Editing Tractography

{{short description|3D visualization of nerve tracts via diffusion MRI}}
{{cs1 config|name-list-style=vanc|display-authors=6}}
{{Infobox diagnostic
| name            = Tractography
| image           = File:Tractography animated lateral view.gif|thumb|
| alt             = 
| caption         = Tractography of human brain|
| pronounce       =  
| purpose         =used to visually represent nerve tracts 
| test of         =
| based on        =
| synonyms        = 
| reference_range =
| calculator      = 
| DiseasesDB      = <!--{{DiseasesDB2|numeric_id}}-->
| ICD10           = <!--{{ICD10|Group|Major|minor|LinkGroup|LinkMajor}} or {{ICD10PCS|code|char1/char2/char3/char4}}-->
| ICD9            = 
| ICDO            =
| MedlinePlus     = <!--article_number-->
| eMedicine       = <!--article_number-->
| MeshID          = 
| OPS301          = <!--{{OPS301|code}}-->
| LOINC           = <!--{{LOINC|code}}-->
}}
In [[neuroscience]], '''tractography''' is a [[3D modeling]] technique used to visually represent [[nerve tract]]s using data collected by [[diffusion MRI]].<ref name="Basser_2000" /> It uses special techniques of [[magnetic resonance imaging]] (MRI) and computer-based diffusion MRI. The results are presented in two- and three-dimensional images called '''tractograms'''.<ref>{{cite book |doi=10.1093/med/9780199541164.001.0001 |title=Atlas of Human Brain Connections |date=2012 | vauthors = Catani M, Thiebaut De Schotten M |isbn=978-0-19-954116-4 }}{{pn|date=September 2023}}</ref>

In addition to the long tracts that connect the [[brain]] to the rest of the body, there are complicated [[neural circuit]]s formed by short connections among different [[Cerebral cortex|cortical]] and [[subcortical]] regions. The existence of these tracts and circuits has been revealed by [[histochemistry]] and [[biology|biological]] techniques on [[post-mortem]] specimens. Nerve tracts are not identifiable by direct exam, [[computed tomography|CT]], or [[MRI]] scans. This difficulty explains the paucity of their description in [[neuroanatomy]] atlases and the poor understanding of their functions.

The most advanced tractography algorithm can produce 90% of the ground truth bundles, but it still contains a substantial amount of invalid results.<ref name="ChallengeNatComm2017">{{cite journal | vauthors = Maier-Hein KH, Neher PF, Houde JC, Côté MA, Garyfallidis E, Zhong J, Chamberland M, Yeh FC, Lin YC, Ji Q, Reddick WE, Glass JO, Chen DQ, Feng Y, Gao C, Wu Y, Ma J, He R, Li Q, Westin CF, Deslauriers-Gauthier S, González JO, Paquette M, St-Jean S, Girard G, Rheault F, Sidhu J, Tax CM, Guo F, Mesri HY, Dávid S, Froeling M, Heemskerk AM, Leemans A, Boré A, Pinsard B, Bedetti C, Desrosiers M, Brambati S, Doyon J, Sarica A, Vasta R, Cerasa A, Quattrone A, Yeatman J, Khan AR, Hodges W, Alexander S, Romascano D, Barakovic M, Auría A, Esteban O, Lemkaddem A, Thiran JP, Cetingul HE, Odry BL, Mailhe B, Nadar MS, Pizzagalli F, Prasad G, Villalon-Reina JE, Galvis J, Thompson PM, Requejo FS, Laguna PL, Lacerda LM, Barrett R, Dell'Acqua F, Catani M, Petit L, Caruyer E, Daducci A, Dyrby TB, Holland-Letz T, Hilgetag CC, Stieltjes B, Descoteaux M | title = The challenge of mapping the human connectome based on diffusion tractography | journal = Nature Communications | volume = 8 | issue = 1 | pages = 1349 | date = November 2017 | pmid = 29116093 | pmc = 5677006 | doi = 10.1038/s41467-017-01285-x | bibcode = 2017NatCo...8.1349M }}</ref>

== MRI technique ==
{{Unreferenced section|date=September 2018}}
[[File:Deterministic Tractography of the Adult Brachial Plexus using Diffusion Tensor Imaging.gif|thumb|DTI of the brachial plexus - see https://doi.org/10.3389/fsurg.2020.00019 for more information]]
<!-- Image with unknown copyright status removed: [[Image:Fallon_Petrovic_DTI_lat3.jpg|right|Image of a streamlined DTI scan of the whole human brain seen from the side: Fallon&Petrovic UC Irvine]] -->
[[Image:DTI-sagittal-fibers.jpg|thumb|240px|Tractographic reconstruction of neural connections by diffusion tensor imaging (DTI)]]
[[File:Ultra-High-Field-MRI-Post-Mortem-Structural-Connectivity-of-the-Human-Subthalamic-Nucleus-Video1.ogv|thumb|240px|MRI tractography of the human [[subthalamic nucleus]]]]
Tractography is performed using data from [[diffusion MRI]]. The free water diffusion is termed "[[isotropic]]" diffusion. If the water diffuses in a medium with barriers, the diffusion will be uneven, which is termed [[anisotropic]] diffusion. In such a case, the relative mobility of the [[molecules]] from the origin has a shape different from a [[sphere]]. This shape is often modeled as an [[ellipsoid]], and the technique is then called [[diffusion tensor imaging]].<ref>{{cite journal | vauthors = Basser PJ, Mattiello J, LeBihan D | title = MR diffusion tensor spectroscopy and imaging | journal = Biophysical Journal | volume = 66 | issue = 1 | pages = 259–267 | date = January 1994 | pmid = 8130344 | pmc = 1275686 | doi = 10.1016/S0006-3495(94)80775-1 | bibcode = 1994BpJ....66..259B }}</ref> Barriers can be many things: cell membranes, axons, myelin, etc.; but in [[white matter]] the principal barrier is the [[myelin]] sheath of [[axons]]. Bundles of axons provide a barrier to perpendicular diffusion and a path for parallel diffusion along the orientation of the fibers.

Anisotropic diffusion is expected to be increased in areas of high mature axonal order. Conditions where the [[myelin]] or the structure of the axon are disrupted, such as [[Physical trauma|trauma]],<ref>{{cite journal | vauthors = Wade RG, Tanner SF, Teh I, Ridgway JP, Shelley D, Chaka B, Rankine JJ, Andersson G, Wiberg M, Bourke G | title = Diffusion Tensor Imaging for Diagnosing Root Avulsions in Traumatic Adult Brachial Plexus Injuries: A Proof-of-Concept Study | journal = Frontiers in Surgery | volume = 7 | pages = 19 | date = 16 April 2020 | pmid = 32373625 | pmc = 7177010 | doi = 10.3389/fsurg.2020.00019 | doi-access = free }}</ref> [[tumors]], and [[inflammation]] reduce anisotropy, as the barriers are affected by destruction or disorganization.

Anisotropy is measured in several ways. One way is by a ratio called [[fractional anisotropy]] (FA). An FA of 0 corresponds to a perfect sphere, whereas 1 is an ideal linear diffusion. Few regions have FA larger than 0.90. The number gives information about how aspherical the diffusion is but says nothing of the direction.

Each anisotropy is linked to an orientation of the predominant axis (predominant direction of the diffusion). Post-processing programs are able to extract this directional information.

This additional information is difficult to represent on 2D grey-scaled images. To overcome this problem, a color code is introduced. Basic colors can tell the observer how the fibers are oriented in a 3D coordinate system, this is termed an "anisotropic map". The software could encode the colors in this way:
* Red indicates directions in the ''X'' axis: right to left or left to right.
* Green indicates directions in the ''Y'' axis: [[Posterior (anatomy)|posterior]] to anterior or from [[anterior]] to posterior.
* Blue indicates directions in the ''Z'' axis: [[Anatomical terms of location|inferior]] to [[Anatomical terms of location|superior]] or vice versa.

The technique is unable to discriminate the "positive" or "negative" direction in the same axis.

== Mathematics ==

Using [[Diffusion MRI|diffusion tensor MRI]], one can measure the [[Diffusion MRI#ADC|apparent diffusion coefficient]] at each [[voxel]] in the image, and after [[Multi-linear regression|multilinear regression]] across multiple images, the whole diffusion tensor can be reconstructed.<ref name="Basser_2000">{{cite journal | vauthors = Basser PJ, Pajevic S, Pierpaoli C, Duda J, Aldroubi A | title = In vivo fiber tractography using DT-MRI data | journal = Magnetic Resonance in Medicine | volume = 44 | issue = 4 | pages = 625–632 | date = October 2000 | pmid = 11025519 | doi = 10.1002/1522-2594(200010)44:4<625::AID-MRM17>3.0.CO;2-O | doi-access =  }}</ref>

Suppose there is a fiber tract of interest in the sample. Following the [[Frenet–Serret formulas]], we can formulate the space-path of the fiber tract as a parameterized curve:

:<math> \frac{d\mathbf{r}(s)}{ds} = \mathbf{T}(s), </math>

where <math>\mathbf{T}(s)</math> is the tangent vector of the curve.  The reconstructed diffusion tensor <math> D </math> can be treated as a matrix, and we can compute its [[eigenvalues]] <math> \lambda_1, \lambda_2, \lambda_3 </math> and [[eigenvectors]] <math> \mathbf{u}_1, \mathbf{u}_2, \mathbf{u}_3 </math>.  By equating the eigenvector corresponding to the largest eigenvalue with the direction of the curve:

:<math> \frac{d\mathbf{r}(s)}{ds} = \mathbf{u}_1(\mathbf{r}(s)) </math>

we can solve for <math> \mathbf{r}(s) </math> given the data for <math> \mathbf{u}_1(s) </math>. This can be done using numerical integration, e.g., using [[Runge–Kutta]], and by interpolating the principal [[eigenvector]]s.

== See also ==
* [[Connectome]]
* [[Diffusion MRI]]
* [[Connectogram]]

== References ==
{{Reflist}}

[[Category:Magnetic resonance imaging]]