We recorded spike and LFP data from microelectrode arrays permanently implanted in each of two monkeys performing reach-to-grasp movements. Two sessions were analyzed from each monkey to assure that the findings reported here were not idiosyncratic to a particular session in either monkey. summarizes the number of single-unit and multi-unit spike recordings, LFP channels and successful trials analyzed in each of the two sessions from each monkey. Whereas for monkey Y all four movement types were incorporated in the analyses described below, a noise transient occurred whenever monkey X grasped the peripheral coaxial cylinder, and for monkey X we therefore excluded this movement type from analysis.
Summary of total number of spike recording (single unit and multi unit), LFP channels and successful trials for each recording session.
LFP Activity in the Time Domain
To examine LFP modulation in the time domain, we formed motor evoked potentials (mEPs) for each channel in each recording session by averaging LFP amplitude across the multiple successful trials of a given movement type with the data aligned on a particular behavioral event. illustrates such mEPs for one channel from array G (top row) and one from array J (bottom row), averaged across all correctly performed trials of each of the four movement types in session Y0225. Separate mEPs for each movement type (sphere-blue, perpendicular cylinder-green, push button-red and coaxial cylinder-cyan) were formed for data aligned at the times of the cue (), the onset of movement () and the beginning of the static hold (). In each frame, a solid vertical line marks the time of alignment, and dashed vertical lines mark the average times of the other two behavioral events.
Variation in motor evoked potentials
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.
For a given movement type, mEP waveforms were generally similar in the two electrodes from different arrays shown in , but differences were observed as well. During movements to the push button (red), for example, positive peaks occurred just after the onset of movement and again prior to the static hold, but the latter peak was relatively larger in the electrode from array J (bottom) than in that from array G (top). Such differences between electrodes suggested some degree of spatial variation in mEPs within the M1 upper extremity representation.
LFP Activity in the Frequency Domain
To examine LFP modulation in the frequency domain, we formed separate time-frequency plots for each channel in each recording session. Time-frequency plots for each movement type were formed separately with the data aligned on each behavioral event. illustrates such plots of LFP activity for the same two single channels in monkey Y’s arrays G and J from which the mEPs are shown in . Here, separate time-frequency plots are shown for each movement type, all aligned at the onset of movement (OM, solid vertical line) with dashed vertical lines marking the average times of the cue (Cue) and static hold (SH). These time-resolved power spectra showed a strong increase in LFP power in the 1–13 Hz range that began promptly after the cue, continued through the onset of movement, and then declined between onset of movement and static hold. A broader increase in LFP power in the 60–170 Hz range began after the cue but before the onset of movement, and continued with variation in intensity to the beginning of the static hold, during which time the monkey reached to and grasped the target object, and then declined afterwards. In contrast, power in the 16–40 Hz range decreased before the onset of movement, and returned to baseline only several hundred milliseconds after the beginning of the static hold. The decrease in 16–40 Hz power was stronger in monkey X than in monkey Y (data not shown). Similar results were observed in the population averages.
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. 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.
Modulation of LFP power in seven frequency bands
Movement-related changes in normalized power were largest in the 1–4 Hz band in both monkeys, increasing up to fourfold by the onset of some movements. Early increases also occurred in power in the 5–13 Hz band, sometimes larger than twofold, though these increases tended to be stronger in monkey X than in monkey Y. (Note that because the calculation of power at each time-point included data from 125 ms before to 125 ms after that time-point, even the time-point nominally 50 ms before the cue incorporated data from up to 75 ms after the cue, and hence could be significantly different from baseline). Power in the 16–24 Hz and 25–40 Hz bands decreased before the onset of movement. These decreases were deeper, faster and returned to baseline more quickly in monkey X than in monkey Y. Also, compared to other bands, normalized power in the 16–24 Hz and 25–40 Hz bands showed relatively little variation depending on movement type. Power in the 41–59 Hz bands was relatively flat in both monkeys. In the 62–98 Hz and 100–170 Hz bands, however, normalized power again showed up to twofold increases by the onset of movement. Movement dependent variation in these high frequency bands persisted longer into the final static hold period than in the low frequency 1–4 Hz and 5–13 Hz bands. Overall, larger modulation of normalized LFP power, with more variation related to movement type, appeared in the lower (1–4 Hz and 5–13 Hz) and the higher (62–98 and 100–170 Hz) frequency bands than in mid-frequency (16–24, 25–40, and 41–59 Hz) bands. Similar results were also observed for the other arrays in both recording sessions from each monkey.
Neuron Spiking Activity
As enumerated in , more spike recordings were available than LFP channels in each recording session. Because previous studies have indicated that multi-unit recordings can provide decoding of direction equivalent to that of single-units (Liu and Newsome, 2006
; Chestek et al., 2009
), in the present analysis we treated single-unit and multi-unit spike recordings equivalently. illustrates spike recordings from two single units recorded in session Y0225, one from array H (2.0 mm deep to the hemispheric surface) and one from array I (1.5 mm deep). Histograms are shown for each movement type aligned separately at the cue, onset of movement and static hold. To permit more direct comparison with frequency domain LFP activity (e.g. ), these histograms were formed using the firing rate averaged in a 250 ms window centered at each 1 ms time step. Population averages showed that, like the two units illustrated in , spiking activity generally increased promptly after the cue, reached near maximal levels by the onset of movement and was declining by the time of the static hold, with variation depending on movement type (data not shown). The two single units illustrated in showed many similarities, discharging a short burst during movements to the coaxial cylinder (cyan), for example, but showing more sustained activity during movements to the push button (red). Clear differences between the two units were present as well. During movements to the push button (red), for example, the firing rate of the single unit from array H (top) showed an initial peak before the onset of movement (OM) which was not present in the single unit from array I (bottom).
Comparing Different Neurophysiological Signals
The neurophysiological signals examined here—LFP activity in the time domain, LFP activity in the frequency domain, and neuron spiking activity—are not directly comparable. Nevertheless, each signal type varied depending on the movement performed, and therefore could contain discriminable information on movement type. We therefore applied LDA to compare the extent to which different movement types could be discriminated using the different neurophysiological signals, quantifying the discriminable information available in each as decoding accuracy. illustrates decoding accuracy in the 250 ms centered on the beginning of the static hold (SH) as a function of the number of channels for each type of signal. For all signal types, decoding accuracy typically increased toward an asymptote as more LFP channels or spike recordings were included.
Decoding accuracy as a function of the number of input channels
For LFP activity in the time domain () 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 ). 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 , 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 (), 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.
illustrates decoding accuracy as a function of the number of spikes recordings used, including both single-unit and multi-unit recordings. Though more than 30 spike recordings were available in each session, decoding accuracies using features from 30 spike recordings were already close to 100%, and the illustrated curves therefore have been truncated at this point. Despite the difference in chance levels between the monkeys (25% for monkey Y; 33% for monkey X), these curves were remarkably similar for both sessions from the two monkeys. In both monkeys, decoding accuracy with a given number of channels was higher using spike recordings than using LFP power in either the 1–4 Hz or 100–170 Hz bands (). In monkey Y but not monkey X, decoding accuracies also were higher using spike recordings than using LFP amplitude in the time domain ().
Spatial Variation in Decoding Accuracy
In addition to variation dependent on movement type, each neurophysiological signal type also showed variation from electrode to electrode, as illustrated in , and . We therefore examined the possibility that such variations were not random, but rather depended on the spatial location of recordings within the M1 upper extremity representation. Given that for each neurophysiological signal we obtained decoding accuracies substantially greater than chance using a relatively small number of channels, we used decoding accuracy in subsets of channels to investigate the extent to which movement-type information varied depending on the spatial location of recording sites. Separate analyses were performed to examine spatiotemporal distribution down the anterior bank of the central sulcus, and along the central sulcus from anterolateral to posteromedial.
Distribution Down the Anterior Bank of the Central Sulcus
To examine variation down the anterior bank of the central sulcus, LDA was performed using channels grouped by depth below the hemispheric surface, as determined by the length of different electrodes on each array. In monkey X each array had electrodes from 1.0 to 6.0 mm in length, but in monkey Y, the shortest electrodes were 1.5 mm long and arrays H and J each had 5 electrodes > 6.0 mm in length. For analysis of distribution in depth, we therefore excluded data from these 10 extra-long electrodes in monkey Y. (We subsequently repeated the analysis including data from the extra-long electrodes and obtained similar results.) Recordings in each monkey then were divided into two groups at an electrode length that provided similar numbers of electrodes in the shallow and deep groups for each monkey. In monkey Y the shallow group of recordings were obtained from electrodes 2.0 to 3.5 mm long, while the deep group were obtained from electrodes 4.0 to 6.0 mm long. In monkey X the shallow group were from electrodes 1.5 to 3.0 mm long, while the deep group were from electrodes 3.5 to 6.0 mm long.
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.
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 ). 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.
Spatiotemporal modulation of neural activity in depth down the anterior bank of the central sulcus
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 () and 1–4 Hz power (), 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 () or spike recordings () 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 ().
We therefore examined the size of the depth effect by calculating η2
for the DEPTH factor in each two-way ANOVA (Stark et al., 2007
). Effect size, η2
, was calculated as the ratio of the sum-of-squares variance in decoding accuracy attributable to depth to the total sum-of-squares variance, and expressed as a percentage. Values of η2
for depth are shown in for data aligned at each of the three behavioral events using each neurophysiological signal type in each session. With few exceptions, the effect of depth was greatest on decoding accuracies obtained using 100–170 Hz power and spikes, still less with 1–4 Hz power, and least with LFP amplitude. Movement-type information thus was distributed relatively evenly to shallow and deep locations with LFP amplitude and 1–4 Hz power, but less information reached deep locations with 100–170 Hz power or spikes.
Effect size (η2) of DEPTH category on the decoding accuracy of different neural signals around different task events
Distribution Along the Central Sulcus
We also used decoding accuracy in subsets of channels to investigate the extent to which movement-type information varied depending on the spatial location of recording sites along the central sulcus. For this analysis, LDA was performed using channels grouped by their parent array. Because array I in monkey Y had only 5 useable LFP channels, to permit accurate comparisons only 5 channels from each array were used in an LDA. To evenly sample all the channels, therefore, the 5 channels used from the other 3 arrays were chosen randomly 100 times and the LDA was repeated for each randomly chosen set. Likewise, because array G in monkey X had only 6 useable LFP channels, LDA was performed for each array in monkey X using 6 channels. To evaluate temporal variation, LDA again was performed repeatedly in 50 ms steps with data aligned at the time of the cue (5 steps), onset of movement (5 steps), and static hold (19 steps).
shows the time course of decoding accuracy for each neurophysiological signal type in each session. Separate curves are shown for each array, as well as for LDA performed using all available LFP channels or spike recordings (). With LFP amplitude (), the decoding accuracy curves for the four arrays rose and fell relatively close together, often crossing one another and showing little systematic separation of different curves across time. Movement-type information contained in LFP amplitude thus was distributed relatively evenly along the central sulcus.
Spatiotemporal modulation of movement-related information along the central sulcus
In contrast, decoding accuracy curves obtained with spike recordings () tended to separate and remain separated for the four arrays, particularly in monkey Y, indicating that different levels of movement-type information were present at different locations along the central sulcus (). In monkey Y, arrays H (cyan) and J (red) tended to have the highest decoding accuracy, followed by array I (orange), with array G (blue) having the lowest. In monkey X, array I (red) showed a time-dependent variation, having decoding accuracy as high as that of array G (cyan) before the onset of movement, but falling close to the level of array H (orange) after the beginning of the static hold. In monkey X, spike firing rates at different locations along the central sulcus thus provided different levels of movement-type information at different times.
In the LFP frequency domain, array-dependent variation in decoding accuracy using 1–4 Hz power was intermediate between that seen with LFP amplitude and that seen with spike recordings (), whereas array-dependent variation in decoding accuracy using 100–170 Hz power was similar to that seen with spike recordings (). With 1–4 Hz power, the decoding accuracy curves tended to separate somewhat before the onset of movement and maintain their relative order through the static hold. The most lateral array in each monkey (array G in monkey Y, array F in monkey X) provided the lowest decoding accuracy. With 100–170 Hz power, the curves for different arrays separated before the onset of movement and maintained their separation through the static hold. Overall, movement-type information transmitted by LFP power thus was more dependent on array location in the 100–170 Hz band than in the 1–4 Hz band.
To better quantify these observations, we performed two-way analyses of variance using TIME and ARRAY as factors. These analyses were similar to the TIME × DEPTH ANOVAs performed above, but here the ARRAY factor had 4 categories for each ANOVA. In all 48 ANOVAs, each main effect and the TIME × ARRAY interaction all were significant (p < 0.00001 or p < 0.0015 after Bonferroni correction for 48 × 3 tests), confirming that decoding accuracy varied with time, with array, and that the variation with time depended on the array in all cases.
We then calculated η2 for the ARRAY factor to quantify the percentage of the variation in decoding accuracy attributable to array location for data aligned at each of the three behavioral events using each neurophysiological signal type in each session. Values of η2 shown in confirm that the effect of array location along the central sulcus was generally greatest for spike recordings and 100–170 Hz LFP power, less for 1–4 Hz power and least for LFP amplitude. In all sessions except X0917, spike recordings showed the greatest effect of array location around the static hold. LFP power in the 100–170 Hz band showed comparable array location effects around the onset of movement and static hold, whereas the other two signal types tended to show their greatest array effect around the onset of movement. Although discriminable information on movement-type thus was distributed relatively evenly by LFP amplitude, with 100–170 Hz power or spikes different locations along the central sulcus showed different levels of movement-type information.
Effect size (η2) of ARRAY on the decoding accuracy of different neural signals around different task events