ORCID Profile
0000-0002-0173-6090
Current Organisation
Monash University
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Central Nervous System | Medical Biotechnology Diagnostics (incl. Biosensors) | Medical Biotechnology | Biomedical engineering | Biomedical Instrumentation | Psychology | Biomedical imaging | Biomedical instrumentation | Developmental Psychology and Ageing
Diagnostic Methods | Medical Instruments | Expanding Knowledge in the Physical Sciences | Neurodegenerative Disorders Related to Ageing | Expanding Knowledge in the Biological Sciences | Expanding Knowledge in the Medical and Health Sciences |
Publisher: IEEE
Date: 09-2006
Publisher: Cold Spring Harbor Laboratory
Date: 03-08-2021
DOI: 10.1101/2021.08.02.454708
Abstract: Background: Functional [18F]-fluorodeoxyglucose positron emission tomography (FDG-fPET) is a new approach for measuring glucose uptake in the human brain. The goal of FDG-fPET is to maintain a constant plasma supply of radioactive FDG in order to track, with high temporal resolution, the dynamic uptake of glucose during neuronal activity that occurs in response to a task or at rest. FDG-fPET has most often been applied in simultaneous BOLD-fMRI/FDG-fPET (blood oxygenation level dependent functional MRI fluorodeoxyglucose functional positron emission tomography) imaging. BOLD-fMRI/FDG-fPET provides the capability to image the two primary sources of energetic dynamics in the brain, the cerebrovascular haemodynamic response and cerebral glucose uptake. Findings: In this Data Note, we describe an open access dataset, Monash DaCRA fPET-fMRI, which contrasts three radiotracer administration protocols for FDG-fPET: bolus, constant infusion, and hybrid bolus/infusion. Participants (n=5 in each group) were randomly assigned to each radiotracer administration protocol and underwent simultaneous BOLD-fMRI/FDG-fPET scanning while viewing a flickering checkerboard. The Bolus group received the full FDG dose in a standard bolus administration the Infusion group received the full FDG dose as a slow infusion over the duration of the scan, and the Bolus-Infusion group received 50% of the FDG dose as bolus and 50% as constant infusion. We validate the dataset by contrasting plasma radioactivity, grey matter mean uptake, and task-related activity in the visual cortex. Conclusions: The Monash DaCRA fPET-fMRI dataset provides significant re-use value for researchers interested in the comparison of signal dynamics in fPET, and its relationship with fMRI task-evoked activity.
Publisher: Springer Nature Switzerland
Date: 2022
Publisher: Wiley
Date: 09-03-2011
DOI: 10.1002/MRM.22825
Abstract: Recent advances in high field magnetic resonance technology have increased the interest in the phase of the complex data. Processed phase images are derived from the phase signal by removing the bias field and phase wraps from the initial data. However, the usefulness of this data has been hindered by artifacts at the brain/non-brain surface, particularly in cortical regions. A method is proposed that efficiently removes surface artifacts by performing Gaussian filtering with spatially varying parameters of unwrapped or complex filtered phase images. The proposed method is shown to produce improved images, revealing underlying structure and detail that are otherwise obscured by surface artifacts in images produced by traditional phase processing methods.
Publisher: IEEE
Date: 12-2009
Publisher: Cold Spring Harbor Laboratory
Date: 09-10-2021
DOI: 10.1101/2021.10.06.463445
Abstract: Parcellation of whole brain tractograms is a critical step to study brain white matter structures and connectivity patterns. The existing methods based on supervised classification of streamlines into predefined streamline bundle types are not designed to explore sub-bundle structures, and methods with manually designed features are expensive to compute streamline-wise similarities. To resolve these issues, we propose a novel atlas-free method that learns a latent space using a deep recurrent auto-encoder. The method efficiently embeds any length of streamlines to fixed-size feature vectors, named streamline embedding, for tractogram parcellation using unsupervised clustering in the latent space. The method was evaluated on the ISMRM 2015 tractography challenge dataset with discrimination of major bundles using unsupervised clustering and streamline querying based on similarity. The learnt latent streamline and bundle representations open the possibility of quantitative studies of arbitrary granularity of sub-bundle structures using generic data mining techniques.
Publisher: Springer Science and Business Media LLC
Date: 24-06-2020
DOI: 10.1007/S12021-019-09430-1
Abstract: Mastering the "arcana of neuroimaging analysis", the obscure knowledge required to apply an appropriate combination of software tools and parameters to analyse a given neuroimaging dataset, is a time consuming process. Therefore, it is not typically feasible to invest the additional effort required generalise workflow implementations to accommodate for the various acquisition parameters, data storage conventions and computing environments in use at different research sites, limiting the reusability of published workflows. We present a novel software framework, Abstraction of Repository-Centric ANAlysis (Arcana), which enables the development of complex, "end-to-end" workflows that are adaptable to new analyses and portable to a wide range of computing infrastructures. Analysis templates for specific image types (e.g. MRI contrast) are implemented as Python classes, which define a range of potential derivatives and analysis methods. Arcana retrieves data from imaging repositories, which can be BIDS datasets, XNAT instances or plain directories, and stores selected derivatives and associated provenance back into a repository for reuse by subsequent analyses. Workflows are constructed using Nipype and can be executed on local workstations or in high performance computing environments. Generic analysis methods can be consolidated within common base classes to facilitate code-reuse and collaborative development, which can be specialised for study-specific requirements via class inheritance. Arcana provides a framework in which to develop unified neuroimaging workflows that can be reused across a wide range of research studies and sites.
Publisher: Springer International Publishing
Date: 2020
Publisher: Cold Spring Harbor Laboratory
Date: 06-12-2019
DOI: 10.1101/865709
Abstract: Dysregulation of iron in the cerebral motor areas has been hypothesized to occur in in iduals with Amyotrophic Lateral Sclerosis (ALS). There is still limited knowledge regarding iron dysregulation in the progression of ALS pathology. Our objectives were to use magnetic resonance based Quantitative Susceptibility Mapping (QSM) to investigate the association between iron dysregulation in the motor cortex and clinical manifestations in patients with limb-onset ALS, and to examine changes in the iron concentration in the motor cortex in these patients over a six-month period. Iron concentration was investigated using magnetic resonance based -QSM in the primary motor cortex and the pre-motor area in thirteen limb-onset ALS patients (including five lumbar onset, six cervical onset and two flail arm patients), and eleven age and sex-matched healthy controls. Nine ALS patients underwent follow-up scans at six months. Significantly increased QSM was observed in the left posterior primary motor area (p = 0.02, Cohen’s d = 0.9) and right anterior primary motor area (p = 0.02, Cohen’s d = 0.92) in all in iduals with limb-onset ALS compared to healthy controls. Increased QSM was observed in the primary motor and pre-motor area at baseline in patients with lumbar onset ALS patients, but not cervical limb-onset ALS patients, compared to healthy controls. No significant change in QSM was observed at the six-month follow-up scans in the ALS patients. The findings suggest that iron dysregulation can be detected in the motor cortex in limb-onset ALS, which does not appreciably change over a further 6 months. In iduals with lumbar onset ALS appear to be more susceptible to motor cortex iron dysregulation compared to the in iduals with cervical onset ALS. Importantly, this study highlights the potential use of QSM as a radiological indicator in disease diagnosis, and in clinical trials in limb-onset ALS and its subtypes. Serial measurement of QSM in the motor cortex in limb-onset ALS was performed QSM changes in the motor cortex in ALS sub-groups were investigated Higher QSM was observed in the motor cortex in Lumbar ALS relative to controls QSM is sensitive to iron dysregulation in the motor cortex in limb-onset ALS
Publisher: Springer International Publishing
Date: 2022
Publisher: Elsevier BV
Date: 10-2021
Publisher: Springer International Publishing
Date: 2022
Publisher: Wiley
Date: 15-02-2021
DOI: 10.1002/JMRI.27530
Abstract: Amyotrophic lateral sclerosis (ALS) results in progressive impairment of upper and lower motor neurons. Increasing evidence from both in vivo and ex vivo studies suggest that iron accumulation in the motor cortex is a neuropathological hallmark in ALS. An in vivo neuroimaging marker of iron dysregulation in ALS would be useful in disease diagnosis and prognosis. Magnetic resonance imaging (MRI), with its unique capability to generate a variety of soft tissue contrasts, provides opportunities to image iron distribution in the human brain with millimeter to sub‐millimeter anatomical resolution. Conventionally, MRI T1‐weighted, T2‐weighted, and T2*‐weighted images have been used to investigate iron dysregulation in the brain in vivo. Susceptibility weighted imaging has enhanced contrast for para‐magnetic materials that provides superior sensitivity to iron in vivo. Recently, the development of quantitative susceptibility mapping (QSM) has realized the possibility of using quantitative assessments of magnetic susceptibility measures in brain tissues as a surrogate measurement of in vivo brain iron. In this review, we provide an overview of MRI techniques that have been used to investigate iron dysregulation in ALS in vivo. The potential uses, strengths, and limitations of these techniques in clinical trials, disease diagnosis, and prognosis are presented and discussed. We recommend further longitudinal studies with appropriate cohort characterization to validate the efficacy of these techniques. We conclude that quantitative iron assessment using recent advances in MRI including QSM holds great potential to be a sensitive diagnostic and prognostic marker in ALS. The use of multimodal neuroimaging markers in combination with iron imaging may also offer improved sensitivity in ALS diagnosis and prognosis that could make a major contribution to clinical care and treatment trials. 2 Stage 3
Publisher: IEEE
Date: 2008
Publisher: AME Publishing Company
Date: 07-2020
DOI: 10.21037/QIMS-20-187
Publisher: Wiley
Date: 16-06-2023
DOI: 10.1002/JMRI.28866
Abstract: 5 3
Publisher: Wiley
Date: 03-01-2021
DOI: 10.1002/HBM.24497
Abstract: Head motion is a major source of image artefacts in neuroimaging studies and can lead to degradation of the quantitative accuracy of reconstructed PET images. Simultaneous magnetic resonance‐positron emission tomography (MR‐PET) makes it possible to estimate head motion information from high‐resolution MR images and then correct motion artefacts in PET images. In this article, we introduce a fully automated PET motion correction method, MR‐guided MAF, based on the co‐registration of multicontrast MR images. The performance of the MR‐guided MAF method was evaluated using MR‐PET data acquired from a cohort of ten healthy participants who received a slow infusion of fluorodeoxyglucose ([18‐F]FDG). Compared with conventional methods, MR‐guided PET image reconstruction can reduce head motion introduced artefacts and improve the image sharpness and quantitative accuracy of PET images acquired using simultaneous MR‐PET scanners. The fully automated motion estimation method has been implemented as a publicly available web‐service.
Publisher: Elsevier BV
Date: 06-2020
Publisher: IEEE
Date: 08-2007
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2023
Publisher: IEEE
Date: 08-2007
Publisher: Cold Spring Harbor Laboratory
Date: 22-10-2018
DOI: 10.1101/447649
Abstract: Mastering the “arcana of neuroimaging analysis”, the obscure knowledge required to apply an appropriate combination of software tools and parameters to analyse a given neuroimaging dataset, is a time consuming process. Therefore, it is not typically feasible to invest the additional effort required generalise workflow implementations to accommodate for the various acquisition parameters, data storage conventions and computing environments in use at different research sites, limiting the reusability of published workflows. We present a novel software framework, Abstraction of Repository-Centric ANAlysis (Arcana) , which enables the development of complex, “end-to-end” workflows that are adaptable to new analyses and portable to a wide range of computing infrastructures. Analysis templates for specific image types (e.g. MRI contrast) are implemented as Python classes, which define a range of potential derivatives and analysis methods. Arcana retrieves data from imaging repositories, which can be BIDS datasets, XNAT instances or plain directories, and stores selected derivatives and associated provenance back into a repository for reuse by subsequent analyses. Workflows are constructed using Nipype and can be executed on local workstations or in high performance computing environments. Generic analysis methods can be consolidated within common base classes to facilitate code-reuse and collaborative development, which can be specialised for study-specific requirements via class inheritance. Arcana provides a framework in which to develop unified neuroimaging workflows that can be reused across a wide range of research studies and sites.
Publisher: Elsevier BV
Date: 2010
DOI: 10.1016/J.NEUROIMAGE.2009.09.071
Abstract: Phase contrast imaging holds great potential for in vivo biodistribution studies of paramagnetic molecules and materials. However, in vivo quantification of iron storage and other paramagnetic materials requires improvements in reconstruction and processing of MR complex images. To achieve this, we have developed a framework including (i) an optimal coil sensitivity smoothing filter for phase imaging determined at the maximal signal to noise ratio, (ii) a phase optimised and a complex image optimised reconstruction approach, and (iii) a magnitude and phase correlation test criterion to determine the low pass filter parameter for background phase removal. The method has been evaluated using 3T and 7T MRI data containing cortical regions, the basal ganglia including the caudate, and the midbrain including the substantia nigra. The optimised reconstruction improves phase image contrast and noise suppression compared with conventional reconstruction approaches, and the correlation test criterion provides an objective method for separation of the local phase signal from the background phase measurements. Phase values of several brain regions of interest have been calculated, including gray matter (-1.23 Hz at 7T and -0.55 Hz at 3T), caudate (-3.8 Hz at 7T), and the substantia nigra (-6.2 Hz at 7T).
Publisher: Cold Spring Harbor Laboratory
Date: 11-06-2019
DOI: 10.1101/667352
Abstract: Functional Positron Emission Tomography (fPET) provides a method to track molecular dynamics in the human brain. With a radioactively labelled glucose-analogue, [18F]-flurodeoxyglucose (FDG-fPET), it is now possible to index the dynamics of glucose metabolism with temporal resolutions approaching those of functional magnetic resonance imaging (fMRI). This direct measure of glucose uptake has enormous potential for understanding normal and abnormal brain function, and probing the effects of metabolic and neurodegenerative diseases. Further, new advances in hybrid MR-PET hardware makes it possible to capture fluctuations in glucose and blood oxygenation simultaneously using fMRI and FDG-fPET. The temporal resolution and signal-to-noise of the FDG-fPET images is critically dependent upon the administration of the radioactive tracer. In this work we present two alternative continuous infusion protocols and compare them to a traditional bolus approach. We detail a method for acquiring blood s les, time-locking PET, MRI and experimental stimulus, and administrating the non-traditional tracer delivery. By applying a visual stimulus, we demonstrate cortical maps of the glucose-response to external stimuli on an in idual level with a temporal resolution of 16-seconds. Radiotracer infusion protocols for positron emission tomography (PET) provide improved temporal resolution over bolus administration. Here, we describe radiotracer administration for two protocols, constant infusion and bolus plus infusion protocol. We compare this to the standard bolus administration protocol. Using [18-F] fluorodeoxyglucose PET (FDG-PET) as an ex le, we show that temporal resolutions of approximately 16sec are achievable using these protocols.
Publisher: Wiley
Date: 03-02-2020
DOI: 10.1002/IMA.22401
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2019
Publisher: IEEE
Date: 11-2018
Publisher: Elsevier BV
Date: 09-2021
Publisher: Cold Spring Harbor Laboratory
Date: 24-10-2018
DOI: 10.1101/451468
Abstract: Studies of task-evoked brain activity are the cornerstone of cognitive neuroscience, and unravel the spatial and temporal brain dynamics of cognition in health and disease. Blood oxygenation level dependent functional magnetic resonance imaging (BOLD-fMRI) is one of the most common methods of studying brain function in humans. BOLD-fMRI indirectly infers neuronal activity from regional changes in blood oxygenation and is not a quantitative metric of brain function. Regional variation in glucose metabolism, measured using [18-F] fluorodeoxyglucose positron emission tomography (FDG-PET), provides a more direct and interpretable measure of neuronal activity. However, while the temporal resolution of BOLD-fMRI is in the order of seconds, standard FDG-PET protocols provide a static snapshot of glucose metabolism. Here, we develop a novel experimental design for measurement of task-evoked changes in regional blood oxygenation and glucose metabolism with high temporal resolution. Over a 90-min simultaneous BOLD-fMRI/FDG-PET scan, [18F] FDG was constantly infused to 10 healthy volunteers, who viewed a flickering checkerboard presented in a hierarchical block design. Dynamic task-related changes in blood oxygenation and glucose metabolism were examined with temporal resolution of 2.5sec and 1-min, respectively. Task-related, temporally coherent brain networks of haemodynamic and metabolic connectivity were maximally related in the visual cortex, as expected. Results demonstrate that the hierarchical block design, together with the infusion FDG-PET technique, enabled both modalities to track task-related neural responses with high temporal resolution. The simultaneous MR-PET approach has the potential to provide unique insights into the dynamic haemodynamic and metabolic interactions that underlie cognition in health and disease.
Publisher: Oxford University Press (OUP)
Date: 30-01-2023
Abstract: Dysfunction of fronto-striato-thalamic (FST) circuits is thought to contribute to dopaminergic dysfunction and symptom onset in psychosis, but it remains unclear whether this dysfunction is driven by aberrant bottom-up subcortical signalling or impaired top-down cortical regulation. We used spectral dynamic causal modelling of resting-state functional MRI to characterize the effective connectivity of dorsal and ventral FST circuits in a s le of 46 antipsychotic-naïve first-episode psychosis patients and 23 controls and an independent s le of 36 patients with established schizophrenia and 100 controls. We also investigated the association between FST effective connectivity and striatal 18F-DOPA uptake in an independent healthy cohort of 33 in iduals who underwent concurrent functional MRI and PET. Using a posterior probability threshold of 0.95, we found that midbrain and thalamic connectivity were implicated as dysfunctional across both patient groups. Dysconnectivity in first-episode psychosis patients was mainly restricted to the subcortex, with positive symptom severity being associated with midbrain connectivity. Dysconnectivity between the cortex and subcortical systems was only apparent in established schizophrenia patients. In the healthy 18F-DOPA cohort, we found that striatal dopamine synthesis capacity was associated with the effective connectivity of nigrostriatal and striatothalamic pathways, implicating similar circuits to those associated with psychotic symptom severity in patients. Overall, our findings indicate that subcortical dysconnectivity is evident in the early stages of psychosis, that cortical dysfunction may emerge later in the illness, and that nigrostriatal and striatothalamic signalling are closely related to striatal dopamine synthesis capacity, which is a robust marker for psychosis.
Publisher: Elsevier BV
Date: 06-2021
Publisher: IEEE
Date: 08-2007
Publisher: Wiley
Date: 26-10-2010
DOI: 10.1002/MRM.22197
Abstract: Accelerated parallel MRI has advantage in imaging speed, and its image quality has been improved continuously in recent years. This paper introduces a two-dimensional infinite impulse response model of inverse filter to replace the finite impulse response model currently used in generalized autocalibrating partially parallel acquisitions class image reconstruction methods. The infinite impulse response model better characterizes the correlation of k-space data points and better approximates the perfect inversion of parallel imaging process, resulting in a novel generalized image reconstruction method for accelerated parallel MRI. This k-space-based reconstruction method includes the conventional generalized autocalibrating partially parallel acquisitions class methods as special cases and has a new infinite impulse response data estimation mechanism for effective improvement of image quality. The experiments on in vivo MRI data show that the proposed method significantly reduces reconstruction errors compared with the conventional two-dimensional generalized autocalibrating partially parallel acquisitions method, particularly at the high acceleration rates.
Publisher: Cold Spring Harbor Laboratory
Date: 03-05-2021
DOI: 10.1101/2020.05.01.071662
Abstract: Simultaneous FDG-PET/fMRI ([18F]-fluorodeoxyglucose positron emission tomography functional magnetic resonance imaging) provides the capacity to image two sources of energetic dynamics in the brain – glucose metabolism and haemodynamic response. Functional fMRI connectivity has been enormously useful for characterising interactions between distributed brain networks in humans. Metabolic connectivity based on static FDG-PET has been proposed as a biomarker for neurological disease but static FDG-PET cannot be used to estimate subjectlevel measures of connectivity , only across-subject covariance . Here, we applied high-temporal resolution constant infusion fPET to measure subject-level metabolic connectivity simultaneously with fMRI connectivity. fPET metabolic connectivity was characterised by fronto-parietal connectivity within and between hemispheres. fPET metabolic connectivity showed moderate similarity with fMRI primarily in superior cortex and frontoparietal regions. Significantly, fPET metabolic connectivity showed little similarity with static FDG-PET metabolic covariance, indicating that metabolic brain connectivity is a non-ergodic process whereby in idual brain connectivity cannot be inferred from group level metabolic covariance. Our results highlight the complementary strengths of fPET and fMRI in measuring the intrinsic connectivity of the brain, and open up the opportunity for novel fundamental studies of human brain connectivity as well as multi-modality biomarkers of neurological diseases.
Publisher: Cold Spring Harbor Laboratory
Date: 09-07-2020
Publisher: Springer International Publishing
Date: 2022
Publisher: Oxford University Press (OUP)
Date: 03-06-2021
Publisher: Wiley
Date: 22-12-2022
DOI: 10.1002/NBM.4225
Abstract: The suppression of motion artefacts from MR images is a challenging task. The purpose of this paper was to develop a standalone novel technique to suppress motion artefacts in MR images using a data‐driven deep learning approach. A simulation framework was developed to generate motion‐corrupted images from motion‐free images using randomly generated motion profiles. An Inception‐ResNet deep learning network architecture was used as the encoder and was augmented with a stack of convolution and ups ling layers to form an encoder‐decoder network. The network was trained on simulated motion‐corrupted images to identify and suppress those artefacts attributable to motion. The network was validated on unseen simulated datasets and real‐world experimental motion‐corrupted in vivo brain datasets. The trained network was able to suppress the motion artefacts in the reconstructed images, and the mean structural similarity (SSIM) increased from 0.9058 to 0.9338. The network was also able to suppress the motion artefacts from the real‐world experimental dataset, and the mean SSIM increased from 0.8671 to 0.9145. The motion correction of the experimental datasets demonstrated the effectiveness of the motion simulation generation process. The proposed method successfully removed motion artefacts and outperformed an iterative entropy minimization method in terms of the SSIM index and normalized root mean squared error, which were 5–10% better for the proposed method. In conclusion, a novel, data‐driven motion correction technique has been developed that can suppress motion artefacts from motion‐corrupted MR images. The proposed technique is a standalone, post‐processing method that does not interfere with data acquisition or reconstruction parameters, thus making it suitable for routine clinical practice.
Publisher: IEEE
Date: 2005
Publisher: Elsevier BV
Date: 04-2019
DOI: 10.1016/J.NEUROIMAGE.2019.01.003
Abstract: Studies of task-evoked brain activity are the cornerstone of cognitive neuroscience, and unravel the spatial and temporal brain dynamics of cognition in health and disease. Blood oxygenation level dependent functional magnetic resonance imaging (BOLD-fMRI) is one of the most common methods of studying brain function in humans. BOLD-fMRI indirectly infers neuronal activity from regional changes in blood oxygenation and is not a quantitative metric of brain function. Regional variation in glucose metabolism, measured using [18-F] fluorodeoxyglucose positron emission tomography (FDG-PET), provides a more direct and interpretable measure of neuronal activity. However, while the temporal resolution of BOLD-fMRI is in the order of seconds, standard FDG-PET protocols provide a static snapshot of glucose metabolism. Here, we develop a novel experimental design for measurement of task-evoked changes in regional blood oxygenation and glucose metabolism with high temporal resolution. Over a 90-min simultaneous BOLD-fMRI/FDG-PET scan, [18F] FDG was constantly infused to 10 healthy volunteers, who viewed a flickering checkerboard presented in a hierarchical block design. Dynamic task-related changes in blood oxygenation and glucose metabolism were examined with temporal resolution of 2.5sec and 1-min, respectively. Task-related, temporally coherent brain networks of haemodynamic and metabolic connectivity were jointly coupled in the visual cortex, as expected. Results demonstrate that the hierarchical block design, together with the infusion FDG-PET technique, enabled both modalities to track task-related neural responses with high temporal resolution. The simultaneous MR-PET approach has the potential to provide unique insights into the dynamic haemodynamic and metabolic interactions that underlie cognition in health and disease.
Publisher: IEEE
Date: 10-2017
Publisher: IEEE
Date: 2007
Publisher: Cold Spring Harbor Laboratory
Date: 15-03-2021
DOI: 10.1101/2021.03.11.21253426
Abstract: Dysfunction of fronto-striato-thalamic (FST) circuits is thought to contribute to dopaminergic dysfunction and symptom onset in psychosis, but it remains unclear whether this dysfunction is driven by aberrant bottom-up subcortical signaling or impaired top-down cortical regulation. Here, we used spectral dynamic causal modelling (DCM) of resting-state functional magnetic resonance imaging (fMRI) to characterize the effective connectivity of dorsal and ventral FST circuits in a s le of 46 antipsychotic-naïve first-episode psychosis (FEP) patients and 23 controls and an independent s le of 36 patients with established schizophrenia (SCZ) patients and 100 controls. We found that midbrain and thalamic connectivity were implicated across both patient groups. Dysconnectivity in FEP patients was mainly restricted to the subcortex, with positive symptom severity being associated with midbrain connectivity. Dysconnectivity between the cortex and subcortical systems was only apparent in SCZ patients. In another independent s le of 33 healthy in iduals who underwent concurrent fMRI and [ 18 F]DOPA positron emission tomography, we found that striatal dopamine synthesis capacity was associated with the effective connectivity of nigrostriatal and striatothalamic pathways, implicating similar circuits as those associated with psychotic symptom severity in patients. Our findings thus indicate that subcortical dysconnectivity is salient in the early stages of psychosis, that cortical dysfunction may emerge later in the illness, and that nigrostriatal and striatothalamic signaling are closely related to striatal dopamine synthesis capacity, which is a robust risk marker for psychosis.
Publisher: Springer Science and Business Media LLC
Date: 06-11-2018
Publisher: Springer International Publishing
Date: 2018
Publisher: Springer International Publishing
Date: 2018
Publisher: Wiley
Date: 04-08-2018
DOI: 10.1002/HBM.24314
Publisher: Elsevier BV
Date: 07-2020
Publisher: Springer Science and Business Media LLC
Date: 15-10-2021
DOI: 10.1038/S41597-021-01042-2
Abstract: Understanding how the living human brain functions requires sophisticated in vivo neuroimaging technologies to characterise the complexity of neuroanatomy, neural function, and brain metabolism. Fluorodeoxyglucose positron emission tomography (FDG-PET) studies of human brain function have historically been limited in their capacity to measure dynamic neural activity. Simultaneous [18 F]-FDG-PET and functional magnetic resonance imaging (fMRI) with FDG infusion protocols enable examination of dynamic changes in cerebral glucose metabolism simultaneously with dynamic changes in blood oxygenation. The Monash vis-fPET-fMRI dataset is a simultaneously acquired FDG-fPET/BOLD-fMRI dataset acquired from n = 10 healthy adults (18–49 yrs) whilst they viewed a flickering checkerboard task. The dataset contains both raw (unprocessed) images and source data organized according to the BIDS specification. The source data includes PET listmode, normalization, sinogram and physiology data. Here, the technical feasibility of using opensource frameworks to reconstruct the PET listmode data is demonstrated. The dataset has significant re-use value for the development of new processing pipelines, signal optimisation methods, and to formulate new hypotheses concerning the relationship between neuronal glucose uptake and cerebral haemodynamics.
Publisher: Cold Spring Harbor Laboratory
Date: 02-07-2021
DOI: 10.1101/2021.07.02.450445
Abstract: The trans-neural propagation of phosphorylated 43-kDa transactive response DNA-binding protein (pTDP-43) contributes to neurodegeneration in Amyotrophic Lateral Sclerosis (ALS). We investigated whether Network Diffusion Model (NDM), a biophysical model of spread of pathology via the brain connectome, could capture the severity and progression of neurodegeneration (atrophy) in ALS. We measured degeneration in limb-onset ALS patients (n=14 at baseline, 12 at 6-months, and 9 at 12 months) and controls (n=12 at baseline) using FreeSurfer analysis on the structural T1-weighted Magnetic Resonance Imaging (MRI) data. The NDM was simulated on the canonical structural connectome from the IIT Human Brain Atlas. To determine whether NDM could predict the atrophy pattern in ALS, the accumulation of pathology modelled by NDM was correlated against atrophy measured using MRI. The cross-sectional analyses revealed that the network diffusion seeded from the inferior frontal gyrus (pars triangularis and pars orbitalis) significantly predicts the atrophy pattern in ALS compared to controls. Whereas, atrophy over time with-in the ALS group was best predicted by seeding the network diffusion process from the inferior temporal gyrus at 6-month and caudal middle frontal gyrus at 12-month. Our findings suggest the involvement of extra-motor regions in seeding the spread of pathology in ALS. Importantly, NDM was able to recapitulate the dynamics of pathological progression in ALS. Understanding the spatial shifts in the seeds of degeneration over time can potentially inform further research in the design of disease modifying therapeutic interventions in ALS.
Publisher: IEEE
Date: 2008
Publisher: Springer Science and Business Media LLC
Date: 21-03-2022
Publisher: Public Library of Science (PLoS)
Date: 11-08-2022
DOI: 10.1371/JOURNAL.PONE.0272736
Abstract: Emerging evidences suggest that the trans-neural propagation of phosphorylated 43-kDa transactive response DNA-binding protein (pTDP-43) contributes to neurodegeneration in Amyotrophic Lateral Sclerosis (ALS). We investigated whether Network Diffusion Model (NDM), a biophysical model of spread of pathology via the brain connectome, could capture the severity and progression of neurodegeneration (atrophy) in ALS. We measured degeneration in limb-onset ALS patients (n = 14 at baseline, 12 at 6-months, and 9 at 12 months) and controls (n = 12 at baseline) using FreeSurfer analysis on the structural T1-weighted Magnetic Resonance Imaging (MRI) data. The NDM was simulated on the canonical structural connectome from the IIT Human Brain Atlas. To determine whether NDM could predict the atrophy pattern in ALS, the accumulation of pathology modelled by NDM was correlated against atrophy measured using MRI. In order to investigate whether network spread on the brain connectome derived from healthy in iduals were significant findings, we compared our findings against network spread simulated on random networks. The cross-sectional analyses revealed that the network diffusion seeded from the inferior frontal gyrus (pars triangularis and pars orbitalis) significantly predicts the atrophy pattern in ALS compared to controls. Whereas, atrophy over time with-in the ALS group was best predicted by seeding the network diffusion process from the inferior temporal gyrus at 6-month and caudal middle frontal gyrus at 12-month. Network spread simulated on the random networks showed that the findings using healthy brain connectomes are significantly different from null models. Our findings suggest the involvement of extra-motor regions in seeding the spread of pathology in ALS. Importantly, NDM was able to recapitulate the dynamics of pathological progression in ALS. Understanding the spatial shifts in the seeds of degeneration over time can potentially inform further research in the design of disease modifying therapeutic interventions in ALS.
Publisher: Springer Science and Business Media LLC
Date: 13-07-2023
Publisher: Oxford University Press (OUP)
Date: 2022
DOI: 10.1093/GIGASCIENCE/GIAC031
Abstract: “Functional” [18F]-fluorodeoxyglucose positron emission tomography (FDG-fPET) is a new approach for measuring glucose uptake in the human brain. The goal of FDG-fPET is to maintain a constant plasma supply of radioactive FDG in order to track, with high temporal resolution, the dynamic uptake of glucose during neuronal activity that occurs in response to a task or at rest. FDG-fPET has most often been applied in simultaneous BOLD-fMRI/FDG-fPET (blood oxygenation level–dependent functional MRI fluorodeoxyglucose functional positron emission tomography) imaging. BOLD-fMRI/FDG-fPET provides the capability to image the 2 primary sources of energetic dynamics in the brain, the cerebrovascular haemodynamic response and cerebral glucose uptake. In this Data Note, we describe an open access dataset, Monash DaCRA fPET-fMRI, which contrasts 3 radiotracer administration protocols for FDG-fPET: bolus, constant infusion, and hybrid bolus/infusion. Participants (n = 5 in each group) were randomly assigned to each radiotracer administration protocol and underwent simultaneous BOLD-fMRI/FDG-fPET scanning while viewing a flickering checkerboard. The bolus group received the full FDG dose in a standard bolus administration, the infusion group received the full FDG dose as a slow infusion over the duration of the scan, and the bolus-infusion group received 50% of the FDG dose as bolus and 50% as constant infusion. We validate the dataset by contrasting plasma radioactivity, grey matter mean uptake, and task-related activity in the visual cortex. The Monash DaCRA fPET-fMRI dataset provides significant reuse value for researchers interested in the comparison of signal dynamics in fPET, and its relationship with fMRI task-evoked activity.
Publisher: arXiv
Date: 2018
Publisher: Springer International Publishing
Date: 2019
Publisher: Oxford University Press (OUP)
Date: 03-02-2021
Abstract: Simultaneous [18F]-fluorodeoxyglucose positron emission tomography functional magnetic resonance imaging (FDG-PET/fMRI) provides the capacity to image 2 sources of energetic dynamics in the brain—glucose metabolism and the hemodynamic response. fMRI connectivity has been enormously useful for characterizing interactions between distributed brain networks in humans. Metabolic connectivity based on static FDG-PET has been proposed as a biomarker for neurological disease, but FDG-sPET cannot be used to estimate subject-level measures of “connectivity,” only across-subject “covariance.” Here, we applied high-temporal resolution constant infusion functional positron emission tomography (fPET) to measure subject-level metabolic connectivity simultaneously with fMRI connectivity. fPET metabolic connectivity was characterized by frontoparietal connectivity within and between hemispheres. fPET metabolic connectivity showed moderate similarity with fMRI primarily in superior cortex and frontoparietal regions. Significantly, fPET metabolic connectivity showed little similarity with FDG-sPET metabolic covariance, indicating that metabolic brain connectivity is a nonergodic process whereby in idual brain connectivity cannot be inferred from group-level metabolic covariance. Our results highlight the complementary strengths of fPET and fMRI in measuring the intrinsic connectivity of the brain and open up the opportunity for novel fundamental studies of human brain connectivity as well as multimodality biomarkers of neurological diseases.
Publisher: IEEE
Date: 2005
Publisher: Cold Spring Harbor Laboratory
Date: 23-09-2019
DOI: 10.1101/778357
Abstract: Functional positron emission tomography (fPET) is a neuroimaging method involving continuous infusion of 18-F-fluorodeoxyglucose (FDG) radiotracer during the course of the PET examination. Compared with the conventional bolus administered static FDG PET which provides only a snapshot of the averaged glucose uptake into the brain in a limited dynamic time window, fPET offers a significantly wider time window to study the dynamics of glucose uptake. Several earlier studies have applied fPET to investigate brain FDG uptake and study its relationship with functional magnetic resonance imaging (fMRI). However, due to the unique characteristics of fPET signals, modelling of the fPET signal is a complex task and poses challenges for accurate interpretation of the results. This study applies independent component analysis (ICA) to analyze resting state fPET data, and to compare the performance of ICA and general linear modelling (GLM) for estimation of brain activation in response to tasks. The fPET signal characteristics were compared using GLM and ICA methods to model the fPET visual activation data. Our aim was to evaluate GLM and ICA methods for analyzing task fPET datasets and present ICA method in the analysis of resting state fPET datasets. Using both simulation and in-vivo experimental datasets, we show that both methods can successfully identify task related brain activation. We report fPET metabolic resting state brain networks analyzed using the fPET ICA method in a cohort of healthy subjects. Functional PET provides a unique method to map dynamic changes of glucose uptake in the resting human brain and in response to extrinsic stimulation.
Publisher: Springer Science and Business Media LLC
Date: 21-10-2020
DOI: 10.1038/S41597-020-00699-5
Abstract: Simultaneous [18 F]-fluorodeoxyglucose positron emission tomography and functional magnetic resonance imaging (FDG-PET/fMRI) provides the capability to image two sources of energetic dynamics in the brain – cerebral glucose uptake and the cerebrovascular haemodynamic response. Resting-state fMRI connectivity has been enormously useful for characterising interactions between distributed brain regions in humans. Metabolic connectivity has recently emerged as a complementary measure to investigate brain network dynamics. Functional PET (fPET) is a new approach for measuring FDG uptake with high temporal resolution and has recently shown promise for assessing the dynamics of neural metabolism. Simultaneous fMRI/fPET is a relatively new hybrid imaging modality, with only a few biomedical imaging research facilities able to acquire FDG PET and BOLD fMRI data simultaneously. We present data for n = 27 healthy young adults (18–20 yrs) who underwent a 95-min simultaneous fMRI/fPET scan while resting with their eyes open. This dataset provides significant re-use value to understand the neural dynamics of glucose metabolism and the haemodynamic response, the synchrony, and interaction between these measures, and the development of new single- and multi-modality image preparation and analysis procedures.
Publisher: Springer Science and Business Media LLC
Date: 11-05-2021
Publisher: Elsevier BV
Date: 09-2007
DOI: 10.1016/J.COMPMEDIMAG.2007.04.005
Abstract: Dynamic magnetic resonance imaging (MRI) acquires a sequence of images for the visualization of the temporal variation of tissue or organs. Keyhole methods such as Fourier keyhole (FK) and keyhole SVD (KSVD) are the most popular methods for image reconstruction in dynamic MRI. This paper provides a class of adaptive keyhole methods, called adaptive FK (AFK) and adaptive KSVD (AKSVD), for dynamic MRI reconstruction. The proposed methods are based on the conventional Fourier encoding and SVD encoding schemes. Instead of the conventional keyhole methods' duplication of un-acquired data from the reference images, the proposed methods use a temporal model to depict the inter-frame dynamic changes and to estimate the un-acquired data in each successive frame. Because the model is online identified from the acquired data, the proposed methods do not require the pre-imaging process, the navigator signals, and any prior knowledge of the imaged objects. Furthermore, the new methods use the conventional keyhole encoding schemes without the bias to any particular object characters, and the temporal model for updating information is in the general form of AR process without the preference to any particular motion types. Hence, the proposed methods are designed as a generic approach to dynamic MRI, other than for any specific class of objects. Studies on dynamic MRI data set show that the new methods can produce images with much lower reconstruction error than the conventional FK and KSVD.
Publisher: MyJove Corporation
Date: 22-10-2019
DOI: 10.3791/60259
Abstract: Functional positron emission tomography (fPET) provides a method to track molecular targets in the human brain. With a radioactively-labelled glucose analogue,
Publisher: Springer International Publishing
Date: 2021
Publisher: IEEE
Date: 11-2018
Publisher: Elsevier BV
Date: 12-2020
Publisher: Cold Spring Harbor Laboratory
Date: 24-10-2018
DOI: 10.1101/450676
Abstract: Head motion is a major source of image artefacts in neuroimaging studies and can lead to degradation of the quantitative accuracy of reconstructed PET images. Simultaneous Magnetic Resonance-Positron Emission Tomography (MR-PET) makes it possible to estimate head motion information from high-resolution MR images and then correct motion artefacts in PET images. In this paper, we introduce a fully automated PET motion correction method, MR-guided MAF, based on the co-registration of multi-contrast MR images. The performance of the MR-guided MAF method was evaluated using MR-PET data acquired from a cohort of ten healthy participants who received a slow infusion of fluorodeoxyglucose ([18-F]FDG). Compared with conventional methods, MR guided PET image reconstruction can reduce head motion introduced artefacts and improve the image sharpness and quantitative accuracy of PET images acquired using simultaneous MR-PET scanners. The fully automated motion estimation method has been implemented as a publicly available web-service.
Publisher: Springer Science and Business Media LLC
Date: 02-11-2022
DOI: 10.1007/S10278-022-00721-9
Abstract: Magnetic resonance imaging (MRI) provides excellent soft-tissue contrast for clinical diagnoses and research which underpin many recent breakthroughs in medicine and biology. The post-processing of reconstructed MR images is often automated for incorporation into MRI scanners by the manufacturers and increasingly plays a critical role in the final image quality for clinical reporting and interpretation. For image enhancement and correction, the post-processing steps include noise reduction, image artefact correction, and image resolution improvements. With the recent success of deep learning in many research fields, there is great potential to apply deep learning for MR image enhancement, and recent publications have demonstrated promising results. Motivated by the rapidly growing literature in this area, in this review paper, we provide a comprehensive overview of deep learning-based methods for post-processing MR images to enhance image quality and correct image artefacts. We aim to provide researchers in MRI or other research fields, including computer vision and image processing, a literature survey of deep learning approaches for MR image enhancement. We discuss the current limitations of the application of artificial intelligence in MRI and highlight possible directions for future developments. In the era of deep learning, we highlight the importance of a critical appraisal of the explanatory information provided and the generalizability of deep learning algorithms in medical imaging.
Publisher: Springer Science and Business Media LLC
Date: 22-06-2022
DOI: 10.1007/S12021-022-09593-4
Abstract: Parcellation of whole brain tractograms is a critical step to study brain white matter structures and connectivity patterns. The existing methods based on supervised classification of streamlines into predefined streamline bundle types are not designed to explore sub-bundle structures, and methods with manually designed features are expensive to compute streamline-wise similarities. To resolve these issues, we propose a novel atlas-free method that learns a latent space using a deep recurrent auto-encoder trained in an unsupervised manner. The method efficiently embeds any length of streamlines to fixed-size feature vectors, named streamline embedding, for tractogram parcellation using non-parametric clustering in the latent space. The method was evaluated on the ISMRM 2015 tractography challenge dataset with discrimination of major bundles using clustering algorithms and streamline querying based on similarity, as well as real tractograms of 102 subjects Human Connectome Project. The learnt latent streamline and bundle representations open the possibility of quantitative studies of arbitrary granularity of sub-bundle structures using generic data mining techniques.
Publisher: Hindawi Limited
Date: 2012
DOI: 10.1155/2012/608501
Abstract: Amyotrophic lateral sclerosis (ALS) is a rapidly progressing neurodegenerative disorder which is incurable to date. As there are many ongoing studies with therapeutic candidates, it is of major interest to develop biomarkers not only to facilitate early diagnosis but also as a monitoring tool to predict disease progression and to enable correct randomization of patients in clinical trials. Magnetic resonance imaging (MRI) has made substantial progress over the last three decades and is a practical, noninvasive method to gain insights into the pathology of the disease. Disease-specific MRI changes therefore represent potential biomarkers for ALS. In this paper we give an overview of structural and functional MRI alterations in ALS with the focus on task-free resting-state investigations to detect cortical network failures.
Publisher: Elsevier BV
Date: 02-2021
Publisher: Elsevier BV
Date: 05-2020
Publisher: IEEE
Date: 04-2017
Publisher: arXiv
Date: 2022
Start Date: 2023
End Date: 2027
Funder: Australian Research Council
View Funded ActivityStart Date: 2021
End Date: 2023
Funder: Australian Research Council
View Funded ActivityStart Date: 2011
End Date: 2013
Funder: Australian Research Council
View Funded ActivityStart Date: 2018
End Date: 2021
Funder: Australian Research Council
View Funded ActivityStart Date: 02-2024
End Date: 01-2028
Amount: $1,056,049.00
Funder: Australian Research Council
View Funded ActivityStart Date: 01-2019
End Date: 12-2022
Amount: $673,460.00
Funder: Australian Research Council
View Funded ActivityStart Date: 2022
End Date: 12-2024
Amount: $518,700.00
Funder: Australian Research Council
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