The proposed method of using head motion parameters and BOLD signals to estimate respiratory variation waveforms can be extended to clinical populations with respiratory disorders by incorporating specific features or patterns characteristic of these disorders. For instance, in populations with conditions like asthma or chronic obstructive pulmonary disease (COPD), there may be distinct respiratory patterns or irregularities that can be identified and integrated into the machine learning model. By training the model on data from individuals with respiratory disorders and adjusting the algorithm to recognize and account for these unique patterns, the method can be tailored to accurately estimate respiratory fluctuations in clinical populations.