Grand efforts in neuroscience are working toward mapping the connectomes of
many new species, including the near completion of the Drosophila melanogaster.
It is important to ask whether these models could benefit artificial
intelligence. In this work we ask two fundamental questions: (1) where and when
biological connectomes can provide use in machine learning, (2) which design
principles are necessary for extracting a good representation of the
connectome. Toward this end, we translate the motor circuit of the C. Elegans
nematode into artificial neural networks at varying levels of biophysical
realism and evaluate the outcome of training these networks on motor and
non-motor behavioral tasks. We demonstrate that biophysical realism need not be
upheld to attain the advantages of using biological circuits. We also establish
that, even if the exact wiring diagram is not retained, the architectural
statistics provide a valuable prior. Finally, we show that while the C. Elegans
locomotion circuit provides a powerful inductive bias on locomotion problems,
its structure may hinder performance on tasks unrelated to locomotion such as
visual classification problems.

本文通过将矛线虫的运动回路转化为具有不同生理真实性水平的人工神经网络，评估这些网络在动态和非动态行为任务上的训练结果。研究表明，即使不保持生物特性的真实性，也可以获得使用生物电路的优势。建立生物电路结构的统计学提供了有价值的先验知识。同时，矛线虫的运动回路对于运动问题具有强大的归纳偏差，但其结构可能会妨碍其他与运动无关的任务。

生物连接组作为人工神经网络架构的表征

Biological connectomes as a representation for the architecture of artificial neural networks

Numerous social, medical, engineering and biological challenges can be framed
as graph-based learning tasks. Here, we propose a new feature based approach to
network classification. We show how dynamics on a network can be useful to
reveal patterns about the organization of the components of the underlying
graph where the process takes place. We define generalized assortativities on
networks and use them as generalized features across multiple time scales.
These features turn out to be suitable signatures for discriminating between
different classes of networks. Our method is evaluated empirically on
established network benchmarks. We also introduce a new dataset of human brain
networks (connectomes) and use it to evaluate our method. Results reveal that
our dynamics based features are competitive and often outperform state of the
art accuracies.

该研究提出了一种基于特征的新方法来进行网络分类，通过在网络上的动态特征进行分类，并在人类大脑网络上进行了实证研究，结果表明该方法具有优异的性能。

基于动态特性的图分类特征

Dynamics Based Features For Graph Classification

Resting-state functional Magnetic Resonance Imaging (R-fMRI) holds the
promise to reveal functional biomarkers of neuropsychiatric disorders. However,
extracting such biomarkers is challenging for complex multi-faceted
neuropatholo-gies, such as autism spectrum disorders. Large multi-site datasets
increase sample sizes to compensate for this complexity, at the cost of
uncontrolled heterogeneity. This heterogeneity raises new challenges, akin to
those face in realistic diagnostic applications. Here, we demonstrate the
feasibility of inter-site classification of neuropsychiatric status, with an
application to the Autism Brain Imaging Data Exchange (ABIDE) database, a large
(N=871) multi-site autism dataset. For this purpose, we investigate pipelines
that extract the most predictive biomarkers from the data. These R-fMRI
pipelines build participant-specific connectomes from functionally-defined
brain areas. Connectomes are then compared across participants to learn
patterns of connectivity that differentiate typical controls from individuals
with autism. We predict this neuropsychiatric status for participants from the
same acquisition sites or different, unseen, ones. Good choices of methods for
the various steps of the pipeline lead to 67% prediction accuracy on the full
ABIDE data, which is significantly better than previously reported results. We
perform extensive validation on multiple subsets of the data defined by
different inclusion criteria. These enables detailed analysis of the factors
contributing to successful connectome-based prediction. First, prediction
accuracy improves as we include more subjects, up to the maximum amount of
subjects available. Second, the definition of functional brain areas is of
paramount importance for biomarker discovery: brain areas extracted from large
R-fMRI datasets outperform reference atlases in the classification tasks.

研究使用 R-fMRI 从人们的脑功能区构建个体特定的连通图谱，能够提高自闭症谱系障碍患者的神经心理状态的识别准确率，其数据集可以从多个不同处获取，且所提取的功能脑区明显比参考图谱更具有生物标志物发现的重要性。

从多中心静息态数据中提取可再生的生物标志物：以自闭症为例

Deriving reproducible biomarkers from multi-site resting-state data: An  Autism-based example

This manuscript considers the following "graph classification" question:
given a collection of graphs and associated classes, how can one predict the
class of a newly observed graph? To address this question we propose a
statistical model for graph/class pairs. This model naturally leads to a set of
estimators to identify the class-conditional signal, or "signal-subgraph,"
defined as the collection of edges that are probabilistically different between
the classes. The estimators admit classifiers which are asymptotically optimal
and efficient, but differ by their assumption about the "coherency" of the
signal-subgraph (coherency is the extent to which the signal-edges "stick
together" around a common subset of vertices). Via simulation, the best
estimator is shown to be not just a function of the coherency of the model, but
also the number of training samples. These estimators are employed to address a
contemporary neuroscience question: can we classify "connectomes"
(brain-graphs) according to sex? The answer is yes, and significantly better
than all benchmark algorithms considered. Synthetic data analysis demonstrates
that even when the model is correct, given the relatively small number of
training samples, the estimated signal-subgraph should be taken with a grain of
salt. We conclude by discussing several possible extensions.

本文考虑了图分类的问题并提出了一种建模方法以及相关的分类器，该方法可以用于将大脑图像分为男性和女性。