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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Neurosci. 6 (7), 750-757 (2003).</li><li>Bassi, L., 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=Probabilistic+diffusion+tractography+of+the+optic+radiations+and+visual+function+in+preterm+infants+at+term+equivalent+age.">Probabilistic diffusion tractography of the optic radiations and visual function in preterm infants at term equivalent age.</a> Brain. 131 (2), 573-582 (2008).</li><li>Hofer, S., Karaus, A., Frahm, J. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Reconstruction+and+dissection+of+the+entire+human+visual+pathway+using+diffusion+tensor+MRI.">Reconstruction and dissection of the entire human visual pathway using diffusion tensor MRI.</a> Front Neuroanat. 4, 1-7 (2010).</li><li>Fujita, N., 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=Lateral+Geniculate+Nucleus:+Anatomic+and+Functional+Identification+by+Use+of+MR+Imaging.">Lateral Geniculate Nucleus: Anatomic and Functional Identification by Use of MR Imaging.</a> Am. 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...

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 border="1" fo:keep-together.within-page="1" fo:keep-with-next.within-page="always">
	<tbody>
		<tr>
			<td>Magnetom Tim Trio 3T MRI</td>
			<td>Siemens (Erlangen, Germany)</td>
			<td></td>
			<td></td>
		</tr>
		<tr>
			<td>FMRIB&#8217;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.

This section provides a summary of results obtained using two different algorithms of tractography, deterministic and probabilistic. LGN volumes in PD space in which masks were originally drawn, as well as in all other spaces used in this study, are recorded in Table 2, and LGN tracing is illustrated in Figure 4. The results reported here are based on runs that used a standard sphere as the LGN ROI. Standard LGN volume was 461 mm3</s...

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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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

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 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 ...

Simultaneous ex vivo Functional Testing of Two Retinas by in vivo Electroretinogram System

An In vivo electroretinogram (ERG) signal is composed of several overlapping components originating from different retinal cell types, as well as noise from extra-retinal sources. Ex vivo ERG provides an efficient method to dissect the function of retinal cells directly from an intact isolated retina of animals or donor eyes. In addition, ex vivo ERG can be used to test the efficacy and safety of potential therapeutic agents on retina tissue from animals or humans. We show here how commercially available in vivo ERG systems can be used to conduct ex vivo ERG recordings from isolated mouse retinas. We combine the light stimulation, electronic and heating units of a standard in vivo system with custom-designed specimen holder, gravity-controlled perfusion system and electromagnetic noise shielding to record low-noise ex vivo ERG signals simultaneously from two retinas with the acquisition software included in commercial in vivo systems. Further, we demonstrate how to use this method in combination with pharmacological treatments that remove specific ERG components in order to dissect the function of certain retinal cell types.

Simultaneous ex vivo Functional Testing of Two Retinas by in vivo Electroretinogram System

Ex vivo ERG can be used to record electrical activity of retinal cells directly from isolated intact retinas of animals or humans. We demonstrate here how common in vivo ERG systems can be adapted for ex vivo ERG recordings in order to dissect the electrical activity of retinal cells.

An In vivo electroretinogram (ERG) signal is composed of several overlapping components originating from different retinal cell types, as well as noise ...

Analyzing Dendritic Morphology in Columns and Layers

In many regions of the central nervous systems, such as the fly optic lobes and the vertebrate cortex, synaptic circuits are organized in layers and columns to facilitate brain wiring during development and information processing in developed animals. Postsynaptic neurons elaborate dendrites in type-specific patterns in specific layers to synapse with appropriate presynaptic terminals. The fly medulla neuropil is composed of 10 layers and about 750 columns; each column is innervated by dendrites of over 38 types of medulla neurons, which match with the axonal terminals of some 7 types of afferents in a type-specific fashion. This report details the procedures to image and analyze dendrites of medulla neurons. The workflow includes three sections: (i) the dual-view imaging section combines two confocal image stacks collected at orthogonal orientations into a high-resolution 3D image of dendrites; (ii) the dendrite tracing and registration section traces dendritic arbors in 3D and registers dendritic traces to the reference column array; (iii) the dendritic analysis section analyzes dendritic patterns with respect to columns and layers, including layer-specific termination and planar projection direction of dendritic arbors, and derives estimates of dendritic branching and termination frequencies. The protocols utilize custom plugins built on the open-source MIPAV (Medical Imaging Processing, Analysis, and Visualization) platform and custom toolboxes in the matrix laboratory language. Together, these protocols provide a complete workflow to analyze the dendritic routing of Drosophila medulla neurons in layers and columns, to identify cell types, and to determine defects in mutants.

Here, we show how to analyze dendritic routing of Drosophila medulla neurons in columns and layers. The workflow includes a dual-view imaging technique to improve the image quality and computational tools for tracing, registering dendritic arbors to the reference column array and for analyzing the dendritic structures in 3D space.

In many regions of the central nervous systems, such as the fly optic lobes and the vertebrate cortex, synaptic circuits are organized in layers and ...

Imaging Neural Activity in the Primary Somatosensory Cortex Using Thy1-GCaMP6s Transgenic Mice

The mammalian brain exhibits marked symmetry across the sagittal plane. However, detailed description of neural dynamics in symmetric brain regions in adult mammalian animals remains elusive. In this study, we describe an experimental procedure for measuring calcium dynamics through dual optical windows above bilateral primary somatosensory corticies (S1) in Thy1-GCaMP6s transgenic mice using 2-photon (2P) microscopy. This method enables recordings and quantifications of neural activity in bilateral mouse brain regions one at a time in the same experiment for a prolonged period in vivo. Key aspects of this method, which can be completed within an hour, include minimally invasive surgery procedures for creating dual optical windows, and the use of 2P imaging. Although we only demonstrate the technique in the S1 area, the method can be applied to other regions of the living brain facilitating the elucidation of structural and functional complexities of brain neural networks.

Imaging Neural Activity in the Primary Somatosensory Cortex Using Thy1-GCaMP6s Transgenic Mice

We describe an experimental procedure for measuring neuronal activity through dual optical windows above bilateral primary somatosensory corticies (S1) in Thy1-GCaMP6s transgenic mice using 2-photon (2P) microscopy in vivo.

The mammalian brain exhibits marked symmetry across the sagittal plane. However, detailed description of neural dynamics in symmetric brain regions in ...

Analyzing Neural Activity and Connectivity Using Intracranial EEG Data with SPM Software

Measuring neural activity and connectivity associated with cognitive functions at high spatial and temporal resolutions is an important goal in cognitive neuroscience. Intracranial electroencephalography (EEG) can directly record electrical neural activity and has the unique potential to accomplish this goal. Traditionally, averaging analysis has been applied to analyze intracranial EEG data; however, several new techniques are available for depicting neural activity and intra- and inter-regional connectivity. Here, we introduce two analytical protocols we recently applied to analyze intracranial EEG data using the Statistical Parametric Mapping (SPM) software: time-frequency SPM analysis for neural activity and dynamic causal modeling of induced responses for intra- and inter-regional connectivity. We report our analysis of intracranial EEG data during the observation of faces as representative results. The results revealed that the inferior occipital gyrus (IOG) showed gamma-band activity at very early stages (110 ms) in response to faces, and both the IOG and amygdala showed rapid intra- and inter-regional connectivity using various types of oscillations. These analytical protocols have the potential to identify the neural mechanisms underlying cognitive functions with high spatial and temporal profiles.

We present two analytical protocols that can be used to analyze intracranial electroencephalography data using the Statistical Parametric Mapping (SPM) software: time-frequency statistical parametric mapping analysis for neural activity, and dynamic causal modeling of induced responses for intra- and inter-regional connectivity.

Measuring neural activity and connectivity associated with cognitive functions at high spatial and temporal resolutions is an important goal in cognitive ...

Diffusion Tensor Magnetic Resonance Imaging in Chronic Spinal Cord Compression

Chronic spinal cord compression is the most common cause of spinal cord impairment in patients with nontraumatic spinal cord damage. Conventional magnetic resonance imaging (MRI) plays an important role in both confirming the diagnosis and evaluating the degree of compression. However, the anatomical detail provided by conventional MRI is not sufficient to accurately estimate neuronal damage and/or assess the possibility of neuronal recovery in chronic spinal cord compression patients. In contrast, diffusion tensor imaging (DTI) can provide quantitative results according to the detection of water molecule diffusion in tissues. In the present study, we develop a methodological framework to illustrate the application of DTI in chronic spinal cord compression disease. DTI fractional anisotropy (FA), apparent diffusion coefficients (ADCs), and eigenvector values are useful for visualizing microstructural pathological changes in the spinal cord. Decreased FA and increases in ADCs and eigenvector values were observed in chronic spinal cord compression patients compared to healthy controls. DTI could help surgeons understand spinal cord injury severity and provide important information regarding prognosis and neural functional recovery. In conclusion, this protocol provides a sensitive, detailed, and noninvasive tool to evaluate spinal cord compression.

Here, we present a protocol for the application of diffusion tensor imaging parameters to evaluate spinal cord compression.

Chronic spinal cord compression is the most common cause of spinal cord impairment in patients with nontraumatic spinal cord damage. Conventional magnetic ...

Advanced Diffusion Imaging in The Hippocampus of Rats with Mild Traumatic Brain Injury

Mild traumatic brain injury (mTBI) is the most common type of acquired brain injury. Since patients with traumatic brain injury show a tremendous variability and heterogeneity (age, gender, type of trauma, other possible pathologies, etc.), animal models play a key role in unraveling factors that are limitations in clinical research. They provide a standardized and controlled setting to investigate the biological mechanisms of injury and repair following TBI. However, not all animal models mimic the diffuse and subtle nature of mTBI effectively. For example, the commonly used controlled cortical impact (CCI) and lateral fluid percussion injury (LFPI) models make use of a craniotomy to expose the brain and induce widespread focal trauma, which are not commonly seen in mTBI. Therefore, these experimental models are not valid to mimic mTBI. Thus, an appropriate model should be used to investigate mTBI. The Marmarou weight drop model for rats induces similar microstructural alterations and cognitive impairments as seen in patients who sustain mild trauma; therefore, this model was selected for this protocol. Conventional computed tomography and magnetic resonance imaging (MRI) scans commonly show no damage following a mild injury, because mTBI induces often only subtle and diffuse injuries. With diffusion weighted MRI, it is possible to investigate microstructural properties of brain tissue, which can provide more insight into the microscopic alterations following mild trauma. Therefore, the goal of this study is to obtain quantitative information of a selected region-of-interest (i.e., hippocampus) to follow up disease progression after obtaining a mild and diffuse brain injury.

The overall goal of this procedure is to obtain quantitative microstructural information of the hippocampus in a rat with mild traumatic brain injury. This is done using an advanced diffusion-weighted magnetic resonance imaging protocol and region-of-interest based analysis of parametric diffusion maps.

Mild traumatic brain injury (mTBI) is the most common type of acquired brain injury. Since patients with traumatic brain injury show a tremendous ...

Labeling and Imaging of Amyloid Plaques in Brain Tissue Using the Natural Polyphenol Curcumin

Deposition of amyloid beta protein (A&#946;) in extra- and intracellular spaces is one of the hallmark pathologies of Alzheimer&#39;s disease (AD). Therefore, detection of the presence of A&#946; in AD brain tissue is a valuable tool for developing new treatments to prevent the progression of AD. Several classical amyloid binding dyes, fluorochrome, imaging probes, and A&#946;-specific antibodies have been used to detect A&#946; histochemically in AD brain tissue. Use of these compounds for A&#946; detection is costly and time consuming. However, because of its intense fluorescent activity, high-affinity, and specificity for A&#946;, as well as structural similarities with traditional amyloid binding dyes, curcumin (Cur) is a promising candidate for labeling and imaging of A&#946; plaques in postmortem brain tissue. It is a natural polyphenol from the herb Curcuma longa. In the present study, Cur was used to histochemically label A&#946; plaques from both a genetic mouse model of 5x familial Alzheimer&#39;s disease (5xFAD) and from human AD tissue within a minute. The labeling capability of Cur was compared to conventional amyloid binding dyes, such as thioflavin-S (Thio-S), Congo red (CR), and Fluoro-jade C (FJC), as well as A&#946;-specific antibodies (6E10 and A11). We observed that Cur is the most inexpensive and quickest way to label and image A&#946; plaques when compared to these conventional dyes and is comparable to A&#946;-specific antibodies. In addition, Cur binds with most A&#946; species, such as oligomers and fibrils. Therefore, Cur could be used as the most cost-effective, simple, and quick fluorochrome detection agent for A&#946; plaques.

Curcumin is an ideal fluorophore for labeling and imaging of amyloid beta protein plaques in brain tissue due to its preferential binding to amyloid beta protein as well as its structural similarities with other traditional amyloid binding dyes. It can be used to label and image amyloid beta protein plaques more efficiently and inexpensively than traditional methods.

Deposition of amyloid beta protein (A&#946;) in extra- and intracellular spaces is one of the hallmark pathologies of Alzheimer&#39;s disease (AD). ...