There has been great interest in the application of the BOLD effect in studying the physiology of skeletal muscle [1]. Compare to the BOLD signal in the brain, however, the muscle hemodynamics during exercise is more complicated because it is controlled by both local and systemic factors such as the sympathetic nervous system (SNS) [2]. In order the separate the local vascular effect of BOLD as observed in the brain from systemic cardiovascular effects during exercise, this study is designed to elucidate BOLD signal characteristics during low-level exercise with minimal involvement of the SNS. Motion artifact is one of the obstacles for functional studies of skeletal muscle. We implemented individual Independent Component Analysis (ICA), which allowed us to simultaneously identify the muscle groups and their hemodynamic behaviors, while teasing out the “motion component” during an exercise paradigm (single leg dynamic plantar-flexion). Our results may help future studies.The subjects included 9 healthy adults (mean age = 51 years, 1 female). Using a Siemens 3T scanner (Magnetom Trio), gradient echo EPI of the calf muscles during exercise were acquired with the following parameters: voxel size = 2.5 x 2.5 mm, number of slices = 10, slice thickness = 5.0 mm, FOV= 160 mm², flip angle = 70 deg, bandwidth = 2112 Hz/Px, TE = 25 ms and TR = 3 s. The exercise paradigm consisted of a 1-min baseline, a 14-min dynamic plantar-flexion exercise and a 5-min recovery. Using an MRI compatible, custom made exercise machine, all subjects performed plantar-flexion with a 2-kg load at a pace of 1/3 Hz. To reduce the motion artifact, the exercise was performed only during the 2211-ms interval when no image acquisition was performed within each TR. MRI data were first motion-corrected before preprocessed using SPM8 (The Welcome Trust Centre for Neuroimaging, University College London, UK). The single subject ICA analysis was performed according to the methods outlined elsewhere [3, 4].Fig. 1 shows representative “motion component” from two subjects. For both subjects, it’s seen located on the surface of the leg with its temporal pattern more correlated with the exercise paradigm and distributed in low frequency region of the corresponding power spectrum. Accordingly, Fig. 2 shows the 3 muscle groups identified by ICA from one subject and their corresponding time-course of BOLD signal. We validated the selected IC with ROI analysis by manually segmenting the three muscle groups and their BOLD signal time-courses are shown on the right column.All three ICA components captured the muscle groups through their hemodynamic characteristics during the exercise paradigm. ICA requires no prior physiology or anatomy knowledge, and was able to differentiate and identify different muscles based on their unique hemodynamic characteristics during exercise. This data driven method can overcome the problem of subject dependent variations in utilization of muscle groups, muscle strength and conditions. Our results support the notion of an intimate causal connection between the hemodynamic characteristic and muscle physiological conditions during exercise [5]. As such, the current study demonstrates a novel and straight forward methodology to investigate muscle exercise physiology and related diseases such as peripheral arterial disease (PAD).

As seen in Fig. 2, the BOLD signals in the three muscle groups exhibited a similar character: a rapid initial rise (usually within 30 seconds since onset) and followed by a drop to below the baseline level (negative BOLD) that usually sustained to the end of the exercise. This negative BOLD effect in the muscle is interesting as it indicates that a higher oxygen extraction than at rest by the skeletal muscle during steady state exercise at a level that systemic sympathetic activities are not necessary as indicated by the blood pressure and heart rate. Concurrent studies performed on a separate day in these same subjects showed that popliteal artery blood flow increases with this exercise paradigm (~ 100% increase above baseline), consistent with previously published studies [6]. In addition, a transient reactive hyperemia occurred rapidly as indicated by the initial rise of the BOLD signal, which requires further investigation.

Single subject ICA allowed for automatically segmenting muscle groups with respect to the matching hemodynamic characteristics while removing motion artifacts. Application of the single-subject ICA, we revealed an intriguing hemodynamic respond characteristic with a prominent initial transient increase and followed by a sustained negative BOLD signal. With further investigations on assessing its feasibility in differentiating BOLD behavior in healthy and diseased subjects, it can be a valuable tool for clinical applications.