Mind wandering is an ubiquitous phenomenon in everyday life. In the cognitive neurosciences, mind wandering has been associated with several distinct neural processes, most notably increased activity in the default mode network (DMN), suppressed activity within the anti-correlated (task-positive) network (ACN), and changes in neuromodulation. By using an integrative multimodal approach combining machine-learning techniques with modeling of latent cognitive processes, we show that mind wandering in humans is characterized by inefficiencies in executive control (task-monitoring) processes. This failure is predicted by a single-trial signature of (co)activations in the DMN, ACN, and neuromodulation, and accompanied by a decreased rate of evidence accumulation and response thresholds in the cognitive model.

Mind wandering reflects the shift in attentional focus from task-related cognition driven by external stimuli toward self-generated and internally-oriented thought processes. Although such task-unrelated thoughts (TUTs) are pervasive and detrimental to task performance, their underlying neural mechanisms are only modestly understood. To investigate TUTs with high spatial and temporal precision, we simultaneously measured fMRI, EEG, and pupillometry in healthy adults while they performed a sustained attention task with experience sampling probes. Features of interest were extracted from each modality at the single-trial level and fed to a support vector machine that was trained on the probe responses. Compared to task-focused attention, the neural signature of TUTs was characterized by weaker activity in the default mode network but elevated activity in its anticorrelated network, stronger functional coupling between these networks, widespread increase in alpha, theta, delta, but not beta, frequency power, predominantly reduced amplitudes of late, but not early, event-related potentials, and larger baseline pupil size. Particularly, information contained in dynamic interactions between large-scale cortical networks was predictive of transient changes in attentional focus above other modalities. Together, our results provide insight into the spatiotemporal dynamics of TUTs and the neural markers that may facilitate their detection.

Repetitive transcranial magnetic stimulation (rTMS) is an increasingly used, non‐invasive brain stimulation technique in neuroscience research and clinical practice with a broad spectrum of suggested applications. Among other parameters, the choice of stimulus intensity and intracranial electric field strength substantially impacts rTMS outcome. This review provides a systematic overview of the intensity selection approaches and stimulation intensities used in human rTMS studies. We also examined whether studies report sufficient information to reproduce stimulus intensities for basic science research models. We performed a systematic review by focusing on original studies published between 1991 and 2020. We included conventional (e.g., 1 or 10 Hz) and patterned protocols (e.g., continuous or intermittent theta burst stimulation). We identified 3,784 articles in total, and we manually processed a representative portion (20%) of randomly selected articles. The majority of the analyzed studies (90% of entries) used the motor threshold (MT) approach and stimulation intensities from 80% to 120% of the MT. For continuous and intermittent theta burst stimulation, the most frequent stimulation intensity was 80% of the active MT. Most studies (92% of entries) did not report sufficient information to reproduce the stimulation intensity. Only a minority of studies (1.03% of entries) estimated the rTMS‐induced electric field strengths. We formulate easy‐to‐follow recommendations to help scientists and clinicians report relevant information on stimulation intensity. Future standardized reporting guidelines may facilitate the use of basic science approaches aiming at better understanding the molecular, cellular, and neuronal mechanisms of rTMS.

Transcranial direct current stimulation (tDCS) above the left dorsolateral prefrontal cortex (lDLPFC) has been widely used to improve symptoms of major depressive disorder (MDD). However, the effects of different stimulation protocols in the entire frontal lobe have not been investigated in a large sample including patient data. We used 38 head models created from structural magnetic resonance imaging data of 19 healthy adults and 19 MDD patients and applied computational modeling to simulate the spatial distribution of tDCS-induced electric fields (EFs) in 20 frontal regions. We evaluated effects of seven bipolar and two multi-electrode 4 × 1 tDCS protocols. For bipolar montages, EFs were of comparable strength in the lDLPFC and in the medial prefrontal cortex (MPFC). Depending on stimulation parameters, EF cortical maps varied to a considerable degree, but were found to be similar in controls and patients. 4 × 1 montages produced more localized, albeit weaker effects. White matter anisotropy was not modeled. The relationship between EF strength and clinical response to tDCS could not be evaluated. In addition to lDLPFC stimulation, excitability changes in the MPFC should also be considered as a potential mechanism underlying clinical efficacy of bipolar montages. MDD-associated anatomical variations are not likely to substantially influence current flow. Individual modeling of tDCS protocols can substantially improve cortical targeting. We make recommendations for future research to explicitly test the contribution of lDLPFC vs. MPFC stimulation to therapeutic outcomes of tDCS in this disorder.