Corticokinematic Coherence

Background

Non-invasive functional mapping of primary sensorimotor (SM1) cortical areas is typically based on functional brain imaging, e.g., magnetoencephalography (MEG) and functional magnetic resonance imaging (fMRI). Results can be used, e.g., to investigate cortical processing and effects of training, rehabilitation, and plasticity at cortical level. In addition, the results can be useful in planning of the brain surgery.

Figure 1. Single-trial MEG (over SM1 cortex), accelerometer, and EMG signals from a single subject during active and passive movements (a). Schematic drawing showing the experimental setup for the passive conditions (b). Left-hemisphere group-level (n = 15) CKC maps (c). Movements touching and without touching of a table are shown separately. All nodes significantly different from the node with the maximum value are hidden. CKC maximum co-localize with the anatomical SM1 hand area. Modified from Piitulainen et al. 2013a.

Figure 2. Single-trial MEG (over SM1 cortex), reference and EMG signals in a single subject during free, squeeze and fixed-pinch hand-actions (a). Source locations based on the cross-correlograms for acceleration and EMG in free task, pressure signal in squeeze task, and force signal in fixed-pinch task for five subjects superimposed on individual MRI (b). Mean group-level (n = 15) source locations at the “hand knob” superimposed standard Montreal Neurological Institute brain. Modified from Piitulainen et al. 2013b.

Corticokinematic coherence (CKC) reflects coupling between primary sensorimotor (SM1) cortex MEG and kinematic signals, e.g. acceleration (Bourguignon et al. 2011) and velocity (Jerbi et al. 2007); kinetic signals, e.g. force and pressure (Piitulainen et al. 2013b); and electromyographic (EMG) signals (Piitulainen et al. 2013b) of executed hand movements or observed hand movements (Bourguignon et al., 2012a) at the movement frequency, typically at 2–5 Hz, and its harmonics. Basically, all signals detecting the rhythmicity of the hand movements can be applied both for isometric (Piitulainen et al. 2013b) or dynamic contractions (Bourguignon et al., 2011; Bourguignon et al., 2012b; Bourguignon et al. 2012a; Jerbi et al., 2007, Piitulainen et al. 2013a,b), as long as the contractions are fast, rhythmic and continuous (for 2–4 min).

CKC is not restricted to voluntary movements. Passive movements generated by another person result in similar coherence level and cortical neural generators than those obtained during active movements (Piitulainen et al. 2013a). Therefore, CKC seems to be mainly driven by proprioceptive, and in some degree by cutaneous, feedback to the SM1 cortex. Although, the CKC peaks in the SM1 cortex, CKC is associated with large coherent cortical network (Bourguignon et al. 2012b).

CKC is typically strong, with typical coherence values ranging from 0.2–0.8, and has nearly 100% success rate in the SM1 cortex localization among the subjects in the CKC studies (Bourguignon et al., 2011; Bourguignon et al., 2012b, Piitulainen et al. 2013a,b). Therefore, we have proposed CKC as a tool for functional mapping of the human SM1 cortex (Bourguignon et al., 2011), and included in a multimodal scheme for SM1 hand area mapping (Bourguignon et al. 2013b). Importantly, passive movements can be applied for patients with difficulty to perform voluntary movements (Piitulainen et al. 2013a).

We have designed and built a novel a movement actuator to elicit passive finger or toe movements with millisecond accuracy (Figure 3; Piitulainen et al. 2015). The movements are produced by pneumatic artificial muscle (PAM), elastic actuator that shortens with increasing air pressure. PAM stimulator provides a robust and reliable tool to track proprioceptive afference to the cortex and to locate the SM1 cortex (Piitulainen et al. 2015).

Analysis of CKC

CKC data are typically analyzed by computing coherence between a movement kinematic signal and brain signals recorded with MEG, using ~2-s long epochs to afford a ~0.5 Hz spectral resolution. The cortical sources responsible for the encoding or integration of the reference signal can be uncovered with source reconstruction performed on the cross-spectrum or cross-correlogram, or by directly computing coherence in the source space using e.g. dynamic imaging of coherent sources (DICS, Gross et al., 2001). If CKC is to be computed with an EMG signal, this signal should be high-pass filtered (at ~20 Hz) and rectified so as to reveal the action-related modulation of the muscle activity.

References

Bourguignon M, De Tiège X, de Beeck MO, Ligot N, Paquier P, Van Bogaert P, Goldman S, Hari R, Jousmäki V 2013a. The pace of prosodic phrasing couples the listener's cortex to the reader's voice. Hum Brain Mapp 34:314–26.

Bourguignon M, De Tiège X, Op de Beeck M, Pirotte B, Van Bogaert P, Goldman S, Hari R, Jousmäki V 2011. Functional motor-cortex mapping using corticokinematic coherence.Neuroimage 55:1475–9.

Bourguignon M, De Tiège X, de Beeck MO, Van Bogaert P, Goldman S, Jousmäki V, Hari R 2012a. Primary motor cortex and cerebellum are coupled with the kinematics of observed hand movements. Neuroimage 66C:500–7.

Bourguignon M, Jousmäki V, Marty B, Wens V, Op de Beeck M, Van Bogaert P, Nouali M, Metens T, Lubicz B, Lefranc F, Bruneau M, De Witte O, Goldman S, De Tiège X 2013b. Comprehensive functional mapping scheme for non-invasive primary sensorimotor cortex mapping. Brain Topogr 26:511–23.

Bourguignon M, Jousmäki V, Op de Beeck M, Van Bogaert P, Goldman S, De Tiège X 2012b. Neuronal network coherent with hand kinematics during fast repetitive hand movements. Neuroimage 59:1684-91.

Bourguignon M, Piitulainen H, Hari R, Jousmäki V. 2015a. Corticokinematic coherence mainly reflects movement-induced proprioceptive feedback. NeuroImage 106: 382–90

Bourguignon M, Whitmarsh S, Piitulainen H, Hari R, Jousmäki V, Lundqvist D. 2015b. Corticokinematic-coherence-based mapping of the primary sensorimotor cortex in the presence of artifacts. Clinical Neurophysiology, S1388–2457(15)00746-4

Gross J, Kujala J, Hämäläinen M, Timmermann L, Schnitzler A, Salmelin R 2001. Dynamic imaging of coherent sources: Studying neural interactions in the human brain. Proc Natl Acad Sci U S A 98(2):694–699.

Piitulainen H, Bourguignon M, De Tiège X, Hari R, Jousmäki V 2013a. Corticokinematic coherence during active and passive finger movements. Neuroscience 238:361–70.

Piitulainen H, Bourguignon M, De Tiège X, Hari R, Jousmäki V 2013b. Coherence between magnetoencephalography and hand-action-related acceleration, force, pressure, and electromyogram. Neuroimage 72:83–90.

Piitulainen H, Bourguignon M, Hari R, Jousmäki V. 2015. MEG-compatible pneumatic stimulator to elicit passive finger and toe movements. NeuroImage 112: 310–7

Contributors

CKC method has been developed in a collaborative work by:

Brain Research Unit, OV Lounasmaa Laboratory, Aalto University School of Science, Espoo, Finland

Laboratoire de Cartographie Fonctionnelle du Cerveau, ULB-Hôpital Erasme, Brussels, Belgium

Main contributors

• Riitta Hari
  MD PhD
  Aalto Distinguished Professor
• Veikko Jousmäki
  PhD, docent, senior scientist;
  Director of Aalto Neuroimaging Infrastructure
• Mathieu Bourguignon
  PhD, postdoctoral scientist
• Harri Piitulainen
  PhD, postdoctoral scientist
• Xavier de Tiège
  MD PhD

Acknowledgements for the financial support

Mathieu Bourguignon FRIA (FRS-FNRS, Belgium).

Xavier De Tiège Clinicien-Chercheur Spécialiste at the FRS-FNRS, Belgium.

Veikko Jousmäki “Brains Back to Brussels” the Institut d'Encouragement de la Recherche Scientifique et de l'Innovation de Bruxelles (Brussels, Belgium),

Riitta Hari ERC Advanced Grant #232946,

the Fonds de la Recherche Scientifique (FRS-FNRS, Belgium, Research Convention 3.4611.08)

the Academy of Finland (National Centers of Excellence Program 2006–2011).

Harri Piitulainen Academy of Finland Postdoctoral Researcher grant (#13266133)

The SalWe Research Program for Mind and Body (Tekes – the Finnish Funding
Agency for Technology and Innovation grant 1104/10)

We also acknowledge Helge Kainulainen and Ronny Schreiber at the Brain Research Unit (Aalto University School of Science, Espoo, Finland) for technical support.