Researchers

Adnan Shah

Main Lab Location:

CiNet (Main bldg.)

Specific Research Topic:

Signal Processing and Data Analysis in MRI

Phone: 

+81-80-9098-7329

Mailing Address:

1-4 Yamadaoka, Suita, Osaka 565-0871, Japan

Email: 


I am interested in signal processing and data analysis of brain imaging data acquired with ultra-high-field 7-Tesla MR imaging for neuroimaging and neuroscience research. Functional neuroimaging plays a key role in mapping brain functions and can determine the alterations in brain circuits due to mental disorders. The acquired functional brain images are information rich but noisy data, and are of enormous interest to researchers. These images offer ways to uncover brain dynamics through the indirect signatures of neural activity. Features such as functional and dynamic connectivity analysis to assess neuronal synchrony among brain regions, task related activity localization, and hemodynamic response function estimation are some of the different ways to explore brain dynamics.

My research focuses on developing new techniques as well as improving the existing data analysis pipeline for brain imaging data acquired with ultra-high-field 7-Tesla MR imaging.

Selected Publications:

AK Seghouane, A Shah, CM Ting. fMRI hemodynamic response function estimation in autoregressive noise by avoiding the drift. Digital Signal Processing, 66:29-41, 2017

A Shah. A model-free de-drifting approach for detecting BOLD activities in fMRI data. Journal of Signal Processing Systems, 79(2):133-143, 2015

A Shah and AK Seghouane. Estimation of hemodynamic response functions for un-delineated overlapping ROIs in fMRI data based on sparse dictionary learning. IEEE 10th International Symposium on Biomedical Imaging, pp. 1516-1519, 2013

AK Seghouane and A Shah. Functional brain connectivity as revealed by singular spectrum analysis. International Conference of the IEEE Engineering in Medicine and Biology Society, pp. 5186?5189, 2012

AK Seghouane and A Shah. HRF estimation in fMRI data with an unknown drift matrix by iterative minimization of the Kullback-Leibler divergence. IEEE Transactions on Medical Imaging. 31(2):192-206, 2012