Derek Beaton

Crowdsourced estimation of cognitive decline and resilience in Alzheimer's disease

Identifying accurate biomarkers of cognitive decline is essential for advancing early diagnosis and prevention therapies in Alzheimers disease. The Alzheimers disease DREAM Challenge was designed as a computational crowdsourced project to benchmark the current state‐of‐the‐art in predicting cognitive outcomes in Alzheimers disease based on high dimensional, publicly available genetic and structural imaging data. This meta‐analysis failed to identify a meaningful predictor developed from either data modality, suggesting that alternate approaches should be considered for prediction of cognitive performance.

An ExPosition of multivariate analysis with the singular value decomposition in R

ExPosition is a new comprehensive R package providing crisp graphics and implementing multivariate analysis methods based on the singular value decomposition (svd). The core techniques implemented in ExPosition are: principal components analysis, (metric) multidimensional scaling, correspondence analysis, and several of their recent extensions such as barycentric discriminant analyses (e.g., discriminant correspondence analysis), multi-table analyses (e.g.,multiple factor analysis, Statis, and distatis), and non-parametric resampling techniques (e.g., permutation and bootstrap). Several examples highlight the major differences between ExPosition and similar packages. Finally, the future directions of ExPosition are discussed.

Differences in Human Cortical Gene Expression Match the Temporal Properties of Large-Scale Functional Networks

We explore the relationships between the cortex functional organization and genetic expression (as provided by the Allen Human Brain Atlas). Previous work suggests that functional cortical networks (resting state and task based) are organized as two large networks (differentiated by their preferred information processing mode) shaped like two rings. The first ring–Visual-Sensorimotor-Auditory (VSA)–comprises visual, auditory, somatosensory, and motor cortices that process real time world interactions. The second ring–Parieto-Temporo-Frontal (PTF)–comprises parietal, temporal, and frontal regions with networks dedicated to cognitive functions, emotions, biological needs, and internally driven rhythms. We found–with correspondence analysis–that the patterns of expression of the 938 genes most differentially expressed across the cortex organized the cortex into two sets of regions that match the two rings. We confirmed this result using discriminant correspondence analysis by showing that the genetic profiles of cortical regions can reliably predict to what ring these regions belong. We found that several of the proteins–coded by genes that most differentiate the rings–were involved in neuronal information processing such as ionic channels and neurotransmitter release. The systematic study of families of genes revealed specific proteins within families preferentially expressed in each ring. The results showed strong congruence between the preferential expression of subsets of genes, temporal properties of the proteins they code, and the preferred processing modes of the rings. Ionic channels and release-related proteins more expressed in the VSA ring favor temporal precision of fast evoked neural transmission (Sodium channels SCNA1, SCNB1 potassium channel KCNA1, calcium channel CACNA2D2, Synaptotagmin SYT2, Complexin CPLX1, Synaptobrevin VAMP1). Conversely, genes expressed in the PTF ring favor slower, sustained, or rhythmic activation (Sodium channels SCNA3, SCNB3, SCN9A potassium channels KCNF1, KCNG1) and facilitate spontaneous transmitter release (calcium channel CACNA1H, Synaptotagmins SYT5, Complexin CPLX3, and synaptobrevin VAMP2).

Qualitatively Distinct Factors Contribute to Elevated Rates of Paranoia in Autism and Schizophrenia

A converging body of clinical and empirical reports indicates that autism features elevated rates of paranoia comparable to those of individuals with paranoid schizophrenia. However, the distinct developmental courses and symptom manifestations of these two disorders suggest that the nature of paranoid ideation may differ between them in important and meaningful ways. To evaluate this hypothesis, we compared patterns of responses on the Paranoia Scale between actively paranoid individuals with schizophrenia (SCZP), individuals with schizophrenia who were not actively paranoid (SCZNP), adults with an Autism Spectrum Disorder (ASD), and healthy controls. Despite an overall similar level of heightened paranoia in the ASD and SCZP groups, discriminant correspondence analysis (DiCA) revealed that these groups were characterized by unique underlying factors. Paranoia in the SCZP group was defined by a factor based upon victimization, suspicion, and threat of harm. Whereas paranoia in the ASD group was partially characterized by this factor, it was distinguished from SCZP by an additional pattern of responses reflective of increased social cynicism. These findings indicate that paranoia in ASD is supported by qualitative factors distinct from schizophrenia and highlight mechanistic differences in the formation of paranoid ideation that may inform the development of disorder-specific treatments.

Unique aspects of impulsive traits in substance use and overeating: specific contributions of common assessments of impulsivity

Abstract Background: Impulsivity is a complex trait often studied in substance abuse and overeating disorders, but the exact nature of impulsivity traits and their contribution to these disorders are still debated. Thus, understanding how to measure impulsivity is essential for comprehending addictive behaviors. Objectives: Identify unique impulsivity traits specific to substance use and overeating. Methods: Impulsive Sensation Seeking (ImpSS) and Barratt’s Impulsivity scales (BIS) Scales were analyzed with a non-parametric factor analytic technique (discriminant correspondence analysis) to identify group-specific traits on 297 individuals from five groups: Marijuana (n = 88), Nicotine (n = 82), Overeaters (n = 27), Marijuauna + Nicotine (n = 63), and Controls (n = 37). Results: A significant overall factor structure revealed three components of impulsivity that explained respectively 50.19% (pperm < 0.0005), 24.18% (pperm < 0.0005), and 15.98% (pperm < 0.0005) of the variance. All groups were significantly different from one another. When analyzed together, the BIS and ImpSS produce a multi-factorial structure that identified the impulsivity traits specific to these groups. The group specific traits are (1) Control: low impulse, avoids thrill-seeking behaviors; (2) Marijuana: seeks mild sensation, is focused and attentive; (3) Marijuana + Nicotine: pursues thrill-seeking, lacks focus and attention; (4) Nicotine: lacks focus and planning; (5) Overeating: lacks focus, but plans (short and long term). Conclusions: Our results reveal impulsivity traits specific to each group. This may provide better criteria to define spectrums and trajectories – instead of categories – of symptoms for substance use and eating disorders. Defining symptomatic spectrums could be an important step forward in diagnostic strategies.

Revisiting PLS Resampling: Comparing Significance Versus Reliability Across Range of Simulations

pls as a general multivariate method has been applied to many types of data with various covariance structures, signal strengths, numbers of observations and numbers of variables. We present a simulation framework that can cover a wide spectrum of applications by generating realistic data sets with predetermined effect sizes and distributions. In standard implementations of pls, permutation tests are used to assess effect significance, with or without procrustes rotation for matching effect subspaces. This approach is dependent on signal amplitude (effect size) and, as such, is vulnerable to the presence of outliers with strong amplitudes. Moreover, our simulations show that in cases when the overall effect size is weak, the rate of false positives—and to a lesser extent—false negatives, is quite high. From the applications point of view, such as linking genotypes and phenotypes, it is often more important to detect reliable effects, even when they are very weak. Reliability in such cases is measured by the ability to observe the same effects supported by the same patterns of variables, no matter which sets of observations (subjects) are used.11pc]Please check if inserted author affiliations is okay. We implemented split-half reliability testing with thresholds based on null distributions and compared the results to the more familiar significance testing.

Integrating Partial Least Squares Correlation and Correspondence Analysis for Nominal Data

We present an extension of pls—called partial least squares correspondence analysis (plsca)—tailored for the analysis of nominal data. As the name indicates, plsca combines features of pls (analyzing the information common to two tables) and correspondence analysis (ca, analyzing nominal data). We also present inferential techniques for plsca such as bootstrap, permutation, and \({\chi }^{2}\) omnibus tests. We illustrate plsca with two nominal data tables that store (respectively) behavioral and genetics information.

Semantically defined subdomains of functional neuroimaging literature and their corresponding brain regions

The functional neuroimaging literature has become increasingly complex and thus difficult to navigate. This complexity arises from the rate at which new studies are published and from the terminology that varies widely from study‐to‐study and even more so from discipline‐to‐discipline. One way to investigate and manage this problem is to build a “semantic space” that maps the different vocabulary used in functional neuroimaging literature. Such a semantic space will also help identify the primary research domains of neuroimaging and their most commonly reported brain regions. In this work, we analyzed the multivariate semantic structure of abstracts in Neurosynth and found that there are six primary domains of the functional neuroimaging literature, each with their own preferred reported brain regions. Our analyses also highlight possible semantic sources of reported brain regions within and across domains because some research topics (e.g., memory disorders, substance use disorder) use heterogeneous terminology. Furthermore, we highlight the growth and decline of the primary domains over time. Finally, we note that our techniques and results form the basis of a “recommendation engine” that could help readers better navigate the neuroimaging literature.

The Latent Semantic Space and Corresponding Brain Regions of the Functional Neuroimaging Literature

The functional neuroimaging literature has become increasingly complex and thus difficult to navigate. This complexity arises from the rate at which new studies are published and from the terminology that varies widely from study-to-study and even more so from discipline-to-discipline. One way to investigate and manage this problem is to build a “semantic space” that maps the different vocabulary used in functional neuroimaging literature. Such a semantic space will also help identify the primary research domains of neuroimaging and their most commonly reported brain regions. In this work, we analyzed the multivariate semantic structure of abstracts in Neurosynth and found that there are six primary domains of the functional neuroimaging literature each with their own preferred reported brain regions. Our analyses also highlight possible semantic sources of reported brain regions within and across domains because some research topics (e.g., memory disorders, substance use disorder) use heterogeneous terminology. Furthermore, we highlight the growth and decline of the primary domains over time. Finally, we note that our techniques and results form the basis of a “recommendation engine” that could help readers better navigate the neuroimaging literature.

Generalization of the minimum covariance determinant algorithm for categorical and mixed data types

The minimum covariance determinant (MCD) algorithm is one of the most common techniques to detect anomalous or outlying observations. The MCD algorithm depends on two features of multivariate data: the determinant of a matrix (i.e., geometric mean of the eigenvalues) and Mahalanobis distances (MD). While the MCD algorithm is commonly used, and has many extensions, the MCD is limited to analyses of quantitative data and more specifically data assumed to be continuous. One reason why the MCD does not extend to other data types such as categorical or ordinal data is because there is not a well-defined MD for data types other than continuous data. To address the lack of MCD-like techniques for categorical or mixed data we present a generalization of the MCD. To do so, we rely on a multivariate technique called correspondence analysis (CA). Through CA we can define MD via singular vectors and also compute the determinant from CA’s eigenvalues. Here we define and illustrate a generalized MCD on categorical data and then show how our generalized MCD extends beyond categorical data to accommodate mixed data types (e.g., categorical, ordinal, and continuous). We illustrate this generalized MCD on data from two large scale projects: the Ontario Neurodegenerative Disease Research Initiative (ONDRI) and the Alzheimer’s Disease Neuroimaging Initiative (ADNI), with genetics (categorical), clinical instruments and surveys (categorical or ordinal), and neuroimaging (continuous) data. We also make R code and toy data available in order to illustrate our generalized MCD.