Learning stable and predictive network-based patterns of schizophrenia and its clinical symptoms.
Learning stable and predictive network-based patterns of schizophrenia and its clinical symptoms.
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DOI：
10.1038/s41537-017-0022-8
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发表时间：
2017
影响因子：
5.4
通讯作者：
Dursun SM
中科院分区：
文献类型：
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作者：
Gheiratmand M;Rish I;Cecchi GA;Brown MRG;Greiner R;Polosecki PI;Bashivan P;Greenshaw AJ;Ramasubbu R;Dursun SM
精神分裂症通常与大脑连接中断有关。然而，识别精神分裂症和相关症状严重程度的特定神经影像特征仍然是一个具有挑战性的开放问题，需要大规模数据驱动的分析，不仅强调统计显着性，而且强调跨多个数据集、背景和队列的稳定性。对以前未见过的受试者的准确预测或概括对于任何有用的精神分裂症生物标志物也至关重要。为了建立基于功能网络特征模式的预测模型，我们研究了体素水平和低分辨率超体素水平的全脑功能磁共振成像功能网络。针对 FBIRN fMRI 数据集上的听觉奇怪任务数据 (n = 95)，我们考虑了节点度和链接权重网络特征，并评估了区分患者与对照的统计显着特征集的稳定性和泛化准确性。我们还首次将稀疏多元回归（弹性网络）应用于全脑功能连接特征，以获得症状严重程度的稳定预测特征。全脑链接权重特征在识别患者方面达到了 74% 的准确率，并且比体素节点度更稳定。链接权重特征预测了几种阴性和阳性症状量表的严重程度，包括注意力不集中和奇怪的行为。最显着、稳定和有区别的功能连接变化涉及丘脑和初级运动/初级感觉皮层之间以及楔前叶 (BA7) 和丘脑、壳核和布罗德曼区域 BA9 和 BA44 之间的相关性增加。楔前叶以及 BA6 和初级感觉皮层也参与预测多种症状的严重程度。总体而言，所提出的多步骤方法可能有助于识别更可靠的多变量模式，从而准确预测精神分裂症及其症状的严重程度。根据功能磁共振成像 (fMRI) 数据进行的脑网络分析可能有助于诊断精神分裂症并预测症状严重程度。检测神经影像模式需要跨多个数据集进行大规模分析。 Mina Gheiratmand 和阿尔伯塔大学的同事以及 IBM T.J.沃森研究中心分析了来自功能生物医学信息学研究网络的脑成像数据，该研究旨在测试精神分裂症和分裂情感障碍患者以及健康对照者在不同功能磁共振成像机器上获得的脑部扫描结果的可重复性。他们根据研究参与者进行共同听觉测试时收集的数据，研究了不同分辨率水平的大脑网络。研究人员表明，利用功能网络中的连接强度，他们可以在多个神经成像位点以 74% 的准确率区分精神分裂症患者和对照组。他们观察到丘脑和初级运动和感觉皮层之间以及楔前叶和其他大脑区域之间最强大和最具辨别力的连接差异。此外，他们可以根据涉及这些区域的连接变化来确定症状的严重程度。这种寻找精神分裂症及其严重程度的客观、可靠的神经影像生物标志物的新方法可用于诊断、评估疾病进展和治疗效果。
Schizophrenia is often associated with disrupted brain connectivity. However, identifying specific neuroimaging-based patterns pathognomonic for schizophrenia and related symptom severity remains a challenging open problem requiring large-scale data-driven analyses emphasizing not only statistical significance but also stability across multiple datasets, contexts and cohorts. Accurate prediction on previously unseen subjects, or generalization, is also essential for any useful biomarker of schizophrenia. In order to build a predictive model based on functional network feature patterns, we studied whole-brain fMRI functional networks, both at the voxel level and lower-resolution supervoxel level. Targeting Auditory Oddball task data on the FBIRN fMRI dataset (n = 95), we considered node-degree and link-weight network features and evaluated stability and generalization accuracy of statistically significant feature sets in discriminating patients vs. controls. We also applied sparse multivariate regression (elastic net) to whole-brain functional connectivity features, for the first time, to derive stable predictive features for symptom severity. Whole-brain link-weight features achieved 74% accuracy in identifying patients and were more stable than voxel-wise node-degrees. Link-weight features predicted severity of several negative and positive symptom scales, including inattentiveness and bizarre behavior. The most-significant, stable and discriminative functional connectivity changes involved increased correlations between thalamus and primary motor/primary sensory cortex, and between precuneus (BA7) and thalamus, putamen, and Brodmann areas BA9 and BA44. Precuneus, along with BA6 and primary sensory cortex, was also involved in predicting severity of several symptoms. Overall, the proposed multi-step methodology may help identify more reliable multivariate patterns allowing for accurate prediction of schizophrenia and its symptoms severity. Brain network analyses from functional magnetic resonance imaging (fMRI) data may help diagnose schizophrenia and predict symptom severity. Detecting neuroimaging patterns requires large-scale analysis across multiple data sets. Mina Gheiratmand and colleagues from the University of Alberta, along with researchers at the IBM T.J. Watson Research Center analyzed brain imaging data from the Function Biomedical Informatics Research Network, a study designed to test the reproducibility of brain scan results taken on different fMRI machines from people with schizophrenia and schizoaffective disorders, as well as healthy controls. They studied brain networks at different levels of resolution from data gathered while study participants conducted a common auditory test. The researchers showed that they could discriminate between patients with schizophrenia and controls with 74% accuracy across multiple neuroimaging sites using the strength of connection in a functional network. They observed the most robust and discriminative connectivity differences between the thalamus and primary motor and sensory cortices as well as between the precuneus and other brain regions. Moreover, they could determine symptom severity based on the connectivity changes involving these areas. This new approach towards finding objective, reliable neuroimaging biomarkers for schizophrenia and its severity could be used for diagnosis and to assess disease progression and therapeutic efficacy.
影响因子：
--
作者：
Libby, Laura A;Ragland, J Daniel
通讯作者：
Ragland, J Daniel