Cerebellar-Prefrontal Network Connectivity and Negative Symptoms in Schizophrenia

Abstract

Objective: The interpretability of results in psychiatric neuroimaging is significantly limited by an overreliance on correlational relationships. Purely correlational studies cannot alone determine whether behavior-imaging relationships are causal to illness, functionally compensatory processes, or purely epiphenomena. Negative symptoms (e.g., anhedonia, amotivation, and expressive deficits) are refractory to current medications and are among the foremost causes of disability in schizophrenia. The authors used a two-step approach in identifying and then empirically testing a brain network model of schizophrenia symptoms.

Methods: In the first cohort (N=44), a data-driven resting-state functional connectivity analysis was used to identify a network with connectivity that corresponds to negative symptom severity. In the second cohort (N=11), this network connectivity was modulated with 5 days of twice-daily transcranial magnetic stimulation (TMS) to the cerebellar midline.

Results: A breakdown of connectivity in a specific dorsolateral prefrontal cortex-to-cerebellum network directly corresponded to negative symptom severity. Restoration of network connectivity with TMS corresponded to amelioration of negative symptoms, showing a statistically significant strong relationship of negative symptom change in response to functional connectivity change.

Conclusions: These results demonstrate that a connectivity breakdown between the cerebellum and the right dorsolateral prefrontal cortex is associated with negative symptom severity and that correction of this breakdown ameliorates negative symptom severity, supporting a novel network hypothesis for medication-refractory negative symptoms and suggesting that network manipulation may establish causal relationships between network markers and clinical phenomena.

Keywords: Brain Imaging Techniques; Schizophrenia.

Conflict of interest statement

Conflicts of interest:

DO: Served on Scientific Advisory Board for Neurocrine Inc in 2016 AP-L: Serves on the scientific advisory boards for Nexstim, Neuronix, Starlab Neuroscience, Neuroelectrics, and Neosync; and is listed as an inventor on several issued and pending patents on the real-time integration of transcranial magnetic stimulation with electroencephalography and magnetic resonance imaging.

JDS: Serves on the scientific advisory board for Cadent, consults with Biogen, Biohaven, and Pfizer, and holds the license with the General Hospital Corporation to the Brief Ataxia Rating Scale and the Cerebellar Cognitive Affective / Schmahmann Syndrome scale.

Figures

Figure 1.

Multivariate distance matrix regression (MDMR) of negative symptoms in schizophrenia. (a) The MDMR method (after Shezad et al.16). Symptom scales and resting state fMRI data is collected from each participant. For each voxel in the brain atlas: the voxel is used as a seed region to create a connectivity map for each participant. Those maps are compared to each other to create a subject-wise similarity matrix. The symptom scale scores for each participant are then combined with the connectivity similarity matrix to produce a pseudo-F statistic which describes the symptom predictor’s ability to describe the similarity of the functional connectivity. A permutation test of the subject labels can be used to test the significance of this pseudo-F statistic. Each MDMR voxel wise result is then combined to produce a map of each voxels’ connectivity pattern’s ability to predict a symptom score. (b) MDMR results for negative symptoms in the network discovery cohort, voxelwise thresholded p<.005, cluster corrected at p<0.05. Bilateral dorso-lateral prefrontal cortex regions are identified. (c) MDMR results demonstrate the locations where the variability in connectivity from that region co-varies with symptom scales. In a post-hoc analysis, a seed-region was placed in the right dorsolateral prefrontal cortex in all subjects and then this seed-based connectivity map was correlated with symptom severity to identify locations where increasing connectivity to dorsolateral prefrontal cortex corresponds to better symptoms (red) and decreased connectivity corresponds to worse symptoms (blue). Thus, regions in blue correspond to locations where connectivity breakdown with dorsolateral prefrontal cortex corresponds to symptom worsening. The strongest dorsolateral prefrontal cortex connectivity breakdown to symptom severity correlation was observed in the midline cerebellum (MNI x-9, y-96, z-27).

Figure 2.

One week of twice daily cerebellar intermittent theta burst modulates dorsolateral prefrontal cortex-cerebellar functional connectivity in schizophrenia patients. (a) TMS protocol overview: symptom scales and resting-state fMRI data at baseline and at 1 week followup after TMS. TMS was delivered to the midline cerebellum, two times a day separated by 4 hours, in a randomized trial with active and sham arms. (b) Change (followup-baseline) in PANSS negative symptoms is correlated with change (followup-baseline) in MDMR-identified right dorsolateral prefrontal cortex-cerebellar network functional connectivity.

Figure 3.

Voxelwise analysis of change in functional connectivity from MDMR-identified right dorsolateral prefrontal cortex correlated with decrease in PANSS negative symptom score (red: increasing connectivity, blue: decreasing connectivity). Map is voxelwise thresholded at p<.001.>