To avoid potentially noxious prey, predators need to discriminate between palatable and unpalatable prey species. Unpalatable prey often exhibits visual warning signals, which can consist of multiple components, such as color and pattern. Although the role of particular components of visual warning signals in predator discrimination learning has been intensively studied, the importance of different components for predator memory is considerably less understood. In this study, we tested adult wild-caught great tits (Parus major) to find out, which components of prey visual warning signals are important when the birds learn to discriminate between palatable and unpalatable prey, and when they remember their experience over a longer time period. Birds were trained to discriminate between palatable and unpalatable artificial prey items that differed in both color and pattern. After 4 wk, the birds were retested in 3 groups: the first group was presented with the same prey as in the training, the second group was tested with the two prey types differing only in color, and the third group could use only the pattern as a discrimination trait. The results suggest that the birds remember their experience with unpalatable prey even after the period of 4 wk. Although the color appears to be more important than the pattern, the combination of both signal components is more effective for prey recognition after several weeks than either the color or pattern alone.

• Keywords
• avian predators, color, discrimination learning, long-term memory, multicomponent signals, pattern, warning coloration,
• Publication type
• Journal Article MeSH

Anorexigenic neuropeptides have shown a remarkable potential in the treatment of neurodegenerative disorders, such as Alzheimer's disease (AD). One of the strong anorexigenic neuropeptides is called cocaine- and amphetamine-regulated transcript peptide (CARTp), which is the third most abundant transcript in the hypothalamus. Previously, we introduced a novel palmitoylated analog of 2-SS-CART(61-102), a specific analog of natural CART(61-102) with two disulfide bridges, with anorexigenic and neuroprotective properties. This study explores the impact of 2-SS-CART(61-102) and its palmitoylated analog, palm-2-SS-CART(61-102), on the early progression of Tau pathology characteristic of AD, utilizing the THY-Tau22 transgenic mouse model. Chronic subcutaneous treatment with CARTp analogs improved short-term spatial memory in the Y-maze, reduced the number of neurofibrillary tangles (NFT) in the hippocampal CA1 region, and decreased the level of GFAP + astrocytes in the hippocampus of THY-Tau22 mice. Furthermore, treatment with CARTp analogs showed increased levels of synaptic markers in the hippocampus. A beneficial effect on these attributes makes CARTp analogs promising for AD therapy.

• Keywords
• Alzheimer's disease, Anorexigenic neuropeptides, CART peptide, Neuroinflammation, THY-Tau22 mice, Tau pathology,
• MeSH
• Astrocytes drug effects   pathology   metabolism   MeSH
• Maze Learning drug effects   MeSH
• Hippocampus * drug effects   pathology   metabolism   MeSH
• Cocaine- and Amphetamine-Regulated Transcript Protein MeSH
• Disease Models, Animal MeSH
• Mice, Inbred C57BL MeSH
• Mice, Transgenic MeSH
• Mice MeSH
• Neurofibrillary Tangles pathology   metabolism   drug effects   MeSH
• Neuroprotective Agents * pharmacology   therapeutic use   MeSH
• Spatial Memory drug effects   MeSH
• Nerve Tissue Proteins * pharmacology   therapeutic use   chemistry   MeSH
• tau Proteins * genetics   metabolism   MeSH
• Tauopathies * pathology   drug therapy   metabolism   MeSH
• Animals MeSH
• Check Tag
• Male MeSH
• Mice MeSH
• Animals MeSH
• Publication type
• Journal Article MeSH
• Names of Substances
• Cocaine- and Amphetamine-Regulated Transcript Protein MeSH
• Neuroprotective Agents * MeSH
• Nerve Tissue Proteins * MeSH
• tau Proteins * MeSH

INTRODUCTION: Information and communication technologies (ICT) now play a vital role in addressing a wide range of personal and societal needs across various domains. This cross-sectional study investigates the association between cognitive abilities and ICT use among older adults in the Czech Republic and Republic of Slovenia. METHODS: We used data from the Survey of Health, Ageing and Retirement in Europe wave 8 (2019) for the Czechia and Slovenia. Cognitive abilities were measured through 10-word list learning tasks and numeracy tests, while digital engagement was assessed by the frequency of internet use in the past 7 days. RESULTS: Descriptive analyses and regression modeling revealed that higher cognitive abilities were significantly associated with greater ICT use in both Czechia and Slovenia, even after excluding individuals with probable cognitive impairments. This association remained robust after controlling for age, education, gender, and living arrangements. In addition to advancing age, lower cognitive functioning emerged as a key predictor of digital exclusion. Notably, by age 85, only 30% of cognitively healthy individuals in Czechia and 20% in Slovenia reported using internet. DISCUSSION: Cognitive functioning is a significant and independent predictor of ICT use among older adults in both Czechia and Slovenia. Even after accounting for demographic and social factors, individuals with higher cognitive abilities were more likely to engage with digital technologies. These findings highlight the importance of integrating cognitive health into digital inclusion strategies targeting older populations, particularly in aging societies where technological access is essential for social participation and well-being.

• Keywords
• 10-word memory test, SHARE, cognitive impairment, internet usage, older adults,
• Publication type
• Journal Article MeSH

Sleep spindles, an oscillatory brain activity occurring during light non-rapid eye movement (NREM) sleep, are important for memory consolidation and cognitive functions. Accurate detection is important for understanding the role of spindles in sleep state physiology and brain health and for better understanding sleep and neurological disorders. However, manual spindle labeling of electroencephalography (EEG) data is time-consuming and impractical for most clinical and research settings and intracranial EEG (iEEG) presents additional challenges for spindle identification due to its unique signal characteristics and recording environment. This study introduces a novel, precise, and automatic spindle detection method for iEEG using a dual-head architecture to enhance performance, robustness, and ease of use. Our approach achieves a detection F1 score of 0.67 on a challenging iEEG dataset and 0.69 on the publicly available scalp EEG DREAMS dataset. Compared to existing methods such as SUMO, A7, and YASA, our model demonstrates superior performance in detecting, segmenting, and characterizing sleep spindles. This model contributes to open science and advances automated sleep spindle classification in iEEG. This will advance the development of more precise diagnostic and research tools and facilitate a deeper understanding of the role of sleep spindles in cognitive processes and neurological health.

• Keywords
• Dual-head architecture, Intracranial EEG (iEEG), Machine Learning, Signal segmentation, Sleep spindle detection,
• MeSH
• Deep Learning * MeSH
• Electroencephalography * methods   MeSH
• Electrocorticography * methods   MeSH
• Humans MeSH
• Brain * physiology   MeSH
• Signal Processing, Computer-Assisted * MeSH
• Scalp physiology   MeSH
• Sleep * physiology   MeSH
• Sleep Stages * physiology   MeSH
• Check Tag
• Humans MeSH
• Publication type
• Journal Article MeSH

PURPOSE: To adopt different deep learning (DL) techniques for Essential tremor (ET) and Parkinson's tremor (PST) classification, a collaborative approach to address the misdiagnosis of healthy and PSD patients based on ET and PST's frequency patterns and severity. METHODS: This classification uses a comprehensive PDBioStamp time-series dataset to classify tremors based on action and rest tremors of healthy and post-stroke depression (PSD) patients. Combination of DL models, including generative adversarial network (GAN) to generate synthetic data, Autoencoder to reduce the size dimensionality of data and learn a latent representation, long short-term memory (LSTM) to capture temporal features and essential characteristics to improve the performance of tremor classification. RESULTS: The performance of the models is evaluated using evaluation metrics, such as training and testing accuracy, F1 score, loss by varying different epochs, and different optimizers. Moreover, the performance of the model was compared using different state-of-the-art works. CONCLUSION: The ET and PST classification result indicates that the proposed combination of GAN, autoencoder, and LSTM outperformed with 80.0 training, 80.3 testing accuracy, 0.82 F1 score, and 0.89 AUC, which is higher than existing DL models. The proposed collaborative approach helps doctors improve their diagnosis for ET and PST tremor patients. This classification helps doctors to identify PSD and healthy control patients.

• Keywords
• Autoencoder, Deep learning, Essential tremor, GAN, LSTM, Machine learning, Parkinson’s disease,
• MeSH
• Deep Learning * MeSH
• Essential Tremor * diagnosis   classification   physiopathology   MeSH
• Humans MeSH
• Neural Networks, Computer * MeSH
• Parkinson Disease * diagnosis   physiopathology   classification   MeSH
• Tremor * classification   diagnosis   MeSH
• Check Tag
• Humans MeSH
• Male MeSH
• Female MeSH
• Publication type
• Journal Article MeSH

Both Alzheimer's (AD) and Parkinson's disease (PD) are often associated with memory dysfunction, but their pathophysiological underpinnings differ. The current research aimed to differentiate specific profiles of memory impairment due to AD versus PD. We used controlled learning and cued recall paradigm based on the Memory Binding Test (MBT) in 'clinically cognitively normal' controls (CN; n = 161), in patients with amnestic mild cognitive impairment due to AD (AD-aMCI; n = 50) and due to PD (PD-MCI; n = 22), and in PD with normal cognition (n = 18) as based on performance in the neuropsychological battery to prevent circularity in diagnostic decision-making. We applied analysis of covariance (ANCOVA) and Receiver Operating Characteristic (ROC) analysis to determine between-group differences and detection potential of the MBT. We found statistically large between-group differences with worse memory performance in paired cued recall conditions in AD-aMCI .050). The detection potential of MBT paired cued recall for differentiating memory impairment in AD-aMCI from CN yielded an AUC of 90% (95% CI, 85-96) and an AUC of 91% (95% CI, 81->99) between AD-aMCI and PD-MCI. Associative memory and binding impairment are most pronounced in AD-aMCI in comparison to PD-MCI and controls. Overall, the MBT is an efficient tool for the differential diagnosis of memory impairment due to the two most common neurodegenerative diseases.

• Keywords
• Alzheimer's disease, Parkinson's disease, cued recall, episodic memory, mild cognitive impairment, preclinical, validity,
• Publication type
• Journal Article MeSH

Artificial neural networks are limited in the number of patterns that they can store and accurately recall, with capacity constraints arising from factors such as network size, architectural structure, pattern sparsity, and pattern dissimilarity. Exceeding these limits leads to recall errors, eventually leading to catastrophic forgetting, which is a major challenge in continual learning. In this study, we characterize the theoretical maximum memory capacity of single-layer feedforward networks as a function of these parameters. We derive analytical expressions for maximum theoretical memory capacity and introduce a grid-based construction and sub-sampling method for pattern generation that takes advantage of the full storage potential of the network. Our findings indicate that maximum capacity scales as (N/S) S , where N is the number of input/output units and S the pattern sparsity, under threshold constraints related to minimum pattern differentiability. Simulation results validate these theoretical predictions and show that the optimal pattern set can be constructed deterministically for any given network size and pattern sparsity, systematically outperforming random pattern generation in terms of storage capacity. This work offers a foundational framework for maximizing storage efficiency in neural network systems and supports the development of data-efficient, sustainable AI.

• Keywords
• constructive algorithms, data-efficient AI, memory capacity, neural network, sustainable AI,
• Publication type
• Journal Article MeSH

BACKGROUND AND HYPOTHESIS: Cognitive impairment is a key contributor to disability and poor outcomes in schizophrenia, yet it is not adequately addressed by currently available treatments. Thus, it is important to search for preventable or treatable risk factors for cognitive impairment. Here, we hypothesized that obesity-related neurostructural alterations will be associated with worse cognitive outcomes in people with first episode of psychosis (FEP). STUDY DESIGN: This observational study presents cross-sectional data from the Early-Stage Schizophrenia Outcome project. We acquired T1-weighted 3D MRI scans in 440 participants with FEP at the time of the first hospitalization and in 257 controls. Metabolic assessments included body mass index (BMI), waist-to-hip ratio (WHR), serum concentrations of triglycerides, cholesterol, glucose, insulin, and hs-CRP. We chose machine learning-derived brain age gap estimate (BrainAGE) as our measure of neurostructural changes and assessed attention, working memory and verbal learning using Digit Span and the Auditory Verbal Learning Test. STUDY RESULTS: Among obesity/metabolic markers, only WHR significantly predicted both, higher BrainAGE (t(281)=2.53, p=0.012) and worse verbal learning (t(290) = -2.51, P = .026). The association between FEP and verbal learning was partially mediated by BrainAGE (average causal mediated effects, ACME = -0.04 [-0.10, -0.01], P = .022) and the higher BrainAGE in FEP was partially mediated by higher WHR (ACME = 0.08 [0.02, 0.15], P = .006). CONCLUSIONS: Central obesity-related brain alterations were linked with worse cognitive performance already early in the course of psychosis. These structure-function links suggest that preventing or treating central obesity could target brain and cognitive impairments in FEP.

• Keywords
• body mass, brain structure, cognition, metabolic syndrome, schizophrenia,
• Publication type
• Journal Article MeSH

PURPOSE: The evidence suggests that adherence to the Mediterranean diet (MD) may be beneficial in preventing cognitive decline. We aimed to explore this association in a Central European population. METHODS: A total of 6,028 men and women from the Czech arm of the HAPIEE study were included in the analysis. Dietary data were collected using a food frequency questionnaire, and MD score (MDS) was calculated based on nine food groups. The MDS (range 0-17 points) was classified into three groups: low (0-7), medium (8-10), high (11-17). Cognitive function was measured using four tests assessing verbal memory and learning, verbal fluency, and attention, mental speed and concentration, and composite score, each of them converted to z-score. The associations between MDS and cognitive function were analyzed using multivariate linear regression in men and women. RESULTS: There were no significant associations in men. By contrast, women with a dietary score of 8-10 points (B = 0.05, 95% CI: -0.002; 0.097), and those with a score of 11-17 points (B = 0.08, 95% CI: 0.016; 0.140) had a higher composite cognitive score than women in lowest adherence group. Regarding specific domains, women in the highest adherence group had significantly better immediate verbal memory (B = 0.12, 95% CI: 0.031; 0.205) and delayed recall (B = 0.12, 95% CI: 0.027; 0.212), respectively, than those in the lowest adherence group. CONCLUSION: Higher adherence to the MDS was associated with better cognitive functioning in verbal memory and composite cognitive score in Czech women. The results suggest that the Mediterranean diet may help to improve cognitive functioning.

• Keywords
• Aging, Cognition, Cognitive decline, Diet, Dietary habits, Mediterranean,
• MeSH
• Diet Surveys MeSH
• Adult MeSH
• Cognition * MeSH
• Cognitive Dysfunction * prevention & control   epidemiology   MeSH
• Middle Aged MeSH
• Humans MeSH
• Cross-Sectional Studies MeSH
• Aged MeSH
• Diet, Mediterranean * statistics & numerical data   MeSH
• Check Tag
• Adult MeSH
• Middle Aged MeSH
• Humans MeSH
• Male MeSH
• Aged MeSH
• Female MeSH
• Publication type
• Journal Article MeSH
• Geographicals
• Czech Republic epidemiology   MeSH

The spatial orientation of mammals and birds has been intensively studied for many years, but the cognitive mechanism of spatial orientation and memory used by squamates remains poorly understood. Our study evaluated the learning and memory abilities of leopard geckos (Eublepharis macularius) in an adapted Morris water maze. The animals learned during the training phase consisted of 20 trials. To assess long-term memory, we retested geckos twice after several months. The geckos remembered the learned information in a short re-test after two months, but after four months, they required retraining to find the platform. We hypothesise that the duration of memory corresponds with short-term changes in semi-desert environments within one season, while disruption of memory performance after a six-month gap may simulate the more extensive seasonal change in spatial relationships in their natural environment. Moreover, during the winter period, geckos exhibit low activity, which can be connected with decreased frequency of foraging trips. Therefore, the memory loss after four months may reflect the low level of memory jogging. The motivation during the experiment was the crucial parameter of learning and memory processes. In later phases, geckos were less motivated to perform the task. Finally, they relearned the spatial orientation task, but they moved more slowly as the experiment progressed.

• Keywords
• Morris water maze, Squamata, cognition, memory, orientation, reptile learning, spatial navigation,
• Publication type
• Journal Article MeSH