I am a Research Fellow interested in large-scale neural dynamics underlying cognitive functioning. What principles organize brain dynamics and what type of patterns do these dynamics give rise to? I have a particular interest in the role of synchronous neural activity in motor coordination. We investigate neural dynamics by means of time-frequency techniques and multivariate statistics in EEG and EMG. We also use computational models to identify the key mechanisms underlying the experimental findings. In addition to basic neuroscience research, I recently started applying these techniques to understand the changes in large-scale brain dynamics in people with mood disorders such as depression.
Neural synchronization of muscle activity yields robust patterns of coherent activity across multiple muscles and at different frequencies. These muscle networks can be studied using graph theory to investigate the neural implementation of muscle synergies. Based on non-invasive recordings, muscle networks provide a unique window into spinal circuitry and their role in coordinated movement and posture. This complements complex brain networks by mapping spinal pathways underlying human sensorimotor function. We recently published the methodology to extract muscle networks from EMG activity (Boonstra et al., 2015, Scientific Reports).
Adaptive motor control
In this research project supported by the NWO Innovational Research Incentives Scheme Veni, we focus on how spontaneous reorganisation of brain activity allows us to change our behaviour. We show that human subjects who perform rapid movements between force targets reveal a change in the frequency of synchronous brain rhythms when they make a movement error. This reorganization in synchronous brain activity likely reflects changed information processing involved in parsing prediction errors and updating motor commands (Mehrkanoon et al., 2014, NeuroImage).
Cortical activity reveals complex dynamics even at rest. We study resting-state EEG data using a novel method based on multivariate time–frequency interdependence. By capturing patterns of neural synchronisation at frequencies between 5-45 Hz, we reconstruct the principal resting-state network dynamics in human EEG data and the low dimensional linear subspace in which they unfold (Mehrkanoon et al, 2013, Brain Topogr).
Boonstra TW, Farmer SF, Breakspear M (2016). Using computational neuroscience to define common input to spinal motor neurons. Frontiers in Human Neuroscience 10, 313.
Mehrkanoon S, Boonstra TW, Breakspear M, Hinder M, Summers JJ (2016). Upregulation of cortico-cerebellar functional connectivity after motor learning. NeuroImage 128: 252–263.
Boonstra TW, Danna-Dos-Santos A, Xie HB, Roerdink M, Stins JF, Breakspear M (2015). Muscle networks: Connectivity analysis of EMG activity during postural control. Scientific Reports 5:17830.
Mehrkanoon S, Breakspear M, Boonstra TW (2014). The reorganization of corticomuscular coherence during a transition between sensorimotor states. NeuroImage 100: 692-702.
Mehrkanoon S, Breakspear M, Boonstra TW (2014). Low-dimensional dynamics of resting-state cortical activity. Brain Topogr 27: 338-352.
Heitmann, S, Boonstra TW, Breakspear M (2013). A dendritic mechanism for decoding traveling waves: Principles and applications to motor cortex. PLoS Comp Biol 9: e1003260.
Boonstra TW, Breakspear M (2012). Neural mechanisms of intermuscular coherence: Implications for the rectification of surface electromyography. J Neurophysiol 107: 796-807.