脑转储:如何公开可用的fMRI可以帮助告知神经网络架构

Gabriele Fariello
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引用次数: 2

摘要

只提供摘要形式。连接组学是神经信息学的一个新兴学科,研究大脑在解剖学和功能上是如何连接的。整个神经科学界的许多项目已经解决了从纳米尺度(如激光扫描光学显微镜和半自动电子显微镜)到微观尺度(神经元和神经元簇)到宏观尺度(功能磁共振成像)确定相互连接的问题。绘制宏观尺度所需的数据量在可管理的几太字节范围内,并且目前大部分存在,而收集一立方毫米的突触级数据已经接近几拍字节的规模,并且还需要几年的时间。在这两个极端之间,最有可能找到最快、最具代表性的途径来绘制人类神经元连接的有用图谱。尽管在有关神经元连接的许多主题上提供了极其丰富的信息,但较小规模方法的主要局限性是:1)获得一个样本所需的大量时间和精力,这可能无法广泛代表典型的人类大脑;2)它们的高度侵入性或破坏性使它们难以适应活体人类。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Brain dump: How publicly available fMRI can help inform neuronal network architecture
Summary form only given. Connectomics is an emergent discipline of Neuroinformatics that studies how the brain is connected, both anatomically and functionally. A number of projects throughout the neuroscience community have tackled the problem of determining interconnectivity from the nano scale - such as those enabled by laser-scanning light microscopy and semi-automated electron microscopy - to the micro scale of neurons and neuron clusters, to the macro scale of fMRI. The amount of data required to map out the macro scale is in the manageable multi-terabyte range, and largely exists today, while gathering one milometer cubed of synaptic-level data already approaches the multi-petabyte scale and is a few years away. Between these two extremes most likely lies the fastest and most representative path to a useful map of human neuronal connectivity. Although extremely informative on may topics concerning neuronal connectivity, the of the main limitations of the smaller scale approaches are 1) the considerable amount of time and energy needed to have one sample which will likely not be widely representative of a typical human brain and 2) their highly invasive or destructive nature renders them poorly adaptable to living humans.
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