EFTA02693161.pdf

NemoImage 47 (2009) 1691-1700 Contents lists available at ScienceDirect Neurolmage journal homepage: www.elseviet.com!locatelynimg Decoding center-out hand velocity from MEG signals during visuomotor adaptation Trent J. Bradberry Feng Rang'.1, Jose L Contreras-Vidal &schen Department of &Geminating Unarnity of Ala04md. College Park. MD 20742. USA Graduate Program in Neuroscience and Cognitive Science. University of Marylamt College Park. MD 20742. USA Department of Kinesiology. University of Maryforut College Park. MD 20742. USA ARTICLE INFO ABSTRACT Ankle history: During reaching or drawing, the primate cortex carries information about the current and upcoming position Received 4 March 2009 of the hand. Researchers have decoded hand position. velocity. and acceleration during center-out reaching Revised 5 May 2009 or drawing tasks from neural recordings acquired invasively at the microscale and mesoscale levels. Here we Accepted 8 June 2009 report that we can continuously decode information about hand velocity at the macroscale level from Available online 16 June 2009 magnetoencephalography (MEG) data acquired from the scalp during a center-out drawing task with an Keywords: imposed hand-cursor rotation. The grand mean (ri = 5) correlation coefficients (CCs) between measured and Magnemencephalography decoded velocity profiles were 0.48. 0.40. 0.38. and 0.28 for the horizontal dimension of movement and 0.32. Hand movement decoding 0.49. 0.56, and 023 for the vertical dimension of movement where the order of the CCs indicates pre- Conical network exposure. early-exposure. late-exposure, and post-exposure to the hand-cursor rotation. By projecting the Visual rotation sensor contributions to decoding onto whole-head scalp maps. we found that a macroscale sensorimotor Visuomotor adaptation network carries information about detailed hand velocity and that contributions from sensors over central Brain-computer interface and parietal scalp areas change due to adaptation to the rotated environment. Moreover, a 3-D linear estimation of distributed current sources using standardized low-resolution brain electromagnetic tomography (sLORETA) permitted a more detailed investigation into the cortical network that encodes for hand velocity in each of the adaptation phases. Beneficial implications of these findings include a non- invasive methodology to examine the neural correlates of behavior on a macroscale with high temporal resolution and the potential to provide continuous, complex control of a non-invasive neuromotor prosthesis for movement-impaired individuals. 0 2009 Elsevier Int. All rights reserved. Introduction control signals related to hand movement from neural data (Schwartz et al.. 2001). Researchers have demonstrated the ability to decode In the last several decades, great strides have been made in hand kinematics at the microscale from neuronal signals acquired revealing how the primate cortex may encode the current and with microwires or microelectrode arrays seated into small patches of upcoming position of the hand in space during reaching or drawing sensorimotor cortical tissue and to use this information to drive a (Scott 2008). In addition to contributing to the body of neuroscientific cursor or robotic arm (Wessberg et al.. 2000; Serruya et al.. 2002: knowledge, these discoveries have begun to beneficially impact Taylor et al.. 2002: Hochberg et al.. 2006: Santhanam et at. 2006: society. Greater elucidation of the neural code for hand movement Truccolo et al., 2008: Velliste et al.. 2008: Mulliken et al., 2008). Other has served as an impetus to the development of brain-controlled intracranial studies have analyzed neural data at the mesoscale with prostheses for the movement-impaired population. Prior to the coarser spatial resolution but wider spatial extent from local field advent of brain-controlled prostheses, several seminal discoveries potential (LFP) recordings. For example, hand movement direction laid a foundation with arguably the most momentous discovery being and two-dimensional trajectories have been decoded from LFPs that ofa population vector code for the direction of hand movement in (Mehring el al.. 2003. 2004; Leuthardt et al.. 2004; Rickert et at. three-dimensions (Georgopoulos et al.. 1986: Kettner et al.. 1988). At 2005: Scherberger et al., 2.005; Schalk et al., 2007: Pistohl et al., 2008; the beginning of this century. researchers launched the field of brain- Sanchez et al., 2008). controlled neuromotor prostheses with the application of the In the late 1990s, pioneering work on the macroscale began to population vector algorithm as well as other methods to extract relate scalp potentials acquired non-invasively to hand movement (Kelso et at, 1998: 0•Suilleabhain et al., 1999). Some recent non- invasive studies have demonstrated the presence of a macroscale • Corresponding author. E-mad address: r remNeared edu (TJ. Bradbern4 network that carries the neural code for detailed hand movement. For ' Present address: Department SCognalive Sciences: University of Calikania. instance, hand movement direction has been decoded from electro- California 92697. USA. encephalography ( EEG) and MEG data (I lammon et al.. 2008; Walden 10534119/g - see front matter O 2009 Elsevier Inc. All rights reserved. doi:10 MI6/ iwuroimage.2009.06 023 EFTA_R1_02036240 EFTA02693161 1692 TJ. Bradbury er at / Numlinage 47 (2009) 1691-1700 et al.. 2008). and hand position and velocity have been decoded from rate of 1 kHz. The MEG system used coaxial type first-order MEG data collected during continuous joystick and trackball move- gradiometers with a magnetic field resolution of 4 ft/Hz" or 0.8 ments (Georgopoulos et al., 2005: Jerbi et al. 2007). However, with the (ft/cm)/ Hzu2 in the white noise region. On-line. electronic circuits exception of Hammon et al.. these non-invasive studies have band-pass and notch-filtered the MEG data from 1-100 Hz and 60 Hz constrained subjects to small finger and wrist movements as opposed respectively. to multi-joint drawing or reaching movements. Also, most impor- tantly. the tasks employed for non-invasive decoding of hand position Adaptation confirmation and velocity have not incorporated discrete center-out movements. To examine our hypothesis that hand kinematics of natural. multi- To quantitatively confirm the occurrence of adaptation. the mean joint, center-out movements are decodable from non-invasive neural initial directional error (IDE) was calculated across subjects for each signals, we aimed to continuously decode hand velocity from MEG phase of the task. A vector from the center location of the screen data collected during a two-dimensional drawing task. Currently only (home) to the position of the pen at 80 ms after the pen completely invasive studies have continuously decoded hand velocity during left the center circle determined the initial direction of the planned discrete center-out movements. Since MEG coupled with our decod- movement trajectory. The IDE was calculated as the angular difference ing method facilitates the ability to examine sensor involvement on a between this vector and a vector extending from the home location to macroscale with high temporal resolution, we also sought to create the target. Four separate t-tests were performed between the IDE in snapshots of sensor importance in a network covering multiple brain pre-exposure and zero. IDE in pre-exposure and early-exposure. IDE in regions across time during adaptation to a hand-cursor rotation. pre-exposure and late-exposure. and IDE in pre-exposure and post- Furthermore, we aimed to examine the importance of estimated exposure. current sources in the network using sLORETA to determine whether they corroborated non-decoding visuomotor adaptation studies that Signal pre-processing employed other imaging modalities like EEG (Contreras-Vidal and Kerick, 2004), positron emission tomography (PET) (Inoue et al.. Data from each MEG sensor were first standardized according to 2000: Ghilardi et al.. 2000; Krakauer et al.. 2004). and functional Eq. ( I ): magnetic resonance imaging (fMRI) (Graydon et al.. 2005; Seidler et al., 2006). S„[t] = salt] gn for all n from I to N ( 1) Slk Materials and methods where S„Iti and s„ItI are respectively the standardized and measured Experimental procedure and data collection magnetic field strength of sensor n at time r, s, and SD„ are the mean and standard deviation of s„ respectively. and N is the number of The Institutional Review Board of the University of Maryland at sensors. The kinematic data were resampled from 60 Hz to 1 kHz by College Park approved the following experimental procedure. After using a polyphase filter with a factor of 5/ 3. For computational giving informed consent, five healthy, right-handed subjects drew efficiency. the MEG and kinematic data were then decimated from center-out lines with an optic pen on a glass panel positioned in kHz to 100 Hz by applying a low-pass anti-aliasing filter with a cutoff front of them while they lay supine with their heads in an MEG frequency of 40 Hz and then downsampling. The best decoding results recording dewar located inside a magnetically shielded room in the were obtained when both the MEG and kinematic data were Kanazawa Institute of Technology (KIT)-Maryland MEG laboratory at subsequently filtered with a zero-phase. fourth-order, low-pass the University of Maryland (Fig. IA). Cushions were positioned in Butterworth filter with a cutoff frequency of 15 Hz. The data for the dewar and under the right elbow to minimize movement of the each phase of the task were pre-processed separately. head and upper limb respectively. The distance between the glass panel and each subject's head was adjusted for comfort (approxi- Decoding model mately 35 cm from nose tip to the center of the panel). A black curtain occluded the subjects' vision of their hands while visual In the subsequent analyses. we only considered hand velocity feedback was provided on a screen located in front of them that based on our previous work that revealed better decoding of hand displayed the position of the pen tip as a cursor. Subjects were velocity than hand position from MEG signals (Bradberry et al.. 2008). instructed to position the pen tip in a circle (0.5 cm diameter) To continuously decode hand velocity from the MEG signals, a linear located in the middle of the screen, wait for one of four circle decoding model was used (Fig. 2) (Georgopoulos et al.. 2005): targets (03 cm diameter) to appear in the corner of the screen at N L 45. 135. 225. or 315°. wait for the target to change color, and then xitl — x(t - 11=E t bffir calt — lc] (2) draw a straight line to the target as fast as possible. The inter-trial n.1 k =0 delay was randomized between 2 and 2.5 s. Working space dimensions were a 10/ 10 cm virtual square. After 40 trials ( pre- exposure), the cursor was rotated 60' counterclockwise (exposure). The exposure phase consisted of 240 trials with the early-exposure Yltl - y[t —11 = E E bniy.S„Ir — kj (3) n-1 t=0 phase composed of the first 40 trials and the late-exposure phase composed of the last 40 trials. After the exposure phase, the original where x(rj and All are the horizontal and vertical position of the pen orientation of the cursor was restored, and 20 more trials were at time sample r respectively. N is the number of MEG sensors. L is the collected and labeled as the post-exposure phase. The number of number of time lags, S„lt — kl is the magnetic field strength measured trials analyzed in the pre-exposure phase was reduced from 40 to at MEG sensor n at time lag k and the b variables are coefficients 36 because the behavioral performance during several initial trials obtained through multiple regression. By varying the number of lags of some subjects was poor due to lack of familiarization with the and sensors independently in a step-wise fashion, the optimal number task To maintain consistency, the number of trials analyzed in the of lags (L= 20. corresponding to 200 ms) and the best sensors early- and late-exposure phases was also reduced from 40 to 36. (N=62; from central and posterior scalp regions) were determined A video camera sampled the movement of the pen tip at 60 Hz. and experimentally. The data for each phase of the task were decoded whole-head MEG data were acquired from 157 channels at a sampling separately. EFTA_R 1_02036241 EFTA02693162  Tr lbodbeiry er al ' Net:mintage. 47 (2009) lit -1700 1693 A • Target Cursor . Home B Pre-Exposure Early-Exposure Late-Exposure Post-Exposure UL,P C 60 40 - a 20 a) V 0 -20- -40- -60- -80 Pre Early Late Post Phase of Task Fig I.Center-out drawing experimental setupand kinerrutin. :A; in the lust and wwnd p.Invb.asubject is shown lying with his head inside the MEG recording dewar and drawing with an optic pen on a sheet of glass.A black curtain used to ocdude vision of the upper limbs is additionally shown in the second panel. The third panel illuurates the subject's view or the computer screen where visual feedback of the pen position (cursor), center location (home). and peripheral targets was displayed. (B) The superimposed pen (black) and cursor :gray) paths for one representative subject confirmed the occurrence of adaptation. Dissociation between the pen (hand) and cursor (eye) movements due to hand-cursor rotation was evident. (C) The mean SD of the IDE calculated across subjects for each phase of the task further confirmed adaptation. Assessment of decoding accuracy approximately 12 s of continuous data, or four trials). m — 1 parts were used for training, and the remaining part was used for testing. The M-fold cross-validation was used to assess the decoding accuracy. procedure was considered complete when each of them combinations In this procedure, the data were divided into m parts (each with of training and testing data were exhausted. and the mean CC between EFTA_R 1_02036242 EFTA02693163 1694 T.J. Bra*my er Nearofmne 47 (2009) 1691-1700 MEG Sensor I MEG Sensor 62 Time Sensor Weights for X Velocity Sensor Weights for Y Velocity E — Decoded — Measured E 4 Nrr.r. ,r r Velocey Reconstrudion from MEG Data at 4100 ms (snow above) Y velocity Reconstruceon from MEG Oats at 1.100 rre (Maim above) x Velocity Recomuructon from MEG Data from mi b 6200 ms V velocity Recorstrucson from MEG Data from tlf) b 1-200 rra Fig. 2. Didactic model of the linear decoding method. The top raster plot contains time series of 62 MEG sensors extracted 100 ms prior to the current velocity sample of interest. Through multiple linear regression. sensor weights were computed separately for x and y velocity that transformed 11w top raster plot to the lower left and right raster plots. The transformed time series of the sensors were then summed to produce the reconSnucted velocity profiles (red) that overlay the measured velocity profiles (black). The upper velocity profiles are associated with the MEG data shown In the example (100 ms prior to the current velocity sample of interest) and the lower ones with MEG data from0 to 200 ms prior to the anent velocity sample of interest. .l measured and decoded hand velocity was computed across folds. Prior to (from Eqs. X and ; .) vector magnitude were projected onto a time computing the CC, the kinematic signals were smoothed with a fourth- series ( — 200 to 0 ms in increments of 10 ms) of scalp maps for each order, low-pass Butterworth filter with a cutoff frequency of 0.6 HL Cross- phase of the task. These spatial renderings of sensor contributions validation was executed with m= 9 for all phases of the task except for were produced by the topoplot function of EEG AB version 6.01b, an post-exposure where m = 5. For Fig. 3B, standardized velocity profiles open-source MATLAB toolbox for electrophysiological data proces- were computed with I% ' with s, replaced by a velocity profile. sing (Deli-nine and Maketg. 2004: 'thy wen uccd edu eegial) ), that performs biharmonic spline interpolation of the sensor values Sensor sensitivity curves before plotting them (Sandwell. 198?). To examine which time lags were the most important for decoding. for each scalp map. the A curve depicting the relationship between decoding accuracy and percentage of reconstruction contribution for a phase of the task was the number of sensors was computed for the x and y dimensions of computed as hand velocity for each subject for each phase of the task. A similar N method to examine this relationship has been used to analyze 4)b„ + bfly 2 neuronal recordings (tiancliti ci al., 20114). First, for each subject and each phase of the task, each sensor was assigned a rank according GTI=100%x fa a tN for all i from 0 to L (5) to I q. '4': E E Veenk. + No 2 k=0 `Al bm^kw- + bmitLY for all n from I to N 4) where %I, is the percentage of reconstruction contribution for a scalp R" Ma + 1 / map at time lag i. where R„ is the rank of sensor n and M is the number of folds of the Comparison of scalp maps across adaptation cross-validation procedure. Second, the decoding model was iteratively executed with only the highest-ranked sensor, the four highest-ranked Right-tailed. paired t-tests determined statistically significant sensors, the seven highest-ranked sensors. etc. until all sensors were (p<0.05) changes in sensor contributions between phases of the used. For each phase of the task the mean SD of the CCs computed task. Three contrasts between the scalp maps were computed for across subjects was plotted against the number of sensors. Finally, each increases from baseline (pre-exposure): early-exposure - pre-expo- plot was fitted to a double-exponential curve. and the coefficient of sure. late-exposure - pre-exposure. and post-exposure - pre-expo- determination. le. was calculated as a measure of the goodness of fit. sure: and three contrasts were computed for decreases from baseline: pre-exposure - early-exposure. pre-exposure - late-exposure, and Scalp snaps of sensor contributions pre-exposure - post-exposure. The resultant r scores were converted to z scores and then rendered onto scalp maps with the topoplot To graphically assess the relative contributions of scalp regions to function of EEGLAB Mdkelg, 21.11.14) with increases and the reconstruction of hand velocity, the across-subject means of the b decreases represented with hot and cool colors respectively. EFTA_R1_02036243 EFTA02693164  T.J. Brodbeny cl ot / NeurfOrnoge 47 (2009) 1691-1700 1695 Conical source localization plotted onto an axial slice of the brain (z = 55 mm) from the Colin27 volume (Holmes et al.. 1998). the MRI template that best illustrated To better estimate the cortical sources of hand velocity encoding in our regions of interest. All reported coordinates of regions of interest each phase of the task, we used standardized low-resolution brain are in Talairach space. electromagnetic tomography (sLORETA) software version 20081104 (Pascual-Marqui. 2002; http:kwww.uzh.ch. keyinst/loretatilm). Results sLORETA computes instantaneous. 3-D linear, distributed and discrete solutions for the MEG/EEG inverse problem, which compare well with Hand kinematics confirmed adaptation respect to linear inverse algorithms like minimum norm solution. weighted minimum norm solution, and weighted resolution optimi- During early-exposure to the cursor rotation, we observed curved zation (Pascual-Maryut. 2002). These solutions are computed within a hand paths due to the subjects' effort to counteract the imposed rotation three-shell spherical head model that uses a lead field computed with (Fig. IB). Hand paths became straighter in late-exposure as subjects a boundary element method applied to the MNI52 template (Fuchs et adapted to the novel environment. In post-exposure, after-effects, which al.. 2002). The head model includes scalp. skull, and brain compart- consisted of hand paths curved in the opposite direction from those in ments. The brain compartment is restricted to the conical matter of a early-exposure, indicated that adaptation had occurred. We also head model co-registered to the Talairach brain atlas (Talairach and confirmed the occurrence of adaptation quantitatively by computing Tournoux. 1988). This compartment includes 6239 voxels at 5 mm the mean IDE across subjects for each phase of the task and comparing it resolution with each voxel containing a current dipole representing between phases (Fig. IC). The IDE was not significantly different from the integrated activity within the corresponding spatial vicinity. The zero in pre-exposure (two-tailed [-test: p =0.34). The IDE increased in sensor coordinates of the MEG helmet that were entered into sLORETA early-exposure relative to pre-exposure. decreased in late-exposure had been previously measured in the KIT-Maryland MEG laboratory. relative to early-exposure, and increased again in post-exposure relative To identify sources that were sensitive to velocity encoding, we to pre-exposure (one-tailed, paired t-tests, p<0.001). found the sources that best correlated with the most meaningful sensors from the decoding analysis using the following method. Pre- MEG signals contained decidable hand velocity information processed MEG signals from all 157 channels for each subject and each phase of the task were fed to sLORETA to estimate current sources. We employed a linear decoding model (Fos. (2) and (3)) to These MEG signals had been pre-processed in the same manner as for reconstruct the horizontal (x) and vertical (y) velocity components of decoding: standardized. downsampled. and low-pass filtered. From hand movement from the activity of the MEG sensors (Fig. 2). The the scalp map with the highest percentage of reconstruction mean CC of x velocity decreased during each consecutive phase of the contribution ( — 100 ms). the fifteen sensor weights possessing the adaptation task (Fig. 3A). Interestingly the mean CC of y velocity highest values were selected. The CCs were then computed between increased until post-exposure at which point it drastically decreased. the squared time series from the fifteen sensors with the 6239 time In terms of individual subjects. the mean CC ranged from 023 to 0.56 series from the sLORETA solutions and averaged across subjects. Each (Table I), and examples of smoothed, reconstructed hand velocity CC was multiplied by the magnitude of the regression weight b (from profiles matched the measured velocity profiles well (Fig. 38). Fos. (2) and (3)) vector of the sensor in the correlation analysis. The reason that fifteen sensors were chosen for the correlation analysis Number of sensors and decoding accuracy were exponentially related was because of the observation that the sensor sensitivity curves began to plateau around fifteen sensors (Fig. 4). Next the highest 5% of The linear decoding model produced one weight per sensor per the CCs (weighted by b) were set to the value one, and the rest of the time lag: therefore, the importance of the contribution of a sensor to CCs were set to zero. Finally these binary-thresholded CCs were the decoding process at a particular time lag could be considered the A B X Velocity for Lete•Exposure Y Velocity for Late-Exposure 2 2 • 0.8 X Velocity =1 9 Velocity r e 0 8 .2 a%0 19S44\r hea-- n ca T.) 0 el 2 2 E 0.6 ▪ 0.trzatt-IV-N -2 8 • 0.4 X O 0 O rocrwegased340S a P 2 ca -2 tn Oi 7, ▪( 0.2 t"' 2 g 44\es vair— • 0 cz 8 -2 a4 Ea ly Late Post r Phase of Exposure veiVeAerV At N Fa t 2 0 8 o 4 e 12 8- o 4 B 12 Time (s) Time (s) Fig. 3. Decoding accuracy for hand velocity. (A) The across-subject mean SD of the CCs between measured and decoded hand velocity profiles it plotted separately for x (horizontal. black) and y (vertical. white) velocity breach phase affix task. (B) Examples of smoothed and standardized measured (black) and decoded (gray) hand velocity profiles for late- exposure exhibited high decoding accur.xy.Tbe left and right columns contain x and y velocity profiles respectively. Each row contains data fora single subject, and the CC between the measured and decoded velocity is listed to the left of each plot. EFTA_R 1_02036244 EFTA02693165 1696 74. Brodberry tr of Neurohnege 47 (2009) 1691-1700 Early-Exposure Late-Exposure Post-Exposure 08 06 0 — 0.4 X 0.2 O 0 0 •0 2 1 13 25 37 49 61 13 25 37 49 61 1 13 25 37 49 61 1 13 25 37 49 61 mean SD 0.8 08 0.8 ............ 0.6 06 0.6 CC for Y Velocity 0.4 ..... • 0.4 .......... 0.4 ,.•••• ,...... .. 02 02 0.2 0 0 0 -0 2 R2 = 0.95 -0.2 R2 = 0.99 -0.2 13 25 37 49 61 13 25 37 49 61 1 13 25 37 49 61 1 13 25 37 49 61 Number of Sensors Number of Sensors Number of Sensors Number of Sensors Flg.4. Decoding amirary vs. number of sensors. The top and bottom rows comma of mean s() (gray; of the CCs anon tobycts vs. the number of sensor for x and y velocity respectively. Columns organize the plots by phase of the task. R' values between the mean CC curve and a fitted double-exponential curve are displayed at the bottom of each plot. vector magnitude of its regression weights at that time lag. We ranked means of the vector magnitudes of the sensor weights onto a time the sensors and reran the decoding procedure with the most series (-200 to 0 ms in increments of 10 ms) of scalp maps for each important sensor, the four most important sensors. the seven most phase of the adaptation task The scalp maps for each phase of the task important sensors. etc. until all sensors were used. These sensor resembled each other. so only those for pre-exposure are shown sensitivity curves of mean CC vs. the number of sensors fit a double- (Fig. SA). A network of sensors over central and posterior scalp areas exponential function well (R2 =0.95-1.00) (Fit:. •1). For all phases of contributed to decoding hand velocity with a salient member of the the task the curves peaked then plateaued, or nearly plateaued, near network over the contralateral motor area. Although the scalp maps of 15 sensors. the different phases appeared similar upon visual inspection, we investigated the presence of statistically significant increases and A macroscale sensorimotor network encoded hand velocity decreases in early-, late-. and post-exposure relative to baseline (pre- exposure). We observed notable focal differences between phases of To graphically assess the relative contributions of scalp regions to the task in scalp areas over mediolateral premotor and posterior the reconstruction of hand velocity• we projected the across-subject parietal cortices in particular (Fig. 58). To better estimate the cortical Table 1 Mean and SO (in parentheses) of CCs for each subject during each phase of the visuomotor adaptation task. Pre Early Late Post X Vel Y Vel X Vet Y Vel X Vel Y Vet X Vel Y Vel Subject I 064 (0.09) 0.47 (036) 0.44 (QM 0.62 (0.13) 0.53 (0.13) 0.73 (0.12) 0.10 (021) -0.02 (0.13) Subject 2 0.45 (0.16) 029 (0.14) 0.56 (0.10) OAS (021) 0.40 (0.18) 032 (011) Q10 (007) 0.37 (0.13) Subject 3 0.48 (0.14) 023 (021) 046 (0.16) 053(0.18) 0.49 (0121 0.63 (024) 042 (0.16) 026 (0.14) Subject 4 0.60 (0.08) 0.33 (022) 021 (0.20) 023 (0.11) 021 (0.111) 0.44 (015) 035 (0.07) 0.46 (0.13) Subject 5 0.17 (021) 026 (0.30) 026 (0.13) 0.56 (0.14) 024 (015) 0.47 (022) 0.17 (0.32) 0.02 (0.13) Grand mean 0.48 (0.15) 032(008) 0.40 (0.121 0.49 (0.13) 038 (0.12) 036 (OLIO) 028 (0.17) 023 (0.17) EFTA R1_02036245 EFTA02693166  y. Brndbeny el at I Neurohnoge 47 (7009) let -1700 A Pre -exposure 12.4% 12.8% 12.1% 10.8% 10 3' 8.6% 8.0 ®• -70 ms -80 ms -90 ms -100 ms -110 ny, -120 ms -130 r Sensor Contribution B Early -exposure — Pre -exposure • • • 411 • • • Late -exposure — Pre-exposure • • • • • • • Post -exposure — Pre -exposure ®• -70 ins -80 ms • -90 ms -3 -2 • -I -100 ms 0 1 •• • 2 Ito m5 3 -120ms .130 ms z score C Pre Early Late Post Fitt Sensonrnocor networks associated with hand velocity during visumnotor adaptation. :A: Int clas.ehmles oldie scii.ui ‘‘c:);it :meat decoding model revealed the importance of neural regions when interpolated and protected onto a time series ( 200 to 0 ins in increments of 10 msl of scalp maps for the pre-exposure phase (other phases were similar). Light and dark colon represent high and low contributors respectively. The highest sensor weighting of the MEC signals led the velocity output by 100 ms, so the display of scalp maps are centered around — 100 ms. The percentage of reconstruction contribution (kr) is displayed above each scalp map. Due to space limitations, only seven of the twenty-one scalp maps arc shown. (BI The rows respectively contain the z scores of differences between early- and pre-exposure. late- and pre- exposure, and post- and pre-exposure Increased ( t ) and derreased ( ) contributions of sensors are napped to hot and cool colon respectively. IC) The estimated conical sources involved in hand velocity encoding during the task were represented on an axial slice from an MRI template (z= 55). The sources and their Talairach coordinates (x. y. z) were the PrC (-41. — 1145), PoC (-45. —17.55).51'3(30, —46.55), PCu (3. —61. 55),IPI. (-41. — 41.55).5MA (5, — 2.55). MEC (19.18.55 and —24.20.55). and SEC (19.12.55). EFTA_R1_02036246 EFTA02693167 1698 El.. Ona:Chewy et at / NeurOMage 47 (2009) 1691-1700 sources that gave rise to the scalp maps at — 100 ms (the highest current movement (Mehring et al.. 2004: Paninski et al., 2003), this percentage of reconstruction contribution), we correlated the fifteen finding is not unexpected. In our previous report leading up to this best sensors with the sources estimated by sLORETA. After weighting study (Bradberry et al.. 2008). we discovered that hand velocity was the CCs by the vector magnitudes of the sensor weights, the top 5% better decoded than position (post-publication analysis: two-tailed, were binary-thresholded and plotted on an axial slice (Fig. 5C). In all paired t-test; p<0.0001). This is another confirmatory finding, given phases of the task the contralateral precentral gyrus (PIG) and that the motor cortex represents velocity better than position as has postcentral gyrus ( PoG) and the ipsilateral superior parietal lobule been demonstrated, in particular, by studies aimed at decoding (SPL) and precuneus (Ku) encoded for hand velocity. The contral- kinematic parameters for neuroprosthetic control (Schwartz et al.. ateral