Cerebral changes and disrupted gray-matter cortical networks in asymptomatic older adults at risk for Alzheimer's disease

The diagnostic value of cerebrospinal fluid (CSF) biomarkers is well established in Alzheimer's disease, but our current knowledge about how abnormal CSF levels affect cerebral integrity, at local and network levels, is incomplete in asymptomatic older adults. Here, we have collected CSF samples and performed structural magnetic resonance imaging scans in cognitively normal elderly as part of a cross-sectional multicenter study (SIGNAL project). To identify group differences in cortical thickness, white matter volume, and properties of structural networks, participants were split into controls (N = 20), positive amyloid-? (A?1?42 +) (N = 19), and positive phosphorylated tau (N = 18). The A?1?42 + group exhibited thickening of middle temporal regions, while positive phosphorylated tau individuals showed thinning in the superior parietal and orbitofrontal cortices. Subjects with abnormal CSF biomarkers further showed regional white matter atrophy and more segregated cortical networks, the A?1?42 + group showing heightened isolation of cingulate and temporal cortices. Collectively, these findings highlight the relevance of combining structural brain imaging and connectomics for in vivo tracking of Alzheimer's disease lesions in asymptomatic stages.This work was supported by research grants from the Spanish Ministry of Economy and Competitiveness (SAF2011-25463 to J.L.C., PSI2014-55747-R to M.A.), the Carlos III Institute of Health, Spain (PI11/02425 and PI14/01126 to J.F.; PI11/3035 and PI14/1561 to A.L.; PI08/0139, PI12/02288 and PI16/01652 to P.S.J.), jointly funded by Fondo Europeo de Desarrollo Regional (FEDER), Unión Europea, “Una manera de hacer Europa”, the Joint Programming in Neurodegenerative Disease Research (DEMTEST to P.S.J.), “Marató TV3” (project 20141210 to J.F. and 20142610 to A.L.), the Regional Ministry of Innovation, Science and Enterprise, Junta de Andalucia (P12- CTS-2327 to J.C.L.), and the CIBERNED program (Signal project)

Development of Gaussian Learning Algorithms for Early Detection of Alzheimer\u27s Disease

Alzheimer’s disease (AD) is the most common form of dementia affecting 10% of the population over the age of 65 and the growing costs in managing AD are estimated to be $259 billion, according to data reported in the 2017 by the Alzheimer\u27s Association. Moreover, with cognitive decline, daily life of the affected persons and their families are severely impacted. Taking advantage of the diagnosis of AD and its prodromal stage of mild cognitive impairment (MCI), an early treatment may help patients preserve the quality of life and slow the progression of the disease, even though the underlying disease cannot be reversed or stopped. This research aims to develop Gaussian learning algorithms, natural language processing (NLP) techniques, and mathematical models to effectively delineate the MCI participants from the cognitively normal (CN) group, and identify the most significant brain regions and patterns of changes associated with the progression of AD. The focus will be placed on the earliest manifestations of the disease (early MCI or EMCI) to plan for effective curative/therapeutic interventions and protocols. Multiple modalities of biomarkers have been found to be significantly sensitive in assessing the progression of AD. In this work, several novel multimodal classification frameworks based on proposed Gaussian Learning algorithms are created and applied to neuroimaging data. Classification based on the combination of structural magnetic resonance imaging (MRI), positron emission tomography (PET), and cerebrospinal fluid (CSF) biomarkers is seen as the most reliable approach for high-accuracy classification. Additionally, changes in linguistic complexity may provide complementary information for the diagnosis and prognosis of AD. For this research endeavor, an NLP-oriented neuropsychological assessment is developed to automatically analyze the distinguishing characteristics of text data in MCI group versus those in CN group. Early findings suggest significant linguistic differences between CN and MCI subjects in terms of word usage, vocabulary, recall, fragmented sentences. In summary, the results obtained indicate a high potential of the neuroimaging-based classification and NLP-oriented assessment to be utilized as a practically computer aided diagnosis system for classification and prediction of AD and its prodromal stages. Future work will ultimately focus on early signs of AD that could help in the planning of curative and therapeutic intervention to slow the progression of the disease