Human Cognitive Functions Research Articles

MAPbrain: a multi-omics atlas of the primate brain.

The brain is the central hub of the entire nervous system. Its development is a lifelong process guided by a genetic blueprint. Understanding how genes influence brain development is critical for deciphering the formation of human cognitive functions and the underlying mechanisms of neurological disorders. Recent advances in multi-omics techniques have now made it possible to explore these aspects comprehensively. However, integrating and analyzing extensive multi-omics data presents significant challenges. Here, we introduced MAPbrain (http://bigdata.ibp.ac.cn/mapBRAIN/), a multi-omics atlas of the primate brain. This repository integrates and normalizes both our own lab's published data and publicly available multi-omics data, encompassing 21 million brain cells from 38 key brain regions and 436 sub-regions across embryonic and adult stages, with 164 time points in humans and non-human primates. MAPbrain offers a unique, robust, and interactive platform that includes transcriptomics, epigenomics, and spatial transcriptomics data, facilitating a comprehensive exploration of brain development. The platform enables the exploration of cell type- and time point-specific markers, gene expression comparison between brain regions and species, joint analyses across transcriptome and epigenome, and navigation of cell types across species, brain regions, and development stages. Additionally, MAPbrain provides an online integration module for users to navigate and analyze their own data within the platform.

Challenging large language models’ “intelligence” with human tools: A neuropsychological investigation in Italian language on prefrontal functioning

The Artificial Intelligence (AI) research community has used ad-hoc benchmarks to measure the “intelligence” level of Large Language Models (LLMs). In humans, intelligence is closely linked to the functional integrity of the prefrontal lobes, which are essential for higher-order cognitive processes. Previous research has found that LLMs struggle with cognitive tasks that rely on these prefrontal functions, highlighting a significant challenge in replicating human-like intelligence. In December 2022, OpenAI released ChatGPT, a new chatbot based on the GPT-3.5 model that quickly gained popularity for its impressive ability to understand and respond to human instructions, suggesting a significant step towards intelligent behaviour in AI. Therefore, to rigorously investigate LLMs' level of “intelligence,” we evaluated the GPT-3.5 and GPT-4 versions through a neuropsychological assessment using tests in the Italian language routinely employed to assess prefrontal functioning in humans. The same tests were also administered to Claude2 and Llama2 to verify whether similar language models perform similarly in prefrontal tests. When using human performance as a reference, GPT-3.5 showed inhomogeneous results on prefrontal tests, with some tests well above average, others in the lower range, and others frankly impaired. Specifically, we have identified poor planning abilities and difficulty in recognising semantic absurdities and understanding others' intentions and mental states. Claude2 exhibited a similar pattern to GPT-3.5, while Llama2 performed poorly in almost all tests. These inconsistent profiles highlight how LLMs' emergent abilities do not yet mimic human cognitive functioning. The sole exception was GPT-4, which performed within the normative range for all the tasks except planning. Furthermore, we showed how standardised neuropsychological batteries developed to assess human cognitive functions may be suitable for challenging LLMs’ performance.

Scientific problems of ensuring technological sovereignty in the field of artificial intelligence technologies

The article discusses the scientific problems of ensuring technological sovereignty in the field of artificial intelligence technologies. One of the key scientific problems is computer modeling of human cognitive functions with finite resources, which provide the ability to process and analyze finite–dimensional and infinite-dimensional data along with finite-dimensional ones. A well–known solution to this problem is the A.N. Tikhonov regularization method, due to the high complexity and cost of implementation of which, less complex and expensive empirically constructed foreign INS are used to build AI solutions, but without guarantees of error-free results. The consequence of this is a rather low level of implementation of AI solutions in industry and in the field of information technology and a practical lack of data on the economic effect. It is proposed to consider the economic efficiency of domestic AI solutions as the main criterion for the need for their development.

Effects of natural reduced water on cognitive functions in older adults: A RCT study

Oxidative stress and diabetes increase the risk of cognitive decline and dementia. Natural reduced water contains active hydrogen (hydrogen radicals), eliminates reactive oxygen species, and has antidiabetic effects. However, whether natural reduced water affects human cognitive function is unknown. Therefore, we implemented a double-blind intervention experiment in which participants consumed 1 L of natural reduced water or tap water daily for 6 months. The participants were healthy older adults living in Japan. The intervention group showed significant improvements in cognitive functions of attention function (p < 0.01) and short-term memory (p < 0.05). These results indicate that the continuous intake of natural reduced water improves several cognitive functions.

What drives the corporate payoffs of using generative artificial intelligence?

Artificial Intelligence, a set of technologies that aim to replicate human cognitive functions, has seen remarkable improvements over the last decade. In particular, generative AI (GenAI), a subset of AI able to generate content tasks based on Large Language Models (LLM), has recently gained momentum. Based on an extensive analysis of generative AI use cases in large enterprises, we find that Gen AI shows strong labor productivity improvements across metrics such as throughput time, unit cost, and task effectiveness. However, the distribution of gains is asymmetric in favor of a few companies. While the current distribution of gains does not provide evidence of a power law effect, the current asymmetry reflects differences in AI resources/capabilities across companies - mainly data access, AI talent, or AI governance.

Artificial Intelligence in Employee Learning Process: Insights from Generation Z

Abstract Artificial intelligence, as a field of computer science focused on developing technologies that simulate intelligent behaviours and human cognitive functions, undoubtedly has huge potential to transform all business activities, including the process of employee learning. However, different generations have varying attitudes toward the rapid advancement of technology and the increasing possibilities offered by artificial intelligence. The general purpose of this research is to gain insights into the attitudes of Generation Z regarding the use of AI in the context of the employee learning process. Empirical research was conducted on a sample of 264 respondents from Slovenia and Bosnia and Herzegovina. In addition to descriptive statistics, Cronbach's alpha, Shapiro-Wilk, and Mann-Whitney tests were used to test hypotheses. Generally, the research findings indicate that the upcoming generation of the workforce considers artificial intelligence a significant factor in improving the employee learning process. The study contributes to human resource management literature because it brings new insights into Generation Z attitudes, whose participation in the active workforce will significantly increase in the coming years.

Unmodulated 40 Hz Stimulation as a Therapeutic Strategy for Aging: Improvements in Metabolism, Frailty, and Cognitive Function in Senescence-Accelerated Prone 10 Mice.

With aging populations in many countries, including Japan, efforts to mitigate the aging-related decline in physical function have gained importance not only for improving individual quality of life but also for mitigating the effects of this loss of function on society. Impaired glucose tolerance, muscle weakness, and cognitive decline are well-known effects of aging. These interrelated factors can create a vicious cycle because impaired glucose tolerance can accelerate muscle weakness and cognitive decline. Unmodulated 40 Hz (u40Hz) stimulation is imperceptible to the human ear and has been reported to improve cognitive function in humans and mice. However, research on the effects of u40Hz stimulation is still limited. This study aimed to report the effects of u40Hz stimulation on glucose tolerance and muscle strength in senescence-accelerated prone (SAMP)-10 mice, a model of accelerated aging. SAMP-10 mice underwent five weeks of u40Hz stimulation followed by glucose-tolerance tests, cognitive and behavioral assessments, and frailty evaluations. In comparison with the control group, the u40Hz-stimulation group showed mitigation of age-related decline in glucose tolerance, a better frailty index (FI), and notably preserved muscle strength. Microarray analysis of stimulated muscle tissue revealed significant upregulation of β-oxidation genes and genes functioning downstream of peroxisome proliferator-activated receptor gamma, and significant downregulation of clock genes. These findings indicate the beneficial effects of u40Hz stimulation on glucose tolerance, muscle strength, and cognitive function, warranting further research in this area.

The environmental model of mindfulness

This paper introduces the environmental model of mindfulness, a novel perspective that conceptualizes mindfulness as an adaptive set of cognitive styles influenced by specific socioecological contexts. Extending upon existing conceptualizations of mindfulness as a cultivated trait or practice, the environmental model describes how mindfulness can also result from cognitive adaptations to immediate-return environments. Through an interdisciplinary synthesis of cognitive psychology, anthropology, and environmental studies, four key factors are identified that foster mindful cognitive styles in immediate-return environments: immediate feedback, observational learning, nature exposure, and interconnected self-construal. By examining Indigenous communities living in immediate-return environments, this paper demonstrates how modern, delayed-return environments may inadvertently suppress innate mindful awareness, suggesting that mindfulness practices might be attempts to recalibrate toward more natural cognitive styles. The environmental model bridges Western medical and Eastern ethical perspectives on mindfulness, offering a unifying framework that acknowledges cognitive, environmental, and cultural influences. This approach not only enriches our understanding of human cognitive functioning but also broadens cultural perspectives on mindfulness, highlighting its manifestations across diverse sociocultural contexts. The model has profound implications for mindfulness research and interventions, providing a foundation for more ecologically valid and culturally sensitive approaches to fostering wellbeing. It invites a fundamental reconsideration of the relationship between human cognition and the environment, potentially revolutionizing our approach to mental health and cognitive enhancement in an increasingly complex world. This paper stands as a call to action for researchers, clinicians, and policymakers to rethink fundamental assumptions about human cognition and design environments that support our innate capacity for mindful awareness.

AI in microbiome research: Where have we been, where are we going?

Artificial intelligence (AI), a subdiscipline of computer science that develops machines or software that mimic characteristically human cognitive functions, is currently undergoing a revolution. In this commentary article, I will give my perspective on the evolution of the field and my thoughts on how AI may impact microbiome research in the next decade.

Mushroom-legume-based alternative chicken nuggets: Physico-chemical and sensory properties

The consumer demand for meat analogs (MAs) is on the rise. Soy proteins are frequently used as the primary ingredient in MAs, but soybean may negatively impact human health and cognitive functions. The aim of this research was to develop alternative chicken nuggets using mushroom and chickpea flour. Effect of different sources of mushroom and starch in the textural and sensory quality of nuggets were determined. Phoenix mushroom was chosen based on nuggets' textural and sensory qualities. Different ratios of mushroom and chickpea flour (0:70, 15:55, 55:15, and 70:0, w/w) were tested to prepare the nuggets. Based on protein, textural and sensory qualities compared to chicken and commercial plant-based nuggets, the mushroom to flour ratio of 55:15 was chosen. The results showed that tapioca starch-containing nuggets had a better texture and were more palatable to consumers. The processed nuggets demonstrated lower protein (6.88 vs 11.72%, dry wt. basis) but better cooking yield (87.65 vs 85.29%), moisture retention (77.86 vs 75.16%), fat retention (90.55 vs 88.39%) and higher consumer acceptability than commercially available soy-based nuggets. The results of the study suggest that mushroom and chickpea flour may replace soy protein in meat analogs.

In silico prediction of physicochemical properties and drug-likeness of omega-3 fatty acids

Abstract Omega-3 fatty acids, including alpha-linolenic acid (ALA), eicosapentaenoic acid (EPA), and docosahexaenoic acid (DHA), are recognized for their crucial roles in human health, particularly cardiovascular and cognitive function. In this study, we employed computational methodologies, leveraging the SwissADME platform and ADMETLab 3.0, to predict and cross-validate the physicochemical properties and drug-likeness of these essential fatty acids. SwissADME predictions indicated molecular weights of 278.43 g/mol for ALA, 302.45 g/mol for EPA, and 328.49 g/mol for DHA, with consensus Log Po/w values of 5.09, 5.50, and 5.72 respectively, and varying degrees of water solubility. However, predictions from ADMETLab 3.0 were almost similar: ALA with a molecular weight of 278.22 g/mol, EPA at 302.22 g/mol, and DHA at 328.24 g/mol. Significant discrepancies were observed in lipophilicity, with ADMETLab 3.0 predicting Log Po/w values of 6.461 for ALA, 6.477 for EPA, and 7.006 for DHA, higher than those from SwissADME. Additionally, water solubility predictions from ADMETLab 3.0 showed ALA with a Log S of −5.034, EPA at −4.4, and DHA at −4.638, which differed from SwissADME’s estimates. These differences reflect variations in computational approaches and algorithms. Comparison with literature data revealed general alignment in physicochemical properties, such as water solubility and lipophilicity. Furthermore, assessment of drug-likeness according to Lipinski’s rule demonstrated compliance for all three fatty acids, albeit with variations in other criteria such as Ghose, Veber, Egan, and Muegge rules. These findings underscore the reliability and applicability of computational approaches in elucidating the physicochemical properties and drug-likeness of omega-3 fatty acids, offering valuable insights for pharmaceutical research and therapeutic applications.

Deployment of AI Tools and Technologies on Academic Integrity and Research

Academic integrity is a set of ethical ideals and values that guide the behavior of individuals in academic and educational settings. It encompasses honesty, trustworthiness, fairness, and a commitment to upholding the highest standards of ethical conduct in the quest for knowledge, learning, and research. Academic integrity is essential in maintaining the trustworthiness, reputation, and effectiveness of educational institutions and scholarly communities. Whereas, AI, or Artificial Intelligence, is a broad field of computer science that focuses on creating frameworks, software, or machines that can perform tasks that would typically require human intelligence. These tasks include problem-solving, learning from experience, understanding natural language, recognizing patterns, and making choices. AI systems aim to mimic or replicate human cognitive functions, and they can range from simple rule-based systems to highly complex, autodidactic neural networks. AI can significantly impact academic integrity and research in both positive and potentially challenging ways.

Electroencephalography as a new diagnostic technology for mild psychoneurological disorders

Owing to the increase in the information load in modem society, assessing the clinical characteristics of borderline psychopathological conditions is significantly difficult because we cannot objectively assess them using functional examination methods. To demonstrate the possibility of mathematical analysis of electroencephalography as a new method of clinical electroencephalographic diagnostics for clinically mild conditions, we examined the electroencephalogram indicators of patients who voluntarily and for the first time sought medical help with minimally pronounced clinical manifestations of psychoneurological diseases. The results obtained allowed us to determine that using modern methods of mathematical processing of electroencephalographic signals, we can identify objective differences in the activity indicators of individual cortical structures. The conducted studies allow us to provide practical medicine with a new objective approach to studying human cognitive function.

IMPACT OF STRESS ON EMOTIONAL HEALTH AND COGNITIVE FUNCTION

Background. Emotional stress can have both a positive effect, which is aimed at adaptation, and a negative one, which affects the higher integrative functions of the brain, and also leads to the development of numerous diseases. In this regard, the problem of establishing the influence of stress factors on the emotional state and cognitive function becomes relevant, which creates the prerequisites for a detailed analysis of the scientific data. Aim: to investigate the impact of chronic stress on emotional health and to determine the impact of stressful factors on human cognitive functions. Material and methods. The review included 63 articles, which have been selected using the following keywords: «chronic stress», «cortisol», «cognitive functions», «emotions», «memory», in the databases of scientific medical data PubMed, Scopus and Web of Science. An analysis of the existing research results on the impact of stress on emotional health and cognitive functions was carried out. Results. Stress causes a multiple effect on the human nervous system, leading to structural changes in different parts of the brain such as atrophy and reduction of brain volume and mass with long-term consequences for the nervous system resulting in impaired cognitive abilities and memory. Alteration of neuronal plasticity, caused by chronic stress, due to dendrite atrophy and decreased spinal density may underlie the depressive disorders. Additionally, chronic inflammation, which also results from prolonged stress, can develop depression and disturb cognitive functions. The hippocampus contains the high density of glucocorticoids receptors, thus increased basal concentration of cortisol may result in functional and structural changes in the hippocampus with atrophy and impaired neurogenesis. Chronic stress can affect cognitive function both acutely and chronically. The acute effect is caused by beta-adrenergic effects, while the chronic effect is caused by long-term changes in gene expression mediated by steroid hormones. Conclusion. Chronic stress with an increased basal concentration of glucocorticoids affects the hippocampus leading to impaired memory, cognition, and thinking, also increases risk of depression, anxiety disorders.

The Complex Relationship between Sleep and Cognitive Reserve: A Narrative Review Based on Human Studies.

Sleep and brain/cognitive/neural reserve significantly impact well-being and cognition throughout life. This review aims to explore the intricate relationship between such factors, with reference to their effects on human cognitive functions. The specific goal is to understand the bidirectional influence that sleep and reserve exert on each other. Up to 6 February 2024, a methodical search of the literature was conducted using the PubMed database with terms related to brain, cognitive or neural reserve, and healthy or disturbed sleep. Based on the inclusion criteria, 11 articles were selected and analyzed for this review. The articles focus almost exclusively on cognitive reserve, with no explicit connection between sleep and brain or neural reserve. The results evidence sleep's role as a builder of cognitive reserve and cognitive reserve's role as a moderator in the effects of physiological and pathological sleep on cognitive functions. In conclusion, the findings of the present review support the notion that both sleep and cognitive reserve are critical factors in cognitive functioning. Deepening comprehension of the interactions between them is essential for devising strategies to enhance brain health and resilience against age- and pathology-related conditions.

Investigating the neural mechanisms of transcranial direct current stimulation effects on human cognition: current issues and potential solutions.

Transcranial direct current stimulation (tDCS) has been studied extensively for its potential to enhance human cognitive functions in healthy individuals and to treat cognitive impairment in various clinical populations. However, little is known about how tDCS modulates the neural networks supporting cognition and the complex interplay with mediating factors that may explain the frequently observed variability of stimulation effects within and between studies. Moreover, research in this field has been characterized by substantial methodological variability, frequent lack of rigorous experimental control and small sample sizes, thereby limiting the generalizability of findings and translational potential of tDCS. The present manuscript aims to delineate how these important issues can be addressed within a neuroimaging context, to reveal the neural underpinnings, predictors and mediators of tDCS-induced behavioral modulation. We will focus on functional magnetic resonance imaging (fMRI), because it allows the investigation of tDCS effects with excellent spatial precision and sufficient temporal resolution across the entire brain. Moreover, high resolution structural imaging data can be acquired for precise localization of stimulation effects, verification of electrode positions on the scalp and realistic current modeling based on individual head and brain anatomy. However, the general principles outlined in this review will also be applicable to other imaging modalities. Following an introduction to the overall state-of-the-art in this field, we will discuss in more detail the underlying causes of variability in previous tDCS studies. Moreover, we will elaborate on design considerations for tDCS-fMRI studies, optimization of tDCS and imaging protocols and how to assure high-level experimental control. Two additional sections address the pressing need for more systematic investigation of tDCS effects across the healthy human lifespan and implications for tDCS studies in age-associated disease, and potential benefits of establishing large-scale, multidisciplinary consortia for more coordinated tDCS research in the future. We hope that this review will contribute to more coordinated, methodologically sound, transparent and reproducible research in this field. Ultimately, our aim is to facilitate a better understanding of the underlying mechanisms by which tDCS modulates human cognitive functions and more effective and individually tailored translational and clinical applications of this technique in the future.

Neuromodulation of inhibitory control using phase-lagged transcranial alternating current stimulation

BackgroundTranscranial alternating current stimulation (tACS) is a prominent non-invasive brain stimulation method for modulating neural oscillations and enhancing human cognitive function. This study aimed to investigate the effects of individualized theta tACS delivered in-phase and out-of-phase between the dorsal anterior cingulate cortex (dACC) and left dorsolateral prefrontal cortex (lDLPFC) during inhibitory control performance.MethodsThe participants engaged in a Stroop task with phase-lagged theta tACS over individually optimized high-density electrode montages targeting the dACC and lDLPFC. We analyzed task performance, event-related potentials, and prestimulus electroencephalographic theta and alpha power.ResultsWe observed significantly reduced reaction times following out-of-phase tACS, accompanied by reduced frontocentral N1 and N2 amplitudes, enhanced parieto-occipital P1 amplitudes, and pronounced frontocentral late sustained potentials. Out-of-phase stimulation also resulted in significantly higher prestimulus frontocentral theta and alpha activity.ConclusionsThese findings suggest that out-of-phase theta tACS potently modulates top-down inhibitory control, supporting the feasibility of phase-lagged tACS to enhance inhibitory control performance.

Effects of inter-stimulus and inter-trial intervals on somatosensory gating

Aim of the study Sensory gating is a human higher cognitive function that serves to suppress excessive sensory information and prevent brain overactivity. To elucidate this function, a paired-pulse stimulation paradigm has been used while recording electroencephalography (EEG), and evaluated as an amplitude ratio of responses to a second stimulus (S2) over responses to the first stimulus (S1). The present study investigated the effects of the inter-stimulus interval (ISI) and inter-trial interval (ITI) on somatosensory gating using somatosensory-evoked potentials (SEPs). Methods In Experiment 1, ISI was set at five conditions: 200, 400, 600, 800, and 1000 ms. In Experiment 2, ITI was set at four conditions: 1, 2, 4, and 8 s. Results ISI affected the S2/S1 amplitude ratios of P22 and N27 at C3’ and N30 at Fz, and these S2/S1 amplitude ratios decreased the most under the 200 and 400-ms conditions. ITI affected the S2/S1 amplitude ratios of P22, N27, and N60 at C3', and especially, the somatosensory gating did not work under the 1-s condition. These results suggest that not all SEP components are modulated in the same manner with changing ISI and ITI. The effects of ISI and ITI independently affected the somatosensory gating. Conclusions Based on our findings, preferable parameters are 200-400 ms for ISI and 4 s or longer for ITI to evaluate the functional mechanisms on somatosensory gating in SEPs.

Decoupling of gray and white matter functional networks in cognitive impairment induced by occupational aluminum exposure

Aluminum (Al) is a low-toxic, accumulative substance with neurotoxicity properties that adversely affect human cognitive function. This study aimed to investigate the neurobiological mechanisms underlying cognitive impairment resulting from occupational Al exposure. Resting-state functional magnetic resonance imaging was conducted on 54 individuals with over 10 years of Al exposure. Al levels were measured, and cognitive function was assessed using the Montreal Cognitive Assessment (MoCA). Subsequently, the K-means clustering algorithm was employed to identify functional gray matter (GM) and white matter (WM) networks. Two-sample t-tests were conducted between the cognition impairment group and the control group. Al exhibited a negative correlation with MoCA scores. Participants with cognitive impairment demonstrated reduced functional connectivity (FC) between the middle cingulum network (WM1) and anterior cingulum network (WM2), as well as between the executive control network (WM6) and limbic network (WM10). Notably, decreased FCs were observed between the executive control network (GM5) and WM1, WM4, WM6, and WM10. Additionally, the FC of GM5-GM4 and WM1-WM2 negatively correlated with Trail Making Test Part A (TMT-A) scores. Prolonged Al accumulation detrimentally affects cognition, primarily attributable to executive control and limbic network disruptions.

Artificial Intelligence in Medicine: A New Frontier

Artificial intelligence (AI) refers to the engineering and science of making intelligent machines through algorithms or rules, mimicking human cognitive functions, such as learning and problem-solving. AI has several branches, such as machine learning and deep learning, which can add intelligence to applications. Machine learning is the study of algorithms that allow computer programs to improve automatically through experience. Deep learning algorithms learn from an extensive, multi-layered collection of interconnected processes and expose these processors to many examples. In the coming years, the integration of AI in routine medical care is expected to revolutionize Medicine, potentially improving patient care and quality of life. The time required for a diagnosis can be greatly reduced, and diagnostic efficiency can be significantly enhanced when AI assists clinicians. Large language model chatbots are capable of clinical expert-level medical note-taking, consultation, and questionanswering. Chatbotscan generate human-like text, may help diagnose diseases based on medical records, and may suggest treatment options or plans. Artificial intelligence algorithms, particularly deep learning, have demonstrated remarkable progress in radiological image analysis and diagnosis and may improve radiologists’ efficiency. These algorithms may also improve diagnostic accuracy in dermatology, histopathology, fundoscopy, endoscopy, and other medical images. Natural language processing and ambient clinical intelligence automate administrative duties like recording patient visits in electronic health records, streamlining clinical workflow, and freeing up doctors to spend more time with patients. AI may also help with new drug discoveries, precision medicine, and clinical research. AI developments can revolutionize several healthcare-related fields and pave the way for a more individualized, accurate, predictive, and portable future. Bangladesh J Medicine 2024; 35: 54-60