There are many active research projects accessing and applying shared ADNI data. Use the search above to find specific research focuses on the active ADNI investigations. This information is requested annually as a requirement for data access.
| Principal Investigator | |
| Principal Investigator's Name: | Valentina Bordin |
| Institution: | Politecnico di Milano |
| Department: | Electronics, Information and Bioengineering |
| Country: | |
| Proposed Analysis: | I’m a PhD student from Politecnico di Milano with a main research focus around White Matter Hyperintensities (WMHs). These areas of abnormal intensity on magnetic resonance imaging (MRI), are generally associated to pathological changes in the white matter axonal microstructure or to alterations in the interstitial fluid (Wardlaw et al., 2015). Despite being considered as a consequence of advancing age, several studies have indicated the important associations of WMHs with neuropathological conditions such as Alzheimer’s disease, Parkinson’s disease, multiple sclerosis and progressive cognitive impairment (Wardlaw et al., 2013)(Griffanti et al., 2016). Numerous attempts have been made to automatically segment and quantify their volume (Caligiuri et al., 2015). However, the use of machine learning techniques has always raised the issue of providing manually labelled gold standards, whose creation, alongside the need of training procedures, has made the process of automatic segmentation cumbersome and time consuming. Our recent analysis (Bordin et al., 2021) focused on BIANCA (Griffanti et al., 2016) – a fully automated and supervised WMH segmentation tool, developed by the Oxford Centre for Functional MRI of the Brain (FIMRIB) – has successfully exploited the Whitehall (Filippini et al., 2014) (WH) and UK Biobank (Alfaro-Almagro et al., 2018) (UKB) cohorts to develop an analysis and training pipeline specifically tailored towards the increase of the tool performance and generalisation capabilities. The resulting training set is publicly available (https://issues.dpuk.org/eugeneduff/wmh_harmonisation) but has not yet been tested on a different population from the ones involved in its development. Hence, with this research study we aim to lay the foundation for a proper validation of our former results. We aim to test the pipeline on both healthy and Alzheimer’s disease subjects acquired with different protocols and scanners with respect to the WH and UKB cohorts. Additionally, we would like to compare the performances obtained by our method with that of common harmonisation approaches such as ComBat (Fortin et al., 2018), which directly estimate the corrections between different datasets. For this reason, we would enormously benefit from the imaging data collected by the Alzheimer’s Disease Neuroimaging Initiative. Good segmentation outcomes, comparable with our previous study (Bordin et al., 2021), would suggest widespread applicability for the designed approach. Alfaro-Almagro, F., Jenkinson, M., Bangerter, N.K., Andersson, J.L.R., Griffanti, L., Douaud, G., Sotiropoulos, S.N., Jbabdi, S., Hernandez-Fernandez, M., Vallee, E., Vidaurre, D., Webster, M., McCarthy, P., Rorden, C., Daducci, A., Alexander, D.C., Zhang, H., Dragonu, I., Matthews, P.M., Miller, K.L., Smith, S.M., 2018. Image processing and Quality Control for the first 10,000 brain imaging datasets from UK Biobank. NeuroImage 166, 400–424. https://doi.org/10.1016/j.neuroimage.2017.10.034 Bordin, V., Bertani, I., Mattioli, I., Sundaresan, V., McCarthy, P., Suri, S., Zsoldos, E., Filippini, N., Mahmood, A., Melazzini, L., Laganà, M.M., Zamboni, G., Singh-Manoux, A., Kivimäki, M., Ebmeier, K.P., Baselli, G., Jenkinson, M., Mackay, C.E., Duff, E.P., Griffanti, L., 2021. Integrating large-scale neuroimaging research datasets: Harmonisation of white matter hyperintensity measurements across Whitehall and UK Biobank datasets. NeuroImage 237, 118189. https://doi.org/10.1016/j.neuroimage.2021.118189 Caligiuri, M.E., Perrotta, P., Augimeri, A., Rocca, F., Quattrone, A., Cherubini, A., 2015. Automatic Detection of White Matter Hyperintensities in Healthy Aging and Pathology Using Magnetic Resonance Imaging: A Review. Neuroinformatics 13, 261–276. https://doi.org/10.1007/s12021-015-9260-y Filippini, N., Zsoldos, E., Haapakoski, R., Sexton, C.E., Mahmood, A., Allan, C.L., Topiwala, A., Valkanova, V., Brunner, E.J., Shipley, M.J., Auerbach, E., Moeller, S., Uğurbil, K., Xu, J., Yacoub, E., Andersson, J., Bijsterbosch, J., Clare, S., Griffanti, L., Hess, A.T., Jenkinson, M., Miller, K.L., Salimi-Khorshidi, G., Sotiropoulos, S.N., Voets, N.L., Smith, S.M., Geddes, J.R., Singh-Manoux, A., Mackay, C.E., Kivimäki, M., Ebmeier, K.P., 2014. Study protocol: the Whitehall II imaging sub-study. BMC Psychiatry 14, 159. https://doi.org/10.1186/1471-244X-14-159 Fortin, J.-P., Cullen, N., Sheline, Y.I., Taylor, W.D., Aselcioglu, I., Cook, P.A., Adams, P., Cooper, C., Fava, M., McGrath, P.J., McInnis, M., Phillips, M.L., Trivedi, M.H., Weissman, M.M., Shinohara, R.T., 2018. Harmonization of cortical thickness measurements across scanners and sites. NeuroImage 167, 104–120. https://doi.org/10.1016/j.neuroimage.2017.11.024 Griffanti, L., Zamboni, G., Khan, A., Li, L., Bonifacio, G., Sundaresan, V., Schulz, U.G., Kuker, W., Battaglini, M., Rothwell, P.M., Jenkinson, M., 2016. BIANCA (Brain Intensity AbNormality Classification Algorithm): A new tool for automated segmentation of white matter hyperintensities. NeuroImage 141, 191–205. https://doi.org/10.1016/j.neuroimage.2016.07.018 Wardlaw, J.M., Smith, E.E., Biessels, G.J., Cordonnier, C., Fazekas, F., Frayne, R., Lindley, R.I., O’Brien, J.T., Barkhof, F., Benavente, O.R., Black, S.E., Brayne, C., Breteler, M., Chabriat, H., DeCarli, C., de Leeuw, F.-E., Doubal, F., Duering, M., Fox, N.C., Greenberg, S., Hachinski, V., Kilimann, I., Mok, V., Oostenbrugge, R. van, Pantoni, L., Speck, O., Stephan, B.C.M., Teipel, S., Viswanathan, A., Werring, D., Chen, C., Smith, C., van Buchem, M., Norrving, B., Gorelick, P.B., Dichgans, M., 2013. Neuroimaging standards for research into small vessel disease and its contribution to ageing and neurodegeneration. Lancet Neurol. 12, 822–838. https://doi.org/10.1016/S1474-4422(13)70124-8 Wardlaw, J.M., Valdés Hernández, M.C., Muñoz‐Maniega, S., 2015. What are White Matter Hyperintensities Made of?: Relevance to Vascular Cognitive Impairment. J. Am. Heart Assoc. 4. https://doi.org/10.1161/JAHA.114.001140 |
| Additional Investigators | |
| Investigator's Name: | Giulia Mazzetti |
| Proposed Analysis: | Giulia Mazzetti is a bioengineering student from Politecnico di Milano working on her MSc thesis. In the context of the proposed research, she will conduct the data mining and pre-processing steps necessary to prepare both FLAIR and T1-weighted MRI scans for the following step of automatic WMH segmentation. Next, the imaging data will be fed to BIANCA (Griffanti et al., 2016), after using using the 'mixed' training set generated from WH and UKB (Bordin et al., 2021). The manual segmentation of a subset of the ADNI population will allow us to evaluate the obtained performances and compare them to the existing literature. Good outcomes would suggest widespread applicability for the designed approach. Eventually, the percentage WMH volume will be extracted for every subject of the dataset and will be compared with age-matched populations affected by the same neurogenerative condition, to test the consistency of the obtained results. The same data (i.e., WMH percentage volume) will also be processed using ComBat (Fortin et al., 2018) to determine if the batch effect correction provided by this statistical harmonization method is comparable to that obtained using our analysis and training pipeline (Bordin et al., 2021). Bordin, V., Bertani, I., Mattioli, I., Sundaresan, V., McCarthy, P., Suri, S., Zsoldos, E., Filippini, N., Mahmood, A., Melazzini, L., Laganà, M.M., Zamboni, G., Singh-Manoux, A., Kivimäki, M., Ebmeier, K.P., Baselli, G., Jenkinson, M., Mackay, C.E., Duff, E.P., Griffanti, L., 2021. Integrating large-scale neuroimaging research datasets: Harmonisation of white matter hyperintensity measurements across Whitehall and UK Biobank datasets. NeuroImage 237, 118189. https://doi.org/10.1016/j.neuroimage.2021.118189 Fortin, J.-P., Cullen, N., Sheline, Y.I., Taylor, W.D., Aselcioglu, I., Cook, P.A., Adams, P., Cooper, C., Fava, M., McGrath, P.J., McInnis, M., Phillips, M.L., Trivedi, M.H., Weissman, M.M., Shinohara, R.T., 2018. Harmonization of cortical thickness measurements across scanners and sites. NeuroImage 167, 104–120. https://doi.org/10.1016/j.neuroimage.2017.11.024 Griffanti, L., Zamboni, G., Khan, A., Li, L., Bonifacio, G., Sundaresan, V., Schulz, U.G., Kuker, W., Battaglini, M., Rothwell, P.M., Jenkinson, M., 2016. BIANCA (Brain Intensity AbNormality Classification Algorithm): A new tool for automated segmentation of white matter hyperintensities. NeuroImage 141, 191–205. https://doi.org/10.1016/j.neuroimage.2016.07.018 |
