Lourens J. Waldorp

Comorbidity: A network perspective

The pivotal problem of comorbidity research lies in the psychometric foundation it rests on, that is, latent variable theory, in which a mental disorder is viewed as a latent variable that causes a constellation of symptoms. From this perspective, comorbidity is a (bi)directional relationship between multiple latent variables. We argue that such a latent variable perspective encounters serious problems in the study of comorbidity, and offer a radically different conceptualization in terms of a network approach, where comorbidity is hypothesized to arise from direct relations between symptoms of multiple disorders. We propose a method to visualize comorbidity networks and, based on an empirical network for major depression and generalized anxiety, we argue that this approach generates realistic hypotheses about pathways to comorbidity, overlapping symptoms, and diagnostic boundaries, that are not naturally accommodated by latent variable models: Some pathways to comorbidity through the symptom space are more likely than others; those pathways generally have the same direction (i.e., from symptoms of one disorder to symptoms of the other); overlapping symptoms play an important role in comorbidity; and boundaries between diagnostic categories are necessarily fuzzy.

The Small World of Psychopathology

Background Mental disorders are highly comorbid: people having one disorder are likely to have another as well. We explain empirical comorbidity patterns based on a network model of psychiatric symptoms, derived from an analysis of symptom overlap in the Diagnostic and Statistical Manual of Mental Disorders-IV (DSM-IV). Principal Findings We show that a) half of the symptoms in the DSM-IV network are connected, b) the architecture of these connections conforms to a small world structure, featuring a high degree of clustering but a short average path length, and c) distances between disorders in this structure predict empirical comorbidity rates. Network simulations of Major Depressive Episode and Generalized Anxiety Disorder show that the model faithfully reproduces empirical population statistics for these disorders. Conclusions In the network model, mental disorders are inherently complex. This explains the limited successes of genetic, neuroscientific, and etiological approaches to unravel their causes. We outline a psychosystems approach to investigate the structure and dynamics of mental disorders.

Brain responses strongly correlate with Weibull image statistics when processing natural images.

The visual appearance of natural scenes is governed by a surprisingly simple hidden structure. The distributions of contrast values in natural images generally follow a Weibull distribution, with beta and gamma as free parameters. Beta and gamma seem to structure the space of natural images in an ecologically meaningful way, in particular with respect to the fragmentation and texture similarity within an image. Since it is often assumed that the brain exploits structural regularities in natural image statistics to efficiently encode and analyze visual input, we here ask ourselves whether the brain approximates the beta and gamma values underlying the contrast distributions of natural images. We present a model that shows that beta and gamma can be easily estimated from the outputs of X-cells and Y-cells. In addition, we covaried the EEG responses of subjects viewing natural images with the beta and gamma values of those images. We show that beta and gamma explain up to 71% of the variance of the early ERP signal, substantially outperforming other tested contrast measurements. This suggests that the brain is strongly tuned to the images beta and gamma values, potentially providing the visual system with an efficient way to rapidly classify incoming images on the basis of omnipresent low-level natural image statistics.

A new method for constructing networks from binary data

Network analysis is entering fields where network structures are unknown, such as psychology and the educational sciences. A crucial step in the application of network models lies in the assessment of network structure. Current methods either have serious drawbacks or are only suitable for Gaussian data. In the present paper, we present a method for assessing network structures from binary data. Although models for binary data are infamous for their computational intractability, we present a computationally efficient model for estimating network structures. The approach, which is based on Ising models as used in physics, combines logistic regression with model selection based on a Goodness-of-Fit measure to identify relevant relationships between variables that define connections in a network. A validation study shows that this method succeeds in revealing the most relevant features of a network for realistic sample sizes. We apply our proposed method to estimate the network of depression and anxiety symptoms from symptom scores of 1108 subjects. Possible extensions of the model are discussed.

Spatiotemporal EEG/MEG source analysis based on a parametric noise covariance model

A method is described to incorporate the spatiotemporal noise covariance matrix into a spatiotemporal source analysis. The essential feature is that the estimation problem is split into two parts. First, a model is fitted to the observed noise covariance matrix. This model is a Kronecker product of a spatial and a temporal matrix. The spatial matrix models the spatial covariances by a function dependent on sensor distance. The temporal matrix models the temporal covariances as lag dependent. In the second part, sources are estimated given this noise model, which can be done very efficiently due to the Kronecker formulation. An application to real electroencephalogram (EEG) data shows that the noise model fits the data very well. Simulation results show that the resulting source estimates are more precise than those obtained from a standard analysis neglecting the noise covariance. In addition, the estimated standard errors of the source parameter estimates are far more precise than those obtained from a standard analysis. Finally, the source parameter standard errors are used to investigate the effects of temporal sampling. It is shown that increasing the sampling by a factor x, decreases the standard errors of all source parameters with the square root of x.

Simpson's paradox in psychological science: a practical guide

The direction of an association at the population-level may be reversed within the subgroups comprising that population—a striking observation called Simpsons paradox. When facing this pattern, psychologists often view it as anomalous. Here, we argue that Simpsons paradox is more common than conventionally thought, and typically results in incorrect interpretations—potentially with harmful consequences. We support this claim by reviewing results from cognitive neuroscience, behavior genetics, clinical psychology, personality psychology, educational psychology, intelligence research, and simulation studies. We show that Simpsons paradox is most likely to occur when inferences are drawn across different levels of explanation (e.g., from populations to subgroups, or subgroups to individuals). We propose a set of statistical markers indicative of the paradox, and offer psychometric solutions for dealing with the paradox when encountered—including a toolbox in R for detecting Simpsons paradox. We show that explicit modeling of situations in which the paradox might occur not only prevents incorrect interpretations of data, but also results in a deeper understanding of what data tell us about the world.

How preparation changes the need for top–down control of the basal ganglia when inhibiting premature actions

Goal-oriented signals from the prefrontal cortex gate the selection of appropriate actions in the basal ganglia. Key nodes within this fronto-basal ganglia action regulation network are increasingly engaged when one anticipates the need to inhibit and override planned actions. Here, we ask how the advance preparation of action plans modulates the need for fronto-subcortical control when a planned action needs to be withdrawn. Functional magnetic resonance imaging data were collected while human participants performed a stop task with cues indicating the likelihood of a stop signal being sounded. Mathematical modeling of go trial responses suggested that participants attained a more cautious response strategy when the probability of a stop signal increased. Effective connectivity analysis indicated that, even in the absence of stop signals, the proactive engagement of the full control network is tailored to the likelihood of stop trial occurrence. Importantly, during actual stop trials, the strength of fronto-subcortical projections was stronger when stopping had to be engaged reactively compared with when it was proactively prepared in advance. These findings suggest that fronto-basal ganglia control is strongest in an unpredictable environment, where the prefrontal cortex plays an important role in the optimization of reactive control. Importantly, these results further indicate that the advance preparation of action plans reduces the need for reactive fronto-basal ganglia communication to gate voluntary actions.

The sensory consequences of speaking: parametric neural cancellation during speech in auditory cortex

When we speak, we provide ourselves with auditory speech input. Efficient monitoring of speech is often hypothesized to depend on matching the predicted sensory consequences from internal motor commands (forward model) with actual sensory feedback. In this paper we tested the forward model hypothesis using functional Magnetic Resonance Imaging. We administered an overt picture naming task in which we parametrically reduced the quality of verbal feedback by noise masking. Presentation of the same auditory input in the absence of overt speech served as listening control condition. Our results suggest that a match between predicted and actual sensory feedback results in inhibition of cancellation of auditory activity because speaking with normal unmasked feedback reduced activity in the auditory cortex compared to listening control conditions. Moreover, during self-generated speech, activation in auditory cortex increased as the feedback quality of the self-generated speech decreased. We conclude that during speaking early auditory cortex is involved in matching external signals with an internally generated model or prediction of sensory consequences, the locus of which may reside in auditory or higher order brain areas. Matching at early auditory cortex may provide a very sensitive monitoring mechanism that highlights speech production errors at very early levels of processing and may efficiently determine the self-agency of speech input.

Estimated generalized least squares electromagnetic source analysis based on a parametric noise covariance model [EEG/MEG]

Estimated generalized least squares (EGLS) electromagnetic source analysis is used to downweight noisy and correlated data. Standard EGLS requires many trials to accurately estimate the noise covariances and, thus, the source parameters. Alternatively, the noise covariances can be modeled parametrically. Only the parameters of the model describing the noise covariances need to be estimated and, therefore, less trials are required. This method is referred to as parametric EGLS (PEGLS). In this paper, PEGLS is developed and its performance is tested in a simulation study and in a pseudoempirical study.

Mind the Gap: a psychometric approach to the reduction problem

Cognitive neuroscience involves the simultaneous analysis of behavioral and neurological data. Common practice in cognitive neuroscience, however, is to limit analyses to the inspection of descriptive measures of association (e.g., correlation coefficients). This practice, often combined with little more than an implicit theoretical stance, fails to address the relationship between neurological and behavioral measures explicitly. This article argues that the reduction problem, in essence, is a measurement problem. As such, it should be solved by using psychometric techniques and models. We show that two influential philosophical theories on this relationship, identity theory and supervenience theory, can be easily translated into psychometric models. Upon such translation, they make explicit hypotheses based on sound theoretical and statistical foundations, which renders them empirically testable. We examine these models, show how they can elucidate our conceptual framework, and examine how they may be used to study foundational questions in cognitive neuroscience. We illustrate these principles by applying them to the relation between personality test scores, intelligence tests, and neurological measures.