Measuring heterogeneity in normative models as the effective number of deviation patterns

Normative modeling is an increasingly popular method for characterizing the ways in which clinical cohorts deviate from a reference population, with respect to one or more biological features. In this paper, we extend the normative modeling framework with an approach for measuring the amount of heterogeneity in a cohort. This heterogeneity measure is based on the Representational Rényi Heterogeneity method, which generalizes diversity measurement paradigms used across multiple scientific disciplines. We propose that heterogeneity in the normative modeling setting can be measured as the effective number of deviation patterns; that is, the effective number of coherent patterns by which a sample of data differ from a distribution of normative variation. We show that lower effective number of deviation patterns is associated with the presence of systematic differences from a (non-degenerate) normative distribution. This finding is shown to be consistent across (A) application of a Gaussian process model to synthetic and real-world neuroimaging data, and (B) application of a variational autoencoder to well-understood database of handwritten images.

Measuring Heterogeneity in Normative Models as the Effective Number of Deviation Patterns

Normative modeling is an increasingly popular method for characterizing the ways in which clinical cohorts deviate from a reference population, with respect to one or more biological features. In this paper, we extend the normative modeling framework with an approach for measuring the amount of heterogeneity in a cohort. This heterogeneity measure is based on the Representational Renyi Heterogeneity method, which generalizes diversity measurement paradigms used across multiple scientific disciplines. We propose that heterogeneity in the normative modeling setting can be measured as the effective number of deviation patterns; that is, the effective number of coherent patterns by which a sample of data differ from a distribution of normative variation. We show that lower effective number of deviation patterns is associated with the presence of systematic differences from a (non-degenerate) normative distribution. This finding is shown to be consistent across (A) application of a Gaussian process model to synthetic and real-world neuroimaging data, and (B) application of a variational autoencoder to well-understood database of handwritten images.

ICTs Supportive and Therapeutic Contribution in Psychoemotional Disorders in Childhood and Adolescence

<p>This paper aims at reviewing the supportive and therapeutic contribution of ICTs on the most common psychoemotional disorders of childhood and adolescence. The various approaches to classifying behavioral and emotional dysregulations in children and adolescents have been reviewed and the boundaries between normative variation and clinically significant presentations have been determined. Furthermore, we present the major categories of mental illnesses in children and adolescents investigating the etiology, the diagnostic criteria, the epidemiology and treatments by critically reviewing the outcomes of relevant studies. The findings indicate that significant steps have been made in this field, but it is vital to underline the need for more extended investigation in school aged children and adolescents for this purpose.</p>

Normative Variation of P3a and P3b from a Large Sample

The P3a and P3b components were elicited in 120 (60 females, 60 males) young adults using a visual three-stimulus event-related brain potential (ERP) oddball paradigm in which subjects responded to an infrequent target. The major purpose of the paper was to provide a statistically strong characterization of these related P300 subcomponents. P3a components were obtained from the infrequently presented distracter stimulus, which was a large blue square. P3b components were obtained from the target stimulus, which was a blue circle that differed slightly in diameter from the standard stimulus blue circle. Amplitude measures demonstrated that P3a was maximum at Cz, and P3b was maximum at Pz; latency measures increased for both potentials from frontal to parietal recording sites. P3a and P3b from females were larger and later than those from male subjects, with topographic and appreciable individual difference variability observed. P3a was generally unrelated to response time. P3b amplitude was negatively correlated over right frontal areas with P3b latency and positively correlated over right parietal areas to response time. Theoretical implications are discussed.