In addition to the potentially biased visual inspection of the vector fields, an unbiased statistical statement about the reconstruction accuracy is given by the mean residual: the mean value of the mean residuals of all pixels is 15% lower for the results of the ROFL algorithm than the results of the DFT algorithm.

#### 3.2.2. Agreement of Model and Data

Since the ROFL algorithm performs a least squares fit in all image pixel, the actual sum of residuals χ 2 (as a result of the optimization process) was accessible. For the DFT algorithm the residual map can also be calculated, yet this requires additional computations which take even longer than the runtime of the algorithm itself. **Figure 9** shows maps of the residuum χ 2 (**Figure 9A**), the relative thickness d (**Figure 9B**) and the fiber orientation map (**Figure 9C**) for a selected region of interest.

As χ 2 is a measure of the difference between the model and the experimental data, it is expected to increase for artifacts such as dust particles which rotate with the polarization filters. Such artifacts were indeed observed in experimental results (cf. **Figure 9A**, where dust particles are indicated by green arrows). For one pixel, a dust particle corrupts one of the 18 obtained values during filter rotation. Still it does not necessarily have a strong effect on the resulting fit values as the corresponding relative thicknesses and fiber orientations do not show signs of artifacts. The χ 2 measure, however, is sensitive to such artifacts.

In addition, we found areas of increased residuals (cf. red arrows in **Figure 9**) we were able to assign to crossing fiber regions. In the fiber orientation map, crossings stood out as the center of an abrupt change of colors (**Figure 9C**). Also, the optimization process resulted in an abrupt change of the relative thickness, even with values of d > 1 for which the relationship between the retardation and inclination is not bijective anymore (**Figure 9B**). Due to the bad agreement between the model and the data, the residuum finally increased strongly (cf. red arrows in **Figure 9**).

### 4. DISCUSSION AND OUTLOOK

We introduced the least-squares algorithm ROFL for the reconstruction of fiber orientation and the extraction of the relative section thickness from measurements with a tiltable specimen stage in 3D-Polarized Light Imaging (3D-PLI). This method requires only one additional assumption besides the polarimetric model: the refractive index of the brain tissue. This represents a substantial improvement as compared to other histological imaging techniques which strongly rely on parameter dependent image processing pipelines to extract three-dimensional fiber orientations. To our knowledge, no other histological imaging technique is currently capable of deriving three-dimensional information from a biophysical model. This compensates for the disadvantage of the difficulties accompanying the 3D re-alignment of serial high-resolution brain section images as required for 3D-PLI anaylsis.

The working principle of the ROFL algorithm was proven for simulated data for which it resulted in a significant improvement of the orientation reconstruction. It was opposed to a previously implemented analytical algorithm based on a discrete Fourier

transformation (DFT). The reconstruction with ROFL became more reliable, in particular for low relative section thicknesses. The noise instability of the DFT algorithm for very flat fibers was already observed by Wiese et al. (2014) and is explainable by the fact that for very flat fibers the gradient ∂ sin δ ∂α becomes small (for in-plane fibers with α = 0 ◦ it even becomes zero: ∂ sin δ ∂α |α=0 ◦ = 0). This makes the DFT algorithm prone to noise effects in this case. In fact, the simulations proved that the DFT algorithm is not capable of reconstructing in-plane orientations. On the contrary, the presented fitting algorithm still enabled a reliable reconstruction for in-plane fibers. The random orientations computed by the DFT algorithm for small relative section thicknesses of d < 0.05 were not surprising, as for d < 0.05, the maximal possible retardation value is also |sin δ| = 0.05 and the retardation values of the different tilting positions become almost indistinguishable. In this case, the ROFL algorithm provides a superior reconstruction. In the case of very steep fibers, the ROFL algorithm also outperformed DFT significantly. Still, the reconstruction accuracy decreases strongly for very steep fibers also for the ROFL algorithm. This originates from the signal itself: for α = 90◦ , the amplitude of the sinusoidal signals are almost zero and the measured signals resemble a constant function with random noise (cf. Dohmen et al., 2015 for a theoretical description of this issue). While the reconstruction of out-of-plane fibers remains challenging, orientation vectors with |α| > 80◦ only make up approx. 3% of all possible orientations. Therefore, we can conclude that ROFL accomplishes a very robust orientation reconstruction with an average accuracy of 2◦ for the vast majority of possible fiber orientations for relative section thicknesses of d > 0.2 on synthetic data.

ROFL was applied to 230 consecutive human brain sections subjected to 3D-PLI. The results were 3D reconstructed to demonstrate the robustness and reproducibility of our approach. The obtained modality volumes of retardation, relative section thickness and fiber orientation were characterized by high coherence across the sections. Anatomical structures, such as fiber tracts, white/gray matter borders, vessels could be reconstructed with high precision. The transmittance volume displayed brightness variations, yet the strength of our approach is that the determination of the fiber orientation and relative section thickness is independent of the transmittance due to the normalization. By eyesight, no major differences were observed in the vector fields resulting from the ROFL and DFT approaches. In the stratum sagittale, both approaches resulted in very similar orientations with very minor differences. The computed inclinations were on average α ≈ 65◦ and the computed relative section thicknesses d = 0.6. This combination actually corresponds to the minimum of the orientation reconstruction error of the DFT algorithm obtained from the simulated datasets, which suggests that a strong agreement with the ROFL algorithm can be expected. At the boundary of white and gray matter regions, the ROFL algorithm resulted in less inclined fiber orientations than DFT but also only very minor differences were observable.

The inclination histograms, however, yielded a very important finding: whereas, the frequency of the computed inclination angles decreased significantly for in-plane fibers with α ≈ 0 ◦ for the DFT algorithm, the ROFL algorithm was still capable of reconstructing the whole spectrum of possible inclinations. The same behavior was observed for simulated data, which proves a systematic bias of the DFT algorithm. The ROFL result is also more plausible as for the observed ROI in **Figure 8** the inclination histogram obtained from ROFL agrees far better with the inclination histogram of uniformly distributed orientations. Still, it has to be noted that fiber orientations in a small ROI of the brain cannot expected to be perfectly uniformly distributed due to the convoluted structure of the human brain. The observed inclination differences between ROFL and DFT are hard to observe based on orientation vectors, as the actual inclination differences of up to 10◦ for in-plane orientations can barely be distinguished by eye. As the difference between the measured and predicted light intensities according to the 3D-PLI model also decreases significantly for the parameters obtained from ROFL compared to DFT, ROFL yields more accurate results than DFT also from a purely statistical perspective.

Furthermore, the ROFL algorithm enables a direct way to evaluate the agreement of model and data based on the residuum map which would otherwise have to be computed additionally. This kind of map was exploited to identify measurement artifacts and, even more importantly, crossing fiber regions which were not classifiable by previous 3D-PLI analysis approaches (Axer et al., 2011a; Kleiner et al., 2012; Wiese et al., 2014). Fiber crossings currently pose the greatest complication for 3D-PLI analysis as partial volume effects are still present in brain tissue at voxel sizes of 64 × 64 × 70µm3 . A voxel containing fibers with different courses (i.e., orientations) will inevitably result in a 3D-PLI measurement composed of superimposed birefringence signals. This might lead to a biased orientation interpretation. To give an example, a voxel comprising crossing out-of-plane and in-plane fibers, the ROFL algorithm will preferably reconstruct the orientation which causes a larger signal, in this case the inplane orientation. To overcome this issue, different strategies are currently being explored: (i) Modeling of crossing fiber structures and subsequent simulations of tilting measurements utilizing the simPLI simulation platform introduced by Dohmen et al. (2015). By this means the behavior of the ROFL algorithm in case of wellknown crossing fiber constellations can be further investigated. (ii) Realizing the idea of oblique imaging for in-plane resolutions at the micrometer scale. Preliminary measurements of a mouse section using a prototypic "tilting" polarizing microscope built up at an optical bench (pixel size: 1.3 × 1.3µm) already revealed the benefit of smaller voxel sizes.

While the obtained parameters show a high coherence across the volume, no quantitative statement about the reliability of the resulting parameters is possible at this point. Hence, future studies need to investigate the uncertainty of the fitted parameters. For Diffusion Tensor Imaging, for example, bootstrapping approaches were used to obtain orientation confidence maps (Jones, 2003; Heim et al., 2004; Whitcher et al., 2008). Especially, the uncertainty of the relative section thickness is of interest, as this parameter is as an indicator for myelin and might even be a reliable measure of the local myelin density. As a matter of fact, the relative section thickness is proportional to the local birefringence 1n which was clearly attributed to the myelin sheath in earlier studies (Goethlin, 1913; Schmidt, 1923; Schmitt and Bear, 1937; de Campos Vidal et al., 1980). Since myelin density information is of outmost interest for the research of degenerative brain diseases characterized by locally altered myelination (e.g., multiple sclerosis), future studies will address the correlation between the relative section thickness and myelin density.

The improved reconstruction comes at the cost of computation time, which increases by a factor of 5,000. Yet, as processing a single brain section takes about 3 min using four compute nodes on JURECA (Jülich Supercomputing Centre, 2016) with the ROFL algorithm in its current implementation, even the computation of whole brains becomes feasible. Still, the computation time could further be reduced by utilizing GPU ressources (Przybylski et al., 2017).

To conclude, the present approach has opened up a new way to determine physical tissue properties from oblique measurements in microscopic 3D-PLI. Cortical, subcortical, and white matter regions could be characterized coherently across brain sections in terms of fiber orientation and birefringence strength. This is a prerequisite for subsequent volume-based connectivity analysis.

#### AUTHOR CONTRIBUTIONS

DS: study design, algorithm implementation, simulation, processing of experimental data, evaluation, writing, discussion.

#### REFERENCES

SM and MS: 3D reconstruction, discussion. NS: 3D visualization. MM: provided the brain sample and anatomical interpretations. KA and TL: writing, discussion. MA: study design, discussion, writing.

#### FUNDING

This project has received funding from the European Union's Horizon 2020 Research and Innovation Programme under Grant Agreement No. 7202070 (HBP SGA1) and Grant Agreement No. 785907 (HBP SGA2).

#### ACKNOWLEDGMENTS

We would like to thank Marcus Cremer and Patrick Nysten for the preparation of the examined brain sections and Felix Matuschke for fruitful discussions about the statistical analysis. We also thank David Gräßel who significantly helped in identifying anatomical structures. The authors gratefully acknowledge the computing time granted by the JARA-HPC Vergabegremium and provided on the JARA-HPC Partition part of the supercomputer JURECA at Forschungszentrum Jülich.

#### SUPPLEMENTARY MATERIAL

The Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fnana. 2018.00075/full#supplementary-material

means of independent component analysis. Neuroimage 49, 1241–1248. doi: 10.1016/j.neuroimage.2009.08.059

Goodman, J. (2000). Statistical Optics. New York, NY: Wiley.

**Conflict of Interest Statement:** The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Copyright © 2018 Schmitz, Muenzing, Schober, Schubert, Minnerop, Lippert, Amunts and Axer. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

# The Superoanterior Fasciculus (SAF): A Novel White Matter Pathway in the Human Brain?

Szabolcs David1 \*, Anneriet M. Heemskerk 1 , Francesco Corrivetti 2 , Michel Thiebaut de Schotten3 , Silvio Sarubbo4 , Francesco Corsini 4 , Alessandro De Benedictis 5 , Laurent Petit 6 , Max A. Viergever 1 , Derek K. Jones 7 , Emmanuel Mandonnet 2 , Hubertus Axer 8 , John Evans 7 , Tomás Paus ˇ 9,10 and Alexander Leemans 1

1 Image Sciences Institute, University Medical Center Utrecht and Utrecht University, Utrecht, Netherlands, 2Department of Neurosurgery, Lariboisière Hospital, APHP, Paris, France, 3Centre for Neuroimaging Sciences, Institute of Psychiatry, King's College London, London, United Kingdom, 4Structural and Functional Connectivity Lab Project, Department of Emergency, Division of Neurosurgery, "S. Chiara" Hospital, Azienda Provinciale per i Servizi Sanitari (APSS), Trento, Italy, 5Department of Neurosciences, Division of Neurosurgery, "Bambino Gesù" Children Hospital, IRCCS, Rome, Italy, 6Groupe d'Imagerie Neurofonctionnelle (GIN), Institut des Maladies Neurodégératives (IMN)-UMR5293-CNRS, CEA, Université de Bordeaux, Bordeaux, France, 7Cardiff University Brain Research Imaging Centre, School of Psychology, Cardiff, United Kingdom, 8Hans Berger Department of Neurology, Jena University Hospital, Friedrich-Schiller University Jena, Jena, Germany, 9Bloorview Research Institute, Holland Bloorview Kids Rehabilitation Hospital, Toronto, ON, Canada, 10Departments of Psychology and Psychiatry, University of Toronto, Toronto, ON, Canada

#### Edited by:

Francisco Clasca, Autonomous University of Madrid, Spain

#### Reviewed by:

Miguel Ángel García-Cabezas, Boston University, United States Francesco Sammartino, The Ohio State University, United States

> \*Correspondence: Szabolcs David<EMAIL_ADDRESS>
Received: 25 November 2018 Accepted: 07 February 2019 Published: 05 March 2019

#### Citation:

David S, Heemskerk AM, Corrivetti F, Thiebaut de Schotten M, Sarubbo S, Corsini F, De Benedictis A, Petit L, Viergever MA, Jones DK, Mandonnet E, Axer H, Evans J, Paus T and Leemans A (2019) The Superoanterior Fasciculus (SAF): A Novel White Matter Pathway in the Human Brain? Front. Neuroanat. 13:24. doi: 10.3389/fnana.2019.00024 Fiber tractography (FT) using diffusion magnetic resonance imaging (dMRI) is widely used for investigating microstructural properties of white matter (WM) fiber-bundles and for mapping structural connections of the human brain. While studying the architectural configuration of the brain's circuitry with FT is not without controversy, recent progress in acquisition, processing, modeling, analysis, and visualization of dMRI data pushes forward the reliability in reconstructingWM pathways.Despite being aware ofthe well-known pitfalls in analyzing dMRI data and several other limitations of FT discussed in recent literature, we presentthe superoanterior fasciculus (SAF), a novel bilateral fibertract inthe frontal region of the human brain that—to the best of our knowledge—has not been documented. The SAF has a similar shapetothe anterior part ofthe cingulum bundle, but it is locatedmore frontally. To minimize the possibility that these FT findings are based on acquisition or processing artifacts, different dMRI data sets and processing pipelines have been used to describe the SAF. Furthermore,we evaluatedthe configuration oftheSAFwith complementarymethods, such as polarized light imaging (PLI) and human brain dissections. The FT results of the SAF demonstrate a long pathway, consistent across individuals, while the human dissections indicate fiber pathways connectingthe postero-dorsal withthe antero-dorsal cortices ofthe frontal lobe.

Keywords: brain, diffusion MRI, fiber tractography, dissection, polarized light imaging, neuroanatomy, superoanterior fasciculus

### INTRODUCTION

Fiber tractography (FT) based on diffusion magnetic resonance imaging (dMRI; Jeurissen et al., 2017) is widely used for investigating microstructural properties of white matter (WM) fiber-bundles (Alexander et al., 2017), and for mapping structural connections of the human brain (Wakana et al., 2004; Sotiropoulos and Zalesky, 2017). Since the first endeavors of FT

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(Basser et al., 1994, 2000; Conturo et al., 1999; Jones et al., 1999; Mori et al., 1999; Catani et al., 2002), many studies have contributed to the improvement of data quality and the level of detail. Recent advances in MRI hardware and acquisition (Anderson, 2005; Moeller et al., 2010; Setsompop et al., 2013, 2018; Sotiropoulos et al., 2013; Andersson et al., 2016) have improved significantly dMRI data in terms of spatial resolution, angular resolution signal-to-noise ratio, and geometrical distortion reduction.  Besides the traditional diffusion tensor model, more sophisticated methods have been developed to resolve crossing fibers (Lin et al., 2003; Tuch, 2004; Tournier et al., 2004; Wu and Alexander, 2007; Dell'Acqua et al., 2013; Tax et al., 2014; Jensen et al., 2016). Together, all these improvements allow for more reliable FT results, thereby resolving complex fiber architecture, smaller branches of fiber bundles or minor fiber-pathways (Tournier et al., 2008). Although dMRI based tractography is a promising technique, there are many well-known pitfalls and limitations in acquiring and analyzing dMRI data (Jones and Cercignani, 2010; Jones et al., 2013b; O'Donnell and Pasternak, 2015). Thus, dMRI is an indirect approach for measuring the underlying WM pathway properties, as it is an approximation of the average diffusion within a given voxel. Modeling the diffusion results in a proxy of main directions, and thus the exact architecture of the pathways cannot be determined unambiguously. The modeling errors can cause tracts to stop prematurely or jump from one WM structure to another, resulting in false negative or false positive connections. The strength of connectivity, when assessed via probabilistic FT, is unreliable due to its sensitivity to data quality (Mesri et al., 2016).

Despite recent efforts to increase the accuracy of FT by either pruning the tractograms using anatomical information (Smith et al., 2012; Roine et al., 2015) or by including microstructural information to disambiguate between pathways (Smith et al., 2013), the International Society for Magnetic Resonance in Medicine (ISMRM) Tractography Challenge in 2015 demonstrated that some data-processing pipelines could result in large errors as the reconstructed tracts produced an average ratio of 1:4 in false positivefalse negative connections and 45% in bundle overlap when compared with pre-defined, ground-truth streamlines (Maier-Hein et al., 2017).

In another study, the sensitivity (true connections) and specificity (avoidance of false connections) of dMRI tractography was investigated by combining tracer studies and high quality dMRI data (Thomas et al., 2014). This research showed a sensitivity-specificity trade-off, i.e., the number of true connections increased simultaneously with the number of false connections. Additionally, the anatomical accuracy of the tracts depended on the studied pathway, the acquisition and the processing pipeline. This stresses the fact that comparing results across studies can suffer from the differences in acquisition settings and processing methods. Therefore, reconstructing fiber pathways from dMRI FT should be performed with great care and additional support from other methods is welcome.

Nonetheless, being aware of the recognized issues, we present the description of a novel fiber tract, the superoanterior fasciculus (SAF), in the frontal lobe which—to the best of our knowledge—has not been documented before with dMRI based tractography with this level of detail. The tract is slightly curved and follows the same arc profile of the cingulum bundle, but is located more frontally and above the frontal part of the cingulum; it can be found in both hemispheres.

Traditionally, the description of new WM fiber bundles went through several stages. For example, WM histology and bundle degeneration studies (Dejerine and Dejerine-Klumpke, 1895) and dissection (Ludwig and Klingler, 1956; Heimer, 1983) studies supported the presence of a structure in post mortem brains, which was confirmed later via dMRI-based FT in the living human brain. Conturo et al. (1999) laid the foundations for selection and analysis of tracts based on ''region of interest (ROI)'', which opened up a new era of tract-based investigations from neuroscience (Lebel et al., 2008; Thiebaut de Schotten et al., 2011a, 2012) to clinical applications (Thiebaut de Schotten et al., 2005; Bartolomeo et al., 2007; Deprez et al., 2012). The access to large samples made the construction of in vivo WM tract atlases and guidelines feasible (Mori et al., 2005; Wakana et al., 2007; Hua et al., 2008). In this work, we reconstruct the proposed structure using specific ROIs along with modeling of the diffusion MR signal based on constrained spherical deconvolution (CSD; Tournier et al., 2004; Tournier et al., 2007).

To minimize the possibility that these findings are based on acquisition or processing artifacts, we used datasets from different projects: young Adult human connectome project (HCP; Glasser et al., 2013; Sotiropoulos et al., 2013; Van Essen et al., 2013), Avon Longitudinal Study of Parents and Children (ALSPAC; Golding, 2004), Multiple Acquisitions for Standardization of Structural Imaging Validation and Evaluation (MASSIVE; Froeling et al., 2017), an in-house dataset (Jeurissen et al., 2011), and robust processing pipelines to show these trajectories. While the main focus of this research was based on dMRI, we complemented our findings with other non-MRI techniques, such as polarized light imaging (PLI) microscopy (Axer et al., 2011) and human brain dissections. Preliminary results of this work on the SAF have been presented at the 2015 ISMRM meeting in Toronto, ON, Canada (Heemskerk et al., 2015).

#### MATERIALS AND METHODS

In the methods section, we describe first the datasets that were used for the investigation. We then explain the dMRI processing, FT and analysis steps. Finally, we describe the PLI and dissection methods.

### Diffusion MRI

#### Datasets

Four dMRI datasets acquired on different platforms were used to study the tract. The datasets represent a large diversity in data quality and number of samples, which helps to generalize the conclusions.

#### **Dataset 1**

Minimally processed dMRI data from the HCP S500 release were used. Briefly, the data were acquired with a 1.25 mm isotropic voxel size; three shells with b = 1,000, 2,000 and 3,000 s/mm2 in 90 DW volumes and six non-weighted images per shell. Every participant for whom all the 90 b = 1,000 s/mm2 and 90 b = 3,000 s/mm2 images were available along with all 18 b = 0 s/mm2 volumes, and were not listed among participants with known anatomical anomalies or data quality issues were included in the analysis. The IDs of the excluded participants are listed on the HCP wiki page (HCP wiki, 2018). The selection resulted in 409 healthy participants (243 females and 166 males), between the age of 22 and 36 years. Low b-value images were analyzed with DTI and CSD models separately using 90 b = 1,000 s/mm2 and nine unweighted volumes, while the higher b-shell was processed with CSD only using 90 b = 3,000 s/mm2 and 18 unweighted volumes. For a subset of 10 participants we also analyzed the b = 1,000 s/mm2 , 2,000 s/mm2 and 3,000 s/mm2 shells with both CSD and DTI models.

#### **Dataset 2**

Ten dMRI datasets were used from the ALSPAC cohort, each consisting of 30 diffusion gradients with b = 1,200 s/mm2 , and 2.4 mm isotropic voxel size as described by Björnholm et al. (2017).

#### **Dataset 3**

The dataset is described in the work of Jeurissen et al. (2011). In summary, one participant's dMRI consisted of 60 diffusion directions, 2.4 mm isotropic voxel size (resampled to 1 mm isotropic) and b = 3,000 s/mm2 .

#### **Dataset 4**

The dataset from the MASSIVE (Froeling et al., 2017) acquisition was used. In short, we used a subset of 22 b = 0 s/mm2 volumes and 250 b = 3,000 s/mm2 volumes and an isotropic resolution of 2.5 mm3 of one participants.

#### Image Processing and Signal Modeling

All datasets were preprocessed with ExploreDTI v.4.8.6 (Leemans and Jones, 2009; Leemans et al., 2009), except for the HCP dataset, which was already preprocessed (see Dataset 1 below). Further modeling (DTI and CSD) and defining ROI configurations were also performed with ExploreDTI.

#### **Dataset 1**

Briefly, FSL (Jenkinson et al., 2012) tools topup (Andersson et al., 2003) and eddy (Andersson and Sotiropoulos, 2016) were used to correct for head motion and geometrical distortions arise from eddy currents and susceptibility artifacts. Finally, the DWIs were aligned to the structural T1 image. For the full description, see the work of Sotiropoulos et al. (2013). DTI estimation of the low b-value shell for the full population was performed using REKINDLE (Tax et al., 2015). The fiber orientation distribution (FOD) in each voxel was estimated using CSD (Tournier et al., 2004, 2007) with the recursive calibration method (peak ratio threshold = 0.01; Tax et al., 2014) with maximum harmonic degree of Lmax = 8, in the high and low b-value shells separately. For a subset of 10 subjects, we performed additional analyses to test the effects of b-value and choice of diffusion modeling. For this, we used the following settings: (a) CSD with b = 2,000 s/mm2 ; (b) CSD with b = 1,000 s/mm2 ; (c) CSD with Lmax = 6; (d) CSD with calibration of the response function based on FA > 0.8; and (e) DTI estimation using REKINDLE.

#### **Dataset 2**

Motion-distortion correction and Gaussian anisotropic smoothing (Van Hecke et al., 2010) were performed with ExploreDTI. The FOD in each voxel was estimated using CSD with recursive calibration (peak ratio threshold = 0.01) and Lmax = 8.

#### **Dataset 3**

Motion-distortion correction was performed with ExploreDTI. The dataset was resampled to 1 mm isotropic voxel size to increase the level of detail (Dyrby et al., 2014). FODs were estimated using CSD with recursive calibration (peak ratio threshold = 0.01), Lmax = 8.

#### **Dataset 4**

Dataset 4 was motion, distortion corrected and resampled to 1 mm isotropic voxel size. Similar to the previous datasets, the FOD was estimated using CSD with recursive calibration (peak ratio threshold = 0.01), Lmax = 8.

#### Fiber Tractography

For all four datasets, the deterministic FT framework of Jeurissen et al. (2011) was used for the CSD approach, with the following parameter settings: FOD threshold = 0.1, angle deviation = 45◦ , step size = 1 mm and minimal tract length = 20 mm. Wholebrain FT was performed with uniform distribution of seed points defined at a 2 mm resolution. For DTI based FT, the settings are: step size = 1 mm, minimal tract length = 20 mm, angle deviation = 45◦ , FA seed and tracking threshold of 0.2 and uniform seed point resolution of 2 mm.

#### ROI Configuration for Reconstructing the SAF

Automated, large-scale tract selection on the HCP dataset was obtained by atlas-based tractography segmentation (Lebel et al., 2008). On the template dataset, we defined 4 Boolean ''AND'' and ''NOT'' ROIs: 2 axial AND, 1 sagittal NOT and 1 coronal NOT ROIs (**Figure 1**). The 2 axial AND ROIs were placed as follows: the first one was located at the height of approximately half the genu of the corpus callosum (CC) and the second ROI was placed 10 slices (equals to 12.5 mm in the HCP dataset) superior. Both AND ROIs included the medial frontal area and excluded the cingulum. A NOT ROI was placed midsagittal to exclude fiber pathways from the CC. Additionally, one coronal NOT ROI was placed posterior of the frontal lobe and below the CC to exclude fibers from the inferior fronto-occipital fasciculus (iFOF).

FIGURE 1 | Positions of the two AND (in green) and two NOT (in red) regions of interest (ROIs) are shown with a direction encoded color (DEC)-FA map. This ROI configuration was used for the tract selection of the superoanterior fasciculus (SAF) in both hemispheres.

#### Consistency Evaluation of the SAF **Within Cohort**

For each of the 409 participants from the HCP cohort, the visitation mask of the reconstructed SAF was normalized to the Montreal Neurological Institute (MNI) template (Fonov et al., 2011), using the transform functions provided by the HCP team. The resulting map was thresholded at 1 and binarized. The group map displays the fraction of participants for whom the tract mask of the SAF is present in each voxel of the MNI space.

#### **Across Acquisition Protocols**

The tractography results of participants from the four different datasets were compared to investigate the consistency of the SAF configuration across different acquisition methods.

#### **Across Processing Settings**

We evaluated the effects of different processing settings on the tractography results for 10 subjects of the HCP dataset.

#### Polarized Light Microscopy

PLI is a microscopy method that uses the birefringent properties of myelin sheath to quantify the main fiber orientation in histological sections (Larsen et al., 2007). In this work, we used data previously involved in the investigation of the anterior cingulum (Axer et al., 2011). One of the studied brains was sectioned as to expose the area of interest but was not investigated previously. For details on acquisition and processing see the corresponding study (Axer et al., 2011).  Each of these four blocks were serially sliced and analyzed. Sections were obtained with a cryostat microtome (CM3050 S, Leica Microsystems, Bensheim, Germany) at a thickness of 100 µm. Aqueous mounting medium AquatexTM (Merck, Darmstadt, Germany) was used for mounting. The histological sections were placed between two coupled crossed polars which can be rotated. Birefringence in the tissue is able to twist some of the light so that it can pass through the second polarizer and be imaged. The orientation of the nerve fibers influences the transmission of plane-polarized light at different azimuths. A CCD camera (Axiocam HR, Carl Zeiss, Göttingen, Germany, basic resolution of 1,388 × 1,040 pixel) was used to capture the light. The brain slices were digitized under azimuths from 0 to 80◦ using two polars only. These sequences were used to estimate the inclination of fibers (in z-direction). The same slices were digitized under azimuths from 0 to 160◦ in steps of 20◦ using a quarter wave plate additionally. These sequences were used to estimate the direction of the fibers in xy-direction.

### Dissection (Paris)

Post-mortem brain dissection was performed in the laboratory of neuroanatomy of Lariboisière Hospital in Paris, France by two neurosurgeons (EM, FCorr).

Five human cerebral hemispheres, obtained from fresh autopsy, were fixed in 10% formalin solution for at least 3 weeks. These specimens were frozen at −18◦C for 2 weeks and then unfrozen at room temperature. This process (Klingler technique's; Klingler, 1935) induces water crystallization in the brain tissue that, by spreading along the WM fibers, facilitates visualization and dissection of the subcortical WM fibers.

The dissection was performed with light microscopy (OPMI pico, Carl Zeiss, Inc., Oberkochen, Germany) and video recorded with a high-resolution digital camera (Karl Storz GmbH, Tuttlingen, Germany).

Initial observation of the configurational shape of sulci and gyri of the mesial cortical surface of frontal lobe was always performed before dissection. The dissection always started from the cingulate fibers extending to the entire mesial and lateral surface of the frontal lobe.

#### Dissection (Trento)

Dissections below were performed by three neurosurgeons (SS, FCors, and AB) in the context of the Structural and Functional Connectivity Lab (SFC-Lab) Project, which was approved by the Ethical Committee of the Azienda Provinciale per i Servizi Sanitari (APSS) of Trento, Italy.

Three right hemispheres were prepared for Klingler's dissection (Klingler, 1935) according to the protocol previously reported (Sarubbo et al., 2015; De Benedictis et al., 2014, 2016). It starts with an injection of formalin 10% in carotids and vertebral arteries, then an immersion in formalin 10% for 40 days, and finally the progressive freezing at −80◦ and de-freezing procedure. After the first de-frost process and removal of arachnoids and vessels, we started the micro-dissection under microscope with 4× magnification with wooden spatulas, approaching at the medial and ventral WM of the frontal lobe from the latero-dorsal cortical surface and leaving intact the gray matter at the tip of the gyri exposed to highlight the territories of terminations.

### RESULTS

### Tract Description

The architectural configuration of the SAF is shown in **Figure 2** where dataset 3 was used with CSD based FT. The complex fiber architecture in the frontal area can be appreciated from the FODs overlaid on the sagittal view in **Figures 2A–C** with the dotted yellow line identifying the interface between regions with locally different dominant fiber populations [''blue'' vs. ''green'' on the principal direction encoded color (DEC) map]. A medial and frontal view of the bilateral tract configuration is given in **Figures 2D,E**, respectively. Note that the SAF resembles the trajectory of the cingulum bundle (in red) but it is located more anteriorly, superiorly, and laterally.

The SAF group composite map of the HCP participants (dataset 1) using CSD modeling with b = 3,000 s/mm2 is shown in **Figure 3**. Again, the bilateral tracts follow a similar trajectory as the cingulum, but more frontally [i.e., in front of the cingulate sulcus or within the superior frontal gyrus (SFG)] and slightly more laterally. The SAF pathways appear to spread from the rostrum of the CC to the ascending ramus of the cingulate sulcus and are medial

FIGURE 2 | Location of the SAF: the axial (A) and sagittal (B) view of part of the region that intersects the SAF (blue region indicated by the arrows). The complex fiber architecture in this area can be appreciated from the fiber orientation distributions (FODs) overlaid on the sagittal view in (C) with the dotted yellow line identifying the interface between regions with different dominant fiber populations ("blue" vs. "green" on the DEC-FA map). Sagittal (D) and coronal (E) views of the SAF configuration. For displaying purposes, only one of the bilateral SAFs is shown. The cingulum bundle is shown in red to provide anatomical reference. Grayscale background is the T1-weighted magnetic resonance imaging (MRI) of the subject.

fraction of subjects for which the tract is present in a given voxel.

to the corona radiata. **Supplementary Figures S1, S2** show the population level map using the same ROIs, but with the 1,000 s/mm2 b-value dataset and modeled with DTI and CSD, respectively.

#### Tract Consistency

#### Across Participants

The percentage overlap map in **Figure 3** and **Supplementary Figure S2** show that the main portion of the SAF is present for 90% of the participants, which is in the same range as other tracts (Thiebaut de Schotten et al., 2011b). The extent of the reconstructed SAF varied substantially across participants (see **Figure 4**). For some, a very extended and broad structure was found (**Figure 4**, bottom row), while in others we found only a narrow portion of the SAF (**Figure 4**, top row). The length of the streamlines also varied across participants, but also within the same individual. It is often observed that streamlines stop abruptly due to the absence of a corresponding FOD peak larger than the predefined threshold.

#### Across Data Acquisitions

**Figure 5** reveals that the SAF was present in all samples that we analyzed. This observation excludes the possibility that the SAF is merely a scanner-specific artifact. While there are differences in acquisition settings between the different acquisition sites, there is no impact on the extent of the SAF. An example is shown in **Figure 6**, where the effect of using different b-values for the data acquisition on the SAF reconstruction is depicted. Low b-values result in less complex FODs leading to shorter and fewer streamlines, but CSD based modeling can still reveal the SAF configuration. CSD based tract coverage from low b-value data shows large overlap with the high b-value based CSD tracts, revealing that proper modeling of crossing fibers is imperative for reconstruction of the SAF. This can be appreciated as well in the population map in **Supplementary Figure S2**, in which DTI based FT cannot deal with complex fiber pathway configurations.

#### Across Processing Strategies

Modeling and processing steps also affected the extent of the reconstructed SAF (**Figure 7**). DTI analysis (**Figure 7A**) resulted in either no pathways at all or in some spurious ones. CSD-based modeling showed either shorter SAF pathways (Lmax = 6, **Figure 7C**) or minor variations (FA calibration, **Figure 7D**). According to the population-level maps in **Supplementary Figure S1**, DTI-based reconstructions of the SAF cover only the most frontal parts of the tract, which is also depicted in **Figure 2** with the blue color. In this location of the SAF, the locally dominant direction of diffusion is tangential with the SAF, irrespective of any other crossing pathways. Clearly the biggest impact on showing the SAF configuration was the modeling of crossing fibers, in our example using CSD over DTI.

#### PLI

**Figure 8** shows the ensemble of the four blocks color-coded by the main in-plane orientation per voxel. In the ROI of where the SAF is expected, a variety of colors and therefore locally dominant orientations were present. Nonetheless, a similar shape (indicated by the white arrows) as with tractography can be appreciated.

#### Dissection (Paris)

We were able to isolate, in four of the five specimens, a series of fibers spreading over from the cingulate fibers that seemed to have the same anteroposterior orientation as the

FIGURE 4 | Consistency within the HCP cohort. The right SAF and cingulum is depicted in sagittal view for nine subjects from the HCP dataset highlighting the large variability in extent of the SAF. The cingulum is shown in red to provide anatomical reference. Tracts are plotted on top of the DEC-FA map.

FT results of the SAF. These fibers were located above the cingulum in correspondence with the frontal gyrus (**Figure 9**). The careful dissection of the fiber complex revealed several different fibers: U-shaped fibers from cingulate, U-shaped fibers belonging to SFG and more lateral fibers from corona radiata (**Figure 9**). All of them had a vertical orientation; therefore, no correspondence to the anteroposterior orientation of the putative SAF.

### Dissection (Trento)

After exploration of the sulco-gyral anatomy (**Figure 10A**) we proceeded with a gentle peel out of the gray matter of the depth and lateral surface of all the gyri of the dorsal portion of the frontal and parietal lobes (**Figure 10B**). Then we removed the U-fibers connecting the most anterior thirds of the middle and superior frontal gyri, at the level of the superior frontal sulcus (SFS; **Figure 10C**). After a progressive and cautious removal of the U-fibers connecting the lateral and medial cortices facing at the border of the SFS, we exposed a thin layer of fibers with an antero-posterior course (in all the three hemispheres dissected; **Figure 10D**). We followed back with a gentle microdissection this layer of fibers exposing the posterior territories of terminations within the dorsal third of the pre-central gyrus. Anteriorly we exposed this thin bundle with an arching course, that follows the physiologic curvature of the frontal anterior cortices, and we exposed the territories of terminations within: the frontal pole, the fronto-orbital cortex and the fronto-orbitolateral cortex.

FIGURE 5 | Consistency across acquisition protocols. For all four datasets an example of the SAF configuration is shown in sagittal view: right SAF and right cingulum for (A) dataset 1 and (B) dataset 3. Left SAF and left cingulum is shown for (C) dataset 2 and (D) dataset 4. The cingulum is shown in red to provide anatomical reference. Tracts are plotted on top of the DEC-FA map.

It was not possible to follow these fibers further back (i.e., behind the central sulcus) in any of the specimen because: (1) we did not find clear signs of continuity; and (2) the dense and intermingled crossing area with the vertical fibers ascending and descending to the pre- and post-central gyri.

This thin layer of fibers is located in the most medial and dorsal portion of frontal WM and connects the postero-dorsal with the antero-dorsal cortices of the frontal lobe (**Figure 11A**). We performed also the microdissection of the most ventral components of the superior longitudinal fascicle (namely, SLF II and III; **Figures 11B–D**), accordingly to the recent description by Fernández-Miranda et al. (2015); these SLF components occupy the mediodorsal and ventral WM of the frontal lobe and are clearly

FIGURE 7 | Consistency of the SAF configuration across different modeling and processing strategies for the same subject of the HCP cohort. In (A), the DTI model was used, resulting in only few and likely spurious tract pathways, whereas the CSD models in (B–D) produce similar and plausible SAF trajectories, but with differences due to processing settings: (B) recursive calibration and Lmax = 8; (C) recursive calibration and Lmax = 6; and (D) FA calibration and Lmax = 8. Tracts are plotted on top of the DEC-FA map and shown sagittal.

distinguishable from the SAF fibers by location, direction and course.

### DISCUSSION

By taking advantage of advanced dMRI methodology, we have identified a consistent bilateral pathway, referred to as the SAF, via FT in the frontal lobe, which has not been described before (Catani et al., 2012; Makris et al., 2005; Thiebaut de Schotten et al., 2011b). Reproducibility across multiple participants, different data samples and acquisition settings boosted our confidence that this finding is not based on imaging artifacts.

FT shows that the SAF can be consistently reconstructed with FT. However, the presence of a long association fiber is not fully supported by dissection, which necessitates hypothesizing about why this pathway is still found with FT. An explanation could be that a series of consecutive U-shaped fibers that connect adjacent gyri emerge to form a long pathway (see **Figures 8**, **12**). This possibility is supported by the work of Maldonado et al. (2012), where they propose that the dorsal component of SLF is primarily composed of U-shaped fibers. However, dissection also revealed a distinct WM structure with the same orientation as the proposed fiber bundle after the explicit removal of U-fibers. One may argue that the fibers are part of the dorsal component of the SLF system (i.e., SLF I), but considering the common anatomical definitions and the territories of termination of the SLF, it is unlikely the case. **Figures 11C,D** highlights the different and well-known components of the SLF. Important to note that this regions is challenging for FT and dissection as well due to the high number of crossings with the descending and ascending (i.e., vertical) pathways.

Independent investigations are still necessary to decide whether the suggested structure is indeed a new one or a component of an already defined tract. A possible description based on new nomenclature that it is part of the large dorsal system of horizontal fibers connecting frontal and parietal cortices as a branch of the mesial longitudinal system (MesLS; Mandonnet et al., 2018), which can be further divided into

FIGURE 8 | Polarized light imaging (PLI) results. (A) Four separate blocks of the same brain sliced in sagittal plane, where the fiber orientation maps are depicted correspond to the (B) color scheme circle. White arrows are indicating the location of the proposed pathway. Part (C) shows an example of formalin fixed human cadaver brain.

an inner branch, which is the cingulum per se and an outer branch, the SAF. Note, such an outer branch is close to what Wang et al. (2016) named as supracingulate or paracingulate pathways. **Supplementary Figure S3** shows the population of the SAF map CSD based modeling and high b-value data along with the cingulum from the JHU-ICBM DTI based WM atlas (Mori et al., 2005; Wakana et al., 2007; Hua et al., 2008) demonstrating that the SAF is not part of the cingulum.

While the agreement between dissection and FT is imperfect, it is important to describe the structure and recognize its occurrence. First, as this is a consistent finding, other researchers will likely discover and investigate the presence of this FT structure. Second, investigating the structural properties of the sub-compartments can improve the understanding of the U-fibers and rule out false structures, which were accidentally reconstructed from U-fibers. Third, this adds to the discussion of the value of FT, thereby raising additional awareness of the pitfalls of FT (Jones and Cercignani, 2010; Parker et al., 2013; O'Donnell and Pasternak, 2015), and the concerns in the field of connectomics (Hagmann et al., 2010; Fornito et al., 2013; Zalesky et al., 2016).

In the following sections, we will discuss that our FT results are plausible from the point of dMRI-based tractography, place our results in the context of other FT and dissection studies, provide suggestions for future investigations, and stimulate the debate about the validity of dMRI-based FT.

### Diffusion MRI

Recent developments in acquisition and data processing boosted the reliability of dMRI and increased the inherently low accuracy of mapping WM pathways with FT. The investigations in solving crossing fibers were imperative, as the reported structure would not or hardly be detected since the main diffusion direction here is along the forceps (see **Figure 2**). However, one of the pitfalls of dMRI is that it is an indirect measure of the underlying WM pathways and reflects the net displacement of water molecules along all structures within a large voxel. Different structural architectures can lead to the same diffusion profile (Jbabdi and Johansen-Berg, 2011), making it hard to reconstruct unambiguously the intrinsic fiber arrangement.

#### Impact of Modeling

The choice of diffusion signal modeling was critical as the SAF cannot be revealed with DTI. The tract is formed mostly from locally non-dominant fiber populations. Therefore, it can be mapped only with techniques, which can resolve crossing fibers. The single location where the tract is locally dominant is the most anterior part with up-down orientation, as depicted in blue in **Figures 1A–C**, and is also the only location revealed by DTI based population level tract mapping shown in **Supplementary Figure S1**. Following this line of reasoning, yet a system of non-dominant, interwoven fiber bundles are to be found with the help of high data quality and advanced modeling.

Another example of mapping a challenging fiber population without any well-known underlying architecture is showcased in the MyConnectome project (Poldrack et al., 2015), featuring longitudinal, high quality MRI data of a single adult male. In a small region of the CC, an anterior–posterior oriented set of fibers are dominating locally, yet the left-right oriented CC fibers are still present and continuous. This rare finding is not expected in the CC, but is remarkably strong in that

FIGURE 9 | Dissection results in two different hemispheres and color overlays representing the proposed structure. Top row: (A) the original image and (B) possible trajectory of the SAF overlaid in blue, the cingulum is indicated in red as the anatomical reference. Bottom row: (C) original image and (D) several U-shaped fibers highlighted in red, which may form part of the SAF via FT.

the front-back oriented diffusion was larger than the left-right orientated diffusion and, hence, changed the color in the DEC map from red to green. Similar to the SAF, it is difficult to investigate parts of pathways where the underlying FOD profiles exhibit non-dominant peaks tangential to the pathway of interest.

#### Tract Consistency

CSD tractography results obtained with low and high b-value data show a high similarity of 90% for the core of the SAF, which is outstanding given the fact that WM fiber bundles vary in their size and position, especially for this is a small and inferior bundle. Compared with other larger bundles, the similarity is in line with previously reported overlap values of >90% (cingulum), >90% (core CC), >75% (CC), and >75% (iFOF; Thiebaut de Schotten et al., 2011b).

The presence of the SAF across different data samples and acquisition settings is indicative that the results are not merely based on acquisition artifacts. As FODs are sharper, more and longer streamlines are found at higher b-values as this will aid the resolving power of the different fiber populations within a voxel. Although we can show the SAF in case of all the participants, finding the complete trajectory is not trivial. The SAF can be incomplete, for example, when the FOD only contains the main peak of the crossing fibers and does not contain the minor second peak. While we were able to determine the main part of the SAF configuration, the dorsal terminations were hard to obtain, which was also an issue during dissection. Future studies, especially at higher spatial and angular resolution, may alleviate this question.

#### Validation

A conceptual limitation of FT (Jeurissen et al., 2017) is the inability to resolve the functionality and directionality of anatomical links, where the existence of a physical connection is necessary to complete the cortical functions. Such functional confirmation can arise from the use cortico-cortical (Matsumoto et al., 2004, 2006, 2012) or axono-cortical evoked potentials (Yamao et al., 2014; Mandonnet et al., 2016), generally obtained with invasive techniques involving implants or electrodes during surgery.

The PLI results show an overall pathway that can resemble the presented structure; however, it is not clearly one consistent pathway. A drawback of PLI, however, is that it cannot distinguish between differently oriented crossing fibers, making it hard to be unambiguous about the exact pathway.

On the one hand, the brain dissections in this work could not clearly verify the existence of the SAF. On the other hand, the dissection results could not rule out the existence of the SAF given the limitations of this approach, especially in regions where multiple fiber systems exhibit crossing configurations. The brain data used for the FT results in this work are obtained from

FIGURE 10 | (A) The main sulci and gyri of the lateral frontal region of a right hemisphere are indicated, after removal of vessels, pia mater, and arachnoid membranes. (B) Decortication of the SFG and MFG allows identifying the U fibers under the SFS (blue pin, pink circle). (C) Exposition of U-fibers between di SFG and MFG (pink arrows). (D) Exposition of a thin layer of fibers with an antero-posterior course under the SFS (pink arrow). CS, central sulcus; IFG, inferior frontal gyrus; IFS, inferior frontal sulcus; MFG, middle frontal gyrus; Op, opercular part of the IFG; Orb, orbital part of the IFG; PreCG, precentral gyrus; PreCS, precentral sulcus; PostCG, postcentral gyrus; S, superior; SFS, superior frontal sulcus; SFG, superior frontal gyrus; Tr, triangular part of the IFG.

healthy volunteers. Hence, ex vivo investigations (dissection and PLI) on the same brains were not feasible. As such, a direct comparison was not possible.

### Complexity of Fiber Bundles

Interaction between FT and brain dissection methods drives both the validation of tracts, but also the discovery of true fiber bundles, thereby increasing our understanding of the design of the brain's architecture (Yeatman et al., 2014; Meola et al., 2015; De Benedictis et al., 2016; Wu et al., 2016b; Hau et al., 2017). Over the years, more and more (parts of) WM bundles were revealed using FT, which were previously undetectable with the formerly existing techniques (Jbabdi and Johansen-Berg, 2011) such as the lateral projections of the CC, IFOF or the Aslant fiber bundle (Thiebaut de Schotten et al., 2012). However, the validity of some of these tracts is still debated. Yeatman et al. (2014) used tractography to rediscover the vertical occipital fasciculus (VOF); a bundle that caused controversies among neuroanatomists in the 19th century. These tractography findings were further confirmed via dissection by Wu et al. (2016c). In this context, the identification of the SAF with FT raises similar concerns and fuels the ongoing debate about validation with other approaches.

Other researchers have also proposed new or redefined WM structures. Track density imaging (TDI) on 7T dMRI (Calamante et al., 2010, 2011, 2012) recently demonstrated finer details of thalamocortical connections (Choi et al., 2018) and revealed the fiber system of the septum pellucidum area (Cho et al., 2015). In both studies the super-resolution ability of the voxelwise fiber count was used to generate images with high anatomical contrast and therefore exposed the aforementioned bundles. However, due to the noise sensitivity of the technique, the validity of structures revealed by TDI remains an open question (Dhollander et al., 2012, 2014).

In addition to new bundles, several studies have shown subcompartments of known WM pathways, which are validated by histology.  De Benedictis et al.

FIGURE 11 | Dissection of a right hemisphere according to Klingler's technique. (A) Exposition of the most ventral components of the anterior indirect component of the SLF (SLF II and SLF III; green area). (B) Thin layer of fibers connecting the postero-dorsal with the antero-dorsal cortices of the frontal lobe (white area, red arrow). (C) Dissection of the main components the SLF: indirect anterior (green area), indirect posterior (blue area); direct (yellow area). (D) U-fibers (blue pins), dorsal frontal fibers (white pins), frontal terminations of the AF, respectively in the ventral premotor cortex (green pins) and the pars opercularis of the IFG (red pins). AF, arcuate fasciculus; SLF, superior longitudinal fascicle.

(2016) used a microdissection approach to reveal and validate the presence of both homotopic as well as heterotopic fibers in the anterior half of the CC.

The cingulum, which has a similar shape and is in close approximation of the SAF, is also a multi-component bundle. The cingulum bundle consists of many short fibers as well as longer fibers that together have many different connections (Bajada et al., 2017). The cingulum is usually depicted as a continuous structure using FT although thorough research is showing a division in at least three subparts, each with their own distinct diffusion metrics (Jones et al., 2013a; Wu et al., 2016b). Given the similarity with the cingulum and the knowledge that many bundles have complex and multicomponent fiber organization, it seems plausible that the SAF also has a multicomponent organization.

#### Future Directions

This study describes a new pathway identified with dMRI based FT, called the SAF, which may have several implications in in vivo neuroimaging studies about structural brain connectivity. If the SAF is considered to be a genuine new brain pathway, further research is needed to better understand the origin and the function of this structure. Investigation of this area at higher spatial resolution could be one avenue as recent work has shown that this coincides with a better understanding of complex crossing fiber structures (Schilling et al., 2017). This approach would then ideally be combined with dissection. We can expect more, previously not shown and locally non-dominant structures to be discovered as Jeurissen et al. (2013) showed that most of the WM in the human brain contains multiple populations. Other imaging techniques, such as optical coherence tomography (Huang et al., 1991) or optogenetics (Deisseroth, 2010) may provide complementary information that could verify the existence of the SAF.

We observed a large variability in the cross-sectional area of the SAF and future analysis of the structure should also entail examining common properties related to microstructure, shape, demographics, and even changes in pathological conditions. Furthermore, it is well-known that the frontal lobe is generally a challenging region to investigate by most MRI methods, because of the susceptibility induced distortions, which are further emphasized by EPI sequences caused by the air-tissue interfaces of the sinuses.

In the past, we have seen examples in which part of anatomical knowledge is abandoned from the mainstream, e.g., in case of the VOF or the IFOF. Since the influential work of Andreas Vesalius (De humani corporis fabrica, 1543, Padua Italy) anatomists are drawing, dissecting and reconstructing WM pathways with increasing precision as technology advances. It would not be unexpected if the SAF could reemerge from textbooks of human brain anatomy in a similar way.

#### CONCLUSION

We have proposed the existence of the SAF in the human brain, a new WM bundle identified with dMRI based FT. We showed that it is consistent within several cohorts, across different acquisition settings and processing strategies. However, there are still uncertainties about the true underlying anatomy of the structure as evidenced by our complementary PLI and brain dissection findings.

### DATA AVAILABILITY

All datasets generated for this study are included in the manuscript and the supplementary files.

### ETHICS STATEMENT

#### Dissection (Paris)

The study was approved by the local ethics committee of Pôle Neurosciences of Lariboisière Hospital. This study complied with the ethical standards.

#### REFERENCES

Alexander, D. C., Dyrby, T. B., Nilsson, M., and Zhang, H. (2017). Imaging brain microstructure with diffusion MRI: practicality and applications. NMR Biomed. doi: 10.1002/nbm.3841 [Epub ahead of print].

### Dissection (Trento)

Dissections were performed in the context of the Structural and Functional Connectivity Lab (SFC-Lab) Project, regularly approved by the Ethical Commitee of the APSS di Trento (authorization N◦ 1837 released on September 26th 2013 and renewed with amendment on September 7th 2018).

### PLI

The brains were taken from persons without history of neurologic or psychiatric disease, who donated their body for anatomical study. All brains were collected from the body donor program of the Institute of Anatomy at the Technical University Aachen (RWTH).

### AUTHOR CONTRIBUTIONS

SD, AH, and AL: protocol design and data analysis. SD, AH, FCorr, MTS, SS, FCors, AB, LP, EM, and AL: data interpretation. FCorr, MTS, SS, FCors, AB, LP, and EM: neuroanatomy. HA: PLI. SD, AH, FCorr, MTS, SS, FCors, AB, LP, EM, HA, TP, and AL: manuscript preparation. AH, FCorr, MTS, SS, FCors, AB, LP, MV, DJ, EM, HA, JE, TP, and AL: manuscript revision. AL: principal investigator.

### FUNDING

The research of AL, AH and SD is supported by VIDI Grant 639.072.411 from the Netherlands Organization for Scientific Research (NWO). The UK Medical Research Council and the Wellcome Trust (Grant ref: 092731) and the University of Bristol provided core support for ALSPAC. Acquisition of MRI data was funded by the National Institutes of Health (R01MH085772 to TP).

### ACKNOWLEDGMENTS

We are extremely grateful to the families who took part in this study and the ALSPAC team, including midwifes, interviewers, computer and laboratory technicians, clerical workers, research scientists, volunteers, managers, receptionists and nurses. A pre-print of this work has been released at bioRxiv (David et al., 2018).

### SUPPLEMENTARY MATERIAL

The Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fnana. 2019.00024/full#supplementary-material

Andersson, J. L. R., Graham, M. S., Zsoldos, E., and Sotiropoulos, S. N. (2016). Incorporating outlier detection and replacement into a non-parametric framework for movement and distortion correction of diffusion MR

Anderson, A. W. (2005). Measurement of fiber orientation distributions using high angular resolution diffusion imaging. Magn. Reson. Med. 54, 1194–1206. doi: 10.1002/mrm.20667

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**Conflict of Interest Statement**: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Copyright © 2019 David, Heemskerk, Corrivetti, Thiebaut de Schotten, Sarubbo, Corsini, De Benedictis, Petit, Viergever, Jones, Mandonnet, Axer, Evans, Paus and Leemans. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

# A Missing Connection: A Review of the Macrostructural Anatomy and Tractography of the Acoustic Radiation

#### Chiara Maffei1,2 \*, Silvio Sarubbo3 and Jorge Jovicich2,4

1 Harvard Medical School, Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, MA, United States, 2 Center for Mind/Brain Sciences - CIMeC, University of Trento, Trento, Italy, 3 Division of Neurosurgery, Structural and Functional Connectivity Lab Project, S. Chiara Hospital, Trento Azienda Provinciale per i Servizi Sanitari (APSS), Trento, Italy, 4 Department of Psychology and Cognitive Sciences, University of Trento, Trento, Italy

The auditory system of mammals is dedicated to encoding, elaborating and transporting acoustic information from the auditory nerve to the auditory cortex. The acoustic radiation (AR) constitutes the thalamo-cortical projection of this system, conveying the auditory signals from the medial geniculate nucleus (MGN) of the thalamus to the transverse temporal gyrus on the superior temporal lobe. While representing one of the major sensory pathways of the primate brain, the currently available anatomical information of this white matter bundle is quite limited in humans, thus constituting a notable omission in clinical and general studies on auditory processing and language perception. Tracing procedures in humans have restricted applications, and the in vivo reconstruction of this bundle using diffusion tractography techniques remains challenging. Hence, a more accurate and reliable reconstruction of the AR is necessary for understanding the neurobiological substrates supporting audition and language processing mechanisms in both health and disease. This review aims to unite available information on the macroscopic anatomy and topography of the AR in humans and non-human primates. Particular attention is brought to the anatomical characteristics that make this bundle difficult to reconstruct using non-invasive techniques, such as diffusion-based tractography. Open questions in the field and possible future research directions are discussed.

Keywords: acoustic radiation, auditory system, sensory pathways, auditory pathways, auditory tract, diffusionbased tractography

**Abbreviations:** AC, auditory cortex; AR, acoustic radiation; CC, corpus callosum; CR, corona radiata; CSD, constrained spherical deconvolution; dMRI, diffusion magnetic resonance imaging; DWI, diffusion weighted imaging; EC, external capsule; EEG, electroencephalography; FA, fractional anisotropy; FS, sylvian fissure; HC, healthy cotrols; HG, Heschl's gyrus; Ic, inferior culliculus; IC, internal capsule; ICPL, posterior limb of the internal capsule; ICSL, sub-lenticular part of the internal capsule; ILF, inferior longitudinal fasciculus; LGN, lateral geniculate nucleus; MEG, magnetoencephalography; MGN, medial geniculate nucleus; ODF, orientation distribution function; OR, optic radiation; ROI, region of interest; PAC, primary auditory cortex; SNR, signal-to-noise ratio; STG, superior temporal gyrus; STP, superior temporal plane; TH, thalamus; WM, white matter; XRT, X-ray therapy.

#### Edited by:

Ricardo Insausti, University of Castilla La Mancha, Spain

#### Reviewed by:

David Reser, Monash University, Australia Hisayuki Ojima, Tokyo Medical and Dental University, Japan

#### \*Correspondence:

Chiara Maffei<EMAIL_ADDRESS>
Received: 04 April 2018 Accepted: 15 February 2019 Published: 07 March 2019

#### Citation:

Maffei C, Sarubbo S and Jovicich J (2019) A Missing Connection: A Review of the Macrostructural Anatomy and Tractography of the Acoustic Radiation. Front. Neuroanat. 13:27. doi: 10.3389/fnana.2019.00027

### INTRODUCTION

fnana-13-00027 March 5, 2019 Time: 19:7 # 2

The acoustic radiation (AR) represents a highly-myelinated group of axonal projections and constitutes one of the primary sensory pathways of the primate brain, carrying auditory information from the thalamus to the cortex. The connectivity pattern of these fibers has been described in some detail in cytoarchitectonic and myeloarchitectonic studies of non-human primates (Polyak, 1932; Mesulam and Pandya, 1973; Morel et al., 1993; Hackett et al., 1998) and, at a more macroscopic level, in a few histological studies in humans (Flechsig, 1920; Pfeifer, 1920; Rademacher et al., 2002; Bürgel et al., 2006). However, the information obtained from non-human primate studies cannot be transferred directly to the human brain. Furthermore, such studies have focused mostly on the cytoarchitectonic aspects of the auditory cortices and their intrinsic connectivity, with little emphasis on the anatomical course of the AR itself. In humans, tracing studies are impossible in vivo and have restricted applications in post-mortem brains (Mesulam, 1979; Tardif and Clarke, 2001), while limited information can be drawn from old myeloarchitectonical post-mortem studies.

The advent of diffusion magnetic resonance imaging (dMRI) (Basser et al., 1994) and tractography (Mori et al., 1999) has made it possible to investigate the anatomy of the major white matter (WM) bundles of the human brain in vivo and non-invasively (Catani et al., 2002; Catani and de Schotten, 2008; Lawes et al., 2008). However, the AR constitutes a notable exception in this sense. This primary sensory bundle is largely absent from most tractography studies investigating audition and language and from human WM atlases (Thiebaut de Schotten et al., 2011). This is mainly due to the intrinsic anatomical characteristics of these fibers, which go beyond the current limits of dMRI tractography methods (Behrens et al., 2007; Jones and Cercignani, 2010; Daducci et al., 2016). Therefore, the diffusion-based tractography reconstruction of the AR remains highly challenging at present, discouraging its in vivo anatomical investigation in humans.

However, overcoming or circumventing these methodological considerations is essential, as successful in vivo reconstruction of the human auditory tract is of great importance for both clinical applications (e.g., pre-surgical mapping) and basic neurobiological research. Reliably revealing the 3D characteristics of this tract would help in correlating the anatomical and functional aspects of audition and in the study of human-only cognitive functions, such as language, both in healthy and pathological conditions.

The main aim of this review is to emphasize the paramount need for characterizing the human AR in both clinical and scientific contexts, which has yet to be done for a number of reasons that we discuss. This review collates the available information from primate studies on the anatomy, topography, and course of the AR, with particular emphasis on the anatomical features that make this tract extremely challenging to study, even for state-of-the-art dMRI tractography techniques. Additionally, recent attempts to reconstruct the AR using diffusion-based tractography methods will be discussed. Finally, open questions in the field will be presented and possible future research directions considered.

### THE ACOUSTIC RADIATION IN PRIMATES

The auditory system of mammals is a complex network of parallel and overlapping axonal projections that connect subcortical nuclei and cortical regions. It encodes and transmits stimuli coming from the acoustic environment, enabling an organism to detect a sound in its environment, determine the direction from which it originated, discriminate among potential sources, and thereby, react or communicate with conspecifics. **Figure 1** provides a schematic representation of the human ascending auditory system and its sub-cortical relays, as it is commonly described in both the scientific literature and neuroanatomy textbooks (Moller, 2006; Brugge, 2013). The thalamo-cortical projections of this system, which connects the medial geniculate nucleus (MGN) to auditory cortex, constitute the AR (**Figure 1**).

Anatomical knowledge of the human AR mainly comes from pioneering investigations at the beginning of the 20th century (Dejerine and Dejerine-Klumpke, 1895; Flechsig, 1920; Pfeifer, 1920). Since these early studies, very limited additional information on this structure has been reported for humans (Rademacher et al., 2001, 2002; Bürgel et al., 2006). Most of the anatomical and functional organization of the mammalian auditory system has been inferred from animal studies, mainly non-human primates and cats (Morel and Kaas, 1992; Hashikawa et al., 1995; Hackett et al., 1998; Kaas and Hackett, 2000; Hackett et al., 2001; Jones, 2003; de la Mothe et al., 2006; Lee and Winer, 2008). However, these studies primarily focus on the topographical mapping between the thalamus and auditory cortex without documenting the spatial connection pattern and course of these fibers (Jones, 2003; Hackett, 2011). As a consequence, most neuroanatomical books only report schematic drawings of this pathway (e.g., **Figure 1**). Furthermore, although comparative studies have shown similar features across human and non-human primate brains, cortical and subcortical architectonic differences, as well as cognitive dissimilarities,

extra-lemniscal pathway by dashed lines. CN, cochlear nucleus; SOC, superior olivary complex; LL, lateral lemniscus; Ic, inferior culliculus; MGN, medial geniculate nucleus; AR, acoustic radiation.

exist (Galaburda and Sanides, 1980; Galaburda and Pandya, 1983; Schmahmann et al., 2007; Passingham, 2009; Thiebaut de Schotten et al., 2012). Thus, it is important to understand the commonalities among primate species and to identify potentially unique aspects of the human auditory system that might be related to our ability to perceive and process languagespecific stimuli. In vivo diffusion imaging techniques constitute a powerful tool in investigating these topics.

In the next section, we briefly review the AR microstructural topography, as described in animal studies, and its macrostructural anatomy, which has been gleaned from human research. Our goal is to provide a more complete anatomical profile of this bundle across species and to highlight the existing gap of topographical information between invasive and non-invasive studies. In particular, we focus on the AR in vivo imaging literature, reviewing recent attempts to visualize this tract using tractography techniques.

### The Acoustic Radiation in Invasive Studies

The axonal connections between the thalamus and the auditory cortex have been investigated in animals using different invasive techniques. These fibers stem from the medial geniculate nucleus of the thalamus (MGN), as first described by von Monakow (1882), and contact a specific area on the posterior part of the Sylvian fissure (Minkowski, 1923; Polyak, 1932), which has been described as a "rudimentary transverse temporal gyrus" (Walker, 1937) in monkeys. In most species, three major divisions of the MGN are identified: ventral (or principal), dorsal (or posterior), and medial (or magnocellular) (Winer et al., 2001; Jones, 2003). Each of these divisions has unique connections to different cortical regions. The ventral division receives input from the central nucleus of the inferior culliculus (Ic) and almost exclusively projects to what is defined as the core region of the auditory cortex (Mesulam and Pandya, 1973; Burton and Jones, 1976; Morel and Kaas, 1992; Morel et al., 1993; Hashikawa et al., 1995; Rauschecker et al., 1997). This region is distinguished by dense immunoreactivity for the calciumbinding protein parvalbumin, as most of its inputs come from the ventral MGN parvalbumin immunoreactive cells (Molinari et al., 1995), even if some connections with the other MGN divisions appear to exist (Luethke et al., 1989; Morel et al., 1993). This region occupies a portion of the caudal superior temporal plane (postero-medial part of the Heschl's gyrus in humans) and it is characterized by a dense population of small granule cells (e.g., koniocortex) with a well-developed layer IV (Merzenich and Brugge, 1973; Seldon, 1981). Both the ventral MGN and the core region show a tonotopical organization in which the representation of frequencies is spatially organized. This suggests a topographical organization of fibers connecting similar frequency domains in these two structures (Burton and Jones, 1976; Molinari et al., 1995). The core region is surrounded by a secondary narrow belt region and a third, more lateral region that occupies the lateral surface of the superior temporal gyrus (Hackett et al., 1998; Kaas and Hackett, 2000). This latter "parabelt" region is generally considered to be a higher-order auditory region or auditory association cortex that integrates auditory with non-auditory multisensory information (Hackett et al., 1998). These regions are less responsive to pure tone sounds, preferring more complex sounds, and do not show the clear tonotopical organization typical of the core region (Rauschecker et al., 1995; Jones, 2003). The dorsal and medial divisions of the MGN constitute the major inputs to these secondary auditory association regions. These nuclei receive inputs from the external nucleus of the Ic, as well as from lower brainstem relays, and bypass the core region to project to secondary auditory and other cortical regions (Rauschecker et al., 1997; Jones, 2003; Winer and Lee, 2007).

Projections from the ventral MGN to the core region correspond to the most direct classic auditory pathway, also called the lemniscal pathway. These parallel connections are tonotopically organized and their neurons show sharp responses to tones (Morel et al., 1993). Direct projections from the other MGN divisions to secondary auditory cortical regions are part of the extralemniscal (or non-lemniscal) auditory pathway. These fibers are separate from but lie adjacent to those of the lemniscal pathway in the ascending auditory system. In addition, their subdivision continues at the cortical level, where the non-lemniscal pathway has stronger and more diffuse connections with regions surrounding the core region (Lee and Sherman, 2010). These pathways are less tonotopically organized and their neurons demonstrate fewer sharp responses to sounds.

Classical studies in non-human primates focus on the topography of the connectivity between the MGN and the auditory projection cortical territory but provide no information on either the course and extension of the AR tract itself or its relationship with the other WM pathways of the brain. Using Marchi axonal degeneration, Polyak (1932) provided a detailed description of the course of this tract in rhesus macaques (**Figure 2**). He describes a dense bundle of closely assembled fibers that leaves the MGN, turns laterally and crosses the most ventral portion of the internal capsule (IC) immediately above the lateral geniculate nucleus (LGN) of the thalamus (**Figure 2**). At this level, these fibers are distinguishable from somatosensory fibers because of their nearly horizontal orientation. The AR then bends ventrally and reaches the external capsule (EC) by passing through the ventral edge of the posterior putamen. Once there it meets other projection and association bundles before finally reaching the WM of the superior temporal convolution close to the Sylvian fissure (**Figure 2**). He describes the AR as a regularly arranged projection system, where fibers lie parallel to one another until gradually diverging only when approaching the cortex.

Classical topographical descriptions in humans (**Figure 3**) provide a very similar description (Dejerine and Dejerine-Klumpke, 1895; Flechsig, 1920; Pfeifer, 1920) of the AR, which may be summarized as follows. The AR leaves the MGN and travels in an antero-lateral direction; it then passes through the posterior portion of the IC, proceeds along the corona radiata and curves around the inferior portion of the circular sulcus of the insula before entering the transverse temporal gyrus of Heschl (HG) in a ventral-to-dorsal direction (Pfeifer, 1920).

At this macrostructural level, both animal and human studies delineate a bundle with a transverse orientation that lies adjacent to and crosses over other main WM bundles before reaching the auditory cortex. In both animals and humans, the AR intermingles with the fibers of the IC in its most posterior portion. Furthermore, Dejerine and Dejerine-Klumpke (1895) described the close proximity of the AR to the optic radiation (OR) at the stemming point in the thalamus, defining this region as the "carrefour sensitive" (sensory intersection). Polyak (1932) also studied this region, stating that the OR and AR, although lying close together, are completely separate: the AR is located more anterior and crosses at a right angle above the OR, which follows a posterior direction in the sagittal plane. As will be discussed in the following section, this configuration and certain other anatomical features of the AR pose serious challenges to its 3D tractography reconstruction.

The myeloarchitectonic maps from Dejerine and Dejerine-Klumpke (1895) and Flechsig (1920), while being of invaluable historical significance, cannot be used to extract precise anatomical information that can be applied to modern brain atlases or neuroimaging studies. More recently, radiological information about the AR anatomical organization was obtained in human post mortem myelin-stained sections (Rademacher et al., 2002; Bürgel et al., 2006). These studies confirm the classical topographical description of the acoustic fibers and are of great importance as they represent the main reference framework for in vivo imaging studies of this brain region and provide the opportunity to investigate inter-subject variability and hemispheric asymmetry. Previous studies have found that the AR does not enter the lenticular nucleus, but rather runs

human brain (coronal view). (B) Coronal cut through the middle section of the thalamus. (C) Axial section of the brain; cut through the inferior thalamus. In the three panels, fibers belonging to the acoustic radiation have been highlighted in green and fibers of the internal capsule in pink. The acoustic radiation projects from the thalamus (TH) to the first temporal circonvolution (T1) and passes through the sub-lenticular and posterior segment of the internal capsule. This map clearly highlights the crossing between these two fiber systems. CC, Corpus callosum; EC, external capsule; ICPL, Posterior limb of the internal capsule; ICSL, sub-lenticular part of the internal capsule; CR, corona radiata; LGN, lateral geniculate nucleus; MGN, medial geniculate nucleus; T1, first temporal circonvolution; TH, thalamus (adapted from Dejerine and Dejerine-Klumpke, 1895; https://archive.org, public domain).

dorsally to the OR, crossing the temporal isthmus as it ascends to the auditory cortex (Pfeifer, 1920; Polyak, 1932; Bürgel et al., 2006). Fanning of these fibers in HG creates a hat-like structure that covers the posterior end of the lenticular nucleus (Rademacher et al., 2002).

According to Flechsig (1920), these fibers are divisible into two bundles, one of which ascends near the Ec and enters the auditory cortex from the superior-posterior side. The other bundle courses for some distance in the company of the OR before passing behind and below the fossa sylvii where it pierces the bases of the middle and inferior temporal gyri to reach the transverse temporal gyrus or gyri. Similarly, early evidence from animal studies suggests that the AR is subdivided into dorsal and ventral components (von Monakow, 1882), although, Rademacher et al. (2002) found more recently only a single and heavily myelinated bundle.

Overall, the macrostructural description of the AR in humans resembles the description of this tract in non-human primates. The origin and termination of this bundle, together with the extension and relationship to other WM bundles, is maintained. However, the macro- and micro-anatomical correspondence between the different cortical regions in monkeys and humans is not straightforward (Baumann et al., 2013). Compared to nonhuman primates, the human cortical surface of the auditory regions demonstrates additional gyri and higher inter-subject and interhemispheric variability (Galaburda et al., 1978; Hackett et al., 2001), both of which may affect the AR anatomy. The human auditory cortex also shows higher differentiation into

sub-regions as compared to non-human primates, and the core region is larger than the belt region (the opposite is true for monkeys) (Fullerton and Pandya, 2007). Both the differences in cortical anatomy between non-human primates and humans and the major variability of cortical and subcortical structures across subjects and hemispheres in humans (Bürgel et al., 2006) raise interesting questions about the possibility of a relationship between such morphological differences and human-only language abilities.

### The Acoustic Radiation in Non-invasive Tractography Studies

Diffusion MRI (dMRI) tractography allows for the investigation of WM architecture in the human brain non-invasively in vivo. Since its first applications (Mori et al., 1999), most of the well-known WM bundles of the human brain have been reconstructed using diffusion-based tractography methods (Catani and Thiebaut de Schotten, 2008; Lawes et al., 2008). Despite its potentials, dMRI tractography has several important limitations that have been discussed in depth in the literature (e.g., Jones and Cercignani, 2010; Thomas et al., 2014; Maier-Hein et al., 2017). In principle, these limitations affect most tractography reconstructions, but here we focus on how they particularly affect the 3D reconstruction of the AR. Reconstructing the AR three-dimensionally is highly challenging at present due to the anatomical features described in the previous sections: its relatively small size, transversal orientation, and location in a region with a high density of crossing fibers. This largely prevents the inclusion of this particular tract in most tractography investigations.

As shown in the previous section (see **Figures 2**, **3**), in its medio-lateral course from the MGN to the HG, the AR lies in a nearly horizontal position and, for this reason, crosses some of the major fiber systems of the human brain: internal capsule, external capsule, and posterior thalamic radiation (Maffei et al., 2018). Resolving the fiber crossing is a wellknown challenge in dMRI (Tuch et al., 2002; Dell'Acqua and Catani, 2012; Jeurissen et al., 2013). The classic tensor model (Basser et al., 1994) is capable of characterizing only one main fiber orientation per voxel and it has been shown to constantly fail in regions where voxels contain complex fiber architectures (Behrens et al., 2007; Jbabdi and Johansen-Berg, 2011). The impact of this limitation is particularly evident for non-dominant tracts, given that the orientation produced by the tensor will be closest to the largest contributing direction in most cases. This effect is amplified at the low resolution of commonly available diffusion protocols due to within-voxel partial volume averaging effects (Tournier et al., 2011). When implemented in tractography studies, the diffusion tensor model has proven to be incapable of detecting the 3D profile of the AR. Streamlines are either truncated when entering voxels containing major inferior-superior orientations or erroneously embedded in the reconstruction of these major projection bundles, with no visible streamlines contacting the HG (Behrens et al., 2007; Crippa et al., 2010; Berman et al., 2013). This has likely been the primary factor preventing the investigation of the auditory system by means of diffusion-based tractography. Some studies have used the diffusion tensor to investigate the WM microstructure of the auditory system, limiting the structural investigation of the auditory pathways to the extraction of mean quantitative diffusion measures [e.g., fractional anisotropy (FA)] from specific regions of interest (ROI) (Chang et al., 2004; Lee et al., 2007; Lin et al., 2008; Wu et al., 2009). However ROIbased analysis can lead to inaccurate results, especially for WM tracts that are extremely variable across subjects, such as the AR (Rademacher et al., 2002). Therefore, it is typically preferable to map the exact anatomy of such WM tracts in individual subjects/patients.

To address the intrinsic limitations of the tensor formalization, more advanced models have been introduced that can better account for fibers crossing, by modeling more than one fiber population per voxel (Tuch, 2004; Tournier et al., 2008; Descoteaux et al., 2009). These models open up the possibility of propagating streamlines through crossing fiber regions, thus allowing the reconstruction of non-dominant WM bundles, such as the AR. However, together with the low-level diffusion model employed, other parameters play a role in the accurate reconstruction of WM bundles, such as the tractography parameters chosen and the strategy to define inclusion ROIs. Here we report studies that reconstruct the AR 3D tractography profile in vivo using multi-fiber-based models (**Figure 4**), taking into consideration the different combinations of acquisition parameters, diffusion models and ROIs selection strategies that they used (**Table 1**).

Behrens et al. (2007) were able to visualize the course of the AR from the MGN to the cortex using the ball-and-stick model and probabilistic tractography (Behrens et al., 2003) (**Figure 4A**). The authors demonstrate how this multi-fiber model can reconstruct the AR, overcoming the limitations of the tensor model. Three following studies were then able to demonstrate the reliability of this model for successful in vivo reconstruction of the profile of the auditory tract in both healthy subjects and those with tinnitus (Crippa et al., 2010; Javad et al., 2014; Profant et al., 2014). In these studies, different combinations of inclusion ROIs were used to isolate the AR, including the Ic, the MGN, and both functionally and manually defined HG. Nevertheless, the profile of the 3D tractography reconstruction looks visually similar across the four studies and shows connections between the posterior thalamus and the auditory region WM. However, artifacts are visible along the inferior-superior axis in the middle part of the AR at the level of the crossing with the IC and these false positive reconstructions are likely to be related to the probabilistic nature of the diffusion model and tractography algorithm used. Despite the inclusion of false positive signals in the reconstructions, the ball-and-stick model has the important advantage of being accessible to low b-value (b = 1000 s/mm2 ) diffusion protocols which makes it suitable for clinical investigations of the AR. Berman et al. (2013) (**Figure 4B**) used a solid-angle q-ball model (Aganj et al., 2010) and probabilistic tractography to successfully reconstruct the auditory connections between the MGN and HG. This reconstruction looks anatomically very accurate and free of false positive artifacts. However, q-ball methods require higher b-value diffusion data (b ≥ 3000 s/mm2 ). In these models,

FIGURE 4 | The tractography reconstruction of the acoustic radiation (AR) in three different studies. (A) 3D volumetric reconstruction of the right AR in one subject using a multi-tensor model and probabilistic tractography. Voxels are color-coded from 10 (red) to 50 (yellow) samples passing through the voxel (adapted with permission from Behrens et al., 2007). (B) The panel shows the streamlines of the right AR in axial view, as reconstructed in one subject using q-ball imaging and probabilistic tractography. The location of the thalamus and auditory cortex (AC) are specified by the blue boxes. The figure also shows the orientation distribution functions (ODF) corresponding to the inferior longitudinal fasciculus (ILF), highlighted by the green arrow (adapted with permissions from Berman et al., 2013). (C) Left: 3D tractography reconstruction of the right AR in one subject using constrained spherical deconvolution models and probabilistic tractography. A 3D rendering of the thalamus is also shown in gray and the borders of HG in green. Right: Klinger's post-mortem blunt dissection of the right AR (modified from Maffei et al., 2018). In all three panels the location of thalamus (blue arrow) and auditory cortex (white arrow) are highlighted.

the angular resolution of the reconstructed diffusion profiles is increased and the crossing fiber configurations are correctly represented (Tournier et al., 2013), although they pose limitations to its use in clinical populations. Our group (Maffei et al., 2018) used ultra-high b-value Human Connectome Project diffusion data-sets (Fan et al., 2015) and spherical deconvolution (Tournier et al., 2008) to reconstruct AR streamlines using probabilistic tractography. We compared the results to Klinger's post-mortem blunt micro-dissections (**Figure 4C**), a method based on a brain freezing technique optimized to reveal WM (Ludwig and Klingler, 1956). This approach has been used in several studies to evaluate tractography accuracy (Fernaìndez-Miranda et al., 2015; De Benedictis et al., 2016; Pascalau et al., 2018). The obtained reconstructions agreed with the AR anatomy revealed in the post-mortem dissections and no additional exclusion regions were needed to isolate the AR profile. However, the ultra-high b-values used in this study (b ≤ 10,000 s/mm2 ) are very rarely achievable, even in research settings. More recently, Yeh et al. (2018) published a population-averaged atlas of several WM connections, including the AR, using q-space diffeomorphic reconstruction (QSDR) (Yeh and Tseng, 2011) and deterministic tractography for multiple fiber orientations. In their approach, the high angular and spatial resolution of the data (1.25 mm isotropic, and b ≤ 3000 s/mm2 ) and the large sample (842 subjects) allowed them to reduce the rate of false positive artifacts. However, the AR profile shown in this study, while correctly originating at the posterior thalamus, does not reach the expected auditory cortex on the superior side of the temporal lobe.

Overall, the reconstructed 3D profiles shown in these studies are in accordance with the macrostructural landmarks defined by classic anatomical studies: streamlines originate in the posterior thalamus and course in an antero-lateral direction to terminate in the temporal lobe (Dejerine and Dejerine-Klumpke, 1895). However, AR reconstructions are still highly variable across studies. In particular, while showing similar profiles at the thalamic stemming region, reconstructions differ as they approach the cortex, either falling short of reaching the HG (Crippa et al., 2010; Yeh et al., 2018) or creating false positive artifacts at the intersection with vertically oriented fibers (Javad et al., 2014; Maffei et al., 2018). Moreover, disagreement about the relationship with neighboring tracts exists. Berman et al. (2013) suggest that AR streamlines cross the inferior longitudinal fasciculus (ILF), while Behrens et al. (2007) and Javad et al. (2014) claim that they cross the OR. In a recent work from our group, we did not find that the AR is in close proximity to the ILF (Maffei et al., 2018), supporting older studies that report no crossing between the AR and OR (Pfeifer, 1920; Polyak, 1932), as described above. In addition to variability across studies, low reproducibility across subjects is also reported. Some groups have been able to reconstruct AR tracts on both hemispheres on 100% of subjects (Behrens et al., 2007; Profant et al., 2014; Maffei et al., 2017). In contrast, even when using similar diffusion models (e.g., ball-and-stick), other studies report reconstructions successful in both hemispheres in much lower proportions, such as 35–50% (Crippa et al., 2010) or 71–86% (Javad et al., 2014).

We suggest that the present variability and low reproducibility in the reconstructed AR profile are related to a combination of some of the specific characteristics of the AR that make its tractographic reconstruction quite challenging, even for state of the art tractography techniques: its anatomical location, small size, and inter-individual anatomical variability.

As alluded to in the previous section, the AR constitutes a compact but relatively short and small bundle that lies horizontally in a region with a high density of vertical fibers. Even if multi-fiber models proved capable of representing this crossing, the degree to which this crossing can be accurately resolved in the final 3D reconstruction also depends on the tractography algorithm used and the intrinsic angular resolution of the dMRI data (Tournier et al., 2013). For example, using a higher b-value (Berman et al., 2013; Maffei et al., 2018) might help improve accuracy of results relative to those obtained with lower b-value data (Crippa et al., 2010). However, in these studies several exclusion ROI have been employed to either constrain tractography (Behrens et al., 2007) or clean the results (Crippa et al., 2010). This renders the accuracy of

TABLE 1 | The table reports the main acquisition and tractography parameters used to reconstruct the AR in the listed studies.

The acquisition parameters column provides the number of diffusion encoding directions, the b-factor (s/mm2 ), and the spatial resolution (mm). The inclusion ROIs column indicates whether seed and target regions were selected manually or with other methods. Some tractography studies performed reconstructions in both directions and results were summed. The success rate column reports the percentage of subjects showing successful AR tractography reconstructions in both hemispheres. DWI, diffusion MRI encoding directions; CG, Cingulate Gyrus; CSD, Constrained Spherical Deconvolution; HG, Heschl's Gyrus (gray and white matter); HG-WM, Heschl's Gyrus (only white matter); Ic, Inferior Culliculus; MGN, Medial Geniculate Nucleus; QSDR, q-space diffeomorphic reconstruction; TH, Thalamus; HC, healthy controls.

the final reconstructions sensitive to the selection of these ROI, complicating comparisons across studies.

The use of different ROI selection strategies can strongly affect the resulting tractography reconstructions. The HG is a complex structure that shows large variability in sulcal landmarks across subjects and hemispheres (Rademacher et al., 2001), whereas the MGN is a very small structure, varying from 74 to 183 mm3 (Kitajima et al., 2015), making it difficult to locate in neuroimaging data and also highly variable across individuals and hemispheres (Rademacher et al., 2002). Therefore, this variability poses difficulties in the selection of the ROIs used to initiate the tractography reconstruction or to perform the virtual dissections. For example, while reliable automatic segmentation tools for the entire thalamus are available in different public software packages (e.g., FSL1 , Freesurfer2 ), it is far more challenging to automatically segment smaller structures, such as the MGN, due to both their size and their lower MRI contrast with neighboring WM. Alternative solutions exist but are also challenging. For example, subject-specific manual segmentations of MGN can lead to high anatomical accuracy, but they are very time intensive and, therefore, costly to do in large subject cohorts. Brain atlases also can be used to define the MGN, however given the small size and inter-subject variability, atlas-driven segmentations are unlikely to provide a good anatomical match for all subjects. The development of more accurate automatic parcellation techniques for the thalamic nuclei is expected to improve the accuracy of seed-to-target definition and, thus, of the resulting tractography reconstructions. Recently, a new and promising probabilistic atlas of the thalamic nuclei has been proposed based on a combination of ex vivo MRI and histology (Iglesias et al., 2018).

Future research in the field should focus on how to improve the sensitivity and reproducibility of the tractography reconstruction of this bundle. This could be achieved by inputting prior anatomical knowledge in the tractography process, as it is implemented in global-tractography-based frameworks (Yendiki et al., 2011) or more recently developed bundlespecific algorithms (Rheault et al., 2019). Parallel to these advances at the tractography level, there have been efforts in validating tractography results at a micro-anatomical scale. As this validation process progresses, we expect to expand our knowledge of the exact boundaries of the human AR and, consequently, better inform its tractography reconstruction and improve accuracy of tractography results. A more accurate tractography investigation of the AR could expand our structural and functional knowledge of the auditory system, as proposed in the next section.

1https://fsl.fmrib.ox.ac.uk/fsl/

2http://surfer.nmr.mgh.harvard.edu/

The reliable in vivo reconstruction of the AR in humans may help the exploration of the neuro-anatomical and functional mechanisms underlying auditory processing and language comprehension. The precise characterization of the AR can provide information useful for clinical applications, such as in diagnosis and treatment of hearing and speech disorders, recovery from injury, and performance of interventions that can damage the AR, such as brain surgery or radiation treatments. This section provides a brief review of basic and clinical research areas that could benefit from an improved characterization of the AR.

#### Language and Auditory Perception

The ability to communicate through speech is quintessentially human. However, the anatomical organization and the functional mechanisms underlying speech comprehension in the brain are still not understood completely. The acoustic information that reaches the primary auditory cortex via the AR fibers is processed within neural networks that depend on cortico-cortical shortand long-range connections involving temporal, parietal and frontal regions, as schematized in the dual-stream model (Hickok and Poeppel, 2007; Saur et al., 2008; Friederici, 2009). Within this processing network, it is unclear where language-specific processing starts and whether the auditory cortex is involved in speech-specific analysis. Some theories suggest that the left auditory cortex is specialized in processing temporal cues that are fundamental for speech comprehension (Zatorre et al., 2002; Poeppel, 2003) and that this language-specific encoding might actually start at the subcortical level (Hornickel et al., 2009). The diffusion-based reconstruction of the AR, and of the auditory pathways at large, could help address the structural-functional relationship of speech perception. At the structural level, it would be interesting to understand whether the AR exhibits a degree of leftward lateralization in its volume, as demonstrated for some of the other WM bundles implicated in language processing (Catani et al., 2007). Reports on the macroscopic volumetric asymmetry of cortical auditory regions have been known for some time (Von Economo and Horn, 1930; Galaburda et al., 1978; Geschwind and Galaburda, 1985; Penhune et al., 1996), but only one study specifically investigated the hemispheric lateralization of the AR (Bürgel et al., 2006). At the functional level, the recent association of tractography and neurophysiological techniques [such as magnetoencephalography (MEG) and electroencephalography (EEG)] opens interesting possibilities for investigating these topics. EEG metrics have been recently correlated with diffusion metrics in the investigation of the OR (Renauld et al., 2016). Similarly, EEG/MEG- and tractography-derived measures could be combined to investigate the relationship between temporal cortical regions and auditory function in both healthy subjects and patients.

On a finer scale, AR streamline terminations could be combined with functional MRI to provide critical insights into the subdivision of the auditory cortex, the borders of which are still not clearly defined using in vivo neuroimaging methods (Baumann et al., 2013). In this sense tractography could be used to investigate the topographical organization of the AR with respect to the different subdivisions of the auditory cortex. The different auditory cortical regions show a hierarchical organization in information processing (Tardif and Clarke, 2001), from highly specialized core regions to more integrated tertiary para-belt regions. This is confirmed by functional MRI studies, which suggest a gradient of increasingly more complex and abstracted processing from primary to higher-order auditory regions (Rauschecker et al., 1995; Humphries et al., 2014). Direct connections from MGN to secondary regions have been shown in monkeys (Rauschecker et al., 1997), suggesting that this functional organization might be maintained in the topographical organization of the AR fibers. At its present stage, tractography can locate and delineate the profile of major WM bundles with some accuracy, but it is very difficult to achieve precise site-to-site connectivity analysis with it; this limits the in vivo investigation of the WM topographical organization of the human brain. However, some studies use diffusion tractography to parcellate functionally different cortical regions (Rushworth et al., 2006; Anwander et al., 2007) and investigate the topographical organization of major bundles (Lee et al., 2016), and new methods have been proposed to advance the use of tractography for this purpose (Aydogan and Shi, 2016). In this scenario, it would be interesting to investigate whether some language-specific connections exist inside the AR and whether these project to higher-order languagespecific cortical regions. Moreover, this would also allow for the investigation of whether the tonotopical organization of the primary auditory cortex is reflected in its thalamocortical connections, as was recently shown in the mouse brain (Hackett et al., 2011). As dMRI acquisition (in particular, spatial resolution), diffusion modeling and tractography techniques improve, we will be able to bridge the gap between the micro-anatomical knowledge we have of the thalamocortical connections in animals and the macro-anatomical description in humans.

#### Language and Hearing Disorders

Damage to the auditory regions, most often the result of brain infarct or traumatic injury, has been associated generally with rare auditory syndromes, such as verbal auditory agnosia (Shivashankar et al., 2001), environmental auditory agnosia (Taniwaki et al., 2000), and cerebral (or central) deafness (Griffiths, 2002). However, until now, only one study investigated the extent of WM damage to the AR in a patient suffering from verbal auditory agnosia (Maffei et al., 2017). Investigating the extent of damage to the AR in patients suffering speech-related comprehension deficits would potentially enhance our understanding of the involvement of the AR in language processing.

Also, there is evidence that AR infarct can cause auditory hallucination (Woo et al., 2014), and that the extra-lemniscal pathway might be implicated in tinnitus perception (Moller et al., 1992). At present, studies investigating the auditory

pathways in these patients relied on WM ROI measurements (Lee et al., 2007; Lin et al., 2008), which only outline a portion of the underlying WM bundles and may not be representative of the entire tract. Being able to better understand the dynamics and location of such changes in the auditory pathways could help inform pathophysiological treatment strategies, such as repetitive transcranial stimulation (Langguth et al., 2010).

In congenitally and early deaf subjects, volumetric studies have outlined differences in gray and white matter of the auditory regions, compared to hearing subjects (Shibata, 2007; Kim et al., 2009), but to the best of our knowledge, no specific study on the AR in deaf subjects has been conducted to date. In addition to providing more detailed information on the anatomical changes occurring in the brain as a consequence of sensory deprivation, tractography of the AR may serve as an additional early diagnostic as well as complementary treatment tool in monitoring data in congenital hearing loss. This would help avoid delayed diagnosis that might lead to poor speech outcomes (Dedhia et al., 2018). Moreover, AR tractography reconstruction might be fundamental in assessing auditory pathway integrity before and after cochlear implantation, potentially predicting implant success (Huang et al., 2015).

The structural-functional relationship in language and hearing disorders can be further investigated by combining tractography reconstructions and more recently developed diffusion measures (Raffelt et al., 2012; Calamuneri et al., 2018) within both classical and more advanced tractography frameworks (Daducci et al., 2016). The application of these methods in clinical populations in the context of hearing disorders may help characterize axonal and myelination diseases (Ohno and Ikenaka, 2018) and auditory neuropathies (Moser and Starr, 2016; Ohno and Ikenaka, 2018).

Tractography reconstruction of the AR could help us investigate the anatomy of this tract in patients with hearing and/or language disorders, understand whether these fibers undergo structural reorganization in the case of auditory deprivation, and clarify the extent of AR damage in poststroke lesion profiles. This may be critical for shedding light on the functional-structural relationships of linguistic and nonlinguistic sound processing in the human brain.

#### Brain Surgical Planning

Investigating the functional and anatomical characteristics of the auditory fibers reaching the cortex, especially in relation to their implications for language function, would be important for surgical planning, such as in the case of tumor or epilepsy surgery (Wu et al., 2007; Farshidfar et al., 2014). The 3D reconstruction of major WM bundles is employed to plan and guide resections during surgery, and a functional atlas of human WM to drive well balanced onco-functional resections has been proposed recently (Sarubbo et al., 2015). In this context, diffusion-based virtual dissections have focused almost exclusively on language and sensory-motor structures (Chen et al., 2015). Possible reasons why AR fibers have not received much attention in the neurosurgical literature include the possibility that most of the non-linguistic auditory processing may happen at the brain-stem level and that auditory information is conveyed to both hemispheres, so that extensive bilateral damage is necessary for complete deafness (Griffiths, 2002). However, different sub-modal aspects of auditory processing, for example, those related to music perception and/or speech comprehension, might depend on the integrity of these projections (Hayashi and Hayashi, 2007; Baird et al., 2014). For cases of temporal lobe resection, the reliable virtual reconstruction of the AR might be critical for minimizing post-operative deficits in these domains. Also, it might serve in pre-operative assessments for cochlear implantation, as hearing recovery after implantation is influenced by the integrity of subcortical pathways (Vlastarakos et al., 2010).

#### Brain Radiation Oncology Planning

Today, X-ray therapy (XRT) is the standard of care for most brain tumors. However, XRT can damage normal brain tissue, causing neurocognitive deficits in different cognitive domains (Makale et al., 2016). The consideration of neuroimaging techniques for treatment planning is gaining importance as it helps avoid such complications by minimizing the unnecessary absorption of radiation in sensitive regions outside the tumor. Nevertheless, further improvement of radiation treatment will require tailored radiotherapy based on intra-treatment response (Wong et al., 2017).

Additionally, studies have shown XRT side effects in both gray (Bahrami et al., 2017) and white matter, although it is still largely unclear how variable sensitivity to radiation injury is across various regions of the brain (Connor et al., 2017). Kawasaki et al. (2017) show that tractography can help evaluate how radiation from XRT differentially affects WM regions and pathways. This knowledge, together with an understanding of how damage to such regions and pathways affects cognitive processes, could be used in the future to further optimize radiation treatment planning.

#### CONCLUSION

The anatomical and functional organization of the auditory system is still not well understood, particularly in humans. Successful in vivo tractographic reconstruction of the human auditory tracts is of great importance for clinical applications (e.g., pre-surgical mapping), as well as for basic research (e.g., language and auditory systems). This review outlines how the characterization of the AR has been limited by the methods used in the past and how advances in MRI acquisition and diffusion tractography methods offer the possibility to improve the characterization of this important WM tract. A few exciting potential research areas are suggested that would investigate anatomy and function concurrently in the same individual, both in health (e.g., the role of these tracts in language processing) and in disease (e.g., how the integrity of this tract relates to cognitive deficits). However, in order to obtain reliable reconstructions of the AR across subjects and protocols, additional work is needed to better understand how diffusion MRI acquisition

and tractography reconstruction strategies affect the AR 3D characterization and to validate tractography reconstructions at a micro-anatomical scale. Furthermore, as diffusion tractography is blind to the directionality of reconstructed fibers, the AR bundle could include both thalamo-cortical and corticothalamic projections, and therefore more studies are needed to differentiate between the afferent or efferent nature of these connections. Although these methodological challenges apply to diffusion MRI tractography in general, here we have focused on their relevance to the AR, a tract that has proven to be rather elusive for the reasons herein reviewed.

#### REFERENCES

### AUTHOR CONTRIBUTIONS

JJ conceived the review. SS revised the manuscript for neuroanatomical content. CM drafted the manuscript and designed the figures. All authors contributed to the final manuscript.

### ACKNOWLEDGMENTS

We thank Albert Galaburda for his insightful comments on a previous version of this manuscript.

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**Conflict of Interest Statement:** The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

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