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Individual Detection of Patients with Parkinson Disease Using Support Vector Machine Analysis of Diffusion Tensor Imaging Data: Initial Results - AJNR News Digest
March-April 2018
Brain

Individual Detection of Patients with Parkinson Disease Using Support Vector Machine Analysis of Diffusion Tensor Imaging Data: Initial Results

Haller Pic

Sven Haller

I chose to research this topic because I am convinced that neurodegenerative disorders are associated with systematic structural and functional modifications in the brain; however, this might not be evident to the naked eye, particularly at early stages of Parkinson disease. Computer-aided pattern recognition approaches might detect such subtle abnormalities associated with neurodegenerative diseases at the individual level and may therefore aid in the clinical diagnosis of individual patients.

Although the field of pattern recognition of brain imaging is progressing, the application to clinical routine is still limited, notably due to medicolegal issues. Each diagnostic test and technique cannot be 100% accurate. It must, therefore, be clarified how to handle false-positive and false-negative results. This medicolegal and ethical issue is similar to the discussion of self-driving cars. Who is responsible if a computer-driven car causes an accident?

Feedback regarding this research has been positive, and the fact that the field of pattern recognition of neurodegenerative diseases is rapidly expanding demonstrates the interest in this domain. Our own group published related papers for the diagnosis of mild cognitive impairment, including one in AJNR.1

Except for the multiple studies using the ADNI dataset, most existing scientific studies in the domain of pattern recognition in neurodegenerative diseases use a single imaging parameter, with relatively small sample sizes and 1 or a few scanners after careful harmonization.

However, in a clinical setting, there are different scanners with different protocols; typical MRI protocols include several sequences (eg, T1 for gray matter, T2/FLAIR/DWI/DTI for white matter, T2*/SWI for microbleeds, etc), clinical data, and sometimes follow-up imaging studies.

In order to bring pattern recognition to the next and clinically relevant level, it would be important to integrate all of this information into the classification analysis in order to improve the accuracy and robustness of classification. This is conceptually simple and straightforward yet very difficult to implement and optimize practically.

Moreover, there are a few very fundamental points that should be considered. First, there is normal interindividual variability of anatomy. Second, there is variability between imaging and clinical markers (eg, brain atrophy or metabolic changes) and clinical symptoms. Third, the same underlying pathology may cause different clinical symptoms, such as the accumulation of Lewy bodies in Parkinson disease and Lewy body dementia. Fourth, there is interindividual variability in the resilience to neurodegenerative diseases. Fifth, neurodegenerative diseases are not mutually exclusive and may coexist (ie, there is often evidence of dopaminergic dysfunction in Parkinson disease, but in the elderly age group, coexisting microvascular changes are very common and the coexistence of both pathologies might be supra-additive).

As a consequence, we may not expect a simple, linear correlation between imaging findings and clinical symptoms, yet we must take into account those factors described above that induce variability at the individual level. Therefore, the results of such pattern recognition analyses should be considered probabilistic results and always be carefully interpreted within the clinical context.

We have ongoing follow-up projects in the domains of both Parkinson disease and mild cognitive impairment/dementia, and we hope to publish the results soon in AJNR.

Reference

  1. Haller S, Missonnier P, Herrmann FR, et al. Individual classification of mild cognitive impairment subtypes by support vector machine analysis of white matter DTI. AJNR Am J Neuroradiol 2013;34:283–91, 10.3174/ajnr.A3223.

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