Search

Non-human primates (NHPs) have become key for translational research in noninvasive brain stimulation (NIBS). However, in order to create comparable stimulation conditions to humans it is vital to evaluate and match electric fields across species. Numerical models to simulate electric fields are an important tool for experimental planning in NHPs and translation to human studies. It is thus essential to evaluate how anatomical details in NHP models affect NIBS electric fields. Here, we create a highly accurate head model of a non-human primate (NHP) from 10.5 T MR data. We systematically evaluate how muscle tissue and head size (due to different MRI fields of view) affect simulation results in transcranial electric and magnetic stimulation (TES and TMS). Our findings indicate that the inclusion of muscle can affect TES electric field strength up to 29.5% while TMS is largely unaffected. Additionally, comparing a full head model to a cropped head model illustrates the impact of head size on electric fields for both TES and TMS. We find opposing effects between TES and TMS with an increase up to 13.5% for TES and a decrease up to 21.5% for TMS for the cropped head model compared to the full head model. Our results provide important insights into the level of anatomical detail needed for NHP head models and can inform future translational efforts for NIBS studies.HighlightsWe created a high resolution non-human primate FEM head model from 10.5 T MR data.We ran transcranial electric and magnetic stimulation (TES and TMS) simulations to evaluate the effects of muscle and head size on the induced electric field in the brain.We simulated both isotropic and anisotropic muscle conductivities.Muscle tissue can greatly affect simulation results for TES (increase electric field strength by up to 29.5%), but not for TMS.There are opposing, but large effects of using a full head model versus a cropped head model for both TES (increase up to 13.5%) and TMS (decrease of up to 21.5%)

Generating realistic volume conductor models for forward calculations in electroencephalography (EEG) is not trivial and several factors contribute to the accuracy of such models, two of which are its anatomical accuracy and the accuracy with which electrode positions are known. Here, we investigate effects of anatomical accuracy by comparing forward solutions from SimNIBS, a tool which allows state-of-the-art anatomical modeling, with well-established pipelines in MNE-Python and FieldTrip. We also compare different ways of specifying electrode locations when digitized positions are not available such as transformation of measured positions from standard space and transformation of a manufacturer layout.Substantial effects of anatomical accuracy were seen throughout the entire brain both in terms of field topography and magnitude with SimNIBS generally being more accurate than the pipelines in MNE-Python and FieldTrip. Topographic and magnitude effects were particularly pronounced for MNE-Python which uses a three-layer boundary element method (BEM) model. We attribute these mainly to differences in skull and cerebrospinal fluid (CSF) modeling. Effects of electrode specification method were evident in occipital and posterior areas when using a transformed manufacturer layout whereas transforming measured positions from standard space generally resulted in smaller errors.We suggest modeling the anatomy of the volume conductor as accurately possible and we hope to facilitate this by making it easy to export simulations from SimNIBS to MNE-Python and FieldTrip for further analysis. Likewise, if digitized electrode positions are not available, a set of measured positions on a standard head template may be preferable to those specified by the manufacturer.

During the past decade, it became clear that the electric field elicited by non-invasive brain stimulation (NIBS) techniques such as transcranial direct current stimulation (tDCS) and transcranial magnetic stimulation (TMS) are substantially influenced by variations in individual head and brain anatomy. In addition to structural variations in the healthy, several psychiatric disorders are characterized by anatomical alterations that are likely to further constrain the intracerebral effects of NIBS. Here, we present high-resolution realistic head models derived from structural magnetic resonance imaging data of 19 healthy adults and 19 patients diagnosed with major depressive disorder (MDD). By using a freely available software package for modelling the electric fields induced by different NIBS protocols, we show that our head models are well-suited for assessing inter-individual and between-group variability in the magnitude and focality of tDCS-induced electric fields for two protocols targeting the left dorsolateral prefrontal cortex.

Cortical lesions constitute a key manifestation of multiple sclerosis and contribute to clinical disability and cognitive impairment. Yet it is unknown whether local cortical lesions and cortical lesion subtypes contribute to domain-specific impairments attributable to the function of the lesioned cortex. In this cross-sectional study, we assessed how cortical lesions in the primary sensorimotor hand area relate to corticomotor physiology and sensorimotor function of the contralateral hand. Fifty relapse-free patients with relapsing-remitting or secondary-progressive multiple sclerosis and 28 healthy age- and sex-matched participants underwent whole-brain 7 T MRI to map cortical lesions. Brain scans were also used to estimate normalized brain volume, pericentral cortical thickness, white matter lesion fraction of the corticospinal tract, infratentorial lesion volume and the cross-sectional area of the upper cervical spinal cord. We tested sensorimotor hand function and calculated a motor and sensory composite score for each hand. In 37 patients and 20 healthy controls, we measured maximal motor-evoked potential amplitude, resting motor threshold and corticomotor conduction time with transcranial magnetic stimulation and the N20 latency from somatosensory-evoked potentials. Patients showed at least one cortical lesion in the primary sensorimotor hand area in 47 of 100 hemispheres. The presence of a lesion was associated with worse contralateral sensory (P = 0.014) and motor (P = 0.009) composite scores. Transcranial magnetic stimulation of a lesion-positive primary sensorimotor hand area revealed a decreased maximal motor-evoked potential amplitude (P < 0.001) and delayed corticomotor conduction (P = 0.002) relative to a lesion-negative primary sensorimotor hand area. Stepwise mixed linear regressions showed that the presence of a primary sensorimotor hand area lesion, higher white-matter lesion fraction of the corticospinal tract, reduced spinal cord cross-sectional area and higher infratentorial lesion volume were associated with reduced contralateral motor hand function. Cortical lesions in the primary sensorimotor hand area, spinal cord cross-sectional area and normalized brain volume were also associated with smaller maximal motor-evoked potential amplitude and longer corticomotor conduction times. The effect of cortical lesions on sensory function was no longer significant when controlling for MRI-based covariates. Lastly, we found that intracortical and subpial lesions had the largest effect on reduced motor hand function, intracortical lesions on reduced motor-evoked potential amplitude and leucocortical lesions on delayed corticomotor conduction. Together, this comprehensive multilevel assessment of sensorimotor brain damage shows that the presence of a cortical lesion in the primary sensorimotor hand area is associated with impaired corticomotor function of the hand, after accounting for damage at the subcortical level. The results also provide preliminary evidence that cortical lesion types may affect the various facets of corticomotor function differentially. Madsen et al. show that patients with multiple sclerosis with cortical lesions in the sensorimotor hand area (visible on 7 T MRI) have reduced sensorimotor hand function and reduced corticomotor output. These results indicate that cortical lesions lead to domain-specific functional impairment and local cortical dysfunction.

Despite advances in data augmentation and transfer learning, convolutional neural networks (CNNs) difficultly generalise to unseen domains. When segmenting brain scans, CNNs are highly sensitive to changes in resolution and contrast: even within the same MRI modality, performance can decrease across datasets. Here we introduce SynthSeg, the first segmentation CNN robust against changes in contrast and resolution. SynthSeg is trained with synthetic data sampled from a generative model conditioned on segmentations. Crucially, we adopt a domain randomisation strategy where we fully randomise the contrast and resolution of the synthetic training data. Consequently, SynthSeg can segment real scans from a wide range of target domains without retraining or fine-tuning, which enables straightforward analysis of huge amounts of heterogeneous clinical data. Because SynthSeg only requires segmentations to be trained (no images), it can learn from labels obtained by automated methods on diverse populations (e.g., ageing and diseased), thus achieving robustness to a wide range of morphological variability. We demonstrate SynthSeg on 5,000 scans of six modalities (including CT) and ten resolutions, where it exhibits unparalleled generalisation compared with supervised CNNs, state-of-the-art domain adaptation, and Bayesian segmentation. Finally, we demonstrate the generalisability of SynthSeg by applying it to cardiac MRI and CT scans.

Severe traumatic brain injury (TBI) produces shearing forces on long-range axons and brain vessels, causing axonal and vascular injury. To examine whether microbleeds and axonal injury colocalize after TBI, we performed whole-brain susceptibility-weighted imaging (SWI) and diffusion tensor imaging (DTI) in 14 patients during the subacute phase after severe TBI. SWI was used to determine the number and volumes of microbleeds in five brain regions: the frontotemporal lobe; parieto-occipital lobe; midsagittal region (cingular cortex, parasagittal white matter, and corpus callosum); deep nuclei (basal ganglia and thalamus); and brainstem. Averaged fractional anisotropy (FA) and mean diffusivity (MD) were measured to assess microstructural changes in the normal appearing white matter attributed to axonal injury in the same five regions. Regional expressions of microbleeds and microstructure were used in a partial least-squares model to predict the impairment of consciousness in the subacute stage after TBI as measured with the Coma Recovery Scale-Revised (CRS-R). Only in the midsagittal region, the expression of microbleeds was correlated with regional changes in microstructure as revealed by DTI. Microbleeds and microstructural DTI-based metrics of deep, but not superficial, brain regions were able to predict individual CRS-R. Our results suggest that microbleeds are not strictly related to axonal pathology in other than the midsagittal region. While each measure alone was predictive, the combination of both metrics scaled best with individual CRS-R. Structural alterations in deep brain structures are relevant in terms of determining the severity of impaired consciousness in the acute stage after TBI.

Comparing electric field simulations from individualized head models against in-vivo recordings is important for direct validation of computational field modeling for transcranial brain stimulation and brain mapping techniques such as electro- and magnetoencephalography. This also helps to improve simulation accuracy by pinning down the factors having the largest influence on the simulations. Here we compare field simulations from four different automated pipelines, against intracranial voltage recordings in an existing dataset of 14 epilepsy patients. We show that ignoring uncertainty in the simulations leads to a strong bias in the estimated linear relationship between simulated and measured fields. In addition, even though the simulations between the pipelines differ notably, this is not reflected in the correlation with the measurements. We discuss potential reasons for this apparent mismatch and propose a new Bayesian regression analysis of the data that yields unbiased estimates enabling robust conclusions to be reached.

Numerical simulation of the electric fields induced by non-invasive brain stimulation (NIBS), using realistic anatomical head models has gained interest in recent years for understanding the NIBS effects in individual subjects. Although automated tools for generating the head models and performing the electric field simulations have become available, individualized modelling is still not a standard practice in NIBS studies. This is likely partly explained by the lack of robustness and usability of the previously available software tools, and partly by the still developing understanding of the link between physiological effects and electric field distributions in the brain. To facilitate individualized modelling in NIBS, we have introduced the SimNIBS (Simulation of NIBS) software package, providing easy-to-use automated tools for electric field modelling. In this chapter, we give an overview of the modelling pipeline in SimNIBS 2.1, with step-by-step examples of how to run a simulation. Furthermore, we demonstrate a set of scripts for extracting average electric fields for a group of subjects, and finally demonstrate the accuracy of automated placement of standard electrode montages on the head model. SimNIBS 2.1 is freely available at www.simnibs.org .

In this paper we propose a method for whole brain parcellation using the type of generative parametric models typically used in tissue classification. Compared to the non-parametric, multi-atlas segmentation techniques that have become popular in recent years, our method obtains state-of-the-art segmentation performance in both cortical and subcortical structures, while retaining all the benefits of generative parametric models, including high computational speed, automatic adaptiveness to changes in image contrast when different scanner platforms and pulse sequences are used, and the ability to handle multi-contrast (vector-valued intensities) MR data. We have validated our method by comparing its segmentations to manual delineations both within and across scanner platforms and pulse sequences, and show preliminary results on multi-contrast test-retest scans, demonstrating the feasibility of the approach.

Transcranial current stimulation (tCS or tES) protocols yield results that are highly variable across individuals. Part of this variability results from differences in the electric field (E-field) induced in subjects’ brains during stimulation. The E-field determines how neurons respond to stimulation, and it can be used as a proxy for predicting the concurrent effects of stimulation, like changes in cortical excitability, and, ultimately, its plastic effects. While the use of multichannel systems with small electrodes has provided a more precise tool for delivering tCS, individually variable anatomical parameters like the shape and thickness of tissues affect the E-field distribution for a specific electrode montage. Therefore, using the same montage parameters across subjects does not lead to the homogeneity of E-field amplitude over the desired targets. Here we describe a pipeline that leverages individualized head models combined with montage optimization algorithms to reduce the variability of the E-field distributions over subjects in tCS. We will describe the different steps of the pipeline – namely, MRI segmentation and head model creation, target specification, and montage optimization – and discuss their main advantages and limitations.