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Fig. 1 | Alzheimer's Research & Therapy

Fig. 1

From: Subject classification and cross-time prediction based on functional connectivity and white matter microstructure features in a rat model of Alzheimer’s using machine learning

Fig. 1

Experimental timeline. MRI: fMRI and diffusion MRI data were collected at 2, 6, 13, and 21 weeks after icv-STZ injection. N = 24 rats were included in total (12 STZ / 12 CTL) as reflected in the diffusion MRI datasets at 13 weeks. A lower number of datasets at other timepoints are due to poor data quality (2, 6 weeks) or missing datasets at 21 weeks due to an MRI system upgrade. For fMRI, two runs per rat were acquired for each MRI session which increased the number of datasets. The 4 timepoints were further grouped into early and late time groups and finally the pooled dataset. Sample sizes (STZ/CTL) in the 3 datasets for fMRI and diffusion MRI are as follows. Early: 47/47 (fMRI), 21/24 (diffusion); late: 34/34 (fMRI), 19/19 (diffusion); pooled: 81/81 (fMRI), 40/43 (diffusion). PET: FDG-PET data were also collected at the four timepoints in a subset of rats (N = 20 total) and were used to assess group differences in regional brain glucose uptake. Dataset numbers at each timepoint vary due to PET scanner unavailability (especially fewer datasets at 2 weeks) or missing MRI at 21 weeks which also prompted dropping the PET scan acquisition

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