CHAPTER 1 GENERAL INTRODUCTION
1.4 E XPERIMENTAL S ETUPS AND D ATA S PECIFICATIONS
Datasets used in this dissertation were individually collected through experimental paradigms for motor BMIs in three research groups. One of the datasets was acquired by Motor Lab of Schwartz group at the University of Pittsburgh and permitted by Dr. Jeong-Woo Sohn and Dr. Andrew Schwartz for our studies used in this dissertation [91]. Others were collected from Northwestern University and were available in a data repository of the Database for Reaching Experiments and Models (DREAM) through the Collaborative Research in Computational Neuroscience (CRCNS), as public datasets [63]–[65].
This section briefly covers the data descriptions, including animal experiments and data specifications.
1.4.1 Experimental Setups and Behavioral Tasks for Animals
Individual datasets obtained from three rhesus monkeys (Macaca mulatta) were used for reasonably evaluating our approaches in this dissertation. One of two 96-channel intracortical microelectrode arrays (MEA) (Utah array, Blackrock Microsystems, Salk Lake City, USA) was chronically implanted in the M1 arm area of all the monkeys (monkey C, M, and F). Although not yet used in this dissertation, the other MEA was also chronically inserted in the dorsal premotor cortex (PMd) in monkey M or the
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ventral premotor cortex (PMv) in monkey F, respectively. All the procedures for animal care and surgical protocols were approved by the Institutional Animal Care and Use Committee (IACUC) in Northwestern University (monkey C and M) and the University of Pittsburgh (monkey F), respectively.
Single neuronal units were recorded, simultaneously with behavioral data acquisition, using different acquisition systems for each monkey: Cerebus (Blackrock Microsystems, Salk Lake City, USA) in monkeys C and M, and OmniPlex (Plexon Inc, Dallas, Texas, USA) in monkey F. In monkeys C, M, and F, 172, 67, and 143 single units were offline sorted, respectively. Furthermore, I excluded units with sparse (the mean firing rate in a single trial < 5 %) or no spikes in each study, depending on the bin width of the preset firing rates, and thus only analyzed 101, 48, and 79 units in monkeys C, M, and F, respectively.
All monkeys performed 2D reaching tasks in which they moved a computer cursor using arm movements. Monkey C and M were seated in a chair while controlling a two-link manipulandum.
Cursor and hand movements were constrained within a workspace of 20 cm × 20 cm. Monkey C performed a center-out reaching task and was instructed to move the computer cursor toward one of the equidistant targets radially distributed from the center (Fig. 1-6 (A)). Eight targets with 2 cm square spaced at 45º intervals over a circle with a radius of 10 cm were presented on the computer screen. Each trial started with the illuminated target at the center of the circle. After a hold time of about 0.5 seconds, one of the outer targets on the circle was presented while the center target disappeared. And immediately, it signals the monkey to move the cursor. Here, if the cursor reaches the target within a limited time of 1.5 seconds and holds it between 0.2 and 0.4 seconds, monkey C acquires a liquid reward. Monkey M performed a randomly sequential reaching task and was instructed to move the computer cursor from a starting location toward a pseudo-randomly placed target (Fig. 1-6 (B)). In contrast to the task of monkey C reaching one target in a trial, this task presented four targets sequentially in a single trial.
The target with 2 cm × 2 cm square was pseudo-randomly located within an annulus centered on the current target; the monkey should move the cursor toward the illuminated target. Each trial started with the illuminated target randomly located within the circle. Upon reaching the target, the next cursor was triggered within the current target 100 ms later. And then, the next target was appeared on average 96 ms after being triggered. If the cursor reaches the four targets sequentially, monkey M acquires a liquid reward.
Monkey F, in contrast to Monkeys C and M, performed a center-out and out-center reaching task in three-dimensional spaces (Fig. 1-6 (C)). Similar to monkey C, monkey F was instructed to move the computer cursor from a starting location toward one of the radial targets equidistant from the center.
Twenty-six different targets were presented in a virtual reality environment, evenly distributed on the surface of a spherical workspace. The arm position was monitored and recorded with optical tracking systems (Northern Digital Inc., Waterloo, Canada).
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Monkeys C, M, and F performed 175, 498, and 468 trials, respectively. Furthermore, bad trials with failed tasks or kinematic data that were contaminated with noise were excluded, leaving 175, 358, and 451 trials in monkey C, M, and F, respectively, for our data analysis.
1.4.2 Arm Movements and Kinematic Tuning Properties
I first investigated the mean firing rates of neural activity recorded from each monkey. Note that the firing rates were digitized as bin width of 20 ms without overlapping. Figure 1-7 illustrates the distribution of the mean firing rates for each single-trial (22.9±23 Hz in monkey C, 14.5±11.5 Hz in monkey M, and 19.7±13.4 Hz in monkey F). The total mean firing rates of monkey C were significantly higher than that of monkey M (p = 0.013, one-way ANOVA, Turkey-Kramer correction), whereas there was no difference with that of monkey F (p = 0.36).
I next examined the tuning properties of individual neurons for kinematic parameters of arm movements. PDs can be estimated by a cosine tuning model assuming the linear relationship between neural firing rates and movement directions, which can be defined as:
𝑧𝑛(𝑡) = 𝛽0+ 𝛃T𝐱(𝑡) + 𝜖(𝑡) (1.7) where 𝑧𝑛(𝑡) denotes neural firing rates from neuron n at time t, a vector x is the unit vector for movement directions. 𝜖 indicates an error term that follows a Gaussian distribution. PDs can be computed by 𝛃/‖𝛃‖, 𝛽0 indicates the offset. Figure 1-8 (A) depicts the distribution of PDs for each monkey. The uniformities of PDs are 0.81, 0.81, and 0.91, for monkey C, M, and F, respectively, where
Figure 1-5 Examples of arm movement trajectory for each monkey. Gray filled squares indicate traces of pseudo-randomly located target. For monkeys C and F, each colored line denotes a single- trial trajectory for different targets. For monkey M, colored line denotes different sequential sub- trials for a single trial.
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the uniformity corresponds to the Euclidean norm of the average PD. The average PDs are 75.2±38.2º, -11.1±54.3º, and {azimuth: 75.2±58.8º and elevation: 23.7±31.9º} for monkey C, M, and F, respectively, which suggest that population neurons are closely related to that directions. Figure 1-8 (B) represents neuronal distribution for PDs binned by the interval as 45º. For monkey C, neurons were significantly tuned at a high ratio of 16.8% in the range of 0º to 22.5º, for monkey M, neurons were tuned at a high ratio of 33.3 % in the range of -90º to 45º. Also, for monkey F, neurons were tuned at a high ratio of 15.3 % in the range of {0º to 22.5º for azimuth and 22.5º to 45º for elevation}. Particularly, monkey C showed a higher proportion of neurons tuned in all directions than other monkeys.
Figure 1-9 illustrates the tuning quality (r2) for kinematic variables and the proportion of neurons.
The tuning model of each kinematic variable follows an equal form with Eq 1.7. For monkey C, velocity Figure 1-6 Mean firing rate for each monkey. The solid red line denotes the total median firing rate, and the dark solid line is the total mean firing rate. The individual circle indicates the mean firing rates in a single trial.
Figure 1-7 Tuning quality and proportion of neurons to each kinematic variable. The left y- axis denotes the tuning quality (r2) of kinematic parameters on the linear model. The right y-axis and the solid red line denote the proportion of neurons related to kinematic variables.
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and speed revealed significantly higher r2 than direction (p = 0.02 for velocity, p = 0.015 for speed), acceleration (p < 0.01 for velocity and speed), and jerk (p < 0.01 for velocity and speed), whereas there was no difference between velocity and speed (p = 0.8). There were related 90.8 %, 84.0 %, 79.4 %, 71.8 %, and 33.6 % of all neurons to kinematic variables, including velocity, direction, speed, acceleration, and jerk, respectively. For monkey M, velocity and direction revealed significantly higher r2 than speed (p < 0.01 for velocity and direction), acceleration (p < 0.01 for velocity and direction), and jerk (p < 0.01 for velocity and direction), whereas there was no difference between velocity and direction (p = 0.71). There were related 98.0 %, 96.1 %, 82.4 %, 90.2 %, and 58.8 % of all neurons to kinematic variables, including velocity, direction, speed, acceleration, and jerk, respectively. For monkey F, speed revealed significantly higher r2 than direction (p = 0.04), acceleration (p < 0.01 for speed), and jerk (p < 0.01 for speed), whereas there was no difference between speed and velocity (p = 0.65). There were related 92.4 %, 91.5 %, 78.8 %, 83.9 %, and 61.9 % of all neurons to kinematic variables, including velocity, direction, speed, acceleration, and jerk, respectively.
Figure 1-8 (A) PD distribution of each monkey. A dark solid line denotes PD for each neuron. The solid red line indicates the averaged PD. (B) Neuronal distribution for PDs binned by the interval as 45º.
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