Exp Brain Res (2007) 180:345–354 DOI 10.1007/s00221-007-0858-7

R E SEARCH ART I CLE

How does the brain respond to unimodal and bimodal sensory demand in movement of the lower extremity? Lewis A. Wheaton · J. C. Mizelle · Larry W. Forrester · Ou Bai · Hiroshi Shibasaki · Richard F. Macko

Received: 16 August 2006 / Accepted: 5 January 2007 / Published online: 26 January 2007 © Springer-Verlag 2007

Abstract Numerous electroencephalography (EEG) studies have shown that neurophysiological signals change in response to visual and sensory adaptations in upper extremity tasks. However, this has not been clearly studied in the lower extremity. In this study, we evaluated how sensory loading aVects brain activations related to knee movement. Thirty-two channel EEG was recorded while ten subjects performed knee extension in four diVerent conditions: no weight and no visual target (NWNT), weight aYxed to the ankle and no visual target (WNT), no weight and a visual target

L. A. Wheaton (&) · L. W. Forrester · R. F. Macko Department of Veterans AVairs and Veterans AVairs Medical Center, Baltimore Geriatric Research, Education and Clinical Center (GRECC), 10 North Greene Street, Baltimore, MD 21201, USA e-mail: [email protected] J. C. Mizelle · L. W. Forrester Department of Physical Therapy and Rehabilitation Science, University of Maryland School of Medicine, Baltimore, MD, USA L. W. Forrester · R. F. Macko Department of Neurology, University of Maryland School of Medicine, Baltimore, MD, USA O. Bai Human Motor Control Section, Medical Neurology Branch, National Institute of Neurological Disorders and Stroke, National Institutes of Health, Bethesda, MD, USA H. Shibasaki Takeda General Hospital, Kyoto, Japan

(NWT), and both weight and target (WT). Surface electromyography (EMG) was recorded from the vastus medialis and vastus lateralis muscles to determine onset of the movement. EEG was epoched from ¡4.5 s before to 1 s after EMG onset. Epochs were averaged to acquire movement-related cortical potentials (MRCPs) of each task condition. MRCP amplitude during the pre-movement period from ¡2 s to EMG onset was evaluated at electrodes over motor, sensory, frontal, and parietal areas. The amplitude of the pre-movement potentials for the conditions was diVerent across areas of interest. Over the motor area, NWNT had lower amplitude than any other condition and WT had higher amplitude than any other condition. There was no diVerence between unimodal NWT and WNT conditions. Mesial frontal and parietal areas showed larger MRCP to the bimodal condition than either unimodal or NWNT conditions. The parietal cortex was the only region that showed a diVerence between unimodal conditions with greater amplitude for NWT condition. Information concerning added sensory demand is processed by the motor cortex in a way that may be indiVerent to the type of modality, but is inXuenced by the quantity of modalities at the level of the knee. Other brain structures such as parietal and premotor cortices respond based on the modality type to help plan appropriate strategies for motor control in response to sensory manipulations. This suggests that additional task demands in motor training may create a rich sensory environment that may be beneWcial in promoting optimal neuromotor recovery. Keywords Motor planning · Sensorimotor integration · Motor control · Multisensory integration · Electroencephalography

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Introduction Movement-related cortical potentials (MRCPs) are a commonly studied neurophysiologic marker for cortical activity related to motor tasks for many types of movement. The MRCP is an electrophysiological signal of involvement of cortical regions before and during a movement (Deecke and Kornhuber 1978; Deecke et al. 1980; Shibasaki et al. 1980). It reXects the synchronous activity of post-synaptic potentials generated by apical dendrites of large pyramidal neurons arranged perpendicularly to the cortical surface. DiVerent periods of the MRCP have been described with simple single movements, generally put into three diVerent periods (Shibasaki et al. 1980; Tarkka and Hallett 1990; Tarkka et al. 1993). The early period of MRCP, the Bereitschaftspotential (BP), is characterized by a slowly rising negativity characteristically seen beginning about 2 s before a movement. This component is followed by a steeper negative slope (NS⬘). Following this is the motor potential (MP), which peaks immediately after movement onset. Rehabilitation techniques are thought to promote neuronal plasticity, increasing potentials related to motor planning and execution (for example, Wiese et al. 2005). Current trends of rehabilitation therapy point to use of robotics promote neuroplastic mechanisms of motor recovery (Guger et al. 2001; Fasoli et al. 2004). Robotic technology commonly employs the use of visual feedback of the patient’s actual movements for goal establishment and error correction to supplement movement therapy, which clearly changes the type of task being performed. As well, the added weight of any external device aYxed to a limb can signiWcantly aVect neuronal mechanisms required to move. Given that visual cuing and mass loading can substantially aVect neurophysiological processes and are commonly implemented in movement therapies, it is important to evaluate their relative contributions to motor control. The eVects of weighted loads on movement have been evaluated in the upper extremity. Several studies have been performed in normal subjects to evaluate how weight aVects pre-movement cortical potentials. MRCPs are capable of distinguishing increasing inertial loads to the Wnger by showing increased amplitude of pre-movement potentials over the frontal and precentral areas (Slobounov et al. 1999). Inertial loads appear to aVect unilateral, but not bilateral arm movement MRCP amplitudes, as increasing motor control demands of bilateral movement may supersede unimodal sensory complexity (Kristeva et al. 1990). Additionally, visually guided tasks have been studied to determine their eVect on cortical potentials. In a visuo-

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motor Go/NoGo choice reaction time paradigm, a distinction was made in representations of cognitive activity in the rostral premotor cortex and motor related activity in the caudal premotor cortex using electrocorticography (Matsumoto et al. 2003). As well, visuomotor adaptation requires activation of frontalparietal and anterior cingulate areas (Contreras-Vidal and Kerick 2004). Prefrontal and sensorimotor brain activation is evident in tasks that require Wnger movement with visual guidance compared to simple Wnger extension (Touge et al. 2003). Such work has profoundly inXuenced paradigms aimed at upper extremity motor rehabilitation (Schaechter 2004). However, little work has been done to evaluate these activations related to lower extremity movement. Results from the upper extremity may not translate fully to the lower extremity as diVerent brain areas contribute to upper and lower extremity motor control. Finger movements show more lateralized activity than lower limb joints, which implies diVerences in specialization of the brain for lower extremity movements (Kapreli et al. 2006). As well, isolated knee movement tasks show higher supplementary motor area, ipsilateral primary motor cortex activations than elbow movements (Luft et al. 2002). A similar result of involvement of ipsilateral motor and premotor activity in planning ankle movements had been shown, which is diVerent from traditional studies using the hand (Ciccarelli et al. 2005). In consideration of the studies to date, we sought to evaluate how brain activity changes with respect to increasing sensory demand during knee movement in healthy subjects. SpeciWcally, we will assess this by studying three diVerent sensory conditions above a simple knee extension. First, we assessed whether unimodal visual and sensorimotor demands (i.e. moving to a visual target or having an external load placed at the level of the ankle) showed discernible brain activity. As well, we assess the impact of bimodal sensory demands (i.e. both visual target and weight) on brain activity. We hypothesize that activations will be diVerent for increasing sensory demand (from no added modality to unimodal to bimodal) over motor cortical areas. Furthermore, we expect that visually targeted movements will require additional parietal and premotor activations.

Materials and methods Subjects Ten right-handed normal volunteers, six males and four females (mean age 33.4 years § 8.7) participated

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in this study. Participants had no known history of neurological events, no signiWcant visual problems, and were able to extend the knee repetitively with a weight added to the ankle. The local institutional review board approved these experimental procedures. Subjects were notiWed of the task demands before the study, and informed consent was obtained.

347 Table 1 Condition order variability for the current study Subject number

Order of study

S1-2 S3-4 S5-6 S7-8 S9-10

NWNT–NWT–WNT–WT WNT–WT–NWNT–NWT WT–NWT–WNT–NWNT NWT–WT–NWNT–WNT WT–WNT–NWT–NWNT

Experimental procedures Subjects were seated in an upright armchair raised on a platform such that the legs were freely hanging and the feet did not touch the Xoor. As well, shoes were removed and shorts were worn to further allow for comfort of the legs and assist in recording muscle activity during the study. The right leg was Wtted with a rigid ankle-foot orthosis to restrict extraneous dorsi-/plantar Xexion and in-/eversion of the ankle. Subjects were instructed to perform self-paced extensions of the right knee under four experimental conditions. The Wrst was a simple knee extension to a comfortable height without any added sensory elements (NWNT). A second experimental variant was knee extension to a comfortable height with a 3.2 kg cuV weight strapped to the ankle (WNT). Third, subjects were asked to extend the knee without a weight to a stationary target (a colored cotton tipped applicator) in the right visual space placed 40 cm above the Xoor and 75 cm away from the edge of the chair (NWT). The knee was extended until a target on the foot was aligned to the stationary target. The foot target (a second colored cotton tipped applicator) was aYxed to the surface localization of the right Wfth metatarsophalangeal joint, consistent with the location of the Wfth metatarsal head on the ventral surface of the foot. In the fourth variant, subjects performed knee extension to the same visual target with the 3.2 kg ankle cuV weight (WT). Each condition was performed for 10 min, in successive 5 min blocks with 2 min of rest between blocks. To control for any order eVects, Wve order variants were made with two subjects per variant (Table 1). During the target conditions, subjects were asked to Wxate on the target continuously. During the non-target conditions, the stationary target was removed from sight and subjects were to Wxate on a point in horizontal space. As well, during the non-target conditions, subjects were asked to make brisk movements to a similar, comfortable height. During the target conditions, subjects were asked to make brisk, but accurate movements to the target. For each movement, subjects were instructed to move once every 10–15 s and refrain from blinking, swallowing, and making other extraneous movements. Subjects were given approximately 2 min of rest between conditions.

EEG and EMG recordings EEG signals were recorded using a standard 32-channel tin electrode cap (Electro-cap, Eaton, OH, USA), and EEG data were captured using a 32-channel EEG ampliWer with Synamps2 and recorded in the Acquire module of Scan 4.2 (Neuro Scan Labs, El Paso, TX, USA). EEG was referenced to the right ear and recorded at DC-200 Hz with a sampling rate of 1,000 Hz. Surface EMG was recorded from the vastus medialis and vastus lateralis of the right leg to determine movement onset. EMG was Wltered at 5–200 Hz with the sampling rate of 1,000 Hz. Data processing and analysis Initial analysis was made using the oVline Edit feature of Scan 4.2. OVline, EEG was Wltered at DC-30 Hz for analysis. Based on the onset of the EMG, epochs of the continuous data were made from ¡4.5 s before to 1 s after EMG onset. Upon visual inspection, epochs with artifacts were rejected. Baseline was corrected for ¡4.5 to ¡4.0 s before EMG onset. Averages per person and per condition were made to determine MRCP onset and amplitudes. Further analysis was performed using MATLAB version 7.0.1 (Math Works, Inc, Natick, MA, USA). Onset of the MRCP was identiWed for each condition as the Wrst time point when the signal deviated from a 95% conWdence interval from baseline for 500 ms. Analysis was focused on a priori selected areas. Because of the extensive spatial topography of individual electrodes in a standard 32 channel montage, regions of interest were not used. Instead, four electrodes of interest were chosen for analysis: motor, CZ; mesial premotor, FCZ; parietal, P3; mesial parietal CPZ. These electrodes were speciWcally chosen as they provide assessment of not only motor control from electrode CZ, but aspects of higher-level motor control including visual feedback (anterior cingulate, electrode FCZ) (Debaere et al. 2003), visuospatial attention (precuneus, electrode CPZ) (Natale et al. 2006), and planning in object-centered space (posterior parietal, electrode P3) (Grefkes and Fink 2005), all eVects that

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Fig. 1 Single subject average MRCP recorded from electrode CZ (mesial motor area) for each condition tested

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this study design sought to characterize. Electrodes such as C3 and C4 (over the hand motor areas) are commonly used in such analyses, but are outside of our current interest as we seek to evaluate primary leg motor areas and high level sensory processing. All electrodes were used to obtain topographical maps of the distribution of each component of MRCP. Two factor analysis of variance (ANOVA) with four levels (four conditions and two time periods) was performed on each electrode of interest to assess diVerences in the MRCP amplitudes during the two consecutive 1,000 ms blocks prior to EMG onset for each condition. Post hoc Bonferonni corrected t tests were used to determine the signiWcance of the eVect. The analysis was performed to detect signiWcant diVerences between the diVerent task-type conditions across the time of interest within the same electrode of interest.

Results MRCP onset identiWed over electrode CZ was similar for each condition, beginning at ¡3.7 s before EMG onset. An example average MRCP of one subject is seen in Fig. 1. Overall, the MRCP began with an initial slow positive BP (Fig. 2). This was followed by the beginning of the NS⬘ period appearing at around ¡0.75 s before EMG onset for the NWNT, WNT, NWT conditions, and at ¡0.45 s before movement for the WT condition. Based on the voltage plots, the transition from BP to NS⬘ was delayed for each condition based on the timing of a change from the slope derived from the BP at ¡1.2 s before EMG onset (dashed lines, Fig. 2). Figure 3 displays the head maps for each condition. Repeated measures ANOVA across conditions revealed a signiWcant main eVect of condition for electrodes CZ (F = 12.43, P = 0.0001), P3 (F = 22.82,

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P = 0.0001), and CPZ (F = 4.8, P = 0.003), and FCZ (F = 42.84, P = 0.03). As well, a signiWcant main eVect of time was seen for CZ (F = 5.77, P = 0.02), FCZ (F = 8.08, P = 0.005), but not P3 (F = 0.1, P = 0.9) or CPZ (F = 0.15, P = 0.7). There was no interaction eVect present for any electrode. Averaging based on the bootstrapping technique (Efron and Tibshirani 1993) repeated 1,000 times validated the results at each electrode of interest and condition. To assess how conditions diVered, post hoc Bonferonni corrected t tests were computed at each electrode within the two 1,000 ms time bins for comparison during the pre-movement time of interest. For the Wrst time bin at electrode CZ (mesial motor area), the NWNT condition showed a signiWcantly smaller potential than either unimodal condition (NWT, P = 0.0001; WNT, P = 0.005) and the bimodal condition (WT, P = 0.00001). The unimodal conditions showed no signiWcant diVerences from each other (NWT compared to WNT, P = 0.43). Both unimodal conditions were signiWcantly smaller than the bimodal condition (NWT vs. WT, P = 0.0006; WNT vs. WT, P = 0.0002, Fig. 2). In the second time bin, the only signiWcant diVerences that existed were between NWNT and WT (P = 0.00001), and the unimodal versus bimodal conditions (NWT versus WT, P = 0.0001; WNT vs. WT, P = 0.0022). For the Wrst time bin at electrode P3 (posterior parietal cortex), the NWNT condition MRCP amplitude was signiWcantly smaller than the NWT condition (P = 0.0001) but not the WNT condition (P = 0.85). The NWT condition was signiWcantly larger than the WNT condition (P = 0.0001) and the WT condition (P = 0.00001). There was no statistical diVerence between the WT and the WNT conditions (P = 0.09, Fig. 2). For the second time bin, the NWNT condition was diVerent from the NWT condition (P = 0.007) but

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Fig. 2 Group (n = 10) averaged MRCP recorded over each electrode used in statistical analyses. Dashed lines indicate the onset of deviation of the waveform from a slope deWning the BP (transition from BP to NS⬘) Fig. 3 Spatial head plots of the group averaged MRCP seen at 4 time points for each condition. Each head represents an average of 500 ms

not the WNT condition (P = 0.74). The NWT condition showed no Bonferroni corrected statistical diVerence from the WNT (P = 0.01) and WT (P = 0.01) conditions. There was no statistical diVerence between the WT and the WNT conditions (P = 0.07).

For the Wrst time bin, at electrode CPZ (mesial parietal cortex), three comparisons showed signiWcant diVerences. MRCP amplitude in the WT condition was signiWcantly larger than any other condition: NWNT, P = 0.0008; NWT, P = 0.004; WNT, P = 0.0002 (Fig. 3).

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The same statistical diVerences also existed at the second time bin. The WT condition was signiWcantly diVerent from the NWNT (P = 0.001), NWT (P = 0.002), and WNT conditions (P = 0.001). For the Wrst time bin at FCZ (mesial frontal cortex), the WT condition was signiWcantly larger than any other condition: WNT, P = 0.002; NWT, P = 0.004; WNT, P = 0.0008 (Fig. 2). In the second time bin, however, these diVerences failed to meet Bonferroni corrected thresholds for signiWcance (WT vs. NWNT, P = 0.01; WT vs. NWT, P = 0.05; WT vs. WNT, P = 0.02). To further assess any spatial distinctions between each condition, principal components analysis (PCA) was run on all the epoched datasets each condition for a time period of ¡1.5 to ¡0.5 s before movement onset. This range was chosen as it was the time frame of NS⬘ and BP transition, thus we should be able to detect this component from noise. Figure 4 shows the

topography and contribution of the overall variance of the components. In the NWNT condition, the largest component consisted of a radial dipole localized over the mesial frontal area, which accounted for 91% of the variance. This component was similar in the WT condition. In the NWT condition the localization of the maximal component is more anterior, and accounts for 95% of the variance. In the WNT condition, the maximal mesial frontal component accounted for much less of the variance (74%). A second radial component localized to the mesial frontal area was seen, which accounted for 13% of the variance in the WNT condition and to a lesser extent in the other conditions. In each case, the Wrst component shows transitions to that appear to reXect the shift from BP to NS⬘, however these appear to be later in the targeting conditions (NWT and WT). The second component generally showed slowly drifting activity.

Fig. 4 Head plots of the two principal components that accounted for the most variance in each of the datasets. Below each headplot, waveforms of the components illustrate the changes in

deXection of the waveform (arrows) particularly for the Wrst components that contributed most to the potentials seen

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Discussion MRCPs can distinguish between task complexity (Cui and Deecke 1999; Wheaton et al. 2005) and increasing levels of motor demand (Slobounov et al. 1999, 2004). While these studies have been restricted to the upper extremity, we chose to evaluate the eVects of task complexity, by increasing task-speciWc sensory demand, on cortical activation during knee movement. Recordings from the motor cortex revealed increased amplitude based on increasing numbers of added sensory modality (no added modality, unimodal, and bimodal sensory addition), but not between the unimodal tasks (NWT vs. WNT). The posterior parietal cortex pre-movement amplitudes diVered based on the presence of the target, where both the NWNT and WNT conditions showed smaller MRCP than the NWT and WT conditions. The mesial frontal and parietal cortical premovement amplitudes appear to diVer based on higher levels of absolute cortical demand, where baseline and unimodal tasks were signiWcantly smaller than the most demanding WT task. These demonstrate that kneespeciWc cortical representations of motor control spread over wide areas of the cortex, with incremental increases in activations commensurate with greater task complexity. Several considerations and limitations to the paradigm should be considered. Because there were four conditions studied in this paradigm, we felt it essential to control for any order eVects. Due to the large number of possible orders among the four conditions, we chose the orders based on a mix of condition type (Table 1). The existence of other orders may inXuence the results, which we are unable to fully assess. We limited our analysis to the pre-movement period of the MRCP, as it is likely that added weight and targeting would alter movement time between task conditions. Additionally, the movement is biphasic, including both extension and Xexion of the knee, and likely involves substantial musculature of the posterior compartment of the quadriceps, from which we did not record EMG. Thus, if we analyzed post-EMG onset potentials, it would be impossible to compare slow, deliberate accurate extension in the WT condition with hold and release of extension (or deliberate Xexion) in the NWNT condition. For this study, we were unable to quantify accuracy of the knee movement in the targeted conditions. However, each subject was repeatedly encouraged to align the Wxed target in space to the target on the foot as closely as possible. As well, each subject was visually monitored to be sure that they were Wxated on the markers, and attempting to perform the task correctly. As the pre-movement components of

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the MRCP likely relate to intention (Toma and Hallett 2003), we suggest that this measure reliably assesses intention without the necessity of detecting targeting error. The organization of the primary sensory and motor cortices can be conceptualized as containing overlapping representational maps of the sensorimotor apparatus of the body (Graziano et al. 2002). Brain plasticity can involve local reorganization of these representational maps. The ability for cortical reorganization to occur as a result of altered sensory input has been observed in human sensorimotor cortex. For example, a ventral extension of activation during hand movement into the face area of motor cortex has been shown in some patients after capsular stroke (Weiller et al. 1993). Many areas of the brain associated with the processing of sensory information may directly aVect motor function and be useful in restoring motor function from the lesioned motor cortex (Galea and Darian-Smith 1994). This eVect has been seen in hand recovery after stroke, and suggests extensive recruitment of cortical networks in restoration of motor function (Pineiro et al. 2001). The present work demonstrates that by modulating task related sensory constraints at the leg, we are able to elicit speciWc activity in non-primary motor areas that are known to contribute to functional recovery after stroke. Results from this research are important for developing a foundational understanding of lower extremity motor control, and how region speciWc cortical activity may be modulated by sensorimotor-related constraints. Perhaps rehabilitation strategies that exploit task-related CNS processing can be eVective, as they use the manipulation of rich sensory environments as a potential means to activate broad regions of brain to facilitate leg motor control. Because all movement conditions in this study are predicated on a simple monoarticular knee extension in the sagittal plane, we are able to manipulate the number and type of sensory feedback modalities, thereby determining the inXuence of sensorimotor processing complexity. Our deWnition of complexity is based on the understanding that by adding a constraint to even a simple movement, here a visual target or additional mass, the underlying structure of the movement has been made more complex from the baseline movement (NWNT). Furthermore, by increasing the number of constraints (0 < 1 < 2), we consider that the complexity of a multi-constraint movement (WT) has been increased over a movement with a single constraint (NWT or WNT). This increasing level of complexity is similar to that seen in other studies assessing motor control (Winstein et al. 1997).

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The BP is commonly a slow rising negative potential. In this study, we consistently saw an increasing positive BP and a decreasing positive (increasing negative) NS⬘, preceding the traditional negative MP. The basis for the negativity commonly seen is that thalamocortical aVerents synapse onto superWcial apical dendrites which cause a superWcial negative and deep positive dipole (Toma and Hallett 2003). Electrocorticographic recordings of the brain, which involve electrode grids attached subdurally, will often show potential variants, such as the positive BP (Hamano et al. 1997; Yazawa et al. 1997; Yazawa et al. 1998; Matsumoto et al. 2003). Such potentials are not readily seen in EEG recordings, even in recordings during foot movements, similar to our experimental design (Shibasaki et al. 1981). These distributions could be reXecting a Weld of a tangential dipole which is actually located in a distant area. This eVect is unlikely as there is no negative representation occurring at other regions of the headmap (Fig. 3). It is possible that the positive potential may be reXective of a dipole orientation where we see surface positive initially (BP), which rotates to eventual negativity (NS⬘, MP), as the NS⬘ is a negativegoing potential in this result. Since this pattern was present in each subject and for each condition, and stable based on the bootstrapping procedure, we feel that this is consistently reXected in knee movements. Future analysis (i.e. dipole localization) must be done to better evaluate the possibility of rotating dipoles in mesial brain activation. The results indicate that the leg motor area (CZ) shows increasing activity based on the number of taskspeciWc sensory adaptations, but not type of modality, where increasing numbers of modalities revealed increasing pre-movement amplitude. We suspect that in the unimodal conditions, the leg must move more deliberately compared to the condition where no added sensory modality is placed on the movement. As well, in the bimodal condition, the leg reaches a maximal amount of sensorimotor demand in this study, which involves the most motor cortex activity. This is consistent with the notion that the amount of motoric demand speciWcally inXuences the amount of motor cortex activity (Nakai et al. 2003). While this eVect was seen, we must consider what neuronal substrate may reXect changes in weight and vision over the motor cortex. It has been suggested that increasing force robustly increases activation of the motor cortex (Slobounov et al. 1999). It may be that there were two diVerent changes occurring over the motor cortex in the two-unimodal conditions, both of which may modulate the amplitude of the potential with diVerent neuronal mechanisms. One, involving

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increased musculature in response to weight where the task is internally guided (WNT), and secondly, increased premotor cortical planning required for appropriate targeting where the task is externally guided (NWT, as will be discussed later). PCA analysis revealed two components over the mesial motor area (73.9 and 13.3%) in the WNT condition, which is unlike the single main component seen for the NWNT and NWT conditions. Hence, the added weight may generate an added mesial component to assist in motor generation, which is critical to increasing the capability of the limb to lift the added mass. This increased motor activity may not be required in visual tasks, which may involve other areas. While increases in motor demand and increases in targeting-related activity (as will be discussed later) cause changes in motor cortex activity reXected in EEG, it is postulated that the main modulator of weight representation is in the motor cortex. Targeting may also modify motor cortex amplitude, as a more anterior main component was seen for the targeting condition, which may aVect recordings over CZ. We suggest that other non-primary motor areas largely responsible for variations in type of sensory modality representation. Higher MRCP pre-movement amplitudes were seen over the posterior parietal cortex (P3) in conditions where targeting was necessary (NWT and WT). It is known that the posterior parietal cortex plays a large role in creating a spatial representation of the outside world and planning movements in object-centered coordinates (Grefkes et al. 2004; Grefkes and Fink 2005). We suspect that this trend holds for our lower extremity task as well, where the movement is now externally guided. Making a movement to a speciWc part of visual space requires accurate transformation of visuospatial coordinates into a body-centered representation which can be used to move to the target. Appropriate movement planning in this task necessitates the repeated formation of accurate egocentric visuospatial representations, a process thought to mainly involve the posterior parietal cortex (Buneo et al. 2002). The mesial parietal (CPZ) and mesial premotor (FCZ) areas display signiWcant activation in the most complex condition (WT) compared to the other condition. The spatial map of this condition clearly involves activity of posterior and frontal areas (Fig. 3). Parietal activity may be enhanced activity of the precuneus, and frontal activity by the anterior cingulate, which are thought to be involved in motor tasks that are particularly demanding (Crutcher et al. 2004; Wenderoth et al. 2005). In this task, both precise computation of visual coordinates and the compensation for added

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weight are necessary for accurate performance. The cingulate has been speciWcally implicated in externally guided movements requiring visual feedback, and may play a role in movement optimization compared to movements where no targeting is required (Debaere et al. 2003). It is also conceivable that the frontal activity may be related to additional attention dedicated to the task. A parietofrontal network (involving precuneus and superior frontal sulcus) is thought to be involved in movements demanding visuospatial attention (Natale et al. 2006). Research has shown that an area adjacent to the intersection of the precentral sulcus and the superior frontal sulcus can mediate visuomotor hand movements (Amiez et al. 2006). Taken together with evidence that identiWes parietal and premotor activity with movements demanding visuospatial accuracy (AstaWev et al. 2003), we suggest that both areas are predominantly involved in this type of task. However, the PCA results may depict an alternative story. This analysis revealed a mesial radial component (similar to NWNT) which accounted for 95% of variance. Kristeva et al. (1990) found that unilateral movements were sensitive to inertial loading, while bilateral movements were not. An application of this Wnding may be that movements involving higher aspects of motor control supersede that needed for adjustment to inertial loading. It may be that absolute motor demand in the WT condition may produce increased motor cortex activation above the eVects of the unimodal sensory manipulation, thereby creating a large motor component. This large motor activation may cause widespread voltage conduction to other areas, such as parietal and frontal. Nevertheless, we are able to diVerentiate between conditions based on the MRCP over many cortical regions. Several other studies have shown that parietal and premotor areas are active during similar task conditions (Ikeda et al. 1999; Toni et al. 2001; Matsumoto et al. 2003; Contreras-Vidal and Kerick 2004). It is likely that the PCA analysis is not able to diVerentiate between two non-orthogonal activations (mesial parietal and mesial premotor). Thus, the two activities appear as a single, broad, mesial sensorimotor component. It remains to be seen with further analysis (such as dipole source modeling) if mesial parietal and premotor activities are inXuencing the voltage seen in electrodes overlying these regions as we suspect. Movement rehabilitation therapy often involves sensorimotor facilitation of the limbs during movement, as well as other movement techniques designed to induce speciWc visual and/or motor adaptations. While many studies have suggested that both modalities may augment cortical activity for movements of the upper limb, little work has been done to evaluate this in the lower

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extremity. Our results indicate that while the use of one modality or two modalities (speciWcally weight and visual target) may signiWcantly modify motor cortex activity during knee movement, such tasks also involve many brain areas necessary for successful accomplishment of the more complex task. Such additional task demands create a rich sensory environment which may facilitate restoration of damaged networks and positively inXuence motor recovery (Jones et al. 1999; Biernaskie and Corbett 2001; Farrell et al. 2001; Nygren and Wieloch 2005; Fung et al. 2006). Further investigation into the eVectiveness of these types of task manipulations must be made to determine their usefulness in motor recovery of lower extremity motor function. Acknowledgments The study was supported by the Baltimore Veterans AVairs Geriatrics Research, Education, and Clinical Center; Veterans AVairs Research Enhancement Award Program (REAP); and the Veterans AVairs Center for Excellence in Exercise and Robotics for Neurological Disorders.

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