Neural Components Research Articles

The effect of botulinum toxin-A on neural and non-neural components of wrist hyper-resistance in adults with stroke or cerebral palsy.

BackgroundBotulinum toxin‐A (BoNT) is widely used to manage focal upper limb spasticity and is effective in reducing resistance to passive movement, as measured with the modified Ashworth scale. Discrimination and quantification of the underlying neural and non‐neural components of hyper‐resistance may further improve understanding of the effect of BoNT.ObjectiveTo explore the effects of BoNT on neural (NC), non‐neural elastic (EC), and viscous (VC) components of resistance to passive wrist extension in adults with stroke or cerebral palsy and the association between the effects on wrist hyper‐resistance components and clinical spasticity, pain and motor function scales.DesignPre‐experimental study with pre‐ and post‐intervention measurements at 6 and 12 weeks.SettingAn outpatient clinic of a hospital.ParticipantsAdults with chronic stroke or cerebral palsy indicated for BoNT treatment for hyper‐resistance in the wrist (N = 18).InterventionsBoNT injections in the wrist and/or finger flexor muscles.Main Outcome MeasuresWrist hyper‐resistance components, using the NeuroFlexor, and clinical scales (modified Ashworth scale, Tardieu scale, passive wrist extension, pain, Fugl‐Meyer motor assessment of the upper extremity, and action research arm test).ResultsNC was significantly reduced 6 and 12 weeks post‐intervention (median −11.96 Newton, P < .001 and median −9.34 Newton, P = .001, respectively); non‐neural EC and VC showed no change. NC reduction 6 weeks post‐intervention correlated significantly with BoNT dose (Pearson correlation coefficient r p = −0.56). No significant correlations were found between change scores in wrist hyper‐resistance components and clinical scales.ConclusionsBoNT affected the neural component of resistance to passive wrist extension, while leaving the non‐neural elastic and viscous components unaffected. This instrumented approach to quantify the effects of BoNT in the wrist and finger flexor muscles on the components of wrist hyper‐resistance may have an added value for BoNT treatment evaluation in clinical practice.

Neural Entrainment Meets Behavior: The Stability Index as a Neural Outcome Measure of Auditory-Motor Coupling.

Understanding rhythmic behavior in the context of coupled auditory and motor systems has been of interest to neurological rehabilitation, in particular, to facilitate walking. Recent work based on behavioral measures revealed an entrainment effect of auditory rhythms on motor rhythms. In this study, we propose a method to compute the neural component of such a process from an electroencephalographic (EEG) signal. A simple auditory-motor synchronization paradigm was used, where 28 healthy participants were instructed to synchronize their finger-tapping with a metronome. The computation of the neural outcome measure was carried out in two blocks. In the first block, we used Generalized Eigendecomposition (GED) to reduce the data dimensionality to the component which maximally entrained to the metronome frequency. The scalp topography pointed at brain activity over contralateral sensorimotor regions. In the second block, we computed instantaneous frequency from the analytic signal of the extracted component. This returned a time-varying measure of frequency fluctuations, whose standard deviation provided our “stability index” as a neural outcome measure of auditory-motor coupling. Finally, the proposed neural measure was validated by conducting a correlation analysis with a set of behavioral outcomes from the synchronization task: resultant vector length, relative phase angle, mean asynchrony, and tempo matching. Significant moderate negative correlations were found with the first three measures, suggesting that the stability index provided a quantifiable neural outcome measure of entrainment, with selectivity towards phase-correction mechanisms. We address further adoption of the proposed approach, especially with populations where sensorimotor abilities are compromised by an underlying pathological condition. The impact of using stability index can potentially be used as an outcome measure to assess rehabilitation protocols, and possibly provide further insight into neuropathological models of auditory-motor coupling.

Roles of the ventral hippocampus and medial prefrontal cortex in spatial reversal learning and attentional set-shifting

Neural components enabling flexible cognition and behavior are well-established, and depend mostly on proper intercommunication within the prefrontal cortex (PFC) and striatum. However, dense projections from the ventral hippocampus (vHPC) alter the functioning of the medial PFC (mPFC). Dysfunctional hippocampo-prefrontal connectivity negatively affects the integrity of flexible cognition, especially in patients with schizophrenia. In this study, we aimed to test the role of the vHPC and mPFC in a place avoidance task on a rotating arena using two spatial flexibility task variants – reversal learning and set-shifting. To achieve this, we inactivated each of these structures in adult male Long-Evans rats by performing bilateral local muscimol (a GABAA receptor agonist) injections. A significantly disrupted performance was observed in reversal learning in the vHPC-inactivated, but not in the mPFC-inactivated rats. These results confirm the notion that the vHPC participates in some forms of behavioral flexibility, especially when spatial cues are needed. It seems, rather unexpectedly, that the mPFC is not taxed in these flexibility tasks on a rotating arena.

Cisplatin induces differentiation in teratomas derived from pluripotent stem cells.

IntroductionCurrently, embryonic stem cells (ESCs) and induced pluripotent stem cells (iPSCs) can be induced to differentiate at the cellular level but not to form mature tissues or organs suitable for transplantation. ESCs/iPSCs form immature teratomas after injection into immunodeficient mice. In humans, immature teratomas often transform into fully differentiated mature teratomas after administration of anticancer agents.MethodsWe first investigated the ability of cisplatin to induce changes in mouse ESCs/iPSCs in vitro. Next, we designed experiments to analyze ESC/iPSC-derived immature teratoma tissue in vivo after treatment of cisplatin. Groups of six mice carrying ESC- or iPSC-derived teratomas were given either low or high dose intraperitoneal injection of cisplatin, while the control group received saline for 4 weeks.ResultsTreatment of ESC/iPSC cultures with cisplatin for 3 days caused a dose-related decrease in cell numbers without inducing any morphological changes to the cells. ESC/iPSC-derived teratomas showed lower growth rates with a significantly higher mature components ratio in a concentration dependent manner after cisplatin treatment (P < 0.05); however, immunohistochemical analyses demonstrated a significantly reduced PCNA labelling index and an increase in an apoptosis marker on immature neural components (P < 0.05) along with emergence of h-Caldesmon+ mature smooth muscle cells in treated mice. Moreover, newly differentiated components not found in the control group, such as mature adipose tissue, cartilage, and pancreas, as well as striated muscle, salivary glands, gastric mucosa with fundic glands, and hair follicles emerged. The identities of these components were confirmed by immunostaining for specific markers.ConclusionsCisplatin has the ability to reduce immature components in ESC/iPSC-derived teratomas, presumably through apoptosis, and also to induce them to differentiate.

Chronic resistance training: is it time to rethink the time course of neural contributions to strength gain?

Resistance training enhances muscular force due to a combination of neural plasticity and muscle hypertrophy. It has been well documented that the increase in strength over the first few weeks of resistance training (i.e. acute) has a strong underlying neural component and further enhancement in strength with long-term (i.e. chronic) resistance training is due to muscle hypertrophy. For obvious reasons, collecting long-term data on how chronic-resistance training affects the nervous system not feasible. As a result, the effect of chronic-resistance training on neural plasticity is less understood and has not received systematic exploration. Thus, the aim of this review is to provide rationale for investigating neural plasticity beyond acute-resistance training. We use cross-sectional work to highlight neural plasticity that occurs with chronic-resistance training at sites from the brain to spinal cord. Specifically, intra-cortical circuitry and the spinal motoneuron seem to be key sites for this plasticity. We then urge the need to further investigate the differential effects of acute versus chronic-resistance training on neural plasticity, and the role of this plasticity in increased strength. Such investigations may help in providing a clearer definition of the continuum of acute and chronic-resistance training, how the nervous system is altered during this continuum and the causative role of neural plasticity in changes in strength over the continuum of resistance training.

Applying Spike-density component analysis for high-accuracy auditory event-related potentials in children

ObjectiveOverlapping neurophysiological signals are the main obstacle preventing from using cortical auditory event-related potentials (AEPs) in clinical settings. Children AEPs are particularly affected by this problem, as their cerebral cortex is still maturing. To overcome this problem, we applied a new version of Spike-density Component Analysis (SCA), an analysis method recently developed, to isolate with high accuracy the neural components of auditory responses of 8-year-old children. MethodsElectroencephalography was used with 33 children to record AEPs to auditory stimuli varying in spectrotemporal features. Three different analysis approaches were adopted: the standard AEP analysis procedure, SCA with template-match (SCA-TM), and SCA with half-split average consistency (SCA-HSAC). ResultsSCA-HSAC most successfully allowed the extraction of AEPs for each child, revealing that the most consistent components were P1 and N2. An immature N1 component was also detected. ConclusionSuperior accuracy in isolating neural components at the individual level was demonstrated for SCA-HSAC over other SCA approaches even for children AEPs. SignificanceReliable methods of extraction of neurophysiological signals at the individual level are crucial for the application of cortical AEPs for routine diagnostic exams in clinical settings both in children and adults.

Inferring Association Between Alcohol Addiction and Defendant's Emotion Based on Sound at Court.

Alcohol addiction can lead to health and social problems. It can also affect people's emotions. Emotion plays a key role in human communications. It is important to recognize the people's emotions at the court and infer the association between the people's emotions and the alcohol addiction. However, it is challenging to recognize people's emotions efficiently in the courtroom. Furthermore, to the best of our knowledge, no existing work is about the association between alcohol addiction and people's emotions at court. In this paper, we propose a deep learning framework for predicting people's emotions based on sound perception, named ResCNN-SER. The proposed model combines several neural network-based components to extract the features of the speech signals and predict the emotions. The evaluation shows that the proposed model performs better than existing methods. By applying ResCNN-SER for emotion recognition based on people's voices at court, we infer the association between alcohol addiction and the defendant's emotion at court. Based on the sound source data from 54 trial records, we found that the defendants with alcohol addiction tend to get angry or fearful more easily at court comparing with defendants without alcohol addiction.

Revisiting the Neural Architecture of Adolescent Decision-Making: Univariate and Multivariate Evidence for System-Based Models.

Understanding adolescent decision-making is significant for informing basic models of neurodevelopment as well as for the domains of public health and criminal justice. System-based theories posit that adolescent decision-making is guided by activity related to reward and control processes. While successful at explaining behavior, system-based theories have received inconsistent support at the neural level, perhaps because of methodological limitations. Here, we used two complementary approaches to overcome said limitations and rigorously evaluate system-based models. Using decision-level modeling of fMRI data from a risk-taking task in a sample of 2000+ decisions across 51 human adolescents (25 females, mean age = 15.00 years), we find support for system-based theories of decision-making. Neural activity in lateral PFC and a multivariate pattern of cognitive control both predicted a reduced likelihood of risk-taking, whereas increased activity in the NAcc predicted a greater likelihood of risk-taking. Interactions between decision-level brain activity and age were not observed. These results garner support for system-based accounts of adolescent decision-making behavior.SIGNIFICANCE STATEMENT Adolescent decision-making behavior is of great import for basic science, and carries equally consequential implications for public health and criminal justice. While dominant psychological theories seeking to explain adolescent decision-making have found empirical support, their neuroscientific implementations have received inconsistent support. This may be partly because of statistical approaches used by prior neuroimaging studies of system-based theories. We used brain modeling, an approach that predicts behavior from brain activity, of univariate and multivariate neural activity metrics to better understand how neural components of psychological systems guide decision behavior in adolescents. We found broad support for system-based theories such that neural systems involved in cognitive control predicted a reduced likelihood to make risky decisions, whereas value-based systems predicted greater risk-taking propensity.

Efficacy of Low-Level Laser Therapy in Management of Temporomandibular Joint Pain: A Double Blind and Placebo Controlled Trial.

Stomatognathic system is an interaction of the muscles of mastication, dentition, neural component, and temporomandibular joint. Any dysfunction in this system may lead to temporomandibular disorders (TMDs). Various non-surgical modalities have been employed for treating TMDs. The aim of the study was to objectively evaluate the effects of low-level laser therapy (LLLT) in treatment of patients with TMDs. Sixty individuals diagnosed with TMDs were divided randomly into two groups (Group I-placebo and Group II -LLLT). A series of 20 sessions of LLLT applied both in closed mouth and maximum mouth opening position were given over a period of 08weeks. Assessment was done in terms of improvement in mouth opening, pain, clicking, and deviation of mandible. The data collected were analyzed statistically. The results showed improvement in the pain reduction, improvement in the maximum mouth opening, reduction in deviation, and clicking in both groups but better treatment outcome in the low-level laser group. Though conservative measures improved the symptoms in TMD but LLLT has shown better results in comparison with the placebo group. Being non-surgical can be employed in combination with other modes for effectively treating such disorders. Keywords: Pain, Dysfunction, Temporomandibular joint disorders (TMDs), Low-level laser therapy (LLLT).

Deep Innovation Protection: Confronting the Credit Assignment Problem in Training Heterogeneous Neural Architectures

Deep reinforcement learning approaches have shown impressive results in a variety of different domains, however, more complex heterogeneous architectures such as world models require the different neural components to be trained separately instead of end-to-end. While a simple genetic algorithm recently showed end-to-end training is possible, it failed to solve a more complex 3D task. This paper presents a method called Deep Innovation Protection (DIP) that addresses the credit assignment problem in training complex heterogenous neural network models end-to-end for such environments. The main idea behind the approach is to employ multiobjective optimization to temporally reduce the selection pressure on specific components in multi-component network, allowing other components to adapt. We investigate the emergent representations of these evolved networks, which learn to predict properties important for the survival of the agent, without the need for a specific forward-prediction loss.

A New Protocol of Computer-Assisted Image Analysis Highlights the Presence of Hemocytes in the Regenerating Cephalic Tentacles of Adult Pomacea canaliculata

In humans, injuries and diseases can result in irreversible tissue or organ loss. This well-known fact has prompted several basic studies on organisms capable of adult regeneration, such as amphibians, bony fish, and invertebrates. These studies have provided important biological information and helped to develop regenerative medicine therapies, but important gaps concerning the regulation of tissue and organ regeneration remain to be elucidated. To this aim, new models for studying regenerative biology could prove helpful. Here, the description of the cephalic tentacle regeneration in the adult of the freshwater snail Pomacea canaliculata is presented. In this invasive mollusk, the whole tentacle is reconstructed within 3 months. Regenerating epithelial, connective, muscular and neural components are already recognizable 72 h post-amputation (hpa). Only in the early phases of regeneration, several hemocytes are retrieved in the forming blastema. In view of quantifying the hemocytes retrieved in regenerating organs, granular hemocytes present in the tentacle blastema at 12 hpa were counted, with a new and specific computer-assisted image analysis protocol. Since it can be applied in absence of specific cell markers and after a common hematoxylin-eosin staining, this protocol could prove helpful to evidence and count the hemocytes interspersed among regenerating tissues, helping to unveil the role of immune-related cells in sensory organ regeneration.

Neuromorphic Devices: Artificial Neuron and Synapse Devices Based on 2D Materials (Small 20/2021)

In article number 2100640, Stephen J. Pearton, Gwan-Hyoung Lee, Jihyun Kim, and co-workers review the recent progress on 2D-material-based neuromorphic devices. Artificial neuromorphic devices, including synapse and neuron devices, have been classified according to their working principles. The unique properties of 2D materials make it possible to emulate biological neural components, enabling to realize the neuromorphic devices that emulate advanced neural functions with high energy efficiency and excellent performance.

A Neuro-Fuzzy Model for Conflict Prediction in the Niger Delta Region of Nigeria

This paper proposes a soft computing system driven by Neural Networks, Fuzzy Logic and Principal Component Analysis (PCA) for the prediction of conflict in the Niger Delta (ND) region of Nigeria. Identifying conflicts along the level of severity with which they arise in oil bearing host communities (OBHC) is of primary importance to government and other stakeholders, as the accompanying conflict risks mitigation course administered could be planned based on the level of severity of the conflict situations. The system is implemented using MATLAB and Microsoft Excel running on Microsoft Windows 10 operating system. The data set chosen for classification and experimental simulation is based on a statistical data obtained from a three-year field study of the nine states of the ND region of Nigeria. The average training and testing errors of 0.015514 and 0.053247 were obtained at 50 epochs for the model using a hybrid algorithm. PCA reduced the dimension of the original data set at a Cronbach’s alpha of over 0.8 with a 10-fold cross validation, thereby reducing the computational complexity and inference time of the model. The model predicted the conflict risk with an average accuracy of 92.85% and this compared favourably with domain experts conventional conflict prediction approaches. The result obtained gives a promising conclusion that the model is effective in predicting at high level of accuracy, the degree of conflict and presents a veritable decision support for conflict resolution and mediation agencies and stakeholders.

Prediction of Chromatography Conditions for Purification in Organic Synthesis Using Deep Learning.

In this research, a process for developing normal-phase liquid chromatography solvent systems has been proposed. In contrast to the development of conditions via thin-layer chromatography (TLC), this process is based on the architecture of two hierarchically connected neural network-based components. Using a large database of reaction procedures allows those two components to perform an essential role in the machine-learning-based prediction of chromatographic purification conditions, i.e., solvents and the ratio between solvents. In our paper, we build two datasets and test various molecular vectorization approaches, such as extended-connectivity fingerprints, learned embedding, and auto-encoders along with different types of deep neural networks to demonstrate a novel method for modeling chromatographic solvent systems employing two neural networks in sequence. Afterward, we present our findings and provide insights on the most effective methods for solving prediction tasks. Our approach results in a system of two neural networks with long short-term memory (LSTM)-based auto-encoders, where the first predicts solvent labels (by reaching the classification accuracy of 0.950 ± 0.001) and in the case of two solvents, the second one predicts the ratio between two solvents (R2 metric equal to 0.982 ± 0.001). Our approach can be used as a guidance instrument in laboratories to accelerate scouting for suitable chromatography conditions.

Regulation of Drosophila Long-Term Courtship Memory by Ecdysis Triggering Hormone.

Endocrine state is an important determinant of learning and memory in animals. In Drosophila, rejection of male courtship overtures by mated females leads to an aversive response manifested as courtship memory. Here we report that ecdysis triggering hormone (ETH) is an obligatory enabler of long-term courtship memory (LTM). ETH deficiency suppresses LTM, whereas augmented ETH release reduces the minimum training period required for LTM induction. ETH receptor knockdown either in the mushroom body (MB) γ lobe or in octopaminergic dorsal-anterior-lateral (DAL) neurons impairs memory performance, indicating its direct action in these brain areas. Consistent with these findings, brain exposure to ETH mobilizes calcium in MB γ lobe neuropils and DAL neurons. ETH receptor (ETHR) knockdown in the corpus allatum (CA) to create juvenile hormone (JH) deficiency also suppresses LTM, as does knockdown of the JH receptor Met in the MB γ lobe, indicating a convergence of ETH and JH signaling in this region of the brain. Our findings identify endocrine-enabled neural circuit components in the brain that are critical for persistent behavioral changes resulting from aversive social experience.

Single-trial FRPs: A Machine Learning Approach Towards the Study of the Neural Underpinning of Reading Disorders

Understanding the neural underpinning of reading disorders, such as dyslexia, is a fundamental question in developmental neuroscience. However, identifying and isolating informative neural components elicited during free-naming paradigms (i.e. unprompted and unconstrained naming tasks) has proven a challenging methodological task. These methodological barriers have hindered the study of the neural underpinnings of reading disorders. In this paper, we proposed a machine learning approach for detecting neural components during free-naming, overcoming much of the current methodological challenges. We propose a new neural-based metric to differentiate groups of children with dyslexia (DYS) and their chronological age controls (CAC) in a free-naming task. Our approach combines electroencephalography (EEG) and eye-tracking measures to generate single-trial fixation-related potentials (sFRPs) and formulate an optimization problem to extract naming-related neural components, informative of group differences. Our approach is validated on a real dataset involving children with dyslexia and CAC performing a Rapid-Automatized Naming (RAN) task. Our results demonstrate the validity of the proposed metric as an indicator of the neural-based markers of reading disorders. Importantly, our proposed framework provides a novel approach that can facilitate the study of neural correlates of reading disorders under paradigms current methods are unable to.

Towards EEG-based Emotion Recognition During Video Viewing

Emotions affect our decisions, experiences, preferences, and perceptions. Understanding the neural underpinning of human emotions is a fundamental goal of neuroscience research. Moreover, EEG-based emotion recognition is a key component towards the development of affective-aware intelligent systems. However, characterizing the neural basis of emotions elicited during video viewing has been proven a challenging task. In this paper, we propose a novel machine-learning approach to isolate neural components in EEG signals that are informative of the affective content of emotionally-loaded videos. Based on these components, we define a set of neural metrics and evaluate them as potential indicators of the overall emotional content of each video. We demonstrate the predictive power of the proposed metrics, on the DEAP benchmark dataset for EEG-based emotion recognition. Our results provide novel empirical evidence that the neural components extracted by our method can serve as an informative metric in EEG-based emotion recognition during video viewing and achieving a 4-fold increase in predictive power compared to traditional frequency-based metrics. Moreover, each extracted component is associated with a spatial and a temporal profile, that allows researchers to inspect and interpret the spatiotemporal origins of the underlying neural activity. Thus, our method a framework that facilitates the study of neural correlates of emotion during video viewing.

Evaluation of the neuroprotective effects of Vitamin E on the rat substantia nigra neural cells exposed to electromagnetic field: An ultrastructural study

ABSTRACT Electromagnetic fields (EMFs) could induce oxidative stress (OS) in human tissues. Lipid peroxidation (LPO) is the main hallmark of OS that harms neural cell components, primarily lipids in the myelin sheaths and membranes. Vitamin E is a lipophilic antioxidant that protects cells from OS-related damages and inhibits the LPO process. In this study, male rats were assigned into three groups of Control, EMF, and EMF+ Vitamin E. The EMF producer equipment produced an alternate current of 50 Hz, 3 Mili Tesla (mT). At the end of the experiment, half of the substantia nigra in every sample was used for measurement of the malondialdehyde (MDA) level as the end-product of the LPO and activity of superoxide dismutase (SOD) enzyme. The next half of the tissue was prepared for transmission electron microscopy (TEM). In the EMF group, MDA level was enhanced and SOD value decreased significantly compared to the control group, but Vitamin E could restore these changes. In rats undergone EMF, heterochromatic nucleus and destruction in some portions of the nuclear membrane were detected. The segmental separation or destruction of myelin sheath lamellae was observed in nerve fibers. In treated animals, the nucleus was round, less heterochromatic, with a regular membrane. Separation of myelin sheath lamellae in some nerve fibers was slighter than the radiation group. Considering the results, EMF exposure induces LPO and triggers ultrastructural changes in the cell membranes, nucleus, and myelin sheath of substantia nigra cells, but Vitamin E consumption weakens these neuropathological alterations.

Static Stretching Reduces Motoneuron Excitability: The Potential Role of Neuromodulation.

Prolonged static muscle stretching transiently reduces maximal muscle force, and this force loss has a strong neural component. In this review, we discuss the evidence suggesting that stretching reduces the motoneuron's ability to amplify excitatory drive. We propose a hypothetical model in which stretching causes physiological relaxation, reducing the brainstem-derived neuromodulatory drive necessary to maximize motoneuron discharge rates.

LONG-TERM EFFECT OF ANTIHYPERTENSIVE AGENTS ON THE NEURAL BAROREFLEX SENSITIVITY-A SUBSTUDY OF THE PARIS PROSPECTIVE STUDY III

Objective: The baroreflex is a crucial mechanism regulating moment-to-moment blood pressure (BP) to ensure appropriate flow and pressure to the brain and other organs. All antihypertensive treatments, because of their BP lowering effect, interact with the autonomic nervous system by stimulating the sympathetic response. In addition, specific drugs may further interact with the BRS because of their specific mode of action. Whether antihypertensive agents have remnant effect is still unknown. Design and method: In the Paris Prospective Study III, spontaneous baroreflex, carotid stiffness and pharmacological drugs intake were available in 7967 adults (mean age of 60 ± 6 years, 62% men). The neural component of the baroreflex sensitivity (nBRS) was obtained with a cross-spectral analysis of variations in carotid distention rate and R-R intervals. Individuals with a BP lowering medication (BP-treated) were paired to non-BP treated individuals (controls) with similar cardiovascular comorbidities using a propensity score matching procedure (n = 1182 pairs). Results: Sympathetic activity was higher in the BP-treated group compared to their matched-controls, as shown by the increased spectral power of distention rate in the low frequency band (log LF distrate: 4.106 ± 0.530 vs 4.055 ± 0.514, p = 0.018). Amongst pharmacological classes of BP lowering agents, only agents acting on the angiotensin system (ACEi-ARB) were associated with nBRS. Compared to their matched controls (n = 331 pairs), ACEi-ARB users had lower nBRS (2.79 ± 0.66 (ln) vs. 2.90 ± 0.62 (ln), p = 0.03). After adjustment for carotid stiffness (stdbeta = 0.25, p &lt; 0.001) and systolic pressure (stdbeta = -0.20, p &lt; 0.001), ACEi-ARB remained significantly associated with nBRS (stdbeta = -0.09, p = 0.025). Conclusions: In this community-based population study, antihypertensive treatments induced activation of the autonomic nervous system independently of cardiovascular comorbidities. Taken as a whole, BP treatments did not have any effect on the neural component of the baroreflex loop, with the exception of ACEi-ARB which were negatively associated with nBRS. This effect was independent of BP and stiffness, which may suggest an inhibition of sympathetic activity by ACEi-ARB.