Multimodal identification of the mouse brain using simultaneous Ca 2+ imaging and fMRI.
Francesca MandinoCorey HorienXilin ShenGabriel Desrosiers-GregoireWendy LuoMarija MarkicevicR xsTodd ConstableXenophon PapademetrisMallar M ChakravartyRichard F BetzelEvelyn M R LakePublished in: bioRxiv : the preprint server for biology (2024)
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 workhorse in this arena, BOLD (blood-oxygen-level-dependent) 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.
Keyphrases
- resting state
- functional connectivity
- magnetic resonance imaging
- high resolution
- systematic review
- single molecule
- electronic health record
- type diabetes
- computed tomography
- big data
- magnetic resonance
- deep learning
- air pollution
- bioinformatics analysis
- metabolic syndrome
- machine learning
- climate change
- subarachnoid hemorrhage
- replacement therapy
- combination therapy