Precise and Rapid Whole-Head Segmentation from Magnetic Resonance Images of Older Adults using Deep Learning.

Whole-head segmentation from Magnetic Resonance Images (MRI) establishes the foundation for individualized computational models using finite element method (FEM). This foundation paves the path for computer-aided solutions in fields, particularly in non-invasive brain stimulation. Most current automatic head segmentation tools are developed using healthy young adults. Thus, they may neglect the older population that is more prone to age-related structural decline such as brain atrophy. In this work, we present a new deep learning method called GRACE, which stands for General, Rapid, And Comprehensive whole-hEad tissue segmentation. GRACE is trained and validated on a novel dataset that consists of 177 manually corrected MR-derived reference segmentations that have undergone meticulous manual review. Each T1-weighted MRI volume is segmented into 11 tissue types, including white matter, grey matter, eyes, cerebrospinal fluid, air, blood vessel, cancellous bone, cortical bone, skin, fat, and muscle. To the best of our knowledge, this work contains the largest manually corrected dataset to date in terms of number of MRIs and segmented tissues. GRACE outperforms five freely available software tools and a traditional 3D U-Net on a five-tissue segmentation task. On this task, GRACE achieves an average Hausdorff Distance of 0.21, which exceeds the runner-up at an average Hausdorff Distance of 0.36. GRACE can segment a whole-head MRI in about 3 seconds, while the fastest software tool takes about 3 minutes. In summary, GRACE segments a spectrum of tissue types from older adults T1-MRI scans at favorable accuracy and speed. The trained GRACE model is optimized on older adult heads to enable high-precision modeling in age-related brain disorders. To support open science, the GRACE code and trained weights are made available online and open to the research community at https://github.com/lab-smile/GRACE.

The impact of a tDCS and cognitive training intervention on task-based functional connectivity.

Declines in several cognitive domains, most notably processing speed, occur in non-pathological aging. Given the exponential growth of the older adult population, declines in cognition serve as a significant public health issue that must be addressed. Promising studies have shown that cognitive training in older adults, particularly using the useful field of view (UFOV) paradigm, can improve cognition with moderate to large effect sizes. Additionally, meta-analyses have found that transcranial direct current stimulation (tDCS), a non-invasive form of brain stimulation, can improve cognition in attention/processing speed and working memory. However, only a handful of studies have looked at concomitant tDCS and cognitive training, usually with short interventions and small sample sizes. The current study assessed the effect of a tDCS (active versus sham) and a 3-month cognitive training intervention on task-based functional connectivity during completion of the UFOV task in a large older adult sample (N = 153). We found significant increased functional connectivity between the left and right pars triangularis (the ROIs closest to the electrodes) following active, but not sham tDCS. Additionally, we see trending behavioral improvements associated with these functional connectivity changes in the active tDCS group, but not sham. Collectively, these findings suggest that tDCS and cognitive training can be an effective modulator of task-based functional connectivity above and beyond a cognitive training intervention alone.

Impact of electrode selection on modeling tDCS in the aging brain.

Background: Person-specific computational models can estimate transcranial direct current stimulation (tDCS) current dose delivered to the brain and predict treatment response. Artificially created electrode models derived from virtual 10-20 EEG measurements are typically included in these models as current injection and removal sites. The present study directly compares current flow models generated via artificially placed electrodes ("artificial" electrode models) against those generated using real electrodes acquired from structural MRI scans ("real" electrode models) of older adults. Methods: A total of 16 individualized head models were derived from cognitively healthy older adults (mean age = 71.8 years) who participated in an in-scanner tDCS study with an F3-F4 montage. Visible tDCS electrodes captured within the MRI scans were segmented to create the "real" electrode model. In contrast, the "artificial" electrodes were generated in ROAST. Percentage differences in current density were computed in selected regions of interest (ROIs) as examples of stimulation targets within an F3-F4 montage. Main results: We found significant inverse correlations (p < 0.001) between median current density values and brain atrophy in both electrode pipelines with slightly larger correlations found in the artificial pipeline. The percent difference (PD) of the electrode distances between the two models predicted the median current density values computed in the ROIs, gray, and white matter, with significant correlation between electrode distance PDs and current density. The correlation between PD of the contact areas and the computed median current densities in the brain was found to be non-significant. Conclusions: This study demonstrates potential discrepancies in generated current density models using real versus artificial electrode placement when applying tDCS to an older adult cohort. Our findings strongly suggest that future tDCS clinical work should consider closely monitoring and rigorously documenting electrode location during stimulation to model tDCS montages as closely as possible to actual placement. Detailed physical electrode location data may provide more precise information and thus produce more robust tDCS modeling results.

Machine-learning defined precision tDCS for improving cognitive function.

BACKGROUND: Transcranial direct current stimulation (tDCS) paired with cognitive training (CT) is widely investigated as a therapeutic tool to enhance cognitive function in older adults with and without neurodegenerative disease. Prior research demonstrates that the level of benefit from tDCS paired with CT varies from person to person, likely due to individual differences in neuroanatomical structure. OBJECTIVE: The current study aims to develop a method to objectively optimize and personalize current dosage to maximize the functional gains of non-invasive brain stimulation. METHODS: A support vector machine (SVM) model was trained to predict treatment response based on computational models of current density in a sample dataset (n = 14). Feature weights of the deployed SVM were used in a weighted Gaussian Mixture Model (GMM) to maximize the likelihood of converting tDCS non-responders to responders by finding the most optimum electrode montage and applied current intensity (optimized models). RESULTS: Current distributions optimized by the proposed SVM-GMM model demonstrated 93% voxel-wise coherence within target brain regions between the originally non-responders and responders. The optimized current distribution in original non-responders was 3.38 standard deviations closer to the current dose of responders compared to the pre-optimized models. Optimized models also achieved an average treatment response likelihood and normalized mutual information of 99.993% and 91.21%, respectively. Following tDCS dose optimization, the SVM model successfully predicted all tDCS non-responders with optimized doses as responders. CONCLUSIONS: The results of this study serve as a foundation for a custom dose optimization strategy towards precision medicine in tDCS to improve outcomes in cognitive decline remediation for older adults.

DOMINO: Domain-aware loss for deep learning calibration.

Deep learning has achieved the state-of-the-art performance across medical imaging tasks; however, model calibration is often not considered. Uncalibrated models are potentially dangerous in high-risk applications since the user does not know when they will fail. Therefore, this paper proposes a novel domain-aware loss function to calibrate deep learning models. The proposed loss function applies a class-wise penalty based on the similarity between classes within a given target domain. Thus, the approach improves the calibration while also ensuring that the model makes less risky errors even when incorrect. The code for this software is available at https://github.com/lab-smile/DOMINO.

Electric Field Strength From Prefrontal Transcranial Direct Current Stimulation Determines Degree of Working Memory Response: A Potential Application of Reverse-Calculation Modeling?

BACKGROUND: Transcranial direct current stimulation (tDCS) for working memory is an enticing treatment, but there is mixed evidence to date. OBJECTIVES: We tested the effects of electric field strength from uniform 2 mA dosing on working memory change from prestimulation to poststimulation. Second, we statistically evaluated a reverse-calculation method of individualizing tDCS dose and its effect on normalizing electric field at the cortex. MATERIALS AND METHODS: We performed electric field modeling on a data set of 28 healthy older adults (15 women, mean age = 73.7, SD = 7.3) who received ten sessions of active 2 mA tDCS (N = 14) or sham tDCS (N = 14) applied over bilateral dorsolateral prefrontal cortices (DLPFC) in a triple-blind design. We evaluated the relationship between electric field strength and working memory change on an N-back task in conditions of above-median, high electric field from active 2 mA (N = 7), below-median, low electric field from active 2 mA (N = 7), and sham (N = 14) at regions of interest (ROI) at the left and right DLPFC. We then determined the individualized reverse-calculation dose to produce the group average electric field and measured the electric field variance between uniform 2 mA doses vs individualized reverse-calculation doses at the same ROIs. RESULTS: Working memory improvements from pre- to post-tDCS were significant for the above-median electric field from active 2 mA condition at the left DLPFC (mixed ANOVA, p = 0.013). Furthermore, reverse-calculation modeling significantly reduced electric field variance at both ROIs (Levene's test; p < 0.001). CONCLUSIONS: Higher electric fields at the left DLPFC from uniform 2 mA doses appear to drive working memory improvements from tDCS. Individualized doses from reverse-calculation modeling significantly reduce electric field variance at the cortex. Taken together, using reverse-calculation modeling to produce the same, high electric fields at the cortex across participants may produce more effective future tDCS treatments for working memory.

Impact of Transcranial Direct Current Stimulation and Cognitive Training on Frontal Lobe Neurotransmitter Concentrations.

Objective: This study examines the impact of transcranial direct current stimulation (tDCS) combined with cognitive training on neurotransmitter concentrations in the prefrontal cortex. Materials and Methods: Twenty-three older adults were randomized to either active-tDCS or sham-tDCS in combination with cognitive training for 2 weeks. Active-tDCS was delivered over F3 (cathode) and F4 (anode) electrode placements for 20 min at 2 mA intensity. For each training session, 40-min of computerized cognitive training were applied with active or sham stimulation delivered during the first 20-min. Glutamine/glutamate (Glx) and gamma-aminobutyric acid (GABA) concentrations via proton magnetic resonance spectroscopy were evaluated at baseline and at the end of 2-week intervention. Results: Glx concentrations increased from pre- to post-intervention (p = 0.010) in the active versus sham group after controlling for age, number of intervention days, MoCA scores, and baseline Glx concentration. No difference in GABA concentration was detected between active and sham groups (p = 0.650) after 2-week intervention. Conclusion: Results provide preliminary evidence suggesting that combining cognitive training and tDCS over the prefrontal cortex elicits sustained increase in excitatory neurotransmitter concentrations. Findings support the combination of tDCS and cognitive training as a potential method for altering neurotransmitter concentrations in the frontal cortices, which may have implications for neuroplasticity in the aging brain.

Individualized tDCS modeling predicts functional connectivity changes within the working memory network in older adults.

BACKGROUND: Working memory decline has been associated with normal aging. The frontal brain structure responsible for this decline is primarily located in the prefrontal cortex (PFC). Our previous neuroimaging study demonstrated a significant change in functional connectivity between the left dorsolateral PFC (DLPFC) and left ventrolateral PFC (VLPFC) when applying 2 mA tDCS in MRI scanner during an N-Back task. These regions were part of the working memory network. The present study is the first study that utilizes individualized finite element models derived from older adults' MRI to predict significant changes of functional connectivity observed from an acute tDCS application. METHODS: Individualized head models from 15 healthy older adults (mean age = 71.3 years) were constructed to create current density maps. Each head model was segmented into 11 tissue types: white matter, gray matter, CSF, muscle, blood vessels, fat, eyes, air, skin, cancellous, and cortical bone. Electrodes were segmented from T1-weighted images and added to the models. Computed median and maximum current density values in the left DLPFC and left VLPFC regions of interest (ROIs) were correlated with beta values as functional connectivity metrics measured in different timepoint (baseline, during stimulation) and stimulation condition (active and sham). MAIN RESULTS: Positive significant correlations (R2 = 0.523 for max J, R2 = 0.367 for median J, p < 0.05) were found between the beta values and computed current densities in the left DLPFC ROIs for active stimulation, but no significant correlation was found during sham stimulation. We found no significant correlation between connectivity and current densities computed in the left VLPFC for both active and sham stimulation. CONCLUSIONS: The amount of current within the left DLPFC ROIs was found positively correlated with changes in functional connectivity between left DLPFC and left VLPFC during active 2 mA stimulation. Future work may include expansion of number of participants to further test the accuracy of tDCS models used to predict tDCS-induced functional connectivity changes within the working memory network.

A Systematic Review and Meta-Analysis of Transcranial Direct Current Stimulation to Remediate Age-Related Cognitive Decline in Healthy Older Adults.

BACKGROUND: Transcranial direct current stimulation (tDCS) has been proposed as a possible method for remediating age-associated cognitive decline in the older adult population. While tDCS has shown potential for improving cognitive functions in healthy older adults, stimulation outcomes on various cognitive domains have been mixed. METHODS: A systematic search was performed in four databases: PubMed, EMBASE, Web of Science, and PsychInfo. Search results were then screened for eligibility based on inclusion/exclusion criteria to only include studies where tDCS was applied to improve cognition in healthy older adults 65 years and above. Eligible studies were reviewed and demographic characteristics, tDCS dose parameters, study procedures, and cognitive outcomes were extracted. Reported effect sizes for active compared to sham group in representative cognitive domain were converted to Hedges' g. MAIN RESULTS: A total of thirteen studies involving healthy older adults (n=532, mean age=71.2+5.3 years) were included in the meta-analysis. The majority of included studies (94%) targeted the prefrontal cortex with stimulation intensity 1-2 mA using various electrode placements with anodes near the frontal region. Across all studies, we found Hedges' g values ranged from -0.31 to 1.85 as reported group effect sizes of active stimulation compared to sham. CONCLUSION: While observed outcomes varied, overall findings indicated promising effects of tDCS to remediate cognitive aging and thus deserves further exploration. Future characterization of inter-individual variability in tDCS dose response and applications in larger cohorts are warranted to further validate benefits of tDCS for cognition in healthy older adults.

White matter hyperintensities affect transcranial electrical stimulation in the aging brain.

BACKGROUND: White matter hyperintensities (WMH) are estimated to occur in greater than 63% of older adults over the age of 60 years. WMH identified in the T2-weighted FLAIR images can be combined with T1-weighted images to enhance individualized current flow models of older adults by accounting for the presence of WMH and its effects on delivered tES current in the aging brain. METHODS: Individualized head models were derived from T1-weighted images of 130 healthy older adults (mean = 71 years). Lesions segmented from FLAIR acquisition were added to individualized models. Current densities were computed in the brain and compared between models with and without lesions. MAIN RESULTS: Integrating WMH into the models resulted in an overall decrease (up to 7%) in median current densities in the brain outside lesion regions. Changes in current density and total lesion volume was positively correlated (R2 = 0.31, p < 0.0001). CONCLUSIONS: Incorporating WMH into individualized models may increase the accuracy of predicted tES current flow in the aging brain.

Machine learning and individual variability in electric field characteristics predict tDCS treatment response.

BACKGROUND: Transcranial direct current stimulation (tDCS) is widely investigated as a therapeutic tool to enhance cognitive function in older adults with and without neurodegenerative disease. Prior research demonstrates that electric current delivery to the brain can vary significantly across individuals. Quantification of this variability could enable person-specific optimization of tDCS outcomes. This pilot study used machine learning and MRI-derived electric field models to predict working memory improvements as a proof of concept for precision cognitive intervention. METHODS: Fourteen healthy older adults received 20 minutes of 2 mA tDCS stimulation (F3/F4) during a two-week cognitive training intervention. Participants performed an N-back working memory task pre-/post-intervention. MRI-derived current models were passed through a linear Support Vector Machine (SVM) learning algorithm to characterize crucial tDCS current components (intensity and direction) that induced working memory improvements in tDCS responders versus non-responders. MAIN RESULTS: SVM models of tDCS current components had 86% overall accuracy in classifying treatment responders vs. non-responders, with current intensity producing the best overall model differentiating changes in working memory performance. Median current intensity and direction in brain regions near the electrodes were positively related to intervention responses (r=0.811,p<0.001 and r=0.774,p=0.001). CONCLUSIONS: This study provides the first evidence that pattern recognition analyses of MRI-derived tDCS current models can provide individual prognostic classification of tDCS treatment response with 86% accuracy. Individual differences in current intensity and direction play important roles in determining treatment response to tDCS. These findings provide important insights into mechanisms of tDCS response as well as proof of concept for future precision dosing models of tDCS intervention.

Modeling transcranial electrical stimulation in the aging brain.

BACKGROUND: Varying treatment outcomes in transcranial electrical stimulation (tES) recipients may depend on the amount of current reaching the brain. Brain atrophy associated with normal aging may affect tES current delivery to the brain. Computational models have been employed to compute predicted tES current inside the brain. This study is the largest study that uses computational models to investigate tES field distribution in healthy older adults. METHODS: Individualized head models from 587 healthy older adults (mean = 73.9years, 51-95 years) were constructed to create field maps. Two electrode montages (F3-F4, M1-SO) with 2 mA input current were modeled using ROAST with modified codes. A customized template of healthy older adults, the UFAB-587, was created from the same dataset and used to warp individual brains into the same space. Warped models were analyzed to determine the relationship between computed field measures, brain atrophy and age. MAIN RESULTS: Computed field measures were inversely correlated with brain atrophy (R2 = 0.0829, p = 1.14e-12). Field pattern showed negative correlation with age in brain sub-regions including part of DLPFC and precentral gyrus. Mediation analysis revealed that the negative correlation between age and current density is partially mediated by brain-to-CSF ratio. CONCLUSIONS: Computed field measures showed decreasing amount of tES current reaching the brain with increasing atrophy. Therefore, adjusting current dose by modifying tES stimulation parameters in older adults based on degree of atrophy may be necessary to achieve desired stimulation benefits. Results from this study may inform future tES application in healthy older adults.

Effects of in-Scanner Bilateral Frontal tDCS on Functional Connectivity of the Working Memory Network in Older Adults.

Working memory is an executive memory process essential for everyday decision-making and problem solving that declines with advanced age. Transcranial direct current stimulation (tDCS) is a non-invasive form of brain stimulation that has demonstrated potential for improving working memory performance in older adults. However, the neural mechanisms underlying effects of tDCS on working memory are not well understood. This mechanistic study investigated the acute and after-effects of bilateral frontal (F3/F4) tDCS at 2 mA for 12-min on functional connectivity of the working memory network in older adults. We hypothesized active tDCS over sham would increase frontal connectivity during working memory performance. The study used a double-blind within-subject 2 session crossover design. Participants performed an functional magnetic resonance imaging (fMRI) N-Back working memory task before, during, and after active or sham stimulation. Functional connectivity of the working memory network was assessed within and between stimulation conditions (FDR < 0.05). Active tDCS produced a significant increase in functional connectivity between left ventrolateral prefrontal cortex (VLPFC) and left dorsolateral PFC (DLPFC) during stimulation, but not after stimulation. Connectivity did not significantly increase with sham stimulation. In addition, our data demonstrated both state-dependent and time-dependent effects of tDCS working memory network connectivity in older adults. tDCS during working memory performance produces a selective change in functional connectivity of the working memory network in older adults. These data provide important mechanistic insight into the effects of tDCS on brain connectivity in older adults, as well as key methodological considerations for tDCS-working memory studies.

Benchmarking transcranial electrical stimulation finite element models: a comparison study.

OBJECTIVE: To compare field measure differences in simulations of transcranial electrical stimulation (tES) generated by variations in finite element (FE) models due to boundary condition specification, use of tissue compartment smoothing filters, and use of free or structured tetrahedral meshes based on magnetic resonance imaging (MRI) data. APPROACH: A structural MRI head volume was acquired at 1 mm3 resolution and segmented into ten tissue compartments. Predicted current densities and electric fields were computed in segmented models using modeling pipelines involving either an in-house (block) or a commercial platform commonly used in previous FE tES studies involving smoothed compartments and free meshing procedures (smooth). The same boundary conditions were used for both block and smooth pipelines. Differences caused by varying boundary conditions were examined using a simple geometry. Percentage differences of median current density values in five cortical structures were compared between the two pipelines for three electrode montages (F3-right supraorbital, T7-T8 and Cz-Oz). MAIN RESULTS: Use of boundary conditions commonly used in previous tES FE studies produced asymmetric current density profiles in the simple geometry. In head models, median current density differences produced by the two pipelines, using the same boundary conditions, were up to 6% (isotropic) and 18% (anisotropic) in structures targeted by each montage. Tangential electric field measures calculated via either pipeline were within the range of values reported in the literature, when averaged over cortical surface patches. SIGNIFICANCE: Apparently equivalent boundary settings may affect predicted current density outcomes and care must be taken in their specification. Smoothing FE model compartments may not be necessary, and directly translated, voxellated tissue boundaries at 1 mm3 resolution may be sufficient for use in tES FE studies, greatly reducing processing times. The findings here may be used to inform future current density modeling studies.

Effects of Transcranial Direct Current Stimulation Paired With Cognitive Training on Functional Connectivity of the Working Memory Network in Older Adults.

BACKGROUND: Working memory, a fundamental short-term cognitive process, is known to decline with advanced age even in healthy older adults. Normal age-related declines in working memory can cause loss of independence and decreased quality of life. Cognitive training has shown some potential at enhancing certain cognitive processes, although, enhancements are variable. Transcranial direct current stimulation (tDCS), a form of non-invasive brain stimulation, has shown promise at enhancing working memory abilities, and may further the benefits from cognitive training interventions. However, the neural mechanisms underlying tDCS brain-based enhancements remain unknown. OBJECTIVE/HYPOTHESIS: Assess the effects of a 2-week intervention of active-tDCS vs. sham paired with cognitive training on functional connectivity of the working memory network during an N-Back working memory task. METHODS: Healthy older adults (N = 28; mean age = 74 ± 7.3) completed 10-sessions of cognitive training paired with active or sham-tDCS. Functional connectivity was evaluated at baseline and post-intervention during an N-Back task (2-Back vs. 0-Back). RESULTS: Active-tDCS vs. sham demonstrated a significant increase in connectivity between the left dorsolateral prefrontal cortex and right inferior parietal lobule at post-intervention during 2-Back. Target accuracy on 2-Back was significantly improved for active vs. sham at post-intervention. CONCLUSION: These results suggest pairing tDCS with cognitive training enhances functional connectivity and working memory performance in older adults, and thus may hold promise as a method for remediating age-related cognitive decline. Future studies evaluating optimal dose and long-term effects of tDCS on brain function will help to maximize potential clinical impacts of tDCS paired with cognitive training in older adults. CLINICAL TRIAL REGISTRATION: www.ClinicalTrials.gov, identifier NCT02137122.

Methods to monitor accurate and consistent electrode placements in conventional transcranial electrical stimulation.

BACKGROUND: Inaccurate electrode placement and electrode drift during a transcranial electrical stimulation (tES) session have been shown to alter predicted field distributions in the brain and thus may contribute to a large variation in tES study outcomes. Currently, there is no objective and independent measure to quantify electrode placement accuracy/drift in tES clinical studies. OBJECTIVE/HYPOTHESIS: We proposed and tested novel methods to quantify accurate and consistent electrode placements in tES using models generated from a 3D scanner. METHODS: Accurate electrode placements were quantified as Discrepancy in eight tES participants by comparing landmark distances of physical electrode locations F3/F4 to their model counterparts. Distances in models were computed using curve and linear based methods. Variability of landmark locations in a single subject was computed for multiple stimulation sessions to determine consistent electrode placements across four experimenters. MAIN RESULTS: We obtained an average of 0.4 cm in Discrepancy, which was within the placement accuracy/drift threshold (1 cm) for conventional tES electrodes (∼35 cm2) to achieve reliable tES sessions suggested in the literature. Averaged Variability was 5.2%, with F4 electrode location as the least consistent placement. CONCLUSIONS: These methods provide objective feedback for experimenters on their performance in placing tES electrodes. Applications of these methods can be used to monitor electrode locations in tES studies of a larger cohort using F3/F4 montage and other conventional electrode arrangements. Future studies may include co-registering the landmark locations with imaging-derived head models to quantify the effects of electrode accuracy/drift on predicted field distributions in the brain.

Non-invasive Brain Stimulation: Probing Intracortical Circuits and Improving Cognition in the Aging Brain.

The impact of cognitive aging on brain function and structure is complex, and the relationship between aging-related structural changes and cognitive function are not fully understood. Physiological and pathological changes to the aging brain are highly variable, making it difficult to estimate a cognitive trajectory with which to monitor the conversion to cognitive decline. Beyond the information on the structural and functional consequences of cognitive aging gained from brain imaging and neuropsychological studies, non-invasive brain stimulation techniques such as transcranial magnetic stimulation (TMS) and transcranial direct current stimulation (tDCS) can enable stimulation of the human brain in vivo, offering useful insights into the functional integrity of intracortical circuits using electrophysiology and neuromodulation. TMS measurements can be used to identify and monitor changes in cortical reactivity, the integrity of inhibitory and excitatory intracortical circuits, the mechanisms of long-term potentiation (LTP)/depression-like plasticity and central cholinergic function. Repetitive TMS and tDCS can be used to modulate neuronal excitability and enhance cortical function, and thus offer a potential means to slow or reverse cognitive decline. This review will summarize and critically appraise relevant literature regarding the use of TMS and tDCS to probe cortical areas affected by the aging brain, and as potential therapeutic tools to improve cognitive function in the aging population. Challenges arising from intra-individual differences, limited reproducibility, and methodological differences will be discussed.

Low-Frequency Conductivity Tensor Imaging of the Human Head In Vivo Using DT-MREIT: First Study.

We present the first in vivo images of anisotropic conductivity distribution in the human head, measured at a frequency of approximately 10 Hz. We used magnetic resonance electrical impedance tomography techniques to encode phase changes caused by current flow within the head via two independent electrode pairs. These results were then combined with diffusion tensor imaging data to reconstruct full anisotropic conductivity distributions in 5-mm-thick slices of the brains of two participants. Conductivity values recovered in this paper were broadly consistent with literature values. We anticipate that this technique will be of use in many areas of neuroscience, most importantly in functional imaging via inverse electroencephalogram. Future studies will involve pulse sequence acceleration to maximize brain coverage and resolution.

Accelerating acquisition strategies for low-frequency conductivity imaging using MREIT.

We sought to improve efficiency of magnetic resonance electrical impedance tomography data acquisition so that fast conductivity changes or electric field variations could be monitored. Undersampling of k-space was used to decrease acquisition times in spin-echo-based sequences by a factor of two. Full MREIT data were reconstructed using continuity assumptions and preliminary scans gathered without current. We found that phase data were reconstructed faithfully from undersampled data. Conductivity reconstructions of phantom data were also possible. Therefore, undersampled k-space methods can potentially be used to accelerate MREIT acquisition. This method could be an advantage in imaging real-time conductivity changes with MREIT.