TRADITIONAL POSTER - ismrm

Poster Sessions
1142. fMRI Topographic Mapping of the Somatosensory Cortex at 7T Using Multigrid Priors
Selene da Rocha Amaral 1 , Sue Francis 1 , Penny Gowland 1 , Nestor Caticha 2
1 Sir Peter Mansfield Magnetic Centre, University of Nottingham, Nottingham, Notts, United Kingdom; 2 Institute of Physics,
University of Sao Paulo, Sao Paulo, Brazil
We have applied a Bayesian non-parametric multiscale technique, the iterated Multigrid Priors method, to map the digits of the hand in primary
somatosensory cortex for 1mm isotropic spatial resolution data. It is data driven and makes no assumption about the local hemodynamic response as a
function of time or space. It was able to detect an orderly pattern of response phases on the posterior bank of the central sulcus (postcentral gyrus) suggesting
that the method can also be extended for retinotopic mapping studies of visual cortex. We also showed variations in HRs across digits through local
posterior spatial averages.
1143. Support Vector Regression Prediction of Graded FMRI Activity
Yash Shailesh Shah 1 , Douglas C. Noll, Scott J. Peltier
1 Biomedical Engineering, University of Michigan, Ann Arbor, MI, United States
Support Vector Regression is a machine learning technique that learns the mapping from the training set and labels provided. This creates a model which can
then be used to give predictions for all testing sets. The prediction is really quick and hence SVR has potential to be used as a tool for real-time biofeedback
applications to evaluate graded potential. In this study, we have used SVR analysis to evaluate graded activation in multiple neural systems namely the
visual and motor cortex activation. The outputs are encouraging and advocate prospects of using SVR for future work in building real-time biofeedback
applications in which graded activation needs to be evaluated.
1144. A Comparison of SVM and RVM for Real-Time FMRI Applications
Daniel Antonio Perez 1 , Richard Cameron Craddock 2 , George Andrew James 1 , Xiaoping Philip Hu 1
1 The Wallace H. Coulter Department of Biomedical Engineering, Georgia Institute of Technology/Emory University, Atlanta, GA,
United States; 2 School of Electrical and Computer Engineering, Georgia Institute of Technology, Atlanta,, GA, United States
Support vector machines (SVM) and relevance vector machines (RVM) are two machine learning algorithms which have gained popularity due to its
sensitivity to networks of brain activation. Despite their recent extensive use in fMRI research, little contribution has been put forth to compare these
different algorithms. Both models were compared for speed and prediction accuracy. The results revealed that both RVM and SVM are comparable in
classification accuracy. However, RVM is capable of performing the task much faster and with a sparser model. Feature selection was also found to increase
both speed and classification accuracy for both SVM and RVM.
1145. Using Eigenvector Centrality to Measure the Effect of Propofol-Induced Sedation on Functional
Connectivity
Gabriele Lohmann 1 , Wolfgang Heinke 2 , Burkhard Pleger 1 , Joeran Lepsien 1 , Stefan Zysset 3 , Robert Turner 1
1 Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany; 2 Dept. of Anesthesiology and Intensive Medicine,
University of Leipzig, Leipzig, Germany; 3 NordicNeuroLab, Norway
Propofol is an anesthestic agent widely used in clinical practice which is known to affect episodic memory. The exact mechanism causing this effect is still
unclear. Here we investigated whether propofol has a region-specific effect on functional connectivity in fMRI data. Subjects were scanned under the
influence of propofol or a placebo. Functional connectivity was assessed using an algorithm new to fMRI data analysis called 'eigenvector centrality'. Our
results suggest that the well known impairment of episodic memory after propofol infusion is related to an impaired function of cerebellar regions known to
be involved in memory encoding.
1146. The Rényi Entropy in Data-Driven Analysis for Pharmacological MRI
John McGonigle 1 , Andrea L. Malizia 2 , Robin Holmes 3 , Majid Mirmehdi 1
1 Computer Science, University of Bristol, Bristol, United Kingdom; 2 Psychopharmacology Unit, University of Bristol, Bristol, United
Kingdom; 3 Medical Physics, United Bristol Healthcare NHS Trust, Bristol, United Kingdom
Data-driven analysis is useful in pharmacological MRI where there may be no model of neural response available a priori. It is recognised that the signal
complexity of noise will usually be higher than any signal of interest. Renyi entropy may be used to discover the complexity of a time frequency
representation of a voxel time course. Its application here at every voxel in a region of interest across several subjects shows it is capable of discovering drug
effect which is not found when the same analysis is carried out on placebo data.
1147. Functional MRI Constrained EEG Sources Localization for Brain State Classification
Changming Wang 1,2 , Zhihao Li 1 , Gopikrinsha Desphande 1 , Li Yao 2 , Xiaoping Hu 1
1 Biomedical Engineering, Emory Univ./Georgia Tech., Atlanta, GA, United States; 2 Inst. of Cog. Neurosci. & Learning, Beijing
Normal Univ., Beijing, China
We used fMRI to assist single-trial EEG signal classification by transforming scalp EEG into corresponding source activation patterns. The classification
performance for 4 categories visual perception task was around 98%.
1148. Development of an Automated Threshold Technique Based on Reproducibility of FMRI Activation.
Tynan Stevens 1,2 , Steven Beyea, 12 , Ryan D'Arcy 2,3 , David Clarke 4,5 , Chris Bowen, 12 , Gerhard Stroink 1
1 Physics, Dalhousie University, Halifax, NS, Canada; 2 NRC Institute for Biodiagnostics (Atlantic), Halifax, NS, Canada;
3 Neuroscience, Dalhousie University, Halifax, NS, Canada; 4 Neurosurgery, QEII Health Science Center, Halifax, NS, Canada;
5 Surgery, Dalhousie University, Halifax, NS, Canada
Setting activation thresholds remains a challenge in functional MRI. While strategies exist to address the increased chance of false positive activations due to
the large number of voxels in an fMRI image, these methods frequently ignore differences in activation strength between tasks, individuals, and scanners.