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@article{neurolibre:2021,
title={Neurolibre: A preprint server for interactive data analyses},
author={Karakuzu, Agah and DuPre, Elizabeth and Tetrel, Loic and Boudreau, Mathieu and Poline, Jean-Baptiste and Stikov, Nikola and Bellec, Pierre},
journal={NeuroLibre},
volume={1},
number={1},
pages={1--2},
year={2021},
publisher={CONP}
}
@ARTICLE{Kruper2024-ke,
title = "Tractometry of the Human Connectome Project: resources and
insights",
author = "Kruper, John and Hagen, Mckenzie P and Rheault, François and
Crane, Isaac and Gilmore, Asa and Narayan, Manjari and Motwani,
Keshav and Lila, Eardi and Rorden, Chris and Yeatman, Jason D and
Rokem, Ariel",
journal = "Front. Neurosci.",
publisher = "Frontiers Media SA",
volume = 18,
pages = 1389680,
abstract = "Introduction: The Human Connectome Project (HCP) has become a
keystone dataset in human neuroscience, with a plethora of
important applications in advancing brain imaging methods and an
understanding of the human brain. We focused on tractometry of
HCP diffusion-weighted MRI (dMRI) data. Methods: We used an
open-source software library (pyAFQ;
https://yeatmanlab.github.io/pyAFQ) to perform probabilistic
tractography and delineate the major white matter pathways in the
HCP subjects that have a complete dMRI acquisition (n = 1,041).
We used diffusion kurtosis imaging (DKI) to model white matter
microstructure in each voxel of the white matter, and extracted
tract profiles of DKI-derived tissue properties along the length
of the tracts. We explored the empirical properties of the data:
first, we assessed the heritability of DKI tissue properties
using the known genetic linkage of the large number of twin pairs
sampled in HCP. Second, we tested the ability of tractometry to
serve as the basis for predictive models of individual
characteristics (e.g., age, crystallized/fluid intelligence,
reading ability, etc.), compared to local connectome features. To
facilitate the exploration of the dataset we created a new
web-based visualization tool and use this tool to visualize the
data in the HCP tractometry dataset. Finally, we used the HCP
dataset as a test-bed for a new technological innovation: the TRX
file-format for representation of dMRI-based streamlines.
Results: We released the processing outputs and tract profiles as
a publicly available data resource through the AWS Open Data
program's Open Neurodata repository. We found heritability as
high as 0.9 for DKI-based metrics in some brain pathways. We also
found that tractometry extracts as much useful information about
individual differences as the local connectome method. We
released a new web-based visualization tool for
tractometry-``Tractoscope'' (https://nrdg.github.io/tractoscope).
We found that the TRX files require considerably less disk
space-a crucial attribute for large datasets like HCP. In
addition, TRX incorporates a specification for grouping
streamlines, further simplifying tractometry analysis.",
month = jun,
year = 2024,
keywords = "MRI; Open Data; brain; data visualization; diffusion MRI;
heritability; predictive modeling; tractometry",
language = "en"
}

@ARTICLE{Yeatman2012AFQ,
title = "Tract profiles of white matter properties: automating fiber-tract
quantification",
author = "Yeatman, Jason D and Dougherty, Robert F and Myall, Nathaniel J
and Wandell, Brian A and Feldman, Heidi M",
abstract = "Tractography based on diffusion weighted imaging (DWI) data is a
method for identifying the major white matter fascicles (tracts)
in the living human brain. The health of these tracts is an
important factor underlying many cognitive and neurological
disorders. In vivo, tissue properties may vary systematically
along each tract for several reasons: different populations of
axons enter and exit the tract, and disease can strike at local
positions within the tract. Hence quantifying and understanding
diffusion measures along each fiber tract (Tract Profile) may
reveal new insights into white matter development, function, and
disease that are not obvious from mean measures of that tract. We
demonstrate several novel findings related to Tract Profiles in
the brains of typically developing children and children at risk
for white matter injury secondary to preterm birth. First,
fractional anisotropy (FA) values vary substantially within a
tract but the Tract FA Profile is consistent across subjects.
Thus, Tract Profiles contain far more information than mean
diffusion measures. Second, developmental changes in FA occur at
specific positions within the Tract Profile, rather than along
the entire tract. Third, Tract Profiles can be used to compare
white matter properties of individual patients to standardized
Tract Profiles of a healthy population to elucidate unique
features of that patient's clinical condition. Fourth, Tract
Profiles can be used to evaluate the association between white
matter properties and behavioral outcomes. Specifically, in the
preterm group reading ability is positively correlated with FA
measured at specific locations on the left arcuate and left
superior longitudinal fasciculus and the magnitude of the
correlation varies significantly along the Tract Profiles. We
introduce open source software for automated fiber-tract
quantification (AFQ) that measures Tract Profiles of MRI
parameters for 18 white matter tracts. With further validation,
AFQ Tract Profiles have potential for informing clinical
management and decision-making.",
journal = "PLoS One",
volume = 7,
number = 11,
pages = "e49790",
month = nov,
year = 2012,
language = "en"
}


@ARTICLE{RichieHalford2021SGL,
title = "Multidimensional analysis and detection of informative features
in human brain white matter",
author = "Richie-Halford, Adam and Yeatman, Jason D and Simon, Noah and
Rokem, Ariel",
abstract = "The white matter contains long-range connections between
different brain regions and the organization of these connections
holds important implications for brain function in health and
disease. Tractometry uses diffusion-weighted magnetic resonance
imaging (dMRI) to quantify tissue properties along the
trajectories of these connections. Statistical inference from
tractometry usually either averages these quantities along the
length of each fiber bundle or computes regression models
separately for each point along every one of the bundles. These
approaches are limited in their sensitivity, in the former case,
or in their statistical power, in the latter. We developed a
method based on the sparse group lasso (SGL) that takes into
account tissue properties along all of the bundles and selects
informative features by enforcing both global and bundle-level
sparsity. We demonstrate the performance of the method in two
settings: i) in a classification setting, patients with
amyotrophic lateral sclerosis (ALS) are accurately distinguished
from matched controls. Furthermore, SGL identifies the
corticospinal tract as important for this classification,
correctly finding the parts of the white matter known to be
affected by the disease. ii) In a regression setting, SGL
accurately predicts ``brain age.'' In this case, the weights are
distributed throughout the white matter indicating that many
different regions of the white matter change over the lifespan.
Thus, SGL leverages the multivariate relationships between
diffusion properties in multiple bundles to make accurate
phenotypic predictions while simultaneously discovering the most
relevant features of the white matter.",
journal = "PLoS Comput. Biol.",
volume = 17,
number = 6,
pages = "e1009136",
month = jun,
year = 2021,
language = "en"
}


@ARTICLE{Muncy2022GAMs,
title = "General additive models address statistical issues in diffusion
{MRI}: An example with clinically anxious adolescents",
author = "Muncy, Nathan M and Kimbler, Adam and Hedges-Muncy, Ariana M and
McMakin, Dana L and Mattfeld, Aaron T",
abstract = "Statistical models employed to test for group differences in
quantized diffusion-weighted MRI white matter tracts often fail
to account for the large number of data points per tract in
addition to the distribution, type, and interdependence of the
data. To address these issues, we propose the use of Generalized
Additive Models (GAMs) and supply code and examples to aid in
their implementation. Specifically, using diffusion data from 73
periadolescent clinically anxious and no-psychiatric-diagnosis
control participants, we tested for group tract differences and
show that a GAM allows for the identification of differences
within a tract while accounting for the nature of the data as
well as covariates and group factors. Further, we then used these
tract differences to investigate their association with
performance on a memory test. When comparing our high versus low
anxiety groups, we observed a positive association between the
left uncinate fasciculus and memory overgeneralization for
negatively valenced stimuli. This same association was not
evident in the right uncinate or anterior forceps. These findings
illustrate that GAMs are well-suited for modeling diffusion data
while accounting for various aspects of the data, and suggest
that the adoption of GAMs will be a powerful investigatory tool
for diffusion-weighted analyses.",
journal = "Neuroimage Clin",
volume = 33,
pages = "102937",
month = jan,
year = 2022,
keywords = "Adolescence; Anxiety; DWI; GAM; MRI; Uncinate",
language = "en"
}


@ARTICLE{Yeatman2018AFQBrowser,
title = "A browser-based tool for visualization and analysis of diffusion
{MRI} data",
author = "Yeatman, Jason D and Richie-Halford, Adam and Smith, Josh K and
Keshavan, Anisha and Rokem, Ariel",
abstract = "Human neuroscience research faces several challenges with regards
to reproducibility. While scientists are generally aware that
data sharing is important, it is not always clear how to share
data in a manner that allows other labs to understand and
reproduce published findings. Here we report a new open source
tool, AFQ-Browser, that builds an interactive website as a
companion to a diffusion MRI study. Because AFQ-Browser is
portable---it runs in any web-browser---it can facilitate
transparency and data sharing. Moreover, by leveraging new
web-visualization technologies to create linked views between
different dimensions of the dataset (anatomy, diffusion metrics,
subject metadata), AFQ-Browser facilitates exploratory data
analysis, fueling new discoveries based on previously published
datasets. In an era where Big Data is playing an increasingly
prominent role in scientific discovery, so will browser-based
tools for exploring high-dimensional datasets, communicating
scientific discoveries, aggregating data across labs, and
publishing data alongside manuscripts.",
journal = "Nat. Commun.",
volume = 9,
number = 1,
pages = "940",
month = mar,
year = 2018
}
86 changes: 29 additions & 57 deletions paper.md
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---
title: 'NeuroLibre preprint (PDF) template'
title: 'A software ecosystem for brain tractometry processing, analysis, and insight'
tags:
- Preprint
- Jupyter Book
- Reproducible article
- Neuroscience


authors:
- name: Agah Karakuzu
orcid: 0000-0001-7283-271X
- name: John Kruper
affiliation: "1, 2"
- name: Nikola Stikov
orcid: 0000-0002-8480-5230

- name: Ariel Rokem
affiliation: "1, 2"

affiliations:
- name: NeuroPoly Lab, Institute of Biomedical Engineering, Polytechnique Montreal, Montreal, Canada
- name: Department of Psychology, University of Washington, Seattle, WA, USA
index: 1
- name: Montreal Heart Institute, University of Montréal, Montréal, Canada
- name: eScience Institute, University of Washington, Seattle, WA, USA
index: 2
date: 26 March 2021

date: 15 October 2024
bibliography: paper.bib
---

# Summary

This document is intended to provide:

* Author list & affiliations
* A brief overview of the submission

Given that the actual preprint content will be generated in the `Jupyter Book` format by combining `Jupyter Notebooks` and `Markdown` files found at the `content` folder, we suggest keeping this document as brief as possible (about 1-2 pages). Nevertheles, it is at author's discretion to provide a longer `preprint.md` for creating the PDF that'll accompany the NeuroLibre Book.

# Mathematics

Single dollars ($) are required for inline mathematics e.g. $f(x) = e^{\pi/x}$

Double dollars make self-standing equations:

$$\Theta(x) = \left\{\begin{array}{l}
0\textrm{ if } x < 0\cr
1\textrm{ else}
\end{array}\right.$$

You can also use plain \LaTeX for equations
\begin{equation}\label{eq:fourier}
\hat f(\omega) = \int_{-\infty}^{\infty} f(x) e^{i\omega x} dx
\end{equation}
and refer to \autoref{eq:fourier} from text.

# Citations

Citations to entries in `preprint.bib` should be in
[rMarkdown](http://rmarkdown.rstudio.com/authoring_bibliographies_and_citations.html)
format.

For a quick reference, the following citation commands can be used:
* `@author:2021` -> "Author et al. (2021)"
* `[@author:2021]` -> "(Author et al., 2021)"
* `[@author1:2021; @author2:2001]` -> "(Author1 et al., 2021; Author2 et al., 2021)"

This is an example citation [@neurolibre:2021].

# Figures

Figures can be included like this:

![Caption for example figure.\label{fig:example}](images/example_figure.png)

You can reference figure from text using \autoref{fig:example}.

Figure sizes can be customized by adding an optional second parameter:

![Caption for example figure.](images/example_figure.png){ width=20% }
Tractometry uses diffusion-weighted magnetic resonance imaging (dMRI) to assess
the physical properties of long-range brain connections [@Yeatman2012AFQ].
Here, we present an integrative ecosystem of software that performs all steps
of tractometry: post-processing of dMRI data, delineation of major white matter
pathways, and modeling of the tissue properties within them. This ecosystem
also provides tools that extract insights from these measurements, including
novel implementations of machine learning and statistical analysis methods that
consider the unique structure of tractometry data [@RichieHalford2021SGL,
@Muncy2022GAMs], as well as tools for visualization and interpretation of the
results [@Yeatman2018AFQBrowser, @Kruper2024-ke]. Taken together, these
open-source software tools provide a comprehensive environment for the analysis
of dMRI data.

# Acknowledgements

NeuroLibre is sponsored by Canadian Open Neuroscience Platform (CONP), Brain Canada, Quebec Bioimaging Network, Cancer Computing and Healthy Brains & Healthy Life.
This work was funded by National Institutes of Health grants MH121868,
MH121867, and EB027585, as well as by National Science Foundation grant
1934292. Software development was also supported by the Chan Zuckerberg
Initiative's Essential Open Source Software for Science program, the Alfred P.
Sloan Foundation and the Gordon \& Betty Moore Foundation.


# References

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