Metacognition is often regarded as a sophisticated cognitive process that relies on consciousness. While both conscious and subconscious information impact decision-making, it remains unclear whether subconscious information can elicit post-decision metacognition to the same extent as conscious information. To clarify the postdecision metacognitive process under conscious and subconscious conditions and how they differ between active and passive decisions, 35 volunteers were recruited to participate in an actively making or passively watching decision task under conscious (Con), subconscious (SubCon), and no stimuli (None) conditions that utilized the continuous-flash-suppression paradigm. Brain activities were recorded from all participants using a 64-channel EEG system. In addition to analyzing ERPs, multivariate pattern analysis (MVPA) was employed to perform timeseries decoding using the area under the receiver operating characteristic curve (AUC) as an indicator. While ERPs failed to reveal any significant difference, MVPA indicated that the metacognitive processing for SubCon and None differed from Con in the decoding AUCs. Further characterization of decoding metrics validated that SubCon was also different from None, demonstrating distinct metacognitive processing for subconscious information. Additionally, temporal generalization analysis illustrated the diverse neural dynamics among Con, SubCon, and None conditions, as well as between active and passive decisions. Finally, source localization analysis suggested the possible role of the frontal-parietal lobe in discerning post-decision metacognition. These findings demonstrate that subconscious information elicits distinct metacognitive processes post-decisionmaking, making a significant contribution to our understanding of metacognition.

Haploinsufficiency of the SHANK3 gene is the primary cause of Phelan-McDermid syndrome (PMS), a severe neurodevelopmental disorder with intellectual disability and autism spectrum disorder. We previously reported that founder SHANK3 macaques had behaviors reminiscent of some aspects of PMS. However, insights into behavioral, physiological, and cognitive changes were limited due to the small cohort and mosaicism. We therefore generated a larger, F1 generation of heterozygous SHANK3+/- macaques to conduct more thorough studies. We found sleep disturbances, diminished exploration, atypical social interactions, stereotypical behaviors, and altered brain functional connectivity in SHANK3+/- macaques. Electroencephalogram recordings revealed a markedly diminished response to auditory stimulation. Cognitively, SHANK3+/- monkeys did not show major deficits in working memory tests but exhibited an impaired ability to learn and execute a paired-association memory task. We further developed a multi-task array to systemically evaluate autism-related phenotypes, which revealed the heterogeneity of phenotypes and established potential biomarkers for testing therapeutics.

Microglia, essential in the central nervous system (CNS), were historically considered absent from the peripheral nervous system (PNS). Here, we show a PNS-resident macrophage population that shares transcriptomic and epigenetic profiles as well as an ontogenetic trajectory with CNS microglia. This population (termed PNS microglia-like cells) enwraps the neuronal soma inside the satellite glial cell envelope, preferentially associates with larger neurons during PNS development, and is required for neuronal functions by regulating soma enlargement and axon growth. A phylogenetic survey of 24 vertebrates revealed an early origin of PNS microglia-like cells, whose presence is correlated with neuronal soma size (and body size) rather than evolutionary distance. Consistent with their requirement for soma enlargement, PNS microglia-like cells are maintained in vertebrates with large peripheral neuronal soma but absent when neurons evolve to have smaller soma. Our study thus reveals a PNS counterpart of CNS microglia that regulates neuronal soma size during both evolution and ontogeny.

The extensive adoption of electrophysiological methods in neuroscience has underscored the critical need to accurately distinguish neuronal subtypes from spike waveforms for advanced neurophysiological investigations. Here, we present a protocol to categorize neurons according to their waveform characteristics by combining machine learning algorithms with initial spike sorting. We describe steps for data preprocessing, spike sorting, and subsequent analytical procedures implemented through MATLAB and Python scripts. This protocol provides a framework for researchers aiming to classify distinct cell types with confidence. For complete details on the use and execution of this protocol, please refer to Liu et al.1.

Background: Major Depressive Disorder represents a prevalent and critical mental health issue that highlights the pressing need for innovative therapeutic solutions. Recent research has identified dysfunction within the glutamate system as a crucial element influencing both the onset and management of depressive symptoms. Although TAK-653 is a new positive allosteric modulator of AMPA receptors, its effects have not been rigorously examined in models of depression in primates. Methods: To assess its potential antidepressant properties, a chronic unpredictable mild stress protocol was implemented over 12 weeks to create a monkey model of depression, followed by a two-week treatment period with TAK-653. Results: Behavioral evaluations showed that following stress exposure, the monkeys exhibited reduced motivation for food, increased huddling, diminished movement, and a tendency to remain at the lower levels of their enclosure. They also displayed heightened anxiety in response to external stimuli. Plasma analyses indicated higher levels of cortisol, IL-6, and IL-8 in the stressed monkeys compared to baseline readings, confirming the efficacy of the stress-inducing protocol. Post-treatment with TAK-653 resulted in significant improvements, such as enhanced motivation for food, less huddling behavior, greater activity, and a move towards the upper areas of the enclosure. Additionally, the plasma analysis revealed a marked decrease in cortisol and IL-6 levels, along with an increased expression of BDNF. Conclusions: These findings indicate that TAK-653 effectively alleviates depression-like behaviors in nonhuman primate models, thereby paving the way for a promising new strategy in the treatment of depression.

Non-human primates (NHPs) are extensively utilized to investigate the neural mechanisms underlying face processing; however, measuring their brain activity necessitates a diverse array of technologies. Pupillometry emerges as a convenient, cost-effective, and non-invasive alternative for indirectly assessing brain activity. To evaluate the efficacy of pupillometry in assessing facial and emotional processing in NHPs, this study designed a face fixation task for experimental monkeys (Rhesus macaque) and recorded variations in their pupil size in response to face images with differing characteristics, such as species, emotional expression, viewing angles, and orientation (upright vs. inverted). All face images were balanced with luminance and spatial frequency. A sophisticated eye-tracking system (Eye-link 1000 plus) was employed to observe the pupils and track the viewing trajectories of monkeys as they examined images of faces. Our findings reveal that monkeys exhibited larger pupil sizes in response to carnivore faces (versus human faces, p = 0.035), negative conspecific faces (versus human faces, p = 0.018), and profile viewing angles (versus frontal view angles, p = 0.010). Notably, pupil size recorded during the 500–1000 ms post-stimulus interval was negatively correlated with their gaze durations directed at those images (r = −0.357, p = 0.016). Overall, this study demonstrates that pupillometry effectively captures subtle differences in facial and emotional processing, underscoring its potential as a valuable tool in future cognitive research and the diagnosis of disorders.

At present, there are no definitive biomarkers for major depressive disorder (MDD). Previous studies prompted that neuroimmunoinflammation is involved in the pathogenesis of depression and its factors become potential diagnostic biomarkers. Non-human primates exhibit depression-like behavior similar to humans in chronically stressed environments. Therefore, in the present study, after completing Whole transcriptome sequencing of peripheral blood, neurology-related and inflammatory molecules in plasma and cerebrospinal fluid were measured by Olink proximity extension assay technology simultaneously in 4 natural depressive-like (DL) cynomolgus monkeys and 4 normal controls to screen potential biological markers. Further, postmortem brain tissues and peripheral blood RNA sequencing data from MDD patients available in the Gene Expression Omnibus (GEO) database were used for cross-species validation. Compared to control monkeys, depressive-like monkeys exhibited elevated levels of neurocan (NCAN). RNA sequencing revealed Toll-like receptor 4 (TLR4) and the interacting S100 calcium-binding protein A family as key molecules in the inflammatory gene network. GEO brain tissue data showed up-regulation of S100A8 and S100A9 in the anterior cingulate cortex of MDD patients. These findings suggest that depressive-like monkeys are in a state of chronic low-grade inflammation and identify NCAN and TLR4 inflammatory network molecules as potential biomarkers of MDD.

Timing perception is a fundamental cognitive function that allows organisms to navigate their environment effectively, encompassing both prospective and retrospective timing. Despite significant advancements in understanding how the brain processes temporal information, the neural mechanisms underlying these two forms of timing remain largely unexplored. In this study, it aims to bridge this knowledge gap by elucidating the functional roles of various neuronal populations in the striatum and prefrontal cortex (PFC) in shaping subjective experiences of time. Utilizing a large-scale electrode array, it recorded responses from over 3000 neurons in the striatum and PFC of macaque monkeys during timing tasks. The analysis classified neurons into distinct groups and revealed that retrospective and prospective timings are governed by separate neural processes. Specifically, this study demonstrates that medium spiny neurons (MSNs) in the striatum play a crucial role in facilitating these timing processes. Through cell-type-specific manipulation, it identified D2-MSNs as the primary contributors to both forms of timing. Additionally, the findings indicate that effective processing of timing requires coordination between the PFC and the striatum. In summary, this study advances the understanding of the neural foundations of timing perception and highlights its behavioral implications.

Artificial intelligence (AI) is driving transformative changes in the field of medicine, with its successful application relying on accurate data and rigorous quality standards. By integrating clinical information, pathology, medical imaging, physiological signals, and omics data, AI significantly enhances the precision of research into disease mechanisms and patient prognoses. AI technologies also demonstrate exceptional potential in drug development, surgical automation, and brain-computer interface (BCI) research. Through the simulation of biological systems and prediction of intervention outcomes, AI enables researchers to rapidly translate innovations into practical clinical applications. While challenges such as computational demands, software development, and ethical considerations persist, the future of AI remains highly promising. AI plays a pivotal role in addressing societal issues like low birth rates and aging populations. AI can contribute to mitigating low birth rate issues through enhanced ovarian reserve evaluation, menopause forecasting, optimization of Assisted Reproductive Technologies (ART), sperm analysis and selection, endometrial receptivity evaluation, fertility forecasting, and remote consultations. In addressing the challenges posed by an aging population, AI can facilitate the development of dementia prediction models, cognitive health monitoring and intervention strategies, early disease screening and prediction systems, AI-driven telemedicine platforms, intelligent health monitoring systems, smart companion robots, and smart environments for aging-in-place. AI profoundly shapes the future of medicine.

Yohimbine, a potent alpha2A-adrenergic receptor (α2AAR) antagonist, was found therapeutic potential for type 2 diabetes through improving insulin release. However, the adverse side effects mediated by its actions in the brain hampered its use. Here, based on molecular docking analysis and structural modification, we have developed a novel peripherally acting yohimbine derivative (CDS479-2). CryoEM data found that yohimbine and CDS479-2 have similar interactions with the structure of α2AAR. Importantly, CDS479-2 shows similar α2AAR antagonist activity as yohimbine, but with very limited access to the brain, and thus avoiding the unwanted central effects such as hypertension and anxiety. Acute administration of CDS479-2 by injection or gavage lowered blood glucose levels and improved glucose tolerance in the high-fat diet-induced obesity (DIO) mice, an animal model for human type 2 diabetes. Remarkably, DIO mice received 2 weeks of daily administration of CDS479-2, but not yohimbine, exhibited sustained normoglycaemia, and increased density of the insulin-producing beta cells, in which important proliferation genes were found upregulated. Moreover, the overall protein expression levels of their pancreas were more similar to that of the healthy chow-fed mice. Thus, CDS479-2 may indicate a new direction for type 2 diabetes treatment. Importantly, the strategy we employed in this study will inspire the optimization for drugs that with both peripheral and central targets. ### Competing Interest Statement The authors have declared no competing interest.

When attempting to concurrently perform two distinct cognitive tasks, the performance of either task is frequently compromised. This phenomenon is known as dual-task interference. Although multiple task features have been postulated to influence on dual-task interference, the primary determinant remains unclear. The determinant factor causing dual-task interference is an important issue to understand its mechanism and associated functions including switching tasks and planning task order. The present study investigated this issue using monkeys and three behavioral tasks requiring distinct cognitive processes (spatial working memory, SWM; working memory and long-term memory of objects, PA; object working memory, DMS) and manipulating task pair (SWM and PA or SWM and DMS), task order (fixed or randomized), and task difficulty (different delay lengths). The task introduced first showed better performance as compared with the task introduced second, suggesting the task order as an important factor. However, the performance of the SWM task decreased when preceded by the PA and DMS tasks, while the latter tasks were unaffected when the SWM task was introduced first. This tendency was more obvious in random-order conditions than fixed-order conditions. Further, interference effect increased as task difficulty increased. Although the task order is one determinant, our results show the difference in cognitive process needed for tasks, its complexity, and the demand of working memory resources as more significant determinants for deciding the dominant task in dual-task conditions, indicating importance of neural mechanisms including managing working memory resources and coordinating multiple cognitive processes to understand the cause of dual-task interference.

Major depressive disorder(MDD)is a debilitating mental health condition that ranks second in the global burden of human dis-eases[1].Despite extensive research,the etiology and pathogene-sis of MDD remain poorly understood.Disturbance of the hypothalamic-pituitary-adrenal(HPA)axis mediated by glucocor-ticoid receptor(GR)dysfunction has been recognized to be associ-ated with the occurrence of MDD[2].

INTRODUCTION:More robust non-human primate models of Alzheimer's disease (AD) will provide new opportunities to better understand the pathogenesis and progression of AD. METHODS:We designed a CRISPR/Cas9 system to achieve precise genomic deletion of exon 9 in cynomolgus monkeys using two guide RNAs targeting the 3' and 5' intron sequences of PSEN1 exon 9. We performed biochemical, transcriptome, proteome, and biomarker analyses to characterize the cellular and molecular dysregulations of this non-human primate model. RESULTS:We observed early changes of AD-related pathological proteins (cerebrospinal fluid Aβ42 and phosphorylated tau) in PSEN1 mutant (ie, PSEN1-ΔE9) monkeys. Blood transcriptome and proteome profiling revealed early changes in inflammatory and immune molecules in juvenile PSEN1-ΔE9 cynomolgus monkeys. DISCUSSION:PSEN1 mutant cynomolgus monkeys recapitulate AD-related pathological protein changes, and reveal early alterations in blood immune signaling. Thus, this model might mimic AD-associated pathogenesis and has potential utility for developing early diagnostic and therapeutic interventions. HIGHLIGHTS:A dual-guide CRISPR/Cas9 system successfully mimics AD PSEN1-ΔE9 mutation by genomic excision of exon 9. PSEN1 mutant cynomolgus monkey-derived fibroblasts exhibit disrupted PSEN1 endoproteolysis and increased Aβ secretion. Blood transcriptome and proteome profiling implicate early inflammatory and immune molecular dysregulation in juvenile PSEN1 mutant cynomolgus monkeys. Cerebrospinal fluid from juvenile PSEN1 mutant monkeys recapitulates early changes of AD-related pathological proteins (increased Aβ42 and phosphorylated tau).

Artificial intelligence has had a profound impact on life sciences. This review discusses the application, challenges, and future development directions of artificial intelligence in various branches of life sciences, including zoology, plant science, microbiology, biochemistry, molecular biology, cell biology, developmental biology, genetics, neuroscience, psychology, pharmacology, clinical medicine, biomaterials, ecology, and environmental science. It elaborates on the important roles of artificial intelligence in aspects such as behavior monitoring, population dynamic prediction, microorganism identification, and disease detection. At the same time, it points out the challenges faced by artificial intelligence in the application of life sciences, such as data quality, black-box problems, and ethical concerns. The future directions are prospected from technological innovation and interdisciplinary cooperation. The integration of Bio-Technologies (BT) and Information-Technologies (IT) will transform the biomedical research into AI for Science and Science for AI paradigm.

No well-established biomarkers are available for the clinical diagnosis of major depressive disorder (MDD). Vitamin D-binding protein (VDBP) is altered in plasma and postmortem dorsolateral prefrontal cortex (DLPFC) tissues of MDD patients. Thereby, the role of VDBP as a potential biomarker of MDD diagnosis was further assessed. Total extracellular vesicles (EVs) and brain cell-derived EVs (BCDEVs) were isolated from the plasma of first-episode drug-naïve or drug-free MDD patients and well-matched healthy controls (HCs) in discovery (20 MDD patients and 20 HCs) and validation cohorts (88 MDD patients and 38 HCs). VDBP level in the cerebrospinal fluid (CSF) from chronic glucocorticoid-induced depressed rhesus macaques or prelimbic cortex from lipopolysaccharide (LPS)-induced depressed mice and wild control groups was measured to evaluate its relationship with VDBP in plasma microglia-derived extracellular vesicles (MDEVs). VDBP was significantly decreased in MDD plasma MDEVs compared to HCs, and negatively correlated with HAMD-24 score with the highest diagnostic accuracy among BCDEVs. VDBP in plasma MDEVs was decreased both in depressed rhesus macaques and mice. A positive correlation of VDBP in MDEVs with that in CSF was detected in depressed rhesus macaques. VDBP levels in prelimbic cortex microglia were negatively correlated with those in plasma MDEVs in depressed mice. The main results suggested that VDBP in plasma MDEVs might serve as a prospective candidate biomarker for MDD diagnosis.

On January 29, 2024, Elon Musk made a public announcement via social media about the successful implantation of a Neuralink device in humans. Shortly after, a collaborative team from Xuanwu Hospital and Tsinghua University in China revealed the advancements in their clinical experiment testing the Neural Electronic Opportunity (NEO) – a wireless brain-computer interface (BCI).1 These simultaneous developments sparked widespread interest and renewed enthusiasm for BCIs worldwide.