The hippocampus is involved at the onset of the neuropathological pathways leading to Alzheimer's disease (AD). Individuals with mild cognitive impairment (MCI) are at increased risk of AD. Hippocampal volume has been shown to predict which MCI subjects will convert to AD. Our aim in the present study was to produce a fully automated prognostic procedure, scalable to high throughput clinical and research applications, for the prediction of MCI conversion to AD using 3D hippocampal morphology. We used an automated analysis for the extraction and mapping of the hippocampus from structural magnetic resonance scans to extract 3D hippocampal shape morphology, and we then applied machine learning classification to predict conversion from MCI to AD. We investigated the accuracy of prediction in 103 MCI subjects (mean age 74.1 years) from the longitudinal AddNeuroMed study. Our model correctly predicted MCI conversion to dementia within a year at an accuracy of 80% (sensitivity 77%, specificity 80%), a performance which is competitive with previous predictive models dependent on manual measurements. Categorization of MCI subjects based on hippocampal morphology revealed more rapid cognitive deterioration in MMSE scores (p<0.01) and CERAD verbal memory (p<0.01) in those subjects who were predicted to develop dementia relative to those predicted to remain stable. The pattern of atrophy associated with increased risk of conversion demonstrated initial degeneration in the anterior part of the cornus ammonis 1 (CA1) hippocampal subregion. We conclude that automated shape analysis generates sensitive measurements of early neurodegeneration which predates the onset of dementia and thus provides a prognostic biomarker for conversion of MCI to AD.
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http://dx.doi.org/10.1016/j.neuroimage.2011.01.050 | DOI Listing |
Curr Alzheimer Res
January 2025
Student's Scientific Research Center, School of Medicine, Tehran University of Medical Sciences, Tehran, Iran.
Background: Alzheimer's disease (AD) is a progressive neurodegenerative condition with rising prevalence due to the aging global population. Existing methods for diagnosing AD are struggling to detect the condition in its earliest and most treatable stages. One early indicator of AD is a substantial decrease in the brain's glucose metabolism.
View Article and Find Full Text PDFFront Psychiatry
January 2025
Department of Psychiatry, Third People's Hospital of Zhongshan City, Zhongshan, China.
Objective: To investigate the correlation between BDNF gene polymorphism, BDNF levels, and susceptibility to mild cognitive impairment (MCI).
Methods: In this study, we investigated 107 elderly adults individuals from a community in Zhongshan, Guangdong Province, with an average age of 73.17 ± 7.
J Transl Med
January 2025
Center for Memory Disturbances, Laboratory of Clinical Neurochemistry, Section of Neurology, Department of Medicine and Surgery, University of Perugia, Perugia, 06129, Italy.
Background: Alzheimer's disease (AD) is the most frequent neurodegenerative disorder worldwide. The great variability in disease evolution and the incomplete understanding of the molecular mechanisms underlying AD make it difficult to predict when a patient will convert from prodromal stage to dementia. We hypothesize that metabolic alterations present at the level of the brain could be reflected at a systemic level in blood serum of patients, and that these alterations could be used as prognostic biomarkers.
View Article and Find Full Text PDFSci Rep
January 2025
Department of Medicine, Surgery and Dentistry, Center for Neurodegenerative Diseases (CEMAND), University of Salerno, Fisciano, Italy.
Subtle gait and cognitive dysfunction are common in Parkinson's disease (PD), even before most evident clinical manifestations. Such alterations can be assumed as hypothetical phenotypical and prognostic/progression markers. To compare spatiotemporal gait parameters in PD patients with three cognitive status: cognitively intact (PD-noCI), with subjective cognitive impairment (PD-SCI) and with mild cognitive impairment (PD-MCI) in order to detect subclinical gait differences.
View Article and Find Full Text PDFBiomedicines
January 2025
+Pec Proteomics Research Group (+PPRG), Neuroscience Area, Biomedical Research Institute of Lleida Dr. Pifarré Foundation (IRBLLEIDA), University Hospital Arnau de Vilanova (HUAV), 25198 Lleida, Spain.
: Poor oral health and periodontitis have been epidemiologically linked to cognitive decline and mild cognitive impairment (MCI) in older adults. However, specific metrics directly linking these clinical signs are exceedingly limited. : To address this gap and develop novel tools to help clinicians identify individuals at risk of cognitive decline, we established the PerioMind Colombia Cohort, comprising elderly Colombian subjects who underwent comprehensive neurocognitive and periodontal evaluations.
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