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Analyzing Statistical Inference Maps Using MRI Images for Parkinson’s Disease

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Part of the Lecture Notes in Computer Science book series (LNCS,volume 13258)


Parkinson’s Disease (PD) is one of the most prevalent and studied types of dementia. Traditionally, studies about this neurological disorder have made use of functional SPECT images. Nevertheless, to avoid some of its disadvantages with special focus on its expensive cost and low resolution, in the last years many studies have tried to use another imaging alternatives such as MRI scans able to evaluate subtle changes in the Grey Matter tissue. When analyzing the state of the art on this subject, we have found several shortcomings in the way of proceeding. Therefore, the work presented here presents a qualitative analysis of the regions of interest (ROIs) when using MRI for PD by the computation of statistical significance maps. For that, we have made use of a parametric and a non-parametric approaches using the widely known Statistical Parametric Mapping (SPM) package and the novel Statistical Agnostic Mapping (SAM) proposal. Results obtained suggest that there are no relevant ROIs in GM MRI imaging contrary to other modalities like the FP-CIT SPECT scans evaluated on the striatum region.


  • Statistical Agnostic Mapping
  • Statistical Parametric Mapping
  • Neuroimaging
  • MRI
  • Parkinson’s Disease

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This work was supported by the MCIN/ AEI/10.13039/501100011033/ and FEDER “Una manera de hacer Europa” under the RTI2018-098913-B100 project; by the Consejería de Economía, Innovación, Ciencia y Empleo (Junta de Andalucía) and FEDER under CV20-45250, A-TIC-080-UGR18, B-TIC-586-UGR20 and P20-00525 projects; and by the Ministerio de Universidades under the FPU18/04902 grant given to C. Jimenez-Mesa and the Margarita-Salas grant to J.E. Arco.

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Jimenez-Mesa, C., Castillo-Barnes, D., Arco, J.E., Segovia, F., Ramirez, J., Górriz, J.M. (2022). Analyzing Statistical Inference Maps Using MRI Images for Parkinson’s Disease. In: Ferrández Vicente, J.M., Álvarez-Sánchez, J.R., de la Paz López, F., Adeli, H. (eds) Artificial Intelligence in Neuroscience: Affective Analysis and Health Applications. IWINAC 2022. Lecture Notes in Computer Science, vol 13258. Springer, Cham.

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