Introduction: Adopting healthy lifestyle behaviors is vital for stroke secondary prevention. However, low- and middle-income countries often lack specific guidelines. High-income countries, such as Ireland, have more extensive research and resources, whereas Brazil, which has a higher stroke burden, frequently relies on external evidence. Understanding the perspectives of individuals post-stroke is essential for implementing contextually relevant interventions. Therefore, this study aims to identify similarities and differences in how individuals post-stroke participating in Patient and Public Involvement (PPI) panels in Ireland (high-income) and Brazil (middle-income) prioritize components to be included in an intervention designed to support the adoption of healthy lifestyle behaviors.

Methods: A descriptive, cross-sectional study was conducted with two PPI panels (five individuals post-stroke from each country), who rated core components across six behaviors (healthy diet, medication adherence, mood management, physical activity participation, safe alcohol consumption, and smoking cessation) as “definitely important”, “maybe important”, or “not important”. Frequencies of responses were calculated per behavior. A cross-country similarity was defined when a majority (>50%) from both panels rated a component the same (“definitely important” or “not important”); otherwise, perspectives were considered different. Descriptive statistics were used.

Results: Most components were rated “definitely important” by >50% in both panels, except for healthy diet. No component was largely deemed “not important”. Differences emerged: only the majority of the Irish PPI panel rated “Have medications review with pharmacist/general practitioner” as “definitely important”; only the Brazilian panel rated all healthy diet, physical activity participation, and smoking cessation components as “definitely important”. Only in Ireland did most PPI members rate 18-50% of the components across all behaviors as “maybe important”.

Conclusions: Similarities point to core priorities for post-stroke lifestyle interventions across contexts, while differences highlight the need for cultural adaptation when transferring interventions from high- to middle-income countries.

Introduction: Schizophrenia is a psychiatric disease characterized by deficiencies in multiple cognitive domains, such as attention, working memory, long-term memory, and learning, that are stable throughout the patient’s life. Due to mainly focusing on typical symptomatology relief, in many cases, cognitive impairment is overlooked. However, the medication of choice often consists of antipsychotics, their combination being adjusted to the therapeutic response. Yet data regarding the cognitive effects of antipsychotics in schizophrenia is rather scarce and inconsistent. In this context, we aimed to investigate the impact of various combinations of atypical antipsychotics on the cognitive functions of schizophrenia patients. Methods: Thirty participants with schizophrenia were enrolled in the study (18 men and 12 women). The assessment of study participants was conducted by specialized medical personnel, and the application of the Mini-Mental State Examination (MMSE) and Montreal Cognitive Assessment Scale (MoCA) questionnaires aimed to verify cognitive parameters such as spatial–temporal orientation, attention, immediate and short-term memory, the capacity to perform concrete and abstract operations, motor skills, and language. Results: There were differences between the polypharmacy group and the monotherapy group in copying tasks, visuospatial orientation, and attention for both scales involved in this study. Conclusions: Our results suggested that the polytherapeutic approach based on a combination of atypical antipsychotics could lead to the significant improvement of cognitive functions, including spatial and visual orientation, general attention, and verbal memory.

Introduction: Physical inactivity remains common post-stroke. Although behavior change interventions improve physical activity, many require home visits or Internet access. Telephone calls and activity monitors are low-cost telehealth options, but evidence for monitor use post-stroke is limited. Integrating monitors into multifaceted strategies (e.g., the 5As brief intervention from the HEARTS Technical Package) may enhance outcomes. This study aimed to assess the feasibility of the 5As brief intervention with versus without a physical activity monitor and to explore changes in physical activity levels to inform a future randomized controlled trial (RCT).

Methods: This phase 1 feasibility RCT, with concealed allocation and blinded assessments, was conducted in a university laboratory (Belo Horizonte, Brazil). Twenty-four individuals in the chronic phase post-stroke were included (12 per group) and received the 5As brief intervention from the HEARTS Technical Package. The experimental group (EG) additionally used an activity monitor (Mi Band 7® Smartwatch). Outcomes included feasibility indicators and physical activity level, analyzed descriptively.

Results: Trial feasibility was supported by recruitment (36.4%), retention (83.3% in both groups), and high attendance (99.1% in the EG and 97.0% in the CG). No adverse events were reported. Attrition remained a challenge, with 66.7% of EG participants (n=8) and 50.0% of CG participants (n=6) completing all follow-up assessments. Main costs were related to transportation for in-person assessments. Physical activity levels increased clinically in both groups, with a large within-group effect in the EG (Cohen’s d = 1.01) and a moderate effect in the CG (Cohen’s d = 0.76).

Conclusions: Both interventions were feasible and resulted in clinical improvements in physical activity levels post-stroke, with greater effects observed in the EG. These findings support the design of a future, larger RCT, with attention to retention strategies, follow-up procedures, and formal evaluation of the added value of physical activity monitor use.

Background: Chronic stress is a significant contributing factor to the advancement and complications of type 2 diabetes mellitus, notably the development of neuropathic pain. The amygdala, a key brain region regulating the emotional and affective aspects of pain, is susceptible to oxidative stress and inflammation. This study investigates the oxido-inflammatory changes in the amygdala and related pain behaviour in diabetic rats exposed to chronic unpredictable mild stress (CUMS) and the ameliorative effect of Sorghum bicolor polyphenol extract (SBPE).

Method: HFD/STZ-induced diabetic rats were subjected to CUMS and administered oral treatments of SBPE (200 mg/kg), metformin (250 mg/kg), or a combination of SBPE and metformin for a period of 28 days. Pain-related behaviours, inflammatory, oxidative stress and neurotransmitter-related parameters as well as histological changes in the amygdala were evaluated.

Results: Chronic stress exacerbates anxiety-like behaviour and neuropathic-like pain behaviour in diabetic rats, which was, however, reversed in SBPE and metformin-treated rats. SBPE ameliorated CUMS-induced significant elevation of TNF-α, IL-1β and myeloperoxidase in the amygdala brain tissue of diabetic rats. SBPE treatment significantly reduced malondialdehyde and nitrites and increased GSH, catalase, SOD, and GST in the amygdala. SBPE induced significant modulation of acetylcholinesterase, glutamic acid decarboxylase and arginase activities in the amygdala of diabetic rats. Distorted neuronal morphology of the amygdala in CUMS-diabetic rats was restored in SBPE-treated groups.

Conclusion: Sorghum bicolor polyphenol-rich extract relieves anxiety and neuropathic pain in chronically stressed diabetic rats possibly by inhibition of oxidative stress and inflammation in the amygdala.

Aging is considered a progressive physiological degeneration associated with a decrease in synaptic plasticity and a decline in different functions such as olfaction, taste, attention, memory, and language. The biological mechanisms of aging are not yet fully understood. The aim of the study was to evaluate correlations between age-related changes in cognitive abilities with olfactory and gustatory functions. In our study, 290 participants with an age range from 18 to 86 years were enrolled. All participants were divided into three age groups: young adults (18-29 years, n=135), middle-aged (30-59 years, n=93), and elderly (≥60 years, n=62). Our results showed that odor threshold (OT), discrimination (OD), and identification (OI) significantly decreased in the elderly group compared to the young adults. Moreover, significant differences were also found for OT, OD, and OI between middle-aged and elderly groups. Instead, gustatory function decreased in relation to age in a different manner, since only sweet and sour taste perceptions decreased in elderly participants compared to the young adults’ group. Similarly, cognitive abilities, memory, language, and global attention significantly declined in the elderly group compared to the young adults and middle-aged groups. The decrease in global attention, memory, and language was significantly correlated to OI performance. Interestingly, significant correlations were observed between sour taste perception versus global cognitive abilities, language, and memory.

In conclusion, olfactory function and cognitive abilities showed a slight decline in relation to age and a dramatic decrease after 60 years, while gustatory function decreased more gradually. The decrease in OI could be considered a predictor for global cognitive function, as well as for specific cognitive subdomains such as global attention, language, and memory. Our study highlighted the importance of using olfactory evaluations in clinical practice for the early diagnosis of cognitive decline and for the development of appropriate personalized risk prevention strategies.

Understanding how large-scale brain networks dynamically reorganize to support attention and working memory remains a central challenge in cognitive neuroscience. Emerging evidence suggests that the prefrontal–parietal network flexibly interacts with subcortical and default mode regions to optimize cognitive control, yet the temporal mechanisms underlying this reconfiguration remain unclear.

This study employed a multi-modal experimental design integrating functional MRI, electroencephalography (EEG), and computational modeling to investigate dynamic network transitions during attentional load and memory manipulation tasks. Eighty healthy adults completed a parametric n-back paradigm with real-time neuroimaging. Dynamic causal modeling (DCM) quantified directed connectivity, while graph-theoretical metrics assessed modularity and integration across cortical systems. Additionally, recurrent neural network (RNN) simulations were trained to reproduce observed neural trajectories and predict behavioral accuracy.

Results revealed a robust task-dependent reorganization of the frontoparietal control system, with transient decoupling from the default mode network (DMN) during high-load trials. EEG phase-synchrony analyses indicated theta–gamma coupling between dorsolateral prefrontal cortex and intraparietal sulcus as a predictor of task performance (r = 0.67, p < 0.001). Computational models recapitulated these oscillatory dynamics, suggesting that recurrent feedback mechanisms enable efficient information maintenance.

Our findings provide convergent neurobiological and computational evidence that cognitive flexibility arises from transient, hierarchical synchronization across distributed neural systems. These results advance a mechanistic framework for understanding attention–memory interactions and may inform neuroadaptive interventions for cognitive decline.

The application of machine learning in neuroscience has expanded markedly in recent years, propelled by advances in data acquisition technologies and the proliferation of large-scale neural, imaging, and behavioral datasets. Machine learning techniques have become indispensable for capturing complex, high-dimensional patterns inherent in brain data; however, their deployment varies significantly in the extent to which they prioritize predictive performance over mechanistic insight. This systematic review offers a comprehensive synthesis of ML-based approaches in contemporary computational neuroscience, with a particular focus on the conceptual and methodological distinction between predictive models, aimed at optimizing the decoding or forecasting of neural and behavioral outcomes, and mechanistic models, which seek to elucidate the computational principles and biological architectures underpinning brain function. A systematic review of the peer-reviewed literature was conducted, encompassing studies that applied machine learning techniques to the analysis of neural data derived from neuroimaging, electrophysiological recordings, cellular-level measurements, and multimodal experimental paradigms. The extant literature reveals a strong dominance of predictive modeling, typically utilizing supervised learning and deep neural networks to classify brain states, decode experimental conditions, or predict cognitive and behavioral phenotypes. Conversely, mechanistic machine learning models, often grounded in computational neuroscience traditions such as dynamical systems theory, probabilistic inference, and network modeling, remain comparatively underrepresented, yet they offer essential explanatory value by linking algorithmic components to biophysical mechanisms and system-level dynamics. Emerging hybrid approaches aim to reconcile predictive accuracy with mechanistic transparency, though they remain methodologically heterogeneous and are often limited by insufficient validation. This review delineates critical challenges confronting machine learning in neuroscience, including the opacity of complex models, limited reproducibility, and difficulties in cross-scale integration. It concludes by advocating the development of integrative modeling frameworks that transcend the prediction–explanation dichotomy, thereby fostering a more unified, biologically grounded understanding of brain function.

Introduction: Emergency department providers may be challenged by the presentation of patients who exhibit movement abnormalities that could indicate conditions requiring interventions with potential morbidity and mortality. Crucially, interventions for potential neurological diseases that require immediate intervention (e.g., stroke) are contraindicated in patients exhibiting movements that may be voluntary (e.g., emotional expressions and fabricated symptoms) or functional (e.g., functional movement disorders such as functional tremor or psychogenic nonepileptic seizures). This diagnostic challenge is amplified when evaluating patients who are unable to provide symptom history, so information provided by accompanying persons requires confirmation by corroborating sources. Since accurate and rapid classification of these movement subtypes is essential for proper diagnosis and treatment of patients, we hypothesize that technology incorporating the neural underpinnings of voluntary movements provides a foundation for differentiating subtypes of movements. This difference and these signatures may be quantifiable: Voluntary movements, reflecting complex and specific planning, demonstrate spatial and temporal precision and accuracy, motor smoothness, and longer preparation and execution time.

Methods: We propose that kinematic analysis using three-dimensional position data from three sensors on the thumb, little finger, and wrist can effectively capture these unique temporal and spatial characteristics, allowing differentiation in subtypes of voluntary movements. This protocol and device setup are being implemented to generate preliminary data.

Results: We propose that an algorithm for voluntary movements may appear as follows:

Voluntary movements = The presence of at least one of the following: + malingering + factitious disorder + ululation + applause + . . . .

Conclusion: Our protocol examines the relationship between the neural underpinnings of motor planning in quantifiable kinematic differences among voluntary movements. This distinction, captured by simple, multi-sensor analysis, may provide a foundation for objective and novel diagnostic aid in complex movement disorders and symptoms. This protocol may be a valuable tool to monitor patients participating in clinical trials.

Introduction

Deep learning methods in neuroimaging have largely focused on population-level classification and group-based inference, limiting their applicability for individualised clinical interpretation. Convolutional neural networks (CNNs) are well suited to modelling complex spatial patterns in structural MRI; however, their use for single-subject analysis remains limited. This study introduces a technically novel normative CNN framework designed to enable personalised, single-subject interpretation of T1-weighted structural MRI without reliance on diagnostic group comparisons or case–control statistics.

Methods

A three-dimensional convolutional autoencoder was implemented to learn normative neuroanatomical representations from healthy adult T1-weighted MRI data. Approximately 100 scans were selected from the publicly available IXI dataset to construct the normative reference model. Images underwent standard pre-processing including skull stripping, spatial normalisation, and intensity standardisation. The trained CNN was subsequently applied to individual subjects to generate voxel-wise reconstruction error maps, representing deviations from learned normative structure. Quantitative regional deviation scores were computed by aggregating voxel-level errors within anatomically defined brain regions, enabling subject-specific neuroanatomical profiling.

Results

The proposed framework produces continuous, voxel-wise deviation measures across the entire brain volume from a single T1-weighted MRI scan. Reconstruction error maps provide spatially localised representations of neuroanatomical variability relative to normative structure learned from the IXI reference cohort. Regional aggregation yields quantitative subject-specific deviation profiles, supporting interpretable individual-level assessment. Preliminary analyses demonstrate stable model training and consistent deviation patterns across healthy reference scans, indicating the technical feasibility of CNN-based normative modelling for single-subject inference.

Conclusions

This work presents a single-subject CNN framework for personalised structural MRI analysis. By shifting deep learning applications from group-level classification to normative individual-level deviation mapping, the proposed approach supports personalised medicine and precision neuroimaging paradigms. The framework provides a foundation for future large-scale validation, longitudinal modelling, and clinical translation of personalised neuroimaging methodologies.

Introduction. Unilateral lower-limb amputation often leads to long-term asymmetry in knee muscle function, which may persist despite rehabilitation. The magnitude and profile of impairment depend on rehabilitation timing and which muscle groups are most affected. Earlier prosthetic rehabilitation is typically associated with better functional preservation, whereas delays can lead to selective deficits – particularly in muscles that contribute to high-velocity movements. Flexors and extensors may recover differently, with consequences for joint stability and movement efficiency. We therefore aimed (I) to determine whether knee flexors or extensors show better functional preservation in unilateral transtibial para-athletes and (II) test whether time-to-prosthetic gait and movement velocity are related to inter-limb asymmetry (ILA).

Methods. Eight male unilateral transtibial para-athletes (classifications C2–C4; height 172.9±7.2 cm; body mass without prosthesis 68.9±7.9 kg; BMI 22.9±1.9) were tested. The mean interval from amputation to first prosthetic gait was 4.4±1.5 months (n=8). Concentric knee flexion/extension was assessed on an Isomed 2000 dynamometer at 60, 180, 240, and 300°·s⁻¹. Four athletes were tested bilaterally and four were tested on the intact limb only (retained to augment intact-limb reference values). Outcomes are listed: peak torque (PT, Nm; Nm·kg⁻¹), hamstrings-to-quadriceps ratio (H/Q, %), and inter-limb asymmetry (ILA, % = |PT_intact−PT_prosthetic|/PT_intact×100). Anthropometrics are reported as mean ± SD; isokinetic outcomes are reported as median [IQR]. The exploratory Spearman method tested the delay–ILA association.

Results. Prosthetic-side torque deficits were evident across all velocities. ILA was velocity-dependent, with minima at 240°·s⁻¹ (flexion) and 300°·s⁻¹ (extension), alongside pronounced individual outliers. H/Q was slightly higher on the prosthetic limb, consistent with relatively preserved flexors.

Conclusion. The profile indicates a selective extensor deficit and clear velocity specificity; prioritize early, targeted, velocity-specific extensor strengthening on the prosthetic side. From the results, earlier initiation of prosthetic gait was associated with lower asymmetry.