Hasil untuk "Neurosciences. Biological psychiatry. Neuropsychiatry"

Arturo Tozzi

Physics relies on mathematical spaces carefully matched to the phenomena under study. Phase space in classical mechanics, Hilbert space in quantum theory, configuration spaces in field theory all provide representations in which physical laws, stability and invariants become expressible and testable. In contrast, biology lacks an agreed-upon notion of space capturing context dependence, partial observability, degeneracy and irreversible dynamics. To address this gap, we introduce a unified mathematical space tailored to biological processes where states are represented in locally convex spaces indexed by context, where context includes both environment and history. Within our setting, proximity is defined through families of seminorms rather than a single global metric, allowing biological relevance to vary across conditions. Admissible sets encode biological constraints, observation maps formalize partial observability and many-to-one relations between state and dynamics capture irreversibility without requiring convergence to fixed points. Stabilization is characterized by neighborhood inclusion and degeneracy arises naturally through quotient structures induced by observation. We develop explicit constructions, operators and bounds within this space, yielding quantitative predictions dictated by its structure. A worked example based on EGFR-mutant non-small-cell lung cancer shows how single-cell data can be mapped into our framework, how numerical thresholds can be calibrated from the literature and how testable predictions can be formulated concerning rare tolerant states, context-dependent proximity and early stabilization. Overall, by providing biology with a space playing a role analogous to those used in physics, we aim to support structurally grounded and quantitative analyses of biological systems across contexts.

Eugenio Simao

Motivation: Biological Petri Nets (Bio-PNs) model biochemical pathways where multiple reactions simultaneously affect shared metabolites through convergent production or regulatory coupling. However, classical Petri net independence theory requires transitions to share no places -- a constraint that fails to capture biological reality. This mismatch prevents parallel simulation and incorrectly flags biologically valid models as structurally problematic. Results: To resolve this fundamental limitation, we introduce weak independence -- a novel formalization distinguishing resource conflicts from biological coupling. Building on this theory, we extend the Bio-PN definition from a classical 5-tuple to a 12-tuple by adding regulatory structure, environmental exchange classification, dependency taxonomy, heterogeneous transition types, and biochemical formula tracking. This extended formalism enables systematic classification of three place-sharing modes: competitive (conflict), convergent (superposition), and regulatory (read-only). Validating our approach on 100 diverse BioModels (1,775 species, 2,234 reactions across metabolism, signaling, and gene regulation), we find that 96.93% of transition pairs exhibit weak independence -- confirming that biological networks inherently favor cooperation over competition. Our SHYpn implementation demonstrates the practical impact, achieving up to 2.6x speedup on 30% of evaluated models. Availability and Implementation: Open-source at https://github.com/simao-eugenio/shypn (MIT License).

Chinmay Prabhakar, Suprosanna Shit, Tamaz Amiranashvili et al.

3D spatial graphs play a crucial role in biological and clinical research by modeling anatomical networks such as blood vessels,neurons, and airways. However, generating 3D biological graphs while maintaining anatomical validity remains challenging, a key limitation of existing diffusion-based methods. In this work, we propose a novel 3D biological graph generation method that adheres to structural and semantic plausibility conditions. We achieve this by using a novel projection operator during sampling that stochastically fixes inconsistencies. Further, we adopt a superior edge-deletion-based noising procedure suitable for sparse biological graphs. Our method demonstrates superior performance on two real-world datasets, human circle of Willis and lung airways, compared to previous approaches. Importantly, we demonstrate that the generated samples significantly enhance downstream graph labeling performance. Furthermore, we show that our generative model is a reasonable out-of-the-box link predictior.

Haiteng Zhao, Chang Ma, Fangzhi Xu et al.

The applications of large language models (LLMs) in various biological domains have been explored recently, but their reasoning ability in complex biological systems, such as pathways, remains underexplored, which is crucial for predicting biological phenomena, formulating hypotheses, and designing experiments. This work explores the potential of LLMs in pathway reasoning. We introduce BioMaze, a dataset with 5.1K complex pathway problems derived from real research, covering various biological contexts including natural dynamic changes, disturbances, additional intervention conditions, and multi-scale research targets. Our evaluation of methods such as CoT and graph-augmented reasoning, shows that LLMs struggle with pathway reasoning, especially in perturbed systems. To address this, we propose PathSeeker, an LLM agent that enhances reasoning through interactive subgraph-based navigation, enabling a more effective approach to handling the complexities of biological systems in a scientifically aligned manner. The dataset and code are available at https://github.com/zhao-ht/BioMaze.

Jesus de la Fuente, Robert Lehmann, Carlos Ruiz-Arenas et al.

Predicting the impact of genomic and drug perturbations in cellular function is crucial for understanding gene functions and drug effects, ultimately leading to improved therapies. To this end, Causal Representation Learning (CRL) constitutes one of the most promising approaches, as it aims to identify the latent factors that causally govern biological systems, thus facilitating the prediction of the effect of unseen perturbations. Yet, current CRL methods fail in reconciling their principled latent representations with known biological processes, leading to models that are not interpretable. To address this major issue, we present SENA-discrepancy-VAE, a model based on the recently proposed CRL method discrepancy-VAE, that produces representations where each latent factor can be interpreted as the (linear) combination of the activity of a (learned) set of biological processes. To this extent, we present an encoder, SENA-δ, that efficiently compute and map biological processes' activity levels to the latent causal factors. We show that SENA-discrepancy-VAE achieves predictive performances on unseen combinations of interventions that are comparable with its original, non-interpretable counterpart, while inferring causal latent factors that are biologically meaningful.

Ciyuan Peng, Yuelong Huang, Qichao Dong et al.

State-of-the-art brain graph analysis methods fail to fully encode the small-world architecture of brain graphs (accompanied by the presence of hubs and functional modules), and therefore lack biological plausibility to some extent. This limitation hinders their ability to accurately represent the brain's structural and functional properties, thereby restricting the effectiveness of machine learning models in tasks such as brain disorder detection. In this work, we propose a novel Biologically Plausible Brain Graph Transformer (BioBGT) that encodes the small-world architecture inherent in brain graphs. Specifically, we present a network entanglement-based node importance encoding technique that captures the structural importance of nodes in global information propagation during brain graph communication, highlighting the biological properties of the brain structure. Furthermore, we introduce a functional module-aware self-attention to preserve the functional segregation and integration characteristics of brain graphs in the learned representations. Experimental results on three benchmark datasets demonstrate that BioBGT outperforms state-of-the-art models, enhancing biologically plausible brain graph representations for various brain graph analytical tasks

Xuening Lyu, Rimsa Goperma, Dandan Wang et al.

Abstract Background Niacin Skin-Flushing Response (NSR) has emerged as a promising objective biomarker for the precise diagnosis of mental disorders. However, its diagnostic potential has been constrained by the limitations of traditional statistical approaches. The advent of Artificial Intelligence (AI) offers a transformative opportunity to overcome these challenges. This study presents a novel contribution to the field by establishing an open-access dataset and developing advanced AI-driven tools to enhance the diagnostic accuracy of psychiatric disorders through NSR analysis. Methods This study introduces the world’s first open dataset specifically developed for AI studies of Niacin Skin-Flushing Response (NSR), a physiological biomarker associated with mental illnesses including depression, bipolar disorder, and schizophrenia. Leveraging this dataset, we developed an advanced Machine Learning (ML) approach designed for the broad diagnosis of mental disorders. Distinct from prior studies which are often limited to First Episode Schizophrenia and depend on specific devices, our approach champions device independence. The core of our methodology involves a novel algorithm featuring an Efficient-Unet based Deep Learning model for the precise segmentation of NSR areas. This segmentation is significantly enhanced by runtime data augmentation and trained on a robust train/validation/test dataset split. Subsequently, a Support Vector Machine (SVM) method is employed for psychiatric disorder classification utilizing feature vectors extracted from the segmentation of NSR areas with a 3-scale quantization. The SVM training incorporates 5-fold cross-validation, Synthetic Minority Over-sampling Technique (SMOTE) for managing class imbalance, and hyperparameter tuning to optimize balanced accuracy. Results The established dataset comprises 600 high-quality NSR images from 120 individuals, encompassing a diverse cohort of healthy controls and patients with various mental illnesses. The developed AI tools offer an objective, swift, and highly accurate approach that is demonstrably independent of the diagnosed condition or the specific device used for image acquisition. Comparative results demonstrate that the ML-based diagnostic approach achieves a sensitivity ranging from 60.0 to 65.0% and a specificity from 75.0 to 88.3% across various types of illnesses, further underscoring its broad applicability and device independence. Conclusions This research conclusively demonstrates the significant potential of advanced AI tools in achieving precise diagnosis of psychiatric disorders, potentially surpassing human capabilities in both speed and accuracy. With the provision of the proposed open dataset and the introduction of novel methodologies, this study marks substantial progress in developing an objective and accurate NSR-based screening process for a wide spectrum of psychiatric disorders. Its enhanced applicability and independence from specific devices hold profound potential to substantially advance mental health diagnostics and contribute to improved patient outcomes globally.

Liangpeng Wei, Jiaming Lu, Xin Li et al.

<b>Background/Objectives</b>: Identifying pathological distinctions among mild cognitive impairment (MCI) subtypes is important for differentiating dementia. The purpose of this study is to investigate subtype-specific structural alterations in amnestic MCI (aMCI) and non-amnestic MCI (naMCI) and evaluate their potential as imaging biomarkers for subtype classification. <b>Methods</b>: T1 and DTI MRI data from two independent cohorts were analyzed, including a discovery dataset (58 aMCI, 35 naMCI, and 95 NC) and a replication dataset (61 aMCI, 39 naMCI, and 67 NC). Surface-based morphometry and automated fiber quantification (AFQ) were used to examine cortical thickness and white matter microstructure. Mediation models were used to explore the links between brain structure and cognitive outcomes. A logistic regression model was applied to evaluate classification performance. <b>Results</b>: The aMCI exhibited right hippocampal atrophy. In the naMCI, reduced cortical thickness was observed in the right anterior cingulate cortex (rACC) and opercular inferior frontal gyrus, along with increased fractional anisotropy (FA) in the right inferior fronto-occipital fasciculus (IFOF). These alterations were linked to domain-specific cognitive deficits. Moreover, partial mediation effects of IFOF FA values were observed in the link between rACC thickness and cognitive outcomes. Furthermore, these structural alterations effectively distinguished between aMCI and naMCI, showing stable performance across independent datasets (Accuracy = 0.821, AUC = 0.904). <b>Conclusions</b>: Our findings reveal distinct structural alterations across MCI subtypes, providing deeper insight into the heterogeneous mechanisms of dementia and supporting the potential of imaging markers for the diagnosis of MCI subtypes.

Chunxiao Wei, Guimei Zhang, Xiaoshu Fu et al.

Abstract Olfactomedin 1 (OLFM1) is thought to be involved in neuronal development, synaptic structure and function. However, the expression level of peripheral OLFM1 in Alzheimer’s disease (AD) and its role in AD are unclear. The present study was conducted to assess the relationship of serum OLFM1 with AD and cognitive function. This study comprised 120 patients with AD and 118 healthy controls (HC). Serum OLFM1 levels, cognitive functions, and brain region volumes were evaluated in all participants. The results demonstrated a significant reduction in serum OLFM1 levels in AD patients (749.8 ± 42.3 pg/mL) compared to HC (804.4 ± 45.7 pg/mL). Among participants carrying the APOE ε4 allele, a significant positive correlation was observed between OLFM1 levels and cognitive assessments, including Mini-Mental State Examination (MMSE), Montreal Cognitive Assessment (MoCA), and Memory and Executive Screening (MES). Furthermore, reduced OLFM1 levels were significantly associated with hippocampus (β = 0.005, 95% CI = 0.001–0.011, p = 0.042) and angular gyrus (β = 0.012, 95% CI = 0.001–0.022, p = 0.025) atrophy. The integration of serum OLFM1 with basic clinical characteristics exhibited robust discriminatory power in differentiating AD patients from HC, evidenced by an area under the curve of 0.881 (95% CI = 0.834–0.926). In summary, serum OLFM1 is a potential peripheral biomarker for AD, that correlates with cognitive function and specific brain volumes. In addition, APOE ε4 may modulate the influence of OLFM1 on cognitive function.

Michelle Cherfane, Michelle Cherfane, Michelle Cherfane et al.

ObjectivesThis study evaluates the effectiveness of a brief, culturally tailored educational video in improving stroke-related knowledge among residents of the United Arab Emirates (UAE).MethodsA pre-post intervention study was conducted with 407 UAE residents aged 25 years and older. Participants viewed a 3-min educational video addressing stroke symptoms, risk factors, and preventive strategies. Stroke knowledge was measured using a structured questionnaire immediately before and after the video. Statistical analyses included paired t-tests, repeated measures ANOVA, and linear regression models.ResultsStroke knowledge significantly increased following the intervention (mean score: 20.80 pre-test to 23.53 post-test; p &lt; 0.001), with notable improvements in identifying symptoms and risk factors. Regression analyses indicated that female gender, higher education, and healthy lifestyle practices positively influenced knowledge gains, whereas older age was associated with smaller improvements.ConclusionA brief, culturally relevant audiovisual intervention effectively enhances stroke-related knowledge. Such scalable educational tools should be integrated into global public health strategies to promote earlier stroke recognition and intervention.

Jeremie Courraud, Jeremie Courraud, Jeremie Courraud et al.

IntroductionMutations in dual-specificity tyrosine phosphorylation-regulated kinase 1A (DYRK1A) represent one of the most prevalent monogenic causes of neurodevelopmental disorders (NDDs), often associated with intellectual developmental disorder and autism spectrum disorder. DYRK1A encodes a dual-specificity kinase (tyrosine and serine/threonine) that plays a key role in various cellular processes and is a critical regulator of nervous system development.MethodsFor the first time, we have characterized the DYRK1A interactome and study the consequences of DYRK1A depletion in human neural stem cells (hNSCs).ResultsWe identified 35 protein partners of DYRK1A involved in essential pathways such as cell cycle regulation and DNA repair. Notably, five of these interactors are components of the anaphase-promoting complex (APC), and one is an additional ubiquitin ligase, RNF114 (also known as ZNF313), which is known to target p21. Many of these identified partners are also linked to other human NDDs, and several others (e.g., DCAF7 and GSPT1) may represent novel candidate genes for NDDs. DYRK1A knockdown (KD) in hNSCs using siRNA revealed changes in the expression of genes encoding proteins involved in extracellular matrix composition and calcium binding (e.g., collagens, TGFβ2 and UNC13A). While the majority of genes were downregulated following DYRK1A depletion, we observed an upregulation of early growth factors (EGR1 and EGR3), as well as E2F2 and its downstream targets. In addition, DYRK1A-KD led to a reduction in p21 protein levels, despite an increase in the expression of a minor transcript variant for this gene, and a decrease in ERK pathway activation.DiscussionTogether, the DYRK1A interactome in hNSCs and the gene expression changes induced by its depletion highlight the significant role of DYRK1A in regulating hNSC proliferation. Although the effects on various growth signaling pathways may appear contradictory, the overall impact is a marked reduction in hNSC proliferation. This research underscores the pivotal role of DYRK1A in neurodevelopment and identifies, among DYRK1A’s protein partners and differentially expressed genes, potential novel candidate genes for NDDs and promising therapeutic targets for DYRK1A syndrome.

Xiaoliang Luo, Akilles Rechardt, Guangzhi Sun et al.

Scientific discoveries often hinge on synthesizing decades of research, a task that potentially outstrips human information processing capacities. Large language models (LLMs) offer a solution. LLMs trained on the vast scientific literature could potentially integrate noisy yet interrelated findings to forecast novel results better than human experts. To evaluate this possibility, we created BrainBench, a forward-looking benchmark for predicting neuroscience results. We find that LLMs surpass experts in predicting experimental outcomes. BrainGPT, an LLM we tuned on the neuroscience literature, performed better yet. Like human experts, when LLMs were confident in their predictions, they were more likely to be correct, which presages a future where humans and LLMs team together to make discoveries. Our approach is not neuroscience-specific and is transferable to other knowledge-intensive endeavors.

Jacobo Ruiz, Manas Gupta

The field of artificial intelligence faces significant challenges in achieving both biological plausibility and computational efficiency, particularly in visual learning tasks. Current artificial neural networks, such as convolutional neural networks, rely on techniques like backpropagation and weight sharing, which do not align with the brain's natural information processing methods. To address these issues, we propose the Memory Network, a model inspired by biological principles that avoids backpropagation and convolutions, and operates in a single pass. This approach enables rapid and efficient learning, mimicking the brain's ability to adapt quickly with minimal exposure to data. Our experiments demonstrate that the Memory Network achieves efficient and biologically plausible learning, showing strong performance on simpler datasets like MNIST. However, further refinement is needed for the model to handle more complex datasets such as CIFAR10, highlighting the need to develop new algorithms and techniques that closely align with biological processes while maintaining computational efficiency.

Marina Guillén-Yunta, Ángel García-Aldea, Víctor Valcárcel-Hernández et al.

Allan-Herndon-Dudley syndrome (AHDS) is a rare X-linked disorder that causes severe neurological damage, for which there is no effective treatment. AHDS is due to inactivating mutations in the thyroid hormone transporter MCT8 that impair the entry of thyroid hormones into the brain, resulting in cerebral hypothyroidism. However, the pathophysiology of AHDS is still not fully understood and this is essential to develop therapeutic strategies. Based on evidence suggesting that thyroid hormone deficit leads to alterations in astroglial cells, including gliosis, in this work, we have evaluated astroglial impairments in MCT8 deficiency by means of magnetic resonance imaging, histological, ultrastructural, and immunohistochemical techniques, and by mining available RNA sequencing outputs. Apparent diffusion coefficient (ADC) imaging values obtained from magnetic resonance imaging showed changes indicative of alterations in brain cytoarchitecture in MCT8-deficient patients (n = 11) compared to control subjects (n = 11). Astroglial alterations were confirmed by immunohistochemistry against astroglial markers in autopsy brain samples of an 11-year-old and a 30th gestational week MCT8-deficient subjects in comparison to brain samples from control subjects at similar ages. These findings were validated and further explored in a mouse model of AHDS. Our findings confirm changes in all the astroglial populations of the cerebral cortex in MCT8 deficiency that impact astrocytic metabolic and mitochondrial cellular respiration functions. These impairments arise early in brain development and persist at adult stages, revealing an abnormal distribution, density, morphology of cortical astrocytes, along with altered transcriptome, compatible with an astrogliosis-like phenotype at adult stages. We conclude that astrocytes are potential novel therapeutic targets in AHDS, and we propose ADC imaging as a tool to monitor the progression of neurological impairments and potential effects of treatments in MCT8 deficiency.

Arnault Tauziède-Espariat, Emmanuelle Uro-Coste, Yvan Nicaise et al.

M. A. Allam

Introduction Pharmaceutical slogans presuming a particular antidepressant molecule being the best solely based on a core concept could be proved “not accurate” especially following patients’ actual exposure to the antidepressant for longer than the usual six or twelve weeks’ trials Objectives Reviewing the current situations of SSRI induced anhedonia recognition and its management. Distinguishing anhedonia as a core symptoms of depression from SSRI induced anhedonia and the combination of both. Methods Review of literatures including theses related to the same topic Results Research suggests that, SSRIs might be more effective at treating some symptoms than others. More specifically, it has been suggested that, SSRIs might be more effective at improving symptoms such as low mood and anxiety but not anhedonia (Argyropoulos et al. psych.pharmaology, 2013; 27(10), 869-877). It has been proposed that, catecholaminergic antidepressants might be more effective treatments for anhedonia and emotional blunting in MDD than SSRIs (McCabe et al. Biological psychiatry, 2010; 67(5), 439–445. The primary effect of SSRIs is reduced processing of negative stimuli rather than increased positive stimuli. Emotional blunting is related to SSRI dose and possibly serotonergic effects on the frontal lobes and/or serotonergic modulation of midbrain dopaminergic systems projecting to the prefrontal cortex (PFC). By enhancing serotonergic transmission, SSRIs can activate the inhibitory Gamma Aminobutyric Acid (GABA) interneurons, thereby dampening the noradrenergic and dopaminergic input ( Blier. Int J Neuropsychopharmacol., 2014; 17:997–1008). Management of SSRI induced anhedonia includes lowering the current SSRI dose. Adding non SSRI antidepressant to the current SSRI dose or to a lowered SSRI dose. Gradual discontinuation of the SSRI and switching to another antidepressant with a different profile (SNRI) that might improve the patient’s emotional response (Koenigs. Behav Brain Res., 2009;201:239–43). Bupropion is an antidepressant with less possibility to give rise to emotional blunting. (Tomoko et al. Neuroscience Letters., 2021; 749, 135716. agomelatine(Thome et al. Journal of neural transmission., 2015; 122(1), 3-7.Vortioxetine and others (Bing et al. frontiers in psychiatry., Jan, 2019; 10-17. are of inrerest in this regard. Image 2: Conclusions The most selective SSRI concept assumes that most selective means less affinity to other receptors or secondary binding sites which might suggest less side effects and perhaps being the most efficacious. Not only serotonin but multiple neurotransmitters are in action at the downstream part of a cascade of events underpinning the etiology of MDD. MDD has heterogeneous etiology and this explains why patients respond differently. SSRI induced anhedonia could be tackled and we need to explore how many patients would benefit from that now and have not yet. Disclosure of Interest None Declared

Yen Ting Lin, Nicolas E. Buchler

Gene expression is a biochemical process, where stochastic binding and un-binding events naturally generate fluctuations and cell-to-cell variability in gene dynamics. These fluctuations typically have destructive consequences for proper biological dynamics and function (e.g., loss of timing and synchrony in biological oscillators). Here, we show that gene expression noise counter-intuitively accelerates the evolution of a biological oscillator and, thus, can impart a benefit to living organisms. We used computer simulations to evolve two mechanistic models of a biological oscillator at different levels of gene expression noise. We first show that gene expression noise induces oscillatory-like dynamics in regions of parameter space that cannot oscillate in the absence of noise. We then demonstrate that these noise-induced oscillations generate a fitness landscape whose gradient robustly and quickly guides evolution by mutation towards robust and self-sustaining oscillation. These results suggest that noise can help dynamical systems evolve or learn new behavior by revealing cryptic dynamic phenotypes outside the bifurcation point.

G. Mecacci, W.F.G (Pim) Haselager

Bioethics 13 (1):134–54. doi:10.3138/ijfab.13.1.09. De Haan, S., E. Rietveld, M. Stokhof, and D. Denys. 2013. The phenomenology of deep brain stimulation-induced changes in OCD: an enactive affordance-based model. Frontiers in Human Neuroscience 7:653. doi:10.3389/ fnhum.2013.00653. Goering, S. 2015. Stimulating autonomy: DBS and the prospect of choosing to control ourselves through stimulation. AJOB Neuroscience 6 (4):1–3. doi:10.1080/21507740. 2015.1106274. Goering, S., E. Klein, D. D. Dougherty, and A. S. Widge. 2017. Staying in the loop: Relational agency and identity in next-generation DBS for psychiatry. AJOB Neuroscience 8 (2):59–70. doi:10.1080/21507740.2017. 1320320. Klein, E., S. Goering, J. Gagne, C. V. Shea, R. Franklin, S. Zorowitz, D. D. Dougherty, and A. S. Widge. 2016. Brain-computer interface-based control of closed-loop brain stimulation: attitudes and ethical considerations. Brain-Computer Interfaces 3 (3):140–8. doi:10.1080/ 2326263X.2016.1207497. Klein, E. 2020. Ethics and the emergence of brain-computer interface medicine. In N. F. Ramsey and J. del R. Millan. Handbook of clinical neurology, vol. 168, 329–339. Cambridge, MA: Elsevier. Klein, E. 2015. Models of the patient-machineclinician relationship in closed-loop machine neuromodulation. In S. P. van Rysewyk and M. Pontier. Machine medical ethics, 273–290. Cham, Switzerland: Springer. Mackenzie, C. 2014. Three dimensions of autonomy: A relational analysis. In A. Veltman and M. Piper, Autonomy, oppression and gender, 15–41. New York, NY: Oxford University Press. Mu~ noz, K. A., K. Kostick, C. Sanchez, L. Kalwani, L. Torgerson, R. Hsu, D. Sierra-Mercado, J. O. Robinson, S. Outram, B. A. Koenig, et al. 2020. Researcher perspectives on ethical considerations in adaptive deep brain stimulation trials. Frontiers in Human Neuroscience 14: 489. doi:10.3389/fnhum.2020.578695. Roskies, A. L. 2015. Agency and intervention. Philosophical Transactions of the Royal Society of London. Series B, Biological Sciences 370 (1677):20140215. doi:10.1098/rstb. 2014.0215. Sch€ onau, A., I. Dasgupta, T. Brown, E. Versalovic, E. Klein, and S. Goering. 2021. Mapping the dimensions of agency. AJOB Neuroscience 12 (2-3):172–186.

Luke Y. Prince, Roy Henha Eyono, Ellen Boven et al.

We provide a brief review of the common assumptions about biological learning with findings from experimental neuroscience and contrast them with the efficiency of gradient-based learning in recurrent neural networks. The key issues discussed in this review include: synaptic plasticity, neural circuits, theory-experiment divide, and objective functions. We conclude with recommendations for both theoretical and experimental neuroscientists when designing new studies that could help bring clarity to these issues.

Hayahito Ishikawa, Masahiro Takeshima, Hiroyasu Ishikawa et al.

Abstract Pregabalin is a drug that can cause psychiatric symptoms via pregabalin withdrawal. Prior reports on pregabalin withdrawal have mainly focused on cases with pregabalin dependence or abuse, and little attention has been paid to patients who are prescribed regular doses of pregabalin. Herein, we report three cases of pregabalin withdrawal in patients without psychiatric disorders, taking regular doses of pregabalin, who developed psychiatric symptoms such as insomnia and anxiety after abrupt discontinuation of pregabalin. In addition, we conducted a systematic review of six case reports (previous studies) of pregabalin withdrawal under regular doses of pregabalin. Among the six cases, three patients had no comorbid mental or substance use disorders, the dose of pregabalin ranged from 150 to 600 mg/d, and the duration of pregabalin use ranged from a few weeks to many years. Of these six cases of pregabalin withdrawal, five had psychopathological symptoms, three had vegetative symptoms, and three had neurologic and physical complications. We concluded that since pregabalin withdrawal can occur even with regular doses and short‐term use, clinicians must carefully reduce pregabalin doses when reducing or discontinuing treatment, paying close attention to withdrawal symptoms. Our case series sheds light on the scant evidence from previous research on physical dependence in patients who are taking regular doses of pregabalin. Furthermore, our cases were also valuable in demonstrating that pregabalin withdrawal can occur even after a relatively short period of 2 months.