Unveiling Functional Biomarkers in Schizophrenia: Insights from Region of Interest Analysis Using Machine Learning.

Background: Schizophrenia is a complex and disabling mental disorder that represents one of the most important challenges for neuroimaging research. There were many attempts to understand these basic mechanisms behind the disorder, yet we know very little. By employing machine learning techniques with age-matched samples from the auditory oddball task using multi-site functional magnetic resonance imaging (fMRI) data, this study aims to address these challenges.

LRE-MMF: A novel multi-modal fusion algorithm for detecting neurodegeneration in Parkinson's disease among the geriatric population.

Parkinson's disease (PD) is a prevalent neurological disorder characterized by progressive dopaminergic neuron loss, leading to both motor and non-motor symptoms. Early and accurate diagnosis is challenging due to the subtle and variable nature of early symptoms. This study aims to address these diagnostic challenges by proposing a novel method, Localized Region Extraction and Multi-Modal Fusion (LRE-MMF), designed to enhance diagnostic accuracy through the integration of structural MRI (sMRI) and resting-state functional MRI (rs-fMRI) data.

From bean to brain: Coffee, gray matter, and neuroprotection in neurological disorders spectrum.

Coffee is a popular drink enjoyed around the world, and scientists are very interested in studying how it affects the human brain. This chapter looks at lots of different studies to understand how drinking coffee might change the brain and help protect it from neurodegenerative disorders especially like schizophrenia. With the help of available literature a link between the coffee mechanism and neurodegenerative disorders is established in this chapter.

Investigating the association between symptoms and functional activity in brain regions in schizophrenia: A cross-sectional fmri-based neuroimaging study.

Schizophrenia is a persistent neurological disorder profoundly affecting cognitive, emotional, and behavioral functions, prominently characterized by delusions, hallucinations, disordered speech, and abnormal motor activity. These symptoms often present diagnostic challenges due to their overlap with other forms of psychosis. Therefore, the implementation of automated diagnostic methodologies is imperative.

Investigating the symptomatic and morphological changes in the brain based on pre and post-treatment: A critical review from clinical to neuroimaging studies on schizophrenia.

Pharmacological treatment for schizophrenia has been a long-standing concern. As a severe neuropsychological illness, schizophrenia is always a challenging disorder to unravel its pathophysiology. Since it exhibits both positive and negative symptoms, such as hallucination and delusion, as well as social isolation and cognitive impairment, following the symptomatic changes is a crucial task for clinicians.

Ensemble learning-based early detection of influenza disease.

Across the world, the seasonal disease influenza is a respiratory illness that impacts all age groups in many ways. Its symptoms are fever, chills, aches, pains, headaches, fatigue, cough, and weakness. Seasonal influenza can cause mild to severe illness and lead to death at times.

A two-staged NLP-based framework for assessing the sentiments on Indian supreme court judgments.

Topic modeling is a powerful technique for uncovering hidden patterns in large documents. It can identify themes that are highly connected and lead to a certain region while accounting for temporal and spatial complexity. In addition, sentiment analysis can determine the sentiments of media articles on various issues.

Probing the Neuro-psychological Changes Observed with the Administration of COVID-19 Drugs.

The COVID-19 virus caused countless significant alterations in the human race, the most challenging of which was respiratory and neurological disorders. Several studies were conducted to find a robust therapy for the virus, which led to a slew of additional health issues. This study aims to understand the changes in the neurological system brought about by COVID-19 drugs and highlights the drug-drug interaction between COVID-19 drugs and psychiatric drugs.

Identification of brain regions associated with working memory deficit in schizophrenia.

Schizophrenia, a severe psychological disorder, shows symptoms such as hallucinations and delusions. In addition, patients with schizophrenia often exhibit a deficit in working memory which adversely impacts the attentiveness and the behavioral characteristics of a person. Although several clinical efforts have already been made to study working memory deficit in schizophrenia, in this paper, we investigate the applicability of a machine learning approach for identification of the brain regions that get affected by schizophrenia leading to the dysfunction of the working memory.

Mean deviation based identification of activated voxels from time-series fMRI data of schizophrenia patients.

Schizophrenia is a serious mental illness affecting different regions of the brain, which causes symptoms such as hallucinations and delusions. Functional magnetic resonance imaging (fMRI) is the most popular technique to study the functional activation patterns of the brain. The fMRI data is four-dimensional, composed of 3D brain images over time.