Community-supported open-source software for computational neuroanatomy

社区支持的计算神经解剖学开源软件

• 批准号: 10735068
• 负责人: Eleftherios Garyfallidis
• 金额: $ 55.8万
• 依托单位: TRUSTEES OF INDIANA UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2018
• 资助国家: 美国
• 起止时间: 2018-08-01 至 2027-07-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10735068
• 关键词: Acceleration; Address; Adoption; Algorithmic Software; Algorithms; Anatomy; Area; Axon; Basic Science; Behavior; Brain; Brain imaging; Brain region; Clinical; Cognitive; Collection; Communities; Computer Analysis; Computer software; Computing Methodologies; Data; Data Analyses; Data Set; Detection; Development; Diffusion; Diffusion Magnetic Resonance Imaging; Disease; Goals; Health; High Performance Computing; Hour; Human; Image; Individual; Individual Differences; Libraries; Magnetic Resonance Imaging; Maps; Measures; Methodology; Methods; Modernization; Motion; Neuroanatomy; Neurologic; Neurons; Neurosciences; Outcome; Pathway interactions; Performance; Phase; Play; Population; Predisposition; Process; Property; Pythons; Quality Control; Research; Research Methodology; Research Personnel; Resources; Sensitivity and Specificity; Software Tools; Speed; Structure; Structure-Activity Relationship; Techniques; Technology; Time; Tissues; Translating; Validation; Work; analytical method; base; brain pathway; brain research; clinical application; clinical translation; cluster computing; connectome; connectome data; digital imaging; human data; image processing; improved; in vivo; innovation; insight; interest; large datasets; large scale data; method development; neural; neuroimaging; next generation; nonhuman primate; novel; open source; parallel computer; physical property; secondary analysis; tool; tractography; white matter

This project catalyzes research on brain networks by developing computational methods and software tools for analysis of diffusion MRI (dMRI) data and validating them in vivo. Understanding brain networks and their relation to neural computation is a major challenge in contemporary neuroscience. The proper function of brain networks is also inextricably tied to neurological, cognitive and psychiatric health. The networks that connect distinct regions in the brain are composed of large bundles that contain the axons of millions of neurons. DMRI is the only currently available method to measure the trajectory and physical properties of these bundles in the human brain non-invasively and a large body of research using dMRI has substantially contributed to our understanding of the way in which differences in brain connections contribute to individual differences across a spectrum of behaviors and clinical conditions. Progress in research and methods development has also translated into increasing use of dMRI in clinical applications. The project is led by the founders of the Diffusion Imaging in Python (DIPY) software project who have been working to invigorate the neuroimaging user and developer community by developing, implementing, disseminating, and maintaining important software tools. The proposed project pursues a next generation phase of development, in which the overall objective is to generate a platform to better utilize dMRI data in accordance with BRAIN 2.0 targets. To address current barriers to progress, we plan to address the following aims in the current proposal: Aim 1 will develop new tractometry methods that enhance the interpretability of dMRI data; Aim 2 will introduce new pre-processing algorithms including methods for susceptibility correction; Aim 3 will focus on improving computational performance using parallel computing within a single node (e.g., via use of graphical processing units) and across nodes (i.e., distributed computing); Aim 4 will focus on the validation of DIPY methods using publicly available human and non-human primate data. Overall, this work will enable impactful brain research and will facilitate subsequent clinical adoption of advanced computational methods using dMRI data.

该项目通过开发计算方法促进了对大脑网络的研究， 用于分析扩散MRI（dMRI）数据并在体内验证它们的软件工具。 理解大脑网络及其与神经计算的关系是一个重大挑战， 当代神经科学大脑网络的正常功能也与 神经、认知和精神健康。连接不同地区的网络 大脑由包含数百万神经元轴突的大束组成。DMRI是 目前唯一可用的方法来测量这些物体的轨迹和物理特性， 束在人类大脑中的非侵入性和大量的研究使用dMRI已经 大大有助于我们理解大脑中的差异 连接有助于在一系列行为和临床上的个体差异 条件研究和方法开发方面的进展也转化为越来越多的 在临床应用中使用dMRI。该项目由扩散成像的创始人领导， Python（DIPY）软件项目，他们一直致力于激励神经成像用户， 通过开发、实施、传播和维护重要的 软件工具。拟议的项目追求下一代发展阶段，其中 总体目标是生成一个平台，以便更好地利用dMRI数据 BRAIN 2.0目标。为了解决目前阻碍进展的障碍，我们计划解决以下问题 目标1将开发新的牵引测量方法，以增强 dMRI数据的可解释性;目标2将引入新的预处理算法，包括 磁化率校正方法;目标3将侧重于提高计算性能 使用单个节点内的并行计算（例如，通过使用图形处理单元）和 跨节点（即，目标4将侧重于验证DIPY方法 使用公开的人类和非人类灵长类动物数据。总的来说，这项工作将使 有影响力的大脑研究，并将促进后续的临床采用先进的 使用dMRI数据的计算方法。

项目成果

An analysis-ready and quality controlled resource for pediatric brain white-matter research.

• DOI: 10.1038/s41597-022-01695-7
• 发表时间: 2022-10-12
• 期刊: SCIENTIFIC DATA
• 影响因子: 9.8
• 作者: Richie-Halford, Adam;Cieslak, Matthew;Ai, Lei;Caffarra, Sendy;Covitz, Sydney;Franco, Alexandre R.;Karipidis, Iliana I.;Kruper, John;Milham, Michael;Avelar-Pereira, Barbara;Roy, Ethan;Sydnor, Valerie J.;Yeatman, Jason D.;Satterthwaite, Theodore D.;Rokem, Ariel;Abbott, Nicholas J.;Abbott, Nicholas J.;Anderson, John A. E.;Gagana, B.;Bleile, MaryLena;Bloomfield, Peter S.;Bottom, Vince;Bourque, Josiane;Boyle, Rory;Brynildsen, Julia K.;Calarco, Navona;Castrellon, Jaime J.;Chaku, Natasha;Chen, Bosi;Chopra, Sidhant;Coffey, Emily B. J.;Colenbier, Nigel;Cox, Daniel J.;Crippen, James Elliott;Crouse, Jacob J.;David, Szabolcs;Leener, Benjamin De;Delap, Gwyneth;Deng, Zhi-De;Dugre, Jules Roger;Eklund, Anders;Ellis, Kirsten;Ered, Arielle;Farmer, Harry;Faskowitz, Joshua;Finch, Jody E.;Flandin, Guillaume;Flounders, Matthew W.;Fonville, Leon;Frandsen, Summer B.;Garic, Dea;Garrido-Vasquez, Patricia;Gonzalez-Escamilla, Gabriel;Grogans, Shannon E.;Grotheer, Mareike;Gruskin, David C.;Guberman, Guido I.;Haggerty, Edda Briana;Hahn, Younghee;Hall, Elizabeth H.;Hanson, Jamie L.;Harel, Yann;Vieira, Bruno Hebling;Hettwer, Meike D.;Hobday, Harriet;Horien, Corey;Huang, Fan;Huque, Zeeshan M.;James, Anthony R.;Kahhale, Isabella;Kamhout, Sarah L. H.;Keller, Arielle S.;Khera, Harmandeep Singh;Kiar, Gregory;Kirk, Peter Alexander;Kohl, Simon H.;Korenic, Stephanie A.;Korponay, Cole;Kozlowski, Alyssa K.;Kraljevic, Nevena;Lazari, Alberto;Leavitt, Mackenzie J.;Li, Zhaolong;Liberati, Giulia;Lorenc, Elizabeth S.;Lossin, Annabelle Julina;Lotter, Leon D.;Lydon-Staley, David M.;Madan, Christopher R.;Magielse, Neville;Marusak, Hilary A.;Mayor, Julien;McGowan, Amanda L.;Mehta, Kahini P.;Meisler, Steven Lee;Michael, Cleanthis;Mitchell, Mackenzie E.;Morand-Beaulieu, Simon;Newman, Benjamin T.;Nielsen, Jared A.;O'Mara, Shane M.;Ojha, Amar;Omary, Adam;ozarslan, Evren;Parkes, Linden;Peterson, Madeline;Pines, Adam Robert;Pisanu, Claudia;Rich, Ryan R.;Sahoo, Ashish K.;Samara, Amjad;Sayed, Farah;Schneider, Jonathan Thore;Shaffer, Lindsay S.;Shatalina, Ekaterina;Sims, Sara A.;Sinclair, Skyler;Song, Jae W.;Hogrogian, Griffin Stockton;Tooley, Ursula A.;Tripathi, Vaibhav;Turker, Hamid B.;Valk, Sofie Louise;Wall, Matthew B.;Walther, Cheryl K.;Wang, Yuchao;Wegmann, Bertil;Welton, Thomas;Wiesman, Alex I.;Wiesman, Andrew G.;Wiesman, Mark;Winters, Drew E.;Yuan, Ruiyi;Zacharek, Sadie J.;Zajner, Chris;Zakharov, Ilya;Zammarchi, Gianpaolo;Zhou, Dale;Zimmerman, Benjamin;Zoner, Kurt;Satterthwaite, Theodore D.;Rokem, Ariel
• 通讯作者: Rokem, Ariel

On the generalizability of diffusion MRI signal representations across acquisition parameters, sequences and tissue types: Chronicles of the MEMENTO challenge.

• DOI: 10.1016/j.neuroimage.2021.118367
• 发表时间: 2021-10-15
• 期刊: NeuroImage
• 影响因子: 5.7
• 作者: De Luca A;Ianus A;Leemans A;Palombo M;Shemesh N;Zhang H;Alexander DC;Nilsson M;Froeling M;Biessels GJ;Zucchelli M;Frigo M;Albay E;Sedlar S;Alimi A;Deslauriers-Gauthier S;Deriche R;Fick R;Afzali M;Pieciak T;Bogusz F;Aja-Fernández S;Özarslan E;Jones DK;Chen H;Jin M;Zhang Z;Wang F;Nath V;Parvathaneni P;Morez J;Sijbers J;Jeurissen B;Fadnavis S;Endres S;Rokem A;Garyfallidis E;Sanchez I;Prchkovska V;Rodrigues P;Landman BA;Schilling KG
• 通讯作者: Schilling KG