OBJECTIVE: The main obstacle in interpreting EEG/MEG data in terms of brain connectivity is the fact that because of volume conduction, the activity of a single brain source can be observed in many channels. Here, we present an approach which is insensitive to false connectivity arising from volume conduction. METHODS: We show that the (complex) coherency of non-interacting sources is necessarily real and, hence, the imaginary part of coherency provides an excellent candidate to study brain interactions. Although the usual magnitude and phase of coherency contain the same information as the real and imaginary parts, we argue that the Cartesian representation is far superior for studying brain interactions. The method is demonstrated for EEG measurements of voluntary finger movement. RESULTS: We found: (a) from 5 s before to movement onset a relatively weak interaction around 20 Hz between left and right motor areas where the contralateral side leads the ipsilateral side; and (b) approximately 2-4 s after movement, a stronger interaction also at 20 Hz in the opposite direction. CONCLUSIONS: It is possible to reliably detect brain interaction during movement from EEG data. SIGNIFICANCE: The method allows unambiguous detection of brain interaction from rhythmic EEG/MEG data.
OBJECTIVE: The main obstacle in interpreting EEG/MEG data in terms of brain connectivity is the fact that because of volume conduction, the activity of a single brain source can be observed in many channels. Here, we present an approach which is insensitive to false connectivity arising from volume conduction. METHODS: We show that the (complex) coherency of non-interacting sources is necessarily real and, hence, the imaginary part of coherency provides an excellent candidate to study brain interactions. Although the usual magnitude and phase of coherency contain the same information as the real and imaginary parts, we argue that the Cartesian representation is far superior for studying brain interactions. The method is demonstrated for EEG measurements of voluntary finger movement. RESULTS: We found: (a) from 5 s before to movement onset a relatively weak interaction around 20 Hz between left and right motor areas where the contralateral side leads the ipsilateral side; and (b) approximately 2-4 s after movement, a stronger interaction also at 20 Hz in the opposite direction. CONCLUSIONS: It is possible to reliably detect brain interaction during movement from EEG data. SIGNIFICANCE: The method allows unambiguous detection of brain interaction from rhythmic EEG/MEG data.
Authors: Maria M G Koenis; Nico Romeijn; Giovanni Piantoni; Ilse Verweij; Ysbrand D Van der Werf; Eus J W Van Someren; Cornelis J Stam Journal: Hum Brain Mapp Date: 2011-11-11 Impact factor: 5.038
Authors: Michael Kometer; Thomas Pokorny; Erich Seifritz; Franz X Volleinweider Journal: Psychopharmacology (Berl) Date: 2015-08-01 Impact factor: 4.530
Authors: Vincent Wens; Brice Marty; Alison Mary; Mathieu Bourguignon; Marc Op de Beeck; Serge Goldman; Patrick Van Bogaert; Philippe Peigneux; Xavier De Tiège Journal: Hum Brain Mapp Date: 2015-09-02 Impact factor: 5.038