Abstract

Lesion analysis plays a central role in cognitive neuroscience, complementing functional activation studies by identifying brain regions that are necessary for the performance of particular functions. Most existing methods for lesion analysis are inherently univariate, considering the relationship between injury and behavior independently between locations in the brain. We propose to develop and adapt methods for detecting multivariate relationships between brain injury and behavior. These methods will be applied to resolve puzzling results from an existing dataset of aphasic stroke patients, and will be provided to the community in the form of user-friendly cross-platform software.

Public Health Relevance

We propose to develop advanced methods and software for the analysis of brain-behavior relationships in stroke, with application to an existing dataset of patients with language impairments.

  Funding Agency

Agency
National Institute of Health (NIH)
Institute
National Institute on Deafness and Other Communication Disorders (NIDCD)
Type
Exploratory/Developmental Grants (R21)
Project #
1R21DC011074-01
Application #
7976163
Study Section
Language and Communication Study Section (LCOM)
Program Officer
Shekim, Lana O
Project Start
2010-08-01
Project End
2012-07-31
Budget Start
2010-08-01
Budget End
2011-07-31
Support Year
1
Fiscal Year
2010
Total Cost
$240,000
Indirect Cost

  Institution

Name
University of Pennsylvania
Department
Neurology
Type
Schools of Medicine
DUNS #
042250712
City
Philadelphia
State
PA
Country
United States
Zip Code
19104

  Publications

Xie, Long; Dolui, Sudipto; Das, Sandhitsu R et al. (2016) A brain stress test: Cerebral perfusion during memory encoding in mild cognitive impairment. Neuroimage Clin 11:388-397
Wang, Ze; Li, Yin; Childress, Anna Rose et al. (2014) Brain entropy mapping using fMRI. PLoS One 9:e89948
Zhang, Yongsheng; Kimberg, Daniel Y; Coslett, H Branch et al. (2014) Multivariate lesion-symptom mapping using support vector regression. Hum Brain Mapp 35:5861-76
Wang, Ze (2014) Support vector machine learning-based cerebral blood flow quantification for arterial spin labeling MRI. Hum Brain Mapp 35:2869-75
Zhang, Yongsheng; Kimberg, Daniel Y; Coslett, H Branch et al. (2014) Support vector regression based multivariate lesion-symptom mapping. Conf Proc IEEE Eng Med Biol Soc 2014:5599-602
Wang, Ze; Das, Sandhitsu R; Xie, Sharon X et al. (2013) Arterial spin labeled MRI in prodromal Alzheimer's disease: A multi-site study. Neuroimage Clin 2:630-6
Wang, Ze (2012) Improving cerebral blood flow quantification for arterial spin labeled perfusion MRI by removing residual motion artifacts and global signal fluctuations. Magn Reson Imaging 30:1409-15
Li, Zhengjun; Kadivar, Aniseh; Pluta, John et al. (2012) Test-retest stability analysis of resting brain activity revealed by blood oxygen level-dependent functional MRI. J Magn Reson Imaging 36:344-54
Li, Zhengjun; Zhu, Yisheng; Childress, Anna Rose et al. (2012) Relations between BOLD fMRI-derived resting brain activity and cerebral blood flow. PLoS One 7:e44556
Wang, Ze (2011) Fixed-point algorithms for constrained ICA and their applications in fMRI data analysis. Magn Reson Imaging 29:1288-303