ArticleEuropean radiology2022
Machine learning based on the multimodal connectome can predict the preclinical stage of Alzheimer's disease: a preliminary study.
Article in European radiology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers, 1 of them a synthesis that pooled it.
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Who cites it
15 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Artificial intelligence role in advancement of human brain connectome studies.Frontiers in neuroinformatics · 2024Pooled it
- Single-Cell and Multi-Omics-Based Characterization of Gastric Cancer Identifies TPP1 as a Potential Target for Gastric Cancer Progression and Treatment.Oncology research · 2026Article
- Research progress on brain network imaging biomarkers of subjective cognitive decline.Frontiers in neuroscience · 2025Review
- Altered brain network dynamics and functional connectivity in subjective cognitive decline: an edge-centric network study.Frontiers in aging neuroscience · 2025Article
- Translational Connectomics: overview of machine learning in macroscale Connectomics for clinical insights.BMC neurology · 2024Review
- Assessing cognitive impairment and disability in older adults through the lens of whole brain white matter patterns.Alzheimer's & dementia : the journal of the Alzheimer's Association · 2024Article
- The characteristic pattern of functional connectivity density influenced by postmenopausal females in the preclinical stage of dementia.Cerebral cortex (New York, N.Y. : 1991) · 2024Article
- Sex-Related Disparities in the Resting State Functional Connectivity of the Locus Coeruelus and Salience Network in Preclinical Alzheimer's Disease.International journal of molecular sciences · 2023Article
- Syndrome Pattern Recognition Method Using Sensed Patient Data for Neurodegenerative Disease Progression Identification.Diagnostics (Basel, Switzerland) · 2023Article
- The Use of Artificial Intelligence in the Management of Neurodegenerative Disorders; Focus on Alzheimer's Disease.Galen medical journal · 2023Article
- Neuroimaging modalities in the detection of Alzheimer's disease-associated biomarkers.Psychoradiology · 2023Review
- Tensor-Based Multi-Modality Feature Selection and Regression for Alzheimer's Disease Diagnosis.Computer science & information technology · 2022Article
- Rich-Club Organization Disturbances of the Individual Morphological Network in Subjective Cognitive Decline.Frontiers in aging neuroscience · 2022Article
- Structural and Functional Brain Connectivity Uniquely Contribute to Episodic Memory Performance in Older Adults.Frontiers in aging neuroscience · 2022Article
- Recent contributions to the field of subjective cognitive decline in aging: A literature review.Alzheimer's & dementia (Amsterdam, Netherlands)Review
Corrections and comments
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Authors and funding
8 authors.
Funding
Abstract
objectivesSubjective cognitive decline (SCD) may be a preclinical stage of Alzheimer's disease (AD). Neuroimaging studies suggest that abnormal brain connectivity plays an important role in the pathophysiology of SCD. However, most previous studies focused on single modalities only. Multimodal combinations can more effectively utilize various information and little is known about their diagnostic value in SCD.
methodsOne hundred ten SCD individuals and well-matched healthy controls (HCs) were recruited in this study (the primary sample: 35 SCD and 36 HC; the validation sample: 21 SCD and 18 HC). Multimodal imaging data were used to construct functional, anatomical, and morphological networks, respectively. These networks were used in combination with a multiple kernel learning-support vector machine to predict SCD individuals. We validated our model on another independent sample. Multiple linear regression (MLR) analyses were conducted to investigate the relationships among network metrics, cognition, and pathological biomarkers.
resultsWe found that the characteristics identified from the multimodal network were primarily located in the default mode network (DMN) and salience network (SN), achieving an accuracy of 88.73% (an accuracy of 79.49% for an independent sample) based on the integration of the three modalities. MLR analyses showed that increased AV45 SUVRs were significantly associated with impaired memory function, the enhanced functional connectivity, and the decreased morphological connectivity.
conclusionThis study suggests that abnormal multimodal connections within DMN and SN can be used as effective biomarkers to identify SCD and provide insight into understanding the pathophysiological mechanisms underlying SCD. KEY POINTS: • Multimodal brain networks improve the detection accuracy of SCD. • Abnormal connections within DMN and SN can be used as effective biomarkers for the identification of SCD.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.