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Abstract Cortico-cortical paired associative stimulation (ccPAS) is an effective transcranial magnetic stimulation (TMS) method for inducing associative plasticity between interconnected brain areas in humans. Prior ccPAS studies have focused on protocol’s aftereffects. Here, we investigated physiological changes induced “online” during ccPAS administration. We tested 109 participants receiving ccPAS over left ventral premotor cortex (PMv) and primary motor cortex (M1) using a standard procedure (90 paired-pulses with 8-ms interstimulus interval, repeated at 0.1 Hz frequency). On each paired-pulse, we recorded a motor-evoked potential (MEP) to continuously trace the emergence of corticomotor changes. Participant receiving forward-ccPAS (on each pair, a first TMS pulse was administered over PMv, second over M1, i.e., PMv-to-M1) showed a gradual and linear increase in MEP size that did not reach a plateau at the end of the protocol and was greater in participants with low motor threshold. Participants receiving reverse-ccPAS (i.e., M1-to-PMv) showed a trend toward inhibition. Our study highlights the facilitatory and inhibitory modulations that occur during ccPAS administration and suggest that online MEP monitoring could provide insights into the malleability of the motor system and protocol’s effectiveness. Our findings open interesting prospects about ccPAS potential optimization in experimental and clinical settings.

Abstract Schizophrenia is among the most debilitating neuropsychiatric disorders. However, clear neurophysiological markers that would identify at-risk individuals represent still an unknown. The aim of this study was to investigate possible alterations in the resting alpha oscillatory activity in normal population high on schizotypy trait, a physiological condition known to be severely altered in patients with schizophrenia. Direct comparison of resting-state EEG oscillatory activity between Low and High Schizotypy Group (LSG and HSG) has revealed a clear right hemisphere alteration in alpha activity of the HSG. Specifically, HSG shows a significant slowing down of right hemisphere posterior alpha frequency and an altered distribution of its amplitude, with a tendency towards a reduction in the right hemisphere in comparison to LSG. Furthermore, altered and reduced connectivity in the right fronto-parietal network within the alpha range was found in the HSG. Crucially, a trained pattern classifier based on these indices of alpha activity was able to successfully differentiate HSG from LSG on tested participants further confirming the specific importance of right hemispheric alpha activity and intrahemispheric functional connectivity. By combining alpha activity and connectivity measures with a machine learning predictive model optimized in a nested stratified cross-validation loop, current research offers a promising clinical tool able to identify individuals at-risk of developing psychosis (i.e., high schizotypy individuals).

Abstract An amendment to this paper has been published and can be accessed via a link at the top of the paper.