Frontiers in Neuroscience最新文献

Background: The diagnosis of primary trigeminal neuralgia (PTN) in radiology lacks the gold standard and largely depends on the identification of neurovascular compression (NVC) using magnetic resonance imaging (MRI) water imaging sequences. However, relying on this imaging sign alone often fails to accurately distinguish the symptomatic side of the nerve from asymptomatic nerves, and may even lead to incorrect diagnoses. Therefore, it is essential to develop a more effective diagnostic tool to aid radiologists in the diagnosis of TN.

Purpose: This study aims to establish a radiomics-based machine learning model integrating multi-region of interest (multiple-ROI) MRI and anatomical data, to improve the accuracy in differentiating symptomatic from asymptomatic nerves in PTN.

Methods: A retrospective analysis of MRI data and clinical anatomical data was conducted on 140 patients with clinically confirmed PTN. Symptomatic nerves of TN patients were defined as the positive group, while asymptomatic nerves served as the negative group. The ipsilateral Meckel's cavity (MC) was included in both groups. Through dimensionality reduction analysis, four radiomics features were selected from the MC and 24 radiomics features were selected from the trigeminal cisternal segment. Thirteen anatomical features relevant to TN were identified from the literature, and analyzed using univariate logistic regression and multivariate logistic regression. Four features were confirmed as independent risk factors for TN. Logistic regression (LR) models were constructed for radiomics model and clinical anatomy, and a combined model was developed by integrating the radiomics score (Rad-Score) with the clinical anatomy model. The models' performance was evaluated using receiver operating characteristic curve (ROC) curves, calibration curves, and decision curve analysis (DCA).

Results: The four independent clinical anatomical factors identified were: degree of neurovascular compression, site of neurovascular compression site, thickness of the trigeminal nerve root, and trigeminal pons angle (TPA). The final combined model, incorporating radiomics and clinical anatomy, achieved an area under the curve (AUC) of 0.91/0.90 (95% CI: 0.87-0.95/0.81-0.96) and an accuracy of approximately 82% in recognizing symptomatic and normal nerves.

Conclusion: The combined radiomics and anatomical model provides superior recognition efficiency for the symptomatic nerves in PTN, offering valuable support for radiologists in diagnosing TN.

Neurodevelopmental disorders such as attention deficit and/or hyperactivity disorder (ADHD) and schizophrenia are characterized by core impairment in executive functions (EF). Despite the development of various behavioral interventions to enhance EF, the evidence is still scarce. Alternatively, non-invasive brain stimulation tools such as transcranial alternating current stimulation (tACS) has emerged as a potential strategy to alleviate cognitive deficits. Previous studies have demonstrated the safety, feasibility, and efficacy of one single tACS session in different clinical populations. However, the effects of tACS appear limited and need to be sustained to be considered an effective cognitive neurorehabilitation tool. Recent studies have used home-based, repeated tACS sessions in individuals with neurodegenerative diseases. To our knowledge, the safety and feasibility of such an intensive protocol remains to be tested in a younger population with neurodevelopmental disorders. Using a randomized double-blind sham-controlled design, we administered home-based, repeated tACS sessions to seven individuals aged 14-25 with 22q11.2 deletion syndrome (22q11.2DS), which confers an increased risk for neurodevelopmental disorders. We aimed to assess the safety, tolerability, and feasibility of tACS. Findings from this ongoing clinical trial revealed a favorable safety profile, with frequent yet transient and mainly mild adverse effects. The intervention proved to be feasible, shown by very high adherence rates and positive user experiences. Future studies should therefore investigate whether prolonged exposure to tACS can lead to long-lasting cognitive outcomes.

Clinical trial registration: ClinicalTrials.gov, identifier NCT05664412.

Objective: Mild cognitive impairment (MCI) is a common non-motor manifestation of Parkinson's disease, commonly referred to as PD-MCI. However, there is a lack of comprehensive data regarding the role of glial cell line-derived neurotrophic factor (GDNF) and cerebral white matter damage in the pathogenesis of PD-MCI. The objective of this study is to investigate the association between alterations in GDNF levels and cerebral white matter damage in individuals diagnosed with PD-MCI, as well as to explore their potential involvement in cognitive progression.

Methods: Neuropsychological assessments were conducted on 105 patients with Parkinson's disease and 45 healthy volunteers to examine various cognitive domains. An enzyme-linked immunosorbent assay (ELISA) was employed to measure serum levels of GDNF. Additionally, all participants underwent 3.0T magnetic resonance imaging (MRI) to acquire diffusion tensor images (DTI), and a voxel-based analysis (VBA) approach was utilized to compare the fractional anisotropy (FA) values of white matter in the brain.

Results: There was a significant correlation between the right corpus callosum, right cingulate gyrus, and the Digit Span Backward Test (DSB-T) as well as the Trail Making Test A (TMT-A), both of which assess attention and working memory functions. The left internal capsule exhibited a significant correlation with the Trail Making Test B (TMT-B) and the Clock Drawing Test (CDT), which evaluate executive function. Additionally, the right cingulate gyrus showed a significant association with scores on the Auditory Verbal Learning Test-HuaShan (AVLT-H), assessing memory function. Abnormal fiber structures that demonstrated significant correlations with serum GDNF levels included the left internal capsule, left corticospinal tract, right corpus callosum, and right cingulate gyrus.

Conclusion: The decrease in serum GDNF levels among PD-MCI patients exhibiting impairments in attention and working memory function was significantly correlated with alterations in the corpus callosum (knee) and posterior cingulate gyrus. Furthermore, the reduction of serum GDNF levels in PD-MCI patients with impaired executive function is associated with changes in the internal capsule (forelimb) projection fibers. Additionally, the decline of serum GDNF levels in PD-MCI patients experiencing memory function impairment is related to alterations in the right cingulate gyrus.

While exoskeleton technology is becoming more and more common for gait rehabilitation in children with neurological disorders, evaluation of gait performance still faces challenges and concerns. The reasoning behind evaluating the spinal locomotor output is that, while exoskeleton's guidance forces create the desired walking kinematics, they also affect sensorimotor interactions, which may lead to an abnormal spatiotemporal integration of activity in particular spinal segments and the risk of abnormalities in gait recovery. Therefore, traditional indicators based on kinematic or kinetic characteristics for optimizing exoskeleton controllers for gait rehabilitation may be supplemented by performance measures associated with the neural control mechanisms. The purpose of this study on a sample of children was to determine the basic features of lower limb muscle activity and to implement a method for assessing the neuromechanics of spinal locomotor output during exoskeleton-assisted gait. To this end, we assessed the effects of a robotic exoskeleton (ExoAtlet Bambini) on gait performance, by recording electromyographic activity of leg muscles and analyzing the corresponding spinal motor pool output. A slower walking setting (about 0.2 m/s) was chosen on the exoskeleton. The results showed that, even with slower walking, the level of muscle activation was roughly comparable during exoskeleton-assisted gait and normal walking. This suggests that, despite full assistance for leg movements, the child's locomotor controllers can interpret step-related afferent information promoting essential activity in leg muscles. This is most likely explained by the active nature of stepping in the exoskeleton (the child was not fully relaxed, experienced full foot loading and needed to maintain the upper trunk posture). In terms of the general muscle activity patterns, we identified notable variations for the proximal leg muscles, coactivation of the lumbar and sacral motor pools, and weak propulsion from the distal extensors at push-off. These changes led to the lack of characteristic lumbosacral oscillations of the center of motoneuron activity, normally associated with the pendulum mechanism of bipedal walking. This work shows promise as a useful technique for analyzing exoskeleton performance to help children develop their natural gait pattern and to guide system optimization in the future for inclusion into clinical care.

Background: Functional magnetic resonance imaging (fMRI) is widely used to depict neural activity and understand human brain function. Studies show that functional networks in gray matter undergo complex transformations from neonatal age to childhood, supporting rapid cognitive development. However, white matter functional networks, given the much weaker fMRI signal, have not been characterized until recently, and changes in white matter functional networks in the developing brain remain unclear.

Purpose: Aims to examine and compare white matter functional networks in neonates and 8-year-old children.

Methods: We acquired resting-state fMRI data on 69 full-term healthy neonates and 38 healthy 8-year-old children using a same imaging protocol and studied their brain white matter functional networks using a similar pipeline. First, we utilized the ICA method to extract white matter functional networks. Next, we analyzed the characteristics of the white matter functional networks from both time-domain and frequency-domain perspectives, specifically, intra-network functional connectivity (intra-network FC), inter-network functional connectivity (inter-network FC), and fractional amplitude of low-frequency fluctuation (fALFF). Finally, the differences in the above functional networks' characteristics between the two groups were evaluated. As a supplemental measure and to confirm with literature findings on gray matter functional network changes in the developing brain, we also studied and reported functional networks in gray matter.

Results: White matter functional networks in the developing brain can be depicted for both the neonates and the 8-year-old children. White matter intra-network FC within the optic radiations, corticospinal tract, and anterior corona radiata was lower in 8-year-old children compared to neonates (p < 0.05). Inter-network FC between cerebral peduncle (CP) and anterior corona radiation (ACR) was higher in 8-year-olds (p < 0.05). Additionally, 8-year-olds showed a greater distribution of brain activity energy in the high-frequency range of 0.01-0.15 Hz. Significant developmental differences in brain white matter functional networks exist between the two group, characterized by increased inter-network FC, decreased intra-network FC, and higher high-frequency energy distribution. Similar findings were also observed in gray matter functional networks.

Conclusion: White matter functional networks can be reliably measured in the developing brain, and the differences in these networks reflect functional differentiation and integration in brain development.

[This corrects the article DOI: 10.3389/fnins.2023.1267647.].

This paper reviews the effects of gut microbiota in regulating neurodegenerative diseases through controlling gut-brain axis. Specific microbial populations and their metabolites (short-chain fatty acids and tryptophan derivatives) regulate neuroinflammation, neurogenesis and neural barrier integrity. We then discuss ways by which these insights lead to possible interventions - probiotics, prebiotics, dietary modification, and fecal microbiota transplantation (FMT). We also describe what epidemiological and clinical studies have related certain microbiota profiles with the courses of neurodegenerative diseases and how these impact the establishment of microbiome-based diagnostics and individualized treatment options. We aim to guide microbial ecology research on this key link to neurodegenerative disorders and also to highlight collaborative approaches to manage neurological health by targeting microbiome-related factors.

The decision-making process involves various cognitive procedures influenced by the interplay between cognition, motivation, and attention, forming a complex neural framework. Attention is a fundamental cognitive element within decision-making mechanisms, and one of the conditions affecting the attentional system is attention deficit hyperactivity disorder (ADHD). Decision-making impairments in ADHD have significant economic consequences, necessitating effective policies and interventions to address this critical issue. Research from computational models and neuroscience suggests how cognitive functions' workings and problems affect decision-making and provide insights into the neural implications of decision-making. This article explores the intersection of decision-making, ADHD, and neuroeconomics, highlighting research gaps, potential contributions, and implications for future policies.

Introduction: The normal transition to sleep is characterized by a reduction in higher frequency activity and an increase in lower frequency activity in frontal brain regions. In sleep onset insomnia these changes in activity are weaker and may prolong the transition to sleep.

Methods: Using a wearable device, we compared 30min of short duration repetitive transcranial electric stimulation (SDR-tES) at 0.75Hz, prior to going to bed, with an active control at 25Hz in the same individuals.

Results: Treatment with 0.75Hz significantly reduced sleep onset latency (SOL) by 53% when compared with pre-treatment baselines and was also significantly more effective than stimulation with 25Hz which reduced SOL by 30%. Reductions in SOL with 25Hz stimulation displayed order effects suggesting the possibility of placebo. No order effects were observed with 0.75Hz stimulation. The decrease in SOL with 0.75Hz treatment was proportional to an individual's baseline wherein those suffering from the longest pre-treated SOLs realized the greatest benefits. Changes in SOL were correlated with left/right frontal EEG signal coherence around the stimulation frequency, providing a possible mechanism and target for more focused treatment. Stimulation at both frequencies also decreased perceptions of insomnia symptoms measured with the Insomnia Severity Index, and comorbid anxiety measured with the State Trait Anxiety Index.

Discussion: Our study identifies a new potential treatment for sleep onset insomnia that is comparably effective to current state-of-practice options including pharmacotherapy and cognitive behavioral therapy and is safe, effective, and can be delivered in the home.

Introduction: Multi-channel electrophysiology systems for recording of neuronal activity face significant data throughput limitations, hampering real-time, data-informed experiments. These limitations impact both experimental neurobiology research and next-generation neuroprosthetics.

Methods: We present a novel solution that leverages the high integration density of 22nm fully-depleted silicon-on-insulator technology to address these challenges. The proposed highly integrated programmable System-on-Chip (SoC) comprises 68-channel 0.41 μW/Ch recording frontends, spike detectors, 16-channel 0.87-4.39 μW/Ch action potentials and 8-channel 0.32 μW/Ch local field potential codecs, as well as a multiply-accumulate-assisted power-efficient processor operating at 25 MHz (5.19 μW/MHz). The system supports on-chip training processes for compression, training, and inference for neural spike sorting. The spike sorting achieves an average accuracy of 91.48 or 94.12% depending on the utilized features. The proposed programmable SoC is optimized for reduced area (9 mm²) and power. On-chip processing and compression capabilities free up the data bottlenecks in data transmission (up to 91% space saving ratio), and moreover enable a fully autonomous yet flexible processor-driven operation.

Discussion: Combined, these design considerations overcome data-bottlenecks by allowing on-chip feature extraction and subsequent compression.