Feasibility of Acquiring Neuroimaging Data from Adults with Acquired Brain Injuries before and after a Yoga Intervention.

BACKGROUND: To date, no one has prospectively evaluated yoga intervention-induced changes in brain structure or function in adults with acquired brain injuries (ABI). Thus, this study was conducted to test the feasibility of acquiring neuroimaging data from adults with ABI before and after a yoga intervention. METHODS: This was a single-arm intervention feasibility study that included 12 adults with chronic (i.e., greater than 6 months post-injury) ABI and self-reported limitations in balance. Neuroimaging data were acquired before and after yoga. The yoga intervention was completed once per week for eight weeks. Feasibility objectives and benchmarks were established a priori. RESULTS: Most feasibility objectives and benchmarks were achieved. The goal of recruiting 12 participants was successfully achieved, and 75% of participants were retained throughout the study (goal of 80%). All imaging feasibility benchmarks were met; rs-fMRI and fNIRS data were acquired safely, data were of acceptable quality, and data pre-processing procedures were successful. Additionally, improvements were detected in balance after yoga, as group-level balance was significantly better post-yoga compared to pre-yoga, p = 0.043. CONCLUSIONS: These findings indicate it is feasible to acquire neuroimaging data from adults with ABI before and after a yoga intervention. Thus, future prospective studies are warranted.

Detecting the Inverted-U in fMRI Studies of Schizophrenia: A Comparison of Three Analysis Methods.

OBJECTIVE: Cognitive tasks are used to probe neuronal activity during functional magnetic resonance imaging (fMRI) to detect signs of aberrant cognitive functioning in patients diagnosed with schizophrenia (SZ). However, nonlinear (inverted-U-shaped) associations between neuronal activity and task difficulty can lead to misinterpretation of group differences between patients and healthy comparison subjects (HCs). In this paper, we evaluated a novel method for correcting these misinterpretations based on conditional performance analysis. METHOD: Participants included 25 HCs and 27 SZs who performed a working memory (WM) task (N-back) with 5 load conditions while undergoing fMRI. Neuronal activity was regressed onto: 1) task load (i.e., parametric task levels), 2) marginal task performance (i.e., performance averaged over all load conditions), or 3) conditional task performance (i.e., performance within each load condition). RESULTS: In most regions of interest, conditional performance analysis uniquely revealed inverted-U-shaped neuronal activity in both SZs and HCs. After accounting for conditional performance differences between groups, we observed few difference in both the pattern and level of neuronal activity between SZs and HCs within regions that are classically associated with WM functioning (e.g., posterior dorsolateral prefrontal and parietal association cortices). However, SZs did show aberrant activity within the anterior dorsolateral prefrontal cortex. CONCLUSIONS: Interpretations of differences in neuronal activity between groups, and of associations between neuronal activity and performance, should be considered within the context of task performance. Whether conditional performance-based differences reflect compensation, dedifferentiation, or other processes is not a question that is easily resolved by examining activation and performance data alone.

Latent Variable Modeling and Adaptive Testing for Experimental Cognitive Psychopathology Research.

The adaptation of experimental cognitive tasks into measures that can be used to quantify neurocognitive outcomes in translational studies and clinical trials has become a key component of the strategy to address psychiatric and neurological disorders. Unfortunately, while most experimental cognitive tests have strong theoretical bases, they can have poor psychometric properties, leaving them vulnerable to measurement challenges that undermine their use in applied settings. Item response theory-based computerized adaptive testing has been proposed as a solution but has been limited in experimental and translational research due to its large sample requirements. We present a generalized latent variable model that, when combined with strong parametric assumptions based on mathematical cognitive models, permits the use of adaptive testing without large samples or the need to precalibrate item parameters. The approach is demonstrated using data from a common measure of working memory-the N-back task-collected across a diverse sample of participants. After evaluating dimensionality and model fit, we conducted a simulation study to compare adaptive versus nonadaptive testing. Computerized adaptive testing either made the task 36% more efficient or score estimates 23% more precise, when compared to nonadaptive testing. This proof-of-concept study demonstrates that latent variable modeling and adaptive testing can be used in experimental cognitive testing even with relatively small samples. Adaptive testing has the potential to improve the impact and replicability of findings from translational studies and clinical trials that use experimental cognitive tasks as outcome measures.