Covert speech involves imagining speaking without audible sound or any movements.Decoding covert speech from electroencephalogram (EEG) is challenging due to a limited understanding of neural pronunciation mapping and the low signal-to-noise ratio of the signal. In this study, we developed a large-scale multi-utterance speech EEG dataset from 57 right-handed native English-speaking subjects, each performing covert and overt speech tasks by repeating the same word in five utterances within a ten-second duration. Given the spatio-temporal nature of the neural activation process during speech pronunciation, we developed a Functional Areas Spatio-temporal Transformer (FAST), an effective framework for converting EEG signals into tokens and utilizing transformer architecture for sequence encoding. Our results reveal distinct and interpretable speech neural features by the visualization of FAST-generated activation maps across frontal and temporal brain regions, with each word being covertly spoken, providing new insights into the discriminative features of the neural representation of covert speech. This is the first report of such a study, which provides interpretable evidence for speech decoding from EEG.

BCI biosensors enable continuous monitoring of neural activity, but existing systems face challenges in scalability, latency, and reliable integration with cloud infrastructure. This work presents a cloud-aware, real-time cognitive grid architecture for multimodal BCI biosensors, validated at the system level through a full physical prototype. The system integrates the BioAmp EXG Pill for signal acquisition with an RP2040 microcontroller for local preprocessing using edge-resident TinyML deployment for on-device feature/inference feasibility coupled with environmental context sensors to augment signal context for downstream analytics talking to the external world via Wi-Fi/4G connectivity. A tiered data pipeline was implemented: SD card buffering for raw signals, Redis for near-real-time streaming, PostgreSQL for structured analytics, and AWS S3 with Glacier for long-term archival. End-to-end validation demonstrated consistent edge-level inference with bounded latency, while cloud-assisted telemetry and analytics exhibited variable transmission and processing delays consistent with cellular connectivity and serverless execution characteristics; packet loss remained below 5%. Visualization was achieved through Python 3.10 using Matplotlib GUI, Grafana 10.2.3 dashboards, and on-device LCD displays. Hybrid deployment strategies-local development, simulated cloud testing, and limited cloud usage for benchmark capture-enabled cost-efficient validation while preserving architectural fidelity and latency observability. The results establish a scalable, modular, and energy-efficient biosensor framework, providing a foundation for advanced analytics and translational BCI applications to be explored in subsequent work, with explicit consideration of both edge-resident TinyML inference and cloud-based machine learning workflows.

Portable biosensor hardware can now sustain continuous multimodal physiological acquisition at the edge, yet the analytical layer that converts raw signals into deployment-consistent inference remains the main bottleneck for practical embedded systems. This study addresses that bottleneck by presenting the machine-learning layer of the Real-time Cognitive Grid, the analytical companion to the previously reported hardware architecture, which equips a fixed-wiring biosensor assembly with real-time physiological-state classification through an asymmetric edge-cloud workflow. The proposed framework assigns analytical responsibility across tiers: a locked 17-feature schema comprising 5 EMG features, 6 EEG spectral features, 2 cross-modal features, 2 HRV features, 1 EOG feature, and 1 EEG quality indicator governs window-bounded inference on the Arduino Nano RP2040 Connect with an LDA edge artefact requiring approximately 716 B RAM, whereas the cloud tier supports public-dataset pretraining, hardware-aligned refinement, multimodal fusion, deployment comparison, and feature-importance analysis under the same schema contract. To evaluate analytical consistency across physiological diversity, five public repositories covering stress physiology (WESAD), affective EEG (DEAP), inertial activity recognition (PAMAP2), sEMG gesture decoding (EMG Gestures), and motor-imagery EEG (EEGMMIDB) were evaluated under subject-disjoint GroupKFold (k=5) protocols. To test whether the same contract survives translation to the physical rig, the hardware branch was evaluated under session-disjoint GroupKFold across five bench-acquired sessions. Unimodal performance was strongest in sEMG- and IMU-dominant tasks, whereas multimodal fusion improved macro-F1 by up to 0.141 over the strongest unimodal baseline in WESAD and by 0.109 in PAMAP2. In the hardware branch, the deployed edge LDA artefact reached 0.9435 macro-F1 with 0.9470 accuracy, while the retained cloud Random Forest reached 0.8792 macro-F1 with 0.8799 accuracy; feature-importance analysis further showed that the final 17-feature branch was dominated by EMG descriptors, with EEG spectral terms contributing secondary support and hardware-exclusive variables remaining weak under the present bench regime. These results show that a compact multimodal sensing assembly can be elevated beyond passive signal capture into an intelligent portable biosensor that performs context-aware interpretation with minimal user intervention, supported by a reproducible analytical workflow that remains coherent across heterogeneous benchmark repositories, hardware-specific refinement, and microcontroller-class deployment, thereby establishing cross-session bench feasibility as a structured basis for future multi-subject wearable validation.

Background: BCI and AI-driven image segmentation are revolutionizing precision neurosurgery by enhancing surgical accuracy, reducing human error, and improving patient outcomes. Methods: This systematic review explores the integration of AI techniques—particularly DL and CNNs—with neuroimaging modalities such as MRI, CT, EEG, and ECoG for automated brain mapping and tissue classification. Eligible clinical and computational studies, primarily published between 2015 and 2025, were identified via PubMed, Scopus, and IEEE Xplore. The review follows PRISMA guidelines and is registered with the OSF (registration number: J59CY). Results: AI-based segmentation methods have demonstrated Dice similarity coefficients exceeding 0.91 in glioma boundary delineation and tumor segmentation tasks. Concurrently, BCI systems leveraging EEG and SSVEP paradigms have achieved information transfer rates surpassing 22.5 bits/min, enabling high-speed neural decoding with sub-second latency. We critically evaluate real-time neural signal processing pipelines and AI-guided surgical robotics, emphasizing clinical performance and architectural constraints. Integrated systems improve targeting precision and postoperative recovery across select neurosurgical applications. Conclusions: This review consolidates recent advancements in BCI and AI-driven medical imaging, identifies barriers to clinical adoption—including signal reliability, latency bottlenecks, and ethical uncertainties—and outlines research pathways essential for realizing closed-loop, intelligent neurosurgical platforms.

Semiconductor quantum dots (QDs) have been used in a variety of applications ranging from optoelectronics to biodiagnostic fields, primarily due to their size dependent fluorescent nature. CdSe nanocrystals (NCs) are generally synthesized via a hot injection method in an organic solvent. However, such NCs are insoluble in water and therefore preclude the direct usage toward biological systems. Thus, the preparation of more biocompatible water-soluble QDs with a high photoluminescent quantum yield (PLQY) is extremely important for imaging applications. Although previous literature has detailed on the synthesis of CdSe NCs in water, they suffer from poor size distribution and very low PLQY. The complex formation mechanism of CdSe NCs in an aqueous environment adversely affects the quality of NCs due to the presence of OH-, H+, and H2O moieties. Here in this article, we have presented the facile hydrothermal approach to obtain size tunable (2.9-5.1 nm), aqueous CdSe NCs with a narrow emission profile having similar to 40 nm fwhm with 56% PLQY. Physicochemical properties of the synthesized water-soluble CdSe NCs were studied with the help of UV-vis, PL, XRD, FTIR, XPS, and HR-TEM analysis. Furthermore, the surface of the synthesized CdSe NCs was modified with d-glucosamine via EDC and NHS coupling to obtain a stable, biocompatible bioimaging probe. Furthermore, we demonstrated that their successful bioconjugation with glucosamine could facilitate effective internalization into the cellular matrix.

Stress and depression have emerged as prevalent challenges in contemporary society, deeply intertwined with the complexities of modern life. This paper delves into the multifaceted nature of these phenomena, exploring their intricate relationship with various socio-cultural, technological, and environmental factors through the application of neurosymbolic AI to social media content. Through a quantitative and qualitative analysis of results, we elucidate the profound impact of technological advancements on information processing, work culture, and social dynamics, highlighting the role of digital connectivity in exacerbating stressors. Economic pressures and social isolation further compound these challenges, contributing to a pervasive sense of unease and disconnection. Environmental stressors, including climate change, add another layer of complexity, fostering existential concerns about the future. Moreover, the persistent stigma surrounding mental health perpetuates a cycle of silence and suffering, hindering access to support and resources. Addressing these issues necessitates a holistic approach, encompassing societal changes, policy interventions, and individual coping strategies.

The substitution of semiconductor quantum dots (QDs) by a small number of transition-metal ions with magnetic properties gives rise to magnetic-doped semiconductors. With a balance of optical and magnetic properties, these magnetic semiconductors are widely used in spintronics, bioimaging and magnetic resonance imaging (MRI) applications. To facilitate their usage in bio-applications, it is critical to synthesize water-soluble magnetic QDs with a stabilized structure while maintaining their optical and magnetic properties. Here in our work, we have developed a facile substituted synthetic route to achieve Cr-doped CdSe (Cr-CdSe) via hydrothermal method. The effects of doping on the structural, optical, and magnetic properties of Cr-CdSe were studied using X-ray diffraction, UV-visible spectroscopy, and photoluminescence lifetime. We then explored their chemical nature and change in morphology with an increase in doping concentration via X-ray photoelectron spectroscopy and transmission electron microscopy. Water-soluble QDs have been used as bioimaging probes for the past few decades due to their strong fluorescence, photostability and improved tissue or cellular penetration. However, incorporating magnetic material into a fluorescent entity harnesses the ability to control the strengths of both modalities, which enhances diagnostic accuracy and facilitates its application in bio-systems, especially in early accurate diagnosis. Finally, we demonstrate the competency of Cr-CdSe as a dual-imaging probe with fluorescent cellular imaging and MRI applications.

There have been multiple technological advancements that promise to gradually enable devices to measure and record signals with high resolution and accuracy in the domain of brain–computer interfaces (BCIs). Multimodal BCIs have been able to gain significant traction given their potential to enhance signal processing by integrating different recording modalities. In this review, we explore the integration of multiple neuroimaging and neurophysiological modalities, including electroencephalography (EEG), magnetoencephalography (MEG), functional magnetic resonance imaging (fMRI), electrocorticography (ECoG), and single-unit activity (SUA). This multimodal approach leverages the high temporal resolution of EEG and MEG with the spatial precision of fMRI, the invasive yet precise nature of ECoG, and the single-neuron specificity provided by SUA. The paper highlights the advantages of integrating multiple modalities, such as increased accuracy and reliability, and discusses the challenges and limitations of multimodal integration. Furthermore, we explain the data acquisition approaches for each of these modalities. We also demonstrate various software programs that help in extracting, cleaning, and refining the data. We conclude this paper with a discussion on the available literature, highlighting recent advances, challenges, and future directions for each of these modalities.

Functional magnetic resonance imaging (fMRI) is a commonly used technique to measure neural activation. Its application has been particularly important in identifying underlying neurodegenerative conditions such as Parkinson’s, Alzheimer’s, and Autism. Recent analysis of fMRI data models the brain as a graph and extracts features by graph neural networks (GNNs). However, the unique characteristics of fMRI data require a special design of GNN. Tailoring GNN to generate effective and domain-explainable features remains challenging. In this paper, we propose a contrastive dual-attention block and a differentiable graph pooling method called ContrastPool to better utilize GNN for brain networks, meeting fMRI-specific requirements. We apply our method to 5 resting-state fMRI brain network datasets of 3 diseases and demonstrate its superiority over state-of-the-art baselines. Our case study confirms that the patterns extracted by our method match the domain knowledge in neuroscience literature, and disclose direct and interesting insights. Our contributions underscore the potential of ContrastPool for advancing the understanding of brain networks and neurodegenerative conditions. The source code is available at https://github.com/AngusMonroe/ContrastPool.

One of the major constituents of mitochondrial membranes is the phospholipids, which play a key role in maintaining the structure and the functions of the mitochondria. However, mitochondria do not synthesize most of the phospholipids in situ, necessitating the presence of phospholipid import pathways. Even for the phospholipids, which are synthesized within the inner mitochondrial membrane (IMM), the phospholipid precursors must be imported from outside the mitochondria. Therefore, the mitochondria heavily rely on the phospholipid transport pathways for its proper functioning. Since, mitochondria are not part of a vesicular trafficking network, the molecular mechanisms of how mitochondria receive its phospholipids remain a relevant question. One of the major ways that hydrophobic phospholipids can cross the aqueous barrier of inter or intraorganellar spaces is by apposing membranes, thereby decreasing the distance of transport, or by being sequestered by lipid transport proteins (LTPs). Therefore, with the discovery of LTPs and membrane contact sites (MCSs), we are beginning to understand the molecular mechanisms of phospholipid transport pathways in the mitochondria. In this review, we will present a brief overview of the recent findings on the molecular architecture and the importance of the MCSs, both the intraorganellar and interorganellar contact sites, in facilitating the mitochondrial phospholipid transport. In addition, we will also discuss the role of LTPs for trafficking phospholipids through the intermembrane space (IMS) of the mitochondria. Mechanistic insights into different phospholipid transport pathways of mitochondria could be exploited to vary the composition of membrane phospholipids and gain a better understanding of their precise role in membrane homeostasis and mitochondrial bioenergetics.

Traumatic brain injury (TBI) causes multiple cerebrovascular disruptions and oxidative stress. These pathological mechanisms are often accompanied by serious impairment of cerebral blood flow autoregulation and neuronal and glial degeneration. Background/Objectives: Multiple biochemical cascades are triggered by brain damage, resulting in reactive oxygen species production alongside blood loss and hypoxia. However, most currently available early antioxidant therapies lack capacity and hence sufficient efficacy against TBI. The aim of this study was to test a novel catalytic antioxidant nanoparticle to alleviate the damage occurring in blast TBI. Methods: TBI was elicited in an open blast rat model, in which the rats were exposed to the effects of an explosive blast. Key events of the post-traumatic chain in the brain parenchyma were studied using immunohistochemistry. The application of a newly developed biologically compatible catalytic superoxide dismutase mimetic carbon-based nanocluster, a poly-ethylene-glycol-functionalized hydrophilic carbon cluster (PEG-HCC), was tested post-blast to modulate the components of the TBI process. Results: The PEG-HCC was shown to significantly ameliorate neuronal loss in the brain cortex, the dentate gyrus, and hippocampus when administered shortly after the blast. There was also a significant increase in endothelial activity to repair blood-brain barrier damage as well as the modulation of microglial and astrocyte activity and an increase in inducible NO synthase in the cortex. Conclusions: We have demonstrated qualitatively and quantitatively that the previously demonstrated antioxidant properties of PEG-HCCs have a neuroprotective effect after traumatic brain injury following an explosive blast, acting at multiple levels of the pathological chain of events elicited by TBI.

Reactive oxygen species (ROS) contain at least one oxygen atom and one or more unpaired electrons and include singlet oxygen, superoxide anion radical, hydroxyl radical, hydroperoxyl radical, and free nitrogen radicals. Intracellular ROS can be formed as a consequence of several factors, including ultra-violet (UV) radiation, electron leakage during aerobic respiration, inflammatory responses mediated by macrophages, and other external stimuli or stress. The enhanced production of ROS is termed oxidative stress and this leads to cellular damage, such as protein carbonylation, lipid peroxidation, deoxyribonucleic acid (DNA) damage, and base modifications. This damage may manifest in various pathological states, including ageing, cancer, neurological diseases, and metabolic disorders like diabetes. On the other hand, the optimum levels of ROS have been implicated in the regulation of many important physiological processes. For example, the ROS generated in the mitochondria (mitochondrial ROS or mt-ROS), as a byproduct of the electron transport chain (ETC), participate in a plethora of physiological functions, which include ageing, cell growth, cell proliferation, and immune response and regulation. In this current review, we will focus on the mechanisms by which mt-ROS regulate different pathways of host immune responses in the context of infection by bacteria, protozoan parasites, viruses, and fungi. We will also discuss how these pathogens, in turn, modulate mt-ROS to evade host immunity. We will conclude by briefly giving an overview of the potential therapeutic approaches involving mt-ROS in infectious diseases.

Humans can direct attentional resources to a single sound occurring simultaneously among others to extract the most behaviourally relevant information present. To investigate this cognitive phenomenon in a precise manner, we used frequency-tagging to separate neural auditory steady-state responses (ASSRs) that can be traced back to each auditory stimulus, from the neural mix elicited by multiple simultaneous sounds. Using a mixture of 2 frequency-tagged melody streams, we instructed participants to selectively attend to one stream or the other while following the development of the pitch contour. Bottom-up attention towards either stream was also manipulated with salient changes in pitch. Distributed source analyses of magnetoencephalography measurements showed that the effect of ASSR enhancement from top-down driven attention was strongest at the left frontal cortex, while that of bottom-up driven attention was dominant at the right temporal cortex. Furthermore, the degree of ASSR suppression from simultaneous stimuli varied across cortical lobes and hemisphere. The ASSR source distribution changes from temporal-dominance during single-stream perception, to proportionally more activity in the frontal and centro-parietal cortical regions when listening to simultaneous streams. These findings are a step forward to studying cognition in more complex and naturalistic soundscapes using frequency-tagging.

The important roles of bacterial outer membrane vesicles (OMVs) in various diseases and their emergence as a promising platform for vaccine development and targeted drug delivery necessitates the development of imaging techniques suitable for quantifying their biodistribution with high precision. To address this requirement, we aimed to develop an OMV specific radiolabeling technique for positron emission tomography (PET). A novel bacterial strain (E. coli BL21(DE3) ΔnlpI, ΔlpxM) was created for efficient OMV production, and OMVs were characterized using various methods. SpyCatcher was anchored to the OMV outer membrane using autotransporter-based surface display systems. Synthetic SpyTag-NODAGA conjugates were tested for OMV surface binding and 64Cu labeling efficiency. The final labeling protocol shows a radiochemical purity of 100% with a ~ 29% radiolabeling efficiency and excellent serum stability. The in vivo biodistribution of OMVs labeled with 64Cu was determined in mice using PET/MRI imaging which revealed that the biodistribution of radiolabeled OMVs in mice is characteristic of previously reported data with the highest organ uptakes corresponding to the liver and spleen 3, 6, and 12 h following intravenous administration. This novel method can serve as a basis for a general OMV radiolabeling scheme and could be used in vaccine- and drug-carrier development based on bioengineered OMVs.

The oxysterol 27-hydroxycholesterol (27OHC) is produced by the enzyme sterol 27-hydroxylase (Cyp27A1) and is mainly catabolized to 7α-Hydroxy-3-oxo-4-cholestenoic acid (7-HOCA) by the enzyme cytochrome P-450 oxysterol 7α-hydroxylase (Cyp7B1). 27OHC is mostly produced in the liver and can reach the brain by crossing the blood-brain barrier. A large body of evidence shows that CYP27A1 overexpression and high levels of 27OHC have a detrimental effect on the brain, causing cognitive and synaptic dysfunction together with a decrease in glucose uptake in mice. In this work, we analyzed two mouse models with high levels of 27OHC: Cyp7B1 knock-out mice and CYP27A1 overexpressing mice. Despite the accumulation of 27OHC in both models, Cyp7B1 knock-out mice maintained intact learning and memory capacities, neuronal morphology, and brain glucose uptake over time. Neurons treated with the Cyp7B1 metabolite 7-HOCA did not show changes in synaptic genes and 27OHC-treated Cyp7B1 knock-out neurons could not counteract 27OHC detrimental effects. This suggests that 7-HOCA and Cyp7B1 deletion in neurons do not mediate the neuroprotective effects observed in Cyp7B1 knock-out animals. RNA-seq of neuronal nuclei sorted from Cyp7B1 knock-out brains revealed upregulation of genes likely to confer neuroprotection to these animals. Differently from Cyp7B1 knock-out mice, transcriptomic data from CYP27A1 overexpressing neurons showed significant downregulation of genes associated with synaptic function and several metabolic processes. Our results suggest that the differences observed in the two models may be mediated by the higher levels of Cyp7B1 substrates such as 25-hydroxycholesterol and 3β-Adiol in the knock-out mice and that CYP27A1 overexpressing mice may be a more suitable model for studying 27-OHC-specific signaling. We believe that future studies on Cyp7B1 and Cyp27A1 will contribute to a better understanding of the pathogenic mechanisms of neurodegenerative diseases like Alzheimer’s disease and may lead to potential new therapeutic approaches.

Traumatic brain injury (TBI) is one of the most common causes of disability and mortality worldwide, creating a large socioeconomic burden annually. Secondary injury physiopathology is known to play a prominent role in exacerbating neurodegeneration post‐TBI and is potentially preventable by therapies. However, due to the heterogeneity of TBI and the complexity of the pathological mechanisms that ensue, there are currently no effective disease‐modifying treatments to prevent TBI‐associated disability and mortality. Nanotechnology has emerged in recent decades as a promising platform for the development of multifunctional neuroprotective agents for TBI. Herein, current multifunctional innovations are explored in this review in nanotechnology, which target the secondary injury pathological mechanisms of TBI and show promise in improving future post‐TBI management. Also, potential new directions for the future development of TBI treatment are discussed.

Surface functionalization has a prominent influence on tuning/manipulating the physicochemical properties of nanometer scaled materials. Ultrasmall sized nanoclusters with very few atoms have received enormous attention due to their bright fluorescence, biocompatibility, lower toxicity, good colloidal stability and strong photostability. These properties make them suitable for diagnostic applications. In this work, we intend to study the effect of surface functional ligands on their biodistribution both in vitro and in vivo organelle systems for bioimaging applications.

Functional metal nanomaterials, especially in the nanocluster (NC) size regime, with strong fluorescence, aqueous colloidal stability, and low toxicity, necessitate their application potential in biology and environmental science. Here, we successfully report a simple cost-effective method for red-/green-color-emitting protein/amino-acid-mediated Cu NCs in an aqueous medium. As-synthesized Cu NCs were characterized through UV-Vis absorption spectroscopy, fluorescence spectroscopy, time-resolved photoluminescence, dynamic light scattering, zeta potential, transmission electron microscopy and X-ray photoelectron spectroscopy. The optical properties of both Cu NCs responded linearly to the variation in pH in the neutral and alkaline ranges, and a robust pH reversible nature (between pH 7 and 11) was observed that could be extended to rapid, localized pH sensor development. However, a contrasting pH response nature between protein–Cu NCs and amino acid–Cu NCs was recorded. The alteration in protein secondary structure and strong binding nature of the surfactants were suggested to explain this behavior. Furthermore, we investigated their use as an efficient optical probe for fluoride ion detection. The limit of detection for protein–Cu NCs is 6.74 µM, whereas the limit of detection for amino acid–Cu NCs is 4.67 µM. Thus, it is anticipated that ultrasmall Cu NCs will exhibit promise in biological and environmental sensing applications.