Electrical and Electronic Engineering - Research Publications
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ItemA Silk Fibroin Bio-Transient Solution Processable Memristor(NATURE PORTFOLIO, 2017-11-07)Today's electronic devices are fabricated using highly toxic materials and processes which limits their applications in environmental sensing applications and mandates complex encapsulation methods in biological and medical applications. This paper proposes a fully resorbable high density bio-compatible and environmentally friendly solution processable memristive crossbar arrays using silk fibroin protein which demonstrated bipolar resistive switching ratio of 104 and possesses programmable device lifetime characteristics before the device gracefully bio-degrades, minimizing impact to environment or to the implanted host. Lactate dehydrogenase assays revealed no cytotoxicity on direct exposure to the fabricated device and support their environmentally friendly and biocompatible claims. Moreover, the correlation between the oxidation state of the cations and their tendency in forming conductive filaments with respect to different active electrode materials has been investigated. The experimental results and the numerical model based on electro-thermal effect shows a tight correspondence in predicting the memristive switching process with various combinations of electrodes which provides insight into the morphological changes of conductive filaments in the silk fibroin films.
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ItemAutomated differentiation of pre-diagnosis Huntington's disease from healthy control individuals based on quadratic discriminant analysis of the basal ganglia: The IMAGE-HD study(ACADEMIC PRESS INC ELSEVIER SCIENCE, 2013-03)We investigated two measures of neural integrity, T1-weighted volumetric measures and diffusion tensor imaging (DTI), and explored their combined potential to differentiate pre-diagnosis Huntington's disease (pre-HD) individuals from healthy controls. We applied quadratic discriminant analysis (QDA) to discriminate pre-HD individuals from controls and we utilised feature selection and dimension reduction to increase the robustness of the discrimination method. Thirty six symptomatic HD (symp-HD), 35 pre-HD, and 36 control individuals participated as part of the IMAGE-HD study and underwent T1-weighted MRI, and DTI using a Siemens 3 Tesla scanner. Volume and DTI measures [mean diffusivity (MD) and fractional anisotropy (FA)] were calculated for each group within five regions of interest (ROI; caudate, putamen, pallidum, accumbens and thalamus). QDA was then performed in a stepwise manner to differentiate pre-HD individuals from controls, based initially on unimodal analysis of motor or neurocognitive measures, or on volume, MD or FA measures from within the caudate, pallidum and putamen. We then tested for potential improvements to this model, by examining multi-modal MRI classifications (volume, FA and MD), and also included motor and neurocognitive measures, and additional brain regions (i.e., accumbens and thalamus). Volume, MD and FA differed across the three groups, with pre-HD characterised by significant volumetric reductions and increased FA within caudate, putamen and pallidum, relative to controls. The QDA results demonstrated that the differentiation of pre-HD from controls was highly accurate when both volumetric and diffusion data sets from basal ganglia (BG) regions were used. The highest discriminative accuracy however was achieved in a multi-modality approach and when including all available measures: motor and neurocognitive scores and multi-modal MRI measures from the BG, accumbens and thalamus. Our QDA findings provide evidence that combined multi-modal imaging measures can accurately classify individuals up to 15 years prior to onset when therapeutic intervention is likely to have maximal effects in slowing the trajectory of disease development.
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ItemA review of β-amyloid neuroimaging in Alzheimer's disease(Frontiers Research Foundation, 2014)Alzheimer's disease (AD) is the most common cause of dementia worldwide. As advancing age is the greatest risk factor for developing AD, the number of those afflicted is expected to increase markedly with the aging of the world's population. The inability to definitively diagnose AD until autopsy remains an impediment to establishing effective targeted treatments. Neuroimaging has enabled in vivo visualization of pathological changes in the brain associated with the disease, providing a greater understanding of its pathophysiological development and progression. However, neuroimaging biomarkers do not yet offer clear advantages over current clinical diagnostic criteria for them to be accepted into routine clinical use. Nonetheless, current insights from neuroimaging combined with the elucidation of biochemical and molecular processes in AD are informing the ongoing development of new imaging techniques and their application. Much of this research has been greatly assisted by the availability of transgenic mouse models of AD. In this review we summarize the main efforts of neuroimaging in AD in humans and in mouse models, with a specific focus on ß-amyloid, and discuss the potential of new applications and novel approaches.
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ItemFiltering induces correlation in fMRI resting state data(ACADEMIC PRESS INC ELSEVIER SCIENCE, 2013-01-01)Correlation-based functional MRI connectivity methods typically impose a temporal sample independence assumption on the data. However, the conventional use of temporal filtering to address the high noise content of fMRI data may introduce sample dependence. Violation of the independence assumption has ramifications for the distribution of sample correlation which, if unaccounted for, may invalidate connectivity results. To enable the use of temporal filtering for noise suppression while maintaining the integrity of connectivity results, we derive the distribution of sample correlation between filtered timeseries as a function of the filter frequency response. Corrected distributions are also derived for statistical inference tests of sample correlation between filtered timeseries, including Fisher's z-transformation and the Student's t-test. Crucially, the proposed corrections are valid for any unknown true correlation and arbitrary filter specifications. Empirical simulations demonstrate the potential for temporal filtering to artificially induce connectivity by introducing sample dependence, and verify the utility of the proposed corrections in mitigating this effect. The importance of our corrections is exemplified in a resting state fMRI connectivity analysis: seed-voxel correlation maps generated from filtered data using uncorrected test variates yield an unfeasible number of connections to the left primary motor cortex, suggesting artificially induced connectivity, while maps acquired from filtered data using corrected test variates exhibit bilateral connectivity in the primary motor cortex, in conformance with expected results as seen in the literature.
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ItemA method for shape analysis and segmentation in MRI(IEEE, 2008-12-01)
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ItemA multistage parallel magnetic resonance image reconstruction method(IEEE, 2008-12-01)