Hasil untuk "Neurophysiology and neuropsychology"

Andrew Zillgitt, David E Burdette, Atheel Yako et al.

Clobazam (CLB) and cenobamate (CNB) are commonly used antiseizure medications (ASMs) in the treatment of patients with drug-resistant epilepsy (DRE). However, concomitant use of these two ASMs may lead to significant treatment-related adverse events (TRAE). Furthermore, these TRAE may be exacerbated in individuals with genetic polymorphisms involving the P450 system. In patients with DRE, epilepsy surgery, including neuromodulation, may lead to improved seizure control and a reduction in systemic TRAE from ASMs. This case report describes a patient with drug-resistant idiopathic generalized epilepsy (IGE) who experienced persistent excessive somnolence correlated with elevated N-desmethylclobazam (N-CLB) levels. Pharmacogenetic testing revealed poor metabolism of CYP2C19, and N-CLB levels remained elevated and detectable for nearly one year after the discontinuation of treatment with CLB and CNB. Responsive neurostimulator (RNS) implantation within the bilateral centromedian nuclei (CMN) of the thalamus resulted in seizure freedom until N-CLB levels fell, after which there was an 83–93 % reduction in the frequency of generalized tonic-clonic seizures (GTC).

Giovanni M. Di Liberto, Emily Y. J. Ip

Understanding the neural basis of speech communication is essential for uncovering how sounds are translated into meaning, how that changes with development, ageing, and speech-related deficits, as well as contributing to brain-computer interfaces research. While traditional neurophysiological studies have relied on simplified, controlled paradigms, recent advances have shifted the field toward more ecologically-valid approaches. Here, we examine the impact of continuous speech research and discuss the potential of speech interaction neurophysiology. We present a discussion on how realistic paradigms challenge conventional methods, offering richer insights into neural encoding, functional brain mapping, and neural entrainment. At the same time, they introduce significant analytical and technical complexities, particularly when incorporating social interaction. We discuss the evolving landscape of experimental designs, from discrete to continuous stimuli and from socially-isolated listening to dynamic, multi-agent communication. By synthesising findings across studies, we highlight how naturalistic speech paradigms contribute to refining theories of language processing and open new avenues for research. In doing so, this review critically evaluates of whether the move toward realism in speech neurophysiology represents a technological trend or a transformative leap in understanding the neural underpinnings of speech communication.

Gene Tangtartharakul, Katherine R. Storrs

Visual Language Models (VLMs) show remarkable performance in visual reasoning tasks, successfully tackling college-level challenges that require high-level understanding of images. However, some recent reports of VLMs struggling to reason about elemental visual concepts like orientation, position, continuity, and occlusion suggest a potential gulf between human and VLM vision. Here we use the toolkit of neuropsychology to systematically assess the capabilities of three state-of-the-art VLMs across visual domains. Using 51 tests drawn from six clinical and experimental batteries, we characterise the visual abilities of leading VLMs relative to normative performance in healthy adults. While the models excel in straightforward object recognition tasks, we find widespread deficits in low- and mid-level visual abilities that would be considered clinically significant in humans. These selective deficits, profiled through validated test batteries, suggest that an artificial system can achieve complex object recognition without developing foundational visual concepts that in humans require no explicit training.

Neil Natarajan, Sruthi Viswanathan, Xavier Roberts-Gaal et al.

Static solutions don't serve a dynamic mind. Thus, we advocate a shift from static mental health diagnostic assessments to continuous, artificial intelligence (AI)-driven assessment. Focusing on Attention-Deficit/Hyperactivity Disorder (ADHD) as a case study, we explore how generative AI has the potential to address current capacity constraints in neuropsychology, potentially enabling more personalized and longitudinal care pathways. In particular, AI can efficiently conduct frequent, low-level experience sampling from patients and facilitate diagnostic reconciliation across care pathways. We envision a future where mental health care benefits from continuous, rich, and patient-centered data sampling to dynamically adapt to individual patient needs and evolving conditions, thereby improving both accessibility and efficacy of treatment. We further propose the use of mental health digital twins (MHDTs) - continuously updated computational models that capture individual symptom dynamics and trajectories - as a transformative framework for personalized mental health care. We ground this framework in empirical evidence and map out the research agenda required to refine and operationalize it.

Runkai Zhang, Hua Yu, John Q. Gan et al.

Scalp electroencephalography (EEG) and intracranial EEG (iEEG) are vital for epilepsy diagnosis and treatment. Their unified analysis offers the potential to harness the complementary strengths of each modality but is challenging due to variations in recording montages, amplitude and signal-to-noise ratio (SNR), and frequency components. To address the aforementioned challenges, this paper introduces EpiNT, a novel Transformer-based pre-trained model for unified EEG and iEEG analysis. EpiNT employs channel-independent modeling with masked autoencoders (MAE) and vector quantization (VQ), along with a frequency domain mapping quantizer to capture crucial frequency features. Pre-trained on over 2,700 hours of multi-modal clinical neurophysiological data from 1,199 patients, EpiNT outperformed both randomly initialized models and other pre-trained methods on six downstream classification tasks, demonstrating robust representation learning capabilities. This work presents a promising approach for unified epilepsy neurophysiology analysis.

Vassilis Lyberatos, Spyridon Kantarelis, Ioanna Zioga et al.

This study investigates emotional expression and perception in music performance using computational and neurophysiological methods. The influence of different performance settings, such as repertoire, diatonic modal etudes, and improvisation, as well as levels of expressiveness, on performers' emotional communication and listeners' reactions is explored. Professional musicians performed various tasks, and emotional annotations were provided by both performers and the audience. Audio analysis revealed that expressive and improvisational performances exhibited unique acoustic features, while emotion analysis showed stronger emotional responses. Neurophysiological measurements indicated greater relaxation in improvisational performances. This multimodal study highlights the significance of expressivity in enhancing emotional communication and audience engagement.

Anna Csilla Kegyes-Brassai, R. Pierson-Bartel, Gergo Bolla et al.

Alzheimer’s disease (AD) is the leading cause of dementia, often associated with impaired sleep quality and disorganized sleep structure. This study aimed to characterize changes in sleep macrostructure and K-complex density in AD, in relation to neuropsychological performance and brain structural changes. We enrolled 30 AD and 30 healthy control participants, conducting neuropsychological exams, brain MRI, and one-night polysomnography. AD patients had significantly reduced total sleep time (TST), sleep efficiency, and relative durations of non-rapid eye movement (NREM) stages 2 (S2), 3 (S3), and rapid eye movement (REM) sleep (p < 0.01). K-complex (KC) density during the entire sleep period and S2 (p < 0.001) was significantly decreased in AD. We found strong correlations between global cognitive performance and relative S3 (p < 0.001; r = 0.86) and REM durations (p < 0.001; r = 0.87). TST and NREM stage 1 (S1) durations showed a moderate negative correlation with amygdaloid and hippocampal volumes (p < 0.02; r = 0.51–0.55), while S3 and REM sleep had a moderate positive correlation with cingulate cortex volume (p < 0.02; r = 0.45–0.61). KC density strongly correlated with global cognitive function (p < 0.001; r = 0.66) and the thickness of the anterior cingulate cortex (p < 0.05; r = 0.45–0.47). Our results indicate significant sleep organization changes in AD, paralleling cognitive decline. Decreased slow wave sleep and KCs are strongly associated with cingulate cortex atrophy. Since sleep changes are prominent in early AD, they may serve as prognostic markers or therapeutic targets.

Zhang Y, Wang H, Yang J et al.

Yating Zhang,1 Hongyan Wang,1 Jie Yang,2 Sanchun Wang,1 Weifang Tong,1 Bo Teng1 1Department of Otorhinolaryngology Head and Neck Surgery, the Second Hospital of Jilin University, Changchun, Jilin Province, People’s Republic of China; 2Department of Neurology, the First Hospital of Jilin University, Changchun, Jilin Province, People’s Republic of ChinaCorrespondence: Bo Teng, Department of Otorhinolaryngology Head and Neck Surgery, the Second Hospital of Jilin University, No. 218 Ziqiang Street, Nanguan District, Changchun, Jilin Province, 130000, People’s Republic of China, Email tengbo1975@163.comPurpose: This investigation sought to elucidate the genetic underpinnings that connect obesity indicators, circulating blood lipid levels, adipokines levels and obstructive sleep apnea syndrome (OSAS), employing a bidirectional two-sample Mendelian randomization (MR) analysis that utilizes data derived from extensive genome-wide association studies (GWAS).Methods: We harnessed genetic datasets of OSAS available from the FinnGen consortium and summary data of four obesity indices (including neck circumference), seven blood lipid (including triglycerides) and eleven adipokines (including leptin) from the IEU OpenGWAS database. We primarily utilized inverse variance weighted (IVW), weighted median, and MR-Egger methods, alongside MR-PRESSO and Cochran’s Q tests, to validate and assess the diversity and heterogeneity of our findings.Results: After applying the Bonferroni correction, we identified significant correlations between OSAS and increased neck circumference (Odds Ratio [OR]: 3.472, 95% Confidence Interval [CI]: 1.954– 6.169, P= 2.201E-05) and decreased high-density lipoprotein (HDL) cholesterol levels (OR: 0.904, 95% CI: 0.858– 0.952, P= 1.251E-04). Concurrently, OSAS was linked to lower leptin levels (OR: 1.355, 95% CI: 1.069– 1.718, P= 0.012) and leptin receptor levels (OR: 0.722, 95% CI: 0.530– 0.996, P= 0.047). Sensitivity analyses revealed heterogeneity in HDL cholesterol and leptin indicators, but further multiplicative random effects IVW method analysis confirmed these correlations as significant (P< 0.05) without notable heterogeneity or horizontal pleiotropy in other instrumental variables.Conclusion: This investigation compellingly supports the hypothesis that OSAS could be a genetic predisposition for elevated neck circumference, dyslipidemia, and adipokine imbalance. These findings unveil potential genetic interactions between OSAS and metabolic syndrome, providing new pathways for research in this domain. Future investigations should aim to delineate the specific biological pathways by which OSAS impacts metabolic syndrome. Understanding these mechanisms is critical for developing targeted prevention and therapeutic strategies.Keywords: sleep disorders, metabolic syndrome, causal inference, GWAS

Joe Bathelt, Kathleen Rastle, J. S. H. Taylor

Michael Pichat, Enola Campoli, William Pogrund et al.

Neuropsychology of artificial intelligence focuses on synthetic neural cog nition as a new type of study object within cognitive psychology. With the goal of making artificial neural networks of language models more explainable, this approach involves transposing concepts from cognitive psychology to the interpretive construction of artificial neural cognition. The human cognitive concept involved here is categorization, serving as a heuristic for thinking about the process of segmentation and construction of reality carried out by the neural vectors of synthetic cognition.

Matthew Avaylon, Ryan Ly, Andrew Tritt et al.

Across many domains, large swaths of digital assets are being stored across distributed data repositories, e.g., the DANDI Archive [8]. The distribution and diversity of these repositories impede researchers from formally defining terminology within experiments, integrating information across datasets, and easily querying, reusing, and analyzing data that follow the FAIR principles [15]. As such, it has become increasingly important to have a standardized method to attach contextual metadata to datasets. Neuroscience is an exemplary use case of this issue due to the complex multimodal nature of experiments. Here, we present the HDMF External Resources Data (HERD) standard and related tools, enabling researchers to annotate new and existing datasets by mapping external references to the data without requiring modification of the original dataset. We integrated HERD closely with Neurodata Without Borders (NWB) [2], a widely used data standard for sharing and storing neurophysiology data. By integrating with NWB, our tools provide neuroscientists with the capability to more easily create and manage neurophysiology data in compliance with controlled sets of terms, enhancing rigor and accuracy of data and facilitating data reuse.

Michael Pichat, Enola Campoli, William Pogrund et al.

We propose a neuropsychological approach to the explainability of artificial neural networks, which involves using concepts from human cognitive psychology as relevant heuristic references for developing synthetic explanatory frameworks that align with human modes of thought. The analogical concepts mobilized here, which are intended to create such an epistemological bridge, are those of categorization and similarity, as these notions are particularly suited to the categorical "nature" of the reconstructive information processing performed by artificial neural networks. Our study aims to reveal a unique process of synthetic cognition, that of the categorical convergence of highly activated tokens. We attempt to explain this process with the idea that the categorical segment created by a neuron is actually the result of a superposition of categorical sub-dimensions within its input vector space.

B. Frauscher, C. Bénar, J. Engel et al.

Since the discovery of the human electroencephalogram (EEG), neurophysiology techniques have become indispensable tools in our armamentarium to localize epileptic seizures. New signal analysis techniques and the prospects of artificial intelligence and big data will offer unprecedented opportunities to further advance the field in the near future, ultimately resulting in improved quality of life for many patients with drug-resistant epilepsy. This article summarizes selected presentations from Day 1 of the two-day symposium "Neurophysiology, Neuropsychology, Epilepsy, 2022: Hills We Have Climbed and the Hills Ahead". Day 1 was dedicated to highlighting and honoring the work of Dr. Jean Gotman, a pioneer in EEG, intracranial EEG, simultaneous EEG/ functional magnetic resonance imaging, and signal analysis of epilepsy. The program focused on two main research directions of Dr. Gotman, and was dedicated to "High-frequency oscillations, a new biomarker of epilepsy" and "Probing the epileptic focus from inside and outside". All talks were presented by colleagues and former trainees of Dr. Gotman. The extended summaries provide an overview of historical and current work in the neurophysiology of epilepsy with emphasis on novel EEG biomarkers of epilepsy and source imaging and concluded with an outlook on the future of epilepsy research, and what is needed to bring the field to the next level.

M. Smith, G. Risse, Viviane Sziklas et al.

This article summarizes selected presentations from a session titled "Cognition and Sensory Systems in Healthy and Diseased Subjects", held to highlight and honor the work of Dr. Marilyn Jones-Gotman. The session was part of a two-day symposium, "Neurophysiology, Neuropsychology, Epilepsy, 2022: Hills We Have Climbed and the Hills Ahead". The session presented research on epilepsy and sensory systems by colleagues and former trainees of Dr. Jones-Gotman. The extended summaries provide an overview of historical and current work in the neuropsychology of epilepsy, neuropsychological and neuroimaging approaches to understanding brain organization, sex differences in brain mechanisms underlying neurological disorders, dietary influences on brain function and cognition, and expertise in olfactory training and language experiences and their implications for brain organization and structure.

Felicia A. Hardi, Leigh G. Goetschius, Scott Tillem et al.

Unstable and unpredictable environments are linked to risk for psychopathology, but the underlying neural mechanisms that explain how instability relate to subsequent mental health concerns remain unclear. In particular, few studies have focused on the association between instability and white matter structures despite white matter playing a crucial role for neural development. In a longitudinal sample recruited from a population-based study (N = 237), household instability (residential moves, changes in household composition, caregiver transitions in the first 5 years) was examined in association with adolescent structural network organization (network integration, segregation, and robustness of white matter connectomes; Mage = 15.87) and young adulthood anxiety and depression (six years later). Results indicate that greater instability related to greater global network efficiency, and this association remained after accounting for other types of adversity (e.g., harsh parenting, neglect, food insecurity). Moreover, instability predicted increased depressive symptoms via increased network efficiency even after controlling for previous levels of symptoms. Exploratory analyses showed that structural connectivity involving the left fronto-lateral and temporal regions were most strongly related to instability. Findings suggest that structural network efficiency relating to household instability may be a neural mechanism of risk for later depression and highlight the ways in which instability modulates neural development.

Steve Chung, Jurriaan M. Peters, Kamil Detyniecki et al.

Nasal administration of treatments for neurologic conditions, including rescue therapies to treat seizure clusters among people with epilepsy, represents a meaningful advance in patient care. Nasal anatomy and physiology underpin the multiple advantages of nasal administration but also present challenges that must be addressed in any successful nasal formulation. Nasal cavity anatomy is complex, with a modest surface area for absorption that limits the dose volume of an intranasal formulation. The mucociliary clearance mechanism and natural barriers of the nasal epithelia must be overcome for adequate absorption. An extensive vasculature and the presence of olfactory nerves in the nasal cavity enable both systemic and direct-to-brain delivery of drugs targeting the central nervous system. Two intranasal benzodiazepine rescue therapies have been approved by the US Food and Drug Administration for seizure-cluster treatment, in addition to the traditional rectal formulation. Nasal sprays are easy to use and offer the potential for quick and consistent bioavailability. This review aims to increase the clinician’s understanding of nasal anatomy and physiology and of the formulation of intranasal rescue therapies and to facilitate patient education and incorporate intranasal rescue therapies for seizure clusters (also known as acute repetitive seizures) into their seizure action plans.

Kirti Singhal, Surekha Chukkali

AbstractDespite the advances in studies conducted among healthcare professionals to explore the impact of the pandemic on their mental health, a large population still continues to display COVID-19 related psychological complaints. There has been recent awareness of moral injury related guilt and shame among doctors and nurses. However, the factors associated with moral injury have not received much attention, due to which the issue still persists. This study aims to explore the role of guilt-shame proneness, and locus of control in predicting moral injury among healthcare professionals. MISS-HP, PGI Locus of Control, and GASP scales were administered to a sample of 806 healthcare professionals. Pearson correlation coefficient indicated a significant positive relationship between moral injury and guilt-shame proneness, as well as the locus of control. Regression analysis indicated a significant role of guilt-shame proneness and locus of control in predicting moral injury. In conclusion, while studying moral injury, it becomes equally important to consider these factors to understand the concept better.

Ugurcan Mugan, Seiichiro Amemiya, Paul S. Regier et al.

Current theories suggest that adaptive decision-making necessitates the interaction between multiple decision-making systems. The computational definitions of different models of decision-making suggest interactions with task demands and complexity. We review these computational theories and derive experimental predictions that will shed light on the underlying neurobiological mechanisms. We use a well-established multi-strategy task and novel neurophysiological analyses from hippocampus and striatum as a case study in the interaction between task structure and navigational complexity. This approach reveals how task structure and navigational complexity interact with each other to identify differences between habitual and planned action choices.

Hernan Ceferino Vazquez

Artificial Intelligence (AI) has been rapidly advancing and has demonstrated its ability to perform a wide range of cognitive tasks, including language processing, visual recognition, and decision-making. Part of this progress is due to LLMs (Large Language Models) like those of the GPT (Generative Pre-Trained Transformers) family. These models are capable of exhibiting behavior that can be perceived as intelligent. Most authors in Neuropsychology consider intelligent behavior to depend on a number of overarching skills, or Executive Functions (EFs), which rely on the correct functioning of neural networks in the frontal lobes, and have developed a series of tests to evaluate them. In this work, we raise the question of whether LLMs are developing executive functions similar to those of humans as part of their learning, and we evaluate the planning function and working memory of GPT using the popular Towers of Hanoi method. Additionally, we introduce a new variant of the classical method in order to avoid that the solutions are found in the LLM training data (dataleakeage). Preliminary results show that LLMs generates near-optimal solutions in Towers of Hanoi related tasks, adheres to task constraints, and exhibits rapid planning capabilities and efficient working memory usage, indicating a potential development of executive functions. However, these abilities are quite limited and worse than well-trained humans when the tasks are not known and are not part of the training data.

Julian Fuhrer, Alejandro Blenkmann, Tor Endestad et al.

Brain activity differs vastly between sleep, cognitive tasks, and action. Information theory is an appropriate concept to analytically quantify these brain states. Based on neurophysiological recordings, this concept can handle complex data sets, is free of any requirements about the data structure, and can infer the present underlying brain mechanisms. Specifically, by utilizing algorithmic information theory, it is possible to estimate the absolute information contained in brain responses. While current approaches that apply this theory to neurophysiological recordings can discriminate between different brain states, they are limited in directly quantifying the degree of similarity or encoded information between brain responses. Here, we propose a method grounded in algorithmic information theory that affords direct statements about responses' similarity by estimating the encoded information through a compression-based scheme. We validated this method by applying it to both synthetic and real neurophysiological data and compared its efficiency to the mutual information measure. This proposed procedure is especially suited for task paradigms contrasting different event types because it can precisely quantify the similarity of neuronal responses.