Association between dynamic resting-state functional connectivity and ketamine plasma levels in visual processing networks.
Marie SpiesManfred KlöblAnna HöflichAllan HummerThomas VanicekPaul MichenthalerGeorg S KranzAndreas HahnDietmar WinklerChristian WindischbergerSiegfried KasperRupert LanzenbergerPublished in: Scientific reports (2019)
Numerous studies demonstrate ketamine's influence on resting-state functional connectivity (rsFC). Seed-based and static rsFC estimation methods may oversimplify FC. These limitations can be addressed with whole-brain, dynamic rsFC estimation methods. We assessed data from 27 healthy subjects who underwent two 3 T resting-state fMRI scans, once under subanesthetic, intravenous esketamine and once under placebo, in a randomized, cross-over manner. We aimed to isolate only highly robust effects of esketamine on dynamic rsFC by using eight complementary methodologies derived from two dynamic rsFC estimation methods, two functionally defined atlases and two statistical measures. All combinations revealed a negative influence of esketamine on dynamic rsFC within the left visual network and inter-hemispherically between visual networks (p < 0.05, corrected), hereby suggesting that esketamine's influence on dynamic rsFC is highly stable in visual processing networks. Our findings may be reflective of ketamine's role as a model for psychosis, a disorder associated with alterations to visual processing and impaired inter-hemispheric connectivity. Ketamine is a highly effective antidepressant and studies have shown changes to sensory processing in depression. Dynamic rsFC in sensory processing networks might be a promising target for future investigations of ketamine's antidepressant properties. Mechanistically, sensitivity of visual networks for esketamine's effects may result from their high expression of NMDA-receptors.
Keyphrases
- resting state
- functional connectivity
- pain management
- major depressive disorder
- magnetic resonance imaging
- poor prognosis
- computed tomography
- machine learning
- clinical trial
- randomized controlled trial
- chronic pain
- high dose
- electronic health record
- big data
- long non coding rna
- subarachnoid hemorrhage
- bipolar disorder
- deep learning
- artificial intelligence
- contrast enhanced
- case control