TRADITIONAL POSTER - ismrm

Poster Sessions
Setting appropriate thresholds is particularly pertinent in presurgical mapping, as knowledge of the location and extent of functional cortex can affect
surgical decisions. In this work, we demonstrate an automated threshold technique based on test-retest imaging and receiver-operator characteristic curves,
which produces individualized threshold levels optimized for reproducibility of the observed activation.
1149. Semiparametric Paradigm Free Mapping: Automatic Detection and Characterization of FMRI BOLD
Responses and Physiological Fluctuations Without Prior Information
Cesar Caballero-Gaudes 1 , Natalia Petridou, 12 , Susan Francis 1 , Penny Gowland 1
1 Sir Peter Mansfield Magnetic Resonance Centre, University of Nottingham, Nottingham, Nottinghamshire, United Kingdom;
2 University Medical Centre Utrecht, Utrecht, Netherlands
In recent work we showed that by means of sparse estimation techniques the spatial and temporal evolution of single-trial BOLD responses can be
automatically detected without any prior knowledge of the stimulus timing and without thresholding: paradigm free mapping (PFM). However, fMRI time
series also contain physiological and instrumental fluctuations which can hinder the detection of BOLD responses associated to neuronal activity.
Physiological fluctuations can be removed prior to PFM via high-pass filtering, or by RETROICOR, RVT or RVHRCOR, but these techniques must be
employed in a pre-processing stage and require the additional recording of physiological respiratory and cardiac waveforms. Here, extending on our previous
work, we present a novel technique which by decomposing the fMRI signal enables automatic detection of fMRI BOLD responses without prior stimulus
information and automatic fitting of significant frequency fluctuations present in the signal, such as non-neuronal cardiac and respiratory fluctuations
(semiparametric PFM, sPFM). This technique is based on a semiparametric linear representation of the fMRI signal which is recursively fitted using a
morphological component analysis algorithm. The feasibility of this technique was evaluated in simulations and real fMRI data acquired at 7T, and its
performance validated to RETROICOR.
1150. Spatial Registration of Support Vector Machine Models for Multi-Session and Group Real-Time
FMRI
Andrew Fischer 1 , Jonathan Lisinski 2 , Pearl Chiu 2 , Brooks King-Casas 2 , Stephen LaConte 2
1 Rice University, Houston, TX, United States; 2 Neuroscience, Baylor College of Medicine, Houston, TX, United States
A pattern-based rt-fMRI system capable of multi-session and group-based models enables progressive training and testing across sessions, and potentially
enables the use of group models for rehabilitation/therapy using multi-voxel targets built from databases of recovered individuals. Here we investigate
alignment strategies to verify that there is not a significant tradeoff between classification accuracy and rt-fMRI computational demands. Our results
demonstrate the feasibility of a model-to-scan alignment system for real-time fMRI in which the least demanding computational approach does not lead to a
compromise of classification accuracy. This work also demonstrates the feasibility of using group SVM models in real-time experiments.
1151. Constrained CCA with Different Novel Linear Constraints and a Nonlinear Constraint in FMRI
Dietmar Cordes 1 , Rajesh Nandy 2 , Mingwu Jin 1
1 Radiology, University of Colorado Denver, Aurora, CO, United States; 2 Biostatistics and Psychology, UCLA, Los Angeles, CA,
United States
Multivariate statistical analysis has recently become popular in fMRI data analysis as such methods can capture better the spatial dependencies between
neighboring voxels. One such method is local canonical correlation analysis (CCA) where one looks at the joint time courses of a group of neighboring
voxels. It is known that CCA without any constraints can lead to significant artifacts and an increase in false activations. Here, we investigate different novel
linear constraints and a nonlinear constraint for CCA and propose a method that rectifies the weakness of conventional CCA mentioned above.
1152. An Optimized Clustering Technique for Functional Parcellation of Hippocampus
Arabinda Mishra 1 , James C. Gatenby 1 , Allen T. Newton 1 , John C. Gore 1 , Baxter P. Rogers 1
1 Radiology & Radiological Science, VUIIS, Nashville, TN, United States
Functional sub-divisions of important anatomic regions in the human brain are normally done based on disparities in structural connectivity patterns or
functional connectivity maps. However, quantification of functional heterogeneity, and determining the appropriate number of sub-regions, has rarely been a
focus of study. This work evaluates the use of self organized maps (SOM) to classify the functionally different regions in the hippocampus, which exhibits
functional and sometimes anatomical differences in patients with disorders such as schizophrenia and bipolar disorder etc. Using voxel based connectivity
maps we successfully parcellated left hippocampus and found performance of SOM to be superior in comparison to kmeans clustering.
1153. Spatiotemporal Dynamics of Low Frequency Fluctuations in BOLD FMRI of Rats and Humans
Waqas Majeed 1 , Matthew Magnuson 1 , Shella Keilholz 1
1 Biomedical Engineering, Georgia Institute of Technology / Emory University, Atlanta, GA, United States
Presence of propagating spatiotemporal waves in low frequency fluctuations (LFFs) has recently reported using high temporal resolution single slice BOLD
fMRI of the rat brain. We have developed a novel method for automatic detection of such patterns and some initial findings for multslice rat and human data
are presented in this abstract.
1154. A Statistical Method for Computing BOLD Activations in Multi-Echo Time FMRI Data Sets and
Identifying Likely Non-BOLD Task Related Signal Change
Andrew Scott Nencka 1 , Daniel L. Shefchik 1 , James S. Hyde 1 , Andrzej Jesmanowicz 1 , Daniel B. Rowe 2
1 Department of Biophysics, Medical College of Wisconsin, Milwaukee, WI, United States; 2 Department of Mathematics, Statistics
and Computer Science, Marquette University, Milwaukee, WI, United States
The T2* contrast mechanism associated with the BOLD signal is well known, as is its echo time (TE) dependence. In this abstract, we present a method for
analyzing data acquired with interleaved echo times. Based upon the expected BOLD TE behavior, the ratio of the regression coefficients for the task related
columns of the design matrix may be used to identify voxels which exhibit BOLD-like responses.