non-Gaussian Diffusion Research Articles

A comparison study of monoexponential and fractional order calculus diffusion models and 18F-FDG PET in differentiating benign and malignant solitary pulmonary lesions and their pathological types.

ObjectiveTo evaluate the application value of monoexponential, fractional order calculus (FROC) diffusion models and PET imaging to distinguish between benign and malignant solitary pulmonary lesions (SPLs) and malignant SPLs with different pathological types and explore the correlation between each parameter and Ki67 expression.MethodsA total of 112 patients were enrolled in this study. Prior to treatment, all patients underwent a dedicated thoracic 18F-FDG PET/MR examination. Five parameters [including apparent diffusion coefficient (ADC) derived from the monoexponential model; diffusion coefficient (D), a microstructural quantity (μ), and fractional order parameter (β) derived from the FROC model and maximum standardized uptake value (SUVmax) derived from PET] were compared between benign and malignant SPLs and different pathological types of malignant SPLs. Independent sample t test, Mann-Whitney U test, DeLong test and receiver operating characteristic (ROC) curve analysis were used for statistical evaluation. Pearson correlation analysis was used to calculate the correlations between Ki-67 and ADC, D, μ, β, and SUVmax.ResultsThe ADC and D values were significantly higher and the μ and SUVmax values were significantly lower in the benign group [1.57 (1.37, 2.05) μm2/ms, 1.59 (1.52, 1.72) μm2/ms, 5.06 (3.76, 5.66) μm, 5.15 ± 2.60] than in the malignant group [1.32 (1.03, 1.51) μm2/ms, 1.43 (1.29, 1.52) μm2/ms, 7.06 (5.87, 9.45) μm, 9.85 ± 4.95]. The ADC, D and β values were significantly lower and the μ and SUVmax values were significantly higher in the squamous cell carcinoma (SCC) group [1.29 (0.66, 1.42) μm2/ms, 1.32 (1.02, 1.42) μm2/ms, 0.63 ± 0.10, 9.40 (7.76, 15.38) μm, 11.70 ± 5.98] than in the adenocarcinoma (AC) group [1.40 (1.28, 1.67) μm2/ms, 1.52 (1.44, 1.64) μm2/ms, 0.70 ± 0.10, 5.99 (4.54, 6.87) μm, 8.76 ± 4.18]. ROC curve analysis showed that for a single parameter, μ exhibited the best AUC value in discriminating between benign and malignant SPLs groups and AC and SCC groups (AUC = 0.824 and 0.911, respectively). Importantly, the combination of monoexponential, FROC models and PET imaging can further improve diagnostic performance (AUC = 0.872 and 0.922, respectively). The Pearson correlation analysis showed that Ki67 was positively correlated with μ value and negatively correlated with ADC and D values (r = 0.402, -0.346, -0.450, respectively).ConclusionThe parameters D and μ derived from the FROC model were superior to ADC and SUVmax in distinguishing benign from malignant SPLs and adenocarcinoma from squamous cell carcinoma, in addition, the combination of multiple parameters can further improve diagnostic performance. The non-Gaussian FROC diffusion model is expected to become a noninvasive quantitative imaging technique for identifying SPLs.

Reciprocal Space Study of Brownian Yet Non-Gaussian Diffusion of Small Tracers in a Hard-Sphere Glass

The simultaneous presence of normal (Brownian) diffusion and non-Gaussian statistics of particle displacements has been identified as a recurring motif for a broad spectrum of physical and biological systems. While not yet fully understood, it is generally accepted that a key ingredient for observing this Brownian yet non-Gaussian (BNG) diffusion is that the environment hosting the particles appears stationary and homogenous on the small length and time scales, while displaying significant fluctuations on larger distances and/or longer time scales. To date, most of the experimental studies on systems displaying BNG diffusion have been performed in direct space, usually via a combination of optical microscopy and particle tracking to quantify the particle’s self-diffusion. Here, we demonstrate that a reciprocal space analysis of the density fluctuations caused by the particle motion as a function of the wave vector enables the investigation of BNG diffusion in situations where single-particle tracking is impossible. To accomplish this aim, we use confocal differential dynamic microscopy (ConDDM) to study the BNG dynamics of diluted sub-resolution tracers diffusing in a glassy matrix of larger hard spheres. We first elucidate the nontrivial connection between the tracer self-diffusion and collective relaxation of the resulting density fluctuations. We find that the experimentally determined intermediate scattering functions are in excellent agreement with the recent predictions of a “diffusing diffusivity” model of BNG diffusion, whose analytical predictions are available only in reciprocal space. Our results show that studying BNG diffusion in reciprocal space can be an invaluable strategy to access the fast, anomalous dynamics occurring at very small scales in crowded environments.

Diffusion Kurtosis Imaging of Neonatal Spinal Cord in Clinical Routine.

Diffusion kurtosis imaging (DKI) has undisputed advantages over the more classical diffusion magnetic resonance imaging (dMRI) as witnessed by the fast-increasing number of clinical applications and software packages widely adopted in brain imaging. However, in the neonatal setting, DKI is still largely underutilized, in particular in spinal cord (SC) imaging, because of its inherently demanding technological requirements. Due to its extreme sensitivity to non-Gaussian diffusion, DKI proves particularly suitable for detecting complex, subtle, fast microstructural changes occurring in this area at this early and critical stage of development, which are not identifiable with only DTI. Given the multiplicity of congenital anomalies of the spinal canal, their crucial effect on later developmental outcome, and the close interconnection between the SC region and the brain above, managing to apply such a method to the neonatal cohort becomes of utmost importance. This study will (i) mention current methodological challenges associated with the application of advanced dMRI methods, like DKI, in early infancy, (ii) illustrate the first semi-automated pipeline built on Spinal Cord Toolbox for handling the DKI data of neonatal SC, from acquisition setting to estimation of diffusion measures, through accurate adjustment of processing algorithms customized for adult SC, and (iii) present results of its application in a pilot clinical case study. With the proposed pipeline, we preliminarily show that DKI is more sensitive than DTI-related measures to alterations caused by brain white matter injuries in the underlying cervical SC.

Neurite Exchange Imaging (NEXI): A minimal model of diffusion in gray matter with inter-compartment water exchange

Biophysical models of diffusion in white matter have been center-stage over the past two decades and are essentially based on what is now commonly referred to as the “Standard Model” (SM) of non-exchanging anisotropic compartments with Gaussian diffusion. In this work, we focus on diffusion MRI in gray matter, which requires rethinking basic microstructure modeling blocks. In particular, at least three contributions beyond the SM need to be considered for gray matter: water exchange across the cell membrane – between neurites and the extracellular space; non-Gaussian diffusion along neuronal and glial processes – resulting from structural disorder; and signal contribution from soma. For the first contribution, we propose Neurite Exchange Imaging (NEXI) as an extension of the SM of diffusion, which builds on the anisotropic Kärger model of two exchanging compartments. Using datasets acquired at multiple diffusion weightings (b) and diffusion times (t) in the rat brain in vivo, we investigate the suitability of NEXI to describe the diffusion signal in the gray matter, compared to the other two possible contributions. Our results for the diffusion time window 20–45 ms show minimal diffusivity time-dependence and more pronounced kurtosis decay with time, which is well fit by the exchange model. Moreover, we observe lower signal for longer diffusion times at high b. In light of these observations, we identify exchange as the mechanism that best explains these signal signatures in both low-b and high-b regime, and thereby propose NEXI as the minimal model for gray matter microstructure mapping. We finally highlight multi-b multi-t acquisition protocols as being best suited to estimate NEXI model parameters reliably. Using this approach, we estimate the inter-compartment water exchange time to be 15 – 60 ms in the rat cortex and hippocampus in vivo, which is of the same order or shorter than the diffusion time in typical diffusion MRI acquisitions. This suggests water exchange as an essential component for interpreting diffusion MRI measurements in gray matter.

Investigation of breast cancer microstructure and microvasculature from time-dependent DWI and CEST in correlation with histological biomarkers

We investigated the associations of time-dependent DWI, non-Gaussian DWI, and CEST parameters with histological biomarkers in a breast cancer xenograft model. 22 xenograft mice (7 MCF-7 and 15 MDA-MB-231) were scanned at 4 diffusion times [Td = 2.5/5 ms with 11 b-values (0–600 s/mm2) and Td = 9/27.6 ms with 17 b-values (0–3000 s/mm2), respectively]. The apparent diffusion coefficient (ADC) was estimated using 2 b-values in different combinations (ADC0–600 using b = 0 and 600 s/mm2 and shifted ADC [sADC200–1500] using b = 200 and 1500 s/mm2) at each of those diffusion times. Then the change (Δ) in ADC/sADC between diffusion times was evaluated. Non-Gaussian diffusion and intravoxel incoherent motion (IVIM) parameters (ADC0, the virtual ADC at b = 0; K, Kurtosis from non-Gaussian diffusion; f, the IVIM perfusion fraction) were estimated. CEST images were acquired and the amide proton transfer signal intensity (APT SI) were measured. The ΔsADC9–27.6 (between {text{sADC}}_{{9,{text{ms}}}}^{200{-}1500} and {text{sADC}}_{{27.6,{text{ms}}}}^{200{-}1500} and ΔADC2.5_sADC27.6 (between {text{ADC}}_{{2.5, {text{ms}}}}^{0{-}600} and {text{sADC}}_{{27.6,{text{ms}}}}^{200{-}1500}) was significantly larger for MCF-7 groups, and ΔADC2.5_sADC27.6 was positively correlated with Ki67max and APT SI. ADC0 decreased significantly in MDA-MB-231 group and K increased significantly with Td in MCF-7 group. APT SI and cellular area had a moderately strong positive correlation in MDA-MB-231 and MCF-7 tumors combined, and there was a positive correlation in MDA-MB-231 tumors. There was a significant negative correlation between APT SI and the Ki-67-positive ratio in MDA-MB-231 tumors and when combined with MCF-7 tumors. The associations of ΔADC2.5_sADC27.6 and API SI with Ki-67 parameters indicate that the Td-dependent DW and CEST parameters are useful to predict the histological markers of breast cancers.

Fickian Non-Gaussian Diffusion in Glass-Forming Liquids.

Fickian yet non-Gaussian diffusion (FnGD), a most intriguing open issue in soft matter, is generically associated with some dynamical and/or structural heterogeneity of the environment. Here we investigate the features of FnGD in glass-forming liquids, the epitome of dynamical heterogeneity, drawing on experiments on hard-sphere colloidal suspensions and simulations of a simple model of molecular liquid. We demonstrate that FnGD strengthens on approaching the glass transition, by identifying distinct timescales for Fickianity, τ_{F}, and for restoring of Gaussianity, τ_{G}>τ_{F}, as well as their associated length scales, ξ_{F} and ξ_{G}. We find τ_{G}∝τ_{F}^{γ} with γ≃1.8 for both systems. In the deep FnGD regime, the displacement distributions display exponential tails. We show that, in simulations, the time-dependent decay lengths l(t) at different temperatures all collapse onto a power-law master curve [l(t)/(ξ_{G})]∝(t/τ_{G})^{α}, with α=0.33. A similar collapse, if less sharp, is also found in experiments, seemingly with the same exponent α. We further discuss the connections of the timescales and length scales characterizing FnGD with structural relaxation and dynamic heterogeneity.

Renal functional and interstitial fibrotic assessment with non-Gaussian diffusion kurtosis imaging

ObjectivesTo evaluate the application value of diffusion kurtosis imaging (DKI) for monitoring renal function and interstitial fibrosis.MethodsForty-two patients suspected of having primary nephropathy, hypertension or diabetes with impaired renal function were examined with DKI. DKI metrics of renal cortex and medulla on both sides of each patient were measured, including mean kurtosis (MK), axial kurtosis (Ka), radial kurtosis (Kr), mean diffusivity (MD) and fractional anisotropy (FA). The differences in DKI metrics between stable and impaired estimated glomerular filtration rate (eGFR) patients as well as between mild and severe interstitial fibrosis patients were compared. Correlations of DKI metrics with clinical indicators and pathology were analyzed. Diagnostic performance of DKI to assess the degree of renal dysfunction was analyzed.ResultsCortical MK, parenchymal Ka, MD and medullary FA were different in stable vs impaired eGFR patients and mild vs severe interstitial fibrosis patients (all p < .05). Negative correlation was found between Ka and eGFR (cortex: r = − 0.579; medulla: r = − 0.603), between MD and interstitial fibrosis (cortex: r = − 0.899; medulla: r = − 0.770), and positive correlation was found between MD and eGFR (cortex: r = 0.411; medulla: r = 0.344), between Ka and interstitial fibrosis (cortex: r = 0.871; medulla: r = 0.844) (all p < .05). DKI combined with mean arterial blood pressure (MAP) and urea showed good diagnostic power for assessing the degree of renal dysfunction (sensitivity: 90.5%; specificity: 89.5%).ConclusionsNoninvasive DKI has certain application value for monitoring renal function and interstitial fibrosis.

DKI enhances the sensitivity and interpretability of age-related DTI patterns in the white matter of UK biobank participants

Studies of healthy brain aging traditionally report diffusivity patterns associated with white matter degeneration using diffusion tensor imaging (DTI), which assumes that diffusion measured at typical b-values (approximately 1000 s/mm2) is Gaussian. Diffusion kurtosis imaging (DKI) is an extension of DTI that measures non-Gaussian diffusion (kurtosis) to better capture microenvironmental processes by incorporating additional data at a higher b-value. In this study, using diffusion data (b-values of 1000 and 2000 s/mm2) from 700 UK Biobank participants aged 46–80, we investigate (1) the extent of novel information gained from adding diffusional kurtosis to diffusivity observations in aging, and (2) how conventional DTI metrics in aging compare with diffusivity metrics derived from DKI, which are corrected for kurtosis. We establish a pattern of lower kurtosis alongside higher diffusivity among older adults, with kurtosis generally being more sensitive to age than diffusivity. We also find discrepancies between diffusivity metrics derived from DTI and DKI, emphasizing the importance of accounting for non-Gaussian diffusion when interpreting age-related diffusivity patterns.

In vivo Correlation Tensor MRI reveals microscopic kurtosis in the human brain on a clinical 3T scanner

Diffusion MRI (dMRI) has become one of the most important imaging modalities for noninvasively probing tissue microstructure. Diffusional Kurtosis MRI (DKI) quantifies the degree of non-Gaussian diffusion, which in turn has been shown to increase sensitivity towards, e.g., disease and orientation mapping in neural tissue. However, the specificity of DKI is limited as different sources can contribute to the total intravoxel diffusional kurtosis, including: variance in diffusion tensor magnitudes (Kiso), variance due to diffusion anisotropy (Kaniso), and microscopic kurtosis (μK) related to restricted diffusion, microstructural disorder, and/or exchange. Interestingly, μK is typically ignored in diffusion MRI signal modelling as it is assumed to be negligible in neural tissues. However, recently, Correlation Tensor MRI (CTI) based on Double-Diffusion-Encoding (DDE) was introduced for kurtosis source separation, revealing non negligible μK in preclinical imaging. Here, we implemented CTI for the first time on a clinical 3T scanner and investigated the sources of total kurtosis in healthy subjects. A robust framework for kurtosis source separation in humans is introduced, followed by estimation of μK (and the other kurtosis sources) in the healthy brain. Using this clinical CTI approach, we find that μK significantly contributes to total diffusional kurtosis both in grey and white matter tissue but, as expected, not in the ventricles. The first μK maps of the human brain are presented, revealing that the spatial distribution of μK provides a unique source of contrast, appearing different from isotropic and anisotropic kurtosis counterparts. Moreover, group average templates of these kurtosis sources have been generated for the first time, which corroborated our findings at the underlying individual-level maps. We further show that the common practice of ignoring μK and assuming the multiple Gaussian component approximation for kurtosis source estimation introduces significant bias in the estimation of other kurtosis sources and, perhaps even worse, compromises their interpretation. Finally, a twofold acceleration of CTI is discussed in the context of potential future clinical applications. We conclude that CTI has much potential for future in vivo microstructural characterizations in healthy and pathological tissue.

Brownian non-Gaussian polymer diffusion and queuing theory in the mean-field limit

We link the Brownian non-Gaussian diffusion of a polymer center of mass (CM) to a microscopic cause: the polymerization/depolymerization phenomenon occurring when the polymer is in contact with a monomer chemostat. The anomalous behavior is triggered by the polymer critical point, separating the dilute and the dense phase in the grand canonical ensemble. In the mean-field limit we establish contact with queuing theory and show that the kurtosis of the polymer CM diverges alike a response function when the system becomes critical, a result which holds for general polymer dynamics (Zimm, Rouse, reptation). Both the equilibrium and nonequilibrium behaviors are solved exactly as a reference study for novel stochastic modeling and experimental setup.

A model-system of Fickian yet non-Gaussian diffusion: light patterns in place of complex matter.

Fickian yet non-Gaussian Diffusion (FnGD), widely observed for colloidal particles in a variety of complex and biological fluids, emerges as a most intriguing open issue in Soft Matter. To fully monitor FnGD and advance its understanding, recording many trajectories over a large time range is crucial, which makes experiments challenging. Here we exploit a recently introduced experimental model of finely tunable FnGD: a quasi-2d system of Brownian beads in water moving in a heterogeneous energy landscape generated by a static and spatially random optical force field (speckle pattern). By performing experiments at different optical power, we succeed in monitoring the evolution as well as the precursors of FnGD. Fickian scaling of the mean square displacement is always attained after a subdiffusive regime while the displacement distributions keep on being non-Gaussian, which allows for measuring a characteristic length- and time-scale for the onset of FnGD, ξf and tf. We find that ξf stays constant, whereas tf grows as the inverse of the long-time diffusion coefficient tf ∝ D-1 for increasing the optical power. Deviations from the standard Gaussian shape of the displacement distribution are neatly characterized on a broad range of times, focusing on the excess probability at small displacements and on the decay-length of the distinctive exponential tails. Such deviations are fully built in the subdiffusive regime and, at the FnGD onset, grow with the optical power. As time goes on, the small-displacement probability narrows and the exponential tails progressively break up, with a tendency to recover the Gaussian behaviour. Overall, both subdiffusion and FnGD become more marked and persistent on increasing the optical power, suggesting a strict relation between these two regimes. As clearly demonstrated by our results, the adopted model-system represents a privileged stage for in-depth study of FnGD and opens the way to unveil the nature of this phenomenon through finely tuned and well-controlled experiments.

Polymers critical point originates Brownian non-Gaussian diffusion

We demonstrate that size fluctuations close to polymers critical point originate the non-Gaussian diffusion of their center of mass. Static universal exponents γ and ν-depending on the polymer topology, on the dimension of the embedding space, and on equilibrium phase-concur to determine the potential divergency of a dynamic response, epitomized by the center-of-mass kurtosis. Prospects in experiments and stochastic modeling brought about by this result are briefly outlined.

Altered Microstructure of Cerebral Gray Matter in Neuromyelitis Optica Spectrum Disorder-Optic Neuritis: A DKI Study.

The purpose of this study was to analyze microstructural alterations in cerebral gray matter using non-Gaussian diffusion kurtosis imaging (DKI) in neuromyelitis optica spectrum disorder (NMOSD) patients with optic neuritis (NMOSD-ON). DKI was performed in 14 NMOSD-ON patients and 22 normal controls (NCs). DKI-derived metrics, including mean kurtosis (MK), radial kurtosis (RK), axial kurtosis (AK), fractional anisotropy (FA), and mean diffusivity (MD), were voxel-wisely compared by two-sample t-tests with gaussian random field (GRF) correction between the two groups. The correlations between altered DKI metrics and clinical features were analyzed. Compared with NCs, NMOSD-ON patients showed significantly decreased MK and RK both in the left inferior temporal gyrus (ITG), and decreased AK in the bilateral calcarine (CAL). While increased MD in the left fusiform gyrus (FFG), right CAL, and right hippocampus (HIP)/parahippocampal gyrus (PHG) were found. Furthermore, correlation analysis showed that mean deviation was negatively correlated with AK values of bilateral CAL and positively correlated with MD values of right CAL (q < 0.05, false discovery rate (FDR) corrected). For NMOSD-ON patients, microstructural abnormalities in the occipital visual cortex are correlated with clinical disability. These findings may provide complementary information to understand the neuropathological mechanisms underlying the impairments of cerebral gray matter in NMOSD-ON.

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.

Colossal Brownian yet non-Gaussian diffusion in a periodic potential: Impact of nonequilibrium noise amplitude statistics.

Last year, Białas et al. [Phys. Rev. E 102, 042121 (2020)] studied an overdamped dynamics of nonequilibrium noise driven Brownian particle dwelling in a spatially periodic potential and discovered a novel class of Brownian, yet non-Gaussian diffusion. The mean square displacement of the particle grows linearly with time and the probability density for the particle position is Gaussian; however, the corresponding distribution for the increments is non-Gaussian. The latter property induces the colossal enhancement of diffusion, significantly exceeding the well known effect of giant diffusion. Here, we considerably extend the above predictions by investigating the influence of nonequilibrium noise amplitude statistics on the colossal Brownian, yet non-Gaussian diffusion. The tail of amplitude distribution crucially impacts both the magnitude of diffusion amplification and the Gaussianity of the position and increments statistics. Our results carry profound consequences for diffusive behavior in nonequilibrium settings such as living cells in which diffusion is a central transport mechanism.

Transport and Diffusion Enhancement in Experimentally Realized Non-Gaussian Correlated Ratchets.

Living cells are known to generate non-Gaussian active fluctuations significantly larger than thermal fluctuations owing to various active processes. Understanding the effect of these active fluctuations on various physicochemical processes, such as the transport of molecular motors, is a fundamental problem in nonequilibrium physics. Therefore, we experimentally and numerically studied an active Brownian ratchet comprising a colloidal particle in an optically generated asymmetric periodic potential driven by non-Gaussian noise having finite-amplitude active bursts, each arriving at random and decaying exponentially. We find that the particle velocity is maximum for relatively sparse bursts with finite correlation time and non-Gaussian distribution. These occasional kicks, which produce Brownian yet non-Gaussian diffusion, are more efficient for transport and diffusion enhancement of the particle than the incessant kicks of active Ornstein-Uhlenbeck noise.

The strength of corticomotoneuronal drive underlies ALS split phenotypes and reflects early upper motor neuron dysfunction.

BackgroundSplit phenotypes, (split hand, elbow, leg, and foot), are probably unique to ALS, and are characterized by having a shared peripheral input of both affected and unaffected muscles. This implies an anatomical origin rostral to the spinal cord, primarily within the cerebral cortex. Therefore, split phenotypes are a potential marker of ALS upper motor neuron pathology. However, to date, reports documenting upper motor neuron dysfunction in split phenotypes have been limited to using transcranial magnetic stimulation and cortical threshold tracking techniques. Here, we consider several other potential methodologies that could confirm a primary upper motor neuron pathology in split phenotypes.MethodsWe review the potential of: 1. measuring the compound excitatory post‐synaptic potential recorded from a single activated motor unit, 2. cortical‐muscular coherence, and 3. new advanced modalities of neuroimaging (high‐resolution imaging protocols, ultra‐high field MRI platforms [7T], and novel Non‐Gaussian diffusion models).ConclusionsWe propose that muscles involved in split phenotypes are those functionally involved in the human motor repertoire used particularly in complex activities. Their anterior horn cells receive the strongest corticomotoneuronal input. This is also true of the weakest muscles that are the earliest to be affected in ALS. Descriptions of split hand in non‐ALS cases and proposals that peripheral nerve or muscle dysfunction may be causative are contentious. Only a few carefully controlled cases of each form of split phenotype, using upper motor neuron directed methodologies, are necessary to prove our postulate.

Diffusion MRI in Peripheral Nerves: Optimized b Values and the Role of Non-Gaussian Diffusion.

Background Diffusion-weighted imaging (DWI) provides specific in vivo information about tissue microstructure, which is increasingly recognized for various applications outside the central nervous system. However, standard sequence parameters are commonly adopted from optimized central nervous system protocols, thus potentially neglecting differences in tissue-specific diffusional behavior. Purpose To characterize the optimal tissue-specific diffusion imaging weighting scheme over the b domain in peripheral nerves under physiologic and pathologic conditions. Materials and Methods In this prospective cross-sectional study, 3-T MR neurography of the sciatic nerve was performed in healthy volunteers (n = 16) and participants with type 2 diabetes (n = 12). For DWI, 16 b values in the range of 0-1500 sec/mm2 were acquired in axial and radial diffusion directions of the nerve. With a region of interest-based approach, diffusion-weighted signal behavior as a function of b was estimated using standard monoexponential, biexponential, and kurtosis fitting. Goodness of fit was assessed to determine the optimal b value for two-point DWI/diffusion tensor imaging (DTI). Results Non-Gaussian diffusional behavior was observed beyond b values of 600 sec/mm2 in the axial and 800 sec/mm2 in the radial diffusion direction in both participants with diabetes and healthy volunteers. Accordingly, the biexponential and kurtosis models achieved a better curve fit compared with the standard monoexponential model (Akaike information criterion >99.9% in all models), but the kurtosis model was preferred in the majority of cases. Significant differences between healthy volunteers and participants with diabetes were found in the kurtosis-derived parameters Dk and K. The results suggest an upper bound b value of approximately 700 sec/mm2 for optimal standard DWI/DTI in peripheral nerve applications. Conclusion In MR neurography, an ideal standard diffusion-weighted imaging/diffusion tensor imaging protocol with b = 700 sec/mm2 is suggested. This is substantially lower than in the central nervous system due to early-occurring non-Gaussian diffusion behavior and emphasizes the need for tissue-specific b value optimization. Including higher b values, kurtosis-derived parameters may represent promising novel imaging markers of peripheral nerve disease. ©RSNA, 2021 Online supplemental material is available for this article. See also the editorial by Jang and Du in this issue.

The predictive value of DKI in breast cancer: Does tumour subtype affect pathological response evaluations?

PurposeTo explore the differences in quantitative parameters based on diffusion weighted imaging (DWI) and diffusion kurtosis imaging (DKI) between different immunohistochemical indicator statuses and their predictive value for neoadjuvant chemotherapy (NAC) among different phenotypes of breast cancer. MethodsEighty-one breast cancer patients who underwent NAC were enrolled in this retrospective study. Correlations between diffusion parameters and immunohistochemical indicators were determined using Spearman's test, and receiver operating characteristic (ROC) curves were constructed to assess the apparent diffusion coefficient (ADC), mean diffusivity (MD), and mean kurtosis (MK) in predicting the pathologic complete response (PCR). ResultsCorrelations were observed between MK values and hormone receptor (HR) expression (oestrogen receptor (ER): r = 0.315 and progesterone receptor (PR): r = 0.268). The parameters ADC(0,1000), MK, and MD all showed correlations with Ki67 expression (r = 0.276, 0.316 and − 0.224, respectively). ER and Ki67 expression and the parameters MD and MK were significantly different between the PCR and non-PCR groups (AUC = 0.783, 0.688, 0.649 and 0.684, respectively). After splitting patients into subgroups, no significant differences were observed between the PCR and non-PCR groups with human epidermal growth factor receptor 2 (HER2) + and triple-negative (TN) breast cancer. However, we were surprised to find that ADC(0, 1000), MD, and MK were significantly different between different remission groups with HR+/HER2+ subtypes, and the AUCs of each parameter reached 0.794, 0.825, and 0.712, respectively. ConclusionMK was correlated with HR expression. ADC(0, 1000) and DKI were correlated with Ki67 expression. ADC(0, 1000) and the non-Gaussian diffusion model are suitable for predicting PCR in patients with HR+/HER2+ breast cancer before NAC.

Magnetic resonance quantification of non-Gaussian water diffusion in hepatic fibrosis staging: a pilot study of diffusion kurtosis imaging to identify reversible hepatic fibrosis.

BackgroundThis study aimed to evaluate the diagnostic accuracy of diffusion kurtosis imaging (DKI) in differentiating early hepatic fibrosis (HF) from normal liver and advanced HF in rabbits.MethodsA total of 35 healthy New Zealand white rabbits were included in the study. A model of HF was established in 30 rabbits through subcutaneous injections of 50% carbon tetrachloride (CCl4)/olive oil, while 5 rabbits received saline injections. The gradually increased doses of CCl4 were 0.1, 0.2, and 0.3 mL/kg in weeks 1 to 3, weeks 4 to 6, and weeks 7 to 10, respectively. Two injections were given each week. Two rabbits in the experimental group died. All rabbits underwent DKI with three b values (0, 500, and 1,000 s/mm2) at week 5 (n=8), week 6 (n=9), week 7 (n=8), and week 10 (n=8). Approximately 2 liver lobes per rabbit were selected for histopathology. Mean diffusivity (MD) and mean kurtosis (MK) were calculated. Discrimination capacities of DKI parameters were analyzed and compared by receiver operating characteristic (ROC) analysis.ResultsThe meta-analysis of histological data in viral hepatitis (METAVIR) scoring system was used to classify liver lobes into the control group (F0, n=0), early HF group (F1–F2, n=28), and advanced HF group (F3–F4, n=28). MD and MK values were significantly different among the three groups (all P<0.05). MD value was negatively correlated with increased fibrosis level, while MK value was positively correlated with increased fibrosis level (ρ=−0.540, 0.614; P<0.05). The area under ROC curves (AUCs) for MD and MK were 0.886 and 0.875, respectively, for characterization of F0 and F1–F2, and 0.975 and 0.957 for F0 and F3–F4. AUC for MK was 0.751 for characterization of F1–F2 and F3–F4. MD performed better than MK for characterization of F0 and F1–F2 as well as F0 and F3–F4. MK showed good differentiation performance between F1–F2 and F3–F4.ConclusionsOur results showed that DKI contributed to discriminating reversible early HF from normal liver and advanced HF and as a result, showed promise for use in HF diagnosis.