Biology subjects

Lindquist, M. A.

Publications and source records attributed to Lindquist, M. A..

4 recordsLinked to original sources

bioRxiv2018

Fatigue induces long lasting detrimental changes in motor skill learning

Fatigue due to physical exertion is a ubiquitous phenomenon in everyday life and especially common in a range of neurological diseases. While the effect of fatigue on limiting skill execution are well known, its influence on learning new skills is unclear. This is of particular interest as it is common practice to train athletes, musicians or perform rehabilitation exercises up to and beyond a point of fatigue. In a series of experiments, we describe how fatigue impairs motor skill learning beyond its effects on task execution. The negative effects on learning are evidenced by impaired task acquisition on subsequent practice days even in the absence of fatigue. Further, we found that this effect is in part mediated centrally and can be alleviated by altering motor cortex function. Thus, the common practice of training while, or beyond, fatigue levels should be carefully reconsidered, since this affects overall long-term skill learning.

neuroscience↗

bioRxiv2018

Multiple brain networks mediating stimulus-pain relationships in humans

The brain transforms nociceptive input into a complex pain experience comprised of sensory, affective, motivational, and cognitive components. However, it is still unclear how pain arises from nociceptive input, and which brain networks coordinate to generate pain experiences. We introduce a new high-dimensional mediation analysis technique to estimate distributed, network-level patterns mediating the relationship between stimulus intensity and pain. In a large-scale analysis of functional magnetic resonance imaging data (N=284), we identify both traditional mediators in somatosensory brain regions and additional mediators located in prefrontal, midbrain, striatal, and default-mode regions unrelated to nociception in standard analyses. The whole brain mediators are specific for pain vs. aversive sounds and are organized in five functional networks. Brain mediators explain 32% more within-subject variance of single-trial pain ratings than previous brain-based models. Our results provide a new, broader view of the networks underlying pain experience, as well as distinct targets for interventions.

neuroscience↗

bioRxiv2018

Effect Size and Power in fMRI Group Analysis

Multi-subject functional magnetic resonance imaging (fMRI) analysis is often concerned with determining whether there exists a significant population-wide activation in a comparison between two or more conditions. Typically this is assessed by testing the average value of a contrast of parameter estimates (COPE) against zero in a general linear model (GLM) analysis. In this work we investigate several aspects of this type of analysis. First, we study the effects of sample size on the sensitivity and reliability of the group analysis, allowing us to evaluate the ability of small sampled studies to effectively capture population-level effects of interest. Second, we assess the difference in sensitivity and reliability when using volumetric or surface based data. Third, we investigate potential biases in estimating effect sizes as a function of sample size. To perform this analysis we utilize the task-based fMRI data from the 500-subject release from the Human Connectome Project (HCP). We treat the complete collection of subjects (N = 491) as our population of interest, and perform a single-subject analysis on each subject in the population. We investigate the ability to recover population level effects using a subset of the population and standard analytical techniques. Our study shows that sample sizes of 40 are generally able to detect regions with high effect sizes (Cohens d > 0.8), while sample sizes closer to 80 are required to reliably recover regions with medium effect sizes (0.5 < d < 0.8). We find little difference in results when using volumetric or surface based data with respect to standard mass-univariate group analysis. Finally, we conclude that special care is needed when estimating effect sizes, particularly for small sample sizes.

neuroscience↗

bioRxiv2016

A Bayesian Heteroscedastic GLM with Application to fMRI Data with Motion Spikes

We propose a voxel-wise general linear model with autoregressive noise and heteroscedastic noise innovations (GLMH) for analyzing functional magnetic resonance imaging (fMRI) data. The model is analyzed from a Bayesian perspective and has the benefit of automatically down-weighting time points close to motion spikes in a data-driven manner. We develop a highly efficient Markov Chain Monte Carlo (MCMC) algorithm that allows for Bayesian variable selection among the regressors to model both the mean (i.e., the design matrix) and variance. This makes it possible to include a broad range of explanatory variables in both the mean and variance (e.g., time trends, activation stimuli, head motion parameters and their temporal derivatives), and to compute the posterior probability of inclusion from the MCMC output. Variable selection is also applied to the lags in the autoregressive noise process, making it possible to infer the lag order from the data simultaneously with all other model parameters. We use both simulated data and real fMRI data from OpenfMRI to illustrate the importance of proper modeling of heteroscedasticity in fMRI data analysis. Our results show that the GLMH tends to detect more brain activity, compared to its homoscedastic counterpart, by allowing the variance to change over time depending on the degree of head motion.

neuroscience↗