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Pearson MJ, Wagstaff R, Williams RJ. Choroid plexus volumes and auditory verbal learning scores are associated with conversion from mild cognitive impairment to Alzheimer's disease. Brain Behav 2024; 14:e3611. [PMID: 38956818 PMCID: PMC11219301 DOI: 10.1002/brb3.3611] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/29/2024] [Revised: 05/30/2024] [Accepted: 06/01/2024] [Indexed: 07/04/2024] Open
Abstract
PURPOSE Mild cognitive impairment (MCI) can be the prodromal phase of Alzheimer's disease (AD) where appropriate intervention might prevent or delay conversion to AD. Given this, there has been increasing interest in using magnetic resonance imaging (MRI) and neuropsychological testing to predict conversion from MCI to AD. Recent evidence suggests that the choroid plexus (ChP), neural substrates implicated in brain clearance, undergo volumetric changes in MCI and AD. Whether the ChP is involved in memory changes observed in MCI and can be used to predict conversion from MCI to AD has not been explored. METHOD The current study used data from the Alzheimer's Disease Neuroimaging Initiative (ADNI) database to investigate whether later progression from MCI to AD (progressive MCI [pMCI], n = 115) or stable MCI (sMCI, n = 338) was associated with memory scores using the Rey Auditory Verbal Learning Test (RAVLT) and ChP volumes as calculated from MRI. Classification analyses identifying pMCI or sMCI group membership were performed to compare the predictive ability of the RAVLT and ChP volumes. FINDING The results indicated a significant difference between pMCI and sMCI groups for right ChP volume, with the pMCI group showing significantly larger right ChP volume (p = .01, 95% confidence interval [-.116, -.015]). A significant linear relationship between the RAVLT scores and right ChP volume was found across all participants, but not for the two groups separately. Classification analyses showed that a combination of left ChP volume and auditory verbal learning scores resulted in the most accurate classification performance, with group membership accurately predicted for 72% of the testing data. CONCLUSION These results suggest that volumetric ChP changes appear to occur before the onset of AD and might provide value in predicting conversion from MCI to AD.
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Affiliation(s)
- Michael J. Pearson
- Faculty of HealthCharles Darwin UniversityDarwinNorthern TerritoryAustralia
| | - Ruth Wagstaff
- Faculty of HealthCharles Darwin UniversityDarwinNorthern TerritoryAustralia
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Lee AJ, Stark JH, Hayes SM. Baseline Frontoparietal Gray Matter Volume Predicts Executive Function Performance in Aging and Mild Cognitive Impairment at 24-Month Follow-Up. J Alzheimers Dis 2024; 100:357-374. [PMID: 38875035 DOI: 10.3233/jad-231468] [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] [Indexed: 06/16/2024]
Abstract
Background Executive dysfunction in mild cognitive impairment (MCI) has been associated with gray matter atrophy. Prior studies have yielded limited insight into associations between gray matter volume and executive function in early and late amnestic MCI (aMCI). Objective To examine the relative importance of predictors of executive function at 24 months and relationships between baseline regional gray matter volume and executive function performance at 24-month follow-up in non-demented older adults. Methods 147 participants from the Alzheimer's Disease Neuroimaging Initiative (mean age = 70.6 years) completed brain magnetic resonance imaging and neuropsychological testing and were classified as cognitively normal (n = 49), early aMCI (n = 60), or late aMCI (n = 38). Analyses explored the importance of demographic, APOEɛ4, biomarker (p-tau/Aβ42, t-tau/Aβ42), and gray matter regions-of-interest (ROI) variables to 24-month executive function, whether ROIs predicted executive function, and whether relationships varied by baseline diagnostic status. Results Across all participants, baseline anterior cingulate cortex and superior parietal lobule volumes were the strongest predictors of 24-month executive function performance. In early aMCI, anterior cingulate cortex volume was the strongest predictor and demonstrated a significant interaction such that lower volume related to worse 24-month executive function in early aMCI. Educational attainment and inferior frontal gyrus volume were the strongest predictors of 24-month executive function performance for cognitively normal and late aMCI groups, respectively. Conclusions Baseline frontoparietal gray matter regions were significant predictors of executive function performance in the context of aMCI and may identify those at risk of Alzheimer's disease. Anterior cingulate cortex volume may predict executive function performance in early aMCI.
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Affiliation(s)
- Ann J Lee
- Department of Psychology, The Ohio State University, Columbus, OH, USA
| | - Jessica H Stark
- Department of Psychology, The Ohio State University, Columbus, OH, USA
| | - Scott M Hayes
- Department of Psychology, The Ohio State University, Columbus, OH, USA
- Chronic Brain Injury Initiative, The Ohio State University, Columbus, OH, USA
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García-Gutiérrez F, Marquié M, Muñoz N, Alegret M, Cano A, de Rojas I, García-González P, Olivé C, Puerta R, Orellana A, Montrreal L, Pytel V, Ricciardi M, Zaldua C, Gabirondo P, Hinzen W, Lleonart N, García-Sánchez A, Tárraga L, Ruiz A, Boada M, Valero S. Harnessing acoustic speech parameters to decipher amyloid status in individuals with mild cognitive impairment. Front Neurosci 2023; 17:1221401. [PMID: 37746151 PMCID: PMC10512723 DOI: 10.3389/fnins.2023.1221401] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/12/2023] [Accepted: 08/08/2023] [Indexed: 09/26/2023] Open
Abstract
Alzheimer's disease (AD) is a neurodegenerative condition characterized by a gradual decline in cognitive functions. Currently, there are no effective treatments for AD, underscoring the importance of identifying individuals in the preclinical stages of mild cognitive impairment (MCI) to enable early interventions. Among the neuropathological events associated with the onset of the disease is the accumulation of amyloid protein in the brain, which correlates with decreased levels of Aβ42 peptide in the cerebrospinal fluid (CSF). Consequently, the development of non-invasive, low-cost, and easy-to-administer proxies for detecting Aβ42 positivity in CSF becomes particularly valuable. A promising approach to achieve this is spontaneous speech analysis, which combined with machine learning (ML) techniques, has proven highly useful in AD. In this study, we examined the relationship between amyloid status in CSF and acoustic features derived from the description of the Cookie Theft picture in MCI patients from a memory clinic. The cohort consisted of fifty-two patients with MCI (mean age 73 years, 65% female, and 57% positive amyloid status). Eighty-eight acoustic parameters were extracted from voice recordings using the extended Geneva Minimalistic Acoustic Parameter Set (eGeMAPS), and several ML models were used to classify the amyloid status. Furthermore, interpretability techniques were employed to examine the influence of input variables on the determination of amyloid-positive status. The best model, based on acoustic variables, achieved an accuracy of 75% with an area under the curve (AUC) of 0.79 in the prediction of amyloid status evaluated by bootstrapping and Leave-One-Out Cross Validation (LOOCV), outperforming conventional neuropsychological tests (AUC = 0.66). Our results showed that the automated analysis of voice recordings derived from spontaneous speech tests offers valuable insights into AD biomarkers during the preclinical stages. These findings introduce novel possibilities for the use of digital biomarkers to identify subjects at high risk of developing AD.
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Affiliation(s)
| | - Marta Marquié
- Ace Alzheimer Center Barcelona, Universitat Internacional de Catalunya, Barcelona, Spain
- Networking Research Center on Neurodegenerative Diseases (CIBERNED), Instituto de Salud Carlos III, Madrid, Spain
| | - Nathalia Muñoz
- Ace Alzheimer Center Barcelona, Universitat Internacional de Catalunya, Barcelona, Spain
| | - Montserrat Alegret
- Ace Alzheimer Center Barcelona, Universitat Internacional de Catalunya, Barcelona, Spain
- Networking Research Center on Neurodegenerative Diseases (CIBERNED), Instituto de Salud Carlos III, Madrid, Spain
| | - Amanda Cano
- Ace Alzheimer Center Barcelona, Universitat Internacional de Catalunya, Barcelona, Spain
- Networking Research Center on Neurodegenerative Diseases (CIBERNED), Instituto de Salud Carlos III, Madrid, Spain
| | - Itziar de Rojas
- Ace Alzheimer Center Barcelona, Universitat Internacional de Catalunya, Barcelona, Spain
- Networking Research Center on Neurodegenerative Diseases (CIBERNED), Instituto de Salud Carlos III, Madrid, Spain
| | - Pablo García-González
- Ace Alzheimer Center Barcelona, Universitat Internacional de Catalunya, Barcelona, Spain
| | - Clàudia Olivé
- Ace Alzheimer Center Barcelona, Universitat Internacional de Catalunya, Barcelona, Spain
| | - Raquel Puerta
- Ace Alzheimer Center Barcelona, Universitat Internacional de Catalunya, Barcelona, Spain
| | - Adelina Orellana
- Ace Alzheimer Center Barcelona, Universitat Internacional de Catalunya, Barcelona, Spain
- Networking Research Center on Neurodegenerative Diseases (CIBERNED), Instituto de Salud Carlos III, Madrid, Spain
| | - Laura Montrreal
- Ace Alzheimer Center Barcelona, Universitat Internacional de Catalunya, Barcelona, Spain
| | - Vanesa Pytel
- Ace Alzheimer Center Barcelona, Universitat Internacional de Catalunya, Barcelona, Spain
| | - Mario Ricciardi
- Ace Alzheimer Center Barcelona, Universitat Internacional de Catalunya, Barcelona, Spain
| | | | | | - Wolfram Hinzen
- Department of Translation and Language Sciences, Universitat Pompeu Fabra, Barcelona, Spain
- Institut Català de Recerca i Estudis Avançats (ICREA), Barcelona, Spain
| | - Núria Lleonart
- Ace Alzheimer Center Barcelona, Universitat Internacional de Catalunya, Barcelona, Spain
| | - Ainhoa García-Sánchez
- Ace Alzheimer Center Barcelona, Universitat Internacional de Catalunya, Barcelona, Spain
| | - Lluís Tárraga
- Ace Alzheimer Center Barcelona, Universitat Internacional de Catalunya, Barcelona, Spain
- Networking Research Center on Neurodegenerative Diseases (CIBERNED), Instituto de Salud Carlos III, Madrid, Spain
| | - Agustín Ruiz
- Ace Alzheimer Center Barcelona, Universitat Internacional de Catalunya, Barcelona, Spain
- Networking Research Center on Neurodegenerative Diseases (CIBERNED), Instituto de Salud Carlos III, Madrid, Spain
| | - Mercè Boada
- Ace Alzheimer Center Barcelona, Universitat Internacional de Catalunya, Barcelona, Spain
- Networking Research Center on Neurodegenerative Diseases (CIBERNED), Instituto de Salud Carlos III, Madrid, Spain
| | - Sergi Valero
- Ace Alzheimer Center Barcelona, Universitat Internacional de Catalunya, Barcelona, Spain
- Networking Research Center on Neurodegenerative Diseases (CIBERNED), Instituto de Salud Carlos III, Madrid, Spain
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Yang L, Lu J, Li D, Xiang J, Yan T, Sun J, Wang B. Alzheimer's Disease: Insights from Large-Scale Brain Dynamics Models. Brain Sci 2023; 13:1133. [PMID: 37626490 PMCID: PMC10452161 DOI: 10.3390/brainsci13081133] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/27/2023] [Revised: 07/21/2023] [Accepted: 07/25/2023] [Indexed: 08/27/2023] Open
Abstract
Alzheimer's disease (AD) is a degenerative brain disease, and the condition is difficult to assess. In the past, numerous brain dynamics models have made remarkable contributions to neuroscience and the brain from the microcosmic to the macroscopic scale. Recently, large-scale brain dynamics models have been developed based on dual-driven multimodal neuroimaging data and neurodynamics theory. These models bridge the gap between anatomical structure and functional dynamics and have played an important role in assisting the understanding of the brain mechanism. Large-scale brain dynamics have been widely used to explain how macroscale neuroimaging biomarkers emerge from potential neuronal population level disturbances associated with AD. In this review, we describe this emerging approach to studying AD that utilizes a biophysically large-scale brain dynamics model. In particular, we focus on the application of the model to AD and discuss important directions for the future development and analysis of AD models. This will facilitate the development of virtual brain models in the field of AD diagnosis and treatment and add new opportunities for advancing clinical neuroscience.
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Affiliation(s)
- Lan Yang
- College of Computer Science and Technology, Taiyuan University of Technology, Taiyuan 030024, China; (L.Y.); (J.L.); (D.L.); (J.X.); (J.S.)
| | - Jiayu Lu
- College of Computer Science and Technology, Taiyuan University of Technology, Taiyuan 030024, China; (L.Y.); (J.L.); (D.L.); (J.X.); (J.S.)
| | - Dandan Li
- College of Computer Science and Technology, Taiyuan University of Technology, Taiyuan 030024, China; (L.Y.); (J.L.); (D.L.); (J.X.); (J.S.)
| | - Jie Xiang
- College of Computer Science and Technology, Taiyuan University of Technology, Taiyuan 030024, China; (L.Y.); (J.L.); (D.L.); (J.X.); (J.S.)
| | - Ting Yan
- Teranslational Medicine Research Center, Shanxi Medical University, Taiyuan 030001, China;
| | - Jie Sun
- College of Computer Science and Technology, Taiyuan University of Technology, Taiyuan 030024, China; (L.Y.); (J.L.); (D.L.); (J.X.); (J.S.)
| | - Bin Wang
- College of Computer Science and Technology, Taiyuan University of Technology, Taiyuan 030024, China; (L.Y.); (J.L.); (D.L.); (J.X.); (J.S.)
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Shaji S, Palanisamy R, Swaminathan R. Structural irregularities in MR corpus callosal images and their association with cerebrospinal fluid biomarkers in Mild Cognitive Impairments. Neurosci Lett 2023; 810:137329. [PMID: 37301466 DOI: 10.1016/j.neulet.2023.137329] [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: 01/14/2023] [Revised: 05/15/2023] [Accepted: 06/05/2023] [Indexed: 06/12/2023]
Abstract
In this study, irregularity measures from MR images of corpus callosal brain structures in healthy and Mild Cognitive Impairment (MCI) conditions are extracted and their association with Cerebrospinal Fluid (CSF) biomarkers are analyzed. For this, MR images of healthy controls, Early MCI (EMCI) and Late MCI (LMCI) subjects are considered from a public database. The considered images are preprocessed and corpus callosal structure is segmented. Structural irregularity measures are extracted from the segmented regions using Fourier analysis. Statistical tests are performed to identify the significant features which can characterize the MCI stages. Association of these measures with CSF amyloid beta and tau concentrations are further investigated. Results demonstrate that Fourier spectral analysis is able to characterize the non-periodic variations in the corpus callosal structures of healthy, EMCI and LMCI MR images. The callosal irregularity measures increase as the disease progresses from healthy to LMCI. Phosphorylated tau concentrations in CSF demonstrate a positive correlation with irregularity measures across the diagnostic groups. Significant association of callosal measures and amyloid beta levels are found to be absent in MCI stages. As corpus callosal structural irregularities due to early MCI condition and their association with CSF markers remain uncharacterized in the literature, this study seems to be clinically significant for the timely intervention of pre-symptomatic MCI stages.
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Affiliation(s)
- Sreelakshmi Shaji
- Non-Invasive Imaging and Diagnostic Laboratory, Biomedical Engineering Group, Department of Applied Mechanics, Indian Institute of Technology Madras, Chennai, India.
| | - Rohini Palanisamy
- Indian Institute of Information Technology, Design and Manufacturing, Kancheepuram, Chennai, India.
| | - Ramakrishnan Swaminathan
- Non-Invasive Imaging and Diagnostic Laboratory, Biomedical Engineering Group, Department of Applied Mechanics, Indian Institute of Technology Madras, Chennai, India.
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Göschel L, Kurz L, Dell'Orco A, Köbe T, Körtvélyessy P, Fillmer A, Aydin S, Riemann LT, Wang H, Ittermann B, Grittner U, Flöel A. 7T amygdala and hippocampus subfields in volumetry-based associations with memory: A 3-year follow-up study of early Alzheimer's disease. Neuroimage Clin 2023; 38:103439. [PMID: 37253284 PMCID: PMC10236463 DOI: 10.1016/j.nicl.2023.103439] [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: 03/02/2023] [Revised: 05/09/2023] [Accepted: 05/15/2023] [Indexed: 06/01/2023]
Abstract
INTRODUCTION The hippocampus is the most prominent single region of interest (ROI) for the diagnosis and prediction of Alzheimer's disease (AD). However, its suitability in the earliest stages of cognitive decline, i.e., subjective cognitive decline (SCD), remains uncertain which warrants the pursuit of alternative or complementary regions. The amygdala might be a promising candidate, given its implication in memory as well as other psychiatric disorders, e.g. depression and anxiety, which are prevalent in SCD. In this 7 tesla (T) magnetic resonance imaging (MRI) study, we aimed to compare the contribution of volumetric measurements of the hippocampus, the amygdala, and their respective subfields, for early diagnosis and prediction in an AD-related study population. METHODS Participants from a longitudinal study were grouped into SCD (n = 29), mild cognitive impairment (MCI, n = 23), AD (n = 22) and healthy control (HC, n = 31). All participants underwent 7T MRI at baseline and extensive neuropsychological testing at up to three visits (baseline n = 105, 1-year n = 78, 3-year n = 39). Analysis of covariance (ANCOVA) was used to assess group differences of baseline volumes of the amygdala and the hippocampus and their subfields. Linear mixed models were used to estimate the effects of baseline volumes on yearly changes of a z-scaled memory score. All models were adjusted to age, sex and education. RESULTS Compared to the HC group, individuals with SCD showed smaller amygdala ROI volumes (range across subfields -11% to -1%), but not hippocampus ROI volumes (-2% to 1%) except for the hippocampus-amygdala-transition-area (-7%). However, cross-sectional associations between baseline memory and volumes were smaller for amygdala ROIs (std. ß [95% CI] ranging between 0.16 [0.08; 0.25] and 0.46 [0.31; 0.60]) than hippocampus ROIs (between 0.32 [0.19; 0.44] and 0.53 [0.40; 0.67]). Further, the association of baseline volumes with yearly memory change in the HC and SCD groups was similarly weak for amygdala ROIs and hippocampus ROIs. In the MCI group, volumes of amygdala ROIs were associated with a relevant yearly memory decline [95% CI] ranging between -0.12 [-0.24; 0.00] and -0.26 [-0.42; -0.09] for individuals with 20% smaller volumes than the HC group. However, effects were stronger for hippocampus ROIs with a corresponding yearly memory decline ranging between -0.21 [-0.35; -0.07] and -0.31 [-0.50; -0.13]. CONCLUSION Volumes of amygdala ROIs, as determined by 7T MRI, might contribute to objectively and non-invasively identify patients with SCD, and thus aid early diagnosis and treatment of individuals at risk to develop dementia due to AD, however associations with other psychiatric disorders should be evaluated in further studies. The amygdala's value in the prediction of longitudinal memory changes in the SCD group remains questionable. Primarily in patients with MCI, memory decline over 3 years appears to be more strongly associated with volumes of hippocampus ROIs than amygdala ROIs.
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Affiliation(s)
- Laura Göschel
- Charité - Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Department of Neurology, Charitéplatz 1, 10117 Berlin, Germany; Charité - Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin and Humboldt-Universität zu Berlin, NCRC - Neuroscience Clinical Research Center, Charitéplatz 1, 10117 Berlin, Germany.
| | - Lea Kurz
- Charité - Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Department of Neurology, Charitéplatz 1, 10117 Berlin, Germany; Charité - Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin and Humboldt-Universität zu Berlin, NCRC - Neuroscience Clinical Research Center, Charitéplatz 1, 10117 Berlin, Germany
| | - Andrea Dell'Orco
- Charité - Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Department of Neurology, Charitéplatz 1, 10117 Berlin, Germany; Charité - Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin and Humboldt-Universität zu Berlin, NCRC - Neuroscience Clinical Research Center, Charitéplatz 1, 10117 Berlin, Germany; Charité - Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Department of Neuroradiology, Charitéplatz 1, 10117 Berlin, Germany
| | - Theresa Köbe
- Deutsches Zentrum für Luft- und Raumfahrt e.V. Projektträger (DLR-PT), Berlin, Germany
| | - Peter Körtvélyessy
- Charité - Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Department of Neurology, Charitéplatz 1, 10117 Berlin, Germany; German Center for Neurodegenerative Diseases (DZNE), site Magdeburg, Leipziger Strasse 44, 39120 Magdeburg, Germany
| | - Ariane Fillmer
- Physikalisch-Technische Bundesanstalt (PTB), Braunschweig and Berlin, Germany
| | - Semiha Aydin
- Physikalisch-Technische Bundesanstalt (PTB), Braunschweig and Berlin, Germany
| | - Layla Tabea Riemann
- Physikalisch-Technische Bundesanstalt (PTB), Braunschweig and Berlin, Germany; Institute for Applied Medical Informatics, University Hospital Hamburg-Eppendorf, Hamburg, Germany
| | - Hui Wang
- Charité - Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Department of Neurology, Charitéplatz 1, 10117 Berlin, Germany; Charité - Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin and Humboldt-Universität zu Berlin, NCRC - Neuroscience Clinical Research Center, Charitéplatz 1, 10117 Berlin, Germany; Department of Neurology and Pain Treatment, Immanuel Klinik Rüdersdorf, University Hospital of the Brandenburg Medical School Theodor Fontane, Rüdersdorf bei Berlin, Germany
| | - Bernd Ittermann
- Physikalisch-Technische Bundesanstalt (PTB), Braunschweig and Berlin, Germany
| | - Ulrike Grittner
- Charité - Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Institute of Biometry and Clinical Epidemiology, Charitéplatz 1, 10117 Berlin, Germany
| | - Agnes Flöel
- Department of Neurology, University Medicine Greifswald, Greifswald, Germany; German Center for Neurodegenerative Diseases (DZNE), Standort Rostock/Greifswald, Germany
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Stocks J, Heywood A, Popuri K, Beg MF, Rosen H, Wang L. Longitudinal Spatial Relationships Between Atrophy and Hypometabolism Across the Alzheimer's Disease Continuum. J Alzheimers Dis 2023; 92:513-527. [PMID: 36776061 DOI: 10.3233/jad-220975] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/10/2023]
Abstract
BACKGROUND The A/T/N framework allows for the assessment of pathology-specific markers of MRI-derived structural atrophy and hypometabolism on 18FDG-PET. However, how these measures relate to each other locally and distantly across pathology-defined A/T/N groups is currently unclear. OBJECTIVE To determine the regions of association between atrophy and hypometabolism in A/T/N groups both within and across time points. METHODS We examined multivariate multimodal neuroimaging relationships between MRI and 18FDG-PET among suspected non-Alzheimer's disease pathology (SNAP) (A-T/N+; n = 14), Amyloid Only (A+T-N-; n = 24) and Probable AD (A+T+N+; n = 77) groups. Sparse canonical correlation analyses were employed to model spatially disjointed regions of association between MRI and 18FDG-PET data. These relationships were assessed at three combinations of time points -cross-sectionally, between baseline visits and between month 12 (M-12) follow-up visits, as well as longitudinally between baseline and M-12 follow-up. RESULTS In the SNAP group, spatially overlapping relationships between atrophy and hypometabolism were apparent in the bilateral temporal lobes when both modalities were assessed at the M-12 timepoint. Amyloid-Only subjects showed spatially discordant distributed atrophy-hypometabolism relationships at all time points assessed. In Probable AD subjects, local correlations were evident in the bilateral temporal lobes when both modalities were assessed at baseline and at M-12. Across groups, hypometabolism at baseline correlated with non-local, or distant, atrophy at M-12. CONCLUSION These results support the view that local concordance of atrophy and hypometabolism is the result of a tau-mediated process driving neurodegeneration.
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Affiliation(s)
- Jane Stocks
- Department of Psychiatry and Behavioral Sciences, Feinberg School of Medicine, Northwestern University, Chicago, IL, USA
| | - Ashley Heywood
- Department of Psychiatry and Behavioral Sciences, Feinberg School of Medicine, Northwestern University, Chicago, IL, USA
| | - Karteek Popuri
- School of Engineering Science, Simon Fraser University, Canada.,Memorial University of Newfoundland, Department of Computer Science, St. John's, NL, Canada
| | | | - Howie Rosen
- School of Medicine, University of California, San Francisco, CA, USA
| | - Lei Wang
- Department of Psychiatry and Behavioral Sciences, Feinberg School of Medicine, Northwestern University, Chicago, IL, USA.,Department of Psychiatry and Behavioral Health, Ohio State University Wexner Medical Center, Columbus, OH, USA
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Chen Z, Liu Y, Zhang Y, Li Q. Orthogonal latent space learning with feature weighting and graph learning for multimodal Alzheimer's disease diagnosis. Med Image Anal 2023; 84:102698. [PMID: 36462372 DOI: 10.1016/j.media.2022.102698] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/10/2022] [Revised: 10/18/2022] [Accepted: 11/17/2022] [Indexed: 11/23/2022]
Abstract
Recent studies have shown that multimodal neuroimaging data provide complementary information of the brain and latent space-based methods have achieved promising results in fusing multimodal data for Alzheimer's disease (AD) diagnosis. However, most existing methods treat all features equally and adopt nonorthogonal projections to learn the latent space, which cannot retain enough discriminative information in the latent space. Besides, they usually preserve the relationships among subjects in the latent space based on the similarity graph constructed on original features for performance boosting. However, the noises and redundant features significantly corrupt the graph. To address these limitations, we propose an Orthogonal Latent space learning with Feature weighting and Graph learning (OLFG) model for multimodal AD diagnosis. Specifically, we map multiple modalities into a common latent space by orthogonal constrained projection to capture the discriminative information for AD diagnosis. Then, a feature weighting matrix is utilized to sort the importance of features in AD diagnosis adaptively. Besides, we devise a regularization term with learned graph to preserve the local structure of the data in the latent space and integrate the graph construction into the learning processing for accurately encoding the relationships among samples. Instead of constructing a similarity graph for each modality, we learn a joint graph for multiple modalities to capture the correlations among modalities. Finally, the representations in the latent space are projected into the target space to perform AD diagnosis. An alternating optimization algorithm with proved convergence is developed to solve the optimization objective. Extensive experimental results show the effectiveness of the proposed method.
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Affiliation(s)
- Zhi Chen
- Knowledge and Data Engineering Laboratory of Chinese Medicine, School of Information and Software Engineering, University of Electronic Science and Technology of China, Chengdu 610054, China
| | - Yongguo Liu
- Knowledge and Data Engineering Laboratory of Chinese Medicine, School of Information and Software Engineering, University of Electronic Science and Technology of China, Chengdu 610054, China.
| | - Yun Zhang
- Knowledge and Data Engineering Laboratory of Chinese Medicine, School of Information and Software Engineering, University of Electronic Science and Technology of China, Chengdu 610054, China
| | - Qiaoqin Li
- Knowledge and Data Engineering Laboratory of Chinese Medicine, School of Information and Software Engineering, University of Electronic Science and Technology of China, Chengdu 610054, China
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Zhang D, Liu S, Huang Y, Gao J, Liu W, Liu W, Ai K, Lei X, Zhang X. Altered Functional Connectivity Density in Type 2 Diabetes Mellitus with and without Mild Cognitive Impairment. Brain Sci 2023; 13:brainsci13010144. [PMID: 36672125 PMCID: PMC9856282 DOI: 10.3390/brainsci13010144] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/07/2022] [Revised: 01/06/2023] [Accepted: 01/11/2023] [Indexed: 01/18/2023] Open
Abstract
Although disturbed functional connectivity is known to be a factor influencing cognitive impairment, the neuropathological mechanisms underlying the cognitive impairment caused by type 2 diabetes mellitus (T2DM) remain unclear. To characterize the neural mechanisms underlying T2DM-related brain damage, we explored the altered functional architecture patterns in different cognitive states in T2DM patients. Thirty-seven T2DM patients with normal cognitive function (DMCN), 40 T2DM patients with mild cognitive impairment (MCI) (DMCI), and 40 healthy controls underwent neuropsychological assessments and resting-state functional MRI examinations. Functional connectivity density (FCD) analysis was performed, and the relationship between abnormal FCD and clinical/cognitive variables was assessed. The regions showing abnormal FCD in T2DM patients were mainly located in the temporal lobe and cerebellum, but the abnormal functional architecture was more extensive in DMCI patients. Moreover, in comparison with the DMCN group, DMCI patients showed reduced long-range FCD in the left superior temporal gyrus (STG), which was correlated with the Rey auditory verbal learning test score in all T2DM patients. Thus, DMCI patients show functional architecture abnormalities in more brain regions involved in higher-level cognitive function (executive function and auditory memory function), and the left STG may be involved in the neuropathology of auditory memory in T2DM patients. These findings provide some new insights into understanding the neural mechanisms underlying T2DM-related cognitive impairment.
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Affiliation(s)
- Dongsheng Zhang
- Department of MRI, Shaanxi Provincial People’s Hospital, Xi’an 710068, China
| | - Shasha Liu
- Department of MRI, Shaanxi Provincial People’s Hospital, Xi’an 710068, China
| | - Yang Huang
- Department of MRI, Shaanxi Provincial People’s Hospital, Xi’an 710068, China
| | - Jie Gao
- Department of MRI, Shaanxi Provincial People’s Hospital, Xi’an 710068, China
| | - Weirui Liu
- Department of MRI, Shaanxi Provincial People’s Hospital, Xi’an 710068, China
| | - Wanting Liu
- Department of MRI, Shaanxi Provincial People’s Hospital, Xi’an 710068, China
| | - Kai Ai
- Department of Clinical Science, Philips Healthcare, Xi’an 710000, China
| | - Xiaoyan Lei
- Department of MRI, Shaanxi Provincial People’s Hospital, Xi’an 710068, China
| | - Xiaoling Zhang
- Department of MRI, Shaanxi Provincial People’s Hospital, Xi’an 710068, China
- Correspondence: ; Tel.: +86-13087581380
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10
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Gürler Z, Gharsallaoui MA, Rekik I. Template-based graph registration network for boosting the diagnosis of brain connectivity disorders. Comput Med Imaging Graph 2023; 103:102140. [PMID: 36470102 DOI: 10.1016/j.compmedimag.2022.102140] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/09/2022] [Revised: 10/11/2022] [Accepted: 11/02/2022] [Indexed: 11/20/2022]
Abstract
Brain graphs are powerful representations to explore the biological roadmaps of the human brain in its healthy and disordered states. Recently, a few graph neural networks (GNNs) have been designed for brain connectivity synthesis and diagnosis. However, such non-Euclidean deep learning architectures might fail to capture the neural interactions between different brain regions as they are trained without guidance from any prior biological template-i.e., template-free learning. Here we assume that using a population-driven brain connectional template (CBT) that captures well the connectivity patterns fingerprinting a given brain state (e.g., healthy) can better guide the GNN training in its downstream learning task such as classification or regression. To this aim we design a plug-in graph registration network (GRN) that can be coupled with any conventional graph neural network (GNN) so as to boost its learning accuracy and generalizability to unseen samples. Our GRN is a graph generative adversarial network (gGAN), which registers brain graphs to a prior CBT. Next, the registered brain graphs are used to train typical GNN models. Our GRN can be integrated into any GNN working in an end-to-end fashion to boost its prediction accuracy. Our experiments showed that GRN remarkably boosted the prediction accuracy of four conventional GNN models across four neurological datasets.
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Affiliation(s)
- Zeynep Gürler
- BASIRA lab, Faculty of Computer and Informatics, Istanbul Technical University, Istanbul, Turkey
| | - Mohammed Amine Gharsallaoui
- BASIRA lab, Faculty of Computer and Informatics, Istanbul Technical University, Istanbul, Turkey; Ecole Polytechnique de Tunisie, Tunisia
| | - Islem Rekik
- BASIRA lab, Faculty of Computer and Informatics, Istanbul Technical University, Istanbul, Turkey; Computing, Imperial-X Translation and Innovation Hub, Imperial College London, London, UK.
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11
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Pölsterl S, Wachinger C. Identification of causal effects of neuroanatomy on cognitive decline requires modeling unobserved confounders. Alzheimers Dement 2022; 19:1994-2005. [PMID: 36419215 DOI: 10.1002/alz.12825] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/06/2022] [Revised: 09/16/2022] [Accepted: 09/19/2022] [Indexed: 11/27/2022]
Abstract
INTRODUCTION Carrying out a randomized controlled trial to estimate the causal effects of regional brain atrophy due to Alzheimer's disease (AD) is impossible. Instead, we must estimate causal effects from observational data. However, this generally requires knowing and having recorded all confounders, which is often unrealistic. METHODS We provide an approach that leverages the dependencies among multiple neuroanatomical measures to estimate causal effects from observational neuroimaging data without the need to know and record all confounders. RESULTS Our analyses of N = 732 $N=732$ subjects from the Alzheimer's Disease Neuroimaging Initiative demonstrate that using our approach results in biologically meaningful conclusions, whereas ignoring unobserved confounding yields results that conflict with established knowledge on cognitive decline due to AD. DISCUSSION The findings provide evidence that the impact of unobserved confounding can be substantial. To ensure trustworthy scientific insights, future AD research can account for unobserved confounding via the proposed approach.
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Affiliation(s)
- Sebastian Pölsterl
- The Lab for Artificial Intelligence in Medical Imaging (AI-Med), Department of Child and Adolescent Psychiatry, Ludwig-Maximilians-Universität, Munich, Germany
| | - Christian Wachinger
- The Lab for Artificial Intelligence in Medical Imaging (AI-Med), Department of Child and Adolescent Psychiatry, Ludwig-Maximilians-Universität, Munich, Germany.,Technical University of Munich, School of Medicine, Department of Radiology, Munich, Germany
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12
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Jitsuishi T, Yamaguchi A. Searching for optimal machine learning model to classify mild cognitive impairment (MCI) subtypes using multimodal MRI data. Sci Rep 2022; 12:4284. [PMID: 35277565 PMCID: PMC8917197 DOI: 10.1038/s41598-022-08231-y] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/16/2021] [Accepted: 03/03/2022] [Indexed: 12/13/2022] Open
Abstract
The intervention at the stage of mild cognitive impairment (MCI) is promising for preventing Alzheimer's disease (AD). This study aims to search for the optimal machine learning (ML) model to classify early and late MCI (EMCI and LMCI) subtypes using multimodal MRI data. First, the tract-based spatial statistics (TBSS) analyses showed LMCI-related white matter changes in the Corpus Callosum. The ROI-based tractography addressed the connected cortical areas by affected callosal fibers. We then prepared two feature subsets for ML by measuring resting-state functional connectivity (TBSS-RSFC method) and graph theory metrics (TBSS-Graph method) in these cortical areas, respectively. We also prepared feature subsets of diffusion parameters in the regions of LMCI-related white matter alterations detected by TBSS analyses. Using these feature subsets, we trained and tested multiple ML models for EMCI/LMCI classification with cross-validation. Our results showed the ensemble ML model (AdaBoost) with feature subset of diffusion parameters achieved better performance of mean accuracy 70%. The useful brain regions for classification were those, including frontal, parietal lobe, Corpus Callosum, cingulate regions, insula, and thalamus regions. Our findings indicated the optimal ML model using diffusion parameters might be effective to distinguish LMCI from EMCI subjects at the prodromal stage of AD.
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Affiliation(s)
- Tatsuya Jitsuishi
- Department of Functional Anatomy, Graduate School of Medicine, Chiba University, 1-8-1 Inohana, Chuo-ku, Chiba, 260-8670, Japan
| | - Atsushi Yamaguchi
- Department of Functional Anatomy, Graduate School of Medicine, Chiba University, 1-8-1 Inohana, Chuo-ku, Chiba, 260-8670, Japan.
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13
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Wu BS, Zhang YR, Li HQ, Kuo K, Chen SD, Dong Q, Liu Y, Yu JT. Cortical structure and the risk for Alzheimer's disease: a bidirectional Mendelian randomization study. Transl Psychiatry 2021; 11:476. [PMID: 34526483 PMCID: PMC8443658 DOI: 10.1038/s41398-021-01599-x] [Citation(s) in RCA: 21] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/10/2021] [Revised: 08/16/2021] [Accepted: 08/27/2021] [Indexed: 02/08/2023] Open
Abstract
Progressive loss of neurons in a specific brain area is one of the manifestations of Alzheimer's disease (AD). Much effort has been devoted to investigating brain atrophy and AD. However, the causal relationship between cortical structure and AD is not clear. We conducted a bidirectional two-sample Mendelian randomization analysis to investigate the causal relationship between cortical structure (surface area and thickness of the whole cortex and 34 cortical regions) and AD risk. Genetic variants used as instruments came from a large genome-wide association meta-analysis of cortical structure (33,992 participants of European ancestry) and AD (AD and AD-by-proxy, 71,880 cases, 383,378 controls). We found suggestive associations of the decreased surface area of the temporal pole (OR (95% CI): 0.95 (0.9, 0.997), p = 0.04), and decreased thickness of cuneus (OR (95% CI): 0.93 (0.89, 0.98), p = 0.006) with higher AD risk. We also found a suggestive association of vulnerability to AD with the decreased surface area of precentral (β (SE): -43.4 (21.3), p = 0.042) and isthmus cingulate (β (SE): -18.5 (7.3), p = 0.011). However, none of the Bonferroni-corrected p values of the causal relationship between cortical structure and AD met the threshold. We show suggestive evidence of an association of the atrophy of the temporal pole and cuneus with higher AD risk. In the other direction, there was a suggestive causal relationship between vulnerability to AD and the decreased surface area of the precentral and isthmus cingulate. Our findings shed light on the associations of cortical structure with the occurrence of AD.
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Affiliation(s)
- Bang-Sheng Wu
- Department of Neurology and Institute of Neurology, Huashan Hospital, State Key Laboratory of Medical Neurobiology and MOE Frontiers Center for Brain Science, Shanghai Medical College, Fudan University, Shanghai, China
| | - Ya-Ru Zhang
- Department of Neurology and Institute of Neurology, Huashan Hospital, State Key Laboratory of Medical Neurobiology and MOE Frontiers Center for Brain Science, Shanghai Medical College, Fudan University, Shanghai, China
| | - Hong-Qi Li
- Department of Neurology and Institute of Neurology, Huashan Hospital, State Key Laboratory of Medical Neurobiology and MOE Frontiers Center for Brain Science, Shanghai Medical College, Fudan University, Shanghai, China
| | - Kevin Kuo
- Department of Neurology and Institute of Neurology, Huashan Hospital, State Key Laboratory of Medical Neurobiology and MOE Frontiers Center for Brain Science, Shanghai Medical College, Fudan University, Shanghai, China
| | - Shi-Dong Chen
- Department of Neurology and Institute of Neurology, Huashan Hospital, State Key Laboratory of Medical Neurobiology and MOE Frontiers Center for Brain Science, Shanghai Medical College, Fudan University, Shanghai, China
| | - Qiang Dong
- Department of Neurology and Institute of Neurology, Huashan Hospital, State Key Laboratory of Medical Neurobiology and MOE Frontiers Center for Brain Science, Shanghai Medical College, Fudan University, Shanghai, China
| | - Yong Liu
- School of Artificial Intelligence, Beijing University of Posts and Telecommunications, Beijing, China.
| | - Jin-Tai Yu
- Department of Neurology and Institute of Neurology, Huashan Hospital, State Key Laboratory of Medical Neurobiology and MOE Frontiers Center for Brain Science, Shanghai Medical College, Fudan University, Shanghai, China.
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14
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Yang Z, Wan X, Zhao X, Rong Y, Wu Y, Cao Z, Xie Q, Luo M, Liu Y. Brain neurometabolites differences in individuals with subjective cognitive decline plus: a quantitative single- and multi-voxel proton magnetic resonance spectroscopy study. Quant Imaging Med Surg 2021; 11:4074-4096. [PMID: 34476190 DOI: 10.21037/qims-20-1254] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/19/2020] [Accepted: 04/23/2021] [Indexed: 11/06/2022]
Abstract
Background Subjective cognitive decline plus could be an extremely early phase of Alzheimer's disease; however, changes of N-acetylaspartate, myoinositol, and N-acetylaspartate/myoinositol is still unknown at this stage. This study aimed to explore brain neurometabolic alterations in patients with subjective cognitive decline plus using quantitative single-voxel and multi-voxel 1H-magnetic resonance spectroscopy. Methods A total of 91 participants were enrolled and underwent a GE 3.0-T magnetic resonance imaging, including 33 elderly controls, 27 patients with subjective cognitive decline plus, and 31 patients with amnestic mild cognitive impairment (MCI). Single-voxel and multi-voxel 1H-magnetic resonance spectroscopy were used to investigate the differences in neurometabolite levels among the three groups. Results Compared with elderly controls, patients with subjective cognitive decline plus showed significant decline in N-acetylaspartate and N-acetylaspartate/myoinositol values in multiple regions, and amnestic MCI participants demonstrated more significant decreased N-acetylaspartate and N-acetylaspartate/myoinositol levels in multiple regions. The combined concentrations of N-acetylaspartate with myoinositol showed an excellent discrimination between those with subjective cognitive decline plus and elderly controls as compared to that obtained using N-acetylaspartate/myoinositol ratios with the area under the receiver operating characteristic curve of 0.895 and 0.860, respectively. Likewise, the combined area under the curve for differentiating patients with subjective cognitive decline plus from amnestic MCI was obtained using the combined levels of N-acetylaspartate with myoinositol was 0.892. This was also higher than the combined area under the curve of 0.836 obtained using N-acetylaspartate/myoinositol ratios. Moreover, N-acetylaspartate levels in the left hippocampus and left posterior cingulate cortex (PCC) was positively related to the Auditory Verbal Learning Test delayed recall scores in patients with subjective cognitive decline plus, whereas only the N-acetylaspartate/myoinositol ratio was positively related to this scale scores in the left hippocampus. Conclusions Quantitative single-voxel and multi-voxel 1H-magnetic resonance spectroscopy can provide valuable information to detect alterative brain neurometabolites characteristics in patients with subjective cognitive decline plus. N-acetylaspartate concentrations may be used as one of the earliest neuroimaging markers at this stage, while N-acetylaspartate/myoinositol ratio could be more suitable for monitoring Alzheimer's disease progression.
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Affiliation(s)
- Zhongxian Yang
- Medical Imaging Center, Shenzhen Hospital, Southern Medical University, Shenzhen, China.,The Third School of Clinical Medicine, Southern Medical University, Guangzhou, China.,Medical Imaging Center, the Second Affiliated Hospital, Medical College of Shantou University, Shantou, China
| | - Xing Wan
- Medical Imaging Center, Shenzhen Hospital, Southern Medical University, Shenzhen, China.,The Third School of Clinical Medicine, Southern Medical University, Guangzhou, China
| | - Xinzhu Zhao
- Medical Imaging Center, Shenzhen Hospital, Southern Medical University, Shenzhen, China.,The Third School of Clinical Medicine, Southern Medical University, Guangzhou, China
| | - Yu Rong
- Department of Neurology, the People's Hospital of Gaozhou City, Maoming, China
| | - Yi Wu
- Department of Neurology, Shantou Central Hospital and Affiliated Shantou Hospital of Sun Yat-sen University, Shantou, China
| | - Zhen Cao
- Medical Imaging Center, the Second Affiliated Hospital, Medical College of Shantou University, Shantou, China
| | - Qiuxia Xie
- Medical Imaging Center, Shenzhen Hospital, Southern Medical University, Shenzhen, China.,The Third School of Clinical Medicine, Southern Medical University, Guangzhou, China
| | - Min Luo
- Medical Imaging Center, Shenzhen Hospital, Southern Medical University, Shenzhen, China.,The Third School of Clinical Medicine, Southern Medical University, Guangzhou, China
| | - Yubao Liu
- Medical Imaging Center, Shenzhen Hospital, Southern Medical University, Shenzhen, China.,The Third School of Clinical Medicine, Southern Medical University, Guangzhou, China
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15
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Chen W, Li S, Ma Y, Lv S, Wu F, Du J, Wu H, Wang S, Zhao Q. A simple nomogram prediction model to identify relatively young patients with mild cognitive impairment who may progress to Alzheimer's disease. J Clin Neurosci 2021; 91:62-68. [PMID: 34373060 DOI: 10.1016/j.jocn.2021.06.026] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/08/2020] [Revised: 05/16/2021] [Accepted: 06/14/2021] [Indexed: 12/25/2022]
Abstract
AIM Construct a clinical predictive model based on easily accessible clinical features and imaging data to identify patients 65 years of age and younger with mild cognitive impairment(MCI) who may progress to Alzheimer's disease(AD). METHODS From the ADNI database, patients with MCI who were less than or equal to 65 years of age and who had been followed for 6-60 months were selected.We collected demographic data, neuropsychological test scale scores, and structural magnetic images of these patients. Clinical characteristics were then screened, and VBM and SBM analyses were performed using structural nuclear magnetic images to obtain imaging histology characteristics. Finally, predictive models were constructed combining the clinical and imaging histology characteristics. RESULTS The constructed nomogram has a cross-validated AUC of 0.872 in the training set and 0.867 in the verification set, and the calibration curve fits well.We also provide an online model-based forecasting tool. CONCLUSION The model has good performance and uses convenience,it should be able to provide assistance in clinical work to screen relatively young MCI patients who may progress to AD.
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Affiliation(s)
- Wenhong Chen
- Department of Neurology, China-Japan Union Hospital of Jilin University, Changchun, China
| | - Songtao Li
- Department of Neurology, China-Japan Union Hospital of Jilin University, Changchun, China
| | - Yangyang Ma
- Department of Neurology, China-Japan Union Hospital of Jilin University, Changchun, China
| | - Shuyue Lv
- Department of Neurology, China-Japan Union Hospital of Jilin University, Changchun, China
| | - Fan Wu
- Department of Neurology, China-Japan Union Hospital of Jilin University, Changchun, China
| | - Jianshi Du
- Department of Vascular Surgery, China-Japan Union Hospital of Jilin University, Changchun, China
| | - Honglin Wu
- Department of Gastroenterology, China-Japan Union Hospital of Jilin University, Changchun, China
| | - Shuai Wang
- Department of Gastroenterology, China-Japan Union Hospital of Jilin University, Changchun, China
| | - Qing Zhao
- Department of Neurology, China-Japan Union Hospital of Jilin University, Changchun, China.
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16
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Rong S, Li Y, Li B, Nie K, Zhang P, Cai T, Mei M, Wang L, Zhang Y. Meynert nucleus-related cortical thinning in Parkinson's disease with mild cognitive impairment. Quant Imaging Med Surg 2021; 11:1554-1566. [PMID: 33816191 DOI: 10.21037/qims-20-444] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/23/2022]
Abstract
Background Cognitive impairment in Parkinson's disease (PD) involves the cholinergic system and cholinergic neurons, especially the nucleus basalis of Meynert (NBM/Ch4) located in the basal forebrain (BF). We analyzed associations between NBM/Ch4 volume and cortical thickness to determine whether the NBM/Ch4-innervated neocortex shows parallel atrophy with the NBM/Ch4 as disease progresses in PD patients with cognitive impairment (PD-MCI). Methods We enrolled 35 PD-MCI patients, 48 PD patients with normal cognition (PD-NC), and 33 age- and education-matched healthy controls (HCs), with all participants undergoing neuropsychological assessment and structural magnetic resonance imaging (MRI). Correlation analyses between NBM/Ch4 volume and cortical thickness and correlation coefficient comparisons were conducted within and across groups. Results In the PD-MCI group, NBM/Ch4 volume was positively correlated with cortical thickness in the bilateral posterior cingulate, parietal, and frontal and left insular regions. Based on correlation coefficient comparisons, the atrophy of NBM/Ch4 was more correlated with the cortical thickness of right posterior cingulate and precuneus, anterior cingulate and medial orbitofrontal lobe in PD-MCI versus HC, and the right medial orbitofrontal lobe and anterior cingulate in PD-NC versus HC. Further partial correlations between cortical thickness and NBM/Ch4 volume were significant in the right medial orbitofrontal (PD-NC: r=0.3, P=0.045; PD-MCI: r=0.51, P=0.003) and anterior cingulate (PD-NC: r=0.41, P=0.006; PD-MCI: r=0.43, P=0.013) in the PD groups and in the right precuneus (r=0.37, P=0.04) and posterior cingulate (r=0.46, P=0.008) in the PD-MCI group. Conclusions The stronger correlation between NBM/Ch4 and cortical thinning in PD-MCI patients suggests that NBM/Ch4 volume loss may play an important role in PD cognitive impairment.
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Affiliation(s)
- Siming Rong
- Department of Neurology, Guangdong Neuroscience Institute, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, Guangzhou, China
| | - Yan Li
- Department of Neurology, Guangdong Neuroscience Institute, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, Guangzhou, China
| | - Bing Li
- Department of Neurology, Guangdong Neuroscience Institute, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, Guangzhou, China
| | - Kun Nie
- Department of Neurology, Guangdong Neuroscience Institute, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, Guangzhou, China
| | - Piao Zhang
- Department of Neurology, Guangdong Neuroscience Institute, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, Guangzhou, China
| | - Tongtong Cai
- Department of Neurology, Guangdong Neuroscience Institute, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, Guangzhou, China
| | - Mingjin Mei
- Department of Neurology, Guangdong Neuroscience Institute, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, Guangzhou, China
| | - Lijuan Wang
- Department of Neurology, Guangdong Neuroscience Institute, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, Guangzhou, China
| | - Yuhu Zhang
- Department of Neurology, Guangdong Neuroscience Institute, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, Guangzhou, China
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17
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He B, Wang L, Xu B, Zhang Y. Association between CSF Aβ42 and amyloid negativity in patients with different stage mild cognitive impairment. Neurosci Lett 2021; 754:135765. [PMID: 33667602 DOI: 10.1016/j.neulet.2021.135765] [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: 07/07/2020] [Revised: 02/16/2021] [Accepted: 02/18/2021] [Indexed: 11/30/2022]
Abstract
Whether the cerebrospinal fluid (CSF) biomarkers of amyloid-positive and amyloid-negative patients with mild cognitive impairment (MCI) or Alzheimer's disease (AD) are significantly different is still unknown. The purpose of this study is to compare the differences in CSF total tau, P-tau and Aβ42 in patients with amyloid-positive positron emission tomography (PET) and amyloid-negative PET, and to explore related risk factors in cognitive normal (CN), early MCI (EMCI), late MCI (LMCI) and AD. 558 participants (140 CN; 233 EMCI; 125 LMCI; 60 AD) were recruited in this study from the AD Neuroimaging Initiative (ADNI) database. The associations between CSF biomarkers were assessed by partial correlation analysis. The relations between significant variables were determined by multinomial logistic regression. Compared with amyloid-positive PET patients, patients with amyloid-negative PET had higher CSF Aβ42 and lower P-tau in the whole samples. The concentration of Aβ42 in the positive amyloid PET was significantly different in different groups, but not the negative amyloid PET (CN vs. LMCI; CN vs. AD; EMCI vs. AD, all P < 0.05). When amyloid PET was positive, a weak correlation was found between the levels of Aβ42 and P-tau only in CN group. However, a moderate degree of correlation between Aβ42 and P-tau was found in EMCI and LMCI when amyloid PET was negative. After covariates adjustment, CSF Aβ42 was significantly associated with EMCI [adjusted odds ratio (OR) = 0.99, 95 % confidence interval (CI) = 0.99-1.00, P = 0.02) and LMCI (adjusted OR = 0.99, 95 % CI = 0.99-1.00, P = 0.007)] in patients with negative amyloid PET, not in patients with positive amyloid PET. Our findings highlight that Aβ42 had strong correlations with other biomarkers and might help reduce risk of EMCI or LMCI in patients with amyloid negativity.
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Affiliation(s)
- Bingjie He
- Department of Rehabilitation, Panyu Health Management Center (Panyu Rehabilitation Hospital), Guangzhou, China
| | - Lijun Wang
- Department of Neurology, Institute of Neurology, Ruijin Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Bingdong Xu
- Department of Neurology, The First Affiliated Hospital of Jinan University, Guangzhou, China
| | - Yusheng Zhang
- Department of Neurology, The First Affiliated Hospital of Jinan University, Guangzhou, China.
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18
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Villa C, Lavitrano M, Salvatore E, Combi R. Molecular and Imaging Biomarkers in Alzheimer's Disease: A Focus on Recent Insights. J Pers Med 2020; 10:jpm10030061. [PMID: 32664352 PMCID: PMC7565667 DOI: 10.3390/jpm10030061] [Citation(s) in RCA: 21] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/25/2020] [Revised: 06/28/2020] [Accepted: 07/07/2020] [Indexed: 12/15/2022] Open
Abstract
Alzheimer’s disease (AD) is the most common neurodegenerative disease among the elderly, affecting millions of people worldwide and clinically characterized by a progressive and irreversible cognitive decline. The rapid increase in the incidence of AD highlights the need for an easy, efficient and accurate diagnosis of the disease in its initial stages in order to halt or delay the progression. The currently used diagnostic methods rely on measures of amyloid-β (Aβ), phosphorylated (p-tau) and total tau (t-tau) protein levels in the cerebrospinal fluid (CSF) aided by advanced neuroimaging techniques like positron emission tomography (PET) and magnetic resonance imaging (MRI). However, the invasiveness of these procedures and the high cost restrict their utilization. Hence, biomarkers from biological fluids obtained using non-invasive methods and novel neuroimaging approaches provide an attractive alternative for the early diagnosis of AD. Such biomarkers may also be helpful for better understanding of the molecular mechanisms underlying the disease, allowing differential diagnosis or at least prolonging the pre-symptomatic stage in patients suffering from AD. Herein, we discuss the advantages and limits of the conventional biomarkers as well as recent promising candidates from alternative body fluids and new imaging techniques.
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Affiliation(s)
- Chiara Villa
- School of Medicine and Surgery, University of Milano-Bicocca, 20900 Monza, Italy
- Correspondence: (C.V.); (R.C.)
| | - Marialuisa Lavitrano
- School of Medicine and Surgery, University of Milano-Bicocca, 20900 Monza, Italy
- Institute for the Experimental Endocrinology and Oncology, National Research Council (IEOS-CNR), 80131 Naples, Italy;
| | - Elena Salvatore
- Department of Neurosciences and Reproductive and Odontostomatological Sciences, Federico II University, 80131 Naples, Italy;
| | - Romina Combi
- School of Medicine and Surgery, University of Milano-Bicocca, 20900 Monza, Italy
- Correspondence: (C.V.); (R.C.)
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19
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Guo S, Xiao B, Wu C. Identifying subtypes of mild cognitive impairment from healthy aging based on multiple cortical features combined with volumetric measurements of the hippocampal subfields. Quant Imaging Med Surg 2020; 10:1477-1489. [PMID: 32676366 DOI: 10.21037/qims-19-872] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/18/2023]
Abstract
Background Mild cognitive impairment (MCI) is subtle cognitive decline with an estimated 10-15% yearly conversion rate toward Alzheimer's disease (AD). It remains unexplored in brain cortical association areas in different lobes and its changes with progression and conversion of MCI. Methods Brain structural magnetic resonance (MR) images were collected from 102 stable MCI (sMCI) patients. One hundred eleven were converted MCI (cMCI) patients, and 109 were normal control (NC). The cortical surface features and volumes of subcortical hippocampal subfields were calculated using the FreeSurfer software, followed by an analysis of variance (ANOVA) model, to reveal the differences between the NC-sMCI, NC-cMCI, and sMCI-cMCI groups. Afterward, the support vector machine-recursive feature elimination (SVM-RFE) method was applied to determine the differences between the groups. Results The experimental results showed that there were progressive degradations in either range or degree of the brain structure from NC to sMCI, and then to cMCI. The SVM classifier obtained accuracies with 64.62%, 78.96%, and 70.33% in the sMCI-NC, cMCI-NC, and cMCI-sMCI groups, respectively, using the volumes of hippocampal subfields independently. The combination of the volumes from the hippocampal subfields and cortical measurements could significantly increase the performance to 71.86%, 84.64%, and 76.86% for the sMCI-NC, cMCI-NC, and cMCI-sMCI classifications, respectively. Also, the brain regions corresponding to the dominant features with strong discriminative power were widely located in the temporal, frontal, parietal, olfactory cortexes, and most of the hippocampal subfields, which were associated with cognitive decline, memory impairment, spatial navigation, and attention control. Conclusions The combination of cortical features with the volumes of hippocampal subfields could supply critical information for MCI detection and its conversion.
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Affiliation(s)
- Shengwen Guo
- Department of Biomedical Engineering, School of Material Science and Engineering, South China University of Technology, Guangzhou, China
| | - Benheng Xiao
- Department of Biomedical Engineering, School of Material Science and Engineering, South China University of Technology, Guangzhou, China
| | - Congling Wu
- Department of Biomedical Engineering, School of Material Science and Engineering, South China University of Technology, Guangzhou, China
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Zhang C, Kong M, Wei H, Zhang H, Ma G, Ba M. The effect of ApoE ε 4 on clinical and structural MRI markers in prodromal Alzheimer's disease. Quant Imaging Med Surg 2020; 10:464-474. [PMID: 32190571 PMCID: PMC7063277 DOI: 10.21037/qims.2020.01.14] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/27/2019] [Accepted: 01/15/2020] [Indexed: 12/18/2022]
Abstract
BACKGROUND Apolipoprotein E (ApoE) ε 4 has been identified as the strongest genetic risk factor for Alzheimer's disease (AD). However, the importance of ApoE ε 4 on clinical and biological heterogeneity of AD is still to be determined, particularly at the prodromal stage. Here, we evaluate the association of ApoE ε 4 with clinical cognition and neuroimaging regions in mild cognitive impairment (MCI) participants based on the AT (N) system, which is increasingly essential for developing a precise assessment of AD. METHODS We stratified 178 A+T+MCI participants (prodromal AD) into ApoE ε 4 (+) and ApoE ε 4 (-) according to ApoE genotype from the Alzheimer's Disease Neuroimaging Initiative (ADNI). We determined Aβ-positivity (A+) by the standardized uptake values ratios (SUVR) means of florbetapir-PET-AV45 (the cut-off value of 1.1) and fibrillar tau-positivity (T+) by cerebrospinal fluid (CSF) phosphorylated-tau at threonine 181 position (p-Tau) (cut-off value of 23 pg/mL). We evaluated the effect of ApoE ε 4 status on cognitive conditions and brain atrophy from structural magnetic resonance imaging (MRI) scans. A multivariate analysis of variance was used to compare the differences of cognitive scores and brain atrophy from structural MRI regions of interest (ROIs) between both groups. Furthermore, we performed a linear regression model to assess the correlation between signature ROIs of structural MRI and cognitive scores in the prodromal AD participants. RESULTS ApoE ε 4 (+) prodromal AD participants had lower levels of CSF Aβ 1-42, higher levels of t-Tau, more memory and global cognitive impairment, and faster decline of global cognition, compared to ApoE ε 4 (-) prodromal AD. ApoE ε 4 (+) prodromal AD participants had a thinner cortical thickness of bilateral entorhinal, smaller subcortical volume of the left amygdala, bilateral hippocampus, and left ventral diencephalon (DC) relative to ApoE ε 4 (-) prodromal AD. Furthermore, the cortical thickness average of bilateral entorhinal was highly correlated with memory and global cognition. CONCLUSIONS ApoE ε 4 status in prodromal AD participants has an important effect on clinical cognitive domains. After ascertaining the ApoE ε 4 status, specific MRI regions can be correlated to the cognitive domain and will be helpful for precise assessment in prodromal AD.
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Affiliation(s)
- Chunhua Zhang
- Department of Neurology, the Affiliated Yantai Yuhuangding Hospital of Qingdao University, Yantai 264000, China
| | - Min Kong
- Department of Neurology, Yantaishan Hospital, Yantai 264000, China
| | - Hongchun Wei
- Department of Neurology, the Affiliated Yantai Yuhuangding Hospital of Qingdao University, Yantai 264000, China
| | - Hua Zhang
- Department of Neurology, the First Affiliated Hospital of Chongqing Medical University, Chongqing 400016, China
| | - Guozhao Ma
- Department of Neurology, East Hospital, Tongji University School of Medicine, Shanghai 200120, China
| | - Maowen Ba
- Department of Neurology, the Affiliated Yantai Yuhuangding Hospital of Qingdao University, Yantai 264000, China
| | - for the Alzheimer’s Disease Neuroimaging Initiative
- Department of Neurology, the Affiliated Yantai Yuhuangding Hospital of Qingdao University, Yantai 264000, China
- Department of Neurology, Yantaishan Hospital, Yantai 264000, China
- Department of Neurology, the First Affiliated Hospital of Chongqing Medical University, Chongqing 400016, China
- Department of Neurology, East Hospital, Tongji University School of Medicine, Shanghai 200120, China
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Pichet Binette A, Gonneaud J, Vogel JW, La Joie R, Rosa-Neto P, Collins DL, Poirier J, Breitner JCS, Villeneuve S, Vachon-Presseau E. Morphometric network differences in ageing versus Alzheimer's disease dementia. Brain 2020; 143:635-649. [PMID: 32040564 PMCID: PMC7009528 DOI: 10.1093/brain/awz414] [Citation(s) in RCA: 28] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/04/2019] [Revised: 10/21/2019] [Accepted: 11/15/2019] [Indexed: 12/21/2022] Open
Abstract
Age being the main risk factor for Alzheimer's disease, it is particularly challenging to disentangle structural changes related to normal brain ageing from those specific to Alzheimer's disease. Most studies aiming to make this distinction focused on older adults only and on a priori anatomical regions. Drawing on a large, multi-cohort dataset ranging from young adults (n = 468; age range 18-35 years), to older adults with intact cognition (n = 431; age range 55-90 years) and with Alzheimer's disease (n = 50 with late mild cognitive impairment and 71 with Alzheimer's dementia, age range 56-88 years), we investigated grey matter organization and volume differences in ageing and Alzheimer's disease. Using independent component analysis on all participants' structural MRI, we first derived morphometric networks and extracted grey matter volume in each network. We also derived a measure of whole-brain grey matter pattern organization by correlating grey matter volume in all networks across all participants from the same cohort. We used logistic regressions and receiver operating characteristic analyses to evaluate how well grey matter volume in each network and whole-brain pattern could discriminate between ageing and Alzheimer's disease. Because increased heterogeneity is often reported as one of the main features characterizing brain ageing, we also evaluated interindividual heterogeneity within morphometric networks and across the whole-brain organization in ageing and Alzheimer's disease. Finally, to investigate the clinical validity of the different grey matter features, we evaluated whether grey matter volume or whole-brain pattern was related to clinical progression in cognitively normal older adults. Ageing and Alzheimer's disease contributed additive effects on grey matter volume in nearly all networks, except frontal lobe networks, where differences in grey matter were more specific to ageing. While no networks specifically discriminated Alzheimer's disease from ageing, heterogeneity in grey matter volumes across morphometric networks and in the whole-brain grey matter pattern characterized individuals with cognitive impairments. Preservation of the whole-brain grey matter pattern was also related to lower risk of developing cognitive impairment, more so than grey matter volume. These results suggest both ageing and Alzheimer's disease involve widespread atrophy, but that the clinical expression of Alzheimer's disease is uniquely associated with disruption of morphometric organization.
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Affiliation(s)
- Alexa Pichet Binette
- Department of Psychiatry, Faculty of Medicine, McGill University, Montreal, Qc, H3A 1Y2, Canada
- Douglas Mental Health University Institute, Montreal, Qc, H4H 1R3, Canada
| | - Julie Gonneaud
- Douglas Mental Health University Institute, Montreal, Qc, H4H 1R3, Canada
| | - Jacob W Vogel
- McConnell Brain Imaging Center, Montreal Neurological Institute, Montreal, Qc, H3A 2B4, Canada
| | - Renaud La Joie
- Department of Neurology, Memory and Aging Center, University of California San Francisco, San Francisco, CA, 94158, USA
| | - Pedro Rosa-Neto
- Department of Psychiatry, Faculty of Medicine, McGill University, Montreal, Qc, H3A 1Y2, Canada
- Douglas Mental Health University Institute, Montreal, Qc, H4H 1R3, Canada
| | - D Louis Collins
- McConnell Brain Imaging Center, Montreal Neurological Institute, Montreal, Qc, H3A 2B4, Canada
| | - Judes Poirier
- Department of Psychiatry, Faculty of Medicine, McGill University, Montreal, Qc, H3A 1Y2, Canada
- Douglas Mental Health University Institute, Montreal, Qc, H4H 1R3, Canada
| | - John C S Breitner
- Department of Psychiatry, Faculty of Medicine, McGill University, Montreal, Qc, H3A 1Y2, Canada
- Douglas Mental Health University Institute, Montreal, Qc, H4H 1R3, Canada
| | - Sylvia Villeneuve
- Department of Psychiatry, Faculty of Medicine, McGill University, Montreal, Qc, H3A 1Y2, Canada
- Douglas Mental Health University Institute, Montreal, Qc, H4H 1R3, Canada
- McConnell Brain Imaging Center, Montreal Neurological Institute, Montreal, Qc, H3A 2B4, Canada
| | - Etienne Vachon-Presseau
- Department of Anesthesia, Faculty of Medicine, McGill University, Montreal, Qc, H3A 1G1, Canada
- Faculty of Dentistry, McGill University, Montreal, Qc, H3A 1G1, Canada
- Alan Edwards Centre for Research on Pain (AECRP), McGill University, Montreal, Qc, H3A 1G1, Canada
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Jiang L, Cao X, Jiang J, Li T, Wang J, Yang Z, Li C. Atrophy of hippocampal subfield CA2/3 in healthy elderly men is related to educational attainment. Neurobiol Aging 2019; 80:21-28. [PMID: 31077957 DOI: 10.1016/j.neurobiolaging.2019.03.019] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/20/2018] [Revised: 03/10/2019] [Accepted: 03/27/2019] [Indexed: 11/16/2022]
Abstract
A higher education level is a protective factor against cognitive decline in elders; however, the underlying neural mechanisms remain unclear. Modulated by both aging and education, the hippocampus is a starting point for understanding the long-lasting effect of education on the aging of human brain. Because the hippocampus possesses functionally heterogeneous subfields and exhibits sex differences, we examined hippocampal subfields in men and women separately. We performed both cross-sectional (n = 143) and longitudinal (n = 51) analyses on healthy participants aged 65-75 years, who underwent structural magnetic resonance imaging. Volumes of the hippocampi and their subfields were estimated by automated segmentation. We found significantly positive correlations between educational attainment and the volume of hippocampal CA2/3 in men but not in women. The longitudinal analysis focusing on this region validated the above results by showing that a higher education level attenuated the progression of atrophy during a 15-month follow-up period in the CA2/3 region in men. These findings suggest that, in men, education plays a role in the aging of specific hippocampal subfields.
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Affiliation(s)
- Lijuan Jiang
- Shanghai Key Laboratory of Psychotic Disorders, Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Xinyi Cao
- Shanghai Key Laboratory of Psychotic Disorders, Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Jiangling Jiang
- Shanghai Key Laboratory of Psychotic Disorders, Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Ting Li
- Department of Geriatric Psychiatry, Shanghai Changning Mental Health Center, Shanghai, China
| | - Jijun Wang
- Shanghai Key Laboratory of Psychotic Disorders, Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai, China; Center for Excellence in Brain Science and Intelligence Technology (CEBSIT), Chinese Academy of Science, Shanghai, China; Institute of Psychology and Behavioral Science, Shanghai Jiao Tong Universit, Shanghai, China
| | - Zhi Yang
- Shanghai Key Laboratory of Psychotic Disorders, Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai, China; Institute of Psychology and Behavioral Science, Shanghai Jiao Tong Universit, Shanghai, China; Brain Science and Technology Research Center, Shanghai Jiao Tong University, Shanghai, China.
| | - Chunbo Li
- Shanghai Key Laboratory of Psychotic Disorders, Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai, China; Center for Excellence in Brain Science and Intelligence Technology (CEBSIT), Chinese Academy of Science, Shanghai, China; Institute of Psychology and Behavioral Science, Shanghai Jiao Tong Universit, Shanghai, China; Brain Science and Technology Research Center, Shanghai Jiao Tong University, Shanghai, China.
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Pan Z, Huang K, Huang W, Kim KH, Wu H, Yu Y, Kim KN, Yi S, Shin DA, Vora D, Gragnaniello C, Phan K, Tasiou A, Winder MJ, Koga H, Azimi P, Kang SY, Ha Y. The risk factors associated with delirium after lumbar spine surgery in elderly patients. Quant Imaging Med Surg 2019; 9:700-710. [PMID: 31143661 DOI: 10.21037/qims.2019.04.09] [Citation(s) in RCA: 23] [Impact Index Per Article: 4.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/12/2023]
Abstract
Background To prospectively explore the incidence and risk factors for postoperative delirium in elderly patients following lumbar spine surgery. Methods This prospective study enrolled 148 consecutive patients over the age of 65 who were scheduled to undergo spine surgery. Patients were screened for delirium using the short Confusion Assessment Method (CAM) postoperatively. Patient demographics and relevant medical information were collected. Logistic regression analysis was used to identify the risk factors associated with postoperative delirium. Results Eighty-three patients (56.1%) who underwent lumbar spine surgery (not coexisting with cervical or thoracic spine surgery) were enrolled in our study. Post-operative delirium was noted in 14.5% of patients over 65 years old. The presence of preoperative Parkinsonism was significantly higher in the delirium group (41.7% vs. 8.5%, P=0.002), as was a higher preoperative C-reactive protein (CRP) (7.0±15.2 vs. 1.3±2.3 mg/L, P=0.017) when compared with the non-delirium group. Of the risk factors, male sex [odds ratio (OR) =0.10, 95% confidence interval (CI): 0.01-0.66, P=0.017], Parkinsonism (OR =5.83, 95% CI: 1.03-32.89, P=0.046), and lower baseline MMSE score (OR =0.71, 95% CI: 0.52-0.97, P=0.032) were independently associated with postoperative delirium in elderly patients undergoing lumbar spine surgery. Conclusions Post-operative delirium occurred in 14.5% of elderly patients who underwent lumbar spine surgery. Male sex, Parkinsonism, and lower baseline MMSE score were identified as independent risk factors for postoperative delirium in elderly patients following lumbar surgery.
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Affiliation(s)
- Zhimin Pan
- Department of Neurosurgery, Spine and Spinal Cord Institute, Yonsei University College of Medicine, Seoul 03722, Republic of Korea.,Department of Neurosurgery, Xuanwu Hospital, Capital Medical University, Beijing 100053, China
| | - Kai Huang
- Department of Orthopedics, Zhabei Central Hospital of Jing'an District, Shanghai 200070, China
| | - Wei Huang
- Department of Clinical Laboratory, Jiangxi Province Children's Hospital, Nanchang 330006, China
| | - Ki Hoon Kim
- Department of Neurology, Yonsei University College of Medicine, Seoul 03722, Republic of Korea
| | - Hao Wu
- Department of Neurosurgery, Xuanwu Hospital, Capital Medical University, Beijing 100053, China
| | - Yanghong Yu
- Department of Radiology, Jiangxi Province Children's Hospital, Nanchang 330006, China
| | - Keung Nyun Kim
- Department of Neurosurgery, Spine and Spinal Cord Institute, Yonsei University College of Medicine, Seoul 03722, Republic of Korea
| | - Seong Yi
- Department of Neurosurgery, Spine and Spinal Cord Institute, Yonsei University College of Medicine, Seoul 03722, Republic of Korea
| | - Dong Ah Shin
- Department of Neurosurgery, Spine and Spinal Cord Institute, Yonsei University College of Medicine, Seoul 03722, Republic of Korea
| | - Darshan Vora
- Department of Neurosurgery, George Washington University, Washington, DC 20037, USA
| | | | - Kevin Phan
- NeuroSpine Surgery Research Group (NSURG), Sydney, Australia; Prince of Wales Clinical School, University of New South Wales, Randwick, NSW 2031, Australia
| | - Anastasia Tasiou
- Department of Neurosurgery, University Hospital of Larissa, Larissa 41110, Greece
| | - Mark J Winder
- Department of Neurosurgery, St Vincent's Public and Private Hospitals, Darlinghurst 2010, NSW, Australia
| | - Hisashi Koga
- PELD Center, Iwai Orthopaedic Medical Hospital, Tokyo 133-0056, Japan
| | - Parisa Azimi
- Functional Neurosurgery Research Center of Shohada Tajrish Hospital, Shahid Beheshti University of Medical Sciences, Tehran, Iran
| | - Suk Yun Kang
- Department of Neurology, Dongtan Sacred Heart Hospital, Hallym University College of Medicine, Hwaseong 18450, Republic of Korea
| | - Yoon Ha
- Department of Neurosurgery, Spine and Spinal Cord Institute, Yonsei University College of Medicine, Seoul 03722, Republic of Korea
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