Edwin Thanarajah Sharmili, Han Cheol E, Rotarska-Jagiela Anna, Singer Wolf, Deichmann Ralf, Maurer Konrad, Kaiser Marcus, Uhlhaas Peter J
Department of Neurology, University Hospital of Cologne, Cologne, Germany; Department of Neurophysiology, Max-Planck Institute for Brain Research, Frankfurt am Main, Germany; Max-Planck Institute for Metabolism Research, Cologne, Germany.
Department of Electronics and Information Engineering, Korea University, Sejong, South Korea; Department of Bio-Convergence Engineering, Korea University, Seoul, South Korea; Department of Brain and Cognitive Sciences, Seoul National University, Seoul, South Korea.
Front Psychiatry. 2016 Jun 30;7:114. doi: 10.3389/fpsyt.2016.00114. eCollection 2016.
The graph theoretical analysis of structural magnetic resonance imaging (MRI) data has received a great deal of interest in recent years to characterize the organizational principles of brain networks and their alterations in psychiatric disorders, such as schizophrenia. However, the characterization of networks in clinical populations can be challenging, since the comparison of connectivity between groups is influenced by several factors, such as the overall number of connections and the structural abnormalities of the seed regions. To overcome these limitations, the current study employed the whole-brain analysis of connectional fingerprints in diffusion tensor imaging data obtained at 3 T of chronic schizophrenia patients (n = 16) and healthy, age-matched control participants (n = 17). Probabilistic tractography was performed to quantify the connectivity of 110 brain areas. The connectional fingerprint of a brain area represents the set of relative connection probabilities to all its target areas and is, hence, less affected by overall white and gray matter changes than absolute connectivity measures. After detecting brain regions with abnormal connectional fingerprints through similarity measures, we tested each of its relative connection probability between groups. We found altered connectional fingerprints in schizophrenia patients consistent with a dysconnectivity syndrome. While the medial frontal gyrus showed only reduced connectivity, the connectional fingerprints of the inferior frontal gyrus and the putamen mainly contained relatively increased connection probabilities to areas in the frontal, limbic, and subcortical areas. These findings are in line with previous studies that reported abnormalities in striatal-frontal circuits in the pathophysiology of schizophrenia, highlighting the potential utility of connectional fingerprints for the analysis of anatomical networks in the disorder.
近年来，结构磁共振成像（MRI）数据的图论分析在表征脑网络的组织原则及其在精神疾病（如精神分裂症）中的改变方面引起了广泛关注。然而，临床人群中网络的表征可能具有挑战性，因为组间连通性的比较受多种因素影响，如连接的总数和种子区域的结构异常。为克服这些局限性，本研究对3T下慢性精神分裂症患者（n = 16）和年龄匹配的健康对照参与者（n = 17）的扩散张量成像数据进行了全脑连接指纹分析。采用概率纤维束成像来量化110个脑区的连通性。一个脑区的连接指纹代表其与所有目标区域的相对连接概率集，因此，与绝对连通性测量相比，受白质和灰质总体变化的影响较小。通过相似性测量检测到连接指纹异常的脑区后，我们测试了每组之间其相对连接概率。我们发现精神分裂症患者的连接指纹改变，与失连接综合征一致。虽然内侧前额叶回仅显示连通性降低，但额下回和壳核的连接指纹主要包含与额叶、边缘叶和皮质下区域相对增加的连接概率。这些发现与先前报道精神分裂症病理生理学中纹状体 - 额叶回路异常的研究一致，突出了连接指纹在分析该疾病解剖网络方面潜在的实用性。
