The ‘wet mind’: water and functional neuroimaging

Thinking about the action potential: the nerve signal as a window to the physical principles guiding neuronal excitability

Ever since the work of Edgar Adrian, the neuronal action potential has been considered as an electric signal, modeled and interpreted using concepts and theories lent from electronic engineering. Accordingly, the electric action potential, as the prime manifestation of neuronal excitability, serving processing and reliable "long distance" communication of the information contained in the signal, was defined as a non-linear, self-propagating, regenerative, wave of electrical activity that travels along the surface of nerve cells. Thus, in the ground-breaking theory and mathematical model of Hodgkin and Huxley (HH), linking Nernst's treatment of the electrochemistry of semi-permeable membranes to the physical laws of electricity and Kelvin's cable theory, the electrical characteristics of the action potential are presented as the result of the depolarization-induced, voltage- and time-dependent opening and closure of ion channels in the membrane allowing the passive flow of charge, particularly in the form of Na+ and K+ -ions, into and out of the neuronal cytoplasm along the respective electrochemical ion gradient. In the model, which treats the membrane as a capacitor and ion channels as resistors, these changes in ionic conductance across the membrane cause a sudden and transient alteration of the transmembrane potential, i.e., the action potential, which is then carried forward and spreads over long(er) distances by means of both active and passive conduction dependent on local current flow by diffusion of Na+ ion in the neuronal cytoplasm. However, although highly successful in predicting and explaining many of the electric characteristics of the action potential, the HH model, nevertheless cannot accommodate the various non-electrical physical manifestations (mechanical, thermal and optical changes) that accompany action potential propagation, and for which there is ample experimental evidence. As such, the electrical conception of neuronal excitability appears to be incomplete and alternatives, aiming to improve, extend or even replace it, have been sought for. Commonly misunderstood as to their basic premises and the physical principles they are built on, and mistakenly perceived as a threat to the generally acknowledged explanatory power of the "classical" HH framework, these attempts to present a more complete picture of neuronal physiology, have met with fierce opposition from mainstream neuroscience and, as a consequence, currently remain underdeveloped and insufficiently tested. Here we present our perspective that this may be an unfortunate state of affairs as these different biophysics-informed approaches to incorporate also non-electrical signs of the action potential into the modeling and explanation of the nerve signal, in our view, are well suited to foster a new, more complete and better integrated understanding of the (multi)physical nature of neuronal excitability and signal transport and, hence, of neuronal function. In doing so, we will emphasize attempts to derive the different physical manifestations of the action potential from one common, macroscopic thermodynamics-based, framework treating the multiphysics of the nerve signal as the inevitable result of the collective material, i.e., physico-chemical, properties of the lipid bilayer neuronal membrane (in particular, the axolemma) and/or the so-called ectoplasm or membrane skeleton consisting of cytoskeletal protein polymers, in particular, actin fibrils. Potential consequences for our view of action potential physiology and role in neuronal function are identified and discussed.

Estimating axon radius using diffusion-relaxation MRI: calibrating a surface-based relaxation model with histology

Axon radius is a potential biomarker for brain diseases and a crucial tissue microstructure parameter that determines the speed of action potentials. Diffusion MRI (dMRI) allows non-invasive estimation of axon radius, but accurately estimating the radius of axons in the human brain is challenging. Most axons in the brain have a radius below one micrometer, which falls below the sensitivity limit of dMRI signals even when using the most advanced human MRI scanners. Therefore, new MRI methods that are sensitive to small axon radii are needed. In this proof-of-concept investigation, we examine whether a surface-based axonal relaxation process could mediate a relationship between intra-axonal T2 and T1 times and inner axon radius, as measured using postmortem histology. A unique in vivo human diffusion-T1-T2 relaxation dataset was acquired on a 3T MRI scanner with ultra-strong diffusion gradients, using a strong diffusion-weighting (i.e., b = 6,000 s/mm2) and multiple inversion and echo times. A second reduced diffusion-T2 dataset was collected at various echo times to evaluate the model further. The intra-axonal relaxation times were estimated by fitting a diffusion-relaxation model to the orientation-averaged spherical mean signals. Our analysis revealed that the proposed surface-based relaxation model effectively explains the relationship between the estimated relaxation times and the histological axon radius measured in various corpus callosum regions. Using these histological values, we developed a novel calibration approach to predict axon radius in other areas of the corpus callosum. Notably, the predicted radii and those determined from histological measurements were in close agreement.

Incorporating multiple magnetic resonance diffusion models to differentiate low- and high-grade adult gliomas: a machine learning approach

BACKGROUND

Accurate grading of gliomas is a challenge in imaging diagnosis. This study aimed to evaluate the performance of a machine learning (ML) approach based on multiparametric diffusion-weighted imaging (DWI) in differentiating low- and high-grade adult gliomas.

METHODS

A model was developed from an initial cohort containing 74 patients with pathology-confirmed gliomas, who underwent 3 tesla (3T) diffusion magnetic resonance imaging (MRI) with 21 b values. In all, 112 histogram features were extracted from 16 parameters derived from seven diffusion models [monoexponential, intravoxel incoherent motion (IVIM), diffusion kurtosis imaging (DKI), fractional order calculus (FROC), continuous-time random walk (CTRW), stretched-exponential, and statistical]. Feature selection and model training were performed using five randomly permuted five-fold cross-validations. An internal test set (15 cases of the primary dataset) and an external cohort (n=55) imaged on a different scanner were used to validate the model. The diagnostic performance of the model was compared with that of a single DWI model and DWI radiomics using accuracy, sensitivity, specificity, and the area under the curve (AUC).

RESULTS

Seven significant multiparametric DWI features (two from the stretched-exponential and FROC models, and three from the CTRW model) were selected to construct the model. The multiparametric DWI model achieved the highest AUC (0.84, versus 0.71 for the single DWI model, P<0.05), an accuracy of 0.80 in the internal test, and both AUC and accuracy of 0.76 in the external test.

CONCLUSIONS

Our multiparametric DWI model differentiated low- (LGG) from high-grade glioma (HGG) with better generalization performance than the established single DWI model. This result suggests that the application of an ML approach with multiple DWI models is feasible for the preoperative grading of gliomas.

Quantitative measurement of diffusion-weighted imaging signal using expression-controlled aquaporin-4 cells: Comparative study of 2-compartment and diffusion kurtosis imaging models

The purpose of this study was to compare parameter estimates for the 2-compartment and diffusion kurtosis imaging models obtained from diffusion-weighted imaging (DWI) of aquaporin-4 (AQP4) expression-controlled cells, and to look for biomarkers that indicate differences in the cell membrane water permeability. DWI was performed on AQP4-expressing and non-expressing cells and the signal was analyzed with the 2-compartment and diffusion kurtosis imaging models. For the 2-compartment model, the diffusion coefficients (Df, Ds) and volume fractions (Ff, Fs, Ff = 1-Fs) of the fast and slow compartments were estimated. For the diffusion kurtosis imaging model, estimates of the diffusion kurtosis (K) and corrected diffusion coefficient (D) were obtained. For the 2-compartment model, Ds and Fs showed clear differences between AQP4-expressing and non-expressing cells. Fs was also sensitive to cell density. There was no clear relationship with the cell type for the diffusion kurtosis imaging model parameters. Changes to cell membrane water permeability due to AQP4 expression affected DWI of cell suspensions. For the 2-compartment and diffusion kurtosis imaging models, Ds was the parameter most sensitive to differences in AQP4 expression.

Technical Note: Temperature and concentration dependence of water diffusion in polyvinylpyrrolidone solutions

Objective

The goal of this work is to provide temperature and concentration calibration of water diffusivity in polyvinylpyrrolidone (PVP) solutions used in phantoms to assess system bias and linearity in apparent diffusion coefficient (ADC) measurements.

Method

ADC measurements were performed for 40 kDa (K40) PVP of six concentrations (0%, 10%, 20%, 30%, 40%, and 50% by weight) at three temperatures (19.5°C, 22.5°C, and 26.4°C), with internal phantom temperature monitored by optical thermometer (±0.2°C). To achieve ADC measurement and fit accuracy of better than 0.5%, three orthogonal diffusion gradients were calibrated using known water diffusivity at 0°C and system gradient nonlinearity maps. Noise‐floor fit bias was also controlled by limiting the maximum b‐value used for ADC calculation of each sample. The ADC temperature dependence was modeled by Arrhenius functions of each PVP concentration. The concentration dependence was modeled by quadratic function for ADC normalized by the theoretical water diffusion values. Calibration coefficients were obtained from linear regression model fits.

Results

Measured phantom ADC values increased with temperature and decreasing PVP concentration, [PVP]. The derived Arrhenius model parameters for [PVP] between 0% and 50%, are reported and can be used for K40 ADC temperature calibration with absolute ADC error within ±0.016 μm²/ms. Arrhenius model fit parameters normalized to water value scaled with [PVP] between 10% and 40%, and proportional change in activation energy increased faster than collision frequency. ADC normalization by water diffusivity, D_W, from the Speedy–Angell relation accounted for the bulk of temperature dependence (±0.035 μm²/ms) and yielded quadratic calibration for ADC_PVP/D_W = (12.5 ± 0.7) ·10⁻⁵·[PVP]² − (23.2 ± 0.3)·10⁻³·[PVP]+1, nearly independent of PVP molecular weight and temperature.

Conclusion

The study provides ground‐truth ADC values for K40 PVP solutions commonly used in diffusion phantoms for scanning at ambient room temperature. The described procedures and the reported calibration can be used for quality control and standardization of measured ADC values of PVP at different concentrations and temperatures.

Potential Pitfalls of Using Fractional Anisotropy, Axial Diffusivity, and Radial Diffusivity as Biomarkers of Cerebral White Matter Microstructure

Fractional anisotropy (FA), axial diffusivity (AD), and radial diffusivity (RD) are commonly used as MRI biomarkers of white matter microstructure in diffusion MRI studies of neurodevelopment, brain aging, and neurologic injury/disease. Some of the more frequent practices include performing voxel-wise or region-based analyses of these measures to cross-sectionally compare individuals or groups, longitudinally assess individuals or groups, and/or correlate with demographic, behavioral or clinical variables. However, it is now widely recognized that the majority of cerebral white matter voxels contain multiple fiber populations with different trajectories, which renders these metrics highly sensitive to the relative volume fractions of the various fiber populations, the microstructural integrity of each constituent fiber population, and the interaction between these factors. Many diffusion imaging experts are aware of these limitations and now generally avoid using FA, AD or RD (at least in isolation) to draw strong reverse inferences about white matter microstructure, but based on the continued application and interpretation of these metrics in the broader biomedical/neuroscience literature, it appears that this has perhaps not yet become common knowledge among diffusion imaging end-users. Therefore, this paper will briefly discuss the complex biophysical underpinnings of these measures in the context of crossing fibers, provide some intuitive “thought experiments” to highlight how conventional interpretations can lead to incorrect conclusions, and suggest that future studies refrain from using (over-interpreting) FA, AD, and RD values as standalone biomarkers of cerebral white matter microstructure.

Correlation Tensor MRI deciphers underlying kurtosis sources in stroke

Noninvasively detecting and characterizing modulations in cellular scale micro-architecture remains a desideratum for contemporary neuroimaging. Diffusion MRI (dMRI) has become the mainstay methodology for probing microstructure, and, in ischemia, its contrasts have revolutionized stroke management. Diffusion kurtosis imaging (DKI) has been shown to significantly enhance the sensitivity of stroke detection compared to its diffusion tensor imaging (DTI) counterparts. However, the interpretation of DKI remains ambiguous as its contrast may arise from competing kurtosis sources related to the anisotropy of tissue components, diffusivity variance across components, and microscopic kurtosis (e.g., arising from cross-sectional variance, structural disorder, and restriction). Resolving these sources may be fundamental for developing more specific imaging techniques for stroke management, prognosis, and understanding its pathophysiology. In this study, we apply Correlation Tensor MRI (CTI) - a double diffusion encoding (DDE) methodology recently introduced for deciphering kurtosis sources based on the unique information captured in DDE's diffusion correlation tensors - to investigate the underpinnings of kurtosis measurements in acute ischemic lesions. Simulations for the different kurtosis sources revealed specific signatures for cross-sectional variance (representing neurite beading), edema, and cell swelling. Ex vivo CTI experiments at 16.4 T were then performed in an experimental photothrombotic stroke model 3 h post-stroke (N = 10), and successfully separated anisotropic, isotropic, and microscopic non-Gaussian diffusion sources in the ischemic lesions. Each of these kurtosis sources provided unique contrasts in the stroked area. Particularly, microscopic kurtosis was shown to be a primary "driver" of total kurtosis upon ischemia; its large increases, coupled with decreases in anisotropic kurtosis, are consistent with the expected elevation in cross-sectional variance, likely linked to beading effects in small objects such as neurites. In vivo experiments at 9.4 T at the same time point (3 h post ischemia, N = 5) demonstrated the stability and relevance of the findings and showed that fixation is not a dominant confounder in our findings. In future studies, the different CTI contrasts may be useful to address current limitations of stroke imaging, e.g., penumbra characterization, distinguishing lesion progression form tissue recovery, and elucidating pathophysiological correlates.

The thermodynamic theory of action potential propagation: a sound basis for unification of the physics of nerve impulses

The thermodynamic theory of action potential propagation challenges the conventional understanding of the nerve signal as an exclusively electrical phenomenon. Often misunderstood as to its basic tenets and predictions, the thermodynamic theory is virtually ignored in mainstream neuroscience. Addressing a broad audience of neuroscientists, we here attempt to stimulate interest in the theory. We do this by providing a concise overview of its background, discussion of its intimate connection to Albert Einstein's treatment of the thermodynamics of interfaces and outlining its potential contribution to the building of a physical brain theory firmly grounded in first principles and the biophysical reality of individual nerve cells. As such, the paper does not attempt to advocate the superiority of the thermodynamic theory over any other approach to model the nerve impulse, but is meant as an open invitation to the neuroscience community to experimentally test the assumptions and predictions of the theory on their validity.

Spatiotemporal characterisation of ischaemic lesions in transient stroke animal models using diffusion free water elimination and mapping MRI with echo time dependence

BACKGROUND AND PURPOSE

The excess fluid as a result of vasogenic oedema and the subsequent tissue cavitation obscure the microstructural characterisation of ischaemic tissue by conventional diffusion and relaxometry MRI. They lead to a pseudo-normalisation of the water diffusivity and transverse relaxation time maps in the subacute and chronic phases of stroke. Within the context of diffusion MRI, the free water elimination and mapping method (FWE) with echo time dependence has been proposed as a promising approach to measure the amount of free fluid in brain tissue robustly and to eliminate its biasing effect on other biomarkers. In this longitudinal study of transient middle cerebral artery occlusion (MCAo) in the rat brain, we investigated the use of FWE MRI with echo time dependence for the characterisation of the tissue microstructure and explored the potential of the free water fraction as a novel biomarker of ischaemic tissue condition.

METHODS

Adult rats received a transient MCAo. Diffusion- and transverse relaxation-weighted MRI experiments were performed longitudinally, pre-occlusion and on days 1, 3, 4, 5, 6, 7 and 10 after MCAo on four rats. Histology was performed for non-stroke and 1, 3 and 10 days after MCAo on three different rats at each time point.

RESULTS

The free water fraction was homogeneously increased in the ischaemic cortex one day after stroke. Between three and ten days after stroke, the core of the ischaemic tissue showed a progressive normalisation in the amount of free water, whereas the inner and outer border zones of the ischaemic cortex depicted a large, monotonous increase with time. The specific lesions in brain sections were verified by H&E and immunostaining. The tissue-specific diffusion and relaxometry MRI metrics in the ischaemic cortex were significantly different compared to their conventional counterpart.

CONCLUSIONS

Our results demonstrate that the free water fraction in FWE MRI with echo time dependence is a valuable biomarker, sensitive to the progressive degeneration in ischaemic tissue. We showed that part of the heterogeneity previously observed in conventional parameter maps can be accounted for by a heterogeneous distribution of free water in the tissue. Our results suggest that the temporal evolution of the free fluid fraction map at the core and inner border zone can be associated with the pathological changes linked to the evolution of vasogenic oedema. Namely, the homogeneous increase in free water one day after stroke and its tendency to normalise in the core of the ischaemic cortex starting three days after stroke, followed by a progressive increase in free water at the inner border zone from three to ten days after stroke. Finally, the monotonous increase in free fluid in the outer border zone of the cortex reflects the formation of fluid-filled cysts.

Exploring cell membrane water exchange in aquaporin-4-deficient ischemic mouse brain using diffusion-weighted MRI

BACKGROUND

Aquaporin-4 is a membrane channel protein that is highly expressed in brain astrocytes and facilitates the transport of water molecules. It has been suggested that suppression of aquaporin-4 function may be an effective treatment for reducing cellular edema after cerebral infarction. It is therefore important to develop clinically applicable measurement systems to evaluate and better understand the effects of aquaporin-4 suppression on the living body.

METHODS

Animal models of focal cerebral ischemia were created by surgically occluding the middle cerebral artery of wild-type and aquaporin-4 knockout mice, after which multi-b-value multi-diffusion-time diffusion-weighted imaging measurements were performed. Data were analyzed with both the apparent diffusion coefficient (ADC) model and a compartmental water-exchange model.

RESULTS

ADCs were estimated for five different b value ranges. The ADC of aquaporin-4 knockout mice in the contralateral region was significantly higher than that of wild-type mice for each range. In contrast, aquaporin-4 knockout mice had significantly lower ADC than wild-type mice in ischemic tissue for each b-value range. Genotype-dependent differences in the ADC were particularly significant for the lowest ranges in normal tissue and for the highest ranges in ischemic tissue. The ADCs measured at different diffusion times were significantly different for both genotypes. Fitting of the water-exchange model to the ischemic region data found that the water-exchange time in aquaporin-4 knockout mice was approximately 2.5 times longer than that in wild-type mice.

CONCLUSIONS

Multi-b-value multi-diffusion-time diffusion-weighted imaging may be useful for in vivo research and clinical diagnosis of aquaporin-4-related diseases.

Statistical Evaluation of Different Mathematical Models for Diffusion Weighted Imaging of Prostate Cancer Xenografts in Mice

Purpose

To evaluate fitting quality and repeatability of four mathematical models for diffusion weighted imaging (DWI) during tumor progression in mouse xenograft model of prostate cancer.

Methods

Human prostate cancer cells (PC-3) were implanted subcutaneously in right hind limbs of 11 immunodeficient mice. Tumor growth was followed by weekly DWI examinations using a 7T MR scanner. Additional DWI examination was performed after repositioning following the fourth DWI examination to evaluate short term repeatability. DWI was performed using 15 and 12 b-values in the ranges of 0-500 and 0-2000 s/mm², respectively. Corrected Akaike information criteria and F-ratio were used to evaluate fitting quality of each model (mono-exponential, stretched exponential, kurtosis, and bi-exponential).

Results

Significant changes were observed in DWI data during the tumor growth, indicated by ADC_m, ADC_s, and ADC_k. Similar results were obtained using low as well as high b-values. No marked changes in model preference were present between the weeks 1−4. The parameters of the mono-exponential, stretched exponential, and kurtosis models had smaller confidence interval and coefficient of repeatability values than the parameters of the bi-exponential model.

Conclusion

Stretched exponential and kurtosis models showed better fit to DWI data than the mono-exponential model and presented with good repeatability.

Macro- and micro-structural cerebellar and cortical characteristics of cognitive empathy towards fictional characters in healthy individuals

Few investigations have analyzed the neuroanatomical substrate of empathic capacities in healthy subjects, and most of them have neglected the potential involvement of cerebellar structures. The main aim of the present study was to investigate the associations between bilateral cerebellar macro- and micro-structural measures and levels of cognitive and affective trait empathy (measured by Interpersonal Reactivity Index, IRI) in a sample of 70 healthy subjects of both sexes. We also estimated morphometric variations of cerebral Gray Matter structures, to ascertain whether the potential empathy-related peculiarities in cerebellar areas were accompanied by structural differences in other cerebral regions. At macro-structural level, the volumetric differences were analyzed by Voxel-Based Morphometry (VBM)- and Region of Interest (ROI)-based approaches, and at a micro-structural level, we analyzed Diffusion Tensor Imaging (DTI) data, focusing in particular on Mean Diffusivity and Fractional Anisotropy. Fantasy IRI-subscale was found to be positively associated with volumes in right cerebellar Crus 2 and pars triangularis of inferior frontal gyrus. The here described morphological variations of cerebellar Crus 2 and pars triangularis allow to extend the traditional cortico-centric view of cognitive empathy to the cerebellar regions and indicate that in empathizing with fictional characters the cerebellar and frontal areas are co-recruited.

Advanced non-invasive MRI of neuroplasticity in ischemic stroke: Techniques and applications

Ischemic stroke represents a serious medical condition which could cause survivors suffer from long-term and even lifetime disabilities. After a stroke attack, the brain would undergo varying degrees of recovery, in which the central nervous system could be reorganized spontaneously or with the help of appropriate rehabilitation. Magnetic resonance imaging (MRI) is a non-invasive technique which can provide comprehensive information on structural, functional and metabolic features of brain tissue. In the last decade, there has been an increased technical advancement in MR techniques such as voxel-based morphological analysis (VBM), diffusion magnetic resonance imaging (dMRI), functional magnetic resonance imaging (fMRI), arterial spin-labeled perfusion imaging (ASL), magnetic sensitivity weighted imaging (SWI), quantitative sensitivity magnetization (QSM) and magnetic resonance spectroscopy (MRS) which have been proven to be a valuable tool to study the brain tissue reorganization. Due to MRI indices of neuroplasticity related to neurological outcome could be translated to the clinic. The ultimate goal of this review is to equip readers with a fundamental understanding of advanced MR techniques and their corresponding clinical application for improving the ability to predict neuroplasticity that are most suitable for stroke management.

Higher b-values improve the correlation between diffusion MRI and the cortical microarchitecture

PURPOSE

In diffusion MRI (dMRI), it remains unclear to know how much increase of b-value is conveying additional biological meaning. We tested the correlations between cortical microarchitecture and diffusion metrics computed from standard (1000 s/mm²), high (3000 s/mm²), to very high (5000 s/mm²) b-value dMRI.

METHODS

Healthy volunteers were scanned with a dMRI pulse sequence that was first optimized together with a T1-WI and T2-WI. Averaged cortical surface map of estimated myelin (T1-WI/T2-WI) was compared with surface maps of mean diffusivity (MD) computed from each b-value (MD₁₀₀₀, MD₃₀₀₀, and MD₅₀₀₀) and to surface map of mean kurtosis (MK computed from the 0-, 1000-, to 3000-s/mm² shells) in 360 cortical parcels using Spearman correlations, multiple linear regressions, and Akaike information criteria (AIC).

RESULTS

Surface map from MD₁₀₀₀ showed variations not related to myelin but the MD₃₀₀₀ and MD₅₀₀₀ maps inversely mirrored estimated myelin map; lower MD values being observed in more myelinated cortical areas. MK mirrored myelinated cortical areas. Quantitatively, Spearman correlations between myelin and MD became more and more negative as long as b-values increased while the correlation was positive between myelin and MK. Multiple regression models confirmed negative associations between myelin and MD that were significantly better from MD₁₀₀₀ to MD₃₀₀₀ and MD₅₀₀₀ (R² = 0.33, p < 0.001; R² = 0.43, p < 0.001; and R² = 0.50, p < 0.001) and positive association between myelin and MK (R² = 0.53, p < 0.001). Comparisons of the 3 statistical models showed the best performances with MK and MD₅₀₀₀ (AIC_MK < AIC_MD5000 < AIC_MD3000 < AIC_MD1000).

CONCLUSION

Higher b-values are more closely related to subtle cellular variations of the cortical microarchitecture.

Blood oxygenation level dependent magnetic resonance imaging and diffusion weighted MRI imaging for benign and malignant breast cancer discrimination

PURPOSE

The purpose of this study is to assess Blood oxygenation level dependent Magnetic Resonance Imaging (BOLD-MRI) and Diffusion Weighted Magnetic Resonance Imaging (DW-MRI) in the differentiation of benign and malignant breast lesions.

METHODS

Fifty-nine breast lesions (26 benign and 33 malignant lesions) pathologically proven in 59 patients were included in this retrospective study. As BOLD parameters were estimated basal signal S₀ and the relaxation rate R2*, diffusion and perfusion parameters were derived by DWI (pseudo-diffusion coefficient (Dp), perfusion fraction (fp) and tissue diffusivity (Dt)). Wilcoxon-Mann-Whitney U test and Receiver operating characteristic (ROC) analyses were calculated and area under ROC curve (AUC) was obtained. Moreover, pattern recognition approaches (linear discrimination analysis (LDA), support vector machine, k-nearest neighbours, decision tree) with least absolute shrinkage and selection operator (LASSO) method and leave one out cross validation approach were considered.

RESULTS

A significant discrimination was obtained by the standard deviation value of S0, as BOLD parameter, that reached an AUC of 0.76 with a sensitivity of 65%, a specificity of 85% and an accuracy of 76%. No significant discrimination was obtained considering diffusion and perfusion parameters. Considering LASSO results, the features to use as predictors were all extracted parameters except that the mean value of R2* and the best result was obtained by a LDA that obtained an AUC = 0.83, with a sensitivity of 88%, a specificity of 77% and an accuracy of 83%.

CONCLUSIONS

Good performance to discriminate benign and malignant lesions could be obtained using BOLD and DWI derived parameters with a LDA classification approach. However, these findings should be proven on larger and several dataset with different MR scanners.

Diffusion and perfusion MRI of normal, preeclamptic and growth-restricted mice models reveal clear fetoplacental differences

Diffusion-weighted MRI on rodents could be valuable to evaluate pregnancy-related dysfunctions, particularly in knockout models whose biological nature is well understood. Echo Planar Imaging’s sensitivity to motions and to air/water/fat heterogeneities, complicates these studies in the challenging environs of mice abdomens. Recently developed MRI methodologies based on SPatiotemporal ENcoding (SPEN) can overcome these obstacles, and deliver diffusivity maps at ≈150 µm in-plane resolutions. The present study exploits these capabilities to compare the development in wildtype vs vascularly-altered mice. Attention focused on the various placental layers—deciduae, labyrinth, trophoblast, fetal vessels—that the diffusivity maps could resolve. Notable differences were then observed between the placental developments of wildtype vs diseased mice; these differences remained throughout the pregnancies, and were echoed by perfusion studies relying on gadolinium-based dynamic contrast-enhanced MRI. Longitudinal monitoring of diffusivity in the animals throughout the pregnancies also showed differences between the development of the fetal brains in the wildtype and vascularly-altered mice, even if these disparities became progressively smaller as the pregnancies progressed. These results are analyzed on the basis of the known physiology of normal and preeclamptic pregnancies, as well as in terms of the potential that they might open for the early detection of disorders in human pregnancies.

Diffusion functional MRI reveals global brain network functional abnormalities driven by targeted local activity in a neuropsychiatric disease mouse model

Diffusion functional magnetic resonance imaging (DfMRI) has been proposed as an alternative functional imaging method to detect brain activity without confounding hemodynamic effects. Here, taking advantage of this DfMRI feature, we investigated abnormalities of dynamic brain function in a neuropsychiatric disease mouse model (glial glutamate transporter-knockdown mice with obsessive-compulsive disorder [OCD]-related behavior). Our DfMRI approaches consisted of three analyses: resting state brain activity, functional connectivity, and propagation of neural information. We detected hyperactivation and biased connectivity across the cortico-striatal-thalamic circuitry, which is consistent with known blood oxygen-level dependent (BOLD)-fMRI patterns in OCD patients. In addition, we performed ignition-driven mean integration (IDMI) analysis, which combined activity and connectivity analyses, to evaluate neural propagation initiated from brain activation. This analysis revealed an unbalanced distribution of neural propagation initiated from intrinsic local activation to the global network, while these were not detected by the conventional method with BOLD-fMRI. This abnormal function detected by DfMRI was associated with OCD-related behavior. Together, our comprehensive DfMRI approaches can successfully provide information on dynamic brain function in normal and diseased brains.

On the Origins of Diffusion MRI Signal Changes in Stroke

Magnetic resonance imaging (MRI) is a leading diagnostic technique especially for neurological studies. However, the physical origin of the hyperintense signal seen in MR images of stroke immediately after ischemic onset in the brain has been a matter of debate since it was first demonstrated in 1990. In this article, we hypothesize and provide evidence that changes in the glial cells, comprising roughly one-half of the brain's cells and therefore a significant share of its volume, accompanying ischemia, are the root cause of the MRI signal change. Indeed, a primary function of the glial cells is osmoregulation in order to maintain homeostasis in the neurons and nerve fibers for accurate and consistent function. This realization also impacts our understanding of signal changes in other tissues following ischemia. We anticipate that this paradigm shift will facilitate new and improved models of MRI signals in tissues, which will, in turn, impact clinical utility.

High-resolution diffusion MRI studies of development in pregnant mice visualized by novel spatiotemporal encoding schemes

This study introduces an MRI approach to map diffusion of water in vivo with high resolution under challenging conditions; the approach's potential is then used in diffusivity characterizations of embryos and fetoplacental units in pregnant mice, as well as of newborn mice in their initial postnatal period. The method relies on performing self-referenced spatiotemporal encoded MRI acquisitions, which can achieve the motional and susceptibility immunities needed to target challenging regions such as a mouse's abdominal cavity in a single shot. When suitably combined with zooming-in and novel interleaving procedures, these scans can overcome the inhomogeneity and sensitivity challenges arising upon targeting ≈100 μm in-plane resolutions, and thereby enable longitudinal development studies of abdominal organs that have hitherto eluded in vivo diffusion-weighted imaging. This is employed here to follow processes related to embryonic implantation and placentation, including the final stages of mouse gastrulation, the development of white matter in fetal brains, the maturation of fetal spines, and the evolution of the different layers making up mouse hemochorial placentas. The protocol's ability to extract diffusivity information in challenging regions as a function of embryonic mouse development is thus demonstrated, and its usefulness as a tool for visualizing pregnancy-related developmental changes in rodents is discussed.

Monitoring Response to Neoadjuvant Chemotherapy of Primary Osteosarcoma Using Diffusion Kurtosis Magnetic Resonance Imaging: Initial Findings

Objective

To determine whether diffusion kurtosis imaging (DKI) is effective in monitoring tumor response to neoadjuvant chemotherapy in patients with osteosarcoma.

Materials and Methods

Twenty-nine osteosarcoma patients (20 men and 9 women; mean age, 17.6 ± 7.8 years) who had undergone magnetic resonance imaging (MRI) and DKI before and after neoadjuvant chemotherapy were included. Tumor volume, apparent diffusion coefficient (ADC), mean diffusivity (MD), mean kurtosis (MK), and change ratio (ΔX) between pre- and post-treatment were calculated. Based on histologic response, the patients were divided into those with good response (≥ 90% necrosis, n = 12) and those with poor response (< 90% necrosis, n = 17). Several MRI parameters between the groups were compared using Student's t test. The correlation between image indexes and tumor necrosis was determined using Pearson's correlation, and diagnostic performance was compared using receiver operating characteristic curves.

Results

In good responders, MD_post, ADC_post, and MK_post values were significantly higher than in poor responders (p < 0.001, p < 0.001, and p = 0.042, respectively). The ΔMD and ΔADC were also significantly higher in good responders than in poor responders (p < 0.001 and p = 0.01, respectively). However, no significant difference was observed in ΔMK (p = 0.092). MD_post and ΔMD showed high correlations with tumor necrosis rate (r = 0.669 and r = 0.622, respectively), and MD_post had higher diagnostic performance than ADC_post (p = 0.037) and MK_post (p = 0.011). Similarly, ΔMD also showed higher diagnostic performance than ΔADC (p = 0.033) and ΔMK (p = 0.037).

Conclusion

MD is a promising biomarker for monitoring tumor response to preoperative chemotherapy in patients with osteosarcoma.

Determination of optimized set of b-values for Apparent Diffusion Coefficient mapping in liver Diffusion-Weighted MRI

In this manuscript we derive the Cramér-Rao Lower Bound (CRLB) of the monoexponential diffusion-weighted signal model under a realistic noise assumption, and propose a formulation to obtain optimized sets of b-values that maximize the noise performance of the Apparent Diffusion Coefficient (ADC) maps given a target ADC and a signal-to-noise ratio. Therefore, for various sets of parameters (S₀ and ADC), signal-to-noise ratios (SNR) and noise distribution, we computed optimized sets of b-values using CRLB-based analysis in two different ways: (i) through a greedy algorithm where b-values from a pool of candidates were added iteratively to the set, and (ii) through a two b-value search algorithm were all two b-value combinations of the pool of candidates were tested. Further, optimized sets of b-values were computed from synthetic data, phantoms, and in-vivo liver diffusion-weighted imaging (DWI) experiments to validate the CRLB-based analysis. The optimized sets of b-values obtained through the proposed CRLB-based analysis showed good agreement with the optimized sets obtained experimentally from synthetic, phantoms, and in-vivo liver data. The variance of the ADC maps decreased when employing the optimized set of b-values compared to various sets of b-values proposed in the literature for in-vivo liver DWI, although differences of notable magnitude between noise models and optimization strategies were not found. In addition, the higher b-values decreased for lower SNR under the Rician noise distribution. Optimization of the set b-values is critical to maximize the noise performance (i.e., maximize the precision and minimize the variance) of the estimated ADC maps in diffusion-weighted MRI. Hence, the proposed approach may help to optimize and standardize liver diffusion-weighted MRI acquisitions by computing optimized set of b-values for a given set of parameters.

Non-Gaussian models of diffusion weighted imaging for detection and characterization of prostate cancer: a systematic review and meta-analysis

The importance of Diffusion Weighted Imaging (DWI) in prostate cancer (PCa) diagnosis have been widely handled in literature. In the last decade, due to the mono-exponential model limitations, several studies investigated non-Gaussian DWI models and their utility in PCa diagnosis. Since their results were often inconsistent and conflicting, we performed a systematic review of studies from 2012 examining the most commonly used Non-Gaussian DWI models for PCa detection and characterization. A meta-analysis was conducted to assess the ability of each Non-Gaussian model to detect PCa lesions and distinguish between low and intermediate/high grade lesions. Weighted mean differences and 95% confidence intervals were calculated and the heterogeneity was estimated using the I² statistic. 29 studies were selected for the systematic review, whose results showed inconsistence and an unclear idea about the actual usefulness and the added value of the Non-Gaussian model parameters. 12 studies were considered in the meta-analyses, which showed statistical significance for several non-Gaussian parameters for PCa detection, and to a lesser extent for PCa characterization. Our findings showed that Non-Gaussian model parameters may potentially play a role in the detection and characterization of PCa but further studies are required to identify a standardized DWI acquisition protocol for PCa diagnosis.

Functional MRI Readouts From BOLD and Diffusion Measurements Differentially Respond to Optogenetic Activation and Tissue Heating

Functional blood-oxygenation-level-dependent (BOLD) MRI provides a brain-wide readout that depends on the hemodynamic response to neuronal activity. Diffusion fMRI has been proposed as an alternative to BOLD fMRI and has been postulated to directly rely on neuronal activity. These complementary functional readouts are versatile tools to be combined with optogenetic stimulation to investigate networks of the brain. The cell-specificity and temporal precision of optogenetic manipulations promise to enable further investigation of the origin of fMRI signals. The signal characteristics of the diffusion fMRI readout vice versa may better resolve network effects of optogenetic stimulation. However, the light application needed for optogenetic stimulation is accompanied by heat deposition within the tissue. As both diffusion and BOLD are sensitive to temperature changes, light application can lead to apparent activations confounding the interpretation of fMRI data. The degree of tissue heating, the appearance of apparent activation in different fMRI sequences and the origin of these phenomena are not well understood. Here, we disentangled apparent activations in BOLD and diffusion measurements in rats from physiological activation upon sensory or optogenetic stimulation. Both, BOLD and diffusion fMRI revealed similar signal shapes upon sensory stimulation that differed clearly from those upon heating. Apparent activations induced by high-intensity light application were dominated by T₂^∗-effects and resulted in mainly negative signal changes. We estimated that even low-intensity light application used for optogenetic stimulation reduces the BOLD response close to the fiber by up to 0.4%. The diffusion fMRI signal contained T₂, T₂^∗ and diffusion components. The apparent diffusion coefficient, which reflects the isolated diffusion component, showed negative changes upon both optogenetic and electric forepaw stimulation. In contrast, positive changes were detected upon high-intensity light application and thus ruled out heating as a major contributor to the diffusion fMRI signal.

Prostate cancer aggressive prediction: preponderant diagnostic performances of intravoxel incoherent motion (IVIM) imaging and diffusion kurtosis imaging (DKI) beyond ADC at 3.0 T scanner with gleason score at final pathology

PURPOSE

To explore the preponderant diagnostic performances of IVIM and DKI in predicting the Gleason score (GS) of prostate cancer.

METHODS

Diffusion-weighted imaging data were postprocessed using monoexponential, lVIM and DK models to quantitate the apparent diffusion coefficient (ADC), molecular diffusion coefficient (D), perfusion-related diffusion coefficient (Dstar), perfusion fraction (F), apparent diffusion for Gaussian distribution (Dapp), and apparent kurtosis coefficient (Kapp). Spearman's rank correlation coefficient was used to explore the relationship between those parameters and the GS, Kruskal-Wallis test, and Mann-Whitney U test were performed to compare the above parameters between the different groups, and a receiver-operating characteristic (ROC) curve was used to analyze the differential diagnosis ability. The interpretation of the results is in view of histopathologic tumor tissue composition.

RESULTS

The area under the ROC curves (AUCs) of ADC, F, D, Dapp, and Kapp in differentiating GS ≤ 3 + 4 and GS > 3 + 4 PCa were 0.744 (95% CI 0.581-0.868), 0.726 (95% CI 0.563-0.855), 0.732 (95% CI 0.569-0.860), and 0.752 (95% CI 0.590-0.875), 0.766 (95% CI 0.606-0.885), respectively, and those in differentiating GS ≤ 7 and GS > 7 PCa were 0.755 (95% CI 0.594-0.877), 0.734 (95% CI 0.571-0.861), 0.724 (95% CI0.560-0.853), and 0.716 (95% CI 0.552-0.847), 0.828 (95% CI 0.676-0.929), respectively. All the P values were less than 0.05. There was no significant difference in the AUC for the detection of different GS groups by using those parameters.

CONCLUSION

Both the IVIM and DKI models are beneficial to predict GS of PCa and indirectly predict its aggressiveness, and they have a comparable diagnostic performance with each other as well as ADC.

Imaging of Hypoxic Tumor: Correlation between Diffusion-weighted MR Imaging and 18F-fluoroazomycin Arabinoside Positron Emission Tomography in Head and Neck Carcinoma

We investigated the usefulness of diffusion-weighted imaging (DWI) for detecting changes in the structure of hypoxic cells by evaluating the correlation between ¹⁸F-fluoroazomycin arabinoside (FAZA) positron emission tomography activity and DWI parameters in head and neck carcinoma. The diffusion coefficient corresponding to the slow compartment of a two-compartment model had a significant positive correlation with FAZA activity (ρ = 0.58, P = 0.016), whereas the diffusional kurtosis from diffusion kurtosis imaging had a significant negative correlation (ρ = -0.62, P = 0.008), which suggests that those DWI parameters might be useful as indicators for changes in cell structure.

Anomalous water dynamics in brain: a combined diffusion magnetic resonance imaging and neutron scattering investigation

Water diffusion is an optimal tool for investigating the architecture of brain tissue on which modern medical diagnostic imaging techniques rely. However, intrinsic tissue heterogeneity causes systematic deviations from pure free-water diffusion behaviour. To date, numerous theoretical and empirical approaches have been proposed to explain the non-Gaussian profile of this process. The aim of this work is to shed light on the physics piloting water diffusion in brain tissue at the micrometre-to-atomic scale. Combined diffusion magnetic resonance imaging and first pioneering neutron scattering experiments on bovine brain tissue have been performed in order to probe diffusion distances up to macromolecular separation. The coexistence of free-like and confined water populations in brain tissue extracted from a bovine right hemisphere has been revealed at the micrometre and atomic scale. The results are relevant for improving the modelling of the physics driving intra- and extracellular water diffusion in brain, with evident benefit for the diffusion magnetic resonance imaging technique, nowadays widely used to diagnose, at the micrometre scale, brain diseases such as ischemia and tumours.

Cerebral hunger maps in rodents and humans by diffusion weighted MRI

We design, implement and validate a novel image processing strategy to obtain in vivo maps of hunger stimulation in the brain of mice, rats and humans, combining Diffusion Weighted Magnetic Resonance Imaging (DWI) datasets from fed and fasted subjects. Hunger maps were obtained from axial/coronal (rodents/humans) brain sections containing the hypothalamus and coplanar cortico-limbic structures using Fisher's Discriminant Analysis of the combined voxel ensembles from both feeding situations. These maps were validated against those provided by the classical mono-exponential diffusion model as applied over the same subjects and conditions. Mono-exponential fittings revealed significant Apparent Diffusion Coefficient (ADC) decreases through the brain regions stimulated by hunger, but rigorous parameter estimations imposed the rejection of considerable number of pixels. The proposed approach avoided pixel rejections and provided a representation of the combined DWI dataset as a pixel map of the "Hunger Index" (HI), a parameter revealing the hunger score of every pixel. The new methodology proved to be robust both, by yielding consistent results with classical ADC maps and, by reproducing very similar HI maps when applied to newly acquired rodent datasets. ADC and HI maps demonstrated similar patterns of activation by hunger in hypothalamic and cortico-limbic structures of the brain of rodents and humans, albeit with different relative intensities, rodents showing more intense activations by hunger than humans, for similar fasting periods. The proposed methodology may be easily extended to other feeding paradigms or even to alternative imaging methods.

Cerebellar Structural Variations in Subjects with Different Hypnotizability

Hypnotizability-the proneness to accept suggestions and behave accordingly-has a number of physiological and behavioral correlates (postural, visuomotor, and pain control) which suggest a possible involvement of cerebellar function and/or structure. The present study was aimed at investigating the association between cerebellar macro- or micro-structural variations (analyzed through a voxel-based morphometry and a diffusion tensor imaging approach) and hypnotic susceptibility. We also estimated morphometric variations of cerebral gray matter structures, to support current evidence of hypnotizability-related differences in some cerebral areas. High (highs, N = 12), and low (lows, N = 37) hypnotizable healthy participants (according to the Stanford Hypnotic Susceptibility Scale, form A) were submitted to a high field (3 T) magnetic resonance imaging protocol. In comparison to lows, highs showed smaller gray matter volumes in left cerebellar lobules IV/V and VI at uncorrected level, with the results in left lobule IV/V maintained also at corrected level. Highs showed also gray matter volumes smaller than lows in right inferior temporal gyrus, middle and superior orbitofrontal cortex, parahippocampal gyrus, and supramarginal parietal gyrus, as well as in left gyrus rectus, insula, and middle temporal cortex at uncorrected level. Results of right inferior temporal gyrus survived also at corrected level. Analyses on micro-structural data failed to reveal any significant association. The here found morphological variations allow to extend the traditional cortico-centric view of hypnotizability to the cerebellar regions, suggesting that cerebellar peculiarities may sustain hypnotizability-related differences in sensorimotor integration and emotional control.

Brain Edema in Chronic Hepatic Encephalopathy

Brain edema is a common feature associated with hepatic encephalopathy (HE). In patients with acute HE, brain edema has been shown to play a crucial role in the associated neurological deterioration. In chronic HE, advanced magnetic resonance imaging (MRI) techniques have demonstrated that low-grade brain edema appears also to be an important pathological feature. This review explores the different methods used to measure brain edema ex vivo and in vivo in animal models and in humans with chronic HE. In addition, an in-depth description of the main studies performed to date is provided. The role of brain edema in the neurological alterations linked to HE and whether HE and brain edema are the manifestations of the same pathophysiological mechanism or two different cerebral manifestations of brain dysfunction in liver disease are still under debate. In vivo MRI/magnetic resonance spectroscopy studies have allowed insight into the development of brain edema in chronic HE. However, additional in vivo longitudinal and multiparametric/multimodal studies are required (in humans and animal models) to elucidate the relationship between liver function, brain metabolic changes, cellular changes, cell swelling, and neurological manifestations in chronic HE.

Layer-specific connectivity revealed by diffusion-weighted functional MRI in the rat thalamocortical pathway

Investigating neural activity from a global brain perspective in-vivo has been in the domain of functional Magnetic Resonance Imaging (fMRI) over the past few decades. The intricate neurovascular couplings that govern fMRI's blood-oxygenation-level-dependent (BOLD) functional contrast are invaluable in mapping active brain regions, but they also entail significant limitations, such as non-specificity of the signal to active foci. Diffusion-weighted functional MRI (dfMRI) with relatively high diffusion-weighting strives to ameliorate this shortcoming as it offers functional contrasts more intimately linked with the underlying activity. Insofar, apart from somewhat smaller activation foci, dfMRI's contrasts have not been convincingly shown to offer significant advantages over BOLD-driven fMRI, and its activation maps relied on significant modelling. Here, we study whether dfMRI could offer a better representation of neural activity in the thalamocortical pathway compared to its (spin-echo (SE)) BOLD counterpart. Using high-end forepaw stimulation experiments in the rat at 9.4 T, and with significant sensitivity enhancements due to the use of cryocoils, we show for the first time that dfMRI signals exhibit layer specificity, and, additionally, display signals in areas devoid of SE-BOLD responses. We find that dfMRI signals in the thalamocortical pathway cohere with each other, namely, dfMRI signals in the ventral posterolateral (VPL) thalamic nucleus cohere specifically with layers IV and V in the somatosensory cortex. These activity patterns are much better correlated (compared with SE-BOLD signals) with literature-based electrophysiological recordings in the cortex as well as thalamus. All these findings suggest that dfMRI signals better represent the underlying neural activity in the pathway. In turn, these advanatages may have significant implications towards a much more specific and accurate mapping of neural activity in the global brain in-vivo.

Comparison of diffusion-weighted MRI and anti-Stokes Raman scattering (CARS) measurements of the inter-compartmental exchange-time of water in expression-controlled aquaporin-4 cells

We performed multi-b and multi-diffusion-time diffusion-weighted magnetic resonance imaging on aquaporin-4-expressing (AQ) and -non-expressing (noAQ) cells, and demonstrated a clear difference between the signals from the two cell types. The data were interpreted using a two-compartment (intra and extracellular spaces) model including inter-compartmental exchange. It was also assumed that restricted diffusion of water molecules inside the cells leads to the intracellular diffusion coefficient being inversely proportional to the diffusion-time. Estimates of the water-exchange-times obtained with this model are compared to those measured using an independent optical imaging technique (coherent anti-Stokes Raman scattering imaging, CARS). For both techniques it was found that the exchange-time estimated for the noAQ cells was significantly longer than that for the AQ cells.

Diffusion effects in myelin sheath free induction decay

Myelin sheath microstructure and composition produce MR signal decay characteristics that can be used to evaluate status and outcome of demyelinating disease. We extend a recently proposed model of neuronal magnetic susceptibility, that accounts for both the structural and inherent anisotropy of the myelin sheath, by including the whole dynamic range of diffusion effects. The respective Bloch-Torrey equation for local spin dephasing is solved with a uniformly convergent perturbation expansion method, and the resulting magnetization decay is validated with a numerical solution based on a finite difference method. We show that a variation of diffusion strengths can lead to substantially different MR signal decay curves. Our results may be used to adjust or control simulations for water diffusion in neuronal structures.

Influence of the extracellular matrix on water mobility in subcortical gray matter

PURPOSE

Water mobility in tissues is related to the microstructure that modulates diffusion and spin relaxation. Previous work has shown that the extracellular matrix (ECM) impacts water diffusion in cartilage. To investigate if similar contributions to image contrast exist for brain, which is characterized by a substantially lower ECM content, diffusion and relaxation were studied in fixed samples from goat and human thalamus before and after enzymatic digestion of ECM compounds. Selected experiments in human corpus callosum were included for comparing subcortical gray matter and white matter.

METHODS

Digestion of matrix components was achieved by treatment with hyaluronidase. Nonlocalized pulsed field gradient measurements were performed with <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mi>b</mml:mi></mml:math> values between 0.6 and 18,000 s/mm² at 3T and temperatures between 0°C and 20°C, in addition to T₁ and T₂ relaxation measurements. The data were fitted to multiexponential models to account for different water compartments. After the measurements, the samples were sliced and stained for ECM-sensitive markers to verify efficient digestion.

RESULTS

Microstructural alterations associated with hyaluronan digestion did not lead to measurable effects on water diffusion or <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:msub><mml:mi>T</mml:mi> <mml:mn>2</mml:mn></mml:msub> </mml:math> . However, T₁ of the main relaxographic component, attributed to intra-/extracellular water, decreased by 7%.

CONCLUSION

Investigations with very strong gradients did not reveal a detectable effect on water diffusion or <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:msub><mml:mi>T</mml:mi> <mml:mn>2</mml:mn></mml:msub> </mml:math> after hyaluronan removal, indicating that the brain ECM content is too low to produce a detectable effect. The subtle alteration of T₁ upon hyaluronidase treatment might reflect a modulation of intercompartmental water exchange properties.

Comparison of Glass Capillary Plates and Polyethylene Fiber Bundles as Phantoms to Assess the Quality of Diffusion Tensor Imaging

Purpose:

The purpose of this study was to evaluate the suitability of two phantoms, one made of capillary plates and the other polyethylene fibers, for assessing the quality of diffusion tensor imaging (DTI).

Methods:

The first phantom was a stack of glass capillary plates with many parallel micropores (CP). The second phantom was a bundle of polyethylene fiber Dyneema held together with a thermal shrinkage tube (Dy). High resolution multi-shot echo planar imaging (EPI) DTI acquisitions were performed at b-values of 0 and 1000 s/mm² and diffusion-times (T_diff) of 37.7 and 97.7 ms on a preclinical 7T MRI scanner. Thirty diffusion-encoding directions were used. The data were used to calculate the fractional anisotropy (FA), mean diffusivity (MD), and angular dispersion (AD). Further acquisitions were performed at b-values from 0 to 8000 s/mm² in 14 steps with the diffusion gradient applied parallel (axial) and perpendicular (radial) to the Z direction. On the other hand, the data acquired with a 3T MRI scanner were used to confirm that measurements on a clinical machine are consistent with the 7T MRI results.

Results:

The dependence of FA, MD and AD on T_diff was smaller for the Dy than for the CPs. The b-value-dependent signal attenuations in the axial direction at T_diff = 37.7 and 97.7 ms were similar for both phantoms. In the radial direction, Dy demonstrated similar b-value attenuation to that of in vivo tissue for both T_diffs, but the attenuation for the CPs was affected by the change in T_diff. Parameter estimates were similar for 3T and 7T MRI.

Conclusion:

The characteristics of the CP indicate that it can be used as a restricted-diffusion dominant phantom, while the characteristics of the Dy suggest that it can be used as a hindered-diffusion dominant phantom. Dy may be more suitable than CP for DTI quality control.

Detection of neural light-scattering activity in vivo: optical transmittance studies in the rat brain

Optical studies of ex vivo brain slices where blood is absent show that neural activity is accompanied by significant intrinsic optical signals (IOS) related to activity-dependent scattering changes in neural tissue. However, the neural scattering signals have been largely ignored in vivo in widely-used IOS methods where absorption contrast from hemoglobin was employed. Changes in scattering were observed on a time scale of seconds in previous brain slice IOS studies, similar to the time scale for the hemodynamic response. Therefore, potential crosstalk between the scattering and absorption changes may not be ignored if they have comparable contributions to IOS. In vivo, the IOS changes linked to neural scattering have been elusive. To isolate neural scattering signals in vivo, we employed 2 implantable optodes for small-separation (2 mm) transmission measurements of local brain tissue in anesthetized rats. This unique geometry enables us to separate neuronal activity-related changes in neural tissue scattering from changes in blood absorption based upon the direction of the signal change. The changes in IOS scattering and absorption in response to up-states of spontaneous neuronal activity in cortical or subcortical structures have strong correlation to local field potentials, but significantly different response latencies. We conclude that activity-dependent neural tissue scattering in vivo may be an additional source of contrast for functional brain studies that provides complementary information to other optical or MR-based systems that are sensitive to hemodynamic contrast.

On modeling

Mapping tissue microstructure with MRI holds great promise as a noninvasive window into tissue organization at the cellular level. Having originated within the realm of diffusion NMR in the late 1970s, this field is experiencing an exponential growth in the number of publications. At the same time, model-based approaches are also increasingly incorporated into advanced MRI acquisition and reconstruction techniques. However, after about two decades of intellectual and financial investment, microstructural mapping has yet to find a single commonly accepted clinical application. Here, we suggest that slow progress in clinical translation may signify unresolved fundamental problems. We outline such problems and related practical pitfalls, as well as review strategies for developing and validating tissue microstructure models, to provoke a discussion on how to bridge the gap between our scientific aspirations and the clinical reality. We argue for recalibrating the efforts of our community toward a more systematic focus on fundamental research aimed at identifying relevant degrees of freedom affecting the measured MR signal. Such a focus is essential for realizing the truly revolutionary potential of noninvasive three-dimensional in vivo microstructural mapping.

Functional Diffusion Magnetic Resonance Imaging

Functional diffusion magnetic resonance imaging (fDMRI) is a noninvasive technique that allows elucidating physiological and anatomical changes at a microscopic scale by detection of water molecular displacements in tissue structures. These displacements likely reflect microstructural changes associated with neuronal or glial cells activation. In this chapter, we will describe the physical and biological concepts of fDMRI and how images of brain activation can be acquired in a preclinical setup.

Optimization of prostate MRI acquisition and post-processing protocol: a pictorial review with access to acquisition protocols

The aim of this review article is to provide insight into the optimization of 1.5-Testla (T) and 3-T prostate magnetic resonance imaging (MRI). An approach for optimization of data quantification, especially diffusion-weighted imaging (DWI), is provided. Benefits and limitations of various pulse sequences are discussed. Importable MRI protocols and access to imaging datasets is provided. Careful optimization of prostate MR acquisition protocol allows the acquisition of high-quality prostate MRI using clinical 1.5-T/3-T MR scanners with an overall acquisition time < 15 min.

A 3D model-based simulation of demyelination to understand its effects on diffusion tensor imaging

Demyelination is the progressive damage to the myelin sheath, a protective covering that surrounds a nerve's axon. Due to its high sensitivity to microscopic tissue changes, diffusion tensor imaging (DTI) is a powerful means of detecting signs of demyelination and axonal injury. In this paper, we present a 3D virtual model capable of simulating the complex Brownian motion of water molecules in a bundle of myelinated axons and glial cells for the purpose of synthesizing DTI data, characterizing and verifying the impact of demyelination on DTI. Our model consists of a highly detailed and realistic 3D representation of biological fiber bundles, with a myelin sheath covering the axons and glial cells in between them. The system simulates the Brownian motion of molecules to extract diffusion data. We perform our experiment for progressive stages of demyelination and demonstrate its effect on DTI measurements.

Predictive and prognostic value of intravoxel incoherent motion (IVIM) MR imaging in patients with advanced cervical cancers undergoing concurrent chemo-radiotherapy

By using the intravoxel incoherent motion (IVIM) model, the diffusion-related coefficient (D) and the perfusion-related parameter (f) can be obtained simultaneously. Here, we explored the application of IVIM MR imaging in predicting long-term prognosis in patients with advanced cervical cancers treated with concurrent chemo-radiotherapy (CCRT). In this study, pelvic MR examinations including an IVIM sequence were performed on 30 women with advanced cervical cancers at three time points (within 2 weeks before, as well as 2 and 4 weeks after, the initiation of CCRT). The performance of tumour size and IVIM-derived parameters in predicting long-term prognosis was evaluated. After a median follow-up of 24 months (range, 10∼34 months), 25/30 (83.33%) patients were alive, and 21/30 (70.00%) remained free of disease. A shrinkage rate of maximum diameter (time point 1 vs. 3) ≥ 58.31% was useful in predicting a good long-term prognosis. The IVIM-derived apparent diffusion coefficient (ADC_IVIM) value at time point 2 and the ADC_IVIM and f values at time point 3 also performed well in predicting a good prognosis, with AUC of 0.767, 0.857 and 0.820, respectively. IVIM MR imaging has great potential in predicting long-term prognosis in patients with advanced cervical cancers treated with CCRT.

Microstructure-informed slow diffusion tractography in humans enhances visualisation of fibre pathways

Conventional fibre tractography methods based on diffusion tensor imaging exploit diffusion anisotropy and directionality in the range of low diffusion weightings (b-values). High b-value Biexponential Diffusion Tensor Analysis reported previously has demonstrated that fractional anisotropy of the slow diffusion component is essentially higher than that of conventional diffusion tensor imaging whereas popular compartment models associate this slow diffusion component with axonal water fraction. One of the primary aims of this study is to elucidate the feasibility and potential benefits of "microstructure-informed" whole-brain slow-diffusion fibre tracking (SDIFT) in humans. In vivo diffusion-weighted images in humans were acquired in the extended range of diffusion weightings≤6000smm^-2 at 3T. Fast and slow diffusion tensors were reconstructed using the bi-exponential tensor decomposition, and a detailed statistical analysis of the relevant whole-brain tensor metrics was performed. We visualised three-dimensional fibre tracts in in vivo human brains using deterministic streamlining via the major eigenvector of the slow diffusion tensor. In particular, we demonstrated that slow-diffusion fibre tracking provided considerably higher fibre counts of long association fibres and allowed one to reconstruct more short association fibres than conventional diffusion tensor imaging. SDIFT is suggested to be useful as a complimentary method capable to enhance reliability and visualisation of the evaluated fibre pathways. It is especially informative in precortical areas where the uncertainty of the mono-exponential tensor evaluation becomes too high due to decreased anisotropy of low b-value diffusion in these areas. Benefits can be expected in assessment of the residual axonal integrity in tissues affected by various pathological conditions, in surgical planning, and in evaluation of cortical connectivity, in particular, between Brodmann's areas.

Modulation of water diffusion by activation-induced neural cell swelling in Aplysia Californica

Diffusion functional magnetic resonance imaging (DfMRI) has been proposed as a method for functional neuroimaging studies, as an alternative to blood oxygenation level dependent (BOLD)-fMRI. DfMRI is thought to more directly reflect neural activation, but its exact mechanism remains unclear. It has been hypothesized that the water apparent diffusion coefficient (ADC) decrease observed upon neural activation results from swelling of neurons or neuron parts. To elucidate the origin of the DfMRI response at cellular level we performed diffusion MR microscopy at 17.2 T in Aplysia californica buccal ganglia and compared the water ADCs at cellular and ganglia levels before and after neuronal activation induced by perfusion with a solution containing dopamine. Neural cell swelling, evidenced from optical microscopy imaging, resulted in an intracellular ADC increase and an ADC decrease at ganglia level. Furthermore, the intracellular ADC increase was found to have a significant positive correlation with the increase in cell size. Our results strongly support the hypothesis that the ADC decrease observed with DfMRI upon neuronal activation at tissue level reflects activation-induced neural cell swelling.

The rapid development of structural plasticity through short water maze training: A DTI study

Diffusion MRI is sensitive to the microstructure of tissue and allows the study of structural plasticity over short time scales of only hours. The initial temporal and spatial progression of this process, however, has yet to be elucidated. With the aim of examining early temporal progression of structural plasticity, we subjected rats to short training periods on a task in the Morris water maze (MWM), a paradigm previously shown to induce rapid changes in diffusion tensor imaging (DTI) indices. Two groups of rats were each divided into subgroups that consecutively completed 1, 2 or 3 sets of short trials (up to 60s) in the MWM. Each set comprised 4 trials (1 from each quadrant of the WMW), with a specific time interval between sets. To assess the effect of the duration of the task on the evolving changes in DTI indices, we allowed a rest of 45min between sets in one group of rats and a 2-h rest in the other. All rats were scanned with a DTI protocol before and 45min after their last trial. We found that a few minutes of training in a new task sufficed to generate changes in diffusion indices. The earliest changes in DTI (measured after one set of trials) progressed with further training (measured after two sets), but within a few more minutes (after three sets) they reached a plateau. Lengthening the duration of the overall task by prolonging the time interval between sessions did not alter this pattern of change, suggesting that at least within this short time scale such changes are task-dependent, but not time dependent. Our results demonstrate the progression of structural neuroplasticity at different stages of exposure to a novel experience, and show that DTI can be used to trace, in vivo, the localization of structural plasticity induced by training.

Water diffusion closely reveals neural activity status in rat brain loci affected by anesthesia

Diffusion functional MRI (DfMRI) reveals neuronal activation even when neurovascular coupling is abolished, contrary to blood oxygenation level—dependent (BOLD) functional MRI (fMRI). Here, we show that the water apparent diffusion coefficient (ADC) derived from DfMRI increased in specific rat brain regions under anesthetic conditions, reflecting the decreased neuronal activity observed with local field potentials (LFPs), especially in regions involved in wakefulness. In contrast, BOLD signals showed nonspecific changes, reflecting systemic effects of the anesthesia on overall brain hemodynamics status. Electrical stimulation of the central medial thalamus nucleus (CM) exhibiting this anesthesia-induced ADC increase led the animals to transiently wake up. Infusion in the CM of furosemide, a specific neuronal swelling blocker, led the ADC to increase further locally, although LFP activity remained unchanged, and increased the current threshold awakening the animals under CM electrical stimulation. Oppositely, induction of cell swelling in the CM through infusion of a hypotonic solution (−80 milliosmole [mOsm] artificial cerebrospinal fluid [aCSF]) led to a local ADC decrease and a lower current threshold to wake up the animals. Strikingly, the local ADC changes produced by blocking or enhancing cell swelling in the CM were also mirrored remotely in areas functionally connected to the CM, such as the cingulate and somatosensory cortex. Together, those results strongly suggest that neuronal swelling is a significant mechanism underlying DfMRI.

It has been reported that neuronal activation results in a decrease of water diffusion in activated neural tissue. This new approach, known as diffusion functional MRI (DfMRI), has high potential for functional imaging of the brain, as the currently widespread blood oxygenation level—dependent (BOLD)-functional MRI (fMRI) method, which is based on neurovascular coupling, remains an indirect marker of neuronal activation. Here, we show that the water apparent diffusion coefficient (ADC) derived from DfMRI increased in specific rat brain regions under anesthetic conditions, reflecting the decreased neuronal activity, especially in regions involved in wakefulness. Electrical stimulation of the central medial (CM) thalamic nucleus exhibiting this anesthesia-induced ADC increase led the animals to transiently wake up. Infusion of the CM with furosemide—a specific blocker of neuronal swelling—led the ADC to increase further locally and increased the current threshold for waking the animals. Conversely, induction of cell swelling in the CM through infusion of a hypotonic solution led to a local ADC decrease and a lower current threshold to wake the animals. Strikingly, the local ADC changes produced by blocking or enhancing cell swelling in the CM were also mirrored remotely in areas functionally connected to the CM, such as the cingulate and somatosensory cortex. Those results strongly suggest that neuronal swelling is a significant mechanism underlying DfMRI.

Semiautomated Workflow for Clinically Streamlined Glioma Parametric Response Mapping

Management of glioblastoma multiforme remains a challenging problem despite recent advances in targeted therapies. Timely assessment of therapeutic agents is hindered by the lack of standard quantitative imaging protocols for determining targeted response. Clinical response assessment for brain tumors is determined by volumetric changes assessed at 10 weeks post-treatment initiation. Further, current clinical criteria fail to use advanced quantitative imaging approaches, such as diffusion and perfusion magnetic resonance imaging. Development of the parametric response mapping (PRM) applied to diffusion-weighted magnetic resonance imaging has provided a sensitive and early biomarker of successful cytotoxic therapy in brain tumors while maintaining a spatial context within the tumor. Although PRM provides an earlier readout than volumetry and sometimes greater sensitivity compared with traditional whole-tumor diffusion statistics, it is not routinely used for patient management; an automated and standardized software for performing the analysis and for the generation of a clinical report document is required for this. We present a semiautomated and seamless workflow for image coregistration, segmentation, and PRM classification of glioblastoma multiforme diffusion-weighted magnetic resonance imaging scans. The software solution can be integrated using local hardware or performed remotely in the cloud while providing connectivity to existing picture archive and communication systems. This is an important step toward implementing PRM analysis of solid tumors in routine clinical practice.