Stimulus-response correlation analysis dissociates spatiotemporal cortical networks supporting speech production

IntroductionUnderstanding the spatiotemporal distribution of cortical activation during language production is a central question in cognitive neuroscience with broad clinical applications. High spatial/temporal resolution recording over multiple brain regions and specific psycholinguistic manipulations with testable behavioral predictions are necessary to separate neural variance attributable to processing stages. ObjectiveWe combine a delayed naming paradigm with intracranial electrophysiology to identify spatiotemporally dissociated cortical networks supporting stages of speech production. MethodsSubjects (20 healthy, 15 intracranial electrophysiology) are presented visual stimuli to name but are instructed to delay response until a go cue at 0/400/1000ms. Exploiting the temporal variance induced by delay, we calculate stimulus/response-locked correlation of high gamma (70-200Hz) activity at each contact. We compare shifts in stimulus-response correlation space to response time (RT) to validate separation of networks. ResultsBehaviorally, the factor delay modulated the effect of lexical selection on RT (p=0.021) but did not modulate the effect of phonological planning (p=0.4), confirming the paradigm separates processing stages. Stimulus-response correlation analyses revealed globally-invariant structure across subjects. Certain regions showed strong stimulus-locking (occipital lobe) or response-locking (motor cortex), but most regions were intermediate. Shifts in correlation space identified dissociated networks that predicted substantial variance in RT from lexical selection (R2=0.64) and phonological density (R2=0.75). Using a leave-one-subject-out cross validation approach, these shifts explained 31.4% of variance in RT at the trial level. ConclusionStimulus-response correlation space reveals stable spatiotemporal signatures of psycholinguistic processing that generalize across individuals. This approach permits hyperalignment of neurophysiological data and functional separation of cortical networks in speech production across subjects.