ORCID Profile
0000-0002-1412-1423
Current Organisations
UNSW Sydney
,
Sydney Local Health District
,
University of New South Wales
,
University of Sydney
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Publisher: Ovid Technologies (Wolters Kluwer Health)
Date: 10-2016
Publisher: Wiley
Date: 07-2018
Publisher: Springer Science and Business Media LLC
Date: 04-07-2018
DOI: 10.1007/S11682-017-9747-2
Abstract: Incidental findings on structural cerebral magnetic resonance imaging (MRI) are common in healthy subjects, and the prevalence increases with age. There is a paucity of data regarding incidental cerebral findings in twins. We examined brain MRI data acquired from community-dwelling older twins to determine the prevalence and concordance of incidental cerebral findings, as well as the associated clinical implications. Participants (n = 400) were drawn from the Older Australian Twins Study. T1-weighted and T2-weighted fluid-attenuated inversion recovery (FLAIR) cerebral MRI scans were systematically reviewed by a trained, blinded clinician. Incidental findings were recorded according to pre-determined categories, and the diagnosis confirmed by an experienced neuroradiologist. Periventricular and deep white matter hyperintensities (WMH) were scored visually. WMH heritability was calculated for those with the twin pair included in the study (n = 320 in iduals monozygotic (MZ) = 92 twin pairs, dizygotic (DZ) = 68 twin pairs). Excluding infarcts and WMH, a total of 47 (11.75%) incidental abnormalities were detected. The most common findings were hyperostosis frontalis interna (8 participants 2%), meningiomas, (6 participants 1.5%), and intracranial lipomas (5 participants 1.25%). Only 3% of participants were referred for follow-up. Four twin pairs, all monozygotic, had lesions concordant with their twin. Periventricular WMH was moderately heritable (0.61, CI 0.43-0.75, p = 7.21E-08) and deep WMH highly heritable (0.80, CI 0.66-0.88, p = 1.76E-13). As in the general population, incidental findings on cerebral MRI in older twins are common, although concordance rates are low. Such findings can alter the clinical outcome of participants, and should be anticipated by researchers when designing trials involving cerebral imaging.
Publisher: BMJ
Date: 17-12-2021
Abstract: To determine the proportional genetic contribution to the variability of cerebral β-amyloid load in older adults using the classic twin design. Participants (n=206) comprising 61 monozygotic (MZ) twin pairs (68 (55.74%) females mean age (SD): 71.98 (6.43) years), and 42 dizygotic (DZ) twin pairs (56 (66.67%) females mean age: 71.14 (5.15) years) were drawn from the Older Australian Twins Study. Participants underwent detailed clinical and neuropsychological evaluations, as well as MRI, diffusion tensor imaging (DTI) and amyloid PET scans. Fifty-eight participants (17 MZ pairs, 12 DZ pairs) had PET scans with 11 Carbon-Pittsburgh Compound B, and 148 participants (44 MZ pairs, 30 DZ pairs) with 18 Fluorine-NAV4694. Cortical amyloid burden was quantified using the centiloid scale globally, as well as the standardised uptake value ratio (SUVR) globally and in specific brain regions. Small vessel disease (SVD) was quantified using total white matter hyperintensity volume on MRI, and peak width of skeletonised mean diffusivity on DTI. Heritability ( h 2 ) and genetic correlations were measured with structural equation modelling under the best fit model, controlling for age, sex, tracer and scanner. The heritability of global amyloid burden was moderate (0.41 using SUVR 0.52 using the centiloid scale) and ranged from 0.20 to 0.54 across different brain regions. There were no significant genetic or environmental correlations between global amyloid burden and markers of SVD. Amyloid deposition, the hallmark early feature of Alzheimer’s disease, is under moderate genetic influence, suggesting a major environmental contribution that may be amenable to intervention.
Publisher: Ovid Technologies (Wolters Kluwer Health)
Date: 03-2018
Publisher: Cold Spring Harbor Laboratory
Date: 22-03-2022
DOI: 10.1101/2022.03.21.22272729
Abstract: Objective: Several movement disorders develop secondary to the use of psychotropic drugs, for which multiple symptom rating scales are in common use. We planned to develop the Unified Drug-Induced Movement Scale (UDIMS) to assess the severity and impact of drug-induced dyskinesia, tremor, drug-induced parkinsonism, akathisia, dystonia and myoclonus with a single instrument. Methods: Based on a literature review, consultation and pilot work, a 12-item instrument was developed, with each item rated on a 0-4 scale. The clinimetric properties of UDIMS were examined in 53 psychiatric patients on psychotropic medications, using established ratings scales for validation. The factor structure of the scale was examined, and the movement disorder correlates of distress and disability were determined. Results: The instrument has good inter-rater reliability. Its correspondence with three other scales - Abnormal Involuntary Movements Scale, Simpson-Angus Scale and Prince Henry Hospital Akathisia Scale - for the relevant items was high. A principal components analysis yielded four factors, considered to represent tremor, parkinsonism, akathisia and dyskinesia. Overall movement-disorder related disability was related to parkinsonism and dyskinesia, while distress to all four components. Conclusions: UDIMS is a reliable and valid scale to quantify a range of drug-induced movement disorders (DIMDs), that obviates the need for the use of multiple rating scales. Its widespread use by both clinicians and researchers, and further refinement based on this, will help promote the detection and treatment of drug-induced movement disorders, thereby reducing both distress and disability.
Publisher: Wiley
Date: 07-2019
Publisher: Ovid Technologies (Wolters Kluwer Health)
Date: 31-12-2021
Publisher: Elsevier BV
Date: 06-2020
Publisher: Elsevier BV
Date: 07-2018
DOI: 10.1016/J.NEUROIMAGE.2018.03.050
Abstract: We present 'UBO Detector', a cluster-based, fully automated pipeline for extracting and calculating variables for regions of white matter hyperintensities (WMH) (available for download at cheba.unsw.edu.au/group/neuroimaging-pipeline). It takes T1-weighted and fluid attenuated inversion recovery (FLAIR) scans as input, and SPM12 and FSL functions are utilised for pre-processing. The candidate clusters are then generated by FMRIB's Automated Segmentation Tool (FAST). A supervised machine learning algorithm, k-nearest neighbor (k-NN), is applied to determine whether the candidate clusters are WMH or non-WMH. UBO Detector generates both image and text (volumes and the number of WMH clusters) outputs for whole brain, periventricular, deep, and lobar WMH, as well as WMH in arterial territories. The computation time for each brain is approximately 15 min. We validated the performance of UBO Detector by showing a) high segmentation (similarity index (SI) = 0.848) and volumetric (intraclass correlation coefficient (ICC) = 0.985) agreement between the UBO Detector-derived and manually traced WMH b) highly correlated (r
No related grants have been discovered for Rebecca Koncz.