Kaisu Lankinen

This study investigates how EEG lead configurations affect TMS-induced EEG artifacts and finds that certain configurations can minimize both pulse and decay artifacts.

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

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.

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.