TRADITIONAL POSTER - ismrm

Poster Sessions
1155. Eigenspace Minimum L1-Norm Beamformer Reconstruction of Functional Magnetic Resonance
Inverse Imaging of Visuomotor Processing
Shr-Tai Liou 1 , Hsiao-Wen Chung 1 , Wei-Tang Chang 2 , Fa-Hsuan Lin 2,3
1 Graduate Institute of Biomedical Electronics and Bioinformatics, National Taiwan University, Taipei, Taiwan; 2 Institute of
Biomedical Engineering, National Taiwan University, Taipei, Taiwan; 3 A. A. Martinos Center, Massachusetts General Hospital,
Charlestown, MA, United States
We propose the eigenspace L1-norm beamformer, a new novel technique for ultrafast MR inverse imaging (InI) reconstruction. This method minimizes the
amplitude of the beamformer output quantified by the L1-norm of the spatial filter coefficients. We tested this method to reconstruct functional MR InI
measurements using a visuomotor task. Results show that the eigenspace L1-norm beamformer can detect BOLD contrast functional activity and provide
higher spatial resolution than linear constrained minimum variance (LCMV) beamformer in both motor and visual cortices.
1156. Filtering FMRI Using a SOCK
Kaushik Bhaganagarapu 1,2 , Graeme D. Jackson 1,3 , David F. Abbott 1,2
1 Brain Research Institute, Florey Neuroscience Institutes (Austin), Melbourne, Victoria, Australia; 2 Department of Medicine, The
University of Melbourne, Melbourne, Victoria, Australia; 3 Departments of Medicine and Radiology, The University of Melbourne,
Melbourne, Victoria, Australia
BOLD fMRI is restricted by low signal to noise and various artifacts varying from motion to physiological noise. Independent components analysis (ICA) is
a data-driven analysis approach that is being used to filter fMRI of such noise. However, one of the problems with ICA remains the interpretation of the
results. Recently, we developed an automatic classifier (Spatially Organised Component Klassifikator - SOCK), which uses spatial criteria to help
distinguish plausible biological phenomena from noise. We utilize SOCK to automatically filter a conventional fMRI block-design language study and
successfully show the significance of activation obtained increases as a result of SOCK.
1157. Real Time FRMI: Machine Learning or ROIs?
Thomas WJ Ash 1 , T Adrian Carpenter 1 , Guy B. Williams 1
1 Wolfson Brain Imaging Centre, University of Cambridge, Cambridge, Cambridgeshire, United Kingdom
The first applications of real time fMRI used voxel intensity averaging over a ROI to provide feedback, whereas recent work has shown that machine
learning tools may improve performance. We conduct a comparison between the two techniques, and find that support vector machines (SVM) outperform
averaging over a ROI no matter how restricted an ROI we use. Further to this, we find that SVM performance does not decrease as sharply as ROI
averaging when block length is decreased.
1158. Using Dynamically Adaptive Imaging with FMRI to Rapidly Characterize Neural Representations
Rhodri Cusack 1 , Michele Veldsman 1 , Lorina Naci 2 , Daniel Mitchell 1
1 MRC CBU, Cambridge, Cambs, United Kingdom; 2 University of Cambridge, Cambridge, United Kingdom
Dynamically Adaptive Imaging (DAI) is a new real-time paradigm for fMRI. BOLD data were analyzed using our open-source real-time software and used
to iteratively and automatically adapt the stimuli presented to the volunteer. DAI was applied to investigate feature coding in ventral visual cortex. Pictures
of objects were presented on a screen. We performed an iterative search, in which the outcome of the experiment was the neural neighborhood of stimuli that
evoked the most similar pattern of neural response to a referent stimulus. DAI converged rapidly and found object-specific tuning to complex conjunctions
of sensory and semantic features.
1159. A Novel Artifact Reduction Strategy for Retaining and Detecting Changes in Muscle Activity in the
MR Environment
Jaimie B. Dougherty 1 , Christopher J. Conklin 2 , Karen Moxon 1 , Scott Faro 2 , Feroze Mohamed 2
1 Drexel Univesrity, Philadelphia, Pa, United States; 2 Temple University, Philadelphia, Pa
Combined EMG and fMRI is very desirable. Detecting changes in muscle activity associated with changes in cortical activity can greatly improve our
understanding of neuroplastic changes and the affects of treatments in neuromuscular conditions. This work proposes a robust wavelet-based artifact
reduction strategy that allows for the distinction between two muscular conditions in an MR environment. This work also introduces the use of the EMG
parameter median frequency as a covariate in a motor fatigue study to better refine image analysis.
1160. Stockwell Coherence of the Motor Resting State Reduces Within-Subject Variability Caused by
Inadvertent Body Movements
Ali Mohammad Golestani 1 , Bradley G. Goodyear 2,3
1 Electrical & Computer Engineering, University of Calgary, Calgary, AB, Canada; 2 Radiology & Clinical Neuroscience, University of
Calgary, Calgary, AB, Canada; 3 Seaman Family MR Research Centre, Calgary, AB, Canada
Resting-state fMRI analysis techniques that determine the similarity between time varying signals of seed and target regions assume the signals are
stationary; however, the resting-state varies between subjects and is susceptible to unwanted brain activity due to inadvertent movements or cognition. In this
study, we introduce a time-frequency approach based on the Stockwell transform to temporally resolve coherence between resting-state signals. We
demonstrate S-Coherence can reduce the contribution of unwanted hand movements in the determination of the resting-state connectivity within the motor
network, and hence reduce within-subject variability in comparison with existing techniques (temporal cross-correlation and coherence).