The work is supported in part by the Natural Sciences and Engineering Research Council of Canada (NSERC). The authors thank the participants, Dr. Rick Thompson for his assistance in recruiting the albinism patients, Denis Romanovsky for his help running some of the analyses and modifying a figure, M&#243;nica Giraldo Chica for her knowledge and advice with tractography, Joy Williams for her help in MRI acquisition, and Aman Goyal for his MRI analysis expertise.

Ethics Statement: The current research study has been approved by the Human Participants Review Committee (HPRC) at York University, Toronto. All participants gave informed written consent.
1. Subject Preparation
Note: Eleven participants with OCA, aged 36 &#177; 4 years (6 females) were compared to ten age-matched controls, aged 32 &#177; 4 years (6 females). Participant history is recorded in Table 1.
<ol>
	<li>Ask each participant to fill out and sign a consent form that lists MRI safety guidelines and imaging protocol.</li>
	<li>For each participant, provide earplugs for the ears. Position participant supine and head first in the magnet, and landmark above the eyes at the eyebrow level. Secure participant&#39;s head with cushions to reduce head motion. Give the participant a squeeze bulb for patient alert.</li>
</ol>
2. Structural MRI Parameters
Note: All imaging is acquired on a 3T MRI scanner using a 32-channel head coil. During a single session per subject:
<ol>
	<li>Acquire a high resolution T1-weighted anatomical using a 3D-MPRAGE sequence covering the entire brain with the following parameters: acquisition time 4 min 26 sec, field of view 256 mm, 256 matrix, 192 slices with slice thickness of 1 mm, with a resulting isotropic voxel size of 1.0 mm3, TR = 1900 ms, TE (echo time) = 2.52 ms with an inversion time of 900 ms and flip angle of 9&#176;, 1 average, parallel imaging (iPat GRAPPA, acceleration factor of 2).</li>
	<li>Acquire a DTI sequence covering the cortex, with slices in transverse orientation following the anterior commissure/posterior commissure (AC-PC) line, using the following parameters:acquisition time 8 min 5 sec, field of view 192 mm, 128 matrix, voxels 1.5 1.5 mm in-plane, 56 contiguous (no gap) slices with 2 mm thickness, TR = 6900 ms, TE = 86 ms, 64 directions, b-value of 1000 s/mm2 (reference image with low b-value of 0 sec/mm2), 1 average, parallel imaging (iPat GRAPPA) with an acceleration factor of 3.</li>
	<li>Acquire 30-40 PD-weighted images in a coronal orientation, parallel to the brain stem,covering from the anterior extent of the pons to the posterior portion of the inferior colliculus.
	<ol>
		<li>Use the Turbo spin echo (FAST spin echo) pulse sequence and the following parameters:acquisition time 1 min 29 sec per scan, field of view 192 mm, 256 matrix, 30-40 slices with thickness of 1 mm, resulting voxel size 0.75 0.75 1 mm3, TR = 3,000 msec, TE = 22 msec, turbo factor of 5, refocusing flip angle of 120&#176;, 1 average, parallel imaging (iPat GRAPPA) with an acceleration factor of 2. 
		Note: S12 was scanned using the following parameters: field of view 180 mm, 512 matrix, 30 slices with 1 mm thick slices, resulting voxel size 0.4 x 0.4 x 1.0 mm3. All other parameters remained the same. Acquisition time 2 min 47 sec.</li>
	</ol>
	</li>
	<li>Pre-process all scans by converting raw DICOM to NIfTI format using the program dcm2nii.</li>
</ol>
3. LGN Delineation
Note: The LGN is a small subcortical structure located deep in the brain, hence high-resolution PD images are required to determine its anatomical boundaries. In these scans, the LGN appears as an area of high signal intensity relative to the surrounding WM tracts, facilitating its detection21. The identified anatomical LGN is then used as a seed region for tractography.
<ol>
	<li>While blind to group membership, manually trace right and left LGN masks three times each on averaged PD images interpolated to twice the resolution and half the voxel size (original 256 x 256 matrix, 0.75 x 0.75 x 1 mm3 voxel size).
	<ol>
		<li>To obtain high resolution PD images use the freely available FLIRT function and other software tools within FMRIB&#39;s Software Library (FSL,&#160;http://www.fmrib.ox.ac.uk/fsl/). Upsample, concatenate, motion correct and average PD images for each participant as previously described elsewhere22.</li>
		<li>Load the high resolution PD image in FSLView and click on the Tools tab to select the Single option (or press <img alt="Figure 1" src="/files/ftp_upload/53759/53759symbol1.jpg" /> ) to enlarge the image.</li>
		<li>Click the File tab to select the Create Mask option, and use the toolbar on the top left of the screen to trace the LGN in each slice. If desired, change the contrast of the image by dragging along the min/max in the toolbar to facilitate LGN detection.</li>
	</ol>
	</li>
	<li>Merge these regions of interest (ROIs) into a median mask using the fslmerge command.</li>
	<li>Combine all raters&#39; median masks into a single median mask using the same command.</li>
</ol>
4. V1 Segmentation
<ol>
	<li>Run &#34;recon-all&#34; command in FreeSurfer23 (v5.3.0) on brains in native anatomical space (T1-weighted images) for automated processing.</li>
	<li>Convert the appropriate outputs in the newly created mri folder (orig.mgz, brain.mgz, rawavg.mgz, T1.mgz) to NIfTI using &#34;mri_convert&#34;.</li>
	<li>Use BET brain extraction in the FSL GUI to correct the skull-stripped output brain (brain.nii.gz) in FreeSurfer space if necessary. Choose the Run standard brain extraction using bet2 option (default). Lower the threshold if image is missing brain tissue, or increase if non-brain tissue is captured (default threshold 0.5). Select the Output brain-extracted image and Output binary brain mask image (the latter may be used for manual corrections) in the advanced options.</li>
	<li>Convert output V1 parcellation to a volumetric mask using &#34;label2surf&#34; and &#34;surf2volume&#34; commands.</li>
</ol>
5. Pre-tracking Registrations
Note: For the next steps, call the FSL GUI to open each of the following tools.
<ol>
	<li>Use BET brain extraction and select the Bias field &#38; neck cleanup option to skull-strip rawavg.nii.gz, located in the mri folder created by &#34;recon-all&#34;. Adjust threshold as necessary.</li>
	<li>Run FLIRT linear registration to bring brains in FreeSurfer and native anatomical space to diffusion space.
	<ol>
		<li>Select brain.nii.gz, output of recon-all (FreeSurfer space), or a subject&#39;s brain extracted T1 (native anatomical space) as the input image, and an Eddy corrected and brain extracted diffusion-weighted image (DWI) as the reference image. Then click &#34;Go&#34;. 
		Note: This step creates two outputs, the input brain registered to the reference image (.nii.gz) and a transformation matrix (.mat). Apart from registration, the latter file is required for tractography when seed space is not diffusion. Use the output transformation matrices (.mat) created in this step for tractography as explained in 7.4.2.</li>
	</ol>
	</li>
	<li>Similar to 5.2, run FLIRT linear registration to bring participants&#39; PD brains to FreeSurfer space and native anatomical space.</li>
	<li>Prepare seed masks for tractography:
	<ol>
		<li>Apply FLIRT transformation from Utils in the FLIRT linear registration toolbox. Use the .mat output as the transformation matrix, the original LGN mask as the input and brain.nii.gz (FreeSurfer space) or T1_brain.nii.gz (native anatomical space) (see 5.2) as the reference volume. Select the Nearest Neighbor interpolation method from the advanced options.</li>
	</ol>
	</li>
	<li>Using brain.nii.gz files only, prepare target masks for tractography:
	<ol>
		<li>Register FreeSurfer brains to native anatomical space and create target masks by applying transformation to V1 masks (see 5.2, 5.4.1) using Tri-Linear interpolation. Click &#34;Go&#34;.</li>
	</ol>
	</li>
</ol>
6. LGN Normalization&#160;
<ol>
	<li>Use FNIRT non-linear registration as described previously at http://fsl.fmrib.ox.ac.uk/fsl/fslwiki/FNIRT to bring participants&#8217; non-extracted brains in native anatomical space to MNI space, using the Montreal Neurological Institute whole brain template (MNI152). 
	Note: Non-linear registration of original anatomical images is recommended for this step, as registrations were more accurate when FNIRT was applied to non-extracted T1s compared to FLIRT on extracted brains.</li>
	<li>Apply transformation to LGN masks in anatomical space (original LGN previously transformed to native anatomical space in 5.4) using nearest-neighbour interpolation as described in 5.4.1 to bring masks to MNI space.</li>
	<li>Average all LGN masks in MNI space across both groups using AFNI&#39;s &#34;3dMean&#34; command.</li>
	<li>Use &#34;fslmaths -thr&#34; to apply a threshold to the mean mask in MNI space.</li>
	<li>Calculate the radius of the mean mask in MNI space using V = 4/3&#960;r3 (assume a sphere).</li>
	<li>Record the centre of mass coordinates of each individual LGN mask in native anatomical space using the command &#34;fslstats -C&#34;.</li>
	<li>Create spherical ROIs of identical volumes across participants:
	<ol>
		<li>Use &#34;fslmaths&#34; to create an ROI point with the coordinates of the appropriate individual LGN mask in native anatomical space as recorded in 6.6</li>
		<li>Using &#34;fslmaths&#34;, apply the radius of the mean mask in MNI space to create a sphere around the ROI point in native anatomical space.</li>
	</ol>
	</li>
	<li>Use these standardized masks as seeds for tractography.</li>
</ol>
7. Probabilistic Tractography (FSL 5.0.4)
Note: For the next steps, call the Fdt_gui to access each of the following tools.
<ol>
	<li>Correct for distortions in DWIs with Eddy current correction. Select the Eddy current correction option from the menu at the top of the Diffusion Toolbox window and upload the DWI as the input, leaving the default reference volume (0).</li>
	<li>Brain extract the images with BET as described in 4.3.</li>
	<li>Select the DTIFIT Reconstruction diffusion tensors option from the menu. Specify an input directory containing the following files: diffusion weighted data, nodif_brain_mask (output of BET), bvec and bval (must be renamed to bvecs and bvals; text files containing information about diffusion image acquisition parameters, output of DICOM to NIfTI conversion of diffusion data). Click &#34;Go&#34; to run dtifit, which fits a diffusion tensor model at each voxel, creating files for post-processing.</li>
	<li>Next, select the BedpostX (estimation of diffusion parameters) option from the menu. Use the same input directory as for DTIFIT. Click &#39;Go&#39; to generate all files required for tractography.</li>
	<li>From the same menu, choose ProbtrackX for probabilistic tracking and run it for each hemisphere separately. Keep default basic options (5,000 samples, 0.2 curvature and loopcheck applied) and select modified Euler for computing probabilistic streamlines from advanced options for increased accuracy.
	<ol>
		<li>Select the output of BedpostX containing .merged files as the BEDPOSTX directory.</li>
		<li>Select single mask as seed space and load the transformed LGN mask (in native anatomical space) as the seed image, T1 (brain in native anatomical space) to diffusion transformation matrix as the seed to diffusion transform, and V1 (in native anatomical space) in &#34;optional targets&#34; (all but exclusion masks) as the target.</li>
		<li>Use default mesh convention (Caret) and load the brain in native anatomical space (T1 image) as the surface reference image.</li>
	</ol>
	</li>
	<li>Repeat ProbtrackX for probabilistic tracking using the standard spherical ROIs (created in step 6) as seed regions for tractography as described in 7.5.2. Upload ROIs in the same way transformed LGN (anatomical space) were uploaded in 7.5.2.</li>
	<li>Re-run tractography (7.5), this time with seed (non-normalized) and target masks in FreeSurfer space with the addition of FreeSurfer&#39;s contralateral white matter border mask as an exclusion mask, to avoid any crossing over and ensure direct ipsilateral connections. Check the Surface option from the ProbtrackX toolbox and select FreeSurfer as mesh convention. 
	Note: It is important to emphasize that tractography is always run from diffusion space, but Probtrackx for probabilistic tracking allows the input of seed and target masks in a different space, along with a transformation matrix to diffusion space. In this study, probabilistic tractography was run with masks in both native anatomical and FreeSurfer space (Figure 2).</li>
</ol>
8. Deterministic Tractography (DSI Studio)
<ol>
	<li>Open Eddy corrected diffusion-weighted images in DSI Studio24 by clicking on Step 1: Open Source Images. Load bvec and bval files onto a b-table window that is automatically opened to create a source (.src) file.</li>
	<li>Load the generated Source files onto the reconstruction window to modify the default reconstructed brain masks as necessary.</li>
	<li>Then, select DTI as the reconstruction method25 and run it on the source files to produce fiber information files (.fib).</li>
	<li>Bring Participants&#39; PD brains to diffusion space using FLIRT linear registration.</li>
	<li>Apply transformation to LGN masks using nearest neighbor interpolation as described in 5.4.1.</li>
	<li>Open .fib files in the program&#39;s tracking window.</li>
	<li>Run tracking for each hemisphere separately, using LGN in diffusion space as the seed and Region 17 (V1) from Brodmann atlas available from DSI Studio as the terminative region. Load the LGN mask by clicking the Regions tab and Open Region. Select the Seed option under Type in the Region List on the left of the screen. To load the V1 mask from the atlas, click on Atlas from the toolbar in the Region List and select the appropriate atlas.</li>
	<li>In each run, set the contralateral WM (named left/right-cerebral-white-matter) mask from FreeSurfer segmentation atlas (see Region List box in the tracking window) as a region of avoidance (ROA).</li>
	<li>Repeat tracking (8.7-8.8) using spherical ROIs in diffusion space instead of individual LGN as seed regions for tractography. 
	Note: The spherical ROIs have the same volume across all subjects and are centered on the center of mass of each LGN.</li>
	<li>Repeat LGN normalization, section 6, only this time registering brains in diffusion space to standard MNI space, and applying transformations to LGN in diffusion space (original LGN previously transformed to diffusion space in 8.4-8.5) to bring masks to standard MNI space. Calculate the volume of the spherical ROI as the mean volume of all LGN across subjects in MNI space. 
	Note: Tracking parameters can be modified by the user. For most runs, default tracking parameters were applied. For some individuals (A5, A7, S12), anisotropy threshold (default 0.14-0.15) was lowered (0.10-0.12) and angular threshold (default 60) was increased (65-85) for nicer visualization. A schematic of the technique is shown in Figure 3.</li>
</ol>
9. Statistical Analysis&#160;&#8211; TBSS (FSL)
Note: Tract-based spatial statistics is a voxelwise statistical analysis of participants&#8217; FA maps16 obtained with dtifit26. It is extensively used for statistics on diffusion data. This voxelwise approach overcomes potential alignment and smoothing problems seen in VBM-style FA analysis and provides whole brain investigation, unattainable through tractography-based approaches16.
<ol>
	<li>Run &#34;tbss_1_preproc&#34; on the FA data located in a newly created TBSS directory.</li>
	<li>Run &#34;tbss_2_reg&#34; &#8211; T to apply non-linear registration, bringing each participant&#39;s FA data into common space (FMRIB58_FA, target image in TBSS).</li>
	<li>Create a mean FA skeleton with the centers of all common tracts among participants using &#34;tbss_3_postreg -S&#34;.</li>
	<li>Run &#34;tbss_4_prestats 0.2&#34; to project each participant&#39;s aligned FA map onto the mean skeleton of all aligned FA maps.</li>
	<li>Create design.con and design.mat files, ensuring that the order of the matrix is consistent with the order in which TBSS pre-processed the FA data.</li>
	<li>Run &#34;randomise&#34;, using the T2 option, which is recommended for TBSS as it acts on a skeleton (a reduced subset of the 3D data), and 5,000 pre-mutations, which gives more accurate p-values.</li>
</ol>
10. Statistical Analysis&#160;&#8211; SPSS&#160;
<ol>
	<li>Extracting FA Values from Deterministic Data 
	Note: Deterministic-based FA values were derived from DSI Studio output statistics text files. These values represent the mean FA within the generated tracts, which in this case correspond to the region of the OR.
	<ol>
		<li>Run fiber tracking in DSI studio.</li>
		<li>Save the &#39;statistics&#39; text files created by DSI Studio for each generated set of tracts and record the &#39;FA mean&#39; values from them.</li>
	</ol>
	</li>
	<li>Extracting FA Values from Probabilistic Data 
	Note: Probabilistic-based FA values are derived from ProbtrackX2 output fdt_paths files. These are 3D tract density images that in this study cover the area corresponding to the OR.
	<ol>
		<li>Use FLIRT linear registration to bring each participant&#39;s fdt_paths files to diffusion space.</li>
		<li>Binarize the output masks using &#34;fslmaths - bin&#34;.</li>
		<li>For each participant, multiply the mask by their FA map from dtifit using &#34;fslmaths -mul&#34;.</li>
		<li>Run &#34;fslmeants&#34; command to find the mean FA from each tract mask.</li>
	</ol>
	</li>
	<li>Running Analyses with SPSS (Using Deterministic and Probabilistic 
	Data) 
	Note: Statistical analysis is performed using SPSS 20 for Mac. Since hemisphere is a within-subject variable, a generalized linear model (GENLIN) with which the effects in each side of the brain can be looked at separately, is applied. Specifically, the generalized estimating equation (GEE) is used.
	<ol>
		<li>In separate tests, set each of mean FA and streamline count (waytotal or percentage generated streamlines, PGSL) as the dependent variable. 
		Note: In this study, streamline count is based on way-total values. Waytotal describes the total number of generated streamlines that have not been rejected by inclusion/exclusion criteria27. The number of generated streamlines (NGSL), which refers to the total number of streamlines sent, is equal to the number of voxels in the seed mask multiplied by the number of samples drawn from each voxel (5,000 in this case). Percentage generated streamlines (PGSL), waytotal divided by NGSL times 100, is a measure of successful connectivity between the seed and the target.</li>
		<li>Study the influence of group and gender on LGN to V1 connectivity by setting them as independent variables in all tests. 
		Note: Main effects as well as two- and three-way interactions were studied. It is important to note that these individual tests are not conditioned to each other, so the significance of one main effect or interaction is independent of the other.</li>
		<li>Use age as a covariate for all tests. Also, use LGN volume as a covariate for tests with mean FA and waytotal as the dependent variables, but omit it from tests with PGSL as the dependent variable. 
		Note: Total brain volume was found to be an insignificant covariate and was therefore omitted from stats.</li>
		<li>Select the Bonferroni correction method to adjust for multiple comparisons28 (level of significance p &#60;0.05).</li>
	</ol>
	</li>
</ol>

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J. Neuroradiol. 22 (9), 1719-1726 (2001).</li><li>McKetton, L., Joy, W., Viviano, J. D., Y&#252;cel, Y. H., Gupta, N., Schneider, K. A. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=High+resolution+structural+magnetic+resonance+imaging+of+the+human+subcortex+in+vivo+and+postmortem.">High resolution structural magnetic resonance imaging of the human subcortex in vivo and postmortem.</a> J. Vis. Exp. , (2015).</li><li>Fischl, B. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=FreeSurfer.">FreeSurfer.</a> NeuroImage. 62 (2), 774-781 (2012).</li><li>Yeh, F. C., Verstynen, T. D., Wang, Y., Fern&#225;ndez-Miranda, J. C., Tseng, W. Y. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Deterministic+Diffusion+Fiber+Tracking+Improved+by+Quantitative+Anisotropy.">Deterministic Diffusion Fiber Tracking Improved by Quantitative Anisotropy.</a> PLoS One. 8 (11), 807-813 (2013).</li><li>Jiang, H., van Zijl, P. C., Kim, J., Pearlson, G. D., Mori, S. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=DtiStudio:+resource+program+for+diffusion+tensor+computation+and+fiber+bundle+tracking.">DtiStudio: resource program for diffusion tensor computation and fiber bundle tracking.</a> Comput. Methods. Programs. Biomed. 81 (2), 106-116 (2006).</li><li>Smith, S. M., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Advances+in+functional+and+structural+MR+image+analysis+and+implementation+as+FSL.">Advances in functional and structural MR image analysis and implementation as FSL.</a> NeuroImage. 23 (1), 208-219 (2004).</li><li>Galantucci, S., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=White+matter+damage+in+primary+progressive+aphasias:+a+diffusion+tensor+tractography+study.">White matter damage in primary progressive aphasias: a diffusion tensor tractography study.</a> J. Neurol. 134, 3011-3029 (2011).</li><li>Cabin, R. J., Mitchell, R. J. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=To+Bonferroni+or+not+to+Bonferroni:+when+and+how+are+the+questions.">To Bonferroni or not to Bonferroni: when and how are the questions.</a> Bull. Ecol. Soc. Am. 81 (3), 246-248 (2000).</li><li>Kaiser, P. K. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Prospective+evaluation+of+visual+acuity+assessment:+a+comparison+of+snellen+versus+ETDRS+charts+in+clinical+practice+(An+AOS+Thesis).">Prospective evaluation of visual acuity assessment: a comparison of snellen versus ETDRS charts in clinical practice (An AOS Thesis).</a> Trans. Am. Ophthalmol. Soc. 107, 311-324 (2009).</li><li>Farahibozorg, S., Hashemi-Golpayegani, S. M., Ashburner, J. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Age+and+sex-related+variations+in+the+brain+white+matter+fractal+dimension+throughout+adulthood:+An+MRI+study.">Age and sex-related variations in the brain white matter fractal dimension throughout adulthood: An MRI study.</a> Clin. Neuroradiol. 25 (1), 19-32 (2014).</li><li>Tian, L., Wang, J., Yan, C., He, Y. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Hemisphere+and+gender-related+differences+in+small+world+brain+networks:+a+resting+state+functional+MRI+study.">Hemisphere and gender-related differences in small world brain networks: a resting state functional MRI study.</a> NeuroImage. 54 (1), 191-202 (2011).</li><li>Ge, Y., Grossman, R. I., Babb, J. S., Rabin, M. L., Mannon, L. J., Kolson, D. L. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Age-related+total+gray+matter+and+white+matter+changes+in+normal+adult+brain.+Part+1:+volumetric+MR+imaging+analysis.">Age-related total gray matter and white matter changes in normal adult brain. Part 1: volumetric MR imaging analysis.</a> Am. J. Neuroradiol. 23 (8), 1327-1333 (2002).</li><li>Zhang, L., Dean, D., Liu, J. Z., Sahgal, V., Wang, X., Yue, G. H. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Quantifying+degeneration+of+white+matter+in+normal+aging+using+fractal+dimension.">Quantifying degeneration of white matter in normal aging using fractal dimension.</a> Neurobiol. Aging. 28 (10), 1543-1555 (2007).</li><li>Jones, D. K., Knosche, T. R., Turner, R. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=White+matter+integrity,+fiber+count,+and+other+fallacies:+The+do's+and+don'ts+of+diffusion+MRI.">White matter integrity, fiber count, and other fallacies: The do's and don'ts of diffusion MRI.</a> NeuroImage. 73, 239-254 (2013).</li><li>Coenen, V. A., Huber, K. K., Krings, T., Weidemann, J., Gilsbach, J. M., Rohde, V. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Diffusion-weighted+imaging-guided+resection+of+intracerebral+lesions+involving+the+optic+radiation.">Diffusion-weighted imaging-guided resection of intracerebral lesions involving the optic radiation.</a> Neurosurg. Rev. 28 (3), 188-195 (2005).</li><li>Andrews, T. J., Halperm, S. D., Purves, D. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Correlated+size+variations+in+human+visual+cortex,+lateral+geniculate+nucleus,+and+optic+tract.">Correlated size variations in human visual cortex, lateral geniculate nucleus, and optic tract.</a> J. Neurosci. 17 (8), 2859-2865 (1997).</li><li>Bridge, H., Thomas, O., Jbabdi, S., Cowey, A. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Changes+in+connectivity+after+visual+cortical+brain+damage+underlie+altered+visual+function.">Changes in connectivity after visual cortical brain damage underlie altered visual function.</a> Brain. 131, 1433-1444 (2008).</li><li>Asman, A. J., Landman, B. 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The authors declare no conflict of interest.

Altered WM and, more specifically, decreased connectivity in albinism compared to controls were expected. Thus, the reduced FA in the right hemisphere of albinism compared to controls as well as the decreased connectivity in male patients with albinism reported here are in line with our prediction. Gender and hemisphere effects are not completely clear, although research on the healthy brain that suggests decreased WM complexity in the left hemisphere of males compared to females30-31 could explain some of the gender- and hemisphere-related differences observed in this study. Reduced connectivity in male albinism compared to female albinism may be in part due to the younger average age of females in both groups (7-8 years &#60; males), as WM volume was reported to be decreased in mid-adulthood in the healthy brain32-33. Gender differences may also be due to the small sample size and the uneven number of males versus females in each group (5 males OCA, 4 controls). The increased connectivity observed in female albinism compared to controls of the same gender was not expected, and might suggest some sort of a compensatory mechanism in albinism. However, this finding could rather be attributed to the great variability within a small population sample.
Mean FA and streamline count are used to explore LGN to V1 connectivity. FA is one of the most commonly reported measures derived from diffusion data. It is most accurately described as a quantification of how strongly directional diffusion is in a region. It is important to note that group differences in FA measures do not necessarily imply differences in white matter integrity. Since this study involves a clinical condition with a potential effect on white matter, such interpretation can be justified. However, while reduced integrity suggests structural damage or decline, FA in a region may be lowered due to fewer barriers such as larger axon diameters and less densely packed axons, as well as due to less effective boundaries resulting from increased membrane permeability34. The term &#39;streamline count&#39;, is preferred over &#39;fiber count&#39;, since the number of reconstructed streamlines may not be a true representation of the number of actual fibers due to variations in pathway features such as length, curvature and branching, as well as experimental conditions such as signal-to-noise ratio (SNR)34.
Tractography is the only non-invasive technique for mapping WM in vivo. In the deterministic approach, termination occurs when anisotropy drops below a certain level, or when there is an abrupt angulation. However, most voxels contain fibers in more than one direction, and the deterministic approach is limited to either identifying the dominant tract in a voxel or an &#34;average&#34; of all directions within a voxel. It thus fails to provide a true representation of neuronal pathways. Probabilistic tractography allows the resolution of two fiber models in a single voxel, providing a more reliable representation of a tract, and tracking in areas of low anisotropy17. The probabilistic algorithm was therefore particularly advantageous in this study, which investigated the trajectory of the OR, a highly branching WM structure (particularly the portion of Meyer&#39;s loop), terminating in the gray matter of V1. It is important to note that probabilistic tractography refers to a stochastic probability of having the same pathway emerge over multiple permutations, rather than the misleading notion of tractography representing the true anatomy of a fiber. Although probabilistic tractography is believed to follow the true course of nerve fibers more closely, deterministic tractography was run to compare the usefulness and reproducibility of both techniques. Since trends were detected using the probabilistic approach, similarities seen in the deterministic run only further strengthened the validity of the results.
Apart from its increasing use in research on mapping anatomical connectivity in the brain, tractography has shown promising advances in neurosurgical panning. Visualization of OR during resection of tumours by neuronavigationally transferring DWI data into the operative field was reported to help in successfully removing lesions and keeping the visual fields intact35. Nevertheless, DTI has limitations such as relatively poor spatial resolution and impaired data quality when structures with low fiber density such as the OR are studied20. Though probabilistic tractography was the preferred technique, the current study aimed to compare the two algorithms. Both tracking algorithms revealed some common findings, increasing the reliability of the results.
LGN volume ranges from 112-276 mm3 in healthy brains8 and correlates with volumes of the visual cortex and the optic tract, but not with overall brain volume36. Since tractography depends on the number of voxels in the seed mask, LGN volume should be normalized across participants to prevent bias in quantitative comparisons between tracts37. We controlled for differences in LGN size using two different methods. In the first method, spherical ROIs of a standard size were created. To achieve this, a mean volume across all subjects of their LGN masks transformed into MNI space was calculated. Using the radius of the mean mask in MNI space, a sphere was centered on the center of mass of each LGN in either anatomical or diffusion space. Using a standard sphere ROI with a volume larger than the individual LGN volumes ensures the inclusion of all voxels that are part of this structure in all participants. While using a larger mask size would usually increase the risk for false positives, such concern does not apply when seeding in a structure such as the LGN, which differs in connectivity patterns from surrounding nuclei. Hence, voxels belonging to other structures will generate streamlines that do not follow the expected paths and such paths will therefore be discarded18.The second method we used to control for LGN volume was to run statistical analyses using LGN volume as a covariate, but this did not significantly alter the statistical results. Another possible approach for LGN normalization is the addition and removal of voxels around individual LGN masks to obtain a standard size across subjects using a MATLAB script. This approach does not require any additional registrations and therefore limits the degree of deformation of the brain images and increases accuracy. It also retains the LGN outline rather than creating a sphere around the LGN region. However, to minimize human error, multi-atlas segmentation may be used instead of manually tracing the LGN masks. Using a set of atlases accounts for individual structural variability and therefore yields a more accurate representation of a subcortical region than a single atlas38.
This novel in vivo study investigates interdependent development among different parts of the primary visual pathway in human albinism. It thus provides further insight into the etiology of this condition and improved diagnosis through identification of disorder-specific signatures. Both probabilistic and deterministic tractography algorithms used here showed comparable patterns of altered LGN to V1 connectivity in human albinism. Though no generalization to the large population can be made due to the small sample size and the large inter-subject variation in structures of interest, this study demonstrates the usefulness of tractography in detecting trends within the sample population, suggesting the importance of further research in the field.

Albinism is a genetic condition primarily characterized by overt hypopigmentation observed in affected individuals. It is caused by inherited mutations to genes involved in melanin synthesis1. Albinism appears in two main forms: oculo-cutaneous albinism (OCA), an autosomal recessive trait presenting both ocular and cutaneous features; and ocular albinism (OA), an X-linked trait more prevalent in males and characterized primarily by the ocular symptoms2. Melanin in the retinal pigment epithelium (RPE) is crucial for proper development of the central visual pathway. Its absence in albinism therefore results in visual impairments, including photophobia, nystagmus, reduced visual acuity and loss of binocular vision2-3. Visual acuity has been linked to foveal morphology, which is altered in albinism4. In humans, a retinal line of decussation lies along the nasotemporal border through the fovea, with fibers from nasal retina crossing to the other hemisphere and those from temporal retina extending ipsilaterally. The degree of reduced visual function in albinism has been linked to the level of hypopigmentation. Specifically, pigmentation is inversely proportional to the shift into temporal retina of the line of decussation5. As a result of the shift in line of decussation into the temporal retina, crossing of optic nerve fibers is increased&#160;&#8211; a characteristic common across all species3.
Structural MRI studies on humans have shown narrower optic chiasms in albinism compared to controls, which is likely the result of increased crossing of RGCs observed in albinism6-8. The retina and optic chiasm express axonal guidance cues such as Eph family receptors and their ligands9 and are therefore candidate sites for misrouting10.
A study on monkeys with induced glaucoma revealed a significant decrease in the number of LGN parvalbumin-immunoreactive relay neurons and LGN volume11. This suggests a correlation between LGN size and the number of white matter (WM) trajectories traveling through the OR to V1. A post mortem study on human albinism also revealed smaller LGN with fused M and P layers12. High-resolution structural MRI confirmed significant reduction in volume of LGN in albinism8. Taken together, these findings suggest that decreased LGN volume may result in a reduced number of neurons in the LGN, and in turn in decreased connectivity between LGN and V1.
Examining patterns of anatomical connectivity in humans has been limited. Dissection, tracer injection and lesion induction are invasive techniques that can only be used post mortem, and usually involve a very small number of patients. Previous studies using carbocyanine dye DiI injections demonstrated neuronal connectivity between V1 and V2 (secondary visual cortex)13, as well as within the hippocampal complex in aldehyde-fixed post-mortem human brains14. Labeling fibers in this way is restricted to distances of only tens of millimeters from the point of injection14. Diffusion tensor imaging, DTI, is an MRI modality developed in early-mid 1990s to identify fiber tract direction and organization. It is a non-invasive method that allows mapping of large WM pathways in the living brain. DTI is sensitive to the diffusion of water molecules in biological tissue15. In the brain, the diffusion of water is anisotropic (uneven) due to barriers such as membranes and myelin. WM has high diffusion anisotropy, meaning the diffusion is greater parallel to than perpendicular to the orientation of the fibers16. Fractional anisotropy (FA) is a scalar quantity that describes the preference of molecules to diffuse in an anisotropic manner. FA values range from 0-1, from low to high anisotropy (cerebrospinal fluid (CSF) &#60;gray matter (GM) &#60;WM)16.
Streamline (deterministic) and probabilistic fiber tracking are two different algorithms for 3D path reconstruction. Deterministic tractography uses a line propagation method, connecting neighboring voxels in a defined seed region. Two stop criteria used in this algorithm are the turning angle and the FA value. Therefore, tract tracing between neighbouring voxels is unlikely at large turning angles. The algorithm would therefore also progresses only if the FA in a voxel exceeds a specific threshold, limiting its effectiveness in accurately defining pathways near gray matter, where anisotropy drops. Probabilistic tractography, on the other hand, yields a connectivity map describing the probability of a voxel to be part of a tract between two regions of interest (ROIs) and thus progresses into gray matter such as V117. Using this MRI application, key WM structures like the OR can be delineated, as shown in previous studies18-20.
This study therefore uses diffusion data and tractography to explore the effect of axonal misrouting on retino-geniculo-cortical connectivity. Based on previously reported reductions in LGN volumes in human albinism8, we predict that fiber projections from LGN to V1 are also reduced (Figure 1).

<table><tbody><tr><td>Magnetom Tim Trio 3T MRI</td><td>Siemens (Erlangen, Germany)</td><td></td><td></td></tr><tr><td>FMRIB&amp;rsquo;s Software Library (FSL)</td><td></td><td></td><td>http://www.fmrib.ox.ac.uk/fsl/</td></tr><tr><td>FreeSurfer</td><td></td><td></td><td>http://surfer.nmr.mgh.harvard.edu</td></tr><tr><td>DSI Studio</td><td></td><td></td><td>http://dsi-studio.labsolver.org</td></tr><tr><td>SPSS</td><td></td><td></td><td></td></tr></tbody></table>

measuring connectivity in the primary visual pathway in human albinism using diffusion tensor imaging and tractography

In albinism, the number of ipsilaterally projecting retinal ganglion cells (RGCs) is significantly reduced. The retina and optic chiasm have been proposed as candidate sites for misrouting. Since a correlation between the number of lateral geniculate nucleus (LGN) relay neurons and LGN size has been shown, and based on previously reported reductions in LGN volumes in human albinism, we suggest that fiber projections from LGN to the primary visual cortex (V1) are also reduced. Studying structural differences in the visual system of albinism can improve the understanding of the mechanism of misrouting and subsequent clinical applications. Diffusion data and tractography are useful for mapping the OR (optic radiation). This manuscript describes two algorithms for OR reconstruction in order to compare brain connectivity in albinism and controls.An MRI scanner with a 32-channel head coil was used to acquire structural scans. A T1-weighted 3D-MPRAGE sequence with 1 mm3 isotropic voxel size was used to generate high-resolution images for V1 segmentation. Multiple proton density (PD) weighted images were acquired coronally for right and left LGN localization. Diffusion tensor imaging (DTI) scans were acquired with 64 diffusion directions. Both deterministic and probabilistic tracking methods were run and compared, with LGN as the seed mask and V1 as the target mask. Though DTI provides relatively poor spatial resolution, and accurate delineation of OR may be challenging due to its low fiber density, tractography has been shown to be advantageous both in research and clinically. Tract based spatial statistics (TBSS) revealed areas of significantly reduced white matter integrity within the OR in patients with albinism compared to controls. Pairwise comparisons revealed a significant reduction in LGN to V1 connectivity in albinism compared to controls. Comparing both tracking algorithms revealed common findings, strengthening the reliability of the technique.

Watch this Scientific Journal Video about Measuring Connectivity in the Primary Visual Pathway in Human Albinism Using Diffusion Tensor Imaging and Tractography at JoVE.com

Measuring Connectivity in the Primary Visual Pathway in Human Albinism Using Diffusion Tensor Imaging and Tractography

This manuscript describes deterministic and probabilistic algorithms for white matter (WM) reconstruction, used to examine differences in optic radiation (OR) connectivity between albinism and controls. Although probabilistic tractography follows the true course of nerve fibers more closely, deterministic tractography was run to compare the reliability and reproducibility of both techniques.

In albinism, the number of ipsilaterally projecting retinal ganglion cells (RGCs) is significantly reduced. The retina and optic chiasm have been proposed ...

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Nanyang Technological University

Research

JoVE Journal

Neuroscience

12.2K Views.  York University. This manuscript describes deterministic and probabilistic algorithms for white matter (WM) reconstruction, used to examine differences in optic radiation (OR) connectivity between albinism and controls. Although probabilistic tractography follows the true course of nerve fibers more closely, deterministic tractography was run to compare the reliability and reproducibility of both techniques.

Video: Measuring Connectivity in the Primary Visual Pathway in Human Albinism Using Diffusion Tensor Imaging and Tractography

Probing the Brain in Autism Using fMRI and Diffusion Tensor Imaging

Newly emerging theories suggest that the brain does not function as a cohesive unit in autism, and this discordance is reflected in the behavioral symptoms displayed by individuals with autism. While structural neuroimaging findings have provided some insights into brain abnormalities in autism, the consistency of such findings is questionable. Functional neuroimaging, on the other hand, has been more fruitful in this regard because autism is a disorder of dynamic processing and allows examination of communication between cortical networks, which appears to be where the underlying problem occurs in autism. Functional connectivity is defined as the temporal correlation of spatially separate neurological events1. Findings from a number of recent fMRI studies have supported the idea that there is weaker coordination between different parts of the brain that should be working together to accomplish complex social or language problems2,3,4,5,6. One of the mysteries of autism is the coexistence of deficits in several domains along with relatively intact, sometimes enhanced, abilities. Such complex manifestation of autism calls for a global and comprehensive examination of the disorder at the neural level. A compelling recent account of the brain functioning in autism, the cortical underconnectivity theory,2,7 provides an integrating framework for the neurobiological bases of autism. The cortical underconnectivity theory of autism suggests that any language, social, or psychological function that is dependent on the integration of multiple brain regions is susceptible to disruption as the processing demand increases. In autism, the underfunctioning of integrative circuitry in the brain may cause widespread underconnectivity. In other words, people with autism may interpret information in a piecemeal fashion at the expense of the whole. Since cortical underconnectivity among brain regions, especially the frontal cortex and more posterior areas 3,6, has now been relatively well established, we can begin to further understand brain connectivity as a critical component of autism symptomatology.
A logical next step in this direction is to examine the anatomical connections that may mediate the functional connections mentioned above. Diffusion Tensor Imaging (DTI) is a relatively novel neuroimaging technique that helps probe the diffusion of water in the brain to infer the integrity of white matter fibers. In this technique, water diffusion in the brain is examined in several directions using diffusion gradients. While functional connectivity provides information about the synchronization of brain activation across different brain areas during a task or during rest, DTI helps in understanding the underlying axonal organization which may facilitate the cross-talk among brain areas. This paper will describe these techniques as valuable tools in understanding the brain in autism and the challenges involved in this line of research.

Neuroimaging techniques, such as functional MRI and Diffusion Tensor Imaging have become increasingly useful in characterizing the cognitive and neural deficits in autism. An examination of brain connectivity in autism at a network level along with adaptations for scanning children with developmental disabilities is presented.

Newly emerging theories suggest that the brain does not function as a cohesive unit in autism, and this discordance is reflected in the behavioral ...

Medicine

Time-lapse Live Imaging of Clonally Related Neural Progenitor Cells in the Developing Zebrafish Forebrain

Precise patterns of division, migration and differentiation of neural progenitor cells are crucial for proper brain development and function1,2. To understand the behavior of neural progenitor cells in the complex in vivo environment, time-lapse live imaging of neural progenitor cells in an intact brain is critically required. In this video, we exploit the unique features of zebrafish embryos to visualize the development of forebrain neural progenitor cells in vivo. We use electroporation to genetically and sparsely label individual neural progenitor cells. Briefly, DNA constructs coding for fluorescent markers were injected into the forebrain ventricle of 22 hours post fertilization (hpf) zebrafish embryos and electric pulses were delivered immediately. Six hours later, the electroporated zebrafish embryos were mounted with low melting point agarose in glass bottom culture dishes. Fluorescently labeled neural progenitor cells were then imaged for 36hours with fixed intervals under a confocal microscope using water dipping objective lens. The present method provides a way to gain insights into the in vivo development of forebrain neural progenitor cells and can be applied to other parts of the central nervous system of the zebrafish embryo.

The present video demonstrates a method which takes advantage of the combination of electroporation and confocal microscopy to perform live imaging on individual neural progenitor cells in the developing zebrafish forebrain. In vivo analysis of the development of forebrain neural progenitor cells at a clonal level can be achieved in this way.

Precise patterns of division, migration and differentiation of neural progenitor cells are crucial for proper brain development and function1,2. To ...

Co-analysis of Brain Structure and Function using fMRI and Diffusion-weighted Imaging

The study of complex computational systems is facilitated by network maps, such as circuit diagrams. Such mapping is particularly informative when studying the brain, as the functional role that a brain area fulfills may be largely defined by its connections to other brain areas. In this report, we describe a novel, non-invasive approach for relating brain structure and function using magnetic resonance imaging (MRI). This approach, a combination of structural imaging of long-range fiber connections and functional imaging data, is illustrated in two distinct cognitive domains, visual attention and face perception. Structural imaging is performed with diffusion-weighted imaging (DWI) and fiber tractography, which track the diffusion of water molecules along white-matter fiber tracts in the brain (Figure 1). By visualizing these fiber tracts, we are able to investigate the long-range connective architecture of the brain. The results compare favorably with one of the most widely-used techniques in DWI, diffusion tensor imaging (DTI). DTI is unable to resolve complex configurations of fiber tracts, limiting its utility for constructing detailed, anatomically-informed models of brain function. In contrast, our analyses reproduce known neuroanatomy with precision and accuracy. This advantage is partly due to data acquisition procedures: while many DTI protocols measure diffusion in a small number of directions (e.g., 6 or 12), we employ a diffusion spectrum imaging (DSI)1, 2 protocol which assesses diffusion in 257 directions and at a range of magnetic gradient strengths. Moreover, DSI data allow us to use more sophisticated methods for reconstructing acquired data. In two experiments (visual attention and face perception), tractography reveals that co-active areas of the human brain are anatomically connected, supporting extant hypotheses that they form functional networks. DWI allows us to create a "circuit diagram" and reproduce it on an individual-subject basis, for the purpose of monitoring task-relevant brain activity in networks of interest.

We describe a novel approach for simultaneous analysis of brain function and structure using magnetic resonance imaging (MRI). We assess brain structure with high-resolution diffusion-weighted imaging and white-matter fiber tractography. Unlike standard structural MRI, these techniques allow us to directly relate anatomical connectivity to functional properties of brain networks.

The study of complex computational systems is facilitated by network maps, such as circuit diagrams. Such mapping is particularly informative when ...

Diffusion Tensor Magnetic Resonance Imaging in the Analysis of Neurodegenerative Diseases

Diffusion tensor imaging (DTI) techniques provide information on the microstructural processes of the cerebral white matter (WM) in vivo. The present applications are designed to investigate differences of WM involvement patterns in different brain diseases, especially neurodegenerative disorders, by use of different DTI analyses in comparison with matched controls. 	
DTI data analysis is performed in a variate fashion, i.e. voxelwise comparison of regional diffusion direction-based metrics such as fractional anisotropy (FA), together with fiber tracking (FT) accompanied by tractwise fractional anisotropy statistics (TFAS) at the group level in order to identify differences in FA along WM structures, aiming at the definition of regional patterns of WM alterations at the group level. Transformation into a stereotaxic standard space is a prerequisite for group studies and requires thorough data processing to preserve directional inter-dependencies. The present applications show optimized technical approaches for this preservation of quantitative and directional information during spatial normalization in data analyses at the group level. On this basis, FT techniques can be applied to group averaged data in order to quantify metrics information as defined by FT. Additionally, application of DTI methods, i.e. differences in FA-maps after stereotaxic alignment, in a longitudinal analysis at an individual subject basis reveal information about the progression of neurological disorders. Further quality improvement of DTI based results can be obtained during preprocessing by application of a controlled elimination of gradient directions with high noise levels.	
In summary, DTI is used to define a distinct WM pathoanatomy of different brain diseases by the combination of whole brain-based and tract-based DTI analysis.

Diffusion tensor imaging (DTI) basically serves as an MRI-based tool to identify in vivo the microstructure of the brain and pathological processes due to neurological disorders within the cerebral white matter. DTI-based analyses allow for application to brain diseases both at the group level and in single subject data.

Diffusion tensor imaging (DTI) techniques provide information on the microstructural processes of the cerebral white matter (WM) in vivo. The present ...

DTI of the Visual Pathway - White Matter Tracts and Cerebral Lesions

DTI is a technique that identifies white matter tracts (WMT) non-invasively in healthy and non-healthy patients using diffusion measurements. Similar to visual pathways (VP), WMT are not visible with classical MRI or intra-operatively with microscope. DTI will help neurosurgeons to prevent destruction of the VP while removing lesions adjacent to this WMT. We have performed DTI on fifty patients before and after surgery between March 2012 to January 2014. To navigate we used a 3DT1-weighted sequence. Additionally, we performed a T2-weighted and DTI-sequences. The parameters used were, FOV: 200 x 200 mm, slice thickness: 2 mm, and acquisition matrix: 96 x 96 yielding nearly isotropic voxels of 2 x 2 x 2 mm. Axial MRI was carried out using a 32 gradient direction and one b0-image. We used Echo-Planar-Imaging (EPI) and ASSET parallel imaging with an acceleration factor of 2 and b-value of 800 s/mm&#178;. The scanning time was less than 9 min.
The DTI-data obtained were processed using a FDA approved surgical navigation system program which uses a straightforward fiber-tracking approach known as fiber assignment by continuous tracking (FACT). This is based on the propagation of lines between regions of interest (ROI) which is defined by a physician. A maximum angle of 50, FA start value of 0.10 and ADC stop value of 0.20 mm&#178;/s were the parameters used for tractography.
There are some limitations to this technique. The limited acquisition time frame enforces trade-offs in the image quality. Another important point not to be neglected is the brain shift during surgery. As for the latter intra-operative MRI might be helpful. Furthermore the risk of false positive or false negative tracts needs to be taken into account which might compromise the final results.

Diffusion tensor imaging (DTI) was performed to try to depict the major parts of the visual pathway. The goal was to use an FDA approved standard commercial workstation which could be used for everyday routine in order to try to reduce postoperative damage of the visual pathway in the patients.

DTI is a technique that identifies white matter tracts (WMT) non-invasively in healthy and non-healthy patients using diffusion measurements. Similar to ...

Diffusion Imaging in the Rat Cervical Spinal Cord

Magnetic resonance imaging (MRI) is the state of the art approach for assessing the status of the spinal cord noninvasively, and can be used as a diagnostic and prognostic tool in cases of disease or injury. Diffusion weighted imaging (DWI), is sensitive to the thermal motion of water molecules and allows for inferences of tissue microstructure. This report describes a protocol to acquire and analyze DWI of the rat cervical spinal cord on a small-bore animal system. It demonstrates an imaging setup for the live anesthetized animal and recommends a DWI acquisition protocol for high-quality imaging, which includes stabilization of the cord and control of respiratory motion. Measurements with diffusion weighting along different directions and magnitudes (b-values) are used. Finally, several mathematical models of the resulting signal are used to derive maps of the diffusion processes within the spinal cord tissue that provide insight into the normal cord and can be used to monitor injury or disease processes noninvasively.

The goal of this protocol is to obtain high-quality diffusion weighted magnetic resonance imaging (DWI) of the rat spinal cord for noninvasive characterization of tissue microstructure. This protocol describes optimizations of the MRI sequence, radiofrequency coil, and analysis methods to enable DWI images free from artifacts.

Magnetic resonance imaging (MRI) is the state of the art approach for assessing the status of the spinal cord noninvasively, and can be used as a ...

Fiber Connections of the Supplementary Motor Area Revisited: Methodology of Fiber Dissection, DTI, and Three Dimensional Documentation

The purpose of this study is to show the methodology for the examination of the white matter connections of the supplementary motor area (SMA) complex (pre-SMA and SMA proper) using a combination of fiber dissection techniques on cadaveric specimens and magnetic resonance (MR) tractography. The protocol will also describe the procedure for a white matter dissection of a human brain, diffusion tensor tractography imaging, and three-dimensional documentation. The fiber dissections on human brains and the 3D documentation were performed at the University of Minnesota, Microsurgery and Neuroanatomy Laboratory, Department of Neurosurgery. Five postmortem human brain specimens and two whole heads were prepared in accordance with Klingler's method. Brain hemispheres were dissected step by step from lateral to medial and medial to lateral under an operating microscope, and 3D images were captured at every stage. All dissection results were supported by diffusion tensor imaging. Investigations on the connections in line with Meynert's fiber tract classification, including association fibers (short, superior longitudinal fasciculus I and frontal aslant tracts), projection fibers (corticospinal, claustrocortical, cingulum, and frontostriatal tracts), and commissural fibers (callosal fibers) were also conducted.

The purpose of this study is to show each step of the fiber dissection technique on human cadaveric brains, the 3D documentation of these dissections, and the diffusion tensor imaging of the anatomically dissected fiber pathways.

The purpose of this study is to show the methodology for the examination of the white matter connections of the supplementary motor area (SMA) complex ...

Mapping Regional Homogeneity and Functional Connectivity of the Visual Cortex in Resting-State fMRI

A combined regional homogeneity (ReHo) and functional connectivity (FC) method, a type of noninvasive functional magnetic resonance imaging (fMRI) method, has been used to evaluate synchronous neuronal activity changes in retinitis pigmentosa (RP). The purpose of this study is to describe our method for analysis of intra- and interregional synchronizations of changes in neuronal activity in RP patients. The advantages of the combined ReHo and FC method are that it is both noninvasive and sufficiently sensitive to investigate changes in cerebral synchronous neuronal activity changes in vivo. Here, 16 RP patients and 14 healthy controls closely matched in age, sex, and education underwent resting-state fMRI scans. Two sample t-tests were conducted to compare ReHo and FC across groups. Our results showed that visual network disconnection and reorganization of the retino-thalamocortical pathway and dorsal visual stream occurred in the RP patients. Here, we describe the details of this method, its use, and the impact of its key parameters in a step-by-step manner.

We present a protocol for analyzing functional magnetic resonance imaging data to investigate spontaneous neural activity alterations in retinitis pigmentosa patient using a combined regional homogeneity and functional connectivity method.

A combined regional homogeneity (ReHo) and functional connectivity (FC) method, a type of noninvasive functional magnetic resonance imaging (fMRI) method, ...

Role of Diffusion MRI Tractography in Endoscopic Endonasal Skull Base Surgery

Endoscopic endonasal surgery has gained a prominent role in the management of complex skull base tumors. It allows the resection of a large group of benign and malignant lesions through a natural anatomical extra-cranial pathway, represented by the nasal cavities, avoiding brain retraction and neurovascular manipulation. This is reflected by the patients' prompt clinical recovery and the low risk of permanent neurological sequelae, representing the main caveat of conventional skull base surgery. This surgery must be tailored to each specific case, considering its features and relationship with surrounding neural structures, mostly based on preoperative neuroimaging. Advanced MRI techniques, such as tractography, have been rarely adopted in skull base surgery due to technical issues: lengthy and complicated processes to generate reliable reconstructions for inclusion in the neuronavigation system. 
This paper aims to present the protocol implemented in the institution and highlights the synergistic collaboration and teamwork between neurosurgeons and the neuroimaging team (neurologists, neuroradiologists, neuropsychologists, physicists, and bioengineers) with the final goal of selecting the optimal treatment for each patient, improving the surgical results and pursuing the advancement of personalized medicine in this field.

We present a protocol to integrate diffusion MRI tractography in patient work-up to endoscopic endonasal surgery for a skull base tumor. The methods for adopting these neuroimaging studies in the pre- and intra-operative phases are described.

Endoscopic endonasal surgery has gained a prominent role in the management of complex skull base tumors. It allows the resection of a large group of ...

Using Diffusion Tensor Imaging in Traumatic Brain Injury

Source: Laboratories of Jonas T. Kaplan and Sarah I. Gimbel&#8212;University of Southern California
Traditional brain imaging techniques using MRI are very good at visualizing the gross structures of the brain. A structural brain image made with MRI provides high contrast of the borders between gray and white matter, and information about the size and shape of brain structures. However, these images do not detail the underlying structure and integrity of white matter networks in the brain, which consist of axon bundles that interconnect local and distant brain regions.
Diffusion MRI uses pulse sequences that are sensitive to the diffusion of water molecules. By measuring the direction of diffusion, it is possible to make inferences about the structure of white matter networks in the brain. Water molecules within an axon are constrained in their movements by the cell membrane; instead of randomly moving in every direction with equal probability (isotropic movement), they are more likely to move in certain directions, in parallel with the axon (anisotropic movement; Figure 1). Therefore, measures of diffusion anisotropy are thought to reflect properties of the white matter such as fiber density, axon thickness, and degree of myelination. One common measure is fractional anisotropy (FA). FA values range from 0, which represents completely isotropic movement, to 1, reflecting maximum anisotropy.
<img alt="Figure 1" src="/files/ftp_upload/10276/10276fig1.jpg" /> 
Figure 1: Diffusion anisotropy. When the direction of diffusion is unconstrained and random, movement is measured in all directions equally. This is isotropic diffusion (A). When water molecules are contained within the axon of a neuron, diffusion is anisotropic, tending to occur more frequently along the direction of the axon (B). <a href="https://www-jove-com.remotexs.ntu.edu.sg/files/ftp_upload/10276/10276fig1large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
In this experiment we will use diffusion tensor imaging (DTI) to measure white matter integrity in traumatic brain injury (TBI). TBI occurs when an external force injures the brain, such as a blow to the head or a sudden movement like the kind that might occur in a car accident. This type of brain injury from mechanical forces is associated with diffuse axonal injury-damage to white matter throughout the brain. Because it is an injury affecting white matter integrity, standard neuroimaging techniques may not reveal the damage. However, measures of diffusion are especially sensitive to these anatomical changes. Following a study by Kraus et al.1, we compare a group of healthy controls to a group of people with TBI and use diffusion imaging to measure the effect of TBI on cerebral white matter. Furthermore, we will test the relationship between white matter integrity and cognitive function using an attention task.2 This study uses a region of interest (ROI) approach focusing on three white matter tracts: the splenium of the corpus callosum, the anterior corona radiata, and the superior longitudinal fasciculus (Figure 2).
<img alt="Figure 2" src="/files/ftp_upload/10276/10276fig2.jpg" /> 
Figure 2: Regions of interest. The three ROIs, defined from the ICBM DTI-81 atlas, are shown here in horizontal slices through the brain. In green is the splenium of the corpus callosum. The splenium is the most posterior part of the corpus callosum. In blue is the anterior corona radiata. The superior longitudinal fasciculus is shown in red. <a href="https://www-jove-com.remotexs.ntu.edu.sg/files/ftp_upload/10276/10276fig2large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>

Diffusion Tensor Imaging in Traumatic Brain Injury

Source: Laboratories of Jonas T. Kaplan and Sarah I. Gimbel&#8212;University of Southern California
Traditional brain imaging techniques using MRI are ...