Search

Abstract Segmentation of brain structures on magnetic resonance imaging (MRI) is a highly relevant neuroimaging topic, as it is a prerequisite for different analyses such as volumetry or shape analysis. Automated segmentation facilitates the study of brain structures in larger cohorts when compared with manual segmentation, which is time-consuming. However, the development of most automated methods relies on large and manually annotated datasets, which limits the generalizability of these methods. Recently, new techniques using synthetic images have emerged, reducing the need for manual annotation. Here we provide a dataset composed of label maps built from publicly available ultra-high resolution ex vivo MRI from 10 whole hemispheres, which can be used to develop segmentation methods using synthetic data. The label maps are obtained with a combination of manual labels for the hypothalamic regions and automated segmentations for the rest of the brain, and mirrored to simulate entire brains. We also provide the pre-processed ex vivo scans, as this dataset can support future projects to include other structures after these are manually segmented.

Non-human primates (NHPs) have become key for translational research in noninvasive brain stimulation (NIBS). However, in order to create comparable stimulation conditions for humans it is vital to study the accuracy of current modeling practices 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 whether and to what extent the anatomical details of NHP models agree with current modeling practices when calculating NIBS electric fields. Here, we create highly accurate head models of two non-human primates (NHP) MR data. We evaluate how muscle tissue and head field of view (depending on MRI parameters) affect simulation results in transcranial electric and magnetic stimulation (TES and TMS). Our findings indicate that the inclusion of anisotropic muscle can affect TES electric field strength up to 22% while TMS is largely unaffected. Additionally, comparing a full head model to a cropped head model illustrates the impact of head field of view on electric fields for both TES and TMS. We find opposing effects between TES and TMS with an increase up to 24.8% for TES and a decrease up to 24.6% 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.

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 the coarse representation of the anatomy used in this model, in particular differences in skull and cerebrospinal fluid (CSF). 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.

Transcranial focused Ultrasound Stimulation (TUS) at low intensities is emerging as a novel non-invasive brain stimulation method with higher spatial resolution than established transcranial stimulation methods and the ability to selectively stimulate also deep brain areas. Accurate control of the focus position and strength of the TUS acoustic waves is important to enable a beneficial use of the high spatial resolution and to ensure safety. As the human skull causes strong attenuation and distortion of the waves, simulations of the transmitted waves are needed to accurately determine the TUS dose distribution inside the cranial cavity. The simulations require information of the skull morphology and its acoustic properties. Ideally, they are informed by computed tomography (CT) images of the individual head. However, suited individual imaging data is often not readily available. For this reason, we here introduce and validate a head template that can be used to estimate the average effects of the skull on the TUS acoustic wave in the population.The template was created from CT images of the heads of 29 individuals of different ages (between 20–50 years), gender and ethnicity using an iterative non-linear co-registration procedure. For validation, we compared acoustic and thermal simulations based on the template to the average of the simulation results of all 29 individual datasets. Acoustic simulations were performed for a model of a focused transducer driven at 500 kHz, placed at 24 standardized positions by means of the EEG 10–10 system. Additional simulations at 250 kHz and 750 kHz at 16 of the positions were used for further confirmation. The amount of ultrasound-induced heating at 500 kHz was estimated for the same 16 transducer positions. Our results show that the template represents the median of the acoustic pressure and temperature maps from the individuals reasonably well in most cases. This underpins the usefulness of the template for the planning and optimization of TUS interventions in studies of healthy young adults. Our results further indicate that the amount of variability between the individual simulation results depends on the position. Specifically, the simulated ultrasound-induced heating inside the skull exhibited strong interindividual variability for three posterior positions close to the midline, caused by a high variability of the local skull shape and composition. This should be taken into account when interpreting simulation results based on the template.

Background and objective: Transcranial direct current stimulation (tDCS) has wide ranging applications in neuro-behavioural and physiological research, and in neurological rehabilitation. However, it is currently limited by substantial inter-subject variability in responses, which may be explained, at least in part, by anatomical differences that lead to variability in the electric field (E-field) induced in the cortex. Here, we tested whether the variability in the E-field in the stimulated cortex during anodal tDCS, estimated using computational simulations, explains the variability in tDCS induced changes in GABA, a neurophysiological marker of stimulation effect. Methods: Data from five previously conducted MRS studies were combined. The anode was placed over the left primary motor cortex (M1, 3 studies, N = 24) or right temporal cortex (2 studies, N = 32), with the cathode over the contralateral supraorbital ridge. Single voxel spectroscopy was performed in a 2x2x2cm voxel under the anode in all cases. MRS data were acquired before and either during or after 1 mA tDCS using either a sLASER sequence (7T) or a MEGA-PRESS sequence (3T). sLASER MRS data were analysed using LCModel, and MEGA-PRESS using FID-A and Gannet. E-fields were simulated in a finite element model of the head, based on individual structural MR images, using SimNIBS. Separate linear mixed effects models were run for each E-field variable (mean and 95th percentile; magnitude, and components normal and tangential to grey matter surface, within the MRS voxel). The model included effects of time (pre or post tDCS), E-field, grey matter volume in the MRS voxel, and a 3-way interaction between time, E-field and grey matter volume. Additionally, we ran a permutation analysis using PALM to determine whether E-field anywhere in the brain, not just in the MRS voxel, correlated with GABA change. Results: In M1, higher mean E-field magnitude was associated with greater anodal tDCS-induced decreases in GABA (t(24) = 3.24, p = 0.003). Further, the association between mean E-field magnitude and GABA change was moderated by the grey matter volume in the MRS voxel (t(24) = −3.55, p = 0.002). These relationships were consistent across all E-field variables except the mean of the normal component. No significant relationship was found between tDCS-induced GABA decrease and E-field in the temporal voxel. No significant clusters were found in the whole brain analysis. Conclusions: Our data suggest that the electric field induced by tDCS within the brain is variable, and is significantly related to anodal tDCS-induced decrease in GABA, a key neurophysiological marker of stimulation. These findings strongly support individualised dosing of tDCS, at least in M1. Further studies examining E-fields in relation to other outcome measures, including behaviour, will help determine the optimal E-fields required for any desired effects.