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Gi Y, Oh G, Jo Y, Lim H, Ko Y, Hong J, Lee E, Park S, Kwak T, Kim S, Yoon M. Study of multistep Dense U-Net-based automatic segmentation for head MRI scans. Med Phys 2024; 51:2230-2238. [PMID: 37956307 DOI: 10.1002/mp.16824] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/21/2023] [Revised: 09/25/2023] [Accepted: 10/22/2023] [Indexed: 11/15/2023] Open
Abstract
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.
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Affiliation(s)
- Yongha Gi
- Department of Bio-medical Engineering, Korea University, Seoul, Republic of Korea
| | - Geon Oh
- Department of Bio-medical Engineering, Korea University, Seoul, Republic of Korea
| | - Yunhui Jo
- Institute of Global Health Technology (IGHT), Korea University, Seoul, Republic of Korea
| | - Hyeongjin Lim
- Department of Bio-medical Engineering, Korea University, Seoul, Republic of Korea
| | - Yousun Ko
- Department of Bio-medical Engineering, Korea University, Seoul, Republic of Korea
| | - Jinyoung Hong
- Department of Bio-medical Engineering, Korea University, Seoul, Republic of Korea
| | - Eunjun Lee
- Department of Bio-medical Engineering, Korea University, Seoul, Republic of Korea
| | - Sangmin Park
- Department of Bio-medical Engineering, Korea University, Seoul, Republic of Korea
- Field Cure Ltd., Seoul, Republic of Korea
| | - Taemin Kwak
- Department of Bio-medical Engineering, Korea University, Seoul, Republic of Korea
- Field Cure Ltd., Seoul, Republic of Korea
| | - Sangcheol Kim
- Department of Bio-medical Engineering, Korea University, Seoul, Republic of Korea
- Field Cure Ltd., Seoul, Republic of Korea
| | - Myonggeun Yoon
- Department of Bio-medical Engineering, Korea University, Seoul, Republic of Korea
- Field Cure Ltd., Seoul, Republic of Korea
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Hamajima H, Gomez-Tames J, Uehara S, Otaka Y, Tanaka S, Hirata A. Computation of group-level electric field in lower limb motor area for different tDCS montages. Clin Neurophysiol 2023; 150:69-78. [PMID: 37023635 DOI: 10.1016/j.clinph.2023.03.009] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/27/2022] [Revised: 01/31/2023] [Accepted: 03/09/2023] [Indexed: 03/29/2023]
Abstract
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.
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Huang Y, Moreno R, Malani R, Meng A, Swinburne N, Holodny AI, Choi Y, Rusinek H, Golomb JB, George A, Parra LC, Young RJ. Deep Learning Achieves Neuroradiologist-Level Performance in Detecting Hydrocephalus Requiring Treatment. J Digit Imaging 2022; 35:1662-1672. [PMID: 35581409 PMCID: PMC9712867 DOI: 10.1007/s10278-022-00654-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/05/2021] [Revised: 04/11/2022] [Accepted: 05/02/2022] [Indexed: 11/25/2022] Open
Abstract
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.
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Affiliation(s)
- Yu Huang
- Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, NY 10065 USA
| | - Raquel Moreno
- Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, NY 10065 USA
| | - Rachna Malani
- Department of Neurology, Memorial Sloan Kettering Cancer Center, New York, NY 10065 USA
- Brain Tumor Center, Memorial Sloan Kettering Cancer Center, New York, NY 10065 USA
| | - Alicia Meng
- Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, NY 10065 USA
- Brain Tumor Center, Memorial Sloan Kettering Cancer Center, New York, NY 10065 USA
| | - Nathaniel Swinburne
- Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, NY 10065 USA
- Brain Tumor Center, Memorial Sloan Kettering Cancer Center, New York, NY 10065 USA
| | - Andrei I. Holodny
- Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, NY 10065 USA
| | - Ye Choi
- Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, NY 10065 USA
| | - Henry Rusinek
- Department of Radiology, Grossman School of Medicine, New York University, New York, NY 10016 USA
- Department of Psychiatry, Grossman School of Medicine, New York University, New York, NY 10016 USA
| | - James B. Golomb
- Department of Psychiatry, Grossman School of Medicine, New York University, New York, NY 10016 USA
- Department of Neurology, Grossman School of Medicine, New York University, New York, NY 10016 USA
| | - Ajax George
- Department of Radiology, Grossman School of Medicine, New York University, New York, NY 10016 USA
| | - Lucas C. Parra
- Department of Biomedical Engineering, City College of New York, 160 Convent Ave, Steinman Hall Room 401, New York, NY 10031 USA
| | - Robert J. Young
- Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, NY 10065 USA
- Brain Tumor Center, Memorial Sloan Kettering Cancer Center, New York, NY 10065 USA
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Vrba D, Malena L, Albrecht J, Fricova J, Anders M, Rokyta R, Rodrigues D, Vrba J. Numerical analysis of transcranial magnetic stimulation application in patients with orofacial pain. IEEE Trans Neural Syst Rehabil Eng 2022; 30:590-599. [PMID: 35239486 DOI: 10.1109/tnsre.2022.3156703] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/05/2022]
Abstract
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.
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Feasibility of Reconstructing Source Functional Connectivity with Low-Density EEG. Brain Topogr 2021; 34:709-719. [PMID: 34415477 PMCID: PMC8556201 DOI: 10.1007/s10548-021-00866-w] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/11/2021] [Accepted: 08/02/2021] [Indexed: 10/24/2022]
Abstract
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.
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Hirsch L, Huang Y, Parra LC. Segmentation of MRI head anatomy using deep volumetric networks and multiple spatial priors. J Med Imaging (Bellingham) 2021; 8:034001. [PMID: 34159222 DOI: 10.1117/1.jmi.8.3.034001] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/29/2020] [Accepted: 05/19/2021] [Indexed: 11/14/2022] Open
Abstract
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.
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Affiliation(s)
- Lukas Hirsch
- City College New York, Department of Biomedical Engineering, New York City, New York, United States
| | - Yu Huang
- Memorial Sloan Kettering Cancer Center, Department of Radiology, New York City, New York, United States
| | - Lucas C Parra
- City College New York, Department of Biomedical Engineering, New York City, New York, United States
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Næss S, Halnes G, Hagen E, Hagler DJ, Dale AM, Einevoll GT, Ness TV. Biophysically detailed forward modeling of the neural origin of EEG and MEG signals. Neuroimage 2020; 225:117467. [PMID: 33075556 DOI: 10.1016/j.neuroimage.2020.117467] [Citation(s) in RCA: 23] [Impact Index Per Article: 5.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/27/2020] [Revised: 09/28/2020] [Accepted: 10/12/2020] [Indexed: 12/22/2022] Open
Abstract
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.
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Affiliation(s)
- Solveig Næss
- Department of Informatics, University of Oslo, Oslo 0316, Norway
| | - Geir Halnes
- Faculty of Science and Technology, Norwegian University of Life Sciences, 1432 Ås, Norway
| | - Espen Hagen
- Department of Physics, University of Oslo, Oslo 0316, Norway
| | - Donald J Hagler
- Department of Radiology, University of California, La Jolla, CA 92093, USA
| | - Anders M Dale
- Department of Radiology, University of California, La Jolla, CA 92093, USA; Department of Neurosciences, University of California, La Jolla, CA 92093, USA
| | - Gaute T Einevoll
- Faculty of Science and Technology, Norwegian University of Life Sciences, 1432 Ås, Norway; Department of Physics, University of Oslo, Oslo 0316, Norway.
| | - Torbjørn V Ness
- Faculty of Science and Technology, Norwegian University of Life Sciences, 1432 Ås, Norway.
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Braun V, Blackmore J, Cleveland RO, Butler CR. Transcranial ultrasound stimulation in humans is associated with an auditory confound that can be effectively masked. Brain Stimul 2020; 13:1527-1534. [PMID: 32891872 PMCID: PMC7710976 DOI: 10.1016/j.brs.2020.08.014] [Citation(s) in RCA: 58] [Impact Index Per Article: 14.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/30/2020] [Revised: 08/24/2020] [Accepted: 08/27/2020] [Indexed: 01/07/2023] Open
Abstract
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. Transcranial ultrasound stimulation elicits auditory signals in humans. Healthy human participants can distinguish stimulation from non-stimulation trials. Auditory masking reduces detection rates. Skull vibrations are present during transcranial ultrasound stimulation. The auditory signal is likely due to bone conduction at the pulse repetition frequency.
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Affiliation(s)
- Verena Braun
- Nuffield Department of Clinical Neurosciences, University of Oxford, UK
| | | | | | - Christopher R Butler
- Nuffield Department of Clinical Neurosciences, University of Oxford, UK; Department of Brain Sciences, Imperial College London, UK; Departamento de Neurología, Pontificia Universidad Católica de Chile, Santiago, Chile.
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Farcito S, Lloyd BA, Thielscher A, Puonti O, Montanaro H, Saturnino GB, Nielsen JD, Madsen CG, Siebner HR, Neufeld E, Kuster N. Accurate anatomical head segmentations: a data set for biomedical simulations. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2020; 2019:6118-6123. [PMID: 31947240 DOI: 10.1109/embc.2019.8857041] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Abstract
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.
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Combined behavioral and electrophysiological evidence for a direct cortical effect of prefrontal tDCS on disorders of consciousness. Sci Rep 2020; 10:4323. [PMID: 32152347 PMCID: PMC7062738 DOI: 10.1038/s41598-020-61180-2] [Citation(s) in RCA: 46] [Impact Index Per Article: 11.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/28/2019] [Accepted: 02/19/2020] [Indexed: 12/11/2022] Open
Abstract
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.
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Gomez-Tames J, Asai A, Hirata A. Significant group-level hotspots found in deep brain regions during transcranial direct current stimulation (tDCS): A computational analysis of electric fields. Clin Neurophysiol 2019; 131:755-765. [PMID: 31839398 DOI: 10.1016/j.clinph.2019.11.018] [Citation(s) in RCA: 30] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/18/2019] [Revised: 10/07/2019] [Accepted: 11/04/2019] [Indexed: 12/20/2022]
Abstract
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.
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Affiliation(s)
- Jose Gomez-Tames
- Department of Electrical and Mechanical Engineering, Nagoya Institute of Technology, Nagoya, Aichi 466-8555, Japan.
| | - Akihiro Asai
- Department of Electrical and Mechanical Engineering, Nagoya Institute of Technology, Nagoya, Aichi 466-8555, Japan
| | - Akimasa Hirata
- Department of Electrical and Mechanical Engineering, Nagoya Institute of Technology, Nagoya, Aichi 466-8555, Japan; Center of Biomedical Physics and Information Technology, Nagoya Institute of Technology, Nagoya, Aichi 466-8555, Japan
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Lenchik L, Heacock L, Weaver AA, Boutin RD, Cook TS, Itri J, Filippi CG, Gullapalli RP, Lee J, Zagurovskaya M, Retson T, Godwin K, Nicholson J, Narayana PA. Automated Segmentation of Tissues Using CT and MRI: A Systematic Review. Acad Radiol 2019; 26:1695-1706. [PMID: 31405724 PMCID: PMC6878163 DOI: 10.1016/j.acra.2019.07.006] [Citation(s) in RCA: 62] [Impact Index Per Article: 12.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/12/2019] [Revised: 07/17/2019] [Accepted: 07/17/2019] [Indexed: 01/10/2023]
Abstract
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.
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Affiliation(s)
- Leon Lenchik
- Department of Radiology, Wake Forest School of Medicine, Medical Center Boulevard, Winston-Salem, NC 27157.
| | - Laura Heacock
- Department of Radiology, NYU Langone, New York, New York
| | - Ashley A Weaver
- Department of Biomedical Engineering, Wake Forest School of Medicine, Winston-Salem, North Carolina
| | - Robert D Boutin
- Department of Radiology, University of California Davis School of Medicine, Sacramento, California
| | - Tessa S Cook
- Department of Radiology, University of Pennsylvania, Philadelphia Pennsylvania
| | - Jason Itri
- Department of Radiology, Wake Forest School of Medicine, Medical Center Boulevard, Winston-Salem, NC 27157
| | - Christopher G Filippi
- Department of Radiology, Donald and Barbara School of Medicine at Hofstra/Northwell, Lenox Hill Hospital, NY, New York
| | - Rao P Gullapalli
- Department of Radiology, University of Maryland School of Medicine, Baltimore, Maryland
| | - James Lee
- Department of Radiology, University of Kentucky, Lexington, Kentucky
| | | | - Tara Retson
- Department of Radiology, University of California San Diego, San Diego, California
| | - Kendra Godwin
- Medical Library, Memorial Sloan Kettering Cancer Center, New York, New York
| | - Joey Nicholson
- NYU Health Sciences Library, NYU School of Medicine, NYU Langone Health, New York, New York
| | - Ponnada A Narayana
- Department of Diagnostic and Interventional Imaging, McGovern Medical School, University of Texas Health Science Center at Houston, Houston, Texas
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Spadea MF, Pileggi G, Zaffino P, Salome P, Catana C, Izquierdo-Garcia D, Amato F, Seco J. Deep Convolution Neural Network (DCNN) Multiplane Approach to Synthetic CT Generation From MR images—Application in Brain Proton Therapy. Int J Radiat Oncol Biol Phys 2019; 105:495-503. [DOI: 10.1016/j.ijrobp.2019.06.2535] [Citation(s) in RCA: 31] [Impact Index Per Article: 6.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/28/2018] [Revised: 06/18/2019] [Accepted: 06/21/2019] [Indexed: 10/26/2022]
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Huang Y, Datta A, Bikson M, Parra LC. ROAST: An Open-Source, Fully-Automated, Realistic Volumetric-Approach-Based Simulator For TES. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2019; 2018:3072-3075. [PMID: 30441043 DOI: 10.1109/embc.2018.8513086] [Citation(s) in RCA: 37] [Impact Index Per Article: 7.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/06/2022]
Abstract
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/.
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Huang Y, Datta A, Bikson M, Parra LC. Realistic volumetric-approach to simulate transcranial electric stimulation-ROAST-a fully automated open-source pipeline. J Neural Eng 2019; 16:056006. [PMID: 31071686 PMCID: PMC7328433 DOI: 10.1088/1741-2552/ab208d] [Citation(s) in RCA: 196] [Impact Index Per Article: 39.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/20/2022]
Abstract
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.
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Affiliation(s)
- Yu Huang
- Department of Biomedical Engineering, City College of the City University of New York, New York, NY 10031, United States of America. Research & Development, Soterix Medical Inc., New York, NY 10001, United States of America
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Cencini M, Tosetti M, Buonincontri G. An Aristotelian View on MR-Based Attenuation Correction (ARISTOMRAC): Combining the Four Elements. IEEE TRANSACTIONS ON RADIATION AND PLASMA MEDICAL SCIENCES 2019. [DOI: 10.1109/trpms.2019.2903593] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/14/2023]
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17
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Indahlastari A, Chauhan M, Sadleir RJ. Benchmarking transcranial electrical stimulation finite element models: a comparison study. J Neural Eng 2019; 16:026019. [PMID: 30605892 DOI: 10.1088/1741-2552/aafbbd] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/12/2022]
Abstract
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.
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Affiliation(s)
- Aprinda Indahlastari
- Department of Clinical and Health Psychology, Center for Cognitive Aging and Memory, McKnight Brain Institute, University of Florida, Gainesville, FL, United States of America
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Bikson M, Brunoni AR, Charvet LE, Clark VP, Cohen LG, Deng ZD, Dmochowski J, Edwards DJ, Frohlich F, Kappenman ES, Lim KO, Loo C, Mantovani A, McMullen DP, Parra LC, Pearson M, Richardson JD, Rumsey JM, Sehatpour P, Sommers D, Unal G, Wassermann EM, Woods AJ, Lisanby SH. Rigor and reproducibility in research with transcranial electrical stimulation: An NIMH-sponsored workshop. Brain Stimul 2018; 11:465-480. [PMID: 29398575 PMCID: PMC5997279 DOI: 10.1016/j.brs.2017.12.008] [Citation(s) in RCA: 123] [Impact Index Per Article: 20.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/06/2017] [Revised: 12/01/2017] [Accepted: 12/21/2017] [Indexed: 12/21/2022] Open
Abstract
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.
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Affiliation(s)
- Marom Bikson
- Department of Biomedical Engineering, The City College of the City University of New York, United States
| | - Andre R Brunoni
- Laboratory of Neurosciences (LIM-27), Department and Institute of Psychiatry, University of Sao Paulo, Sao Paulo, Brazil
| | - Leigh E Charvet
- Department of Neurology, New York University School of Medicine, New York, NY, United States
| | - Vincent P Clark
- Department of Psychology, University of New Mexico, Albuquerque, NM, United States
| | - Leonardo G Cohen
- Human Cortical Physiology and Neurorehabilitation Section, National Institute of Neurological Disorders and Stroke, Bethesda, MD, United States
| | - Zhi-De Deng
- Experimental Therapeutics and Pathophysiology Branch, National Institute of Mental Health, Bethesda, MD, United States
| | - Jacek Dmochowski
- Department of Biomedical Engineering, The City College of the City University of New York, United States
| | - Dylan J Edwards
- Non-invasive Brain Stimulation and Human Motor Control Laboratory, Burke Rehabilitation and Research, Burke-Cornell Medical Research Facility, White Plains, New York and School of Medicine and Health Sciences, Edith Cowan University, Perth, Australia
| | - Flavio Frohlich
- Department of Psychiatry, Cell Biology and Physiology, Biomedical Engineering, and Neurology, Carolina Center for Neurostimulation, University of North Carolina School of Medicine, Chapel Hill, NC, United States
| | - Emily S Kappenman
- Department of Psychology, San Diego State University, San Diego, CA, United States
| | - Kelvin O Lim
- Department of Psychiatry, University of Minnesota, Minneapolis Veterans Administration Health Care System, and Defense Veterans Brain Injury Center, Minneapolis, MN, United States
| | - Colleen Loo
- School of Psychiatry and Black Dog Institute, University of New South Wales, Sydney, Australia
| | - Antonio Mantovani
- Department of Physiology, Pharmacology and Neuroscience, City College of the City University of New York, New York, NY, United States
| | - David P McMullen
- Division of Translational Research, National Institute of Mental Health, Bethesda, MD, United States
| | - Lucas C Parra
- Department of Biomedical Engineering, The City College of the City University of New York, United States
| | - Michele Pearson
- Division of Translational Research, National Institute of Mental Health, Bethesda, MD, United States
| | - Jessica D Richardson
- Department of Speech and Hearing Sciences, University of New Mexico, Albuquerque, NM, United States
| | - Judith M Rumsey
- Division of Translational Research, National Institute of Mental Health, Bethesda, MD, United States.
| | - Pejman Sehatpour
- Department of Psychiatry, Columbia University, New York, NY, United States
| | - David Sommers
- Scientific Review Branch, National Institute of Mental Health, Bethesda, MD, United States
| | - Gozde Unal
- Department of Biomedical Engineering, The City College of the City University of New York, United States
| | - Eric M Wassermann
- Behavioral Neurology Unit, National Institute of Neurological Disorders and Stroke, Bethesda, MD, United States
| | - Adam J Woods
- Department of Clinical and Health Psychology, Center for Cognitive Aging and Memory, McKnight Brain Institute, University of Florida, Gainesville, FL, United States
| | - Sarah H Lisanby
- Division of Translational Research, National Institute of Mental Health, Bethesda, MD, United States
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Montesano G, Allegrini D, Colombo L, Rossetti LM, Pece A. Features of the normal choriocapillaris with OCT-angiography: Density estimation and textural properties. PLoS One 2017; 12:e0185256. [PMID: 29020026 PMCID: PMC5636074 DOI: 10.1371/journal.pone.0185256] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/18/2016] [Accepted: 09/08/2017] [Indexed: 12/24/2022] Open
Abstract
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.
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Affiliation(s)
- Giovanni Montesano
- University of Milan, School of Ophthalmology, Milan, Italy
- ASST Santi Paolo e Carlo–Eye Clinic, Milan, Italy
- City, University of London, Optometry and Visual Sciences, London, United Kingdom
- * E-mail:
| | - Davide Allegrini
- Eye clinic, Humanitas Gavazzeni, Humanitas University, Milan, Italy
| | | | - Luca M. Rossetti
- University of Milan, School of Ophthalmology, Milan, Italy
- ASST Santi Paolo e Carlo–Eye Clinic, Milan, Italy
| | - Alfredo Pece
- Eye Clinic, Melegnano Hospital, Vizzolo Predabissi, Italy
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Huang Y, Liu AA, Lafon B, Friedman D, Dayan M, Wang X, Bikson M, Doyle WK, Devinsky O, Parra LC. Measurements and models of electric fields in the in vivo human brain during transcranial electric stimulation. eLife 2017; 6:18834. [PMID: 28169833 PMCID: PMC5370189 DOI: 10.7554/elife.18834] [Citation(s) in RCA: 321] [Impact Index Per Article: 45.9] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/16/2016] [Accepted: 02/06/2017] [Indexed: 11/13/2022] Open
Abstract
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
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Affiliation(s)
- Yu Huang
- Department of Biomedical Engineering, City College of the City University of New York, New York, United States
| | - Anli A Liu
- Comprehensive Epilepsy Center, New York University School of Medicine, New York, United States
| | - Belen Lafon
- Department of Biomedical Engineering, City College of the City University of New York, New York, United States
| | - Daniel Friedman
- Comprehensive Epilepsy Center, New York University School of Medicine, New York, United States
| | - Michael Dayan
- Department of Neurology, Mayo Clinic, Rochester, United States
| | - Xiuyuan Wang
- Comprehensive Epilepsy Center, New York University School of Medicine, New York, United States
| | - Marom Bikson
- Department of Biomedical Engineering, City College of the City University of New York, New York, United States
| | - Werner K Doyle
- Comprehensive Epilepsy Center, New York University School of Medicine, New York, United States
| | - Orrin Devinsky
- Comprehensive Epilepsy Center, New York University School of Medicine, New York, United States
| | - Lucas C Parra
- Department of Biomedical Engineering, City College of the City University of New York, New York, United States
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21
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Huang Y, Parra LC, Haufe S. The New York Head-A precise standardized volume conductor model for EEG source localization and tES targeting. Neuroimage 2016; 140:150-62. [PMID: 26706450 PMCID: PMC5778879 DOI: 10.1016/j.neuroimage.2015.12.019] [Citation(s) in RCA: 138] [Impact Index Per Article: 17.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/15/2015] [Revised: 10/12/2015] [Accepted: 12/12/2015] [Indexed: 12/01/2022] Open
Abstract
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.
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Affiliation(s)
- Yu Huang
- Department of Biomedical Engineering, City College of the City University of New York, New York, NY 10031, USA
| | - Lucas C Parra
- Department of Biomedical Engineering, City College of the City University of New York, New York, NY 10031, USA.
| | - Stefan Haufe
- Laboratory for Intelligent Imaging and Neural Computing, Columbia University, New York, NY 10027, USA; Machine Learning Department, Technische Universität Berlin, 10587, Berlin, Germany.
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Cancelli A, Cottone C, Tecchio F, Truong DQ, Dmochowski J, Bikson M. A simple method for EEG guided transcranial electrical stimulation without models. J Neural Eng 2016; 13:036022. [PMID: 27172063 DOI: 10.1088/1741-2560/13/3/036022] [Citation(s) in RCA: 30] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/08/2023]
Abstract
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.
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Affiliation(s)
- Andrea Cancelli
- Laboratory of Electrophysiology for Translational neuroScience (LET'S)-ISTC-CNR, Italy. Institute of Neurology, Catholic University, Rome, Italy
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23
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Bikson M, Truong DQ, Mourdoukoutas AP, Aboseria M, Khadka N, Adair D, Rahman A. Modeling sequence and quasi-uniform assumption in computational neurostimulation. PROGRESS IN BRAIN RESEARCH 2015; 222:1-23. [PMID: 26541374 DOI: 10.1016/bs.pbr.2015.08.005] [Citation(s) in RCA: 37] [Impact Index Per Article: 4.1] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/14/2022]
Abstract
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.
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Affiliation(s)
- Marom Bikson
- Department of Biomedical Engineering, The City College of New York, CUNY, New York, NY, USA.
| | - Dennis Q Truong
- Department of Biomedical Engineering, The City College of New York, CUNY, New York, NY, USA
| | | | - Mohamed Aboseria
- Department of Biomedical Engineering, The City College of New York, CUNY, New York, NY, USA
| | - Niranjan Khadka
- Department of Biomedical Engineering, The City College of New York, CUNY, New York, NY, USA
| | - Devin Adair
- Department of Biomedical Engineering, The City College of New York, CUNY, New York, NY, USA
| | - Asif Rahman
- Department of Biomedical Engineering, The City College of New York, CUNY, New York, NY, USA
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