poster.bib

@article{alexander2017hbn,
  title   = {An open resource for transdiagnostic research in pediatric mental health and learning disorders},
  volume  = {4},
  url     = {https://doi.org/10.1038/sdata.2017.181},
  journal = {Scientific Data},
  author  = {Alexander and others},
  month   = dec,
  year    = {2017},
  pages   = {170181}
}

@ARTICLE{yeatman2012,
  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 {Kruper2021evaluating,
	author = {Kruper, John and others},
	title = {Evaluating the reliability of human brain white matter tractometry},
	journal = {Aperture},
	year = {2021},
	volume = {1},
	pages= {10.52294/e6198273-b8e3-4b63-babb-6e6b0da10669},
	doi = {10.1101/2021.02.24.432740},
	URL = {https://www.biorxiv.org/content/early/2021/02/24/2021.02.24.432740},
}


@ARTICLE{RichieHalfordCieslak2022HBNPOD2,
	title     = "An analysis-ready and quality controlled resource for pediatric
				 brain white-matter research",
	author    = "Richie-Halford, Adam and others",
	abstract  = "We created a set of resources to enable research based on
				 openly-available diffusion MRI (dMRI) data from the Healthy
				 Brain Network (HBN) study. First, we curated the HBN dMRI data
				 (N = 2747) into the Brain Imaging Data Structure and
				 preprocessed it according to best-practices, including denoising
				 and correcting for motion effects, susceptibility-related
				 distortions, and eddy currents. Preprocessed, analysis-ready
				 data was made openly available. Data quality plays a key role in
				 the analysis of dMRI. To optimize QC and scale it to this large
				 dataset, we trained a neural network through the combination of
				 a small data subset scored by experts and a larger set scored by
				 community scientists. The network performs QC highly concordant
				 with that of experts on a held out set (ROC-AUC = 0.947). A
				 further analysis of the neural network demonstrates that it
				 relies on image features with relevance to QC. Altogether, this
				 work both delivers resources to advance transdiagnostic research
				 in brain connectivity and pediatric mental health, and
				 establishes a novel paradigm for automated QC of large datasets.",
	journal   = "Scientific Data",
	publisher = "Nature Publishing Group",
	volume    =  9,
	number    =  1,
	pages     = "1--27",
	month     =  oct,
	year      =  2022,
	language  = "en"
  }

  @article{richford2021sgl,
    Author = {Richie-Halford, Adam and Yeatman, Jason D. and Simon, Noah and bf Rokem, Ariel},
    title = {Multidimensional analysis and detection of informative features in human brain white matter},
    journal = {{P}{L}o{S} Computational Biology},
    volume={in press},
    year = {2021},
    month = {06},
    volume = {17},
    url = {https://doi.org/10.1371/journal.pcbi.1009136},
    pages = {1-24},
    number = {6},
    note={PMC5838108[pmc}
	}