Fine-tuning neural excitation/inhibition for tailored ketamine use in treatment-resistant depression
The glutamatergic modulator ketamine has been shown to rapidly reduce depressive symptoms in patients with treatment-resistant major depressive disorder (TRD). Although its mechanisms of action are not fully understood, changes in cortical excitation/inhibition (E/I) following ketamine administration are well documented in animal models and could represent a potential biomarker of treatment response. Here, we analyse neuromagnetic virtual electrode timeseries collected from the primary somatosensory cortex in 18 unmedicated patients with TRD and in an equal number of age-matched healthy controls during a somatosensory 'airpuff' stimulation task. These two groups were scanned as part of a clinical trial of ketamine efficacy under three conditions: a) baseline; b) 6-9 hours following subanesthetic ketamine infusion; and c) 6-9 hours following placebo-saline infusion. We obtained estimates of E/I interaction strengths by using Dynamic Causal Modelling (DCM) on the timeseries, thereby allowing us to pinpoint, under each scanning condition, where each subject's dynamics lie within the Poincar\'e diagram - as defined in dynamical systems theory. We demonstrate that the Poincar\'e diagram offers classification capability for TRD patients, in that the further the patients' coordinates were shifted (by virtue of ketamine) toward the stable (top-left) quadrant of the Poincar\'e diagram, the more their depressive symptoms improved. The same relationship was not observed by virtue of a placebo effect - thereby verifying the drug-specific nature of the results. We show that the shift in neural dynamics required for symptom improvement necessitates an increase in both excitatory and inhibitory coupling. We present accompanying MATLAB code made available in a public repository, thereby allowing for future studies to assess individually-tailored treatments of TRD.
Code (0)
등록된 구현이 없습니다.
Similar Papers 제목 키워드 기반
Emergence of robust looming selectivity via coordinated inhibitory neural computations
In the locust's lobula giant movement detector neural pathways, four categories of inhibition, i.e., global inhibition, self-inhibition, lateral inhibition, and feed-forward inhibition, have been functionally explored in…
Bio-inspired learnable divisive normalization for ANNs
In this work we introduce DivNormEI, a novel bio-inspired convolutional network that performs divisive normalization, a canonical cortical computation, along with lateral inhibition and excitation that is tailored for in…
Decoderimage-classificationImage ClassificationObject RecognitionDependence of Orientation Tuning on Recurrent Excitation and Inhibition in a Network Model of V1
One major role of primary visual cortex (V1) in vision is the encoding of the orientation of lines and contours. The role of the local recurrent network in these computations is, however, still a matter of debate. To add…
Maximum Likelihood Estimation for Hawkes Processes with self-excitation or inhibition
In this paper, we present a maximum likelihood method for estimating the parameters of a univariate Hawkes process with self-excitation or inhibition. Our work generalizes techniques and results that were restricted to t…
Different roles for inhibition in the rhythm-generating respiratory network
Unraveling the interplay of excitation and inhibition within rhythm-generating networks remains a fundamental issue in neuroscience. We use a biophysical model to investigate the different roles of local and long-range i…
Rhythm