Electroencephalogram based Causality Graph Analysis in Behavior Tasks of Parkinson’s Disease Patients

Electroencephalographic (EEG) signals of the human brains represent electrical activities for a number of channels recorded over a the scalp. The main purpose of this thesis is to investigate the interactions and causality of different parts of a brain using EEG signals recorded during a performance subjects of verbal fluency tasks. Subjects who have Parkinson's Disease (PD) have difficulties with mental tasks, such as switching between one behavior task and another. The behavior tasks include phonemic fluency, semantic fluency, category semantic fluency and reading fluency. This method uses verbal generation skills, activating different Broca's areas of the Brodmann's areas (BA44 and BA45). Advanced signal processing techniques are used in order to determine the activated frequency bands in the granger causality for verbal fluency tasks. The graph learning technique for channel strength is used to characterize the complex graph of Granger causality. Also, the support vector machine (SVM) method is used for training a classifier between two subjects with PD and two healthy controls. Neural data from the study was recorded at the Colorado Neurological Institute (CNI). The study reveals significant difference between PD subjects and healthy controls in terms of brain connectivities in the Broca's Area BA44 and BA45 corresponding to EEG electrodes. The results in this thesis also demonstrate the possibility to classify based on the flow of information and causality in the brain of verbal fluency tasks. These methods have the potential to be applied in the future to identify pathological information flow and causality of neurological diseases.

Towards Closed-loop Deep Brain Stimulation: Behavior Recognition from Human STN

Deep brain stimulation (DBS) provides significant therapeutic benefit for movement disorders such as Parkinson’s disease (PD). Current DBS devices lack real-time feedback (thus are open loop) and stimulation parameters are adjusted during scheduled visits with a clinician. A closed-loop DBS system may reduce power consumption and side effects by adjusting stimulation parameters based on patient’s behavior. Thus behavior detection is a major step in designing such systems. Various physiological signals can be used to recognize the behaviors. Subthalamic Nucleus (STN) Local field Potential (LFP) is a great candidate signal for the neural feedback, because it can be recorded from the stimulation lead and does not require additional sensors. This thesis proposes novel detection and classification techniques for behavior recognition based on deep brain LFP. Behavior detection from such signals is the vital step in developing the next generation of closed-loop DBS devices. LFP recordings from 13 subjects are utilized in this study to design and evaluate our method. Recordings were performed during the surgery and the subjects were asked to perform various behavioral tasks. Various techniques are used understand how the behaviors modulate the STN. One method studies the time-frequency patterns in the STN LFP during the tasks. Another method measures the temporal inter-hemispheric connectivity of the STN as well as the connectivity between STN and Pre-frontal Cortex (PFC). Experimental results demonstrate that different behaviors create different m odulation patterns in STN and it’s connectivity. We use these patterns as features to classify behaviors. A method for single trial recognition of the patient’s current task is proposed. This method uses wavelet coefficients as features and support vector machine (SVM) as the classifier for recognition of a selection of behaviors: speech, motor, and random. The proposed method is 82.4% accurate for the binary classification and 73.2% for classifying three tasks. As the next step, a practical behavior detection method which asynchronously detects behaviors is proposed. This method does not use any priori knowledge of behavior onsets and is capable of asynchronously detect the finger movements of PD patients. Our study indicates that there is a motor-modulated inter-hemispheric connectivity between LFP signals recorded bilaterally from STN. We utilize a non-linear regression method to measure this inter-hemispheric connectivity and to detect the finger movements. Our experimental results using STN LFP recorded from eight patients with PD demonstrate this is a promising approach for behavior detection and developing novel closed-loop DBS systems.

Computational models for improved diagnosis and prognosis of stroke using robot-based biomarkers

Stroke is a leading cause of death and permanent disability worldwide, affecting millions of individuals. Traditional clinical scores for assessment of stroke-related impairments are inherently subjective and limited by inter-rater and intra-rater reliability, as well as floor and ceiling effects. In contrast, robotic technologies provide objective, highly repeatable tools for quantification of neurological impairments following stroke. KINARM is an exoskeleton robotic device that provides objective, reliable tools for assessment of sensorimotor, proprioceptive and cognitive brain function by means of a battery of behavioral tasks. As such, KINARM is particularly useful for assessment of neurological impairments following stroke. This thesis introduces a computational framework for assessment of neurological impairments using the data provided by KINARM. This is done by achieving two main objectives. First, to investigate how robotic measurements can be used to estimate current and future abilities to perform daily activities for subjects with stroke. We are able to predict clinical scores related to activities of daily living at present and future time points using a set of robotic biomarkers. The findings of this analysis provide a proof of principle that robotic evaluation can be an effective tool for clinical decision support and target-based rehabilitation therapy. The second main objective of this thesis is to address the emerging problem of long assessment time, which can potentially lead to fatigue when assessing subjects with stroke. To address this issue, we examine two time reduction strategies. The first strategy focuses on task selection, whereby KINARM tasks are arranged in a hierarchical structure so that an earlier task in the assessment procedure can be used to decide whether or not subsequent tasks should be performed. The second strategy focuses on time reduction on the longest two individual KINARM tasks. Both reduction strategies are shown to provide significant time savings, ranging from 30% to 90% using task selection and 50% using individual task reductions, thereby establishing a framework for reduction of assessment time on a broader set of KINARM tasks. All in all, findings of this thesis establish an improved platform for diagnosis and prognosis of stroke using robot-based biomarkers.

Prospective longitudinal voxel-based morphometry study of major depressive disorder in young individuals at high familial risk

BACKGROUND Previous neuroimaging studies indicate abnormalities in cortico-limbic circuitry in mood disorder. Here we employ prospective longitudinal voxel-based morphometry to examine the trajectory of these abnormalities during early stages of illness development. METHOD Unaffected individuals (16-25 years) at high and low familial risk of mood disorder underwent structural brain imaging on two occasions 2 years apart. Further clinical assessment was conducted 2 years after the second scan (time 3). Clinical outcome data at time 3 was used to categorize individuals: (i) healthy controls ('low risk', n = 48); (ii) high-risk individuals who remained well (HR well, n = 53); and (iii) high-risk individuals who developed a major depressive disorder (HR MDD, n = 30). Groups were compared using longitudinal voxel-based morphometry. We also examined whether progress to illness was associated with changes in other potential risk markers (personality traits, symptoms scores and baseline measures of childhood trauma), and whether any changes in brain structure could be indexed using these measures. RESULTS Significant decreases in right amygdala grey matter were found in HR MDD v. controls (p = 0.001) and v. HR well (p = 0.005). This structural change was not related to measures of childhood trauma, symptom severity or measures of sub-diagnostic anxiety, neuroticism or extraversion, although cross-sectionally these measures significantly differentiated the groups at baseline. CONCLUSIONS These longitudinal findings implicate structural amygdala changes in the neurobiology of mood disorder. They also provide a potential biomarker for risk stratification capturing additional information beyond clinically ascertained measures.

Report to Congress: benefits of safety belts and motorcycle helmets. Based on data from the Crash Outcome Data Evaluation System (CODES).

Mode of access: Internet.

Age-Related Olfactory Decline is Associated with the BDNF Val66met Polymorphism : Evidence from a Population-Based Study

The present study investigates the effect of the brain-derived neurotrophic factor (BDNF) val66met polymorphism on change in olfactory function in a large scale, longitudinal population-based sample (n = 836). The subjects were tested on a 13 item force-choice odor identification test on two test occasions over a 5-year-interval. Sex, education, health-related factors, and semantic ability were controlled for in the statistical analyses. Results showed an interaction effect of age and BDNF val66met on olfactory change, such that the magnitude of olfactory decline in the older age cohort (70–90years old at baseline) was larger for the val homozygote carriers than for the met carriers. The older met carriers did not display larger age-related decline in olfactory function compared to the younger group. The BDNF val66met polymorphism did not affect the rate of decline in the younger age cohort (45–65years). The findings are discussed in the light of the proposed roles of BDNF in neural development and maintenance.

Closed-Loop Deep Brain Stimulation: Bidirectional Neuroprosthetics for Tremor and BCI

Thesis (Ph.D.)--University of Washington, 2016-06

Equity in access to outpatient rehabilitation services for children with traumatic brain injury and public insurance

Thesis (Master's)--University of Washington, 2016-06

Referral to rehabilitation following traumatic brain injury: a model for understanding inequities in access

Identifying inequities in access to health care requires critical scrutiny of the patterns and processes of care decisions. This paper describes a conceptual model. derived from social problems theory. which is proposed as a useful framework for explaining patterns of post-acute care referral and in particular, individual variations in referral to rehabilitation after traumatic brain injury (TBI). The model is based on three main components: (1) characteristics of the individual with TBI, (2) activities of health care professionals and the processes of referral. and (3) the contexts of care. The central argument is that access to rehabilitation following TBI is a dynamic phenomenon concerning the interpretations and negotiations of health care professionals. which in turn are shaped by the organisational and broader health care contexts. The model developed in this paper provides opportunity to develop a complex analysis of post-acute care referral based on patient factors, contextual factors and decision-making processes. It is anticipated that this framework will have utility in other areas examining and understanding patterns of access to health care. (C) 2002 Elsevier Science Ltd. All rights reserved.

Classifying client goals in community-based ABI rehabilitation: a taxonomy for profiling service delivery and conceptualizing outcomes

Purpose: To develop, confirm and trial a framework for analysing the content of goals set within community-based rehabilitation. This framework (taxonomy) is proposed as a tool to assist in service evaluation and outcome exploration. Method: Qualitative thematic analysis and categorization of 1765 rehabilitation goal statements in a four phase process of synthesis, refinement, verification and application. Results: A taxonomy of goal content was developed comprising 21 categories within five domains, utilizing 125 descriptors. The taxonomy demonstrated good inter-rater consistency and was able to discriminate between similar but related data sets comprising goal statements. Conclusion: Structured analysis of the content of goal setting (particularly in community rehabilitation) utilizing a framework such as the proposed taxonomy has considerable potential as a 'window' into service delivery to broaden the parameters of existing service evaluation and to more clearly link outcome exploration to intervention.

Human brain regions required for the dividing and switching of attention between two features of a single object

This combined PET and ERP study was designed to identify the brain regions activated in switching and divided attention between different features of a single object using matched sensory stimuli and motor response. The ERP data have previously been reported in this journal [64]. We now present the corresponding PET data. We identified partially overlapping neural networks with paradigms requiring the switching or dividing of attention between the elements of complex visual stimuli. Regions of activation were found in the prefrontal and temporal cortices and cerebellum. Each task resulted in different prefrontal cortical regions of activation lending support to the functional subspecialisation of the prefrontal and temporal cortices being based on the cognitive operations required rather than the stimuli themselves. (C) 2003 Elsevier Science B.V. All rights reserved.

Speeding up the EM algorithm for mixture model-based segmentation of magnetic resonance images

Mixture models implemented via the expectation-maximization (EM) algorithm are being increasingly used in a wide range of problems in pattern recognition such as image segmentation. However, the EM algorithm requires considerable computational time in its application to huge data sets such as a three-dimensional magnetic resonance (MR) image of over 10 million voxels. Recently, it was shown that a sparse, incremental version of the EM algorithm could improve its rate of convergence. In this paper, we show how this modified EM algorithm can be speeded up further by adopting a multiresolution kd-tree structure in performing the E-step. The proposed algorithm outperforms some other variants of the EM algorithm for segmenting MR images of the human brain. (C) 2004 Pattern Recognition Society. Published by Elsevier Ltd. All rights reserved.

Investigation of factors related to employment outcome following traumatic brain injury: a critical review and conceptual model

Purpose : The purpose of this article is to critically review the literature to examine factors that are most consistently related to employment outcome following traumatic brain injury (TBI), with a particular focus on metacognitive skills. It also aims to develop a conceptual model of factors related to employment outcome. Method : The first stage of the review considered 85 studies published between 1980 and December 2003 which investigated factors associated with employment outcome following TBI. English-language studies were identified through searches of Medline and PsycINFO, as well as manual searches of journals and reference lists. The studies were evaluated and rated by two independent raters (Kappa = 0.835) according to the quality of their methodology based upon nine criteria. Fifty studies met the criteria for inclusion in the second stage of the review, which examined the relationship between a broad range of variables and employment outcome. Results : The factors most consistently associated with employment outcome included pre-injury occupational status, functional status at discharge, global cognitive functioning, perceptual ability, executive functioning, involvement in vocational rehabilitation services and emotional status. Conclusions : A conceptual model is presented which emphasises the importance of metacognitive, emotional and social environment factors for improving employment outcome.

The application of proteomics to the human alcoholic brain

Alcoholism results in changes in the human brain that reinforce the cycle of craving and dependency, and these changes are manifest in the pattern of expression of proteins in key cells and brain areas. Described here is a proteomics-based approach aimed at determining the identity of proteins in the superior frontal cortex (SFC) of the human brain that show different levels of expression in autopsy samples taken from healthy and long-term alcohol abuse subjects. Soluble protein fractions constituting pooled samples combined from SFC biopsies of four well-characterized chronic alcoholics (mean consumption > 80 g ethanol/day throughout adulthood) and four matched controls (< 20 g/day) were generated. Two-dimensional electrophoresis was performed in triplicate on alcoholic and control samples and the resultant protein profiles analyzed for differential expression. Overall, 182 proteins differed by the criterion of twofold or more between case and control samples. Of these, 139 showed significantly lower expression in alcoholics, 35 showed significantly higher expression, and 8 were new or had disappeared. To date, 63 proteins have been identified using MALDI-MS and MS-MS. The finding that the expression level of differentially expressed proteins is preponderantly lower in the alcoholic brain is supported by recent results from parallel studies using microarray mRNA transcript.

Referral to rehabilitation following traumatic brain injury: practitioners and the process of decision-making

The study aimed to examine the factors influencing referral to rehabilitation following traumatic brain injury (TBI) by using social problems theory as a conceptual model to focus on practitioners and the process of decision-making in two Australian hospitals. The research design involved semi-structured interviews with 18 practitioners and observations of 10 team meetings, and was part of a larger study on factors influencing referral to rehabilitation in the same settings. Analysis revealed that referral decisions were influenced primarily by practitioners' selection and their interpretation of clinical and non-clinical patient factors. Further, practitioners generally considered patient factors concurrently during an ongoing process of decision-making, with the combinations and interactions of these factors forming the basis for interpretations of problems and referral justifications. Key patient factors considered in referral decisions included functional and tracheostomy status, time since injury, age, family, place of residence and Indigenous status. However, rate and extent of progress, recovery potential, safety and burden of care, potential for independence and capacity to cope were five interpretative themes, which emerged as the justifications for referral decisions. The subsequent negotiation of referral based on patient factors was in turn shaped by the involvement of practitioners. While multi-disciplinary processes of decision-making were the norm, allied health professionals occupied a central role in referral to rehabilitation, and involvement of medical, nursing and allied health practitioners varied. Finally, the organizational pressures and resource constraints, combined with practitioners' assimilation of the broader efficiency agenda were central factors shaping referral. (C) 2004 Elsevier Ltd. All rights reserved.