Abstract
Many recent studies have shown that Fractal Dimension (FD), a ratio for figuring out the complexity of a system given its measurements, can be used as an useful index to provide information about certain brain disease. Our research focuses on the Alzheimer’s disease changes in white and grey brain matters detected through the FD indexes of their contours. Data used in this study were obtained from the Alzheimer’s Disease (AD) Neuroimaging Initiative database (Normal Condition, N = 57, and Alzheimer’s Disease, N = 60). After standard preprocessing pipeline, the white and grey matter 3D FD indexes are computed for the two groups. A statistical analysis shows that only grey matter 3D FD indexes are able to differentiate healthy and AD subjects. Although white matter 3D FD indexes do not, it is remarkable that their presence enhance the separation capability of previous ones. In order to valuate the classification capability of these indexes on healthy and AD subjects, we define several Neural Networks models. The performances of these models vary according to the statistical analysis and reach their best performances when each 3D FD input index is changed into a sequence of 2D FD indexes of (a subset of) the horizontal slices of the white and grey matter volumes.
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The data used in this study belong to the Alzheimer’s Disease Neuroimaging Initiative (ADNI), a neuroscience consortium of universities and research institutes, and are available through the ADNI database (adni.loni.usc.edu) after approval of a data request application. For more information about how to access ADNI data see http://adni.loni.usc.edu/datasamples/accessdata/.
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Acknowledgements
Data collection and sharing for this project was funded by the Alzheimer's Disease Neuroimaging Initiative (ADNI) (National Institutes of Health Grant U01 AG024904) and DOD ADNI (Department of Defense award number W81XWH-12-2-0012). ADNI is funded by the National Institute on Aging, the National Institute of Biomedical Imaging and Bioengineering, and through generous contributions from the following: AbbVie, Alzheimer’s Association; Alzheimer’s Drug Discovery Foundation; Araclon Biotech; BioClinica, Inc.; Biogen; Bristol-Myers Squibb Company; CereSpir, Inc.; Cogstate; Eisai Inc.; Elan Pharmaceuticals, Inc.; Eli Lilly and Company; EuroImmun; F. Hoffmann-La Roche Ltd and its affiliated company Genentech, Inc.; Fujirebio; GE Healthcare; IXICO Ltd.; Janssen Alzheimer Immunotherapy Research & Development, LLC.; Johnson & Johnson Pharmaceutical Research & Development LLC.; Lumosity; Lundbeck; Merck & Co., Inc.; Meso Scale Diagnostics, LLC.; NeuroRx Research; Neurotrack Technologies; Novartis Pharmaceuticals Corporation; Pfizer Inc.; Piramal Imaging; Servier; Takeda Pharmaceutical Company; and Transition Therapeutics. The Canadian Institutes of Health Research is providing funds to support ADNI clinical sites in Canada. Private sector contributions are facilitated by the Foundation for the National Institutes of Health (www.fnih.org). The grantee organization is the Northern California Institute for Research and Education, and the study is coordinated by the Alzheimer’s Therapeutic Research Institute at the University of Southern California. ADNI data are disseminated by the Laboratory for Neuro Imaging at the University of Southern California.
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*Data used in preparation of this article were obtained from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) database (adni.loni.usc.edu). As such, the investigators within the ADNI contributed to the design and implementation of ADNI and/or provided data but did not participate in analysis or writing of this report. A complete listing of ADNI investigators can be found at: http://adni.loni.usc.edu/wp-content/uploads/how_to_apply/ADNI_Acknowledgement_List.pdf.
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Di Marco, N., di Palma, A., Frosini, A. et al. A study on the predictive strength of fractal dimension of white and grey matter on MRI images in Alzheimer’s disease. Ann Math Artif Intell 92, 201–214 (2024). https://doi.org/10.1007/s10472-023-09885-8
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DOI: https://doi.org/10.1007/s10472-023-09885-8