Reconstructing brain connectivity at sufficient resolution for computational models designed to study the biophysical mechanisms underlying cognitive processes is extremely challenging. For such a purpose, a mesoconnectome that includes laminar and cell-class specificity would be a major step forward. We analyzed the ability of gene expression patterns to predict cell-class and layer-specific projection patterns and assessed the functional annotations of the most predictive groups of genes. To achieve our goal we used publicly available volumetric gene expression and connectivity data and we trained computational models to learn and predict cell-class and layer-specific axonal projections using gene expression data. Predictions were done in two ways, namely predicting projection strengths using the expression of individual genes and using the co-expression of genes organized in spatial modules, as well as predicting binary forms of projection. For predicting the strength of projections, we found that ridge (L2-regularized) regression had the highest cross-validated accuracy with a median r2 score of 0.54 which corresponded for binarized predictions to a median area under the ROC value of 0.89. Next, we identified 200 spatial gene modules using a dictionary learning and sparse coding approach. We found that these modules yielded predictions of comparable accuracy, with a median r2 score of 0.51. Finally, a gene ontology enrichment analysis of the most predictive gene groups resulted in significant annotations related to postsynaptic function. Taken together, we have demonstrated a prediction workflow that can be used to perform multimodal data integration to improve the accuracy of the predicted mesoconnectome and support other neuroscience use cases.

中文翻译：

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使用基因表达数据预测特定于细胞类的小鼠Mesoconnectome。

以足够的分辨率为设计用于研究认知过程基础的生物物理机制的计算模型重建大脑连接性具有极大的挑战性。为此，包括层流和细胞类特异性的中观连接体将是向前迈出的重要一步。我们分析了基因表达模式预测细胞类和层特异性投影模式的能力，并评估了最具预测性的基因组的功能注释。为了实现我们的目标，我们使用了公开可用的体积基因表达和连通性数据，并训练了计算模型，以使用基因表达数据来学习和预测细胞类别和特定层的轴突投影。预测是通过两种方式完成的，即利用单个基因的表达和空间模块中组织的基因的共表达来预测投射强度，以及预测投射的二进制形式。为了预测投影的强度，我们发现岭（L2正规化）回归具有最高的交叉验证准确性，中位数r2得分为0.54，这对应于二进制化的预测，对应于ROC值低于0.89的中位数。接下来，我们使用字典学习和稀疏编码方法确定了200个空间基因模块。我们发现，这些模块产生的预测结果具有可比的准确性，中位r2得分为0.51。最后，对最具预测性的基因组的基因本体论富集分析导致与突触后功能相关的重要注释。在一起