Thiebaut de Schotten Michel, Urbanski Marika, Valabregue Romain, Bayle Dimitri J, Volle Emmanuelle
Natbrainlab, Department of Forensic and Neurodevelopmental Sciences, Institute of Psychiatry, King's College London, London, UK; Université Pierre et Marie Curie-Paris 6, Centre de Recherche de l'Institut du Cerveau et de la Moelle épinière (CRICM), UMRS 975, Paris, France; Inserm, U 975, Paris, France; CNRS, UMR 7225, Paris, France.
Université Pierre et Marie Curie-Paris 6, Centre de Recherche de l'Institut du Cerveau et de la Moelle épinière (CRICM), UMRS 975, Paris, France; Inserm, U 975, Paris, France; Service de Médecine et de Réadaptation, Hôpitaux de Saint-Maurice, Saint-Maurice, France.
Cortex. 2014 Jul;56:121-37. doi: 10.1016/j.cortex.2012.12.007. Epub 2012 Dec 19.
Exploring brain connectivity is fundamental to understanding the functional architecture of the cortex. In our study we employed tractography-based parcellation, combined with the principal component analysis statistical framework, to divide the occipital lobes into seven areas in a group of eighteen healthy participants. Tractography-based parcellation is a method based on diffusion imaging tractography, which segregates the living human brain into distinctive areas showing sharp differences in their anatomical connectivity. The results were compared to covarying functional networks involving distinct areas within the occipital lobes, that we obtained using resting state functional magnetic resonance imaging (fMRI), as well as to other existing subdivisions of the occipital lobes. Our results showed similarities with functional imaging data in healthy controls and cognitive profiles in brain-damaged patients, although several differences with cytoarchitectonic, myelogenetic, myeloarchitectonic and functional maps were reported. While the similarities are encouraging, the potential validity and limitations of the differences observed are discussed. Taken together these results suggest that tractography-based parcellation may provide a new promising anatomical subdivision of the living human brain based on its anatomical connectivity, which may benefit the understanding of clinical-neuroanatomical dissociations and functional neuroimaging results.
探索脑连接性是理解皮质功能结构的基础。在我们的研究中,我们采用基于纤维束成像的脑区划分方法,并结合主成分分析统计框架,将一组18名健康参与者的枕叶划分为7个区域。基于纤维束成像的脑区划分是一种基于扩散成像纤维束成像的方法,它将活体人类大脑分隔成在解剖连接上表现出明显差异的不同区域。我们将结果与通过静息态功能磁共振成像(fMRI)获得的涉及枕叶内不同区域的共变功能网络进行比较,同时也与枕叶的其他现有细分进行比较。我们的结果显示出与健康对照者的功能成像数据以及脑损伤患者的认知特征存在相似性,尽管也报告了与细胞构筑、髓鞘发生、髓鞘构筑和功能图谱的一些差异。虽然这些相似性令人鼓舞,但我们也讨论了所观察到的差异的潜在有效性和局限性。综合这些结果表明,基于纤维束成像的脑区划分可能基于活体人类大脑的解剖连接性提供一种新的、有前景的解剖细分方法,这可能有助于理解临床神经解剖学分离和功能神经成像结果。