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

Abstract

Schizophrenia is associated with diverse neuroimage-derived phenotypes such as increased ventricular size, gray matter reduction, reduced white matter anisotropy, and functional differences at rest and while performing a task. Well-established functional MRI analysis methods, including observation of statistically significant differences in first-order regional activation as well as second-order functional connectivity modeling based on correlation of blood oxygenation level dependent (BOLD) signal time-series between pairs of spatially distributed regions, either account for activity in individual regions or pairwise coactivation of regions, respectively. Neither of these methods integrate both first-order and second-order terms to obtain a comprehensive picture of neural dynamics. While it is challenging to incorporate both of these into one framework, the pairwise maximum entropy model (MEM), also called the Ising model, captures both individual and pairwise activity into a single quantity known as energy, which is inversely related to the probability of state occurrence. Here, we apply the MEM framework to task functional MRI data collected on 23 adolescent onset schizophrenia (AOS) since AOS is a more severe form of schizophrenia with more severe cognitive impairments and poorer outcome, in comparison with 53 healthy control subjects while performing the Penn Conditional Exclusion Test (PCET), which measures abstraction and mental flexibility, aspects of executive function that have repeatedly been shown to be impaired in AOS. Accuracy of PCET performance was significantly reduced among AOS compared to controls as expected. Average cumulative energy achieved for a participant over the course of the fMRI negatively correlated with task performance, and the association was stronger than any first-order association. The AOS image-derived phenotype was characterized by spending more time in higher energy brain states that represent lower probability of occurrence and were associated with impaired executive function, suggesting less stable neural dynamics among AOS compared to controls, who spent more time in lower energy states that are more stable. The energy landscapes in both conditions featured attractors that corresponded to two distinct subnetworks, namely fronto-temporal and parieto-motor. Attractor basins were larger in the controls than in AOS; moreover, fronto-temporal basin size was significantly correlated with cognitive performance in controls, although not among the AOS group. These findings demonstrate that the MEM can integrate and reduce the dimensionality of neuroimaging data, providing information about functional brain states while emphasizing rather than obscuring clinical and cognitive associations, and may shed light on the organizing principles of neural dynamics in patients and controls.

Diagnostically distinct resting state fMRI energy distributions: A subject-specific maximum entropy modeling study

“…The mean activation level is by definition zero after z-scoring, resulting in a B matrix with 50% “on” states and 50% “off” states (i.e., 50% of entries in each row of matrix B are 1 and 50% are 0). This threshold, zero, has previously been determined to be the most optimal threshold choice in terms of fit of MEM to empirical observations (14).…”

“…The mean activation level is by definition zero after z-scoring, resulting in a B matrix with 50% "on" states and 50% "off" states (i.e., 50% of entries in each row of matrix B are 1 and 50% are 0). This threshold, zero, has previously been determined to be the most optimal threshold choice in terms of fit of MEM to empirical observations (14). A) The preprocessed BOLD signals in amplitude of arbitrary units for a single subject for the 8 bilateral nodes: POS1, p32m aTPOJ3, and PGi in both hemispheres.…”

“…Temporal snapshots of this time-evolving system at multiple scales can be considered states in the same sense used in the Ising model, though these states are binary simplifications of the full underlying biological activity. Several previous studies on healthy individuals have shown that concatenated functional magnetic resonance imaging (fMRI) state occurrence patterns across individuals can be predicted using an Ising model (7)(8)(9)(10), but only a few (11)(12)(13) have attempted to interpret the parameters of the model biologically or have applied it to psychiatric disorders (14).…”