Causal mapping of self-motion networks in the human brain
Dary, Z.; Lagarde, S.; Medina Villalon, S.; Dary, H.; Leonard, J.; Bartolomei, F.; Lopez, C.
Show abstract
Although functional neuroimaging studies using caloric and galvanic vestibular stimulation have identified a distributed cortical network involved in human vestibular processing, including the posterior insula, parietal operculum, temporo-parietal junction, cingulate and frontal areas, the causal contribution of specific brain regions to vestibular self-motion perception remains poorly understood. Invasive electrical brain stimulation (EBS) during stereoelectroencephalography (SEEG) offers a unique opportunity to causally map the cortical networks underlying vestibular self-motion perception in humans. Here, we retrospectively analyzed EBS-induced vestibular percepts in 354 patients with drug-resistant epilepsy undergoing SEEG. A total of 19,708 stimulations (11,004 at 50 Hz and 8,704 at 1 Hz) yielded 3,015 clinical responses. Vestibular self-motion illusions were defined as sensations of vertigo, dizziness, whole-body rotation, or translation occurring without corresponding physical movement. Stimulation sites were assigned to the seven large-scale functional networks of the Schaefer-Yeo atlas to characterize the network organization of vestibular self-motion perception. Vestibular self-motion sensations were elicited in 46 patients during 86 EBS delivered outside epileptogenic and lesional regions. Percepts ranged from nonspecific vertigo and dizziness (54.7%) to more explicit rotational (30.2%) and translational (15.1%) self-motion illusions. Vestibular responses were most commonly evoked by stimulation of the insula, medial temporal regions, cingulate cortex, inferior frontal gyrus, and premotor cortices. Network-level mapping using the Schaefer-Yeo atlas revealed a non-uniform distribution of vestibular sites across large-scale functional networks, with the highest representation within the salience/ventral attention, visual, and dorsal attention networks. Together, these findings provide causal evidence that vestibular self-motion perception emerges from activity within a distributed cortical network involved in attentional control and multisensory processing. Beyond advancing our understanding of human vestibular processing, these findings may have clinical relevance for disorders involving altered self-motion perception, including vestibular epilepsy and functional neurological disorders.
Matching journals
The top 7 journals account for 50% of the predicted probability mass.
Similar papers in this journal
- Pre-stimulus Brain States Predict and Control Variability in Stimulation Responses 95%
- The effects of direct brain stimulation in humans depend on frequency, amplitude, and white-matter proximity. 93%
- Simultaneous stereo-EEG and high-density scalp EEG recordings to study the effects of intracerebral stimulation parameters 93%
Similar papers in this journal
Similar papers in this journal
- Thalamic deep brain stimulation modulates circadian and infradian cycles of seizure risk in epilepsy 93%
- Dual mechanisms of ictal high frequency oscillations in human rhythmic onset seizures 93%
- Entrainment of local synchrony reveals a causal role for high-beta right frontal oscillations in human visual consciousness 93%
Similar papers in this journal
- Multisensory Flicker Modulates Widespread Brain Networks and Reduces Interictal Epileptiform Discharges in Humans 93%
- Integrating electric field modelling and neuroimaging to explain inter-individual variability of tACS effects 92%
- Rhythmic interactions between the mediodorsal thalamus and prefrontal cortex precede human visual perception 92%
Similar papers in this journal
- Individual-specific resting-state networks predict language dominance in drug-resistant epilepsy 95%
- Synchronizability predicts effective responsive neurostimulation for epilepsy prior to treatment 93%
- Stimulation better targets fast ripple generating networks in super-responders to the responsive neurostimulator system (RNS) 93%
"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.