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Alzheimer’s disease (AD) is a biological, clinical continuum that covers the preclinical, prodromal, and clinical phases of the disease. Early diagnosis and identification of the stages of Alzheimer’s disease (AD) are crucial in clinical practice. Ideally, biomarkers should reflect the underlying process (pathological or otherwise), be reproducible and non-invasive, and allow repeated measurements over time. However, the currently known biomarkers for AD are not suitable for differentiating the stages and predicting the trajectory of disease progression. Some objective parameters extracted using electroencephalography (EEG) and functional magnetic resonance imaging (fMRI) are widely applied to diagnose the stages of the AD continuum. While electroencephalography (EEG) has a high temporal resolution, fMRI has a high spatial resolution. Combined EEG and fMRI (EEG–fMRI) can overcome single-modality drawbacks and obtain multi-dimensional information simultaneously, and it can help explore the hemodynamic changes associated with the neural oscillations that occur during information processing. This technique has been used in the cognitive field in recent years. This review focuses on the different techniques available for studying the AD continuum, including EEG and fMRI in single-modality and multi-modality settings, and the possible future directions of AD diagnosis using EEG–fMRI.

Fibril formation by amyloidogenic proteins and peptides is considered the cause of a number of incurable diseases. One of the most known amyloid diseases is Alzheimer’s disease (AD). Traditionally, amyloidogenic beta peptides Aβ40 and Aβ42 (Aβs) are considered as main causes of AD and the foremost targets in AD fight. The main efforts in pharmacology are aimed at reducing Aβs concentration to prevent their accumulation, aggregation, formation of senile plaques, neuronal death, and neurodegeneration. However, a number of publications have demonstrated certain beneficial physiological effects of Aβs. Simultaneously, it is indicated that the effects of Aβs turn into pathological due to the development of certain diseases in the body. The accumulation of C- and N-terminal truncated Aβs under diverse conditions is supposed to play a role in AD development. The significance of transformation of glutamate residue at positions 3 or 11 of Aβs catalyzed by glutaminyl cyclase making them more degradation resistant, hydrophobic, and prone to aggregation, as well as the participation of dipeptidyl peptidase IV in these transformations are discussed. The experimental data presented confirm the maintenance of physiological, nonaggregated state of Aβs by plant preparations. In conclusion, this review suggests that in the fight against AD, instead of removing Aβs, preference should be given to the treatment of common diseases. Glutaminyl cyclase and dipeptidyl peptidase IV can be considered as targets in AD treatment. Flavonoids and plant preparations that possess antiamyloidogenic propensity are proposed as beneficial neuroprotective, anticancer, and antidiabetic food additives.

Artificial intelligence (AI) is increasingly being used in the medical field, specifically for brain cancer imaging. In this review, we explore how AI-powered medical imaging can impact the diagnosis, prognosis, and treatment of brain cancer. We discuss various AI techniques, including deep learning and causality learning, and their relevance. Additionally, we examine current applications that provide practical solutions for detecting, classifying, segmenting, and registering brain tumors. Although challenges such as data quality, availability, interpretability, transparency, and ethics persist, we emphasise the enormous potential of intelligent applications in standardising procedures and enhancing personalised treatment, leading to improved patient outcomes. Innovative AI solutions have the power to revolutionise neuro-oncology by enhancing the quality of routine clinical practice.

Functional near-infrared spectroscopy (fNIRS) and its interaction with machine learning (ML) is a popular research topic for the diagnostic classification of clinical disorders due to the lack of robust and objective biomarkers. This review provides an overview of research on psychiatric diseases by using fNIRS and ML. Article search was carried out and 45 studies were evaluated by considering their sample sizes, used features, ML methodology, and reported accuracy. To our best knowledge, this is the first review that reports diagnostic ML applications using fNIRS. We found that there has been an increasing trend to perform ML applications on fNIRS-based biomarker research since 2010. The most studied populations are schizophrenia ( n  = 12), attention deficit and hyperactivity disorder ( n  = 7), and autism spectrum disorder ( n  = 6) are the most studied populations. There is a significant negative correlation between sample size (>21) and accuracy values. Support vector machine (SVM) and deep learning (DL) approaches were the most popular classifier approaches (SVM = 20) (DL = 10). Eight of these studies recruited a number of participants more than 100 for classification. Concentration changes in oxy-hemoglobin (ΔHbO) based features were used more than concentration changes in deoxy-hemoglobin (ΔHb) based ones and the most popular ΔHbO-based features were mean ΔHbO ( n  = 11) and ΔHbO-based functional connections ( n  = 11). Using ML on fNIRS data might be a promising approach to reveal specific biomarkers for diagnostic classification.

Giant intracranial aneurysms represent a very challenging aspect of aneurysmal pathophysiology with very high mortality and morbidity if left untreated. Their variety in clinical presentation (subarachnoid hemorrhage, cranial nerve palsy, etc.) and pathological and imaging properties (location, anatomy, presence of collateral circulation) pose serious questions regarding the best treatment option. Admirable advances have been achieved in surgical techniques, while endovascular modalities with flow diversion techniques have become widely used. However, there is still lack of data regarding whether a single endovascular technique can be the universal treatment for such cases. In this review, we aim to summarize the current funds of knowledge concerning giant intracranial aneurysms and the role of endovascular management in their treatment.

Cerebral vasospasm and delayed cerebral ischemia represent a very challenging aspect of cerebrovascular pathophysiology, most commonly subarachnoid hemorrhage, with significantly high mortality if left untreated. Considerable advances have been made in medical treatment and prompt diagnosis, while newer endovascular modalities have recently been proposed for cases of resistant cerebral vasospasm. However, there is still paucity of data regarding which and whether a single endovascular technique is non inferior to the pharmacological standard of care. In this review, we aim to summarize the current funds of knowledge concerning cerebral vasospasm and the emerging role of the endovascular techniques for its treatment.

Childhood epilepsy affects up to 1 % of children. It has been shown that 30 % of patients are resistant to drug treatments, making further investigation of other potential treatment strategies necessary. One such approach is the ketogenic diet (KD) showing promising results and potential benefits beyond the use of current antiepileptic drugs. This study aims to investigate the effects of KD on inflammation and oxidative stress, as one of the main suggested mechanisms of neuroprotection, in children with epilepsy. This narrative review was conducted using the Medline and Google Scholar databases, and by searching epilepsy, drug-resistant epilepsy, child, children, ketogenic, ketogenic diet, diet, ketogenic, keto, ketone bodies (BHB), PUFA, gut microbiota, inflammation, inflammation mediators, neurogenic inflammation, neuroinflammation, inflammatory marker, adenosine modulation, mitochondrial function, MTOR pathway, Nrf2 pathway, mitochondrial dysfunction, PPARɣ, oxidative stress, ROS/RNS, and stress oxidative as keywords. Compelling evidence underscores inflammation and oxidative stress as pivotal factors in epilepsy, even in cases with genetic origins. The ketogenic diet effectively addresses these factors by reducing ROS and RNS, enhancing antioxidant defenses, improving mitochondrial function, and regulating inflammatory genes. Additionally, KD curbs pro-inflammatory cytokine and chemokine production by dampening NF-κB activation, inhibiting the NLRP3 inflammasome, increasing brain adenosine levels, mTOR pathway inhibition, upregulating PPARɣ expression, and promoting a healthy gut microbiota while emphasizing the consumption of healthy fats. KD could be considered a promising therapeutic intervention in patients with epilepsy particularly in drug-resistant epilepsy cases, due to its targeted approach addressing oxidative stress and inflammatory mechanisms.