Re-Executability Assessment of the Recent Autism Literature David Kennedy, Christian Haselgrove, Steve Hodge, Leah Honor, Jean Frazier University of Massachusetts Medical School, Worcester, MA, USA Introduction: There is concern about the stats of reproducibility in science in general and neuroimaging neuroscience in particular (Gorgolewski and Poldrack 2016; Button et al. 2013). A particularly germain concern was expressed by Insel and colleagues in lamenting: “a profusion of statistically significant, but minimally differentiating, biological findings; ‘approximate replications’ of these findings in a way that neither confirms nor refutes them” (Kapur, Phillips, and Insel 2012). The replication of a specific finding (or reproducibility of a specific analysis), as reflected in a publication, has many details and nuances to it (Kennedy et al. 2019). Often, we are searching for the ‘generalizability’ of a finding: does the finding hold true when using ‘similar’ data and a ‘similar’ analysis. In this poster, we: 1) develop a specification for what constitutes an assessment of the re-executability for a given publication, in each of the domains of: data, software, execution environment, statistics and results; 2) codify this assessment in survey form; and 3) apply the survey to a subset of the autism neuroimaging literature published recently (~2018). From the results of this survey, we can begin to generalize the state of the re-executability of the recent autism neuroimaging literature, in order to identify trends and opportunities for the enhancement of the re-executability status in support of greater overall generalizability (and hence reproducibility) of the literature. Methods Survey Development: In order to assess the prospects of re-execution of a given paper, we assess 1) the availability of the starting data, 2) the precision of the analysis description (both data processing and statistical assessment), and 3) the availability of the detailed complete results (in order to verify accuracy of re-execution). Regarding the ‘availability of the starting data’, we assess if the publication indicates how someone (other than the authors themselves) could appropriately access the data. The ‘precision of the analysis description’ ultimately asks if a reader who is reasonably skilled in the necessary domains, could precisely carry out the prescribed analysis steps. Specifically, are the software versions, operating system and complete parameters somehow made available to the reader? The ‘detailed complete results’ assesses if the publication indicates how to obtain the complete results, in order to both verify that the re-execution generates the same result and to overcome the limitations of selected summary only being presented, which impedes a more complete meta analysis of the literature. Literature Identification: On January 23, 2019, the following pubmed query was executed: (("autistic disorder"[MeSH Terms] OR ("autistic"[All Fields] AND "disorder"[All Fields]) OR "autistic disorder"[All Fields] OR "autism"[All Fields]) AND ("magnetic resonance imaging"[MeSH Terms] OR ("magnetic"[All Fields] AND "resonance"[All Fields] AND "imaging"[All Fields]) OR "magnetic resonance imaging"[All Fields] OR "mri"[All Fields])) AND ("2014/01/25"[PDat] : "2019/01/23"[PDat] AND "humans"[MeSH Terms]) This is the expansion of the general query for ‘autism AND MRI, qualified to select publications between 1/25/2014 - 1/23/2019 and where the MeSH term includes ‘human’. This query generated 811 resultant publications at the time of the query. Figure 1: Overview of the survey design. Data Availability: Sixteen of the 50 (32%) publications make reference to the availability of the data used in the publication. However, publications that indicate availability are reusing data from the large repositories, whereas the publications that do not indicate data availability are principally locally conducted studies. Thus, this indicates that a large fraction