Open Access. Powered by Scholars. Published by Universities.®
Neuroscience and Neurobiology Commons™
Open Access. Powered by Scholars. Published by Universities.®
- Institution
- Publication
- Publication Type
Articles 1 - 6 of 6
Full-Text Articles in Neuroscience and Neurobiology
Toward A Multimodal Computer-Aided Diagnostic Tool For Alzheimer’S Disease Conversion, Danilo Pena, Jessika Suescun, Mya Schiess, Timothy M. Ellmore, Luca Giancardo, Alzheimer’S Disease Neuroimaging Initiative
Toward A Multimodal Computer-Aided Diagnostic Tool For Alzheimer’S Disease Conversion, Danilo Pena, Jessika Suescun, Mya Schiess, Timothy M. Ellmore, Luca Giancardo, Alzheimer’S Disease Neuroimaging Initiative
Publications and Research
Alzheimer’s disease (AD) is a progressive neurodegenerative disorder. It is one of the leading sources of morbidity and mortality in the aging population AD cardinal symptoms include memory and executive function impairment that profoundly alters a patient’s ability to perform activities of daily living. People with mild cognitive impairment (MCI) exhibit many of the early clinical symptoms of patients with AD and have a high chance of converting to AD in their lifetime. Diagnostic criteria rely on clinical assessment and brain magnetic resonance imaging (MRI). Many groups are working to help automate this process to improve the clinical workflow. Current …
Using Deep Learning For Children Brain Image Analysis, Rafael Toche Pizano
Using Deep Learning For Children Brain Image Analysis, Rafael Toche Pizano
Computer Science and Computer Engineering Undergraduate Honors Theses
Analyzing the correlation between brain volumetric/morphometry features and cognition/behavior in children is important in the field of pediatrics as identifying such relationships can help identify children who may be at risk for illnesses. Understanding these relationships can not only help identify children who may be at risk of illnesses, but it can also help evaluate strategies that promote brain development in children. Currently, one way to do this is to use traditional statistical methods such as a correlation analysis, but such an approach does not make it easy to generalize and predict how brain volumetric/morphometry will impact cognition/behavior. One of …
Prediction Of Alzheimer’S Disease-Specific Phospholipase C Gamma-1 Snv By Deep Learning-Based Approach For High-Throughput Screening, Sung Hyun Kim, Sumin Yang, Key Hwan Lim, Euiseng Ko, Hyun Jun Jang, Mingon Kang, Pann Ghill Suh, Jae Yeol Joo
Prediction Of Alzheimer’S Disease-Specific Phospholipase C Gamma-1 Snv By Deep Learning-Based Approach For High-Throughput Screening, Sung Hyun Kim, Sumin Yang, Key Hwan Lim, Euiseng Ko, Hyun Jun Jang, Mingon Kang, Pann Ghill Suh, Jae Yeol Joo
Computer Science Faculty Research
© 2021 National Academy of Sciences. All rights reserved. Exon splicing triggered by unpredicted genetic mutation can cause translational variations in neurodegenerative disorders. In this study, we discover Alzheimer’s disease (AD)-specific single-nucleotide variants (SNVs) and abnormal exon splicing of phospholipase c gamma-1 (PLCγ1) gene, using genome-wide association study (GWAS) and a deep learning-based exon splicing prediction tool. GWAS revealed that the identified single-nucleotide variations were mainly distributed in the H3K27ac-enriched region of PLCγ1 gene body during brain development in an AD mouse model. A deep learning analysis, trained with human genome sequences, predicted 14 splicing sites in human PLCγ1 gene, …
Image And Video-Based Autism Spectrum Disorder Detection Via Deep Learning, Mindi Ruan
Image And Video-Based Autism Spectrum Disorder Detection Via Deep Learning, Mindi Ruan
Graduate Theses, Dissertations, and Problem Reports
People with Autism Spectrum Disorder (ASD) show atypical attention to social stimuli and aberrant gaze when viewing images of the physical world. However, it is unknown how they perceive the world from a first-person perspective. In this study, we used machine learning to classify photos taken in three different categories (people, indoors, and outdoors) as either having been taken by individuals with ASD or by peers without ASD. Our classifier effectively discriminated photos from all three categories but was particularly successful at classifying photos of people with >80% accuracy. Importantly, the visualization of our model revealed critical features that led …
Ai-Human Collaboration Via Eeg, Adam Noack
Ai-Human Collaboration Via Eeg, Adam Noack
All College Thesis Program, 2016-2019
As AI becomes ever more competent and integrated into our lives, the issue of AI-human goal misalignment looms larger. This is partially because there is often a rift between what humans explicitly command and what they actually mean. Most contemporary AI systems cannot bridge this gap. In this study we attempted to reconcile the goals of human and machine by using EEG signals from a human to help a simulated agent complete a task.
How Deep Is The Feature Analysis Underlying Rapid Visual Categorization?, Sven Eberhardt, Jonah Cader, Thomas Serre
How Deep Is The Feature Analysis Underlying Rapid Visual Categorization?, Sven Eberhardt, Jonah Cader, Thomas Serre
MODVIS Workshop
Rapid categorization paradigms have a long history in experimental psychology: Characterized by short presentation times and fast behavioral responses, these tasks highlight both the speed and ease with which our visual system processes natural object categories. Previous studies have shown that feed-forward hierarchical models of the visual cortex provide a good fit to human visual decisions. At the same time, recent work has demonstrated significant gains in object recognition accuracy with increasingly deep hierarchical architectures: From AlexNet to VGG to Microsoft CNTK – the field of computer vision has championed both depth and accuracy. But it is unclear how well …