Jyrki Ahveninen

The paper examines depth-dependent coupling between neuronal activity and fMRI in the human auditory cortex during naturalistic music listening, revealing spectrolaminar organization of neurovascular coupling.

This study uses MEG to show that trigger sounds in misophonia elicit smaller evoked responses and different oscillatory patterns in the right auditory cortex compared to non-trigger sounds.

The paper characterizes the auditory cortex's function through dynamic coactivation patterns identified in fMRI data, showing consistency between resting-state and task conditions.

This paper improves voxel targeting accuracy in MRS by correcting motion-induced displacement and quantifying anatomical targeting using a voxel-ROI overlap metric.

The paper uses MEG data and a machine learning approach to investigate causal connectivity between inferior frontal gyrus and temporal areas during semantic prediction, finding significant feedback effects.

The paper maps individual auditory cortex subareas using functional network analysis and fMRI, showing high intraindividual reproducibility and meaningful interindividual variability.

The paper decodes auditory working memory content from EEG aftereffects of auditory-cortical TMS using multivariate pattern analysis.

This study uses multilayer network analysis to examine functional connectivity across cortical depths in resting-state 7T fMRI, finding that superficial layers dominate information transfer while deeper layers drive clustering, particularly in frontotemporal and limbic regions.

The paper investigates whether visual stimuli drive high-frequency activity in human auditory cortex and finds no significant increase in high-frequency activity despite cross-sensory modulations.

This study uses intracranial EEG and machine learning to show dynamic, activity-silent mechanisms maintain auditory working memory in humans.

The paper compares auditory and visual aspects of multisensory working memory using bimodally matched stimuli and finds auditory matching is more accurate than visual in certain conditions.

This study uses 7T fMRI to compare brain activity during speech production and perceptual categorization, finding that left motor networks are involved in both tasks.

The paper uses MEG and computational modeling to show that cross-sensory visual input to human auditory cortex is feedback-type, consistent with findings in non-human primates.

This paper develops a distributed sEEG source-modeling approach for analyzing intracranial recordings, showing it effectively captures variance and provides consistent results for event-related potentials and high broadband gamma activity.

This study examines individual variability in the functional organization of human and monkey auditory cortex using intrinsic connectivity and task-based fMRI, finding greater variability in non-primary regions, compared to visual areas, and left hemisphere lateralization.

The paper investigates how the brain represents auditory distance using reverberation cues, finding neural activity in posterior auditory cortex that is independent of intensity and binaural cues.

The paper introduces a high-resolution numerical method for solving forward problems in neurophysiological recordings, showing improved accuracy and speed compared to existing methods.

This study examines how feature-based attention affects motion detection across visual and auditory modalities, finding that combined audiovisual cues improve detection accuracy compared to unimodal cues.