The human auditory system excels at detecting patterns needed for processing speech and music. According to predictive coding, the brain predicts incoming sounds, compares predictions to sensory input and generates a prediction error whenever a mismatch between the prediction and sensory input occurs. Predictive coding can be indexed in electroencephalography (EEG) with the mismatch negativity (MMN) and P3a, two components of event‐related potentials (ERP) that are elicited by infrequent deviant sounds (e.g., differing in pitch, duration and loudness) in a stream of frequent sounds. If these components reflect prediction error, they should also be elicited by omitting an expected sound, but few studies have examined this. We compared ERPs elicited by infrequent randomly occurring omissions (unexpected silences) in tone sequences presented at two tones per second to ERPs elicited by frequent, regularly occurring omissions (expected silences) within a sequence of tones presented at one tone per second. We found that unexpected silences elicited significant MMN and P3a, although the magnitude of these components was quite small and variable. These results provide evidence for hierarchical predictive coding, indicating that the brain predicts silences and sounds.
This investigation looks to use graph signal processing to generate a functional graph over the anatomical leg and assess the extracted functional information and its viability in modeling underlying physiological factors. The generated graph is constructed on the edge dimensions of node to node coherences and fractal dimension differences. The resultant graph structure is then analyzed to observe if extracted functional data shows alignment with underlying structures via a generalized linear mixed-effect mode
Lower leg resting-state BOLD images (n=8 males, 4 endurance, 4 power athletes), were acquired after 30min of rest, allowing for blood-flow normalization. BOLD images were motion corrected and the gastrocnemius and soleus manually segmented using an anatomical reference for their differing twitch fibre profiles. Voxel-wise BOLD mono- and bi-fractal dimension were computed using the scaled windowed variance approach, with linear detrending, removing scanner induced low-frequency variations. The bi-fractal dimension was significantly different between endurance and power groups in both muscles. Specific bi-fractal components more readily distinguished soleus and gastrocnemius for the endurance (I) and power (II) group, when at rest.
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