Tu Jiaxin Cindy, Kim Jung-Hoon, Lu Chenyan, H Luckett Patrick, Adeyemo Babatunde, Shimony Joshua S, Elison Jed T, Eggebrecht Adam T, Wheelock Muriah D
Mallinckrodt Institute of Radiology, Washington University in St. Louis, St. Louis, MO, United States.
Developing Brain Institute, Children's National Hospital, Washington, DC, United States.
Imaging Neurosci (Camb). 2025 Sep 3;3. doi: 10.1162/IMAG.a.129. eCollection 2025.
Spatial similarity of functional connectivity profiles across matching anatomical locations in individuals is often calculated to delineate individual differences in functional networks. Likewise, spatial similarity is assessed across average functional connectivity profiles of groups to evaluate the maturity of functional networks during development. Despite its widespread use, spatial similarity is limited to comparing two samples at a time. In this study, we employed a variational autoencoder to embed functional connectivity profiles from various anatomical locations, individuals, and group averages for simultaneous comparison. We demonstrate that our variational autoencoder, with pre-trained weights, can project new functional connectivity profiles from the vertex space to a latent space with as few as two dimensions, yet still retain meaningful global and local structures in the data. Functional connectivity profiles from various functional networks occupy distinct compartments of the latent space. Moreover, the variability of functional connectivity profiles from the same anatomical location is readily captured in the latent space. We believe that this approach could be useful for visualization and exploratory analyses in precision functional mapping.
通常会计算个体中匹配解剖位置的功能连接图谱的空间相似性，以描绘功能网络中的个体差异。同样，也会评估群体平均功能连接图谱之间的空间相似性，以评估发育过程中功能网络的成熟度。尽管其应用广泛，但空间相似性一次仅限于比较两个样本。在本研究中，我们使用变分自编码器来嵌入来自不同解剖位置、个体和群体平均值的功能连接图谱，以便进行同时比较。我们证明，我们的变分自编码器在预训练权重的情况下，可以将新的功能连接图谱从顶点空间投影到低至二维的潜在空间，同时仍保留数据中有意义的全局和局部结构。来自各种功能网络的功能连接图谱占据潜在空间的不同区域。此外，来自相同解剖位置的功能连接图谱的变异性在潜在空间中很容易被捕捉到。我们认为这种方法可能有助于精确功能映射中的可视化和探索性分析。
