心理学
无血性
背外侧前额叶皮质
精神分裂症(面向对象编程)
神经科学
前额叶皮质
神经影像学
连接体
静息状态功能磁共振成像
精神科
功能连接
认知
作者
Roscoe O. Brady,Irene Gonsalvez,Ivy Lee,Döst Öngür,Larry J. Seidman,Jeremy D. Schmahmann,Shaun M. Eack,Matcheri S. Keshavan,Álvaro Pascual‐Leone,Mark A. Halko
标识
DOI:10.1176/appi.ajp.2018.18040429
摘要
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.
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