Frequency preference and attention effects across cortical depths in the human primary auditory cortex

Abstract

Columnar arrangements of neurons with similar preference have been suggested as the fundamental processing units of the cerebral cortex. Within these columnar arrangements, feed-forward information enters at middle cortical layers whereas feedback information arrives at superficial and deep layers. This interplay of feedforward and feedback processing is at the core of perception and behavior. Here we provide in vivo evidence consistent with a columnar organization of the processing of sound frequency in the human auditory cortex. We measure submillimeter functional responses to sound frequency sweeps at high magnetic fields (7 tesla) and show that frequency preference is stable through cortical depth in primary auditory cortex. Furthermore, we demonstrate that-in this highly columnar cortex-task demands sharpen the frequency tuning in superficial cortical layers more than in middle or deep layers. These findings are pivotal to understanding mechanisms of neural information processing and flow during the active perception of sounds.A uditory perception starts in our ears, where hair cells at different places in the cochlea respond to different sound frequencies. The spatially ordered arrangement of neural responses to frequencies (tonotopy) that arises from this transduction mechanism is preserved in subcortical (1, 2) and cortical stages of processing, where neuronal populations form multiple tonotopic maps (3, 4). At the cortical level, tonotopic maps describe systematic changes along the surface. In the orthogonal direction (i.e., perpendicular to the cortical surface), aggregates of neurons with parallel axons have been reported (5, 6). These anatomical observations of cortical microcolumns inspired invasive electrophysiological investigations in cats (7), demonstrating that frequency preference is constant across cortical depth (i.e., frequency columns). Since this early study, frequency columns have been observed in a variety of animals (3,(8)(9)(10), and a columnar organization has been suggested for other acoustic properties (10, 11). Despite this anatomical and physiological evidence from animal models, the role of cortical columns in auditory perception is not understood (6, 12, 13). To unravel intracolumnar computations, it is of fundamental importance to analyze the transformation of information across cortical depths. Differences in cell types and in patterns of input and output projections suggest a distinct role of cortical layers in neural information processing (5). In particular, behavioral demands and ongoing brain states can modulate the functional properties of layer 2/3 neurons, suggesting that supragranular neuronal populations may be of fundamental relevance for the processing of sensory information in a context-dependent manner (14). Recordings in the primary auditory cortex of animals have shown differences across layers in response latency (15, 16), in frequency selectivity (i.e., tuning width) (8, 16), and in the complexity of neuronal preference to acoustic information (i.e., recepti...

Laminar fMRI: Applications for cognitive neuroscience

“…Finally, De Martino et al (2015) successfully imaged columnar structures exhibiting frequency tuning in the primary auditory cortex. Together, these examples demonstrate that high spatial resolution fMRI can provide important insights into neural response properties across cortical depths, and columnar structures of the human brain.…”

A critical assessment of data quality and venous effects in ultra-high-resolution fMRI

“…14 15 While a number of ultra-high-resolution fMRI studies have been conducted (see De Martino et al, 2018; 16 Dumoulin et al, 2018; Lawrence et al, 2017 for reviews), ultra-high-resolution fMRI has not yet become a 17 mainstream technique in neuroscience. Part of the reason is the limited availability of ultra-high-field MR 18 scanners (7 Tesla or higher), which provide increases in signal-to-noise ratio and contrast-to-noise ratio 19 critical for high-resolution imaging. However, the prevalence of 7T scanners is growing rapidly (Trattnig et 20 al., 2018).…”

“…We also 18 increased the density of surface vertices using mris_mesh_subdivide. This bisected each edge and 19 resulted in a doubling of the number of vertices. Finally, to reduce computational burden, we truncated 20 the surfaces to include only posterior portions of cortex (since this is where functional measurements are 21 made).…”

“…We then 18 regularized the fieldmaps by performing 3D local linear regression using an Epanechnikov kernel with 19 radius 5 mm; we used values in the magnitude component of the fieldmap as weights in the regression in 20 order to improve robustness of the field estimates. This regularization procedure removes noise from the 21 fieldmaps and imposes some degree of spatial smoothness.…”

“…The exact same procedures associated with volume-based pre-processing are 16 performed, except that the final spatial interpolation is performed at the locations of the vertices of the 6 17 depth-dependent surfaces. Thus, the only difference between volume-and surface-based pre-processing 18 is that the data are prepared either on a regular 3D grid (volume) or an irregular manifold of densely 19 spaced vertices (surface). The use of simple interpolation to map volumetric data onto surface 20 representations (as opposed to incorporating spatial kernels tailored to the cortical surface (Grova et al, 21 2006)) helps maximize spatial resolution and avoids making strong assumptions about cortical topology.…”