Biology subjects

Couto, B. A. N.

Publications and source records attributed to Couto, B. A. N..

3 recordsLinked to original sources

bioRxiv2025

REDUCED ALPHA-BAND PHASE COHERENCE AND CORTICAL COMPLEXITY IN FIBROMYALGIA: A TMS-EEG EXPLORATORY STUDY

ObjectivesCortico-spinal excitability of the primary motor cortex (M1) is reduced in fibromyalgia, and repetitive transcranial magnetic stimulation (TMS) targeting M1 normalizes these changes and relieves symptoms. TMS combined with electroencephalography (TMS-EEG) allows the measurement of M1 excitability and its connectivity to other regions, which may help clarify neurophysiological effects in fibromyalgia. We assessed cortical excitability, oscillatory activity, and complexity in individuals with fibromyalgia compared to pain-free healthy controls. MethodsGlobal and local mean field power, peak-to-peak amplitude, event-related spectral perturbation, intertrial coherence (ITC), natural frequency, and perturbational complexity index (PCIst) of the EEG response after left-M1 TMS were compared between groups (n=18 fibromyalgia; n=15 controls). Pain intensity, interference, relief of current therapy, mood, and quality of life were assessed in individuals with fibromyalgia. ResultsCompared with controls, individuals with fibromyalgia showed a reduction in the alpha-band ITC in middle and right parieto-occipital areas (P<0.05). Middle-parieto-occipital ITC negatively correlated with reported pain relief (rho=-0.552, p=0.019). The PCIst was lower in fibromyalgia compared with controls (P<0.01) and correlated with higher pain interference in general activity (rho=-0.486, p=0.042). ConclusionIndividuals with fibromyalgia showed abnormal cortical connectivity compared with asymptomatic controls. SignificanceTMS-EEG measurements may provide insights on brain connectivity relevant for therapy.

neuroscience↗

bioRxiv2025

The specific spatiotemporal evolution of TMS-evoked potentials reflects the engagement of cortical circuits

Transcranial Magnetic Stimulation (TMS) evokes electroencephalographic (EEG) responses that can persist for hundreds of milliseconds. While the first 80 ms after the pulse are widely accepted to reflect genuine cortical responses to TMS, later components have mainly been attributed to the effects of sensory co-stimulations. Here we reappraise this view by investigating the target-specificity of the spatiotemporal evolution of TMS-evoked potentials (TEPs). To this end, we compared TEPs elicited by targeting the premotor and primary motor cortices in 16 healthy subjects, under conditions designed to optimize TMS effectiveness on the cortex while minimizing peripheral confounds. As a counterfactual, we conducted the same comparison on the EEG responses evoked by realistic sham TMS and high-intensity somatosensory scalp stimulation. We found that EEG responses to motor and premotor TMS can exhibit distinct spatiotemporal evolutions, lasting up to 300 ms, both at the group and single-subject levels. These differences were absent or marginally detectable in both realistic sham TMS and high-intensity somatosensory scalp stimulation. Our findings suggest that, when effectiveness is optimized and peripheral confounds are controlled, TMS elicits specific long-lasting genuine EEG responses that reflect the initial engagement of specific cortical targets. These results challenge previous assumptions and highlight how TMS-EEG can be reliably used to assess large-scale properties within corticothalamic networks.

physiology↗

bioRxiv2025

Extracting Reproducible Components from Electroencephalographic Responses to Transcranial Magnetic Stimulation with Group Task-Related Component Analysis

Transcranial magnetic stimulation combined with electroencephalography (TMS-EEG) is a powerful technique for investigating human cortical circuits. However, characterizing TMS-evoked potentials (TEPs) at the group level typically relies on grand averaging across stimulus repetitions (trials) and subjects - an approach that assumes a level of spatial and temporal consistency that is often lacking in TEPs. Here, we introduce an adaptation of Group Task-Related Component Analysis (gTRCA), a novel multivariate signal decomposition method, to automatically extract TEP components that are maximally reproducible across both trials and subjects. Following the validation of a new permutation-based statistical test for gTRCA using simulated data, the method was applied to two independent TMS-EEG datasets, in which stimulation was targeted to the primary motor cortex (M1) in cohorts of 16 and 22 healthy participants. We found that gTRCA reliably identified TEP components that were reproducible at the group level. Notably, the main gTRCA component captured the key spatial, temporal, and spectral features of motor TEPs, remained robust despite reduced number of stimuli and participants, and was consistent across different recordings. These findings demonstrate that gTRCA affords a more reliable characterization of TEPs at the group level, thereby facilitating the translation of TMS-EEG research into clinical practice.

neuroscience↗