no code implementations • 18 Apr 2023 • Zijin Gu, Keith Jamison, Mert R. Sabuncu, Amy Kuceyeski
Furthermore, aTLfaces and FBA1 had higher activation in response to maximal synthetic images compared to maximal natural images.
1 code implementation • 5 Dec 2022 • Zijin Gu, Keith Jamison, Amy Kuceyeski, Mert Sabuncu
In this work, we propose a novel approach for this task, which we call Cortex2Image, to decode visual stimuli with high semantic fidelity and rich fine-grained detail.
1 code implementation • 4 Feb 2022 • Zijin Gu, Keith Jamison, Mert Sabuncu, Amy Kuceyeski
Our approach shows the potential to use previously collected, deeply sampled data to efficiently create accurate, personalized encoding models and, subsequently, personalized optimal synthetic images for new individuals scanned under different experimental conditions.
1 code implementation • NeurIPS 2020 • Meenakshi Khosla, Gia H. Ngo, Keith Jamison, Amy Kuceyeski, Mert R. Sabuncu
Using concurrent eye-tracking and functional Magnetic Resonance Imaging (fMRI) recordings from a large cohort of human subjects watching movies, we first demonstrate that leveraging gaze information, in the form of attentional masking, can significantly improve brain response prediction accuracy in a neural encoding model.
1 code implementation • 7 Aug 2020 • Gia H. Ngo, Meenakshi Khosla, Keith Jamison, Amy Kuceyeski, Mert R. Sabuncu
Resting-state functional MRI (rsfMRI) yields functional connectomes that can serve as cognitive fingerprints of individuals.
2 code implementations • 7 Aug 2020 • Gia H. Ngo, Meenakshi Khosla, Keith Jamison, Amy Kuceyeski, Mert R. Sabuncu
Resting-state functional MRI (rsfMRI) yields functional connectomes that can serve as cognitive fingerprints of individuals.
1 code implementation • 29 Jun 2020 • Meenakshi Khosla, Gia H. Ngo, Keith Jamison, Amy Kuceyeski, Mert R. Sabuncu
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.
no code implementations • 16 Aug 2019 • Meenakshi Khosla, Keith Jamison, Amy Kuceyeski, Mert R. Sabuncu
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 brain.
no code implementations • 30 Dec 2018 • Meenakshi Khosla, Keith Jamison, Gia H. Ngo, Amy Kuceyeski, Mert R. Sabuncu
Here, we present an overview of various unsupervised and supervised machine learning applications to rs-fMRI.
1 code implementation • 11 Sep 2018 • Meenakshi Khosla, Keith Jamison, Amy Kuceyeski, Mert R. Sabuncu
The specificty and sensitivity of resting state functional MRI (rs-fMRI) measurements depend on pre-processing choices, such as the parcellation scheme used to define regions of interest (ROIs).
no code implementations • 11 Jun 2018 • Meenakshi Khosla, Keith Jamison, Amy Kuceyeski, Mert Sabuncu
Resting-state functional MRI (rs-fMRI) scans hold the potential to serve as a diagnostic or prognostic tool for a wide variety of conditions, such as autism, Alzheimer's disease, and stroke.