Mandino Francesca, Horien Corey, Shen Xilin, Desrosiers-Grégoire Gabriel, Luo Wendy, Markicevic Marija, Constable R Todd, Papademetris Xenophon, Chakravarty Mallar M, Betzel Richard F, Lake Evelyn M R
Department of Radiology and Biomedical Imaging, Yale University School of Medicine, New Haven, CT, USA.
Interdepartmental Neuroscience Program, Yale University School of Medicine, New Haven, CT, USA.
Commun Biol. 2025 Apr 26;8(1):665. doi: 10.1038/s42003-025-08037-4.
Individual differences in neuroimaging are of interest to clinical and cognitive neuroscientists based on their potential for guiding the personalized treatment of various heterogeneous neurological conditions and diseases. Despite many advantages, the prevailing modality in this field-blood-oxygen-level-dependent (BOLD) functional magnetic resonance imaging (fMRI)-suffers from low spatiotemporal resolution and specificity as well as a propensity for noise and spurious signal corruption. To better understand individual differences in BOLD-fMRI data, we can use animal models where fMRI, alongside complementary but more invasive contrasts, can be accessed. Here, we apply simultaneous wide-field fluorescence calcium imaging and BOLD-fMRI in mice to interrogate individual differences using a connectome-based identification framework adopted from the human fMRI literature. This approach yields high spatiotemporal resolution cell-type specific signals (here, from glia, excitatory, as well as inhibitory interneurons) from the whole cortex. We found mouse multimodal connectome-based identification to be successful and explored various features of these data.
神经成像中的个体差异受到临床和认知神经科学家的关注，因为它们有可能指导各种异质性神经疾病的个性化治疗。尽管有许多优点，但该领域目前流行的模式——血氧水平依赖（BOLD）功能磁共振成像（fMRI）——存在时空分辨率低、特异性差以及易受噪声和虚假信号干扰的问题。为了更好地理解BOLD-fMRI数据中的个体差异，我们可以使用动物模型，在这些模型中可以同时进行fMRI以及补充性但更具侵入性的对比。在这里，我们在小鼠中同时应用宽场荧光钙成像和BOLD-fMRI，使用从人类fMRI文献中采用的基于连接组的识别框架来探究个体差异。这种方法从整个皮质产生高时空分辨率的细胞类型特异性信号（这里来自神经胶质细胞、兴奋性神经元以及抑制性中间神经元）。我们发现基于小鼠多模态连接组的识别是成功的，并探索了这些数据的各种特征。
