Current methods for early diagnosis of Alzheimer's Dementia include structured questionnaires, structured interviews, and various cognitive tests. Language difficulties are a major problem in dementia as linguistic skills break down. Current methods do not provide robust tools to capture the true nature of language deficits in spontaneous speech. Early detection of Alzheimer's Dementia (AD) from spontaneous speech overcomes the limitations of earlier approaches as it is less time consuming, can be done at home, and is relatively inexpensive. In this work, we re-implement the existing NLP methods, which used CNN-LSTM architectures and targeted features from conversational transcripts. Our work sheds light on why the accuracy of these models drops to 72.92% on the ADReSS dataset, whereas, they gave state of the art results on the DementiaBank dataset. Further, we build upon these language input-based recurrent neural networks by devising an end-to-end deep learning-based solution that performs a binary classification of Alzheimer's Dementia from the spontaneous speech of the patients. We utilize the ADReSS dataset for all our implementations and explore the deep learning-based methods of combining acoustic features into a common vector using recurrent units. Our approach of combining acoustic features using the Speech-GRU improves the accuracy by 2% in comparison to acoustic baselines. When further enriched by targeted features, the Speech-GRU performs better than acoustic baselines by 6.25%. We propose a bi-modal approach for AD classification and discuss the merits and opportunities of our approach.
Introduction: Network analysis allows investigators to explore the many facets of brain networks, particularly the proliferation of disease, using graph theory to model the disease movement. One of the hypotheses behind the disruption in brain networks in Alzheimer’s disease (AD) is the abnormal accumulation of beta-amyloid plaques and tau protein tangles. In this study, the potential use of percolation centrality to study the movement of beta-amyloids, as a feature of given PET image-based networks, is studied. The PET image-based network construction is possible using a public access database - Alzheimer’s Disease Neuroimaging Initiative, which provided 551 scans. For each image, the Julich atlas provides 121 regions of interest, which are the network nodes. Besides, the influential nodes for each scan are calculated using the collective influence algorithm.Results: Analysis of variance (p¡0.05) yields the region of interest GM Superior parietal lobule 7A L, for which percolation centrality is significant irrespective of the tracer type. Pairwise variance analysis between the clinical groups provides five and twelve Regions of Interest for AV45 and PiB. Multivariate linear regression between the percolation centrality values for nodes and psychometric assessment scores reveals Mini-Mental State Examination is a reliable metric. Finally, a ranking of the regions of interest is made based on the collective influence algorithm to indicate the anatomical areas strongly influencing the beta-amyloid network. Through this study, it is possible to use percolation centrality values to indicate the regions of interest that reflect the disease’s spread.
Background The study of brain networks, particularly the spread of disease, is made easier thanks to the network theory. The aberrant accumulation of beta-amyloid plaques and tau protein tangles in Alzheimer’s disease causes disruption in brain networks. The evaluation scores, such as the mini-mental state examination (MMSE) and neuropsychiatric inventory questionnaire, which provide a clinical diagnosis, are affected by this build-up. Purpose The percolation of beta-amyloid/tau tangles and their impact on cognitive tests are still unspecified. Methods Percolation centrality could be used to investigate beta-amyloid migration as a characteristic of positron emission tomography (PET)-image-based networks. The PET-image-based network was built utilizing a public database containing 551 scans published by the Alzheimer’s Disease Neuroimaging Initiative. Each image in the Julich atlas has 121 zones of interest, which are network nodes. Furthermore, the influential nodes for each scan are computed using the collective influence algorithm. Results For five nodal metrics, analysis of variance (ANOVA; P < .05) reveals the region of interest (ROI) in gray matter (GM) Broca’s area for Pittsburgh compound B (PiB) tracer type. The GM hippocampus area is significant for three nodal metrics in the case of florbetapir (AV45). Pairwise variance analysis of the clinical groups reveals five to twelve statistically significant ROIs for AV45 and PiB, respectively, that can distinguish between pairs of clinical situations. Based on multivariate linear regression, the MMSE is a trustworthy evaluation tool. Conclusion Percolation values suggest that around 50 of the memory, visual-spatial skills, and language ROIs are critical to the percolation of beta-amyloids within the brain network when compared to the other extensively used nodal metrics. The anatomical areas rank higher with the advancement of the disease, according to the collective influence algorithm.
Network analysis allows investigators to explore the many facets of brain networks, particularly the proliferation of disease using graph theory to model the disease movement. The disruption in brain networks in Alzheimer's disease (AD) is due to the abnormal accumulation of beta-amyloid plaques and tau protein tangles. In this study, the potential use of percolation centrality to study the movement of beta-amyloid plaques, as a feature of given PET image-based networks, is studied. The PET image-based network construction is possible using the public access database - Alzheimer's Disease Neuroimaging Initiative, which provided 1522 scans, of which 429 are of AD patients, 583 of patients with mild cognitive impairment, and 510 of cognitively normal. For each image, the Julich atlas provides 121 regions of interest/network nodes. Additionally, the influential nodes for each scan are calculated using the collective influence algorithm. Through this study, it is possible to use percolation centrality values to indicate the regions of interest that reflect the disease's spread and show potential use for early AD diagnosis. Analysis of variance (ANOVA) shows the regions of interest for which percolation centrality is a valid measure, irrespective of the tracer type. A multivariate linear regression between the percolation centrality values for each of the nodes and psychometric assessment scores reveals that models Mini-Mental State Examination (MMSE) scores performed better than ones with Neuropsychiatric Inventory Questionnaire (NPIQ) scores as the target variable. Similar to ANOVA, the multivariate linear regression yields regions of interest for which percolation centrality is a good differentiator. Finally, a ranking of the regions of interest is made based on the collective influence algorithm to indicate the anatomical areas strongly influencing the beta-amyloid network.
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