Visualization of latent components assessed in O*Net occupations (VOLCANO): A robust method for standardized conversion of occupational labels to ratio scale format.

Occupations are typically characterized in nominal form, a format that limits options for hypothesis testing and data analysis. We drew upon ratings of knowledge, skills, and abilities for 966 occupations listed in the US Department of Labor's Occupational Classification Network (O*NET) database to create an accessible, standardized multidimensional space in which occupations can be quantitatively localized and compared. Principal component analysis revealed that the occupation space comprises three main dimensions that correspond to (1) the required amount of education and training, (2) the degree to which an occupation falls within a science, technology, engineering, and mathematics (STEM) discipline versus social sciences and humanities, and (3) whether occupations are more mathematically or health related. Additional occupational spaces reflecting cognitive versus labor-oriented categories were created for finer-grained characterization of dimensions within occupational sets defined by higher or lower required educational preparation. Data-driven groupings of related occupations were obtained with hierarchical cluster analysis (HCA). Proof-of-principle was demonstrated with a real-world dataset (470 participants from the Nathan Kline Institute - Rockland Sample; NKI-RS), whereby verbal and non-verbal abilities-as assessed by standardized testing-were related to the STEM versus social sciences and humanities dimension. Visualization of Latent Components Assessed in O*Net Occupations (VOLCANO) is provided to the research community as a freely accessible tool, along with a Shiny app for users to extract quantitative scores along the relevant dimensions. VOLCANO brings much-needed standardization to unwieldy occupational data. Moreover, it can be used to create new occupational spaces customized to specific research domains.

Partial Least Squares Regression Analysis of Alzheimer's Disease Biomarkers, Modifiable Health Variables, and Cognitive Change in Older Adults with Mild Cognitive Impairment.

BACKGROUND: Prior work has shown that certain modifiable health, Alzheimer's disease (AD) biomarker, and demographic variables are associated with cognitive performance. However, less is known about the relative importance of these different domains of variables in predicting longitudinal change in cognition. OBJECTIVE: Identify novel relationships between modifiable physical and health variables, AD biomarkers, and slope of cognitive change over two years in a cohort of older adults with mild cognitive impairment (MCI). METHODS: Metrics of cardiometabolic risk, stress, inflammation, neurotrophic/growth factors, and AD pathology were assessed in 123 older adults with MCI at baseline from the Alzheimer's Disease Neuroimaging Initiative (mean age = 73.9; SD = 7.6; mean education = 16.0; SD = 3.0). Partial least squares regression (PLSR)-a multivariate method which creates components that best predict an outcome-was used to identify whether these physiological variables were important in predicting slope of change in episodic memory or executive function over two years. RESULTS: At two-year follow-up, the two PLSR models predicted, respectively, 20.0% and 19.6% of the variance in change in episodic memory and executive function. Baseline levels of AD biomarkers were important in predicting change in both episodic memory and executive function. Baseline education and neurotrophic/growth factors were important in predicting change in episodic memory, whereas cardiometabolic variables such as blood pressure and cholesterol were important in predicting change in executive function. CONCLUSION: These data-driven analyses highlight the impact of AD biomarkers on cognitive change and further clarify potential domain specific relationships with predictors of cognitive change.

Comparing the stability and reproducibility of brain-behaviour relationships found using Canonical Correlation Analysis and Partial Least Squares within the ABCD Sample.

Introduction: Canonical Correlation Analysis (CCA) and Partial Least Squares Correlation (PLS) detect associations between two data matrices based on computing a linear combination between the two matrices (called latent variables; LVs). These LVs maximize correlation (CCA) and covariance (PLS). These different maximization criteria may render one approach more stable and reproducible than the other when working with brain and behavioural data at the population-level. This study compared the LVs which emerged from CCA and PLS analyses of brain-behaviour relationships from the Adolescent Brain Cognitive Development (ABCD) dataset and examined their stability and reproducibility. Methods: Structural T1-weighted imaging and behavioural data were accessed from the baseline Adolescent Brain Cognitive Development dataset (N > 9000, ages = 9-11 years). The brain matrix consisted of cortical thickness estimates in different cortical regions. The behavioural matrix consisted of 11 subscale scores from the parent-reported Child Behavioral Checklist (CBCL) or 7 cognitive performance measures from the NIH Toolbox. CCA and PLS models were separately applied to the brain-CBCL analysis and brain-cognition analysis. A permutation test was used to assess whether identified LVs were statistically significant. A series of resampling statistical methods were used to assess stability and reproducibility of the LVs. Results: When examining the relationship between cortical thickness and CBCL scores, the first LV was found to be significant across both CCA and PLS models (singular value: CCA = .13, PLS = .39, p < .001). LV1 from the CCA model found that covariation of CBCL scores was linked to covariation of cortical thickness. LV1 from the PLS model identified decreased cortical thickness linked to lower CBCL scores. There was limited evidence of stability or reproducibility of LV1 for both CCA and PLS. When examining the relationship between cortical thickness and cognitive performance, there were 6 significant LVs for both CCA and PLS (p < .01). The first LV showed similar relationships between CCA and PLS and was found to be stable and reproducible (singular value: CCA = .21, PLS = .43, p < .001). Conclusion: CCA and PLS identify different brain-behaviour relationships with limited stability and reproducibility when examining the relationship between cortical thickness and parent-reported behavioural measures. However, both methods identified relatively similar brain-behaviour relationships that were stable and reproducible when examining the relationship between cortical thickness and cognitive performance. The results of the current study suggest that stability and reproducibility of brain-behaviour relationships identified by CCA and PLS are influenced by characteristics of the analyzed sample and the included behavioural measurements when applied to a large pediatric dataset.

A change of mind: Globus pallidus activity and effective connectivity during changes in choice selections.

The basal ganglia are a group of interconnected subcortical nuclei that plays a key role in multiple motor and cognitive processes, in a close interplay with several cortical regions. Two conflicting theories postulate that the basal ganglia pathways can either foster or suppress the cortico-striatal output or, alternatively, they can stabilize or destabilize the cortico-striatal circuit dynamics. These different approaches significantly impact the understanding of observable behaviours and cognitive processes in healthy, as well as clinical populations. We investigated the predictions of these models in healthy participants (N = 28), using dynamic causal modeling of fMRI BOLD activity to estimate time- and context-dependent changes in the indirect pathway effective connectivity, in association with repetitions or changes of choice selections. We used two multi-option tasks that required the participants to adapt to uncontrollable environmental changes, by performing sequential choice selections, with and without value-based feedbacks. We found that, irrespective of the task, the trials that were characterized by changes in choice selections (switch trials) were associated with a neural response that mostly overlapped with a network commonly described for the encoding of uncertainty. More interestingly, dynamic causal modeling and family-wise model comparison identified with high likelihood a directed causal relation from the external to the internal part of the globus pallidus (i.e., the short indirect pathway in the basal ganglia), in association with the switch trials. This finding supports the hypothesis that the short indirect pathway in the basal ganglia drives instability in the network dynamics, resulting in changes in choice selection.

An insula-driven network computes decision uncertainty and promotes abstinence in chronic cocaine users.

The anterior insular cortex (AIC) and its interconnected brain regions have been associated with both addiction and decision-making under uncertainty. However, the causal interactions in this uncertainty-encoding neurocircuitry and how these neural dynamics impact relapse remain elusive. Here, we used model-based fMRI to measure choice uncertainty in a motor decision task in 61 individuals with cocaine use disorder (CUD) and 25 healthy controls. CUD participants were assessed before discharge from a residential treatment program and followed for up to 24 weeks. We found that choice uncertainty was tracked by the AIC, dorsal anterior cingulate cortex (dACC) and ventral striatum (VS), across participants. Stronger activations in these regions measured pre-discharge predicted longer abstinence after discharge in individuals with CUD. Dynamic causal modeling revealed an AIC-to-dACC-directed connectivity modulated by uncertainty in controls, but a dACC-to-AIC connectivity in CUD participants. This reversal was mostly driven by early relapsers (<30 days). Furthermore, CUD individuals who displayed a stronger AIC-to-dACC excitatory connection during uncertainty encoding remained abstinent for longer periods. These findings reveal a critical role of an AIC-driven, uncertainty-encoding neurocircuitry in protecting against relapse and promoting abstinence.

Individual differences in working memory capacity and workload capacity.

We investigated the relationship between working memory capacity (WMC) and workload capacity (WLC). Each participant performed an operation span (OSPAN) task to measure his/her WMC and three redundant-target detection tasks to measure his/her WLC. WLC was computed non-parametrically (Experiments 1 and 2) and parametrically (Experiment 2). Both levels of analyses showed that participants high in WMC had larger WLC than those low in WMC only when redundant information came from visual and auditory modalities, suggesting that high-WMC participants had superior processing capacity in dealing with redundant visual and auditory information. This difference was eliminated when multiple processes required processing for only a single working memory subsystem in a color-shape detection task and a double-dot detection task. These results highlighted the role of executive control in integrating and binding information from the two working memory subsystems for perceptual decision making.