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Sleep irregularity has been linked to multiple deleterious consequences in clinical populations or community adults and adolescents, but little is known about young adults. In this study, we explored the relationships between two measures of sleep regularity and a wide range of factors (lifestyle behaviors, subjective sleep, clinical outcomes, and academic performance) in a sample of female, university students in the United Arab Emirates. A total of 176 participants were recruited. Objective estimates of sleep–wake patterns were obtained using seven-day wrist actigraphy and data were used to calculate daily sleep regularity with the Sleep Regularity Index (SRI) and weekly sleep regularity with the social jetlag (SJL). Subjective sleep measures were also acquired using the Pittsburgh Sleep Quality Index (PSQI), Dysfunctional Beliefs and Attitudes about Sleep (DBAS), and daytime napping frequency. Self-reported night-time technology use frequency was ascertained using the Technology Use Questionnaire (TUQ). Psychological health was assessed using the Hospital Anxiety and Depression Scale. Objective physical health measurements for body mass index, fasting blood glucose and blood pressure were obtained. No significant associations emerged between sleep regularity and psychological physical health, or academic performance. However, significant relationships were detected between SRI and daytime napping frequency ( p -value = 0.0017), PSQI ( p -value = 0.0337), and DBAS ( p -value = 0.0176), suggesting that daily irregular sleep patterns are associated with more frequent daytime napping, greater dysfunctional sleep beliefs, and poorer subjective sleep quality. Conversely, SJL was significantly associated with the DBAS ( p -value = 0.0253), and the TUQ ( p -value = 0.0208), indicating that weekly irregular sleep patterns are linked to greater dysfunctional sleep beliefs and increased nighttime technology use. In conclusion, efforts to educate and cultivate sustainable and consistent sleep–wake patterns amongst university students are needed, which can be achieved by raising awareness, promoting good sleep health habits, and minimizing excessive bedtime technology.

The characterization of the distinct dynamic functional connectivity (dFC) patterns that activate in the brain during rest can help to understand the underlying time-varying network organization. The presence and behavior of these patterns (known as meta-states) have been widely studied by means of functional magnetic resonance imaging (fMRI). However, modalities with high-temporal resolution, such as electroencephalography (EEG), enable the characterization of fast temporally evolving meta-state sequences. Mild cognitive impairment (MCI) and dementia due to Alzheimer’s disease (AD) have been shown to disrupt spatially localized activation and dFC between different brain regions, but not much is known about how they affect meta-state network topologies and their network dynamics. The main hypothesis of the study was that MCI and dementia due to AD alter normal meta-state sequences by inducing a loss of structure in their patterns and a reduction of their dynamics. Moreover, we expected that patients with MCI would display more flexible behavior compared to patients with dementia due to AD. Thus, the aim of the current study was twofold: (i) to find repeating, distinctly organized network patterns (meta-states) in neural activity; and (ii) to extract information about meta-state fluctuations and how they are influenced by MCI and dementia due to AD. To accomplish these goals, we present a novel methodology to characterize dynamic meta-states and their temporal fluctuations by capturing aspects based on both their discrete activation and the continuous evolution of their individual strength. These properties were extracted from 60-s resting-state EEG recordings from 67 patients with MCI due to AD, 50 patients with dementia due to AD, and 43 cognitively healthy controls. First, the instantaneous amplitude correlation (IAC) was used to estimate instantaneous functional connectivity with a high temporal resolution. We then extracted meta-states by means of graph community detection based on recurrence plots (RPs), both at the individual- and group-level. Subsequently, a diverse set of properties of the continuous and discrete fluctuation patterns of the meta-states was extracted and analyzed. The main novelty of the methodology lies in the usage of Louvain GJA community detection to extract meta-states from IAC-derived RPs and the extended analysis of their discrete and continuous activation. Our findings showed that distinct dynamic functional connectivity meta-states can be found on the EEG time-scale, and that these were not affected by the oscillatory slowing induced by MCI or dementia due to AD. However, both conditions displayed a loss of meta-state modularity, coupled with shorter dwell times and higher complexity of the meta-state sequences. Furthermore, we found evidence that meta-state sequencing is not entirely random; it shows an underlying structure that is partially lost in MCI and dementia due to AD. These results show evidence that AD progression is associated with alterations in meta-state switching, and a degradation of dynamic brain flexibility.

Glioblastoma is characterized by diffuse infiltration, making accurate detection of residual disease essential for improving prognostication and guiding treatment. This study evaluates whether the volume of predicted infiltration, generated by a machine learning (ML) model trained on radiomic features from postoperative magnetic resonance imaging (MRI), is an independent prognostic factor. We analyzed a total of 114 glioblastoma patients, 89 from a retrospective multicenter cohort and 25 from a prospective cohort, who underwent gross total resection and had an early postoperative MRI. A previously published voxel-wise ML model estimated tumor infiltration probability in the non-enhancing peritumoral region using conventional MRI sequences. High-risk of recurrence regions (HRoR) were delineated from the probability maps, and their volumes were quantified. Associations with residual FLAIR volume, clinical variables (age, Karnofsky Performance Status), and survival outcomes (overall survival [OS], progression-free survival [PFS]) were evaluated using Cox regression and Kaplan–Meier analysis. In the retrospective cohort, multivariate Cox modeling confirmed that higher HRoR volume was independently associated with shorter OS (HR = 1.51; 95% CI, 1.12–2.05; p  = 0.008), with no association found for PFS. A robust cutoff of 1.6 cm³ stratified patients into high- and low-risk groups with significantly different OS (456 vs. 678 days; p  = 0.038). This threshold was validated in a prospective cohort (326 vs. 525 days; p  = 0.039). ML-derived HRoR mapping provides independent prognostic value and may improve risk stratification after surgery in glioblastoma. These findings support its potential clinical integration for personalized follow-up and treatment.

The c-VEP stimulus protocol is distinctly different from other major classes of evoked responses, such as the event-related potential (ERP) and steady-state visual evoked potential (SSVEP) (Martínez-Cagigal et al., 2021). The ERP protocol, typically based on an oddball paradigm, operates at a much slower pace, with a typical stimulus onset asynchrony (SOA) of approximately 250 ms (4 Hz) compared to the significantly faster SOA of at least 16 ms (60 Hz) used in c-VEP. Similarly, while the SSVEP paradigm is also relatively fast as compared to ERP, SSVEP protocols rely on a frequency-tagging approach, where stimuli are restricted to periodic signals with specific frequencies and phases. In contrast, the c-VEP protocol employs a noise-tagging approach, allowing for a much wider range of stimulus sequences, including non-periodic patterns, while also demonstrating greater resilience to narrowband interferences. Moreover, recent evidence has revealed that, from an information-theoretic perspective, the maximum information transfer rate achievable via the visual-evoked pathway in c-VEP-based BCIs significantly exceeds that of SSVEP-based systems (Shi et al., 2024).Furthermore, the c-VEP field places significant emphasis on designing stimulus sequences to ensure that the resulting neural responses are (near-)orthogonal, thereby facilitating and enhancing decoding performance. Initially, the field tested carefully selected pseudorandom binary codes from telecommunications, such as the well-established m-sequence and Gold codes (Martínez-Cagigal et al., 2021). At present, researchers are increasingly exploring alternative noise codes with advantageous properties, aiming to improve signal-to-noise ratio (SNR), enhance decoding accuracy, or increase user comfort (e.g., Martínez-Cagigal et al. (2023)). However, achieving optimal structure of discernibility in the stimulus domain does not necessarily carry over to the response space, where the real-time command decoding ultimately takes place.Complementing the rapid and optimized stimulus presentation, dedicated decoding approaches have been developed to capitalize on the rapid, repeated but pseudorandom presentation of flashes. These methods significantly reduce, and in some cases even eliminate, the need for subject-specific training data to calibrate classifiers (e.g., Thielen et al. (2021)). Additionally, advances in decoding techniques, including dynamic stopping, asynchronous operation, and non-control state detection, enable quick, reliable, and intuitive selection of target classes during online BCI use. In this Research Topic, Ahmadi et al. ( 2024) introduced a novel Bayesian dynamic stopping method aimed at optimizing the trial duration during online use. One of the innovative features of their method is its ability to intuitively fine-tune and control its behavior based on the specific application requirements.Moreover, earlier c-VEP studies primarily relied on binary noise codes that encoded stimuli through contrast reversals, typically at full contrast, alternating between black and white (Martínez-Cagigal et al., 2021). While these approaches achieved high performance, the community quickly recognized the tradeoff with maintaining a high level of user comfort. Currently, research increasingly focuses not only on identifying optimal stimulus sequences, such as non-binary codes (e.g., Martínez-Cagigal et al. (2023)) or those using white noise codes (e.g., Miao et al. (2024)), but also on improving the visual characteristics of the stimuli, for example by employing burst codes and textured stimuli (Dehais et al., 2024). Optimizing both the stimulus sequences and their appearance contributes to enhanced BCI performance as well as an improved user experience. In this Research Topic, Fernandez-Rodriguez et al. ( 2023) explored the effects of varying spatial frequencies in checkerboard-like stimuli on both performance and user experience in c-VEP-based BCIs. Their findings again stress the importance of customizing visual stimuli to optimize both system performance and user satisfaction.Finally, research on c-VEP-based BCIs has often focused on the speller application for communication as a benchmark (Martínez-Cagigal et al., 2021). However, this emphasis overlooks the vast potential of c-VEP for a wide range of applications beyond the standard controlled lab environment. Many promising uses of c-VEP-based BCIs exist that could offer significant societal benefits. In this Research Topic, we highlight two such innovative applications. First, Huang et al. ( 2023) explored the potential of c-VEP for biometric authentication by integrating it into a mild-burdened cognitive task. Second, Moreno-Calderon et al. ( 2023) introduced a multiplayer competitive video game that uses a c-VEP-BCI to implement the classic game of 'Connect Four'.In conclusion, the four studies featured in this Research Topic mark significant advancements in addressing the challenges associated with c-VEP-based BCIs. By optimizing stimulus protocols, refining decoding techniques, and demonstrating practical real-world applications, these contributions lay the foundation for a new generation of BCIs that are more reliable, faster, user-friendly, and accessible to a broader range of users and use cases. Continued interdisciplinary collaboration will be crucial in transforming these innovations into impactful, plug-and-play solutions in the near future.

Alzheimer’s disease (AD) is a neurodegenerative disorder with high prevalence, known for its highly disabling symptoms. The aim of this study was to characterize the alterations in the irregularity and the complexity of the brain activity along the AD continuum. Both irregularity and complexity can be studied applying entropy-based measures throughout multiple temporal scales. In this regard, multiscale sample entropy (MSE) and refined multiscale spectral entropy (rMSSE) were calculated from electroencephalographic (EEG) data. Five minutes of resting-state EEG activity were recorded from 51 healthy controls, 51 mild cognitive impaired (MCI) subjects, 51 mild AD patients (ADMIL), 50 moderate AD patients (ADMOD), and 50 severe AD patients (ADSEV). Our results show statistically significant differences (p-values < 0.05, FDR-corrected Kruskal–Wallis test) between the five groups at each temporal scale. Additionally, average slope values and areas under MSE and rMSSE curves revealed significant changes in complexity mainly for controls vs. MCI, MCI vs. ADMIL and ADMOD vs. ADSEV comparisons (p-values < 0.05, FDR-corrected Mann–Whitney U-test). These findings indicate that MSE and rMSSE reflect the neuronal disturbances associated with the development of dementia, and may contribute to the development of new tools to track the AD progression.

•First participant-level mega-analysis of frontal-midline (FM) theta neurofeedback in 168 adults.•FM-theta upregulation is frequency-specific and reliably distinguishes neurofeedback from active control.•Learning emerges early, stabilizes across sessions, and shows rapid feedback-driven modulation at the block level.•Sex and clinical characteristics predict individual differences in FM-theta neurofeedback learning.•Responder status is associated with clinical characteristics, highlighting substantial individual variability. Frontal-midline theta (FM-theta) neurofeedback is a promising approach for enhancing executive control, yet fundamental questions remain regarding its learning dynamics and the sources of interindividual variability, including how FM-theta self-regulation develops over time and which factors shape neurofeedback responsiveness. In this first large-scale mega-analysis of EEG-based neurofeedback, raw participant-level data from five independent international FM-theta neurofeedback studies were aggregated (N = 168). Learning trajectories were assessed using session-to-session and within-session indices across training segments shared by all studies, and first-to-last session differences capturing study-specific training gains. Analyses were conducted for standard FM-theta (4–8 Hz) and individualized FM-theta centered on participant-specific executive control theta peaks, with frequency specificity evaluated against non-theta control bands. Neurofeedback outcomes were compared with those of an active control group. Individual predictors of neurofeedback success, and exploratory responder profiles were also examined. Participants receiving neurofeedback showed significantly greater FM-theta upregulation than the active control group for both standard and individualized bands, evident at both session-averaged and within-session levels. Learning effects emerged early, stabilized across sessions, and were expressed primarily as robust within-session modulation and reliable first-to-last session increases. Individualized FM-theta effects were more heterogeneous and study-dependent than standard FM-theta effects. Predictor analyses indicated that female sex and lower educational attainment were associated with greater neurofeedback success. Exploratory responder analyses revealed substantial interindividual variability, with non-responders more frequently reporting or suspecting psychiatric disorders. Together, these findings characterize FM-theta neurofeedback learning as an early-stabilizing, within-session–driven process and provide a framework for optimizing protocol design and future work.

Diabetic retinopathy (DR) is characterized by the presence of red lesions (RLs), such as microaneurysms and hemorrhages, and bright lesions, such as exudates (EXs). Early DR diagnosis is paramount to prevent serious sight damage. Computer-assisted diagnostic systems are based on the detection of those lesions through the analysis of fundus images. In this paper, a novel method is proposed for the automatic detection of RLs and EXs. As the main contribution, the fundus image was decomposed into various layers, including the lesion candidates, the reflective features of the retina, and the choroidal vasculature visible in tigroid retinas. We used a proprietary database containing 564 images, randomly divided into a training set and a test set, and the public database DiaretDB1 to verify the robustness of the algorithm. Lesion detection results were computed per pixel and per image. Using the proprietary database, 88.34% per-image accuracy (ACCi), 91.07% per-pixel positive predictive value (PPVp), and 85.25% per-pixel sensitivity (SEp) were reached for the detection of RLs. Using the public database, 90.16% ACCi, 96.26% PPV_p, and 84.79% SEp were obtained. As for the detection of EXs, 95.41% ACCi, 96.01% PPV_p, and 89.42% SE_p were reached with the proprietary database. Using the public database, 91.80% ACCi, 98.59% PPVp, and 91.65% SEp were obtained. The proposed method could be useful to aid in the diagnosis of DR, reducing the workload of specialists and improving the attention to diabetic patients.

Obstructive sleep apnea (OSA) and insomnia are the two most prevalent sleep disorders, often co-occurring in a condition termed comorbid insomnia and sleep apnea (COMISA). While autonomic nervous system (ANS) dysfunction resulting from each of these disorders has been separately established through heart rate variability (HRV) analysis, the specific overnight ANS alterations due to COMISA have not been explored. This study aims to characterize nocturnal ANS alterations attributable to COMISA through time and frequency HRV analysis, distinguishing them from those of isolated insomnia or OSA. A total of 5,335 electrocardiograms from the Sleep Heart Health Study (SHHS) dataset were included in this research. Based on overnight polysomnography and sleep questionnaires, participants were categorized into No-OSA (2,738 subjects), Insomnia (190 subjects), OSA (2,260 subjects), or COMISA (147 subjects) groups. Classic time and frequency HRV features, along with specific frequency measures, were computed to characterize HRV behavior throughout the whole night, in both wakefulness and sleep periods. The analysis revealed that COMISA-specific ANS dysfunction manifests as reduced parasympathetic activity during wakefulness and heightened sympathetic activation during sleep. While primary ANS dysfunctions seem to result from recurrent apneic events affecting frequency features present in both OSA and COMISA, insomnia significantly alters mean heart rate during sleep, thus being the only feature distinguishing these two conditions. In conclusion, the combined effects of OSA and insomnia induce specific ANS dysfunction at night, highlighting the need for further HRV studies to better understand the impact of COMISA on ANS and its cardiovascular implications. Clinical trial registration : The clinical trial identifier of the original SHHS database is NCT00005275, https://sleepdata.org/datasets/shhs .

The discrimination of early Alzheimer’s disease (AD) and its prodromal form (i.e., mild cognitive impairment, MCI) from cognitively healthy control (HC) subjects is crucial since the treatment is more effective in the first stages of the dementia. The aim of our study is to evaluate the usefulness of a methodology based on electroencephalography (EEG) to detect AD and MCI. EEG rhythms were recorded from 37 AD patients, 37 MCI subjects and 37 HC subjects. Artifact-free trials were analyzed by means of several spectral and nonlinear features: relative power in the conventional frequency bands, median frequency, individual alpha frequency, spectral entropy, Lempel–Ziv complexity, central tendency measure, sample entropy, fuzzy entropy, and auto-mutual information. Relevance and redundancy analyses were also conducted through the fast correlation-based filter (FCBF) to derive an optimal set of them. The selected features were used to train three different models aimed at classifying the trials: linear discriminant analysis (LDA), quadratic discriminant analysis (QDA) and multi-layer perceptron artificial neural network (MLP). Afterwards, each subject was automatically allocated in a particular group by applying a trial-based majority vote procedure. After feature extraction, the FCBF method selected the optimal set of features: individual alpha frequency, relative power at delta frequency band, and sample entropy. Using the aforementioned set of features, MLP showed the highest diagnostic performance in determining whether a subject is not healthy (sensitivity of 82.35% and positive predictive value of 84.85% for HC vs. all classification task) and whether a subject does not suffer from AD (specificity of 79.41% and negative predictive value of 84.38% for AD vs. all comparison). Our findings suggest that our methodology can help physicians to discriminate AD, MCI and HC.

Attention deficit hyperactivity disorder (ADHD) is a neurodevelopmental condition that affects cognitive, academic, behavioral, emotional, and social functioning, primarily in children. Despite its high prevalence, current pharmacological treatments are not effective in 30% of cases and show poor long-term adherence. Non-pharmacological interventions can complement medication-based treatments to improve results. Among these therapies, neurofeedback (NFB) and respiratory biofeedback (R-BFB) have shown promise in treating ADHD symptoms. Moreover, median nerve stimulation (MNS) can help to enhance the efficacy of these treatments, but it has never been explored in this context. This study aimed to: (1) investigate the effectiveness of a combined R-BFB and NFB intervention to treat ADHD, and (2) explore the potential benefits of MNS in enhancing the proposed intervention. Sixty children with ADHD participated in the study, divided into two experimental groups. The active group received MNS, and the sham group received sham MNS. Both groups performed the NFB/R-BFB treatment. Clinical assessments (i.e., Conner's parent rating scale) and electroencephalography (EEG) measurements were taken before the intervention, immediately after treatment, and one month later. The results showed that the combined therapy significantly improved behavioral problems, anxiety, hyperactivity, and impulsivity-hyperactivity. Moreover, MNS enhanced the positive effects of the intervention, as the active group achieved higher improvement compared to the sham group. EEG analysis revealed significant changes in spontaneous brain activity, with an increase in frontal theta power ( = 0.0125) associated with reduced anxiety, which might explain the clinical outcomes. These changes were maintained 1 month after the intervention ( = 0.0325). Correlations between EEG changes and clinical outcomes were observed, suggesting a potential relationship between neurophysiological markers and ADHD symptoms measured by standardized scales. The study findings suggest that the proposed R-BFB/NFB intervention may be an effective non-pharmacological therapy for ADHD, with the additional application of MNS potentially enhancing its effects.