Modeling the cell-type-specific mesoscale murine connectome with anterograde tracing experiments.
Modeling the cell-type-specific mesoscale murine connectome with anterograde tracing experiments.
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DOI：
10.1162/netn_a_00337
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发表时间：
2023
影响因子：
4.7
通讯作者：
Mihalas, Stefan
中科院分区：
文献类型：
--
作者：
Koelle, Samson;Mastrovito, Dana;Whitesell, Jennifer D.;Hirokawa, Karla E.;Zeng, Hongkui;Meila, Marina;Harris, Julie A.;Mihalas, Stefan
艾伦小鼠大脑连接图谱由针对不同结构和类别的投射神经元的顺行追踪实验组成。除了在 C57BL/6 野生型小鼠中进行的区域顺行追踪之外，大部分实验都是使用转基因 Cre 系进行的。这允许访问细胞类别特定的全脑连接信息，其中类别由转基因系定义。然而，尽管实验数量很大，但它并没有接近覆盖它们存在的每个区域的所有现有细胞类别。在这里，我们研究了我们可以在多大程度上填补这些空白，并估计细胞类特定的连接函数，给出简化的假设，即附近体素具有平滑变化的投影，但这些投影张量可以根据投影细胞的区域和类别而急剧变化。本文描述了 Cre-line 示踪剂实验到代表源结构和目标结构之间连接强度的类特定连接矩阵的转换。我们引入并验证了一种用于创建连接矩阵的新颖统计模型。我们扩展了之前用于填补空间空白的 Nadaraya-Watson 核学习方法，以填补细胞级连接信息中的空白。为此，我们基于类特定的平均区域化投影构建了一个“细胞类空间”，并结合 3D 空间和该抽象空间中的平滑，以在相似的神经元类之间共享信息。使用这种方法，我们使用多个分辨率级别构建了一组连接矩阵，其中假设连接不连续。我们表明，从该模型获得的连接显示出预期的细胞类型和结构特定的连接。我们还表明，可以使用一组稀疏因子来分解野生型连接矩阵，并分析该潜变量模型的信息量。虽然哺乳动物模型中的标准连接研究侧重于区域之间的整体连接强度，但在描述局部电路水平的功能时，人们越来越关注细胞类别之间的特定连接类型。最近描述了此类在皮质丘脑系统中的重要性，我们现在研究它们对于估计全脑介观连接性的重要性。即使在我们相对较大的数据集中，跨单元类别的连通性数据也是稀疏的，因此我们引入了一种方法来更可靠地跨类别推断并估计特定于连接类型的区域间连通性。我们观察到这种复杂的连接性可以通过一组相对较小的因素来描述。虽然不完整，但该连接矩阵代表了小鼠中尺度连接模型的分类和定量改进。
The Allen Mouse Brain Connectivity Atlas consists of anterograde tracing experiments targeting diverse structures and classes of projecting neurons. Beyond regional anterograde tracing done in C57BL/6 wild-type mice, a large fraction of experiments are performed using transgenic Cre-lines. This allows access to cell-class-specific whole-brain connectivity information, with class defined by the transgenic lines. However, even though the number of experiments is large, it does not come close to covering all existing cell classes in every area where they exist. Here, we study how much we can fill in these gaps and estimate the cell-class-specific connectivity function given the simplifying assumptions that nearby voxels have smoothly varying projections, but that these projection tensors can change sharply depending on the region and class of the projecting cells. This paper describes the conversion of Cre-line tracer experiments into class-specific connectivity matrices representing the connection strengths between source and target structures. We introduce and validate a novel statistical model for creation of connectivity matrices. We extend the Nadaraya-Watson kernel learning method that we previously used to fill in spatial gaps to also fill in gaps in cell-class connectivity information. To do this, we construct a “cell-class space” based on class-specific averaged regionalized projections and combine smoothing in 3D space as well as in this abstract space to share information between similar neuron classes. Using this method, we construct a set of connectivity matrices using multiple levels of resolution at which discontinuities in connectivity are assumed. We show that the connectivities obtained from this model display expected cell-type- and structure-specific connectivities. We also show that the wild-type connectivity matrix can be factored using a sparse set of factors, and analyze the informativeness of this latent variable model. While standard connectivity studies in mammalian models focus on the overall connection strength between areas, there is an increasing focus on specific connection types between cell classes when describing functions at a local circuit level. Having recently described the importance of such classes in the cortico-thalamic system, we now investigate their importance for estimating brain-wide mesoscopic connectivity. Even within our relatively large dataset, the connectivity data across cell classes is sparse, and so we introduce a method to more reliably extrapolate across classes and estimate connection-type-specific inter-areal connectivity. We observe that this complex connectivity may be described via a relatively small set of factors. While not complete, this connectivity matrix represents a categorical and quantitative improvement in mouse mesoscale connectivity models.
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影响因子：
64.8
作者：
Harris JA;Mihalas S;Hirokawa KE;Whitesell JD;Choi H;Bernard A;Bohn P;Caldejon S;Casal L;Cho A;Feiner A;Feng D;Gaudreault N;Gerfen CR;Graddis N;Groblewski PA;Henry AM;Ho A;Howard R;Knox JE;Kuan L;Kuang X;Lecoq J;Lesnar P;Li Y;Luviano J;McConoughey S;Mortrud MT;Naeemi M;Ng L;Oh SW;Ouellette B;Shen E;Sorensen SA;Wakeman W;Wang Q;Wang Y;Williford A;Phillips JW;Jones AR;Koch C;Zeng H
通讯作者：
Zeng H
影响因子：
64.8
作者：
通讯作者：
--
胆碱能系统全脑图谱的生成和基底前脑胆碱能神经元的介观投影组分析。
DOI：
10.1073/pnas.1703601115
发表时间：
2018-01-09
影响因子：
11.1
作者：
Li, Xiangning;Yu, Bin;Qiu, Zilong
通讯作者：
Qiu, Zilong
影响因子：
5.7
作者：
Groeneboom, Piet;Jongbloed, Geurt
通讯作者：
Jongbloed, Geurt
DOI：
10.1073/pnas.0308531101
发表时间：
2004-03-23
影响因子：
11.1
作者：
Brunet, JP;Tamayo, P;Mesirov, JP
通讯作者：
Mesirov, JP