Reedy, E. | Search bioRxiv

bioRxiv2026

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.

neuroscience↗

bioRxiv2018

Prediction of malaria mosquito species and population age structure using mid-infrared spectroscopy and supervised machine learning

Despite the global efforts made in the fight against malaria, the disease is resurging. One of the main causes is the resistance that Anopheles mosquitoes, vectors of the disease, have developed to insecticides. Anopheles must survive for at least 12 days to possibly transmit malaria. Therefore, to evaluate and improve malaria vector control interventions, it is imperative to monitor and accurately estimate the age distribution of mosquito populations as well as total population sizes. However, estimating mosquito age is currently a slow, imprecise, and labour-intensive process that can only distinguish under-from over-four-day-old female mosquitoes. Here, we demonstrate a machine-learning based approach that utilizes mid-infrared spectra of mosquitoes to characterize simultaneously, and with unprecedented accuracy, both age and species identity of females of the malaria vectors Anopheles gambiae and An. arabiensis mosquitoes within their respective populations. The prediction of the age structures was statistically indistinguishable from true modelled distributions. The method has a negligible cost per mosquito, does not require highly trained personnel, is substantially faster than current techniques, and so can be easily applied in both laboratory and field settings. Our results show that, with larger mid-infrared spectroscopy data sets, this technique can be further improved and expanded to vectors of other diseases such as Zika and Dengue.

bioinformatics↗