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

Abstract

Changes of cardiac-induced regional pulsatility can be associated with specific regions of brain volumetric changes, and these are related with cognitive alterations. Thus, mapping of cardiac pulsatility over the entire brain can be helpful to assess these relationships. A total of 108 subjects (age: 66.5 ± 8.4 years, 68 females, 52 healthy controls, 11 subjective cognitive decline, 17 impaired without complaints, 19 MCI and 9 AD) participated. The pulsatility map was obtained directly from resting-state functional MRI time-series data at 3T. Regional brain volumes were segmented from anatomical MRI. Multidomain neuropsychological battery was performed to test memory, language, attention and visuospatial construction. The Montreal Cognitive Assessment (MoCA) was also administered. The sparse partial least square (SPLS) method, which is desirable for better interpreting high-dimensional variables, was applied for the relationship between the entire brain voxels of pulsatility and 45 segmented brain volumes. A multiple holdout SPLS framework was used to optimize sparsity for assessing the pulsatility-volume relationship model and to test the reliability by fitting the models to 9 different splits of the data. We found statistically significant associations between subsets of pulsatility voxels and subsets of segmented brain volumes by rejecting the omnibus null hypothesis (any of 9 splits has p < 0.0056 (=0.05/9) with the Bonferroni correction). The pulsatility was positively associated with the lateral ventricle, choroid plexus, inferior lateral ventricle, and 3rd ventricle and negatively associated with hippocampus, ventral DC, and thalamus volumes for the first pulsatility-volume relationship. The pulsatility had an additional negative relationship with the amygdala and brain stem volumes for the second pulsatility-volume relationship. The spatial distribution of correlated pulsatility was observed in major feeding arteries to the brain regions, ventricles, and sagittal sinus. The indirect mediating pathways through the volumetric changes were statistically significant between the pulsatility and multiple cognitive measures ( p < 0.01). Thus, the cerebral pulsatility, along with volumetric measurements, could be a potential marker for better understanding of pathophysiology and monitoring disease progression in age-related neurodegenerative disorders.

An imaging‐based method of mapping multi‐echo BOLD intracranial pulsatility

“…We note that the acquired multi‐TE BOLD data had a long TR compared to the more rapid acquisition times that are currently available. Undersampling the cardiac signal that is contained in the BOLD data leads to aliasing; nevertheless, the network was able to generate BOLD pulsatility maps with high image similarity to ground truth, in line with prior studies that have demonstrated extraction of cardiac‐related signals from long‐TR BOLD images 7,10,11 . We have assumed that the neural network trained on FFT input images contained subharmonic pulsatility features.…”

“…Using phase contrast MRI, it is possible to calculate time‐resolved blood flow velocities directly from perforating cerebral arteries 5,6 . BOLD MRI is a popular functional neuroimaging technique, which provides additional physiological contrast that includes autonomic, respiratory, and cardiac related signals 2,7–14 . Intracranial BOLD pulsatility reflects flow‐related blood volume changes and spin history inflow effects 15 .…”

“…12 Model-based, data-driven, and deep learning methods have helped to spur new research approaches to study physiological sources in BOLD data; recent work shows how to extract the cardiac trace directly from the BOLD data without the external plethysmograph cardiac trace recording. 10,17,18 For instance, Aslan et al aggregate signals across slices and trained a neural network to predict a robust cardiac trace. 18 Another convolutional neural network approach by Salas et al extracted the respiratory trace from BOLD data.…”

“…5,6 BOLD MRI is a popular functional neuroimaging technique, which provides additional physiological contrast that includes autonomic, respiratory, and cardiac related signals. 2,[7][8][9][10][11][12][13][14] Intracranial BOLD pulsatility reflects flow-related blood volume changes and spin history inflow effects. 15 The pseudo-periodic cardiac influences are evident even when BOLD volume acquisition time is longer than the cardiac period, that is, undersampling of a cardiac source.…”

WHOCARES: data-driven WHOle-brain CArdiac signal REgression from highly accelerated simultaneous multi-Slice fMRI acquisitions

“…From a clinical perspective, cardiac pulsatility has recently shown its application in the definition of pathophysiological biomarkers and monitoring of disease progression in age-related neurodegenerative disorders (Kim et al (2021)), which might benefit from WHOCARES implementation. Another example may be the case of stroke patients where, sometimes, the heart-rate shows pathologicalrelated modifications and changes in cardiac pulsatility (Geurts et al (2019)).…”