Forensic gait analysis — Morphometric assessment from surveillance footage

© 2019 Elsevier B.V. Following the technological rise of surveillance cameras and their subsequent proliferation in public places, the use of information gathered by such means for investigative and evaluative purposes sparked a large interest in the forensic community and within policing scenarios. In particular, it is suggested that analysis of the body, especially the assessment of gait characteristics, can provide useful information to aid the investigation. This paper discusses the influences upon gait to mitigate some of the limitations of surveillance footage, including those due to the varying anatomical differences between individuals. Furthermore, the differences between various techniques applied to assess gait are discussed, including biometric gait recognition, forensic gait analysis, tracking technology, and marker technology. This review article discusses the limitations of the current methods for assessment of gait; exposing gaps within the literature in regard to various influences impacting upon the gait cycle. Furthermore, it suggests a ‘morphometric’ technique to enhance the available procedures to potentially facilitate the development of standardised protocols with supporting statistics and database. This in turn will provide meaningful information to forensic investigation, intelligence-gathering processes, and potentially as an additional method of forensic evaluation of evidence

Doctor of Philosophy

dissertationFalls are one of the most disabling features of aging and are increasingly common in persons with balance-impairments such as Parkinson's disease (PD). Falls can cause physical injuries such as fractures and/or head injuries leading to functional incapacity, increased risk of nursing home admission, and higher mortality rate. Acute muscle fatigue has been shown to exacerbate fall-correlated end-points such as postural control in healthy young and elderly individuals. The majority of studies investigating these effects, however, have focused on static stance postural control, or tasks that fail to incorporate more functional movements such as those requiring components of anticipatory and reactive postural control. The purpose of this study was to document the effects of acute lower extremity muscle fatigue on anticipatory and reactive postural control in persons with PD and to compare those results to the impact of fatigue on healthy elderly and young populations. Additionally, this investigation sought to gain insight into the chronology for postural control recovery following acute muscle fatigue. This dissertation has yielded a background on acute muscle fatigue, followed by a systematic review of the evidence on the effects of muscle fatigue on anticipatory and reactive postural control in healthy older individuals. The focus of the paper then shifts to components of an experimentally designed cohort study examining the effects of acute muscle fatigue on a centrally initiated movement task and a peripherally directed lean-induced fall in persons with PD and neurologically healthy adults. Results indicated that both anticipatory and reactive postural control are altered following acute muscle fatiguing exercise in neurologically healthy young and older adults. Amelioration of fatigue effects is extended beyond 30 minutes for most measures. Recovery occurs more readily for reactive postural control than anticipatory postural control. No statistically significant results were found from fatigue effects on postural control in the full cohort of persons with PD. However, a supplementary analysis revealed that postural control is altered in persons with PD who exercised beyond a minimal threshold of energy expenditure. More research is needed with larger sample sizes and improved construct validity for muscle fatigue in this cohort. The results of this study should serve to heighten awareness regarding the potential negative effects of acute muscle fatigue, including the possibility of falls in clinical and community based exercise settings for older adults at risk for falls

Cell replacement therapy for Parkinson's disease

AbstractParkinson's disease (PD) is a progressive neurodegenerative disorder in which the degeneration of dopaminergic neurons projecting from the substantia nigra to the striatum is a key pathological feature of the disease. Although pharmacological dopamine replacement is generally very effective in early disease, it is only a symptomatic therapy and can have significant side effects with long term use. One of the key strategies in a more restorative approach to PD therapy involves replacement of this degenerating nigro-striatal dopaminergic network with cells and several possible cell sources are being explored. While much experience and some success have been gained with fetal ventral mesencephalic (FVM) tissue transplants, the rapidly advancing stem cell field is providing attractive alternative options which circumvent many of the ethical and practical problems inherent in trials with FVM tissue. Of these embryonic stem cells and induced pluripotent stem cells seem the most promising. However further development and optimisation of the safety and efficacy of the techniques involved in generating and manipulating these, as well as other, cell sources will be essential before any further clinical trials are carried out

Obesity is associated with insufficient behavioral adaptation

Obesity is one of the major health concerns nowadays according to the World Health Organisation (WHO global status report on noncommunicable diseases 2010). Thus, there is an urgent need for understanding obesity-associated alterations in food-related and general cognition and their underlying structural and functional correlates within the central nervous system (CNS). Neuroscientific research of the past decade has mainly focussed on obesity-related differences within homeostatic and hedonic processing of food stimuli. Therein, alterations during anticipation and consumption of food-reward stimuli in obese compared with lean subjects have been highlighted. This points at an altered adaptation of eating behavior in obese individuals. This thesis investigates if adaptation of behavior is attenuated in obese compared to lean individuals in learning-related processes beyond the food domain. In five consecutive experimental studies, we show that obese participants reveal reduced adaptation of behavior within and outside the food context. With the help of MRI, we relate these behavioral findings to alterations in structure and function of the fronto-striatal dopaminergic system in obesity. In more detail, reduced behavioral adaptation seems to be associated with attenuated utilization of negative prediction errors in obese individuals. Within the brain, this relates to reduced functional coupling between subcortical dopaminergic target regions (ventral striatum) and executive cortical structures (supplementary motor area) in obesity, as revealed by fMRI analysis

Imaging the subthalamic nucleus in Parkinson’s disease

This thesis is comprised of a set of work that aims to visualize and quantify the anatomy, structural variability, and connectivity of the subthalamic nucleus (STN) with optimized neuroimaging methods. The study populations include both healthy cohorts and individuals living with Parkinson's disease (PD). PD was chosen specifically due to the involvement of the STN in the pathophysiology of the disease. Optimized neuroimaging methods were primarily obtained using ultra-high field (UHF) magnetic resonance imaging (MRI). An additional component of this thesis was to determine to what extent UHF-MRI can be used in a clinical setting, specifically for pre-operative planning of deep brain stimulation (DBS) of the STN for patients with advanced PD. The thesis collectively demonstrates that i, MRI research, and clinical applications must account for the different anatomical and structural changes that occur in the STN with both age and PD. ii, Anatomical connections involved in preparatory motor control, response inhibition, and decision-making may be compromised in PD. iii. The accuracy of visualizing and quantifying the STN strongly depends on the type of MR contrast and voxel size. iv, MRI at a field strength of 3 Tesla (T) can under certain circumstances be optimized to produce results similar to that of 7 T at the expense of increased acquisition time

Analysis of Brain Magnetic Resonance Images: Voxel-Based Morphometry and Pattern Classification Approaches

This thesis aims to examine two types of elaboration techniques of brain magnetic resonance imaging (MRI) data: the voxel-based morphometry (VBM) and the support vector machine (SVM) approaches. While the VBM is a standard and well-established mass-univariate method, the SVM multivariate analysis has been rarely implemented to investigate brain MRI data. An improvement of our knowledge on the pattern classication approach is necessary to be achieved, both to assess its exploratory capability and to point out advantages and disadvantages with respect to the more largely used VBM approach. Despite these methods are potentially suitable to investigate a large variety of neurological and neuropsychiatric disorders, in the present study they have been employed with the purpose of detecting neuroanatomical and gender-related abnormalities in children with autism spectrum disorders (ASD). In fact, the dierences in the neuroanatomy of young children with ASD are an intriguing and still poor investigated issue. After a description of the physical principles of nuclear magnetic resonance and an overview of magnetic resonance imaging, we specied the two algorithms that represent the object of the current study: voxel-based morphometry and support vector machines classication methods. Hence, we described the theoretical principles they are based on, pointing out schemes and procedures employed to implement these analysis approaches. Then, we examined the application of VBM and SVM methods to an opportunely chosen sample of MRI data. A total of 152 structural MRI scans were selected. Specically, our dataset was composed by 76 ASD children and 76 matched controls in the 2-7 year age range. The images were preprocessed applying the SPM8 algorithm, based on the dieomorphic anatomical registration through exponentiated lie algebra (DARTEL) procedure. The resulting grey matter (GM) segments were analyzed by applying the conventional voxel-wise two-sample t-test VBM analysis and employing the stringent family-wise error (FWE) rate correction according to random gaussian elds theory. The same preprocessed GM segments were then analyzed using the SVM pattern classication approach, that presents the advantage of intrinsically taking into account interregional correlations. Moreover, this technique would allow investigations about the predictive value of structural MRI scans. In fact, the SVM classication capability can be quantied in terms of the area under the receiver operating characteristic curve (AUC). The leave-pair-out cross- validation protocol has been adopted to evaluate the classication performance. The recursive feature elimination (RFE) procedure has been implemented both to reduce the large number of features in the classication problem and to enhance the classication capability. The SVM-RFE allows also to localize the most discriminant voxels and to visualize them in a discrimination map. However, the pattern classication method was not employed to predict the class membership of undiagnosed subjects, but as a gure of merit allowing to determine an optimal threshold on the discrimination maps, where possible between-group structural dierences are encoded. With the aim of strengthening the SVM-based methods applied to brain data and to guarantee reliability and reproducibility of the results, we set up the following tests: 1. We evaluated the consistency among all discrimination maps, each obtained from one of the SVM leave-pair-out cross-validation steps, within the chosen range of number of retained features employed. 2. We assessed the dependency on the population of the training set within the cross- validation procedure. In this way we became able to check for the stability of our statistical results with respect to the number of subjects employed during the learning phase. Furthermore, we can evaluate the classication performances for dierent cross- validation schemes. Among the results we obtained, we found that SVMs applied to GM scans correctly discriminate ASD male and female individuals with respect to controls with an AUC above the 87% with a fraction of retained voxels in the 0.4-29% range. By choosing as operative point of the system that corresponding to the lower amount of signicant voxels (0.4% of the total number of voxels) we obtained a sensitivity of 82% and a specicity of 80%. The resulting discrimination maps showed some signicant regions where an excess of GM characterizes the ASD subjects with respect to the matched control group. These regions seemed to be consistent with those obtained from the VBM analysis, nevertheless the SVM analysis highlighted a larger number of interesting gender-specic discriminating regions. Hence, multivariate methods based on the SVM could contribute not only to distinguish ASD from control children, but also to disentangle the gender specicity of ASD brain alterations, consistently with respect to the mass-univariate approach. Achieving a better AUC could make possible to employ the pattern recognition approach not only to individuate brain regions discriminating between patients and controls, but also to predict the class membership of undiagnosed subjects, thus facilitating the early diagnosis of the ASD pathology

Longitudinal magnetic resonance imaging of cognitive impairment in Parkinson’s disease

Parkinson’s disease (PD) is a neurodegenerative movement disorder characterized by slowness of movement, rigidity, and tremor. However, most patients additionally develop cognitive impairment and eventual dementia (PDD), which becomes the most burdensome aspect of the disease. Pathological processes associated with Parkinson’s extend beyond the classic neurodegenerative changes of neuronal damage in the substantia nigra and the aggregation of misfolded alpha-synuclein protein, leading to the relatively recent understanding of Parkinson’s as a multi-system disorder. Cognitive impairment in PD can vary in the timing of presentation, but dementia eventuates in about 80% of patients. A more mild manifestation of cognitive impairment, also known as the “Mild Cognitive Impairment” or “PD-MCI”, is found in over a third of newly diagnosed Parkinson’s disease patients. Identifying individuals with PD-MCI early in the disease process may eventually facilitate the implementation of novel therapeutic options prior to development of the debilitating stage, dementia. Currently, there are no objective or clinically useful markers for cognitive impairment in PD. However, recent neuroimaging techniques have shown promise in this regard. Magnetic resonance imaging (MRI) is a non-invasive medical imaging technique that may potentially be used to objectively characterize the structural and functional changes in the brain in relation to cognitive impairment in PD. In this thesis, 138 participants meeting the UK Parkinson’s Disease Society’s criteria for idiopathic PD and 50 matched healthy controls completed extensive neuropsychological testing. On the basis of this testing, participants were classified as having normal cognition (PDN=79), mild cognitive impairment (PD-MCI=36), or dementia (PDD=23). Participants also completed an MRI scanning session. These participants were then followed up with the same neuropsychological battery and MRI scanning approximately every two years, with some completed assessments up to six years after baseline. Using a three tesla MRI scanner, three types of MRI data were acquired for each participant: (1) structural T1-weighted images to assess cortical thickness and surface area, (2) MR spectroscopy (MRS) to explore the metabolic changes of the posterior cingulate cortex, and (3) resting-state functional MRI to evaluate functional connectivity of the default mode network. In order to properly model the longitudinal nature of the study, I used Bayesian generalized linear multilevel models to analyse the three MRI data types. The analysis was aimed at evaluating the within- and between-subject association of the MRI-derived metrics and participants’ cognitive impairment. Analysis of structural MRI scans (cortical thickness “CTh” and surface area “SA”) showed strong association with cognition and cognitive decline over time. Baseline cognitive ability was associated significantly with cortical thinning and surface area reduction. However, most importantly, longitudinal assessment showed that cognitive deterioration of PD patients was associated with reduced cortical thickness and surface area in several brain regions. These structural findings, particularly the longitudinal ones, indicate the potential role of both CTh and SA as predictive markers for cognitive impairment in PD. After accounting for age, sex, and motor impairments, none of the MRS-derived metabolites extracted from the posterior cingulate cortex (PCC) showed significant group differences at baseline. Similarly, metabolite changes overtime did not significantly associate with declining cognitive ability of the study participants. These findings indicate that MRS of the PCC is not a clinically useful marker of cognitive impairment in PD. Resting state functional connectivity (RS-fMRI) of the default mode network (DMN) revealed no significant relationship between baseline nor decline in cognitive ability over time and DMN functional connectivity. While DMN dysfunction is strongly related to cognitive impairment and decline in Alzheimer’s disease, the current findings suggest that DMN functional connectivity does not hold the same promise in PD. Hence, it also appears that DMN connectivity does not provide clinically useful information about cognitive status or decline over time in PD. In this thesis, posterior cingulate MRS and DMN connectivity did not provide clinically reliable information about cognitive impairment in PD. However, both cortical thickness and surface area showed reliable and robust association with cognitive ability in PD, at cross section and over time. These results suggest that longitudinal structural MRI measurements may hold promise as outcome measures, along with complimentary clinical and cognitive assessments, in future PD-modifying therapeutic trials