forked from RylanSchaeffer/Stanford-LaTeX-Poster-Template
-
Notifications
You must be signed in to change notification settings - Fork 0
/
Copy pathposter.bib
103 lines (97 loc) · 11.4 KB
/
poster.bib
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
@article{cieslakQSIPrepIntegrativePlatform2021,
title = {{QSIPrep}: an integrative platform for preprocessing and reconstructing diffusion {MRI} data},
volume = {18},
issn = {1548-7091, 1548-7105},
shorttitle = {{QSIPrep}},
url = {https://www.nature.com/articles/s41592-021-01185-5},
doi = {10.1038/s41592-021-01185-5},
language = {en},
number = {7},
urldate = {2023-08-21},
journal = {Nature Methods},
author = {Cieslak, Matthew and Cook, Philip A. and He, Xiaosong and Yeh, Fang-Cheng and Dhollander, Thijs and Adebimpe, Azeez and Aguirre, Geoffrey K. and Bassett, Danielle S. and Betzel, Richard F. and Bourque, Josiane and Cabral, Laura M. and Davatzikos, Christos and Detre, John A. and Earl, Eric and Elliott, Mark A. and Fadnavis, Shreyas and Fair, Damien A. and Foran, Will and Fotiadis, Panagiotis and Garyfallidis, Eleftherios and Giesbrecht, Barry and Gur, Ruben C. and Gur, Raquel E. and Kelz, Max B. and Keshavan, Anisha and Larsen, Bart S. and Luna, Beatriz and Mackey, Allyson P. and Milham, Michael P. and Oathes, Desmond J. and Perrone, Anders and Pines, Adam R. and Roalf, David R. and Richie-Halford, Adam and Rokem, Ariel and Sydnor, Valerie J. and Tapera, Tinashe M. and Tooley, Ursula A. and Vettel, Jean M. and Yeatman, Jason D. and Grafton, Scott T. and Satterthwaite, Theodore D.},
month = jul,
year = {2021},
pages = {775--778},
file = {Accepted Version:/Users/howardchiu/Zotero/storage/FED48RMK/Cieslak et al. - 2021 - QSIPrep an integrative platform for preprocessing.pdf:application/pdf},
}
@article{kruperEvaluatingReliabilityHuman2021,
title = {Evaluating the {Reliability} of {Human} {Brain} {White} {Matter} {Tractometry}},
volume = {2021},
url = {https://apertureneuro.org/article/77465-evaluating-the-reliability-of-human-brain-white-matter-tractometry},
doi = {10.52294/e6198273-b8e3-4b63-babb-6e6b0da10669},
abstract = {The validity of research results depends on the reliability of analysis methods. In recent years, there have been concerns about the validity of research that uses diffusion-weighted MRI (dMRI) to understand human brain white matter connections
in vivo
, in part based on the reliability of analysis methods used in this field. We defined and assessed three dimensions of reliability in dMRI-based tractometry, an analysis technique that assesses the physical properties of white matter pathways: (1) reproducibility, (2) test-retest reliability, and (3) robustness. To facilitate reproducibility, we provide software that automates tractometry (
https://yeatmanlab.github.io/pyAFQ
). In measurements from the Human Connectome Project, as well as clinical-grade measurements, we find that tractometry has high test-retest reliability that is comparable to most standardized clinical assessment tools. We find that tractometry is also robust: showing high reliability with different choices of analysis algorithms. Taken together, our results suggest that tractometry is a reliable approach to analysis of white matter connections. The overall approach taken here both demonstrates the specific trustworthiness of tractometry analysis and outlines what researchers can do to establish the reliability of computational analysis pipelines in neuroimaging.},
number = {1},
urldate = {2023-08-21},
journal = {Aperture Neuro},
author = {Kruper, John and Yeatman, Jason D. and Richie-Halford, Adam and Bloom, David and Grotheer, Mareike and Caffarra, Sendy and Kiar, Gregory and Karipidis, Iliana I. and Roy, Ethan and Chandio, Bramsh Q. and Garyfallidis, Eleftherios and Rokem, Ariel},
month = nov,
year = {2021},
pages = {25},
file = {Full Text:/Users/howardchiu/Zotero/storage/HTRMNYXW/Kruper et al. - 2021 - Evaluating the Reliability of Human Brain White Ma.pdf:application/pdf},
}
@article{grotheerHumanWhiteMatter2023,
title = {Human white matter myelinates faster in utero than ex utero},
volume = {120},
issn = {0027-8424, 1091-6490},
url = {https://pnas.org/doi/10.1073/pnas.2303491120},
doi = {10.1073/pnas.2303491120},
abstract = {The formation of myelin, the fatty sheath that insulates nerve fibers, is critical for healthy brain function. A fundamental open question is what impact being born has on myelin growth. To address this, we evaluated a large (
n
= 300) cross-sectional sample of newborns from the Developing Human Connectome Project (dHCP). First, we developed software for the automated identification of 20 white matter bundles in individual newborns that is well suited for large samples. Next, we fit linear models that quantify how T1w/T2w (a myelin-sensitive imaging contrast) changes over time at each point along the bundles. We found faster growth of T1w/T2w along the lengths of all bundles before birth than right after birth. Further, in a separate longitudinal sample of preterm infants (
N
= 34), we found lower T1w/T2w than in full-term peers measured at the same age. By applying the linear models fit on the cross-section sample to the longitudinal sample of preterm infants, we find that their delay in T1w/T2w growth is well explained by the amount of time they spent developing in utero and ex utero. These results suggest that white matter myelinates faster in utero than ex utero. The reduced rate of myelin growth after birth, in turn, explains lower myelin content in individuals born preterm and could account for long-term cognitive, neurological, and developmental consequences of preterm birth. We hypothesize that closely matching the environment of infants born preterm to what they would have experienced in the womb may reduce delays in myelin growth and hence improve developmental outcomes.},
language = {en},
number = {33},
urldate = {2024-05-07},
journal = {Proceedings of the National Academy of Sciences},
author = {Grotheer, Mareike and Bloom, David and Kruper, John and Richie-Halford, Adam and Zika, Stephanie and Aguilera González, Vicente A. and Yeatman, Jason D. and Grill-Spector, Kalanit and Rokem, Ariel},
month = aug,
year = {2023},
pages = {e2303491120},
file = {Grotheer et al_2023_Human white matter myelinates faster in utero than ex utero.pdf:/Users/howardchiu/Zotero/storage/Z5ARUVML/Grotheer et al_2023_Human white matter myelinates faster in utero than ex utero.pdf:application/pdf},
}
@article{richie-halfordMultidimensionalAnalysisDetection2021,
title = {Multidimensional analysis and detection of informative features in human brain white matter},
volume = {17},
issn = {1553-7358},
url = {https://dx.plos.org/10.1371/journal.pcbi.1009136},
doi = {10.1371/journal.pcbi.1009136},
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.},
language = {en},
number = {6},
urldate = {2024-06-13},
journal = {PLOS Computational Biology},
author = {Richie-Halford, Adam and Yeatman, Jason D. and Simon, Noah and Rokem, Ariel},
editor = {Toro, Roberto},
month = jun,
year = {2021},
pages = {e1009136},
file = {Richie-Halford et al_2021_Multidimensional analysis and detection of informative features in human brain.pdf:/Users/howardchiu/Zotero/storage/JCMLGIHL/Richie-Halford et al_2021_Multidimensional analysis and detection of informative features in human brain.pdf:application/pdf},
}
@article{rasmussenNovelMaturationIndex2017,
title = {A novel maturation index based on neonatal diffusion tensor imaging reflects typical perinatal white matter development in humans},
volume = {56},
issn = {0736-5748, 1873-474X},
url = {https://onlinelibrary.wiley.com/doi/10.1016/j.ijdevneu.2016.12.004},
doi = {10.1016/j.ijdevneu.2016.12.004},
abstract = {Abstract
Human birth presents an abrupt transition from intrauterine to extrauterine life. Here we introduce a novel Maturation Index (MI) that considers the relative importance of gestational age at birth and postnatal age at scan in a General Linear Model. The MI is then applied to Diffusion Tensor Imaging (DTI) in newborns for characterizing typical white matter development in neonates. DTI was performed cross‐sectionally in 47 neonates (gestational age at birth = 39.1 ± 1.6 weeks [GA], postnatal age at scan = 25.5 ± 12.2 days [SA]). Radial diffusivity (RD), axial diffusivity (AD) and fractional anisotropy (FA) along 27 white matter fiber tracts were considered. The MI was used to characterize inflection in maturation at the time of birth using GLM estimated rates of change before and after birth. It is proposed that the sign (positive versus negative) of MI reflects the period of greatest maturation rate. Two general patterns emerged from the MI analysis. First, RD and AD (but not FA) had positive MI on average across the whole brain (average MI
AD
= 0.31 ± 0.42, average MI
RD
= 0.22 ± 0.34). Second, significant regions of negative MI in RD and FA (but not AD) were observed in the inferior corticospinal regions, areas known to myelinate early. Observations using the proposed method are consistent with proposed models of the white matter maturation process in which pre‐myelination is described by changes in AD and RD due to oligodendrocyte proliferation while true myelination is characterized by changes in RD and FA due to myelin formation.},
language = {en},
number = {1},
urldate = {2024-09-15},
journal = {International Journal of Developmental Neuroscience},
author = {Rasmussen, Jerod M. and Kruggel, Frithjof and Gilmore, John H. and Styner, Martin and Entringer, Sonja and Consing, Kirsten N.Z. and Potkin, Steven G. and Wadhwa, Pathik D. and Buss, Claudia},
month = feb,
year = {2017},
pages = {42--51},
file = {Rasmussen et al_2017_A novel maturation index based on neonatal diffusion tensor imaging reflects.pdf:/Users/howardchiu/Zotero/storage/PBAAQANI/Rasmussen et al_2017_A novel maturation index based on neonatal diffusion tensor imaging reflects.pdf:application/pdf},
}