Functional Delineation of Prefrontal Networks Underlying Working Memory in Schizophrenia: A Cross-data-set Examination

Abstract

Background: Working memory (WM) impairment in schizophrenia substantially impacts functional outcome. Although the dorsolateral pFC has been implicated in such impairment, a more comprehensive examination of brain networks comprising pFC is warranted. The present research used a whole-brain, multi-experiment analysis to delineate task-related networks comprising pFC. Activity was examined in schizophrenia patients across a variety of cognitive demands. Methods: One hundred schizophrenia patients and 102 healthy controls completed one of four fMRI tasks: a Sternberg verbal WM task, a visuospatial WM task, a Stroop set-switching task, and a thought generation task (TGT). Task-related networks were identified using multi-experiment constrained PCA for fMRI. Effects of task conditions and group differences were examined using mixed-model ANOVA on the task-related time series. Correlations between task performance and network engagement were also performed. Results: Four spatially and temporally distinct networks with pFC activation emerged and were postulated to subserve (1) internal attention, (2) auditory–motor attention, (3) motor responses, and (4) task energizing. The “energizing” network—engaged during WM encoding and diminished in patients—exhibited consistent trend relationships with WM capacity across different data sets. The dorsolateral-prefrontal-cortex-dominated “internal attention” network exhibited some evidence of hypoactivity in patients, but was not correlated with WM performance. Conclusions: Multi-experiment analysis allowed delineation of task-related, pFC-anchored networks across different cognitive constructs. Given the results with respect to the early-responding “energizing” network, WM deficits in schizophrenia may arise from disruption in the “energization” process described by Donald Stuss' model of pFC functions.

Functional Brain Networks Underlying Autobiographical Event Simulation: An Update

“…This was a network of regions including superior and rostrolateral prefrontal cortex, thalamus, caudate, and lateral occipital cortex that overlaps anatomically with the frontoparietal resting-state network. This MAIN has previously been reported as active when: (1) maintaining information during the delay periods in working memory paradigms (Sanford et al, 2020;Sanford & Woodward, 2021), (2) evaluating whether statements are self-referential (Lariviere et al, 2017), and (3) silently stating the function of a viewed object (Sanford et al, 2020; see Supplementary Figure S4). Here, we observed activation for all conditions late in construction, and throughout the elaboration and scale rating phases.…”

The Pattern-Based Anatomy and Cognitive Mode associated with the Language Network in fMRI

“…First, the reported anatomical configuration is directly sensitive to how well the task-induced BOLD changes match an assumed hemodynamic response (HDR) model. The direct observation of the network-level task-induced BOLD changes elicited by the task timing and task conditions are not observable when applying the assumed to-be-matched model, but are readily retrievable using a finite impulse response (FIR) model of task timing [4][5][6][7][8][9][10][11][12] . As is argued above, these observed task-induced BOLD changes and their associated anatomical depiction are valuable for interpretation of network function, and may or may not match well to an assumed model shape.…”

“…As is argued above, these observed task-induced BOLD changes and their associated anatomical depiction are valuable for interpretation of network function, and may or may not match well to an assumed model shape. Second, despite each cognitive process (e.g., sustained attention, response, response re-evaluation) having its own pattern of task-induced BOLD changes, multiple cognitive processes could partially match a synthetic HDR pattern model, thereby conflating them anatomically and interpretationally 8,9,11,12 . The fMRI-CPCA methodology does not require contrasts, as different networks underlying distinct cognitive processes (such as response and visual\auditory perception) are captured on different dimensions in the analysis, and the task-induced BOLD changes for control and experimental conditions can be directly observed simultaneously for each network separately.…”

“…Functional magnetic resonance imaging (fMRI) reliably detects whole-brain configurations of blood-oxygen-level-dependent (BOLD) signal, and the most well-known studies of these networks involved analysis of resting-state fMRI data 1, 2 . However, a set of task-based fMRI networks is also available 3 , and how these overlap with resting state networks has been put forward in a number of publications [4][5][6][7][8][9][10][11][12] . One of the most well-known of these task-based fMRI networks is referred to as the language network (LN) 10,13,14 .…”

A Closer Look at Transitions between the Generative and Evaluative Phases of Creative Thought

“…A final modern fMRI analysis technique that may prove fruitful in studying generative and evaluative phase transitions is finite impulse response-based constrained principal component analysis (FIR-CPCA; Choi et al, 2017;Metzak et al, 2011;Sanford et al, 2020;Sanford & Woodward, 2021). Finite impulse response (FIR) models estimate the average change in BOLD signal across task-specific scans relative to all other scans, in contrast to canonical hemodynamic response models that assume the shape and timing of the BOLD response.…”