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Lerch O, Ferreira D, Stomrud E, van Westen D, Tideman P, Palmqvist S, Mattsson-Carlgren N, Hort J, Hansson O, Westman E. Predicting progression from subjective cognitive decline to mild cognitive impairment or dementia based on brain atrophy patterns. Alzheimers Res Ther 2024; 16:153. [PMID: 38970077 PMCID: PMC11225196 DOI: 10.1186/s13195-024-01517-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/05/2023] [Accepted: 06/24/2024] [Indexed: 07/07/2024]
Abstract
BACKGROUND Alzheimer's disease (AD) is a progressive neurodegenerative disorder where pathophysiological changes begin decades before the onset of clinical symptoms. Analysis of brain atrophy patterns using structural MRI and multivariate data analysis are an effective tool in identifying patients with subjective cognitive decline (SCD) at higher risk of progression to AD dementia. Atrophy patterns obtained from models trained to classify advanced AD versus normal subjects, may not be optimal for subjects at an early stage, like SCD. In this study, we compared the accuracy of the SCD progression prediction using the 'severity index' generated using a standard classification model trained on patients with AD dementia versus a new model trained on β-amyloid (Aβ) positive patients with amnestic mild cognitive impairment (aMCI). METHODS We used structural MRI data of 504 patients from the Swedish BioFINDER-1 study cohort (cognitively normal (CN), Aβ-negative = 220; SCD, Aβ positive and negative = 139; aMCI, Aβ-positive = 106; AD dementia = 39). We applied multivariate data analysis to create two predictive models trained to discriminate CN individuals from either individuals with Aβ positive aMCI or AD dementia. Models were applied to individuals with SCD to classify their atrophy patterns as either high-risk "disease-like" or low-risk "CN-like". Clinical trajectory and model accuracy were evaluated using 8 years of longitudinal data. RESULTS In predicting progression from SCD to MCI or dementia, the standard, dementia-based model, reached 100% specificity but only 10.6% sensitivity, while the new, aMCI-based model, reached 72.3% sensitivity and 60.9% specificity. The aMCI-based model was superior in predicting progression from SCD to MCI or dementia, reaching a higher receiver operating characteristic area under curve (AUC = 0.72; P = 0.037) in comparison with the dementia-based model (AUC = 0.57). CONCLUSION When predicting conversion from SCD to MCI or dementia using structural MRI data, prediction models based on individuals with milder levels of atrophy (i.e. aMCI) may offer superior clinical value compared to standard dementia-based models.
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Affiliation(s)
- Ondrej Lerch
- Memory Clinic, Department of Neurology, Second Faculty of Medicine, Charles University and Motol University Hospital, Prague, 15006, Czech Republic.
- Division of Clinical Geriatrics, Centre for Alzheimer Research, Department of Neurobiology, Care Sciences, and Society, Karolinska Institutet, Stockholm, 14183, Sweden.
| | - Daniel Ferreira
- Division of Clinical Geriatrics, Centre for Alzheimer Research, Department of Neurobiology, Care Sciences, and Society, Karolinska Institutet, Stockholm, 14183, Sweden
- Department of Radiology, Mayo Clinic, Rochester, MN, 55902, USA
| | - Erik Stomrud
- Clinical Memory Research Unit, Department of Clinical Sciences Malmö, Lund University, Malmö, 20502, Sweden
- Memory Clinic, Skåne University Hospital, Malmö, 21428, Sweden
| | - Danielle van Westen
- Diagnostic Radiology, Institution for Clinical Sciences Lund, Lund University, Lund, 22184, Sweden
| | - Pontus Tideman
- Clinical Memory Research Unit, Department of Clinical Sciences Malmö, Lund University, Malmö, 20502, Sweden
- Memory Clinic, Skåne University Hospital, Malmö, 21428, Sweden
| | - Sebastian Palmqvist
- Clinical Memory Research Unit, Department of Clinical Sciences Malmö, Lund University, Malmö, 20502, Sweden
- Memory Clinic, Skåne University Hospital, Malmö, 21428, Sweden
| | - Niklas Mattsson-Carlgren
- Clinical Memory Research Unit, Department of Clinical Sciences Malmö, Lund University, Malmö, 20502, Sweden
- Memory Clinic, Skåne University Hospital, Malmö, 21428, Sweden
| | - Jakub Hort
- Memory Clinic, Department of Neurology, Second Faculty of Medicine, Charles University and Motol University Hospital, Prague, 15006, Czech Republic
| | - Oskar Hansson
- Clinical Memory Research Unit, Department of Clinical Sciences Malmö, Lund University, Malmö, 20502, Sweden
- Memory Clinic, Skåne University Hospital, Malmö, 21428, Sweden
| | - Eric Westman
- Division of Clinical Geriatrics, Centre for Alzheimer Research, Department of Neurobiology, Care Sciences, and Society, Karolinska Institutet, Stockholm, 14183, Sweden
- Department of Neuroimaging, Centre for Neuroimaging Sciences, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, SE58AF, UK
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Feng Z, Wang J, Xu L, Wu J, Li H, Wang Z, Duan M. Relationship Between Excessive Daytime Sleepiness and Caudate Nucleus Volume in Patients with Subjective Cognitive Decline: A Study from the SILCODE Using the Volbrain. J Alzheimers Dis Rep 2024; 8:935-944. [PMID: 39114552 PMCID: PMC11305844 DOI: 10.3233/adr-230101] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/04/2023] [Accepted: 04/23/2024] [Indexed: 08/10/2024] Open
Abstract
Background Excessive daytime sleepiness (EDS) and caudate nucleus volume alterations have been linked to Alzheimer's disease (AD), but their relationship remains unclear under the context of subjective cognitive decline (SCD). Objective This study aimed to investigate the relationship between EDS and caudate nucleus volume in patients with SCD. Methods The volume of entire brain was measured in 170 patients with SCD, including 37 patients with EDS and 133 non-EDS, from the Sino Longitudinal Study on Cognitive Decline (SILCODE). Participants underwent a comprehensive assessment battery, including neuropsychological and clinical evaluations, blood tests, genetic analysis for APOE ɛ4, and structural MRI scans analyzed using the fully automated segmentation tool, volBrain. Results Patients with EDS had significantly increased volume in the total and left caudate nucleus compared to non-EDS. The most significant cognitive behavioral factor associated with caudate nucleus volume in the EDS was the Auditory Verbal Learning Test-recognition. Conclusions These findings suggest that EDS may be associated with alterations in caudate nucleus volume, particularly in the left hemisphere, in the context of SCD. Further research is necessary to understand the underlying mechanisms of this relationship and its implications for clinical management.
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Affiliation(s)
- Ziqian Feng
- MOE Key Lab for Neuroinformation, School of Life Science and Technology, The Clinical Hospital of Chengdu Brain Science Institute, University of Electronic Science and Technology of China, Chengdu, China
- Department of Geriatrics, The Fourth People’s Hospital of Chengdu, Chengdu, China
- Zunyi Medical and Pharmaceutical College, Zunyi, Guizhou, China
- Zunyi Medical University, Zunyi, Guizhou, China
| | - Jiayu Wang
- MOE Key Lab for Neuroinformation, School of Life Science and Technology, The Clinical Hospital of Chengdu Brain Science Institute, University of Electronic Science and Technology of China, Chengdu, China
- Department of Geriatrics, The Fourth People’s Hospital of Chengdu, Chengdu, China
- Zunyi Medical University, Zunyi, Guizhou, China
| | - Lisi Xu
- MOE Key Lab for Neuroinformation, School of Life Science and Technology, The Clinical Hospital of Chengdu Brain Science Institute, University of Electronic Science and Technology of China, Chengdu, China
- Department of Geriatrics, The Fourth People’s Hospital of Chengdu, Chengdu, China
| | - Jiajing Wu
- MOE Key Lab for Neuroinformation, School of Life Science and Technology, The Clinical Hospital of Chengdu Brain Science Institute, University of Electronic Science and Technology of China, Chengdu, China
- Zunyi Medical University, Zunyi, Guizhou, China
| | - Hongyi Li
- MOE Key Lab for Neuroinformation, School of Life Science and Technology, The Clinical Hospital of Chengdu Brain Science Institute, University of Electronic Science and Technology of China, Chengdu, China
- Department of Geriatrics, The Fourth People’s Hospital of Chengdu, Chengdu, China
| | - Ziqi Wang
- MOE Key Lab for Neuroinformation, School of Life Science and Technology, The Clinical Hospital of Chengdu Brain Science Institute, University of Electronic Science and Technology of China, Chengdu, China
- Department of Geriatrics, The Fourth People’s Hospital of Chengdu, Chengdu, China
| | - Mingjun Duan
- MOE Key Lab for Neuroinformation, School of Life Science and Technology, The Clinical Hospital of Chengdu Brain Science Institute, University of Electronic Science and Technology of China, Chengdu, China
- Department of Geriatrics, The Fourth People’s Hospital of Chengdu, Chengdu, China
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Arrondo P, Elía-Zudaire Ó, Martí-Andrés G, Fernández-Seara MA, Riverol M. Grey matter changes on brain MRI in subjective cognitive decline: a systematic review. Alzheimers Res Ther 2022; 14:98. [PMID: 35869559 PMCID: PMC9306106 DOI: 10.1186/s13195-022-01031-6] [Citation(s) in RCA: 12] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/14/2022] [Accepted: 06/15/2022] [Indexed: 11/25/2022]
Abstract
Introduction People with subjective cognitive decline (SCD) report cognitive deterioration. However, their performance in neuropsychological evaluation falls within the normal range. The present study aims to analyse whether structural magnetic resonance imaging (MRI) reveals grey matter changes in the SCD population compared with healthy normal controls (HC). Methods Parallel systematic searches in PubMed and Web of Science databases were conducted, following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. Quality assessment was completed using the Newcastle-Ottawa Scale (NOS). Results Fifty-one MRI studies were included. Thirty-five studies used a region of interest (ROI) analysis, 15 used a voxel-based morphometry (VBM) analysis and 10 studies used a cortical thickness (CTh) analysis. Ten studies combined both, VBM or CTh analysis with ROI analysis. Conclusions Medial temporal structures, like the hippocampus or the entorhinal cortex (EC), seemed to present grey matter reduction in SCD compared with HC, but the samples and results are heterogeneous. Larger sample sizes could help to better determine if these grey matter changes are consistent in SCD subjects. Supplementary Information The online version contains supplementary material available at 10.1186/s13195-022-01031-6.
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Parker AF, Ohlhauser L, Scarapicchia V, Smart CM, Szoeke C, Gawryluk JR. A Systematic Review of Neuroimaging Studies Comparing Individuals with Subjective Cognitive Decline to Healthy Controls. J Alzheimers Dis 2022; 86:1545-1567. [DOI: 10.3233/jad-215249] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/13/2022]
Abstract
Background: Individuals with subjective cognitive decline (SCD) are hypothesized to be the earliest along the cognitive continuum between healthy aging and Alzheimer’s disease (AD), although more research is needed on this topic. Given that treatment approaches may be most effective pre-clinically, a primary objective of emerging research is to identify biological markers of SCD using neuroimaging methods. Objective: The current review aimed to comprehensively present the neuroimaging studies on SCD to date. Methods: PubMed and PsycINFO databases were searched for neuroimaging studies of individuals with SCD. Quality assessments were completed using the Appraisal tool for Cross-Sectional Studies. Results: In total, 62 neuroimaging studies investigating differences between participants with SCD and healthy controls were identified. Specifically, the number of studies were as follows: 36 MRI, 6 PET, 8 MRI/PET, 4 EEG, 7 MEG, and 1 SPECT. Across neuroimaging modalities, 48 of the 62 included studies revealed significant differences in brain structure and/or function between groups. Conclusion: Neuroimaging methods can identify differences between healthy controls and individuals with SCD. However, inconsistent results were found within and between neuroimaging modalities. Discrepancies across studies may be best accounted for by methodological differences, notably variable criteria for SCD, and differences in participant characteristics and risk factors for AD. Clinic based recruitment and cross-sectional study design were common and may bias the literature. Future neuroimaging investigations of SCD should consistently incorporate the standardized research criteria for SCD (as recommended by the SCD-Initiative), include more details of their SCD sample and their symptoms, and examine groups longitudinally.
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Affiliation(s)
- Ashleigh F. Parker
- Department of Psychology, University of Victoria, BC, Canada
- Institute on Aging and Lifelong Health, University of Victoria, BC, Canada
| | - Lisa Ohlhauser
- Department of Psychology, University of Victoria, BC, Canada
- Institute on Aging and Lifelong Health, University of Victoria, BC, Canada
| | - Vanessa Scarapicchia
- Department of Psychology, University of Victoria, BC, Canada
- Institute on Aging and Lifelong Health, University of Victoria, BC, Canada
| | - Colette M. Smart
- Department of Psychology, University of Victoria, BC, Canada
- Institute on Aging and Lifelong Health, University of Victoria, BC, Canada
| | - Cassandra Szoeke
- Department of Medicine, University of Melbourne, Melbourne, Australia
| | - Jodie R. Gawryluk
- Department of Psychology, University of Victoria, BC, Canada
- Institute on Aging and Lifelong Health, University of Victoria, BC, Canada
- Division of Medical Sciences, University of Victoria, BC, Canada
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