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This article provides a comprehensive overview of how policy makers, practitioners, and scholars can fruitfully use behavioral science to tackle public administration, management, and policy issues. The article systematically reviews 109 articles in the public administration discipline that are inspired by the behavioral sciences to identify emerging research trajectories, significant gaps, and promising applied research directions. In an attempt to systematize and take stock of the nascent behavioral public administration scholarship, the authors trace it back to the seminal works of three Nobel Laureates—Herbert Simon, Daniel Kahneman, and Richard Thaler—and their work on bounded rationality, cognitive biases, and nudging, respectively. The cognitive biases investigated by the studies reviewedfall into the categories of accessibility, loss aversion, and overconfidenceloptimism. Nudging and choic architecture are discussed as viable strategies for leveraging these cognitive traps in an attempt to alter behavior for the better, among both citizens and public servants.

This article tests a broad range of cognitive biases branching out from prospect theory in the context of public policy and management. Results illuminate systematic deviations from rationality. In experiments 1 through 5, the framing of outcomes influenced decisions across policy and management domains. In experiment 6, public employees were prone to an anchoring bias when setting standards for responsiveness. Experiment 7 shows that public workers tend to put more effort into activities that affect higher percentages of beneficiaries, even if the absolute number of affected clients is constant. Experiments 8 and 9 suggest that public employees are more likely to stick to a suboptimal status quo as the number of superior alternatives increases. Experiment 10 provides evidence of an asymmetric dominance effect: decisions changed when a decoy was present. This article contributes to behavioral public administration by replicating and extending previous trials.

Neuronal activity in the brain reflects an excitation–inhibition balance that is regulated predominantly by glutamatergic and GABAergic neurotransmission, and often disturbed in neuropsychiatric disorders. Here, we tested the effects of a single oral dose of two anti-glutamatergic drugs (dextromethorphan, an NMDA receptor antagonist; perampanel, an AMPA receptor antagonist) and an L-type voltage-gated calcium channel blocker (nimodipine) on transcranial magnetic stimulation (TMS)-evoked electroencephalographic (EEG) potentials (TEPs) and TMS-induced oscillations (TIOs) in 16 healthy adults in a pseudorandomized, double-blinded, placebo-controlled crossover design. Single-pulse TMS was delivered to the hand area of left primary motor cortex. Dextromethorphan increased the amplitude of the N45 TEP, while it had no effect on TIOs. Perampanel reduced the amplitude of the P60 TEP in the non-stimulated hemisphere, and increased TIOs in the beta-frequency band in the stimulated sensorimotor cortex, and in the alpha-frequency band in midline parietal channels. Nimodipine and placebo had no effect on TEPs and TIOs. The TEP results extend previous pharmaco-TMS-EEG studies by demonstrating that the N45 is regulated by a balance of GABAAergic inhibition and NMDA receptor-mediated glutamatergic excitation. In contrast, AMPA receptor-mediated glutamatergic neurotransmission contributes to propagated activity reflected in the P60 potential and midline parietal induced oscillations. This pharmacological characterization of TMS-EEG responses will be informative for interpreting TMS-EEG abnormalities in neuropsychiatric disorders with pathological excitation–inhibition balance.

Transcranial magnetic stimulation (TMS) combined with electroencephalography (EEG) provides a powerful framework to probe and modulate human cortical and corticospinal excitability. In recent years, brain state-dependent EEG–TMS paradigms have gained increasing interest by synchronizing stimulation to ongoing neural activity. However, traditional approaches relying on single oscillatory features or fixed thresholds have yielded heterogeneous and often inconsistent results, motivating the adoption of machine learning (ML) and artificial intelligence (AI) methods to model brain state in a multivariate, data-driven manner. This review synthesizes current ML and deep learning (DL) approaches aimed at predicting cortical and corticospinal excitability from pre-stimulus EEG. We contextualize these methods within brain state-dependent EEG–TMS frameworks based on oscillatory phase, power, and network-level features, and within evolving definitions of brain state that move beyond local biomarkers toward distributed, large-scale, and dynamically evolving neural representations. The reviewed studies span feature-engineered models, data-driven decoding approaches, and emerging adaptive closed-loop frameworks. Finally, we discuss key methodological challenges, translational barriers, and future directions toward personalized, interpretable, and fully closed-loop neuromodulation systems.

Transcallosal connectivity between the hand areas of the two primary motor cortices (M1) is important for coordination of unimanual and bimanual hand motor function. Effective connectivity of this M1-M1 pathway can be tested in the form of short-interval interhemispheric inhibition (SIHI) using dual-coil transcranial magnetic stimulation (TMS). Recently, we and others have demonstrated that the phase of the ongoing sensorimotor µ-rhythm has significant impact on corticospinal excitability as measured by motor evoked potential (MEP) amplitude, and repetitive TMS of the high-excitability state (trough of the µ-rhythm) but not other states resulted in long-term potentiation-like MEP increase. Here, we tested to what extent the phase of the ongoing µ-rhythm in the two M1 affects long-term change in SIHI. In healthy subjects we applied cortico-cortical paired associative stimulation (ccPAS) in four different µ-phase conditions in the left conditioning M1 and right test M1 (trough-trough, trough-positive peak, positive peak-trough, random phase). We found long-term strengthening of SIHI but no differential effect of phase conditions. Findings point to a distinct regulation of plasticity of corticospinal versus M1-M1 connectivity. The observed ccPAS-induced strengthening of effective M1-M1 connectivity (SIHI) may be utilized for therapeutic applications that potentially benefit from modification of interhemispheric excitation/inhibition balance.

We tested previous post-hoc findings indicating a relationship between functional connectivity (FC) in the motor network and corticospinal excitability (CsE), in a real-time EEG-TMS experiment in healthy participants. We hypothesized that high FC between left and right motor cortex predicts high CsE. FC was quantified in real-time by single-trial phase-locking value (stPLV), and TMS single pulses were delivered based on the current FC. CsE was indexed by motor-evoked potential (MEP) amplitude in a hand muscle. Possible confounding factors (pre-stimulus μ-power and phase, interstimulus interval) were evaluated post hoc. MEPs were significantly larger during high FC compared to low FC. Post hoc analysis revealed that the FC condition showed a significant interaction with μ-power in the stimulated hemisphere. Further, inter-stimulus interval (ISI) interacted with high vs. low FC conditions. In summary, FC was confirmed to be predictive of CsE, but should not be considered in isolation from μ-power and ISI. Moreover, FC was complementary to μ-phase in predicting CsE. Motor network FC is another marker of real-time accessible CsE beyond previously established markers, in particular phase and power of the μ rhythm, and may help define a more robust composite biomarker of high/low excitability states of human motor cortex. •Functional connectivity (FC) was evaluated and targeted by real-time EEG-TMS.•FC adds network perspective to EEG-TMS, complementing local measures (power, phase).•Single-trial left M1–right M1 μ-oscillation FC predicts corticospinal excitability.

•Optimized sham TMS-EEG is introduced and tested.•Sham combined auditory and supramaximal electrical somatosensory stimulation.•Subjects reported equal sensory perception during sham and real TMS.•Subtraction revealed evoked EEG potentials and beta-band power specific to real TMS.•The optimized sham procedure is relevant in research and therapeutic settings. Electroencephalography (EEG) is increasingly used to investigate brain responses to transcranial magnetic stimulation (TMS). A relevant issue is that TMS is associated with considerable auditory and somatosensory stimulation, causing peripherally evoked potentials (PEPs) in the EEG, which contaminate the direct cortical responses to TMS (TEPs). All previous attempts to control for PEPs suffer from significant limitations. To design an optimized sham procedure to control all sensory input generated by subthreshold real TMS targeting the hand area of the primary motor cortex (M1), enabling reliable separation of TEPs from PEPs. In 23 healthy (16 female) subjects, we recorded EEG activity evoked by an optimized sham TMS condition which masks and matches auditory and somatosensory co-stimulation during the real TMS condition: auditory control was achieved by noise masking and by using a second TMS coil that was placed on top of the real TMS coil and produced a calibrated sound pressure level. Somatosensory control was obtained by electric stimulation (ES) of the scalp with intensities sufficient to saturate somatosensory input. ES was applied in both the sham and real TMS conditions. Perception of auditory and somatosensory inputs in the sham and real TMS conditions were compared by psychophysical testing. Transcranially evoked EEG signal changes were identified by subtraction of EEG activity in the sham condition from EEG activity in the real TMS condition. Perception of auditory and somatosensory inputs in the sham vs. real TMS conditions was comparable. Both sham and real TMS evoked a series of similar EEG signal deflections and induced broadband power increase in oscillatory activity. Notably, the present procedure revealed EEG potentials and a transient increase in beta band power at the site of stimulation that were only present in the real TMS condition. The results validate the effectiveness of our optimized sham approach. Despite the presence of typical responses attributable to sensory input, the procedure provided evidence for direct cortical activation by subthreshold TMS of M1. The findings are relevant for future TMS-EEG experiments that aim at measuring regional brain target engagement controlled by an optimized sham procedure.

Measuring the brain’s response to transcranial magnetic stimulation (TMS) with electroencephalography (EEG) offers unique insights into the cortical circuits activated following stimulation, particularly in non-motor regions where less is known about TMS physiology. However, the mechanisms underlying TMS-evoked EEG potentials (TEPs) remain largely unknown. We assessed TEP sensitivity to changes in excitatory neurotransmission mediated by n-methyl-d-aspartate (NMDA) receptors following stimulation of non-motor regions. In fourteen male volunteers, resting EEG and TEPs from prefrontal (PFC) and parietal (PAR) cortex were measured before and after administration of either dextromethorphan (NMDA receptor antagonist) or placebo across two sessions in a double-blinded pseudo-randomised crossover design. At baseline, there were amplitude differences between PFC and PAR TEPs across a wide time range (15–250 ms), however the signals were correlated after ~80 ms, suggesting early peaks reflect site-specific activity, whereas late peaks reflect activity patterns less dependent on the stimulated sites. Early TEP peaks were not reliably altered following dextromethorphan compared to placebo, although findings were less clear for later peaks, and low frequency resting oscillations were reduced in power. Our findings suggest that early TEP peaks (<80 ms) from PFC and PAR reflect stimulation site specific activity that is largely insensitive to changes in NMDA receptor-mediated neurotransmission.

Brain responses to transcranial magnetic stimulation (TMS) as measured with electroencephalography (EEG) have so far been assessed either by TMS-evoked EEG potentials (TEPs), mostly reflecting phase-locked neuronal activity, or time-frequency-representations (TFRs), reflecting oscillatory power arising from a mixture of both evoked (i.e., phase-locked) and induced (i.e., non-phase-locked) responses. Single-pulse TMS of the human primary motor cortex induces a specific pattern of oscillatory changes, characterized by an early (30–200 ms after TMS) synchronization in the α- and β-bands over the stimulated sensorimotor cortex and adjacent lateral frontal cortex, followed by a late (200–400 ms) α- and β-desynchronization over the stimulated and contralateral sensorimotor cortex. As GABAergic inhibition plays an important role in shaping oscillatory brain activity, we sought here to understand if GABAergic inhibition contributes to these TMS-induced oscillations. We tested single oral doses of alprazolam, diazepam, zolpidem (positive modulators of the GABAA receptor), and baclofen (specific GABAB receptor agonist). Diazepam and zolpidem enhanced, and alprazolam tended to enhance while baclofen decreased the early α-synchronization. Alprazolam and baclofen enhanced the early β-synchronization. Baclofen enhanced the late α-desynchronization, and alprazolam, diazepam and baclofen enhanced the late β-desynchronization. The observed GABAergic drug effects on TMS-induced α- and β-band oscillations were not explained by drug-induced changes on corticospinal excitability, muscle response size, or resting-state EEG power. Our results provide first insights into the pharmacological profile of TMS-induced oscillatory responses of motor cortex. •The response to TMS of M1 is composed of evoked and induced oscillatory activity.•TMS induced early α-/β-synchronization and late α-/β-desynchronization in M1.•GABAAergic vs. GABABergic drugs had opposite effects on early α-synchronization.•GABAAergic and GABABergic drugs enhanced the late β-desynchronization.

•Oscillatory phase of sensorimotor µ-rhythm predicts corticospinal excitability.•Circular regression maps the high excitability state to the early rising phase.•This contradicts the hypothesis of the trough corresponding to highest excitability.•Pre-processing with different spatial filter transforms did not affect the result.•Prediction accuracy was not improved using personalized 64-channel EEG montages. Alpha oscillations are thought to reflect alternating cortical states of excitation and inhibition. Studies of perceptual thresholds and evoked potentials have shown the scalp EEG negative phase of the oscillation to correspond to a short-lasting low-threshold and high-excitability state of underlying visual, somatosensory, and primary motor cortex. The negative peak of the oscillation is assumed to correspond to the state of highest excitability based on biophysical considerations and considerable effort has been made to improve the extraction of a predictive signal by individually optimizing EEG montages. Here, we investigate whether it is the negative peak of sensorimotor µ-rhythm that corresponds to the highest corticospinal excitability, and whether this is consistent between individuals. In 52 adult participants, a standard 5-channel surface Laplacian EEG montage was used to extract sensorimotor µ-rhythm during transcranial magnetic stimulation (TMS) of primary motor cortex. Post-hoc trials were sorted from 800 TMS-evoked motor potentials (MEPs) according to the pre-stimulus EEG (estimated instantaneous phase) and MEP amplitude (as an index of corticospinal excitability). Different preprocessing transformations designed to improve the accuracy by which µ-alpha phase predicts excitability were also tested. By fitting a sinusoid to the MEP amplitudes, sorted according to pre-stimulus EEG-phase, we found that excitability was highest during the early rising phase, at a significant delay with respect to the negative peak by on average 45° or 10 ms. The individual phase of highest excitability was consistent across study participants and unaffected by two different EEG-cleaning methods that utilize 64 channels to improve signal quality by compensating for individual noise level and channel covariance. Personalized transformations of the montage did not yield better prediction of excitability from µ-alpha phase. The relationship between instantaneous phase of a brain oscillation and fluctuating cortical excitability appears to be more complex than previously hypothesized. In TMS of motor cortex, a standard surface Laplacian 5-channel EEG montage is effective in extracting a predictive signal and the phase corresponding to the highest excitability appears to be consistent between individuals. This is an encouraging result with respect to the clinical potential of therapeutic personalized brain interventions in the motor system. However, it remains to be investigated, whether similar results can be obtained for other brain areas and brain oscillations targeted with EEG and TMS.