Biomedical Engineering - Research Publications

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    Compressed sensing effects on quantitative analysis of undersampled human brain sodium MRI
    Blunck, Y ; Kolbe, SC ; Moffat, BA ; Ordidge, RJ ; Cleary, JO ; Johnston, LA (Wiley, 2020-03)
    Purpose The clinical application of sodium MRI is hampered due to relatively low image quality and associated long acquisition times. Compressed sensing (CS) aims at a reduction of measurement time, but has been found to encompass quantitative estimation bias when used in low SNR x‐Nuclei imaging. This work analyses CS in quantitative human brain sodium MRI from undersampled acquisitions and provides recommendations for tissue sodium concentration (TSC) estimation. Methods CS reconstructions from 3D radial acquisitions of 5 healthy volunteers were investigated over varying undersampling factors (USFs) and CS penalty weights on different sparsity domains, Wavelet, Discrete Cosine Transform (DCT), and Identity. Resulting images were compared with highly sampled and undersampled NUFFT‐based images and evaluated for image quality (i.e. structural similarity), image intensity bias, and its effect on TSC estimates in gray and white matter. Results Wavelet‐based CS reconstructions show highest image quality with stable TSC estimates for most USFs. Up to an USF of 4, images showed good structural detail. DCT and Identity‐based CS enable good image quality, however show a bias in TSC with a reduction in estimates across USFs. Conclusions The image intensity bias is lowest in Wavelet‐based reconstructions and enables an up to fourfold acquisition speed up while maintaining good structural detail. The associated acquisition time reduction can facilitate a translation of sodium MRI into clinical routine.
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    Longitudinal hippocampal volumetric changes in mice following brain infarction
    Brait, VH ; Wright, DK ; Nategh, M ; Oman, A ; Syeda, WT ; Ermine, CM ; O'Brien, KR ; Werden, E ; Churilov, L ; Johnston, LA ; Thompson, LH ; Nithianantharajah, J ; Jackman, KA ; Brodtmann, A (NATURE PORTFOLIO, 2021-05-13)
    Hippocampal atrophy is increasingly described in many neurodegenerative syndromes in humans, including stroke and vascular cognitive impairment. However, the progression of brain volume changes after stroke in rodent models is poorly characterized. We aimed to monitor hippocampal atrophy occurring in mice up to 48-weeks post-stroke. Male C57BL/6J mice were subjected to an intraluminal filament-induced middle cerebral artery occlusion (MCAO). At baseline, 3-days, and 1-, 4-, 12-, 24-, 36- and 48-weeks post-surgery, we measured sensorimotor behavior and hippocampal volumes from T2-weighted MRI scans. Hippocampal volume-both ipsilateral and contralateral-increased over the life-span of sham-operated mice. In MCAO-subjected mice, different trajectories of ipsilateral hippocampal volume change were observed dependent on whether the hippocampus contained direct infarction, with a decrease in directly infarcted tissue and an increase in non-infarcted tissue. To further investigate these volume changes, neuronal and glial cell densities were assessed in histological brain sections from the subset of MCAO mice lacking hippocampal infarction. Our findings demonstrate previously uncharacterized changes in hippocampal volume and potentially brain parenchymal cell density up to 48-weeks in both sham- and MCAO-operated mice.
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    Continuous monitoring of plant sodium transport dynamics using clinical PET
    Ruwanpathirana, GP ; Plett, DC ; Williams, RC ; Davey, CE ; Johnston, LA ; Kronzucker, HJ (BMC, 2021-01-19)
    BACKGROUND: The absorption, translocation, accumulation and excretion of substances are fundamental processes in all organisms including plants, and have been successfully studied using radiotracers labelled with 11C, 13N, 14C and 22Na since 1939. Sodium is one of the most damaging ions to the growth and productivity of crops. Due to the significance of understanding sodium transport in plants, a significant number of studies have been carried out to examine sodium influx, compartmentation, and efflux using 22Na- or 24Na-labeled salts. Notably, however, most of these studies employed destructive methods, which has limited our understanding of sodium flux and distribution characteristics in real time, in live plants. Positron emission tomography (PET) has been used successfully in medical research and diagnosis for decades. Due to its ability to visualise and assess physiological and metabolic function, PET imaging has also begun to be employed in plant research. Here, we report the use of a clinical PET scanner with a 22Na tracer to examine 22Na-influx dynamics in barley plants (Hordeum vulgare L. spp. Vulgare-cultivar Bass) under variable nutrient levels, alterations in the day/night light cycle, and the presence of sodium channel inhibitors. RESULTS: 3D dynamic PET images of whole plants show readily visible 22Na translocation from roots to shoots in each examined plant, with rates influenced by both nutrient status and channel inhibition. PET images show that plants cultivated in low-nutrient media transport more 22Na than plants cultivated in high-nutrient media, and that 22Na uptake is suppressed in the presence of a cation-channel inhibitor. A distinct diurnal pattern of 22Na influx was discernible in curves displaying rates of change of relative radioactivity. Plants were found to absorb more 22Na during the light period, and anticipate the change in the light/dark cycle by adjusting the sodium influx rate downward in the dark period, an effect not previously described experimentally. CONCLUSIONS: We demonstrate the utility of clinical PET/CT scanners for real-time monitoring of the temporal dynamics of sodium transport in plants. The effects of nutrient deprivation and of ion channel inhibition on sodium influx into barley plants are shown in two proof-of-concept experiments, along with the first-ever 3D-imaging of the light and dark sodium uptake cycles in plants. This method carries significant potential for plant biology research and, in particular, in the context of genetic and treatment effects on sodium acquisition and toxicity in plants.
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    Correcting for Non-stationarity in BOLD-fMRI Connectivity Analyses
    Davey, CE ; Grayden, DB ; Johnston, LA (FRONTIERS MEDIA SA, 2021-02-24)
    In this work fMRI BOLD datasets are shown to contain slice-dependent non-stationarities. A model containing slice-dependent, non-stationary signal power is proposed to address time-varying signal power during BOLD data acquisition. The impact of non-stationary power on functional MRI connectivity is analytically derived, establishing that pairwise connectivity estimates are scaled by a function of the time-varying signal power, with magnitude upper bound by 1, and that the variance of sample correlation is increased, thereby inducing spurious connectivity. Consequently, we make the observation that time-varying power during acquisition of BOLD timeseries has the propensity to diminish connectivity estimates. To ameliorate the impact of non-stationary signal power, a simple correction for slice-dependent non-stationarity is proposed. Our correction is analytically shown to restore both signal stationarity and, subsequently, the integrity of connectivity estimates. Theoretical results are corroborated with empirical evidence demonstrating the utility of our correction. In addition, slice-dependent non-stationary variance is experimentally determined to be optimally characterized by an inverse Gamma distribution. The resulting distribution of a voxel's signal intensity is analytically derived to be a generalized Student's-t distribution, providing support for the Gaussianity assumption typically imposed by fMRI connectivity methods.
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    White matter changes following experimental pediatric traumatic brain injury: an advanced diffusion-weighted imaging investigation
    Zamani, A ; O'Brien, TJ ; Kershaw, J ; Johnston, LA ; Semple, BD ; Wright, DK (Springer (part of Springer Nature), 2021-01-07)
    Pediatric traumatic brain injury (pTBI) is a major community health concern. Due to ongoing maturation, injury to the brain at a young age can have devastating consequences in later life. However, how pTBI affects brain development, including white matter maturation, is still poorly understood. Here, we used advanced diffusion weighted imaging (DWI) to assess chronic white matter changes after experimental pTBI. Mice at post-natal day 21 sustained a TBI using the controlled cortical impact model and magnetic resonance imaging (MRI) was performed at 6 months post-injury using a 4.7 T Bruker scanner. Four diffusion shells with 81 directions and b-values of 1000, 3000, 5000, and 7000s/mm2 were acquired and analyzed using MRtrix3 software. Advanced DWI metrics, including fiber density, fiber cross-section and a combined fiber density and cross-section measure, were investigated together with three track-weighted images (TWI): the average pathlength map, mean curvature and the track density image. These advanced metrics were compared to traditional diffusion tensor imaging (DTI) metrics which indicated that TBI injured mice had reduced fractional anisotropy and increased radial diffusivity in the white matter when compared to age-matched sham controls. Consistent with previous findings, fiber density and TWI metrics appeared to be more sensitive to white matter changes than DTI metrics, revealing widespread reductions in fiber density and TWI metrics in pTBI mice compared to sham controls. These results provide additional support for the use of advanced DWI metrics in assessing white matter degeneration following injury and highlight the chronic outcomes that can follow pTBI.
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    QSMART: Quantitative susceptibility mapping artifact reduction technique
    Yaghmaie, N ; Syeda, WT ; Wu, C ; Zhang, Y ; Zhang, TD ; Burrows, EL ; Brodtmann, A ; Moffat, BA ; Wright, DK ; Glarin, R ; Kolbe, S ; Johnston, LA (ACADEMIC PRESS INC ELSEVIER SCIENCE, 2021-05-01)
    PURPOSE: Quantitative susceptibility mapping (QSM) is a novel MR technique that allows mapping of tissue susceptibility values from MR phase images. QSM is an ill-conditioned inverse problem, and although several methods have been proposed in the field, in the presence of a wide range of susceptibility sources, streaking artifacts appear around high susceptibility regions and contaminate the whole QSM map. QSMART is a post-processing pipeline that uses two-stage parallel inversion to reduce the streaking artifacts and remove banding artifact at the cortical surface and around the vasculature. METHOD: Tissue and vein susceptibility values were separately estimated by generating a mask of vasculature driven from the magnitude data using a Frangi filter. Spatially dependent filtering was used for the background field removal step and the two susceptibility estimates were combined in the final QSM map. QSMART was compared to RESHARP/iLSQR and V-SHARP/iLSQR inversion in a numerical phantom, 7T in vivo single and multiple-orientation scans, 9.4T ex vivo mouse data, and 4.7T in vivo rat brain with induced focal ischemia. RESULTS: Spatially dependent filtering showed better suppression of phase artifacts near cortex compared to RESHARP and V-SHARP, while preserving voxels located within regions of interest without brain edge erosion. QSMART showed successful reduction of streaking artifacts as well as improved contrast between different brain tissues compared to the QSM maps obtained by RESHARP/iLSQR and V-SHARP/iLSQR. CONCLUSION: QSMART can reduce QSM artifacts to enable more robust estimation of susceptibility values in vivo and ex vivo.
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    A gyrification analysis approach based on Laplace Beltrami eigenfunction level sets
    Shishegar, R ; Pizzagalli, F ; Georgiou-Karistianis, N ; Egan, GF ; Jahanshad, N ; Johnston, LA (Elsevier, 2021-04-01)
    An accurate measure of the complexity of patterns of cortical folding or gyrification is necessary for understanding normal brain development and neurodevelopmental disorders. Conventional gyrification indices (GIs) are calculated based on surface curvature (curvature-based GI) or an outer hull surface of the cortex (outer surface-based GI). The latter is dependent on the definition of the outer hull surface and a corresponding function between surfaces. In the present study, we propose the Laplace Beltrami-based gyrification index (LB-GI). This is a new curvature-based local GI computed using the first three Laplace Beltrami eigenfunction level sets. As with outer surface-based GI methods, this method is based on the hypothesis that gyrification stems from a flat surface during development. However, instead of quantifying gyrification with reference to corresponding points on an outer hull surface, LB-GI quantifies the gyrification at each point on the cortical surface with reference to their surrounding gyral points, overcoming several shortcomings of existing methods. The LB-GI was applied to investigate the cortical maturation profile of the human brain from preschool to early adulthood using the PING database. The results revealed more detail in patterns of cortical folding than conventional curvature-based methods, especially on frontal and posterior tips of the brain, such as the frontal pole, lateral occipital, lateral cuneus, and lingual. Negative associations of cortical folding with age were observed at cortical regions, including bilateral lingual, lateral occipital, precentral gyrus, postcentral gyrus, and superior frontal gyrus. The results also indicated positive significant associations between age and the LB-GI of bilateral insula, the medial orbitofrontal, frontal pole and rostral anterior cingulate regions. It is anticipated that the LB-GI will be advantageous in providing further insights in the understanding of brain development and degeneration in large clinical neuroimaging studies.
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    Predicting individual improvement in schizophrenia symptom severity at 1-year follow-up: Comparison of connectomic, structural, and clinical predictors
    Kottaram, A ; Johnston, LA ; Tian, Y ; Ganella, EP ; Laskaris, L ; Cocchi, L ; McGorry, P ; Pantelis, C ; Kotagiri, R ; Cropley, V ; Zalesky, A (Wiley, 2020-08-15)
    In a machine learning setting, this study aims to compare the prognostic utility of connectomic, brain structural, and clinical/demographic predictors of individual change in symptom severity in individuals with schizophrenia. Symptom severity at baseline and 1‐year follow‐up was assessed in 30 individuals with a schizophrenia‐spectrum disorder using the Brief Psychiatric Rating Scale. Structural and functional neuroimaging was acquired in all individuals at baseline. Machine learning classifiers were trained to predict whether individuals improved or worsened with respect to positive, negative, and overall symptom severity. Classifiers were trained using various combinations of predictors, including regional cortical thickness and gray matter volume, static and dynamic resting‐state connectivity, and/or baseline clinical and demographic variables. Relative change in overall symptom severity between baseline and 1‐year follow‐up varied markedly among individuals (interquartile range: 55%). Dynamic resting‐state connectivity measured within the default‐mode network was the most accurate single predictor of change in positive (accuracy: 87%), negative (83%), and overall symptom severity (77%) at follow‐up. Incorporating predictors based on regional cortical thickness, gray matter volume, and baseline clinical variables did not markedly improve prediction accuracy and the prognostic utility of these predictors in isolation was moderate (<70%). Worsening negative symptoms at 1‐year follow‐up were predicted by hyper‐connectivity and hypo‐dynamism within the default‐mode network at baseline assessment, while hypo‐connectivity and hyper‐dynamism predicted worsening positive symptoms. Given the modest sample size investigated, we recommend giving precedence to the relative ranking of the predictors investigated in this study, rather than the prediction accuracy estimates.
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    Microstructural correlates of 23Na relaxation in human brain at 7 Tesla.
    Kolbe, SC ; Syeda, W ; Blunck, Y ; Glarin, R ; Law, M ; Johnston, LA ; Cleary, JO (Elsevier, 2020-05-01)
    23Na provides the second strongest MR-observable signal in biological tissue and exhibits bi-exponential T2∗ relaxation in micro-environments such as the brain. There is significant interest in developing 23Na biomarkers for neurological diseases that are associated with sodium channel dysfunction such as multiple sclerosis and epilepsy. We have previously reported methods for acquisition of multi-echo sodium MRI and continuous distribution modelling of sodium relaxation properties as surrogate markers of brain microstructure. This study aimed to compare 23Na T2∗ relaxation times to more established measures of tissue microstructure derived from advanced diffusion MRI at 7 T. Six healthy volunteers were scanned using a 3D multi-echo radial ultra-short TE sequence using a dual-tuned 1H/23Na birdcage coil, and a high-resolution multi-shell, high angular resolution diffusion imaging sequence using a 32-channel 1H receive coil. 23Na T2∗ relaxation parameters [mean T2∗ (T2∗mean) and fast relaxation fraction (T2∗ff)] were calculated from a voxel-wise continuous gamma distribution signal model. White matter (restricted anisotropic diffusion) and grey matter (restricted isotropic diffusion) density were calculated from multi-shell multi-tissue constrained spherical deconvolution. Sodium parameters were compared with white and grey matter diffusion properties. Sodium T2∗mean and T2∗ff showed little variation across a range of white matter axonal fibre and grey matter densities. We conclude that sodium T2∗ relaxation parameters are not greatly influenced by relative differences in intra- and extracellular partial volumes. We suggest that care be taken when interpreting sodium relaxation changes in terms of tissue microstructure in healthy tissue.