Cardiac-induced cerebral pulsatility, brain structure, and cognition in middle and older-aged adults

Figures

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  Fig. 1.. The cardiac phase measurement. (a) With an external measurement of cardiac cycle. The cardiac pulses synchronized with the scanner triggers determine the phase of the cardiac cycle at the time that each slice is acquired. Green arrows indicate R–R interval and blue bars show the acquisition of each image. Filled red circles are the calculated phase of the first slice of each volume and open circles are the phase for other slices. (b) Without an external measurement of cardiac cycle. The signal intensity changes in the main vessels can be followed by cardiac cycles (black sinusoid). The Hilbert transform generates signal S(t) that is constructed from a real-valued cardiac-induced sinusoidal input signal; S(t) = s(t) + i* h(t), where, S(t) is the analytic signal constructed from s(t) and its Hilbert transform, s(t) is the input signal, and h(t) is the Hilbert Transform of the input signal. The cardiac phase was obtained from the angle of the complex (real and imaginary) value for each MRI acquisition (red circles).

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  Fig. 2.. Multiple holdout framework. (a) Regularization parameter optimization framework. (b) Permutation for statistical significance. (1) Nine spilt datasets were generated. For each spilt data, (2) the regularization parameters were optimized with 100 further randomly split data of trainingsub and validation from the training dataset. The subscript sub was used to distinguish from the name “training” previously used in (1). The correlation between the projection of validation data onto the weighting vectors by SPLS from trainingsub was obtained for 40 × 40 grid search. (3) Grids of correlation values from 100 subsamples were averaged. The highest correlation was selected from the grid search as the optimal constraint parameters. (4) The weighting vectors were obtained by SPLS from the training dataset with optimal regularization parameters, and the correlation between the pulsatility and volume components was calculated by projecting the weighting vectors onto the holdout dataset. (5) The same correlation procedure was applied to 10,000 randomly permuted null datasets. (6) p-value was calculated by the fraction of permutation values obtained in (5) that are at least as extreme as the…

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  Fig. 3.. (a) ROI for large vessels transformed from the MNI template to individual’s space. (b) The normalized first component of cardiac pulsatile signal obtained from the ROI of a. (c) The comparison of cardiac phase calculated from our method vs. pulse-oximeter from 4 separated runs. Different colors indicate four different runs. (d) Pulsatility map calculated from one MCI subject. Pixels with larger than 3σ (99.73%) of the null distribution are displayed. High signal intensity indicates high pulsatility.

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  Fig. 4.. (a) The averaged volume weight vectors for the first pulsatility-volume relationship are displayed. The lateral, interior lateral and 3rd ventricles and choroid plexus are found to be positively associated with the pulsatility, while hippocampus, ventral DC, and thalamus show negative relationship with the pulsatility (b) Additionally, the amygdala and brain stem volumes are negatively associated for the second pulsatility-volume relationship. Age shows small positive relationship. Blue labels: 1. Hippocampus, 2. Lateral ventricle, 3. Inferior lateral ventricle, 4. Ventral DC, 5. 3rd ventricle, 7. Brain stem 8. Amygdala, 9. Thalamus, 13. Choroid plexus, 46. age. Labels for other volume variables are listed in supplementary materials (Table S1). (c) Spatial distribution of the averaged cerebral pulsatility correlated with brain volume for the first pulsatility-volume relationship. The statistically significant voxels were displayed (p < 0.01). The pulsatility of arteries shows positive relationship, while that of ventricles and venous sinus shows negative correlation with the brain volumes. (d) Spatial distribution of the pulsatility for the second pulsatility-volume relationship.…