Department of Neurological Surgery, Vickie and Jack Farber Institute for Neuroscience, Thomas Jefferson University, 909 Walnut Street, Third Floor, Philadelphia, PA 19107, USA; Jefferson Integrated Magnetic Resonance Imaging Center, Department of Radiology, Thomas Jefferson University, 909 Walnut Street, First Floor, Philadelphia, PA 19107, USA.
Victor Horsley Department of Neurosurgery, National Hospital for Neurology and Neurosurgery, 33 Queen Square, London WC1N 3BG, UK; Unit of Functional Neurosurgery, UCL Queen Square Institute of Neurology, 33 Queen Square, London WC1N 3BG, UK.
Neuroimage. 2021 Dec 1;244:118649. doi: 10.1016/j.neuroimage.2021.118649. Epub 2021 Oct 11.
Advances in computational neuroimaging techniques have expanded the armamentarium of imaging tools available for clinical applications in clinical neuroscience. Non-invasive, in vivo brain MRI structural and functional network mapping has been used to identify therapeutic targets, define eloquent brain regions to preserve, and gain insight into pathological processes and treatments as well as prognostic biomarkers. These tools have the real potential to inform patient-specific treatment strategies. Nevertheless, a realistic appraisal of clinical utility is needed that balances the growing excitement and interest in the field with important limitations associated with these techniques. Quality of the raw data, minutiae of the processing methodology, and the statistical models applied can all impact on the results and their interpretation. A lack of standardization in data acquisition and processing has also resulted in issues with reproducibility. This limitation has had a direct impact on the reliability of these tools and ultimately, confidence in their clinical use. Advances in MRI technology and computational power as well as automation and standardization of processing methods, including machine learning approaches, may help address some of these issues and make these tools more reliable in clinical use. In this review, we will highlight the current clinical uses of MRI connectomics in the diagnosis and treatment of neurological disorders; balancing emerging applications and technologies with limitations of connectivity analytic approaches to present an encompassing and appropriate perspective.
计算神经影像学技术的进步扩展了临床神经科学中可用于临床应用的成像工具组合。非侵入性、活体脑 MRI 结构和功能网络映射已被用于识别治疗靶点、定义需要保留的功能区，以及深入了解病理过程和治疗方法以及预后生物标志物。这些工具具有为特定患者提供治疗策略的实际潜力。然而，需要对临床实用性进行现实评估，在平衡该领域日益增长的兴奋和兴趣与这些技术相关的重要局限性的同时，也需要考虑到临床实用性。原始数据的质量、处理方法的细节以及应用的统计模型都会影响结果及其解释。数据采集和处理缺乏标准化也导致了可重复性问题。这一限制直接影响了这些工具的可靠性，最终影响了其在临床应用中的可信度。MRI 技术和计算能力的进步以及处理方法的自动化和标准化，包括机器学习方法，可能有助于解决其中的一些问题，并使这些工具在临床应用中更加可靠。在这篇综述中，我们将重点介绍 MRI 连接组学在神经疾病的诊断和治疗中的当前临床应用；平衡新兴的应用和技术与连接分析方法的局限性，提出一个全面和适当的观点。
