The neuroradiology of upper motor neuron degeneration: PLS, HSP, ALS

Three different short-interval intracortical inhibition methods in early diagnosis of amyotrophic lateral sclerosis

Objectives: To compare the utility of conventional amplitude measurements of short-interval intracortical inhibition (A-SICI) with two threshold-tracking (T-SICI) methods, as aids to early diagnosis of amyotrophic lateral sclerosis (ALS). The new parallel threshold-tracking method (T-SICIp) was compared with the previously used serial tracking method (T-SICIs). Methods: 112 consecutive patients referred with the suspicion of ALS and 40 healthy controls were prospectively included. Based on clinical follow-up, patients were divided into 67 patients with motor neuron disease (MND) comprising progressive muscular atrophy (PMA) as well as ALS, and 45 patient controls. SICI was recorded from first dorsal interosseus muscle using the three different protocols. Results: MND patients had significantly reduced T-SICIp, T-SICIs and A-SICI, compared with healthy controls and patient controls, while healthy and patient controls were similar. Paradoxically, T-SICIp was least affected in MND patients with the most upper motor neuron (UMN) signs (Spearman ρ = 0.537, P < 0.0001) whereas there was no correlation for T-SICIs or A-SICI. T-SICIp also provided the best discrimination between patient controls and MND as determined by the receiver operating characteristic (ROC) curves. For patients with no UMN signs, area under ROC curve for 2-3ms inter-stimulus intervals was 0.931 for T-SICIp, 0.771 for T-SICIs and 0.786 for A-SICI. Conclusions: SICI is a sensitive measure for detection of cortical involvement in ALS patients. T-SICIp has higher sensitivity and specificity than T-SICIs and A-SICI, particularly in patients without any upper motor neuron signs.

Clusters of anatomical disease-burden patterns in ALS: a data-driven approach confirms radiological subtypes

Amyotrophic lateral sclerosis (ALS) is associated with considerable clinical heterogeneity spanning from diverse disability profiles, differences in UMN/LMN involvement, divergent progression rates, to variability in frontotemporal dysfunction. A multitude of classification frameworks and staging systems have been proposed based on clinical and neuropsychological characteristics, but disease subtypes are seldom defined based on anatomical patterns of disease burden without a prior clinical stratification. A prospective research study was conducted with a uniform imaging protocol to ascertain disease subtypes based on preferential cerebral involvement. Fifteen brain regions were systematically evaluated in each participant based on a comprehensive panel of cortical, subcortical and white matter integrity metrics. Using min–max scaled composite regional integrity scores, a two-step cluster analysis was conducted. Two radiological clusters were identified; 35.5% of patients belonging to ‘Cluster 1’ and 64.5% of patients segregating to ‘Cluster 2’. Subjects in Cluster 1 exhibited marked frontotemporal change. Predictor ranking revealed the following hierarchy of anatomical regions in decreasing importance: superior lateral temporal, inferior frontal, superior frontal, parietal, limbic, mesial inferior temporal, peri-Sylvian, subcortical, long association fibres, commissural, occipital, ‘sensory’, ‘motor’, cerebellum, and brainstem. While the majority of imaging studies first stratify patients based on clinical criteria or genetic profiles to describe phenotype- and genotype-associated imaging signatures, a data-driven approach may identify distinct disease subtypes without a priori patient categorisation. Our study illustrates that large radiology datasets may be potentially utilised to uncover disease subtypes associated with unique genetic, clinical or prognostic profiles.