Swathi N, Prabha S
Department of ECE, Hindustan Institute of Technology and Science, Chennai, India.
Curr Med Imaging. 2023;19(2):115-125. doi: 10.2174/2211555204666220131112639.
The importance of identifying the structural and functional abnormalities in the brain in the early prediction and diagnosis of schizophrenia has attracted the attention of neuroimaging scientists and clinicians.
The purpose of this study is to structure a review paper that recognizes specific biomarkers of the schizophrenic brain.
Neuroimaging can be used to characterize brain structure, function, and chemistry by different non-invasive techniques such as computed tomography, magnetic resonance imaging, magnetic resonance spectroscopy, and positron emission tomography. The abnormalities in the brain can be used to discriminate psychic disorder like schizophrenia from others. To find disease-related brain alterations in neuroimaging, structural neuroimaging studies provide the most consistent evidence in most of the studies. The review discusses the major issues and findings in structural neuroimaging studies of schizophrenia. In particular, the data is collected from different papers that concentrated on the brain affected regions of different subjects and made a conclusion out of it.
In this work, a detailed survey has been done to find structural abnormalities in the brain from different neuroimaging techniques. Several image processing methods are used to acquire brain images. Different Machine learning techniques, Optimization methods, and Pattern recognition methods are used to predict the disease with specific biomarkers, and their results are emphasized. Thus, in this work, deep learning is also highlighted, which shows a promising role in obtaining neuroimaging data to characterize disease-related alterations in brain structure.
在精神分裂症的早期预测和诊断中，识别大脑结构和功能异常的重要性已引起神经影像学科学家和临床医生的关注。
本研究的目的是构建一篇综述文章，以识别精神分裂症大脑的特定生物标志物。
神经影像学可通过不同的非侵入性技术来表征大脑结构、功能和化学性质，如计算机断层扫描、磁共振成像、磁共振波谱和正电子发射断层扫描。大脑中的异常可用于将精神分裂症等精神障碍与其他疾病区分开来。为了在神经影像学中发现与疾病相关的大脑改变，在大多数研究中，结构神经影像学研究提供了最一致的证据。本综述讨论了精神分裂症结构神经影像学研究中的主要问题和发现。特别是，数据收集自不同的论文，这些论文集中于不同受试者的大脑受影响区域并据此得出结论。
在这项工作中，已经进行了详细的调查，以从不同的神经影像学技术中发现大脑结构异常。使用了几种图像处理方法来获取大脑图像。使用不同的机器学习技术、优化方法和模式识别方法来通过特定生物标志物预测疾病，并强调了它们的结果。因此，在这项工作中，深度学习也得到了突出，它在获取神经影像学数据以表征大脑结构中与疾病相关的改变方面显示出有前景的作用。
