Abstract
Objective:
The interpretability of results in psychiatric neuroimaging is significantly limited by an overreliance on correlational relationships. Purely correlational studies cannot alone determine if behavior-imaging relationships are causal to illness, functionally compensatory processes, or purely epiphenomena. Here we take a two-step approach to identifying and then empirically testing a brain network model of schizophrenia symptoms. Negative symptoms (e.g. anhedonia, amotivation, and expressive deficits) are refractory to current medications and are one of the foremost causes of disability in this illness.
Method:
We used two-stage method: In the first cohort (n=44), we used a data-driven resting state functional connectivity analysis to identify a network whose connectivity corresponds to negative symptom severity. Then in a second cohort (n=11) we modulated this network connectivity with 5 days of twice daily transcranial magnetic stimulation to the cerebellar midline (vermis VIIb).
Results:
A breakdown of connectivity in a specific dorsolateral prefrontal cortex to cerebellum network directly corresponds to negative symptom severity. Restoration of network connectivity with TMS corresponds to amelioration of negative symptoms, showing a strong relationship of functional connectivity change to negative symptom change (r=0.809,p=0003).
Conclusion:
Our results demonstrate that a connectivity breakdown between the cerebellum and right dorsolateral prefrontal cortex is associated with negative symptom severity and that correction of this breakdown ameliorates negative symptom severity. Our results support a novel network hypothesis for medication refractory negative symptoms and indicates network manipulation may establish causal relationships between network markers and clinical phenomena.