Neuroscience and Neurobiology Commons^™

Human Claustrum Activation During Pain, Zoravar S. Sidhu, David A. Seminowicz, Brent W. Stewart

Undergraduate Student Research Internships Conference

Chronic pain is one of the principal causes of disability in the world. Many if not all of us deal with or know of someone who deals with chronic pain. Interestingly, it appears that a mysterious part of the brain known as the claustrum has a hand in chronic pain. Although little is known about the actual function of the claustrum, it is hypothesized to synchronize cortical networks during tasks which have a cognitive load.

Recent literature has indicated that during chronic pain conditions, neural network recruitment is modified. In addition, an analysis of an existing fMRI data set determined …

Variance Partitioning Reveals Consistent Representation Of Object Boundary Contours In Lo Across Different Datasets, Mark D. Lescroart, Utkarsh Singhal

MODVIS Workshop

No abstract provided.

Predictive Power And Validity Of Connectome Predictive Modeling: A Replication And Extension, Michael Wang, Joaquin Goni, Enrico Amico

The Summer Undergraduate Research Fellowship (SURF) Symposium

Neuroimaging, particularly functional magnetic resonance imaging (fMRI), is a rapidly growing research area and has applications ranging from disease classification to understanding neural development. With new advancements in imaging technology, researchers must employ new techniques to accommodate the influx of high resolution data sets. Here, we replicate a new technique: connectome-based predictive modeling (CPM), which constructs a linear predictive model of brain connectivity and behavior. CPM’s advantages over classic machine learning techniques include its relative ease of implementation and transparency compared to “black box” opaqueness and complexity. Is this method efficient, powerful, and reliable in the prediction of behavioral measures …

Mapping The Spatio-Temporal Dynamics Of Vision In The Human Brain, Aude Oliva

MODVIS Workshop

Recognition of objects and scenes is a fundamental function of the human brain, necessitating a complex neural machinery that transforms low level visual information into semantic content. Despite significant advances in characterizing the locus and function of key visual areas, integrating the temporal and spatial dynamics of this processing stream has posed a decades-long challenge to human neuroscience. In this talk I will describe a brain mapping approach to combine magnetoencephalography (MEG), functional MRI (fMRI) measurements, and convolutional neural networks (CNN) by representational similarity analysis to yield a spatially and temporally integrated characterization of neuronal representations when observers perceive visual …