DOI: 10.5061/dryad.h44j0zpj8
title: The photosensitive phase acts as a sensitive window for seasonal multisensory neuroplasticity in male and female starlings

description: Traditionally, research unraveling seasonal neuroplasticity in songbirds
 has focused on the male song control system and testosterone. We
 longitudinally monitored the song and neuroplasticity in male and female
 starlings during multiple photoperiods using Diffusion Tensor and
 Fixel-Based techniques. These exploratory data-driven whole-brain methods
 resulted in a population-based tractogram uncovering microstructural
 sexual dimorphisms in the song control system and beyond. Male brains
 showed microstructural hemispheric asymmetries, whereas females had higher
 interhemispheric connectivity, which could not be attributed to brain size
 differences. Only females with large brains sing but differ from males in
 their song behavior by showing involvement of the hippocampus. Both sexes
 experienced multisensory neuroplasticity in the song control, auditory and
 visual system, and the cerebellum, mainly during the photosensitive
 period. This period with low gonadal hormones might represent a ‘sensitive
 window’ during which different sensory and motor systems in telencephalon
 and cerebellum can be seasonally re-shaped in both sexes.
description: <strong>1. MRI Data Processing</strong>
 Whole-brain volume was
 manually delineated on the T2-weighted 3D anatomical RARE scan, which
 covered the entire brain, including telencephalon, diencephalon,
 mesencephalon and metencephalon. These volumes were used as a measure of
 brain size, used in further statistical analysis. DW-images were
 prepared for voxel-based analysis using MRtrix3 version 3.0 (Tournier et
 al., 2012). We used an in-house algorithm to convert the Bruker 2dseq
 files to nifti files, which are compatible with other software programs
 such as SPM and MRtrix3. This step includes a signal scale correction,
 since the DWI data is acquired in three separate sequential scans.
 Furthermore, all DW-images were scaled by factor a 10 in 3 dimensions, to
 enable proper processing of small brains in software programs designed to
 process human data like SPM and MRtrix3. Voxel-based analysis requires
 that all images are spatially normalized to the same template, to enable
 voxel-wise comparisons. A simplified overview of the different MRI data
 processing steps is given in figure 1B-D. <strong>1.1 DTI
 processing</strong> Preprocessing
 the diffusion data for voxel-based analysis was performed using MRtrix3
 (Tournier et al., 2012). First, diffusion gradient orientations were
 checked and automatically corrected to match the coordinate frame of
 MRtrix3 and ensure the best global ‘connectivity’ (Jeurissen et al.,
 2014a). Since the DTI data is acquired in three separate sequential scans,
 we further corrected for intensity differences between scans by rescaling
 the diffusion scans based on their b0 images. Preprocessing of the
 individual DW-images included the following steps: denoising (Veraart et
 al., 2016), correction for Gibbs ringing (Kellner et al., 2016), motion
 and distortion correction using FSL (Andersson and Sotiropoulos, 2016;
 Jenkinson et al., 2012), bias field correction using ANTS (Advanced
 Normalization Tool; (Avants et al., 2010)), creating an automated
 whole-brain mask for whole-brain extraction that were manually checked,
 upsampling to isotropic voxels of 1.75 mm. These preprocessed
 diffusion-weighted images were used to calculate individual diffusion maps
 (FA, MD, AD, RD). The transformation parameters derived from building the
 FOD template (see 5.5.2) were applied to the diffusion maps to warp them
 into the template space to perform voxel-based analysis. Next, these
 images were smoothed to double voxel size (3.5 x 3.5 x 3.5 mm³). Finally,
 all normalized diffusion maps were averaged to create an FA template that
 is used as a background to display the statistical results.
 <strong>1.2 Fixel
 based analysis</strong> For
 calculation of the fiber-based metrics, we followed the preprocessing
 steps as defined in (Raffelt et al., 2017). Fixel based analysis follows
 the same preprocessing steps as DTI processing up until the bias field
 correction. Apparent fiber density analysis differs from DTI analysis by
 the fact that it is related to the diffusion-weighted signal intensity
 within a given voxel. Therefore, global intensity normalization is
 performed to ensure robust voxel-wise comparison across subjects. Within
 this step, we used the default FA threshold of 0.4 to create an
 approximate WM mask, which is used to normalize the median white matter
 b=0 intensity across all subjects (Raffelt et al., 2012). For each image,
 a white matter response function was estimated for spherical deconvolution
 using the unsupervised Dhollander algorithm (Dhollander et al., 2019).
 Next, the average of all individual response functions was calculated and
 used for constrained spherical deconvolution to estimate FOD images
 (Jeurissen et al., 2014b). These FOD images were normalized to create an
 unbiased study-based FOD template, which involves linear and non-linear
 registration (Raffelt et al., 2011). Next, the
 fixels in the FOD template are thresholded at 0.15, identifying the
 template white matter fixels to be included in further analysis. This
 threshold is lower than the default threshold of 0.25 for the human brain,
 as this threshold is too high for the songbird brain and excludes many of
 the genuine white matter fibers. We are aware that choosing a lower
 threshold comes with the risk of introducing noisy fixels, especially
 within grey matter, and take this into account in the interpretation of
 the results. The estimated
 transformation parameters or warps of each subject to the template were
 used to transform the individual FOD maps into template space without FOD
 reorientation, so that the apparent FD can be estimated prior to
 reorienting the fixels. In the next step, we compare the fixels within a
 spatially matching voxel of the individual subject and the template white
 matter fixels, to identify which fixels correspond to each other and
 subsequently assign the corresponding FD value (Raffelt et al.,
 2017). Next to FD, we
 also computed a fixel-based metric related to the macroscopic morphology
 in fiber bundle cross-section (FC). Fiber bundle cross-section (FC)
 information relies solely on the transformation parameters or Jacobian
 determinants generated during the construction of the population template,
 similar to other morphometry analyses like voxel-based morphometry
 (Ashburner and Friston, 2000). Morphological differences in the plane
 perpendicular to the fixel orientation could reflect differences in the
 number of axons, myelination, or the spacing between axons. For group
 statistical analysis, the FC values were logarithmically transformed to
 log FC to ensure that the data are centered around zero and normally
 distributed. Positive values indicate then expansion, whereas negative
 values reflect shrinkage of a fiber bundle relative to the template
 (Raffelt et al., 2017).
description: In each group, one scan failed due to excessive movement of the
 animal (subject ssw at SD12 and subject GoRR at LD4). In the male group,
 one animal died at the third time point (subject sblw). Of this animal we
 included MRI data of the first two time points.