Abstract
In a dynamic environment, we seek to enhance behavioral responses by anticipating future events. Previous studies have shown that the probability distribution of the timing of future events could shape our expectation of event timing; furthermore, the modulation of alpha oscillation is known to be a critical neural factor. However, a link between the modulation of alpha oscillation by temporal expectation and single neural activity is missing. In this study, we investigated how temporal expectation modulated frontal neural activities and behavioral reaction time by recording neural activity from the frontal eye field smooth pursuit eye movement region of monkeys while they performed a smooth pursuit eye movement task. We found that the temporal expectation reduced the coherence between the neural spiking and alpha frequency of the local field potential, along with the trial-by-trial correlation between the neural spiking activity and pursuit latency. This result suggests that the desynchronization of alpha oscillation by temporal expectation would be related to the decorrelation of population neural activity, which could be the neural source of reaction time enhancement by temporal expectation. Introduction
When a sprinter is waiting for the starting signal, the sprinter develops an expectation of the timing of the signal and allocates attention to react to the sound as soon as possible. When the expectation matches the actual timing, the reaction time is faster than when the expectation does not match the timing. Similarly, in our daily lives, we often develop expectations to anticipate a certain future event, and previous studies have shown that human behavioral performance depends on the development of temporal expectation 1 , 2 . The developed temporal expectation itself is often relatively precise, as if the brain represents the actual passage of time and calculates the probability of event occurrence even though the event has not happened yet. Previous studies have shown that the modulation of behavioral performance and single neural activity indeed follow this hazard rate 3 . A seminal study by Ghose and Maunsell showed that the modulation of sensory neural responses in macaque area V4 followed the hazard rate of stimulus change timing 4 . Janssen et al. showed that macaque lateral intraparietal area (LIP) neural responses are modulated according to the probability of random wait time between target onset and ‘go’ signal in the delayed saccade task 5 . Additionally, alpha/gamma frequency oscillation and neural activity in the primary visual cortex are known to be modulated by temporal expectation 6 , 7 . These studies showed that the hazard rate and expectation of stimulus timing are represented in the neural responses of the visual and parietal cortical areas. On the other hand, human EEG studies have suggested the importance of occipital alpha oscillation in temporal expectation. They consistently reported that temporal expectation improved behavioral responses by reducing the power of alpha oscillations 8 , 9 , 10 , 11 . Apparently, the anticipation of the future event through temporal expectation is one of our cognitive strategies for using our limited resources efficiently and modulating both behavioral performance and neural responses. Despite many studies reporting the role of alpha oscillation in temporal attention and expectation, information remains limited about how alpha modulation is linked to spiking activities. Additionally, most previous studies reported the functional role of temporal expectation in the parietal and occipital sensory cortical areas. Therefore, it is not clear whether the modulation of neural activity by temporal expectation is limited to sensory cortical regions. In both human and animal studies, an important behavioral signature of temporal expectation is the modulation of reaction time. Therefore, neural mechanisms that control the behavioral reaction time would be tightly related to how temporal expectation modulates behavioral responses. In a smooth pursuit eye movement, neural mechanisms of reaction time have been studied through the investigation of the trial-by-trial correlation between neural activity and behavior. Recent studies showed that a substantial amount of behavioral reaction time variation in smooth pursuit eye movements could be explained by the correlated latency variation in the area MT 12 and frontal eye field smooth eye movement (FEFSEM) region 13 . In FEFSEM, we found that a brief local field potential (LFP) wave of 5 ~ 15 Hz (which overlaps with a nominal alpha oscillation) demonstrated a strong relationship with the behavioral reaction time variation, which suggested that this component might represent a neural latency covariation in a local neural population 13 . Therefore, the desynchronization of alpha oscillation by temporal expectation may be related to the reduction in neural latency covariation. The reduction of neural latency covariation by temporal expectation subsequently might result in the reduced neuron-pursuit latency correlation because the correlated neural variation in each node of the smooth pursuit circuit would contribute to the behavioral variations 12 , 13 , 14 , 15 . In the smooth pursuit eye movement system, human and nonhuman primates can also develop a temporal expectation of motion onset timing; the temporal expectation was verified by showing that the timing of anticipatory eye movement follows the hazard rate 16 . In a typical smooth pursuit paradigm, the timing of target motion is randomized, following a particular probability distribution (typically, a uniform distribution) to prevent the subjects from anticipating the upcoming target motion. This experimental design naturally enables the subjects to develop a temporal expectation following the hazard rate shaped from the randomized onset timing of the pursuit target. In this study, we took advantage of the temporal expectation shaped in the smooth pursuit eye movement paradigm and investigated its relationships with alpha oscillation and neural activity found in FEFSEM. Based on our previous result 13 , suggesting that the 5 ~ 15 Hz LFP wave (alpha oscillation) represents neural latency covariation in FEFSEM, we hypothesized that the temporal expectation would decorrelate the neural activity by desynchronizing the alpha oscillation. Then, the single neural activity should be desynchronized from the alpha oscillation by the temporal expectation, and the trial-by-trial correlation between single neural activity and pursuit latency should be reduced. Materials and methods
We trained two rhesus macaques on a step-ramp smooth pursuit task. The step-ramp smooth pursuit task was developed to minimize the occurrence of saccadic eye movements during the initiation of pursuit 17 . In this task, the pursuit target was displaced eccentrically (step) and started moving toward the center of the screen at a constant speed (ramp). Prior to the commencement of behavioral conditioning, we performed two separate surgeries under the sterile surgical protocol. In the first surgery, we implanted a stainless steel cylindrical head holder on the skull using titanium straps, screws, and dental acrylic. Simultaneously, we also implanted a stainless-steel recording chamber over the cross-section of the arcuate sulcus and central sulcus to obtain access to the anterior bank of the arcuate sulcus. After the animal fully recovered from the surgical procedure, we performed a second surgery for implanting a scleral search coil on the right eye to monitor eye movements. As soon as the animals recovered from the surgery, we controlled the water consumption gradually and began behavioral training. After they were well trained on the step-ramp pursuit task, we performed a craniotomy inside the chamber to obtain access to the FEFSEM region. Experiments were performed at Duke University, and all methods were approved in advance by the Institutional Animal Care and Use Committee at Duke University. The methods conformed to the National Institutes of Health Guide for the Care and Use of Laboratory Animals. All animals were not sacrificed after the experiment and were maintained in healthy conditions. The data presented here were used in a previous study that analyzed other aspects of the data for different purposes 13 . The study is reported in accordance with ARRIVE guidelines. Experimental design and data acquisition
Visual stimuli were presented on a gamma-corrected 24-inch CRT color monitor (Sony Trinitron, GDM-FW900). The spatial resolution of the screen was 2304 by 1440 pixels, and the screen covered 44 by 29° of the visual field. The vertical refresh rate of the monitor was 80 Hz; therefore, the visual stimuli were updated every 12.5 ms. All the stimuli were presented on a gray background (29 cd/m2), covering a luminance range between 0 and 56 cd/m2. We monitored the eye position using the scleral search coil technique, where magnetically induced voltage changes related to the eye position and velocity signals were obtained from the implanted coil after the voltages were decomposed into horizontal and vertical components through a phase detection circuit. The eye velocity was obtained by differentiating and employing a high-pass filter on the eye position at a cutoff frequency of 25 Hz through an analog circuit. The horizontal and vertical eye position and velocity traces were sampled at 1 kHz and saved for offline analysis. In a daily recording, we introduced one to three single electrodes or tetrodes into FEFSEM and recorded the action potential and LFP. We used quartz-insulated platinum-tungsten electrodes, and the impedance of a single electrode was 2~4 MΩ, whereas the impedance of the tetrode was 1~2 MΩ (Thomas Recording GmbH). The neural signals were first amplified by a Tetrode Mini-matrix system (Thomas Recording GmbH) with a gain of 19. The input impedance of the preamplifier was 1 GΩ, which was sufficiently large compared to the impedance of single electrodes or tetrodes. This guaranteed the distortion to be minimal for low-frequency LFP phases 18 . The recorded voltage signals were transmitted through high- and low-pass filters with cutoff frequencies of 150 Hz for spikes and 170 Hz for LFP, respectively. LFPs were digitized at a sampling rate of 2 kHz. Whenever recorded voltage signals crossed a predefined threshold, we digitized a 1.2 ms duration of the spike waveforms at a sampling rate of 40 kHz. In addition, these signals were saved for online and offline analysis. We isolated single neural activity online using a window discriminator or principal component analysis. We later performed a more accurate offline spike sorting procedure, which was composed of principal component analysis and visual inspection of spike waveforms. The sorted spikes were sampled at 1 ms temporal resolution, and we visually inspected the rasters of every neuron to spot apparent errors in spike sorting. All offline spike sorting was performed using a Plexon Offline Sorter (Plexon Inc.). Digitization, filtering, amplification, and online spike sorting were performed in a Plexon MAP system (Plexon Inc.). When we found neurons that showed oculomotor responses, we tested whether the neuron was more responsive to a smooth pursuit or to a saccade. We ran a procedure where saccade and smooth pursuit trials were randomly interleaved. The saccade trials required the animals to make 15° amplitude saccadic eye movements in eight directions (45° intervals), where a 0.3° by 0.3° black circular dot was used as a saccade target. In pursuit trials, the animals were asked to track a random dot stimulus. Random dot patches were made from 120 spots inside a circular window with a 4° diameter. They had the same mean luminance with a gray background because we included equal numbers of black and white dots. The contrast of the dot patch defined by the luminance ratio between black and white dots was 100%. After a random duration (from 1100 to 1900 ms) of fixation on a fixation target (0.3° by 0.3° black circular dot), a random dot patch stimulus appeared at the center of the screen. Immediately after stimulus onset, the dots inside the circular window moved into one of eight directions (45° interval) with 15 deg/s speeds for 100 ms. Subsequently, the random dot patch moved in the same direction and speed with the pattern motion for 650 ms. We measured the saccade and pursuit direction tunings online to identify the preferred pursuit direction. We recorded the neural data if a given neural response was tuned to the direction of the smooth pursuit and proceeded into the second, main experiment. In the later offline analysis, we discarded any neuron that showed more pronounced responses to saccade than those to pursuit. In the main experiment, the monkeys performed a step-ramp smooth pursuit eye movement task 17 . The overall experimental design appears in Fig. 1 a. At the start of each trial, a white circular fixation point appeared at the center of the screen. After a random duration between 1100 and 1900 ms, a 0.6° by 0.6° yellow circular dot appeared as the pursuit target. The pursuit target appeared eccentrically to the center of the screen. Then, it started moving toward the center and continued to move at a constant speed for 770~870 ms. In each session, we chose two to four pursuit directions that provided reasonable compromises among the preferred directions of neurons under recording, along with one to two speeds (10°/s, 20°/s). The sizes of the steps in step-ramp pursuit were 1° and 3° for the two speeds. Figure 1
Experimental design and the effect of temporal expectation on smooth pursuit behaviors. (a) Temporal structure of the step-ramp smooth pursuit task. (b) The average pursuit latency as a function of the fixation duration. As the fixation duration increases, the latency decreases. Error bars are standard errors. (c) The average pursuit speed as a function of the fixation duration. The fixation duration did not modulate the pursuit speed. Error bars are standard errors. (d) The probability distribution of fixation duration and hazard rate. Light and dark gray shaded areas denote the two fixation duration conditions. (e) Average latency and speed differences in the two groups sorted by fixation duration. Both latency and speed differences were significant; however, the latency difference was more pronounced. Error bars are standard errors. (f) Scatter plot of mean pursuit latencies in short and long fixation duration conditions. The dotted line is the unity line. (g) Scatter plot of mean pursuit speeds in short fixation duration vs. those in long fixation duration conditions.

The effect of temporal expectation on the correlations of frontal neural activity with alpha oscillation and sensory-motor latency

Credit: Purdue University/Chris Adam
WEST LAFAYETTE, Ind. – The same technology that helps treat traumatic brain injuries in athletes and soldiers may one day help doctors determine the effectiveness of vaccines on patients. This technology is among the neuroscience innovations and research in the spotlight as Purdue University’s Center for Paralysis Research and Plexon Neurotechnology Systems present the Seminar for Neurotrauma and Diseases. The seminar series, which runs through April, features speakers from Purdue, Harvard Medical School and the medical industry. “Purdue has an incredible environment for developing advances in neuroscience and neurotrauma,” said Riyi Shi, the Purdue Mari Hulman George Endowed Professor of Applied Neuroscience in the College of Veterinary Medicine and professor of biomedical engineering. “We have very strong expertise in engineering, veterinary medicine and science. Our collaborative approach brings about unique engineering solutions for modern medical problems. “Through this Purdue-Plexon alliance, an example of an academic and corporate partnership, the new Seminar of Neurotrauma and Diseases, sponsored by Plexon Neurotechnology Systems, will result in immediate and long-lasting impact for neurotrauma research that will transcend scientific and geographic boundaries.”
Some of the topics being presented during the seminar series are novel approaches to treating traumatic brain injuries, newly discovered potential biomarkers for neurological diseases and integrative neuroscience techniques for medical imaging. “The topics for this seminar series are germane to what we do and the equipment we produce,” said Andrew Klein, vice president of sales and marketing at Plexon “We team up with researchers like those at Purdue and work with Purdue’s neuroscience community and Purdue Research Foundation to help answer questions and understand what happens in the brain.”
The seminar series is powered by Purdue’s Center for Paralysis Research, working closely with both the Purdue Institute for Integrative Neuroscience and Purdue Institute for Drug Discovery. “This series is just another example of how the Center for Paralysis Research is consistently striving to push exciting new basic research ideas to the forefront of modern medicine and beyond, and we are excited to collaborate with industry leaders like Plexon to help accomplish that mission,” Shi said. Shi is a neuroscience innovator who, along with several Purdue colleagues, has developed a drug that has gained FDA approval for the treatment of multiple sclerosis. Recently, he has discovered a new treatment using a World War II chemical agent antidote. The drug shows success in eliminating toxins in the brain that are linked to Parkinson’s disease. It is one of several technologies Shi patented with the help of the Purdue Research Foundation Office of Technology Commercialization. This office operates as one of the most comprehensive technology transfer programs among leading research universities in the U.S.
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About Purdue Center for Paralysis Research
The Center for Paralysis Research (CPR) is an interdisciplinary basic science and neuroengineering group located within Purdue University College of Veterinary Medicine. CPR has an unmatched record for moving basic research into clinical development, with three technologies having successfully completed FDA Phase 1 trials and one treatment already approved for multiple sclerosis patients. The center investigates innovative ways to solve problems of the nervous system and welcomes ideas that challenge traditional dogma. About Purdue University
Purdue University is a top public research institution developing practical solutions to today’s toughest challenges. Ranked the No. 5 Most Innovative University in the United States by U.S. News & World Report, Purdue delivers world-changing research and out-of-this-world discovery. Committed to hands-on and online, real-world learning, Purdue offers a transformative education to all. Committed to affordability and accessibility, Purdue has frozen tuition and most fees at 2012-13 levels, enabling more students than ever to graduate debt-free. See how Purdue never stops in the persistent pursuit of the next giant leap at purdue.edu. About Plexon Neurotechnology Systemsa
Plexon is a pioneer and leading innovator of custom high-performance solutions for data acquisition, behavioral research, and advanced analysis specifically designed for neuroscience research. Our equipment and solutions lay the foundation for work performed around the globe in fields such as basic neuroscience, Brain-Machine Interfaces (BMI), neurodegenerative diseases, addictive behaviors, and neuroprosthetics. Plexon’s mission is to be an investigator’s most dependable research partner.

Neuroscience seminar highlights technologies for brain injuries, vaccines

| ADVANTEST CORPORATION,., Wentworth Laboratories, htt high tech trade GmbH, JEM America Corp, Inc, Celadon Systems, Rucker Kolls Inc
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Probe Card Market - Industry Trends and Forecast to 2026 | ADVANTEST CORPORATION,., Wentworth Laboratories, htt high tech trade GmbH, JEM America Corp, Inc, Celadon Systems, Rucker Kolls Inc

A causal role for frontal cortico-cortical coordination in social action monitoring

Neurescence is a biotechnology company that provides a miniature microscope for functional imaging of neuronal circuits. The company's microscope uses brain imaging technology to cure brain diseases such as alzheimer's, epilepsy, and depression. It combines the power of optical imaging technology with machine learning and quantum machine learning techniques to enhance neurodegenerative, motor, and mood disorder drug development. The company was founded in 2015 and is based in Toronto, Canada. In November 2022, Neurescence was acquired by Bruker. The terms of the transaction were not disclosed.

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Through this partnership , Plexon Inc. will be a non-exclusive distributor of Neurescence products , broadening use within academic labs and research facilities throughout North America .

Neurescence Inc Launches Distribution Sales Partnership with Plexon Inc.
May 7, 2021.

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Plexon offers software solutions. The Company develops data acquisition, behavioral research, and advanced analysis solutions for the neuroscience research industry, and also provides adaptors, headstages, commutators, connectors, headstage tester units, and other accessories. The company is headquartered in Dallas, Texas.

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