Nonlinear Weighting Ensemble Learning Model to Diagnose Parkinson's Disease Using Multimodal Data

This work was supported by the FEDER/Junta deAndalucia-Consejeria de Transformacion Economica, Industria, Conocimiento y Universidades/Proyecto (B-TIC-586-UGR20); the MCIN/AEI/10.13039/501100011033/ and FEDER \Una manerade hacer Europa" under the RTI2018-098913-B100 project, by the Consejeria de Economia, Innovacion,Ciencia y Empleo (Junta de Andalucia) and FEDER under CV20-45250, A-TIC-080-UGR18 and P20-00525 projects. Grant by F.J.M.M. RYC2021-030875-I funded by MCIN/AEI/10.13039/501100011033 and European Union NextGenerationEU/PRTR. Work by D.C.B. is supported by the MCIN/AEI/FJC2021-048082-I Juan de la Cierva Formacion'. Work by J.E.A. is supported by Next Generation EU Fund through a Margarita Salas Grant, and work by C.J.M. is supported by Ministerio de Universidades under the FPU18/04902 grant.Parkinson's Disease (PD) is the second most prevalent neurodegenerative disorder among adults. Although its triggers are still not clear, they may be due to a combination of different types of biomarkers measured through medical imaging, metabolomics, proteomics or genetics, among others. In this context, we have proposed a Computer-Aided Diagnosis (CAD) system that combines structural and functional imaging data from subjects in Parkinson's Progression Markers Initiative dataset by means of an Ensemble Learning methodology trained to identify and penalize input sources with low classification rates and/or high-variability. This proposal improves results published in recent years and provides an accurate solution not only from the point of view of image preprocessing (including a comparison between different intensity preservation techniques), but also in terms of dimensionality reduction methods (Isomap). In addition, we have also introduced a bagging classification schema for scenarios with unbalanced data.As shown by our results, the CAD proposal is able to detect PD with 96.48% of balanced accuracy, and opens up the possibility of combining any number of input data sources relevant for PD.FEDER/Junta deAndalucia-Consejeria de Transformacion Economica, Industria, Conocimiento y Universidades/Proyecto B-TIC-586-UGR20MCIN/AEI P20-00525FEDER \Una manerade hacer Europa RYC2021-030875-IJunta de AndaluciaEuropean Union (EU) Spanish Government RTI2018-098913-B100, CV20-45250, A-TIC-080-UGR18European Union (EU)Juan de la Cierva FormacionNext Generation EU Fund through a Margarita Salas GrantMinisterio de Universidades FPU18/0490

Psychometric development of an instrument for the diagnosis and assessment of anosognosia in Alzheimer\u27s disease

The purpose this study was to develop a psychometrically sound paper-n-pencil questionnaire for the measuring and diagnosing of anosognosia in Alzheimer\u27s disease (AD). Anosognosia is defined as the lack of awareness one has towards one\u27s own state. It manifests within AD as an unawareness of symptoms the individual is experiencing. The initial 43-item questionnaire was administered to 67 AD patients (age: u = 72.66, SD = 3.40), with 41 females and 26 males. A Cronbach\u27s-alpha of 0.89 was obtained showing the questionnaire had excellent internal reliability. The 43 items in the original questionnaire were reduced to 10 using the internal reliability analysis. The 10-item questionnaire was administered to a new group of 83 AD patients (age: u = 75.83, SD = 3.83), with 58 females and 25 males. Internal reliability of the new questionnaire remained high with an obtained Cronbach\u27s-alpha of 0.87.Correlations between the sample population 10-item questionnaire score and the Mini Mental State Exam (r = -0.24, p \u3c 0.05) and Geriatric Depression Scale (r = -0.30, p \u3c 0.05) showed a low but significant correlation. The 8-Point Clock Drawing (r = - 0.04, p \u3e 0.05), and Zarit\u27s Caregiver Burden Scale (r = 0.04, p \u3e 0.05) showed no correlation. Using 19 of the patients a one-way Intraclass Coefficient (ICC) was used to determine inter-rater agreement (alpha = 0.63). Twenty-one of the patients were used for the purpose of test-retest and resulted in a Pearson-r correlation of r = 0.70 (p \u3c 0.000). Forty-three normal subjects were enrolled in the study (age: u = 73.95, SD = 3.90) with 23 females and 20 males. Using the normals mean + 2SD a cutoff score of 12 was obtained as the point where an AD patient was diagnosed as having anosognosia. Using the cutoff value there were 42 AD patients who had anosognosia which was 51% of the sample population.The questionnaire was found to be reliable though further studies would be needed to confirm the results by expanding the sample size and using more generalized inclusion criteria. Nevertheless, the questionnaire showed little relationship to the other questionnaires administered during the study. This helps to show the questionnaire is measuring a unique phenomenon which is not related to other standard diagnostic questionnaires used with AD patients

Optimized semi-quantitative analysis of dopamine transporter SPECT to support visual image interpretation in the diagnosis of parkinsonian syndromes

Zur Differenzierung von neurodegenerativen und nicht-neurodegenerativen Ursachen eines klinisch unklaren Parkinsonsyndroms wird die Dopamintransporter-SPECT (DAT-SPECT) eingesetzt. Neben der visuellen Bildinterpretation unterstützt die semi-quantitative Analyse der striatalen Dopamintransporter-Verfügbarkeit die Befundung. Die vorliegende Dissertationsschrift fasst drei Studien zusammen, die klinisch relevante Parameter der Bildentstehung und Bildverarbeitung in der semi-quantitativen Analyse der DAT-SPECT identifizierten, optimierten und hinsichtlich ihrer diagnostischen Genauigkeit untersuchten. In der ersten Studie wurde eine vollautomatische Methode zur Abgrenzung der äußeren Kopfkontur als Teil der Schwächungskorrektur nach Chang implementiert und gegenüber einer klinisch etablierten halbautomatischen Methode validiert. Die Auswertung eines multizentrischen Datensatzes ergab, dass beide Methoden zur Kopfabgrenzung sowohl vergleichbare semi-quantitative Werte als auch eine vergleichbare diagnostische Genauigkeit lieferten. Damit kann die vollautomatische Methode für den Einsatz in der klinischen Versorgung empfohlen werden, da keine Interaktion durch den Nutzer erforderlich ist. Die zweite Studie untersuchte zwei Methoden zur semi-quantitativen Abschätzung der Tracer Bindung hinsichtlich ihrer diagnostischen Genauigkeit. Der auflösungsunabhängige specific uptake size index (SUSI) zeigte bei Datenerhebung an unterschiedlichen Kamerasystemen eine höhere diagnostische Genauigkeit als der Standardparameter, das sogenannte specific binding ratio (SBR). Dies ist besonders relevant für multizentrische Studien. Sobald jedoch nur ein Kamerasystem eingesetzt wurde, ist der Standardparameter SBR dem SUSI vorzuziehen, da dieser bei vergleichbarer diagnostischer Performance weniger anfällig gegenüber einer fehlerhaften Abschätzung der nicht-spezifischen Tracer-Bindung in der Referenzregion ist. Ziel der dritten Studie war die Untersuchung des Einflusses der Größe der Normaldatenbank (NDB) auf die diagnostische Genauigkeit einer semi-quantitativen Auswertung der DAT-SPECT. Dabei erfolgte eine Simulation von unterschiedlichen Größen der NDB (n=5, 10, 15, …, 50) durch zufälliges Ziehen aus dem Pool an Kontrollen und Validierung der jeweiligen NDB in der Gesamtkohorte anhand von Klassifizierungsgenauigkeit, Sensitivität und Spezifität. Die Analyse ergab, dass ein Mindestumfang von 25 bis 30 DAT-SPECT-Datensätzen zur Bildung einer NDB notwendig ist. Eine Vergrößerung der NDB über 40 Fälle hinaus führt hingegen zu keiner weiteren relevanten Steigerung der diagnostischen Genauigkeit.Dopamine transporter SPECT (DAT-SPECT) is an established method to differentiate neurodegenerative and non-neurodegenerative causes in clinically uncertain parkinsonian syndromes. Besides visual image interpretation, semi-quantitative analysis of the striatal dopamine transporter availability is used to support medical diagnosis. The present doctoral thesis summarizes three studies that identified, optimized and validated clinically relevant, semi-quantitative parameters of DAT-SPECT image acquisition and processing with reference to their diagnostic accuracy. The first study proposed a fully automatic segmentation method of the outer head contour as a part of attenuation correction according to Chang and validated this method to a well-established semi-automatic method. Both methods for head delineation showed comparable semi-quantitative properties as well as comparable diagnostic accuracy based on multi-center patient data. For this reason, we suggest to use the fully automatic method in clinical patient care since no user interaction is required. A direct comparison of two semi-quantitative methods for estimation of tracer binding in reference to diagnostic accuracy was the aim of the second study. The spatial resolution independent specific uptake size index (SUSI) provided a higher diagnostic accuracy compared to the commonly used parameter, the specific binding ratio (SBR), when image acquisition is performed at various camera systems. This is highly relevant for multi-center image acquisition. However, in single-camera/mono-center settings SBR should be favored over SUSI, since SBR seemed to be less sensitive towards errors of the estimate of non-specific tracer uptake in the reference region with comparable diagnostic performance to SUSI. Rationale of the third study was to evaluate the impact of the size of the normal database (NDB) on the diagnostic performance of semi-quantitative analysis in DAT-SPECT. For it, simulation of NDB with different sizes (n=5, 10, 15, …, 50) by randomly selecting subjects from the subcohort of normal controls was implemented and validation of each particular NDB based on the overall cohort was done concerning diagnostic accuracy, sensitivity and specificity as performance measures. The study results suggested that 25 to 30 DAT-SPECT data sets should be the minimum size of NDB. Increasing the size of NDB beyond 40 data sets provided only very small further improvement in diagnostic accuracy

Deep learning of brain asymmetry digital biomarkers to support early diagnosis of cognitive decline and dementia

Early identification of degenerative processes in the human brain is essential for proper care and treatment. This may involve different instrumental diagnostic methods, including the most popular computer tomography (CT), magnetic resonance imaging (MRI) and positron emission tomography (PET) scans. These technologies provide detailed information about the shape, size, and function of the human brain. Structural and functional cerebral changes can be detected by computational algorithms and used to diagnose dementia and its stages (amnestic early mild cognitive impairment - EMCI, Alzheimer’s Disease - AD). They can help monitor the progress of the disease. Transformation shifts in the degree of asymmetry between the left and right hemispheres illustrate the initialization or development of a pathological process in the brain. In this vein, this study proposes a new digital biomarker for the diagnosis of early dementia based on the detection of image asymmetries and crosssectional comparison of NC (normal cognitively), EMCI and AD subjects. Features of brain asymmetries extracted from MRI of the ADNI and OASIS databases are used to analyze structural brain changes and machine learning classification of the pathology. The experimental part of the study includes results of supervised machine learning algorithms and transfer learning architectures of convolutional neural networks for distinguishing between cognitively normal subjects and patients with early or progressive dementia. The proposed pipeline offers a low-cost imaging biomarker for the classification of dementia. It can be potentially helpful to other brain degenerative disorders accompanied by changes in brain asymmetries

Classification of dental x-ray images

Forensic dentistry is concerned with identifying people based on their dental records. Forensic specialists have a large number of cases to investigate and hence, it has become important to automate forensic identification systems. The radiographs acquired after a person is deceased are called the Post-mortem (PM) radiographs, and the radiographs acquired while the person is alive are called the Ante-mortem (AM) radiographs. Dental biometrics automatically analyzes dental radiographs to identify the deceased individuals. While, ante mortem (AM) identification is usually possible through comparison of many biometric identifiers, postmortem (PM) identification is impossible using behavioral biometrics (e.g. speech, gait). Moreover, under severe circumstances, such as those encountered in mass disasters (e.g. airplane crashes and natural disasters such as Tsunami) most physiological biometrics may not be employed for identification, because of the decay of soft tissues of the body to unidentifiable states. Under such circumstances, the best candidates for postmortem biometric identification are the dental features because of their survivability and diversity.;In my work, I present two different techniques to classify periapical images as maxilla (upper jaw) or mandible (lower jaw) images and we show a third technique to classify dental bitewing images as horizontally flipped/rotated or horizontally un-flipped/un-rotated. In our first technique I present an algorithm to classify whether a given dental periapical image is of a maxilla (upper jaw) or a mandible (lower jaw) using texture analysis of the jaw bone. While the bone analysis method is manual, in our second technique, I propose an automated approach for the identification of dental periapical images using the crown curve detection Algorithm. The third proposed algorithm works in an automated manner for a large number of database comprised of dental bitewing images. Each dental bitewing image in the data base can be classified as a horizontally flipped or un-flipped image in a time efficient manner

Texture analysis of MR images of patients with Mild Traumatic Brain Injury

<p>Abstract</p> <p>Background</p> <p>Our objective was to study the effect of trauma on texture features in cerebral tissue in mild traumatic brain injury (MTBI). Our hypothesis was that a mild trauma may cause microstructural changes, which are not necessarily perceptible by visual inspection but could be detected with texture analysis (TA).</p> <p>Methods</p> <p>We imaged 42 MTBI patients by using 1.5 T MRI within three weeks of onset of trauma. TA was performed on the area of mesencephalon, cerebral white matter at the levels of mesencephalon, corona radiata and centrum semiovale and in different segments of corpus callosum (CC) which have been found to be sensitive to damage. The same procedure was carried out on a control group of ten healthy volunteers. Patients' TA data was compared with the TA results of the control group comparing the amount of statistically significantly differing TA parameters between the left and right sides of the cerebral tissue and comparing the most discriminative parameters.</p> <p>Results</p> <p>There were statistically significant differences especially in several co-occurrence and run-length matrix based parameters between left and right side in the area of mesencephalon, in cerebral white matter at the level of corona radiata and in the segments of CC in patients. Considerably less difference was observed in the healthy controls.</p> <p>Conclusions</p> <p>TA revealed significant changes in texture parameters of cerebral tissue between hemispheres and CC segments in TBI patients. TA may serve as a novel additional tool for detecting the conventionally invisible changes in cerebral tissue in MTBI and help the clinicians to make an early diagnosis.</p

Progressive cortical thinning and subcortical atrophy in dementia with Lewy bodies and Alzheimer's disease.

Patterns of progressive cortical thinning in dementia with Lewy bodies (DLB) remain poorly understood. We examined spatiotemporal patterns of cortical thinning and subcortical atrophy over 12 months in DLB (n = 13), compared with Alzheimer's disease (AD) (n = 23) and healthy control subjects (HC) (n = 33). Rates of temporal thinning in DLB were relatively preserved compared with AD. Volumetric analyses subcortical changes revealed that the AD group demonstrated significantly increased hippocampal atrophy (-5.8%) relative to the HC (-1.7%; p < 0.001) and DLB groups (-2.5%, p = 0.006). Significant lateral ventricular expansion was also observed in AD (8.9%) compared with HC (4.3%; p < 0.001) and DLB (4.7%; p = 0.008) at trend level. There was no significant difference in subcortical atrophy and ventricular expansion between DLB and HC. In the DLB group, increased rates of cortical thinning in the frontal and parietal regions were significantly correlated with decline in global cognition (Mini-Mental State Examination) and motor deterioration (Unified Parkinson's Disease Rating Scale 3), respectively. Overall, AD and DLB are characterized by different spatiotemporal patterns of cortical thinning over time. Our findings warrant further consideration of longitudinal cortical thinning as a potential imaging marker to differentiate DLB from AD.This work was supported by the Sir Jules Thorn Charitable Trust (Grant number 05/JTA), the NIHR Biomedical Research Unit in Dementia and the Biomedical Research Centre awarded to Cambridge University Hospitals NHS Foundation Trust and the University of Cambridge, and the NIHR Biomedical Research Unit in Dementia and the Biomedical Research Centre awarded to Newcastle upon Tyne Hospitals NHS Foundation Trust and the Newcastle University. Elijah Mak was in receipt of a Gates Cambridge PhD studentship. Elijah Mak formulated the research question, performed the statistical analyses, interpreted the results, and wrote the article. Li Su and Guy Williams assisted with the interpretation of the results and provided comments and additional suggestions for revisions of the draft. Rosie Watson recruited and assessed study participants, assisted with the interpretation of the results, and reviewed the article. Michael Firbank designed the imaging protocol, assisted with the interpretation of the results, and reviewed the article. Andrew Blamire obtained funding for the project, designed the imaging protocol, undertook routine quality assurance on the MR system, assisted with the interpretation of the results, and reviewed the article. John O’Brien obtained funding for the project, designed the imaging protocol, assisted with recruitment of study participants, assisted with the interpretation of the results, and reviewed the article. All authors approved the final article.This is the accepted manuscript for a paper published in Neurobiology of Aging Volume 36, Issue 4, April 2015, Pages 1743–1750, DOI: 10.1016/j.neurobiolaging.2014.12.03

Assessment of autonomic symptoms may assist with early identification of mild cognitive impairment with Lewy bodies

Funder: GE Healthcare; Id: http://dx.doi.org/10.13039/100006775Funder: Alzheimer's Research UK; Id: http://dx.doi.org/10.13039/501100002283Funder: NIHR Newcastle Biomedical Research Centre; Id: http://dx.doi.org/10.13039/501100012295Abstract: Objectives: Autonomic symptoms are a common feature of the synucleinopathies, and may be a distinguishing feature of prodromal Lewy body disease. We aimed to assess whether the cognitive prodrome of dementia with Lewy bodies, mild cognitive impairment (MCI) with Lewy bodies (MCI‐LB), would have more severe reported autonomic symptoms than cognitively healthy older adults, with MCI due to Alzheimer's disease (MCI‐AD) also included for comparison. We also aimed to assess the utility of an autonomic symptom scale in differentiating MCI‐LB from MCI‐AD. Methods: Ninety‐three individuals with MCI and 33 healthy controls were assessed with the Composite Autonomic Symptom Score 31‐item scale (COMPASS). Mild cognitive impairment patients also underwent detailed clinical assessment and differential classification of MCI‐AD or MCI‐LB according to current consensus criteria. Differences in overall COMPASS score and individual symptom sub‐scales were assessed, controlling for age. Results: Age‐adjusted severity of overall autonomic symptomatology was greater in MCI‐LB (Ratio = 2.01, 95% CI: 1.37–2.96), with higher orthostatic intolerance and urinary symptom severity than controls, and greater risk of gastrointestinal and secretomotor symptoms. MCI‐AD did not have significantly higher autonomic symptom severity than controls overall. A cut‐off of 4/5 on the COMPASS was sensitive to MCI‐LB (92%) but not specific to this (42% specificity vs. MCI‐AD and 52% vs. healthy controls). Conclusions: Mild cognitive impairment with Lewy bodies had greater autonomic symptom severity than normal ageing and MCI‐AD, but such autonomic symptoms are not a specific finding. The COMPASS‐31 may therefore have value as a sensitive screening test for early‐stage Lewy body disease

Assessment of autonomic symptoms may assist with early identification of mild cognitive impairment with Lewy bodies

Funder: GE Healthcare; Id: http://dx.doi.org/10.13039/100006775Funder: Alzheimer's Research UK; Id: http://dx.doi.org/10.13039/501100002283Funder: NIHR Newcastle Biomedical Research Centre; Id: http://dx.doi.org/10.13039/501100012295Abstract: Objectives: Autonomic symptoms are a common feature of the synucleinopathies, and may be a distinguishing feature of prodromal Lewy body disease. We aimed to assess whether the cognitive prodrome of dementia with Lewy bodies, mild cognitive impairment (MCI) with Lewy bodies (MCI‐LB), would have more severe reported autonomic symptoms than cognitively healthy older adults, with MCI due to Alzheimer's disease (MCI‐AD) also included for comparison. We also aimed to assess the utility of an autonomic symptom scale in differentiating MCI‐LB from MCI‐AD. Methods: Ninety‐three individuals with MCI and 33 healthy controls were assessed with the Composite Autonomic Symptom Score 31‐item scale (COMPASS). Mild cognitive impairment patients also underwent detailed clinical assessment and differential classification of MCI‐AD or MCI‐LB according to current consensus criteria. Differences in overall COMPASS score and individual symptom sub‐scales were assessed, controlling for age. Results: Age‐adjusted severity of overall autonomic symptomatology was greater in MCI‐LB (Ratio = 2.01, 95% CI: 1.37–2.96), with higher orthostatic intolerance and urinary symptom severity than controls, and greater risk of gastrointestinal and secretomotor symptoms. MCI‐AD did not have significantly higher autonomic symptom severity than controls overall. A cut‐off of 4/5 on the COMPASS was sensitive to MCI‐LB (92%) but not specific to this (42% specificity vs. MCI‐AD and 52% vs. healthy controls). Conclusions: Mild cognitive impairment with Lewy bodies had greater autonomic symptom severity than normal ageing and MCI‐AD, but such autonomic symptoms are not a specific finding. The COMPASS‐31 may therefore have value as a sensitive screening test for early‐stage Lewy body disease