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Simulink is a leading modelling language and data-flow environment for Model-Driven Engineering, prevalent in both industrial and educational contexts. Accordingly, there are many standalone publicly-available tools for analyzing and using Simulink models for various purposes. However, Simulink's model format has evolved to a new proprietary format, rendering many of these tools useless. To combat this, we devise an approach, SLX2MDL, that applies transformation rules based on Simulink syntax to transform the new SLX format models to models conforming to the legacy MDL syntax. The resulting approach enables backwards compatibility with existing tools, including previous versions of Simulink itself. Our 4-phase process includes analysis and extraction, merging and transformation of the common elements, transformation of the specialized Stateflow elements, and output production. We position this problem within the literature by comparing and contrasting similar, but insufficient, related approaches. We evaluate and validate SLX2MDL by applying it to 543 standard and publicly available models from an established and curated corpus. Our evaluation demonstrates 100% validity and correctness on these models based on functional equivalence. Further, we evaluate our approach's performance and find it consistent and scalable as model size and complexity increases. 

Despite the known benefits of data‐driven approaches, the lack of approaches for identifying functional neuroimaging patterns that capture both individual variations and inter‐subject correspondence limits the clinical utility of rsfMRI and its application to single‐subject analyses. Here, using rsfMRI data from over 100k individuals across private and public datasets, we identify replicable multi‐spatial‐scale canonical intrinsic connectivity network (ICN) templates via the use of multi‐model‐order independent component analysis (ICA). We also study the feasibility of estimating subject‐specific ICNs via spatially constrained ICA. The results show that the subject‐level ICN estimations vary as a function of the ICN itself, the data length, and the spatial resolution. In general, large‐scale ICNs require less data to achieve specific levels of (within‐ and between‐subject) spatial similarity with their templates. Importantly, increasing data length can reduce an ICN's subject‐level specificity, suggesting longer scans may not always be desirable. We also find a positive linear relationship between data length and spatial smoothness (possibly due to averaging over intrinsic dynamics), suggesting studies examining optimized data length should consider spatial smoothness. Finally, consistency in spatial similarity between ICNs estimated using the full data and subsets across different data lengths suggests lower within‐subject spatial similarity in shorter data is not wholly defined by lower reliability in ICN estimates, but may be an indication of meaningful brain dynamics which average out as data length increases.