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Mapping tonotopic organization in human temporal cortex: representational similarity analysis in EMEG source space.

Su L, Zulfiqar I, Jamshed F, Fonteneau E, Marslen-Wilson W - Front Neurosci (2014)

Bottom Line: We then combined a form of multivariate pattern analysis (representational similarity analysis) with a spatiotemporal searchlight approach to successfully decode information about patterns of neuronal frequency preference and selectivity in bilateral superior temporal cortex.Observed frequency preferences in and around Heschl's gyrus matched current proposals for the organization of tonotopic gradients in primary acoustic cortex, while the distribution of narrow frequency selectivity similarly matched results from the fMRI literature.The spatial maps generated by this novel combination of techniques seem comparable to those that have emerged from fMRI or ECOG studies, and a considerable advance over earlier MEG results.

View Article: PubMed Central - PubMed

Affiliation: Department of Psychiatry, University of Cambridge Cambridge, UK ; Department of Psychology, University of Cambridge Cambridge, UK.

ABSTRACT
A wide variety of evidence, from neurophysiology, neuroanatomy, and imaging studies in humans and animals, suggests that human auditory cortex is in part tonotopically organized. Here we present a new means of resolving this spatial organization using a combination of non-invasive observables (EEG, MEG, and MRI), model-based estimates of spectrotemporal patterns of neural activation, and multivariate pattern analysis. The method exploits both the fine-grained temporal patterning of auditory cortical responses and the millisecond scale temporal resolution of EEG and MEG. Participants listened to 400 English words while MEG and scalp EEG were measured simultaneously. We estimated the location of cortical sources using the MRI anatomically constrained minimum norm estimate (MNE) procedure. We then combined a form of multivariate pattern analysis (representational similarity analysis) with a spatiotemporal searchlight approach to successfully decode information about patterns of neuronal frequency preference and selectivity in bilateral superior temporal cortex. Observed frequency preferences in and around Heschl's gyrus matched current proposals for the organization of tonotopic gradients in primary acoustic cortex, while the distribution of narrow frequency selectivity similarly matched results from the fMRI literature. The spatial maps generated by this novel combination of techniques seem comparable to those that have emerged from fMRI or ECOG studies, and a considerable advance over earlier MEG results.

No MeSH data available.


Related in: MedlinePlus

Frequency preferences in left and right hemisphere superior temporal cortex derived from ssRSA analysis of EMEG data. The search areas were restricted to the regions denoted by the white lines in the upper panels. Green dashed lines (lower panels) show the outlines of Heschl's gyrus (HG). Other anatomical landmarks are superior temporal gyrus (STG), superior temporal sulcus (STS), and planum temporale (PT). The outlines of HG were generated based on the FreeSurfer cortical parcellation (Fischl et al., 2004; Desikan et al., 2006) and on Moerel et al. (2014).
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Figure 7: Frequency preferences in left and right hemisphere superior temporal cortex derived from ssRSA analysis of EMEG data. The search areas were restricted to the regions denoted by the white lines in the upper panels. Green dashed lines (lower panels) show the outlines of Heschl's gyrus (HG). Other anatomical landmarks are superior temporal gyrus (STG), superior temporal sulcus (STS), and planum temporale (PT). The outlines of HG were generated based on the FreeSurfer cortical parcellation (Fischl et al., 2004; Desikan et al., 2006) and on Moerel et al. (2014).

Mentions: Figures 6C,D show the distributions of the standard deviation (SD) of the Gaussian tuning curve for each vertex in bilateral superior temporal cortex. Note that because the unit of the SD is expressed in frequency bands, the SDs for Gaussians with higher frequency preferences will cover larger ranges of frequencies (in Hz). The distributions for the left hemisphere are strongly bimodal, with a narrow selectivity group centered around a peak SD distribution of 3.7, and a broader sensitivity group with a peak SD distribution of 5.2. The right hemisphere (Figure 6D) shows a very different pattern, with most of the region tested being only weakly frequency selective. There is a primarily unimodal distribution, corresponding to the broader sensitivity group in the left hemisphere, with the peak of the SD distribution falling at 4.5. A much smaller set of vertices show stronger selectivity, with SDs falling in the range 2–3. The spatial mapping of frequency preference and selectivity is shown in Figures 7, 9 below.


Mapping tonotopic organization in human temporal cortex: representational similarity analysis in EMEG source space.

Su L, Zulfiqar I, Jamshed F, Fonteneau E, Marslen-Wilson W - Front Neurosci (2014)

Frequency preferences in left and right hemisphere superior temporal cortex derived from ssRSA analysis of EMEG data. The search areas were restricted to the regions denoted by the white lines in the upper panels. Green dashed lines (lower panels) show the outlines of Heschl's gyrus (HG). Other anatomical landmarks are superior temporal gyrus (STG), superior temporal sulcus (STS), and planum temporale (PT). The outlines of HG were generated based on the FreeSurfer cortical parcellation (Fischl et al., 2004; Desikan et al., 2006) and on Moerel et al. (2014).
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4228977&req=5

Figure 7: Frequency preferences in left and right hemisphere superior temporal cortex derived from ssRSA analysis of EMEG data. The search areas were restricted to the regions denoted by the white lines in the upper panels. Green dashed lines (lower panels) show the outlines of Heschl's gyrus (HG). Other anatomical landmarks are superior temporal gyrus (STG), superior temporal sulcus (STS), and planum temporale (PT). The outlines of HG were generated based on the FreeSurfer cortical parcellation (Fischl et al., 2004; Desikan et al., 2006) and on Moerel et al. (2014).
Mentions: Figures 6C,D show the distributions of the standard deviation (SD) of the Gaussian tuning curve for each vertex in bilateral superior temporal cortex. Note that because the unit of the SD is expressed in frequency bands, the SDs for Gaussians with higher frequency preferences will cover larger ranges of frequencies (in Hz). The distributions for the left hemisphere are strongly bimodal, with a narrow selectivity group centered around a peak SD distribution of 3.7, and a broader sensitivity group with a peak SD distribution of 5.2. The right hemisphere (Figure 6D) shows a very different pattern, with most of the region tested being only weakly frequency selective. There is a primarily unimodal distribution, corresponding to the broader sensitivity group in the left hemisphere, with the peak of the SD distribution falling at 4.5. A much smaller set of vertices show stronger selectivity, with SDs falling in the range 2–3. The spatial mapping of frequency preference and selectivity is shown in Figures 7, 9 below.

Bottom Line: We then combined a form of multivariate pattern analysis (representational similarity analysis) with a spatiotemporal searchlight approach to successfully decode information about patterns of neuronal frequency preference and selectivity in bilateral superior temporal cortex.Observed frequency preferences in and around Heschl's gyrus matched current proposals for the organization of tonotopic gradients in primary acoustic cortex, while the distribution of narrow frequency selectivity similarly matched results from the fMRI literature.The spatial maps generated by this novel combination of techniques seem comparable to those that have emerged from fMRI or ECOG studies, and a considerable advance over earlier MEG results.

View Article: PubMed Central - PubMed

Affiliation: Department of Psychiatry, University of Cambridge Cambridge, UK ; Department of Psychology, University of Cambridge Cambridge, UK.

ABSTRACT
A wide variety of evidence, from neurophysiology, neuroanatomy, and imaging studies in humans and animals, suggests that human auditory cortex is in part tonotopically organized. Here we present a new means of resolving this spatial organization using a combination of non-invasive observables (EEG, MEG, and MRI), model-based estimates of spectrotemporal patterns of neural activation, and multivariate pattern analysis. The method exploits both the fine-grained temporal patterning of auditory cortical responses and the millisecond scale temporal resolution of EEG and MEG. Participants listened to 400 English words while MEG and scalp EEG were measured simultaneously. We estimated the location of cortical sources using the MRI anatomically constrained minimum norm estimate (MNE) procedure. We then combined a form of multivariate pattern analysis (representational similarity analysis) with a spatiotemporal searchlight approach to successfully decode information about patterns of neuronal frequency preference and selectivity in bilateral superior temporal cortex. Observed frequency preferences in and around Heschl's gyrus matched current proposals for the organization of tonotopic gradients in primary acoustic cortex, while the distribution of narrow frequency selectivity similarly matched results from the fMRI literature. The spatial maps generated by this novel combination of techniques seem comparable to those that have emerged from fMRI or ECOG studies, and a considerable advance over earlier MEG results.

No MeSH data available.


Related in: MedlinePlus