Studying the human subcortical auditory system non-invasively is challenging due to its small, densely packed structures deep within the brain. Additionally, the elaborate three-dimensional (3-D) structure of the system can be difficult to understand based on currently available 2-D schematics and animal models. Wfe addressed these issues using a combination of histological data, post mortem magnetic resonance imaging (MRI), and in vivo MRI at 7 Tesla. We created anatomical atlases based on state-of-the-art human histology (BigBrain) and postmortem MRI (50 µm). We measured functional MRI (fMRI) responses to natural sounds and demonstrate that the functional localization of subcortical structures is reliable within individual participants who were scanned in two different experiments. Further, a group functional atlas derived from the functional data locates these structures with a median distance below 2 mm. Using diffusion MRI tractography, we revealed structural connectivity maps of the human subcortical auditory pathway both in vivo (1050 µm isotropic resolution) and post mortem (200 µm isotropic resolution). This work captures current MRI capabilities for investigating the human subcortical auditory system, describes challenges that remain, and contributes novel, openly available data, atlases, and tools for researching the human auditory system. Understanding the structure of the human subcortical auditory pathway is a necessary step to research its role in hearing, speech communication, and music. However, due to methodological issues in human research, most of our understanding of the subcortical  auditory pathway arises from research conducted in animal models. This might be problematic because, while the organization of the auditory pathway is largely conserved across mammalian species , the forAlthough MRI has become increasingly powerful at imaging deep brain structures, anatomical investigation of the human subcortical auditory pathway has been primarily conducted in postmortem tissue dissection and staining. Advances in post-mortem human MRI allow for investigating three-dimensional (3-D) brain anatomy with increasingly high resolution (100 µm and below). This points to 'magnetic resonance histology’  as a proTo study the subcortical auditory system in living humans, MRI is the best available tool due to its high spatial resolution. Anatomical in vivo MRI investigations of the human subcortical auditory pathway so far have focused on thalamic nuclei , and theAlthough lower spatial resolution than anatomical MRI, functional MRI (fMRI) has been used to investigate the relevance of subcortical processing of auditory information in humans, but it has been limited by the small size of the structures involved and the relatively low resolution attainable at conventional field strengths (3 Tesla and below) . These aMore recent advances in MRI, especially the increased signal-to-noise ratio (SNR) available at ultra-high magnetic fields (7 Tesla and above), have enabled higher resolution functional imaging of subcortical structures and more advanced localization of human auditory subcortical structures as well as their functional characterization. Using MRI at 7 Tesla (7T), Despite the methodological advances in investigating the human brain, a systematic comparison of their capabilities for imaging the subcortical auditory system has not yet been undertaken. Here, we use publicly available histological data  to segmeWhere histology provides ground truth information about neural anatomy, we show that post mortem MRI can provide similarly useful 3-D anatomical information with less risk of tissue damage and warping. We also show that in vivo functional MRI can reliably identify the subcortical auditory structures within individuals, even across experiments. Overall, we found that each methodology successfully localized each of the small structures of the subcortical auditory system, and while known issues in image registration hindered direct comparisons between methodologies, each method provides complementary information about the human auditory pathway.https://bigbrain.loris.ca; To obtain a spatially accurate reference for all the subcortical auditory structures, we manually segmented publicly available histological data . Thus, our first step was to correctly register the area around the colliculi .The results of our BigBrain subcortical auditory segmentation in corrected MNI space are reported in Magnetic resonance histology—that is the study of tissue at microscopic resolution using MRI—provides several unique advantages over conventional histology: (1) it is non-destructive; (2) it suffers minimal distortion from physical sectioning and dehydration; (3) it yields unique contrast based on water in the tissue and how it is bound (e.g. diffusion); and (4) it produces 3-D data. These advantages make it an ideal medium for visualizing the 3-D organization of the deep brain structures . To deliBased on our segmentations of the subcortical auditory structures in the postmortem MRI data, the resulting 3-D model is presented in Identifying the connectivity between subcortical auditory nuclei is crucial for understanding the structure of the pathway. However, methods for tracing neuronal pathways that are available in other animal models are generally not available in human studies, even post mortem. Diffusion-weighted MRI (dMRI) can be used to measure the orientation and magnitude of molecular motion and infer patterns of white matter in brain tissue (both post mortem and in vivo). Using 200 µm diffusion-weighted MRI data acquired on the same post mortem sample , we modeled diffusion orientations and estimated likely connectivity pathways (or streamlines) using tractography. Constraining the streamlines to only those that pass through auditory structures , we visualized the connectivity map of the subcortical auditory pathway in Connectivity closely resembles the expected pattern of the human subcortical auditory wiring. In particular, streamlines predominantly pass through the lateral lemniscus, the primary subcortical auditory tract. Additional streamlines run through the brachium of the inferior colliculus, connecting the inferior colliculus with the medial geniculate of the thalamus. Many streamlines then course rostrally toward the auditory cortex (not present in this specimen).At the caudal extent of the lateral lemniscus, streamlines pass through the superior olivary complex. Streamlines also run through the root of CNVIII. In total, each expected step along the subcortical auditory pathway is represented in this connectivity map.To investigate the relationship between streamline connectivity and ROI definition strictness, we conducted two additional analyses. In Next, we resampled the diffusion MRI images to an in vivo-like resolution (1.05 mm isotropic). We again estimated fiber ODFs using CSD and estimated white matter connections with deterministic tractography. Using the (undilated but downsampled) anatomically defined ROIs as tractography waypoints, we can visualize streamline estimates connecting subcortical auditory structures . SimilarBecause T2*-weighted GRE imaging is sensitive to blood vessels, we processed our anatomical MR image to highlight brainstem vasculature . These 3-D vasculature images bear striking resemblance to post mortem data acquired with a stereoscopic microscope after full clearing method  , we collVisual inspection and comparison to the MNI dataset  showed tThe difficulty in delineating the CN and SOC from anatomical in vivo MRI data . The left column of Projecting these data to the reference MNI space allowed evaluating the correspondence between in vivo functionally defined regions and histological data  possibly due to partial voluming with the nearby cerebrospinal fluid in combination with thinness (thickness around 0.5 mm) of the dorsal CN as it wraps around inferior cerebellar peduncle see . Nearby,As the next step, we qualitatively investigated if the orientation of the vasculature at the level of the SOC may have an effect on size (and location) of the functionally defined regions. As a visual aid in this evaluation, we overlaid the functionally defined regions with the vasculature image obtained from the postmortem data . This spThe probability of the same voxel to be significantly modulated by sound presentation across subjects increased at the level of the IC and MGB, where the histologically defined regions corresponded (for the large part) to all subjects exhibiting significant responses to sounds. At the threshold of three subjects in the probabilistic maps, the IC seems to extend toward the superior direction, bordering and sometimes including parts of superior colliculus. On the other hand, similarly to what may happen in the SOC, the general directions of the vasculature penetrating the IC and draining blood towards the dorsal surface of brainstem angled in a superior direction  of vascular draining.A quantification of the volume of functionally defined structures is reported in With the successful identification of the subcortical auditory structures with functional MRI, we next sought to estimate the likely connections between these structures in vivo. We analyzed the high spatial and angular resolution diffusion data to estimate streamlines of white matter connectivity following a similar process as the postmortem MRI .While not a measure of actual physical brain connections—and therefore requiring caution in interpretation—connectivity patterns resemble what we would expect to see based on animal model tracer investigations. Overall, the connectivity network appears to be dominated by laterality, in that left hemisphere structures are generally more connected with other left hemisphere structures.However, there are a few notable exceptions to this pattern: the cochlear nuclei and superior olivary complexes are strongly connected bilaterally, which fits with animal research suggesting one-half to two-thirds of ascending auditory connections cross the midline at these early stages. Additionally, there are a small number of connections between left and right inferior colliculi, likely along the anatomical commissure of the inferior colliculus.The auditory pathway includes a number of subcortical structures and connections, but identifying these components in humans has been challenging with existing in vivo imaging methods. We showed that functional localization of the subcortical auditory system is achievable within each participant, and that localization is consistent across experimental sessions. To further facilitate research on the anatomy and function of the human subcortical auditory system, we created 3-D atlases of the human auditory pathway based on gold standard histology, 50 µm isotropic resolution post mortem anatomical MRI, and in vivo functional MRI at 7T. In addition, we created 3-D connectivity maps of the human subcortical auditory pathway using diffusion MRI tractography in a postmortem MRI sample and in living participants.These atlases and connectivity maps are the first fully 3-D representations of the human subcortical auditory pathway and are publicly available to make the localization of subcortical auditory nuclei easier. In particular, the atlases are available in a common reference space (MNI152) to make registration to other MRI data as straightforward as possible. As part of this registration process, we have improved the registration of the brainstem of BigBrain histological data to the MNI space, where the original MNI version presented a significant misregistration of the colliculi , while the overall image contrast facilitated registration with in vivo MRI. High-resolution in vivo functional MRI exhibited greater sensitivity to auditory structures than in vivo anatomical MRI that was even higher resolution. We showed that functional MRI is useful to localize structures throughout the auditory pathway despite their small size. In each participant, we identified voxels significantly responding to sound presentation in regions corresponding to the CN, SOC, IC and MGB. We validated these definition by evaluating both the within-subject reproducibility  and the ability of a probabilistic atlas defined on nine out of our 10 participants to generalize to the left out volunteer.In total, we found that each of the methods described here provides information to the delineation of the human subcortical auditory pathway. Our post mortem and in vivo data suggest that MRI is a capable tool for investigating this system across spatial scales providing a bridge to the gold standard, histology.While not representing specific cells, MRI holds a number of advantages over the gold standard method, histology . First, Probing the connectivity of the human subcortical auditory pathway has been extremely limited, since gold standard (but invasive) tracer studies are largely unavailable for human specimens. In this study, we show that diffusion MRI tractography is sensitive to connections within the human subcortical auditory system, both post mortem and in vivo. In addition to streamlines corresponding to the lateral lemniscus-the major ascending auditory white matter tract-we can see streamlines crossing the midline at the level of the superior olivary complex and the inferior colliculus.increase streamlines terminating within the anatomical ROIs. Dilating the post mortem ROIs and downsampling the data to the in vivo resolution both resulted in greater streamline connectivity between subcortical auditory structures, suggesting that our hypotheses were likely. Thus, while high spatial resolution diffusion-weighted MRI allowed for much finer, higher quality streamline estimates, it also places constraints on tractography analyses that must be accounted for and investigated further.Interestingly, with the highest resolution data (200 µm postmortem diffusion-weighted MRI), we were able to estimate streamlines visually resembling the expected auditory pathway, but missing putative key connections between subcortical auditory structures themselves when using the strictly defined ROIs as tractography seeds. In contrast, the relatively lower resolution in vivo diffusion-weighted MRI produced estimates of connectivity more like what we expected from the literature. We had two hypotheses as to why these results appeared. First, the higher resolution anatomical definition of the nuclei not including the immediately surrounding white matter could miss streamlines that terminate at the immediate proximity of the structures’ borders , this technique faces additional complications introduced by the number of orientations required, the gradient strength  selected, the modeling of diffusion or fiber orientations within each voxel, and the estimation of streamlines across brain regions, especially within the subcortical auditory system . The posOur BigBrain histological segmentations are very similar in volume to those reported previously in the literature , with slPostmortem MRI segmentations differed more greatly, with smaller CN and SOC definitions but larger MGB definitions compared to both the literature and BigBrain histological segmentations. These differences could possibly be caused by the reduced contrast-to-noise ratio in the post mortem MRI data compared to the histological data . This reduced contrast-to-noise ratio may be caused by both reduced differences in magnetic properties between the regions and their surrounding tissues as well as from residual partial volume effects  that may have blurred the borders of the auditory nuclei in the post mortem MRI data. Contrast-to-noise ratio may be ameliorated by different acquisition/reconstruction techniques , and optMore generally, post mortem imaging—whether MRI or histology—is prone to modest deformation of the specimen. Additionally, both post mortem specimens in this paper (BigBrain and post mortem MRI) were from 65-year-old male donors, and age may have additionally affected the volume of the brain structures we investigated.Despite these limitations, the inter-rater and inter-experiment reliability in this study suggest that each method is effective for localizing the subcortical auditory pathway. The reliable functional localization of subcortical auditory structures opens the door to future investigations of more complex human auditory processing. The atlases derived from each localization method is publicly available  to facilitate further investigations into the structure, function, and connectivity of the human subcortical auditory system in vivo. Lastly, the 3-D representations found in this paper and in the available data should be beneficial to others in understanding the immensely complex, but identifiable, structure of the human subcortical auditory pathway.See The experimental procedures were approved by the ethics committee of the Faculty for Psychology and Neuroscience at Maastricht University (reference number: ERCPN-167_09_05_2016), and were performed in accordance with the approved guidelines and the Declaration of Helsinki. Written informed consent was obtained for every participant before conducting the experiments. All participants reported to have normal hearing, had no history of hearing disorder/impairments or neurological disease.Images were acquired on a 7T Siemens MAGNETOM scanner , with 70 mT/m gradients and a head RF coil  at Maastricht University, Maastricht, Netherlands.2; matrix size = 320 × 320; 256 slices; 0.7 mm isotropic voxels; pixel bandwidth = 182 Hz/pixel; first phase encode direction anterior to posterior; second phase encode direction superior to inferior); 2) a Proton Density weighted (PDw) image (0.7 mm iso.) with the same 3-D MPRAGE as for the T1w image but without the inversion pulse ; 3) a T2*-weighted (T2w) anatomical image acquired using a modified 3-D MPRAGE sequence  and 4) a T1-weighted images acquired with a short inversion time (SI-T1w) using a 3-D MPRAGE . To improve transmit efficiency in temporal areas when acquiring these anatomical images we used dielectric pads   = 3, 66 directions and 11 additional b = 0 volumes for every b-value). A total of 462 volumes were obtained (231 in each phase encoding direction anterior-posterior and posterior-anterior) for a total acquisition time of 60 min.In the same session we acquired, for each participant, a diffusion-weighted MRI data set using a multi-band diffusion-weighted spin-echo EPI protocol originating from the 7T Human Connectome Project  , extende2)  coming from seven categories  presented in silent gaps in between the acquisition of functional volumes and were asked to press a button every time the same sound was repeated. The experimental paradigm followed a rapid-event-related design in which sounds were presented with a mean inter stimulus interval of four volumes (minimum three maximum five volumes). The two sessions were identical and each session consisted of twelve functional runs and across the 12 runs each sound was presented three times (i.e. each sounds was presented six times across the two sessions). The 168 sounds were divided in four sets of 42 sounds, each set was presented in three (non consecutive) runs. As a result, the 12 functional runs of each session formed four cross validation sets each one consisting of nine training runs and three testing runs (i.e. 126 training and 42 testing sounds). Note that the testing runs were non overlapping across the cross validations. Catch trials (i.e. sound repetitions) were added to each run, and were excluded from all analyses.2; matrix size = 188 × 188; 46 slices; 1.1 mm isotropic voxels; phase encode direction inferior to superior). Reverses phase encode polarity acquisitions were used for distortion correction. Respiration and cardiac information were collected during acquisition using a respiration belt and pulse oximeter respectively.Functional MRI data were acquired with a 2-D Multi-Band Echo Planar Imaging (2D-MBEPI) sequence  with sli2; matrix size = 188 × 188; 46 slices; 1.1 mm isotropic voxels; phase encode direction inferior to superior). Experiment 2 consisted of two sessions in which participants listened to 96 natural sounds (1 s long) coming from six categories  together with ripples . Some ripple sounds contain a short noise burst ('target') and participants were asked to detect such target in either low-frequency ripples or high-frequency ripples in the two sessions respectively (the target occurrence varied (70 vs. 30 percent) for ripples whose center frequency did or did not match the current attention condition). All sounds were presented in silent gaps in between the acquisition of functional volumes. The experimental paradigm followed a rapid-event-related design in which sounds were presented with a mean inter stimulus interval of four volumes (minimum three maximum five volumes). The two sessions consisted of eight functional runs and across the eight runs each natural sound was presented three times (i.e. each sounds was presented six times across the two sessions) while the ripples were presented seven times per run. The 96 natural sounds were divided in four sets of 24 sounds, each set was presented in two (non consecutive) runs. As a result, the eight functional runs of each session formed four cross validation sets each one consisting of six training runs and two testing runs . Note that the testing runs were non overlapping across the cross validations. In each session of experiment 2, we also collected a lower resolution (1 mm isotropic) anatomical reference images (T1 and PD weighted) using the 3D MPRAGE sequence for alignment purposes and included reverses phase encode polarity acquisitions for distortion correction. Respiration and cardiac information were collected during acquisition using a respiration belt and pulse oximeter respectively.In experiment 2, six of the volunteers that participated in experiment one were recalled and functional data were acquired with the same slice prescription and functional MRI parameters as in experiment 1  gadoteridol. Before the MRI acquisition, the specimen was placed in custom MRI-compatible tube immersed in liquid fluorocarbon.3 spatial resolution over a period of 14 hr, with FOV = 80 × 55 × 45 mm, repetition time (TR) = 50 ms, echo time (TE) = 10 ms, flip angle = 60°, and bandwidth = 78 Hz/pixel.Magnetic resonance imaging was conducted in a 210 mm small-bore Magnex/Agilent MRI at the Duke University Center for In Vivo Microscopy. 3-D gradient echo images were collected at 50 µm3 spatial resolution with 120 diffusion gradient directions at strength b = 4000 s/m2 and 11 b = 0 s/m2 volumes over 208 hr. The FOV was 90 × 55 × 45 mm with TR = 100 ms, TE = 33.6 ms, and bandwidth = 278 Hz/pixel.Diffusion-weighted spin echo images were collected at 200 µmSI-T1w, T1w, T2*w and PDw images (700 µm iso.) were transformed to Talairach space (500 µm iso.) using BrainvoyagerQX version 2.8.4 . IntensiIndividual masks for each 10 brainstems were created semi-automatically using ITK-SNAP version 3.6.0 active contour segmentation mode followed by manual edits. These masks included regions starting from 2 cm below the inferior part of pons to 0.5 cm above the medial geniculate nucleus (MGN), with a lateral extend reaching until the lateral geniculate nucleus (LGN) and 3 cm anterior from MGN, not including cerebellum or large arteries that lie on the surface of brainstem. These brainstem masks were then used with FSL-FNIRT  to warp http://www.bic.mni.mcgill.ca/ServicesAtlases/ICBM152NLin2009). The ICBM 152 reference includes T1w, T2w and PDw data and projecting in vivo and postmortem MRI as well as histology data to this space allowed us also to evaluate the contrast that these commonly used template images have in subcortical auditory areas. To register our in vivo MRI data set to MNI, we used FSL-FNIRT but this time driven by the T1w images (available both in our data set and in the MNI ICBM 152 2009b data).To compare in vivo with postmortem MRI and histology data, we projected the averaged SI-T1w, T1w, T2*w and PDw images to the MNI reference space  could then be transformed into diffusion space using the ‘antsApplyTransforms‘ command, with the affine transform matrix, a super-sampled diffusion image  as the reference image, and denoting the warp as an inverse transform.In vivo and postmortem images were registered non-linearly using ANTs. The in vivo SI-T1w image was warped to the postmortem diffusion b0 image following a rigid, then affine, then non-linear SyN algorithm. This produced an in vivo brainstem image in postmortem diffusion space.The ANTs non-linear registration also created warp and inverse warp transforms that could then be used to transform atlases from one space to another. To preserve the higher resolution of the post mortem MRI when inverse warping postmortem images to in vivo space, we supersampled the in vivo SI-T1w image to 200 µm (matching the post mortem diffusion image) or 50 µm .Finally, to transform the postmortem anatomical image (50 µm) to MNI space, we applied the inverse transform from postmortem anatomical to diffusion space (resampled to 50 µm), then the inverse transform from diffusion space to in vivo space (similarly upsampled to 50 µm), and finally from in vivo space to MNI space using the FSL-FNIRT inverse transform (described above).In what follows we describe the main anatomical observations related to the auditory structures as segmented in the 100 µm histological data. Images were segmented independently by two raters . Overlap between the two raters was high  enters the brainstem where the medulla and the pons meet (the pontomedullary junction). The cochlear component of the vestibulocochlear nerve is composed of spiral ganglion neurons, whose cell bodies are within the cochlea and which carry frequency-specific information to the brainstem.In the BigBrain histology, CNVIII extends primarily laterally  from the pontomedullary junction, bound posteriorly by the cerebellum. Parts of the nerve root are still visible in the images although being cut. It is therefore not labeled in our histological atlas (but see the post mortem MRI atlas below).Once reaching the brainstem, the auditory nerves split into two main routes-one to the anterior ventral cochlear nucleus (AVCN), and one to the posterior ventral cochlear nucleus (PVCN) and then on to the dorsal cochlear nucleus (DCN) . Within In the BigBrain data, the AVCN is situated anterior and medial to the root of CNVIII, while the PVCN continues from the root of CNVIII and extends posteriorly toward the DCN. The DCN is clearly visible as a dark band wrapping around the cerebellar peduncle posteriorly, becoming exposed on the dorsal surface of the pons.The next structure along the auditory pathway is the superior olivary complex (SOC), which in humans is located in the inferior pons. The SOC receives the majority of its ascending inputs from the contralateral cochlear nucleus, although it also receives ipsilateral inputs as well. The contralateral dominance is maintained throughout the remaining ascending pathway. The SOC is comprised of the lateral superior olive (LSO), medial superior olive (MSO), and the medial nucleus of the trapezoid body (MNTB). The size of each of these nuclei varies between species, and it is debated whether the trapezoid body exists in the human SOC  to the AVCN. It then extends about 1 cm rostrally , where it eventually meets the lateral lemniscal tract. The LSO neighbors the MSO near its caudalmost portion, forming a 'V’ shape when viewed axially. In our histological atlas, these two structures are combined into a single SOC segmentation. Cells of the MNTB are not clear to us in this sample, so we do not segment it in our atlas.The inferior colliculus (IC) is a large, spherical structure in the dorsal midbrain and receives ascending inputs from the auditory brainstem via the lateral lemniscus . The darkest staining within these structures corresponds to the central nucleus of the inferior colliculus. An intensity gradient outside of the central nucleus likely corresponds to the external and dorsal nuclei, which were included in our segmentation of the IC. Bounding the IC superiorly is the superior colliculus; medially, the commissure of the IC connecting the two inferior colliculi, as well as the aqueduct and periaqueductal grey; and anteriorly, other midbrain nuclei such as the cuneiform nucleus .The medial geniculate body (MGB) of the thalamus is the final subcortical auditory structure that sends auditory signals to the auditory cortex via the acoustic radiations . The MGBIn the BigBrain sample, the MGB is visible as a dark patch medial to the lateral geniculate nucleus (which can be easily identified by its striations) in a coronal view. Axially, the MGB takes an ovoid shape with a clear dorsolateral boundary next to the brachium of the superior colliculus, which appears light due to lack of cell nuclei being stained. Ventromedially, the MGB is bordered by a light band corresponding to the medial lemniscus. Rostrally, we marked the edge of the MGB where cell staining decreases, at the border with the pulvinar nucleus and ventral posterolateral nucleus of the thalamus.In what follows we describe the anatomical contrast that can be leveraged from these post mortem MRI data in order to identify structures in the auditory brainstem. We then used these segmentations to create an MRI-based atlas of the subcortical auditory system, separate from the BigBrain histology-based atlas.The CNVIII is visible in the post mortem MRI near the pontomedullary junction, extending laterally and anteriorly from the brainstem  can be seen as a dark spot in the T2*-weighted image between the facial nucleus and the trapezoid body , at least through the caudal half of the structure. We have included the portions of this fiber bundle in the segmentation of the medial geniculate, as the auditory fibers connecting the IC and the MGB are quite relevant to MRI connectivity investigations .As with the inferior colliculus, it may be possible to identify separate divisions within the medial geniculate. Within the overall structure, there are two identifiable substructures based on T2*-weighted MR image intensity. Dorsomedially , about half of the medial geniculate has high T2*-weighted contrast and appears bright; the ventrolateral  half appears darker in the T2*-weighted image. These segmentations largely (but not perfectly) align with the ventral and dorsal/medial nuclei of the medial geniculate in the Allen Human Brain Atlas , as wellhttp://neuroelf.net/ in Matlab version 2016a. For alignment across experiments (i.e. to co-register the data of experiment two to the ones collected in experiment 1) we used FSL-FLIRT. In this procedure the alignment between the functional data of the two experiments was tailored to a mask that included the brainstem, thalamus and auditory cortex.In both functional experiments, data were preprocessed using BrainvoyagerQX version 2.8.4 . Slice-sAfter pre-processing, functional images were then transformed to Talairach space using Brainvoyager at a resolution of 0.5 mm isotropic. We have previously used this procedure in order to reveal tonotopic maps in both the inferior colliculus and medial geniculate nucleus  and haveGLM-denoise  was usedSimilarly, the hemodynamic response function (HRF) best characterizing the response of each voxel in the brainstem was obtained using a deconvolution GLM (with nine stick predictors) on the training data. Note that this procedure, while possibly overfitting information in the training data, produces noise regressors and an HRF for each test run  that are not overfitted.The resulting HRF and noise regressors were used in a GLM analysis of the test runs. We combined all test runs  using a fixed effect analysis.https://osf.io/hxekn/) for inspection.Statistical maps of responses to sounds vs silence were corrected for multiple comparisons at the individual level using False Discovery Rate . An additional threshold on the uncorrected p-value of each voxel  was applied to further reduce the number of false positive activation that can be expected when applying FDR. Unless otherwise stated, single subject statistical maps are displayed by color coding voxels that surpass these statistical thresholds. Unthresholded statistical maps are visualized in Figure 10 and are available at the online repository of the data (https://osf.io/hxekn/) for inspection.The functional activation maps of the six participants that took part in both experiments have been analyzed to demonstrate within participant reproducibility of effects. Since the stimuli were different and the number of runs were different, this second experiment shows a generalization of the first experiment, thereby additionally validating the detection of these structures. 3  and 2) summing maps across subjects at each single voxel . The additional clustering allowed us to further control for possible false positives by imposing a neuroanatomically plausible hypothesis . The same procedure was also repeated by leaving one subject out (i.e. we generated probabilistic maps from 9 out of the 10 subjects each time leave one subject out). The leave-one-out probabilistic maps were then back-projected to the anatomical space of the left out subject . Unless otherwise stated, probabilistic maps are displayed with minimum threshold of at least three out of 10 (or nine for the leave one out maps) subjects exhibiting significant responses at each voxel. Unthresholded probabilistic maps are available for inspection at the online repository.To produce group level results, the single subject statistical maps were warped to the reference brainstem (subject 1) by applying the warping field obtained on the anatomical data. After projection to the common space, single subject statistical maps were binarized and converted to a probabilistic map by: 1) applying of a cluster size threshold of 3.37 mmhttps://osf.io/hxekn/) for inspection.We evaluated how well cluster localized on the basis of our probabilistic maps generalize to new data. To compare the functional activation maps with histology data and post mortem MRI data, the probabilistic maps were projected to the MNI space using the warping field obtained from the anatomical dataset.https://bigbrain.loris.ca). We downloaded both the original images and the dataset already aligned to MNI ICBM 152. The nuclei along the auditory pathway  were manually segmented in the histology space image using ITK-SNAP  but corrected the region around the inferior colliculus bilaterally, we followed N steps. First, we defined a region of interest around the inferior colliculus using common anatomical landmarks that were visible in the BigBrain MNI and MNI (2009b) T1, PD, T2 images and where aligned satisfactorily. Second, this region was cut out from the BigBrain MNI and replaced by the same region  in the BigBrain histology space data (before projection to MNI). The convex hulls of the region of interest in the BigBrain histology and in the MNI space were matched using 3-D optimal transport as implemented in Geogram version 1.6.7 . Third, Gradient echo (GRE) MRI is sensitive to vasculature within the imaged tissue. To highlight vasculature in the post mortem brainstem specimen, we computed the minimum intensity projection in coronal sagittal and axial direction from the 50 µm isotropic voxel GRE MRI data over slabs of 1.1 mm in thickness using Nibabel  and NumpBefore analysis, postmortem diffusion volumes were each registered to the first b0 volume using an affine transformation in ANTs version 2.1.0 . To estiWhite matter fiber streamlines were estimated deterministically with DIPY’s EudX method  with 1,0To define regions of interest (ROIs) for the fiber display, the auditory structures manually delineated in the post mortem T2*-weighted MR images were transformed to diffusion space using ANTs, and global streamlines were filtered by considering only the voxels in each one of the ROIs as a seed and further constrained by using all auditory ROIs as tractography waypoints. This resulted in a high-resolution, high-quality auditory-specific subcortical tractogram, which were then visualized in TrackVis 0.6.1 .7T in vivo dMRI data was corrected for distortions with the HCP pipeline . SpecifiSimilar to the post mortem analysis, we estimated diffusion FODs with a CSD model implemented in DIPY with response function FA threshold of 0.5. Peaks were extracted with a minimum separation angle of 25°. White matter connectivity was estimated with deterministic tractography throughout the brainstem and thalamus, again using DIPY’s EudX algorithm  with 1,0For the tractography in the in vivo data we used subcortical auditory ROIs as defined by the analysis of the functional data (i.e. regions that exhibited significant [corrected for multiple comparisons] response to sound presentation in at least three out of 10 subjects). The functional ROIs were transformed to individual diffusion space and used as tractography seeds, with all other auditory ROIs as waypoints, producing a subcortical auditory tractogram for each in vivo subject.https://openneuro.org/datasets/ds001942/versions/1.2.0 (DOI: 10.18112/openneuro.ds001942.v1.2). Atlas segmentations and tractography streamlines are available through the Open Science Framework (https://osf.io/hxekn/). Processing and analysis resources, including links to all data and software used in this paper, are available at https://github.com/sitek/subcortical-auditory-atlas . See Unprocessed in vivo data are available at http://www.pyqtgraph.org/) volume rendering. The t-value maps were clipped to 0–20 range and scaled to 0–255 range. These t-values are 3D volume rendered by assigning the corresponding gray value to each voxel as well as the alpha channel (transparency). Which means that lower values are closer to black and translucent. Animation frames were generated by rotating camera one degree at a time for 360 degrees. Additive rendering was used for 2D projections to provide depth vision .Video animations in  In the interests of transparency, eLife includes the editorial decision letter and accompanying author responses. A lightly edited version of the letter sent to the authors after peer review is shown, indicating the most substantive concerns; minor comments are not usually included.eLife. Your article has been reviewed by four peer reviewers, one of whom is a member of our Board of Reviewing Editors, and the evaluation has been overseen by a Senior Editor. The following individual involved in review of your submission has agreed to reveal their identity: Marta Correia (Reviewer #4).Thank you for submitting your work entitled "Mapping the human subcortical auditory system using histology, post mortem MRI and in vivo MRI at 7T" for consideration by eLife. There was widespread appreciation of the amount of care and work that went into the analyses, and many of the results are impressive, particularly the ex vivo work. At the same time the in vivo part seemed less clear and potentially underpowered, and there was general concern about the correspondence between function and anatomy, especially for the lower brainstem structures.Our decision has been reached after consultation between the reviewers. Based on these discussions and the individual reviews below, we regret to inform you that your work will not be considered further for publication in If you are able to substantially add to the in vivo story and (as you've indicated) provide all of the maps for the broader community, we would consider a new submission under "tools and resources". However, this would need to be a substantially improved paper and would likely go to the same set of reviewers.Reviewer #1:This is a very nicely done atlas of subcortical auditory structures that includes histological identification and connectivity from MRI (post mortem and in vivo). Although the results are large confirmatory, this is a thoroughly impressive technical achievement and the release and publication of these atlases is likely to be impactful.That being said, I understand the reasons for not making the segmentations and streamlines available yet, but the work is difficult to fully judge without seeing these . I think the data and code will need to be made available to the reviewers in some fashion before the work could be accepted.Reviewer #2:This is a helpful comparison of histological, postmortem MR, and in-vivo structural and functional approaches to localizing auditory brainstem and midbrain structures. The authors make use of previously acquired data and public atlases to align estimates of cochlear nuclei, olivary nuclei, inferior colliculi and medial geniculate nuclei across these different datasets, along with estimating the tract connections between them using DTI acquired ex- and in-vivo.Clearly a lot of work has gone into the various components of this project and care has been taken with the analyses. What wasn't clear to me was what – if anything – was the 'message' of the paper, except maybe one that was not intended, e.g., that even when state-of-the-art techniques are used on high quality imaging data, it is extremely difficult to identify smaller auditory nuclei accurately in-vivo. Indeed, even the connectivity of the IC is not straightforward to map out despite much higher quality data than is usually available along with generally careful processing.My major concerns were as follows (note that the authors do mention the majority of these as issues in the Discussion):1) The fMRI-defined nuclei are clearly inaccurate in that they are up to an order of magnitude larger than the actual anatomical structures, and do not conform to the structures' shape. This must stem in part from the surprising decision to use a 3.3mm FWHM smoothing kernel on high-resolution data. It was also surprising that no unmasked group z maps were presented; I can understand the idea behind the conjunction maps (showing x of 10 subject with FDR-corrected activation there), but the pretty minimal overlap in the SOC (3 of 10 subjects) doesn't inspire too much confidence. I also wondered where the cluster threshold of 27 voxels came from , and what the rest of the slices look like through the brainstem .2) The similarity between the estimates of CN and SOC across the BigBrain, ex-vivo, and in-vivo segmentations is near or at zero (as shown by the dice coefficients). Since defining these smaller structures is really the main potential contribution of the paper (given IC and MGB have been previously identified by a number of groups), this is concerning.3) The tractography results also essentially show that the known connections between any of the nuclei cannot be reliably reproduced, especially in-vivo. For instance, in the demonstration subject in Figure 6 as well as the matrix showing total streamlines, the IC is essentially being bypassed, despite the fact that it is (to my knowledge) the major source of input to the MGB. In the Figure 3—figure supplement 1 of the postmortem data, there are also a lot of streamlines that must be incorrect . The large projections going inferiorly from the IOC seeds  are also rather mysterious. Particularly given the statements regarding the 'vastly improved estimation of white matter connections'.In terms of overall contribution, having labelled nuclei in two publicly available postmortem datasets is helpful, and the fix to the incorrect MNI warp of bigbrain is also good to have. I'm less convinced about the contribution of the fMRI data (compared to the previously published studies that they come from), due to the way that they were processed and analyzed. If anything, the DTI tractography results seem to confirm the problems that Thomas et al., 2014, showed with even better diffusion data.Reviewer #3:This manuscript examines the structure of human auditory subcortical structures by recording in-vivo 7T functional magnetic resonance imaging (fMRI), structural MRI, and diffusion MRI (N=10). Ex-vivo 7T structural and diffusion MRI were also recorded in a single post-mortem subject. A subcortical atlas was created from the post-mortem structural MRI and publicly available histology data, and used to validate the functionally identified subcortical regions.All four subcortical auditory structures  were successfully identified using ex-vivo structural MRI, while in-vivo structural MRI could only be used to identify the MGN and IC. Using functional data, the MGN and IC could be identified in majority of the participants, while the SOC and CN could only be identified in a small subset participants (n = 2-3). Ex-vivo tractography showed significant within-structure streamlines while the number of between-structure streamlines was quite modest. On the other hand, in-vivo diffusion imaging revealed significant streamlines between and within structures.In general, the paper makes a useful contribution to the literature, and the figures are lovely. The Introduction, however, could be reorganized to more clearly indicate the gap in knowledge and the research question that this work was designed to address, since the general location of the main subcortical auditory processing nuclei is already well known in humans. One strength of the paper is that these findings replicate the authors' previous work -lending further credence to the idea that subcortical structures can be reliably identified from functional data. However, one limitation is that functional localization was not very strong in lower brainstem nuclei  for majority of the participants . I do wonder if the in-vivo portion of the study is under powered. This issue could be addressed using additional commentary by the authors, although if the N is not supported by a power analysis , the paper could be improved by recording data from a larger sample.Reviewer #4:I was asked to provide a review of the diffusion MRI methods used in this paper. The tractography methods used follow the current state-of-the-art and were implemented using the well validated tools within dipy. The authors are also cautious with their interpretation of results, in line with the known limitations of tractography methods.I don't have any major concerns over the methods used. [Editors’ note: what now follows is the decision letter after the authors submitted for further consideration.]Reviewer #1:This is a very nicely done atlas of subcortical auditory structures that includes histological identification and connectivity from MRI (post mortem and in vivo). Although the results are large confirmatory, this is a thoroughly impressive technical achievement and the release and publication of these atlases is likely to be impactful.That being said, I understand the reasons for not making the segmentations and streamlines available yet, but the work is difficult to fully judge without seeing these . I think the data and code will need to be made available to the reviewers in some fashion before the work could be accepted.We appreciate reviewer #1’s comments and recognize that our code and data must be publicly available in order to fully consider our contributions. We have therefore shared our code on Github and our segmentations and streamlines on the Open Science Framework. In addition, original and processed in vivo data are made available in BIDS format through OpenNeuro and NeuroVault. Here are the links to these addresses for convenience: – in vivo data are available on OpenNeuro:https://openneuro.org/datasets/ds001942/versions/1.2.0 (DOI: 10.18112/openneuro.ds001942.v1.2.0) - Derivatives are available on the Open Science Framework: https://osf.io/c4m82/ - Analysis code, flowcharts and other auxiliary files are available here: https://github.com/sitek/subcortical-auditory-atlas.Reviewer #2:This is a helpful comparison of histological, postmortem MR, and in-vivo structural and functional approaches to localizing auditory brainstem and midbrain structures. The authors make use of previously acquired data and public atlases to align estimates of cochlear nuclei, olivary nuclei, inferior colliculi and medial geniculate nuclei across these different datasets, along with estimating the tract connections between them using DTI acquired ex- and in-vivo.Clearly a lot of work has gone into the various components of this project and care has been taken with the analyses. What wasn't clear to me was what – if anything – was the 'message' of the paper, except maybe one that was not intended, e.g., that even when state-of-the-art techniques are used on high quality imaging data, it is extremely difficult to identify smaller auditory nuclei accurately in-vivo.Reviewer #2 raises a valid critique regarding the outcomes of our manuscript in light of the in vivo results. In the revised version of the manuscript we have expanded our in vivo dataset and analyses to demonstrate the reliability of our functional localization within each individual and the generalization across individuals, as well as improved and clarified our group-level functional atlas. The new analyses for the in vivo functional data are reported in the new Figure 4 together with Figure 4—figure supplements 1-5.In particular, Figure 4 shows single subject statistical maps together with probabilistic maps obtained using a leave-one-subject-out procedure. The overlap between the probabilistic leave-one-subject-out maps and the single subjects is analyzed in Figure 4—figure supplements 1 and 2. This first set of results shows that: a) a cluster of voxels significantly responding to sounds can be identified in each volunteer; b) these clusters co-localize to regions identified probabilistically in the other nine volunteers; and c) the overall response is higher for the IC and MGB and lower for the CN and SOC.We then turned our attention to demonstrate the within-subject reliability of these functional definitions. Figure 4—figure supplement 1 shows the statistical maps obtained in a second experiment for six of the ten participants. These results are reported together with the overlap between the two experiments statistical maps in Figure 4—figure supplement 2.Both within subjects and across subjects, the reliability is higher for the IC and MGB than for the CN and SOC. The between-subject reliability  is most likely affected by the lower accuracy of the anatomical alignment for these structures caused by the absence of anatomical contrast to be used to drive the alignment procedure. This issue is discussed in the manuscript where we state:“Another factor that may have impacted the increased volume of the in vivo probabilistic regions can be the residual anatomical misalignment across subjects that also contributes especially to the lower degree of overlap at CN and SOC.”The within-subject reliability is most likely impacted by the lower statistical power in these regions together with residual alignment errors across experiments, which we also discuss in the revised manuscript where we state:“The higher signal-to-noise ratio in attainable in regions corresponding to the IC and MGB results in highly reproducible functional responses both within and across participants in these regions. Activation clusters identified at the level of CN and SOC in single individuals do reproduce  both within subjects  and across subjects.”It has to be noted that Experiment 1 and 2 have been conducted with up to three years of difference in time between them. Additionally, the experiments had different numbers of runs and sounds presented per run . The overall reproducibility of the results demonstrates that the functional definition of the auditory nuclei is reliable both within and across subjects, something that we now clarify in the manuscript:“In each participant we identified voxels significantly responding to sound presentation in regions corresponding to the CN, SOC, IC and MGB. We validated these definition by evaluating both the within-subject reproducibility  and the ability of a probabilistic atlas defined on nine out of our ten participants to generalize to the left out volunteer.”Nevertheless, discrepancies between the in vivo and the post mortem results in terms of the localization and the overlap of the definitions remain. We believe the discussion on the revised manuscript describes what our thoughts are over these discrepancies that can in short can be attributed to: 1) partial volume that still impact the in vivo data; and 2) alignment between post mortem and in vivo rendered cumbersome by the difference (or absence) of anatomical contrast in the relevant ROIs.Indeed, even the connectivity of the IC is not straightforward to map out despite much higher quality data than is usually available along with generally careful processing.We agree with the reviewer that tractography remains challenging, particularly between the small, dense, deep brainstem nuclei. We discuss some of the challenges below in more detail. Despite these challenges, we were able to successfully estimate connections between auditory structures . A video of the in vivo streamlines in one participant is available as Figure 3—video 1. In fact, the resulting connectivity matrix  closely resembles expected connectivity patterns based on animal research. For instance, there appear to be separate connectivity networks on the left and right above the CN, while there are some commissural connections at the CN, SOC, and IC levels, as expected.While these connection estimates align with our general thinking regarding subcortical auditory connectivity, there is unfortunately no gold standard for human anatomical connectivity, as tracer methods available in animal research are largely unavailable in humans. However, we are encouraged by the present results, which are consistent across participants , and believe auditory neuroscientists should continue to push for improved DWI acquisition and tractography methods to further hone the methodology to study the subcortical auditory system.My major concerns were as follows (note that the authors do mention the majority of these as issues in the Discussion):1) The fMRI-defined nuclei are clearly inaccurate in that they are up to an order of magnitude larger than the actual anatomical structures, and do not conform to the structures' shape. This must stem in part from the surprising decision to use a 3.3mm FWHM smoothing kernel on high-resolution data.We appreciate the reviewer’s careful critique of our methods and have addressed them in our revised manuscript. Regarding spatial smoothing, we feel that the concern of the reviewer stemmed from a misunderstanding caused by the way that the smoothing kernel was reported originally. Namely, we applied 3 voxel FWHM smoothing after transforming the functional data to 0.5 mm iso. resolution in Talairach space. This 3 voxel FWHM spatial smoothing translates to 1.5mm FWHM. We have corrected the reporting in the Materials and methods section where we state:“…functional images were then transformed to Talairach space using Brainvoyager at a resolution of 0.5 mm isotropic. We have previously used this procedure in order to reveal tonotopic maps in both the inferior colliculus and medial geniculate nucleus  and have shown that the upsampling has no consequence on the spatial distribution of the responses. After upsampling, mild spatial smoothing  was also applied.”On top of this, we have now added the comparison between unsmoothed and smoothed maps as Figure 4—figure supplement 5. Mildly smoothed functional data helps bringing out the anatomically plausible clusters by improving our SNR, especially in lower brainstem regions of interest.It was also surprising that no unmasked group z maps were presented; I can understand the idea behind the conjunction maps (showing x of 10 subject with FDR-corrected activation there), but the pretty minimal overlap in the SOC (3 of 10 subjects) doesn't inspire too much confidence.We interpret this statement to mean that the results for CN/SOC, where there is lower overlap across participants, would be better interpreted looking at unthresholded maps. We agree with the reviewer and think that in order to have a full understanding of our data, a better picture can be gathered by looking at all single individual data as well as the group data (both thresholded and unthresholded).For this reason, we now report in Figure 4 all single subject statistical maps (thresholded for significance). The unthresholded data are reported in video format for each individual both thresholded and unthresholded . The single subject data clearly indicate that clusters of significant activity can be identified at the level of CN and SOC in most participants . How well regions defined in a group of subjects allow us to localize regions significantly responding to sounds in a new subject was also assessed by conducting a leave-one-out analysis for each participant and calculating the distance and overlap between each participant’s cluster and the cluster comprised of all other participants’ activation. These are shown in Figure 4—figure supplements 1, 2 and in Figure 10—video 1-10. In addition, our unthresholded and thresholded maps are open to inspection here: https://osf.io/c4m82/See the data used in this figure as 3D volume renders for each 10 subject in Figure 10—videos 1-10 in our updated manuscript, in Figure 10.The reproducibility of these results was evaluated in test-retest reliability analysis by looking at data collected in a separate experiment in six of the ten participants . The distance and overlap between each participant’s clusters of activation across the two experiments are reported in Figure 4—figure supplement 4.See the data used in this figure as 3D volume renders for each 6 subject in Figure 11—videos 1-7 in our updated manuscript, in Figure 11.We have also looked at the group average results for further comparison of the activity patterns in experiment 1 and 2. Group averaged 3D volume renders for fMRI activity can be seen in On top of these, now we are also providing our in-vivo results in 3D volume rendered video format in MNI space to conveniently compare our unthresholded group average statistical maps. We present this video as a figure supplement to Figure 9 of our updated manuscript .Thus, while we agree that CN and SOC present a lower level of SNR which affect in part the resulting overlap (and reliability) when compared to IC and MGB, we believe that a major reason for the lower overlap in these regions can be assigned to misalignment: there is not enough anatomical contrast to align the CN/SOC across participants. Furthermore, while we display the group probabilistic maps a threshold of 3 out of ten volunteers, both the CN and SOC our probabilistic maps have voxels where the overall across participants is up to 5 out of ten volunteers.In conclusion, these results attest to the quality of the data and the validity of the analysis. We believe that our new analyses and figures show the reproducibility of our results, inspiring confidence in the reliability of the in vivo definitions also at the level of the CN and SOC.I also wondered where the cluster threshold of 27 voxels came from ?In the text of the Materials and methods, we describe it as:3 .”“applying of a cluster size threshold of 3.37 mmLater in the same paragraph, we state:3 in volume).”“The additional clustering allowed us to further control for possible false positives by imposing a neuroanatomically plausible hypothesis  too big because one of our regions of interest (superior olivary complex) was reported to be around 6-7mm3. We have deemed 8 voxels (1mm3) and 1 voxel (0.125mm3) too small. Therefore we have decided to use 27 voxels.In more detail, we have decided on number `27` instead of `26` or `28` or others by thinking about the discrete cubical lattice that the data resides in. We have considered the following: a cube of 1 voxel, 2×2×2=8 voxels, 3×3×3=27 voxels, and 4×4×4=64 voxels. We deemed 64 voxels .We agree with the reviewer that 2-D slices are inadequate for viewing 3-D clusters. In addition to Figure 4, we have therefore added 3-D animated functional representations, both at the group level and for individual participants (both thresholded and unthresholded). These animations are available as supplementary videos to the manuscript and at the links above.2) The similarity between the estimates of CN and SOC across the BigBrain, ex-vivo, and in-vivo segmentations is near or at zero (as shown by the dice coefficients). Since defining these smaller structures is really the main potential contribution of the paper (given IC and MGB have been previously identified by a number of groups), this is concerning.Image registration remains an open challenge, particularly across different image modalities . This issue is magnified in the brainstem, which has not generally been the focus of registration methods. Indeed, our BigBrain–MNI registration improvements highlight how important it is to pay attention to subcortical anatomy when assessing registration success. In Thus, when assessing the similarity between BigBrain and ex vivo MRI , the low similarity between the segmentations  indicates that alignment is a critical factor to these metrics.The in vivo data are additionally affected by: a) a further decrease in signal (and contrast) compared to the BigBrain and post mortem MRI samples; and b) partial voluming effects due to the lower spatial resolution.These issues, which are discussed in the manuscript, affect the alignment of the in vivo data . It has to be noted here that these data undergo two steps of alignment in our procedure: 1) to align all participants to one reference participant; 2) to align the average of all participants to the post mortem data.The in vivo definition of the CN and SOC is reproducible both within and across participants as demonstrated by the new analyses reported in the manuscript (as well as above in response to an earlier comment). We believe these analyses address the reviewer concerns. Future work will have to address the possibility of improving the alignment of the CN/SOC both in vivo and between in vivo and post mortem data. As we discuss in the manuscript this requires the exploration of acquisition parameters that would allow the accurate anatomical definition of these structures in vivo.3) The tractography results also essentially show that the known connections between any of the nuclei cannot be reliably reproduced, especially in-vivo. For instance, in the demonstration subject in Figure 6 as well as the matrix showing total streamlines, the IC is essentially being bypassed, despite the fact that it is (to my knowledge) the major source of input to the MGB.While we agree that the results demonstrate how tractography methods are not yet perfect, we do show that the tractography results are consistent across participants . With regards to the IC, the streamline connectivity matrix does in fact show streamlines between the IC and the SOC and MGB on the same side . Indeed, we can visualize streamlines hitting the IC from both below and above in the example participant tractography video, However, it is true that the streamlines largely do not follow the neuroanatomically plausible path towards the IC, in contrast to the post mortem tractography. It is likely that the signal-to-noise ratio of in vivo DWI does not yet disambiguate the arcing white matter connections into the IC from both below and above. These lemniscal and brachial connections do appear in the post mortem tractography dataset, particularly as shown in Figure 4 and Figure 4—figure supplement 2 – the latter of which is downsampled to in vivo resolution, demonstrating that voxel sizes of approximately 1 mm should be adequate for identifying these connections with current analysis methods if the data has sufficiently high SNR.In the Figure 3—figure supplement 1 of the postmortem data, there are also a lot of streamlines that must be incorrect . The large projections going inferiorly from the IOC seeds  are also rather mysterious. Particularly given the statements regarding the 'vastly improved estimation of white matter connections'.We appreciate the reviewer’s concerns regarding these figures. Indeed, we had debated how to represent the streamlines, originally deciding to show all streamlines that pass through auditory structures regardless of anatomical plausibility. However, since we do have a priori knowledge about the auditory system , it is reasonable to exclude streamlines that take anatomically implausible routes. Therefore, we have reanalyzed the tractography data to exclude streamlines that pass through the medulla, which includes the inferior-running streamlines mentioned by the reviewer.In terms of overall contribution, having labelled nuclei in two publicly available postmortem datasets is helpful, and the fix to the incorrect MNI warp of bigbrain is also good to have. I'm less convinced about the contribution of the fMRI data (compared to the previously published studies that they come from), due to the way that they were processed and analyzed. If anything, the DTI tractography results seem to confirm the problems that Thomas et al., 2014, showed with even better diffusion data.The in vivo fMRI data reported in the manuscript have never been published before and we have clarified some misunderstandings in the analysis pipeline. Our new analyses and data show the reproducibility of the in vivo definitions of lower auditory nuclei both within and between subjects. Our opinion is that these results are valuable in order to better understand past and future in vivo studies that have or will evaluate the functional properties of these regions. These analyses have originated from the helpful comments of this reviewer regarding our manuscript of which we are thankful.Reviewer #3:This manuscript examines the structure of human auditory subcortical structures by recording in-vivo 7T functional magnetic resonance imaging (fMRI), structural MRI, and diffusion MRI (N=10). Ex-vivo 7T structural and diffusion MRI were also recorded in a single post-mortem subject. A subcortical atlas was created from the post-mortem structural MRI and publicly available histology data, and used to validate the functionally identified subcortical regions.All four subcortical auditory structures  were successfully identified using ex-vivo structural MRI, while in-vivo structural MRI could only be used to identify the MGN and IC. Using functional data, the MGN and IC could be identified in majority of the participants, while the SOC and CN could only be identified in a small subset participants (n = 2-3). Ex-vivo tractography showed significant within-structure streamlines while the number of between-structure streamlines was quite modest. On the other hand, in-vivo diffusion imaging revealed significant streamlines between and within structures.We thank the reviewer for the comments, but we feel a clarification is needed on the summary above. The reviewer states that the SOC and CN could only be identified in 2 or 3 participants in the in vivo functional data. This is a misunderstanding caused by our choice to only present the functional probabilistic maps (across participants) in the original version of the manuscript. These maps color code voxels based on how many participant show a significant activation in response to sounds at that voxel. The lower probability in the CN and SCO (compared to IC and MGB) does not mean that the CN and SOC are significant only in 2 or 3 participants but rather could be due to the alignment across participants being of lower quality in these regions (as we discuss in the manuscript). In the original version of the manuscript we had described that all our participants showed significant responses at the level of CN and SOC. Now we substantiate this claim with new data, and several new analyses and figures. In particular, the new Figure 4 shows statistical maps in each individual as obtained in the experimental data already reported in the original manuscript.This figure shows that the CN and SOC could be identified on the basis of fMRI data in all participants with the exception the right SOC in S03, the left CN and SOC in S08 and the left CN in S09. We also evaluated the reproducibility of these results by comparing functional definitions in the same participants across experiments . Also see 3D volume rendered videos that show both thresholded and unthresholded maps for all individual participants as reference in our new Figures 9, 10 and 11.Thus, while future work will have to address the possibility of better aligning CN/SOC across participants , we believe our data indicate that with sufficient statistical power CN and SOC can be identified in individual participants data.In general, the paper makes a useful contribution to the literature, and the figures are lovely. The Introduction, however, could be reorganized to more clearly indicate the gap in knowledge and the research question that this work was designed to address, since the general location of the main subcortical auditory processing nuclei is already well known in humans.We have reworked the Abstract, Introduction, and Discussion to make our contributions more clear. Foremost, we conclude the Introduction with the following take-home message:“Where histology provides ground truth information about neural anatomy, we show that post mortem MRI can provide similarly useful three-dimensional anatomical information with less risk of tissue damage and warping. We also show that in vivo functional MRI can reliably identify the subcortical auditory structures within individuals, even across experiments. Overall, we found that each methodology successfully localized each of the small structures of the subcortical auditory system, and while known issues in image registration hindered direct comparisons between methodologies, each method provides complementary information about the human auditory pathway.”Additionally, we added text about post mortem MRI in order to lay the groundwork for our novel use of this modality for investigating the human subcortical auditory system:“Advances in MRI of the post mortem human brain allow for investigating three-dimensional (3-D) anatomy with increasingly high resolution (100 µm and below). This points to "magnetic resonance histology"  as a promising avenue for identifying the small, deep subcortical auditory structures. However, to the best of our knowledge, post mortem MRI has not been utilized within the subcortical auditory system, although it has provided useful information about laminar structure in the auditory cortex .”The Abstract now concludes with:“This work demonstrates the potential of functional MRI for investigating the human subcortical auditory system and contributes novel, openly available tools for researching the human auditory system to understand its structural organization.”To open the Discussion, we have added:“We showed that functional localization of the subcortical auditory system is achievable within each participant, and that localization is consistent across experimental sessions.”One strength of the paper is that these findings replicate the authors' previous work -lending further credence to the idea that subcortical structures can be reliably identified from functional data. However, one limitation is that functional localization was not very strong in lower brainstem nuclei  for majority of the participants .We agree that the lower brainstem nuclei are more challenging to identify due to their small size and deep location. However, as we have already clarified above, we were able to identify all main auditory nuclei in each of our subjects; we have added Figure 5 demonstrating these findings in every participant. In a supplementary figure we also demonstrate the reproducibility of this localization within six of the original ten participants that took part to a new experiment.It is only when combined in a common anatomical space that the results look less strong. We believe that this arises in large part due to ongoing challenges regarding image registration, particularly in lower brainstem regions that are discussed in the revised manuscript.I do wonder if the in-vivo portion of the study is under powered. This issue could be addressed using additional commentary by the authors, although if the N is not supported by a power analysis , the paper could be improved by recording data from a larger sample.We thank the reviewer for this comment that highlights the need for more clarity about our study design. As we now clearly state in the manuscript, this study was tailored to obtain a functional definition of the auditory pathway in each individual , as we already had evidence from previous work in the IC/MGB that anatomical variability would have been a major issue in a potential group study. We now state that:“Leveraging the increased SNR available at 7Ts, we aimed to collect data that would allow a functional definition of the auditory subcortical auditory structures in single individual participants. For this reason, we collected a large quantity of data in all individuals (2 sessions with 12 runs each in Experiment 1 and 2 sessions with 8 runs each in Experiment 2), and all statistical analyses were performed at the single subject level. Group analyses were used to evaluate the correspondence across subjects of individually defined regions  as well as the ability of generalizing to new participants by means of a leave-one-subject-out analysis.”The amount of data necessary to identify these nuclei functionally in an individual was piloted by acquiring data in one session for one individual, as prior data do not exist that would allow for the definition of the statistical power attainable at 7T in regions such as the CN and SOC. Preliminary analysis of the single subject suggested that a single session’s worth of fMRI data was too noisy to specifically identify the auditory structures. We then added a second functional MRI session, doubling the data and providing ample SNR for auditory localization.We were able to functionally localize the subcortical auditory structures in every participant. To further augment the reliability of our findings, we now include a second study including 6 of 10 subjects who participated in Experiment 1. Figure 4—figure supplement 3 shows similar spatial activation patterns between experiments for most participants.We believe that these results demonstrate the efficacy of our acquisition methods for localizing brainstem auditory structures within individuals that are reliable across scanning sessions.The group analysis that follows the definition of the auditory nuclei in each participants allowed us to highlight the difference in anatomical registration that can be obtained across structures in the auditory pathway. Compared to our previous work , we were now able to perform one anatomical alignment procedure for the whole brainstem. This procedure produced satisfactory results for the IC/MGB  but requires further development for the CN/SOC. We believe this is mostly due to the lack of anatomical contrast highlighting these regions in each individual and highlight this as potential future work in the revised manuscript.