Imaging NeuroscienceNeurosciencePeer-reviewed journal article
The Virtual Brain links transcranial magnetic stimulation evoked potentials and inhibitory neurotransmitter changes in major depressive disorder
Timo HofsähsiD, Marius PilleiD, Lucas KerniD, Anuja NegiiD, Jil Mona MeieriD, Petra RitteriD
Abstract
BackgroundTranscranial magnetic stimulation evoked potentials (TEPs) show promise as a biomarker in major depressive disorder (MDD), but the origin of the increased TEP amplitude in these patients remains unclear. Gamma aminobutyric acid (GABA) may be involved, as TEP peak amplitude is known to increase with GABAergic activity in healthy controls. We employed a computational modeling approach to investigate this phenomenon. MethodsWhole-brain simulations in The Virtual Brain (thevirtualbrain.org), employing the Jansen and Rit neural mass model, were optimized to simulate TEPs of healthy individuals (Nsubs=20, 14 females, 24.5{+/-}4.9 years). To mimic MDD-like impaired inhibition, a GABAergic deficit was introduced to the simulations by altering one of two selected inhibitory parameters, the inhibitory synaptic decay rate b or the number of inhibitory synapses C4. The TEP amplitude was quantified and compared for all simulations. ResultsThe inhibitory synaptic decay rate showed a quadratic correlation (r=0.99, p<0.001) and the number of inhibitory synapses a negative exponential correlation (r=0.99, p<0.001) with the TEP amplitude. Moreover, significant correlations between these simulation-derived values and all TEP peaks and troughs were detected (p<0.001). Thus, under local parameter changes, we were able to alter the TEP amplitude towards pathological levels, i.e. creating an MDD-like increase of the global mean field amplitude in line with empirical results. ConclusionsOur model suggests specific GABAergic deficits as the cause of increased TEP amplitude in MDD patients, which may serve as therapeutic targets. This work highlights the potential of whole-brain simulations in the investigation of neuropsychiatric diseases.
- organism: human
- assay: electronic health records
- research focus: benchmarking
- approach: statistical model
- Citations
- 1
- FWCI
- 0.00
- Field percentile
- 6th percentile
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