:1691-702. [DOI: 10.1586/14737175.8.11.1691] [Citation(s) in RCA: 90] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/08/2022]
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