To understand the driving force of CSF motion, researchers have investigated animals and humans using a variety of techniques . Many concluded that CSF pulsations are mainly arterial in origin. On the other hand, CSF flow changes due to respiration have been the subject of only a few MRI studies. However, some researchers have investigated the effects of respiratory motion on CSF flow using MRI techniques [8, 10, 11, 15]. Beckett et al.  used simultaneous multi-slice (SMS) velocity imaging to investigate spinal and brain CSF motion. They reported that the CSF motion in the spine and brain is modulated not only by cardiac motion, but also by respiratory motion. Chen et al.  used SMS EPI technique under respiratory guidance to measure respiratory- and cardiac-modulated CSF velocity and direction. They concluded that, during the inspiratory phase, there is upward (inferior to superior) CSF movement into the cranial cavity and lateral ventricles, with a reversal of direction in the expiratory phase. Yamada et al.  investigated the effect of respiration on CSF movement by using a non-contrast Time-SLIP technique with balanced steady-state-free precession (bSSFP) readout. Their results demonstrated that a substantially greater amount of CSF movement occurs with deep respiration than with cardiac pulsations. Later, Dreha-Kulaczewski et al.  concluded that inspiration is the major regulator of CSF motion. Dreha-Kulaczewski et al.  used a highly under-sampled radial gradient–echo sequence with image reconstruction by regularized nonlinear inversion (NLINV) for observing the effect of respiratory on the CSF motion. Since signal intensity modulation due to the inflow effect was used in their work, separated and direct quantification for the CSF velocities due to the cardiac pulsation and respiration were not performed. In the recent publication, Yildiz et al.  used very similar technique with our present work to quantify and characterize the cardiac and respiratory-induced CSF motions at the level of the foramen magnum. Assessment of intracranial CSF motions was, however, not made in their work. Thus we believe our present work is adding new insights concerning on the cardiac and respiratory-induced CSF motions in the intracranial space. In the present study, we differentiated the cardiac and respiratory components to evaluate CSF motion. One of the simplest ways to separate cardiac and respiratory motions is to understand frequency analysis. Sunohara et al.  developed a method using 2D-PC to analyze the driving force of CSF in terms of power and frequency mapping and successfully analyzed the cardiac and respiratory components of CSF motion, albeit obtaining their images from volunteers engaged in controlled respiration. Our frequency technique was taken further for quantitative analysis of CSF motion related to cardiac and respiratory components. The mathematical algorithm for separating the cardiac and respiratory components of the CSF motion is described in our previous work . Shortly, Fourier transformation was applied to the time series of the obtained velocity data at each voxel. The components of CSF motion were extracted from the frequency spectrum by selecting the particular frequency bands corresponding to the cardiac and respiratory frequencies. In this particular work, the frequency band for the cardiac component was set as 1.0–1.6 Hz, while that for the respiratorion was 0.018–0.3 Hz.
In the present study, CSF motion was separated into respiratory and cardiac components. The amount of CSF displacement was found to be larger in the respiratory component than in the cardiac component in both cranial and caudal directions. Simultaneously, while the cardiac component showed a smaller displacement, the velocity was higher compared to the respiratory component. In other words, the movement of CSF due to the cardiac component was rapid and small, and that due to the respiratory component was slow and large. These results are consistent with those of the visual analysis of CSF reported by Yamada et al.  demonstrating that the influence of the respiratory component on the amount of displacement per unit of time was greater than that of the cardiac component. These findings provide quantitative values for results that will be readily understandable to clinicians who have observed the rapid, short-period, powerful CSF motion synchronized with the heartbeat and the slowly pulsing, long-period CSF motion in clinical practice. The difference in the displacement was significant (p < 0.001) and clear in the Sylvian aqueduct for all respiratory periods. The difference became slightly less clear in the foramen magnum, particularly for longer respiratory periods (p < 0.05 for the 16-s cycle). This may be attributed to the fact that the respiratory process tended to be unstable in the longer period (16 s), and, thus, the individual variation among the volunteers became larger than that in the shorter period.
Time-SLIP enables changes in spin to be visualized. This approximates the results for displacement shown in the present study, showing that CSF moves long distances in accordance with respiratory variations. In the present results, the velocity indicated the rapid movement of CSF with a short period associated with the heartbeat. To summarize CSF motion on the basis of these results, although CSF moves fast as it spreads around the vessels with the heartbeat, it moves over comparatively long distances in accordance with the slower movements of breathing, and this fast movement and movement over long distances may be responsible for physical exchanges in the brain and spinal cord.
However, the physical quantity measured in the present study is the displacement calculated by integrating the CSF velocity in the cranial or caudal direction, unlike the spin traveling distance, which the spin-labeling technique measures. Another important point is that the temporal resolution for data sampling (217 ms/frame) was not high enough to sample the cardiac-driven motion. Assuming a heart rate of 1 Hz, only 4–5 points can cover a cycle of cardiac-driven CSF motion resulting in a lack of waveform sampling accuracy, although the present technique is a quantitative measurement based on the 2D-PC technique, which can measure the fluid velocity with 10% accuracy .