Mark Jarmasz

Motor Area Activity During Mental Rotation Studied by Time-Resolved Single-Trial fMRI

The functional equivalence of overt movements and dynamic imagery is of fundamental importance in neuroscience. Here, we investigated the participation of the neocortical motor areas in a classic task of dynamic imagery, Shepard and Metzlers mental rotation task, by time-resolved single-trial functional Magnetic Resonance Imaging (fMRI). The subjects performed the mental-rotation task 16 times, each time with different object pairs. Functional images were acquired for each pair separately, and the onset times and widths of the activation peaks in each area of interest were compared to the response times. We found a bilateral involvement of the superior parietal lobule, lateral premotor area, and supplementary motor area in all subjects; we found, furthermore, that those areas likely participate in the very act of mental rotation. We also found an activation in the left primary motor cortex, which seemed to be associated with the right-hand button press at the end of the task period.

Exploratory data analysis reveals visuovisual interhemispheric transfer in functional magnetic resonance imaging

We used an exploratory data analysis approach to detect interhemispheric processing of complex visual stimuli in functional magnetic resonance imaging (fMRI). A crossed–uncrossed visual field paradigm was used to elicit interhemispheric transfer of picture/word information. Under the uncrossed (control) condition, the stimuli were presented to the preferential hemispheres (pictures to the left visual field/right hemisphere and words to the right visual field/left hemisphere). Under the crossed condition, the visual field presentation was switched in order to elicit increased interhemispheric processing. Fuzzy cluster analysis revealed significantly more crossed activity in cortical areas near the splenium of the corpus callosum. As expected, examination of the activation revealed smaller responses in perisplenial regions (relative to visual responses in the medial extrastriate regions). The exploratory results were compared with those obtained from parametric and masked analyses. The findings confirm that fMRI can be used to detect interhemispheric transfer of picture/word information. The activation was optimally characterized using exploratory data analysis. Magn Reson Med, 2006.

Neuroanatomy of coprolalia in Tourette syndrome using functional magnetic resonance imaging

OBJECTIVE To determine the neural substrates of phonic tics in Tourette syndrome (TS) using functional magnetic resonance imaging (fMRI) and compare with a proposed tic-generating network (TGN). PATIENTS One with TS and one normal control. METHODS fMRI scans were obtained on the TS patient during which numerous unsuppressed phonic tics occurred and, along with the scanner noise, were recorded on audiotape. The control underwent the same functional MRI sequence but mimicked the tics within predetermined, on-off time blocks. Fuzzy clustering (FC) methods were used to generate the activation maps. RESULTS The TS patient and control showed fMRI activation in the left middle frontal gyrus and right precentral gyrus. The TS patient also had activity in the caudate nucleus, cingulate gyrus, cuneus, left angular gyrus, left inferior parietal gyrus, and occipital gyri. CONCLUSIONS fMRI, using an FC analysis, is a viable technique for studying TS patients with phonic tics. These results give further support to the hypothesis of a tic-generating circuit model. Further studies are required to confirm our data.

Resampling as a cluster validation technique in fMRI.

Exploratory, data‐driven analysis approaches such as cluster analysis, principal component analysis, independent component analysis, or neural network‐based techniques are complementary to hypothesis‐led methods. They may be considered as hypothesis generating methods. The representative time courses they produce may be viewed as alternative hypotheses to the null hypothesis, ie, “no activation.” We present here a resampling technique to validate the results of exploratory fuzzy clustering analysis. In this case an alternative hypothesis is represented by a cluster centroid. For both simulated and in vivo functional magnetic resonance imaging data, we show that by permutation‐based resampling, statistical significance may be computed for each voxel belonging to a cluster of interest without parametric distributional assumptions. J. Magn. Reson. Imaging 2000;11:228–231.

A Pattern Recognition Application Framework for Biomedical Datasets

: Scopira facilitates the development of high-performance applications by providing many useful subsystems, flexible and efficient data models, low-level tools such as memory management and serialization, GUI constructs, high-level visualization modules, and the ability to implement parallel algorithms with MPI. Scopira plug-in extensions have been developed to enable Matlab scripts to easily call any Scopira module, thus facilitating the migration of prototypes to highly efficient C++ applications. Scopira is continuously under development and future capabilities will include the ability to develop distributed programs using agents, applicable to grid-computing data mining applications. Scopira has proven to be a successful programming framework for implementing high-performance biomedical data analysis applications. It is based on C++, an efficient object-oriented language, and the source code is available as an open-source project for other researchers to use and adapt to their own research endeavours. Scopira has been compiled to work on Linux and Windows XP operating systems with a port to the Mac OS under development. Scopira, EvIdent and RDP are freely available for download from www.scopira.org.