Dr. Norman Scheel
Research Associate
Michigan State University
Radiology Human Medicine Faculty
Lansing, MI
USA
Email: | scheelno(at)msu.edu |
Phone: |
Research
Classification of neurodegenerative brain states based on multivariate fMRI data
In medicine, biomarkers associated with a specific disease are often used as diagnostic tools. In particular, the intrinsic functional organization of the human brain, assessed by resting-state fMRI, has gained increasing attention. Given the myriad of parameters of spatio-temporal fMRI-data, one major challenge in this research is to find those features that are most predictive for a given disease.
We use machine learning approaches on resting-state fMRI data to unravel differences in brain connectivity in different subject groups. Especiallly comparing the predictive value of fine-scale and global connectomes leads the way to standardized methods for the identification of resting-state biomarkers.
Publikationen
2018
{D}imensional {C}omplexity of the {R}esting {B}rain in {H}ealthy {A}ging, {U}sing a {N}ormalized MPSE, Frontiers in Human Neuroscience , vol. 12, pp. 451, 2018.
DOI: | 10.3389/fnhum.2018.00451 |
Datei: | fnhum.2018.00451 |
Bibtex: | @article{ScFrMuMa2018, author = {Scheel, Norman and Franke, Eric and M{\"u}nte, Thomas F. and Madany Mamlouk, Amir}, title = {{D}imensional {C}omplexity of the {R}esting {B}rain in {H}ealthy {A}ging, {U}sing a {N}ormalized MPSE}, journal = {Frontiers in Human Neuroscience}, volume = {12}, year = {2018}, pages = {451}, doi = {10.3389/fnhum.2018.00451}, url = {https://doi.org/10.3389/fnhum.2018.00451} } |
2015
On the Selection of Seeds for Resting-State fMRI-Based Prediction of Individual Brain Maturity, Bildverarbeitung für die Medizin (BVM 2015), Informatik aktuell , pp. 371--376, 2015.
Selection of {S}eeds for {R}esting-{S}tate f{MRI}-{B}ased {P}rediction of {I}ndividual {B}rain {M}aturity, Bildverarbeitung für die {M}edizin, Informatik aktuell , pp. 371--376, 2015.
2014
{T}he importance of physiological noise regression in high temporal resolution f{MRI}, in Artificial Neural Networks - ICANN 2014 , Springer, 2014. pp. 540--547.
The importance of physiological noise regression in high temporal resolution fMRI, Artificial Neural Networks - ICANN 2014, LNCS , vol. 8681, pp. 540--547, 2014.