Cortical parcellation based on structural connectivity: A case for generative models
Cortical parcellation based on structural connectivity: A case for generative models
复制标题
基于结构连接的皮质分区：生成模型的案例
DOI：
10.1016/j.neuroimage.2018.01.077
复制
发表时间：
2018
期刊：
影响因子：
5.7
通讯作者：
Knösche
中科院分区：
文献类型：
--
作者：
Tittgemeyer;Rigoux;Knösche
系统神经科学的主要挑战之一是识别大脑网络，并揭示它们对大脑功能的重要性--这导致了“连接体”的概念。目前，在多个尺度的大规模国际努力中，连接被广泛地研究，并且遵循关于它们的连接以及它们的元素的不同的定义。也许定义连接的元素的最有希望的途径起源于这样的概念，即单个大脑区域保持不同的(远距离)连接轮廓。这些连通性模式决定了这些区域的功能属性，并允许对其进行解剖描述和绘制地图。这一基本原理激发了基于连接的大脑皮层定位的概念。在过去的十年中，人脑连接的非侵入性映射导致了定位技术及其应用的发展。不幸的是，这些方法中的许多主要目的是确认众所周知的、现有的体系结构图，为此，不适当地结合了先前的知识，并且经常建立在循环论证的基础上。通常，当前的方法也倾向于忽略连通性测量的特定孔径，以及大脑皮层区域的解剖特殊性，例如空间紧凑性、区域异质性、对象间的可变性、连通性信息的多尺度性质以及潜在的分层组织。然而，从方法论的角度来看，一个以不偏不倚的方式看待所有这些方面的有用框架在技术上是必要的。在这篇评论中，我们首先概述基于连通性的皮质分割的概念，并讨论其前景和局限性，特别是在结构连通性方面。为了提高可靠性和效率，我们强烈主张将基于连通性的皮质分割作为一种建模方法；即，基于(模型)参数推理的数据近似值。因此，可以对分割算法的稳健性进行正式测试--其预测的精度可以被量化，并且可以得到关于结果的潜在泛化的统计数据。这样的框架还允许通过模型选择以假设检验的形式重新表述模型约束问题，并提供了一种根据先验分布整合解剖学知识的形成性方法。
One of the major challenges in systems neuroscience is to identify brain networks and unravel their significance for brain function –this has led to the concept of the ‘connectome’. Connectomes are currently extensively studied in large-scale international efforts at multiple scales, and follow different definitions with respect to their connections as well as their elements.Perhaps the most promising avenue for defining the elements of connectomes originates from the notion that individual brain areas maintain distinct (long-range) connection profiles. These connectivity patterns determine the areas’ functional properties and also allow for their anatomical delineation and mapping. This rationale has motivated the concept of connectivity-based cortex parcellation.In the past ten years,non-invasivemapping of human brain connectivity has led to immense advances in the development of parcellation techniques and their applications. Unfortunately, many of these approaches primarily aim for confirmation of well-known, existing architectonic maps and, to that end, unsuitably incorporate prior knowledge and frequently build on circular argumentation. Often, current approaches also tend to disregard the specific apertures of connectivity measurements, as well as the anatomical specificities of cortical areas, such as spatial compactness, regional heterogeneity, inter-subject variability, the multi-scaling nature of connectivity information, and potential hierarchical organisation. From a methodological perspective, however, a useful framework that regards all of these aspects in an unbiased way is technically demanding.In this commentary, we first outline the concept of connectivity-based cortex parcellation and discuss its prospects and limitations in particular with respect to structural connectivity. To improve reliability and efficiency, we then strongly advocate for connectivity-based cortex parcellation as a modelling approach; that is, an approximation of the data based on (model) parameter inference. As such, a parcellation algorithm can be formally tested for robustness –the precision of its predictions can be quantified and statistics about potential generalization of the results can be derived. Such a framework also allows the question of model constraints to be reformulated in terms of hypothesis testing through model selection and offers a formative way to integrate anatomical knowledge in terms of prior distributions.
登录
查看更多内容
影响因子：
4.3
作者：
Penny W;Sengupta B
通讯作者：
Sengupta B
大脑同皮质的模式
DOI：
--
发表时间：
1961
期刊：
影响因子：
--
作者：
G. Bonin;P. Bailey
通讯作者：
P. Bailey
皮质区域和皮质-皮质连接模式
DOI：
--
发表时间：
2002
期刊：
影响因子：
--
作者：
J. Kaas
通讯作者：
J. Kaas
影响因子：
25
作者：
S. Jbabdi;Timothy Edward John Behrens
通讯作者：
Timothy Edward John Behrens
脑网络科学需要变得具有预测性。
影响因子：
11.7
作者：
C. Hilgetag;Ulrike von Luxburg
通讯作者：
Ulrike von Luxburg