Computational neuroscience

Rhythmic movement is the fundamental motion dynamics characterized by repetitive patterns. Precisely defining onsets in rhythmic movement is essential for a comprehensive analysis of motor functions. Our study introduces an automated method for detecting rat's forelimb foot-strike onsets using deep learning tools. This method demonstrates high accuracy of onset detection by combining two techniques using joint coordinates and behavioral confidence scale. The analysis extends to neural oscillatory responses in the rat's somatosensory cortex, validating the effectiveness of our combined approach...

Trial-averaged metrics, e.g. tuning curves or population response vectors, are a ubiquitous way of characterizing neuronal activity. But how relevant are such trial-averaged responses to neuronal computation itself? Here we present a simple test to estimate whether average responses reflect aspects of neuronal activity that contribute to neuronal processing. The test probes two assumptions implicitly made whenever average metrics are treated as meaningful representations of neuronal activity: Reliability: Neuronal responses repeat consistently enough across trials that they convey a recognizable reflection of the average response to downstream regions...

The compound N -(3-(phenylselanyl)prop-2-yn-1-yl)benzamide (SePB), which combines a selenium atom and a benzamide nucleus in an organic structure, has demonstrated a fast antidepressant-like effect in mice. This action is influenced by the serotonergic system and represents a promising development in the search for novel antidepressant drugs to treat major depressive disorder (MDD), which often resists conventional treatments. This study aimed to further explore the mechanism underlying the antidepressant-like effect of SePB by investigating the involvement of the dopaminergic and noradrenergic systems in the tail suspension test (TST) in mice and evaluating its pharmacokinetic profile in silico...

Treadmill running is a common method of exercise and to study human locomotion. Research has examined the kinematics and kinetics of overground and treadmill running, but there has been less focus on the levels of muscle activity during treadmill running. We investigated if muscle activity is different while running overground compared to running on a variety of treadmills. A total of 11 healthy individuals ran at 3 speeds (2.6, 3.6, 4.5 m/s) under 4 different running conditions (3 treadmills, overground). The three treadmills included a typical home exercise treadmill, a midsize commercial research treadmill, and a large, instrumented research treadmill...

INTRODUCTION: With the help of robot technology, intelligent rehabilitation of patients with lower limb motor dysfunction caused by stroke can be realized. A key factor constraining the clinical application of rehabilitation robots is how to realize pattern recognition of human movement intentions by using the surface electromyography (sEMG) sensors to ensure unhindered human-robot interaction. METHODS: A multilayer CNN-LSTM prediction network incorporating the self-attention mechanism (SAM) is proposed, in this paper, which can extract and learn the periodic and trend characteristics of the sEMG signals, and realize the accurate autoregressive prediction of the human motion information...

INTRODUCTION: The capacity to understand the others' emotional states, particularly if negative (e.g. sadness or fear), underpins the empathic and social brain. Patients who cannot express their emotional states experience social isolation and loneliness, exacerbating distress. We investigated the feasibility of detecting non-invasive scalp-recorded electrophysiological signals that correspond to recalled emotional states of sadness, fear, and joy for potential classification. METHODS: The neural activation patterns of 20 healthy and right-handed participants were studied using an electrophysiological technique...

INTRODUCTION: Repetitive transcranial magnetic stimulation (rTMS) is a widely used therapeutic tool in neurology and psychiatry, but its cellular and molecular mechanisms are not fully understood. Standardizing stimulus parameters, specifically electric field strength, is crucial in experimental and clinical settings. It enables meaningful comparisons across studies and facilitates the translation of findings into clinical practice. However, the impact of biophysical properties inherent to the stimulated neurons and networks on the outcome of rTMS protocols remains not well understood...

Vacuole formation is a key hallmark of age-dependent neurodegeneration in the Drosophila brain. Here, we present a protocol to analyze 3D neurodegenerative vacuoles in the whole-mount Drosophila melanogaster brain. We describe steps for whole-brain dissection, staining, 3D imaging, and z-stack image processing using Fiji ImageJ. We then detail procedures for annotating and 3D-reconstructing neurodegenerative vacuoles with WEBKNOSSOS and Python, and performing statistical analysis in Python. This protocol enables measurement of parameters such as the number and volume of each vacuole...

This article explores the contribution of the double error dynamic asymptote computational associative learning model to understanding the role of mediated learning mechanisms in the generation of spurious associations, as those postulated to characterize schizophrenia. Three sets of simulations for mediated conditioning, mediated extinction, and a mediated enhancement of latent inhibition, a unique model prediction, are presented. For each set of simulations, a parameter that modulates the impact of associative memory retrieval and the dissipation of nonperceptual activated representations through the network was manipulated...

According to experts in neurology, brain tumours pose a serious risk to human health. The clinical identification and treatment of brain tumours rely heavily on accurate segmentation. The varied sizes, forms, and locations of brain tumours make accurate automated segmentation a formidable obstacle in the field of neuroscience. U-Net, with its computational intelligence and concise design, has lately been the go-to model for fixing medical picture segmentation issues. Problems with restricted local receptive fields, lost spatial information, and inadequate contextual information are still plaguing artificial intelligence...

In the realm of motor rehabilitation, Brain-Computer Interface Neurofeedback Training (BCI-NFT) emerges as a promising strategy. This aims to utilize an individual's brain activity to stimulate or assist movement, thereby strengthening sensorimotor pathways and promoting motor recovery. Employing various methodologies, BCI-NFT has been shown to be effective for enhancing motor function primarily of the upper limb in stroke, with very few studies reported in cerebral palsy (CP). Our main objective was to develop an electroencephalography (EEG)-based BCI-NFT system, employing an associative learning paradigm, to improve selective control of ankle dorsiflexion in CP and potentially other neurological populations...

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BACKGROUND: Predicting cognitive decline in those already Aβ positive or Tau positive among the aging population poses clinical challenges. In Alzheimer's disease (AD) research, intra-default mode network (DMN) connections play a pivotal role in diagnosis. This paper proposes metabolic connectivity within the DMN as a supplementary biomarker to the AT(N) framework. METHODS: Extracting data from 1292 subjects in the Alzheimer's Disease Neuroimaging Initiative, we collected paired T1-weighted structural MRI and 18 F-labeled-fluorodeoxyglucose positron emission computed tomography (PET) scans...

Growth-associated protein 43 (GAP-43) is found in the axonal terminal of neurons in the limbic system, which is affected in people with Alzheimer's disease (AD). We assumed GAP-43 may contribute to AD progression and serve as a biomarker. So, in a two-year follow-up study, we assessed GAP-43 changes and whether they are correlated with tensor-based morphometry (TBM) findings in patients with mild cognitive impairment (MCI). We included MCI and cognitively normal (CN) people with available baseline and follow-up cerebrospinal fluid (CSF) GAP-43 and TBM findings from the ADNI database...

Neuroimaging biomarkers have shown high potential to map the disease processes in the application to neurodegenerative diseases (NDD), e.g., diffusion tensor imaging (DTI). For DTI, the implementation of a standardized scanning and analysis cascade in clinical trials has potential to be further optimized. Over the last few years, various approaches to improve DTI applications to NDD have been developed. The core issue of this review was to address considerations and limitations of DTI in NDD: we discuss suggestions for improvements of DTI applications to NDD...

The nested data structure is prevalent for cognitive measure experiments due to repeatedly taken observations from different brain locations within subjects. The analysis methods used for this data type should consider the dependency structure among the repeated measurements. However, the dependency assumption is mainly ignored in the cognitive neuroscience data analysis literature. We consider both statistical, and machine learning methods extended to repeated data analysis and compare distinct algorithms in terms of their advantage and disadvantages...

No abstract text is available yet for this article.

No abstract text is available yet for this article.

No abstract text is available yet for this article.

No abstract text is available yet for this article.