Browsing by Author "Smith, Eric Edward"

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    Adult patient perspectives of the unknowns of living with epilepsy - results from a focus group study.
    (2019-11-24) Lee, Jeanie Y. Y.; Gelfand, Jennifer; Khan, Sundus; Crooks, Rachel E.; Josephson, Colin B.; Wiebe, Samuel; Patten, Scott B.; Korngut, Lawrence; Smith, Eric Edward; Roach, Pamela M.
    Background/Objectives: Epilepsy is one of the most common and debilitating neurological conditions that affects nearly 50 million people worldwide, yet there remains a stigma around this condition, which can impact the information-seeking behaviours of patients. As the Brain and Mental Health Research Clinics develop a website about registry-based research, including patient-facing areas, it is important to understand how patients look for information, and the types of information they are seeking out. The objective of this study was to encourage conversation and understand the patient perspectives of existing knowledge gaps between epilepsy patients and the resources they use to obtain information. Methods: A total of thirteen patients (mean (SD) age = 46.4 (16.1) years) from the Calgary Comprehensive Epilepsy Program Registry and four caregivers participated in one of the three focus groups completed in order to meet our aims. There were eight female and five male patients. A semi-structured guide was used to understand the patients’ experiences, top concerns, informational resources currently used, and resources or knowledge that patients felt are lacking. The focus groups were audio-recorded and transcribed verbatim. Thematic content analysis was conducted by two researchers who independently open-coded the transcripts using NVivo 11. The final analysis was done by team discussion and ongoing analysis of the codes to create themes and sub-themes. Results: The major themes that emerged from the data included: 1) daily management; 2) resources; and 3) medications and treatment. For daily management, the participants reported concerns about the effects of epilepsy on day-to-day activities such as driving, working, and the barriers they face in society due to their perceived lack of awareness and education about seizure management in the general public. The participants felt negatively impacted by the stigma and compared their experience with epilepsy with other disorders such as cancer or diabetes which they feel are much more accepted in society. The geographical location of the patient also plays a role in the support they receive for epilepsy management, with participants citing challenges and feelings of isolation in rural areas. To acquire more information about epilepsy, participants reported that they primarily asked their physicians or searched online. However, despite the conveniences of the internet, some individuals felt the volume and variation of quality of online information was overwhelming. Instead, they would prefer to go to trusted resources that are provided by healthcare professionals or websites affiliated with hospitals or universities. Updated information on medication, side effects, and research are examples of resources the patients would like to see provided on such websites. Conclusion: Overall, it is clear from our focus groups that resources and support for self-management and day-to-day living for individuals with epilepsy is paramount to reduce knowledge gaps. Not only is it important to provide daily management and medication information to patients through trusted organizational resources, but it is equally important to increase public awareness about epilepsy and seizure disorders to reduce the stigma attached to these conditions.
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    Automatic Detection and Quantification of Acute Cerebral Infarct by Fuzzy Clustering and Histographic Characterization on Diffusion Weighted MR Imaging and Apparent Diffusion Coefficient Map
    (2014-03-12) Tsai, Jang-Zern; Peng, Syu-Jyun; Chen, Yu-Wei; Wang, Kuo-Wei; Wu, Hsiao-Kuang; Lin, Yun-Yu; Lee, Ying-Ying; Chen, Chi-Jen; Lin, Huey-Juan; Smith, Eric Edward; Yeh, Poh-Shiow; Hsin, Yue-Loong
    Determination of the volumes of acute cerebral infarct in the magnetic resonance imaging harbors prognostic values. However, semiautomatic method of segmentation is time-consuming and with high interrater variability. Using diffusion weighted imaging and apparent diffusion coefficient map from patients with acute infarction in 10 days, we aimed to develop a fully automatic algorithm to measure infarct volume. It includes an unsupervised classification with fuzzy C-means clustering determination of the histographic distribution, defining self-adjusted intensity thresholds. The proposed method attained high agreement with the semiautomatic method, with similarity index 89.9 ± 6.5%, in detecting cerebral infarct lesions from 22 acute stroke patients. We demonstrated the accuracy of the proposed computer-assisted prompt segmentation method, which appeared promising to replace the laborious, time-consuming, and operator-dependent semiautomatic segmentation.
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    Characterizing Cortical Atrophy in Patients with Cerebral Amyloid Angiopathy: A Cross-Sectional and Longitudinal Analysis
    (2019-05-15) Subotic, Arsenije; Smith, Eric Edward; Pike, G. Bruce; Ismail, Zahinoor
    This study investigated cortical thickness in participants with Cerebral Amyloid Angiopathy (CAA) cross-sectionally and longitudinally, as well as its relationship with cognition and other markers of CAA pathology using Magnetic Resonance Imaging (MRI). CAA participants had lower global thickness compared to healthy controls (HC) (p=0.03) and greater global thickness compared to a group of participants with Alzheimer’s Disease (AD) (p=0.001). Cross-sectionally in CAA, an association was found between thickness and memory scores (p=0.01) and lower thickness and higher white matter hyperintensity volume (WMH) (p=0.04). Longitudinally, CAA participants had a greater rate of thinning compared to HC (p=0.008). No associations were found between thinning over time and cognition and WMH volume at baseline in CAA. Distinct differences from HC and AD suggest cortical thickness is a possible biomarker of CAA pathology and a potential therapeutic target.
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    Diffusion Tensor Imaging and Cognition of Transient Ischemic Attack Patients and Healthy Controls
    (2018-05-11) Tariq, Sana; Barber, Philip A.; Longman, Richard Stewart; Smith, Eric Edward
    Currently, there is no cure for dementia and prevention trials need to be redesigned with a focus on high-risk population and standardized biomarkers, which include clinical, demographic, imaging and neuropsychological considerations. Transient Ischemic Attack (TIA) patients are at an increased risk of dementia due to the presence of vascular risk factors and underlying vascular/neurodegenerative pathology. We hypothesized that at baseline TIA patients will exhibit abnormal microstructural white matter (WM) changes as measured by diffusion tensor imaging (DTI) parameters, and that these parameters will predict worse cognitive outcomes. Our results suggest that TIA patients showed higher axial diffusivity (AxD), mean diffusivity (MD) and radial diffusivity (RD) parameters in the fornix, a tract crucial for memory, and lower fractional anisotropy (FA) overall when compared to healthy controls. TIA patients also performed poorer on tests of executive function, episodic and working memory, and processing speed. DTI parameters of FA and MD predicted performance in tests of executive function and memory but not processing speed. Considering these results, TIA patients are a high-risk population for cognitive change.
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    Expanding the Potential Therapeutic Options for Remote Ischemic Preconditioning: Use in Multiple Sclerosis
    (Frontiers Media, 2018-06-19) Cámara-Lemarroy, Carlos Rodrigo; Metz, Luanne M.; Smith, Eric Edward; Dunn, Jeffrey F.; Yong, Voon Wee E.
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    Unboxing the "Black Box": Learning Interpretable Deep Learning Features of Brain Aging
    (2019-11) Souto Maior Neto, Luis; Frayne, Richard; Pichardo, Samuel; Smith, Eric Edward; Harris, Ashley D.; Beg, Mirza Faisal
    Deep learning (DL) algorithms are state-of-the-art techniques for automatic inference tasks like classification and regression in medical imaging and many other fields. Despite growing interest, DL models have had restricted implementation in practical settings as they are often considered to be “black boxes”. Their inner workings are not easily interpretable by humans, which in medicine has limited wider use. In this work, I apply DL models to predict subject age based on brain magnetic resonance (MR) data. While accurate predictions (< 2 years) are made, the purpose of this work is not to establish prediction accuracy but to better understand brain aging by studying the learned representations of the trained models. I use autoencoder-based models that enable translation between the domain of the model’s internal representations of the data, about which we have little understanding, and the domain of MR images, about which we have expert knowledge. The goal of this research is to investigate whether such DL models are capable of learning representations of age-related features similar to what is already known in literature. I show that such DL models, when trained to predict brain age, are capable of learning known features of brain aging, such as brain atrophy. In addition, this approach may potentially identify new features of aging on brain images.
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    Using Machine Learning for Prognostication of Diagnosis and Identifying Neural Correlates of Impulse Dyscontrol in Preclinical and Prodromal Dementia
    (2019-08-22) Gill, Sascha Charlene; Ismail, Zahinoor; Smith, Eric Edward; Forkert, Nils Daniel; MacMaster, Frank P.
    Introduction: Mild Behavioural impairment (MBI) is a validated syndrome that describes neuropsychiatric symptoms (NPS) in preclinical and prodromal dementia. This thesis uses machine learning (ML) and traditional statistical models to: 1) Explore the utility of NPS for predicting diagnostic status 2) Identify the neural correlates of MBI impulse dyscontrol (ID) domain. Methods: Data from the Alzheimer’s Disease Neuroimaging (ADNI) database were extracted. All subjects enrolled in ADNI were between the age of 55-90 years, English or Spanish speakers, and accompanied by study partners who completed the NPI-Q 1) To address the first objective, the logistic model tree classifier combined with an information gain feature selection was trained to predict follow-up diagnosis (normal cognition [NC], MCI, or AD-dementia) using baseline neuroimaging, neuropsychiatric, and demographic data. 2) To address the second objective, ID was identified as behavioural symptoms of agitation/aggression, irritability, and aberrant motor behaviour. Linear mixed effect models were used to assess if ID was related to regional diffusion tensor imaging (DTI) and volumetric parameters. Additionally, ML modeling used a rule-based classification algorithm combined with an information gain feature selector to predict ID using neuroimaging variables. Results: 1) MBI total scores and volume of the left hippocampus were identified as the most important features to predict follow-up diagnostic status. 2) Cingulum, fornix, inferior/superior fronto-occipital fasciculus, superior cerebellar peduncle, and corpus callosum, were the white matter tracts associated with ID. Grey matter regions associated with ID included the parahippocampal gyrus supramarginal gyrus, superior frontal regions, and hippocampus. Conclusion: NPS are early indicators of neurodegenerative disease and can be used predict cognitive decline and dementia.
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    Validation of the Mild Behavioral Impairment-Checklist in Subjective Cognitive Decline, Mild Cognitive Impairment and Dementia
    (2019-06-17) Hu, Sophie; Ismail, Zahinoor; Patten, Scott B.; Fick, Gordon Hilton; Smith, Eric Edward
    Introduction: Neuropsychiatric symptoms (NPS) are early markers of dementia preceding cognitive impairment. The Mild Behavioral Impairment Checklist (MBI-C) was developed to characterize NPS for pre-dementia patients. This thesis examines the validity and utility of the MBI-C for detecting neuropsychiatric symptoms in relation to cognition. Objectives: This study was conducted in three parts: 1) To compare factors of the MBI-C and Neuropsychiatric Inventory-Questionnaire (NPI-Q) using factor analysis. 2) To determine the association between baseline MBI-C and NPI-Q severity scores with cognition. 3) To determine the predictive utility of MBI-C and NPI-Q severity scores for change in cognition. Methods: Patients diagnosed with subjective cognitive decline, mild cognitive impairment and dementia were sampled from a cognitive neurology clinic. 1) Exploratory factor analysis was conducted to determine MBI-C and NPI-Q domains. 2) The association between baseline MBI-C and NPI-Q severity and cognition, as measured by the Montreal Cognitive Assessment (MoCA), was modelled using linear regression analysis. 3) The association between MBI-C and NPI-Q severity at baseline and change in cognition per six months was modelled using generalized linear mixed models. Results: 1) The MBI-C is a valid five-factor questionnaire with the following domains: apathy, mood/anxiety, impulse dyscontrol, social inappropriateness, and psychosis. Anhedonia and appetite disturbances are features that load onto apathy. The NPI-Q is a one-factor questionnaire in our sample. (2) Higher MBI-C and NPI-Q severity is associated with decreased cognition. MBI prevalence increases with increasing severity of cognitive diagnosis. All MBI-C domains are significantly associated with lower MoCA. Psychosis is most strongly associated and total score is most weakly associated. The MBI-C identifies age, sex and diagnosis-specific estimates. 3) Baseline MBI-C and NPI-Q scores predict cognitive decline over time. Impulse dyscontrol, mood/anxiety and social inappropriateness are most predictive of cognitive decline. Conclusions: Neuropsychiatric symptoms are associated with cognitive decline in pre-dementia and dementia patients. The MBI-C is a valid five-factor questionnaire for detecting NPS and is especially robust in pre-dementia patients. MBI domains are indicative and predictive of cognitive decline and can be targeted for management of NPS. The NPI-Q is not as applicable to pre-dementia and does not fully capture NPS groupings. The MBI-C and NPI-Q act as complements and both should be administered with consideration of patient status.