Publications by authors named "Siddharth Arora"

14 Publications

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Olfactory Testing in Parkinson's Disease & REM Behavior Disorder: A Machine Learning Approach.

Neurology 2021 Feb 24. Epub 2021 Feb 24.

Oxford Parkinson's Disease Centre (OPDC), University of Oxford, UK.

Objective: We sought to identify an abbreviated test of impaired olfaction, amenable for use in busy clinical environments in prodromal (isolated REM sleep Behavior Disorder (iRBD)) and manifest Parkinson's disease (PD).

Methods: 890 PD and 313 control participants in the Discovery cohort study underwent Sniffin' stick odour identification assessment. Random forests were initially trained to distinguish individuals with poor (functional anosmia/hyposmia) and good (normosmia/super-smeller) smell ability using all 16 Sniffin' sticks. Models were retrained using the top 3 sticks ranked by order of predictor importance. One randomly selected 3-stick model was tested in a second independent PD dataset (n=452) and in two iRBD datasets (Discovery n=241; Marburg n=37) before being compared to previously described abbreviated Sniffin' stick combinations.

Results: In differentiating poor from good smell ability, the overall area under the curve (AUC) value associated with the top 3 sticks (Anise/Licorice/Banana) was 0.95 in the development dataset (sensitivity:90%, specificity:92%, positive predictive value:92%, negative predictive value:90%). Internal and external validation confirmed AUCs≥0.90. The combination of 3-stick model determined poor smell and an RBD screening questionnaire score of ≥5, separated iRBD from controls with a sensitivity, specificity, PPV and NPV of 65%, 100%, 100% and 30%.

Conclusions: Our 3-Sniffin'-stick model holds potential utility as a brief screening test in the stratification of individuals with PD and iRBD according to olfactory dysfunction.

Classification Of Evidence: This study provides Class III evidence that a 3-Sniffin'-stick model distinguishes individuals with poor and good smell ability and can be used to screen for individuals with iRBD.
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http://dx.doi.org/10.1212/WNL.0000000000011743DOI Listing
February 2021

Prioritizing Delivery of Cancer Treatment During a COVID-19 Lockdown: The Experience of a Clinical Oncology Service in India.

JCO Glob Oncol 2021 01;7:99-107

Department of Radiation Oncology, Tata Medical Center, Kolkata, India.

Purpose: A COVID-19 lockdown in India posed significant challenges to the continuation of radiotherapy (RT) and systemic therapy services. Although several COVID-19 service guidelines have been promulgated, implementation data are yet unavailable. We performed a comprehensive audit of the implementation of services in a clinical oncology department.

Methods: A departmental protocol of priority-based treatment guidance was developed, and a departmental staff rotation policy was implemented. Data were collected for the period of lockdown on outpatient visits, starting, and delivery of RT and systemic therapy. Adherence to protocol was audited, and factors affecting change from pre-COVID standards analyzed by multivariate logistic regression.

Results: Outpatient consults dropped by 58%. Planned RT starts were implemented in 90%, 100%, 92%, 90%, and 75% of priority level 1-5 patients. Although 17% had a deferred start, the median time to start of adjuvant RT and overall treatment times were maintained. Concurrent chemotherapy was administered in 89% of those eligible. Systemic therapy was administered to 84.5% of planned patients. However, 33% and 57% of curative and palliative patients had modifications in cycle duration or deferrals. The patient's inability to come was the most common reason for RT or ST deviation. Factors independently associated with a change from pre-COVID practice was priority-level allocation for RT and age and palliative intent for systemic therapy.

Conclusion: Despite significant access limitations, a planned priority-based system of delivery of treatment could be implemented.
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http://dx.doi.org/10.1200/GO.20.00433DOI Listing
January 2021

Predicting motor, cognitive & functional impairment in Parkinson's.

Ann Clin Transl Neurol 2019 08 26;6(8):1498-1509. Epub 2019 Jul 26.

Oxford Parkinson's Disease Centre (OPDC), University of Oxford, Oxford, UK.

Objective: We recently demonstrated that 998 features derived from a simple 7-minute smartphone test could distinguish between controls, people with Parkinson's and people with idiopathic Rapid Eye Movement sleep behavior disorder, with mean sensitivity/specificity values of 84.6-91.9%. Here, we investigate whether the same smartphone features can be used to predict future clinically relevant outcomes in early Parkinson's.

Methods: A total of 237 participants with Parkinson's (mean (SD) disease duration 3.5 (2.2) years) in the Oxford Discovery cohort performed smartphone tests in clinic and at home. Each test assessed voice, balance, gait, reaction time, dexterity, rest, and postural tremor. In addition, standard motor, cognitive and functional assessments and questionnaires were administered in clinic. Machine learning algorithms were trained to predict the onset of clinical outcomes provided at the next 18-month follow-up visit using baseline smartphone recordings alone. The accuracy of model predictions was assessed using 10-fold and subject-wise cross validation schemes.

Results: Baseline smartphone tests predicted the new onset of falls, freezing, postural instability, cognitive impairment, and functional impairment at 18 months. For all outcome predictions AUC values were greater than 0.90 for 10-fold cross validation using all smartphone features. Using only the 30 most salient features, AUC values greater than 0.75 were obtained.

Interpretation: We demonstrate the ability to predict key future clinical outcomes using a simple smartphone test. This work has the potential to introduce individualized predictions to routine care, helping to target interventions to those most likely to benefit, with the aim of improving their outcome.
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http://dx.doi.org/10.1002/acn3.50853DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6689691PMC
August 2019

Developing a large scale population screening tool for the assessment of Parkinson's disease using telephone-quality voice.

J Acoust Soc Am 2019 05;145(5):2871

Usher Institute of Population Health Sciences and Informatics, Medical School, University of Edinburgh, Edinburgh, EH16 4UX, United Kingdom.

Recent studies have demonstrated that analysis of laboratory-quality voice recordings can be used to accurately differentiate people diagnosed with Parkinson's disease (PD) from healthy controls (HCs). These findings could help facilitate the development of remote screening and monitoring tools for PD. In this study, 2759 telephone-quality voice recordings from 1483 PD and 15 321 recordings from 8300 HC participants were analyzed. To account for variations in phonetic backgrounds, data were acquired from seven countries. A statistical framework for analyzing voice was developed, whereby 307 dysphonia measures that quantify different properties of voice impairment, such as breathiness, roughness, monopitch, hoarse voice quality, and exaggerated vocal tremor, were computed. Feature selection algorithms were used to identify robust parsimonious feature subsets, which were used in combination with a random forests (RFs) classifier to accurately distinguish PD from HC. The best tenfold cross-validation performance was obtained using Gram-Schmidt orthogonalization and RF, leading to mean sensitivity of 64.90% (standard deviation, SD, 2.90%) and mean specificity of 67.96% (SD 2.90%). This large scale study is a step forward toward assessing the development of a reliable, cost-effective, and practical clinical decision support tool for screening the population at large for PD using telephone-quality voice.
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http://dx.doi.org/10.1121/1.5100272DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6509044PMC
May 2019

Investigating Voice as a Biomarker for Leucine-Rich Repeat Kinase 2-Associated Parkinson's Disease.

J Parkinsons Dis 2018 ;8(4):503-510

The Edmond J. Safra Program in Parkinson's Disease and the Morton and Gloria Shulman Movement Disorders Centre and, Toronto Western Hospital, Toronto, ON, Canada.

We investigate the potential association between leucine-rich repeat kinase 2 (LRRK2) mutations and voice. Sustained phonations ('aaah' sounds) were recorded from 7 individuals with LRRK2-associated Parkinson's disease (PD), 17 participants with idiopathic PD (iPD), 20 non-manifesting LRRK2-mutation carriers, 25 related non-carriers, and 26 controls. In distinguishing LRRK2-associated PD and iPD, the mean sensitivity was 95.4% (SD 17.8%) and mean specificity was 89.6% (SD 26.5%). Voice features for non-manifesting carriers, related non-carriers, and controls were much less discriminatory. Vocal deficits in LRRK2-associated PD may be different than those in iPD. These preliminary results warrant longitudinal analyses and replication in larger cohorts.
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http://dx.doi.org/10.3233/JPD-181389DOI Listing
October 2019

Smartphone motor testing to distinguish idiopathic REM sleep behavior disorder, controls, and PD.

Neurology 2018 10 19;91(16):e1528-e1538. Epub 2018 Sep 19.

From the Oxford Parkinson's Disease Centre (OPDC) (S.A., F.B., C.L., T.R.B., M.R., C.R., J.C.K., J.R., A.L., R.W.-M, M.T.H.), University of Oxford, UK; Engineering and Applied Science (S.A., M.A.L.), Aston University, Birmingham, UK; Somerville College (S.A.), University of Oxford, UK; Nuffield Department of Clinical Neurosciences (F.B., C.L., T.R.B., M.A.L., M.T.H.), University of Oxford, UK; Population Health Sciences (M.A.L.), University of Bristol, UK; andDepartment of Computer Science (A.Z.), Johns Hopkins University, Baltimore; Department of Neurology and Neurophysiology (Z.Z., G.L., M.T.H.), Oxford University Hospitals NHS Trust, UK; Respiratory Support and Sleep Centre (T.Q.), Papworth Hospital, Cambridge, UK; Department of Neurology (G.D.), Royal Hallamshire Hospital, Sheffield, UK; and Media Lab (M.A.L.), Massachusetts Institute of Technology, Cambridge, MA.

Objective: We sought to identify motor features that would allow the delineation of individuals with sleep study-confirmed idiopathic REM sleep behavior disorder (iRBD) from controls and Parkinson disease (PD) using a customized smartphone application.

Methods: A total of 334 PD, 104 iRBD, and 84 control participants performed 7 tasks to evaluate voice, balance, gait, finger tapping, reaction time, rest tremor, and postural tremor. Smartphone recordings were collected both in clinic and at home under noncontrolled conditions over several days. All participants underwent detailed parallel in-clinic assessments. Using only the smartphone sensor recordings, we sought to (1) discriminate whether the participant had iRBD or PD and (2) identify which of the above 7 motor tasks were most salient in distinguishing groups.

Results: Statistically significant differences based on these 7 tasks were observed between the 3 groups. For the 3 pairwise discriminatory comparisons, (1) controls vs iRBD, (2) controls vs PD, and (3) iRBD vs PD, the mean sensitivity and specificity values ranged from 84.6% to 91.9%. Postural tremor, rest tremor, and voice were the most discriminatory tasks overall, whereas the reaction time was least discriminatory.

Conclusions: Prodromal forms of PD include the sleep disorder iRBD, where subtle motor impairment can be detected using clinician-based rating scales (e.g., Unified Parkinson's Disease Rating Scale), which may lack the sensitivity to detect and track granular change. Consumer grade smartphones can be used to accurately separate not only iRBD from controls but also iRBD from PD participants, providing a growing consensus for the utility of digital biomarkers in early and prodromal PD.
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http://dx.doi.org/10.1212/WNL.0000000000006366DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6202945PMC
October 2018

Big data in Parkinson's disease: using smartphones to remotely detect longitudinal disease phenotypes.

Physiol Meas 2018 04 26;39(4):044005. Epub 2018 Apr 26.

Department of Engineering Science, Institute of Biomedical Engineering, University of Oxford, Oxford, United Kingdom.

Objective: To better understand the longitudinal characteristics of Parkinson's disease (PD) through the analysis of finger tapping and memory tests collected remotely using smartphones.

Approach: Using a large cohort (312 PD subjects and 236 controls) of participants in the mPower study, we extract clinically validated features from a finger tapping and memory test to monitor the longitudinal behaviour of study participants. We investigate any discrepancy in learning rates associated with motor and non-motor tasks between PD subjects and healthy controls. The ability of these features to predict self-assigned severity measures is assessed whilst simultaneously inspecting the severity scoring system for floor-ceiling effects. Finally, we study the relationship between motor and non-motor longitudinal behaviour to determine if separate aspects of the disease are dependent on one another.

Main Results: We find that the test performances of the most severe subjects show significant correlations with self-assigned severity measures. Interestingly, less severe subjects do not show significant correlations, which is shown to be a consequence of floor-ceiling effects within the mPower self-reporting severity system. We find that motor performance after practise is a better predictor of severity than baseline performance suggesting that starting performance at a new motor task is less representative of disease severity than the performance after the test has been learnt. We find PD subjects show significant impairments in motor ability as assessed through the alternating finger tapping (AFT) test in both the short- and long-term analyses. In the AFT and memory tests we demonstrate that PD subjects show a larger degree of longitudinal performance variability in addition to requiring more instances of a test to reach a steady state performance than healthy subjects.

Significance: Our findings pave the way forward for objective assessment and quantification of longitudinal learning rates in PD. This can be particularly useful for symptom monitoring and assessing medication response. This study tries to tackle some of the major challenges associated with self-assessed severity labels by designing and validating features extracted from big datasets in PD, which could help identify digital biomarkers capable of providing measures of disease severity outside of a clinical environment.
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http://dx.doi.org/10.1088/1361-6579/aab512DOI Listing
April 2018

Ketamine as a bridging agent in opioid maintenance therapy.

Am J Addict 2018 Jan;27(1):47-48

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http://dx.doi.org/10.1111/ajad.12672DOI Listing
January 2018

Metabolomics reveals distinct, antibody-independent, molecular signatures of MS, AQP4-antibody and MOG-antibody disease.

Acta Neuropathol Commun 2017 Dec 6;5(1):95. Epub 2017 Dec 6.

Nuffield Department of Clinical Neurosciences, John Radcliffe Hospital, University of Oxford, Level 3, West Wing, Headley Way, Oxford, OX3 9DU, UK.

The overlapping clinical features of relapsing remitting multiple sclerosis (RRMS), aquaporin-4 (AQP4)-antibody (Ab) neuromyelitis optica spectrum disorder (NMOSD), and myelin oligodendrocyte glycoprotein (MOG)-Ab disease mean that detection of disease specific serum antibodies is the gold standard in diagnostics. However, antibody levels are not prognostic and may become undetectable after treatment or during remission. Therefore, there is still a need to discover antibody-independent biomarkers. We sought to discover whether plasma metabolic profiling could provide biomarkers of these three diseases and explore if the metabolic differences are independent of antibody titre. Plasma samples from 108 patients (34 RRMS, 54 AQP4-Ab NMOSD, and 20 MOG-Ab disease) were analysed by nuclear magnetic resonance spectroscopy followed by lipoprotein profiling. Orthogonal partial-least squares discriminatory analysis (OPLS-DA) was used to identify significant differences in the plasma metabolite concentrations and produce models (mathematical algorithms) capable of identifying these diseases. In all instances, the models were highly discriminatory, with a distinct metabolite pattern identified for each disease. In addition, OPLS-DA identified AQP4-Ab NMOSD patient samples with low/undetectable antibody levels with an accuracy of 92%. The AQP4-Ab NMOSD metabolic profile was characterised by decreased levels of scyllo-inositol and small high density lipoprotein particles along with an increase in large low density lipoprotein particles relative to both RRMS and MOG-Ab disease. RRMS plasma exhibited increased histidine and glucose, along with decreased lactate, alanine, and large high density lipoproteins while MOG-Ab disease plasma was defined by increases in formate and leucine coupled with decreased myo-inositol. Despite overlap in clinical measures in these three diseases, the distinct plasma metabolic patterns support their distinct serological profiles and confirm that these conditions are indeed different at a molecular level. The metabolites identified provide a molecular signature of each condition which is independent of antibody titre and EDSS, with potential use for disease monitoring and diagnosis.
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http://dx.doi.org/10.1186/s40478-017-0495-8DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5718082PMC
December 2017

Factors affecting media coverage of species rediscoveries.

Conserv Biol 2016 08 13;30(4):914-7. Epub 2016 Apr 13.

School of Geography and the Environment, University of Oxford, South Parks Road, Oxford, OX1 3QY, U.K.

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http://dx.doi.org/10.1111/cobi.12683DOI Listing
August 2016

Cortical and Clonal Contribution of Tbr2 Expressing Progenitors in the Developing Mouse Brain.

Cereb Cortex 2015 Oct 13;25(10):3290-302. Epub 2014 Jun 13.

Department of Physiology, Anatomy and Genetics, University of Oxford, Oxford, UK.

The individual contribution of different progenitor subtypes towards the mature rodent cerebral cortex is not fully understood. Intermediate progenitor cells (IPCs) are key to understanding the regulation of neuronal number during cortical development and evolution, yet their exact contribution is much debated. Intermediate progenitors in the cortical subventricular zone are defined by expression of T-box brain-2 (Tbr2). In this study we demonstrate by using the Tbr2(Cre) mouse line and state-of-the-art cell lineage labeling techniques, that IPC derived cells contribute substantial proportions 67.5% of glutamatergic but not GABAergic or astrocytic cells to all cortical layers including the earliest generated subplate zone. We also describe the laminar dispersion of clonally derived cells from IPCs using a recently described clonal analysis tool (CLoNe) and show that pair-generated cells in different layers cluster closer (142.1 ± 76.8 μm) than unrelated cells (294.9 ± 105.4 μm). The clonal dispersion from individual Tbr2 positive intermediate progenitors contributes to increasing the cortical surface. Our study also describes extracortical contributions from Tbr2+ progenitors to the lateral olfactory tract and ventromedial hypothalamic nucleus.
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http://dx.doi.org/10.1093/cercor/bhu125DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4585488PMC
October 2015

Distinguishing autofluorescence of normal, benign, and cancerous breast tissues through wavelet domain correlation studies.

J Biomed Opt 2011 Aug;16(8):087003

Gujarat University, C.U. Shah Science College, Ahmedabad-380 009, India.

Using the multiresolution ability of wavelets and effectiveness of singular value decomposition (SVD) to identify statistically robust parameters, we find a number of local and global features, capturing spectral correlations in the co- and cross-polarized channels, at different scales (of human breast tissues). The copolarized component, being sensitive to intrinsic fluorescence, shows different behavior for normal, benign, and cancerous tissues, in the emission domain of known fluorophores, whereas the perpendicular component, being more prone to the diffusive effect of scattering, points out differences in the Kernel-Smoother density estimate employed to the principal components, between malignant, normal, and benign tissues. The eigenvectors, corresponding to the dominant eigenvalues of the correlation matrix in SVD, also exhibit significant differences between the three tissue types, which clearly reflects the differences in the spectral correlation behavior. Interestingly, the most significant distinguishing feature manifests in the perpendicular component, corresponding to porphyrin emission range in the cancerous tissue. The fact that perpendicular component is strongly influenced by depolarization, and porphyrin emissions in cancerous tissue has been found to be strongly depolarized, may be the possible cause of the above observation.
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http://dx.doi.org/10.1117/1.3606563DOI Listing
August 2011

Characterizing breast cancer tissues through the spectral correlation properties of polarized fluorescence.

J Biomed Opt 2008 Sep-Oct;13(5):054063

Gujarat University, C. U. Shah Science College, Ahmedabad, 380 009, India.

We study the spectral correlation properties of the polarized fluorescence spectra of normal and cancerous human breast tissues, corresponding to patients belonging to diverse age groups and socioeconomic backgrounds. The emission range in the visible wavelength regime of 500 to 700 nm is analyzed, with the excitation wavelength at 488 nm, where flavin is one of the active fluorophores. The correlation matrices for parallel and perpendicularly polarized fluorescence spectra reveal correlated domains, differing significantly in normal and cancerous tissues. These domains can be ascribed to different fluorophores and absorbers in the tissue medium. The spectral fluctuations in the perpendicular component of the cancerous tissue clearly reveal randomization not present in the normal channel. Random matrix-based predictions for the spectral correlations match quite well with the observed behavior. The eigenvectors of the correlation matrices corresponding to large eigenvalues clearly separate out different tissue types and identify the dominant wavelengths, which are active in cancerous tissues.
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http://dx.doi.org/10.1117/1.2997376DOI Listing
February 2009

Zero delay synchronization of chaos in coupled map lattices.

Phys Rev E Stat Nonlin Soft Matter Phys 2007 Aug 3;76(2 Pt 2):026202. Epub 2007 Aug 3.

Physical Research Laboratory, Navrangpura, Ahmedabad 380 009, India.

We show that two coupled map lattices that are mutually coupled to one another with a delay can display zero delay synchronization if they are driven by a third coupled map lattice. We analytically estimate the parametric regimes that lead to synchronization and show that the presence of mutual delays enhances synchronization to some extent. The zero delay or isochronal synchronization is reasonably robust against mismatches in the internal parameters of the coupled map lattices, and we analytically estimate the synchronization error bounds.
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http://dx.doi.org/10.1103/PhysRevE.76.026202DOI Listing
August 2007