Imaging and the Brain Projects
Understanding how the brain’s physical wiring relates to its functional activity remains a major open question in neuroscience. This project investigates how structural connectivity (mapped using diffusion MRI) aligns with functional connectivity (derived from resting-state fMRI), with a focus on underexplored signals in white matter. Using large-scale datasets from the...
A team of students led by Dr. Tananun Songdechakraiwut (Computer Science) and Dr. Michael Lutz (Neurology) will use a newly released dataset from the Alzheimer’s Disease Sequencing Project Phenotype Harmonization Consortium (ADSP-PHC) to study relationships among cognitive measures, brain imaging, biomarkers, and clinical features of Alzheimer’s disease and related dementias...
A team of students led by researchers in the Quantitative Imaging and Analysis Laboratory will use deep learning to decode the ‘neurocardiac circuits’ that link cardiometabolic health to cognitive aging. By relating heart metrics from cardiac imaging to brain data, students will identify how metabolic health modulates pathways that influence...
A team of researchers associated with the Applied Machine Learning Laboratory will lead a team of students in developing novel machine learning techniques that will be used for improving brain computer interfaces (BCIs) using electroencephalography (EEG) data. Students will learn how to pre-process EEG data, extract EEG features, and train...
A team of students led by an interdisciplinary group including statistician Fan Li, neurologist Brian Mac Grory, and preventive medicine physician/clinical data scientist Jay Lusk will integrate information from diverse real-world datasets to better understand risk factors for cardiovascular diseases such as heart attack and stroke and for cognitive disorders...
The application of deep learning to Alzheimer’s disease (AD) research using MRI is a rapidly evolving field, with existing studies serving primarily as proof of concept. This Data+ project aims to contribute to the development of a deep learning model that integrates MRI-based topological biomarkers for the early detection of...
Alzheimer’s alters brain connectivity beyond local regions. Understanding necessitates a shift from dyadic to higher-order relations naturally expressed in the language of algebraic topology. Led by professors of Computer Science and Neurology, Dr. Tananun Songdechakraiwut, Dr. Michael Lutz, and Dr. Jian Pei, an interdisciplinary team of students will investigate intricate...
A team of students led by researchers in the Social Science Research Institute and Departments of Mathematics and Statistics curated a unique video data set for studying how different aspects of social interactions relate to social and psychiatric variables, like trust, empathy, and scores on clinical social competence and autism...
A team of students led by researchers in the O-Lab for auditory neuroscience determined whether the imagination of speech and nonspeech sounds can be distinguished using on a non-invasive measurement of activation in the brain, electroencephalography (EEG). Students collected and analyzed EEG data from human participants in response to both...
Volumetric segmentation of sub-cortical structures such as the basal ganglia and thalamus is necessary for non-invasive diagnosis and neurosurgery planning. This is a challenging problem due in part to limited boundary information between structures, similar intensity profiles across the different structures, and low contrast data. This work presents a semi-automatic...
The sub-thalamic nucleus (STN) within the sub-cortical region of the Basal ganglia is a crucial targeting structure for Deep brain stimulation (DBS) surgery, in particular for alleviating Parkinson’s disease (PD) symptoms. Volumetric segmentation of such small and complex structure, which is elusive in clinical MRI protocols, is thereby a pre-requisite...
Simi Bleznak (Math/AI), Max Brown (Math/Econ), and Julia Choi (Bio) spent ten weeks Exploring how visual, cognitive, and physical abilities relate to physical performance can provide insight into the development of athletes. Using two rich datasets provided by USA Baseball, the team used linear regression, logistic regression models, and longitudinal...
A team of researchers associated with the Applied Machine Learning Lab in Duke’s ECE department will lead a team of students in developing novel machine learning techniques that will be used for improving brain computer interfaces (BCIs) using electroencephalography (EEG) data. Students will learn how to pre-process EEG data, extract...
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