Dataset Information

Brain MRI Pattern Recognition in Neurodegeneration With Brain Iron Accumulation.

ABSTRACT: Most neurodegeneration with brain iron accumulation (NBIA) disorders can be distinguished by identifying characteristic changes on magnetic resonance imaging (MRI) in combination with clinical findings. However, a significant number of patients with an NBIA disorder confirmed by genetic testing have MRI features that are atypical for their specific disease. The appearance of specific MRI patterns depends on the stage of the disease and the patient's age at evaluation. MRI interpretation can be challenging because of heterogeneously acquired MRI datasets, individual interpreter bias, and lack of quantitative data. Therefore, optimal acquisition and interpretation of MRI data are needed to better define MRI phenotypes in NBIA disorders. The stepwise approach outlined here may help to identify NBIA disorders and delineate the natural course of MRI-identified changes.

SUBMITTER: Lee JH

PROVIDER: S-EPMC7511538 | biostudies-literature | 2020

REPOSITORIES: biostudies-literature

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Brain MRI Pattern Recognition in Neurodegeneration With Brain Iron Accumulation.

Lee Jae-Hyeok JH Yun Ji Young JY Gregory Allison A Hogarth Penelope P Hayflick Susan J SJ

Frontiers in neurology 20200910

Most neurodegeneration with brain iron accumulation (NBIA) disorders can be distinguished by identifying characteristic changes on magnetic resonance imaging (MRI) in combination with clinical findings. However, a significant number of patients with an NBIA disorder confirmed by genetic testing have MRI features that are atypical for their specific disease. The appearance of specific MRI patterns depends on the stage of the disease and the patient's age at evaluation. MRI interpretation can be c ...[more]

PMID: 33013674

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Project description:We explored the performance of structure-based computational analysis in four neurodegenerative conditions [Ataxia (AT, n = 16), Huntington's Disease (HD, n = 52), Alzheimer's Disease (AD, n = 66), and Primary Progressive Aphasia (PPA, n = 50)], all characterized by brain atrophy. The independent variables were the volumes of 283 anatomical areas, derived from automated segmentation of T1-high resolution brain MRIs. The segmentation based volumetric quantification reduces image dimensionality from the voxel level [on the order of [Formula: see text](106)] to anatomical structures [[Formula: see text](102)] for subsequent statistical analysis. We evaluated the effectiveness of this approach on extracting anatomical features, already described by human experience and a priori biological knowledge, in specific scenarios: (1) when pathologies were relatively homogeneous, with evident image alterations (e.g., AT); (2) when the time course was highly correlated with the anatomical changes (e.g., HD), an analogy for prediction; (3) when the pathology embraced heterogeneous phenotypes (e.g., AD) so the classification was less efficient but, in compensation, anatomical and clinical information were less redundant; and (4) when the entity was composed of multiple subgroups that had some degree of anatomical representation (e.g., PPA), showing the potential of this method for the clustering of more homogeneous phenotypes that can be of clinical importance. Using the structure-based quantification and simple linear classifiers (partial least square), we achieve 87.5 and 73% of accuracy on differentiating AT and pre-symptomatic HD patents from controls, respectively. More importantly, the anatomical features automatically revealed by the classifiers agreed with the patterns previously described on these pathologies. The accuracy was lower (68%) on differentiating AD from controls, as AD does not display a clear anatomical phenotype. On the other hand, the method identified PPA clinical phenotypes and their respective anatomical signatures. Although most of the data are presented here as proof of concept in simulated clinical scenarios, structure-based analysis was potentially effective in characterizing phenotypes, retrieving relevant anatomical features, predicting prognosis, and aiding diagnosis, with the advantage of being easily translatable to clinics and understandable biologically.

Dataset Information

Brain MRI Pattern Recognition in Neurodegeneration With Brain Iron Accumulation.

Publications

Brain MRI Pattern Recognition in Neurodegeneration With Brain Iron Accumulation.

Similar Datasets

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