Functional MRI of Auditory Responses in the Zebra Finch Forebrain Reveals a Hierarchical Organisation Based on Signal Strength but Not Selectivity

BACKGROUND: Male songbirds learn their songs from an adult tutor when they are young. A network of brain nuclei known as the 'song system' is the likely neural substrate for sensorimotor learning and production of song, but the neural networks involved in processing the auditory feedback signals necessary for song learning and maintenance remain unknown. Determining which regions show preferential responsiveness to the bird's own song (BOS) is of great importance because neurons sensitive to self-generated vocalisations could mediate this auditory feedback process. Neurons in the song nuclei and in a secondary auditory area, the caudal medial mesopallium (CMM), show selective responses to the BOS. The aim of the present study is to investigate the emergence of BOS selectivity within the network of primary auditory sub-regions in the avian pallium. METHODS AND FINDINGS: Using blood oxygen level-dependent (BOLD) fMRI, we investigated neural responsiveness to natural and manipulated self-generated vocalisations and compared the selectivity for BOS and conspecific song in different sub-regions of the thalamo-recipient area Field L. Zebra finch males were exposed to conspecific song, BOS and to synthetic variations on BOS that differed in spectro-temporal and/or modulation phase structure. We found significant differences in the strength of BOLD responses between regions L2a, L2b and CMM, but no inter-stimuli differences within regions. In particular, we have shown that the overall signal strength to song and synthetic variations thereof was different within two sub-regions of Field L2: zone L2a was significantly more activated compared to the adjacent sub-region L2b. CONCLUSIONS: Based on our results we suggest that unlike nuclei in the song system, sub-regions in the primary auditory pallium do not show selectivity for the BOS, but appear to show different levels of activity with exposure to any sound according to their place in the auditory processing stream

Susceptibility correction for improved tractography using high field DT-EPI

Diffusion Tensor Magnetic Resonance Imaging (DTI) is a well known technique that can provide information about the neuronal fiber structure of the brain. However, since DTI requires a large amount of data, a high speed MRI acquisition technique is needed to acquire these data within a reasonable time. Echo Planar Imaging (EPI) is a technique that provides the desired speed. Unfortunately, the advantage of speed is overshadowed by image artifacts, especially at high fields. EPI artifacts originate from susceptibility differences in adjacent tissues and correction techniques are required to obtain reliable images. In this work, the fieldmap method, which tries to measure distortion effects, is optimized by using a non linear least squares estimator for calculating pixel shifts. This method is tested on simulated data and proves to be more robust against noise compared to previously suggested methods. Another advantage of this new method is that other parameters like relaxation and the odd/even phase difference are estimated. This new way of estimating the field map is demonstrated on a hardware phantom, which consists of parallel bundles made of woven strands of Micro Dyneema fibers. Using a modified EPI-sequence, reference data was measured for the calculation of fieldmaps. This allows one to reposition the pixels in order to obtain images with less distortions. The correction is applied to non-diffusion weighted images as well as diffusion weighted images and fiber tracking is performed on this corrected data.</p

Data acquisition.

<p>Schematical representation of the auditory stimulation design. The entire paradigm was repeated 6 times with alternate presentation of the six different stimuli BOS, reversed BOS, random BOS, familiar CON, BOS ripples, and WN.</p

Experimental auditory stimuli.

<p>Spectrograms (top row) and oscillograms (bottom row) of an example of BOS and three temporal manipulated versions including reversed BOS, random BOS and BOS ripples. The spectrograms show that manipulations are restricted to each song separately. To obtain a better visualisation of the spectrograms, the maximum frequency shown is limited to 10 kHz (actual maximum frequency is 22 kHz).</p

Stimulus and regional selectivity.

<p>(A) The average BOLD signal and (B) the average number of pixels activated in the four regions of interest L2a, L2b, L3/NCM and CMM together with exposure to BOS, reversed BOS, random BOS, CON, BOS ripples and WN. All means are represented with their corresponding standard errors (SEM).</p

Average BOLD signal of one example bird (see online edition for color figure).

<p>The images illustrate the typical activation pattern that was found in all experimental birds. The signal shown here is for all sounds presented and all brain images averaged together. The left panel shows the <i>P</i>-values of significant activated pixels, the right panel shows the signal strength relative to the mean signal difference. The three lines show the division in regions of interest conform <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0003184#pone-0003184-g003" target="_blank">Figure 3</a>.</p