Frontiers in Systems Neuroscience最新文献

This paper proposes that learning in animals occurs thru sleep and is fundamentally driven by dynamic information valuation processes. These take the form of either pain and pleasure sensations or the more nuanced emotions that evolved from them. Acting as value identifiers, these sensations and emotions enable animals, from the simplest to the most complex, to mark valuable experiences for both retention and later recall. In this way, the paper argues that learning itself is made possible. The remainder of the paper explores the cognitive, neurological and behavioral implications of this framework, including several novel, testable hypotheses derived from it.

Attention deficit hyperactivity disorder (ADHD) stands as one of the most prevalent neurodevelopmental disorders, affecting millions worldwide. While traditional pharmacological interventions have been the cornerstone of ADHD treatment, emerging novel methods such as transcranial Direct Current Stimulation (tDCS) and neurofeedback offer promising avenues for addressing the multifaceted challenges of ADHD management. This review paper critically synthesizes the current literature on tDCS and neurofeedback techniques in ADHD treatment, elucidating their mechanisms of action, efficacy, and potential as adjunct or alternative therapeutic modalities. By exploring these innovative approaches, this review aims to deepen our understanding of neurobiological underpinnings of ADHD and pave the way for more personalized and effective interventions, ultimately enhancing the quality of life for individuals grappling with ADHD symptoms.

Introduction: Caffeine is the most widely consumed psychoactive substance, and its stimulant properties are well documented, but few investigations have examined its acute effects on brain and cardiovascular responses during cognitively demanding tasks under ecologically valid conditions.

Method: This study used wearable biosensors and machine learning analysis to evaluate the effects of moderate caffeine (162 mg) on heart rate variability (HRV), entropy, pulse transit time (PTT), blood pressure, and EEG activity. Twelve healthy male participants (20-30 years) completed a within-subjects protocol with pre-caffeine and post-caffeine sessions. EEG was recorded from seven central electrodes (C3, Cz, C4, CP1, CP2, CP5, CP6) using the EMOTIV EPOC Flex system, and heart rate (HR) and blood pressure (BP) were continuously monitored via the Huawei Watch D. Data analysis included power spectral density (PSD) estimation, FOOOF decomposition, and unsupervised k-means clustering.

Results: Paired-sample t-tests assessed physiological and EEG changes. Although systolic and diastolic BP showed a non-significant upward trend, HR decreased significantly after caffeine intake (77 ± 5.3 bpm to 72 ± 2.5 bpm, p = 0.027). There was a significant increase in absolute alpha power suppression (from -5.1 ± 0.8 dB to -6.9 ± 0.9 dB, p = 0.04) and beta power enhancement (-4.7 ± 1.2 dB to -2.3 ± 1/1, p = 0.04). The surface data from FOOOF shows these are real oscillatory changes. Based on the changes in clustering prior and post-caffeine, a machine-learning change in the brain activity differentiated pre/post-caffeine states with unsupervised clustering. The study results show that moderate caffeine resulted in synchronized EEG and cardiovascular changes, indicating increased arousal and cortical activation that are detectable with wearable biosensors and classifiable with machine learning.

Conclusion: A fully integrated, non-invasive methodology based on a wearable device for real-time monitoring of cognitive states holds promise in the context of digital health, fatigue detection, and public health awareness efforts.

Measuring precise emotional tagging for taste information, with or without the use of words, is challenging. While affective taste valence and salience are core components of emotional experiences, traditional behavioral assays for taste preference, which often rely on cumulative consumption, lack the resolution to distinguish between different affective states, such as innate versus learned aversion, which are known to be mediated by distinct neural circuits. To overcome this limitation, we developed an open-source system for high-resolution microstructural analysis of licking behavior in freely moving mice. Our approach integrates traditional lick burst analysis with a proprietary software pipeline that utilizes interlick interval (ILI) distributions and principal component analysis (PCA) to create a multidimensional behavioral profile of the animal. Using this system, we characterized the licking patterns associated with innate appetitive, aversive, and neutral tastants. While conventional burst analysis failed to differentiate between two palatable stimuli (water and saccharin), our multidimensional approach revealed distinct and quantifiable behavioral signatures for each. Critically, this approach successfully dissociates innate and learned aversive taste valences, a distinction that cannot be achieved using standard metrics. By providing the designs for our custom-built setup and analysis software under an open-source license, this study offers a comprehensive and accessible methodology for examining hedonic responses in future studies. This powerful toolkit enhances our understanding of sensory valence processing and provides a robust platform for future investigations of the neurobiology of ingestive behavior.

Psilocybin, a compound found in Psilocybe mushrooms, is emerging as a promising treatment for neurodegenerative and psychiatric disorders, including major depressive disorder. Its potential therapeutic effects stem from promoting neuroprotection, neurogenesis, and neuroplasticity, key factors in brain health. Psilocybin could help combat mild neurodegeneration by increasing synaptic density and supporting neuronal growth. With low risk for addiction and adverse effects, it presents a safe option for long-term use, setting it apart from traditional treatments. Despite their relatively simpler neuronal networks, studies using animal models, such as Drosophila and fish, have provided essential insights on the efficacy and mechanism of action of psilocybin. These models provide foundational information that guides more focused investigations, facilitating high-throughput screening, enabling researchers to quickly explore the compound's effects on neural development, behavior, and underlying genetic pathways. While mammalian models are indispensable for comprehensive studies on psilocybin's pharmacokinetics and its nuanced interactions within the complex nervous systems, small non-mammalian models remain valuable for identifying promising targets and mechanisms at early research stages. Together, these animal systems offer a complementary approach to drive rapid hypothesis generation to refine our understanding of psilocybin as a candidate for not only halting but potentially reversing neurodegenerative processes. This integrative strategy highlights the transformative potential of psilocybin in addressing neurodegenerative disorders, leveraging both small and mammalian models to achieve translational research success.

This article describes a biological neural network model that explains how humans learn to understand large language models and their meanings. This kind of learning typically occurs when a student learns from a teacher about events that they experience together. Multiple types of self-organizing brain processes are involved, including content-addressable memory; conscious visual perception; joint attention; object learning, categorization, and cognition; conscious recognition; cognitive working memory; cognitive planning; neural-symbolic computing; emotion; cognitive-emotional interactions and reinforcement learning; volition; and goal-oriented actions. The article advances earlier results showing how small language models are learned that have perceptual and affective meanings. The current article explains how humans, and neural network models thereof, learn to consciously see and recognize an unlimited number of visual scenes. Then, bi-directional associative links can be learned and stably remembered between these scenes, the emotions that they evoke, and the descriptive language utterances associated with them. Adaptive resonance theory circuits control model learning and self-stabilizing memory. These human capabilities are not found in AI models such as ChatGPT. The current model is called ChatSOME, where SOME abbreviates Self-Organizing MEaning. The article summarizes neural network highlights since the 1950s and leading models, including adaptive resonance, deep learning, LLMs, and transformers.

Techniques used over decades in brain-based neural network modeling are applied to understanding processes involved in psychoanalysis. Behavioral change is interpreted as a transition, using simulated annealing, from a less to a more optimal attractor in a competitive-cooperative dynamical system that includes analogs of the amygdala, prefrontal cortex, and hypothalamus, and the neurotransmitter norepinephrine. The article explores how psychoanalysis can facilitate the quest for the life that is as meaningful as possible. The resulting network theory allows for new understanding of several traditional Freudian concepts. The theory provides insights about the life and death drives. It also helps us understand object and narcissistic libido, and the contrast of healthy forms of libido based on autonomy vs. unhealthy forms based on dependence. This inquiry relates to the balance between self-interest and empathy, mediated by various areas of the limbic system. It illuminates transference, which involves both an emotional and intellectual relationship between the analyst and analysand, mediated by cognitive-emotional interactions in amygdala and orbitofrontal cortex. Sublimation, or redirection of socially inappropriate urges toward more appropriate behaviors, is interpreted via lateral inhibition between representations of similar complex behaviors.

Background: Shift work sleep disorder (SWSD) in nurses is highly prevalent and is increasingly recognized for its profound impact on human health. However, the brain functional network topology, which provides a comprehensive map of the brain's information processing architecture, remains partially understood in nurses with SWSD.

Methods: 45 nurses with SWSD and 45 healthy controls (HCs) underwent a resting-state functional magnetic resonance imaging (rs-fMRI) scan. Graph theoretical analysis was used to investigate alterations in brain functional network topology. Functional network connectivity was further examined in nurses with SWSD relative to HCs. Correlations between network metrics and clinical sleep scores were also examined.

Results: Compared to HCs, the SWSD group exhibited significantly lower global network metrics. Additionally, at the regional level, the SWSD group showed reduced nodal efficiency in specific regions, particularly within the visual processing areas and the caudate nucleus. Functional network connectivity analysis revealed a predominant pattern of weakened connectivity within the limbic network (LN), visual network (VN), default mode network (DMN), subcortical network (SN) and between the LN and SN in the SWSD group, although some inter-network connections were strengthened, predominantly the VN-ventral attention network (VAN), frontoparietal network (FPN)-VN, somatomotor network-VAN, and VN-DMN. Furthermore, poorer sleep quality correlated with reduced local efficiency in the visual cortex and insomnia severity was associated with weakened frontal connectivity.

Conclusions: This study reveals significant alterations in brain functional network topology and predominantly weakened functional connectivity across multiple brain networks, despite some strengthened inter-network links. These neuroimaging changes correlated with clinical measures of sleep disturbance. Our findings highlight compromised brain network organization in SWSD, offering insights into its neural mechanisms and potential biomarkers.

Background: Brain functional reorganization in primary blepharospasm (BSP) remains incompletely understood. This study aimed to add to the increasing knowledge by examining abnormalities in local spontaneous neural activity in this disorder.

Methods: Resting-state functional magnetic resonance imaging data were acquired from 32 medication-naïve patients with BSP and 32 age- and sex-matched healthy controls in this study. The imaging data were analyzed using the amplitude of low frequency fluctuation (ALFF) to measure spontaneous neural activity. Partial correlation analyses between the altered ALFF values and clinical variables (illness duration and Jankovic Rating Scale score) in patients with BSP were further conducted.

Results: Compared to healthy controls, medication-naïve patients with BSP exhibited significantly increased ALFF in the bilateral putamen and left premotor cortex and decreased ALFF in the bilateral thalamus (p < 0.05, threshold-free cluster enhancement with family-wise error correction for multiple comparisons). Furthermore, ALFF values in the left putamen in the patient group were positively correlated with illness duration (r = 0.53, p = 0.002).

Conclusion: Our findings reveal aberrant spontaneous neural activity within key regions of the motor control network in medication-naïve BSP patients. These ALFF alterations, especially the progressive changes observed in the putamen, provide novel insights into BSP neuropathophysiology and highlight the value of studying untreated cohorts to understand the disorder's intrinsic characteristics.