Predicting Clinical Dementia Rating Using Blood RNA Levels

Proteomic Analysis to Identify Prospective Biomarkers of Treatment Outcome After Microvascular Decompression for Trigeminal Neuralgia: A Preliminary Study

Trigeminal neuralgia (TN) is a severe neuropathic facial pain disorder, often caused by vascular or neuronal compression of the trigeminal nerve. In such cases, microvascular decompression (MVD) surgery can be used to treat TN, but pain relief is not guaranteed. The molecular mechanisms that affect treatment response to MVD are not well understood. In this exploratory study, we performed label-free quantitative proteomic profiling of plasma and cerebrospinal fluid samples from patients undergoing MVD for TN, then compared the proteomic profiles of patients graded as responders (n = 7) versus non-responders (n = 9). We quantified 1,090 proteins in plasma and 1,087 proteins in the cerebrospinal fluid, of which 12 were differentially regulated in the same direction in both sample types. Functional analyses of differentially regulated proteins in protein-protein interaction networks suggested pathways of the immune system, axon guidance, and cellular stress response to be associated with response to MVD. These findings suggest potential biomarkers of response to MVD, as well as possible mechanisms of variable treatment success in TN patients. PERSPECTIVE: This exploratory study evaluates proteomic profiles in plasma and cerebrospinal fluid of patients undergoing microvascular decompression surgery for trigeminal neuralgia. Differential expression of proteins between surgery responders versus non-responders may serve as biomarkers to predict surgical success and provide insight into surgical mechanisms of pain relief in trigeminal neuralgia.

Systematic review: fluid biomarkers and machine learning methods to improve the diagnosis from mild cognitive impairment to Alzheimer's disease

Mild cognitive impairment (MCI) is often considered an early stage of dementia, with estimated rates of progression to dementia up to 80-90% after approximately 6 years from the initial diagnosis. Diagnosis of cognitive impairment in dementia is typically based on clinical evaluation, neuropsychological assessments, cerebrospinal fluid (CSF) biomarkers, and neuroimaging. The main goal of diagnosing MCI is to determine its cause, particularly whether it is due to Alzheimer's disease (AD). However, only a limited percentage of the population has access to etiological confirmation, which has led to the emergence of peripheral fluid biomarkers as a diagnostic tool for dementias, including MCI due to AD. Recent advances in biofluid assays have enabled the use of sophisticated statistical models and multimodal machine learning (ML) algorithms for the diagnosis of MCI based on fluid biomarkers from CSF, peripheral blood, and saliva, among others. This approach has shown promise for identifying specific causes of MCI, including AD. After a PRISMA analysis, 29 articles revealed a trend towards using multimodal algorithms that incorporate additional biomarkers such as neuroimaging, neuropsychological tests, and genetic information. Particularly, neuroimaging is commonly used in conjunction with fluid biomarkers for both cross-sectional and longitudinal studies. Our systematic review suggests that cost-effective longitudinal multimodal monitoring data, representative of diverse cultural populations and utilizing white-box ML algorithms, could be a valuable contribution to the development of diagnostic models for AD due to MCI. Clinical assessment and biomarkers, together with ML techniques, could prove pivotal in improving diagnostic tools for MCI due to AD.

Augmenting Imaging Biomarker Performance with Blood-Based Gene Expression Levels for Predicting Alzheimer’s Disease Progression

Background: Structural brain imaging metrics and gene expression biomarkers have previously been used for Alzheimer’s disease (AD) diagnosis and prognosis, but none of these studies explored integration of imaging and gene expression biomarkers for predicting mild cognitive impairment (MCI)-to-AD conversion 1-2 years into the future. Objective: We investigated advantages of combining gene expression and structural brain imaging features for predicting MCI-to-AD conversion. Selection of the differentially expressed genes (DEGs) for classifying cognitively normal (CN) controls and AD patients was benchmarked against previously reported results. Methods: The current work proposes integrating brain imaging and blood gene expression data from two public datasets (ADNI and ANM) to predict MCI-to-AD conversion. A novel pipeline for combining gene expression data from multiple platforms is proposed and evaluated in the two independents patient cohorts. Results: Combining DEGs and imaging biomarkers for predicting MCI-to-AD conversion yielded 0.832-0.876 receiver operating characteristic (ROC) area under the curve (AUC), which exceeded the 0.808-0.840 AUC from using the imaging features alone. With using only three DEGs, the CN versus AD predictive model achieved 0.718, 0.858, and 0.873 cross-validation AUC for the ADNI, ANM1, and ANM2 datasets. Conclusion: For the first time we show that combining gene expression and imaging biomarkers yields better predictive performance than using imaging metrics alone. A novel pipeline for combining gene expression data from multiple platforms is proposed and evaluated to produce consistent results in the two independents patient cohorts. Using an improved feature selection, we show that predictive models with fewer gene expression probes can achieve competitive performance.

Pairwise Correlation Analysis of the Alzheimer's Disease Neuroimaging Initiative (ADNI) Dataset Reveals Significant Feature Correlation

The Alzheimer's Disease Neuroimaging Initiative (ADNI) contains extensive patient measurements (e.g., magnetic resonance imaging [MRI], biometrics, RNA expression, etc.) from Alzheimer's disease (AD) cases and controls that have recently been used by machine learning algorithms to evaluate AD onset and progression. While using a variety of biomarkers is essential to AD research, highly correlated input features can significantly decrease machine learning model generalizability and performance. Additionally, redundant features unnecessarily increase computational time and resources necessary to train predictive models. Therefore, we used 49,288 biomarkers and 793,600 extracted MRI features to assess feature correlation within the ADNI dataset to determine the extent to which this issue might impact large scale analyses using these data. We found that 93.457% of biomarkers, 92.549% of the gene expression values, and 100% of MRI features were strongly correlated with at least one other feature in ADNI based on our Bonferroni corrected α (p-value ≤ 1.40754 × 10-13). We provide a comprehensive mapping of all ADNI biomarkers to highly correlated features within the dataset. Additionally, we show that significant correlation within the ADNI dataset should be resolved before performing bulk data analyses, and we provide recommendations to address these issues. We anticipate that these recommendations and resources will help guide researchers utilizing the ADNI dataset to increase model performance and reduce the cost and complexity of their analyses.

Transmembrane Chloride Intracellular Channel 1 (tmCLIC1) as a Potential Biomarker for Personalized Medicine

Identification of potential pathological biomarkers has proved to be essential for understanding complex and fatal diseases, such as cancer and neurodegenerative diseases. Ion channels are involved in the maintenance of cellular homeostasis. Moreover, loss of function and aberrant expression of ion channels and transporters have been linked to various cancers, and to neurodegeneration. The Chloride Intracellular Channel 1 (CLIC1), CLIC1 is a metamorphic protein belonging to a partially unexplored protein superfamily, the CLICs. In homeostatic conditions, CLIC1 protein is expressed in cells as a cytosolic monomer. In pathological states, CLIC1 is specifically expressed as transmembrane chloride channel. In the following review, we trace the involvement of CLIC1 protein functions in physiological and in pathological conditions and assess its functionally active isoform as a potential target for future therapeutic strategies.

A Comprehensive Review of Computer-Aided Diagnosis of Major Mental and Neurological Disorders and Suicide: A Biostatistical Perspective on Data Mining

The World Health Organization (WHO) suggests that mental disorders, neurological disorders, and suicide are growing causes of morbidity. Depressive disorders, schizophrenia, bipolar disorder, Alzheimer's disease, and other dementias account for 1.84%, 0.60%, 0.33%, and 1.00% of total Disability Adjusted Life Years (DALYs). Furthermore, suicide, the 15th leading cause of death worldwide, could be linked to mental disorders. More than 68 computer-aided diagnosis (CAD) methods published in peer-reviewed journals from 2016 to 2021 were analyzed, among which 75% were published in the year 2018 or later. The Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) protocol was adopted to select the relevant studies. In addition to the gold standard, the sample size, neuroimaging techniques or biomarkers, validation frameworks, the classifiers, and the performance indices were analyzed. We further discussed how various performance indices are essential based on the biostatistical and data mining perspective. Moreover, critical information related to the Transparent reporting of a multivariable prediction model for individual prognosis or diagnosis (TRIPOD) guidelines was analyzed. We discussed how balancing the dataset and not using external validation could hinder the generalization of the CAD methods. We provided the list of the critical issues to consider in such studies.