Biology subjects

Szczepankiewicz, F.

Publications and source records attributed to Szczepankiewicz, F..

13 recordsLinked to original sources

bioRxiv2026

Characterising the diffusion functional signature of negative BOLD with interleaved TMS-fMRI in the human brain

The coupling between brain excitatory activity and positive blood oxygen level-dependent (BOLD) responses is well-established. Although often associated with inhibition, negative BOLD remains partially understood. Moving away from neurovascular coupling, apparent diffusion coefficient (ADC)-fMRI provides a more direct measure of excitatory activity, possibly mediated by transient cellular deformations. While decreases in ADC align with positive BOLD, the possible translation of negative BOLD into positive ADC has not been investigated in humans. Diffusion-weighted fMRI (dfMRI) combines vascular and microstructural contributions. Using interleaved subthreshold transcranial magnetic stimulation (TMS)-fMRI on the primary motor cortex (M1), we induced negative BOLD responses in contralateral M1 and primary somatosensory cortex (S1). This was accompanied by a negative dfMRI response, but no ADC-fMRI response, indicating minimal microstructural fluctuations. In ipsilateral M1/S1, no BOLD response was detected while dfMRI revealed a positive cluster, suggesting sensitivity to subtle neural activity. These findings provide new insights into vascular and neuronal responses underlying subthreshold TMS and negative BOLD.

neuroscience↗

bioRxiv2025

Restriction-weighted q-space trajectory imaging (ResQ): Toward mapping diffusion time effects with tensor-valued diffusion encoding in human prostate cancer xenografts

PurposeTensor-valued diffusion encoding employs gradient waveforms that enable unique sensitivity to microstructural features of tissue, but the interpretation of signal and parameters may be confounded by diffusion-time dependence. We introduce a framework for restriction-weighted q-space trajectory imaging (ResQ) that incorporates diffusion-time effects via the restriction-weighting tensor, and we evaluate it in a longitudinal study of prostate cancer xenografts treated by external radiotherapy. MethodsWe proposed a novel gradient waveform design for tensor-valued encoding with controlled restriction weighting and applied a set of four waveforms at a 9.4 T preclinical MRI system. Mice were inoculated with human prostate cancer cells (LNCaP) and assigned to groups that were untreated controls or treated by external beam irradiation. ResQ produced parameters that describes the diffusion process in terms of the mean diffusivity (D), isotropic diffusional variance (VDi), and microscopic diffusion anisotropy (VDa) as well as their diffusion-time dependence ({Delta}D, {Delta}VDi, {Delta}VDa). Analyses were performed to characterize parameters longitudinally and across groups. To highlight the consequences of ignoring restriction effects, we compared ResQ to analogous parameters estimated by q-space trajectory imaging (QTI). ResultsResQ revealed clear diffusion-time dependence across all tumors, with significant longitudinal differences between treated and untreated groups, most prominent in D, {Delta}D, and VDi. The ResQ signal representation captured the signal dynamics, whereas QTI did not. Neglecting diffusion-time dependence in QTI led to substantial parameter bias, most notably a pronounced overestimation of microscopic diffusion anisotropy. ConclusionDiffusion-time effects are non-negligible in prostate cancer and must be considered when using tensor-valued diffusion encoding. The ResQ framework enables controlled restriction weighting and improved interpretability of diffusion MRI parameters compared to approaches that ignore the effects of restriction. This provides a more principled approach for tensor-valued diffusion encoding and may enable novel imaging biomarkers that disentangle diffusivity, isotropic diffusional variance, microscopic anisotropy, and their diffusion-time dependence.

biophysics↗

bioRxiv2025

ZOnally magnified Oblique Multi-slice (ZOOM) for cardiac diffusion weighted imaging in vivo

PurposeCardiac diffusion weighted imaging (cDWI) commonly utilizes 2D-selective RF-pulses (such as ZOOMit) to avoid aliasing artifacts when reducing the field of view (FOV). These RF-pulses, which may require parallel transmit capability, typically take several 10s of milliseconds, prolonging the echo time (TE) of the imaging sequence. Conversely, slice selection using ZOnally-magnified Oblique Multi-slice (ZOOM) (i.e. tilting the excitation relative to the refocusing RF-pulse) is an alternative for reducing FOV while shortening TE. We hypothesized that ZOOM, with appropriate choices for the RF-pulse tilt angle and thickness, can be reliably used as an alternative to ZOOMit for cardiac Diffusion Tensor Imaging (cDTI). MethodsWe scanned phantoms using full FOV and ZOOM with various parameters chosen from a 2-dimensional grid of angles and thicknesses. We identified a subset of 5 ZOOM parameters for in vivo cDTI experiments. We performed cDTI on healthy volunteers on a Siemens Prisma (6 subjects) and a Connectom (6 subjects) MR system using the 5 identified ZOOM settings, as well as ZOOMit (Prisma only) and no ZOOM (Connectom only). We compared mean diffusivity (MD), fractional anisotropy (FA), and secondary eigenvector angle (|E2A|) between scans. ResultsAverage MD, and standard deviation of MD and FA, were significantly reduced by ZOOM compared to no ZOOM on Connectom. We found no significant differences in any averaged diffusion measures between ZOOMit and ZOOM on Prisma. ConclusionCombining ZOOM with cardiac diffusion weighted imaging allows for TE to be considerably shortened (from 79ms to 67ms) while reducing FOV to avoid aliasing artifacts.

biophysics↗

bioRxiv2025

Evaluating the dependence of ADC-fMRI on haemodynamics inbreath-hold and resting-state conditions

Apparent diffusion coefficient (ADC)-fMRI offers a promising functional contrast, capable of mapping neuronal activity directly in both grey and white matter. However, previous studies have shown that diffusion-weighted fMRI (dfMRI), from which ADC-fMRI derives, is influenced by BOLD effects, leading to a concern that the dfMRI contrast is still rooted in neurovascular rather than neuromorphological coupling. Mitigation strategies have been proposed to remove vascular contributions while retaining neuromorphological coupling, by: i) analysing ADC timecourses calculated from two interleaved diffusion-weightings, known as ADC-fMRI; ii) using b-values of at least 200 s mm-2; and iii) using a sequence compensated for cross-terms with fluctuating background field gradients associated with blood oxygenation. Respiration-induced haemodynamic fluctuations, which are dissociated from neural activity, are an excellent test-bed for the robustness of ADC-fMRI to vascular contributions. In this study, we investigate the association between end-tidal CO2 and ADC-fMRI, in comparison with dfMRI and BOLD, in both breath-hold and resting-state paradigms in the human brain. We confirm a strong dependence of the BOLD signal on respiration, and a pattern of delayed haemodynamic response in white matter. While dfMRI mitigates much of the vascular contribution, it retains some association with respiration, as expected. Conversely, ADC-fMRI is mostly unaffected by vascular contribution, exhibiting minimal correlation between expired CO2 and ADC timeseries, as well as low interand intra-subject reproducibility in correlation maps. These findings validate ADC-fMRI as a predominantly non-vascular contrast sensitive to microstructural dynamics, enabling whole-brain functional imaging unconstrained by vascular confounds.

neuroscience↗

bioRxiv2025

Robust constrained weighted least squares for in vivo human cardiac diffusion kurtosis imaging

SummaryRobust estimation with convexity constraints significantly improves signal fitting for in vivo human cardiac diffusion kurtosis imaging. PurposeCardiac diffusion tensor imaging (cDTI) is an emerging technique to investigate the microstructure of heart tissue. At sufficiently high b-values, additional information on microstructure can be observed, but the data require a representation beyond DTI such as diffusion kurtosis imaging (DKI). cDTI is highly prone to image corruption, which researchers usually attempt to handle with shot-rejection. However, this can be handled more generally with robust estimation techniques. Recent work has also demonstrated the need to perform constrained fitting for DKI, as fitted parameters can otherwise violate necessary constraints on the signal behaviour, causing significant errors in estimated measures. MethodsWe developed robust constrained weighted least squares (RCWLS) by combining robust estimation with convexity constraints specifically for DKI. Using in vivo cardiac DKI data from 11 healthy volunteers collected with a Connectom scanner, we tested various combinations of fitting techniques, with/without robustness and with/without constraints. ResultsRCWLS was the only tested technique that convincingly showed radial kurtosis to be larger than axial kurtosis for all subjects, something that is expected in myocardium due to increased restrictions to diffusion in the plane perpendicular to the primary myocyte direction. RCWLS also showed the best correction of corrupted regions in diffusion parameter maps for individual subjects. ConclusionFitting techniques utilizing both robust estimation and constraints are essential to facilitate applicability of in vivo cardiac DKI.

physiology↗

bioRxiv2025

Cardiac diffusion kurtosis imaging in the human heart in vivo using 300mT/m gradients

PurposeDiffusion tensor imaging (DTI) is commonly used in cardiac diffusion magnetic resonance imaging (dMRI). However, the tissues microstructure (cells, membranes, etc.) restricts the movement of the water molecules, making the spin displacements deviate from Gaussian behaviour. This effect may be observed with diffusion kurtosis imaging (DKI) using sufficiently high b-values (b > 450 s/mm2), which are presently outside the realm of routine cardiac dMRI due to the limited gradient strength of clinical scanners. The Connectom scanner with Gmax = 300 mT/m enables high b-values at echo times (TE) similar to DTI on standard clinical scanners, therefore facilitating cardiac DKI in humans. MethodsCardiac-gated, second-order motion-compensated dMRI was performed with bmax = 1350 s/mm2 in 10 healthy volunteers on a 3T MRI scanner with Gmax = 300 mT/m. The signal was fitted to a cumulant expansion up to and including the kurtosis term and diffusion metrics such as fractional anisotropy (FA), mean diffusivity (MD), mean kurtosis (MK), axial kurtosis (AK), and radial kurtosis (RK) were calculated. ResultsWe demonstrate deviation of the signal from monoexponential decay for b-values > 450 s/mm2 (MK = 0.32 {+/-} 0.03). Radial kurtosis (RK = 0.35 {+/-} 0.04) was observed slightly larger than axial kurtosis (AK = 0.27 {+/-} 0.02), and the difference is statistically significant (RK - AK = 0.08 {+/-} 0.04, p = 2e - 4). ConclusionThis work demonstrates the feasibility of quantifying kurtosis effect in the human heart in vivo (at an echo time shorter than typical TEs reported for cardiac DTI), using high-performance gradient systems (which are 4-8 times stronger than on standard clinical scanners). Our work lays the foundation for exploring new biomarkers in cardiac dMRI beyond DTI.

biophysics↗

bioRxiv2024

Mapping grey and white matter activity in the human brain with isotropic ADC-fMRI

Functional MRI (fMRI) using the blood-oxygen level dependent (BOLD) signal provides valuable insight into grey matter activity. However, uncertainty surrounds the white matter BOLD signal. Apparent diffusion coefficient (ADC) offers an alternative fMRI contrast sensitive to transient cellular deformations during neural activity, facilitating detection of both grey and white matter activity. Further, through minimising vascular contamination, ADC-fMRI has the potential to overcome the limited temporal specificity of the BOLD signal. However, the use of linear diffusion encoding introduces sensitivity to fibre directionality, while averaging over multiple directions comes at great cost to temporal resolution. In this study, we used spherical b-tensor encoding to impart diffusion sensitisation in all directions per shot, providing an ADC-fMRI contrast capable of detecting activity independently of fibre directionality. We provide evidence from two task-based experiments on a clinical scanner that isotropic ADC-fMRI is more temporally specific than BOLD-fMRI, and offers more balanced mapping of grey and white matter activity. We further demonstrate that isotropic ADC-fMRI detects white matter activity independently of fibre direction, while linear ADC-fMRI preferentially detects activity in voxels containing fibres perpendicular to the diffusion encoding direction. Thus, isotropic ADC-fMRI opens avenues for investigation into whole-brain grey and white matter functional connectivity.

neuroscience↗

bioRxiv2022

Meningioma microstructure assessed by diffusion MRI: an investigation of the source of mean diffusivity and fractional anisotropy by quantitative histology

BackgroundMean diffusivity (MD) and fractional anisotropy (FA) obtained with diffusion MRI (dMRI) have been associated with cell density and tissue anisotropy across tumors, but it is unknown whether these associations persist at the microscopic level. PurposeTo quantify the degree to which cell density (CD) and structure anisotropy (SA), as determined from histology, account for the intra-tumor variability of MD and FA in meningioma tumors. Furthermore, to clarify whether histological features other than cell density account for additional intra-tumor variability of MD. Materials and MethodsWe performed ex-vivo dMRI at 200 m isotropic resolution and histological imaging on 16 excised meningioma tumor samples. Diffusion tensor imaging (DTI) was used to map MD and FA, as well as the in-plane FA (FAIP). Histology images were analyzed in terms of cell nuclei density and structure anisotropy (obtained from structure tensor analysis) and were used separately in a regression analysis to predict MD and FAIP, respectively. A convolutional neural network (CNN) was also trained to predict the dMRI maps from histology patches. The association between MRI and histology was analyzed in terms of coefficient of determination (R2). Regions showing unexplained variance (large residuals) were analyzed to identify features apart from cell density and structure anisotropy that could influence MD and FAIP. ResultsCell density assessed by histology poorly explained intra-tumor variability at the mesoscopic level (200 m) in MD (median R2 = 0.06, interquartile range 0.01 - 0.29) or FAIP (median R2 = 0.19, 0.09 - 0.29). Samples with low R2 for FAIP exhibited low variations throughout the samples and thus low explainable variability, however, this was not the case for MD. Across tumors, cell density and structure anisotropy were associated with MD (R2 = 0.58) and FAIP (R2 = 0.82), respectively. In 37% of the samples (6 out of 16), cell density did not explain intra-tumor variability of MD when compared to the degree explained by the CNN. Tumor vascularization, psammoma bodies, microcysts, and tissue cohesivity were associated with bias in MD prediction when solely CD was considered. Our results support that FAIP is high in the presence of elongated and aligned cell structures, but low otherwise. ConclusionCell density and structure anisotropy account for variability in MD and FAIP across tumors but cell density does not explain MD variations within the tumor, which means that low or high values of MD locally may not always reflect high or low tumor cell density. Features beyond cell density need to be considered when interpreting MD. HighlightsO_LICell density accounts for MD variability across but not within meningioma tumors. C_LIO_LIStructure anisotropy accounts for in-plane FA variability across and within tumors C_LIO_LIVascularization, psammoma bodies, and microcysts influence the MD. C_LIO_LIHigh and low meningioma tumor cell density can yield similar MD. C_LIO_LIFeatures beyond cell density need to be considered when interpreting MD. C_LI

neuroscience↗

bioRxiv2021

Accuracy and precision in super-resolution MRI: Enabling spherical tensor diffusion encoding at ultra-high b-values and high resolution

Diffusion MRI (dMRI) is a useful probe of tissue microstructure but suffers from low signal-to-noise ratio (SNR) whenever high resolution and/or high diffusion encoding strengths are used. Low SNR leads not only to poor precision but also poor accuracy of the diffusion-weighted signal, as the rectified noise floor gives rise to a positive signal bias. Recently, super-resolution techniques have been proposed for signal acquisition at a low spatial resolution but high SNR, whereafter a higher spatial resolution is recovered by image reconstruction. In this work, we describe a super-resolution reconstruction framework for dMRI and investigate its performance with respect to signal accuracy and precision. Using strictly controlled phantom experiments, we show that the super-resolution approach improves accuracy by facilitating a more beneficial trade-off between spatial resolution and diffusion encoding strength before the noise floor affects the signal. Moreover, precision is shown to have a less straightforward dependency on acquisition, reconstruction, and intrinsic tissue parameters. Indeed, we find that a gain in precision from super-resolution reconstruction (SRR) is substantial only when some spatial resolution is sacrificed. We also demonstrated the value of SRR in the challenging combination of high resolution and spherical b-tensor encoding at ultrahigh b-values--a configuration that produces a unique contrast that emphasizes tissue in which diffusion is restricted in all directions. We conclude that SRR is most valuable in low-SNR conditions, where it can suppress rectified noise floor effects and recover signal with high accuracy. The in vivo application showcases a vastly superior image contrast when using SRR compared to conventional imaging, facilitating investigations of brain tissue that would otherwise have prohibitively low SNR, resolution or required non-conventional MRI hardware.

neuroscience↗

bioRxiv2021

Neural Networks for parameter estimation in microstructural MRI: a study with a high-dimensional diffusion-relaxation model of white matter microstructure

Specific features of white-matter microstructure can be investigated by using biophysical models to interpret relaxation-diffusion MRI brain data. Although more intricate models have the potential to reveal more details of the tissue, they also incur time-consuming parameter estimation that may con-verge to inaccurate solutions due to a prevalence of local minima in a degenerate fitting landscape. Machine-learning fitting algorithms have been proposed to accelerate the parameter estimation and increase the robustness of the attained estimates. So far, learning-based fitting approaches have been restricted to lower-dimensional microstructural models where dense sets of training data are easy to generate. Moreover, the degree to which machine learning can alleviate the degeneracy problem is poorly understood. For conventional least-squares solvers, it has been shown that degeneracy can be avoided by acquisition with optimized relaxation-diffusion-correlation protocols that include tensor-valued diffusion encoding; whether machine-learning techniques can offset these acquisition require-ments remains to be tested. In this work, we employ deep neural networks to vastly accelerate the fitting of a recently introduced high-dimensional relaxation-diffusion model of tissue microstructure. We also develop strategies for assessing the accuracy and sensitivity of function fitting networks and use those strategies to explore the impact of acquisition protocol design on the performance of the network. The developed learning-based fitting pipelines were tested on relaxation-diffusion data acquired with optimized and sub-sampled acquisition protocols. We found no evidence that machine-learning algorithms can by themselves replace a careful design of the acquisition protocol or correct for a degenerate fitting landscape.

neuroscience↗

bioRxiv2020

Visualizing orientation-specific relaxation-diffusion features mapped onto orientation distribution functions estimated via nonparametric Monte Carlo MRI signal inversion

Diffusion MRI techniques are widely used to study in vivo changes in the human brain connectome. However, to resolve and characterise white matter fibres in heterogeneous diffusion MRI voxels remains a challenging problem typically approached with signal models that rely on prior information and restrictive constraints. We have recently introduced a 5D relaxation-diffusion correlation framework wherein multidimensional diffusion encoding strategies are used to acquire data at multiple echo-times in order to increase the amount of information encoded into the signal and ease the constraints needed for signal inversion. Nonparametric Monte Carlo inversion of the resulting datasets yields 5D relaxation-diffusion distributions where contributions from different sub-voxel tissue environments are separated with minimal assumptions on their microscopic properties. Here, we build on the 5D correlation approach to derive fibre-specific metrics that can be mapped throughout the imaged brain volume. Distribution components ascribed to fibrous tissues are resolved, and subsequently mapped to a dense mesh of overlapping orientation bins in order to define a smooth orientation distribution function (ODF). Moreover, relaxation and diffusion measures are correlated to each independent ODF coordinate, thereby allowing the estimation of orientation-specific relaxation rates and diffusivities. The proposed method is tested on a healthy volunteer, where the estimated ODFs were observed to capture major WM tracts, resolve fibre crossings, and, more importantly, inform on the relaxation and diffusion features along distinct fibre bundles. If combined with fibre-tracking algorithms, the methodology presented in this work may be useful for investigating the microstructural properties along individual white matter pathways.

neuroscience↗

bioRxiv2019

Joint RElaxation-Diffusion Imaging Moments (REDIM) to probe neurite microstructure

Joint relaxation-diffusion measurements can provide new insight about the tissue microstructural properties. Most recent methods have focused on inverting the Laplace transform to recover the joint distribution of relaxation-diffusion. However, as is well-known, this problem is notoriously ill-posed and numerically unstable. In this work, we address this issue by directly computing the joint moments of transverse relaxation rate and diffusivity, which can be robustly estimated. To zoom into different parts of the joint distribution, we further enhance our method by applying multiplicative filters to the joint probability density function of relaxation and diffusion and compute the corresponding moments. We propose an approach to use these moments to compute several novel scalar indices to characterize specific properties of the underlying tissue microstructure. Furthermore, for the first time, we propose an algorithm to estimate diffusion signals that are independent of echo time based on the moments of the marginal probability density function of diffusion. We demonstrate its utility in extracting tissue information not contaminated with multiple intra-voxel relaxation rates. We compare the performance of four types of filters that zoom into tissue components with different relaxation and diffusion properties and demonstrate it on an in-vivo human dataset. Experimental results show that these filters are able to characterize heterogeneous tissue microstructure. Moreover, the filtered diffusion signals are also able to distinguish fiber bundles with similar orientations but different relaxation rates. The proposed method thus allows to characterize the neural microstructure information in a robust and unique manner not possible using existing techniques.

neuroscience↗

bioRxiv2019

The dot-compartment revealed? Diffusion MRI with ultra-strong gradients and spherical tensor encoding in the living human brain

The so-called \"dot-compartment\" is conjectured in diffusion MRI to represent small spherical spaces, such as cell bodies, in which the diffusion is restricted in all directions. Previous investigations inferred its existence from data acquired with directional diffusion encoding which does not permit a straightforward separation of signals from sticks (axons) and signals from dots. Here we combine isotropic diffusion encoding with ultra-strong diffusion gradients (240 mT/m) to achieve high diffusion-weightings with high signal to noise ratio, while suppressing signal arising from anisotropic water compartments with significant mobility along at least one axis (e.g., axons). A dot-compartment, defined to have apparent diffusion coefficient equal to zero and no exchange, would result in a non-decaying signal at very high b-values (b {gtrsim} 7000 s/mm2). With this unique experimental setup, a residual yet slowly decaying, signal above the noise floor for b-values as high as 15000 s/mm2 was seen clearly in the cerebellar grey matter (GM), and in several white matter (WM) regions to some extent. Upper limits of the dot-signal-fraction were estimated to be ~2% in cerebellar GM and ~0.2% in WM. By relaxing the assumption of zero diffusivity, the signal at high b-values in cerebellar GM could be represented more accurately by an isotropic water pool with a low apparent diffusivity of 0.11 {micro}m2/ms and a substantial signal fraction of ~7-16%. This remaining signal at high b-values has potential to serve as a novel and simple marker for isotropically-restricted water compartments in cerebellar GM.

neuroscience↗