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Lashgari, R.

Publications and source records attributed to Lashgari, R..

7 recordsLinked to original sources

Cortical spectral rebalancing underlies pregabalin's efficacy in restless legs syndrome

Restless legs syndrome afflicts nearly half of all patients with end-stage renal disease, imposing profound burdens through relentless insomnia and functional decline. Dopaminergic agents--the established first-line therapy--trigger augmentation, a paradoxical intensification of symptoms that leaves patients worse than when they began treatment. The resultant therapeutic impasse has left a vulnerable population without effective recourse. Here we demonstrate that pregabalin, an 2{delta} calcium channel ligand with established renal safety, provides robust and sustained symptom relief in a randomized, placebo-controlled trial of dialysis patients with uremic restless legs syndrome. Clinical benefits emerged rapidly and intensified over three months, with pregabalin-treated patients showing more than twofold greater likelihood of categorical improvement in disease severity. In complementary animal electrophysiology experiments, pregabalin distinctively suppressed cortical hyperarousal--a hallmark of restless legs syndrome--by rebalancing low- and high-frequency oscillatory activity patterns, particularly in motor regions. This neural signature closely resembled that of gabapentin, another therapeutically effective 2{delta} ligand, suggesting convergent mechanisms. Beyond establishing pregabalin as an urgently needed alternative to dopaminergic therapy, our results demonstrate that cortical oscillatory profiling can reveal mechanistic convergence among candidate therapeutics, offering a rational preclinical screening strategy for neurological disorders before exposure of high-risk patient populations to experimental interventions. This framework may accelerate therapeutic discovery while reducing clinical trial risks in vulnerable populations.

neuroscience↗

A comparative study of rigid-body registration algorithms for the alignment of longitudinal structural MRI of the brain

Longitudinal structural MRI (sMRI) may be used to characterize brain morphological changes over time. A key requirement for this approach is accurate rigid-body alignment of longitudinal sMRI. We have recently developed the automatic temporal registration algorithm (ATRA) for this purpose. ATRA is a landmark-based approach capable of registering dozens of serial sMRI simultaneously in an unbiased manner. The aim of the research presented in this paper was to evaluate the accuracy and inverse-consistency of ATRA in comparison to three commonly used sMRI alignment methods: FSL, FreeSurfer, and ANTS. In the absence of a ground truth, it is only possible to quantitatively determine the degree of discrepancy between two algorithms. We propose that if the discrepancy exceeds a certain threshold, the relative accuracy of the two algorithms could be determined visually. We computed the discrepancy between ATRA and each of the three other methods for the alignment of 150 pairs of sMRI taken roughly one year apart. We visually rated the accuracy of alignments in cases where the discrepancy was greater than .5 mm while the rater was agnostic to the registration method. In those instances, ATRA was considered more accurate than FSL in 46 out of 48 cases, more accurate than FreeSurfer in 6 out of 7 cases, and more accurate than ANTS in all 6 cases. ATRA was also the most inverse-consistent method. In addition to being capable of performing unbiased group-wise registration, ATRA is the most accurate algorithm in comparison to several commonly used rigid-body alignment methods.

neuroscience↗

Sharp wave ripples in macaque V1 and V4 are modulated by top-down visual attention

Sharp-wave ripples (SWRs) are highly synchronous neuronal activity events. They have been predominantly observed in the hippocampus during offline states such as pause in exploration, slow-wave sleep and quiescent wakefulness. SWRs have been linked to memory consolidation, spatial navigation, and spatial decision-making. Recently, SWRs have been reported during visual search, a form of remote spatial exploration, in macaque hippocampus. However, the association between SWRs and multiple forms of awake conscious and goal-directed behavior is unknown. We report that ripple activity occurs in macaque visual areas V1 and V4 during focused spatial attention. The frequency of ripples is modulated by characteristics of the stimuli, by spatial attention directed toward a receptive field, and by the size of the attentional focus. Critically, the monkeys reaction times in detecting behaviorally relevant stimulus changes was affected on trials with SWRs. These results show that ripple activity is not limited to hippocampal activity during offline states, rather they occur in the neocortex during active attentive states and vigilance behaviors.

neuroscience↗

V1 receptive field structure contributes to neuronal response latency

The timing of neuronal responses is considered to be important for information transferring and communication across individual neurons. However, the sources of variabilities in the timing of neuronal responses are not well understood and sometimes over-interpreted. A systematic variability in the response latencies of the primary visual cortex has been reported in presence of drifting grating stimulus. Whereas the response latencies are systematically dependent on stimulus orientation. To understand the underlying mechanism of these systematic latencies, we recorded the neuronal response of the cat visual cortex, area 17, and simulated the response latency of V1 neurons, with two geometric models. We showed that outputs of these two models significantly predict the response latencies of the electrophysiology recording during orientation tasks. The periodic patterns created in the raster plots were dependent on the relative position of the stimulus rotation center and the receptive-field sub-regions. We argue the position of stimulus is contributing to systematic response latencies, dependent on drifting orientation. Therefore, we provide a toolbox based on our geometrical model for determining the exact location of RF sub-regions. Our result indicates that a major source of neuronal variability is the lack of fine-tuning in the task parameters. Considering the simplicity of the orientation selectivity task, we argue fine-tuning of stimulus properties is crucial for deduction of neural variability in higher-order cortical areas and understanding their neural dynamics.

neuroscience↗

Neural Oscillatory Characteristics of Feedback Associated Activity in Globus Pallidus Interna

Neural oscillatory activities in basal ganglia have prominent roles in cognitive processes on local and global scales. However, the characteristics of high frequency oscillatory activities during cognitive tasks have not been extensively explored in human Globus Pallidus internus (GPi). This study aimed to investigate amplitude and interhemispheric coupling of bilateral GPi high gamma bursts in dystonia and Parkinsons Disease (PD) patients, in on and off medication states, after feedback during the Intra-Extra-Dimension shift (IED) task. Bilateral GPi Local Field Potentials (LFP) activity was recorded via externalized DBS electrodes during the IED task. Inter hemisphere phase synchrony was assessed using Inter-Site Phase Clustering (ISPC). Transient high gamma activity ([~]100-150Hz) was observed immediately after feedback in the dystonia patient. Moreover, these bursts were phase synchronous between left and right GPis with an antiphase clustering of phase differences. In contrast, no synchronous high gamma activity was detected in the PD patient with or without dopamine administration. The off-med PD patient displayed enhanced low frequency clusters ameliorated by medication in the on-med state. Furthermore, an increased low frequency activity was observed after feedback of incorrect trials in both disease states. The current study provides a rare report of antiphase homotopic synchrony in human GPi, potentially related to incorporating and processing feedback information. The absence of these activities in off and on-med PD indicates the potential presence of impaired medication independent circuits related to feedback processing. Together, these findings are helpful in pointing to the potential role of GPis synchronized high frequency activity in cognitive tasks and feedback information processing.

neuroscience↗

Distinct signatures of reward value and variance on spiking activity and functional interactions in monkey fronto-parietal areas

Theories of executive function propose that controlled information processing is costly and is allocated according to the behavioral benefits it brings. Computational theories predict that the benefits of new information depend on prior uncertainty, but the cellular effects of uncertainty on the executive network are incompletely understood. Using simultaneous recordings in monkeys, we reveal several mechanisms by which the fronto-parietal network reacts to uncertainty independently of average reward gains. We show that the variance of expected rewards, independently of the value of the rewards, was represented in single neuron and population spiking activity and local field potential (LFP) oscillations. Moreover, uncertainty asymmetrically affected the coherence between spikes and LFPs, selectively suppressing information transmission from the frontal to the parietal lobe but enhancing transmission from the parietal to the frontal lobe, consistent with Bayesian principles of optimal inference under uncertainty.

neuroscience↗

A fully automated spike sorting algorithm using t-distributed neighbor embedding and density based clustering

In this study, a new spike sorting method was developed based on a combination of two methods, t-Distributed Stochastic Neighbor Embedding (t-SNE) and Density-Based Spatial Clustering of Applications with Noise (DBSCAN). Parameters of both methods were simultaneously optimized using a Genetic Algorithm (GA) using a simulated dataset containing 2 to 20 simultaneously recorded neurons. The performance of this method was evaluated using both the simulated dataset as well as real multichannel electrophysiological data. The results indicated that our fully automated algorithm using t-SNE-DBSCAN outperforms other state-of-the-art algorithms and human experts in spike sorting especially when there are a large number of simultaneously recorded units. Our algorithm also determines the noise waveforms and has an overall high sensitivity, precision and accuracy for correctly classifying waveforms belonging to each neuron (all >90%) without the need for manual corrections afterwards. Our method can be a crucial part of the analysis pipeline in particular when manual sorting of units is becoming prohibitive due to the sheer number of recorded neurons per session.

neuroscience↗