A Comprehensive Survey on the Detection, Classification, and Challenges of Neurological Disorders

From Skin and Gut to the Brain: The Infectious Journey of the Human Commensal Fungus Malassezia and Its Neurological Consequences

The human mycobiome encompasses diverse communities of fungal organisms residing within the body and has emerged as a critical player in shaping health and disease. While extensive research has focused on the skin and gut mycobiome, recent investigations have pointed toward the potential role of fungal organisms in neurological disorders. Among those fungal organisms, the presence of the commensal fungus Malassezia in the brain has created curiosity because of its commensal nature and primary association with the human skin and gut. This budding yeast is responsible for several diseases, such as Seborrheic dermatitis, Atopic dermatitis, Pityriasis versicolor, Malassezia folliculitis, dandruff, and others. However recent findings surprisingly show the presence of Malassezia DNA in the brain and have been linked to diseases like Alzheimer's disease, Parkinson's disease, Multiple sclerosis, and Amyotrophic lateral sclerosis. The exact role of Malassezia in these disorders is unknown, but its ability to infect human cells, travel through the bloodstream, cross the blood-brain barrier, and reside along with the lipid-rich neuronal cells are potential mechanisms responsible for pathogenesis. This also includes the induction of pro-inflammatory cytokines, disruption of the blood-brain barrier, gut-microbe interaction, and accumulation of metabolic changes in the brain environment. In this review, we discuss these key findings from studies linking Malassezia to neurological disorders, emphasizing the complex and multifaceted nature of these cases. Furthermore, we discuss potential mechanisms through which Malassezia might contribute to the development of neurological conditions. Future investigations will open up new avenues for our understanding of the fungal gut-brain axis and how it influences human behavior. Collaborative research efforts among microbiologists, neuroscientists, immunologists, and clinicians hold promise for unraveling the enigmatic connections between human commensal Malassezia and neurological disorders.

Unsupervised model for structure segmentation applied to brain computed tomography

This article presents an unsupervised method for segmenting brain computed tomography scans. The proposed methodology involves image feature extraction and application of similarity and continuity constraints to generate segmentation maps of the anatomical head structures. Specifically designed for real-world datasets, this approach applies a spatial continuity scoring function tailored to the desired number of structures. The primary objective is to assist medical experts in diagnosis by identifying regions with specific abnormalities. Results indicate a simplified and accessible solution, reducing computational effort, training time, and financial costs. Moreover, the method presents potential for expediting the interpretation of abnormal scans, thereby impacting clinical practice. This proposed approach might serve as a practical tool for segmenting brain computed tomography scans, and make a significant contribution to the analysis of medical images in both research and clinical settings.

Role of autophagy and proteostasis in neurodegenerative diseases: Exploring the therapeutic interventions

Neurodegenerative disorders are devastating disorders characterized by gradual loss of neurons and cognition or mobility impairment. The common pathological features of these diseases are associated with the accumulation of misfolded or aggregation of proteins. The pivotal roles of autophagy and proteostasis in maintaining cellular health and preventing the accumulation of misfolded proteins, which are associated with neurodegenerative diseases like Huntington's disease (HD), Alzheimer's disease (AD), and Parkinson's disease (PD). This article presents an in-depth examination of the interplay between autophagy and proteostasis, highlighting how these processes cooperatively contribute to cellular homeostasis and prevent pathogenic protein aggregate accumulation. Furthermore, the review emphasises the potential therapeutic implications of targeting autophagy and proteostasis to mitigate neurodegenerative diseases. While advancements in research hold promise for developing novel treatments, the article also addresses the challenges and complexities associated with modulating these intricate cellular pathways. Ultimately, advancing understanding of the underlying mechanism of autophagy and proteostasis in neurodegenerative disorders provides valuable insights into potential therapeutic avenues and future research directions.

Brain Organoids: A Game-Changer for Drug Testing

Neurological disorders are the second cause of death and the leading cause of disability worldwide. Unfortunately, no cure exists for these disorders, but the actual therapies are only able to ameliorate people's quality of life. Thus, there is an urgent need to test potential therapeutic approaches. Brain organoids are a possible valuable tool in the study of the brain, due to their ability to reproduce different brain regions and maturation stages; they can be used also as a tool for disease modelling and target identification of neurological disorders. Recently, brain organoids have been used in drug-screening processes, even if there are several limitations to overcome. This review focuses on the description of brain organoid development and drug-screening processes, discussing the advantages, challenges, and limitations of the use of organoids in modeling neurological diseases. We also highlighted the potential of testing novel therapeutic approaches. Finally, we examine the challenges and future directions to improve the drug-screening process.

Relationship of retinal capillary plexus and ganglion cell complex with mild cognitive impairment and dementia

OBJECTIVE

To investigate relationship of the retinal capillary plexus (RCP) and ganglion cell complex (GCC) with mild cognitive impairment (MCI) and dementia in a community-based study1.

METHODS

This cross-sectional study incorporated the participants of the Jidong Eye Cohort Study. Optical coherence tomography angiography was performed to obtain RCP vessel density and GCC thickness with detailed segments. The Mini-mental State Examination and Montreal Cognitive Assessment were used to assess cognitive status by professional neuropsychologists. Participants were thus divided into three groups: normal, mild cognitive impairment, and dementia. Multivariable analysis was used to measure relationship of ocular parameters with cognitive impairment.

RESULTS

Of the 2678 participants, the mean age was 44.1 ± 11.7 years. MCI and dementia occurred in 197 (7.4%) and 80 (3%) participants, respectively. Compared to the normal group, the adjusted odds ratio (OR) with the 95% confidence interval was 0.76 (0.65-0.90) for the correlation of lower deep RCP with MCI. We found the following items significantly associated with dementia compared with the normal group: a superficial (OR, 0.68 [0.54-0.86]) and deep (OR, 0.75 [0.57-0.99]) RCP, as well as the GCC (OR, 0.68 [0.54-0.85]). Compared to the MCI group, those with dementia had decreased GCC (OR, 0.75 [0.58-0.97]).

CONCLUSIONS

Decreased deep RCP density was associated with MCI. Decreased superficial and deep RCP and the thin GCC were correlated with dementia. These implied that the retinal microvasculature may develop into a promising non-invasive imaging marker to predict severity of cognitive impairment.

Voice pathology detection using optimized convolutional neural networks and explainable artificial intelligence-based analysis

This article proposes a noninvasive computer-aided assessment approach based on optimized convolutional neural network for healthy and pathological voice detection. Firstly, the input voice samples are first transformed into mel-spectrogram time-frequency visual representations and fed for training the CNN model. The time-frequency image captures inherent speech variations beneficial for healthy and pathological voice sample detection. The weights and biases of trained CNN network are further optimized using artificial bee colony (ABC) optimization algorithm resulting in optimum CNN network employed for testing unseen data. The proposed approach is evaluated using three popular and publicly available datasets: SVD, AVPD and VOICED. Experimental results emphasize that proposed ABC optimized CNN model shows improved accuracy performance by 1.02% compared to conventional CNN network illustrating data-independent discriminative representation ability. Finally, gradient-weighted class activation mapping (Grad-CAM) explainable artificial intelligence (XAI) is utilized to make the decision understandable.

Brain tumor detection and screening using artificial intelligence techniques: Current trends and future perspectives

A brain tumor is an abnormal mass of tissue located inside the skull. In addition to putting pressure on the healthy parts of the brain, it can lead to significant health problems. Depending on the region of the brain tumor, it can cause a wide range of health issues. As malignant brain tumors grow rapidly, the mortality rate of individuals with this cancer can increase substantially with each passing week. Hence it is vital to detect these tumors early so that preventive measures can be taken at the initial stages. Computer-aided diagnostic (CAD) systems, in coordination with artificial intelligence (AI) techniques, have a vital role in the early detection of this disorder. In this review, we studied 124 research articles published from 2000 to 2022. Here, the challenges faced by CAD systems based on different modalities are highlighted along with the current requirements of this domain and future prospects in this area of research.

Modulating cyclic nucleotides pathways by bioactive compounds in combatting anxiety and depression disorders

Anxiety and depression disorders are highly prevalent neurological disorders (NDs) that impact up to one in three individuals during their lifetime. Addressing these disorders requires reducing their frequency and impact, understanding molecular causes, implementing prevention strategies, and improving treatments. Cyclic nucleotide monophosphates (cNMPs) like cyclic adenosine monophosphate (cAMP), cyclic guanosine monophosphate (cGMP), cyclic uridine monophosphate (cUMP), and cyclic cytidine monophosphate (cCMP) regulate the transcription of genes involved in neurotransmitters and neurological functions. Evidence suggests that cNMP pathways, including cAMP/cGMP, cAMP response element binding protein (CREB), and Protein kinase A (PKA), play a role in the physiopathology of anxiety and depression disorders. Plant and mushroom-based compounds have been used in traditional and modern medicine due to their beneficial properties. Bioactive compound metabolism can activate key pathways and yield pharmacological outcomes. This review focuses on the molecular mechanisms of bioactive compounds from plants and mushrooms in modulating cNMP pathways. Understanding these processes will support current treatments and aid in the development of novel approaches to reduce the prevalence of anxiety and depression disorders, contributing to improved outcomes and the prevention of associated complications.

Interpretable Classification of Tauopathies with a Convolutional Neural Network Pipeline Using Transfer Learning and Validation against Post-Mortem Clinical Cases of Alzheimer's Disease and Progressive Supranuclear Palsy

Neurodegenerative diseases, tauopathies, constitute a serious global health problem. The etiology of these diseases is unclear and an increase in their incidence has been projected in the next 30 years. Therefore, the study of the molecular mechanisms that might stop these neurodegenerative processes is very relevant. Classification of neurodegenerative diseases using Machine and Deep Learning algorithms has been widely studied for medical imaging such as Magnetic Resonance Imaging. However, post-mortem immunofluorescence imaging studies of the brains of patients have not yet been used for this purpose. These studies may represent a valuable tool for monitoring aberrant chemical changes or pathological post-translational modifications of the Tau polypeptide. We propose a Convolutional Neural Network pipeline for the classification of Tau pathology of Alzheimer's disease and Progressive Supranuclear Palsy by analyzing post-mortem immunofluorescence images with different Tau biomarkers performed with models generated with the architecture ResNet-IFT using Transfer Learning. These models' outputs were interpreted with interpretability algorithms such as Guided Grad-CAM and Occlusion Analysis. To determine the best classifier, four different architectures were tested. We demonstrated that our design was able to classify diseases with an accuracy of 98.41% on average whilst providing an interpretation concerning the proper classification involving different structural patterns in the immunoreactivity of the Tau protein in NFTs present in the brains of patients with Progressive Supranuclear Palsy and Alzheimer's disease.

Multiple Sclerosis Diagnosis Using Machine Learning and Deep Learning: Challenges and Opportunities

Multiple Sclerosis (MS) is a disease that impacts the central nervous system (CNS), which can lead to brain, spinal cord, and optic nerve problems. A total of 2.8 million are estimated to suffer from MS. Globally, a new case of MS is reported every five minutes. In this review, we discuss the proposed approaches to diagnosing MS using machine learning (ML) published between 2011 and 2022. Numerous models have been developed using different types of data, including magnetic resonance imaging (MRI) and clinical data. We identified the methods that achieved the best results in diagnosing MS. The most implemented approaches are SVM, RF, and CNN. Moreover, we discussed the challenges and opportunities in MS diagnosis to improve AI systems to enable researchers and practitioners to enhance their approaches and improve the automated diagnosis of MS. The challenges faced by automated MS diagnosis include difficulty distinguishing the disease from other diseases showing similar symptoms, protecting the confidentiality of the patients' data, achieving reliable ML models that are also easily understood by non-experts, and the difficulty of collecting a large reliable dataset. Moreover, we discussed several opportunities in the field such as the implementation of secure platforms, employing better AI solutions, developing better disease prognosis systems, combining more than one data type for better MS prediction and using OCT data for diagnosis, utilizing larger, multi-center datasets to improve the reliability of the developed models, and commercialization.

Modulating brain integrative actions as a new perspective on pharmacological approaches to neuropsychiatric diseases

A critical aspect of drug development in the therapy of neuropsychiatric diseases is the "Target Problem", that is, the selection of a proper target after not simply the etiopathological classification but rather the detection of the supposed structural and/or functional alterations in the brain networks. There are novel ways of approaching the development of drugs capable of overcoming or at least reducing the deficits without triggering deleterious side effects. For this purpose, a model of brain network organization is needed, and the main aspects of its integrative actions must also be established. Thus, to this aim we here propose an updated model of the brain as a hyper-network in which i) the penta-partite synapses are suggested as key nodes of the brain hyper-network and ii) interacting cell surface receptors appear as both decoders of signals arriving to the network and targets of central nervous system diseases. The integrative actions of the brain networks follow the "Russian Doll organization" including the micro (i.e., synaptic) and nano (i.e., molecular) levels. In this scenario, integrative actions result primarily from protein-protein interactions. Importantly, the macromolecular complexes arising from these interactions often have novel structural binding sites of allosteric nature. Taking G protein-coupled receptors (GPCRs) as potential targets, GPCRs heteromers offer a way to increase the selectivity of pharmacological treatments if proper allosteric drugs are designed. This assumption is founded on the possible selectivity of allosteric interventions on G protein-coupled receptors especially when organized as "Receptor Mosaics" at penta-partite synapse level.