Segmentation of supragranular and infragranular layers in ultra-high resolution 7T ex vivo MRI of the human cerebral cortex

Accurate labeling of specific layers in the human cerebral cortex is crucial for advancing our understanding of neurodevelopmental and neurodegenerative disorders. Leveraging recent advancements in ultra-high resolution ex vivo MRI, we present a novel semi-supervised segmentation model capable of identifying supragranular and infragranular layers in ex vivo MRI with unprecedented precision. On a dataset consisting of 17 whole-hemisphere ex vivo scans at 120 μm, we propose a multi-resolution U-Nets framework (MUS) that integrates global and local structural information, achieving reliable segmentation maps of the entire hemisphere, with Dice scores over 0.8 for supra- and infragranular layers. This enables surface modeling, atlas construction, anomaly detection in disease states, and cross-modality validation, while also paving the way for finer layer segmentation. Our approach offers a powerful tool for comprehensive neuroanatomical investigations and holds promise for advancing our mechanistic understanding of progression of neurodegenerative diseases.

Anatomical details affect electric field predictions for non-invasive brain stimulation in non-human primates

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.

A head template for computational dose modelling for transcranial focused ultrasound stimulation

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.

Evaluating the Influence of Anatomical Accuracy and Electrode Positions on EEG Forward Solutions

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.

Investigations of the subarachnoid space as a potential link between aura and headache in migraine: A case-control MRI study

BACKGROUND

The connection between migraine aura and headache is poorly understood. Some patients experience migraine aura without headache, and patients with migraine aura with headache commonly experience milder headaches with age. The distance between the cerebral cortex and the overlying dura mater has been hypothesized to influence development of headache following aura. We tested this hypothesis by comparing approximated distances between visual cortical areas and overlying dura mater between female patients with migraine aura without headache and female patients with migraine aura with headache.

METHODS

Twelve cases with migraine aura without headache and 45 age-matched controls with migraine aura with headache underwent 3.0 T MRI. We calculated average distances between the occipital lobes, between the calcarine sulci, and between the skull and visual areas V1, V2 and V3a. We also measured volumes of corticospinal fluid between the occipital lobes, between the calcarine sulci, and overlying visual areas V2 and V3a. We investigated the relationship between headache status, distances and corticospinal fluid volumes using conditional logistic regression.

RESULTS

Distances between the occipital lobes, calcarine sulci and between the skull and V1, V2 and V3a did not differ between patients with migraine aura with headache and patients with migraine aura without headache. We found no differences in corticospinal fluid volumes between groups.

CONCLUSION

We found no indication for a connection between visual migraine aura and headache based on cortico-cortical, cortex-to-skull distances, or corticospinal fluid volumes overlying visual cortical areas. Longitudinal studies with imaging sequences optimized for measuring the cortico-dural distance and a larger sample of patients are needed to further investigate the hypothesis.

SynthSeg: Segmentation of brain MRI scans of any contrast and resolution without retraining

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 unparallelled 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.

Linking lesions in sensorimotor cortex to contralateral hand function in multiple sclerosis: a 7 T MRI study

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 (SM1-HAND) relate to corticomotor physiology and sensorimotor function of the contralateral hand. 50 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 (MEP) amplitude, resting motor threshold and corticomotor conduction time with transcranial magnetic stimulation (TMS) and the N20 latency from somatosensory evoked potentials (SSEPs).

Patients showed at least one cortical lesion in the SM1-HAND 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. TMS of a lesion-positive SM1-HAND revealed a decreased maximal MEP amplitude (P < 0.001) and delayed corticomotor conduction (P = 0.002) relative to a lesion-negative SM1-HAND. Stepwise mixed linear regressions showed that the presence of an SM1-HAND 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 SM1-HAND, spinal cord cross-sectional area and normalized brain volume were also associated with smaller maximal MEP 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 MEP amplitude and leukocortical lesions on delayed corticomotor conduction.

Together, this comprehensive multi-level assessment of sensorimotor brain damage shows that the presence of a cortical lesion in SM1-HAND 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.

Mapping cortico-subcortical sensitivity to 4 Hz amplitude modulation depth in human auditory system with functional MRI

Temporal modulations in the envelope of acoustic waveforms at rates around 4 Hz constitute a strong acoustic cue in speech and other natural sounds. It is often assumed that the ascending auditory pathway is increasingly sensitive to slow amplitude modulation (AM), but sensitivity to AM is typically considered separately for individual stages of the auditory system. Here, we used blood oxygen level dependent (BOLD) fMRI in twenty human subjects (10 male) to measure sensitivity of regional neural activity in the auditory system to 4 Hz temporal modulations. Participants were exposed to AM noise stimuli varying parametrically in modulation depth to characterize modulation-depth effects on BOLD responses. A Bayesian hierarchical modeling approach was used to model potentially nonlinear relations between AM depth and group-level BOLD responses in auditory regions of interest (ROIs). Sound stimulation activated the auditory brainstem and cortex structures in single subjects. BOLD responses to noise exposure in core and belt auditory cortices scaled positively with modulation depth. This finding was corroborated by whole-brain cluster-level inference. Sensitivity to AM depth variations was particularly pronounced in the Heschl's gyrus but also found in higher-order auditory cortical regions. None of the sound-responsive subcortical auditory structures showed a BOLD response profile that reflected the parametric variation in AM depth. The results are compatible with the notion that early auditory cortical regions play a key role in processing low-rate modulation content of sounds in the human auditory system.

On the reconstruction of magnetic resonance current density images of the human brain: Pitfalls and perspectives

Magnetic resonance current density imaging (MRCDI) of the human brain aims to reconstruct the current density distribution caused by transcranial electric stimulation from MR-based measurements of the current-induced magnetic fields. So far, the MRCDI data acquisition achieves only a low signal-to-noise ratio, does not provide a full volume coverage and lacks data from the scalp and skull regions. In addition, it is only sensitive to the component of the current-induced magnetic field parallel to the scanner field. The reconstruction problem thus involves coping with noisy and incomplete data, which makes it mathematically challenging. Most existing reconstruction methods have been validated using simulation studies and measurements in phantoms with simplified geometries. Only one reconstruction method, the projected current density algorithm, has been applied to human in-vivo data so far, however resulting in blurred current density estimates even when applied to noise-free simulated data. We analyze the underlying causes for the limited performance of the projected current density algorithm when applied to human brain data. In addition, we compare it with an approach that relies on the optimization of the conductivities of a small number of tissue compartments of anatomically detailed head models reconstructed from structural MR data. Both for simulated ground truth data and human in-vivo MRCDI data, our results indicate that the estimation of current densities benefits more from using a personalized volume conductor model than from applying the projected current density algorithm. In particular, we introduce a hierarchical statistical testing approach as a principled way to test and compare the quality of reconstructed current density images that accounts for the limited signal-to-noise ratio of the human in-vivo MRCDI data and the fact that the ground truth of the current density is unknown for measured data. Our results indicate that the statistical testing approach constitutes a valuable framework for the further development of accurate volume conductor models of the head. Our findings also highlight the importance of tailoring the reconstruction approaches to the quality and specific properties of the available data.

Estimation of individually induced e-field strength during transcranial electric stimulation using the head circumference

BACKGROUND

Head and brain anatomy have been related to e-field strength induced by transcranial electrical stimulation (tES). Individualization based on anatomic factors require high-quality structural magnetic resonance images, which are not always available. Head circumference (HC) can serve as an alternative means, but its linkage to electric field strength has not yet been established.

METHODS

We simulated electric fields induced by tES based on individual T1w- and T2w-images of 47 healthy adults, for four conventional ("standard") and four corresponding focal ("4x1") electrode montages. Associations of electric field strength with individual HC were calculated using linear mixed models.

RESULTS

Larger HC was associated with lower electric field strength across montages. We provide mathematical equations to estimate individual electric field strength based on the HC.

CONCLUSION

HC can be used as an alternative to estimate interindividual differences of the tES-induced electric field strength and to prospectively individualize stimulation dose, e.g., in the clinical context.

Accurate and robust whole-head segmentation from magnetic resonance images for individualized head modeling

Transcranial brain stimulation (TBS) has been established as a method for modulating and mapping the function of the human brain, and as a potential treatment tool in several brain disorders. Typically, the stimulation is applied using a one-size-fits-all approach with predetermined locations for the electrodes, in electric stimulation (TES), or the coil, in magnetic stimulation (TMS), which disregards anatomical variability between individuals. However, the induced electric field distribution in the head largely depends on anatomical features implying the need for individually tailored stimulation protocols for focal dosing. This requires detailed models of the individual head anatomy, combined with electric field simulations, to find an optimal stimulation protocol for a given cortical target. Considering the anatomical and functional complexity of different brain disorders and pathologies, it is crucial to account for the anatomical variability in order to translate TBS from a research tool into a viable option for treatment. In this article we present a new method, called CHARM, for automated segmentation of fifteen different head tissues from magnetic resonance (MR) scans. The new method compares favorably to two freely available software tools on a five-tissue segmentation task, while obtaining reasonable segmentation accuracy over all fifteen tissues. The method automatically adapts to variability in the input scans and can thus be directly applied to clinical or research scans acquired with different scanners, sequences or settings. We show that an increase in automated segmentation accuracy results in a lower relative error in electric field simulations when compared to anatomical head models constructed from reference segmentations. However, also the improved segmentations and, by implication, the electric field simulations are affected by systematic artifacts in the input MR scans. As long as the artifacts are unaccounted for, this can lead to local simulation differences up to 30% of the peak field strength on reference simulations. Finally, we exemplarily demonstrate the effect of including all fifteen tissue classes in the field simulations against the standard approach of using only five tissue classes and show that for specific stimulation configurations the local differences can reach 10% of the peak field strength.

Two Coarse Spatial Patterns of Altered Brain Microstructure Predict Post-traumatic Amnesia in the Subacute Stage of Severe Traumatic Brain Injury

Introduction: Diffuse traumatic axonal injury (TAI) is one of the key mechanisms leading to impaired consciousness after severe traumatic brain injury (TBI). In addition, preferential regional expression of TAI in the brain may also influence clinical outcome. Aim: We addressed the question whether the regional expression of microstructural changes as revealed by whole-brain diffusion tensor imaging (DTI) in the subacute stage after severe TBI may predict the duration of post-traumatic amnesia (PTA). Method: Fourteen patients underwent whole-brain DTI in the subacute stage after severe TBI. Mean fractional anisotropy (FA) and mean diffusivity (MD) were calculated for five bilateral brain regions: fronto-temporal, parieto-occipital, and midsagittal hemispheric white matter, as well as brainstem and basal ganglia. Region-specific calculation of mean FA and MD only considered voxels that showed no tissue damage, using an exclusive mask with all voxels that belonged to local brain lesions or microbleeds. Mean FA or MD of the five brain regions were entered in separate partial least squares (PLS) regression analyses to identify patterns of regional microstructural changes that account for inter-individual variations in PTA. Results: For FA, PLS analysis revealed two spatial patterns that significantly correlated with individual PTA. The lower the mean FA values in all five brain regions, the longer that PTA lasted. A pattern characterized by lower FA values in the deeper brain regions relative to the FA values in the hemispheric regions also correlated with longer PTA. Similar trends were found for MD, but opposite in sign. The spatial FA changes as revealed by PLS components predicted the duration of PTA. Individual PTA duration, as predicted by a leave-one-out cross-validation analysis, correlated with true PTA values (Spearman r = 0.68, p permutation = 0.008). Conclusion: Two coarse spatial patterns of microstructural damage, indexed as reduction in FA, were relevant to recovery of consciousness after TBI. One pattern expressed was consistent with diffuse microstructural damage across the entire brain. A second pattern was indicative of a preferential damage of deep midline brain structures.

Migraine with aura in women is not associated with structural thalamic abnormalities

Migraine with aura is a highly prevalent disorder involving transient neurological disturbances associated with migraine headache. While the pathophysiology is incompletely understood, findings from clinical and basic science studies indicate a potential key role of the thalamus in the mechanisms underlying migraine with and without aura. Two recent, clinic-based MRI studies investigated the volumes of individual thalamic nuclei in migraine patients with and without aura using two different data analysis methods. Both studies found differences of thalamic nuclei volumes between patients and healthy controls, but the results of the studies were not consistent. Here, we investigated whether migraine with aura is associated with changes in thalamic volume by analysing MRI data obtained from a large, cross-sectional population-based study which specifically included women with migraine with aura (N = 156), unrelated migraine-free matched controls (N = 126), and migraine aura-free co-twins (N = 29) identified from the Danish Twin Registry. We used two advanced, validated analysis methods to assess the volume of the thalamus and its nuclei; the MAGeT Brain Algorithm and a recently developed FreeSurfer-based method based on a probabilistic atlas of the thalamic nuclei combining ex vivo MRI and histology. These approaches were very similar to the methods used in each of the two previous studies. Between-group comparisons were corrected for potential effects of age, educational level, BMI, smoking, alcohol, and hypertension using a linear mixed model. Further, we used linear mixed models and visual inspection of data to assess relations between migraine aura frequency and thalamic nuclei volumes in patients. In addition, we performed paired t-tests to compare volumes of twin pairs (N = 29) discordant for migraine with aura. None of our analyses showed any between-group differences in volume of the thalamus or of individual thalamic nuclei. Our results indicate that the pathophysiology of migraine with aura does not involve alteration of thalamic volume.

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.

Limited Colocalization of Microbleeds and Microstructural Changes after Severe Traumatic Brain Injury

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.

A modality-adaptive method for segmenting brain tumors and organs-at-risk in radiation therapy planning

In this paper we present a method for simultaneously segmenting brain tumors and an extensive set of organs-at-risk for radiation therapy planning of glioblastomas. The method combines a contrast-adaptive generative model for whole-brain segmentation with a new spatial regularization model of tumor shape using convolutional restricted Boltzmann machines. We demonstrate experimentally that the method is able to adapt to image acquisitions that differ substantially from any available training data, ensuring its applicability across treatment sites; that its tumor segmentation accuracy is comparable to that of the current state of the art; and that it captures most organs-at-risk sufficiently well for radiation therapy planning purposes. The proposed method may be a valuable step towards automating the delineation of brain tumors and organs-at-risk in glioblastoma patients undergoing radiation therapy.

Effects of transcranial direct current stimulation for treating depression: A modeling study

BACKGROUND

Transcranial direct current stimulation (tDCS) above the left dorsolateral prefrontal cortex (lDLPFC) has been widely used to improve symptoms of major depressive disorder (MDD). However, the effects of different stimulation protocols in the entire frontal lobe have not been investigated in a large sample including patient data.

METHODS

We used 38 head models created from structural magnetic resonance imaging data of 19 healthy adults and 19 MDD patients and applied computational modeling to simulate the spatial distribution of tDCS-induced electric fields (EFs) in 20 frontal regions. We evaluated effects of seven bipolar and two multi-electrode 4 × 1 tDCS protocols.

RESULTS

For bipolar montages, EFs were of comparable strength in the lDLPFC and in the medial prefrontal cortex (MPFC). Depending on stimulation parameters, EF cortical maps varied to a considerable degree, but were found to be similar in controls and patients. 4 × 1 montages produced more localized, albeit weaker effects.

LIMITATIONS

White matter anisotropy was not modeled. The relationship between EF strength and clinical response to tDCS could not be evaluated.

CONCLUSIONS

In addition to lDLPFC stimulation, excitability changes in the MPFC should also be considered as a potential mechanism underlying clinical efficacy of bipolar montages. MDD-associated anatomical variations are not likely to substantially influence current flow. Individual modeling of tDCS protocols can substantially improve cortical targeting. We make recommendations for future research to explicitly test the contribution of lDLPFC vs. MPFC stimulation to therapeutic outcomes of tDCS in this disorder.

Head models of healthy and depressed adults for simulating the effects of non-invasive brain stimulation

During the past decade, it became clear that the effects of 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 effects of 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.

Head models of healthy and depressed adults for simulating the electric fields of non-invasive electric brain stimulation

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.

Fast and sequence-adaptive whole-brain segmentation using parametric Bayesian modeling

Quantitative analysis of magnetic resonance imaging (MRI) scans of the brain requires accurate automated segmentation of anatomical structures. A desirable feature for such segmentation methods is to be robust against changes in acquisition platform and imaging protocol. In this paper we validate the performance of a segmentation algorithm designed to meet these requirements, building upon generative parametric models previously used in tissue classification. The method is tested on four different datasets acquired with different scanners, field strengths and pulse sequences, demonstrating comparable accuracy to state-of-the-art methods on T1-weighted scans while being one to two orders of magnitude faster. The proposed algorithm is also shown to be robust against small training datasets, and readily handles images with different MRI contrast as well as multi-contrast data.

Fast, sequence adaptive parcellation of brain MR using parametric models

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.