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Neffati S, Mekki K, Machhout M. Deep learning-based CAD system for Alzheimer's diagnosis using deep downsized KPLS. Sci Rep 2025; 15:18556. [PMID: 40425715 PMCID: PMC12116745 DOI: 10.1038/s41598-025-03010-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/12/2025] [Accepted: 05/19/2025] [Indexed: 05/29/2025] Open
Abstract
Alzheimer's disease (AD) is the most prevalent type of dementia. It is linked with a gradual decline in various brain functions, such as memory. Many research efforts are now directed toward non-invasive procedures for early diagnosis because early detection greatly benefits the patient care and treatment outcome. Additional to an accurate diagnosis and reduction of the rate of misdiagnosis; Computer-Aided Design (CAD) systems are built to give definitive diagnosis. This paper presents a novel CAD system to determine stages of AD. Initially, deep learning techniques are utilized to extract features from the AD brain MRIs. Then, the extracted features are reduced using a proposed feature reduction technique named Deep Downsized Kernel Partial Least Squares (DDKPLS). The proposed approach selects a reduced number of samples from the initial information matrix. The samples chosen give rise to a new data matrix further processed by KPLS to deal with the high dimensionality. The reduced feature space is finally classified using ELM. The implementation is named DDKPLS-ELM. Reference tests have been performed on the Kaggle MRI dataset, which exhibit the efficacy of the DDKPLS-based classifier; it achieves accuracy up to 95.4% and an F1 score of 95.1%.
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Affiliation(s)
- Syrine Neffati
- Department of Computer Engineering, College of Computer Science and Engineering, University of Ha'il, 2440, Ha'il, Saudi Arabia.
| | - Kawther Mekki
- Department of Computer Engineering, College of Computer Science and Engineering, University of Ha'il, 2440, Ha'il, Saudi Arabia
| | - Mohsen Machhout
- Laboratory of Electronics and Microelectronics, Faculty of Sciences of Monastir, University of Monastir, 5019, Monastir, Tunisia
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Vidal-Piñeiro D, Sørensen Ø, Strømstrad M, Amlien IK, Baaré W, Bartrés-Faz D, Brandmaier AM, Cattaneo G, Düzel S, Ghisletta P, Henson RN, Kühn S, Lindenberger U, Mowinckel AM, Nyberg L, Pascual-Leone A, Roe JM, Solana-Sánchez J, Solé-Padullés C, Watne LO, Wolfers T, the Australian Imaging Biomarkers and Lifestyle flagship study of ageing (AIBL), the Alzheimer’s Disease Neuroimaging Initiative (ADNI), Walhovd KB, Fjell AM. Vulnerability to memory decline in aging - a mega-analysis of structural brain change. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2025:2025.03.27.642988. [PMID: 40196574 PMCID: PMC11974904 DOI: 10.1101/2025.03.27.642988] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Subscribe] [Scholar Register] [Indexed: 04/09/2025]
Abstract
Brain atrophy is a key factor behind episodic memory loss in aging, but the nature and ubiquity of this relationship remains poorly understood. This study leveraged 13 longitudinal datasets, including 3,737 cognitively healthy adults (10,343 MRI scans; 13,460 memory assessments), to determine whether brain change-memory change associations are more pronounced with age and genetic risk for Alzheimer's Disease. Both factors are associated with accelerated brain decline, yet it remains unclear whether memory loss is exacerbated beyond what atrophy alone would predict. Additionally, we assessed whether memory decline aligns with a global pattern of atrophy or stems from distinct regional contributions. Our mega-analysis revealed a nonlinear relationship between memory decline and brain atrophy, primarily affecting individuals with above-average brain structural decline. The associations were stronger in the hippocampus but also spread across diverse cortical and subcortical regions. The associations strengthened with age, reaching moderate associations in participants in their eighties. While APOE ε4 carriers exhibited steeper brain and memory loss, genetic risk had no effect on the change-change associations. These findings support the presence of common biological macrostructural substrates underlying memory function in older age which are vulnerable to multiple age-related factors, even in the absence of overt pathological changes.
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Affiliation(s)
- Didac Vidal-Piñeiro
- Centre for Lifespan Changes in Brain and Cognition, Department of Psychology, University of Oslo, Oslo, Norway
| | - Øystein Sørensen
- Centre for Lifespan Changes in Brain and Cognition, Department of Psychology, University of Oslo, Oslo, Norway
| | - Marie Strømstrad
- Centre for Lifespan Changes in Brain and Cognition, Department of Psychology, University of Oslo, Oslo, Norway
| | - Inge K. Amlien
- Centre for Lifespan Changes in Brain and Cognition, Department of Psychology, University of Oslo, Oslo, Norway
| | - William Baaré
- Danish Research Centre for Magnetic Resonance, Department of Radiology and Nuclear Medicine, Copenhagen University Hospital-Amager and Hvidovre, Copenhagen, Denmark
| | - David Bartrés-Faz
- Department of Medicine, Faculty of Medicine and Health Sciences, Institute of Neurosciences, University of Barcelona, Barcelona, Spain
- Institut Guttmann, Institut Universitari de Neurorehabilitació adscrit a la UAB, Badalona, Barcelona, Spain
- Institut de Recerca Biomèdica August Pi i Sunyer (IDIBAPS), Barcelona, Spain
| | - Andreas M. Brandmaier
- Center for Lifespan Psychology, Max Planck Institute for Human Development, Berlin, Germany
- Department of Psychology, MSB Medical School Berlin, Berlin, Germany
- Max Planck UCL Centre for Computational Psychiatry and Ageing Research, Berling, Germany, and London, UK
| | - Gabriele Cattaneo
- Institut Guttmann, Institut Universitari de Neurorehabilitació adscrit a la UAB, Badalona, Barcelona, Spain
- Fundació Institut d’Investigació en Ciències de la Salut Germans Trias i Pujol, Barcelona, Spain
| | - Sandra Düzel
- Center for Lifespan Psychology, Max Planck Institute for Human Development, Berlin, Germany
| | - Paolo Ghisletta
- Faculty of Psychology and Educational Sciences, University of Geneva, Geneva, Switzerland
| | - Richard N. Henson
- MRC Cognition and Brain Sciences Unit, University of Cambridge, United Kingdom
| | - Simone Kühn
- Center for Lifespan Psychology, Max Planck Institute for Human Development, Berlin, Germany
- Department of Psychiatry and Psychotherapy, Department of Psychiatry, University Medical Center Hamburg-Eppendorf, Germany
- Center for Environmental Neuroscience, Max Planck Institute for Human Development, Germany
| | - Ulman Lindenberger
- Center for Lifespan Psychology, Max Planck Institute for Human Development, Berlin, Germany
- Max Planck UCL Centre for Computational Psychiatry and Ageing Research, Berling, Germany, and London, UK
| | - Athanasia M. Mowinckel
- Centre for Lifespan Changes in Brain and Cognition, Department of Psychology, University of Oslo, Oslo, Norway
| | - Lars Nyberg
- Centre for Lifespan Changes in Brain and Cognition, Department of Psychology, University of Oslo, Oslo, Norway
- Umeå Center for Functional Brain Imaging, Umeå University, Umeå, Sweden
- Department of Medical and Translational Biology, Umeå University, Sweden
- Department of Diagnostics and Intervention, Umeå University, Sweden
| | - Alvaro Pascual-Leone
- Hinda and Arthur Marcus Institute for Aging Research, Deanna and Sidney Wolk Center for Memory Health, Harvard Medical School, Hebrew SeniorLife, Boston, MA, United States
- Department of Neurology, Harvard Medical School, Boston, MA, United States
| | - James M. Roe
- Centre for Lifespan Changes in Brain and Cognition, Department of Psychology, University of Oslo, Oslo, Norway
| | - Javier Solana-Sánchez
- Institut Guttmann, Institut Universitari de Neurorehabilitació adscrit a la UAB, Badalona, Barcelona, Spain
- Fundació Institut d’Investigació en Ciències de la Salut Germans Trias i Pujol, Barcelona, Spain
| | - Cristina Solé-Padullés
- Department of Medicine, Faculty of Medicine and Health Sciences, Institute of Neurosciences, University of Barcelona, Barcelona, Spain
- Institut de Recerca Biomèdica August Pi i Sunyer (IDIBAPS), Barcelona, Spain
| | - Leiv Otto Watne
- Oslo Delirium Research Group, Institute of Clinical Medicine, Campus Ahus, University of Oslo, Norway
- Department of Geriatric Medicine, Akershus University Hospital, Norway
| | - Thomas Wolfers
- Department of Psychiatry and Psychotherapy, German Center for Mental Health, University Clinic Tübingen, Tübingen, Germany
| | | | | | - Kristine B Walhovd
- Centre for Lifespan Changes in Brain and Cognition, Department of Psychology, University of Oslo, Oslo, Norway
- Computational Radiology and Artificial Intelligence, Department of Radiology and Nuclear Medicine, Oslo University Hospital, Norway
| | - Anders M. Fjell
- Centre for Lifespan Changes in Brain and Cognition, Department of Psychology, University of Oslo, Oslo, Norway
- Computational Radiology and Artificial Intelligence, Department of Radiology and Nuclear Medicine, Oslo University Hospital, Norway
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Yingmei H, Chaojie W, Yi Z, Yijie L, Heng Z, Ze F, Weiqing L, Bingyuan C, Feng W. Research progress on brain network imaging biomarkers of subjective cognitive decline. Front Neurosci 2025; 19:1503955. [PMID: 40018359 PMCID: PMC11865231 DOI: 10.3389/fnins.2025.1503955] [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: 09/30/2024] [Accepted: 01/21/2025] [Indexed: 03/01/2025] Open
Abstract
Purpose Subjective cognitive decline (SCD) is an early manifestation of the Alzheimer's disease (AD) continuum, and accurately diagnosing SCD to differentiate it from neurotypical aging in older adults is a common challenge for researchers. Methods This review examines and summarizes relevant studies regarding the neuroimaging of the AD continuum, and comprehensively summarizes and outlines the SCD clinical features characterizing along with the corresponding neuroimaging changes involving structural, functional, and metabolic networks. Results The clinical characteristics of SCD include a subjective decline in self-perceived cognitive function, and there are significant imaging changes, such as reductions in gray matter volume in certain brain regions, abnormalities in the integrity of white matter tracts and diffusion metrics, alterations in functional connectivity between different sub-networks or within networks, as well as abnormalities in brain metabolic networks and cerebral blood flow perfusion. Conclusion The 147 referenced studies in this paper indicate that exploring the structural, functional, and metabolic network changes in the brain related to SCD through neuroimaging aims to enhance the goals and mission of brain science development programs: "Understanding the Brain," "Protecting the Brain," and "Creating the Brain," thereby strengthening researchers' investigation into the mechanisms of brain function. Early diagnosis of SCD, along with prompt intervention, can reduce the incidence of AD spectrum while improving patients' quality of life, even integrating numerous scientific research achievements into unified and established standards and applying them in clinical practice by doctors, thus all encouraging researchers to further investigate SCD issues in older adults.
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Affiliation(s)
- Han Yingmei
- Graduate School of Heilongjiang University of Chinese Medicine, Harbin, China
| | - Wang Chaojie
- Acupuncture and Moxibustion Massage College, Liaoning University of Traditional Chinese Medicine, Shenyang, Liaoning, China
| | - Zhang Yi
- Graduate School of Heilongjiang University of Chinese Medicine, Harbin, China
| | - Li Yijie
- Graduate School of Heilongjiang University of Chinese Medicine, Harbin, China
| | - Zhang Heng
- Graduate School of Heilongjiang University of Chinese Medicine, Harbin, China
| | - Feng Ze
- Graduate School of Heilongjiang University of Chinese Medicine, Harbin, China
| | - Li Weiqing
- Graduate School of Heilongjiang University of Chinese Medicine, Harbin, China
| | - Chu Bingyuan
- Graduate School of Heilongjiang University of Chinese Medicine, Harbin, China
| | - Wang Feng
- Division of CT and MRI, First Affiliated Hospital, Heilongjiang University of Chinese Medicine, Harbin, China
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