Non-Invasive Electrical Impedance Tomography for Multi-Scale Detection of Liver Fat Content

Introduction: Obesity is associated with an increased risk of nonalcoholic fatty liver disease (NAFLD). While Magnetic Resonance Imaging (MRI) is a non-invasive gold standard to detect fatty liver, we demonstrate a low-cost and portable electrical impedance tomography (EIT) approach with circumferential abdominal electrodes for liver conductivity measurements. Methods and Results: A finite element model (FEM) was established to simulate decremental liver conductivity in response to incremental liver lipid content. To validate the FEM simulation, we performed EIT imaging on an ex vivo porcine liver in a non-conductive tank with 32 circumferentially-embedded electrodes, demonstrating a high-resolution output given a priori information on location and geometry. To further examine EIT capacity in fatty liver detection, we performed EIT measurements in age- and gender-matched New Zealand White rabbits (3 on normal, 3 on high-fat diets). Liver conductivity values were significantly distinct following the high-fat diet (p = 0.003 vs. normal diet, n=3), accompanied by histopathological evidence of hepatic fat accumulation. We further assessed EIT imaging in human subjects with MRI quantification for fat volume fraction based on Dixon procedures, demonstrating average liver conductivity of 0.331 S/m for subjects with low Body-Mass Index (BMI 25 kg/m²). Conclusion: We provide both the theoretical and experimental framework for a multi-scale EIT strategy to detect liver lipid content. Our preliminary studies pave the way to enhance the spatial resolution of EIT as a marker for fatty liver disease and metabolic syndrome

Non-Invasive Electrical Impedance Tomography for Multi-Scale Detection of Liver Fat Content

Introduction: Obesity is associated with an increased risk of nonalcoholic fatty liver disease (NAFLD). While Magnetic Resonance Imaging (MRI) is a non-invasive gold standard to detect fatty liver, we demonstrate a low-cost and portable electrical impedance tomography (EIT) approach with circumferential abdominal electrodes for liver conductivity measurements. Methods and Results: A finite element model (FEM) was established to simulate decremental liver conductivity in response to incremental liver lipid content. To validate the FEM simulation, we performed EIT imaging on an ex vivo porcine liver in a non-conductive tank with 32 circumferentially-embedded electrodes, demonstrating a high-resolution output given a priori information on location and geometry. To further examine EIT capacity in fatty liver detection, we performed EIT measurements in age- and gender-matched New Zealand White rabbits (3 on normal, 3 on high-fat diets). Liver conductivity values were significantly distinct following the high-fat diet (p = 0.003 vs. normal diet, n=3), accompanied by histopathological evidence of hepatic fat accumulation. We further assessed EIT imaging in human subjects with MRI quantification for fat volume fraction based on Dixon procedures, demonstrating average liver conductivity of 0.331 S/m for subjects with low Body-Mass Index (BMI 25 kg/m²). Conclusion: We provide both the theoretical and experimental framework for a multi-scale EIT strategy to detect liver lipid content. Our preliminary studies pave the way to enhance the spatial resolution of EIT as a marker for fatty liver disease and metabolic syndrome

White matter microstructural perturbations after total sleep deprivation in depression

BackgroundTotal sleep deprivation (TSD) transiently reverses depressive symptoms in a majority of patients with depression. How TSD modulates diffusion tensor imaging (DTI) measures of white matter (WM) microstructure, which may be linked with TSD’s rapid antidepressant effects, remains uncharacterized.MethodsPatients with depression (N = 48, mean age = 33, 26 women) completed diffusion-weighted imaging and Hamilton Depression Rating (HDRS) and rumination scales before and after >24 h of TSD. Healthy controls (HC) (N = 53, 23 women) completed the same assessments at baseline, and after receiving TSD in a subset of HCs (N = 15). Tract based spatial statistics (TBSS) investigated voxelwise changes in fractional anisotropy (FA) across major WM pathways pre-to-post TSD in patients and HCs and between patients and HCs at baseline. Post hoc analyses tested for TSD effects for other diffusion metrics, and the relationships between change in diffusion measures with change in mood and rumination symptoms.ResultsSignificant improvements in mood and rumination occurred in patients with depression (both p < 0.001), but not in HCs following TSD. Patients showed significant (p < 0.05, corrected) decreases in FA values in multiple WM tracts, including the body of the corpus callosum and anterior corona radiata post-TSD. Significant voxel-level changes in FA were not observed in HCs who received TSD (p > 0.05). However, differential effects of TSD between HCs and patients were found in the superior corona radiata, frontal WM and the posterior thalamic radiation (p < 0.05, corrected). A significant (p < 0.05) association between change in FA and axial diffusivity within the right superior corona radiata and improvement in rumination was found post-TSD in patients.ConclusionTotal sleep deprivation leads to rapid microstructural changes in WM pathways in patients with depression that are distinct from WM changes associated with TSD observed in HCs. WM tracts including the superior corona radiata and posterior thalamic radiation could be potential biomarkers of the rapid therapeutic effects of TSD. Changes in superior corona radiata FA, in particular, may relate to improvements in maladaptive rumination

Regional brain tissue integrity in pediatric obstructive sleep apnea

Children with long-standing obstructive sleep apnea (OSA) show evidence of neural injury and functional deficits in behavioral and cognitive regulatory brain regions that are reflected in symptoms of altered cognitive performance and behaviors. While we earlier showed reduced gray matter volume and increased and reduced regional cortical thicknesses, such structural changes give little indication of the underlying pathology. Brain tissue integrity in pediatric OSA subjects can reflect the nature and extent of injury or structural adaptation, and can be assessed by entropy tissue texture, a measure of local changes in signal intensity patterns from high-resolution magnetic resonance images. We collected high-resolution T1-weighted magnetic resonance images from 10 pediatric OSA (age, 7.9 ± 1.1 years; apnea-hypopnea-index, 8.8 ± 3.0 events/hour; body-mass-index, 20 ± 6.7 kg/m2; 7 male) and 8 healthy controls (age, 8.8 ± 1.6 years; body-mass-index, 19.6 ± 5.9 kg/m2; 5 female). Images were bias-corrected and entropy maps calculated, individual maps were normalized to a common space, smoothed, and compared between groups (ANCOVA; covariates: age, gender; SPM12, uncorrected-threshold p < 0.005). No significant differences in age (p = .48), gender (p = .59), or body-mass-index (p = .63) emerged between groups. In OSA children, several brain sites including the pre-frontal cortex, middle and posterior corpus callosum, thalamus, hippocampus, and cerebellar areas showed reduced entropy values, indicating tissue changes suggestive of acute insults. No regions showed higher entropy values in OSA. Children suffering from OSA display predominantly acute tissue injury in neural regions principally localized within autonomic, respiratory, cognitive, and neuropsychologic control, functions that correspond to previously-reported comorbidities associated with OSA. A range of acute processes, including hypoxia/re-oxygenation, repeated arousals, and episodic hypercarbia, may have contributed to regional brain tissue integrity changes in pediatric OSA

Effect of temporal resolution and serial autocorrelations in event‐related functional MRI

Purpose To assess the impact of colored noise on statistics in event-related functional MRI (fMRI) (visual stimulation using checkerboards) acquired by simultaneous multislice imaging enabling repetition times (TRs) between 2.64 to 0.26 s. Methods T-values within the visual cortex obtained with analysis tools that assume a first-order autoregressive plus white noise process (AR(1)+w) with a fixed AR coefficient versus higher-order AR models with spatially varying AR coefficients were compared. In addition, dependency of T-values on correction of physiological noise (respiration, heart rate) was evaluated. Results Optimal statistical power was obtained for a TR of 0.33 s, but T-values as obtained by AR(1)+w models were strongly dependent on the predefined AR coefficients in fMRI with short TRs which required higher-order AR models to achieve stable statistics. Direct estimation of AR coefficients revealed the highest values within the default mode network while physiological noise had little influence on statistics in cortical structures. Conclusion Colored noise in event-related fMRI obtained at short TRs originates mainly from neural sources and calls for more sophisticated correction of serial autocorrelations which cannot be achieved with standard methods relying on AR(1)+w models with globally fixed AR coefficients

Grand averaged low-resolution connectivity networks across participants of each group.

<p>(A) The brightness of the lines connecting regions is proportional to the connectivity value between the two regions. Same color scaling is used for controls and patients’ plots in the same frequency band. (B) The grand average low-resolution network obtained from patients in the beta2 band showing the labels of the AAL regions associated with each node in the network.</p

Clusters resulting from nonparametric between-groups clustering statistics of the nodal strength in high-resolution networks (all grey matter voxels).

<p>Anatomical brain regions (based on the automated anatomical labeling [AAL] atlas) were determined by the coordinates of the local cluster maximum.</p><p>Clusters resulting from nonparametric between-groups clustering statistics of the nodal strength in high-resolution networks (all grey matter voxels).</p

Evaluating the impact of fast-fMRI on dynamic functional connectivity in an event-based paradigm.

The human brain is known to contain several functional networks that interact dynamically. Therefore, it is desirable to analyze the temporal features of these networks by dynamic functional connectivity (dFC). A sliding window approach was used in an event-related fMRI (visual stimulation using checkerboards) to assess the impact of repetition time (TR) and window size on the temporal features of BOLD dFC. In addition, we also examined the spatial distribution of dFC and tested the feasibility of this approach for the analysis of interictal epileptiforme discharges. 15 healthy controls (visual stimulation paradigm) and three patients with epilepsy (EEG-fMRI) were measured with EPI-fMRI. We calculated the functional connectivity degree (FCD) by determining the total number of connections of a given voxel above a predefined threshold based on Pearson correlation. FCD could capture hemodynamic changes relative to stimulus onset in controls. A significant effect of TR and window size was observed on FCD estimates. At a conventional TR of 2.6 s, FCD values were marginal compared to FCD values using sub-seconds TRs achievable with multiband (MB) fMRI. Concerning window sizes, a specific maximum of FCD values (inverted u-shape behavior) was found for each TR, indicating a limit to the possible gain in FCD for increasing window size. In patients, a dynamic FCD change was found relative to the onset of epileptiform EEG patterns, which was compatible with their clinical semiology. Our findings indicate that dynamic FCD transients are better detectable with sub-second TR than conventional TR. This approach was capable of capturing neuronal connectivity across various regions of the brain, indicating a potential to study the temporal characteristics of interictal epileptiform discharges and seizures in epilepsy patients or other brain diseases with brief events