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A variety of psychiatric, behavioral and cognitive phenotypes have been linked to brain ''functional connectivity" -- the pattern of correlation observed between different brain regions. Most commonly assessed using functional magnetic resonance imaging (fMRI), here, we investigate the connectivity-phenotype associations with functional connectivity measured with electroencephalography (EEG), using phase-coupling. We analyzed data from the publicly available Healthy Brain Network Biobank. This database compiles a growing sample of children and adolescents, currently encompassing 1657 individuals. Among a variety of assessment instruments we focus on ten phenotypic and additional demographic measures that capture most of the variance in this sample. The largest effect sizes are found for age and sex for both fMRI and EEG. We replicate previous findings of an association of Intelligence Quotient (IQ) and Attention Deficit Hyperactivity Disorder (ADHD) with the pattern of fMRI functional connectivity. We also find an association with socioeconomic status, anxiety and the Child Behavior Checklist Score. For EEG we find a significant connectivity-phenotype relationship with IQ. The actual spatial patterns of functional connectivity are quite different between fMRI and source-space EEG. However, within EEG we observe clusters of functional connectivity that are consistent across frequency bands. Additionally we analyzed reproducibility of functional connectivity. We compare connectivity obtained with different tasks, including resting state, a video and a visual flicker task. For both EEG and fMRI the variation between tasks was smaller than the variability observed between subjects. We also found an increase of reliability with increasing frequency of the EEG, and increased sampling duration. We conclude that, while the patterns of functional connectivity are distinct between fMRI and phase-coupling of EEG, they are nonetheless similar in their robustness to the task, and similar in that idiosyncratic patterns of connectivity predict individual phenotypes. • Functional connectivity of EEG and fMRI is analyzed in a large cohort aged 5 to 21. • Patterns of EEG functional connectivity are unique for individual subjects. • Functional Connectivity of EEG and fMRI are both related to similar phenotypes. • The patterns of phase-coupling EEG functional connectivity are different from fMRI. • Combining EEG data of multiple tasks increases reliability of functional connectivity. [ABSTRACT FROM AUTHOR]

Rationale and Objectives: To develop and evaluate an AI algorithm that detects breast cancer in MRI scans up to one year before radiologists typically identify it, potentially enhancing early detection in high-risk women., Materials and Methods: A convolutional neural network (CNN) AI model, pre-trained on breast MRI data, was fine-tuned using a retrospective dataset of 3029 MRI scans from 910 patients. These contained 115 cancers that were diagnosed within one year of a negative MRI. The model aimed to identify these cancers, with the goal of predicting cancer development up to one year in advance. The network was fine-tuned and tested with 10-fold cross-validation. Mean age of patients was 52 years (range, 18-88 years), with average follow-up of 4.3 years (range 1-12 years)., Results: The AI detected cancers one year earlier with an area under the ROC curve of 0.72 (0.67-0.76). Retrospective analysis by a radiologist of the top 10% highest risk MRIs as ranked by the AI could have increased early detection by up to 30%. (35/115, CI:22.2-39.7%, 30% sensitivity). A radiologist identified a visual correlate to biopsy-proven cancers in 83 of prior-year MRIs (83/115, CI: 62.1-79.4%). The AI algorithm identified the anatomic region where cancer would be detected in 66 cases (66/115, CI:47.8-66.5%); with both agreeing in 54 cases (54/115, CI:%37.5-56.4%)., Conclusion: This novel AI-aided re-evaluation of "benign" breasts shows promise for improving early breast cancer detection with MRI. As datasets grow and image quality improves, this approach is expected to become even more impactful., Competing Interests: Declaration of Competing Interest All authors declare no financial or non-financial competing interests., (Copyright © 2024 The Association of University Radiologists. Published by Elsevier Inc. All rights reserved.)

Sensory stimulation of the brain reverberates in its recurrent neuronal networks. However, current computational models of brain activity do not separate immediate sensory responses from intrinsic recurrent dynamics. We apply a vector-autoregressive model with external input (VARX), combining the concepts of "functional connectivity" and "encoding models", to intracranial recordings in humans. We find that the recurrent connectivity during rest is largely unaltered during movie watching. The intrinsic recurrent dynamic enhances and prolongs the neural responses to scene cuts, eye movements, and sounds. Failing to account for these exogenous inputs, leads to spurious connections in the intrinsic "connectivity". The model shows that an external stimulus can reduce intrinsic noise. It also shows that sensory areas have mostly outward, whereas higher-order brain areas mostly incoming connections. We conclude that the response to an external audiovisual stimulus can largely be attributed to the intrinsic dynamic of the brain, already observed during rest., Competing Interests: Declaration of interests The authors declare no competing interests.

This work aims to perform a cross-site validation of automated segmentation for breast cancers in MRI and to compare the performance to radiologists. A three-dimensional (3D) U-Net was trained to segment cancers in dynamic contrast-enhanced axial MRIs using a large dataset from Site 1 (n = 15,266; 449 malignant and 14,817 benign). Performance was validated on site-specific test data from this and two additional sites, and common publicly available testing data. Four radiologists from each of the three clinical sites provided two-dimensional (2D) segmentations as ground truth. Segmentation performance did not differ between the network and radiologists on the test data from Sites 1 and 2 or the common public data (median Dice score Site 1, network 0.86 vs. radiologist 0.85, n = 114; Site 2, 0.91 vs. 0.91, n = 50; common: 0.93 vs. 0.90). For Site 3, an affine input layer was fine-tuned using segmentation labels, resulting in comparable performance between the network and radiologist (0.88 vs. 0.89, n = 42). Radiologist performance differed on the common test data, and the network numerically outperformed 11 of the 12 radiologists (median Dice: 0.85-0.94, n = 20). In conclusion, a deep network with a novel supervised harmonization technique matches radiologists' performance in MRI tumor segmentation across clinical sites. We make code and weights publicly available to promote reproducible AI in radiology., (© 2024. The Author(s) under exclusive licence to Society for Imaging Informatics in Medicine.)

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., Competing Interests: Declaration of competing interest LP is listed as inventor in patents owned by CCNY, and has shares in Soterix Medical Inc. The City University of New York (CUNY) has IP on neuro-stimulation systems and methods with authors NK and MB as inventors. NK is an employee of Synchron Inc and consults for Ceragem Medical. MB has equity in Soterix Medical. MB consults, received grants, assigned inventions, and/or served on the S A B of SafeToddles, Boston Scientific, GlaxoSmithKline, Biovisics, Mecta, Lumenis, Halo Neuroscience, Google-X, i-Lumen, Humm, Allergan (Abbvie), Apple, Ybrain, Ceragem Medical, Remz., (Copyright © 2024 The Authors. Published by Elsevier Inc. All rights reserved.)

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., Competing Interests: Declaration of interests The authors declare no competing interests., (Copyright © 2024 The Authors. Published by Elsevier Inc. All rights reserved.)

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., (© 2024. The Author(s).)

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

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., Competing Interests: Competing interests: LP is listed as inventor in patents owned by CCNY, and has shares in Soterix Medical Inc. The City University of New York (CUNY) has IP on neuro-stimulation systems and methods with authors NK and MB as inventors. NK is an employee of Synchron Inc and consults for Ceragem Medical. MB has equity in Soterix Medical. MB consults, received grants, assigned inventions, and/or served on the S A B of SafeToddles, Boston Scientific, GlaxoSmithKline, Biovisics, Mecta, Lumenis, Halo Neuroscience, Google-X, i-Lumen, Humm, Allergan (Abbvie), Apple, Ybrain, Ceragem Medical, Remz.

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., Competing Interests: The authors have declared that no competing interests exist., (Copyright: © 2023 Velarde et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)

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., (© 2023. Springer Nature Limited.)

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., (© 2023. The Author(s).)

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., Competing Interests: Declaration of competing interest The authors declare the following financial interests/personal relationships which may be considered as potential competing interests: LP is listed as inventor in patents owned by CCNY, and has shares in Soterix Medical Inc., (Copyright © 2023 The Authors. Published by Elsevier Inc. All rights reserved.)

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., (© 2022. The Author(s) under exclusive licence to Society for Imaging Informatics in Medicine.)

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., (© The Author(s) 2022. Published by Oxford University Press on behalf of the National Academy of Sciences.)

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., Competing Interests: Declaration of competing interest M.B and L.C.P. have a financial interest in Soterix Medical LLC and are listed as inventors in intellectual property related to transcranial direct current stimulation. M.S and F.F declare no conflict of interest., (Copyright © 2021 The Authors. Published by Elsevier Inc. All rights reserved.)

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., Competing Interests: Disclosures of Conflicts of Interest: L.H. Grant from NIBIB and NIMH through the NIH BRAIN Initiative (R01 EB02157); consulting fee from City College of New York. Y.H. Consulting fee from City College of New York. S.L. No relevant relationships. C.R.S. No relevant relationships. R.L.G. No relevant relationships. I.D.N. Grant from Alfonso Martín Escudero Foundation. A.G.V.B. No relevant relationships. N.O. No relevant relationships. E.S.K. No relevant relationships. D.L. No relevant relationships. D.A. Consulting fee AEAD850301RG4. S.E.W. RSNA Research and Education Foundation grant no. RF1905 (content is solely the responsibility of the authors and does not necessarily represent the official views of the RSNA R&E Foundation). M.H. No relevant relationships. D.F.M. No relevant relationships. K.P. Funded in part through the NIH/NCI Cancer Center Support Grant P30 CA008748 and the Breast Cancer Research Foundation; ongoing research grants include Digital Hybrid Breast PET/MRI for Enhanced Diagnosis of Breast Cancer (HYPMED) H2020 - Research and Innovation Framework Programme PHC-11-2015 #667211-2, A Body Scan for Cancer Detection using Quantum Technology (CANCERSCAN) H2020-FETOPEN-2018-2019-2020-01 # 828978, Multiparametric 18F-Fluoroestradiol PET/MRI coupled with Radiomics Analysis and Machine Learning for Prediction and Assessment of Response to Neoadjuvant Endocrine Therapy in Patients with Hormone Receptor+/HER2− Invasive Breast Cancer Jubiläumsfonds of the Austrian National Bank # Nr: 18207, Deciphering breast cancer heterogeneity and tackling the hypoxic tumor microenvironment challenge with PET/MRI, MSI and radiomics The Vienna Science and Technology Fund LS19-046, MSKCC 2020 Molecularly Targeted Intra-Operative Imaging Award 07/2020-06/2021, Breast Cancer Research Foundation 06/2019 - 05/2021 PI Mark Robson Co-I, NIH R01 Breast Cancer Intravoxel-Incoherent-Motion MRI Multisite (BRIMM) 09/01/2020-08/30/2025 PI Eric Sigmund Co-I, NIH R01 subaward: Abbreviated Non-Contrast-Enhanced MRI for Breast Cancer Screening 09/01/2023-08/31/2025 PI Brian Hargreaves; payment for lectures, service on speakers bureaus and for travel/accommodations/meeting expenses from the European Society of Breast Imaging (MRI educational course, annual scientific meeting) and Siemens Healthcare (lectures). K.J. No relevant relationships. A.E.E. No relevant relationships. P.E. No relevant relationships. E.A.M. No relevant relationships. H.A.M. Grant from NIBIB and NIMH through the NIH BRAIN Initiative (R01 EB028157). L.C.P. Grant from NIH (R01CA247910). E.J.S. Grant from National Institutes of Health/National Cancer Institute (P30 CA008748)., (2022 by the Radiological Society of North America, Inc.)

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.

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., (Copyright © 2021 The Author(s). Published by Elsevier Inc. All rights reserved.)

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., Competing Interests: Declaration of competing interest We wish to confirm that there are no known conflicts of interest associated with this publication and there has been no significant financial support for this work that could have influenced its outcome., (Copyright © 2021 The Author(s). Published by Elsevier Inc. All rights reserved.)

Experienced teachers pay close attention to their students, adjusting their teaching when students seem lost. This dynamic interaction is missing in online education. We hypothesized that attentive students follow videos similarly with their eyes. Thus, attention to instructional videos could be assessed remotely by tracking eye movements. Here we show that intersubject correlation of eye movements during video presentation is substantially higher for attentive students and that synchronized eye movements are predictive of individual test scores on the material presented in the video. These findings replicate for videos in a variety of production styles, for incidental and intentional learning and for recall and comprehension questions alike. We reproduce the result using standard web cameras to capture eye movements in a classroom setting and with over 1,000 participants at home without the need to transmit user data. Our results suggest that online education could be made adaptive to a student's level of attention in real time., Competing Interests: Competing interest statement: L.C.P. and J.M. are listed as inventors in a related pending patent application. L.C.P. was affiliated with Neuromatters Corp., KCore Analytics Inc., and 3Finches Inc. at the time of the study.

Background: Temporal interference (TI) stimulation of the brain generates amplitude-modulated electric fields oscillating in the kHz range with the goal of non-invasive targeted deep brain stimulation. Yet, the current intensities required in human (sensitivity) to modulate deep brain activity and if superficial brain region are spared (selectivity) at these intensities remains unclear., Objective: We developed an experimentally constrained theory for TI sensitivity to kHz electric field given the attenuation by membrane low-pass filtering property, and for TI selectivity to deep structures given the distribution of modulated and unmodulated electric fields in brain., Methods: The electric field threshold to modulate carbachol-induced gamma oscillations in rat hippocampal slices was determined for unmodulated 0.05-2 kHz sine waveforms, and 5 Hz amplitude-modulated waveforms with 0.1-2 kHz carrier frequencies. The neuronal effects are replicated with a computational network model to explore the underlying mechanisms, and then coupled to a validated current-flow model of the human head., Results: Amplitude-modulated electric fields are stronger in deep brain regions, while unmodulated electric fields are maximal at the cortical regions. Both experiment and model confirmed the hypothesis that spatial selectivity of temporal interference stimulation depends on the phasic modulation of neural oscillations only in deep brain regions. Adaptation mechanism (e.g. GABA b ) enhanced sensitivity to amplitude modulated waveform in contrast to unmodulated kHz and produced selectivity in modulating gamma oscillation (i.e. Higher gamma modulation in amplitude modulated vs unmodulated kHz stimulation). Selection of carrier frequency strongly affected sensitivity to amplitude modulation stimulation. Amplitude modulated stimulation with 100 Hz carrier frequency required ∼5 V/m (corresponding to ∼13 mA at the scalp surface), whereas, 1 kHz carrier frequency ∼60 V/m (∼160 mA) and 2 kHz carrier frequency ∼80 V/m (∼220 mA) to significantly modulate gamma oscillation. Sensitivity is increased (scalp current required decreased) for theoretical neuronal membranes with faster time constants., Conclusion: The TI sensitivity (current required at the scalp) depends on the neuronal membrane time-constant (e.g. axons) approaching the kHz carrier frequency. TI selectivity is governed by network adaption (e.g. GABA b ) that is faster than the amplitude-modulation frequency. Thus, we show neuronal and network oscillations time-constants determine the scalp current required and the selectivity achievable with TI in humans., Competing Interests: Declaration of competing interest MB and LCP are supported by grants from the National Institutes of Health (NIH-NIMH R01MH111896, NIH-NIMH R01MH109289; NIH-NINDS R01NS101362; NIH-NINDS R01NS112996). The City University of New York has inventions on tES with MB and LCP as inventor. MB and LCP have equity in Soterix Medical. MB consults, received grants, assigned inventions, and/or serves on the SAB of Boston Scientific, GlaxoSmithKline, Mecta, Halo Neuroscience, X., (Copyright © 2020 The Authors. Published by Elsevier Inc. All rights reserved.)

While it is well known that vision guides movement, less appreciated is that the motor cortex also provides input to the visual system. Here, we asked whether neural processing of visual stimuli is acutely modulated during motor activity, hypothesizing that visual evoked responses are enhanced when engaged in a motor task that depends on the visual stimulus. To test this, we told participants that their brain activity was controlling a video game that was in fact the playback of a prerecorded game. The deception, which was effective in half of participants, aimed to engage the motor system while avoiding evoked responses related to actual movement or somatosensation. In other trials, subjects actively played the game with keyboard control or passively watched a playback. The strength of visually evoked responses was measured as the temporal correlation between the continuous stimulus and the evoked potentials on the scalp. We found reduced correlation during passive viewing, but no difference between active and sham play. Alpha-band (8-12 Hz) activity was reduced over central electrodes during sham play, indicating recruitment of motor cortex despite the absence of overt movement. To account for the potential increase of attention during gameplay, we conducted a second study with subjects counting screen items during viewing. We again found increased correlation during sham play, but no difference between counting and passive viewing. While we cannot fully rule out the involvement of attention, our findings do demonstrate an enhancement of visual evoked responses during active vision., (© 2020 The Authors. European Journal of Neuroscience published by Federation of European Neuroscience Societies and John Wiley & Sons Ltd.)

Odors can be powerful stimulants. It is well-established that odors provide strong cues for recall of locations, people and events. The effects of specific scents on other cognitive functions are less well-established. We hypothesized that scents with different odor qualities will have a different effect on attention. To assess attention, we used Inter-Subject Correlation of the EEG because this metric is strongly modulated by attentional engagement with natural audiovisual stimuli. We predicted that scents known to be "energizing" would increase Inter-Subject Correlation during watching of videos as compared to "calming" scents. In a first experiment, we confirmed this for eucalyptol and linalool while participants watched animated autobiographical narratives. The result was replicated in a second experiment, but did not generalize to limonene, also considered an "energizing" odorant. In a third, double-blind experiment, we tested a battery of scents including single molecules, as well as mixtures, as participants watched various short video clips. We found a varying effect of odor on Inter-Subject Correlation across the various scents. This study provides a basis for reliably and reproducibly assessing effects of odors on brain activity. Future research is needed to further explore the effect of scent-based up-modulation in engagement on learning and memory performance. Educators, product developers and fragrance brands might also benefit from such objective neurophysiological measures., (Copyright © 2020 DeGuzman, Jain, Tabert and Parra.)

Objective: Interferential stimulation (IFS) has generated considerable interest recently because of its potential to achieve focal electric fields in deep brain areas with transcranial currents. Conventionally, IFS applies sinusoidal currents through two electrode pairs with close-by frequencies. Here we propose to use an array of electrodes instead of just two electrode pairs; and to use algorithmic optimization to identify the currents required at each electrode to target a desired location in the brain., Approach: We formulate rigorous optimization criteria for IFS to achieve either maximal modulation-depth or maximally focal stimulation. We find the solution for optimal modulation-depth analytically and maximize for focal stimulation numerically., Main Results: Maximal modulation is achieved when IFS equals conventional high-definition multi-electrode transcranial electrical stimulation (HD-TES) with a modulated current source. This optimal solution can be found directly from a current-flow model, i.e. the 'lead field' without the need for algorithmic optimization. Once currents are optimized numerically to achieve optimal focal stimulation, we find that IFS can indeed be more focal than conventional HD-TES, both at the cortical surface and deep in the brain. Generally, however, stimulation intensity of IFS is weak and the locus of highest intensity does not match the locus of highest modulation., Significance: This proof-of-principle study shows the potential of IFS over HD-TES for focal non-invasive deep brain stimulation. Future work will be needed to improve on intensity of stimulation and convergence of the optimization procedure.

Background: There is evidence that transcranial direct current stimulation (tDCS) can improve learning performance. Arguably, this effect is related to long term potentiation (LTP), but the precise biophysical mechanisms remain unknown., Hypothesis: We propose that direct current stimulation (DCS) causes small changes in postsynaptic membrane potential during ongoing endogenous synaptic activity. The altered voltage dynamics in the postsynaptic neuron then modify synaptic strength via the machinery of endogenous voltage-dependent Hebbian plasticity. This hypothesis predicts that DCS should exhibit Hebbian properties, namely pathway specificity and associativity., Methods: We studied the effects of DCS applied during the induction of LTP in the CA1 region of rat hippocampal slices and using a biophysical computational model., Results: DCS enhanced LTP, but only at synapses that were undergoing plasticity, confirming that DCS respects Hebbian pathway specificity. When different synaptic pathways cooperated to produce LTP, DCS enhanced this cooperation, boosting Hebbian associativity. Further slice experiments and computer simulations support a model where polarization of postsynaptic pyramidal neurons drives these plasticity effects through endogenous Hebbian mechanisms. The model is able to reconcile several experimental results by capturing the complex interaction between the induced electric field, neuron morphology, and endogenous neural activity., Conclusions: These results suggest that tDCS can enhance associative learning. We propose that clinical tDCS should be applied during tasks that induce Hebbian plasticity to harness this phenomenon, and that the effects should be task specific through their interaction with endogenous plasticity mechanisms. Models that incorporate brain state and plasticity mechanisms may help to improve prediction of tDCS outcomes., Competing Interests: Declaration of competing interest LCP and MB have shares in Soterix Medical Inc and are listed as inventors in patents of the City University of New York related to high-definition tDCS., (Copyright © 2019 Elsevier Inc. All rights reserved.)

Slow oscillations and spindle activity during non-rapid eye movement sleep have been implicated in memory consolidation. Closed-loop acoustic stimulation has previously been shown to enhance slow oscillations and spindle activity during sleep and improve verbal associative memory. We assessed the effect of closed-loop acoustic stimulation during a daytime nap on a virtual reality spatial navigation task in 12 healthy human subjects in a randomized within-subject crossover design. We show robust enhancement of slow oscillation and spindle activity during sleep. However, no effects on behavioral performance were observed when comparing real versus sham stimulation. To explore whether memory enhancement effects were task specific and dependent on nocturnal sleep, in a second experiment with 19 healthy subjects, we aimed to replicate a previous study that used closed-loop acoustic stimulation to enhance memory for word pairs. The methods used were as close as possible to those used in the original study, except that we used a double-blind protocol, in which both subject and experimenter were unaware of the test condition. Again, we successfully enhanced slow oscillation and spindle power, but again did not strengthen associative memory performance with stimulation. We conclude that enhancement of sleep oscillations may be insufficient to enhance memory performance in spatial navigation or verbal association tasks, and provide possible explanations for lack of behavioral replication., (Copyright © 2019 Henin et al.)

Objective: Research in the area of transcranial electrical stimulation (TES) often relies on computational models of current flow in the brain. Models are built based on magnetic resonance images (MRI) of the human head to capture detailed individual anatomy. To simulate current flow on an individual, the subject's MRI is segmented, virtual electrodes are placed on this anatomical model, the volume is tessellated into a mesh, and a finite element model (FEM) is solved numerically to estimate the current flow. Various software tools are available for each of these steps, as well as processing pipelines that connect these tools for automated or semi-automated processing. The goal of the present tool-realistic volumetric-approach to simulate transcranial electric simulation (ROAST)-is to provide an end-to-end pipeline that can automatically process individual heads with realistic volumetric anatomy leveraging open-source software and custom scripts to improve segmentation and execute electrode placement., Approach: ROAST combines the segmentation algorithm of SPM12, a Matlab script for touch-up and automatic electrode placement, the finite element mesher iso2mesh and the solver getDP. We compared its performance with commercial FEM software, and SimNIBS, a well-established open-source modeling pipeline., Main Results: The electric fields estimated with ROAST differ little from the results obtained with commercial meshing and FEM solving software. We also do not find large differences between the various automated segmentation methods used by ROAST and SimNIBS. We do find bigger differences when volumetric segmentation are converted into surfaces in SimNIBS. However, evaluation on intracranial recordings from human subjects suggests that ROAST and SimNIBS are not significantly different in predicting field distribution, provided that users have detailed knowledge of SimNIBS., Significance: We hope that the detailed comparisons presented here of various choices in this modeling pipeline can provide guidance for future tool development. We released ROAST as an open-source, easy-to-install and fully-automated pipeline for individualized TES modeling.

Objective: Speech signals have a remarkable ability to entrain brain activity to the rapid fluctuations of speech sounds. For instance, one can readily measure a correlation of the sound amplitude with the evoked responses of the electroencephalogram (EEG), and the strength of this correlation is indicative of whether the listener is attending to the speech. In this study we asked whether this stimulus-response correlation is also predictive of speech intelligibility., Approach: We hypothesized that when a listener fails to understand the speech in adverse hearing conditions, attention wanes and stimulus-response correlation also drops. To test this, we measure a listener's ability to detect words in noisy speech while recording their brain activity using EEG. We alter intelligibility without changing the acoustic stimulus by pairing it with congruent and incongruent visual speech., Main Results: For almost all subjects we found that an improvement in speech detection coincided with an increase in correlation between the noisy speech and the EEG measured over a period of 30 min., Significance: We conclude that simultaneous recordings of the perceived sound and the corresponding EEG response may be a practical tool to assess speech intelligibility in the context of hearing aids.

Costly signaling theory was developed in both economics and biology and has been used to explain a wide range of phenomena. However, the theory's prediction that signal cost can enforce information quality in the design of new communication systems has never been put to an empirical test. Here we show that imposing time costs on reporting extreme scores can improve crowd wisdom in a previously cost-free rating system. We developed an online game where individuals interacted repeatedly with simulated services and rated them for satisfaction. We associated ratings with differential time costs by endowing the graphical user interface that solicited ratings from the users with "physics," including an initial (default) slider position and friction. When ratings were not associated with differential cost (all scores from 0 to 100 could be given by an equally low-cost click on the screen), scores correlated only weakly with objective service quality. However, introducing differential time costs, proportional to the deviation from the mean score, improved correlations between subjective rating scores and objective service performance and lowered the sample size required for obtaining reliable, averaged crowd estimates. Boosting time costs for reporting extreme scores further facilitated the detection of top performances. Thus, human collective online behavior, which is typically cost-free, can be made more informative by applying costly signaling via the virtual physics of rating devices., Competing Interests: The authors declare no conflict of interest., (Copyright © 2019 the Author(s). Published by PNAS.)

In proportional myographic control, one can control either position or velocity of movement. Here, we propose to use adaptive auto-regressive filters, so as to gradually adjust between the two. We implemented this in an adaptive system with closed-loop feedback, where both the user and the machine simultaneously attempt to follow a cursor on a 2-D arena. We tested this on 15 able-bodied and three limb-deficient participants using an eight-channel myoelectric armband. The human-machine pairs learn to perform smoother cursor movements with a larger range of motion when using the auto-regressive filters, as compared with our previous effortswithmoving-average filters. Importantly, the human-machine system converges to an approximate velocity control strategy resulting in faster and more accuratemovements with lessmuscle effort. The method is not specific tomyoelectriccontroland could be used equally well for motion control using high-dimensional signals from reinnervatedmuscles or direct brain recordings.

To reach a deep target in the brain with transcranial electric stimulation (TES), currents have to pass also through the cortical surface. Thus, it is generally thought that TES cannot achieve focal deep brain stimulation. Recent efforts with interfering waveforms and pulsed stimulation have argued that one can achieve deeper or more intense stimulation in the brain. Here we argue that conventional transcranial stimulation with multiple current sources is just as effective as these new approaches. The conventional multi-electrode approach can be numerically optimized to maximize intensity or focality at a desired target location. Using such optimal electrode configurations we find in a detailed and realistic head model that deep targets may in fact be strongly stimulated, with cerebro-spinal fluid guiding currents deep into the brain., (Copyright © 2018 Elsevier Inc. All rights reserved.)

Transcranial direct current stimulation (tDCS) aims to deliver weak electric current into the brain to modulate neural activities. Based on the volume conductor model of the head, optimization algorithm can be used to determine a specific montage of high-definition electrodes on the scalp to achieve targeted stimulation. However, simultaneous targeting for multiple disconnected regions can rarely be found in the literature. Here we attempted to provide an integrated solution for optimized tDCS to target multiple brain regions (either a single point or brain structures). By improving the "maxintensity" routine previously published in [1], we are able to target two regions of interest (ROI) in the brain simultaneously.For ROIs more than two, we show that the "max-focality" algorithm using weighted least-square in [1] can be further improved by putting the L1-norm constraint on the stimulation current as apenalty term into the cost function. Up to five ROIs can be targeted at the same time without violating the safety criteria. Further analysis shows that, for multiple targets, a trade-off exists between targeting accuracy and the number of electrodes needed. We implemented all these algorithms in Soterix software HD-Targets TM .

Research in the area of transcranial electrical stimulation (TES) often relies on computational models of current flow in the brain. Models are built on magnetic resonance images (MRI) of the human head to capture detailed individual anatomy. To simulate current flow, MRIs have to be segmented, virtual electrodes have to be placed on the scalp, the volume is tessellated into a mesh, and the finite element model is solved numerically to estimate the current flow. Various software tools are available for each step, as well as processing pipelines that connect these tools for automated or semi-automated processing. The goal of the present tool - ROAST - is to provide an end-to-end pipeline that can automatically process individual heads with realistic volumetric anatomy leveraging open-source software (SPM8, iso2mesh and getDP) and custom scripts to improve segmentation and execute electrode placement. When we compare the results on a standard head with other major commercial software for finite element modeling (ScanIP, Abaqus), ROAST only leads to a small difference of 9% in the estimated electric field in the brain. We obtain a larger difference of 47% when comparing results with SimNIBS, an automated pipeline that is based on surface segmentation of the head. We release ROAST as an open-source, fully-automated pipeline at https://www.parralab.org/roast/.

Background: Neuropsychiatric disorders are a leading source of disability and require novel treatments that target mechanisms of disease. As such disorders are thought to result from aberrant neuronal circuit activity, neuromodulation approaches are of increasing interest given their potential for manipulating circuits directly. Low intensity transcranial electrical stimulation (tES) with direct currents (transcranial direct current stimulation, tDCS) or alternating currents (transcranial alternating current stimulation, tACS) represent novel, safe, well-tolerated, and relatively inexpensive putative treatment modalities., Objective: This report seeks to promote the science, technology and effective clinical applications of these modalities, identify research challenges, and suggest approaches for addressing these needs in order to achieve rigorous, reproducible findings that can advance clinical treatment., Methods: The National Institute of Mental Health (NIMH) convened a workshop in September 2016 that brought together experts in basic and human neuroscience, electrical stimulation biophysics and devices, and clinical trial methods to examine the physiological mechanisms underlying tDCS/tACS, technologies and technical strategies for optimizing stimulation protocols, and the state of the science with respect to therapeutic applications and trial designs., Results: Advances in understanding mechanisms, methodological and technological improvements (e.g., electronics, computational models to facilitate proper dosing), and improved clinical trial designs are poised to advance rigorous, reproducible therapeutic applications of these techniques. A number of challenges were identified and meeting participants made recommendations made to address them., Conclusions: These recommendations align with requirements in NIMH funding opportunity announcements to, among other needs, define dosimetry, demonstrate dose/response relationships, implement rigorous blinded trial designs, employ computational modeling, and demonstrate target engagement when testing stimulation-based interventions for the treatment of mental disorders., (Published by Elsevier Inc.)

Transcranial electric stimulation aims to stimulate the brain by applying weak electrical currents at the scalp. However, the magnitude and spatial distribution of electric fields in the human brain are unknown. We measured electric potentials intracranially in ten epilepsy patients and estimated electric fields across the entire brain by leveraging calibrated current-flow models. When stimulating at 2 mA, cortical electric fields reach 0.8 V/m, the lower limit of effectiveness in animal studies. When individual whole-head anatomy is considered, the predicted electric field magnitudes correlate with the recorded values in cortical ( r = 0.86) and depth ( r = 0.88) electrodes. Accurate models require adjustment of tissue conductivity values reported in the literature, but accuracy is not improved when incorporating white matter anisotropy or different skull compartments. This is the first study to validate and calibrate current-flow models with in vivo intracranial recordings in humans, providing a solid foundation to target stimulation and interpret clinical trials.

Background: Direct current stimulation (DCS) affects both neuronal firing rate and synaptic efficacy. The neuronal input/output (I/O) function determines the likelihood that a neuron elicits an action potential in response to synaptic input of a given strength. Changes of the neuronal I/O function by DCS may underlie previous observations in animal models and human testing, yet have not been directly assessed., Objective: Test if the neuronal input/output function is affected by DCS METHODS: Using rat hippocampal brain slices and computational modeling, we provide evidence for how DCS modulates the neuronal I/O function., Results: We show for the first time that DCS modulates the likelihood of neuronal firing for a given and fixed synaptic input. Opposing polarization of soma and dendrite may have a synergistic effect for anodal stimulation, increasing the driving force of synaptic activity while simultaneously increasing spiking probability at the soma. For cathodal stimulation, however, the opposing effects tend to cancel. This results in an asymmetry in the strength of the effects of stimulation for opposite polarities., Conclusions: Our results may explain the asymmetries observed in acute and long term effects of transcranial direct current stimulation., (Copyright © 2016 Elsevier Inc. All rights reserved.)

In source localization of electroencephalograpic (EEG) signals, as well as in targeted transcranial electric current stimulation (tES), a volume conductor model is required to describe the flow of electric currents in the head. Boundary element models (BEM) can be readily computed to represent major tissue compartments, but cannot encode detailed anatomical information within compartments. Finite element models (FEM) can capture more tissue types and intricate anatomical structures, but with the higher precision also comes the need for semi-automated segmentation, and a higher computational cost. In either case, adjusting to the individual human anatomy requires costly magnetic resonance imaging (MRI), and thus head modeling is often based on the anatomy of an 'arbitrary' individual (e.g. Colin27). Additionally, existing reference models for the human head often do not include the cerebro-spinal fluid (CSF), and their field of view excludes portions of the head and neck-two factors that demonstrably affect current-flow patterns. Here we present a highly detailed FEM, which we call ICBM-NY, or "New York Head". It is based on the ICBM152 anatomical template (a non-linear average of the MRI of 152 adult human brains) defined in MNI coordinates, for which we extended the field of view to the neck and performed a detailed segmentation of six tissue types (scalp, skull, CSF, gray matter, white matter, air cavities) at 0.5mm(3) resolution. The model was solved for 231 electrode locations. To evaluate its performance, additional FEMs and BEMs were constructed for four individual subjects. Each of the four individual FEMs (regarded as the 'ground truth') is compared to its BEM counterpart, the ICBM-NY, a BEM of the ICBM anatomy, an 'individualized' BEM of the ICBM anatomy warped to the individual head surface, and FEMs of the other individuals. Performance is measured in terms of EEG source localization and tES targeting errors. Results show that the ICBM-NY outperforms FEMs of mismatched individual anatomies as well as the BEM of the ICBM anatomy according to both criteria. We therefore propose the New York Head as a new standard head model to be used in future EEG and tES studies whenever an individual MRI is not available. We release all model data online at neuralengr.com/nyhead/ to facilitate broad adoption., (Published by Elsevier Inc.)

Despite promising preliminary results in treating fibromyalgia (FM) pain, no neuromodulation technique has been adopted in clinical practice because of limited efficacy, low response rate, or poor tolerability. This phase II open-label trial aims to define a methodology for a clinically effective treatment of pain in FM by establishing treatment protocols and screening procedures to maximize efficacy and response rate. High-definition transcranial direct current stimulation (HD-tDCS) provides targeted subthreshold brain stimulation, combining tolerability with specificity. We aimed to establish the number of HD-tDCS sessions required to achieve a 50% FM pain reduction, and to characterize the biometrics of the response, including brain network activation pain scores of contact heat-evoked potentials. We report a clinically significant benefit of a 50% pain reduction in half (n = 7) of the patients (N = 14), with responders and nonresponders alike benefiting from a cumulative effect of treatment, reflected in significant pain reduction (P = .035) as well as improved quality of life (P = .001) over time. We also report an aggregate 6-week response rate of 50% of patients and estimate 15 as the median number of HD-tDCS sessions to reach clinically meaningful outcomes. The methodology for a pivotal FM neuromodulation clinical trial with individualized treatment is thus supported., Online Registration: Registered in Clinicaltrials.gov under registry number NCT01842009., Perspective: In this article, an optimized protocol for the treatment of fibromyalgia pain with targeted subthreshold brain stimulation using high-definition transcranial direct current stimulation is outlined., (Copyright © 2016 American Pain Society. Published by Elsevier Inc. All rights reserved.)

In electroencephalographic (EEG) source imaging as well as in transcranial current stimulation (TCS), it is common to model the head using either three-shell boundary element (BEM) or more accurate finite element (FEM) volume conductor models. Since building FEMs is computationally demanding and labor intensive, they are often extensively reused as templates even for subjects with mismatching anatomies. BEMs can in principle be used to efficiently build individual volume conductor models; however, the limiting factor for such individualization are the high acquisition costs of structural magnetic resonance images. Here, we build a highly detailed (0.5mm(3) resolution, 6 tissue type segmentation, 231 electrodes) FEM based on the ICBM152 template, a nonlinear average of 152 adult human heads, which we call ICBM-NY. We show that, through more realistic electrical modeling, our model is similarly accurate as individual BEMs. Moreover, through using an unbiased population average, our model is also more accurate than FEMs built from mismatching individual anatomies. Our model is made available in Matlab format.

We review a simple yet versatile approach for the analysis of multichannel data, focusing in particular on brain signals measured with EEG, MEG, ECoG, LFP or optical imaging. Sensors are combined linearly with weights that are chosen to provide optimal signal-to-noise ratio. Signal and noise can be variably defined to match the specific need, e.g. reproducibility over trials, frequency content, or differences between stimulus conditions. We demonstrate how the method can be used to remove power line or cardiac interference, enhance stimulus-evoked or stimulus-induced activity, isolate narrow-band cortical activity, and so on. The approach involves decorrelating both the original and filtered data by joint diagonalization of their covariance matrices. We trace its origins; offer an easy-to-understand explanation; review a range of applications; and chart failure scenarios that might lead to misleading results, in particular due to overfitting. In addition to its flexibility and effectiveness, a major appeal of the method is that it is easy to understand., (Copyright © 2014 Elsevier Inc. All rights reserved.)

When scanning a scene, the target of our search may be in plain sight and yet remain unperceived. Conversely, at other times the target may be perceived in the periphery prior to fixation. There is ample behavioral and neurophysiological evidence to suggest that in some constrained visual-search tasks, targets are detected prior to fixational eye movements. However, limited human data are available during unconstrained search to determine the time course of detection, the brain areas involved, and the neural correlates of failures to detect a foveated target. Here, we recorded and analyzed electroencephalographic (EEG) activity during free-viewing visual search, varying the task difficulty to compare neural signatures for detected and unreported ("missed") targets. When carefully controlled to remove eye-movement-related potentials, saccade-locked EEG shows that: (a) "Easy" targets may be detected as early as 150 ms prior to foveation, as indicated by a premotor potential associated with a button response; (b) object-discriminating occipital activity emerges during the saccade to target; and (c) success and failures to detect a target are accompanied by a modulation in alpha-band power over fronto-central areas as well as altered saccade dynamics. Taken together, these data suggest that target detection during free viewing can begin prior to and continue during a saccade, with failure or success in reporting a target possibly resulting from inhibition or activation of fronto-central processing areas associated with saccade control.

Targeted transcranial stimulation with electric currents requires accurate models of the current flow from scalp electrodes to the human brain. Idiosyncratic anatomy of individual brains and heads leads to significant variability in such current flows across subjects, thus, necessitating accurate individualized head models. Here we report on an automated processing chain that computes current distributions in the head starting from a structural magnetic resonance image (MRI). The main purpose of automating this process is to reduce the substantial effort currently required for manual segmentation, electrode placement, and solving of finite element models. In doing so, several weeks of manual labor were reduced to no more than 4 hours of computation time and minimal user interaction, while current-flow results for the automated method deviated by less than 27.9% from the manual method. Key facilitating factors are the addition of three tissue types (skull, scalp and air) to a state-of-the-art automated segmentation process, morphological processing to correct small but important segmentation errors, and automated placement of small electrodes based on easily reproducible standard electrode configurations. We anticipate that such an automated processing will become an indispensable tool to individualize transcranial direct current stimulation (tDCS) therapy.

High-Definition transcranial Direct Current Stimulation (HD-tDCS) using specialized small electrodes has been proposed as a focal, non-invasive neuromodulatory technique. Here we provide the first evidence of a change in cortical excitability after HD-tDCS of the motor cortex, using TMS motor evoked potential (MEP) as the measure of excitability. Stimulation for 20 minutes at 1 mA with an anode centered over the hand area of the motor cortex and four surrounding return electrodes (anodal 4×1 montage) produced a significant increase in MEP amplitude and variability after stimulation, compared to sham stimulation. Stimulation was well tolerated by all subjects with adverse effects limited to transient sensation under the electrodes. A high-resolution computational model confirmed predictions of increased focality using the 4×1 HD tDCS montage compared to conventional tDCS. Simulations also indicated that variability in placement of the center electrode relative to the location of the target (central sulcus) could account for increasing variability. These results provide support for the careful use of this technique where focal tDCS is desired.

Transcranial current stimulation (tCS) is a promising noninvasive technique to elicit neuromodulation by passing weak electrical currents through scalp electrodes. While significant effort has been devoted towards designing stimulation protocols which "steer" current to regions of interest, previous work has been almost exclusively focused on the magnitude of the electric field, while ignoring the effects of direction. This is despite previous in vitro studies demonstrating that the angle between the field orientation and the cell axis of symmetry has significant effects on the resulting membrane polarization presumably underlying therapeutic effects. To that end, here we examine the impact of the desired electric field orientation on the optimal placement of electrodes for a given target region. Based on high-resolution head models derived from magnetic resonance scans of patients enrolled in a clinical trial examining the use of tCS in rehabilitation after stroke, we derive and employ an optimization algorithm which computes the montage maximizing directed current flow at the target. The results reveal a strong dependence of the optimal montage on the desired orientation; moreover, the magnitude of the induced electric field at the target region varies widely with the preferred direction. This suggests that identifying the desired electric field orientation at the region of interest is a crucial step in the development of rational electrical stimulation paradigms.

In a partial report paradigm, subjects observe during a brief presentation a cluttered field and after some time - typically ranging from 100 ms to a second - are asked to report a subset of the presented elements. A vast buffer of information is transiently available to be broadcasted which, if not retrieved in time, fades rapidly without reaching consciousness. An interesting feature of this experiment is that objective performance and subjective confidence is decoupled. This converts this paradigm in an ideal vehicle to understand the brain dynamics of the construction of confidence. Here we report a high-density EEG experiment in which we infer elements of the EEG response which are indicative of subjective confidence. We find that an early response during encoding partially correlates with perceived confidence. However, the bulk of the weight of subjective confidence is determined during a late, N400-like waveform, during the retrieval stage. This shows that we can find markers of access to internal, subjective states, that are uncoupled from objective response and stimulus properties of the task, and we propose that this can be used with decoding methods of EEG to infer subjective mental states.

The goal of this paper is to improve on single-trial classification of electro-encephalography (EEG) using linear methods. The paper proposes to combine the classification of the spatial distribution of activity with the classification of its temporal profile. The work is based on the idea that a current source in the brain has a reproducible temporal profile with a static spatial projection to the electrodes. This assumption reduces the parameter space of a linear classifier to a rank-one factorial space. The new model limits over-fitting due to the fewer number of parameters, and furthermore, it allows us to declare a prior belief of smoothness on the spatial and temporal profiles of the source. Our experiments show that the method is useful as a classifier with an area under the ROC curve of 0.93 having only 40 target trials available for training. Investigation of the trained classifier encourages us to belief that the method can also be useful as a tool to interpret the activity in the data at hand with respect to experimental events.

Accurate spike timing is emerging as an important concept in the encoding of sensory stimuli. Accurately timed spiking has been recorded in-vivo in the visual and auditory cortex, many layers removed from the primary sensory neurons. This temporal accuracy may be maintained despite noisy synaptic transmission by the simultaneous firing of multiple neurons. Here we show in simulations that a coherent polarization of a population of neurons through extracellular fields can further increase the coherence of the population's firing times. We discuss the potential relevance of such a common input as an external "clock" signal in a spatio-temporal code.