In many circumstances, some crucial elements of the neuronal defense system fail, slowly leading to neurodegenerative diseases. Activating this natural process by administering exogenous agents to counteract unfavourable changes seems promising. Therefore, looking for neuroprotective therapeutics, we have to focus on compounds that inhibit the primary mechanisms leading to neuronal injuries, e.g., apoptosis, excitotoxicity, oxidative stress, and inflammation. Among many compounds considered neuroprotective agents, protein hydrolysates and peptides derived from natural materials or their synthetic analogues are good candidates. They have several advantages, such as high selectivity and biological activity, a broad range of targets, and high safety profile. This review aims to provide biological activities, the mechanism of action and the functional properties of plant-derived protein hydrolysates and peptides. We focused on their significant role in human health by affecting the nervous system and having neuroprotective and brain-boosting properties, leading to memory and cognitive improving activities. We hope our observation may guide the evaluation of novel peptides with potential neuroprotective effects. Research into neuroprotective peptides may find application in different sectors as ingredients in functional foods or pharmaceuticals to improve human health and prevent diseases.
Nicotinamide mononucleotide (NMN), a precursor of the coenzyme nicotinamide adenine dinucleotide (NAD+), has gained wide attention as an anti-aging agent, which plays a significant role in intracellular redox reactions. However, its effectiveness is limited by easy metabolism in the liver and subsequent excretion as nicotinamide, resulting in low bioavailability, particularly in the brain. Additionally, the blood-brain barrier (BBB) further hinders NMN supply to the brain, compromising its potential anti-aging effects. Herein, we developed a biocompatible polydopamine (PDA) platform to deliver NMN for boosting NAD+ levels in the brain for the first time. The lactoferrin (Lf) ligand was covalently attached to the PDA spheres to improve BBB transport efficiency. The resultant PDA-based system, referred to as PDA-Lf-NMN, not only exhibited superior BBB penetration ability but also improved the utilization rate of brain NMN in elevating NAD+ levels compared to NMN alone for both young (3 months) and old (21 months) mice. Moreover, after the old mice were treated with low-dose PDA-Lf-NMN (8 mg kg-1 day-1), they exhibited improved spatial cognition. Importantly, these nanomedicines did not induce any cellular necrosis or apoptosis. It provides a promising avenue for delivering NMN specifically to the brain, boosting NAD+ levels for promoting longevity and treating brain aging-related diseases.
Physical activity can have a profound impact on the cognitive, emotional, and social aspects of our clients' lives, and can even boost and sustain therapy outcomes. And yet, family therapists may not be trained or educated in the importance of physical activity nor how to assess, educate, and collaborate with health professionals to incorporate physical activity in treatment. As the evidence grows on the connection between physical activity and biopsychosocial functioning and more family therapists enter medical and healthcare settings, a framework for physical activity competencies is needed. As such, the purpose of this conceptual and empirical review paper is to (a) review the important brain-boosting benefits of physical activity for mental, cognitive, and social health, (b) provide a framework for the domains for family therapists for incorporating exercise in assessment and treatment, and finally, (c) outline recommendations for practice, education and supervision, and research for family therapists.
Functional foods are thought to strongly influence the structure and function of the brain. Previous studies have reported that brain-boosting diets may enhance neuroprotective functions. Certain foods are particularly rich in nutrients like phytochemicals that are known to support brain plasticity; such foods are commonly referred to as brain foods. In this review, we briefly explore the scientific evidence supporting the neuroprotective activity of a number of phytochemicals with a focus on phenols and polyunsaturated fatty acids such as flavonoid, olive oil, and omega-3 fatty acid. The aim of this study was to systematically examine the primary issues related to phytochemicals in the brain. These include (a) the brain-gut-microbiome axis; (b) the effects of phytochemicals on gut microbiome and their potential role in brain plasticity; (c) the role of polyunsaturated fatty acids in brain health; and (d) the effects of nutrition and exercise on brain function. This review provides evidence supporting the view that phytochemicals from medicinal plants play a vital role in maintaining brain plasticity by influencing the brain-gut-microbiome axis. The consumption of brain foods may have neuroprotective effects, thus protecting against neurodegenerative disorders and promoting brain health.
Neurogenesis continues throughout adulthood. The neurogenic capacity of the brain increases after injury by, e.g., hypoxia-ischemia. However, it is well known that in many cases brain damage does not resolve spontaneously, indicating that the endogenous regenerative capacity of the brain is insufficient. Neonatal encephalopathy leads to high mortality rates and long-term neurologic deficits in babies worldwide. Therefore, there is an urgent need to develop more efficient therapeutic strategies. The latest findings indicate that stem cells represent a novel therapeutic possibility to improve outcome in models of neonatal encephalopathy. Transplanted stem cells secrete factors that stimulate and maintain neurogenesis, thereby increasing cell proliferation, neuronal differentiation, and functional integration. Understanding the molecular and cellular mechanisms underlying neurogenesis after an insult is crucial for developing tools to enhance the neurogenic capacity of the brain. The aim of this review is to discuss the endogenous capacity of the neonatal brain to regenerate after a cerebral ischemic insult. We present an overview of the molecular and cellular mechanisms underlying endogenous regenerative processes during development as well as after a cerebral ischemic insult. Furthermore, we will consider the potential to use stem cell transplantation as a means to boost endogenous neurogenesis and restore brain function.
The world is currently under the threat of COVID pandemic and has focused every dimension of research in finding a cure to this novel disease. In this current situation, people are facing mental stress, agony, fear, depression and other associated symptoms which are taking a toll on their overall mental health. Nanoencapsulation of certain brain boosting polyphenols including quercetin, caffeine, cocoa flavanols and proteins like lectins can become new area of interest in the present scenario. Besides the brain boosting benefits, we have also highlighted the anti- viral activities of these compounds which we assume can play a possible role in combating COVID-19 given to their previous history of action against certain viruses. This review outlines the nanoencapsulation approaches of such synergistic compounds as a novel strategy to take the ongoing research a step ahead and also provides a new insight in bringing the role of nanotechnology in addressing the issues related to COVID pandemic.
The colliding epidemics of non-communicable diseases including diabetes with chronic infectious diseases in Sub-Saharan Africa requires contextualized innovative disease management strategies. This qualitative study conducted in a peri-urban township near Cape Town, South Africa aimed to identify and gain in-depth understanding of contextual and environmental issues pertinent to the patient that could influence Type 2-diabetes mellitus (T2DM) care and self-management. Participants included purposively sampled diabetics or pre-diabetics from the community, PURE study database, facility health club and health care providers. Data collection employed in-depth interviews, focus group discussions (FGDs) using structured interviews and FGD topic guides. Thematic data analysis was done to identify recurrent themes. Themes identified: knowledge and awareness about T2DM; health-seeking behaviour; weight perceptions; healthy lifestyles; self-management; health education needs and health care provider experiences. Patients defined T2DM as a physically and emotionally dangerous disease caused by socio-cultural factors, influenced by the sufferers' food and socio-cultural environment with significance placed on physical, social and emotional effects of T2DM diagnosis. Patient-centred definition of T2DM is key to enhancing T2DM self-management. Patients suggested that personally rewarding benefits of physical activity and healthy diet such as anti-ageing, brain boosting, energy boosting which are commonly harnessed by food, tobacco and beauty industry should be considered in T2DM self-management strategies.
Brain tumors represent one of the most critical neurological conditions, where early and accurate diagnosis is directly correlated with patient survival rates. Manual interpretation of Magnetic Resonance Imaging (MRI) scans is time-intensive, subject to inter-observer variability, and demands significant specialist expertise. This paper proposes a deep learning framework for automated four-class brain tumor classification distinguishing glioma, meningioma, pituitary tumor, and healthy brain tissue from a dataset of 7,023 MRI scans. The proposed system employs a Vision Transformer (ViT-B/16) pretrained on ImageNet-21k as the backbone, augmented with a clinically motivated preprocessing and training pipeline. Contrast Limited Adaptive Histogram Equalization (CLAHE) is applied to enhance local contrast and accentuate tumor boundaries invisible to standard normalization. A two-stage fine-tuning strategy is adopted: the classification head is warmed up with the backbone frozen, followed by full fine-tuning with discriminative learning rates. MixUp and CutMix augmentation is applied per batch to improve generalization. Exponential Moving Average (EMA) of weights and Test-Time Augmentatio
Brain decoding is limited by the availability of labeled neural data, and remains challenging in low-data regimes. To address this issue, we investigate whether and when brain decoding can be boosted by augmenting small fMRI datasets with synthetic data generated by a pretrained model of fMRI responses to stimuli. We use TRIBE v2, a large encoding model pretrained on more than 1000 hours of fMRI responses to video, audio and language. For each dataset, we evaluate systematic grids that show how the performance of image decoders varies with the amount of synthetic data used for training. Our results, based on two datasets (the 7T fMRI Natural Scenes Dataset and 3T fMRI BOLD5000), show up to 68% improvement in Top-10 image-retrieval accuracy compared to decoders trained only on real data. Importantly, the proportion of augmented data required to reach a given image decoding performance needs to be adjusted depending on the data source. Surprisingly, image decoders trained exclusively on synthetic fMRI can perform above chance in some settings, suggesting that TRIBE v2 can support zero-shot brain-to-image decoding. Together, these results show how large-scale models of the fMRI respon
This study proposes an efficient Densely Swin Hybrid (EDSH) framework for brain tumor MRI analysis, designed to jointly capture fine grained texture patterns and long range contextual dependencies. Two tumor aware experimental setups are introduced to address class-specific diagnostic challenges. The first setup employs a Boosted Feature Space (BFS), where independently customized DenseNet and Swint branches learn complementary local and global representations that are dimension aligned, fused, and boosted, enabling highly sensitive detection of diffuse glioma patterns by successfully learning the features of irregular shape, poorly defined mass, and heterogeneous texture. The second setup adopts a hierarchical DenseNet Swint architecture with Deep Feature Extraction have Dual Residual connections (DFE and DR), in which DenseNet serves as a stem CNN for structured local feature learning, while Swin_t models global tumor morphology, effectively suppressing false negatives in meningioma and pituitary tumor classification by learning the features of well defined mass, location (outside brain) and enlargments in tumors (dural tail or upward extension). DenseNet is customized at the inp
Brain--computer interfaces are groundbreaking technology whereby brain signals are used to control external devices. Despite some advances in recent years, electroencephalogram (EEG)-based motor-imagery tasks face challenges, such as amplitude and phase variability and complex spatial correlations, with a need for smaller models and faster inference. In this study, we develop a prototype, called the Lightweight Geometric Learning Brain--Computer Interface (LGL-BCI), which uses our customized geometric deep learning architecture for swift model inference without sacrificing accuracy. LGL-BCI contains an EEG channel selection module via a feature decomposition algorithm to reduce the dimensionality of a symmetric positive definite matrix, providing adaptiveness among the continuously changing EEG signal. Meanwhile, a built-in lossless transformation helps boost the inference speed. The performance of our solution was evaluated using two real-world EEG devices and two public EEG datasets. LGL-BCI demonstrated significant improvements, achieving an accuracy of 82.54% compared to 62.22% for the state-of-the-art approach. Furthermore, LGL-BCI uses fewer parameters (64.9K vs. 183.7K), hig
In recent years, Electroencephalographic analysis has gained prominence in stress research when combined with AI and Machine Learning models for validation. In this study, a lightweight dynamic brain connectivity framework based on Time Varying Directed Transfer Function is proposed, where TV DTF features were validated through ML based stress classification. TV DTF estimates the directional information flow between brain regions across distinct EEG frequency bands, thereby capturing temporal and causal influences that are often overlooked by static functional connectivity measures. EEG recordings from the 32 channel SAM 40 dataset were employed, focusing on mental arithmetic task trials. The dynamic EEG-based TV-DTF features were validated through ML classifiers such as Support Vector Machine, Random Forest, Gradient Boosting, Adaptive Boosting, and Extreme Gradient Boosting. Experimental results show that alpha-TV-DTF provided the strongest discriminative power, with SVM achieving 89.73% accuracy in 3-class classification and with XGBoost achieving 93.69% accuracy in 2 class classification. Relative to absolute power and phase locking based functional connectivity features, alpha
We present Hierarchical Residual Networks (HiResNets), deep convolutional neural networks with long-range residual connections between layers at different hierarchical levels. HiResNets draw inspiration on the organization of the mammalian brain by replicating the direct connections from subcortical areas to the entire cortical hierarchy. We show that the inclusion of hierarchical residuals in several architectures, including ResNets, results in a boost in accuracy and faster learning. A detailed analysis of our models reveals that they perform hierarchical compositionality by learning feature maps relative to the compressed representations provided by the skip connections.
Brain-computer interfaces (BCIs) with speech decoding from brain recordings have broad application potential in fields such as clinical rehabilitation and cognitive neuroscience. However, current decoding methods remain limited to single-language, single-subject, and single neuroimaging modality settings, restricting their clinical applicability and generalizability. Here we propose a joint multilingual, multi-subject and multimodal decoding framework. It maps diverse brain recordings into a unified semantic space defined by a pre-trained multilingual model (PMM), enabling decoding across multiple languages, multiple subjects and multiple neuroimaging modalities. The proposed framework is validated using non-invasive brain recordings from 159 participants across four languages. Experimental results show that it exhibits strong generalization across multilingual, multi-subject, and multimodal settings. More importantly, the proposed framework can promote linguistic fairness, which is vital for underrepresented languages in BCI applications. The unified semantic space enables cross-lingual mapping enhancement, allowing the framework to boost the decoding performance of underrepresent
Predicting disease states from functional brain connectivity is critical for the early diagnosis of severe neurodegenerative diseases such as Alzheimer's Disease and Parkinson's Disease. Existing studies commonly employ Graph Neural Networks (GNNs) to infer clinical diagnoses from node-based brain connectivity matrices generated through node-to-node similarities of regionally averaged fMRI signals. However, recent neuroscience studies found that such node-based connectivity does not accurately capture ``functional connections" within the brain. This paper proposes a novel approach to brain network analysis that emphasizes edge functional connectivity (eFC), shifting the focus to inter-edge relationships. Additionally, we introduce a co-embedding technique to integrate edge functional connections effectively. Experimental results on the ADNI and PPMI datasets demonstrate that our method significantly outperforms state-of-the-art GNN methods in classifying functional brain networks.
The brain's Path Integration (PI) mechanism offers substantial guidance and inspiration for Brain-Inspired Navigation (BIN). However, the PI capability constructed by the Continuous Attractor Neural Networks (CANNs) in most existing BIN studies exhibits significant computational redundancy, and its operational efficiency needs to be improved; otherwise, it will not be conducive to the practicality of BIN technology. To address this, this paper proposes an efficient PI approach using representation learning models to replicate CANN neurodynamic patterns. This method successfully replicates the neurodynamic patterns of CANN-modeled Head Direction Cells (HDCs) and Grid Cells (GCs) using lightweight Artificial Neural Networks (ANNs). These ANN-reconstructed HDC and GC models are then integrated to achieve brain-inspired PI for Dead Reckoning (DR). Benchmark tests in various environments, compared with the well-known NeuroSLAM system, demonstrate that this work not only accurately replicates the neurodynamic patterns of navigation cells but also matches NeuroSLAM in positioning accuracy. Moreover, efficiency improvements of approximately 17.5% on the general-purpose device and 40~50% on