TY - JOUR T1 - Quantifying the distribution of feature values over data represented in arbitrary dimensional spaces JF - bioRxiv DO - 10.1101/2022.11.23.517657 SP - 2022.11.23.517657 AU - Enrique R. Sebastian AU - Julio Esparza AU - Liset M de la Prida Y1 - 2022/01/01 UR - http://biorxiv.org/content/early/2022/11/24/2022.11.23.517657.abstract N2 - Background Identifying the structured distribution (or lack thereof) of a given feature over a point cloud is a general research question. In the neuroscience field, this problem arises while investigating representations over neural manifolds (e.g., spatial coding), in the analysis of neurophysiological signals (e.g., auditory coding) or in anatomical image segmentation.New method We introduce the Structure Index (SI) as a graph-based topological metric to quantify the distribution of feature values projected over data in arbitrary D-dimensional spaces (neurons, time stamps, pixels). The SI is defined from the overlapping distribution of data points sharing similar feature values in a given neighborhood.Results Using model data clouds we show how the SI provides quantification of the degree of local versus global organization of feature distribution. SI can be applied to both scalar and vectorial features permitting quantification of the relative contribution of related variables. When applied to experimental studies of head-direction cells, it is able to retrieve consistent feature structure from both the high- and low-dimensional representations. Finally, we provide two general-purpose examples (sound and image categorization), to illustrate the potential application to arbitrary dimensional spaces.Comparison with existing methods Most methods for quantifying structure depend on cluster analysis, which are suboptimal for continuous features and non-discrete data clouds. SI unbiasedly quantifies structure from continuous data in any dimensional space.Conclusions The method provides versatile applications in the neuroscience and data science fieldsHighlightsThe Structure Index is a graph-based topological metricIt quantifies the distribution of feature values in arbitrary dimensional spacesIt can be applied to both scalar and vectorial featuresWhen applied to the head-direction neural system, it extracts concordant information from high- and low-dimensional representationsIt can be extended to sound and image categorization, expanding the range of applicationsCompeting Interest StatementThe authors have declared no competing interest. ER -