Cerebral Cortex Advance Access published September 2, 2014 Cerebral Cortex doi:10.1093/cercor/bhu193
Frequency Selectivity of Voxel-by-Voxel Functional Connectivity in Human Auditory Cortex Kuwook Cha1,3,4, Robert J. Zatorre1,3,4 and Marc Schönwiesner2,3,4 1 Cognitive Neuroscience Unit, Montréal Neurological Institute, McGill University, Montréal, QC, Canada H3A 2B4, 2Département de Psychologie, Université de Montréal, Montréal, QC, Canada H2V 2S9, 3International Laboratory for Brain, Music, and Sound Research (BRAMS), Montréal, QC, Canada H2V 4P3 and 4Center for Research on Brain, Language and Music (CRBLM), Montréal, QC, Canada H3G 2A8
Address correspondence to Kuwook Cha, 3801 rue University, Room 276, Montréal, QC, Canada H3A 2B4. Email: [email protected]
Keywords: frequency selectivity, functional connectivity, functional magnetic resonance imaging, functional organization, human auditory cortex
Introduction To understand complex computation in the brain, it is necessary to identify the pattern of functional interactions between different regions at various spatial and temporal scales. This requires an understanding of the pattern of temporal coherence of neural activity between individual neurons or different populations of neurons, beyond relating only the magnitude of neural responses to behavioral and cognitive variables. Temporal coherence in neural activity has been studied for decades in terms of synchrony in spiking activity (Phillips et al. 1984), synchronous oscillations of neural populations (Singer and Gray 1995; Fries 2005), and correlations in trial-to-trial variability (or “noise” correlation) (Gawne and Richmond 1993; Lee et al. 1998; Averbeck et al. 2006). Given the invasive nature of this line of research, such fine-scale temporal dynamics in human brains has not been studied much. However, temporal coherence in activity measured functional magnetic resonance imaging (fMRI), referred to as “functional connectivity” (FC), has gained much attention and has provided significant information in the field of systems and cognitive neuroscience (Fox and Raichle 2007; Behrens and Sporns 2012). Although the fluctuations that yield coherent patterns in fMRI are rather very slow ( 0.05; 17.12% [SD = 0.01] of voxels) was replaced with the measure of the center of mass of the amplitudes: the amplitude was averaged with the log-transformed frequency values weighted.
and frequency tuning width, which can serve as indicators of response properties of the core-fields area according to previous studies (Morel et al. 1993; Rauschecker et al. 1995; Wessinger et al. 2001; Petkov et al. 2006; Moerel et al. 2012). The delineation of the core area by this method was generally in agreement with the results of the probabilistic cytoarchitectonic maps (Morosan et al. 2001; Rademacher et al. 2001). Also, this same method identified the core-fields in macaque monkeys that overlap with previous parcellations of AC in the same data (Petkov et al. 2006). Only the voxels that had P-values
Frequency Selectivity of Voxel-by-Voxel Functional Connectivity in Human Auditory Cortex Kuwook Cha1,3,4, Robert J. Zatorre1,3,4 and Marc Schönwiesner2,3,4 1 Cognitive Neuroscience Unit, Montréal Neurological Institute, McGill University, Montréal, QC, Canada H3A 2B4, 2Département de Psychologie, Université de Montréal, Montréal, QC, Canada H2V 2S9, 3International Laboratory for Brain, Music, and Sound Research (BRAMS), Montréal, QC, Canada H2V 4P3 and 4Center for Research on Brain, Language and Music (CRBLM), Montréal, QC, Canada H3G 2A8
Address correspondence to Kuwook Cha, 3801 rue University, Room 276, Montréal, QC, Canada H3A 2B4. Email: [email protected]
Keywords: frequency selectivity, functional connectivity, functional magnetic resonance imaging, functional organization, human auditory cortex
Introduction To understand complex computation in the brain, it is necessary to identify the pattern of functional interactions between different regions at various spatial and temporal scales. This requires an understanding of the pattern of temporal coherence of neural activity between individual neurons or different populations of neurons, beyond relating only the magnitude of neural responses to behavioral and cognitive variables. Temporal coherence in neural activity has been studied for decades in terms of synchrony in spiking activity (Phillips et al. 1984), synchronous oscillations of neural populations (Singer and Gray 1995; Fries 2005), and correlations in trial-to-trial variability (or “noise” correlation) (Gawne and Richmond 1993; Lee et al. 1998; Averbeck et al. 2006). Given the invasive nature of this line of research, such fine-scale temporal dynamics in human brains has not been studied much. However, temporal coherence in activity measured functional magnetic resonance imaging (fMRI), referred to as “functional connectivity” (FC), has gained much attention and has provided significant information in the field of systems and cognitive neuroscience (Fox and Raichle 2007; Behrens and Sporns 2012). Although the fluctuations that yield coherent patterns in fMRI are rather very slow ( 0.05; 17.12% [SD = 0.01] of voxels) was replaced with the measure of the center of mass of the amplitudes: the amplitude was averaged with the log-transformed frequency values weighted.
and frequency tuning width, which can serve as indicators of response properties of the core-fields area according to previous studies (Morel et al. 1993; Rauschecker et al. 1995; Wessinger et al. 2001; Petkov et al. 2006; Moerel et al. 2012). The delineation of the core area by this method was generally in agreement with the results of the probabilistic cytoarchitectonic maps (Morosan et al. 2001; Rademacher et al. 2001). Also, this same method identified the core-fields in macaque monkeys that overlap with previous parcellations of AC in the same data (Petkov et al. 2006). Only the voxels that had P-values
