The supplementary motor area (SMA-proper) plays a key role in the preparation and execution of voluntary movements. Anatomically, SMA-proper is densely reciprocally connected to primary motor cortex (M1), but neuronal coordination within the SMA-M1 network and its modification by external perturbation are not well understood. Here we modulated the SMA-M1 network using MR-navigated multicoil associative transcranial magnetic stimulation in healthy subjects. Changes in corticospinal excitability were assessed by recording motor evoked potential (MEP) amplitude bilaterally in a hand muscle. We found timing-dependent bidirectional Hebbian-like MEP changes during and for at least 30 min after paired associative SMA-M1 stimulation. MEP amplitude increased if SMA stimulation preceded M1 stimulation by 6 ms, but decreased if SMA stimulation lagged M1 stimulation by 15 ms. This associative plasticity in the SMA-M1 network was highly topographically specific because paired associative stimulation of pre-SMA and M1 did not result in any significant MEP change. Furthermore, associative plasticity in the SMA-M1 network was strongly state-dependent because it required priming by near-simultaneous M1 stimulation to occur. We conclude that timing-dependent bidirectional associative plasticity is demonstrated for the first time at the systems level of a human corticocortical neuronal network. The properties of this form of plasticity are fully compatible with spike-timing-dependent plasticity as defined at the cellular level. The necessity of priming may reflect the strong interhemispheric connectivity of the SMA-M1 network. Findings are relevant for better understanding reorganization and potentially therapeutic modification of neuronal coordination in the SMA-M1 network after cerebral lesions such as stroke.

Key points• Homeostatic metaplasticity is an important mechanism for maintaining overall synaptic weight of a neuronal network in the physiological range.• Homeostatic metaplasticity has been demonstrated, so far largely exclusively, for excitatory synaptic neurotransmission.• New non-invasive transcranial magnetic theta burst stimulation (TBS) experiments at the systems level of human motor cortex demonstrate for the first time that homeostatic metaplasticity is also present in inhibitory intracortical circuits.• In addition, manipulation of intracortical inhibition by priming TBS contributes to the homeostatic regulation of metaplasticity in the corticospinal excitatory pathway.• Findings are important for therapeutic applications of non-invasive brain stimulation that aim at correcting excitatory or inhibitory neurotransmission outside the physiological range in humans with neuropsychiatric disorders.Abstract Homeostatic metaplasticity, a fundamental principle for maintaining overall synaptic weight in the physiological range in neuronal networks, was demonstrated at the cellular and systems level predominantly for excitatory synaptic neurotransmission. Although inhibitory networks are crucial for regulating excitability, it is largely unknown to what extent homeostatic metaplasticity of inhibition also exists. Here, we employed intermittent and continuous transcranial magnetic theta burst stimulation (iTBS, cTBS) of the primary motor cortex in healthy subjects for induction of long-term potentiation (LTP)-like and long-term depression (LTD)-like plasticity. We studied metaplasticity by testing the interactions of priming TBS with LTP/LTD-like plasticity induced by subsequent test TBS. Changes in excitatory neurotransmission were measured by the input-output curve of motor-evoked potentials (IO-MEP), and changes in GABA A ergic inhibitory neurotransmission by the IO of short-interval intracortical inhibition (IO-SICI, four conditioning stimulus intensities of 70-100% active motor threshold, interstimulus interval 2.0 ms). Non-primed iTBS increased IO-MEP, while non-primed cTBS decreased IO-MEP. Pairing of identical protocols (iTBS→iTBS, cTBS→cTBS) resulted in suppression of the non-primed TBS effects on IO-MEP, and pairing of different protocols (cTBS→iTBS, iTBS→cTBS) enhanced the test TBS effects on IO-MEP. While non-primed TBS did not result in significant changes of IO-SICI, iTBS→iTBS resulted in IO-SICI decrease, and cTBS→cTBS in IO-SICI increase compared with the non-primed conditions. The changes in SICI induced by priming TBS correlated with the changes in MEP induced by subsequent test TBS. Findings demonstrate that plasticity in both excitatory and inhibitory circuits in the human motor cortex are regulated by homeostatic metaplasticity, and that priming effects on inhibition contribute to the homeostatic regulation of metaplasticity in excitatory circuits.

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Background: Transcranial magnetic stimulation (TMS) plays an important role in treatment of mental and neurological illnesses, and neurosurgery. However, it is difficult to target specific brain regions accurately because the complex anatomy of the brain substantially affects the shape and strength of the electric fields induced by the TMS coil. A volume conductor model can be used for determining the accurate electric fields; however, the construction of subject-specific anatomical head structures is timeconsuming. Objective: The aim of this study is to propose a method to estimate electric fields induced by TMS from only T1 magnetic resonance (MR) images, without constructing a subject-specific anatomical model. Methods: Very large sets of electric fields in the brain of subject-specific anatomical models, which are constructed from T1 and T2 MR images, are computed by a volume conductor model. The relation between electric field distribution and T1 MR images is used for machine learning. Deep neural network (DNN) models are applied for the first time to electric field estimation. Results: By determining the relationships between the T1 MR images and electric fields by DNN models, the process of electric field estimation is markedly accelerated (to 0.03 s) due to the absence of a requirement for anatomical head structure reconstruction and volume conductor computation. Validation shows promising estimation accuracy, and rapid computations of the DNN model are apt for practical applications. Conclusion: The study showed that the DNN model can estimate the electric fields from only T1 MR images and requires low computation time, suggesting the possibility of using machine learning for realtime electric field estimation in navigated TMS.