This study answers fundamental questions of large-scale neural networks in the human brain supporting crossmodal cognition. To reveal how auditory and visual stimuli and motor acts are arbitrarily combined as a result of crossmodal learning and integrated to supramodal symbolic representations, we will study the neural representations of the letters of the Roman alphabet. These consist of four unimodal representations (visual, auditory, and motor representations for writing and speaking) and learned connections between these, that is, the processes that underlie their audiovisual recognition and motor production. Accurate experimental control is facilitated by the fact that letters exhibit all the necessary properties of symbolic crossmodal representations but in a physically simple and exact format carrying no semantic associations that could confound the neurophysiological interpretation of results. Combined 3-Tesla functional magnetic resonance imaging (fMRI) and 306-channel magnetoencephalographic / 128-channel electroencephalographic (MEG/EEG) techniques with simultaneous behavioral recordings will be applied to pinpoint the exact underlying neural mechanisms. This approach combines the advantages of spatially accurate fMRI with temporally specific MEG/EEG, enabling accurate spatiotemporal characterization of brain activity totally noninvasively. To directly observe how large-scale neurocognitive networks evolve during crossmodal associative learning, we will also conduct fMRI/MEG/EEG measurements before and after our subjects are taught (previously unfamiliar) Japanese kana-letters.
The specific aims are to elucidate structure, function, and oscillatory mechanisms of fully established crossmodal neural networks based on previous extensive associative learning (Roman letters) and currently evolving networks representing novel crossmodal associations (Japanese letters). We will characterize the relative roles of deep brain nuclei, cerebellum, medial temporal lobe, and sensory-specific and multisensory association cortices in such networks. The multidimensional experimental design allows isolation of neural mechanisms utilized by perception, working memory, memory encoding, and recall. ? ?
| Erickson, Laura C; Rauschecker, Josef P; Turkeltaub, Peter E (2017) Meta-analytic connectivity modeling of the human superior temporal sulcus. Brain Struct Funct 222:267-285 |
| Nummenmaa, Aapo; McNab, Jennifer A; Savadjiev, Peter et al. (2014) Targeting of white matter tracts with transcranial magnetic stimulation. Brain Stimul 7:80-4 |
| Chang, Wei-Tang; Setsompop, Kawin; Ahveninen, Jyrki et al. (2014) Improving the spatial resolution of magnetic resonance inverse imaging via the blipped-CAIPI acquisition scheme. Neuroimage 91:401-11 |
| Lin, Fa-Hsuan; Ahveninen, Jyrki; Raij, Tommi et al. (2014) Increasing fMRI sampling rate improves Granger causality estimates. PLoS One 9:e100319 |
| Chang, Wei-Tang; Ahlfors, Seppo P; Lin, Fa-Hsuan (2013) Sparse current source estimation for MEG using loose orientation constraints. Hum Brain Mapp 34:2190-201 |
| Lin, Fa-Hsuan; Witzel, Thomas; Raij, Tommi et al. (2013) fMRI hemodynamics accurately reflects neuronal timing in the human brain measured by MEG. Neuroimage 78:372-84 |
| Chang, Wei-Tang; Nummenmaa, Aapo; Witzel, Thomas et al. (2013) Whole-head rapid fMRI acquisition using echo-shifted magnetic resonance inverse imaging. Neuroimage 78:325-38 |
| Zhang, Junpeng; Raij, Tommi; Hamalainen, Matti et al. (2013) MEG source localization using invariance of noise space. PLoS One 8:e58408 |
| Lin, Fa-Hsuan; Vesanen, Panu T; Nieminen, Jaakko O et al. (2013) Noise amplification in parallel whole-head ultra-low-field magnetic resonance imaging using 306 detectors. Magn Reson Med 70:595-600 |
| Ahveninen, Jyrki; Huang, Samantha; Nummenmaa, Aapo et al. (2013) Evidence for distinct human auditory cortex regions for sound location versus identity processing. Nat Commun 4:2585 |
Showing the most recent 10 out of 31 publications
