Direct electrical stimulation as an input gate into brain functional networks: principles, advantages and limitations.
BACKGROUND
While the fundamental and clinical contribution of direct electrical stimulation (DES) of the brain is now well acknowledged, its advantages and limitations have not been re-evaluated for a long time.


METHODS
Here, we critically review exactly what DES can tell us about cerebral function.


RESULTS
First, we show that DES is highly sensitive for detecting the cortical and axonal eloquent structures. Moreover, DES also provides a unique opportunity to study brain connectivity, since each area responsive to stimulation is in fact an input gate into a large-scale network rather than an isolated discrete functional site. DES, however, also has a limitation: its specificity is suboptimal. Indeed, DES may lead to interpretations that a structure is crucial because of the induction of a transient functional response when stimulated, whereas (1) this effect is caused by the backward spreading of the electro-stimulation along the network to an essential area and/or (2) the stimulated region can be functionally compensated owing to long-term brain plasticity mechanisms.


CONCLUSIONS
In brief, although DES is still the gold standard for brain mapping, its combination with new methods such as perioperative neurofunctional imaging and biomathematical modeling is now mandatory, in order to clearly differentiate those networks that are actually indispensable to function from those that can be compensated.
Mandonnet
Emmanuel
E
Unité 678, Inserm/UPMC, 91 boulevard de l'Hôpital, 75634 Paris Cedex 13, France. mandonnet@mac.com
Winkler
Peter A
PA
Duffau
Hugues
H
D000200:Action Potentials / Q000502:physiology; D001369:Axons / Q000502:physiology; D001921:Brain / Q000033:anatomy & histology / Q000502:physiology*; D001931:Brain Mapping / Q000379:methods*; D046690:Deep Brain Stimulation / Q000379:methods*; D004594:Electrophysiology / Q000379:methods*; D006801:Humans; D009415:Nerve Net / Q000033:anatomy & histology / Q000502:physiology*; D016571:Neural Networks, Computer; D009473:Neuronal Plasticity / Q000502:physiology