A trial began when the monkey grasped the central cylinder (mounted coaxially on its mounting rod, which pointed directly at the monkey’s right shoulder), and pulled the cylinder toward itself approximately 1 cm against a small spring load. After a variable initial hold period during which the monkey maintained its pull on the central cylinder, a blue LED was illuminated next to the rod supporting one of the four peripheral objects. (The range of the initial hold period duration differed among sessions: Y0225: 1,034–1531 ms; Y0228: 1,034–1533 ms; X0917: 329–628 ms; X0918: 579–878 ms). Illumination of a blue LED (Cue) instructed the monkey to promptly release the central object, reach to and then grasp the indicated peripheral object. Release of the central object was considered the onset of movement (OM). Upon grasping the peripheral object, the monkey was required to rotate the sphere 45°, to pull the perpendicular cylinder, to depress the push button (12 mm diameter), or to pull the peripheral coaxial cylinder, each against a small spring load (Figure 1B). Appropriate manipulation of each object closed a microswitch, which was indicated to the monkey by illumination of a green LED, also mounted next to the rod supporting the peripheral object. The switch closure marked the beginning of a static hold (SH), during which the monkey was required to maintain the object in its final position for 1000 ms. Thereafter the blue LED was turned off. Such trials were considered successful, and the monkey received a food pellet reward (Bioserv). Following successful completion of a trial, the monkey was free to release the peripheral object and initiate another trial by again pulling on the central coaxial cylinder.

Several months after the present recording sessions had been completed, intracortical microstimulation (ICMS) was performed through each recording electrode. Prior to ICMS sessions, the monkey’s right side was shaved to facilitate identification of small muscle twitches. At the beginning of each ICMS session, the impedance of each recording electrode was measured (range: 0.4 to 2.7 MΩ at 1 kHz using an 18 channel impedance tester, MicroProbes for Life Sciences) to confirm that the electrode and current path remained intact. The monkey was lightly tranquilized with ketamine 5 mg/kg, and atropine 0.04 mg/kg was given to reduce secretions; both medications were repeated every 30 to 60 minutes as needed to maintain a tranquil state in which weak muscle twitches could be identified. ICMS was performed as the monkey sat in a primate chair with the right upper arm resting at its side and the elbow flexed to 90° with the forearm resting on a support. Conventional trains of 12 biphasic (0.2 ms per phase) constant current pulses at 333 Hz were delivered at 3 s intervals via an optically-isolated stimulator (BAK BSI-1). Stimulating current was monitored continuously via a high impedance amplifier (WPI DAM80) as the voltage drop across a 100 Ω series resistor. Current was gradually increased until a muscle twitch or an overt movement was observed by one investigator and confirmed by another, or until a maximum of 40 μA was reached. Threshold was measured as the current at which a response was evoked by 50% of stimulus trains. Each muscle twitch or movement evoked at threshold was assigned to one of the following categories: digits, wrist, elbow, shoulder, face, axial or leg.

Figure 2C shows the responses to ICMS evoked in each monkey. No response was obtained from several electrodes that presumably were not close enough to cortical layer V for a response to be evoked at 40 μA. ICMS confirmed that all four arrays in each monkey were situated in the M1 upper extremity representation. Furthermore, the responses obtained in both monkeys were consistent with the nested-horseshoe somatotopic organization of the upper extremity representation as defined with ICMS in previous studies of macaque M1 (Kwan et al., 1978; Park et al., 2001). A core of distal, digit representation extending down the anterior bank of the central sulcus in each monkey was flanked medially by progressively more proximal representation, and was flanked laterally by a narrower region of proximal representation.

Using the MP algorithm, we examined LFP activity from −1.047 s before to 1 s after three behavioral events: illumination of a blue LED (Cue); onset of movement (OM) when the monkey released the center object; and the beginning of the static hold (SH). All post-MP decomposition computations were performed using custom code written in MATLAB (Mathworks, MA). The MP decomposition yielded a 2048 (time) × 1024 (frequency) array of time-frequency values with a temporal resolution of 1 ms and frequency resolution of ~0.5 (1000/2048) Hz. This data array was further down-sampled by a factor of 4, yielding a temporal resolution of 4 ms and frequency resolution of ~2 Hz.

With LFP data aligned on Cue, OM or SH, power in a given time window was computed by averaging the energy within that time period (T) at a given frequency.

Although in each frame the mEP waveforms for different movement types generally were similar, separation of the traces indicated that within a single channel mEPs varied depending on the movement performed. The same was true of population average mEPs (data not shown). Variation in mEPs related to movement type tended to be least immediately following the cue, slightly greater by the onset of movement, and greatest around the time of static hold. All mEPs showed significant movement-type (4 or 3 levels for monkey Y or X, respectively) × time (3 levels; Cue, OM, SH) interactions (two-way ANOVA, p<0.01), indicating that at some time point(s) during the trials the amplitude of each LFP recording varied depending on the movement type.

In each channel, although similar patterns of LFP power modulation occurred during movements to the different objects, variation related to the different movement types also was evident. At the onset of movement, power in the 1–13 Hz range was particularly strong when movements were made to the perpendicular cylinder, for example, whereas approximately 100 ms prior to the beginning of the static hold power in the 60–170 Hz range showed a short burst when movements were made to the sphere. Furthermore, the time-resolved power spectra differed between channels. The burst of power in the 60–170 Hz range around the onset of movement was stronger in the electrode from array J (bottom) than in that from array G (top), for example, and was relatively more intense for movements to the sphere than for other movement types.

To examine frequency dependent variation in greater detail, for each movement-type we subdivided the frequencies from 1–170 Hz into seven bands and plotted normalized power in each band (relative to a baseline of 1) as a function of time. Figure 5 plots such normalized LFP power as a function of time for data averaged over the 8 channels from a one array in monkey Y (array H) and one in monkey X (array H) in a single session from each monkey. Similar plots were generated for each array in each session to compare more quantitatively how the movement-type related variation in LFP power was modulated depending on the frequency band.

For LFP activity in the time domain (Figure 7A) very similar decoding accuracies were obtained in the two sessions from each monkey, but decoding accuracies were systematically lower for monkey Y. With 5 channels, for example, decoding accuracy at the beginning of the static hold in monkey X was ~74%, but in monkey Y was only ~52%, rising with 20 channels to ~95% and ~68% in monkeys X and Y, respectively. One might attribute this discrepancy to the larger number of movements being decoded in monkey Y (4, chance level of 25%) than in monkey X (3, chance level of 33%). We therefore re-computed decoding accuracy for monkey Y’s two sessions using only the same three movement types decoded in monkey X (dotted curves in Figure 7A). While this increased the decoding accuracies for monkey Y, superior decoding accuracy still was obtained in monkey X using LFP amplitude in the time domain.

In the frequency domain, LFP power in different bands provided different levels of decoding accuracy. As illustrated for session X0918 in Figure 7B, decoding accuracy typically was highest in two bands: 1–4 Hz and 100–170 Hz. Although modulation of LFP power also was substantial in the 5–13 Hz and 62–98 Hz bands (Figure 5), across sessions these two bands provided lower decoding accuracies than the 1–4 Hz and 100–170 Hz bands (data not shown). The 16–24 Hz, 25–40 Hz and 41–59 Hz bands consistently provided the lowest decoding accuracies, often little better than chance even when using all channels. In further analyses (below) we therefore focused on LFP power in the 1–4 Hz and 100–170 Hz bands, treating each as a separate neurophysiological signal.

Even using these two frequency bands, decoding accuracies using LFP power in the frequency domain were lower than those obtained using LFP amplitude in the time domain. For example, decoding accuracies of ~52% were obtained with 5 channels using either 1–4 Hz or 100–170 Hz power, whereas accuracies of ~74% were obtained with 5 channels in the time domain. The same was true with larger numbers of channels. With 25 channels, for example, accuracies of ~72% and ~96% were obtained in the frequency and time domains respectively. Decoding accuracy thus was somewhat lower in the frequency domain than in the time domain.

For each neurophysiological signal in each session, we then performed LDA as a function of time separately for the shallow and deep groups in each monkey. To permit the most direct comparison, we used the lowest common number of channels across all four signal types and depth groups: 10 for monkey Y, and 13 for monkey X. If for any signal type, any group had a greater number of channels available, we randomly selected the lowest common number of channels from the larger set and repeated the LDA 100 times. LDA was performed repeatedly in 50 ms time steps with data aligned separately at the time of the cue (5 steps), onset of movement (5 steps), and the beginning of the static hold (19 steps). In the LFP frequency domain, we examined decoding accuracy using power in the 1–4 Hz power and 100–170 Hz bands separately. Because LFP power in the frequency domain was evaluated in 250 ms windows, for all signal types LDA was performed using data averaged in 250 ms windows centered at each 50 ms time step. Consequently, these analyses incorporate data up to 125 ms before and 125 ms after the nominal time-point.

Figure 8 shows the time course of decoding accuracy for each neurophysiological signal (columns) in each session (rows). Separate curves are shown for the shallow group (blue traces), the deep group (red traces), as well as for LDA performed using all available LFP channels or spike recordings (black lines, total LFP channel and spike recording counts as given in Table 1). In general, decoding accuracy rose promptly after the cue, was higher by the onset of movement, and achieved maximal values around the beginning of the static hold. Exceptions included 1–4 Hz LFP power in monkey Y and 100–170 Hz power in monkey X, where decoding accuracy achieved its highest levels around the onset of movement and tended to decline before the static hold. Decoding accuracy generally declined after the beginning of the static hold for all types of signal. The decline occurred earliest with 1–4 Hz LFP power, later with LFP amplitude, and tended to be only very gradual with 100–170 Hz LFP power or spikes. Indeed in monkey Y, decoding accuracy obtained with 100–170 Hz LFP power or with spikes remained at steady high levels throughout the final hold period. While information about movement type thus appeared rapidly after the cue in all signal types, it persisted longer after the beginning of the static hold in spikes and 100–170 Hz power than in 1–4 Hz power or LFP amplitude.

In addition to this temporal difference, movement-type information in the various neurophysiological signals also differed in spatial distribution down the anterior bank of the central sulcus. For LFP amplitude (Figure 8A) and 1–4 Hz power (Figure 8B), decoding accuracy curves for the shallow and deep groups rose and fell quite close together, although short epochs of separation were observed in some instances. Moreover, decoding accuracies obtained using either the shallow or the deep group attained values almost as high as those obtained using all available recordings. Movement-type information contained in LFP amplitude and in 1–4 Hz power thus was distributed quite similarly in both shallow and deep locations in the anterior bank of the central sulcus.

In contrast, decoding accuracies obtained with either 100–170 Hz LFP power (Figure 8C) or spike recordings (Figure 8D) rapidly became higher for the shallow than the deep groups and remained higher throughout the movement period. In monkey X, however, decoding accuracies obtained with the shallow group of spike recordings fell below accuracies obtained with deep recordings after the beginning of the static hold. In both sessions from both monkeys, using 100–170 Hz LFP power the shallow group provided decoding accuracies comparable to those obtained using all electrodes, while the deep group provided substantially lower accuracies. These observations indicate that both for 100–170 Hz LFP power and for spike recordings, more discriminable movement-type information was available close to the hemispheric surface than deep in the anterior bank of the central sulcus.

In addition to attaining different levels, in monkey X decoding accuracies obtained by the shallow and deep groups using 100–170 Hz LFP power also showed substantially different temporal evolution. In both sessions, decoding accuracies obtained with monkey X’s shallow group rose rapidly after the cue, reached maximal values near the onset of movement, fell somewhat by the time of static hold, and remained relatively flat thereafter. In contrast, decoding accuracies obtained with monkey X’s deep group rose slowly after the cue, did not reach maximal values until near the time of static hold, and fell more rapidly thereafter. Though less dramatic, decoding accuracies obtained with monkey X’s spike recordings also rose faster with the shallow group than with the deep group, and fell faster after the beginning of the static hold as well. Such differences in the temporal evolution of decoding accuracy were not observed in monkey Y. We speculate that these differences between the two monkeys might have been related to the tendency of monkey X to react and to move more quickly than monkey Y.

To further quantify the effect of depth down the anterior bank, we performed two-way analyses of variance using TIME and DEPTH as factors. Two-way ANOVAs were performed separately for decoding accuracies aligned at each of the 3 behavioral events (cue, onset of movement and static hold) for each of the 4 types of neurophysiological signal (LFP amplitude, 1–4 Hz LFP power, 100–170 Hz LFP power, and spikes), in each of the 4 sessions, totaling 48 (=3×4×4) two-way ANOVAs. The TIME factor had 5 categories (50 ms time steps) for cue-aligned ANOVAs, 5 categories for movement onset-aligned ANOVAs and 19 for static hold-aligned ANOVAs. (The last time-point of the static hold-aligned data was nominally 800 ms after the beginning of the static hold and thus incorporated data up to 925 ms after SH, with the hold period lasting 1000 ms.) The DEPTH factor had 2 categories, shallow and deep, for each ANOVA. In all 48 ANOVAs, the main effect of TIME, the main effect of DEPTH and the TIME × DEPTH interaction all were significant (p < 0.00001, or p < 0.0015 after Bonferroni correction for 48 × 3 tests), indicating that decoding accuracy varied with time, with depth, and that the variation with time depended on depth in all cases. Shallow versus deep differences thus were significant even when the difference was relatively small, as for 1–4 Hz LFP power aligned on the cue (Figure 8B).

Two factors may have contributed to this difference in discriminable movement-type information between shallow and deep recordings. First, the transition between Brodmann’s area 4 and area 3a occurs in the depth of the central sulcus. Although single units related to individuated finger movements typically are recorded at depths up to 6 mm down the anterior bank (Schieber and Hibbard, 1993; Poliakov and Schieber, 1999; Schieber and Rivlis, 2005), the transition from area 4 to area 3a may occur at depths as shallow as 4 mm (Park et al., 2001; Rathelot and Strick, 2006). Our deep group thus might have included a substantial fraction of recordings from area 3a, which despite the strong proprioceptive input to area 3a, might not be as informative about movement type as 100–170 Hz power or spike recordings from area 4. That the shallow and deep groups provided comparable decoding accuracies using either LFP amplitude or 1–4 Hz power then would suggest that these signals extended across the boundary between area 4 and area 3a.

A second factor contributing to the effect of depth may be rostro-caudal regionalization within area 4. Squirrel monkeys have spatially separate rostral and caudal M1 zones; within each zone ICMS evokes digit movements more caudally and wrist movements more rostrally (Strick and Preston, 1982a). Under anesthesia, the rostral zone receives somatosensory input largely from deep muscle and joint receptors, whereas in the caudal zone somatosensory input is largely cutaneous (Strick and Preston, 1982b). In macaque monkeys, rostral, intermediate and caudal divisions can be distinguished anatomically within area 4 (Preuss et al., 1997). Although segregation of somatosensory inputs may not follow these regional boundaries in awake macaques (Wong et al., 1978; Lemon, 1981), the caudal portion of M1 in the anterior bank of the central sulcus contains the cortico-motoneuronal (CM) cells that make monosynaptic connections to spinal α-motoneurons, whereas the rostral portion on the crown of the precentral gyrus contains few CM cells (Rathelot and Strick, 2009). Whereas rostral M1 neurons show strong relationships to movement kinematics (Moran and Schwartz, 1999b, a; Schwartz and Moran, 1999); caudal M1 neurons show strong relationships to forces and movement dynamics (Kalaska et al., 1989; Sergio et al., 2005). In humans, the posterior region of area 4 shows more functional activation during motor imagery and attention to action than does the anterior region (Binkofski et al., 2002; Sharma et al., 2008). These regional differences may reflect underlying functional differences between rostral and caudal regions within M1.

This work was funded in part by the National Institute of Neurological Disease and Stroke (NINDS) R01 EB010100, the National Science and Engineering Research Council of Canada (NSERC), the DARPA Revolutionizing Prosthetics program (prime contract N66001-06-C-8005) and REPAIR program (prime contract N66001-10-C-2009). The authors thank Jay Uppalapati and Andrea Moore for technical assistance; Supratim Ray, Ernst Niebur and Piotr Franaszczuk for help with MP software; and Marsha Hayles for editorial comments.