activity
20182020
most citedNeural encoding with visual attention

2 citations · 2 across the 3 of their papers we have counts for

collaborators

5 papers

cs.CV20202 cited

Neural encoding with visual attention

Meenakshi Khosla, Gia H. Ngo, Keith Jamison +2

Visual perception is critically influenced by the focus of attention. Due to limited resources, it is well known that neural representations are biased in favor of attended locatio…

cs.LG2020

From Connectomic to Task-evoked Fingerprints: Individualized Prediction of Task Contrasts from Resting-state Functional Connectivity

Gia H. Ngo, Meenakshi Khosla, Keith Jamison +2

Resting-state functional MRI (rsfMRI) yields functional connectomes that can serve as cognitive fingerprints of individuals. Connectomic fingerprints have proven useful in many mac…

q-bio.NC2020

A shared neural encoding model for the prediction of subject-specific fMRI response

Meenakshi Khosla, Gia H. Ngo, Keith Jamison +2

The increasing popularity of naturalistic paradigms in fMRI (such as movie watching) demands novel strategies for multi-subject data analysis, such as use of neural encoding models…

cs.LG2019

Detecting abnormalities in resting-state dynamics: An unsupervised learning approach

Meenakshi Khosla, Keith Jamison, Amy Kuceyeski +1

Resting-state functional MRI (rs-fMRI) is a rich imaging modality that captures spontaneous brain activity patterns, revealing clues about the connectomic organization of the human…

cs.LG2018

Machine learning in resting-state fMRI analysis

Meenakshi Khosla, Keith Jamison, Gia H. Ngo +2

Machine learning techniques have gained prominence for the analysis of resting-state functional Magnetic Resonance Imaging (rs-fMRI) data. Here, we present an overview of various u…