A model for stimulation of enzyme activity by a competitive inhibitor based on the interaction of terazosin and phosphoglycerate kinase 1.

The drug terazosin (TZ) binds to and can enhance the activity of the glycolytic enzyme phosphoglycerate kinase 1 (PGK1) and can increase ATP levels. That finding prompted studies of TZ in Parkinson's disease (PD) in which decreased neuronal energy metabolism is a hallmark feature. TZ was neuroprotective in cell-based and animal PD models and in large epidemiological studies of humans. However, how TZ might increase PGK1 activity has remained a perplexing question because structural data revealed that the site of TZ binding to PGK1 overlaps with the site of substrate binding, predicting that TZ would competitively inhibit activity. Functional data also indicate that TZ is a competitive inhibitor. To explore the paradoxical observation of a competitive inhibitor increasing enzyme activity under some conditions, we developed a mass action model of TZ and PGK1 interactions using published data on PGK1 kinetics and the effect of varying TZ concentrations. The model indicated that TZ-binding introduces a bypass pathway that accelerates product release. At low concentrations, TZ binding circumvents slow product release and increases the rate of enzymatic phosphotransfer. However, at high concentrations, TZ inhibits PGK1 activity. The model explains stimulation of enzyme activity by a competitive inhibitor and the biphasic dose-response relationship for TZ and PGK1 activity. By providing a plausible mechanism for interactions between TZ and PGK1, these findings may aid development of TZ or other agents as potential therapeutics for neurodegenerative diseases. The results may also have implications for agents that interact with the active site of other enzymes.

Activation of primate frontal eye fields with a CMUT phased array system.

BACKGROUND: There are pushes toward non-invasive stimulation of neural tissues to prevent issues that arise from invasive brain recordings and stimulation. Transcranial Focused Ultrasound (TFUS) has been examined as a way to stimulate non-invasively, but previous studies have limitations in the application of TFUS. As a result, refinement is needed to improve stimulation results. NEW METHOD: We utilized a custom-built capacitive micromachined ultrasonic transducer (CMUT) that would send ultrasonic waves through skin and skull to targets located in the Frontal Eye Fields (FEF) region triangulated from co-registered MRI and CT scans while a non-human primate subject was performing a discrimination behavioral task. RESULTS: We observed that the stimulation immediately caused changes in the local field potential (LFP) signal that continued until stimulation ended, at which point there was higher voltage upon the cue for the animal to saccade. This co-incided with increases in activity in the alpha band during stimulation. The activity rebounded mid-way through our electrode-shank, indicating a specific point of stimulation along the shank. We observed different LFP signals for different stimulation targets, indicating the ability to"steer" the stimulation through the transducer. We also observed a bias in first saccades towards the opposite direction. CONCLUSIONS: In conclusion, we provide a new approach for non-invasive stimulation during performance of a behavioral task. With the ability to steer stimulation patterns and target using a large amount of transducers, the ability to provide non-invasive stimulation will be greatly improved for future clinical and research applications.

Corrigendum: Patterned hippocampal stimulation facilitates memory in patients with a history of head impact and/or brain injury.

[This corrects the article DOI: 10.3389/fnhum.2022.933401.].

Patterned Hippocampal Stimulation Facilitates Memory in Patients With a History of Head Impact and/or Brain Injury.

RATIONALE: Deep brain stimulation (DBS) of the hippocampus is proposed for enhancement of memory impaired by injury or disease. Many pre-clinical DBS paradigms can be addressed in epilepsy patients undergoing intracranial monitoring for seizure localization, since they already have electrodes implanted in brain areas of interest. Even though epilepsy is usually not a memory disorder targeted by DBS, the studies can nevertheless model other memory-impacting disorders, such as Traumatic Brain Injury (TBI). METHODS: Human patients undergoing Phase II invasive monitoring for intractable epilepsy were implanted with depth electrodes capable of recording neurophysiological signals. Subjects performed a delayed-match-to-sample (DMS) memory task while hippocampal ensembles from CA1 and CA3 cell layers were recorded to estimate a multi-input, multi-output (MIMO) model of CA3-to-CA1 neural encoding and a memory decoding model (MDM) to decode memory information from CA3 and CA1 neuronal signals. After model estimation, subjects again performed the DMS task while either MIMO-based or MDM-based patterned stimulation was delivered to CA1 electrode sites during the encoding phase of the DMS trials. Each subject was sorted (post hoc) by prior experience of repeated and/or mild-to-moderate brain injury (RMBI), TBI, or no history (control) and scored for percentage successful delayed recognition (DR) recall on stimulated vs. non-stimulated DMS trials. The subject's medical history was unknown to the experimenters until after individual subject memory retention results were scored. RESULTS: When examined compared to control subjects, both TBI and RMBI subjects showed increased memory retention in response to both MIMO and MDM-based hippocampal stimulation. Furthermore, effects of stimulation were also greater in subjects who were evaluated as having pre-existing mild-to-moderate memory impairment. CONCLUSION: These results show that hippocampal stimulation for memory facilitation was more beneficial for subjects who had previously suffered a brain injury (other than epilepsy), compared to control (epilepsy) subjects who had not suffered a brain injury. This study demonstrates that the epilepsy/intracranial recording model can be extended to test the ability of DBS to restore memory function in subjects who previously suffered a brain injury other than epilepsy, and support further investigation into the beneficial effect of DBS in TBI patients.

Prefrontal cortical plasticity during learning of cognitive tasks.

Training in working memory tasks is associated with lasting changes in prefrontal cortical activity. To assess the neural activity changes induced by training, we recorded single units, multi-unit activity (MUA) and local field potentials (LFP) with chronic electrode arrays implanted in the prefrontal cortex of two monkeys, throughout the period they were trained to perform cognitive tasks. Mastering different task phases was associated with distinct changes in neural activity, which included recruitment of larger numbers of neurons, increases or decreases of their firing rate, changes in the correlation structure between neurons, and redistribution of power across LFP frequency bands. In every training phase, changes induced by the actively learned task were also observed in a control task, which remained the same across the training period. Our results reveal how learning to perform cognitive tasks induces plasticity of prefrontal cortical activity, and how activity changes may generalize between tasks.

Identification of Images of COVID-19 from Chest X-rays Using Deep Learning: Comparing COGNEX VisionPro Deep Learning 1.0™ Software with Open Source Convolutional Neural Networks.

The novel Coronavirus, COVID-19, pandemic is being considered the most crucial health calamity of the century. Many organizations have come together during this crisis and created various Deep Learning models for the effective diagnosis of COVID-19 from chest radiography images. For example, The University of Waterloo, along with Darwin AI-a start-up spin-off of this department, has designed the Deep Learning model 'COVID-Net' and created a dataset called 'COVIDx' consisting of 13,975 images across 13,870 patient cases. In this study, COGNEX's Deep Learning Software, VisionPro Deep Learning™,  is used to classify these Chest X-rays from the COVIDx dataset. The results are compared with the results of COVID-Net and various other state-of-the-art Deep Learning models from the open-source community. Deep Learning tools are often referred to as black boxes because humans cannot interpret how or why a model is classifying an image into a particular class. This problem is addressed by testing VisionPro Deep Learning with two settings, first, by selecting the entire image as the Region of Interest (ROI), and second, by segmenting the lungs in the first step, and then doing the classification step on the segmented lungs only, instead of using the entire image. VisionPro Deep Learning results: on the entire image as the ROI it achieves an overall F score of 94.0%, and on the segmented lungs, it gets an F score of 95.3%, which is better than COVID-Net and other state-of-the-art open-source Deep Learning models.

Working memory capacity is enhanced by distributed prefrontal activation and invariant temporal dynamics.

The amount of information that can be stored in working memory is limited but may be improved with practice. The basis of improved efficiency at the level of neural activity is unknown. To investigate this question, we trained monkeys to perform a working memory task that required memory for multiple stimuli. Performance decreased as a function of number of stimuli to be remembered, but improved as the animals practiced the task. Neuronal recordings acquired during this training revealed two hitherto unknown mechanisms of working memory capacity improvement. First, more prefrontal neurons became active as working memory improved, but their baseline activity decreased. Second, improved working memory capacity was characterized by less variable temporal dynamics, resulting in a more consistent firing rate at each time point during the course of a trial. Our results reveal that improved performance of working memory tasks is achieved through more distributed activation and invariant neuronal dynamics.

Anterior-posterior gradient of plasticity in primate prefrontal cortex.

The functional organization of the primate prefrontal cortex has been a matter of debate with some models speculating dorso-ventral and rostro-caudal specialization while others suggesting that information is represented dynamically by virtue of plasticity across the entire prefrontal cortex. To address functional properties and capacity for plasticity, we recorded from different prefrontal sub-regions and analyzed changes in responses following training in a spatial working memory task. This training induces more pronounced changes in anterior prefrontal regions, including increased firing rate during the delay period, selectivity, reliability, information for stimuli, representation of whether a test stimulus matched the remembered cue or not, and variability and correlation between neurons. Similar results are obtained for discrete subdivisions or when treating position along the anterior-posterior axis as a continuous variable. Our results reveal that anterior aspects of the lateral prefrontal cortex of non-human primates possess greater plasticity based on task demands.

Lateralization of Executive Function: Working Memory Advantage for Same Hemifield Stimuli in the Monkey.

Working memory capacity, the amount of information that may be maintained in mind over a period of seconds, is extremely limited, to a handful of items. Some evidence exists that the number of visual items that may be maintained in working memory is independent for the two hemifields. To test this idea, we trained monkeys to perform visual working memory tasks that required maintenance in memory of the locations and/or shapes of 3-5 visual stimuli. We then tested whether systematic performance differences were present for stimuli concentrated in the same hemifield, vs. distributed across hemifields. We found little evidence to support the expectation that working memory capacity is independent in the two hemifields. Instead, when an advantage of stimulus arrangement was present, it involved multiple stimuli presented in the same hemifield. This conclusion was consistent across variations of the task, performance levels, and apparent strategies adopted by individual subjects. This result suggests that factors such as grouping that favor processing of stimuli in relative proximity may counteract the benefits of independent processing in the two hemispheres. Our results reveal an important property of working memory and place constraints on models of working memory capacity.

Functional specialization of areas along the anterior-posterior axis of the primate prefrontal cortex.

Functional specialization of areas along the anterior-posterior axis of the lateral prefrontal cortex has been speculated but little evidence exists about distinct neurophysiological properties between prefrontal sub-regions. To address this issue we divided the lateral prefrontal cortex into a posterior-dorsal, a mid-dorsal, an anterior-dorsal, a posterior-ventral, and an anterior ventral region. Selectivity for spatial locations, shapes, and colors was evaluated in six monkeys never trained in working memory tasks, while they viewed the stimuli passively. Recordings from over two thousand neurons revealed systematic differences between anterior and posterior regions. In the dorsal prefrontal cortex, anterior regions exhibited the largest receptive fields, longest response latencies, and lowest amount of information for stimuli. In the ventral prefrontal cortex, posterior regions were characterized by a low percentage of responsive neurons to any stimuli we used, consistent with high specialization for stimulus features. Additionally, spatial information was more prominent in the dorsal and color in ventral regions. Our results provide neurophysiological evidence for a rostral-caudal gradient of stimulus selectivity through the prefrontal cortex, suggesting that posterior areas are selective for stimuli even when these are not releant for execution of a task, and that anterior areas are likely engaged in more abstract operations.

Role of Prefrontal Persistent Activity in Working Memory.

The prefrontal cortex is activated during working memory, as evidenced by fMRI results in human studies and neurophysiological recordings in animal models. Persistent activity during the delay period of working memory tasks, after the offset of stimuli that subjects are required to remember, has traditionally been thought of as the neural correlate of working memory. In the last few years several findings have cast doubt on the role of this activity. By some accounts, activity in other brain areas, such as the primary visual and posterior parietal cortex, is a better predictor of information maintained in visual working memory and working memory performance; dynamic patterns of activity may convey information without requiring persistent activity at all; and prefrontal neurons may be ill-suited to represent non-spatial information about the features and identity of remembered stimuli. Alternative interpretations about the role of the prefrontal cortex have thus been suggested, such as that it provides a top-down control of information represented in other brain areas, rather than maintaining a working memory trace itself. Here we review evidence for and against the role of prefrontal persistent activity, with a focus on visual neurophysiology. We show that persistent activity predicts behavioral parameters precisely in working memory tasks. We illustrate that prefrontal cortex represents features of stimuli other than their spatial location, and that this information is largely absent from early cortical areas during working memory. We examine memory models not dependent on persistent activity, and conclude that each of those models could mediate only a limited range of memory-dependent behaviors. We review activity decoded from brain areas other than the prefrontal cortex during working memory and demonstrate that these areas alone cannot mediate working memory maintenance, particularly in the presence of distractors. We finally discuss the discrepancy between BOLD activation and spiking activity findings, and point out that fMRI methods do not currently have the spatial resolution necessary to decode information within the prefrontal cortex, which is likely organized at the micrometer scale. Therefore, we make the case that prefrontal persistent activity is both necessary and sufficient for the maintenance of information in working memory.

Closing the loop for memory prosthesis: detecting the role of hippocampal neural ensembles using nonlinear models.

A major factor involved in providing closed loop feedback for control of neural function is to understand how neural ensembles encode online information critical to the final behavioral endpoint. This issue was directly assessed in rats performing a short-term delay memory task in which successful encoding of task information is dependent upon specific spatio-temporal firing patterns recorded from ensembles of CA3 and CA1 hippocampal neurons. Such patterns, extracted by a specially designed nonlinear multi-input multi-output (MIMO) nonlinear mathematical model, were used to predict successful performance online via a closed loop paradigm which regulated trial difficulty (time of retention) as a function of the "strength" of stimulus encoding. The significance of the MIMO model as a neural prosthesis has been demonstrated by substituting trains of electrical stimulation pulses to mimic these same ensemble firing patterns. This feature was used repeatedly to vary "normal" encoding as a means of understanding how neural ensembles can be "tuned" to mimic the inherent process of selecting codes of different strength and functional specificity. The capacity to enhance and tune hippocampal encoding via MIMO model detection and insertion of critical ensemble firing patterns shown here provides the basis for possible extension to other disrupted brain circuitry.

A nonlinear model for hippocampal cognitive prosthesis: memory facilitation by hippocampal ensemble stimulation.

Collaborative investigations have characterized how multineuron hippocampal ensembles encode memory necessary for subsequent successful performance by rodents in a delayed nonmatch to sample (DNMS) task and utilized that information to provide the basis for a memory prosthesis to enhance performance. By employing a unique nonlinear dynamic multi-input/multi-output (MIMO) model, developed and adapted to hippocampal neural ensemble firing patterns derived from simultaneous recorded CA1 and CA3 activity, it was possible to extract information encoded in the sample phase necessary for successful performance in the nonmatch phase of the task. The extension of this MIMO model to online delivery of electrical stimulation delivered to the same recording loci that mimicked successful CA1 firing patterns, provided the means to increase levels of performance on a trial-by-trial basis. Inclusion of several control procedures provides evidence for the specificity of effective MIMO model generated patterns of electrical stimulation. Increased utility of the MIMO model as a prosthesis device was exhibited by the demonstration of cumulative increases in DNMS task performance with repeated MIMO stimulation over many sessions on both stimulation and nonstimulation trials, suggesting overall system modification with continued exposure. Results reported here are compatible with and extend prior demonstrations and further support the candidacy of the MIMO model as an effective cortical prosthesis.