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Open Access 01-12-2024 | Magnetic Resonance Imaging | Critical Review

Constructing and exploring neuroimaging projects: a survey from clinical practice to scientific research

Authors: Ziyan Chen, Abraham Ayodeji Adegboro, Lan Gu, Xuejun Li

Published in: Insights into Imaging | Issue 1/2024

Abstract

Over the past decades, numerous large-scale neuroimaging projects that involved the collection and release of multimodal data have been conducted globally. Distinguished initiatives such as the Human Connectome Project, UK Biobank, and Alzheimer’s Disease Neuroimaging Initiative, among others, stand as remarkable international collaborations that have significantly advanced our understanding of the brain. With the advancement of big data technology, changes in healthcare models, and continuous development in biomedical research, various types of large-scale projects are being established and promoted worldwide. For project leaders, there is a need to refer to common principles in project construction and management. Users must also adhere strictly to rules and guidelines, ensuring data safety and privacy protection. Organizations must maintain data integrity, protect individual privacy, and foster stakeholders’ trust. Regular updates to legislation and policies are necessary to keep pace with evolving technologies and emerging data-related challenges.

Critical relevance statement

By reviewing global large-scale neuroimaging projects, we have summarized the standards and norms for establishing and utilizing their data, and provided suggestions and opinions on some ethical issues, aiming to promote higher-quality neuroimaging data development.

Key Points

Global neuroimaging projects are increasingly advancing but still face challenges.
Constructing and utilizing neuroimaging projects should follow set rules and guidelines.
Effective data management and governance should be developed to support neuroimaging projects.

Graphical Abstract

de Schotten MT, Croxson PL, Mars RB (2019) Large-scale comparative neuroimaging: Where are we and what do we need? Cortex 118:188–202. https://doi.org/10.1016/j.cortex.2018.11.028CrossRef

Surianarayanan C, Lawrence JJ, Chelliah PR, Prakash E, Hewage C (2023) Convergence of artificial intelligence and neuroscience towards the diagnosis of neurological disorders—a scoping review. Sensors (Basel). https://doi.org/10.3390/s23063062

Basso MA, Frey S, Guerriero KA et al (2021) Using non-invasive neuroimaging to enhance the care, well-being and experimental outcomes of laboratory non-human primates (monkeys). Neuroimage 228:117667. https://doi.org/10.1016/j.neuroimage.2020.117667CrossRefPubMed

Messinger A, Sirmpilatze N, Heuer K et al (2021) A collaborative resource platform for non-human primate neuroimaging. Neuroimage 226:117519. https://doi.org/10.1016/j.neuroimage.2020.117519CrossRefPubMed

Ge J, Yang G, Han M et al (2023) Increasing diversity in connectomics with the Chinese human connectome project. Nat Neurosci 26:163–172. https://doi.org/10.1038/s41593-022-01215-1CrossRefPubMed

Xu Q, Guo L, Cheng J et al (2020) CHIMGEN: a Chinese imaging genetics cohort to enhance cross-ethnic and cross-geographic brain research. Mol Psychiatry 25:517–529. https://doi.org/10.1038/s41380-019-0627-6CrossRefPubMed

Eke DO, Bernard A, Bjaalie JG et al (2022) International data governance for neuroscience. Neuron 110:600–612. https://doi.org/10.1016/j.neuron.2021.11.017CrossRefPubMed

White T, Blok E, Calhoun VD (2022) Data sharing and privacy issues in neuroimaging research: opportunities, obstacles, challenges, and monsters under the bed. Hum Brain Mapp 43:278–291. https://doi.org/10.1002/hbm.25120CrossRefPubMed

Bernier A, Molnar-Gabor F, Knoppers BM (2022) The international data governance landscape. J Law Biosci 9:lsac005. https://doi.org/10.1093/jlb/lsac005CrossRefPubMedPubMedCentral

10.

Abraham R, Schneider J, vom Brocke J (2019) Data governance: a conceptual framework, structured review, and research agenda. Int J Inform Manage 49:424–438. https://doi.org/10.1016/j.ijinfomgt.2019.07.008CrossRef

11.

Wilkinson MD, Dumontier M, Aalbersberg IJ et al (2016) The FAIR guiding principles for scientific data management and stewardship. Sci Data 3:160018. https://doi.org/10.1038/sdata.2016.18CrossRefPubMedPubMedCentral

12.

Baughman RW, Farkas R, Guzman M, Huerta MF (2006) The National Institutes of Health Blueprint for neuroscience research. J Neurosci 26:10329–10331. https://doi.org/10.1523/JNEUROSCI.3979-06.2006CrossRefPubMedPubMedCentral

13.

Mott MC, Austin CP, Bianchi DW et al (2019) The NIH blueprint for neuroscience research seeks community input on future neuroscience investments. J Neurosci 39:774–775. https://doi.org/10.1523/JNEUROSCI.2742-18.2018CrossRefPubMedPubMedCentral

14.

Palmer LJ (2007) UK Biobank: bank on it. Lancet 369:1980–1982. https://doi.org/10.1016/S0140-6736(07)60924-6CrossRefPubMed

15.

News (2010) Human connectome project award $40 million. J Invest Med 58:929–935. https://doi.org/10.2310/JIM.0b013e3182025955

16.

Van Essen DC, Smith SM, Barch DM et al (2013) The WU–Minn human connectome project: an overview. Neuroimage 80:62–79. https://doi.org/10.1016/j.neuroimage.2013.05.041CrossRefPubMed

17.

Van Essen D (2014) The human connectome project: progress and prospects. Biol Psychiat 75:135s. s

18.

Elam JS, Glasser MF, Harms MP et al (2021) The human connectome project: a retrospective. Neuroimage 244:118543. https://doi.org/10.1016/j.neuroimage.2021.118543CrossRefPubMed

19.

McNab JA, Edlow BL, Witzel T et al (2013) The human connectome project and beyond: initial applications of 300 mT/m gradients. Neuroimage 80:234–245. https://doi.org/10.1016/j.neuroimage.2013.05.074CrossRefPubMed

20.

Fan Q, Witzel T, Nummenmaa A et al (2016) MGH-USC human connectome project datasets with ultra-high b-value diffusion MRI. Neuroimage 124:1108–1114. https://doi.org/10.1016/j.neuroimage.2015.08.075CrossRefPubMed

21.

Glasser MF, Coalson TS, Robinson EC et al (2016) A multi-modal parcellation of human cerebral cortex. Nature 536:171–178. https://doi.org/10.1038/nature18933CrossRefPubMedPubMedCentral

22.

Bryce NV, Flournoy JC, Guassi Moreira JF et al (2021) Brain parcellation selection: an overlooked decision point with meaningful effects on individual differences in resting-state functional connectivity. Neuroimage 243:118487. https://doi.org/10.1016/j.neuroimage.2021.118487CrossRefPubMed

23.

Somerville LH, Bookheimer SY, Buckner RL et al (2018) The lifespan human connectome project in development: a large-scale study of brain connectivity development in 5–21-year-olds. Neuroimage 183:456–468. https://doi.org/10.1016/j.neuroimage.2018.08.050CrossRefPubMed

24.

Bookheimer SY, Salat DH, Terpstra M et al (2019) The lifespan human connectome project in aging: an overview. Neuroimage 185:335–348. https://doi.org/10.1016/j.neuroimage.2018.10.009CrossRefPubMed

25.

Sudlow C, Gallacher J, Allen N et al (2015) UK biobank: an open access resource for identifying the causes of a wide range of complex diseases of middle and old age. PLoS Med 12:e1001779. https://doi.org/10.1371/journal.pmed.1001779CrossRefPubMedPubMedCentral

26.

Allen N, Sudlow C, Downey P et al (2012) UK Biobank: current status and what it means for epidemiology. Health Policy Techn 1:123–126. https://doi.org/10.1016/j.hlpt.2012.07.003CrossRef

27.

Littlejohns TJ, Holliday J, Gibson LM et al (2020) The UK Biobank imaging enhancement of 100,000 participants: rationale, data collection, management and future directions. Nat Commun 11:2624. https://doi.org/10.1038/s41467-020-15948-9CrossRefPubMedPubMedCentral

28.

Douaud G, Lee S, Alfaro-Almagro F et al (2022) SARS-CoV-2 is associated with changes in brain structure in UK Biobank. Nature 604:697. https://doi.org/10.1038/s41586-022-04569-5CrossRefPubMedPubMedCentral

29.

Kolin DA, Kulm S, Christos PJ, Elemento O (2020) Clinical, regional, and genetic characteristics of Covid-19 patients from UK Biobank. PLoS One 15:e0241264. https://doi.org/10.1371/journal.pone.0241264CrossRefPubMedPubMedCentral

30.

Conroy M, Sellors J, Effingham M et al (2019) The advantages of UK Biobank’s open-access strategy for health research. J Intern Med 286:389–397. https://doi.org/10.1111/joim.12955CrossRefPubMedPubMedCentral

31.

Han S, Ma Y (2014) Cultural differences in human brain activity: a quantitative meta-analysis. Neuroimage 99:293–300. https://doi.org/10.1016/j.neuroimage.2014.05.062CrossRefPubMed

32.

Noble S, Scheinost D, Constable RT (2021) A guide to the measurement and interpretation of fMRI test–retest reliability. Curr Opin Behav Sci 40:27–32. https://doi.org/10.1016/j.cobeha.2020.12.012CrossRefPubMedPubMedCentral

33.

Casey BJ, Cannonier T, Conley MI et al (2018) The adolescent brain cognitive development (ABCD) study: imaging acquisition across 21 sites. Dev Cogn Neurosci 32:43–54. https://doi.org/10.1016/j.dcn.2018.03.001CrossRefPubMedPubMedCentral

34.

Marek S, Tervo-Clemmens B, Nielsen AN et al (2019) Identifying reproducible individual differences in childhood functional brain networks: an ABCD study. Dev Cogn Neurosci 40:100706. https://doi.org/10.1016/j.dcn.2019.100706CrossRefPubMedPubMedCentral

35.

Pornpattananangkul N, Leibenluft E, Pine DS, Stringaris A (2019) Association between childhood anhedonia and alterations in large-scale resting-state networks and task-evoked activation. JAMA Psychiatry 76:624–633. https://doi.org/10.1001/jamapsychiatry.2019.0020CrossRefPubMedPubMedCentral

36.

Karcher NR, O’Brien KJ, Kandala S, Barch DM (2019) Resting-state functional connectivity and psychotic-like experiences in childhood: results from the adolescent brain cognitive development study. Biol Psychiatry 86:7–15. https://doi.org/10.1016/j.biopsych.2019.01.013CrossRefPubMedPubMedCentral

37.

Volkow ND, Koob GF, Croyle RT et al (2018) The conception of the ABCD study: from substance use to a broad NIH collaboration. Dev Cogn Neurosci 32:4–7. https://doi.org/10.1016/j.dcn.2017.10.002CrossRefPubMed

38.

Petersen RC, Aisen PS, Beckett LA et al (2010) Alzheimer’s disease neuroimaging initiative (ADNI): clinical characterization. Neurology 74:201–209. https://doi.org/10.1212/WNL.0b013e3181cb3e25CrossRefPubMedPubMedCentral

39.

Aisen PS, Petersen RC, Donohue M, Weiner MW, Alzheimer’s Disease Neuroimaging I (2015) Alzheimer’s disease neuroimaging initiative 2 clinical core: progress and plans. Alzheimers Dement 11:734–739. https://doi.org/10.1016/j.jalz.2015.05.005CrossRefPubMed

40.

Weiner MW, Veitch DP, Aisen PS et al (2017) The Alzheimer’s disease neuroimaging initiative 3: continued innovation for clinical trial improvement. Alzheimers Dement 13:561–571. https://doi.org/10.1016/j.jalz.2016.10.006CrossRefPubMed

41.

Franzmeier N, Neitzel J, Rubinski A et al (2020) Functional brain architecture is associated with the rate of tau accumulation in Alzheimer’s disease. Nat Commun https://doi.org/10.1038/s41467-019-14159-1

42.

Lin Q, Rosenberg MD, Yoo K, Hsu TW, O’Connell TP, Chun MM (2018) Resting-state functional connectivity predicts cognitive impairment related to Alzheimer’s disease. Front Aging Neurosci 10:94. https://doi.org/10.3389/fnagi.2018.00094CrossRefPubMedPubMedCentral

43.

Biel D, Luan Y, Brendel M et al (2022) Combining tau-PET and fMRI meta-analyses for patient-centered prediction of cognitive decline in Alzheimer’s disease. Alzheimers Res Ther 14:166. https://doi.org/10.1186/s13195-022-01105-5CrossRefPubMedPubMedCentral

44.

Franzmeier N, Caballero MAA, Taylor ANW et al (2017) Resting-state global functional connectivity as a biomarker of cognitive reserve in mild cognitive impairment. Brain Imaging Behav 11:368–382. https://doi.org/10.1007/s11682-016-9599-1CrossRefPubMed

45.

Gao YR, Sengupta A, Li MW et al (2020) Functional connectivity of white matter as a biomarker of cognitive decline in Alzheimer’s disease. PLoS One. https://doi.org/10.1371/journal.pone.0240513

46.

Xiong Y, Ye CH, Chen Y et al (2022) Altered functional connectivity of basal ganglia in mild cognitive impairment and Alzheimer’s disease. Brain Sci. https://doi.org/10.3390/brainsci12111555

47.

Patel SH, Poisson LM, Brat DJ et al (2017) T2-FLAIR mismatch, an imaging biomarker for IDH and 1p/19q status in lower-grade gliomas: a TCGA/TCIA project. Clin Cancer Res 23:6078–6085. https://doi.org/10.1158/1078-0432.CCR-17-0560CrossRefPubMed

48.

Zhao H, Li W, Lyu P et al (2021) TCGA-TCIA-based CT radiomics study for noninvasively predicting epstein-barr virus status in gastric cancer. AJR Am J Roentgenol 217:124–134. https://doi.org/10.2214/AJR.20.23534CrossRefPubMed

49.

Zanfardino M, Pane K, Mirabelli P, Salvatore M, Franzese M (2019) TCGA-TCIA impact on radiogenomics cancer research: a systematic review. Int J Mol Sci. https://doi.org/10.3390/ijms20236033

50.

Yan J, Sun Q, Tan X et al (2023) Image-based deep learning identifies glioblastoma risk groups with genomic and transcriptomic heterogeneity: a multi-center study. Eur Radiol 33:904–914. https://doi.org/10.1007/s00330-022-09066-xCrossRefPubMed

51.

Liu D, Chen J, Ge H et al (2023) Radiogenomics to characterize the immune-related prognostic signature associated with biological functions in glioblastoma. Eur Radiol 33:209–220. https://doi.org/10.1007/s00330-022-09012-xCrossRefPubMed

52.

Bakas S, Sako C, Akbari H et al (2022) The University of Pennsylvania glioblastoma (UPenn-GBM) cohort: advanced MRI, clinical, genomics, & radiomics. Sci Data 9:453. https://doi.org/10.1038/s41597-022-01560-7CrossRefPubMedPubMedCentral

53.

Calabrese E, Villanueva-Meyer JE, Rudie JD et al (2022) The University of California San Francisco preoperative diffuse glioma MRI dataset. Radiol Artif Intell 4:e220058. https://doi.org/10.1148/ryai.220058CrossRefPubMedPubMedCentral

54.

Des Ligneris M, Bonnet A, Chatelain Y et al (2023) Reproducibility of tumor segmentation outcomes with a deep learning model. International symposium on biomedical imaging (ISBI). Cartagena de Indias, Colombia

55.

Palk A, Illes J, Thompson PM, Stein DJ (2020) Ethical issues in global neuroimaging genetics collaborations. Neuroimage 221:117208. https://doi.org/10.1016/j.neuroimage.2020.117208CrossRefPubMed

56.

Bearden CE, Thompson PM (2017) Emerging global initiatives in neurogenetics: the enhancing neuroimaging genetics through meta-analysis (ENIGMA) consortium. Neuron 94:232–236. https://doi.org/10.1016/j.neuron.2017.03.033CrossRefPubMedPubMedCentral

57.

Schmaal L, Hibar DP, Samann PG et al (2017) Cortical abnormalities in adults and adolescents with major depression based on brain scans from 20 cohorts worldwide in the ENIGMA major depressive disorder working group. Mol Psychiatr 22:900–909. https://doi.org/10.1038/mp.2016.60CrossRef

58.

Schmaal L, Veltman DJ, van Erp TG et al (2016) Subcortical brain alterations in major depressive disorder: findings from the ENIGMA major depressive disorder working group. Mol Psychiatry 21:806–812. https://doi.org/10.1038/mp.2015.69CrossRefPubMed

59.

van Erp TG, Hibar DP, Rasmussen JM et al (2016) Subcortical brain volume abnormalities in 2028 individuals with schizophrenia and 2540 healthy controls via the ENIGMA consortium. Mol Psychiatry 21:585. https://doi.org/10.1038/mp.2015.118CrossRefPubMed

60.

Boedhoe PSW, Heymans MW, Schmaal L et al (2018) An empirical comparison of meta- and mega-analysis with data from the ENIGMA obsessive–compulsive disorder working group. Front Neuroinform 12:102. https://doi.org/10.3389/fninf.2018.00102CrossRefPubMed

61.

Bruin WB, Abe Y, Alonso P et al (2023) The functional connectome in obsessive-compulsive disorder: resting-state mega-analysis and machine learning classification for the ENIGMA-OCD consortium. Mol Psychiatry. https://doi.org/10.1038/s41380-023-02077-0.

62.

Liew SL, Zavaliangos-Petropulu A, Jahanshad N et al (2022) The ENIGMA stroke recovery working group: big data neuroimaging to study brain-behavior relationships after stroke. Human Brain Mapping 43:129–148. https://doi.org/10.1002/hbm.25015CrossRefPubMed

63.

Cheon EJ, Bearden CE, Sun D et al (2022) Cross disorder comparisons of brain structure in schizophrenia, bipolar disorder, major depressive disorder, and 22q11.2 deletion syndrome: aA review of ENIGMA findings. Psychiatry Clin Neurosci 76:140–161. https://doi.org/10.1111/pcn.13337CrossRefPubMedPubMedCentral

64.

Thompson PM, Stein JL, Medland SE et al (2014) The ENIGMA consortium: large-scale collaborative analyses of neuroimaging and genetic data. Brain Imaging Behav 8:153–182. https://doi.org/10.1007/s11682-013-9269-5CrossRefPubMedPubMedCentral

65.

Zugman A, Harrewijn A, Cardinale EM et al (2022) Mega-analysis methods in ENIGMA: the experience of the generalized anxiety disorder working group. Hum Brain Mapp 43:255–277. https://doi.org/10.1002/hbm.25096CrossRefPubMed

66.

Thompson PM, Jahanshad N, Schmaal L et al (2022) The enhancing neuroimaging genetics through meta-analysis consortium: 10 years of global collaborations in human brain mapping. Human Brain Mapping 43:15–22. https://doi.org/10.1002/hbm.25672CrossRefPubMed

67.

Koile E, Cristia A (2021) Toward cumulative cognitive science: a comparison of meta-analysis, mega-analysis, and hybrid approaches. Open Mind 5:154–173. https://doi.org/10.1162/opmi_a_00048CrossRefPubMedPubMedCentral

68.

Amunts K, Ebell C, Muller J, Telefont M, Knoll A, Lippert T (2016) The human brain project: creating a european research infrastructure to decode the human brain. Neuron 92:574–581. https://doi.org/10.1016/j.neuron.2016.10.046CrossRefPubMed

69.

Marcus DS, Wang TH, Parker J, Csernansky JG, Morris JC, Buckner RL (2007) Open access series of imaging studies (OASIS): cross-sectional MRI data in young, middle aged, nondemented, and demented older adults. J Cogn Neurosci 19:1498–1507. https://doi.org/10.1162/jocn.2007.19.9.1498CrossRefPubMed

70.

Shafto MA, Tyler LK, Dixon M et al (2014) The Cambridge Centre for Ageing and Neuroscience (Cam-CAN) study protocol: a cross-sectional, lifespan, multidisciplinary examination of healthy cognitive ageing. BMC Neurol 14:204. https://doi.org/10.1186/s12883-014-0204-1CrossRefPubMedPubMedCentral

71.

Taylor JR, Williams N, Cusack R et al (2017) The Cambridge Centre for Ageing and Neuroscience (Cam-CAN) data repository: structural and functional MRI, MEG, and cognitive data from a cross-sectional adult lifespan sample. Neuroimage 144:262–269. https://doi.org/10.1016/j.neuroimage.2015.09.018CrossRefPubMed

72.

Holmes AJ, Hollinshead MO, O’Keefe TM et al (2015) Brain genomics superstruct project initial data release with structural, functional, and behavioral measures. Sci Data 2:150031. https://doi.org/10.1038/sdata.2015.31CrossRefPubMedPubMedCentral

73.

Parkinson Progression Marker I (2011) The Parkinson progression marker initiative (PPMI). Prog Neurobiol 95:629–635. https://doi.org/10.1016/j.pneurobio.2011.09.005CrossRef

74.

Di Martino A, Yan CG, Li Q et al (2014) The autism brain imaging data exchange: towards a large-scale evaluation of the intrinsic brain architecture in autism. Mol Psychiatry 19:659–667. https://doi.org/10.1038/mp.2013.78CrossRefPubMed

75.

Consortium HD. (2012) The ADHD-200 consortium: a model to advance the translational potential of neuroimaging in clinical neuroscience. Front Syst Neurosci 6:62. https://doi.org/10.3389/fnsys.2012.00062CrossRef

76.

Horien C, Lee K, Westwater ML et al (2022) A protocol for working with open-source neuroimaging datasets. STAR Protoc 3:101077. https://doi.org/10.1016/j.xpro.2021.101077CrossRefPubMedPubMedCentral

77.

Dvir Dov RT, Shenhar Aaron (2003) An empirical analysis of the relationship between project planning and project success. Int J Proj Manag 21:89–95. https://doi.org/10.1016/S0263-7863(02)00012-1CrossRef

78.

Stroganov O, Fedarovich A, Wong E et al (2022) Mapping of UK Biobank clinical codes: challenges and possible solutions. PLoS One. https://doi.org/10.1371/journal.pone.0275816

79.

Eshaghzadeh Torbati M, Minhas DS, Ahmad G et al (2021) A multi-scanner neuroimaging data harmonization using RAVEL and ComBat. Neuroimage 245:118703. https://doi.org/10.1016/j.neuroimage.2021.118703CrossRefPubMed

80.

Gorgolewski KJ, Auer T, Calhoun VD et al (2016) The brain imaging data structure, a format for organizing and describing outputs of neuroimaging experiments. Sci Data 3:160044. https://doi.org/10.1038/sdata.2016.44CrossRefPubMedPubMedCentral

81.

Niso G, Botvinik-Nezer R, Appelhoff S et al (2022) Open and reproducible neuroimaging: from study inception to publication. Neuroimage 263:119623. https://doi.org/10.1016/j.neuroimage.2022.119623CrossRefPubMed

82.

Fezai L, Urruty T, Bourdon P, Fernandez-Maloigne C, Initi AsDN (2023) Deep anonymization of medical imaging. Multimed Tools Appl 82:9533–9547. https://doi.org/10.1007/s11042-022-13686-2CrossRef

83.

Nichols TE, Das S, Eickhoff SB et al (2017) Best practices in data analysis and sharing in neuroimaging using MRI. Nat Neurosci 20:299–303. https://doi.org/10.1038/nn.4500CrossRefPubMedPubMedCentral

84.

Aerts H, Schirner M, Jeurissen B et al (2018) Modeling brain dynamics in brain tumor patients using the virtual brain. eNeuro. https://doi.org/10.1523/ENEURO.0083-18.2018

85.

Aerts H, Schirner M, Dhollander T et al (2020) Modeling brain dynamics after tumor resection using the virtual brain. Neuroimage 213:116738. https://doi.org/10.1016/j.neuroimage.2020.116738CrossRefPubMed

86.

Menze BH, Jakab A, Bauer S et al (2015) The multimodal brain tumor image segmentation benchmark (BRATS). IEEE T Med Imaging 34:1993–2024. https://doi.org/10.1109/Tmi.2014.2377694CrossRef

87.

Bakas S, Akbari H, Sotiras A et al (2017) Data descriptor: advancing the cancer genome atlas glioma MRI collections with expert segmentation labels and radiomic features. Scientific Data. https://doi.org/10.1038/sdata.2017.117

88.

Jwa AS, Poldrack RA (2022) The spectrum of data sharing policies in neuroimaging data repositories. Human Brain Mapping 43:2707–2721. https://doi.org/10.1002/hbm.25803CrossRefPubMedPubMedCentral

89.

Jenkinson M, Beckmann CF, Behrens TE, Woolrich MW, Smith SM (2012) FSL. Neuroimage 62:782–790. https://doi.org/10.1016/j.neuroimage.2011.09.015CrossRefPubMed

90.

Frackowiak RS (2004) Human brain function. Elsevier, Amsterdam

91.

Cox RW (1996) AFNI: software for analysis and visualization of functional magnetic resonance neuroimages. Comput Biomed Res 29:162–173. https://doi.org/10.1006/cbmr.1996.0014CrossRefPubMed

92.

Avants BB, Tustison NJ, Song G, Cook PA, Klein A, Gee JC (2011) A reproducible evaluation of ANTs similarity metric performance in brain image registration. Neuroimage 54:2033–2044. https://doi.org/10.1016/j.neuroimage.2010.09.025CrossRefPubMed

93.

Fischl B (2012) FreeSurfer. Neuroimage 62:774–781. https://doi.org/10.1016/j.neuroimage.2012.01.021CrossRefPubMed

94.

Esteban O, Markiewicz CJ, Blair RW et al (2019) fMRIPrep: a robust preprocessing pipeline for functional MRI. Nat Methods 16:111–116. https://doi.org/10.1038/s41592-018-0235-4CrossRefPubMed

95.

Sikka S, Cheung B, Khanuja R et al (2014) Towards automated analysis of connectomes: the configurable pipeline for the analysis of connectomes (c-pac). In: 5th INCF Congress of Neuroinformatics, Munich

96.

Li X, Bianchini Esper N, Ai L et al (2024) Moving beyond processing- and analysis-related variation in resting-state functional brain imaging. Nat Hum Behav 8:2003–2017. https://doi.org/10.1038/s41562-024-01942-4

97.

Gorgolewski K, Burns CD, Madison C et al (2011) Nipype: a flexible, lightweight and extensible neuroimaging data processing framework in python. Front Neuroinform 5:13. https://doi.org/10.3389/fninf.2011.00013CrossRefPubMedPubMedCentral

98.

Abraham A, Pedregosa F, Eickenberg M et al (2014) Machine learning for neuroimaging with scikit-learn. Front Neuroinform 8:14. https://doi.org/10.3389/fninf.2014.00014CrossRefPubMedPubMedCentral

99.

Bowring A, Maumet C, Nichols TE (2019) Exploring the impact of analysis software on task fMRI results. Hum Brain Mapp 40:3362–3384. https://doi.org/10.1002/hbm.24603CrossRefPubMedPubMedCentral

100.

Luppi AI, Gellersen HM, Liu ZQ et al (2024) Systematic evaluation of fMRI data-processing pipelines for consistent functional connectomics. Nat Commun 15:4745. https://doi.org/10.1038/s41467-024-48781-5CrossRefPubMedPubMedCentral

101.

Desmond JE, Glover GH (2002) Estimating sample size in functional MRI (fMRI) neuroimaging studies: statistical power analyses. J Neurosci Methods 118:115–128. https://doi.org/10.1016/s0165-0270(02)00121-8CrossRefPubMed

102.

Szucs D, Ioannidis JP (2020) Sample size evolution in neuroimaging research: an evaluation of highly-cited studies (1990-2012) and of latest practices (2017–2018) in high-impact journals. Neuroimage 221:117164. https://doi.org/10.1016/j.neuroimage.2020.117164CrossRefPubMed

103.

Marek S, Tervo-Clemmens B, Calabro FJ et al (2022) Reproducible brain-wide association studies require thousands of individuals. Nature 603:654–660. https://doi.org/10.1038/s41586-022-04492-9CrossRefPubMedPubMedCentral

104.

Goddard M (2017) The EU general data protection regulation (GDPR): European regulation that has a global impact. Int J Market Res 59:703–705. https://doi.org/10.2501/Ijmr-2017-050CrossRef

105.

Rosenbaum S (2010) Data governance and stewardship: designing data stewardship entities and advancing data access. Health Serv Res 45:1442–1455. https://doi.org/10.1111/j.1475-6773.2010.01140.xCrossRefPubMedPubMedCentral

106.

Jin H, Luo Y, Li P, Mathew J (2019) A review of secure and privacy-preserving medical data sharing. IEEE Access 7:61656–61669CrossRef

107.

Hudson KL, Collins FS (2015) Bringing the common rule into the 21st century. New Engl J Med 373:2293–2296. https://doi.org/10.1056/NEJMp1512205CrossRefPubMed

108.

Mulgund P, Mulgund BP, Sharman R, Singh R (2021) The implications of the California Consumer Privacy Act (CCPA) on healthcare organizations: lessons learned from early compliance experiences. Health Policy Technol. https://doi.org/10.1016/j.hlpt.2021.100543

109.

United S (1996) Health insurance portability and accountability act of 1996. Public law 104–191. US Statut Large 110:1936–2103

110.

Sarabdeen J, Chikhaoui E, Mohamed Ishak MM (2022) Creating standards for Canadian health data protection during health emergency—an analysis of privacy regulations and laws. Heliyon 8:e09458. https://doi.org/10.1016/j.heliyon.2022.e09458CrossRefPubMedPubMedCentral

111.

Shen FX, Wolf SM, Lawrenz F et al (2024) Ethical, legal, and policy challenges in field-based neuroimaging research using emerging portable MRI technologies: guidance for investigators and for oversight. J Law Biosci 11:lsae008. https://doi.org/10.1093/jlb/lsae008CrossRefPubMedPubMedCentral

112.

Hamet P, Tremblay J (2017) Artificial intelligence in medicine. Metabolism 69S:S36–S40. https://doi.org/10.1016/j.metabol.2017.01.011CrossRefPubMed

113.

Holzinger A, Langs G, Denk H, Zatloukal K, Muller H (2019) Causability and explainability of artificial intelligence in medicine. Wiley Interdiscip Rev Data Min Knowl Discov 9:e1312. https://doi.org/10.1002/widm.1312CrossRefPubMedPubMedCentral

114.

Reyes M, Meier R, Pereira S et al (2020) On the interpretability of artificial intelligence in radiology: challenges and opportunities. Radiol Artif Intell 2:e190043. https://doi.org/10.1148/ryai.2020190043CrossRefPubMedPubMedCentral

115.

Ienca M, Ignatiadis K (2020) Artificial intelligence in clinical neuroscience: methodological and ethical challenges. AJOB Neurosci 11:77–87. https://doi.org/10.1080/21507740.2020.1740352CrossRefPubMed

116.

Liebrenz M, Schleifer R, Buadze A, Bhugra D, Smith A (2023) Generating scholarly content with ChatGPT: ethical challenges for medical publishing. Lancet Digit Health 5:e105–e106. https://doi.org/10.1016/S2589-7500(23)00019-5CrossRefPubMed

117.

Challen R, Denny J, Pitt M, Gompels L, Edwards T, Tsaneva-Atanasova K (2019) Artificial intelligence, bias and clinical safety. BMJ Qual Saf 28:231–237. https://doi.org/10.1136/bmjqs-2018-008370CrossRefPubMedPubMedCentral

118.

Parikh RB, Teeple S, Navathe AS (2019) Addressing bias in artificial intelligence in health care. JAMA 322:2377–2378. https://doi.org/10.1001/jama.2019.18058CrossRefPubMed

119.

Boochever SS (2004) HIS/RIS/PACS integration: getting to the gold standard. Radiol Manage 26:16–24. quiz 5-7PubMed

120.

Rao TS, Radhakrishnan R, Andrade C (2011) Standard operating procedures for clinical practice. Indian J Psychiatry 53:1–3. https://doi.org/10.4103/0019-5545.75542CrossRefPubMedPubMedCentral

121.

Simmons JP, Nelson LD, Simonsohn U (2021) Pre-registration: why and how. J Consum Psychol 31:151–162. https://doi.org/10.1002/jcpy.1208CrossRef

122.

Van ‘t Veer AE, Giner-Sorolla R (2016) Pre-registration in social psychology—a discussion and suggested template. J Exp Soc Psychol 67:2–12. https://doi.org/10.1016/j.jesp.2016.03.004CrossRef

123.

Logg JM, Dorison CA (2021) Pre-registration: weighing costs and benefits for researchers. Organ Behav Hum 167:18–27. https://doi.org/10.1016/j.obhdp.2021.05.006CrossRef

124.

Ross MH (1993) The management of conflict: Interpretations and interests in comparative perspective. Yale University Press, New Haven

125.

Roche WK, Teague P, Colvin AJ (2014) The Oxford handbook of conflict management in organizations. Oxford University Press, New York

Title: Constructing and exploring neuroimaging projects: a survey from clinical practice to scientific research
Authors: Ziyan Chen
Abraham Ayodeji Adegboro
Lan Gu
Xuejun Li
Publication date: 01-12-2024
Publisher: Springer Vienna
Keywords: Magnetic Resonance Imaging
Magnetic Resonance Imaging
Published in: Insights into Imaging / Issue 1/2024
Electronic ISSN: 1869-4101
DOI: https://doi.org/10.1186/s13244-024-01848-9

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Radiomics analysis based on CT for predicting lymph node metastasis and prognosis in duodenal papillary carcinoma

Endovascular treatments of acute pulmonary embolism in the post-fibrinolytic era: an up-to-date review

Coronary CTA-based radiomic signature of pericoronary adipose tissue predict rapid plaque progression

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