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. Although various pharmacological treatments are available in the form of antipsychotics, however, their actual consequences need to be examined with the observable changes in symptoms as well as the unobservable changes in brain functioning. This study is a first of its kind to critically investigate both clinical and neuroimaging studies to find out the changes being observed in schizophrenia patients after clinical intervention with various antipsychotics. We observed several symptomatic changes being reported in clinical studies incorporating clinical trials of various first-generation and second-generation antipsychotic drugs. Alongside, we encapsulated several neuroimaging studies showing functional and structural changes in the brain of schizophrenia patients triggered by a variety of drugs. The basal ganglia, frontal lobe, temporal lobe, cuneus, and middle occipital gyrus are some of the notable brain regions that were observed to show subtle functional and structural changes. This critical review paper may pave the way for future research into the study of the pathological and morphological changes in the brains of schizophrenia patients as they progress through the course of medicinal therapy.

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. Alongside this, the study focuses on the neuropsychological changes in three critical mental disorders, such as schizophrenia, Alzheimer's disease, and Parkinson's disease. The comprehensive and narrative review being performed in this paper, has brought together the relevant work done on the association of COVID-19 drugs and changes in the neurological system. For this study, a systematic search was performed on several databases such as PubMed, Scopus, and Web of Science. This study also consolidates shreds of evidence about the challenges confronted by patients having disorders like Schizophrenia, Alzheimer's disease, and Parkinson's disease. This review is based on the studies done on COVID-19 drugs from mid-2020 to date. We have identified some scopes of crucial future opportunities which could add more depth to the current knowledge on the association of COVID- 19 drugs and the changes in the neurological system. This study may present scope for future work to investigate the pathophysiological changes of these disorders due to COVID-19.

Detection of brain regions responsible for chronic pain in osteoarthritis: an fMRI-based neuroimaging study using deep learning.

Introduction: Chronic pain is a multifaceted condition that has yet to be fully comprehended. It is frequently linked with a range of disorders, particularly osteoarthritis (OA), which arises from the progressive deterioration of the protective cartilage that cushions the bone endings over time. Methods: In this paper, we examine the impact of chronic pain on the brain using advanced deep learning (DL) algorithms that leverage resting-state functional magnetic resonance imaging (fMRI) data from both OA pain patients and healthy controls. Our study encompasses fMRI data from 51 pain patients and 20 healthy subjects. To differentiate chronic pain-affected OA patients from healthy controls, we introduce a DL-based computer-aided diagnosis framework that incorporates Multi-Layer Perceptron and Convolutional Neural Networks (CNN), separately. Results: Among the examined algorithms, we discovered that CNN outperformed the others and achieved a notable accuracy rate of nearly 85%. In addition, our investigation scrutinized the brain regions affected by chronic pain and successfully identified several regions that have not been mentioned in previous literature, including the occipital lobe, the superior frontal gyrus, the cuneus, the middle occipital gyrus, and the culmen. Discussion: This pioneering study explores the applicability of DL algorithms in pinpointing the differentiating brain regions in OA patients who experience chronic pain. The outcomes of our research could make a significant contribution to medical research on OA pain patients and facilitate fMRI-based pain recognition, ultimately leading to enhanced clinical intervention for chronic pain patients.

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. This study proposes a two-stage natural language processing-based model that utilizes Latent Dirichlet Allocation to identify critical topics related to each type of legal case or judgment and the Valence Aware Dictionary Sentiment Reasoner algorithm to assess people's sentiments on those topics. By applying these strategies, this research aims to influence public perception of controversial legal issues. This study is the first of its kind to use topic modeling and sentiment analysis on Indian legal documents and paves the way for a better understanding of legal documents.

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. The task of early detection of influenza is an important research area these days. Various studies show that machine learning techniques have attracted many researchers' attention to the early detection of influenza disease. In this paper, early detection of Influenza disease among all age groups is done using various machine learning techniques. Influenza Research Database and the Human Surveillance Records data sets are used. Data analysis is undertaken, and ensemble-based stacked algorithms are implemented on the whole data set. The performance of different models has been evaluated using different performance metrics. Overall, the study proposes efficient machine learning models that can be implemented to provide a cheaper and quicker diagnostic tool for detecting influenza.

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

Background: 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. Methods: We propose a novel scheme for identification of the affected brain regions from functional magnetic resonance imaging data by deploying group independent component analysis in conjunction with feature extraction based on statistical measures, followed by sequential forward feature selection. The features that show highest accuracy during the classification between healthy and schizophrenia subjects are selected. Results: This study reveals several brain regions like cerebellum, inferior temporal gyrus, superior temporal gyrus, superior frontal gyrus, insula, and amygdala that have been reported in the existing literature, thus validating the proposed approach. We are also able to identify some functional changes in the brain regions, such as Heschl gyrus and the vermian area, which have not been reported in the literature involving working memory studies amongst schizophrenia patients. Conclusions: As our study confirms the results obtained in earlier studies, in addition to pointing out some brain regions not reported in earlier studies, the findings are likely to serve as a cue for clinical investigation, leading to better medical intervention.

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

Background: 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. Each voxel of the 3D brain volume is associated with a time series of signal intensity values. This study aimed to identify the distinct voxels from time-series fMRI data that show high functional activation during a task. Methods: In this study, a novel mean-deviation based approach was applied to time-series fMRI data of 34 schizophrenia patients and 34 healthy subjects. The statistical measures such as mean and median were used to find the functional changes in each voxel over time. The voxels that show significant changes for each subject were selected and thus used as the feature set during the classification of schizophrenia patients and healthy controls. Results: The proposed approach identifies a set of relevant voxels that are used to distinguish between healthy and schizophrenia subjects with high classification accuracy. The study shows functional changes in brain regions such as superior frontal gyrus, cuneus, medial frontal gyrus, middle occipital gyrus, and superior temporal gyrus. Conclusions: This work describes a simple yet novel feature selection algorithm for time-series fMRI data to identify the activated brain voxels that are generally affected in schizophrenia. The brain regions identified in this study may further help clinicians to understand the illness for better medical intervention. It may be possible to explore the approach to fMRI data of other psychological disorders.