Hasil untuk "Neurophysiology and neuropsychology"

Faiz Ghifari Haznitrama, Faeyza Rishad Ardi, Alice Oh

Large language models (LLMs) exhibit a unified "general factor" of capability across 10 benchmarks, a finding confirmed by our factor analysis of 156 models, yet they still struggle with simple, trivial tasks for humans. This is because current benchmarks focus on task completion, failing to probe the foundational cognitive abilities that highlight these behaviors. We address this by introducing the NeuroCognition benchmark, grounded in three adapted neuropsychological tests: Raven's Progressive Matrices (abstract relational reasoning), Spatial Working Memory (maintenance and systematic search), and the Wisconsin Card Sorting Test (cognitive flexibility). Our evaluation reveals that while models perform strongly on text, their performance degrades for images and with increased complexity. Furthermore, we observe that complex reasoning is not universally beneficial, whereas simple, human-like strategies yield partial gains. We also find that NeuroCognition correlates positively with standard general-capability benchmarks, while still measuring distinct cognitive abilities beyond them. Overall, NeuroCognition emphasizes where current LLMs align with human-like intelligence and where they lack core adaptive cognition, showing the potential to serve as a verifiable, scalable source for improving LLMs.

Liming Wang, Saurabhchand Bhati, Cody Karjadi et al.

Early detection of dementia is critical for timely medical intervention and improved patient outcomes. Neuropsychological tests are widely used for cognitive assessment but have traditionally relied on manual scoring. Automatic dementia classification (ADC) systems aim to infer cognitive decline directly from speech recordings of such tests. We propose Demenba, a novel ADC framework based on state space models, which scale linearly in memory and computation with sequence length. Trained on over 1,000 hours of cognitive assessments administered to Framingham Heart Study participants, some of whom were diagnosed with dementia through adjudicated review, our method outperforms prior approaches in fine-grained dementia classification by 21\%, while using fewer parameters. We further analyze its scaling behavior and demonstrate that our model gains additional improvement when fused with large language models, paving the way for more transparent and scalable dementia assessment tools. Code: https://anonymous.4open.science/r/Demenba-0861

Ekaterina Kuzmina, Dmitrii Kriukov, Mikhail Lebedev et al.

Adaptive deep brain stimulation (aDBS) has emerged as a promising treatment for Parkinson disease (PD). In aDBS, a surgically placed electrode sends dynamically altered stimuli to the brain based on neurophysiological feedback: an invasive gadget that limits the amount of data one could collect for optimizing the control offline. As a consequence, a plethora of synthetic models of PD and those of the control algorithms have been proposed. Herein, we introduce the first neurophysiologically realistic benchmark for comparing said models. Specifically, our methodology covers not only conventional basal ganglia circuit dynamics and pathological oscillations, but also captures 15 previously dismissed physiological attributes, such as signal instabilities and noise, neural drift, electrode conductance changes and individual variability - all modeled as spatially distributed and temporally registered features via beta-band activity in the brain and a feedback. Furthermore, we purposely built our framework as a structured environment for training and evaluating deep reinforcement learning (RL) algorithms, opening new possibilities for optimizing aDBS control strategies and inviting the machine learning community to contribute to the emerging field of intelligent neurostimulation interfaces.

Jun-Young Kim, Young-Seok Kweon, Gi-Hwan Shin et al.

Adaptive reasoning enables humans to flexibly adjust inference strategies when environmental rules or contexts change, yet its underlying neural dynamics remain unclear. This study investigated the neurophysiological mechanisms of adaptive reasoning using a card-sorting paradigm combined with electroencephalography and compared human performance with that of a multimodal large language model. Stimulus- and feedback-locked analyses revealed coordinated delta-theta-alpha dynamics: early delta-theta activity reflected exploratory monitoring and rule inference, whereas occipital alpha engagement indicated confirmatory stabilization of attention after successful rule identification. In contrast, the multimodal large language model exhibited only short-term feedback-driven adjustments without hierarchical rule abstraction or genuine adaptive reasoning. These findings identify the neural signatures of human adaptive reasoning and highlight the need for brain-inspired artificial intelligence that incorporates oscillatory feedback coordination for true context-sensitive adaptation.

Chuang Li, Rubing Lin, Yantong Liu et al.

Cognitive impairments in older adults represent a significant public health concern, necessitating accurate diagnostic and monitoring strategies. In this study, the principal cognitive and neuropsychological evaluations employed for the diagnosis and longitudinal observation of cognitive deficits in the elderly are investigated. An analytical review of instruments including the Mini-Mental State Examination (MMSE), Digit Symbol Substitution Test (DSST), Montreal Cognitive Assessment (MoCA), and Trail Making Test (TMT) is conducted. This examination encompasses an assessment of each instrument's methodology, efficacy, advantages, and limitations. The objective is to enhance comprehension of these assessments for the early identification and effective management of conditions such as dementia and mild cognitive impairment, thereby contributing to the advancement of cognitive health within the geriatric population.

Ioannis Schizas, Sabrina Sullivan, Scott E. Kerick et al.

We apply modified diffusion entropy analysis (MDEA) to assess multifractal dimensions of ON time series (ONTS) and complexity synchronization (CS) analysis to infer information transfer among ONs that are part of a network of organ networks (NoONs). The purpose of this paper is to advance the validation, standardization, and repeatability of MDEA and CS analysis of heterogeneous neurophysiological time series data. Results from processing these datasets show that the complexity of brain, heart, and lung ONTS significantly co-vary over time during cognitive task performance but that certain principles, guidelines, and strategies for the application of MDEA analysis need consideration.

Hael F. Abdulrazeq, Anna R. Kimata, Belinda Shao et al.

Responsive neurostimulation (RNS) is a valuable tool in the diagnosis and treatment of medication refractory epilepsy (MRE) and provides clinicians with better insights into patients’ seizure patterns. In this case illustration, we present a patient with bilateral hippocampal RNS for presumed bilateral mesial temporal lobe epilepsy. The patient subsequently underwent a right sided LITT amygdalohippocampotomy based upon chronic RNS data revealing predominance of seizures from that side. Analyzing electrocorticography (ECOG) from the RNS system, we identified the frequency of high amplitude discharges recorded from the left hippocampal lead pre- and post- right LITT amygdalohippocampotomy. A reduction in contralateral interictal epileptiform activity was observed through RNS recordings over a two-year period, suggesting the potential dependency of the contralateral activity on the primary epileptogenic zone. These findings suggest that early targeted surgical resection or laser ablation by leveraging RNS data can potentially impede the progression of dependent epileptiform activity and may aid in preserving neurocognitive networks. RNS recordings are essential in shaping further management decisions for our patient with a presumed bitemporal epilepsy.

M. Biabani, Kevin Walsh, Shou-Han Zhou et al.

Despite the prevalence of attention-deficit hyperactivity disorder (ADHD), efforts to develop a detailed understanding of the neuropsychology of this neurodevelopmental condition are complicated by the diversity of interindividual presentations and the inability of current clinical tests to distinguish between its sensory, attentional, arousal, or motoric contributions. Identifying objective methods that can explain the diverse performance profiles across individuals diagnosed with ADHD has been a long-held goal. Achieving this could significantly advance our understanding of etiological processes and potentially inform the development of personalized treatment approaches. Here, we examine key neuropsychological components of ADHD within an electrophysiological (EEG) perceptual decision-making paradigm that is capable of isolating distinct neural signals of several key information processing stages necessary for sensory-guided actions from attentional selection to motor responses. Using a perceptual decision-making task (random dot motion), we evaluated the performance of 79 children (aged 8–17 years) and found slower and less accurate responses, along with a reduced rate of evidence accumulation (drift rate parameter of drift diffusion model), in children with ADHD (n = 37; 13 female) compared with typically developing peers (n = 42; 18 female). This was driven by the atypical dynamics of discrete electrophysiological signatures of attentional selection, the accumulation of sensory evidence, and strategic adjustments reflecting urgency of response. These findings offer an integrated account of decision-making in ADHD and establish discrete neural signals that might be used to understand the wide range of neuropsychological performance variations in individuals with ADHD.

Alireza F. Nia, Vanessa Tang, Gonzalo Maso Talou et al.

The integration of emotional intelligence in machines is an important step in advancing human-computer interaction. This demands the development of reliable end-to-end emotion recognition systems. However, the scarcity of public affective datasets presents a challenge. In this literature review, we emphasize the use of generative models to address this issue in neurophysiological signals, particularly Electroencephalogram (EEG) and Functional Near-Infrared Spectroscopy (fNIRS). We provide a comprehensive analysis of different generative models used in the field, examining their input formulation, deployment strategies, and methodologies for evaluating the quality of synthesized data. This review serves as a comprehensive overview, offering insights into the advantages, challenges, and promising future directions in the application of generative models in emotion recognition systems. Through this review, we aim to facilitate the progression of neurophysiological data augmentation, thereby supporting the development of more efficient and reliable emotion recognition systems.

Weronika Potok, Onno van der Groen, Sahana Sivachelvam et al.

Stochastic Resonance (SR) describes a phenomenon where an additive noise (stochastic carrier-wave) enhances the signal transmission in a nonlinear system. In the nervous system, nonlinear properties are present from the level of single ion channels all the way to perception and appear to support the emergence of SR. For example, SR has been repeatedly demonstrated for visual detection tasks, also by adding noise directly to cortical areas via transcranial random noise stimulation (tRNS). When dealing with nonlinear physical systems, it has been suggested that resonance can be induced not only by adding stochastic signals (i.e., noise) but also by adding a large class of signals that are not stochastic in nature which cause "deterministic amplitude resonance" (DAR). Here we mathematically show that high-frequency, deterministic, periodic signals can yield resonance-like effects with linear transfer and infinite signal-to-noise ratio at the output. We tested this prediction empirically and investigated whether non-random, high-frequency, transcranial alternating current stimulation applied to visual cortex could induce resonance-like effects and enhance performance of a visual detection task. We demonstrated in 28 participants that applying 80 Hz triangular-waves or sine-waves with tACS reduced visual contrast detection threshold for optimal brain stimulation intensities. The influence of tACS on contrast sensitivity was equally effective to tRNS-induced modulation, demonstrating that both tACS and tRNS can reduce contrast detection thresholds. Our findings suggest that a resonance-like mechanism can also emerge when deterministic electrical waveforms are applied via tACS.

Stephan Goerttler, Fei He, Min Wu

Multivariate signals, which are measured simultaneously over time and acquired by sensor networks, are becoming increasingly common. The emerging field of graph signal processing (GSP) promises to analyse spectral characteristics of these multivariate signals, while at the same time taking the spatial structure between the time signals into account. A central idea in GSP is the graph Fourier transform, which projects a multivariate signal onto frequency-ordered graph Fourier modes, and can therefore be regarded as a spatial analog of the temporal Fourier transform. This chapter derives and discusses key concepts in GSP, with a specific focus on how the various concepts relate to one another. The experimental section focuses on the role of graph frequency in data classification, with applications to neuroimaging. To address the limited sample size of neurophysiological datasets, we introduce a minimalist simulation framework that can generate arbitrary amounts of data. Using this artificial data, we find that lower graph frequency signals are less suitable for classifying neurophysiological data as compared to higher graph frequency signals. Finally, we introduce a baseline testing framework for GSP. Employing this framework, our results suggest that GSP applications may attenuate spectral characteristics in the signals, highlighting current limitations of GSP for neuroimaging.

Daniel Suen, Yen-Chi Chen

Multivariate bounded discrete data arises in many fields. In the setting of dementia studies, such data is collected when individuals complete neuropsychological tests. We outline a modeling and inference procedure that can model the joint distribution conditional on baseline covariates, leveraging previous work on mixtures of experts and latent class models. Furthermore, we illustrate how the work can be extended when the outcome data is missing at random using a nested EM algorithm. The proposed model can incorporate covariate information and perform imputation and clustering. We apply our model on simulated data and an Alzheimer's disease data set.

Giulia Mazzari, Christopher A. Lowry, Dominik Langgartner et al.

Chronic psychosocial stress is a burden of modern society and poses a clear risk factor for a plethora of somatic and affective disorders, of which most are associated with an activated immune status and chronic low-grade inflammation. Preclinical and clinical studies further suggest that a failure in immunoregulation promotes an over-reaction of the inflammatory stress response and, thus, predisposes an individual to the development of stress-related disorders. Therefore, all genetic (i.e., sex) and environmental (i.e., early life adversity; ELA) factors facilitating an adult's inflammatory stress response are likely to increase their stress vulnerability.In the present study we investigated whether repeated subcutaneous (s.c.) administrations with a heat-killed preparation of Mycobacterium vaccae (M. vaccae; National Collection of Type Cultures (NCTC) 11659), an abundant soil saprophyte with immunoregulatory properties, are protective against negative behavioral, immunological and physiological consequences of ELA alone or of ELA followed by chronic psychosocial stress during adulthood (CAS) in male and female mice. ELA was induced by the maternal separation (MS) paradigm, CAS was induced by 19 days of chronic subordinate colony housing (CSC) in males and by a 7-week exposure to the social instability paradigm (SIP) in females.Our data indicate that ELA effects in both sexes, although relatively mild, were to a great extent prevented by subsequent s.c. M. vaccae administrations. Moreover, although the use of different paradigms for males and females impedes a direct comparison, male mice seemed to be more susceptible to CAS than females, with only females benefitting slightly from the stress protective effects of s.c. M. vaccae administrations when given prior to CAS alone. Finally, our data support the hypothesis that female mice are more vulnerable to the additive effects of ELA and CAS than male mice and that s.c. M. vaccae administrations subsequent to ELA but prior to CAS are protective in both sexes.Taken together and considering the limitation that CAS in males and females was induced by different paradigms, our findings are consistent with the hypotheses that murine stress vulnerability during different phases of life is strongly sex dependent and that developing immunoregulatory approaches, such as repeated s.c. administrations with immunoregulatory microorganisms, have potential for prevention/treatment of stress-related disorders.

Courtney J. Wright, Snezana Milosavljevic, Ana Pocivavsek

Sleep is a vital and evolutionarily conserved process, critical to daily functioning and homeostatic balance. Losing sleep is inherently stressful and leads to numerous detrimental physiological outcomes. Despite sleep disturbances affecting everyone, women and female rodents are often excluded or underrepresented in clinical and pre-clinical studies. Advancing our understanding of the role of biological sex in the responses to sleep loss stands to greatly improve our ability to understand and treat health consequences of insufficient sleep. As such, this review discusses sex differences in response to sleep deprivation, with a focus on the sympathetic nervous system stress response and activation of the hypothalamic-pituitary-adrenal (HPA) axis. We review sex differences in several stress-related consequences of sleep loss, including inflammation, learning and memory deficits, and mood related changes. Focusing on women's health, we discuss the effects of sleep deprivation during the peripartum period. In closing, we present neurobiological mechanisms, including the contribution of sex hormones, orexins, circadian timing systems, and astrocytic neuromodulation, that may underlie potential sex differences in sleep deprivation responses.

Joseph P. Riordan

Orientation: Dyadic trauma is contagious. Converging neurosynchronistic constructs and the application of attachment focused-somatic experiencing (AF-SE) to traumatised dyads have revealed phenomena that required examination of the relationship between trauma, attachment and community psychopathology. Research purpose: The phylogenetic impact of trauma on attachment is under-reported in attachment traumatology. The purpose of the study was to introduce the theory of dyadic trauma, and SPA and interpersonal neurosynchronistic phylogenesis (INP) as constructs to explain the relationship between trauma, attachment and community psychopathology. Motivation for the study: Widespread loneliness and loss of social cohesion indicate significant, trauma-driven phylogenetic shifts in secure phylogenetic attachment (SPA). Interpersonal neurosynchronistic constructs emerged to elucidate the phenomena. Research approach/design and method: Conceptualisation based on a synthesis of pertinent research provided for an analysis with theory adaptation as an approach. Secure phylogenetic attachment transposed interpersonally is compromised by maladaptive-interpersonal neurosynchronistic phylogenesis (M-INP). Attachment traumatology was chosen as the domain theory and INP as the method theory. Main findings: Maladaptive-interpersonal neurosynchronistic phylogenesis is complicit in community psychopathology. It was found that INP served as a valuable method theory in generating new insights regarding dyadic trauma, attachment and psychopathology. Three unique categories of attachment, namely SPA, the antithesis of trauma, traumatic and monozygotic attachment were proposed. Implications for practice: Attachment traumatologists are provided with a theoretical model, dyadic trauma and descriptive terminology to elucidate the phylogenetic impact of trauma on attachment. Contribution/value add: Specific nomenclature described the interpersonal neuro-dynamics of INP and its functional role in traumatic attachment thereby indicating a paradigm shift in attachment traumatology.

Professionals misdiagnose autism by ticking off symptoms on a checklist without questioning the causes of said symptoms, and without understanding the innate neurophysiology of the autistic brain. A dysfunctional cingulate gyrus (CG) hyperfocuses attention in the left frontal lobe (logical/analytical) with no ability to access the right frontal lobe (emotional/creative), which plays a central role in spontaneity, social behavior, and nonverbal abilities. Autistic people live in a specialized inner space that is entirely intellectual, free from emotional and social distractions. They have no innate biological way of emotionally connecting with other people. Autistic people process their emotions intellectually, a process that can take 24 hours, by which time it is too late to have felt anything. An inactive amygdala makes it impossible for autistic people to experience fear. Because they do not feel emotion, they have no emotional memories. All memories are of events that happened about which they felt no emotion at the time and feel no emotion when talking about it afterward.

Yuqian Chen, Fan Zhang, Chaoyi Zhang et al.

White matter tract microstructure has been shown to influence neuropsychological scores of cognitive performance. However, prediction of these scores from white matter tract data has not been attempted. In this paper, we propose a deep-learning-based framework for neuropsychological score prediction using microstructure measurements estimated from diffusion magnetic resonance imaging (dMRI) tractography, focusing on predicting performance on a receptive vocabulary assessment task based on a critical fiber tract for language, the arcuate fasciculus (AF). We directly utilize information from all points in a fiber tract, without the need to average data along the fiber as is traditionally required by diffusion MRI tractometry methods. Specifically, we represent the AF as a point cloud with microstructure measurements at each point, enabling adoption of point-based neural networks. We improve prediction performance with the proposed Paired-Siamese Loss that utilizes information about differences between continuous neuropsychological scores. Finally, we propose a Critical Region Localization (CRL) algorithm to localize informative anatomical regions containing points with strong contributions to the prediction results. Our method is evaluated on data from 806 subjects from the Human Connectome Project dataset. Results demonstrate superior neuropsychological score prediction performance compared to baseline methods. We discover that critical regions in the AF are strikingly consistent across subjects, with the highest number of strongly contributing points located in frontal cortical regions (i.e., the rostral middle frontal, pars opercularis, and pars triangularis), which are strongly implicated as critical areas for language processes.

James J Gugger, Alexa E Walter, Drew Parker et al.

Traumatic brain injury is a global public health problem associated with chronic neurological complications and long-term disability. Biomarkers that map onto the underlying brain pathology driving these complications are urgently needed to identify individuals at risk for poor recovery and to inform design of clinical trials of neuroprotective therapies. Neuroinflammation and neurodegeneration are two endophenotypes associated with increases in brain extracellular water content after trauma. The objective of this study was to describe the relationship between a neuroimaging biomarker of extracellular free water content and the clinical features of patients with traumatic brain injury. We analyzed a cohort of 64 adult patients requiring hospitalization for non-penetrating traumatic brain injury of all severities as well as 32 healthy controls. Patients underwent brain MRI and clinical neuropsychological assessment in the subacute (2-weeks) and chronic (6-months) post-injury period, and controls underwent a single MRI. For each subject, we derived a summary score representing deviations in whole brain white matter (1) extracellular free water volume fraction (VF) and (2) free water-corrected fractional anisotropy (fw-FA). The summary specific anomaly score (SAS) for VF was significantly higher in TBI patients in the subacute and chronic post-injury period relative to controls. SAS for VF significantly correlated with neuropsychological functioning in the subacute, but not chronic post-injury period. These findings indicate abnormalities in whole brain white matter extracellular water fraction in patients with TBI and are an important step toward identifying and validating noninvasive biomarkers that map onto the pathology driving disability after TBI.

Karen D. Rudolph, Megan M. Davis, Haley V. Skymba et al.

The adaptive calibration model suggests exposure to highly stressful or highly supportive early environments sensitizes the brain to later environmental input. We examined whether family and peer experiences predict neural sensitivity to social cues in 85 adolescent girls who completed a social feedback task during a functional brain scan and an interview assessing adversity. Whole-brain functional connectivity (FC) analyses revealed curvilinear associations between social experiences and FC between the ventral striatum and regions involved in emotion valuation, social cognition, and salience detection (e.g., insula, MPFC, dACC, dlPFC) during social reward processing, such that stronger FC was found at both very high and very low levels of adversity. Moreover, exposure to adversity predicted stronger FC between the amygdala and regions involved in salience detection, social cognition, and emotional memory (e.g., sgACC, precuneus, lingual gyrus, parahippocampal gyrus) during social threat processing. Analyses also revealed some evidence for blunted FC (VS-PCC for reward; amygdala-parahippocampal gyrus for threat) at very high and low levels of adversity. Overall, results suggest social experiences may play a critical role in shaping neural sensitivity to social feedback during adolescence. Future work will need to elucidate the implications of these patterns of neural function for the development of psychopathology.

Nawal F. Abdel Ghaffar, Reem N. Asiri, Laith N. AL-Eitan et al.

Differences in the sociocultural practice and biases against people with epilepsy (PWE) largely contribute to the development of stigmatization. In this study, we evaluated factors that impact stigma for PWE involved in evolution and maintenance to report changes in the public awareness and cultural practices. We performed a cross-sectional study in which data were collected from a self-administered electronic survey composed of 33 items targeting the population in the Aseer region. Feedback response was obtained from 937 respondents. Of these, 921 participants (98.3%) had heard or read about the disorder previously. Approximately 84.8% believed that epilepsy was one of the brain disorders. 95.8% disagreed that epilepsy was due to a contagious disease. However, 40.1% of the responders were convinced that it was the result of a spiritual reason. Still, more than 9% believed treating PWE should be approached spiritually. About 75% felt that epilepsy could be the results of a test delievered by God. In addition to the clinical impact from seizures in PWE, it carries a social label and public stigma that influences one's social prognosis. Raising awareness through campaigns would improve the knowledge and practices of the population and hence provide a healthier environment for PWE, alleviating feelings of stigma, and improving their quality of life.