Parallel inference of hierarchical latent dynamics in two-photon calcium imaging of neuronal populations

Dynamic latent variable modelling has provided a powerful tool for understanding how populations of neurons compute. For spiking data, such latent variable modelling can treat the data as a set of point-processes, due to the fact that spiking dynamics occur on a much faster timescale than the computational dynamics being inferred. In contrast, for other experimental techniques, the slow dynamics governing the observed data are similar in timescale to the computational dynamics that researchers want to infer. An example of this is in calcium imaging data, where calcium dynamics can have timescales on the order of hundreds of milliseconds. As such, the successful application of dynamic latent variable modelling to modalities like calcium imaging data will rest on the ability to disentangle the deeper- and shallower-level dynamical systems’ contributions to the data. To-date, no techniques have been developed to directly achieve this. Here we solve this problem by extending recent advances using sequential variational autoencoders for dynamic latent variable modelling of neural data. Our system VaLPACa (Variational Ladders for Parallel Autoencoding of Calcium imaging data) solves the problem of disentangling deeper- and shallower-level dynamics by incorporating a ladder architecture that can infer a hierarchy of dynamical systems. Using some built-in inductive biases for calcium dynamics, we show that we can disentangle calcium flux from the underlying dynamics of neural computation. First, we demonstrate with synthetic calcium data that we can correctly disentangle an underlying Lorenz attractor from calcium dynamics. Next, we show that we can infer appropriate rotational dynamics in spiking data from macaque motor cortex after it has been converted into calcium fluorescence data via a calcium dynamics model. Finally, we show that our method applied to real calcium imaging data from primary visual cortex in mice allows us to infer latent factors that carry salient sensory information about unexpected stimuli. These results demonstrate that variational ladder autoencoders are a promising approach for inferring hierarchical dynamics in experimental settings where the measured variable has its own slow dynamics, such as calcium imaging data. Our new, open-source tool thereby provides the neuroscience community with the ability to apply dynamic latent variable modelling to a wider array of data modalities.

Effects of cumulative trauma load on long-term trajectories of life satisfaction and health in a population-based study

Background Lifetime traumatic events are known to have a detrimental long-term impact on both mental and physical health. Yet, heterogeneity in the stress response regarding well-being in adults is not well understood. This study investigates effects of cumulative trauma on latent trajectories of two indices of well-being, subjective health and life satisfaction in a large representative sample by means of latent variable modelling techniques. Methods Data from the pairfam study wave 2–9, a longitudinal representative survey was used (N = 10,825). Individuals reported on lifetime trauma type exposure on wave 7 and indicated levels of life satisfaction and health at each wave. Different types of latent Variable Mixture Models were applied in an iterative fashion. Conditional models investigated effects of cumulative trauma load. Results The best fitting model indicated three latent trajectories for life, and four for health, respectively. Trauma load significantly predicted class membership: Higher exposure was associated with non-stable trajectories for both indices but followed complex patterns of both improving and decreasing life satisfaction and health. Trauma load also explained variability within classes. Conclusions The current study expands on evidence to the long-term development of health and life satisfaction in response to traumatic events from a latent variable modelling perspective. Besides detrimental effect, it also points to functional adaptation after initial decline and increased well-being associated with trauma exposure. Thus, response to traumatic stress is marked by great heterogeneity. Future research should focus on variables beyond exposure to trauma that can further identify individuals prone to trajectories of declining well-being.

PARTIAL LEAST SQUARES (PLS) LATENT VARIABLE MODELLING APPROACH FOR MEASURING DURATION OF ORTHODONTIC TREATMENT

In today's society, the quest for aesthetic perfection is no longer just an aspiration of the young. As a result, there is an increasing number of adult patients who seek for orthodontic treatment to improve not only the function but the appearance of their teeth as well. Patients who are going to wear braces will be curious on how long the orthodontic treatment will take and those who complete treatment on time may be more satisfied. Therefore, this retrospective study aims to model the factors that affect the duration of orthodontic treatment using Partial Least Squares Regression. Demographic profile, patient's severity of malocclusion, treatment planning and patient compliance data are collected from patient's folders who have completed orthodontic treatment. The result from Partial Least Squares (PLS) regression indicates that twelve variables which are patient's age, patient's gender, proposed treatment planning, seven malocclusion characteristics, clinician experience and oral hygiene condition significantly contribute to the treatment duration. This study also demonstrates the application of Variable Importance for Projection (VIP) to select significant predictor variables. The final PLS model with one extracted factor explains 89.96% of the variation in the duration of orthodontic treatment.

Measurement Models in Social Work Research: A Data-Based Illustration of Four Confirmatory Factor Models and Their Conceptual Application

Confirmatory factor analysis (CFA) is a valuable tool for social work researchers to examine validity of measurements and other latent constructs. Best practice recommendations are to specify and evaluate the fit of multiple models, balancing plausibility, parsimony and quantitative indices. However, little attention has been given to the conceptual and theoretical implications of CFA model variations. This article offers a brief report on the state of CFA modelling published in social work research and presents a data-based illustration of four CFA models of a measure of empathy including a single-factor, correlated factors, higher order and bifactor models. We present results from each model and describe the models’ conceptual application with substantive explanation and theoretical application to the measurement of empathy. Syntax for all models in Mplus, R, Stata and EQS programmes are provided for reference. As familiarity with CFA and latent variable modelling methods grows, researchers must understand the theory-based implications of varying measurement models and test which model best represents their data and explain their conceptual application.