Bayesian analysis of functional magnetic resonance imaging data with spatially varying auto‐regressive orders

Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/148250/1/rssc12320.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/148250/2/rssc12320_am.pd

Spectral Diversity in Default Mode Network Connectivity Reflects Behavioral State.

Default mode network (DMN) functional connectivity is thought to occur primarily in low frequencies (<0.1 Hz), resulting in most studies removing high frequencies during data preprocessing. In contrast, subtractive task analyses include high frequencies, as these are thought to be task relevant. An emerging line of research explores resting fMRI data at higher-frequency bands, examining the possibility that functional connectivity is a multiband phenomenon. Furthermore, recent studies suggest DMN involvement in cognitive processing; however, without a systematic investigation of DMN connectivity during tasks, its functional contribution to cognition cannot be fully understood. We bridged these concurrent lines of research by examining the contribution of high frequencies in the relationship between DMN and dorsal attention network at rest and during task execution. Our findings revealed that the inclusion of high frequencies alters between network connectivity, resulting in reduced anticorrelation and increased positive connectivity between DMN and dorsal attention network. Critically, increased positive connectivity was observed only during tasks, suggesting an important role for high-frequency fluctuations in functional integration. Moreover, within-DMN connectivity during task execution correlated with RT only when high frequencies were included. These results show that DMN does not simply deactivate during task execution and suggest active recruitment while performing cognitively demanding paradigms

Assessing the Influence of Different ROI Selection Strategies on Functional Connectivity Analyses of fMRI Data Acquired During Steady-State Conditions

In blood oxygen level dependent (BOLD) functional magnetic resonance imaging (fMRI), assessing functional connectivity between and within brain networks from datasets acquired during steady-state conditions has become increasingly common. However, in contrast to connectivity analyses based on task-evoked signal changes, selecting the optimal spatial location of the regions of interest (ROIs) whose timecourses will be extracted and used in subsequent analyses is not straightforward. Moreover, it is also unknown how different choices of the precise anatomical locations within given brain regions influence the estimates of functional connectivity under steady-state conditions. The objective of the present study was to assess the variability in estimates of functional connectivity induced by different anatomical choices of ROI locations for a given brain network. We here targeted the default mode network (DMN) sampled during both resting-state and a continuous verbal 2-back working memory task to compare four different methods to extract ROIs in terms of ROI features (spatial overlap, spatial functional heterogeneity), signal features (signal distribution, mean, variance, correlation) as well as strength of functional connectivity as a function of condition. We show that, while different ROI selection methods produced quantitatively different results, all tested ROI selection methods agreed on the final conclusion that functional connectivity within the DMN decreased during the continuous working memory task compared to rest

Optimizing Preprocessing and Analysis Pipelines for Single-Subject fMRI: 2. Interactions with ICA, PCA, Task Contrast and Inter-Subject Heterogeneity

A variety of preprocessing techniques are available to correct subject-dependant artifacts in fMRI, caused by head motion and physiological noise. Although it has been established that the chosen preprocessing steps (or “pipeline”) may significantly affect fMRI results, it is not well understood how preprocessing choices interact with other parts of the fMRI experimental design. In this study, we examine how two experimental factors interact with preprocessing: between-subject heterogeneity, and strength of task contrast. Two levels of cognitive contrast were examined in an fMRI adaptation of the Trail-Making Test, with data from young, healthy adults. The importance of standard preprocessing with motion correction, physiological noise correction, motion parameter regression and temporal detrending were examined for the two task contrasts. We also tested subspace estimation using Principal Component Analysis (PCA), and Independent Component Analysis (ICA). Results were obtained for Penalized Discriminant Analysis, and model performance quantified with reproducibility (R) and prediction metrics (P). Simulation methods were also used to test for potential biases from individual-subject optimization. Our results demonstrate that (1) individual pipeline optimization is not significantly more biased than fixed preprocessing. In addition, (2) when applying a fixed pipeline across all subjects, the task contrast significantly affects pipeline performance; in particular, the effects of PCA and ICA models vary with contrast, and are not by themselves optimal preprocessing steps. Also, (3) selecting the optimal pipeline for each subject improves within-subject (P,R) and between-subject overlap, with the weaker cognitive contrast being more sensitive to pipeline optimization. These results demonstrate that sensitivity of fMRI results is influenced not only by preprocessing choices, but also by interactions with other experimental design factors. This paper outlines a quantitative procedure to denoise data that would otherwise be discarded due to artifact; this is particularly relevant for weak signal contrasts in single-subject, small-sample and clinical datasets

The effects of age bias on neural correlates of successful and unsuccessful response inhibition in younger and older adults.

Facilitating communication between generations has become increasingly important. However, individuals often demonstrate a preference for their own age group, which can impact social interactions, and such bias in young adults even extends to inhibitory control. To assess whether older adults also experience this phenomenon, a group of younger and older adults completed a Go/NoGo task incorporating young and old faces, while undergoing functional magnetic resonance imaging. Within the networks subserving successful and unsuccessful response inhibition, patterns of activity demonstrated distinct neural age bias effects in each age group. During successful inhibition, the older adult group demonstrated significantly increased activity to other-age faces, whereas unsuccessful inhibition in the younger group produced significantly enhanced activity to other-age faces. Consequently, the findings of the study confirm that neural responses to successful and unsuccessful inhibition can be contingent on the stimulus-specific attribute of age in both younger and older adults. These findings have important implications in regard to minimizing the emergence of negative consequences, such as ageism, as a result of related implicit biases

The effects of age-bias on neural correlates of successful and unsuccessful response inhibition

Response inhibition is important for adherence to social norms, especially when norms conflict with biases based on one’s social identity. While previous studies have shown that in-group bias generally modulates neural activity related to stimulus appraisal, it is unclear whether and how an in-group bias based on age affects neural information processing during response inhibition. To assess this potential influence, young adults completed a Go/NoGo task incorporating younger face (in-group) and older face (out-group) stimuli while undergoing functional magnetic resonance imaging (fMRI). Our results replicated previous findings by demonstrating higher accuracy in successful Go compared to NoGo trials, as well as the engagement of nodes of the response inhibition network during successful response inhibition, and brain regions comprising the salience network during unsuccessful response inhibition. Importantly, despite a lack of behavioural differences, our results showed that younger and older face stimuli modulated activity in the response inhibition and salience networks during successful and unsuccessful inhibition, respectively. Interestingly, these effects were not uniform across networks. During successful response inhibition, in-group stimuli increased activity in medial prefrontal cortex and temporo-parietal junction, whereas out-group stimuli more strongly engaged pre-supplemental motor area. During unsuccessful response inhibition, in-group stimuli increased activity in posterior insula, whereas out-group stimuli more strongly engaged angular gyrus and intraparietal sulcus. Consequently, the results infer the presence of an age-bias effect in the context of inhibitory control, which has substantial implications for future experimental design and may also provide the means of investigating the neural correlates of implicit beliefs that contribute to ageis