Efficacy of MRI data harmonization in the age of machine learning: a multicenter study across 36 datasets.
Journal
Scientific data
ISSN: 2052-4463
Titre abrégé: Sci Data
Pays: England
ID NLM: 101640192
Informations de publication
Date de publication:
23 Jan 2024
23 Jan 2024
Historique:
received:
06
12
2022
accepted:
27
07
2023
medline:
24
1
2024
pubmed:
24
1
2024
entrez:
23
1
2024
Statut:
epublish
Résumé
Pooling publicly-available MRI data from multiple sites allows to assemble extensive groups of subjects, increase statistical power, and promote data reuse with machine learning techniques. The harmonization of multicenter data is necessary to reduce the confounding effect associated with non-biological sources of variability in the data. However, when applied to the entire dataset before machine learning, the harmonization leads to data leakage, because information outside the training set may affect model building, and potentially falsely overestimate performance. We propose a 1) measurement of the efficacy of data harmonization; 2) harmonizer transformer, i.e., an implementation of the ComBat harmonization allowing its encapsulation among the preprocessing steps of a machine learning pipeline, avoiding data leakage by design. We tested these tools using brain T
Identifiants
pubmed: 38263181
doi: 10.1038/s41597-023-02421-7
pii: 10.1038/s41597-023-02421-7
doi:
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
115Informations de copyright
© 2024. The Author(s).
Références
Alfaro-Almagro, F. et al. Image processing and Quality Control for the first 10,000 brain imaging datasets from UK Biobank. NeuroImage 166, 400–424 (2018).
pubmed: 29079522
doi: 10.1016/j.neuroimage.2017.10.034
Pomponio, R. et al. Harmonization of large MRI datasets for the analysis of brain imaging patterns throughout the lifespan. NeuroImage 208, 116450 (2020).
pubmed: 31821869
doi: 10.1016/j.neuroimage.2019.116450
Radua, J. et al. Increased power by harmonizing structural MRI site differences with the ComBat batch adjustment method in ENIGMA. NeuroImage 218, 116956 (2020).
pubmed: 32470572
doi: 10.1016/j.neuroimage.2020.116956
Thompson, P. M. et al. The ENIGMA Consortium: large-scale collaborative analyses of neuroimaging and genetic data. Brain Imaging Behav. 8, 153–182 (2014).
pubmed: 24399358
pmcid: 4008818
doi: 10.1007/s11682-013-9269-5
Fortin, J. P. et al. Harmonization of cortical thickness measurements across scanners and sites. NeuroImage 167, 104–120 (2018).
pubmed: 29155184
doi: 10.1016/j.neuroimage.2017.11.024
Fortin, J. P. et al. Harmonization of multi-site diffusion tensor imaging data. NeuroImage 161, 149–170 (2017).
pubmed: 28826946
doi: 10.1016/j.neuroimage.2017.08.047
Beer, J. C. et al. Longitudinal ComBat: A method for harmonizing longitudinal multi-scanner imaging data. NeuroImage 220, 117129 (2020).
pubmed: 32640273
doi: 10.1016/j.neuroimage.2020.117129
Keshavan, A. et al. Power estimation for non-standardized multisite studies. NeuroImage 134, 281–294 (2016).
pubmed: 27039700
doi: 10.1016/j.neuroimage.2016.03.051
Pinto, M. S. et al. Harmonization of Brain Diffusion MRI: Concepts and Methods. Front. Neurosci. 14, 396 (2020).
pubmed: 32435181
pmcid: 7218137
doi: 10.3389/fnins.2020.00396
Suckling, J. et al. Components of variance in a multicentre functional MRI study and implications for calculation of statistical power. Hum. Brain Mapp. 29, 1111–1122 (2008).
pubmed: 17680602
doi: 10.1002/hbm.20451
Dansereau, C. et al. Statistical power and prediction accuracy in multisite resting-state fMRI connectivity. NeuroImage 149, 220–232 (2017).
pubmed: 28161310
doi: 10.1016/j.neuroimage.2017.01.072
Yu, M. et al. Statistical harmonization corrects site effects in functional connectivity measurements from multi‐site fMRI data. Hum. Brain Mapp. 39, 4213–4227 (2018).
pubmed: 29962049
pmcid: 6179920
doi: 10.1002/hbm.24241
Han, X. et al. Reliability of MRI-derived measurements of human cerebral cortical thickness: The effects of field strength, scanner upgrade and manufacturer. NeuroImage 32, 180–194 (2006).
pubmed: 16651008
doi: 10.1016/j.neuroimage.2006.02.051
Jovicich, J. et al. Reliability in multi-site structural MRI studies: Effects of gradient non-linearity correction on phantom and human data. NeuroImage 30, 436–443 (2006).
pubmed: 16300968
doi: 10.1016/j.neuroimage.2005.09.046
Takao, H., Hayashi, N. & Ohtomo, K. Effect of scanner in longitudinal studies of brain volume changes. J. Magn. Reson. Imaging 34, 438–444 (2011).
pubmed: 21692137
doi: 10.1002/jmri.22636
Hatton, S. N. et al. White matter abnormalities across different epilepsy syndromes in adults: an ENIGMA-Epilepsy study. Brain 143, 2454–2473 (2020).
pubmed: 32814957
pmcid: 7567169
doi: 10.1093/brain/awaa200
Ingalhalikar, M. et al. Functional Connectivity-Based Prediction of Autism on Site Harmonized ABIDE Dataset. IEEE Trans. Biomed. Eng. 68, 3628–3637 (2021).
pubmed: 33989150
pmcid: 8696194
doi: 10.1109/TBME.2021.3080259
Li, Y., Ammari, S., Balleyguier, C., Lassau, N. & Chouzenoux, E. Impact of Preprocessing and Harmonization Methods on the Removal of Scanner Effects in Brain MRI Radiomic Features. Cancers 13, 3000 (2021).
pubmed: 34203896
pmcid: 8232807
doi: 10.3390/cancers13123000
Luna, A. et al. Maturity of gray matter structures and white matter connectomes, and their relationship with psychiatric symptoms in youth. Hum. Brain Mapp. 42, 4568–4579 (2021).
pubmed: 34240783
pmcid: 8410534
doi: 10.1002/hbm.25565
Maikusa, N. et al. Comparison of traveling‐subject and COMBAT harmonization methods for assessing structural brain characteristics. Hum. Brain Mapp. 42, 5278–5287 (2021).
pubmed: 34402132
pmcid: 8519865
doi: 10.1002/hbm.25615
Orlhac, F. et al. How can we combat multicenter variability in MR radiomics? Validation of a correction procedure. Eur. Radiol. 31, 2272–2280 (2021).
pubmed: 32975661
doi: 10.1007/s00330-020-07284-9
Wachinger, C., Rieckmann, A. & Pölsterl, S. Detect and correct bias in multi-site neuroimaging datasets. Med. Image Anal. 67, 101879 (2021).
pubmed: 33152602
doi: 10.1016/j.media.2020.101879
Wengler, K. et al. Cross‐Scanner Harmonization of Neuromelanin‐Sensitive MRI for Multisite Studies. J. Magn. Reson. Imaging 54, 1189–1199 (2021).
pubmed: 33960063
pmcid: 9036665
doi: 10.1002/jmri.27679
Zavaliangos-Petropulu, A. et al. Diffusion MRI Indices and Their Relation to Cognitive Impairment in Brain Aging: The Updated Multi-protocol Approach in ADNI3. Front. Neuroinformatics 13, 2 (2019).
doi: 10.3389/fninf.2019.00002
Zhu, Y. et al. Application of a Machine Learning Algorithm for Structural Brain Images in Chronic Schizophrenia to Earlier Clinical Stages of Psychosis and Autism Spectrum Disorder: A Multiprotocol Imaging Dataset Study. Schizophr. Bull. sbac030 (2022).
Tafuri, B. et al. The impact of harmonization on radiomic features in Parkinson’s disease and healthy controls: A multicenter study. Front. Neurosci. 16, 1012287 (2022).
pubmed: 36300169
pmcid: 9589497
doi: 10.3389/fnins.2022.1012287
Parekh, P. et al. Sample size requirement for achieving multisite harmonization using structural brain MRI features. NeuroImage 264, 119768 (2022).
pubmed: 36435343
doi: 10.1016/j.neuroimage.2022.119768
Chen, A. A., Luo, C., Chen, Y., Shinohara, R. T. & Shou, H. Privacy-preserving harmonization via distributed ComBat. NeuroImage 248, 118822 (2022).
pubmed: 34958950
doi: 10.1016/j.neuroimage.2021.118822
Lombardi, A. et al. Extensive Evaluation of Morphological Statistical Harmonization for Brain Age Prediction. Brain Sci. 10, 364 (2020).
pubmed: 32545374
pmcid: 7349402
doi: 10.3390/brainsci10060364
Zounek, A. J. et al. Feasibility of radiomic feature harmonization for pooling of [18F]FET or [18F]GE-180 PET images of gliomas. Z. Für Med. Phys. 33, 91–102 (2023).
doi: 10.1016/j.zemedi.2022.12.005
Dai, P. et al. The alterations of brain functional connectivity networks in major depressive disorder detected by machine learning through multisite rs-fMRI data. Behav. Brain Res. 435, 114058 (2022).
pubmed: 35995263
doi: 10.1016/j.bbr.2022.114058
Saponaro, S. et al. Multi-site harmonization of MRI data uncovers machine-learning discrimination capability in barely separable populations: An example from the ABIDE dataset. NeuroImage Clin. 35, 103082 (2022).
pubmed: 35700598
pmcid: 9198380
doi: 10.1016/j.nicl.2022.103082
Du, X. et al. Unraveling schizophrenia replicable functional connectivity disruption patterns across sites. Hum. Brain Mapp. 44, 156–169 (2023).
pubmed: 36222054
doi: 10.1002/hbm.26108
Dudley, J. A. et al. ABCD_Harmonizer: An Open-source Tool for Mapping and Controlling for Scanner Induced Variance in the Adolescent Brain Cognitive Development Study. Neuroinformatics 21, 323–337 (2023).
pubmed: 36940062
doi: 10.1007/s12021-023-09624-8
Johnson, W. E., Li, C. & Rabinovic, A. Adjusting batch effects in microarray expression data using empirical Bayes methods. Biostat. Oxf. Engl. 8, 118–127 (2007).
He, L. et al. Deep Multimodal Learning From MRI and Clinical Data for Early Prediction of Neurodevelopmental Deficits in Very Preterm Infants. Front. Neurosci. 15, 753033 (2021).
pubmed: 34675773
pmcid: 8525883
doi: 10.3389/fnins.2021.753033
Kim, J. I. et al. Classification of Preschoolers with Low-Functioning Autism Spectrum Disorder Using Multimodal MRI Data. J. Autism Dev. Disord. (2022).
Lo Gullo, R. et al. Assessing PD-L1 Expression Status Using Radiomic Features from Contrast-Enhanced Breast MRI in Breast Cancer Patients: Initial Results. Cancers 13, 6273 (2021).
pubmed: 34944898
pmcid: 8699819
doi: 10.3390/cancers13246273
Lopez-Soley, E. et al. Dynamics and Predictors of Cognitive Impairment along the Disease Course in Multiple Sclerosis. J. Pers. Med. 11, 1107 (2021).
pubmed: 34834459
pmcid: 8624684
doi: 10.3390/jpm11111107
Simhal, A. K. et al. Predicting multiscan MRI outcomes in children with neurodevelopmental conditions following MRI simulator training. Dev. Cogn. Neurosci. 52, 101009 (2021).
pubmed: 34649041
pmcid: 8517836
doi: 10.1016/j.dcn.2021.101009
Zhou, X. et al. Multimodal MR Images-Based Diagnosis of Early Adolescent Attention-Deficit/Hyperactivity Disorder Using Multiple Kernel Learning. Front. Neurosci. 15, 710133 (2021).
pubmed: 34594183
pmcid: 8477011
doi: 10.3389/fnins.2021.710133
Mandelbrot, B. B. The fractal geometry of nature. (W.H. Freeman, 1982).
Di Martino, A. et al. Enhancing studies of the connectome in autism using the autism brain imaging data exchange II. Sci. Data 4, 170010 (2017).
pubmed: 28291247
pmcid: 5349246
doi: 10.1038/sdata.2017.10
Di Martino, A. et al. The autism brain imaging data exchange: towards a large-scale evaluation of the intrinsic brain architecture in autism. Mol. Psychiatry 19, 659–667 (2014).
pubmed: 23774715
doi: 10.1038/mp.2013.78
Autism Brain Imaging Data Exchange (ABIDE). https://fcon_1000.projects.nitrc.org/indi/abide/ (2017).
Kang, S. M. & Wildes, R. P. The n-distribution Bhattacharyya coefficient. York Univ. (2015).
Bhattacharyya, A. On a measure of divergence between two statistical populations defined by their probability distributions. Bull Calcutta Math Soc 35, 99–109 (1943).
Cameron, C. et al. The Neuro Bureau Preprocessing Initiative: open sharing of preprocessed neuroimaging data and derivatives. Front. Neuroinformatics 7 (2013).
Bigler, E. D. et al. FreeSurfer 5.3 versus 6.0: are volumes comparable? A Chronic Effects of Neurotrauma Consortium study. Brain Imaging Behav. 14, 1318–1327 (2020).
pubmed: 30511116
doi: 10.1007/s11682-018-9994-x
Chepkoech, J.-L., Walhovd, K. B., Grydeland, H. & Fjell, A. M., for the Alzheimer’s Disease Neuroimaging Initiative. Effects of change in FreeSurfer version on classification accuracy of patients with Alzheimer’s disease and mild cognitive impairment: Effects of Change in FreeSurfer Version. Hum. Brain Mapp. 37, 1831–1841 (2016).
pubmed: 27018380
pmcid: 6867543
doi: 10.1002/hbm.23139
Filip, P. et al. Different FreeSurfer versions might generate different statistical outcomes in case–control comparison studies. Neuroradiology 64, 765–773 (2022).
pubmed: 34988592
pmcid: 8916973
doi: 10.1007/s00234-021-02862-0
Glatard, T. et al. Reproducibility of neuroimaging analyses across operating systems. Front. Neuroinformatics 9, (2015).
Gronenschild, E. H. B. M. et al. The Effects of FreeSurfer Version, Workstation Type, and Macintosh Operating System Version on Anatomical Volume and Cortical Thickness Measurements. PLoS ONE 7, e38234 (2012).
pubmed: 22675527
pmcid: 3365894
doi: 10.1371/journal.pone.0038234
Fischl, B. FreeSurfer. NeuroImage 62, 774–781 (2012).
pubmed: 22248573
doi: 10.1016/j.neuroimage.2012.01.021
Fischl, B. & Dale, A. M. Measuring the thickness of the human cerebral cortex from magnetic resonance images. Proc. Natl. Acad. Sci. 97, 11050–11055 (2000).
pubmed: 10984517
pmcid: 27146
doi: 10.1073/pnas.200033797
Cutting, J. E. & Garvin, J. J. Fractal curves and complexity. Percept. Psychophys. 42, 365–370 (1987).
pubmed: 3684493
doi: 10.3758/BF03203093
Fernández, E. & Jelinek, H. F. Use of Fractal Theory in Neuroscience: Methods, Advantages, and Potential Problems. Methods 24, 309–321 (2001).
pubmed: 11465996
doi: 10.1006/meth.2001.1201
Im, K. et al. Fractal dimension in human cortical surface: Multiple regression analysis with cortical thickness, sulcal depth, and folding area. Hum. Brain Mapp. 27, 994–1003 (2006).
pubmed: 16671080
pmcid: 6871396
doi: 10.1002/hbm.20238
Marzi, C., Giannelli, M., Tessa, C., Mascalchi, M. & Diciotti, S. Toward a more reliable characterization of fractal properties of the cerebral cortex of healthy subjects during the lifespan. Sci. Rep. 10, 16957 (2020).
pubmed: 33046812
pmcid: 7550568
doi: 10.1038/s41598-020-73961-w
Russell, D. A., Hanson, J. D. & Ott, E. Dimension of Strange Attractors. Phys. Rev. Lett. 45, 1175–1178 (1980).
doi: 10.1103/PhysRevLett.45.1175
Losa, G. A. The fractal geometry of life. Riv. Biol. 102, 29–59 (2009).
pubmed: 19718622
Falconer, K. J. Fractal geometry: mathematical foundations and applications. (John Wiley & Sons Inc, 2014).
Goñi, J. et al. Robust estimation of fractal measures for characterizing the structural complexity of the human brain: Optimization and reproducibility. NeuroImage 83, 646–657 (2013).
pubmed: 23831414
doi: 10.1016/j.neuroimage.2013.06.072
Courchesne, E. et al. Normal Brain Development and Aging: Quantitative Analysis at in Vivo MR Imaging in Healthy Volunteers. Radiology 216, 672–682 (2000).
pubmed: 10966694
doi: 10.1148/radiology.216.3.r00au37672
Fjell, A. M. & Walhovd, K. B. Structural Brain Changes in Aging: Courses, Causes and Cognitive Consequences. Rev. Neurosci. 21 (2010).
Hogstrom, L. J., Westlye, L. T., Walhovd, K. B. & Fjell, A. M. The Structure of the Cerebral Cortex Across Adult Life: Age-Related Patterns of Surface Area, Thickness, and Gyrification. Cereb. Cortex 23, 2521–2530 (2013).
pubmed: 22892423
doi: 10.1093/cercor/bhs231
Madan, C. R. & Kensinger, E. A. Predicting age from cortical structure across the lifespan. Eur. J. Neurosci. 47, 399–416 (2018).
pubmed: 29359873
pmcid: 5835209
doi: 10.1111/ejn.13835
Madan, C. R. & Kensinger, E. A. Cortical complexity as a measure of age-related brain atrophy. NeuroImage 134, 617–629 (2016).
pubmed: 27103141
doi: 10.1016/j.neuroimage.2016.04.029
Raznahan, A. et al. How Does Your Cortex Grow? J. Neurosci. 31, 7174–7177 (2011).
pubmed: 21562281
pmcid: 3157294
doi: 10.1523/JNEUROSCI.0054-11.2011
Zheng, F. et al. Age-related changes in cortical and subcortical structures of healthy adult brains: A surface-based morphometry study: Age-Related Study in Healthy Adult Brain Structure. J. Magn. Reson. Imaging 49, 152–163 (2019).
pubmed: 29676856
doi: 10.1002/jmri.26037
Sowell, E. R. et al. Sex Differences in Cortical Thickness Mapped in 176 Healthy Individuals between 7 and 87 Years of Age. Cereb. Cortex 17, 1550–1560 (2007).
pubmed: 16945978
doi: 10.1093/cercor/bhl066
Yagis, E. et al. Effect of data leakage in brain MRI classification using 2D convolutional neural networks. Sci. Rep. 11, 22544 (2021).
pubmed: 34799630
pmcid: 8604922
doi: 10.1038/s41598-021-01681-w
Tampu, I. E., Eklund, A. & Haj-Hosseini, N. Inflation of test accuracy due to data leakage in deep learning-based classification of OCT images. Sci. Data 9, 580 (2022).
pubmed: 36138025
pmcid: 9500039
doi: 10.1038/s41597-022-01618-6
Müller, A. C. & Guido, S. Introduction to machine learning with Python: a guide for data scientists. (O’Reilly Media, Inc, 2016).
Scheda, R. & Diciotti, S. Explanations of Machine Learning Models in Repeated Nested Cross-Validation: An Application in Age Prediction Using Brain Complexity Features. Appl. Sci. 12, 6681 (2022).
doi: 10.3390/app12136681
Varma, S. & Simon, R. Bias in error estimation when using cross-validation for model selection. BMC Bioinformatics 7, 91 (2006).
pubmed: 16504092
pmcid: 1397873
doi: 10.1186/1471-2105-7-91
Chen, T. & Guestrin, C. XGBoost: A Scalable Tree Boosting System. in Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining 785–794, https://doi.org/10.1145/2939672.2939785 (ACM, 2016).
Nichols, T. E. & Holmes, A. P. Nonparametric permutation tests for functional neuroimaging: A primer with examples. Hum. Brain Mapp. 15, 1–25 (2002).
pubmed: 11747097
doi: 10.1002/hbm.1058
Ojala, M. & Garriga, G. C. Permutation Tests for Studying Classifier Performance. J Mach Learn Res 11, 1833–1863 (2010).
Winkler, A. M., Ridgway, G. R., Webster, M. A., Smith, S. M. & Nichols, T. E. Permutation inference for the general linear model. NeuroImage 92, 381–397 (2014).
pubmed: 24530839
doi: 10.1016/j.neuroimage.2014.01.060
Wilcoxon, F. Individual Comparisons by Ranking Methods. Biom. Bull. 1, 80 (1945).
doi: 10.2307/3001968
Brouwer, R. M. et al. Genetic variants associated with longitudinal changes in brain structure across the lifespan. Nat. Neurosci. 25, 421–432 (2022).
pubmed: 35383335
pmcid: 10040206
doi: 10.1038/s41593-022-01042-4
Oschwald, J. et al. Brain structure and cognitive ability in healthy aging: a review on longitudinal correlated change. Rev. Neurosci. 31, 1–57 (2019).
pubmed: 31194693
pmcid: 8572130
doi: 10.1515/revneuro-2018-0096
Chen, A. A. et al. Mitigating site effects in covariance for machine learning in neuroimaging data. Hum. Brain Mapp. 43, 1179–1195 (2022).
pubmed: 34904312
doi: 10.1002/hbm.25688
Steffener, J. Education and age-related differences in cortical thickness and volume across the lifespan. Neurobiol. Aging 102, 102–110 (2021).
pubmed: 33765423
doi: 10.1016/j.neurobiolaging.2020.10.034
Free, S. L., Sisodiya, S. M., Cook, M. J., Fish, D. R. & Shorvon, S. D. Three-dimensional fractal analysis of the white matter surface from magnetic resonance images of the human brain. Cereb. Cortex 6, 830–836 (1996).
pubmed: 8922340
doi: 10.1093/cercor/6.6.830
King, R. D. et al. Fractal dimension analysis of the cortical ribbon in mild Alzheimer’s disease. NeuroImage 53, 471–479 (2010).
pubmed: 20600974
doi: 10.1016/j.neuroimage.2010.06.050
King, R. D. et al. Characterization of Atrophic Changes in the Cerebral Cortex Using Fractal Dimensional Analysis. Brain Imaging Behav. 3, 154–166 (2009).
pubmed: 20740072
pmcid: 2927230
doi: 10.1007/s11682-008-9057-9
Marzi, C., Giannelli, M., Tessa, C., Mascalchi, M. & Diciotti, S. Fractal Analysis of MRI Data at 7 T: How Much Complex Is the Cerebral Cortex? IEEE Access 9, 69226–69234 (2021).
doi: 10.1109/ACCESS.2021.3077370
Marzi, C. et al. Structural Complexity of the Cerebellum and Cerebral Cortex is Reduced in Spinocerebellar Ataxia Type 2. J. Neuroimaging 28, 688–693 (2018).
pubmed: 29975004
doi: 10.1111/jon.12534
Pani, J. et al. Longitudinal study of the effect of a 5-year exercise intervention on structural brain complexity in older adults. A Generation 100 substudy. NeuroImage 119226 (2022).
Pantoni, L. et al. Fractal dimension of cerebral white matter: A consistent feature for prediction of the cognitive performance in patients with small vessel disease and mild cognitive impairment. NeuroImage Clin. 24, 101990 (2019).
pubmed: 31491677
pmcid: 6731209
doi: 10.1016/j.nicl.2019.101990
Nazlee, N., Waiter, G. D. & Sandu, A. Age‐associated sex and asymmetry differentiation in hemispheric and lobar cortical ribbon complexity across adulthood: A UK Biobank imaging study. Hum. Brain Mapp. hbm.26076, https://doi.org/10.1002/hbm.26076 (2022).
Sandu, A.-L. et al. Fractal dimension analysis of MR images reveals grey matter structure irregularities in schizophrenia. Comput. Med. Imaging Graph. 32, 150–158 (2008).
pubmed: 18068333
doi: 10.1016/j.compmedimag.2007.10.005
Sandu, A.-L. et al. Post-adolescent developmental changes in cortical complexity. Behav. Brain Funct. 10, 44 (2014).
pubmed: 25431294
pmcid: 4289042
doi: 10.1186/1744-9081-10-44
Sandu, A.-L. et al. Sexual dimorphism in the relationship between brain complexity, volume and general intelligence (g): a cross-cohort study. Sci. Rep. 12, 11025 (2022).
pubmed: 35773463
pmcid: 9247090
doi: 10.1038/s41598-022-15208-4
Sandu, A.-L., Specht, K., Beneventi, H., Lundervold, A. & Hugdahl, K. Sex-differences in grey–white matter structure in normal-reading and dyslexic adolescents. Neurosci. Lett. 438, 80–84 (2008).
pubmed: 18456405
doi: 10.1016/j.neulet.2008.04.022
Sandu, A.-L. et al. Structural brain complexity and cognitive decline in late life — A longitudinal study in the Aberdeen 1936 Birth Cohort. NeuroImage 100, 558–563 (2014).
pubmed: 24993896
doi: 10.1016/j.neuroimage.2014.06.054
Sandu, A.-L., Paillère Martinot, M.-L., Artiges, E. & Martinot, J.-L. 1910s’ brains revisited. Cortical complexity in early 20th century patients with intellectual disability or with dementia praecox. Acta Psychiatr. Scand. 130, 227–237 (2014).
pubmed: 24400850
doi: 10.1111/acps.12243
Marzi, C. & Diciotti, S. Multicenter dataset of neuroimaging features (part I). Zenodo https://doi.org/10.5281/zenodo.7845311 (2023).
Marzi, C. & Diciotti, S. Multicenter dataset of neuroimaging features (part II). Zenodo https://doi.org/10.5281/zenodo.7845361 (2023).
Marzi, C. & Diciotti, S. Multicenter dataset of simulated neuroimaging features - quadratic relationship with age. Zenodo https://doi.org/10.5281/zenodo.8119042 (2023).