Energy of functional brain states correlates with cognition in adolescent-onset schizophrenia and healthy persons

Abstract

Adolescent-onset schizophrenia (AOS) is rare, under-studied, and associated with more severe cognitive impairments and poorer outcomes than adult-onset schizophrenia. Neuroimaging has shown altered regional activations (first-order effects) and functional connectivity (second-order effects) in AOS compared to controls. The pairwise maximum entropy model (MEM) integrates first- and second-order factors into a single quantity called energy, which is inversely related to probability of occurrence of brain activity patterns. We take a combinatorial approach to study multiple brain-wide MEMs of task-associated components; hundreds of independent MEMs for various sub-systems are fit to 7 Tesla functional MRI scans. Acquisitions were collected from 23 AOS individuals and 53 healthy controls while performing the Penn Conditional Exclusion Test (PCET) for executive function, which is known to be impaired in AOS. Accuracy of PCET performance was significantly reduced among AOS compared to controls. A majority of the models showed significant negative correlation between PCET scores and the total energy attained over the fMRI. Across all instantiations, the AOS group was associated with significantly more frequent occurrence of states of higher energy, assessed with a mixed effects model. An example MEM instance was investigated further using energy landscapes, which visualize high and low energy states on a low-dimensional plane, and trajectory analysis, which quantify the evolution of brain states throughout this landscape. Both supported patient-control differences in the energy profiles. Severity of psychopathology was correlated positively with energy. The MEM's integrated representation of energy in task-associated systems can help characterize pathophysiology of AOS, cognitive impairments, and psychopathology.

Keywords: Adolescent onset schizophrenia; Attractors; Cognitive impairments; Energy landscape analysis; Executive function; Maximum Entropy Model.

Figure 1:

The Penn Conditional Exclusion Task (PCET) used in this study to collect task fMRI data related to executive functions. A) Six task blocks (black boxes) and 7 fixation blocks (white boxes) are shown. The onset time in seconds (numbers in white and black boxes) as well as the corresponding MRI TR (grey box) are shown. Each TR is 3 seconds with total number of TRs = 142; total fMRI duration 7 min and 33 sec. B) Within a task block, 8 stimuli are presented over 48 seconds followed by a fixation block consist of a cross. C) Each stimulus consists of 4 objects with varying features (line thickness, size, and shape). The participant’s responses are provided with feedback (“correct” or “incorrect”).

Figure 2:

A schematic outline of the experimental design. One sample of nodes is pictured, with only four nodes. A. Four example components from GICA are randomly selected. B. An example time series for these nodes for a single subject. C. The four regions are binarized and concatenated across all subjects. Next, an MEM is fit to the data, using the binarized components as the nodal timeseries used to optimize the model parameters. D. The MEM assigns energy values to each brain state, and these values are summed per individual and compared performance on the PCET. The relationship between total energy and cognitive performance across subjects is the main variable of interest compared across node samples. E. For one sample, an energy landscape analysis is performed to study differences between the group-level trajectories on the population level MEM.

Figure 3:

PCET scores distribution between AOS and HC. Controls show a right shift in higher PCET accuracy scores compared to patients who mostly concentrated to the left. Blue: controls. Red: AOS. X-axis is total PCET correct. Y-axis is the number of participants in the group who scored the corresponding amount.

Figure 4:

Histogram showing results of 501 sampling experiments for correlation between energy and PCET score. The yellow bin in each plot indicates the bin that the example model of interest belongs in. Plots in the top row show Pearson correlation, r, across subjects between the total energy that subject achieved over time and that PCET score. Plots in the bottom row show the significance, p, where the red vertical line indicates an α <0.05. Bins further to the right on the bottom plots are more significant due to the negative log operation. The left column shows group level results, the middle column shows results for the HC group, and the right column shows the AOS group results.

Figure 5:

Energy Characteristics by Group for the single example sample of nodes. A. The energy level of the most frequent (modal) energy bin in each subgroup: blue is the control group (N=53), and red is the AOS group (N=23). Time samples are fMRI TR, which are 3 seconds in duration. The yellow regions represent the task blocks, which contain 8 stimuli each. Stimuli are presented every 9 seconds. B. Average Energy versus PCET Score. The energy values averaged across the entire duration of the fMRI (142 timepoints) for each subject (Blue point: healthy controls; Red points: AOS; Green: Combined sample of AOS and HC) show significant negative correlation with PCET scores.

Figure 6:

Energy landscapes for both groups show distinct wells. A) Comparison of energy landscapes for combined model (left) versus separate models for controls (middle) and AOS patients (right). All three manifolds show two primary wells, although the well depths and energy barrier between wells differ across them. Wells (blue regions) represent more probable, lower energy conglomerates of states. Theoretically, an individual’s brain activation states yield a sequence of positions on the energy landscape during cognition. B) Comparison of the well areas between MEM of controls versus AOS. The area under the curve (AUC) was calculated for energy cumulative distribution functions (CDF). The definition of well depends on the choice of a threshold. An example for threshold = −1.2 is marked with green rectangle, i.e. all states that have energy values lower than −1.2 are considered as well and the area under the curve (shown as an inset) calculates how many states form a well . C) Percent difference in AUC of control vs AUC for AOS group for different values of threshold. The total well AUC for control was 32.94% higher than for the AOS group as marked by green point and green rectangle in (B).As can be observed, the total well area in the for the control group is higher than the well area for AOS group for all values of threshold < −0.35.

Figure 7:

Average single trial trajectories for AOS have more variance compared to Controls: A) Individual average control trajectories for 5 controls are shown in cooler colors and thin lines (blue to green) and 5 individual AOS trajectories are shown in warmer colors (pink to yellow). The star (*) represents the start of the trial and the circle marks the end of a trial. The average trajectory over control individuals and AOS individuals are shown as thick blue lines for controls and as red lines for AOS. The filled X represent the stimulus onset during the trial. The ellipsoid encompasses the average standard deviation across all controls or AOS subject trajectories in X (PC1), Y(PC2) and Z (energy) axes. A higher radius of the ellipsoid along X and Y axis as seen for AOS suggests higher spread for AOS trajectories over PC1 and PC2. B) The pairwise Euclidean distances between the trajectories for Control group vs AOS group. The distances in Control group were significantly lower than the distances in the AOS group (4.03 vs 4.24, two sided t-test: −4.4, p= 0.0). C) Variance across the trajectories plotted as a function of TR for the three dimensions, i.e. PC1, PC2 and Energy. The trajectories in PC1 show the highest variance.