Bi-objective approach for computer-aided diagnosis of schizophrenia patients using fMRI data

Chatterjee, Indranath; Agarwal, Manoj; Rana, Bharti; Lakhyani, Navin; Kumar, Naveen

doi:10.1007/s11042-018-5901-0

Cited by 34 publications

(12 citation statements)

References 48 publications

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“…However, the diagnosis of diseases encompassing psychiatric disarray is predominantly based on interviews from patients, symptoms presented, and the existence or absence of representative behavioral signs [1]. Schizophrenia (SZ) is a severe, prolonged disorder of the brain that interrupts normal thinking, speech, and the behavioral characteristics of an individual [2]. The National Institute of Mental Health views SZ as a significant contributor to disease burden, with about 2.4 million people in the United States over the age of 18 effected by it [3].…”

Section: Introductionmentioning

confidence: 99%

Deep Convolutional Neural Network Model for Automated Diagnosis of Schizophrenia Using EEG Signals

et al. 2019

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A computerized detection system for the diagnosis of Schizophrenia (SZ) using a convolutional neural system is described in this study. Schizophrenia is an anomaly in the brain characterized by behavioral symptoms such as hallucinations and disorganized speech. Electroencephalograms (EEG) indicate brain disorders and are prominently used to study brain diseases. We collected EEG signals from 14 healthy subjects and 14 SZ patients and developed an eleven-layered convolutional neural network (CNN) model to analyze the signals. Conventional machine learning techniques are often laborious and subject to intra-observer variability. Deep learning algorithms that have the ability to automatically extract significant features and classify them are thus employed in this study. Features are extracted automatically at the convolution stage, with the most significant features extracted at the max-pooling stage, and the fully connected layer is utilized to classify the signals. The proposed model generated classification accuracies of 98.07% and 81.26% for non-subject based testing and subject based testing, respectively. The developed model can likely aid clinicians as a diagnostic tool to detect early stages of SZ.

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Section: Introductionmentioning

confidence: 99%

Deep Convolutional Neural Network Model for Automated Diagnosis of Schizophrenia Using EEG Signals

et al. 2019

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“…Several researchers across these disciplines make use of neuroimaging techniques to investigate the human brain. These techniques became very popular for the last 25 years, with the invention of functional magnetic resonance imaging (fMRI) (Chatterjee, 2018;Chatterjee et al, 2018). However, other neuroimaging techniques such as MRI (Chatterjee et al, 2020), Computed Tomography (CT), and Proton Emission Tomography (PET) (Nasrallah & Dubroff, 2013) are used widely to identify the neurological problems in the brain.…”

Section: Open Access | Editorial Issn: 2576-828xmentioning

confidence: 99%

“…However, other neuroimaging techniques such as MRI (Chatterjee et al, 2020), Computed Tomography (CT), and Proton Emission Tomography (PET) (Nasrallah & Dubroff, 2013) are used widely to identify the neurological problems in the brain. Few studies (Chatterjee et al, 2018;Liotti & Mayberg, 2001) have already shown promising results with the usage of state-of-the-art neuroimaging techniques. The fMRI has a long history of identifying the neural biomarkers in the brain, be it for a psychological or neurological disorder.…”

Section: Open Access | Editorial Issn: 2576-828xmentioning

confidence: 99%

Integrated neural technologies: Solutions beyond tomorrow

2020

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Neuroscience is an exciting area in which, at a fast rate, revolutionary advances materialise. Neurotechnology is interesting and contentious at the same time, as one of its aims is to "wire" human brains directly into computers. Neurotechnology is defined as the assembly of methods and instruments which allow a direct connection to the nervous system of technical components. These instruments are electrodes, machines or smart prostheses. They are designed to record and/or "translate" impulses from the brain into control instructions, or to modify brain function through the application of electrical or optical stimulation. The emergence of neuro-technologies is interdisciplinary. It supports the amalgamation of neurobiology with atomic, nano- and micro-sciences, as a fascinating path for significant development in the neuroscience domain. It poses a scientific foundation for potential therapeutic strategies.

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“…Each slice consists of brain volume small units which are called a voxel. T herefore, a specific spot of the brain is represented by voxel [12] . So, by comparing the fMRI data of schizophrenia patients and healthy control we can diagnose the disease, by detecting the regions which are active during a particular task in schizophrenia patients as well as in healthy controls [12] .…”

Section: Fmri (Functional Magnetic Resonance Imaging)mentioning

confidence: 99%

“…T herefore, a specific spot of the brain is represented by voxel [12] . So, by comparing the fMRI data of schizophrenia patients and healthy control we can diagnose the disease, by detecting the regions which are active during a particular task in schizophrenia patients as well as in healthy controls [12] . are active during a particular task or reacting to a detectable change in the neural activity at a low frequency (0.01-0.01Hz) [13] .…”

Section: Fmri (Functional Magnetic Resonance Imaging)mentioning

confidence: 99%

A Concise Study of Schizophrenia and Resting-state fMRI data analysis

Chatterjee¹,

Mittal²

2019

Preprint

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Schizophrenia is a severe mental illness in which people experience hallucinations and delusions. Reasons for happening schizophrenia are genetics, occur in a family, and it is heritable in nature. It can happen due to inappropriate development of the brain, due to complications at the time of birth, due to intake of drugs and an increase in the stress level in the environment. T o diagnose critical psychological disorders like schizophrenia, functional magnetic resonance imaging (fMRI) is widely used. T his technique measures the indirect neural activity of the brain tracing the oxygenation level of the blood flow. Resting-state fMRI measures the same in the rest condition of the patient while doing no cognitive task. In this review article, firstly, we describe schizophrenia and a brief description of its occurrence, cause, symptoms, and treatment. Secondly, we describe the fMRI technology followed by the resting-state analysis and finally, we describe briefly the methods of data analysis to diagnose schizophrenia with resting-state fMRI. T his review article may act like a start-up for the research in this domain.

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Bi-objective approach for computer-aided diagnosis of schizophrenia patients using fMRI data

Cited by 34 publications

References 48 publications

Deep Convolutional Neural Network Model for Automated Diagnosis of Schizophrenia Using EEG Signals

Deep Convolutional Neural Network Model for Automated Diagnosis of Schizophrenia Using EEG Signals

Integrated neural technologies: Solutions beyond tomorrow

A Concise Study of Schizophrenia and Resting-state fMRI data analysis

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