January 2016
Volume 57, Issue 1
Open Access
Visual Neuroscience  |   January 2016
New Insights in the Optic Radiations Connectivity in the Human Brain
Author Affiliations & Notes
  • Alessandro Arrigo
    Department of Biomedical Sciences and Morphological and Functional Images University of Messina, Messina, Italy
  • Alessandro Calamuneri
    Department of Neurosciences, University of Messina, Messina, Italy
  • Enricomaria Mormina
    Department of Biomedical Sciences and Morphological and Functional Images University of Messina, Messina, Italy
  • Michele Gaeta
    Department of Biomedical Sciences and Morphological and Functional Images University of Messina, Messina, Italy
  • Angelo Quartarone
    IRCCS Centro Neurolesi Bonino Pulejo, Messina, Italy
  • Silvia Marino
    IRCCS Centro Neurolesi Bonino Pulejo, Messina, Italy
  • Giuseppe Pio Anastasi
    Department of Biomedical Sciences and Morphological and Functional Images University of Messina, Messina, Italy
  • Pasquale Aragona
    Department of Biomedical Sciences and Morphological and Functional Images University of Messina, Messina, Italy
  • Correspondence: Alessandro Arrigo, University of Messina, Department of Biomedical Sciences and Morphological and Functional Images, Via Consolare Valeria, 1 Messina, 98125, Italy; alessandro.arrigo@hotmail.com
Investigative Ophthalmology & Visual Science January 2016, Vol.57, 1-5. doi:10.1167/iovs.15-18082
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      Alessandro Arrigo, Alessandro Calamuneri, Enricomaria Mormina, Michele Gaeta, Angelo Quartarone, Silvia Marino, Giuseppe Pio Anastasi, Pasquale Aragona; New Insights in the Optic Radiations Connectivity in the Human Brain. Invest. Ophthalmol. Vis. Sci. 2016;57(1):1-5. doi: 10.1167/iovs.15-18082.

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      © ARVO (1962-2015); The Authors (2016-present)

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Abstract

Purpose: To study optic radiations connectivity by means of advanced magnetic resonance imaging (MRI) approaches, noninvasively, in vivo, in healthy human brains.

Methods: Sixteen healthy subjects (nine males, age range, 25–40 years) were included in this study. Morphologic and diffusion data were acquired by means of a 3T MRI scanner. Using an advanced tractographic technique, based on probabilistic constrained spherical deconvolution algorithm, postprocessing analyses were performed. Statistical analysis was carried out using the 2-tailed Wilcoxon rank sum test. Outcome measure was the percentage distribution of optic radiations streamlines in different cortical visual areas (V1–V5). The latter were detected by means of Juelich probabilistic histologic atlas.

Results: Average connectivity analyses revealed that the optic radiations are mainly distributed in V1 (47.46% ± 5.5) and V2 (32.45% ± 3.98); furthermore, direct connections with V3 (7.81 ± 3.06), V4 (4.22% ± 1.82), and V5 (8.06% ± 2.65) were also detected.

Conclusions: In the present study, the connectivity profile of optic radiations, obtained by means of algorithms not affected by the limitations of other tractographic techniques, such as diffusion tensor imaging, was shown in healthy human brains. Interestingly, direct connections with V4 were detected for the first time in humans; moreover, further support on the possible existence of V5 connections was provided. Our findings showed new connections between lateral geniculate nuclei and cortical visual areas, giving a further possible comprehension of the phenomena leading to the visual signals elaboration.

Optic radiations are two well-represented white matter fiber bundles which allow direct connection between lateral geniculate nuclei (LGNs) and visual cortex. Each optic radiation is conventionally divided into three different portions: anterior, middle, and posterior.1 The anterior bundle initially runs anterolaterally near the temporal horn of lateral ventricle and, assuming a posterolateral course, forms the Meyer loop.2,3 The middle bundle starts superiorly to the temporal horn and reaches occipital lobe passing near the inferior occipital fasciculus.2,3 The posterior bundle runs near the occipital horn and reaches the superior portion of the calcarine fissure.2,3 Optic radiations emerge from LGNs during VI month of gestation, reaching the occipital cortex under formation.4 Fast growth of optic radiations was demonstrated before birth, although the final development is reached only after birth, carrying out fibers segregation into visual columns according to ocular dominance.5 The current literature reports that optic radiations fibers project to the primary (V1), secondary (V2), and tertiary (V3) visual cortices.6,7 
It is known that LGNs show a very accurate retinotopic map forming three different cellular types: (1) parvocellular layers receiving inputs from cones, (2) magnocellular layers receiving visual signals from rod, and (3) koniocellular cells, which are interspersed between other cells and receive inputs mostly from short wavelength cones.8 
Optic radiations represent eloquent bundles that need to be investigated (e.g., for presurgical planning of temporal or occipital neoplasms). For this reason, and in order to accurately predict iatrogenic visual deficits,9 optic radiations are often reconstructed and analyzed by means of magnetic image resonance (MRI)-based diffusion tensor imaging (DTI) tractography,10,11 From the analysis of anisotropic water diffusion in white matter, this technique permits to reconstruct and visualize the white matter fiber bundles. Although conventional DTI techniques are largely used, several limitations of such approach were demonstrated (i.e., large reconstruction biases and less reliability for fibers with complex configuration; e.g., crossing fibers),12,13 In order to overcome these limitations, several sequences (and related signal modelling) were developed, like Q-ball imaging and diffusion spectrum imaging (DSI). Although powerful in comparison with DTI, these techniques still suffer from some limitations, such as poor angular resolution for Q-ball14 and very long acquisition time for DSI.15 Another modelling technique, known as constrained spherical deconvolution (CSD) has been proven to determine more reliable evaluation of white matter bundles in human brain.1517 
The main purpose of this work is to study the optic radiations by means of probabilistic CSD in order to provide a reliable profile of connections in healthy human brains. 
Materials and Methods
For this study, sixteen right-handed, healthy subjects (nine males and seven females; age range, 25–40 years; mean age 35) were recruited. All subjects did not suffer from any neurologic disease. The research followed the tenets of the Declaration of Helsinki; informed consent was obtained from the subjects included, after explanation of the nature and possible consequences of the study. The study was approved by the institutional review board of IRCCS Bonino Pulejo, Messina, Italy (Scientific Institute for Research, Hospitalization and Health Care). 
Magnetic resonance imaging protocol was performed by means of 3T Achieva Philips MRI (Achieva; Philips Healthcare, Best, The Netherlands) scanner with a 32-channel SENSE head coil (Achieva; Philips Healthcare). For each subject we acquired an anatomical T1-weighted three-dimension (3D) high-resolution Fast Field Echo (field-of-view [FOV] 2403240 mm2, voxel size 13131 mm, slice thickness 1 mm, flip angle 30°, repetition time (TR) 25 ms, echo time (TE) 4.6 ms) and a diffusion weighted dataset obtained by means of dual-phase encoded pulsed gradient spin echo sequence (b = 1500 s/mm2, 64 gradient diffusion directions, FOV 240 × 240 mm2 resulting in isotropic 2-mm voxel resolution, TR 11884 ms, TE 54 ms). Subjects motion and movement-by-susceptibility artifacts were corrected by means of unwarp and diffusion toolboxes available for SPM8 package (in the public domain, www.fil.ion.ucl.ac.uk/spm). To this end, a unique reversed phase-encoded b0 image was acquired, for each subject, before diffusion sequence. Rotational part of estimated affine transformations was applied to update gradient diffusion directions. All analyses were performed in native space, following Jones and colleagues,18 in order to avoid possible misalignment issues. 
All regions of interest (ROIs; LGN, V1, V2, V3, V4, and V5) were detected using Juelich probabilistic histologic atlas19: First of all, anatomical T1-weighted 3D volumes were nonlinearly coregistered to preprocessed diffusion as previously described.20 The New Segment Option of SPM8 tool was used to drive the registration procedure comparing cerebro-spinal fluid (CSF) probability maps extracted from T1 and b0 volumes. Usually, an affine mapping of T1s to fractional anisotropy (FA) maps is performed to coregister structural scans to diffusion data. This approach suffers from two inherent flaws: (1) some nonlinear local geometric distortions still persist on diffusion data even after preprocessing, and (2) FA and T1 maps provide different contrasts in brain tissues, because the former is mostly focused on white matter, while the latter mostly highlights gray matter structures. The use of a nonlinear procedure can reduce misregistration by providing a mapping, which more closely follows the anatomy in diffusion space. On the other side, CSF can be extracted with a rather good accuracy both from diffusion data and T1s; thus, spatial priors that are common to both diffusion and T1 spaces can drive CSF-based registration. We know that partial volume effects could affect CSF; for this reason, we used CSF probability maps instead of crude CSF binary ones for warping T1s to diffusion images. This choice allowed performing a weighted registration by further taking into account potential partial volume inaccuracies that might appear in both maps. Coregistered T1s were then normalized to Montreal Neurological Institute (MNI) stereotactic space by means of FSL utilities FLIRT and FNIRT (in the public domain, http://fsl.fmrib.ox.ac.uk/fsl/fslwiki/). Estimated warping fields were later on inverted and applied to Juelich template ROIs in order to have them represented in native space of our subjects. The 50% atlas was used (i.e., only areas that belonged to the claimed structure with a probability ≥50% were used). These ROIs were eventually visually inspected by a radiologist with 20 years of experience (MG) and refined to prevent possible misalignments; 3 of 16 subjects showed no more than two spurious voxels in their LGN ROIs (two subjects for left LGN and one subject for the right one), which were erased. Regions of interest correspondent to right and left LGNs were used as seeds for the probabilistic CSD based tractography; all tractographic reconstructions were obtained using MRtrix software package (in the public domain, http://jdtournier.github.io/mrtrix-0.2/index.html).21 The following reconstruction parameters were used: maximal spherical harmonics degree of 8, maximum fiber length 150 mm, step size 0.2 mm, minimal fiber orientation distribution function (fODF) amplitude 0.15. The latter parameter constitutes a conservative approach that might cause potential underestimation; however, we preferred to use it in order to keep to a minimum false positive tracts,22 because in this way only voxels with a high probability to belong to white matter are involved in the tracking procedure. Target ROIs were moderately dilated to include gray/white matter boundaries; in this way we ensured that streamlines were able to reach target ROIs for subsequent connectivity analysis. This step could cause an overlapping between different visual area-ROIs (V-ROIs); assigning to a V-ROI those regions that had in the neighborhood, on average, the highest probability of accordance with a specific visual area of the histologic atlas solved these conflicts. All tracts were automatically colored, according to streamlines directions, in green (anterior–posterior direction), blue (cranial–caudal direction), and red (left–right direction). From each LGN, 200,000 streamlines were generated. 
For connectivity analysis, we considered the percentage of streamlines ending in each visual area; all calculations were performed by means of in-house scripts built with MATLAB Software Package (in the public domain, www.mathworks.com/products/matlab/), release 2013. 
Results
From all the recruited subjects we obtained tractographic reconstructions of right and left optic radiations (Fig. 1). Starting from LGNs, these tracts share the same symmetrical course (Fig. 1A) widely involving occipital lobes. For visualization purposes, each bundle reaching a specific target visual area was manually colored and shown separately (Figs. 1B–F). Average connectivity results are shown in Figure 2. Percentages of LGNs connections with each visual area are distributed as follow (mean % ± SD): V1 (right 40.88% ± 3.86; left 54.04% ± 5.02; average 47.46% ± 5.50), V2 (right 35.11% ± 2.88; left 29.78% ± 4.64; average 32.45% ± 3.98), V3 (right 10.11% ± 3.15; left 5.52% ± 2.67; average 7.81% ± 3.06), V4 (right 6.03% ± 2.09; left 2.41% ± 0.93; average 4.22% ± 1.82), and V5 (right 7.87% ± 3.41; left 8.25% ± 2.63; average 8.06% ± 2.65). Significant left lateralization was found only for V1 by means of a 2-tailed Wilcoxon rank sum test (P = 0.010). 
Figure 1
 
(A) Tridimensional axial and perspective views of right and left optic radiations in a representative subject. Each bundle was automatically colored according to its main direction. (BF) Tridimensional axial and perspective views of bundles manually colored according to specific visual areas reached (LGN-V1: yellow, LGN-V2: red, LGN-V3: blue, LGN-V4: green, and LGN-V5: magenta).
Figure 1
 
(A) Tridimensional axial and perspective views of right and left optic radiations in a representative subject. Each bundle was automatically colored according to its main direction. (BF) Tridimensional axial and perspective views of bundles manually colored according to specific visual areas reached (LGN-V1: yellow, LGN-V2: red, LGN-V3: blue, LGN-V4: green, and LGN-V5: magenta).
Figure 2
 
Average connectivity analysis of right (red line) and left (blue line) optic radiations. Percentages of connections with each visual area are distributed as reported in the Results section. Average connectivity profile is further shown with black line. Shaded areas surrounding each line represent estimated SDs.
Figure 2
 
Average connectivity analysis of right (red line) and left (blue line) optic radiations. Percentages of connections with each visual area are distributed as reported in the Results section. Average connectivity profile is further shown with black line. Shaded areas surrounding each line represent estimated SDs.
Discussion
In this study, we detected and analyzed optic radiations in healthy human brain by means of probabilistic CSD tractography. Our connectivity analysis revealed that the most part of LGN connections mainly involved V1 and V2; however, projections to V3, V4, and V5 were detected. Evidences of strong projections to V1 as well as direct connections to V2 were in keeping with current literature.7 These projections formed the bulk of the optic radiations structure, hence representing fundamental brain pathways for visual processing. Our connectivity analysis revealed a left lateralization regarding only LGN connections with V1; because all subjects were right-handed, this finding might be related to a left cortical dominance. 
In addition to these white matter bundles, growing evidences suggested the hypothesis of a wider involvement in several high order functions of LGN, thus supposing the existence of direct connections with other visual areas. Blindsight (i.e., the phenomenon whereby patients may respond to visual stimuli applied to their blind field)23 suggested indeed the existence of larger visual connections starting from LGNs. Recently, direct projections to V3 were identified in humans.6 Our detection of LGN direct connections with V3 were thus in keeping with the findings of Alvarez and colleagues.6 
To the best of our knowledge, our study was the first showing possible direct LGN-V4 projections in humans. It was demonstrated both in humans and monkeys that V4 area plays a central role in high order functions, spatial attention, and object recognition.2426 This area might also contribute to color elaboration27 as well as depth perception.28 We knew that tractography is not sufficient to assess per se the existence of a given connection; however, Roe and colleagues' review29 cited several studies evincing that human V4 might show a similar functional organization to that of monkey, despite functional complexity and specialization differences hold across species. Direct LGN-V4 connections were previously demonstrated to exist in monkeys, following three distinct pathways and respectively involving magnocellular, parvocellular, and koniocellular layers.3034 In addition, besides confirming the existence of LGN-V4 connections in macaque brain, Gattass and colleagues34 further increased V4 functional complexity by proposing that these projections might be bidirectional. Although further functional and dissection studies are needed, our findings shed new lights on the possible V4 functional skills in human brains. 
Lastly, strong LGN connections with V5 were also found in this study. These pathways were previously detected in monkeys by means of tracing studies.35 In addition, a previous study36 carried out in a small human cohort, suggested the existence of LGN-V5 pathways; this consideration was supported by the increase of V5 activation following V1 damage, thus suggesting that V5 has an important role for the functional compensation after such damage. Although these findings were also supported by functional studies performed in humans,37,38 Rabbo and colleagues39 raised several criticisms about the reliability of these tractographic data. In particular, they argued about several technical limitations of tensor-based models, which might have led to inconsistent DTI evidences of LGN-V5 connection, while suggesting to adopt more advanced approaches. By means of probabilistic CSD-based tractography, a method consistently more reliable than DTI-based one,15 we were able to support the hypothesis of the existence of a direct LGN-V5 pathway as claimed by Bridge and colleagues.36 This area was considered as a higher order node for computation and integration of different aspects of visual information, including elaboration of motion and depth perception.40,41 The existence of direct LGN-V5 connections could provide further possible support for understanding how V5 implements the visual functions. 
Our global findings reinforced the hypothesis that LGN network might be the basis both for complex elaboration of visual information as well as the genesis of partially understood visual phenomena, such as blindsight. In the latter case, a recent study demonstrated that LGN could be the key structure for establishing blindsight, because, after injury of V1, it was able to strongly activate other visual areas.42 
Potential limitations of the present study depended on possible tractography biases; it was essential the use of an adequate signal modelling to achieve highly qualitative results. We wanted to use probabilistic CSD-based tractography, because of its stronger reliability in resolving voxels containing complex fibers configurations.15 Indeed, it was previously demonstrated that more than 90% of white matter voxels showed fibers with complex geometries (such as crossing fibers).43 This aspect should be taken into account when reconstructing optic radiations, since their tractographic detection could be affected by the presence of several concurring white matter bundles, like uncinate fasciculus, fronto-occipital fasciculus, superior longitudinal fasciculus, and inferior longitudinal fasciculus.2,3 To further improve the robustness of our tractographic outcomes we adopted restrictive criteria respecting usual standard,22,4446 although we knew we might incur in an underestimation issue. We knew that tractographic findings should be carefully considered47 because CSD-based tractography, as well as other methods, was not enough to assess real existence of white matter pathways; it rather provided high probability of connection between two regions. Another potential pitfall is related to the possible mislocalization of visual areas. For this study we used a probabilistic atlas; it was, however, known that individual differences might cause inaccuracy in exact ROIs definition. This limitation could be overcome by using fMRI-based localization of visual areas.48 For these reasons, further studies involving other techniques should be conducted in order to confirm our findings. 
In conclusion, we reconstructed optic radiations in healthy humans by means of probabilistic CSD, providing reliable data not affected by tensorial models limitations. We provided LGN complete connectivity profiles, reinforcing the strong evidence of extrastriate LGN connections in human brain. Further studies should be conducted in order to better define functional aspects of all these pathways as well as their possible involvement in functional compensation after occipital lobes damage. 
Acknowledgments
Disclosure: A. Arrigo, None; A. Calamuneri, None; E. Mormina, None; M. Gaeta, None; A. Quartarone, None; S. Marino, None; G.P. Anastasi, None; P. Aragona, None 
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Figure 1
 
(A) Tridimensional axial and perspective views of right and left optic radiations in a representative subject. Each bundle was automatically colored according to its main direction. (BF) Tridimensional axial and perspective views of bundles manually colored according to specific visual areas reached (LGN-V1: yellow, LGN-V2: red, LGN-V3: blue, LGN-V4: green, and LGN-V5: magenta).
Figure 1
 
(A) Tridimensional axial and perspective views of right and left optic radiations in a representative subject. Each bundle was automatically colored according to its main direction. (BF) Tridimensional axial and perspective views of bundles manually colored according to specific visual areas reached (LGN-V1: yellow, LGN-V2: red, LGN-V3: blue, LGN-V4: green, and LGN-V5: magenta).
Figure 2
 
Average connectivity analysis of right (red line) and left (blue line) optic radiations. Percentages of connections with each visual area are distributed as reported in the Results section. Average connectivity profile is further shown with black line. Shaded areas surrounding each line represent estimated SDs.
Figure 2
 
Average connectivity analysis of right (red line) and left (blue line) optic radiations. Percentages of connections with each visual area are distributed as reported in the Results section. Average connectivity profile is further shown with black line. Shaded areas surrounding each line represent estimated SDs.
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