Automatic Discrimination of Auditory Stimuli Perceived by the Human Brain Humans are able to perceive small difference of sound frequency but it is still unknown how the difference in frequency information is represented at the level of the primary sensory cortex. Indeed, analysis of fMRI imaging identified tonotopic maps through the auditory pathways to the primary sensory cortex. These maps are unfortunately too coarse to show ultrafine discrimination. Then, the hypothesis is that this small frequency differences are recognised thanks to the information coming from a large set of auditory neurons. To investigate this possibility, a multivoxel pattern discriminating analysis of the response of BOLDfMRI in the bilateral auditory cortex to tonal stimuli with different shift in frequency was performed. Our results suggest that small shifts in the frequency are easily classified compared with big shifts and that multiple areas of the auditory cortex are involved in the tone recognition.

Neurocurrent response functions: A unified approach to MEG source analysis under the continuous stimuli paradigm Abstract   Characterizing the neural dynamics underlying sensory processing is one of the central areas of investigation in systems and cognitive neuroscience. Neuroimaging techniques such as magnetoencephalography (MEG) and Electroencephalography (EEG) have provided significant insights into the neural processing of continuous stimuli, such as speech, thanks to their high temporal resolution. Existing work in the context of auditory processing suggests that certain features of speech, such as the acoustic envelope, can be used as reliable linear predictors of the neural response manifested in MEEG. The corresponding linear filters are referred to as temporal response functions (TRFs). While the functional roles of specific components of the TRF are wellstudied and linked to behavioral attributes such as attention, the cortical origins of the underlying neural processes are not as well understood.  In this work, we address this issue by estimating a linear filter representation of cortical sources directly from neuroimaging data in the context of continuous speech processing. To this end, we introduce NeuroCurrent Response Functions (NCRFs), a set of linear filters, spatially distributed throughout the cortex, that predict the cortical currents giving rise to the observed ongoing MEG (or EEG) data in response to continuous speech. NCRF estimation is cast within a Bayesian framework, which allows unification of the TRF and source estimation problems, and also facilitates the incorporation of prior information on the structural properties of the NCRFs. To generalize this analysis to MEEG recordings which lack individual structural magnetic resonance (MR) scans, NCRFs are extended to freeorientation dipoles and a novel regularizing scheme is put forward to lessen reliance on finetuned coordinate coregistration. We present a fast estimation algorithm, which we refer to as the  ChampLasso  algorithm, by leveraging recent advances in optimization, and demonstrate its utility through application to simulated and experimentally recorded MEG data under auditory experiments. Our simulation studies reveal significant improvements over existing methods that typically operate in a twostage fashion, in terms of spatial resolution, response function reconstruction, and recovering dipole orientations. The analysis of experimentallyrecorded MEG data without MR scans corroborates existing findings, but also delineates the distinct cortical distribution of the underlying neural processes at high spatiotemporal resolution. In summary, we provide a principled modeling and estimation paradigm for MEG source analysis tailored to extracting the cortical origin of electrophysiological responses to continuous stimuli.