Fully automated whole-head segmentation with improved smoothness and continuity, with theory reviewed

Study of multistep Dense U-Net-based automatic segmentation for head MRI scans

BACKGROUND

Despite extensive efforts to obtain accurate segmentation of magnetic resonance imaging (MRI) scans of a head, it remains challenging primarily due to variations in intensity distribution, which depend on the equipment and parameters used.

PURPOSE

The goal of this study is to evaluate the effectiveness of an automatic segmentation method for head MRI scans using a multistep Dense U-Net (MDU-Net) architecture.

METHODS

The MDU-Net-based method comprises two steps. The first step is to segment the scalp, skull, and whole brain from head MRI scans using a convolutional neural network (CNN). In the first step, a hybrid network is used to combine 2.5D Dense U-Net and 3D Dense U-Net structure. This hybrid network acquires logits in three orthogonal planes (axial, coronal, and sagittal) using 2.5D Dense U-Nets and fuses them by averaging. The resultant fused probability map with head MRI scans then serves as the input to a 3D Dense U-Net. In this process, different ratios of active contour loss and focal loss are applied. The second step is to segment the cerebrospinal fluid (CSF), white matter, and gray matter from extracted brain MRI scans using CNNs. In the second step, the histogram of the extracted brain MRI scans is standardized and then a 2.5D Dense U-Net is used to further segment the brain's specific tissues using the focal loss. A dataset of 100 head MRI scans from an OASIS-3 dataset was used for training, internal validation, and testing, with ratios of 80%, 10%, and 10%, respectively. Using the proposed approach, we segmented the head MRI scans into five areas (scalp, skull, CSF, white matter, and gray matter) and evaluated the segmentation results using the Dice similarity coefficient (DSC) score, Hausdorff distance (HD), and the average symmetric surface distance (ASSD) as evaluation metrics. We compared these results with those obtained using the Res-U-Net, Dense U-Net, U-Net++, Swin-Unet, and H-Dense U-Net models.

RESULTS

The MDU-Net model showed DSC values of 0.933, 0.830, 0.833, 0.953, and 0.917 in the scalp, skull, CSF, white matter, and gray matter, respectively. The corresponding HD values were 2.37, 2.89, 2.13, 1.52, and 1.53 mm, respectively. The ASSD values were 0.50, 1.63, 1.28, 0.26, and 0.27 mm, respectively. Comparing these results with other models revealed that the MDU-Net model demonstrated the best performance in terms of the DSC values for the scalp, CSF, white matter, and gray matter. When compared with the H-Dense U-Net model, which showed the highest performance among the other models, the MDU-Net model showed substantial improvements in the HD view, particularly in the gray matter region, with a difference of approximately 9%. In addition, in terms of the ASSD, the MDU-Net model outperformed the H-Dense U-Net model, showing an approximately 7% improvements in the white matter and approximately 9% improvements in the gray matter.

CONCLUSION

Compared with existing models in terms of DSC, HD, and ASSD, the proposed MDU-Net model demonstrated the best performance on average and showed its potential to enhance the accuracy of automatic segmentation for head MRI scans.

Computation of group-level electric field in lower limb motor area for different tDCS montages

OBJECTIVE

Transcranial direct current stimulation (tDCS) injects a weak electric current into the brain via electrodes attached to the scalp to modulate cortical excitability. tDCS is used to rebalance brain activity between affected and unaffected hemispheres in rehabilitation. However, a systematic quantitative evaluation of tDCS montage is not reported for the lower limbs. In this study, we computationally investigated the generated electric field intensity, polarity, and co-stimulation of cortical areas for lower limb targeting using high-resolution head models.

METHODS

Volume conductor models have thus been employed to estimate the electric field in the brain. A total of 18 head models of healthy subjects were used to calculate the group-level electric fields generated from four montages of tDCS for modulation of lower limbs.

RESULTS

C1-C2 montage delivered higher electric field intensities while reaching deeper regions of the lower-limb motor area. It produced a uniform polarization on the same hemisphere target with comparable intensities between hemispheres but with higher variability.

CONCLUSIONS

Proper montage selection allows reaching deeper regions of the lower-limb motor area with uniform polarization.

SIGNIFICANCE

First systematic computational study providing support to tDCS experimental studies using montages for the lower limb while considering polarity factor for balancing brain activity.

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.

Numerical analysis of transcranial magnetic stimulation application in patients with orofacial pain

In this paper, we monitored the accuracy of non-navigated application of repetitive Transcranial Magnetic Stimulation (rTMS) in 10 patients suffering from orofacial pain by using functional magnetic resonance (fMRI), computer modeling and numerical simulation. Through a unique process, each fMRI scan was used to define a Region of Interest (ROI) where the source of the orofacial pain was located, which was to be stimulated using rTMS. For each patient, MRI scans with a spatial resolution of 0.7 mm were converted into an anatomically accurate head model. The head model including the ROI was then co-registered with a model of the stimulation coil in an electromagnetic field numerical simulator. The accuracy of rTMS application was evaluated based on the calculations of electric field intensity distribution in the ROI. The research has yielded unique insight into ROIs (with average volume 904mm3) in patients with orofacial pain and has also extended further possibilities of human head MRI image semi-automatic segmentation. According to the calculations performed, the average ROI volume that was stimulated by an electric field with an intensity of over 80 V/m was only 4.4%, with the maximum ROI volume being 20.5%. Furthermore, a numerical study of the impact of coil rotation and translation was performed. It demonstrated a) the optimal placement of the stimulation coil can significantly increase the volume of the stimulated ROI up to 60% and b) patients with orofacial pain would need precise coil positioning with a navigation error lower than 10 mm. Due to an acceptable proccessing time of up to 6 hours, described numerical simulation opens up new options for precise rTMS treatment planning. This planning platform together withpatient-specific navigated rTMS, could lead to significant increase of treatment outcomes in patients suffering from orofacial pain.

Feasibility of Reconstructing Source Functional Connectivity with Low-Density EEG

OBJECTIVES

Functional connectivity (FC) is increasingly used as target for neuromodulation and enhancement of performance. A reliable assessment of FC with electroencephalography (EEG) currently requires a laboratory environment with high-density montages and a long preparation time. This study investigated the feasibility of reconstructing source FC with a low-density EEG montage towards a usage in real life applications.

METHODS

Source FC was reconstructed with inverse solutions and quantified as node degree of absolute imaginary coherence in alpha frequencies. We used simulated coherent point sources as well as two real datasets to investigate the impact of electrode density (19 vs. 128 electrodes) and usage of template vs. individual MRI-based head models on localization accuracy. In addition, we checked whether low-density EEG is able to capture inter-individual variations in coherence strength.

RESULTS

In numerical simulations as well as real data, a reduction of the number of electrodes led to less reliable reconstructions of coherent sources and of coupling strength. Yet, when comparing different approaches to reconstructing FC from 19 electrodes, source FC obtained with beamformers outperformed sensor FC, FC computed after independent component analysis, and source FC obtained with sLORETA. In particular, only source FC based on beamformers was able to capture neural correlates of motor behavior.

CONCLUSION

Reconstructions of FC from low-density EEG is challenging, but may be feasible when using source reconstructions with beamformers.

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.

Biophysically detailed forward modeling of the neural origin of EEG and MEG signals

Electroencephalography (EEG) and magnetoencephalography (MEG) are among the most important techniques for non-invasively studying cognition and disease in the human brain. These signals are known to originate from cortical neural activity, typically described in terms of current dipoles. While the link between cortical current dipoles and EEG/MEG signals is relatively well understood, surprisingly little is known about the link between different kinds of neural activity and the current dipoles themselves. Detailed biophysical modeling has played an important role in exploring the neural origin of intracranial electric signals, like extracellular spikes and local field potentials. However, this approach has not yet been taken full advantage of in the context of exploring the neural origin of the cortical current dipoles that are causing EEG/MEG signals. Here, we present a method for reducing arbitrary simulated neural activity to single current dipoles. We find that the method is applicable for calculating extracranial signals, but less suited for calculating intracranial electrocorticography (ECoG) signals. We demonstrate that this approach can serve as a powerful tool for investigating the neural origin of EEG/MEG signals. This is done through example studies of the single-neuron EEG contribution, the putative EEG contribution from calcium spikes, and from calculating EEG signals from large-scale neural network simulations. We also demonstrate how the simulated current dipoles can be used directly in combination with detailed head models, allowing for simulated EEG signals with an unprecedented level of biophysical details. In conclusion, this paper presents a framework for biophysically detailed modeling of EEG and MEG signals, which can be used to better our understanding of non-inasively measured neural activity in humans.

Transcranial ultrasound stimulation in humans is associated with an auditory confound that can be effectively masked

Background

Transcranial ultrasound stimulation (TUS) is emerging as a potentially powerful, non-invasive technique for focal brain stimulation. Recent animal work suggests, however, that TUS effects may be confounded by indirect stimulation of early auditory pathways.

Objective

We aimed to investigate in human participants whether TUS elicits audible sounds and if these can be masked by an audio signal.

Methods

In 18 healthy participants, T1-weighted magnetic resonance brain imaging was acquired for 3D ultrasound simulations to determine optimal transducer placements and source amplitudes. Thermal simulations ensured that temperature rises were <0.5 °C at the target and <3 °C in the skull. To test for non-specific auditory activation, TUS (500 kHz, 300 ms burst, modulated at 1 kHz with 50% duty cycle) was applied to primary visual cortex and participants were asked to distinguish stimulation from non-stimulation trials. EEG was recorded throughout the task. Furthermore, ex-vivo skull experiments tested for the presence of skull vibrations during TUS.

Results

We found that participants can hear sound during TUS and can distinguish between stimulation and non-stimulation trials. This was corroborated by EEG recordings indicating auditory activation associated with TUS. Delivering an audio waveform to participants through earphones while TUS was applied reduced detection rates to chance level and abolished the TUS-induced auditory EEG signal. Ex vivo skull experiments demonstrated that sound is conducted through the skull at the pulse repetition frequency of the ultrasound.

Conclusion

Future studies using TUS in humans need to take this auditory confound into account and mask stimulation appropriately.

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Transcranial ultrasound stimulation elicits auditory signals in humans.

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Healthy human participants can distinguish stimulation from non-stimulation trials.

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Auditory masking reduces detection rates.

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Skull vibrations are present during transcranial ultrasound stimulation.

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The auditory signal is likely due to bone conduction at the pulse repetition frequency.

Accurate anatomical head segmentations: a data set for biomedical simulations

Detailed computational anatomical models of the entire head are needed for accurate in silico modeling in a variety of transcranial stimulation applications. Models from different subjects help to understand and account for population variability. To this end, we have developed a new library of head models of 20 individuals, segmented from co-aligned multi-modal medical image data. The acquired image modalities allow to accurately model tissues with different material properties, such as electrical conductivity or spatially varying acoustic properties. The usefulness of the models is illustrated for two example applications.

Combined behavioral and electrophysiological evidence for a direct cortical effect of prefrontal tDCS on disorders of consciousness

Severe brain injuries can lead to long-lasting disorders of consciousness (DoC) such as vegetative state/unresponsive wakefulness syndrome (VS/UWS) or minimally conscious state (MCS). While behavioral assessment remains the gold standard to determine conscious state, EEG has proven to be a promising complementary tool to monitor the effect of new therapeutics. Encouraging results have been obtained with invasive electrical stimulation of the brain, and recent studies identified transcranial direct current stimulation (tDCS) as an effective approach in randomized controlled trials. This non-invasive and inexpensive tool may turn out to be the preferred treatment option. However, its mechanisms of action and physiological effects on brain activity remain unclear and debated. Here, we stimulated 60 DoC patients with the anode placed over left-dorsolateral prefrontal cortex in a prospective open-label study. Clinical behavioral assessment improved in twelve patients (20%) and none deteriorated. This behavioral response after tDCS coincided with an enhancement of putative EEG markers of consciousness: in comparison with non-responders, responders showed increases of power and long-range cortico-cortical functional connectivity in the theta-alpha band, and a larger and more sustained P300 suggesting improved conscious access to auditory novelty. The EEG changes correlated with electric fields strengths in prefrontal cortices, and no correlation was found on the scalp. Taken together, this prospective intervention in a large cohort of DoC patients strengthens the validity of the proposed EEG signatures of consciousness, and is suggestive of a direct causal effect of tDCS on consciousness.

Significant group-level hotspots found in deep brain regions during transcranial direct current stimulation (tDCS): A computational analysis of electric fields

OBJECTIVE

Transcranial direct current stimulation (tDCS) is a neuromodulation scheme that delivers a small current via electrodes placed on the scalp. The target is generally assumed to be under the electrode, but deep brain regions could also be involved due to the large current spread between the electrodes. This study aims to computationally evaluate if group-level hotspots exist in deep brain regions for different electrode montages.

METHODS

We computed the tDCS-generated electric fields (EFs) in a group of subjects using interindividual registration methods that permitted the projection of EFs from individual realistic head models (n = 18) to a standard deep brain region.

RESULTS

The spatial distribution and peak values (standard deviation of 14%) of EFs varied significantly. Nevertheless, group-level EF hotspots appeared in deep brain regions. The caudate had the highest field peaks in particular for F3-F4 montage (70% of maximum cortical EF), while other regions reach field peaks of 50%.

CONCLUSIONS

tDCS at deeper regions may include not only modulation via underlying cortical or subcortical circuits but also modulation of deep brain regions.

SIGNIFICANCE

The presented EF atlas in deep brain regions can be used to explain tDCS mechanism or select the most appropriate tDCS montage.

Automated Segmentation of Tissues Using CT and MRI: A Systematic Review

RATIONALE AND OBJECTIVES

The automated segmentation of organs and tissues throughout the body using computed tomography and magnetic resonance imaging has been rapidly increasing. Research into many medical conditions has benefited greatly from these approaches by allowing the development of more rapid and reproducible quantitative imaging markers. These markers have been used to help diagnose disease, determine prognosis, select patients for therapy, and follow responses to therapy. Because some of these tools are now transitioning from research environments to clinical practice, it is important for radiologists to become familiar with various methods used for automated segmentation.

MATERIALS AND METHODS

The Radiology Research Alliance of the Association of University Radiologists convened an Automated Segmentation Task Force to conduct a systematic review of the peer-reviewed literature on this topic.

RESULTS

The systematic review presented here includes 408 studies and discusses various approaches to automated segmentation using computed tomography and magnetic resonance imaging for neurologic, thoracic, abdominal, musculoskeletal, and breast imaging applications.

CONCLUSION

These insights should help prepare radiologists to better evaluate automated segmentation tools and apply them not only to research, but eventually to clinical practice.

ROAST: An Open-Source, Fully-Automated, Realistic Volumetric-Approach-Based Simulator For TES

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/.

Realistic volumetric-approach to simulate transcranial electric stimulation-ROAST-a fully automated open-source pipeline

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.

Benchmarking transcranial electrical stimulation finite element models: a comparison study

OBJECTIVE

To compare field measure differences in simulations of transcranial electrical stimulation (tES) generated by variations in finite element (FE) models due to boundary condition specification, use of tissue compartment smoothing filters, and use of free or structured tetrahedral meshes based on magnetic resonance imaging (MRI) data.

APPROACH

A structural MRI head volume was acquired at 1 mm³ resolution and segmented into ten tissue compartments. Predicted current densities and electric fields were computed in segmented models using modeling pipelines involving either an in-house (block) or a commercial platform commonly used in previous FE tES studies involving smoothed compartments and free meshing procedures (smooth). The same boundary conditions were used for both block and smooth pipelines. Differences caused by varying boundary conditions were examined using a simple geometry. Percentage differences of median current density values in five cortical structures were compared between the two pipelines for three electrode montages (F3-right supraorbital, T7-T8 and Cz-Oz).

MAIN RESULTS

Use of boundary conditions commonly used in previous tES FE studies produced asymmetric current density profiles in the simple geometry. In head models, median current density differences produced by the two pipelines, using the same boundary conditions, were up to 6% (isotropic) and 18% (anisotropic) in structures targeted by each montage. Tangential electric field measures calculated via either pipeline were within the range of values reported in the literature, when averaged over cortical surface patches.

SIGNIFICANCE

Apparently equivalent boundary settings may affect predicted current density outcomes and care must be taken in their specification. Smoothing FE model compartments may not be necessary, and directly translated, voxellated tissue boundaries at 1 mm³ resolution may be sufficient for use in tES FE studies, greatly reducing processing times. The findings here may be used to inform future current density modeling studies.

Rigor and reproducibility in research with transcranial electrical stimulation: An NIMH-sponsored workshop

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.

Features of the normal choriocapillaris with OCT-angiography: Density estimation and textural properties

The main objective of our work is to perform an in depth analysis of the structural features of normal choriocapillaris imaged with OCT Angiography. Specifically, we provide an optimal radius for a circular Region of Interest (ROI) to obtain a stable estimate of the subfoveal choriocapillaris density and characterize its textural properties using Markov Random Fields. On each binarized image of the choriocapillaris OCT Angiography we performed simulated measurements of the subfoveal choriocapillaris densities with circular Regions of Interest (ROIs) of different radii and with small random displacements from the center of the Foveal Avascular Zone (FAZ). We then calculated the variability of the density measure with different ROI radii. We then characterized the textural features of choriocapillaris binary images by estimating the parameters of an Ising model. For each image we calculated the Optimal Radius (OR) as the minimum ROI radius required to obtain a standard deviation in the simulation below 0.01. The density measured with the individual OR was 0.52 ± 0.07 (mean ± STD). Similar density values (0.51 ± 0.07) were obtained using a fixed ROI radius of 450 μm. The Ising model yielded two parameter estimates (β = 0.34 ± 0.03; γ = 0.003 ± 0.012; mean ± STD), characterizing pixel clustering and white pixel density respectively. Using the estimated parameters to synthetize new random textures via simulation we obtained a good reproduction of the original choriocapillaris structural features and density. In conclusion, we developed an extensive characterization of the normal subfoveal choriocapillaris that might be used for flow analysis and applied to the investigation pathological alterations.

Measurements and models of electric fields in the in vivo human brain during transcranial electric stimulation

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.

DOI:http://dx.doi.org/10.7554/eLife.18834.001

The New York Head-A precise standardized volume conductor model for EEG source localization and tES targeting

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.

A simple method for EEG guided transcranial electrical stimulation without models

OBJECTIVE

There is longstanding interest in using EEG measurements to inform transcranial Electrical Stimulation (tES) but adoption is lacking because users need a simple and adaptable recipe. The conventional approach is to use anatomical head-models for both source localization (the EEG inverse problem) and current flow modeling (the tES forward model), but this approach is computationally demanding, requires an anatomical MRI, and strict assumptions about the target brain regions. We evaluate techniques whereby tES dose is derived from EEG without the need for an anatomical head model, target assumptions, difficult case-by-case conjecture, or many stimulation electrodes.

APPROACH

We developed a simple two-step approach to EEG-guided tES that based on the topography of the EEG: (1) selects locations to be used for stimulation; (2) determines current applied to each electrode. Each step is performed based solely on the EEG with no need for head models or source localization. Cortical dipoles represent idealized brain targets. EEG-guided tES strategies are verified using a finite element method simulation of the EEG generated by a dipole, oriented either tangential or radial to the scalp surface, and then simulating the tES-generated electric field produced by each model-free technique. These model-free approaches are compared to a 'gold standard' numerically optimized dose of tES that assumes perfect understanding of the dipole location and head anatomy. We vary the number of electrodes from a few to over three hundred, with focality or intensity as optimization criterion.

MAIN RESULTS

Model-free approaches evaluated include (1) voltage-to-voltage, (2) voltage-to-current; (3) Laplacian; and two Ad-Hoc techniques (4) dipole sink-to-sink; and (5) sink to concentric. Our results demonstrate that simple ad hoc approaches can achieve reasonable targeting for the case of a cortical dipole, remarkably with only 2-8 electrodes and no need for a model of the head.

SIGNIFICANCE

Our approach is verified directly only for a theoretically localized source, but may be potentially applied to an arbitrary EEG topography. For its simplicity and linearity, our recipe for model-free EEG guided tES lends itself to broad adoption and can be applied to static (tDCS), time-variant (e.g., tACS, tRNS, tPCS), or closed-loop tES.

Modeling sequence and quasi-uniform assumption in computational neurostimulation

Computational neurostimulation aims to develop mathematical constructs that link the application of neuromodulation with changes in behavior and cognition. This process is critical but daunting for technical challenges and scientific unknowns. The overarching goal of this review is to address how this complex task can be made tractable. We describe a framework of sequential modeling steps to achieve this: (1) current flow models, (2) cell polarization models, (3) network and information processing models, and (4) models of the neuroscientific correlates of behavior. Each step is explained with a specific emphasis on the assumptions underpinning underlying sequential implementation. We explain the further implementation of the quasi-uniform assumption to overcome technical limitations and unknowns. We specifically focus on examples in electrical stimulation, such as transcranial direct current stimulation. Our approach and conclusions are broadly applied to immediate and ongoing efforts to deploy computational neurostimulation.