调查精神分裂症症状与脑区功能活动之间的关联:基于 fmri 的横断面神经影像研究

IF 2.1 4区 医学 Q3 CLINICAL NEUROLOGY
Indranath Chatterjee , Bisma Hilal
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引用次数: 0

摘要

精神分裂症是一种严重影响认知、情感和行为功能的持续性神经紊乱,主要表现为妄想、幻觉、言语紊乱和异常运动。由于这些症状与其他形式的精神病重叠,往往给诊断带来困难。因此,实施自动诊断方法势在必行。功能磁共振成像(fMRI)是一种神经成像模式,能够描绘出不同脑区的功能激活。此外,利用不断发展的机器学习技术进行 fMRI 数据分析也取得了显著进展。在这里,我们的研究是一次新的尝试,重点是对精神分裂症的典型症状和非典型症状进行综合评估。我们的目标是发现大脑功能活动的相关变化。我们的研究包括两个不同的 fMRI 数据集(1.5T 和 3T),1.5T 数据集包括 34 名精神分裂症患者,3T 数据集包括 25 名精神分裂症患者,以及同等数量的健康对照组。应用机器学习算法对数据子集进行评估,从而能够深入评估与症状影响有关的当前功能状况。识别出的体素有助于确定受每种症状影响最大的大脑区域,并通过症状强度进行量化。这种严谨的方法产生了各种新发现,同时保持了令人印象深刻的 97% 的分类准确率。通过阐明精神分裂症患者多个脑区激活模式的变化,这项研究有助于理解与精神分裂症相关的脑功能变化。所获得的洞察力可为不同的临床干预提供依据,并为准确评估症状严重程度提供一种方法,从而为精神分裂症的治疗提供新的途径。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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. This research leverages Functional Magnetic Resonance Imaging (fMRI), a neuroimaging modality capable of delineating functional activations across diverse brain regions. Furthermore, the utilization of evolving machine learning techniques for fMRI data analysis has significantly progressive. Here, our study stands as a novel attempt, focusing on the comprehensive assessment of both classical and atypical symptoms of schizophrenia. We aim to uncover associated changes in brain functional activity. Our study encompasses two distinct fMRI datasets (1.5T and 3T), each comprising 34 schizophrenia patients for the 1.5T dataset and 25 schizophrenia patients for the 3T dataset, along with an equal number of healthy controls. Machine learning algorithms are applied to assess data subsets, enabling an in-depth evaluation of the current functional condition concerning symptom impact. The identified voxels contribute to determining the brain regions most influenced by each symptom, as quantified by symptom intensity. This rigorous approach has yielded various new findings while maintaining an impressive classification accuracy rate of 97 %. By elucidating variations in activation patterns across multiple brain regions in individuals with schizophrenia, this study contributes to the understanding of functional brain changes associated with the disorder. The insights gained may inform differential clinical interventions and provide a means of assessing symptom severity accurately, offering new avenues for the management of schizophrenia.

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来源期刊
Psychiatry Research: Neuroimaging
Psychiatry Research: Neuroimaging 医学-精神病学
CiteScore
3.80
自引率
0.00%
发文量
86
审稿时长
22.5 weeks
期刊介绍: The Neuroimaging section of Psychiatry Research publishes manuscripts on positron emission tomography, magnetic resonance imaging, computerized electroencephalographic topography, regional cerebral blood flow, computed tomography, magnetoencephalography, autoradiography, post-mortem regional analyses, and other imaging techniques. Reports concerning results in psychiatric disorders, dementias, and the effects of behaviorial tasks and pharmacological treatments are featured. We also invite manuscripts on the methods of obtaining images and computer processing of the images themselves. Selected case reports are also published.
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