Exploring the frequency and clinical background of the "zebra sign" in amyotrophic lateral sclerosis and multiple system atrophy

Feature selection from magnetic resonance imaging data in ALS: a systematic review

Background:

With the advances in neuroimaging in amyotrophic lateral sclerosis (ALS), it has been speculated that multiparametric magnetic resonance imaging (MRI) is capable to contribute to early diagnosis. Machine learning (ML) can be regarded as the missing piece that allows for the useful integration of multiparametric MRI data into a diagnostic classifier. The major challenges in developing ML classifiers for ALS are limited data quantity and a suboptimal sample to feature ratio which can be addressed by sound feature selection.

Methods:

We conducted a systematic review to collect MRI biomarkers that could be used as features by searching the online database PubMed for entries in the recent 4 years that contained cross-sectional neuroimaging data of subjects with ALS and an adequate control group. In addition to the qualitative synthesis, a semi-quantitative analysis was conducted for each MRI modality that indicated which brain regions were most commonly reported.

Results:

Our search resulted in 151 studies with a total of 221 datasets. In summary, our findings highly resembled generally accepted neuropathological patterns of ALS, with degeneration of the motor cortex and the corticospinal tract, but also in frontal, temporal, and subcortical structures, consistent with the neuropathological four-stage model of the propagation of pTDP-43 in ALS.

Conclusions:

These insights are discussed with respect to their potential for MRI feature selection for future ML-based neuroimaging classifiers in ALS. The integration of multiparametric MRI including DTI, volumetric, and texture data using ML may be the best approach to generate a diagnostic neuroimaging tool for ALS.

Signal intensity of cerebral gyri in corticobasal syndrome on phase difference enhanced magnetic resonance images: Comparison of progressive supranuclear palsy and Parkinson's disease

We evaluated cerebral gyri (CG) on phase difference enhanced imaging (PADRE) of corticobasal syndrome (CBS), progressive supranuclear palsy (PSP), and Parkinson's disease (PD) patients to determine whether it is possible to discriminate among them on an individual basis. Two radiologists reviewed appearance of the normal CG and that of CBS patients on PADRE, and deviations from the appearance of the normal CG were recorded. Next, based on the CG abnormalities, two other reviewers reviewed PADRE images from 12 CBS, 14 PSP, and 30 PD patients. In healthy subjects on the PADRE images, the signal intensity (SI) of the gray matter (GM) was homogeneously, slightly hyperintense to the subcortical white matter (SCWM), and the SI of the SCWM was homogeneously hypointense. In CBS patients, hypointense layer in superficial GM and disappearance of hypointense in SCWM. The frequency of the abnormal findings on PADRE in the blinded manner by two readers was 100% (12/12), 3% (1/30), and 29% (4/14 in Reader 1) or 36% (5/14 in Reader 2) in CBS PD, and PSP patients, respectively. Laterality of the PADRE findings was showed in 12 (100%) CBS patients and 3 (21%) PSP, but not in any PD patients. The previously reported typical findings in CBS on conventional magnetic resonance image (MRIs) were observed in only 42% (5/12) of CBS patients. In conclusion, the abnormal findings in CG on PADRE appears more useful than conventional MRI findings for discriminating CBS from PD on an individual basis.