Thesis

Exploration of Descending Pathways from Primate Primary Motor Cortex
Uncovered by Long-Latency Post-Stimulus Effects
By
Jean Fecteau
Submitted in Partial Fulfillment of the
Requirements for the Degree
Master of Science
Supervised by Professor Marc H. Schieber
Department of Biomedical Engineering
Arts, Sciences and Engineering
Edmund A. Hajim School of Engineering and Applied Sciences
University of Rochester
Rochester, New York
2016

ii
Biographical Sketch
The author was born in Fort Worth, Texas. He attended Rensselaer Polytechnic Institute
in Troy, New York, and graduated with a Bachelor of Science degree in Biomedical
Engineering, with a concentration in Biomaterials and a minor in Music. He began his
Master’s studies in Biomedical Engineering at the University of Rochester in 2014. He
pursued his research in neural control of arm and finger movements under the direction
of Marc H. Schieber. He was a TA for the undergraduate computational physiology
course in the fall of 2014.

iii
Abstract
Corticomotoneuronal (CM) connections, or monosynaptic connections between primate
primary motor cortex (M1) and motoneurons, have been known to exist for many
decades in the field of neuroscience (Fetz and Cheney, 1976). Using single-pulse
microstimulation in M1 and performing a stimulus-triggered average of EMG, one can
record the increase in firing probability of motoneurons, or post-stimulus effects (PStEs),
that arise from electrically activating these descending pathways (Cheney and Fetz,
1985). However, PStEs that arrive at the muscles with longer latencies are poorly
understood, and it is the goal of the present study to quantitatively characterize these
later effects. By modifying techniques previously used in spike-triggered averaging
(Poliakov and Schieber, 1998), and applying novel methods to recordings taken during
microstimulation of M1, we were able to identify a spectrum of long-latency effects up to
60ms after stimulation. Facilitatory PStEs were clearly bimodal, with the first group
occurring at a mean latency of 16ms ± 4ms and the second group occurring at a mean
latency of 46ms ± 5ms. Suppressive PStEs were also bimodal, with the first group
occurring at a mean latency of 14ms ± 2ms and the second group occurring at a mean
latency of 29ms ± 4ms.

iv
Contributors and Funding Sources
This work was supported by a thesis committee consisting of Professors Marc H.
Schieber (advisor) and Laurel H. Carney of the Department of Biomedical Engineering
and Charles J. Duffy of the Department of Neurology. Data used in this work was
provided by the Schieber Lab at the University of Rochester. All other work for the thesis
was completed by the student independently.

v
Table of Contents
Chapter Title Page
Chapter 1 Introduction 1
Chapter 2 Methods 4
Chapter 3 Results 16
Chapter 4 Discussion 24
References 31

vi
List of Tables
Table Title Page
Table 1 Forelimb Implantation Sites and Abbreviations 5
Table 2 Post-Stimulus Effect Group Statistics 21
Table 3 Stimulus-Triggered Average Frequencies 22
Table 4 Estimated Post-Stimulus Effect Latencies 28

vii
List of Figures
Figure Title Page
Figure 1 Diagram of Snippet Extraction for Baseline Estimation 8
Figure 2 Baseline Removal Process 9
Figure 3 Stimulus-Triggered Average Example 10
Figure 4 Examples of Varying Forms of Stimulus-Triggered Averages 13
Figure 5 Distribution of Post-Stimulus Effects by Array 16
Figure 6 Distribution of Post-Stimulus Effects by Muscle 17
Figure 7 Histogram of Post-Stimulus Effects 18
Figure 8 3D Scatter of Post-Stimulus Facilitations 19
Figure 9 3D Scatter of Post-Stimulus Suppressions 19
Figure 10 Binning of Post-Stimulus Effects 20
Figure 11 Histogram of Binned Post-Stimulus Effects 21
Figure 12 Assumed Interaction Between Post-Stimulus Effects 23

1
1. Introduction
The study of fine motor control has been an active area of research since the infancy of
neurology, however our techniques for studying the relationships between the neural bases of
these actions and the observable responses of effectors have only recently begun to more
directly probe the descending connections made between the brain and forelimb muscles.
Pioneering work done by Fetz et al (1976) developed the method of spike-triggered averaging for
uncovering the action of corticospinal (CS) cells on motoneurons. Because the activity of a single
cell on a motoneuron will not depolarize the motoneuron to a level sufficient to generate an action
potential, it is difficult to observe these connections directly. However, this action will, on average,
tend to increase the probability that the motoneuron will generate an action potential. By making
use of recordings made simultaneously from penetrating extracellular microelectrodes in the
primary motor cortex (M1) and electromyographic (EMG) activity from wire electrodes implanted
in forelimb muscles, it is possible to compile a spike-triggered average (SpTA) which captures
this increasing probability to fire as a result of the activity of an M1 cell. This is done by using
each M1 cell spike recorded as a temporal marker, extracting a fixed time segment of the EMG
recording around each temporal marker, and averaging the resulting EMG segments, producing a
SpTA. If a SpTA contains any significant deviations from baseline activity following M1 cell
spikes, referred to as post-spike effects (PSpEs), this can be taken as evidence that the M1 cell
investigated makes a connection with the motoneuron pool innervating the muscle investigated.
In addition to recording passively and waiting for cells to spike, the same microelectrodes
in M1 can be used to deliver small electrical stimuli, a technique referred to as intracortical
microstimulation (ICMS). However, as with the activity of single M1 cells, a stimulus applied of
insufficient strength or duration will not elicit visible action in muscle EMG. Thus, ICMS is
generally applied in prolonged bursts of electrical pulses at 100Hz or greater for several hundred
milliseconds (Griffin et al 2014). Although this approach makes responses salient enough to be
easily recorded and analyzed, there has been some dispute as to whether the patterns of activity
generated resemble patterns observed in natural activity. Recently, there has been evidence to

2
support the claim that this form of stimulation activates descending circuitry in a manner that is
independent of natural movement, a phenomenon dubbed “neural hijacking” in an attempt to
convey the degree to which the stimulation method alters observed muscle activity (Griffin et al
2014, Cheney et al 2012). Delivering single-pulse stimulation separated by approximately 100ms
restricts the spread of current to smaller areas compared to delivering stimulus trains, allowing for
a more controlled investigation of descending pathways (Cheney et al 2012, Cheney and Fetz
1985). Additionally, delivering single-pulse stimuli allows the use of a technique similar to spike-
triggered averaging, instead using the stimuli as the temporal markers for extraction of EMG
segments. The similarly named stimulus-triggered averages (StTAs) would then be examined for
significant post-stimulus effects (PStEs).
While spike-triggered averaging and stimulus-triggered averaging may at first glance
appear to be very similar, ICMS is far less specific with regard to the pathways that are activated
during the generation of recordings. In particular, spike-triggered averaging focuses on the
average effects of the activity of one cell, while ICMS activates a sparse and widely distributed
population of neurons in a sphere around the electrode (Histed 2009). This means that any EMG
activity averaged using stimuli as triggers will reflect a large “grab bag” of pathways descending
from the area of stimulation; there should be evidence of longer-latency activity from pathways
not generally observed when performing a spike-triggered analysis in addition to the
characteristic patterns of activity observed through spike-triggered averaging. However, the
difficulty faced in the inference of oligosynaptic or polysynaptic descending pathways has
deterred investigation into these phenomena whenever they happen to be observed. Even the
most fundamental step to exploring these effects has yet to be taken: there is currently no widely
accepted method for determining whether an observed long-latency deviation is statistically
significant, and therefore should be accepted as a real PStE. This is due to the fact that these
effects tend to be highly variable in comparison to the well-known short-latency effects, both in
onset latency and shape (as will be shown below).

3
In the present study, we endeavored to take the first step toward understanding the
nature and origin of these long-latency effects. Using single-pulse stimuli in Rhesus monkey
(Macaca mulatta) M1 and wire electrodes in the forelimb, we compiled StTAs to examine PStEs
occurring up to 60ms after the time of the stimulus. The statistical method described previously by
Poliakov and Schieber (1998) provided us insight and inspiration to develop our own method for
identifying statistically significant deviations occurring at any time after the stimulus. We
systematically examined the properties of long-latency PStEs, and discuss the neural pathways
that could have generated them.

4
2. Methods
2.1 Subject and Behavioral Task
Rhesus monkey L (weighing 9 kg) was the subject in the present study. All procedures
for the care and use of this non-human primate followed the Guide for the Care and Use of
Laboratory Animals and were approved by the University Committee on Animal Resources at the
University of Rochester, Rochester, New York.
The monkey performed a behavioral task described in detail previously (Rouse and
Schieber 2015) that required the monkey to reach for, grasp, and manipulate one of four objects.
The four objects—a coaxial cylinder, a perpendicular cylinder, a button, and a sphere—were
arranged at 45° intervals on a circle of radius 13cm, with an additional coaxial cylinder at the
center of the circle. Between blocks of trials, the entire apparatus was rotated about the center
object such that different peripheral objects were located at up to eight locations that spanned a
range from 0° (to the monkey’s right on the horizontal meridian) to 157.5° (to the left, 22.5° above
the horizontal meridian) in steps of 22.5°. This was done to dissociate the location to which the
monkey reached from the object the monkey grasped and manipulated.
The monkey initiated each trial by pulling on the central coaxial cylinder. After a variable initial
hold period (1000-2000 ms), blue LEDs were illuminated around the base of the horizontal rod
supporting one of the four peripheral objects, cuing the monkey to reach to, grasp, and
manipulate that object. Upon grasping, the monkey was required to pull the perpendicular
cylinder, pull the coaxial cylinder, push the button, or rotate the sphere. Correct manipulation of
the object would result in the closure of a microswitch, and green LEDs at the base of the
horizontal rod supporting the manipulated object were illuminated as long as the switch was
closed. The monkey then was required to hold the switch closed for 1000 ms to receive a water
reward. Trials of different objects were presented in a pseudorandom block design.
An error was marked for the following events: 1. The monkey failed to release the center object
within 1000 ms of the onset of the blue LED cue; 2. The monkey failed to contact the cued object
within 1000 ms of releasing the center object; 3. The monkey contacted a peripheral object that

5
was not cued; 4. The monkey failed to maintain the hold position on the peripheral object for 1000
ms. If any error occurred, the trial was immediately aborted, and the same object was cued on
subsequent trials until the trial was performed correctly. All aspects of the behavioral task were
controlled by custom software running in TEMPO (Reflective Computing, Olympia, WA).
2.2 Electromyographic (EMG) Recordings
EMG electrodes were made from Teflon-coated 32 gauge multi-stranded stainless steel
wire. The wires were tunneled subcutaneously and implanted in bipolar pairs for 16 forelimb
muscles, using an approach adapted from work done by Cheney and colleagues (Park et al.
2000), and described in detail elsewhere (Davidson et al. 2007). The tips of each bipolar pair of
recording electrodes were separated by 5-10mm, oriented along the long axis of the muscle. The
recorded muscles and abbreviations used are shown in Table 1. Muscles were categorized into 4
groups from proximal to distal in the forelimb.
Table 1: Forelimb Implantation Sites and Abbreviations; Forelimb muscles implanted and the
abbreviations used for each site. Muscles are categorized into 4 groups and organized from
proximal to distal.
Group Muscle Abbreviation
Proximal Arm
(shoulder and elbow)
Deltoid – anterior DLTa
Deltoid – posterior DLTp
Pectoralis major PECmaj
Biceps - short head BCPs
Triceps - lateral head TCPlat
Wrist
Flexor carpi radialis FCR
Flexor carpi ulnaris FCU
Extensor carpi radialis brevis ECRB
Extensor carpi ulnaris ECU
Extrinsic Hand
Flexor digitorum profundus – radial FDPr
Flexor digitorum profundus – ulnar FDPu
Abductor pollicis longus APL
Extensor digitorum communis EDC
Intrinsic Hand
Thenar muscle group Thenar
First dorsal interosseus FDI
Hypothenar muscle group Hypoth

6
Bipolar signals from the electrodes were differentially amplified with headstages (Plexon
HST/8o50, 20x gain, 30 to 30,000 Hz bandpass) and passed through a hardware preamplifier
(Plexon PRA2/EMG-16-002, 50x gain, 300 to 3,000 Hz bandpass, Plexon, Inc., Dallas, TX).
EMG signals then were sampled at 1000 Hz, amplified to a final gain of 1,000x to 20,000x
(National Instruments PXI-6071E), and saved to disc.
2.3 Single-Pulse Intracortical Microstimulation
Floating microelectode arrays (FMAs, MicroProbes for Life Sciences, Gaithersburg, MD)
were implanted in the cerebral cortex of the monkey, using procedures described in detail
previously (Mollazadeh et al. 2011). Six FMAs (16 recording electrodes each, impedance 0.5
MΩ), were implanted in the primary motor cortex (M1) along the anterior bank of the central
sulcus, intended to span the forelimb representation. After recovery from the surgical procedure
the monkey again performed the reach-grasp-manipulate task in daily sessions as EMGs were
recorded as described above. Single-pulse intracortical microstimulation (sICMS) was performed
at individual electrodes. Biphasic pulses (0.2 ms per phase, cathodal first) were delivered by a
TDT IZ2 controlled via an TDT RZ5 processor (Tucker Davis Technologies, Gainesville, FL) at
intervals distributed uniformly from 80 to 120 ms. Only one FMA was connected to the simulator
at a time, while the remaining FMAs were connect to spike recording channels of a Plexon
multiacquisition processor described in detail previously (Mollazadeh et al. 2011). Using Plexon’s
Sort Client, the stimulation artifact was discriminated as an event on one or more spike recording
channels and stored to disc along with the EMG data, so that these events times could be used
off-line as triggers for averaging. Pulses were delivered on a single electrode until ~5000 pulses
had accumulated (typically 7-10 minutes), and then the stimulation was switched to another
electrode. Once sICMS had been delivered to each electrode on a given FMA, the stimulator
was connected to a different FMA. Over several days sICMS was performed through each
microelectrode implanted in M1.

7
2.4 Stimulus-Triggered Averaging of Rectified EMG
For each stimulus time recorded, a snippet of rectified EMG (rEMG) activity from 15ms
before to 60ms after the stimulus was extracted, and a StTA was formed by averaging these
snippets. In the present study, stimulus times were recorded for 96 microelectrodes (6 arrays with
16 microelectrodes each) with 16 EMG recordings, although only 847 of a possible 1536 StTAs
were compiled. This was due to several cases in which overly noisy recordings were produced:
one entire array (16 electrodes) produced only noise, another array produced noise for 3 of 16
electrodes, and 5 of 16 muscles produced noise.
A sweep-selection operation was performed on the extracted rEMG segments to
eliminate contributions of rEMG time segments during which only noise was recorded. This was
done by computing the root mean square (RMS) of each rEMG snippet and comparing it to a
threshold level of activity. If the RMS value of a given rEMG snippet was below the threshold, it
was removed from the StTA. To determine the threshold, a 1s segment of rEMG containing no
stimuli was selected and its RMS was computed. The threshold was then set at 1.25 times this
value. In 33 cases where no such 1s segments were present, the following alternative method
was substituted. An estimate of average activity within each rEMG recording of length N samples
(recorded at 5kHz) was obtained by breaking the recording into k segments of length k samples,
where , taking the root mean square of each segment, and taking an average of these
root mean square values. This average root mean square value was used as the sweep-selection
threshold.
2.5 Removal of Curvilinear Baseline Trend
It was observed that the sweep-selected StTAs were distorted by an underlying
curvilinear baseline trend. In order to accurately examine StTAs for PStEs, we removed the trend
by constructing an estimate of the baseline trend in each StTA and subtracting it from the StTA.
Firstly, a second sweep-selected StTA was compiled in a method similar to the first, except with
the length of the rEMG snippets used extended by an additional Fs/20 + 1 samples both before

8
and after the trigger, where Fs is the sampling frequency of the rEMG channel (Fs=5kHz, 251
samples added before and after, 502 added samples total), and a flat moving average filter of
length 125 (Fs/40, roughly half the length of the added segments) was then applied to this second
StTA. The added length of the second StTA was used to confine edge effects of the filtering
operation to time regions not included in the original StTA. Figure 1 shows these time windows
graphically (brown), and figure 2A shows the resulting average.
Figure 1: Diagram of Snippet Extraction for Baseline Estimation; The rEMG signal (gray) is
broken into snippets around each stimulus time (green). The time periods used for the StTA
(blue) are from 15ms before to 60ms after each stimulus time. The time periods used for the
baseline estimation (brown) contain an additional 251 samples (~50ms) on each end of the StTA
time periods.
To correct for the large stimulus artifact observed at the time of the stimulus, the region of
the second StTA from 1ms before to 1ms after the stimulus time was replaced before filtering.
This was done by computing the average of the second StTA from 5ms before to 3ms before the
stimulus, computing the average of the second StTA from 3ms after to 5ms after the stimulus,

9
and interpolating linearly from the first average to the second average over the 2ms region during
which the stimulus artifact occurred.
The resulting trace was truncated by the Fs/20 + 1 samples added previously at both the
beginning and the end, such that its length matched the length of the original StTA and the
stimulus times were aligned. The truncated trace was then subtracted from the original StTA, with
the initial value at the stimulus time added back to preserve the original baseline level. However,
if a stimulus artifact was present, then the value of StTA at the stimulus time did not accurately
reflect baseline level. In this case, the value of the StTA at 2ms before the stimulus was used.
Figure 2 shows the process of forming the original StTA, the alignment of the truncated, filtered
baseline estimate, and the result of the subtraction.
Figure 2: Baseline Removal Process; First, rEMG snippets are extracted to form a StTA (green
trace in C and D), and rEMG snippets of increased length are extracted to form a second average
(brown trace in A). This trace is passed through a flat moving average filter and truncated such
that only the region overlapping with the original StTA remains (B). The result is an estimate of
the underlying curvilinear baseline of the original StTA (brown line in C). D shows the result of
subtracting this baseline estimate (blue) from the original StTA (green).

10
2.6 Modified Multiple Fragment Statistical Analysis
All StTAs generated by the above procedure produced a trace with some baseline above
0mV. Many contained positive or negative deflections indicative of an increase or decrease,
respectively, in activity from the baseline. These deviations were labeled as post-stimulus effects
(PStEs). An example of the final sweep-selected, baseline corrected StTAs is shown in figure 3,
with a suppressive post-stimulus effect (PStE) occurring at about 12ms after the stimulus time
(marked in green).
Figure 3: Stimulus-Triggered Average Example; An example of a StTA after sweep-selection and
baseline correction. The entire trace is above zero, at a baseline of ~.68mV. A suppressive PStE
is seen at 12ms after the stimulus.
Significant PStEs of StTAs were identified using a modified version of the multiple-
fragment statistical analysis (MFSA) approach described by Poliakov and Schieber (1998). In the

11
original method, MFSA breaks the series of sweep-selected rEMG snippets generated in the
formation of a StTA into several smaller subseries (fragments) and forms a separate triggered
average for each fragment, then uses deviations from baseline in each triggered average to
perform a statistical test for significance. In the present study, the N sweep-selected rEMG
snippets generated in the formation of the StTA were first separated into k fragments containing k
rEMG snippets each, where , and then a triggered average was formed for each
fragment from its k rEMG snippets. These triggered averages were baseline corrected as
described above, except using only the k stimulus times contained in each fragment to generate
the baseline estimate.
In the original MFSA method, a test window is defined within this triggered average to
capture activity over a time span of interest, and a control window is defined to capture baseline
activity over a time span presumed to contain no PStEs. The mean of the control window is then
subtracted from the mean of the test window. This difference is calculated or each fragment
average, and if this difference is statistically different from zero (student’s t-test, P<0.05), then the
neuron is accepted as producing PStEs. While this approach was initially implemented with the
control window taking the form of two smaller windows immediately preceding and following the
test window (Poliakov and Schieber 1998), here we assumed that any data following the trigger
might contain resultant EMG activity, and therefore we chose to use a control window from 15 ms
to 5 ms before the trigger. We defined several testing windows of length 10 ms, with the first from
5 ms to 15 ms after the trigger, at 5 ms increments. This resulted in a total of 10 test windows
distributed evenly from 0 ms to 60 ms after the trigger, each overlapping by 5 ms with its
neighbors. The mean of each test window is compared to the mean of the control window
individually, resulting in 10 separate test statistics. The microelectrode was accepted as
producing effects if any of these test statistics was significant at the P < 0.05 level after Bonferroni
corrections for testing both multiple EMG channels and multiple windows.

12
2.6 Identification of Post-Stimulus Effects
Figure 4 shows several examples of the possible forms that as StTA may take. Trace A
has a low signal-to-noise ratio (SNR) and displays no clear PStEs. This trace did not produce any
information in further analysis. The remaining traces contain varying timings and shapes of
PStEs. Of particular interest were traces B and E, which were both generated using rEMG from
the same muscle, but were stimulated via different electrodes (ie. different locations of cortex).
Trace B shows a positive-negative-positive series of deflections, each of which were analyzed as
a separate PStE. Trace E, however, has a single negative deflection, which was analyzed as only
one PStE.

13
Figure 4: Examples of Varying Forms of Stimulus-Triggered Averages; Trace A has a low SNR
and contains no effects. Trace B has a single short-latency facilitatory PStE (15ms), while traces
C and D have two such short-latency facilitatory PStEs (10ms and 20ms). Trace E has a short-
latency suppressive PStE (15ms). Traces B, C, and D all have long-latency suppressive PStEs
(30-40ms). Traces B and C have long-latency facilitatory PStEs (45ms).
StTAs were formed for each EMG channel by taking a grand average of all the triggered
averages of each fragment generated during the MFSA, then subsequently smoothed with a flat
five-point finite impulse response filter. These grand averages were used in lieu of true StTAs for
two reasons: 1. Full compilation and baseline correction of a StTA adds significant computational
load; 2. The grand averages only varied from the true StTAs by computer rounding error. A time
window of 15 ms before trigger to 5 ms before the trigger was used to calculate a baseline mean

14
and standard deviation of the StTA. For each significant test window, each post-stimulus
facilitation (PStF) and post-stimulus suppression (PStS) present in the window was identified by
first identifying all points where the StTA crossed 2 SD from the baseline mean. Segments of the
StTA between these points that fell above (for a PStF) or below (for a PStS) 2 SD from the
baseline mean were labelled as effects, and the endpoints were labeled as the onset and offset.
The extremum of each PStE was identified as the maximum (for a PStF) or the minimum (for a
PStS) of the effect. The mean percent increase (MPI) of each PStE was determined by averaging
the amplitude of the StTA (equivalent to taking the area of the PStE) from the onset to the offset
of the PStE, subtracting the baseline mean, dividing by the baseline mean, and multiplying by
100. The peak percent increase (PPI) of each PStE was determined by subtracting the baseline
mean from the extremum of the PStE, dividing by the baseline mean, and multiplying by 100. The
peak width at half-maximum (PWHM) of each PStE was determined by calculating half the height
of the PStE above (for a PStF) or below (for a PStS) the baseline mean and measuring the width
of the PStE at this level.
2.7 Selection of identified PStEs
Of the 847 StTAs compiled, 134 contained a test window which tested significant during
the modified MFSA. These 134 StTAs identified 197 post-stimulus facilitations (PStFs) and 195
post-stimulus suppressions (PStSs), however we excluded any false PStEs from further analysis
by only including PStEs that satisfied the following five criteria: 1. The PWHM must be less than
the event width; 2. The effect extremum must occur after 5ms. 3. The PWHM must be greater
than 2ms; 4. The effect width must be greater than 2.5ms; 5. The magnitude of the PPI must be
less than 100. After this exclusion, 99 PStFs and 102 PStSs remained, identified by 109 StTAs.
The first two criteria were selected to exclude false positives generated by the MFSA. In
particular, it is possible for the MFSA to detect a PStE if the StTA remains outside 2SD from the
baseline for the entire duration of the test window. This will result in the half-maximum being
calculated inside 2SD from the baseline, which will generate a larger PWHM than effect width.

15
The first criterion removes these detected effects from further analysis. The second criterion
removes PStEs that occur earlier than is physiologically possible.
Due to a significant stimulus artifact present in many rEMG recordings, it was also
important to question whether any of the PStEs seen were a result of a ringing effect of our
hardware filter. We reasoned that, if any PStEs were in fact an artifact due to ringing, then the
process of full-wave rectification of EMG before averaging could obscure the waveform
generating them. We therefore re-compiled these 109 StTAs without rectifying EMG in order to
examine any underlying oscillations, particularly with frequencies near the edges of our hardware
filter (30Hz and 30kHz). No such oscillations were observed, even when large stimulus artifacts
were present. As an additional measure, these StTAs were also passed through the MFSA and
PStE counting. The latter 3 criteria for counting PStEs were added to exclude the PStEs identified
from non-rectified EMG.

16
3. Results
Figure 5 shows a nonuniform distribution of PStEs detected from stimulation in each
array, with array J generating the most. Although some StTAs from array G contained significant
test windows and identified some PStEs, none passed the PStE filtering described above. As
shown in figure 6, most of the PStEs detected occurred in intrinsic and extrinsic hand muscles.
Since our intent was to implant the FMAs over the hand region of M1, these are expected results.
Figure 5: Distribution of Post-Stimulus Effects by Array; The distribution of PStEs contributed by
each array. Each array contributed approximately equal proportions of PStFs and PStSs. The
largest contribution was from array J, and array G contributed no PStEs. Each array contributed
approximately equal proportions of PStFs and PStSs.

17
Figure 6: Distribution of Post-Stimulus Effects by Muscle; The distribution of PStEs contributed by
each muscle. Largest contributions were from hand muscles, both intrinsic and extrinsic (FDI,
FDPu, Hypoth, FDPr, and FCU). Each muscle contributed approximately equal proportions of
PStFs and PStSs.
Both facilitatory and suppressive effects appear to follow a temporal trend, whereby they
separate into distinct groups according to the time at which their extremum (maximum for PStFs,
minimum for PStSs) occurs after the stimulus. Figure 7 displays a histogram of PStEs, binned by
extremum time. From this perspective, a bimodality of both PStFs and PStSs is evident.

18
Figure 7: Histogram of Post-Stimulus Effects; PStEs are binned according to their extremum
latency. There is a clear bimodality of the PstFs. These groups are centered at roughly 15ms, and
45ms. The PStSs show a clear bimodality. These groups are centered at roughly 15ms and
28ms.
Figures 8 and 9 plot the PStFs and PStSs, respectively, according to their MPI, width,
and extremum latencies. A few more features of the apparent groups can be identified from these
plots. In figure 8, the earliest of PStFs (the group centered at 10ms) are capable of reaching a
higher MPI than later groups (a maximum of 39.8 in the early group, versus a maximum of 10.7 in
the late group). Figure 9 shows that the division between the two PStS groups seen in figure 7 is
far less clear, once MPI and width are considered. If two groups do in fact exist, as suggested by
figure 7, they do not vary from each other in terms of MPI or width.

19
Figure 8: 3D Scatter of Post-Stimulus Facilitations; MPI, width, and extremum time for each of 99
PstFs (A). The width of PStFs is broadly distributed across any clusters (B). The events separate
into two distinct clusters according to the time of their extrema, with one cluster occurring near
15ms and the other occurring near 45ms (C). The earlier cluster achieves a higher maximum MPI
than the later cluster (D).
Figure 9: 3D Scatter of Post-Stimulus Suppressions; MPI, width, and extremum time for each of
102 PstSs (A). PStSs are broadly distributed across a range of widths (B). If the data separate
into two clusters according to extremum time, with the first occurring near 15ms and the second
occurring near 30ms, neither the width nor the MPI of PStSs can be used to clearly separate the
two clusters, as these features do not clearly define clusters (C and D).

20
Following the results above, we defined bins around each of the apparent groups of
PStEs (2 bins for PStFs, 2 bins for PStSs) to count the number of PStE extremum times that fell
into each group. For PStFs, we defined the three extremum time groups as follows: 5-25ms (FS),
and 35-55ms (FL). For PStSs, we defined the two extremum time groups as follows: 9-18ms (SS),
and 20-40ms (SL). These divisions are shown graphically in figure 10. The number of PStEs that
fell into each group is shown graphically in figure 11. The number of PStEs counted for each of
the facilitatory groups was relatively similar, but there was a large disparity in the number of
PStEs counted for the suppressive groups, with SL containing the most. 5 of 201 PStEs did not
have an extremum time that fell into our defined groups and so were not counted.
Figure 10: Binning of Post-Stimulus Effects; PStEs are binned according to their extremum time.
Two groups are defined for PStFs (varying reds) and two groups are defined for PStSs (varying
blues). 5 of 201 total PStEs did not fall into any bin (gray).

21
Figure 11: Histogram of Binned Post-Stimulus Effects; The distribution of PStEs sorted into each
group. There were relatively more short latency PStFs (FS) than long latency PStFs (FL). There
were relatively fewer short latency PStSs (SS) than long latency PStSs (SL).
Table 2: Post-Stimulus Effect Group Statistics; means and standard deviations of calculated
properties of the four groups of PStEs identified.
Effect Group Mean Extremum
Time (ms)
Mean Width (ms) Mean MPI
FS 16 ± 4 7 ± 2 6.2 ± 6.7
FL 46 ± 5 11 ± 4 3.6 ± 2.7
SS 14 ± 2 7 ± 3 -9.8 ± 5.2
SL 29 ± 4 10 ± 4 -7.9 ± 6.0
In order to examine any interdependencies between the occurrences of these different
groups of PStEs, each of 109 StTAs first received a 4-category binary classification according to
whether they contained PStEs that fell into any of the five bins defined above. For example, a
StTA containing PStEs that fell into both the FS and the FL bins would receive a classification of
1100, regardless of how many PStEs fell into each group. We then constructed a 4-way
contingency table, tabulating the number of StTAs that fell into each classification. A chi-square

22
test for independence would be appropriate to determine whether these features are
independent, however our sample size was too small for the chi-square test to be accurate.
Instead, Fisher’s exact test was used to examine dependencies between each possible pair of
the four PStEs (equivalent to testing each of the marginal 2x2 tables), for a total of six tests. Only
three pairs rejected the null hypothesis of independence (P<.05, with Bonferroni correction for 6
tests). For these three pairs, the number StTAs containing both of the effects tested was less
than their expected frequency (table 3), which we interpret as an inhibitory effect of SS on longer-
latency effects. Figure 12 shows the resulting assumed model. It should be noted that these
interactions are based on pairwise comparisons and therefore may not all influence observed
EMG activity simultaneously.
Table 3: Stimulus-Triggered Average Frequencies; Observed and expected frequencies for StTAs
containing both of two effects in three pairs tested for independence. Each of these pairs rejected
the null hypothesis of independence and show observed frequencies lower than their expected
frequencies.
SS and FS SS and SL SS and FL
Expected Frequency 8.9 13.6 6.1
Observed Frequency 1 4 0

23
Figure 12: Assumed Interaction Between Post-Stimulus Effects; Interdependencies of PStEs
revealed by statistical testing. FS and SS exhibit mutual inhibition on one another. Additionally, SS
inhibits both of the long-latency effect groups that follow it. Green: P<.001; Yellow: P<10-4
; Red:
P<10-6

24
4. Discussion
Our results indicate that single-pulse ICMS delivered to the arm/hand region of M1 is
capable of generating at least four groups of PStEs observable with onset latencies less than
60ms, two of which are facilitatory, and two of which are suppressive. Extremum latencies for the
two facilitatory groups occurred at 16ms ± 4ms (FS), and 46ms ± 5ms (FL). Extremum latencies
for the two suppressive groups occurred at 14ms ± 2ms (SS), and 29ms ± 4ms (SL). There were
more short-latency PstFs than long-latency PStFs (59 in FS and 37 in FL), while there were more
long-latency PStSs than short-latency PStSs (19 in SS, 80 in SL).
The earliest effects, FS and SS, are attributed to corticomotoneuronal (CM) pathways
described and analyzed in detail in previous literature (Schieber and Rivlis 2005, Poliakov and
Shieber 1998, Cheney and Fetz 1985). However, the longer-latency effects we have observed
have also been recorded in previous studies, though the pathways responsible for conducting
these signals are poorly understood (Messamore et al 2015). Below, we will discuss potential
candidates for pathways that could have generated the effects we observed and suggest new
areas of focus for the investigation of the origin of these effects.
4.1 Pathways Potentially Contributing to Post-Stimulus Effects
We now will consider the possibility that the stimulus could have activated collateral
pathways descending through other areas from M1 before arriving at motoneuron pools. The
earliest of these PStEs, which we have labeled FS, occurs at latencies similar to those observed
in stimulus-triggered averaging studies recording from the same area in M1, and therefore we
attribute this effect to the CM pathway described in these previous studies (Schieber and Rivlis
2005, Poliakov and Shieber 1998). However, the FS effects we observed are distributed across a
larger time span than expected for CM-derived PStEs. We attribute this partially to the spread of
current of the applied stimulus (resulting in recruitment of a greater number of cells), but it may
also be partly a result of the simultaneous arrival of signals descending by a pathway through the
red nucleus (RN), which we estimate would produce effects of similar latencies. We also
investigated the possibility that longer-latency PStEs could be a result of pathways descending

25
through the ponto-medullary reticular formation (PMRF), and while the SL group we observed
occurred at a latency we estimate would be expected for such pathways, the lack of facilitatory
effects observed during this time window leads us to believe that pathways through PMRF are not
responsible for any of the PStEs we observed. Although the group we labeled FS has been
observed in previous studies to be bimodal when generated by S-ICMS (Messamore et al 2015,
Hudson et al 2015), our data were too sparse to identify this division conclusively. We
documented here the properties of FL, but were unable to identify a plausible origin.
4.2 Probing Descending Pathways
It has long been known that a large population of cells in primate primary motor cortex
(M1) project via the pyramidal tract (PT) to forelimb motoneuron pools, with a subset of these
projections, dubbed corticomotoneuronal (CM) cells, making direct (ie. monosynaptic)
connections with motoneurons (Cheney and Fetz 1985, Schieber and Rivlis 2005). It has also
been known that projections are made from M1 to other regions involved in motor control and
processing, namely the red nucleus (RN) and the ponto-medullary reticular formation (PMRF)
(Humphrey and Rietz 1976, Fisher et al 2012). Additionally, both RN and PMRF project to spinal
motoneuron in ways analogous to the corticospinal (CS) projections from M1, where a subset of
these projections is monosynaptic with respect to the signal pathway toward motoneuron pools
(Mewes and Cheney 1991, Belhaj-Saïf et al 1998, Riddle et al 2009, Baker 2011). These
corticorubral (CRub) and corticoreticular (CRet) projections are likely rarer than other projections
from M1. In a previous study by Humphrey and Rietz (1976), contrasts between CS and CRub
cells were drawn using penetrating electrodes in M1, RN, and PT. In M1, they were able to
identify an average of 7 PT cells per electrode, compared with 2.8 CRub cells per electrode.
Thus, it is possible for electrodes to be placed in locations in M1 where any stimulus delivered
would propagate substantially along CRub pathways. By gathering signal conduction data from
previous studies, it should be possible to estimate the latencies at which signals generated by
ICMS and traveling from M1 through RN will arrive at the muscles. This will allow us to determine
whether it is plausible that the long-latency effects we have observed could have been

26
propagated by these pathways.
4.3 Descending Pathways: Directly from M1
When stimulating in the hand/arm region of M1, it should be expected that PStEs will be
observed at latencies similar to PSpEs observed when recording from the same region,
generated by activation of CM cells. This is a good way to check that the correct region has been
implanted and that the analysis performed is sound; stimulation of this region should activate CM
cells. In previous work, it has been shown that these cells should consistently generate PSpEs
between 6-16ms after the trigger time (Schieber and Rivlis 2005, Poliakov and Shieber 1998). We
observed strong short-latency facilitatory effects at 5-25ms latencies (the FS group). While this
overlaps with previous data, our data indicate that thee effects are distributed over a time period
10ms longer than expected from previous results.
Immediately following this group of responses, we observed short-latency suppressive
effects at 9-18ms latencies (the SS group). It is generally thought that such effects are the result of
inhibitory spinal interneurons, which should increase the time needed for the signal to reach
motoneuron pools and produce an inverted effect (Poliakov and Schieber 2005). This added time
is estimated at ≥1ms, and a large overlap between the group of facilitatory and suppressive
effects is often observed (Perlmutter et al 1998, Poliakov and Schieber 2005). We considered that
this may be a result of the strong activation generated by the stimulus; the FS effect is so large in
magnitude that the SS effect is unable to suppress the signal enough to be detected by our
methods. This is supported by the statistically significant mutual inhibition we observed between
FS and SS (figure 12), indicating that detecting the presence of one effect decreases the likelihood
that the other effect is detected in the same StTA.
4.4 Descending Pathways: Through PMRF
Previous work by Fisher et al (2012) used transcranial magnetic stimulation (TMS) to
stimulate in M1 while recording from PMRF with penetrating microelectrodes in order to
determine signal arrival latencies produced by fibers projecting from M1 to PMRF. Their data
indicate a division of three groups of conducting fibers into fast, medium, and slow conduction

27
velocities, with the slow group holding the largest proportion of cells. Signals conducted by fast
fibers had latencies between 1-3ms, by medium fibers had latencies between 3-7ms, and by slow
fibers had latencies between 7-25ms.
Previous work by Davidson and Buford (2006) used penetrating microelectrodes in
PMRF and wire electrodes implanted in shoulder and arm muscles to perform stimulus-triggered
averaging. Their results indicate that signals generated in PMRF should be expected to arrive in
muscles at 10-15ms latencies.
Assuming that it is most likely that a signal from M1 will be conducted to PMRF by a slow
fiber, and using 1ms as a rough estimate for the segmental latency between M1 and PMRF, we
estimate that a stimulus delivered to M1 and traveling through PMRF should be detectable in
stimulus-triggered averages at latencies between 18-41ms. This overlaps well with our SL group
(suppressive effects occurring at 20-40ms latencies). However, Davidson and Buford (2006)
reported that there was no overall preference between facilitatory and suppressive effects
(despite individual preferences between muscles), whereas our data indicate that only
suppressive effects were observed during the time period when signals traversing PMRF should
arrive in muscles. We therefore conclude that the effects observed in the SL, though appropriately
timed, were not delivered by PMRF-traversing pathways from M1.
4.5 Descending Pathways: Through RN
Previous work by Humphrey and Rietz (1976) used penetrating microelectrodes to
measure fiber conduction velocities between M1 and RN by stimulating in M1 and recording in
RN. They identified two subpopulations of these projections which they labeled fast-conducting
(30-40 m/s) and slow-conducting (12-14 m/s). They also noted that the large majority of these
projections (81%) are of the slow-conducting variety. By examining magnetic resonance imaging
data of the subject from the present study, we were able to gain an estimate of the distance
between our electrodes and RN. We drew three-dimensional line segments between the
reconstructed locations of arrays H, I, and J, and the center of RN. These distances were
29.50mm, 29.61mm, and 29.18mm. Taking the average value of 29.43mm, we approximate the

28
signal latency between M1 and RN to be 0.7-1.0ms for fast-conducting fibers and 2.1-2.5ms for
slow-conducting fibers.
Previous work has identified that projections from RN to motoneuron pools follow an
organization similar to that of the projections from M1 to motoneuron pools, in that there exist
rubromotoneuronal (RubM) cells which descend from RN and synapse directly on motoneurons
(Belhaj-Saïf et al 1998), as well as rubrospinal (RubS) cells which synapse on spinal interneurons
(Mewes and Cheney 1991). From StTA methods, they found that the average signal latency along
RubM pathways for facilitatory effects was 5.7ms, and for suppressive effects was 9.2ms (Belhaj-
Saïf et al 1998). For RubS pathways, average latency for facilitatory effects was 10ms, and for
suppressive effects was 14.4ms (Mewes and Cheney 1991). Using the numbers above and the
value of 1ms for the segmental latency between M1 and RN, we estimated the latencies that
should be expected of signals carried by the various pathways descending through RN (table 4).
Table 4: Estimated Post-Stimulus Effect Latencies; Estimated latencies of PStEs carried along
various descending pathways traversing RN en route from M1 to motoneuron pools.
M1 to RN
Fast-conducting Slow-conducting
RN to
motoneuron
pools
RubM PStF: 7.4-7.7ms
PStS: 10.9-11.2ms
PStF: 8.8-9.2ms
PStS: 12.3-12.7ms
RubS PStF: 11.7-12.0ms
PStS: 16.8-17.1ms
PStF: 13.1-13.5ms
PStS: 17.5-17.9ms
Our estimates align the expected PStF latencies partof the FS group and the expected
PStS latencies with the SS group. If pathways traversing RN contribute to the FS group of PStEs,
then this may also explain the small number of PStFs we observed with MPI values above 20
(figure 8). If PT cells and RN-traversing pathways are activated simultaneously from stimulation
delivered to M1, there should be a small chance for the signals to synergistically act upon the
same muscle, increasing the size of the effect generated.

29
4.6 Other possible sources of PStEs
Our analysis has thus far interpreted the appearance of the two earliest PStE groups ( FS
and SS), leaving the remaining two PStE groups. The remaining two PStE groups SL and FL elude
our present analysis. We have shown above that, although pathways through PMRF should be
expected to deliver signals to muscles with latencies similar to SL, the lack of any facilitatory
features during this time period indicates that it is unlikely that these pathways are the source of
SL. Additionally, none of our estimates have reached the time period covered by FL, leaving this
feature something of a mystery. It is possible that these last two PStEs are a result of
reverberation of signals between different brain regions, only revealed by the distributed,
simultaneous activation of many pathways generated in ICMS conditions.
4.7 Suggestions for future work
To determine whether the pathways investigated above play a role in generating these
effects, recordings made simultaneously from all of these areas during M1 stimulation should be
able to catch the signal as it traverses on its way to motoneuron pools. Additionally, if the cells
responsible for such pathways could be antidromically identified at each step of the pathway,
recreating the entire path of the signal, this would provide further evidence that such long
pathways may be generating these effects. However, the technical difficulty that would be faced in
performing such an experiment is likely a large factor in the lack of research currently surrounding
these phenomena. Instead, it may be better to develop a broader picture of which areas are
activated as a result of M1 stimulation. This may employ the use of functional magnetic
resonance imaging (fMRI) during stimulation of M1, which should be able to generate a rough
guide for where to more precisely record using penetrating electrodes in following studies.
To examine the possibility of reverberation after gaining the fMRI map of candidate
locations to investigate, penetrating electrodes both in M1 and in another candidate region may
be able to record the signal as it reverberates after M1 stimulation. This would require specialized
hardware that could switch between stimulation and recording in M1 within the span of several
milliseconds, otherwise the signal may not be captured as it returns to M1.

30
Additionally, the modality of the earliest group of PStFs must be better established.
Previous studies have shown evidence of a bimodality (Messamore et al 2015, Hudson et al
2015), however this phenomenon has not yet been observed reliably to determine the
circumstances necessary for its generation. In the present study, the PStF group we labeled FS
may in fact be comprised of two subgroups, which could explain why these PStFs occur over
such a broad range of time, but we find our data insufficient to make such a claim. Regardless of
the answer, the question of this bimodality itself indicates a lack of understanding of the
physiological impact of S-ICMS and a need to further explore how these methods generate
responses in descending systems.

31
References
1. Fetz EE, Cheney PD, German DC. Corticomotoneuronal Connections of Precentral Cells
Detected by Post-Spike Averages of EMG Activity in Behaving Monkeys. Brain Research,
1976; 114: 505-510.
2. Griffin DM, Hudson HM, Belhaj-Saf A, Cheney PD. EMG Activation Patterns Associated
with High Frequency, Long-Duration Intracortical Microstimulation of Primary Motor Cortex.
Journal of Neuroscience, 2014; 34: 1647-1656.
3. Cheney PD, Griffin GM, Van Acker III GM. Neural Hijacking: Action of High-Frequency
Electrical Stimulation on Cortical Circuits. The Neuroscientist, 2012; 19: 431-441.
4. Cheney PD, Fetz EE. Comparable Patterns of Muscle Facilitation Evoked by Individual
Corticomotoneuronal (CM) Cells and by Single Intracortical Microstimuli in Primates:
Evidence for Functional Groups of CM Cells. Journal of Neurophysiology, 1985; 53: 786-804.
5. Histed MH, Bonin V, Reid RC. Direct Activation of Sparse, Distributed Populations of
Cortical Neurons by Electrical Stimulation. Neuron, 2009; 63: 508-522.
6. Poliakov AV, Schieber MH. Multiple Fragment Statistical Analysis of Post-Spike Effects in
Spike-Triggered Averages of Rectified EMG. Journal of Neuroscience Methods, 1998; 79:
143-150.
7. Rouse AG, Schieber MH. Spatiotemporal Distribution of Location and Object Effects in
Reach-to-Grasp Kinematics. Journal of Neurophysiology, 2015; 114: 3268-3282.
8. Park MC, Belhaj-Saïf A, Cheney PD. Chronic Recording of EMG Activity from Large
Numbers of Forelimb Muscles in Awake Macaque Monkeys. Journal of Neuroscience,2000;
96: 153-160.
9. Davidson AG, O’Dell R, Chan V, Schieber MH. Comparing Effects in Spike-Triggered
Averages of Rectified EMG Across Different Behaviors. Journal of Neuroscience Methods,
2007; 163: 283-294.
10. Mollazadeh M, Aggarwal V, Davidson AG, Law AJ, Thakor NV, Schieber MH.
Spaciotemporal Variation of Multiple Neurophysiological Signals in the Primary Motor Cortex
During Dexterous Reach-to-Grasp Movements. Journal of Neuroscience, 2011; 31: 15531-
15543.
11. Schieber MH, Rivlis G. A Spectrum From Pure Post-Spike Effects to Synchrony Effects in
Spike-Triggered Averages of Electromyographic Activity During Skilled Finger Movements.
Journal of Neurophysiology, 2005; 94: 3325-3341.
12. Humphrey DR, Rietz RR. Cells of Origin of Corticorubral Projections from the Arm Area of
Primate Motor Cortex and their Synaptic Actions in the Red Nucleus. Brain Research, 1976;
110: 162-169.
13. Fisher KM, Zaaimi B, Baker SN. Reticular Formation Responses to Magnetic Brain
Stimulation of Primary Motor Cortex. Journal of Physiology, 2012; 590: 4045-4060.
14. Mewes K, Cheney PD. Facilitation and Suppression of Wrist and Digit Muscles from
Single Rubromotorneuronal Cells in the Awake Monkey. Journal of Neurophysiology, 1991;
66: 1965-1977.
15. Belhaj-Saïf A, Karrer JH, Cheney PD. Distribution and Characteristics of Poststimulus
Effects in Proximal and Distal Forelimb Muscles from Red Nucleus in the Monkey. Journal of
Neurophysiology, 1998; 79: 1777-1789.
16. Riddle CN, Edgley SA, Baker SN. Direct and Indirect Connections with Upper Limb
Motoneurons from the Primate Reticulospinal Tract. Journal of Neuroscience, 2009; 29: 4993-
4999.
17. Baker SN. The Primate Reticulospinal Tract, Hand Function and Functional Recovery.
Journal of Physiology, 2011; 589: 5503-5512.
18. Histed MH, Bonin V, Reid RC. Direct Activation of Sparse, Distributed Populations of
Cortical Neurons by Electrical Stimulation. Neuron, 2009; 63: 508-522.
19. Perlmutter SI, Maier MA, Fetz EE. Activity of Spinal Interneurons and Their Effects on
Forearm Muscles During Voluntary Wrist Movements in the Monkey. Journal of

32
Neurophysiology, 1998; 80: 2475-2494.
20. Lemon RN. Descending Pathways in Motor Control. Annual Review of Neuroscience,
2008; 31: 195-218.
21. Davidson AG, Buford JA. Bilateral Actions of the Reticulospinal Tract on Arm and
Shoulder Muscles in the Monkey: Stimulus Triggered Averaging. Experimental Brain
Research, 2006; 173: 25-39.
22. Messamore WG, Van Acker III GM, Hudson HM, Zhang HY, Kovac A, Nazzaro J, Cheney
PD. Cortical Effects on Ipsilateral Hindlimb Muscles Revealed with Stimulus-Triggered
Averaging of EMG Activity. Cerebral Cortex, 2015. doi:10.1093/cercor/bhv122.
23. Hudson HM, Griffin DM, Belhaj-Saif A, Cheney PD. Properties of Primary Motor Cortex
Output to Hindlimb Muscles in the Macaque Monkey. Journal of Neurophysiology, 2015; 113:
937-949.

Thesis

Recomendados

Recomendados

Más contenido relacionado

La actualidad más candente

La actualidad más candente (20)

Similar a Thesis

Similar a Thesis (20)

Thesis