DOES BRAIN ACTIVATION DURING FUNCTIONAL MOVEMENT TASKS DIFFERENTIATE BETWEEN GOOD AND BAD MOVERS? AN INTEGRATED NEUROIMAGING ASSESSMENT OF MOTOR CONTROL IN YOUNG ATHLETES

Background: Aberrant frontal and sagittal plane knee motor control biomechanics contribute to increased anterior cruciate ligament (ACL) injury risk. Emergent data further indicates alterations in brain function may underlie ACL injury high risk biomechanics and primary injury. However, technical limitations have limited our ability to assess direct linkages between maladaptive biomechanics and brain function. Hypothesis/Purpose: (1) Increased frontal plane knee range of motion would associate with altered brain activity in regions important for sensorimotor control and (2) increased sagittal plane knee motor control timing error would associate with altered activity in sensorimotor control brain regions. Methods: Eighteen female high-school basketball and volleyball players (14.7 ± 1.4 years, 169.5 ± 7 cm, 65.8 ± 20.5 kg) underwent brain functional magnetic resonance imaging (fMRI) while performing a bilateral, combined hip, knee, and ankle flexion/extension movements against resistance (i.e., leg press) Figure 1(a). The participants completed this task to a reference beat of 1.2 Hz during four movement blocks of 30 seconds each interleaved in between 5 rest blocks of 30 seconds each. Concurrent frontal and sagittal plane range of motion (ROM) kinematics were measured using an MRI-compatible single camera motion capture system. Results: Increased frontal plane ROM was associated with increased brain activity in one cluster extending over the occipital fusiform gyrus and lingual gyrus ( p = .003, z > 3.1). Increased sagittal plane motor control timing error was associated with increased brain activity in multiple clusters extending over the occipital cortex (lingual gyrus), frontal cortex, and anterior cingulate cortex ( p < .001, z > 3.1); see Figure 1 (b). Conclusion: The associations of increased knee frontal plane ROM and sagittal plane timing error with increased activity in regions that integrate visuospatial information may be indicative of an increased propensity for knee injury biomechanics that are, in part, driven by reduced spatial awareness and an inability to adequately control knee abduction motion. Increased activation in these regions during movement tasks may underlie an impaired ability to control movements (i.e., less neural efficiency), leading to compromised knee positions during more complex sports scenarios. Increased activity in regions important for cognition/attention associating with motor control timing error further indicates a neurologically inefficient motor control strategy. [Figure: see text]

Determining OBS clock drift using ambient seismic noise

&lt;p&gt;Accurate timing of seismic records is essential for almost all applications in seismology. Wrong timing of the waveforms may result in incorrect Earth models and/or inaccurate earthquake locations. As such, it may render interpretations of underground processes incorrect. Ocean bottom seismometers (OBSs) experience clock drifts due to their inability to synchronize with a GNSS signal (with the correct reference time), since electromagnetic signals are unable to propagate efficiently in water. As OBSs generally operate in relatively stable ambient temperature, the timing deviation is usually assumed to be linear. Therefore, the time corrections can be estimated through GPS synchronization before deployment and after recovery of the instrument. However, if the instrument has run out of power prior to recovery (i.e., due to the battery being dead at the time of recovery), the timing error at the end of the deployment cannot be determined. In addition, the drift may not be linear, e.g., due to rapid temperature drop while the OBS sinks to the seabed. Here we present an algorithm that recovers the linear clock drift, as well as a potential timing error at the onset.&lt;/p&gt;&lt;p&gt;The algorithm presented in this study exploits seismic interferometry (SI). Specifically, time-lapse (averaged) cross-correlations of ambient seismic noise are computed. As such, virtual-source responses, which are generally dominated by the recorded surface waves, are retrieved. These interferometric responses generate two virtual sources: a causal wave (arriving at a positive time) and an acausal wave (arriving at a negative time). Under favorable conditions, both interferometric responses approach the surface-wave part of the medium's Green's function. Therefore, it is possible to calculate the clock drift for each station by exploiting the time-symmetry between the causal and acausal waves. For this purpose, the clock drift is calculated by measuring the differential arrival times of the causal and acausal waves for a large number of receiver-receiver pairs and computing the drift by carrying-out a least-squares inversion. The methodology described is applied to time-lapse cross-correlations of ambient seismic noise recorded on and around the Reykjanes peninsula, SW Iceland. The stations used for the analysis were deployed in the context of IMAGE (Integrated Methods for Advanced Geothermal Exploration) and consisted of 30 on-land stations and 24 ocean bottom seismometers (OBSs).&amp;#160; The seismic activity was recorded from spring 2014 until August 2015 on an area of around 100 km in diameter (from the tip of the Reykjanes peninsula).&lt;/p&gt;