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459.5 Introduction EEG Results Combing Imaging Modalities We study the transition from discrete to continuous movements by investigating brain activity at 21 different movement rates ranging from 0.5 to 2.5Hz. For this purpose two separate EEG and MEG experiments were performed using a continuation paradigm. One of the subjects also participated in an fMRI study with a similar experimental setup. Independent component analysis (ICA) was used to separate the spatiotemporal signals resulting in either two or three different response types for EEG and MEG, respectively. Using the graywhite matter boundary extracted from an MRI scan as constraint for the locations and directions of primary currents we localized the origins of two of the three MEG response types and found a division of the region of activation identified with fMRI. ICA decomposition consistently revealed two types of spatiotemporal response patterns for all subjects. Samples for each type are shown below for one subject. Due to the columnar organization of the cortex, the primary currents flow in a direction perpendicular to the boundary between the gray and the white matter. This surface can be extracted from T1 weighted MRI scans and serve as a constraint for the calculation of inverse solutions. Methods Fullhead EEG and MEG recording: 84 electrode EEG system SAM Technologies, San Francisco, CA), 143 SQuID MEG system (CTF Inc., Port Coquitlam, BC); Behavior was measured as pressure changes in an air cushion placed under the right index finger; Continuation paradigm: 20 cycles synchronization with an auditory metronome, 20 cycles continuation at the same rate without metronome; 21 different rates: 0.5-2.5Hz in steps of 0.1Hz; 4 subjects in each experiment; Blind source separation was performed using the ICA Toolbox from the Swartz Center for Computational Neuroscience at UCSD; Freesurfer was used to construct the surface that represents the gray-white matter boundary; fMRI from the same subject was used to identify sensory-motor areas; Regions of activity were computed as overlap between forward solutions from the surface normals and patterns extracted from MEG data; First response type: Peak latencies are consistent with the well-known sequence of motor evoked potentials. Peak amplitudes are relatively independent of movement rate. different stimulus rates; Left: The MRI scan is transformed such that the landmarks Bottom: Grand average of the time series across all rates on the subject’s head (nasion, and left and right preauriculars) are located in the same slice); (red) and movement profile (green). A black vertical line indicates the point of peak finger flexion. S3 S4 1.5 S1 SD S2 SD 1 S3 SD S4 SD 0.5 0 Produced Rate (Hz) Produced Rate (Hz) S2 2 S1 S2 2 S3 S4 1.5 0.5 1 1.5 2 Required Rate (Hz) 2.5 3 Second response type: Active brain areas found in fMRI for the subject shown in the MEG results during performance of a similar continuation task (left). Activity is seen in sensory-motor cortex and SMA. On the right activation is constraint to the gray-white matter boundary. The same data overlaid on a 3d-reconstruction of this surface is shown below. Similar type one and type two responses are found in MEG shown on the left and right, respectively. S4 SD 0.5 1 1.5 2 locations and directions of the primary currents in the cortex. MEG Results S3 SD 0.5 Middle: The boundary between the gray and white matter; The amplitude of this pattern shows a systematic dependence on movement rate. Strong activity exists only at low movement frequencies and the pattern vanished at higher rates. S2 SD 1 0 Type III Right: The normal vectors for this surface, representing the S1 SD 0 0 A strong correlation between the patterns identified as MEG responses I and III with the forward solution on the gray white matter surface determines possible source locations. The figures on the left indicate a lateralmedial separation of the type I and III responses. Note that together the areas of activation coincide with the regions identified in fMRI. Conclusions ICA is an effective method for separating temporally and spatially overlapping responses in complex behavioral experiments with consistency across subjects and modalities. The dropout of slow wave activity seen in response type II at higher rates indicates a simplification of the movement related activity and may be associated with a transition between discrete and continuous movements. Using the gray-white matter surface as a constraint for the location and direction of source currents produces anatomically meaningful areas of activity that are consistent with results from fMRI. The distinct split between the origins of response type I and III, and the difference of their activation profile in time shows that combining imaging modalities strongly enhances the spatial and temporal resolution of brain activity recordings. References 2.5 S1 x Middle: Color coded amplitude of the average time series at 3 2.5 Type I Steps towards the combination of modalities: MEG Continuation 3 y Top: Spatial pattern for each type of response; Task Performance EEG Continuation Origin of MEG Responses Type I and III 2.5 3 Required Rate (Hz) Produced versus required rate during continuation. All subjects performed the task adequately, providing at least 50 good cycles for averaging at all rates. Good cycles are those within an interresponse interval of two standard deviations from the mean at each rate. In MEG a third type of response is found in all subjects. The dipolar pattern is shifted medially compared to the type one response, the amplitude is relatively independent of movement rate and a peaks during the extension phase of the movement cycle. Bell, A. J., & Sejnowski, T. J. (1995). An information-maximization approach to blind separation and blind deconvolution. Neural Computation, 7, 1129-1159. Fuchs, A. (2002). Combining Brain Imaging Technologies: Using Brain Surfaces. Biomag 2002, VDE Verlag, Berlin Makeig S., Westerfield M., Jung T.P., Enghoff S., Townsend J., Courchesne E., & Sejnowski T.J. (2002). Dynamic Brain Sources of Visual Evoked Responses. Science, 295, 690-694.\ Dale, A. M., Fischl, B., Sereno, M.I. (1999). Cortical Surface Based Analysis I+II. Neuroimage 9, 179-194, 195-207. Acknowledgement Work supported by NINDS (grant NS39845), NIMH (grants MH42900 and 19116) and the Human Frontier Science Program.