Search Results (34)

The pathological mechanism of autism spectrum disorder (ASD) exhibits dual heterogeneity: abnormal local energy metabolism and brain-wide high-order topological failure. To synergistically characterize these complex signals captured by advanced neuroimaging sensors, we propose the Quantum-Enhanced Graph Convolutional Wide Residual Network (QG-WRN), a modality-specific, decoupled parallel dual-stream architecture. In the classical branch, to accurately capture the spatial distribution of local metabolic abnormalities, we employ a wide residual network (WRN) to extract amplitude of low-frequency fluctuation (ALFF) features, leveraging its expanded feature channels to effectively mine regional neurodynamic properties. Furthermore, to overcome the representational bottlenecks of classical linear operators in parsing hidden, long-range network connections, we introduce quantum computing, exploiting its exponentially expansive state space and intrinsic low-parameter regularization mechanism. Guided by these properties, the quantum branch utilizes a variational quantum graph convolutional (QGCN) module—featuring a trainable circular encoding strategy and a hardware-efficient 4-qubit configuration—with a 2-layer nested message passing structure to process the functional connectivity (FC) matrix, harnessing quantum interference in Hilbert space to parse complex topology while effectively mitigating overfitting on small-sample medical data. A unified training scheme achieves full-dimensional fusion of node activity and topology. Achieving 68.49% accuracy, our method outperforms 10 classic and recent new baselines, providing a powerful computational intelligence tool for sensor-based ASD clinical diagnosis. Furthermore, interpretability analysis successfully maps core disease hubs to standard AAL116 atlas coordinates, providing a powerful tool for computationally aided ASD diagnosis. Full article

Spiking neural networks (SNNs) offer inherent advantages in processing temporal information. However, their network topologies are predominantly algorithm-generated, lacking constraints from biological brain connectivity, which limits their bio-plausibility. In our previous work, we constructed a spiking neural network (SNN) by incorporating the topological structure of functional brain networks derived from fMRI data of healthy subjects and proposed an fMRISNN model. This model was further employed as the reservoir layer of a liquid state machine (LSM) to build a speech recognition framework. In this framework, the Lyon ear model and the BSA were used to encode speech signals into spike sequences; however, this approach suffers from high computational cost and limited adaptability to temporal variations. To address these limitations, we propose an enhanced Mel-frequency cepstral coefficient (MFCC)-driven sparse spike encoding method. For the speech recognition task, we systematically compare the two preprocessing pipelines in terms of spike number, spike sparsity, encoding time, and downstream speech recognition performance. Experimental results show that the proposed method generates substantially fewer spikes, achieves markedly higher sparsity, and requires significantly less encoding time, while maintaining nearly the same recognition accuracy under the same LSM-based framework. These findings indicate that improved speech input representation can enhance the computational efficiency of SNN-based speech recognition without compromising recognition capability. In addition, the fMRISNN model significantly outperforms several baseline models with algorithmically generated topologies. Compared with mainstream models reported in the literature, although the deep convolutional neural network (CNN) still achieves higher absolute recognition accuracy, the fMRISNN exhibits clear advantages in terms of model parameter size and theoretical energy efficiency. Full article

Deep learning models have achieved high accuracy in forecasting problems, but at the cost of large computational energy demand. Brain-inspired spiking neural networks (SNNs) offer a promising, low-power alternative, yet their adoption for time-series forecasting has been limited by information loss from binary spikes and degraded performance in deeper networks. This paper proposes a fully spiking framework that bridges this gap by improving both the encoding and propagation of information in SNNs. The framework introduces a hybrid Delta-Rate encoding mechanism that captures both abrupt changes and gradual trends in time-series data, and a Mem-Spike mechanism that transmits analog membrane potential values to preserve fine-grained information between spiking layers. We further employ residual membrane connections to maintain signal flow in deep spiking networks. Using two public energy load datasets, our enhanced SNNs consistently outperform conventional spiking models, improving prediction accuracy by up to 61.6% and mitigating degradation in multi-layer networks. Notably, it narrows the gap to the selected deep learning baseline (LSTM), achieving comparable accuracy in some settings while requiring only about 10% of the estimated inference energy of that baseline under a common operation-level model. These results show that, within the empirical scope considered here, enhanced conventional SNNs can improve time-series forecasting accuracy while retaining favorable estimated efficiency. Full article

Diet form is increasingly recognized as a welfare-relevant factor in intensive aquaculture, yet the effects of feed cooking on fish behavioral and physiological welfare remain poorly characterized. Juvenile southern catfish (Silurus meridionalis; 6.18 ± 0.52 g) were reared for 6 weeks in an indoor recirculating aquaculture system and fed either raw grass carp (Ctenopharyngodon idella) muscle (fish fed raw muscle, FR) or cooked grass carp muscle (fish fed cooked muscle, FC; 15 min ramp to ~100 °C followed by 2–3 min at ~100 °C). Locomotor activity and anxiety-like behavior were assessed using the open-field test and an annular light–dark preference assay, respectively. Flow-through respirometry further revealed a significantly lower standard metabolic rate (SMR) in FC fish than in FR fish, decreasing from 10.30 to 6.83, which represents a 33.7% reduction. Endocrine and biochemical analyses showed that cooking significantly decreased serum total triiodothyronine (T3) by 23.8%, whereas routine serum biochemical indices remained unchanged. In brain tissue, dopamine (DA) was significantly reduced by 7.2% in the FC group, and RT-qPCR analysis of dopamine-related genes further showed a significant downregulation of the rate-limiting synthesis gene th. These results indicate that cooking primarily downshifts the activity-energy axis in southern catfish and is accompanied by coordinated thyroid and dopaminergic changes. To our knowledge, this is the first integrated study to evaluate the behavioral, metabolic, and neuroendocrine effects of cooked feed in S. meridionalis, providing a short-term phenotypic baseline for assessing welfare-relevant feeding scenarios in aquaculture. Full article

Tachykinins (TKs), a conserved family of neuropeptides, play critical roles in regulating multiple physiological processes such as feeding and energy metabolism in insects. This study identified the TK gene (ApTK) from the Chinese oak silkworm, Antheraea pernyi, an economically important insect species. Bioinformatic analysis showed that ApTK possesses four FX₁GX₂R motifs (X₁ and X₂ represent variable amino acid residues), comprising FMGVR, FYGVR, FIGVR, and FFGMR, in the C-terminus and shares a close phylogenetic relationship with TKs from Bombyx mori and Manduca sexta. Tissue-specific expression profiling demonstrated that ApTK was mainly expressed in the brain and midgut. Starvation–refeeding experiments showed that the expression of ApTK was significantly upregulated during food deprivation and returned to baseline after refeeding, evincing its involvement in hunger signaling. RNA interference (RNAi)-mediated knockdown of ApTK led to a significant increase in larval body weight and increased levels of triglyceride, glycogen, and trehalose, indicating enhanced energy storage. Collectively, these results demonstrate that ApTK acts as a key regulator in restraining feeding and modulating energy homeostasis in A. pernyi. Our findings provide insights into the neuroendocrine mechanisms underlying feeding behavior and energy metabolism in A. pernyi. Full article

The regulator theorem states that, under certain conditions, any optimal controller must embody a model of the system it regulates, grounding the idea that controllers embed, explicitly or implicitly, internal models of the controlled. This principle underpins neuroscience and predictive brain theories like the Free-Energy Principle or Kolmogorov/Algorithmic Agent theory. However, the theorem is only proven in limited settings. Here, we treat the deterministic, closed, coupled world-regulator system (W,R) as a single self-delimiting program p via a constant-size wrapper that produces the world output string x fed to the regulator. We analyze regulation from the viewpoint of the algorithmic complexity of the output, $K\left(x\right)$ (regulation as compression). We define R to be a good algorithmic regulator if it reduces the algorithmic complexity of the readout relative to a null (unregulated) baseline ⌀, i.e., $\Delta =K\left(O_{W,⌀}\right)-K\left(O_{W,R}\right)>0.$ We then prove that the larger $\Delta$ is, the more world-regulator pairs with high mutual algorithmic information are favored. More precisely, a complexity gap $\Delta >0$ yields $Pr((W,R)\mid x)\le C 2^{M(W:R)}{2}^{-\Delta }$, making low $M(W:R)$ exponentially unlikely as $\Delta$ grows. This is an AIT version of the idea that “the regulator contains a model of the world.” The framework is distribution-free, applies to individual sequences, and complements the Internal Model Principle. Beyond this necessity claim, the same coding-theorem calculus singles out a canonical scalar objective and implicates a planner. On the realized episode, a regulator behaves as if it minimized the conditional description length of the readout. Full article

Background: Exercise-induced fatigue (EIF) impairs performance and recovery and may contribute to overreaching/overtraining and adverse health outcomes. Beyond classical explanations (substrate depletion, metabolite accumulation, oxidative stress), accumulating evidence indicates that the gut microbiota modulates fatigue-related physiology through metabolic, immune, barrier, and neurobehavioral pathways. Methods: We conducted a structured narrative review of PubMed and Web of Science covering 1 January 2015 to 30 November 2025 using predefined keywords related to EIF, gut microbiota, recovery, and nutritional interventions. Human studies, animal experiments, and mechanistic preclinical work (in vivo/in vitro) were included when they linked exercise load, microbial features (taxa/functions/metabolites), and fatigue-relevant outcomes. Results: Across models, high-intensity or prolonged exercise is consistently associated with disrupted gut homeostasis, including altered community structure, reduced abundance of beneficial taxa, increased intestinal permeability, and shifts in microbial metabolites (e.g., short-chain fatty acids). Evidence converges on four interconnected microbiota-mediated pathways relevant to EIF: (1) energy availability and metabolic by-product clearance; (2) redox balance and inflammation; (3) intestinal barrier integrity and endotoxemia risk; and (4) central fatigue and exercise motivation via microbiota–gut–brain signaling. Nutritional strategies—particularly targeted probiotics, prebiotics/plant polysaccharides, and selected bioactive compounds—show potential to improve fatigue biomarkers and endurance-related outcomes, although effects appear context-dependent (exercise modality, baseline fitness, diet, and baseline microbiota). Conclusions: Current evidence supports a mechanistic role of the gut microbiota in EIF and highlights microbiota-targeted nutrition as a promising adjunct for recovery optimization. Future work should prioritize causal validation (e.g., fecal microbiota transplantation and metabolite supplementation), athlete-focused randomized trials with standardized fatigue endpoints, and precision approaches that stratify individuals by baseline microbiome features and training load. Full article

Background/Objectives: Glucose Transporter 1 Deficiency Syndrome (GLUT1-DS) is a neurodevelopmental disorder caused by mutations in the gene encoding glucose transporter 1 (GLUT1), which leads to impaired glucose transport into the brain and is characterized by drug-resistant epilepsy. Limited glucose supply disrupts neuronal and astrocytic energy homeostasis, but how hypometabolism translates into network hyperexcitability remains poorly understood. Here, we used induced pluripotent stem cells (iPSCs)-derived brain organoids to examine how reduced metabolic substrate availability shapes epileptiform dynamics in human neuronal circuits from GLUT1-DS. Methods: Brain organoids were generated from a healthy donor or a GLUT1-DS patient and interfaced with multielectrode arrays (MEA) for recording of neuronal activity. A unified Python (v3.10)-based analytical pipeline was developed to quantify spikes, bursts, and power spectral density (PSD) across frequency bands of neuronal activity. Organoids were challenged with reduced glucose, pentylenetetrazol (PTZ), potassium chloride (KCl), and tetrodotoxin (TTX) to assess metabolic and pharmacological modulation of excitability. Results: GLUT1-DS organoids exhibited elevated baseline hyperexcitability compared to healthy control, characterized by increased spike rates, prolonged bursts, increased spikes per burst, and elevated PSD. Reduced glucose availability further amplified these features selectively in GLUT1-DS. Conclusions: Human brain organoids reproduce the pathological coupling between hypometabolism and hyperexcitability in GLUT1-DS. Our platform provides a mechanistic model and quantification tool for evaluating metabolic and anti-epileptic therapeutic strategies. Full article

As the third generation of neural networks, Spiking Neural Networks (SNNs) simulate the event-driven processing mode of the brain, offering superior energy efficiency and biological interpretability compared to traditional deep learning. Combining the architectural strengths of Transformers with SNNs has recently demonstrated high accuracy and significant potential. SNNs process binary spikes and rich temporal information, resulting in lower computational complexity and making them particularly suitable for neuromorphic datasets. However, neuromorphic data typically involve dynamic edges and high-frequency pixel intensity changes. Capturing this frequency information is challenging for traditional spatial methods but is critical for event-driven vision. To address this, we investigate the integration of the Fast Fourier Transform (FFT) into SNNs and propose the Adaptive Spiking Fourier Neural Operator Transformer (ASFNOformer). This architecture adapts the Adaptive Fourier Neural Operator (AFNO)—originally validated in Artificial Neural Networks (ANNs)—specifically for the spiking domain. Unlike standard AFNOs, our module applies FFT across both spatial (H, W) and temporal (T) dimensions, followed by a Multi-Layer Perceptron structure (MLP) mechanism with a block-diagonal weight matrix. This design effectively captures both spatial features and temporal dynamics inherent in event streams. Furthermore, we incorporate Leaky Integrate-and-Fire (LIF) neurons optimized with Learnable Weight Parameters (LWP-LIF) to enhance temporal feature extraction and adaptivity. Experimental results on standard benchmarks indicate that our method reduces the parameter count by approximately 25%. In terms of recognition accuracy, ASFNOformer is comparable to mainstream models on static datasets and demonstrates superior performance on neuromorphic datasets by efficiently capturing frequency features. Notably, ablation studies confirm the model’s generalizability, and when using QKformer as a baseline, our method achieves state-of-the-art (SOTA) performance on the CIFAR10-DVS dataset. This work advances frequency-domain analysis in SNNs, paving the way for efficient deployment on neuromorphic hardware. Full article

Background/Objectives: Traumatic brain injury (TBI) in the pediatric patient results in acute neurophysiological deficits and can have potential long-term sequelae, impacting neurodevelopment. Serum biomarkers are an active area of study for TBI prognosis and diagnosis. Cyclosporin A (CsA), an immunosuppressant drug with neuroprotective qualities, targets mitochondria to stabilize the neurometabolic energy crisis following TBI. The objective of this study was to determine the acute effect of CsA treatment following focal and diffuse TBI on piglet serum biomarkers associated with glial neurofilaments, axonal dysfunction, and neuronal injury. Methods: Biomarker concentrations of GFAP, Nf-L, and UCH-L1 were quantified retrospectively from porcine serum samples (n = 488) at multiple timepoints from three experimental groups: anesthetized sham (n = 10), controlled cortical impact (CCI, n = 49), or rapid, non-impact rotations (RNR, n = 151) of the head. Injured animals received 24 h post-injury intravenous administration of saline or one of four CsA treatment doses (10, 20, 40, or 60 mg/kg/day), and then, were sacrificed. Results: After RNR, GFAP levels significantly increased from baseline at 1 h and recovered by 1 day to healthy reference ranges, while Nf-L increased at 1 day. Multiple CsA treatment doses (10, 40 mg/kg/day) significantly reduced Nf-L levels at 1 day compared to the untreated group. After CCI, GFAP and Nf-L increased at 1 day; there were no significant treatment effects. Conclusions: Focal and diffuse brain injury mechanisms resulted in distinct biomarker timelines. CsA reduced Nf-L levels at 1 day after diffuse TBI, showing promise of acute therapeutic benefit and warranting further investigation in extended timelines. Full article

Background/Objectives: Daily caloric restriction (DCR) is a common dietary weight loss strategy, but leads to metabolic and behavioral adaptations, including maladaptive eating behaviors and dysregulated appetite. Intermittent fasting (IMF) may mitigate these effects by offering diet flexibility during energy restriction. This secondary analysis compared changes in eating behaviors and appetite-related hormones between 4:3 intermittent fasting (4:3 IMF) and DCR and examined their association with weight loss over 12 months. Methods: Adults with overweight or obesity were randomized to 4:3 IMF or DCR for 12 months. Both randomized groups received a matched targeted weekly dietary energy deficit (34%), comprehensive group-based behavioral support, and a prescription to increase moderate-intensity aerobic activity to 300 min/week. Eating behaviors were assessed using validated questionnaires at baseline and months 3, 6, and 12. Fasting levels of leptin, ghrelin, peptide YY, brain-derived neurotrophic factor, and adiponectin were measured at baseline and months 6 and 12. Linear mixed models and Pearson correlations were used to evaluate outcomes. Results: Included in this analysis were 165 adults (mean ± SD; age 42 ± 9 years, BMI 34.2 ± 4.3 kg/m², 74% female) randomized to 4:3 IMF (n = 84) or DCR (n = 81). At 12 months, binge eating and uncontrolled eating scores decreased in 4:3 IMF but increased in DCR (p < 0.01 for between-group differences). Among 4:3 IMF, greater weight loss was associated with decreased uncontrolled eating (r = −0.27, p = 0.03), emotional eating (r = −0.37, p < 0.01), and increased cognitive restraint (r = 0.35, p < 0.01) at 12 months. There were no between-group differences in changes in fasting appetite-related hormones at any time point. Conclusions: Compared to DCR, 4:3 IMF exhibited improved binge eating and uncontrolled eating behaviors at 12 months. This may, in part, explain the greater weight loss achieved by 4:3 IMF versus DCR. Future studies should examine mechanisms underlying eating behavior changes with 4:3 IMF and their long-term sustainability. Full article

Brain ischemia-reperfusion (IR) injury is a critical pathological process that leads to extensive neuronal death, with hippocampal pyramidal cells, particularly those in the cornu Ammonis 1 (CA1) subfield, being highly vulnerable. Until now, human olfactory mitral cell resistance to IR injury has not been directly studied, but olfactory dysfunction in humans is frequently reported in systemic vascular conditions such as ischemic heart failure and may serve as an early clinical marker of neurological or cardiovascular disease. Mitral cells, the principal neurons of the olfactory bulb (OB), exhibit remarkable resistance to IR injury, suggesting the presence of unique molecular adaptations that support their survival under ischemic stress. Several factors may contribute to the resilience of mitral cells. They have a lower susceptibility to excitotoxicity, mitigating the harmful effects of excessive glutamate signaling. Additionally, they maintain efficient calcium homeostasis, preventing calcium overload—a major trigger for cell death in vulnerable neurons. Mitral cells may also express high baseline levels of antioxidant enzymes and their activities, counteracting oxidative stress. Their robust mitochondrial function enhances energy production and reduces susceptibility to metabolic failure. Furthermore, neuroprotective signaling pathways, including phosphatidylinositol-3-kinase (PI3K)/Akt, mitogen-activated protein kinase/extracellular signal-regulated kinase (MAPK/ERK), and nuclear factor erythroid-2-related factor 2 (Nrf2)-mediated antioxidative responses, further bolster their resistance. In addition to these intrinsic mechanisms, the unique microvascular architecture and metabolic support within the olfactory bulb provide an extra layer of protection. By comparing mitral cells to ischemia-sensitive neurons, key vulnerabilities—such as oxidative stress, excitotoxicity, calcium dysregulation, and mitochondrial dysfunction—can be identified and potentially mitigated in other brain regions. Understanding these molecular determinants of neuronal survival may offer valuable insights for developing novel neuroprotective strategies to combat IR injury in highly vulnerable areas, such as the hippocampus and cortex. Full article

Post-exertional malaise (PEM) is a defining condition of myalgic encephalomyelitis (ME/CFS). The concept requires that a provocation causes disabling limitation of cognitive and functional effort (“fatigue”) that does not respond to rest. Cerebrospinal fluid was examined as a proxy for brain metabolite and lipid flux and to provide objective evidence of pathophysiological dysfunction. Two cohorts of ME/CFS and sedentary control subjects had lumbar punctures at baseline (non-exercise) or after submaximal exercise (post-exercise). Cerebrospinal fluid metabolites and lipids were quantified by targeted Biocrates mass spectrometry methods. Significant differences between ME/CFS and control, non-exercise vs. post-exercise, and by gender were examined by multivariate general linear regression and Bayesian regression methods. Differences were found at baseline between ME/CFS and control groups indicating disease-related pathologies, and between non-exercise and post-exercise groups implicating PEM-related pathologies. A new, novel finding was elevated serine and its derivatives sarcosine and phospholipids with a decrease in 5-methyltetrahydrofolate (5MTHF), which suggests general dysfunction of folate and one-carbon metabolism in ME/CFS. Exercise led to consumption of lipids in ME/CFS and controls while metabolites were consumed in ME/CFS but generated in controls. In general, the frequentist and Bayesian analyses generated complementary but not identical sets of analytes that matched the metabolic modules and pathway analysis. Cerebrospinal fluid is unique because it samples the choroid plexus, brain interstitial fluid, and cells of the brain parenchyma. The quantitative outcomes were placed into the context of the cell danger response hypothesis to explain shifts in serine and phospholipid synthesis; folate and one-carbon metabolism that affect sarcosine, creatine, purines, and thymidylate; aromatic and anaplerotic amino acids; glucose, TCA cycle, trans-aconitate, and coenzyme A in energy metabolism; and vitamin activities that may be altered by exertion. The metabolic and phospholipid profiles suggest the additional hypothesis that white matter dysfunction may contribute to the cognitive dysfunction in ME/CFS. Full article

Creatine monohydrate supplementation is widely used by athletes in high-intensity, power-based sports due to its ability to enhance short-term performance by increasing intramuscular phosphocreatine (PCr) stores, which aid in ATP resynthesis during intense muscle contractions. However, emerging evidence suggests that creatine monohydrate offers benefits beyond athletic performance. This narrative review explores the literature supporting the advantages of creatine supplementation in women, vegans, and clinical populations. In women, who typically have lower baseline intramuscular creatine levels, supplementation may help alleviate fatigue-related symptoms associated with the menstrual cycle, particularly during the early follicular and luteal phases. For vegans and vegetarians, who often have reduced creatine stores due to the absence of creatine-rich animal products in their diet, supplementation can improve both physical and cognitive performance while supporting adherence to plant-based diets. Additionally, creatine supplementation holds potential for various clinical populations. It may mitigate muscle wasting in conditions such as sarcopenia and cachexia, support neuroprotection in neurodegenerative diseases such as Parkinson’s and Huntington’s, improve exercise capacity in cardiovascular diseases, and enhance energy metabolism in chronic fatigue syndrome. Creatine may also aid recovery from traumatic brain injury by promoting brain energy metabolism and reducing neuronal damage. In conclusion, creatine monohydrate supplementation can enhance physical performance, cognitive function, and overall health in women, vegans, and clinical populations by addressing creatine deficiencies, improving energy metabolism, and supporting recovery from physical and neurological challenges. Most available evidence supports the effectiveness of creatine monohydrate, which should be considered the preferred form of creatine supplementation over other variants. Additionally, proper creatine dosing is essential to maximize benefits and minimize potential adverse effects that may arise from chronic ingestion of excessively high doses. Full article

Background: Healthy dietary patterns can support the maintenance of cognition and brain health in older age and are negatively associated with cardiometabolic risk. Cardiometabolic risk factors are similarly important for cognition and may play an important role in linking diet to cognition. Aim: This study aimed to explore the relationship between dietary patterns and cognition and to determine whether cardiometabolic health markers moderate these relationships in older adulthood. Design: A cross-sectional analysis of observational data from the baseline of the ACTIVate study. Participants: The cohort included 426 cognitively normal adults aged 60–70 years. Methods: The Australian Eating Survey (AES) Food Frequency Questionnaire was used to collect data on usual dietary intake, along with additional questions assessing intake of dietary oils. Principal component analysis (PCA) was applied to reduce the dimensionality of dietary data. Cardiometabolic risk was quantified using the metabolic syndrome severity score (MetSSS). Tests from the Cambridge Neuropsychological Test Automated Battery (CANTAB) were used to derive composite scores on four cognitive domains: processing speed, executive function, short-term memory, and long-term memory. Results: Three dietary patterns were identified using PCA: a plant-dominant diet, a Western-style diet, and a meat-dominant diet. After controlling for age, sex, total years of education, energy intake, and moderate-to-vigorous physical activity (MVPA), there was a small, negative association between the meat-dominant diets and long-term memory. Subsequent moderation analysis indicated that MetSSS significantly moderated this relationship. Conclusions: Findings highlight the link between diet, cardiometabolic health, and cognitive function in older, cognitively healthy adults. However, longitudinal studies are needed to confirm observations and evaluate the dynamics of diet, cardiometabolic health, and cognitive function over time. Full article