Acknowledgement Work supported by NINDS (grant NS39845), NIMH (grants MH42900 and 19116) and the Human Frontier Science Program 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: Hz 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; Task Performance 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 inter- response interval of two standard deviations from the mean at each rate. 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 Bell, A. J., & Sejnowski, T. J. (1995). An information-maximization approach to blind separation and blind deconvolution. Neural Computation, 7, 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, \ Dale, A. M., Fischl, B., Sereno, M.I. (1999). Cortical Surface Based Analysis I+II. Neuroimage 9, , Introduction 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 gray- white 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. EEG Results ICA decomposition consistently revealed two types of spatiotemporal response patterns for all subjects. Samples for each type are shown below for one subject. First response type: Peak latencies are consistent with the well-known sequence of motor evoked potentials. Peak amplitudes are relatively independent of movement rate. Second response type: 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. Top: Spatial pattern for each type of response; Middle: Color coded amplitude of the average time series at different stimulus rates; Bottom: Grand average of the time series across all rates (red) and movement profile (green). A black vertical line indicates the point of peak finger flexion. MEG Results 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. Similar type one and type two responses are found in MEG shown on the left and right, respectively. Combing Imaging Modalities 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 T 1 weighted MRI scans and serve as a constraint for the calculation of inverse solutions. x y Steps towards the combination of modalities: Left: The MRI scan is transformed such that the landmarks on the subject’s head (nasion, and left and right preauriculars) are located in the same slice); Middle: The boundary between the gray and white matter; Right: The normal vectors for this surface, representing the locations and directions of the primary currents in the cortex. 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. Origin of MEG Responses Type I and III 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 lateral- medial separation of the type I and III responses. Note that together the areas of activation coincide with the regions identified in fMRI. Type I Type III