scholarly journals Distinctive Oculomotor Behaviors in Alzheimer's Disease and Frontotemporal Dementia

2021 ◽  
Vol 12 ◽  
Author(s):  
Carmen Lage ◽  
Sara López-García ◽  
Alexandre Bejanin ◽  
Martha Kazimierczak ◽  
Ignacio Aracil-Bolaños ◽  
...  
Keyword(s):  
Machine Learning ◽  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Eye Tracking ◽  
Frontotemporal Dementia ◽  
Brain Regions ◽  
Middle Temporal Gyrus ◽  
Classification Algorithms ◽  
Learning Approaches ◽  
Machine Learning Classification

Oculomotor behavior can provide insight into the integrity of widespread cortical networks, which may contribute to the differential diagnosis between Alzheimer's disease and frontotemporal dementia. Three groups of patients with Alzheimer's disease, behavioral variant of frontotemporal dementia (bvFTD) and semantic variant of primary progressive aphasia (svPPA) and a sample of cognitively unimpaired elders underwent an eye-tracking evaluation. All participants in the discovery sample, including controls, had a biomarker-supported diagnosis. Oculomotor correlates of neuropsychology and brain metabolism evaluated with 18F-FDG PET were explored. Machine-learning classification algorithms were trained for the differentiation between Alzheimer's disease, bvFTD and controls. A total of 93 subjects (33 Alzheimer's disease, 24 bvFTD, seven svPPA, and 29 controls) were included in the study. Alzheimer's disease was the most impaired group in all tests and displayed specific abnormalities in some visually-guided saccade parameters, as pursuit error and horizontal prosaccade latency, which are theoretically closely linked to posterior brain regions. BvFTD patients showed deficits especially in the most cognitively demanding tasks, the antisaccade and memory saccade tests, which require a fine control from frontal lobe regions. SvPPA patients performed similarly to controls in most parameters except for a lower number of correct memory saccades. Pursuit error was significantly correlated with cognitive measures of constructional praxis and executive function and metabolism in right posterior middle temporal gyrus. The classification algorithms yielded an area under the curve of 97.5% for the differentiation of Alzheimer's disease vs. controls, 96.7% for bvFTD vs. controls, and 92.5% for Alzheimer's disease vs. bvFTD. In conclusion, patients with Alzheimer's disease, bvFTD and svPPA exhibit differentiating oculomotor patterns which reflect the characteristic neuroanatomical distribution of pathology of each disease, and therefore its assessment can be useful in their diagnostic work-up. Machine learning approaches can facilitate the applicability of eye-tracking in clinical practice.

scholarly journals A Machine Learning Approach to Unmask Novel Gene Signatures and Prediction of Alzheimer’s Disease Within Different Brain Regions

2021 ◽  
Author(s):  
Abhibhav Sharma ◽  
Pinki Dey
Keyword(s):  
Machine Learning ◽  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Neurodegenerative Disorder ◽  
Brain Regions ◽  
Middle Temporal Gyrus ◽  
Non Coding Rna ◽  
Machine Learning Approach ◽  
Microarray Datasets ◽  
Rna Genes

AbstractAlzheimer’s disease (AD) is a progressive neurodegenerative disorder whose aetiology is currently unknown. Although numerous studies have attempted to identify the genetic risk factor(s) of AD, the interpretability and/or the prediction accuracies achieved by these studies remained unsatisfactory, reducing their clinical significance. Here, we employ the ensemble of random-forest and regularized regression model (LASSO) to the AD-associated microarray datasets from four brain regions - Prefrontal cortex, Middle temporal gyrus, Hippocampus, and Entorhinal cortex- to discover novel genetic biomarkers through a machine learning-based feature-selection classification scheme. The proposed scheme unrevealed the most optimum and biologically significant classifiers within each brain region, which achieved by far the highest prediction accuracy of AD in 5-fold cross-validation (99% average). Interestingly, along with the novel and prominent biomarkers including CORO1C, SLC25A46, RAE1, ANKIB1, CRLF3, PDYN, numerous non-coding RNA genes were also observed as discriminator, of which AK057435 and BC037880 are uncharacterized long non-coding RNA genes.

scholarly journals The extracellular chaperone Clusterin enhances Tau aggregate seeding in a cellular model

2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Patricia Yuste-Checa ◽  
Victoria A. Trinkaus ◽  
Irene Riera-Tur ◽  
Rahmi Imamoglu ◽  
Theresa F. Schaller ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Disease Progression ◽  
Frontotemporal Dementia ◽  
Brain Regions ◽  
Model System ◽  
Cellular Model ◽  
Tau Pathology ◽  
Extracellular Chaperone ◽  
Tau Aggregate

AbstractSpreading of aggregate pathology across brain regions acts as a driver of disease progression in Tau-related neurodegeneration, including Alzheimer’s disease (AD) and frontotemporal dementia. Aggregate seeds released from affected cells are internalized by naïve cells and induce the prion-like templating of soluble Tau into neurotoxic aggregates. Here we show in a cellular model system and in neurons that Clusterin, an abundant extracellular chaperone, strongly enhances Tau aggregate seeding. Upon interaction with Tau aggregates, Clusterin stabilizes highly potent, soluble seed species. Tau/Clusterin complexes enter recipient cells via endocytosis and compromise the endolysosomal compartment, allowing transfer to the cytosol where they propagate aggregation of endogenous Tau. Thus, upregulation of Clusterin, as observed in AD patients, may enhance Tau seeding and possibly accelerate the spreading of Tau pathology.

scholarly journals Interactions between decision-making and emotion in behavioral-variant frontotemporal dementia and Alzheimer’s disease

2020 ◽  
Vol 15 (6) ◽  
pp. 681-694
Author(s):  
Aurélie L Manuel ◽  
Daniel Roquet ◽  
Ramon Landin-Romero ◽  
Fiona Kumfor ◽  
Rebekah M Ahmed ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Decision Making ◽  
Frontotemporal Dementia ◽  
Delay Discounting ◽  
Animal Studies ◽  
Negative Emotion ◽  
Emotion Processing ◽  
Brain Regions ◽  
Behavioral Variant Frontotemporal Dementia

Abstract Negative and positive emotions are known to shape decision-making toward more or less impulsive responses, respectively. Decision-making and emotion processing are underpinned by shared brain regions including the ventromedial prefrontal cortex (vmPFC) and the amygdala. How these processes interact at the behavioral and brain levels is still unclear. We used a lesion model to address this question. Study participants included individuals diagnosed with behavioral-variant frontotemporal dementia (bvFTD, n = 18), who typically present deficits in decision-making/emotion processing and atrophy of the vmPFC, individuals with Alzheimer’s disease (AD, n = 12) who present with atrophy in limbic structures and age-matched healthy controls (CTRL, n = 15). Prior to each choice on the delay discounting task participants were cued with a positive, negative or neutral picture and asked to vividly imagine witnessing the event. As hypothesized, our findings showed that bvFTD patients were more impulsive than AD patients and CTRL and did not show any emotion-related modulation of delay discounting rate. In contrast, AD patients showed increased impulsivity when primed by negative emotion. This increased impulsivity was associated with reduced integrity of bilateral amygdala in AD but not in bvFTD. Altogether, our results indicate that decision-making and emotion interact at the level of the amygdala supporting findings from animal studies.

scholarly journals Uncovering the prevalence and neural substrates of anhedonia in frontotemporal dementia

Brain ◽  
2021 ◽  
Author(s):  
Siobhán R Shaw ◽  
Hashim El-Omar ◽  
Daniel Roquet ◽  
John R Hodges ◽  
Olivier Piguet ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Frontotemporal Dementia ◽  
Small Region ◽  
Neural Correlates ◽  
Brain Regions ◽  
Semantic Dementia ◽  
Voxel Based Morphometry ◽  
Targeted Interventions ◽  
The Impact

Abstract Much of human behaviour is motivated by the drive to experience pleasure. The capacity to envisage pleasurable outcomes and to engage in goal-directed behaviour to secure these outcomes depends upon the integrity of frontostriatal circuits in the brain. Anhedonia refers to the diminished ability to experience, and to pursue, pleasurable outcomes, and represents a prominent motivational disturbance in neuropsychiatric disorders. Despite increasing evidence of motivational disturbances in frontotemporal dementia (FTD), no study to date has explored the hedonic experience in these syndromes. Here, we present the first study to document the prevalence and neural correlates of anhedonia in FTD in comparison with Alzheimer’s disease, and its potential overlap with related motivational symptoms including apathy and depression. A total of 172 participants were recruited, including 87 FTD, 34 Alzheimer’s disease, and 51 healthy older control participants. Within the FTD group, 55 cases were diagnosed with clinically probable behavioural variant FTD, 24 presented with semantic dementia, and eight cases had progressive non-fluent aphasia (PNFA). Premorbid and current anhedonia was measured using the Snaith-Hamilton Pleasure Scale, while apathy was assessed using the Dimensional Apathy Scale, and depression was indexed via the Depression, Anxiety and Stress Scale. Whole-brain voxel-based morphometry analysis was used to examine associations between grey matter atrophy and levels of anhedonia, apathy, and depression in patients. Relative to controls, behavioural variant FTD and semantic dementia, but not PNFA or Alzheimer’s disease, patients showed clinically significant anhedonia, representing a clear departure from pre-morbid levels. Voxel-based morphometry analyses revealed that anhedonia was associated with atrophy in an extended frontostriatal network including orbitofrontal and medial prefrontal, paracingulate and insular cortices, as well as the putamen. Although correlated on the behavioural level, the neural correlates of anhedonia were largely dissociable from that of apathy, with only a small region of overlap detected in the right orbitofrontal cortices whilst no overlapping regions were found between anhedonia and depression. This is the first study, to our knowledge, to demonstrate profound anhedonia in FTD syndromes, reflecting atrophy of predominantly frontostriatal brain regions specialized for hedonic tone. Our findings point to the importance of considering anhedonia as a primary presenting feature of behavioural variant FTD and semantic dementia, with distinct neural drivers to that of apathy or depression. Future studies will be essential to address the impact of anhedonia on everyday activities, and to inform the development of targeted interventions to improve quality of life in patients and their families.

scholarly journals Comparison of machine learning approaches for enhancing Alzheimer’s disease classification

PeerJ ◽  
2021 ◽  
Vol 9 ◽  
pp. e10549
Author(s):  
Qi Li ◽  
Mary Qu Yang
Keyword(s):  
Machine Learning ◽  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Structural Changes ◽  
Spatial Information ◽  
Neurodegenerative Disorder ◽  
Brain Regions ◽  
Disease Classification ◽  
Support Vector ◽  
The Brain

Alzheimer’s disease (AD) is a progressive neurodegenerative disorder, accounting for nearly 60% of all dementia cases. The occurrence of the disease has been increasing rapidly in recent years. Presently about 46.8 million individuals suffer from AD worldwide. The current absence of effective treatment to reverse or stop AD progression highlights the importance of disease prevention and early diagnosis. Brain structural Magnetic Resonance Imaging (MRI) has been widely used for AD detection as it can display morphometric differences and cerebral structural changes. In this study, we built three machine learning-based MRI data classifiers to predict AD and infer the brain regions that contribute to disease development and progression. We then systematically compared the three distinct classifiers, which were constructed based on Support Vector Machine (SVM), 3D Very Deep Convolutional Network (VGGNet) and 3D Deep Residual Network (ResNet), respectively. To improve the performance of the deep learning classifiers, we applied a transfer learning strategy. The weights of a pre-trained model were transferred and adopted as the initial weights of our models. Transferring the learned features significantly reduced training time and increased network efficiency. The classification accuracy for AD subjects from elderly control subjects was 90%, 95%, and 95% for the SVM, VGGNet and ResNet classifiers, respectively. Gradient-weighted Class Activation Mapping (Grad-CAM) was employed to show discriminative regions that contributed most to the AD classification by utilizing the learned spatial information of the 3D-VGGNet and 3D-ResNet models. The resulted maps consistently highlighted several disease-associated brain regions, particularly the cerebellum which is a relatively neglected brain region in the present AD study. Overall, our comparisons suggested that the ResNet model provided the best classification performance as well as more accurate localization of disease-associated regions in the brain compared to the other two approaches.

scholarly journals Age-Associated Epigenetic Alterations, Somatic Mutations, and Their Crosstalk in Alzheimer’s Disease

2021 ◽  
Vol 5 (Supplement_1) ◽  
pp. 636-637
Author(s):  
Yaroslav Markov ◽  
Kyra Thrush ◽  
Morgan Levine
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Risk Factor ◽  
Somatic Mutations ◽  
Brain Regions ◽  
Major Risk Factor ◽  
Postmortem Brain ◽  
Biological Aging ◽  
Learning Approaches ◽  
Age Related

Abstract Aging is the major risk factor for Alzheimer’s Disease (AD), and as life expectancy increases, neurodegeneration will continue to afflict an ever-increasing proportion of the population. While numerous theories are attempting to explain the drivers behind AD pathology, what unites them is the observation that AD is reliably associated with a progressive buildup of age-related molecular changes. Because of the varying clinical presentations of AD in patients with similar genetic backgrounds, it has been postulated that epigenetics may be implicated in its etiology. Building on our prior work showing that AD pathology is linked to alterations in age-related DNA CpG methylation (DNAme) across various brain regions, we use state-of-the-art machine learning approaches to identify patterns of molecular damage in postmortem brain samples. We show that alterations in DNAme are associated with accelerated biological aging, AD, and the APOE e4 genotype, which is a major risk factor for AD. We also demonstrate that these associations are present in the PFC but not cerebellum -- in line with the current understanding of AD progression in the brain. Finally, we perform whole-exome sequencing and protein mass spectrometry on the same brain samples to test our hypothesis as to whether AD-associated alterations of DNAme are linked with the accumulation of somatic mutations that affect the structural and binding properties of protein epigenetic regulators.

scholarly journals Rapid Biomarker Screening of Alzheimer's Disease by Machine Learning and Graphene-Assisted Raman Spectroscopy

2021 ◽  
Author(s):  
Ziyang Wang ◽  
Jiarong Ye ◽  
Li Ding ◽  
Tomotaroh Granzier-Nakajima ◽  
Shubhang Sharma ◽  
...  
Keyword(s):  
Machine Learning ◽  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Raman Spectroscopy ◽  
Brain Slices ◽  
Rapid Screening ◽  
Monolayer Graphene ◽  
Accurate Identification ◽  
Machine Learning Classification ◽  
The Brain

As the most common cause of dementia, Alzheimer's disease (AD) faces challenges in terms of understanding of pathogenesis, developing early diagnosis and developing effective treatment. Rapid and accurate identification of AD biomarkers in the brain will be critical to provide novel insights of AD. To this end, in the current work, we developed a system that can enable a rapid screening of AD biomarkers by employing Raman spectroscopy and machine learning analyses in AD transgenic animal brains. Specifically, we collected Raman spectra on slices of mouse brains with and without AD, and used machine learning to classify AD and non-AD spectra. By contacting monolayer graphene with the brain slices, we achieved significantly increased accuracy from 77% to 98% in machine learning classification. Further, we identified the Raman signature bands that are most important in classifying AD and non-AD samples. Based on these, we managed to identify AD-related biomolecules, which have been confirmed by biochemical studies. Our Raman-machine learning integrated method is promising to greatly accelerate the study of AD and can be potentially extended to human samples and various other diseases.

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