The Mind Research Network, Lovelace Biomedical and Environmental Research Institute , Albuquerque, NM , USA ; LIAMA Center for Computational Medicine, National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences , Beijing , China.
Front Hum Neurosci. 2013 May 29;7:235. doi: 10.3389/fnhum.2013.00235. eCollection 2013.
Multimodal brain imaging data have shown increasing utility in answering both scientifically interesting and clinically relevant questions. Each brain imaging technique provides a different view of brain function or structure, while multimodal fusion capitalizes on the strength of each and may uncover hidden relationships that can merge findings from separate neuroimaging studies. However, most current approaches have focused on pair-wise fusion and there is still relatively little work on N-way data fusion and examination of the relationships among multiple data types. We recently developed an approach called "mCCA + jICA" as a novel multi-way fusion method which is able to investigate the disease risk factors that are either shared or distinct across multiple modalities as well as the full correspondence across modalities. In this paper, we applied this model to combine resting state fMRI (amplitude of low-frequency fluctuation, ALFF), gray matter (GM) density, and DTI (fractional anisotropy, FA) data, in order to elucidate the abnormalities underlying schizophrenia patients (SZs, n = 35) relative to healthy controls (HCs, n = 28). Both modality-common and modality-unique abnormal regions were identified in SZs, which were then used for successful classification for seven modality-combinations, showing the potential for a broad applicability of the mCCA + jICA model and its results. In addition, a pair of GM-DTI components showed significant correlation with the positive symptom subscale of Positive and Negative Syndrome Scale (PANSS), suggesting that GM density changes in default model network along with white-matter disruption in anterior thalamic radiation are associated with increased positive PANSS. Findings suggest the DTI anisotropy changes in frontal lobe may relate to the corresponding functional/structural changes in prefrontal cortex and superior temporal gyrus that are thought to play a role in the clinical expression of SZ.
多模态脑影像数据在回答科学有趣和临床相关问题方面的作用越来越大。每种脑成像技术都提供了大脑功能或结构的不同视角,而多模态融合则利用了每种技术的优势,并可能揭示隐藏的关系,从而将来自不同神经影像学研究的发现合并。然而,目前大多数方法都集中在两两融合上,而对于 N -way 数据融合以及对多种数据类型之间关系的研究仍然相对较少。我们最近开发了一种称为“mCCA+jICA”的新方法,作为一种新的多模态融合方法,能够研究跨多种模态共享或独特的疾病风险因素,以及模态之间的全部对应关系。在本文中,我们应用该模型整合静息态 fMRI(低频振幅,ALFF)、灰质(GM)密度和 DTI(各向异性分数,FA)数据,以阐明精神分裂症患者(SZ,n=35)相对于健康对照组(HC,n=28)的异常。在 SZ 中鉴定出了模态共有的和模态特有的异常区域,然后用于对七种模态组合的成功分类,这表明 mCCA+jICA 模型及其结果具有广泛的适用性。此外,一对 GM-DTI 成分与阳性和阴性综合征量表(PANSS)的阳性症状子量表显著相关,表明默认模式网络中的 GM 密度变化以及前丘脑辐射中的白质中断与阳性 PANSS 的增加有关。研究结果表明,额叶的弥散张量成像各向异性变化可能与前额叶皮质和颞上回的相应功能/结构变化有关,这被认为与精神分裂症的临床表现有关。