Parietal-Prefrontal Feedforward Connectivity in Association With Schizophrenia Genetic Risk and Delusions

We examined 143 healthy control subjects and 66 patients with schizophrenia as they performed an event-related working memory task during MRI scanning. Participants, who were enrolled as part of the Clinical Brain Disorders Branch Sibling Study (25), were between 18 and 45 years of age (Table 1) and were right-handed. They were interviewed by a research psychiatrist using the Structured Clinical Interview for DSM-IV Axis I Disorders, and completed a neurological examination and a battery of neuropsychological tests. Exclusion criteria included an IQ <70, a history of prolonged substance abuse or significant medical problems, and any abnormalities found by EEG or MRI. All participants gave written consent before participation. The study was approved by the National Institute of Mental Health Institutional Review Board. The individuals in this sample were not part of our earlier study (1).

Blood-oxygen-level-dependent (BOLD) functional MRI data were acquired for each participant after a brief training period (∼10 minutes). The event-related working memory paradigm has been described in detail elsewhere (26). In each trial, participants first encoded two integer numbers presented over 1 second and retained this context information in working memory across a jittered interval of 3–5 seconds. In maintenance trials, participants subsequently responded to cues as to which of the two numbers was “larger” or “smaller” within 2 seconds. In the manipulation trials, participants had to perform a mental subtraction of 2 or 3 on one of the two context numbers before the “larger” or “smaller” evaluation within 2 seconds. There were 28 trials of working memory manipulation and 28 trials of working memory maintenance, counterbalanced for trial type and numerical size order, over about 9 minutes.

T₂*-weighted echo planar imaging (EPI) images with BOLD contrast were obtained with a 3-T MRI scanner (GE, Milwaukee) using a standard GE head coil (64×64×24 matrix with 3.75×3.75×6.0 mm spatial resolution, repetition time=2000 ms, echo time=28 ms, flip angle=90°, field of view=24×24 cm) while participants performed the cognitive task. The first four scans were discarded to allow for signal saturation.

The functional imaging data were preprocessed and analyzed using the general linear model for event-related designs in SPM12 (Wellcome Trust Centre for Neuroimaging, London). The functional images were corrected for differences in acquisition time between slices for each whole-brain volume and realigned to correct for head movement. Six movement parameters (translation: x, y, z; and rotation: roll, pitch, yaw) were included in the statistical model as covariates of no interest. The functional images were normalized to a standard EPI Montreal Neurological Institute template and then spatially smoothed using an isotropic Gaussian kernel of 8 mm full width half maximum.

A random-effects event-related statistical analysis was performed at two levels. In the first level, the onsets and durations of each trial for each task condition were convolved with a canonical hemodynamic response function and modeled using a general linear model on an individual subject basis. The realignment parameters were included as additional regressors of no interest. Data were high-pass filtered at 1/128 Hz. Random-effects analyses at the second (group) level were then conducted based on statistical parameter maps from each individual participant to allow population-level inference. Group-level activation maps were thresholded at a voxel-level whole brain p threshold of <0.05 with family-wise error correction for multiple comparisons unless otherwise stated.

We used dynamic causal modeling (DCM) (27) as implemented in SPM12 (DCM10) to examine how parietal and prefrontal brain regions interacted. We focused on the left parietal (coordinates, −38, −58, 38) and prefrontal (−44, 30, 16) regions implicated previously in context updating dysfunction in delusions (1), and we examined the corresponding parcels defined by the Human Connectome Project (28) in the parietal (the dorsal lateral intraparietal area [LIPd]) and prefrontal (Brodmann’s area 46) cortex encompassing these peaks. Time series during working memory context updating (manipulation) were extracted from each of the two regions of interest for each individual, masked by the conjunction of the respective cortical parcel, the group-level activation mask at p<0.05, voxelwise whole brain family-wise error corrected for multiple comparisons, and an individual subject-level activation at p<0.05. Deterministic bilinear DCM models comprising all seven possible input and pairing combinations of these two regions of interest (29) were then specified (Figure 1B shows this at the dorsolateral prefrontal cortex and parietal cortex, although this also applies to all region-of-interest pairs in the machine learning models below). The strength and direction of regional interactions were then estimated, elucidating how regional neural activity and their interactions were influenced by cognitive inputs during working memory context updating, as well as how these neuronal effects were biophysically linked to form BOLD signals (27). Bayesian model averaging was used to generate weighted task-related connectivity averages in each direction, for each pair of nodes, based on the posterior model probability (30). Using these results, we then examined task-related modulation of regional connectivity across participants and relationships with psychopathology and polygenic risk for schizophrenia (31).

Polygenic risk scores for schizophrenia were calculated for each control subject as a measure of genomic risk for schizophrenia, based on the most recent genome-wide association study (GWAS) (31). We obtained odds ratios of 102,217 index single-nucleotide polymorphisms (SNPs) from a meta-analysis of Psychiatric Genomics Consortium phase 2 (PGC2) GWAS using data sets excluding the discovery sample (PGC 2014, non-Lieber sample PGC2 GWAS). These SNPs were linkage disequilibrium independent (R²<0.1). We then calculated a weighted sum of risk alleles for schizophrenia by summing the imputation probability for the reference allele of the index SNP, weighted by the natural log of the odds ratio of association with schizophrenia, at each independent locus across the whole genome (31, 32). In this analysis, we used the polygenic risk score calculated using the conservative PGC2 GWAS association p value of 5×10⁻⁸.

We defined and tested the validity of each individual patient’s putative optimal (normal) parietal-prefrontal connectivity at working memory context updating. We first created a linear support vector regression (SVR) model, in healthy control subjects, of how feedforward parietal-prefrontal network connectivity during context updating may be predicted by connectivities from prefrontal and parietal regions during simple working memory maintenance, a process that is less dysfunctional in schizophrenia than context updating (21–23). We included 31 parcels in the frontal and parietal cortex that were robustly engaged during working memory maintenance in the patient and control samples (p<0.05, whole-brain voxel-wise family-wise error corrected in each separate patient or control sample). Time series during working memory maintenance were then extracted from the region of interest for each individual, masked by the conjunction of the respective cortical parcel, group-level activation mask at p<0.05, whole brain voxelwise family-wise error corrected for multiple comparisons, and the individual subject-level activation at p<0.05. Deterministic DCM models comprising all possible pairings across these 31 regions of interest (29) were specified, giving 465 pairs of regions. From each pair of regions of interest, we determined the Bayesian model averaging over all seven possible models within which a pair of regions could effectively interact (Figure 1B, and in reference 29). This resulted in a directed matrix of effective connectivity during working memory maintenance, predicting in SVR modeling in healthy control subjects the target parietal-prefrontal feedforward connectivity during working memory context updating. SVR was conducted in MATLAB using the implementation “fitrsvm” with a linear kernel, automatic hyperparameter tuning, and sequential minimal optimization. We examined the in-sample predictive validity using a leave-one-out correlation analysis of the left-out predicted parietal-prefrontal connectivity compared with the actual connectivity. We also examined predictions in more conservative approaches, including fivefold cross-validation in SVR with a held-out 20% test sample, and elastic net regression (33) with alpha at 0.5, also with a held-out 20% test sample.

More importantly, we subsequently tested the validity of this modeling approach in the patients. The SVR models thus derived in healthy control subjects were applied to each patient to determine what that individual patient’s target “normal” feedforward connectivity should be at context updating during working memory manipulation, given the patient’s connectivity pattern during the relatively less dysfunctional working memory maintenance. We examined each patient’s predicted normal connectivity and the deviation from his or her actual connectivity, as well as the extent to which this deviation correlated with delusional psychopathology, hypothesized previously to be associated with the feedforward connectivity deficits. We then tested the extent to which the connectivity-derived support vector predictions may outperform correlations with delusions derived by simply comparing between patient feedforward connectivity and mean control feedforward connectivity, and also by feedforward connectivity deviations across patients and control subjects uninformed by machine learning, the latter generated by random pairings between patients and control subjects over 10,000 permutations.

Across healthy subjects (N=143) and schizophrenia patients (N=66), age and parental education were similar (Table 1). The mean duration of illness among patients was 8.2 years (SD=7.82) and the mean age at onset was 19.5 years (SD=4.2). The patients’ mean total positive and negative symptom score was 54 (SD=24), and their mean dosage of antipsychotic medication was 298 mg/day (SD=389) of chlorpromazine equivalents.

Working memory manipulation was more difficult (less accurate) than working memory maintenance, and patients generally performed more poorly in working memory accuracy and reaction time (Table 1). However, working memory manipulation was disproportionately more dysfunctional than working memory maintenance in patients (working memory process-by-diagnosis interaction, F=63.9, df=1, 217, p<0.001) (Figure 1A).

In this report, we focus on the left LIPd and area 46 regions of interest (Human Connectome Project parcellation notations [28]), which correspond to regions where we previously found parietal and prefrontal effects associated with delusions and context updating in schizophrenia (1). Peaks in the left LIPd and area 46 regions of interest were significantly engaged during context updating in working memory manipulation at the group level in control subjects and in patients (p<0.05, voxel-wise whole-brain family-wise error corrected for multiple comparisons, in each control and patient group) (Figure 1B; see also Table S1 in the online supplement). Effective connectivity across these two regions of interest during context updating in working memory manipulation were engaged at feedforward (parietal to prefrontal) and feedback (prefrontal to parietal) in control subjects (p<0.001; see Table S2 in the online supplement) and in schizophrenia patients (p<0.001; see Table S4 in the online supplement). Connectivity in both directions was relatively reduced in patients between the LIPd and area 46 regions of interest (p<0.001).

Within patients with schizophrenia (N=66), we examined the hypothesized role of reduced parietal-prefrontal feedforward effective connectivity in the left LIPd and area 46 regions at context updating during working memory manipulation in delusional psychopathology, as suggested previously (1). Individuals with a score ≥3 for delusions on the Positive and Negative Syndrome Scale tended to have relatively reduced parietal-prefrontal feedforward effective connectivity (t=1.97, p=0.05) (Figure 1C). Effects in the opposite feedback direction were not significant.

We then tested whether these feedforward parietal-to-prefrontal neural functions were related to putative genetic mechanisms of illness, rather than to confounders associated with medications or chronic ill health, through effects on polygenic risk for schizophrenia in healthy individuals. Left LIPd to area 46 feedforward effective connectivity during working memory manipulation was reduced in association with increased polygenic risk for schizophrenia (r=−0.19, p<0.02) (Figure 1D). Effects in the opposite feedback direction were not significant.

If indeed dysfunction in parietal-prefrontal feedforward effective connectivity during updating of new information into context is related to delusions, the degree to which an individual patient’s connectivity deviates from his or her “optimum” value should also be related to dysfunction with delusions, particularly if the connectivity optimum reflects a relevant target to normalize psychopathology. To define an individual subject’s parietal-prefrontal connectivity optimum during working memory manipulation, we first examined 31 of the most activated parcels in the prefrontal and parietal cortices during working memory maintenance in control subjects (peak activation in each parcel of p<0.05, voxel-wise whole-brain family-wise error corrected for multiple comparisons; see Table S3 in the online supplement). From these brain parcels, we computed the effective connectivity from each pair of nodes in both directions, giving 930 pairwise effective connectivity values during working memory maintenance. As noted previously, working memory maintenance performance accuracy was relatively less dysfunctional in patients (Cohen’s d=0.67 for maintenance and d=2.11 for manipulation, relative to control subjects) (Figure 1A). However, in healthy control subjects (N=143), within the working memory maintenance prefrontal-parietal connectivity patterns in leave-one-out SVR models, feedforward parietal-prefrontal connectivity at LIPd to area 46 during working memory manipulation was robustly predicted (N=143, r>0.9, p<0.001; see Figure 1A in the online supplement). More conservative SVR models trained with 80% of the healthy sample (N=116) and tested on a held-out 20% sample also gave robust predictions (N=27, r>0.80, p<0.001) (Figure 2A). Elastic net regularization and variable selection (with alpha at 0.5), which effectively tackles model fitting where the number of variables exceeds that of subjects (33), yielded connectivity predictions in the held-out sample with an effect size reasonably similar to that of SVR (N=27; r=0.60, p<0.001; see Figure 1B in the online supplement).

We then reasoned that, given working memory maintenance connectivity from patients, we might predict their corresponding optimized working memory manipulation feedforward connectivity, using the SVR model independently derived from healthy control subjects. We tested the extent to which this individualized metric may be clinically significant, and found that patients’ deviations from this metric was associated with delusions (r=0.41, p<0.001) (Figure 2B). On the other hand, deviation between each patient’s connectivity and mean control connectivity did not correlate with psychopathology (p>0.17). Deviation between each patient’s parietal-prefrontal connectivity and randomly paired control subjects’ parietal-prefrontal connectivity also did not correlate with psychopathology, and yielded an average r of 0.01 (p=0.52) over 10,000 iterations, with 2/10,000 instances of performance equal to or better than that of the support vector machine (r≥0.41). We further tested the specificity to delusional psychopathology and found no significant correlation between each patient’s deviation from modeled “optimal” feedforward connectivity with negative syndrome score (p>0.12) or with general symptom score (p>0.8). There was a smaller correlation with positive syndrome score (r=0.25, p=0.03), and, within the positive syndrome, correlation with hallucinations (r=0.26, p=0.02), as well as the larger effect with delusions noted above. In a regression of the connectivity deviation against hallucinations and delusions, the hallucination effect disappeared (p>0.8) and the delusion effect remained (beta=0.41, p=0.01), suggesting a degree of specificity for delusions.