以同調分析探討功能性磁振造影血流反應之時序：理論與驗證

Abstract

功能性磁振造影是一項非侵襲性探察大腦功能的技術。最近的十多年來，被廣泛的運用在測定大腦於特定認知、感覺、或運動作業中所活化的區域。由於這些作業所誘發的血流反應相當緩慢，因此在傳統的功能性磁振造影實驗中較少探討時序方面的資訊。然而經過適當的實驗設計與分析，功能性磁振造影還是可以顯示在作業當中，各活化區域訊號發生的時間順序，這可以反映出這些區域神經活化的時間順序。然而目前所常為人使用的方法，有些需要使用相當快速的取樣速率，如此會減少掃描的截面數；或是需要假設特定的反應模型，不同的模型，可能會導致不同的結果。這些皆會降低探討功能性磁振造影中時間順序的實用性。 本論文是運用同調分析法，於頻域中分析功能性磁振造影中各體素的訊號變化，如此可以偵測出於規律刺激下大腦的活化區域。利用所計算出的相角差，我們可以推算出血流反應的延遲。我們運用的具有相角差的身體感覺刺激實驗，結果顯示出︰同調分析可以偵測出具有不同相角的活化區域來，反觀基於時域的相關係數分析法，對於一些血流反應較緩慢的區域有時會偵測不到。不同刺激間的相角差可以反應在其所對應大腦區域訊號的相角差上，我們可以使用同調分析所獲得這些資料，經過轉換可以反映出其間的時間差，且同調分析所求得的相角差和實驗刺激的相角差具有高度相關性。我們接著使用事件誘發的實驗方式，來得到完整刺激所誘發的血流動力反應函式，再將反應相對於刺激的相角差轉換成彼此的時間差，表示在刺激所誘發的血流動力反應區線圖上，結果顯示這時間差接近於血流反應到達頂點的時間。對於所有有反應的體素做回歸分析，結果顯示相角差和到達頂點的時間具有高度相關性。接著要測試同調分析求得的相角差與到達頂點的時間對於取樣頻率的穩定性，我們將一長序列快速取得的功能性磁振造影信號減少取樣來模擬慢速取樣的結果，結果顯示在較慢的取樣頻率下，同調分析的相角和原本的相角值相關係數較大，誤差較小。

同調分析是偵測規律刺激中大腦活化區域的有力工具，它可以不受不同區域間血流反應延遲的影響來找出活化區域。同時，也可測定血流反應延遲時間，相角差和血流動力反應函式到達頂點的時間有密切相關，而且相角差的估計相當穩定，較不受取樣頻率快慢的影響。在未來，同調分析方法可運在腦血流訊號上，尋求較接近神經活化的時序，並將可運用於臨床，尋找疾病相關的血流反應改變。

Functional MRI (fMRI) is a noninvasive technique to study brain function. During the recent decade, it is used widely to determine the spatial layout of brain activation associated with the specified cognitive or sensorimotor tasks. Temporal information in fMRI is less concerned in conventional fMRI analysis because of the sluggish nature of the hemodynamic response. However, with proper experimental design and detail analysis, fMRI can provide the temporal sequence of cortical activation across brain regions during tasks, reflecting the sequence of the individual localized neural events. Present methods to obtain the temporal information in fMRI rely on either rapid sampling rate or assume a specified model. However, with rapid sampling the slice coverage would be reduced. With the specified model, there will be model dependent limitations. All these factors will impair the practicability of the method. In our study, we used coherence analysis to analyze the fMRI voxel time series in frequency domain. It can detect activated areas under rhythmic stimuli. The calculated phase lag can used to estimate the hemodynamic delay. With the devised somatosensory experiments with phase lags in the stimulations, we at first showed the ability of coherence analysis to detect the regions with different hemodynamic delays; in contrast, conventional correlation coefficient method based on temporal domain characteristics might fail to detect areas with large hemodynamic delay. We also demonstrated that the phase lags between different stimuli could be retrieved from the phase lags in the fMRI signals at the corresponding brain regions. We later adopted the event-related paradigm to derive the evoked hemodynamic response function. We then plotted the time delay calculated from the phase lag of the signal from the stimulus paradigm on the fitted curve, which was located near the peak of the curve. We correlated the phase lag with fitted HRF and found good correlation between phase lag from coherence analysis and the time-to-peak in the HRF. To test the consistency and stability of the estimation of the phase and time-to-peak upon different sampling rates, we downsampled a long series of functional activation data acquired at higher rate to simulate the effect of lower sampling rate. The results showed that the phase estimates had higher correlation and lower error in comparison to the original data. In summary, coherence analysis is a powerful method that can be used to detect activated brain region under rhythmic stimulation irrespective of the hemodynamic delay. And it can also estimate the HRF delay in fMRI at the same time. The phase correlates closely with the time-to-peak in fitted HRF, but is less susceptible to the effect of lower sampling rate. In the future, we would like to apply coherence analysis on perfusion-based fMRI contrast to derive more accurate estimate of neuronal activation sequence. We also employ coherence analysis on clinical data in order to estimate possible disease-related alternation in BOLD response temporal dynamics.