Glioblastoma (GBM) and primary central nervous system lymphoma (PCNSL) often exhibit overlapping appearances on routine MRI, complicating pre-treatment diagnosis. In 1,109 patients from five centers, we constructed standard-space tumor probabilistic maps and derived atlas-anchored spatial features to augment conventional radiomics. The spatial radiomics classifier outperformed radiomics alone (external test area under the ROC curve [AUC], 0.98) with acceptable calibration and decision curve benefit, and SHapley Additive exPlanations (SHAP)-enabled anatomy-grounded interpretation. Aligning tumor localization with the Allen Human Brain Atlas and a normative functional connectome linked GBM-enriched territories to developmental-oncogenic programs and network hubness, whereas PCNSL-enriched territories showed immune-inflammatory/proliferative programs, and associations with network hubness did not survive spatial-autocorrelation correction. These results provide shareable reference maps and an interpretable, multicenter-generalizing tool for GBM-PCNSL differentiation, while offering biological context for diagnosis-specific location susceptibility.

Understanding how spontaneous, rather than experimentally induced, thoughts relate to brain activity remains a major challenge. We combined simultaneous fMRI and EEG recordings with Descriptive Experience Sampling (DES) to link momentary, naturally occurring experiences to their neural signatures during rest. Using machine-learning classification of 240 time-locked samples from eight participants-each completing nine 25-minute resting-state sessions-we reliably distinguished internally from externally oriented experiences (fMRI accuracy = 65.4%, EEG = 62.5%). Externally oriented states showed greater fMRI activity in salience, auditory, and visuospatial networks and lower occipital alpha power in EEG, whereas internally oriented states exhibited the opposite pattern, extending prior DMN-focused accounts of internally directed states. Across modalities, integrated resting-state alpha power correlated negatively with BOLD fluctuations in parietal and occipital regions. These multimodal findings reveal distinct neural signatures of spontaneous experience and demonstrate that coordinated large-scale network dynamics and alpha-band oscillations track the natural alternation between inward and outward focus in the human mind.

Parkinson’s disease (PD) disrupts cortico-basal ganglia communication, producing exaggerated beta-band (13–30 Hz) synchronization expressed as prolonged beta bursts that correlate with bradykinesia and rigidity. In healthy systems, brief beta bursts support flexible motor control, including reactive and proactive motor inhibition. However, in PD it remains unclear whether pathological beta bursts facilitate motor inhibition or if their prolonged nature and temporal inflexibility hinder both movement initiation and the timely engagement of inhibition. We addressed this by examining cortical beta bursts from high-density EEG acquired during a stop-signal task in 14 PD patients in practically defined medication OFF state with beep brain stimulation (DBS) ON and OFF, and 15 age-matched controls. At rest, PD patients exhibited prolonged cortical beta bursts that were normalized by DBS. During stopping, beta burst frequency increased in all groups, but bursts occurred earlier and more broadly across frontal regions in PD patients. DBS shifted burst properties toward the healthy range, improving reactive stopping while also reducing proactive inhibition. These findings suggest that DBS over subthalamic nucleus restores temporal flexibility in cortical beta bursting but may attenuate proactive control, underscoring the need for adaptive DBS approaches that normalize pathologically prolonged bursts without excessively reducing burst features that support proactive adjustments.

Intensive exercise and high-altitude exposure can disrupt neural activity and impair cognitive functioning. Previous research suggests that ketone ester (KE) ingestion may counteract cognitive impairments; however, its impact on neural activity during exercise and hypoxia remains unclear. Therefore, we investigated the impact of KE on electroencephalography (EEG) patterns and cognition during hypoxia and exercise. Twelve healthy males completed three randomized crossover sessions: i) normoxia + placebo, ii) hypoxia + placebo, and iii) hypoxia + KE. Each session included normoxic endurance (ET120') and high-intensity interval training (HIIT80'), followed by a 16-h period including sleep in either normoxia or hypoxia. The next day, participants performed a normoxic 30-min all-out time-trial (TT30'). EEG was recorded during rest and exercise, while cerebral tissue oxygenation index (cTOI) and cognitive performance were evaluated during rest. At rest, KE attenuated hypoxia-induced increases in alpha and beta power and cTOI declines. Nonetheless, cognitive performance remained unaffected. Brain activity rose throughout ET120' and normalized during recovery, while HIIT80' elicited a fluctuating neural response but normalized during recovery. Following TT30', theta, alpha, and gamma power remained elevated during recovery. Altogether, these data, obtained in healthy males, show the potential of KE to stabilize resting-state EEG patterns in hypoxia. Moreover, they shed light on how EEG patterns vary with exercise intensity, with sustained postexercise increases in theta, alpha, and gamma power following high-intensity efforts. These findings suggest that KE can help to preserve neural stability under hypoxia and highlight EEG's potential for monitoring fatigue and tailoring training or recovery strategies.NEW & NOTEWORTHY This study is the first to demonstrate the effects of ketone ester ingestion on hypoxia-induced neural alterations. Moreover, it uniquely combines measurements of cerebral oxygenation, cognitive performance, and electroencephalography (EEG) across low-, high-, and all-out exercise intensities, as well as during rest. Potentially highlighting EEG as a valuable tool for monitoring fatigue and optimizing training strategies.

EEG-based resting-state functional connectivity (FC) has been widely explored as a prognostic tool in stroke recovery, offering a cost-effective alternative to fMRI. However, it remains unclear whether FC measures are reliable biomarkers for predicting stroke recovery. This systematic review provides a comprehensive overview of existing EEG-based FC measures and their clinical relevance in stroke recovery. Specifically, this study aims to identify the most reliable and predictive FC measures of recovery by examining their relationship with longitudinal changes, clinical outcomes, and neurorehabilitation protocols. Results show that while some studies report associations between FC and recovery, no consistent patterns emerge. Significant methodological heterogeneity, such as differences in sensor- vs. source-level analysis, study design, connectivity measures, and reliance on correlational rather than predictive approach, limits the interpretability and comparability of results. Overall, these inconsistencies raise concerns about the reliability of EEG-based FC measures as biomarkers of stroke recovery. Moreover, while most studies focused on motor recovery, emerging evidence suggests FC may also help predict cognitive recovery. Future research should prioritize predictive models tailored to clinical needs, explore multidimensional recovery domains, and establish standardized protocols to enhance methodological consistency. By addressing these challenges and harnessing advanced computational techniques, EEG-based FC holds the potential to transform personalized rehabilitation strategies and optimize outcomes for stroke patients. Given the wide range of analytical scenarios and the absence of a superior method, we propose that a “multiverse” analytical approach could offer valuable insights into the most promising pathways for establishing connectivity as a potential biomarker for stroke recovery.

OBJECTIVE:Resting-state networks (RSNs) consist of coherent spontaneous activity patterns that support a wide range of sensorimotor and higher-order cognitive functions. In schizophrenia (SZ), RSN alterations reflect disruptions in the brain's functional architecture. Given the heterogeneity of SZ, accurate spatial mapping of RSNs at the individual level is crucial for characterizing altered brain connectivity in a more personalized manner. To achieve this, we used single-subject independent component analysis (ICA) to extract RSNs at the individual level, preserving unique functional patterns and accounting for variability among SZ patients. METHODS:We analyzed a resting-state functional magnetic resonance imaging dataset from 74 SZ patients and 74 matched healthy controls (HCs) obtained from the publicly available COINS database. Using single-subject ICA, we extracted 14 distinct RSNs associated with sensory, motor, and higher-order cognitive functions. Voxel-wise statistical comparisons were performed to identify spatial differences between the groups. RESULTS:The SZ group exhibited widespread RSN alterations in regions associated with visual, motor, and cognitive processing. Significant spatial differences were observed within each network, with the most extensive changes occurring in the somatomotor network and three cognitive networks: the cingulo-insular, medial prefrontal, and left frontoparietal networks. Within the default mode network, differences between SZ patients and HC were observed exclusively in visual areas. CONCLUSIONS:Single-subject ICA provides a valuable approach for investigating RSN alterations in SZ and enables a detailed, individualized characterization of functional connectivity disruptions. The extensive connectivity alterations in visual, motor, and cognitive networks highlight the complex interplay among these systems in SZ.

The coupling between electroencephalography (EEG) and blood-oxygen-level-dependent (BOLD) signals has been investigated across numerous studies, but its neurobiological underpinnings remain poorly understood. Resting-state EEG alpha-BOLD coupling follows a characteristic spatial pattern, shifting from negative correlations in sensory regions to positive correlations in association cortices. In this study, we examined neurobiological correlates of resting-state alpha-BOLD coupling. We compared the spatial pattern of the alpha-BOLD coupling map to 82 cortical feature maps, including gene expression profiles of different cell types and receptor subunits as well as structural MRI measures. We identified three statistically significant ( q < 0.05 FDR-corrected) maps: the layer 6 VIP interneuron marker, excitatory layer-5 marker, and NMDA receptor subunit GRIN2C. The three significant gene maps, combined in a multiple linear regression model, explained R 2 = 0.312 of the spatial variance in alpha-BOLD coupling. Analysis of the spatial mismatch between cortical maps and the alpha-BOLD coupling map revealed that the early auditory cortex is the region that consistently diverges from predictions across gene expression and T1/T2 maps. The spatial correspondence between alpha-BOLD coupling and gene expression profiles of specific receptor subunits, neuronal types, and layer-specific populations identifies these as concrete candidates for future computational and experimental studies of alpha-BOLD coupling.

The artificial intelligence-assisted ASPECTS (AI-ASPECTS) system has become an increasingly common tool in clinical practice for assessing acute ischemic stroke (AIS). However, current AI-ASPECTS implementations still rely on the conventional expert-evaluation framework, which uses a simplified two-slice atlas and arbitrarily selected lesion-load thresholds. Our study aimed to develop a refined AI-assisted ASPECTS (Ref-AI-ASPECTS) framework featuring a seamless whole middle cerebral artery (MCA) territory atlas and region-specific, optimally determined lesion-load thresholds, and comprehensively evaluate the performance of this framework across various clinical scenarios for AIS. We enrolled a cohort of 7,655 AIS patients from eleven centers. Modified atlas was created by expanding conventional atlas based on full MCA territory. Ref-AI-ASPECTS with modified atlas and specific lesion-load thresholds was established using a genetic algorithm. The clinical utility of Ref-AI-ASPECTS was assessed by comparing it to the conventional framework (Con-AI-ASPECTS) in terms of correlation with NIHSS scores on admission, dichotomized prediction of mRS at 3 months, and consistency with expert scoring across the training DWI data, external DWI data, expanded CT data, and real-world prospective DWI data. The Ref-AI-ASPECTS frameworks with modified atlas and specific lesion-load thresholds (2

Healthy aging is associated with progressive structural brain decline, yet the loss of functional abilities varies across individuals, which has been linked to reserve mechanisms. Within the framework of complex systems theory, reserve is thought to manifest as resilience when the system is challenged by stressors, such as increases in task difficulty. The cerebellum has been proposed as a potential source of motor reserve, but empirical evidence linking cerebellar structure, function, and resilience remains limited. We conducted a cross-sectional study including 50 young, 80 older, and 30 older-old adults to examine resilience to increasing task demands across cerebellar-specific and general outcomes. Participants completed three motor tasks (pure elbow motion, motor timing, postural stability) and two cognitive tasks (mental rotation, spatial working memory). Structural MRI was acquired to quantify cerebellar grey matter volume within functionally defined regions. Cerebellar-specific motor measures (anticipatory muscle activation and timing variability) were preserved across age groups and remained resilient under increased task demands, including in adults over 80 years of age. In contrast, general sensorimotor performance (postural sway) declined with age and showed reduced resilience. Within the cognitive domain, both cerebellar-specific and general measures showed comparable age-related declines and reduced resilience. Resilience measures were not correlated across tasks, indicating that resilience is task- and domain-specific. Furthermore, cerebellar grey matter volume did not predict resilience in motor or cognitive outcomes. These findings support the cerebellar motor reserve hypothesis, suggesting that cerebellar-dependent motor processes remain resilient despite age-related structural decline. However, resilience appears to be function-specific rather than a generalized individual trait. Overall, the results highlight dissociations between brain structure, function, and resilience, underscoring the selective contribution of the cerebellum to motor preservation in healthy aging.

Despite several age-related processes impacting motor performance, older adults often retain the ability to implicitly adapt to sensory prediction errors. Here, we leverage the fact that implicit adaptation is not attenuated by aging to study the impact of aging on responses to motor errors. In other domains, such as reinforcement learning, aging has been shown to influence how task outcomes or rewards are processed and used to guide subsequent actions, with some studies emphasizing that older adults react more strongly to a miss than to a hit. We aimed to extend these reinforcement learning findings to the motor domain with two preregistered experiments testing whether missing the target leads to larger implicit adaptation in young and older adults to the same extent. In addition, we compared these results to one reinforcement learning task in the motor domain (Boolean feedback after reaching in the absence of visual feedback) and one in the cognitive domain (reward-based decision-making). While we found age-related effects in the cognitive domain, we did not observe a consistent effect of age on the modulation of reaching direction or motor adaptation by task outcomes. These results suggest a domain-specific nature of age-related changes in sensitivity to task outcomes.

Aging is frequently perceived negatively due to its association with declines in brain and motor function yet not all aspects of brain function are equally affected. The cerebellum, a brain region closely linked to motor control, undergoes clear structural changes with age, but the impact of this degeneration on cerebellar function remains debated. The present study thoroughly investigates the impact of age on cerebellar function by measuring cerebellar motor and cognitive performance across the lifespan in 50 young adults (20–35 years), 80 older adults (55–70 years), and 30 older-old adults (over 80 years). Participants completed a test battery comprising seven motor control tasks and one cognitive task each designed to probe both cerebellar-specific and general sensorimotor function. Our results revealed that, despite age-related changes in cerebellar structure, cerebellar-specific functions remained intact in older adults compared to young adults, even among those above 80 years old. In contrast, general sensorimotor measures showed a clear pattern of decline with age. Together, these findings indicate that cerebellar function is largely preserved despite pronounced structural degeneration, providing compelling evidence for the cerebellum’s remarkable functional resilience.

Handwriting is a complex cognitive and motor skill supported by a distributed brain network involving cortical, subcortical, and cerebellar regions responsible for planning, execution, and sensorimotor integration. Beyond its communicative role, handwriting provides biologically meaningful information about brain function and motor control, serving as a sensitive marker of both normal and pathological changes. Age-related alterations, such as reduced fine motor precision, impaired sensory feedback, and cognitive slowing, contribute to the progressive decline in handwriting fluency and legibility. Importantly, distinctive handwriting patterns may be associated with early signs of neurodegenerative diseases, including Parkinson’s disease, Alzheimer’s disease, and Multiple Sclerosis, reflecting disease-specific alterations in motor and cognitive circuits. Advances in digital technology now enable high-resolution, quantitative analysis of handwriting kinematics, offering promising and scalable tools for diagnosis, longitudinal monitoring, and personalized rehabilitation. Furthermore, interventions incorporating fine motor and visuomotor coordination exercises, adaptive writing, and cognitive training may help preserve handwriting abilities and promote adaptive neural changes. In this review, we synthesize current evidence on the neural, behavioral, and technological mechanisms underlying handwriting across aging and neurodegenerative conditions. We provide an integrated overview of neural substrates, age- and disease-related alterations, and emerging digital approaches for assessment and intervention, highlighting their relevance for research and clinical practice. Overall, handwriting has the potential to offer a powerful, non-invasive window into brain health, bridging neuroscience, aging research, and digital medicine.

OBJECTIVE:To assess gait imagery related activations by means of high-density electroencephalography (hdEEG) in a population of early-stage PD patients and age-matched healthy controls. METHODS:Fifteen patients with early-stage PD (Hohen & Yahr range: 1-2.0) and 14 age matched controls were recruited. They were asked to visually imagine walking on a straight pathway and on a straight pathway while crossing a hurdle in the middle. We registered hdEEG in the participants and analyzed α and β bands Event Related Desynchronizations (ERDs). RESULTS:PD patients showed reduced low α and high β activity and more widespread ERDs in the high α range compared to the controls. On the contrary, a similar behavior in the low β band was found between the two groups. CONCLUSIONS:Findings about α activity in PD might indicate an abnormal basal ganglia-sensorimotor interaction (high α), together with attentional and executive functions deficit (low α). Low β ERD modulation is normal in patients with PD, whereas high β ERD results may indicate top-down control impairments. SIGNIFICANCE:By uncovering disparities in gait imagery related activations between the two groups, our protocol could potentially help in better understanding gait pathophysiology in the early stages of PD.

Brain–computer interface (BCI) technology holds promise for improving motor rehabilitation in stroke patients. This review explores the immediate and long-term effects of BCI training, shedding light on the potential benefits and challenges. Clinical studies have demonstrated that BCIs yield significant immediate improvements in motor functions following stroke. Patients can engage in BCI training safely, making it a viable option for rehabilitation. Evidence from single-group studies consistently supports the effectiveness of BCIs in enhancing patients’ performance. Despite these promising findings, the evidence regarding long-term effects remains less robust. Further studies are needed to determine whether BCI-induced changes are permanent or only last for short durations. While evaluating the outcomes of BCI, one must consider that different BCI training protocols may influence functional recovery. The characteristics of some of the paradigms that we discuss are motor imagery-based BCIs, movement-attempt-based BCIs, and brain-rhythm-based BCIs. Finally, we examine studies suggesting that integrating BCIs with other devices, such as those used for functional electrical stimulation, has the potential to enhance recovery outcomes. We conclude that, while BCIs offer immediate benefits for stroke rehabilitation, addressing long-term effects and optimizing clinical implementation remain critical areas for further investigation.

Introduction:Emotion regulation is a key domain of social cognition, and its impairment contributes to poor psychosocial functioning in schizophrenia (SZ). The "Managing Emotions" (ME) branch of the Mayer-Salovey-Caruso Emotional Intelligence Test (MSCEIT) is widely used to assess this ability, yet its neural correlates remain unclear. Methods:We examined resting-state functional connectivity (rsFC) associated with MSCEIT-ME performance in 56 patients with schizophrenia and 56 healthy controls matched for age, sex, and years of education. Seed-based correlation analyses focused on three large-scale networks previously implicated in emotion regulation: the salience network (SN), the language network (LN), and the ventral attention network (VAN). Between-group differences and brain-behavior relationships were tested while controlling for IQ scores on the Wechsler Abbreviated Scale of Intelligence (WASI). False discovery rate Benjamini-Yekutieli (FDR-BY) correction was applied to all analyses. Results:Patients with SZ scored significantly lower on the MSCEIT-ME compared to healthy subjects (HCs). Moreover, SZ patients exhibited reduced left-lateralized rsFC between SN and LN regions relative to HCs. These findings indicate altered language-salience connectivity in schizophrenia and show that, while connectivity is associated with emotion regulation ability in healthy individuals, no significant brain-behavior association was detected in patients. Therefore, the neural mechanisms underlying emotion regulation deficits in schizophrenia remain to be clarified. Conclusion:Schizophrenia was characterized by altered left-lateralized language-salience connectivity. However, because no significant brain-behavior associations were found in patients, the neural basis of emotion-regulation deficits in schizophrenia remains unresolved, highlighting the need for network-level investigations in larger samples.

Aerosol jet printing (AJP) technology has emerged as a transformative tool in neuroprosthetic device development, offering high accuracy and versatility in fabricating complex and miniaturized structures, which are essential for advanced neural interfaces. This review explores the fundamental principles of AJP, highlighting its unique aerosol generation and concentrated deposition mechanisms, which facilitate the use of different materials on a variety of substrates. The advantages of AJP, including its device scalability, ability to print on flexible and stretchable substrates, and compatibility with a wide range of biocompatible materials, are examined in the context of neuroprosthetic applications. Key implementations, such as the fabrication of neural interfaces, the development of microelectrode arrays, and the integration with flexible electronics, are discussed, showcasing the potential of AJP to revolutionize neuroprosthetic devices. Additionally, this review addresses the challenges of biocompatibility and technical limitations, such as the long-term stability of electroconductive traces. The review concludes with a discussion of future directions and innovations, emphasizing the realization of sensorized prosthetic limbs through the incorporation of tactile sensors, the integration of biosensors for monitoring physiological parameters, and the development of intelligent prostheses. These prospects underscore the role of AJP in the advancement of neuroprosthetic applications and its pathway toward clinical translation and commercialization.

A post-injury increase in sensory sensitivity is frequently reported by acquired brain injury patients, including stroke patients. These symptoms are related to poor functional outcomes, but their underlying neural mechanisms remain unclear. Since stroke results in focal lesions that can easily be visualized on imaging, the lesions of stroke survivors can be used to study the neuroanatomy of post-injury sensory hypersensitivity. We used multivariate support vector regression lesion-symptom mapping and indirect structural disconnection mapping to uncover the lesion location and white matter tracts related to post-stroke sensory hypersensitivity. A total of 103 patients were included in the study, of which 47% reported post-stroke sensory hypersensitivity across different sensory modalities. The lesion-symptom and structural connectivity mapping identified the putamen, thalamus, amygdala and insula in the grey matter as well as fronto-insular tracts, and the fronto-striatal tract in the white matter as neural structures potentially involved in post-stroke sensory hypersensitivity. By examining the neuroanatomy of post-stroke sensory hypersensitivity in a large stroke sample, this study offers a significant advancement in our understanding of the neural basis of post-stroke sensory hypersensitivity.

Prospective Memory (PM) is the ability to encode an intention in memory and retrieve it at the right time in the future. After the intention is formed, it must be maintained in memory while simultaneously monitoring the environment until the occurrence of the stimulus associated with its retrieval. Therefore, monitoring and maintenance processes must work in conjunction to subserve PM processing (monitoring/maintenance phase). Several brain regions play a role in PM, such as the anterior prefrontal cortex, inferior parietal lobules, and precuneus. Notably, these regions belong to different brain networks and are differently involved depending on the memory and attentional requests of the PM task. In this study, we investigate the neural bases of PM from a network perspective, using functional connectivity (FC) analysis to identify the networks involved in the attentional and memory mechanisms underlying PM. To this end, we analyzed MEG data collected in two different PM conditions, enhancing either the monitoring (i.e., attention) or the maintenance (i.e., memory) loads of the PM task. To disentangle the neural correlates of these mechanisms from other processes occurring after stimulus presentation, the analysis focused on the prestimulus time window (monitoring/maintenance phase). The monitoring-load condition was characterized by increased inter-network FC of the Dorsal Attention Network (DAN) in the alpha band, a marker of increased top-down monitoring. In contrast, the maintenance-load condition was associated with increased connectivity of the Ventral Attention Network (VAN) with the FrontoParietal Control and the Default-Mode Networks (FPCN and DMN, respectively). Additionally, response times were found to correlate with prestimulus alpha connectivity of different networks in the two conditions. These differences in connectivity within and between networks support the hypothesis that different networks (DAN, or VAN and DMN) and mechanisms (top-down or bottom-up, respectively) are involved in PM processing depending on the features of the PM task.

Aging significantly impacts motor performance, especially in multi-joint movement tasks where the nervous system needs to adequately coordinate mechanical interactions between joints. Effective coordination of multiple joints relies on intact feedforward control to predict movement dynamics in the initial phase of the movement, and on feedback control to fine-tune the execution in the final phase. The effect of aging on these specific control mechanisms remains controversial. Here, we investigated a pure-elbow motion task with a group of 50 young (20–35 years old), 80 old (55–70 years old) and 30 older-old (80 + years old) healthy participants. They performed 30° elbow flexions and extensions under two speed conditions as higher elbow velocities increase interaction torques at the shoulder, and demand greater neuromuscular effort for stabilization. The timing and magnitude of anticipatory EMG activity of the agonist shoulder muscle, necessary to counteract interaction torques, were similar across all age groups. Moreover, increasing elbow velocity did not result in any performance differences between young and older adults, indicating that shoulder stabilization during movement initiation remained intact with age. However, older adults exhibited reduced ability to stabilize the shoulder position until the end of the movement, leading to decreased accuracy with older age. These results suggest that feedforward control, which is essential for shoulder stabilization during movement initiation, is functionally stable during healthy aging and remains resilient to increased motor demands. In contrast, feedback control appears to deteriorate with age, potentially contributing to reduced movement precision in the final phase of the multi-joint movement.