Brain Sciences最新文献

Aside from its immediate traumatic effects, spinal cord injury (SCI) presents multiple secondary complications that can be harmful to those who have been affected by SCI. Among these secondary effects, gut dysbiosis (GD) and the activation of the NOD (nucleotide-binding oligomerization domain) like receptor-family pyrin-domain-containing three (NLRP3) inflammasome are of special interest for their roles in impacting mental health. Studies have found that the state of the gut microbiome is thrown into disarray after SCI, providing a chance for GD to occur. Metabolites such as short-chain fatty acids (SCFAs) and a variety of neurotransmitters produced by the gut microbiome are hampered by GD. This disrupts healthy cognitive processes and opens the door for SCI patients to be impacted by mental health disorders. Additionally, some studies have found an increased presence and activation of the NLRP3 inflammasome and its respective parts in SCI patients. Preclinical and clinical studies have shown that NLRP3 inflammasome plays a key role in the maturation of pro-inflammatory cytokines that can initiate and eventually aggravate mental health disorders after SCI. In addition to the mechanisms of GD and the NLRP3 inflammasome in intensifying mental health disorders after SCI, this review article further focuses on three promising treatments: fecal microbiome transplants, phytochemicals, and melatonin. Studies have found these treatments to be effective in combating the pathogenic mechanisms of GD and NLRP3 inflammasome, as well as alleviating the symptoms these complications may have on mental health. Another area of focus of this review article is exploring how artificial intelligence (AI) can be used to support treatments. AI models have already been developed to track changes in the gut microbiome, simulate drug-gut interactions, and design novel anti-NLRP3 inflammasome peptides. While these are promising, further research into the applications of AI for the treatment of mental health disorders in SCI is needed.

Treatment-resistant depression (TRD) is a substantial burden for psychiatric care, affecting approximately one-third of patients with major depressive disorder (MDD). Adolescent populations with depression are a particularly challenging demographic to treat as early intervention is crucial to prevent treatment resistance, but treatment options are limited. Transcranial magnetic stimulation (TMS) has emerged as a promising non-invasive option for TRD in adults as well as adolescents, offering hope for patients who have not responded to conventional therapies. This review examines the convergence of functional magnetic resonance imaging (fMRI) as a tool to examine how TMS modulates functional connectivity in adolescents with MDD. Such analyses have led to advances in our understanding of the pathophysiology of MDD, TRD, and the mechanisms of TMS. We review this evidence, evaluate methodological approaches, and identify critical gaps in the existing literature, highlighting how neuroimaging-guided TMS protocols offer a promising therapeutic avenue for adolescent TRD, particularly in cases where conventional treatments have proven ineffective.

Most brain development occurs in the "first 1000 days", a critical period from conception to a child's second birthday. Critical brain processes that occur during this time include synaptogenesis, myelination, neural pruning, and the formation of functioning neuronal circuits. Perturbations during the first 1000 days likely contribute to later-life neurodegenerative disease, including sporadic amyotrophic lateral sclerosis (ALS). Neurodevelopment is determined by many events, including the maturation and colonization of the infant microbiome and its metabolites, specifically neurotransmitters, immune modulators, vitamins, and short-chain fatty acids. Successful microbiome maturation and gut-brain axis function depend on maternal factors (stress and exposure to toxins during pregnancy), mode of delivery, quality of the postnatal environment, diet after weaning from breast milk, and nutritional deficiencies. While the neonatal microbiome is highly plastic, it remains prone to dysbiosis which, once established, may persist into adulthood, thereby inducing the development of chronic inflammation and abnormal excitatory/inhibitory balance, resulting in neural excitation. Both are recognized as key pathophysiological processes in the development of ALS.

Acupuncture is a medical tool in which a sterile needle is used to penetrate and stimulate a certain body area (acupoint), inducing a series of sensations such as numbness, dullness, or aching, often referred to as de-qi. But is that all? In this article, we adopt a Bayesian perspective to explore the cognitive and affective aspects of acupuncture beyond needling, specifically, how the body integrates bottom-up sensory signals with top-down predictions of acupuncture perception. We propose that the way in which we discern acupuncture treatment is the result of predictive coding, a probabilistic, inferential process of our brain. Active inference from both prior experience and expectations of acupuncture, when integrated with incoming sensory signals, creates a unique, individual internal generative model of our perception of acupuncture. A Bayesian framework and predictive coding may, therefore, aid in elucidating and quantifying the cognitive components of acupuncture and facilitate understanding of their differential interactions in determining individual expectations of treatment. Thus, a perception-based Bayesian model of acupuncture presented in this article may expand on how we perceive acupuncture treatment, from simply inserting needles into our body to one that encompasses a complex healing process supported by belief and hope of regaining health. By exploring how cognitive factors influence individual responsiveness to acupuncture treatment, this review sheds light on why acupuncture treatment is more effective in some individuals than in others.

Purpose: Determine if performing ultrasound-guided, bilateral, two-level cervical sympathetic chain blocks (2LCSB) (performed on subsequent days) provides durable improvement in symptoms associated with anxiety. Methods: A retrospective chart review was conducted between January 2022 and November 2024. We identified 114 patients who received bilateral, 2LCSB for anxiety symptoms. Generalized Anxiety Disorder 7-Item Scale (GAD-7) outcome measure scores were collected at baseline and three-months post procedure in 71 males and 43 females. Results: Out of 114 patients, 99 patients (86.8%) showed a long-lasting improvement in their GAD-7 scores. Collected GAD-7 forms had a baseline average of 15.52 (14.99 for males and 16.40 for females), which decreased after three months to an average of 7.28 (6.96 for males and 7.81 for females). This represents a 52% average improvement in anxiety symptoms. Conclusions: In individuals treated with bilateral, 2LCSB, GAD-related symptoms were improved by 52% for at least 3 months regardless of initial anxiety severity.

Background/Objectives: Coma prognosis is challenging, as patient presentation can be misleading or uninformative when using behavioral assessments only. Event-related potentials have been shown to provide valuable information about a patient's chance of survival and emergence from coma. Our prior work revealed that the mismatch negativity (MMN) in particular waxes and wanes across 24 h in some coma patients. This "cycling" aspect of the presence/absence of neurophysiological responses may require fine-grained tools to increase the chances of detecting levels of neural processing in coma. This study implements multivariate pattern analysis (MVPA) to automatically quantify patterns of neural discrimination between duration deviant and standard tones over time at the single-subject level in seventeen healthy controls and in three comatose patients. Methods: One EEG recording, containing up to five blocks of an auditory oddball paradigm, was performed in controls over a 12 h period. For patients, two EEG sessions were conducted 3 days apart for up to 24 h, denoted as day 0 and day 3, respectively. MVPA was performed using a support-vector machine classifier. Results: Healthy controls exhibited reliable discrimination or classification performance during the latency intervals associated with MMN and P3a components. Two patients showed some intervals with significant discrimination around the second half of day 0, and all had significant results on day 3. Conclusions: These findings suggest that decoding analyses can accurately classify neural responses at a single-subject level in healthy controls and provide evidence of small but significant changes in auditory discrimination over time in coma patients. Further research is needed to confirm whether this approach represents an improved technology for assessing cognitive processing in coma.

Background: Co-occurring conditions and psychiatric comorbidities are more frequently observed in autistic individuals than in typically developing populations. Objective: The present study aimed to investigate the agreement of parent- and self-reported psychopathological assessment using the Child Behavior Checklist (CBCL/6-18) and the Youth Self Report (YSR/11-18), respectively, in autistic adolescents without intellectual impairment. Methods: 54 autistic adolescents without intellectual impairment (11-18 years; M = 14.73; SD = 2.28) were assessed with a psychiatric and psychological evaluation conducted by expert clinicians also using self- and parent-reported scales and semi-structured interviews (K-SADS PL, CDI, MASC) including CBCL/6-18 and YSR/11-18. Results: According to clinical judgment, over 90% of participants had at least a comorbidity: anxiety (68.5%) and mood disorder (57.4%) were the most frequent. The results indicate significant discrepancies between parent- and self-reports across the three summary scales, which assess emotional and behavioral problems, as well as their combined presentation, often observed in youth with ASD. Specifically, differences were found in Internalizing (p < 0.001), Externalizing (p = 0.013), and Total Problems (p < 0.001) scales. Conclusions: The findings show the lack of agreement in parent- and self-reported scales in our sample. These results suggest the need for a cross- and multi-informant approach to support clinical judgment and understand psychopathological comorbidities of autistic adolescents without intellectual impairment.

Background: The construction industry faces significant safety hazards, frequent accidents, and inadequate management. Studies identify unsafe worker behaviors as the primary cause of construction accidents. However, most research overlooks the psychological state, particularly emotions, of construction workers. Methods: This study designed a behavioral experiment integrating social cognitive neuroscience, collecting real-time EEG data to classify and recognize fear, anger, and neutral emotions. Variance analysis explored differences in safety hazard identification and risk assessment under these emotional states. A total of 22 male participants were involved, with data collection lasting three days. The role of psychological capital in mediating the effects of emotions on unsafe behaviors was also examined. Results: Emotional classification using EEG signals achieved 79% accuracy by combining frequency domain and nonlinear feature extraction. Fear significantly enhanced safety hazard identification accuracy compared to neutral and anger emotions (F = 0.027, p = 0.03). Risk assessment values under fear and anger were higher than under neutral emotion (F = 0.121, p = 0.023). Psychological capital interacted significantly with emotions in hazard identification accuracy (F = 0.68, p = 0.034), response time (F = 2.562, p = 0.003), and risk assessment response time (F = 1.415, p = 0.026). Safety hazard identification correlated with the number of safety trainings (p = 0.002) and safety knowledge lectures attended (p = 0.025). Risk assessment was significantly associated with smoking (p = 0.023), alcohol consumption (p = 0.004), sleep duration (p = 0.017), and safety training (p = 0.024). Conclusions: The findings provide insights into how emotions affect safety hazard identification and risk assessment, offering a foundation for improving emotional regulation, reducing accidents, and enhancing safety management in construction.

Background: Brain-inspired models are commonly employed for artificial intelligence. However, the complex environment can hinder the performance of electronic equipment. Therefore, enhancing the injury resistance of brain-inspired models is a crucial issue. Human brains have self-adaptive abilities under injury, so drawing on the advantages of the human brain to construct a brain-inspired model is intended to enhance its injury resistance. But current brain-inspired models still lack bio-plausibility, meaning they do not sufficiently draw on real neural systems' structure or function.

Methods: To address this challenge, this paper proposes the complex spiking neural network (Com-SNN) as a brain-inspired model, in which the topology is inspired by the topological characteristics of biological functional brain networks, the nodes are Izhikevich neuron models, and the edges are synaptic plasticity models with time delay co-regulated by excitatory synapses and inhibitory synapses. To evaluate the injury resistance of the Com-SNN, two injury-resistance metrics are investigated and compared with SNNs with alternative topologies under the stochastic removal of neuron models to simulate the consequence of stochastic attacks. In addition, the injury-resistance mechanism of brain-inspired models remains unclear, and revealing the mechanism is crucial for understanding the development of SNNs with injury resistance. To address this challenge, this paper analyzes the synaptic plasticity dynamic regulation and dynamic topological characteristics of the Com-SNN under stochastic attacks.

Results: The experimental results indicate that the injury resistance of the Com-SNN is superior to that of other SNNs, demonstrating that our results can help improve the injury resistance of SNNs.

Conclusions: Our results imply that synaptic plasticity is an intrinsic element impacting injury resistance, and that network topology is another element that impacts injury resistance.

Background/Objectives: Prior studies have noted varied, spontaneous motor recovery in rat strains after spinal cord injury (SCI), but systematic comparisons of different locomotor measurements across different severity and sexes are lacking. Hence, we quantified hindlimb utilization in male and female Sprague-Dawley (SD) and Wistar rats following moderate and severe SCI. Methods: Compression SCI was induced using a 15-g clip for 180 s for moderate SCI or a 50-g aneurysm clip for 60 s for severe SCI in male and female SD and Wistar rats. Measures of locomotor performance using the Basso-Beattie-Bresnahan (BBB), CatWalk gait analysis, and horizontal ladder tests were taken postoperatively and weekly for seven weeks. Results: BBB scores indicated greater spontaneous recovery in SD rats, with females showing higher scores than males following moderate and severe SCI. No sex or strain differences were observed in the horizontal ladder test. The CatWalk results indicated greater average hindlimb swing speed in SD rats following moderate SCI, but greater print area was observed in Wistar rats after severe SCI, although female SD rats had greater print area than either male SD or female Wistar rats following moderate SCI. Conclusions: The findings that SD rats, especially females, exhibited greater spontaneous motor recovery following moderate SCI indicate the need to consider the sex and strain of rats when conducting therapeutic testing following moderate SCI. The significance of these findings is that they should facilitate the use of appropriate rat models for translational research in SCI that can be applied to future clinical trials.