Bidirectional brain-body interactions during natural story listening.

Narratives can synchronize neural and physiological signals between individuals, but the relationship between these signals, and the underlying mechanism, is unclear. We hypothesized a top-down effect of cognition on arousal and predicted that auditory narratives will drive not only brain signals but also peripheral physiological signals. We find that auditory narratives entrained gaze variation, saccade initiation, pupil size, and heart rate. This is consistent with a top-down effect of cognition on autonomic function. We also hypothesized a bottom-up effect, whereby autonomic physiology affects arousal. Controlled breathing affected pupil size, and heart rate was entrained by controlled saccades. Additionally, fluctuations in heart rate preceded fluctuations of pupil size and brain signals. Gaze variation, pupil size, and heart rate were all associated with anterior-central brain signals. Together, these results suggest bidirectional causal effects between peripheral autonomic function and central brain circuits involved in the control of arousal.

Transcranial electric stimulation modulates firing rate at clinically relevant intensities.

BACKGROUND: Notwithstanding advances with low-intensity transcranial electrical stimulation (tES), there remain questions about the efficacy of clinically realistic electric fields on neuronal function. OBJECTIVE: To measure electric fields magnitude and their effects on neuronal firing rate of hippocampal neurons in freely moving rats, and to establish calibrated computational models of current flow. METHODS: Current flow models were calibrated on electric field measures in the motor cortex (n = 2 anesthetized rats) and hippocampus. A Neuropixels 2.0 probe with 384 channels was used in an in-vivo rat model of tES (n = 4 freely moving and 2 urethane anesthetized rats) to detect effects of weak fields on neuronal firing rate. High-density field mapping and computational models verified field intensity (1 V/m in hippocampus per 50 µA of applied skull currents). RESULTS: Electric fields of as low as 0.35 V/m (0.25-0.47) acutely modulated average firing rate in the hippocampus. At these intensities, firing rate effects increased monotonically with electric field intensity at a rate of 11.5 % per V/m (7.2-18.3). For the majority of excitatory neurons, firing increased for soma-depolarizing stimulation and diminished for soma-hyperpolarizing stimulation. While more diverse, the response of inhibitory neurons followed a similar pattern on average, likely as a result of excitatory drive. CONCLUSION: In awake animals, electric fields modulate spiking rate above levels previously observed in vitro. Firing rate effects are likely mediated by somatic polarization of pyramidal neurons. We recommend that all future rodent experiments directly measure electric fields to insure rigor and reproducibility.

Robustness of Deep Networks for Mammography: Replication Across Public Datasets.

Deep neural networks have demonstrated promising performance in screening mammography with recent studies reporting performance at or above the level of trained radiologists on internal datasets. However, it remains unclear whether the performance of these trained models is robust and replicates across external datasets. In this study, we evaluate four state-of-the-art publicly available models using four publicly available mammography datasets (CBIS-DDSM, INbreast, CMMD, OMI-DB). Where test data was available, published results were replicated. The best-performing model, which achieved an area under the ROC curve (AUC) of 0.88 on internal data from NYU, achieved here an AUC of 0.9 on the external CMMD dataset (N = 826 exams). On the larger OMI-DB dataset (N = 11,440 exams), it achieved an AUC of 0.84 but did not match the performance of individual radiologists (at a specificity of 0.92, the sensitivity was 0.97 for the radiologist and 0.53 for the network for a 1-year follow-up). The network showed higher performance for in situ cancers, as opposed to invasive cancers. Among invasive cancers, it was relatively weaker at identifying asymmetries and was relatively stronger at identifying masses. The three other trained models that we evaluated all performed poorly on external datasets. Independent validation of trained models is an essential step to ensure safe and reliable use. Future progress in AI for mammography may depend on a concerted effort to make larger datasets publicly available that span multiple clinical sites.

[WITHDRAWN] Predicting breast cancer with AI for individual risk-adjusted MRI screening and early detection.

This paper has been withdrawn by Lukas Hirsch. Major revisions and rewriting in progress.

Transcranial electric stimulation modulates firing rate at clinically relevant intensities.

Notwithstanding advances with low-intensity transcranial electrical stimulation (TES), there remain questions about the efficacy of clinically realistic electric fields on neuronal function. We used Neuropixels 2.0 probe with 384 channels in an in-vivo rat model of TES to detect effects of weak fields on neuronal firing rate. High-density field mapping and computational models verified field intensity (1 V/m in hippocampus per 50 µA of applied skull currents). We demonstrate that electric fields below 0.5 V/m acutely modulate firing rate in 5% of neurons recorded in the hippocampus. At these intensities, average firing rate effects increased monotonically with electric field intensity at a rate of 7 % per V/m. For the majority of excitatory neurons, firing increased for cathodal stimulation and diminished for anodal stimulation. While more diverse, the response of inhibitory neurons followed a similar pattern on average, likely as a result of excitatory drive. Our results indicate that responses to TES at clinically relevant intensities are driven by a fraction of high-responder excitatory neurons, with polarity-specific effects. We conclude that transcranial electric stimulation is an effective neuromodulator at clinically realistic intensities.

Architecture of the brain's visual system enhances network stability and performance through layers, delays, and feedback.

In the visual system of primates, image information propagates across successive cortical areas, and there is also local feedback within an area and long-range feedback across areas. Recent findings suggest that the resulting temporal dynamics of neural activity are crucial in several vision tasks. In contrast, artificial neural network models of vision are typically feedforward and do not capitalize on the benefits of temporal dynamics, partly due to concerns about stability and computational costs. In this study, we focus on recurrent networks with feedback connections for visual tasks with static input corresponding to a single fixation. We demonstrate mathematically that a network's dynamics can be stabilized by four key features of biological networks: layer-ordered structure, temporal delays between layers, longer distance feedback across layers, and nonlinear neuronal responses. Conversely, when feedback has a fixed distance, one can omit delays in feedforward connections to achieve more efficient artificial implementations. We also evaluated the effect of feedback connections on object detection and classification performance using standard benchmarks, specifically the COCO and CIFAR10 datasets. Our findings indicate that feedback connections improved the detection of small objects, and classification performance became more robust to noise. We found that performance increased with the temporal dynamics, not unlike what is observed in core vision of primates. These results suggest that delays and layered organization are crucial features for stability and performance in both biological and artificial recurrent neural networks.

An open-access dataset of naturalistic viewing using simultaneous EEG-fMRI.

In this work, we present a dataset that combines functional magnetic imaging (fMRI) and electroencephalography (EEG) to use as a resource for understanding human brain function in these two imaging modalities. The dataset can also be used for optimizing preprocessing methods for simultaneously collected imaging data. The dataset includes simultaneously collected recordings from 22 individuals (ages: 23-51) across various visual and naturalistic stimuli. In addition, physiological, eye tracking, electrocardiography, and cognitive and behavioral data were collected along with this neuroimaging data. Visual tasks include a flickering checkerboard collected outside and inside the MRI scanner (EEG-only) and simultaneous EEG-fMRI recordings. Simultaneous recordings include rest, the visual paradigm Inscapes, and several short video movies representing naturalistic stimuli. Raw and preprocessed data are openly available to download. We present this dataset as part of an effort to provide open-access data to increase the opportunity for discoveries and understanding of the human brain and evaluate the correlation between electrical brain activity and blood oxygen level-dependent (BOLD) signals.

Semantic novelty modulates neural responses to visual change across the human brain.

Our continuous visual experience in daily life is dominated by change. Previous research has focused on visual change due to stimulus motion, eye movements or unfolding events, but not their combined impact across the brain, or their interactions with semantic novelty. We investigate the neural responses to these sources of novelty during film viewing. We analyzed intracranial recordings in humans across 6328 electrodes from 23 individuals. Responses associated with saccades and film cuts were dominant across the entire brain. Film cuts at semantic event boundaries were particularly effective in the temporal and medial temporal lobe. Saccades to visual targets with high visual novelty were also associated with strong neural responses. Specific locations in higher-order association areas showed selectivity to either high or low-novelty saccades. We conclude that neural activity associated with film cuts and eye movements is widespread across the brain and is modulated by semantic novelty.

Robust enhancement of motor sequence learning with 4 mA transcranial electric stimulation.

BACKGROUND AND OBJECTIVES: Motor learning experiments with transcranial direct current stimulation (tDCS) at 2 mA have produced mixed results. We hypothesize that tDCS boosts motor learning provided sufficiently high field intensity on the motor cortex. METHODS: In a single-blinded design, 108 healthy participants received either anodal (N = 36) or cathodal (N = 36) tDCS at 4 mA total, or no stimulation (N = 36) while they practiced a 12-min sequence learning task. Anodal stimulation was delivered across four electrode pairs (1 mA each), with anodes above the right parietal lobe and cathodes above the right frontal lobe. Cathodal stimulation, with reversed polarities, served as an active control for sensation, while the no-stimulation condition established baseline performance. fMRI-localized targets on the primary motor cortex in 10 subjects were used in current flow models to optimize electrode placement for maximal field intensity. A single electrode montage was then selected for all participants. RESULTS: We found a significant difference in performance with anodal vs. cathodal stimulation (Cohen's d = 0.71) and vs. no stimulation (d = 0.56). This effect persisted for at least 1 h, and subsequent learning for a new sequence and the opposite hand also improved. Sensation ratings were comparable in the active groups and did not exceed moderate levels. Current flow models suggest the new electrode montage can achieve stronger motor cortex polarization than alternative montages. CONCLUSION: The present paradigm shows a medium to large effect size and is well-tolerated. It may serve as a go-to experiment for future studies on motor learning and tDCS.

Deep Learning Achieves Neuroradiologist-Level Performance in Detecting Hydrocephalus Requiring Treatment.

In large clinical centers a small subset of patients present with hydrocephalus that requires surgical treatment. We aimed to develop a screening tool to detect such cases from the head MRI with performance comparable to neuroradiologists. We leveraged 496 clinical MRI exams collected retrospectively at a single clinical site from patients referred for any reason. This diagnostic dataset was enriched to have 259 hydrocephalus cases. A 3D convolutional neural network was trained on 16 manually segmented exams (ten hydrocephalus) and subsequently used to automatically segment the remaining 480 exams and extract volumetric anatomical features. A linear classifier of these features was trained on 240 exams to detect cases of hydrocephalus that required treatment with surgical intervention. Performance was compared to four neuroradiologists on the remaining 240 exams. Performance was also evaluated on a separate screening dataset of 451 exams collected from a routine clinical population to predict the consensus reading from four neuroradiologists using images alone. The pipeline was also tested on an external dataset of 31 exams from a 2nd clinical site. The most discriminant features were the Magnetic Resonance Hydrocephalic Index (MRHI), ventricle volume, and the ratio between ventricle and brain volume. At matching sensitivity, the specificity of the machine and the neuroradiologists did not show significant differences for detection of hydrocephalus on either dataset (proportions test, p > 0.05). ROC performance compared favorably with the state-of-the-art (AUC 0.90-0.96), and replicated in the external validation. Hydrocephalus cases requiring treatment can be detected automatically from MRI in a heterogeneous patient population based on quantitative characterization of brain anatomy with performance comparable to that of neuroradiologists.

Radiologist-Level Performance by Using Deep Learning for Segmentation of Breast Cancers on MRI Scans.

PURPOSE: To develop a deep network architecture that would achieve fully automated radiologist-level segmentation of cancers at breast MRI. MATERIALS AND METHODS: In this retrospective study, 38 229 examinations (composed of 64 063 individual breast scans from 14 475 patients) were performed in female patients (age range, 12-94 years; mean age, 52 years ± 10 [standard deviation]) who presented between 2002 and 2014 at a single clinical site. A total of 2555 breast cancers were selected that had been segmented on two-dimensional (2D) images by radiologists, as well as 60 108 benign breasts that served as examples of noncancerous tissue; all these were used for model training. For testing, an additional 250 breast cancers were segmented independently on 2D images by four radiologists. Authors selected among several three-dimensional (3D) deep convolutional neural network architectures, input modalities, and harmonization methods. The outcome measure was the Dice score for 2D segmentation, which was compared between the network and radiologists by using the Wilcoxon signed rank test and the two one-sided test procedure. RESULTS: The highest-performing network on the training set was a 3D U-Net with dynamic contrast-enhanced MRI as input and with intensity normalized for each examination. In the test set, the median Dice score of this network was 0.77 (interquartile range, 0.26). The performance of the network was equivalent to that of the radiologists (two one-sided test procedures with radiologist performance of 0.69-0.84 as equivalence bounds, P < .001 for both; n = 250). CONCLUSION: When trained on a sufficiently large dataset, the developed 3D U-Net performed as well as fellowship-trained radiologists in detailed 2D segmentation of breast cancers at routine clinical MRI.Keywords: MRI, Breast, Segmentation, Supervised Learning, Convolutional Neural Network (CNN), Deep Learning Algorithms, Machine Learning AlgorithmsPublished under a CC BY 4.0 license. Supplemental material is available for this article.

Weak DCS causes a relatively strong cumulative boost of synaptic plasticity with spaced learning.

BACKGROUND: Electric fields generated during direct current stimulation (DCS) are known to modulate activity-dependent synaptic plasticity in-vitro. This provides a mechanistic explanation for the lasting behavioral effects observed with transcranial direct current stimulation (tDCS) in human learning experiments. However, previous in-vitro synaptic plasticity experiments show relatively small effects despite using strong fields compared to what is expected with conventional tDCS in humans (20 V/m vs. 1 V/m). There is therefore a need to improve the effectiveness of tDCS at realistic field intensities. Here we leverage the observation that effects of learning are known to accumulate over multiple bouts of learning, known as spaced learning. HYPOTHESIS: We propose that effects of DCS on synaptic long-term potentiation (LTP) accumulate over time in a spaced learning paradigm, thus revealing effects at more realistic field intensities. METHODS: We leverage a standard model for spaced learning by inducing LTP with repeated bouts of theta burst stimulation (TBS) in hippocampal slice preparations. We studied the cumulative effects of DCS paired with TBS at various intensities applied during the induction of LTP in the CA1 region of rat hippocampal slices. RESULTS: As predicted, DCS applied during repeated bouts of theta burst stimulation (TBS) resulted in an increase of LTP. This spaced learning effect is saturated quickly with strong TBS protocols and stronger fields. In contrast, weaker TBS and the weakest electric fields of 2.5 V/m resulted in the strongest relative efficacies (12% boost in LTP per 1 V/m applied). CONCLUSIONS: Weak DCS causes a relatively strong cumulative effect of spaced learning on synaptic plasticity. Staturarion may have masked stronger effects sizes in previous in-vitro studies. Relative effect sizes of DCS are now closer in line with human tDCS experiments.

Cognitive processing of a common stimulus synchronizes brains, hearts, and eyes.

Neural, physiological, and behavioral signals synchronize between human subjects in a variety of settings. Multiple hypotheses have been proposed to explain this interpersonal synchrony, but there is no clarity under which conditions it arises, for which signals, or whether there is a common underlying mechanism. We hypothesized that cognitive processing of a shared stimulus is the source of synchrony between subjects, measured here as intersubject correlation (ISC). To test this, we presented informative videos to participants in an attentive and distracted condition and subsequently measured information recall. ISC was observed for electro-encephalography, gaze position, pupil size, and heart rate, but not respiration and head movements. The strength of correlation was co-modulated in the different signals, changed with attentional state, and predicted subsequent recall of information presented in the videos. There was robust within-subject coupling between brain, heart, and eyes, but not respiration or head movements. The results suggest that ISC is the result of effective cognitive processing, and thus emerges only for those signals that exhibit a robust brain-body connection. While physiological and behavioral fluctuations may be driven by multiple features of the stimulus, correlation with other individuals is co-modulated by the level of attentional engagement with the stimulus.

Neural responses to natural visual motion are spatially selective across the visual field, with selectivity differing across brain areas and task.

It is well established that neural responses to visual stimuli are enhanced at select locations in the visual field. Although spatial selectivity and the effects of spatial attention are well understood for discrete tasks (e.g. visual cueing), little is known for naturalistic experience that involves continuous dynamic visual stimuli (e.g. driving). Here, we assess the strength of neural responses across the visual space during a kart-race game. Given the varying relevance of visual location in this task, we hypothesized that the strength of neural responses to movement will vary across the visual field, and it would differ between active play and passive viewing. To test this, we measure the correlation strength of scalp-evoked potentials with optical flow magnitude at individual locations on the screen. We find that neural responses are strongly correlated at task-relevant locations in visual space, extending beyond the focus of overt attention. Although the driver's gaze is directed upon the heading direction at the centre of the screen, neural responses were robust at the peripheral areas (e.g. roads and surrounding buildings). Importantly, neural responses to visual movement are broadly distributed across the scalp, with visual spatial selectivity differing across electrode locations. Moreover, during active gameplay, neural responses are enhanced at select locations in the visual space. Conventionally, spatial selectivity of neural response has been interpreted as an attentional gain mechanism. In the present study, the data suggest that different brain areas focus attention on different portions of the visual field that are task-relevant, beyond the focus of overt attention.

During natural viewing, neural processing of visual targets continues throughout saccades.

Relatively little is known about visual processing during free-viewing visual search in realistic dynamic environments. Free-viewing is characterized by frequent saccades. During saccades, visual processing is thought to be suppressed, yet we know that the presaccadic visual content can modulate postsaccadic processing. To better understand these processes in a realistic setting, we study here saccades and neural responses elicited by the appearance of visual targets in a realistic virtual environment. While subjects were being driven through a 3D virtual town, they were asked to discriminate between targets that appear on the road. Using a system identification approach, we separated overlapping and correlated activity evoked by visual targets, saccades, and button presses. We found that the presence of a target enhances early occipital as well as late frontocentral saccade-related responses. The earlier potential, shortly after 125 ms post-saccade onset, was enhanced for targets that appeared in the peripheral vision as compared to the central vision, suggesting that fast peripheral processing initiated before saccade onset. The later potential, at 195 ms post-saccade onset, was strongly modulated by the visibility of the target. Together these results suggest that, during natural viewing, neural processing of the presaccadic visual stimulus continues throughout the saccade, apparently unencumbered by saccadic suppression.

Conscious processing of narrative stimuli synchronizes heart rate between individuals.

Heart rate has natural fluctuations that are typically ascribed to autonomic function. Recent evidence suggests that conscious processing can affect the timing of the heartbeat. We hypothesized that heart rate is modulated by conscious processing and therefore dependent on attentional focus. To test this, we leverage the observation that neural processes synchronize between subjects by presenting an identical narrative stimulus. As predicted, we find significant inter-subject correlation of heart rate (ISC-HR) when subjects are presented with an auditory or audiovisual narrative. Consistent with our hypothesis, we find that ISC-HR is reduced when subjects are distracted from the narrative, and higher ISC-HR predicts better recall of the narrative. Finally, patients with disorders of consciousness have lower ISC-HR, as compared to healthy individuals. We conclude that heart rate fluctuations are partially driven by conscious processing, depend on attentional state, and may represent a simple metric to assess conscious state in unresponsive patients.

Segmentation of MRI head anatomy using deep volumetric networks and multiple spatial priors.

Purpose: Conventional automated segmentation of the head anatomy in magnetic resonance images distinguishes different brain and nonbrain tissues based on image intensities and prior tissue probability maps (TPMs). This works well for normal head anatomies but fails in the presence of unexpected lesions. Deep convolutional neural networks (CNNs) leverage instead spatial patterns and can learn to segment lesions but often ignore prior probabilities. Approach: We add three sources of prior information to a three-dimensional (3D) convolutional network, namely, spatial priors with a TPM, morphological priors with conditional random fields, and spatial context with a wider field-of-view at lower resolution. We train and test these networks on 3D images of 43 stroke patients and 4 healthy individuals which have been manually segmented. Results: We demonstrate the benefits of each source of prior information, and we show that the new architecture, which we call Multiprior network, improves the performance of existing segmentation software, such as SPM, FSL, and DeepMedic for abnormal anatomies. The relevance of the different priors was compared, and the TPM was found to be most beneficial. The benefit of adding a TPM is generic in that it can boost the performance of established segmentation networks such as the DeepMedic and a UNet. We also provide an out-of-sample validation and clinical application of the approach on an additional 47 patients with disorders of consciousness. We make the code and trained networks freely available. Conclusions: Biomedical images follow imaging protocols that can be leveraged as prior information into deep CNNs to improve performance. The network segmentations match human manual corrections performed in 3D and are comparable in performance to human segmentations obtained from scratch in 2D for abnormal brain anatomies.

Cutaneous sensation of electrical stimulation waveforms.

BACKGROUND: Skin sensation is the primary factor limiting the intensity of transcranial electrical stimulation (tES). It is well established that different waveforms generate different sensations, yet transcranial stimulation has been limited to a relatively small number of prototypical waveforms. OBJECTIVE: We explore whether alternative stimulation waveforms could substantially reduce skin sensation and thus allow for stronger intensities in tES. METHODS: We systematically tested a range of waveforms in a series of 6 exploratory experiments stimulating human adults on the forearm and in one instance on the head. Subjects were asked to rate skin sensation level on a numerical scale from "none" to "extreme". RESULTS: High frequency (>1 kHz) monophasic square wave stimulation was found to decrease in sensation with increasing duty cycle, baseline, and frequency, but the sensation was never lower than for constant current stimulation. For the purpose of injecting a net direct current (DC), a constant current is optimal. For stimulation with alternating current (AC), sensation decreased with increasing frequency, consistent with previous reports. Amplitude modulation did not reduce sensation below stimulation with constant AC amplitude, and biphasic square waveforms produced higher sensation levels than biphasic sinusoidal waveforms. Furthermore, for DC stimulation, sensation levels on the arm were similar to those reported on the head. CONCLUSION: Our comparisons of various waveforms for monophasic and biphasic stimulation indicate that conventional DC and AC waveforms may provide the lowest skin sensations levels for transcutaneous electrical stimulation. These results are likely generalizable to tES applications.

Effects of direct current stimulation on synaptic plasticity in a single neuron.

BACKGROUND: Transcranial direct current stimulation (DCS) has lasting effects that may be explained by a boost in synaptic long-term potentiation (LTP). We hypothesized that this boost is the result of a modulation of somatic spiking in the postsynaptic neuron, as opposed to indirect network effects. To test this directly we record somatic spiking in a postsynaptic neuron during LTP induction with concurrent DCS. METHODS: We performed rodent in-vitro patch-clamp recordings at the soma of individual CA1 pyramidal neurons. LTP was induced with theta-burst stimulation (TBS) applied concurrently with DCS. To test the causal role of somatic polarization, we manipulated polarization via current injections. We also used a computational multi-compartment neuron model that captures the effect of electric fields on membrane polarization and activity-dependent synaptic plasticity. RESULTS: TBS-induced LTP was enhanced when paired with anodal DCS as well as depolarizing current injections. In both cases, somatic spiking during the TBS was increased, suggesting that evoked somatic activity is the primary factor affecting LTP modulation. However, the boost of LTP with DCS was less than expected given the increase in spiking activity alone. In some cells, we also observed DCS-induced spiking, suggesting DCS also modulates LTP via induced network activity. The computational model reproduces these results and suggests that they are driven by both direct changes in postsynaptic spiking and indirect changes due to network activity. CONCLUSION: DCS enhances synaptic plasticity by increasing postsynaptic somatic spiking, but we also find that an increase in network activity may boost but also limit this enhancement.

A double-blind sham-controlled phase 1 clinical trial of tDCS of the dorsolateral prefrontal cortex in cocaine inpatients: Craving, sleepiness, and contemplation to change.

Impaired inhibitory control accompanied by enhanced salience attributed to drug-related cues, both associated with function of the dorsolateral prefrontal cortex (dlPFC), are hallmarks of drug addiction, contributing to worse symptomatology including craving. dlPFC modulation with transcranial direct current stimulation (tDCS) previously showed craving reduction in inpatients with cocaine use disorder (CUD). Our study aimed at assessing feasibility of a longer tDCS protocol in CUD (15 versus the common five/10 sessions) and replicability of previous results. In a randomized double-blind sham-controlled protocol, 17 inpatients with CUD were assigned to either a real-tDCS (right anodal/left cathodal) or a sham-tDCS condition for 15 sessions. Following the previous report, primary outcome measures were self-reported craving, anxiety, depression, and quality of life. Secondary measures included sleepiness, readiness to change drug use, and affect. We also assessed cognitive function including impulsivity. An 88% retention rate demonstrated feasibility. Partially supporting the previous results, there was a trend for self-reported craving to decrease in the real-tDCS group more than the sham-group, an effect that would reach significance with 15 subjects per group. Quality of life and impulsivity improved over time in treatment in both groups. Daytime sleepiness and readiness to change drug use showed significant Group × Time interactions whereby improvements were noted only in the real-tDCS group. One-month follow-up suggested transient effects of tDCS on sleepiness and craving. These preliminary results suggest the need for including more subjects to show a unique effect of real-tDCS on craving and examine the duration of this effect. After replication in larger sample sizes, increased vigilance and motivation to change drug use in the real-tDCS group may suggest fortification of dlPFC-supported executive functions.