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Rochas, V.

Publications and source records attributed to Rochas, V..

2 recordsLinked to original sources

Characterising the diffusion functional signature of negative BOLD with interleaved TMS-fMRI in the human brain

The coupling between brain excitatory activity and positive blood oxygen level-dependent (BOLD) responses is well-established. Although often associated with inhibition, negative BOLD remains partially understood. Moving away from neurovascular coupling, apparent diffusion coefficient (ADC)-fMRI provides a more direct measure of excitatory activity, possibly mediated by transient cellular deformations. While decreases in ADC align with positive BOLD, the possible translation of negative BOLD into positive ADC has not been investigated in humans. Diffusion-weighted fMRI (dfMRI) combines vascular and microstructural contributions. Using interleaved subthreshold transcranial magnetic stimulation (TMS)-fMRI on the primary motor cortex (M1), we induced negative BOLD responses in contralateral M1 and primary somatosensory cortex (S1). This was accompanied by a negative dfMRI response, but no ADC-fMRI response, indicating minimal microstructural fluctuations. In ipsilateral M1/S1, no BOLD response was detected while dfMRI revealed a positive cluster, suggesting sensitivity to subtle neural activity. These findings provide new insights into vascular and neuronal responses underlying subthreshold TMS and negative BOLD.

neuroscience↗

Neural integration and segregation revealed by a joint time-vertex connectome spectral analysis

Brain oscillations are produced by the coordinated activity of large groups of neurons and different rhythms are thought to reflect different modes of information processing. These modes, in turn, are known to occur at different spatial scales. Nevertheless, how these rhythms support different modes of information processing at the brain scale is not yet fully understood. Here we present "Joint Time-Vertex Connectome Spectral Analysis", a framework for characterizing the spectral content of brain activity both in time (temporal frequencies) and in space (spatial connectome harmonics). This method allows us to estimate the contribution of integration (global communication) and segregation (functional specialization) mechanisms at different temporal frequency bands in source-reconstructed M/EEG signals, thus providing a better understanding of the complex interplay between different information processing modes. We validated our method on two different datasets, an auditory steady-state response (ASSR) and a visual grating task. Our results suggest that different information processing mechanisms are carried out at different frequency channels: while integration seems to be a specific mechanism occurring at low temporal frequencies (alpha and theta), segregation is only observed at higher temporal frequencies (high and low gamma). Crucially, the estimated contribution of the integration and segregation mechanisms predicts performance in a behavioral task, demonstrating the neurophysiological relevance of this new framework.

neuroscience↗