Pathological neural networks and artificial neural networks in ALS: diagnostic classification based on pathognomonic neuroimaging features

Abstract

The description of group-level, genotype- and phenotype-associated imaging traits is academically important, but the practical demands of clinical neurology centre on the accurate classification of individual patients into clinically relevant diagnostic, prognostic and phenotypic categories. Similarly, pharmaceutical trials require the precision stratification of participants based on quantitative measures. A single-centre study was conducted with a uniform imaging protocol to test the accuracy of an artificial neural network classification scheme on a cohort of 378 participants composed of patients with ALS, healthy subjects and disease controls. A comprehensive panel of cerebral volumetric measures, cortical indices and white matter integrity values were systematically retrieved from each participant and fed into a multilayer perceptron model. Data were partitioned into training and testing and receiver-operating characteristic curves were generated for the three study-groups. Area under the curve values were 0.930 for patients with ALS, 0.958 for disease controls, and 0.931 for healthy controls relying on all input imaging variables. The ranking of variables by classification importance revealed that white matter metrics were far more relevant than grey matter indices to classify single subjects. The model was further tested in a subset of patients scanned within 6 weeks of their diagnosis and an AUC of 0.915 was achieved. Our study indicates that individual subjects may be accurately categorised into diagnostic groups in an observer-independent classification framework based on multiparametric, spatially registered radiology data. The development and validation of viable computational models to interpret single imaging datasets are urgently required for a variety of clinical and clinical trial applications.

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

“…Certain anatomical areas such as the parietal lobes and occipital lobe may not be characteristic regions of degeneration, yet, as illustrated, may have a role in segregating specific ALS subtypes. This observation is consistent with the emerging machine-learning literature of ALS [ 52 , 53 ] which suggests that feature importance analyses, especially in multi-class classification schemes, may identify brain regions which are not classically associated with ALS [ 54 , 55 ].…”

Segmental alterations of the corpus callosum in motor neuron disease: A DTI and texture analysis in 575 patients

“…This is, with respect to an AUC value of 0.91, an excellent result according to predefined criteria ( Mandrekar, 2010 ) in comparison to other applications of multiparametric MRI classifiers to ALS (review in Thome et al, 2022 ) and supports the use of CC as a neuroimaging marker in ALS. A recent study ( Bede et al, 2022A ) used a perceptron model to discriminate ALS, UMN predominant, and LMN predominant cohorts and found that measures of the forceps minor (anterior CC / fibres of the genu of CC) discriminate these subtypes rather well, in the same line like the results of the current study; approaches of neural network classification based on neuroimaging features have also been reported ( Bede et al, 2022B ).…”

Multimodal in vivo staging in amyotrophic lateral sclerosis using artificial intelligence

“…In conclusion, our results support the enormous academic and clinical potential of AI‐based approaches in ALS 19,47 . Cluster analysis of multimodal parameters associated with the respective criteria for staging in DTI, VOG, and cognitive testing displayed a high congruence of these approaches in patients with ALS.…”

“…In conclusion, our results support the enormous academic and clinical potential of AI-based approaches in ALS. 19,47 Cluster analysis of multimodal parameters associated with the respective criteria for staging in DTI, VOG, and cognitive testing displayed a high congruence of these approaches in patients with ALS. The combination of measures of structural changes in diffusionweighted MRI with measures of cognition and oculomotor function was capable of assessing neuropathological stages 1 and 4 in particular and could provide the basis for a future multimodal extension of the in vivo staging system.…”