Three-dimensional brain fiber tracking modeling in diffusion tensor imaging

This dissertation establishes the foundation for a new 3-D visual interface integrating Magnetic Resonance Imaging (MRI) to Diffusion Tensor Imaging (DTI). The need for such an interface is critical for understanding brain dynamics, and for providing more accurate diagnosis of key brain dysfunctions in terms of neuronal connectivity. ^ This work involved two research fronts: (1) the development of new image processing and visualization techniques in order to accurately establish relational positioning of neuronal fiber tracts and key landmarks in 3-D brain atlases, and (2) the obligation to address the computational requirements such that the processing time is within the practical bounds of clinical settings. The system was evaluated using data from thirty patients and volunteers with the Brain Institute at Miami Children's Hospital. ^ Innovative visualization mechanisms allow for the first time white matter fiber tracts to be displayed alongside key anatomical structures within accurately registered 3-D semi-transparent images of the brain. ^ The segmentation algorithm is based on the calculation of mathematically-tuned thresholds and region-detection modules. The uniqueness of the algorithm is in its ability to perform fast and accurate segmentation of the ventricles. In contrast to the manual selection of the ventricles, which averaged over 12 minutes, the segmentation algorithm averaged less than 10 seconds in its execution. ^ The registration algorithm established searches and compares MR with DT images of the same subject, where derived correlation measures quantify the resulting accuracy. Overall, the images were 27% more correlated after registration, while an average of 1.5 seconds is all it took to execute the processes of registration, interpolation, and re-slicing of the images all at the same time and in all the given dimensions. ^ This interface was fully embedded into a fiber-tracking software system in order to establish an optimal research environment. This highly integrated 3-D visualization system reached a practical level that makes it ready for clinical deployment. ^

Targeted Brain Derived Neurotropic Factors (BDNF) Delivery across the Blood-Brain Barrier for Neuro-Protection Using Magnetic Nano Carriers: An In-Vitro Study

Parenteral use of drugs; such as opiates exert immunomodulatory effects and serve as a cofactor in the progression of HIV-1 infection, thereby potentiating HIV related neurotoxicity ultimately leading to progression of NeuroAIDS. Morphine exposure is known to induce apoptosis, down regulate cAMP response element-binding (CREB) expression and decrease in dendritic branching and spine density in cultured cells. Use of neuroprotective agent; brain derived neurotropic factor (BDNF), which protects neurons against these effects, could be of therapeutic benefit in the treatment of opiate addiction. Previous studies have shown that BDNF was not transported through the blood brain barrier (BBB) in-vivo.; and hence it is not effectivein-vivo. Therefore development of a drug delivery system that can cross BBB may have significant therapeutic advantage. In the present study, we hypothesized that magnetically guided nanocarrier may provide a viable approach for targeting BDNF across the BBB. We developed a magnetic nanoparticle (MNP) based carrier bound to BDNF and evaluated its efficacy and ability to transmigrate across the BBB using an in-vitro BBB model. The end point determinations of BDNF that crossed BBB were apoptosis, CREB expression and dendritic spine density measurement. We found that transmigrated BDNF was effective in suppressing the morphine induced apoptosis, inducing CREB expression and restoring the spine density. Our results suggest that the developed nanocarrier will provide a potential therapeutic approach to treat opiate addiction, protect neurotoxicity and synaptic density degeneration.

Magnetic nanoparticle-based targeted drug delivery for treatment of neuro-AIDS and drug addiction

Brain is one of the safe sanctuaries for HIV and, in turn, continuously supplies active viruses to the periphery. Additionally, HIV infection in brain results in several mild-to-severe neuro-immunological complications termed neuroAIDS. One-tenth of HIV-infected population is addicted to recreational drugs such as opiates, alcohol, nicotine, marijuana, etc. which share common target-areas in the brain with HIV. Interestingly, intensity of neuropathogenesis is remarkably enhanced due to exposure of recreational drugs during HIV infection. Current treatments to alleviate either the individual or synergistic effects of abusive drugs and HIV on neuronal modulations are less effective at CNS level, basically due to impermeability of therapeutic molecules across blood-brain barrier (BBB). Despite exciting advancement of nanotechnology in drug delivery, existing nanovehicles such as dendrimers, polymers, micelles, etc. suffer from the lack of adequate BBB penetrability before the drugs are engulfed by the reticuloendothelial system cells as well as the uncertainty that if and when the nanocarrier reaches the brain. Therefore, in order to develop a fast, target-specific, safe, and effective approach for brain delivery of anti-addiction, anti-viral and neuroprotective drugs, we exploited the potential of magnetic nanoparticles (MNPs) which, in recent years, has attracted significant importance in biomedical applications. We hypothesize that under the influence of external (non-invasive) magnetic force, MNPs can deliver these drugs across BBB in most effective manner. Accordingly, in this dissertation, I delineated the pharmacokinetics and dynamics of MNPs bound anti-opioid, anti-HIV and neuroprotective drugs for delivery in brain. I have developed a liposome-based novel magnetized nanovehicle which, under the influence of external magnetic forces, can transmigrate and effectively deliver drugs across BBB without compromising its integrity. It is expected that the developed nanoformulations may be of high therapeutic significance for neuroAIDS and for drug addiction as well.

Whole-brain patterns of 1H-magnetic resonance spectroscopy imaging in Alzheimer's disease and dementia with Lewy bodies

Acknowledgments We thank Craig Lambert for his help in processing the MRS data. The study was funded by the Sir Jules Thorn Charitable Trust (grant ref: 05/JTA) and was supported by the National Institute for Health Research (NIHR) Newcastle Biomedical Research Centre and the Biomedical Research Unit in Lewy Body Dementia based at Newcastle upon Tyne Hospitals National Health Service (NHS) Foundation Trust and Newcastle University and the NIHR Biomedical Research Centre and Biomedical Research Unit in Dementia based at Cambridge University Hospitals NHS Foundation Trust and the University of Cambridge. The views expressed are those of the authors and not necessarily those of the NHS, the NIHR or the Department of Health.

Source localization of reaction-diffusion models for brain tumors

We propose a mathematically well-founded approach for locating the source (initial state) of density functions evolved within a nonlinear reaction-diffusion model. The reconstruction of the initial source is an ill-posed inverse problem since the solution is highly unstable with respect to measurement noise. To address this instability problem, we introduce a regularization procedure based on the nonlinear Landweber method for the stable determination of the source location. This amounts to solving a sequence of well-posed forward reaction-diffusion problems. The developed framework is general, and as a special instance we consider the problem of source localization of brain tumors. We show numerically that the source of the initial densities of tumor cells are reconstructed well on both imaging data consisting of simple and complex geometric structures.

Brain oscillations and connectivity in autism spectrum disorders (ASD):new approaches to methodology, measurement and modelling

Although atypical social behaviour remains a key characterisation of ASD, the presence ofsensory and perceptual abnormalities has been given a more central role in recentclassification changes. An understanding of the origins of such aberrations could thus prove afruitful focus for ASD research. Early neurocognitive models of ASD suggested that thestudy of high frequency activity in the brain as a measure of cortical connectivity mightprovide the key to understanding the neural correlates of sensory and perceptual deviations inASD. As our review shows, the findings from subsequent research have been inconsistent,with a lack of agreement about the nature of any high frequency disturbances in ASD brains.Based on the application of new techniques using more sophisticated measures of brainsynchronisation, direction of information flow, and invoking the coupling between high andlow frequency bands, we propose a framework which could reconcile apparently conflictingfindings in this area and would be consistent both with emerging neurocognitive models ofautism and with the heterogeneity of the condition.

Guideline-based decision support has a small, non-sustained effect on transthoracic echocardiography ordering frequency.

BACKGROUND: Guidance for appropriate utilisation of transthoracic echocardiograms (TTEs) can be incorporated into ordering prompts, potentially affecting the number of requests. METHODS: We incorporated data from the 2011 Appropriate Use Criteria for Echocardiography, the 2010 National Institute for Clinical Excellence Guideline on Chronic Heart Failure, and American College of Cardiology Choosing Wisely list on TTE use for dyspnoea, oedema and valvular disease into electronic ordering systems at Durham Veterans Affairs Medical Center. Our primary outcome was TTE orders per month. Secondary outcomes included rates of outpatient TTE ordering per 100 visits and frequency of brain natriuretic peptide (BNP) ordering prior to TTE. Outcomes were measured for 20 months before and 12 months after the intervention. RESULTS: The number of TTEs ordered did not decrease (338±32 TTEs/month prior vs 320±33 afterwards, p=0.12). Rates of outpatient TTE ordering decreased minimally post intervention (2.28 per 100 primary care/cardiology visits prior vs 1.99 afterwards, p<0.01). Effects on TTE ordering and ordering rate significantly interacted with time from intervention (p<0.02 for both), as the small initial effects waned after 6 months. The percentage of TTE orders with preceding BNP increased (36.5% prior vs 42.2% after for inpatients, p=0.01; 10.8% prior vs 14.5% after for outpatients, p<0.01). CONCLUSIONS: Ordering prompts for TTEs initially minimally reduced the number of TTEs ordered and increased BNP measurement at a single institution, but the effect on TTEs ordered was likely insignificant from a utilisation standpoint and decayed over time.

Adaptive Brain-Computer Interface Systems For Communication in People with Severe Neuromuscular Disabilities

Brain-computer interfaces (BCI) have the potential to restore communication or control abilities in individuals with severe neuromuscular limitations, such as those with amyotrophic lateral sclerosis (ALS). The role of a BCI is to extract and decode relevant information that conveys a user's intent directly from brain electro-physiological signals and translate this information into executable commands to control external devices. However, the BCI decision-making process is error-prone due to noisy electro-physiological data, representing the classic problem of efficiently transmitting and receiving information via a noisy communication channel.

This research focuses on P300-based BCIs which rely predominantly on event-related potentials (ERP) that are elicited as a function of a user's uncertainty regarding stimulus events, in either an acoustic or a visual oddball recognition task. The P300-based BCI system enables users to communicate messages from a set of choices by selecting a target character or icon that conveys a desired intent or action. P300-based BCIs have been widely researched as a communication alternative, especially in individuals with ALS who represent a target BCI user population. For the P300-based BCI, repeated data measurements are required to enhance the low signal-to-noise ratio of the elicited ERPs embedded in electroencephalography (EEG) data, in order to improve the accuracy of the target character estimation process. As a result, BCIs have relatively slower speeds when compared to other commercial assistive communication devices, and this limits BCI adoption by their target user population. The goal of this research is to develop algorithms that take into account the physical limitations of the target BCI population to improve the efficiency of ERP-based spellers for real-world communication.

In this work, it is hypothesised that building adaptive capabilities into the BCI framework can potentially give the BCI system the flexibility to improve performance by adjusting system parameters in response to changing user inputs. The research in this work addresses three potential areas for improvement within the P300 speller framework: information optimisation, target character estimation and error correction. The visual interface and its operation control the method by which the ERPs are elicited through the presentation of stimulus events. The parameters of the stimulus presentation paradigm can be modified to modulate and enhance the elicited ERPs. A new stimulus presentation paradigm is developed in order to maximise the information content that is presented to the user by tuning stimulus paradigm parameters to positively affect performance. Internally, the BCI system determines the amount of data to collect and the method by which these data are processed to estimate the user's target character. Algorithms that exploit language information are developed to enhance the target character estimation process and to correct erroneous BCI selections. In addition, a new model-based method to predict BCI performance is developed, an approach which is independent of stimulus presentation paradigm and accounts for dynamic data collection. The studies presented in this work provide evidence that the proposed methods for incorporating adaptive strategies in the three areas have the potential to significantly improve BCI communication rates, and the proposed method for predicting BCI performance provides a reliable means to pre-assess BCI performance without extensive online testing.

High-speed label-free functional photoacoustic microscopy of mouse brain in action.

We present fast functional photoacoustic microscopy (PAM) for three-dimensional high-resolution, high-speed imaging of the mouse brain, complementary to other imaging modalities. We implemented a single-wavelength pulse-width-based method with a one-dimensional imaging rate of 100 kHz to image blood oxygenation with capillary-level resolution. We applied PAM to image the vascular morphology, blood oxygenation, blood flow and oxygen metabolism in both resting and stimulated states in the mouse brain.

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.

Performance Evaluation of a Statistical and a Neural Network Model for Nonrigid Shape-Based Registration

Shape-based registration methods frequently encounters in the domains of computer vision, image processing and medical imaging. The registration problem is to find an optimal transformation/mapping between sets of rigid or nonrigid objects and to automatically solve for correspondences. In this paper we present a comparison of two different probabilistic methods, the entropy and the growing neural gas network (GNG), as general feature-based registration algorithms. Using entropy shape modelling is performed by connecting the point sets with the highest probability of curvature information, while with GNG the points sets are connected using nearest-neighbour relationships derived from competitive hebbian learning. In order to compare performances we use different levels of shape deformation starting with a simple shape 2D MRI brain ventricles and moving to more complicated shapes like hands. Results both quantitatively and qualitatively are given for both sets.

Cost-effectiveness of natriuretic peptide-based screening and collaborative care: a report from the STOP-HF (St Vincent's Screening TO Prevent Heart Failure) study

AIMS: Prevention of cardiovascular disease and heart failure (HF) in a cost-effective manner is a public health goal. This work aims to assess the cost-effectiveness of the St Vincent's Screening TO Prevent Heart Failure (STOP-HF) intervention.

METHODS AND RESULTS: This is a substudy of 1054 participants with cardiovascular risk factors [median age 65.8 years, interquartile range (IQR) 57.8:72.4, with 4.3 years, IQR 3.4:5.2, follow-up]. Annual natriuretic peptide-based screening was performed, with collaborative cardiovascular care between specialist physicians and general practitioners provided to patients with BNP levels >50 pg/mL. Analysis of cost per case prevented and cost-effectiveness per quality-adjusted life year (QALY) gained was performed. The primary clinical endpoint of LV dysfunction (LVD) with or without HF was reduced in intervention patients [odds ratio (OR) 0.60; 95% confidence interval (CI) 0.38-0.94; P = 0.026]. There were 157 deaths and/or emergency hospitalizations for major adverse cardiac events (MACE) in the control group vs. 102 in the intervention group (OR 0.68; 95% CI 0.49-0.93; P = 0.01). The cost per case of LVD/HF prevented was €9683 (sensitivity range -€843 to €20 210), whereas the cost per MACE prevented was €3471 (sensitivity range -€302 to €7245). Cardiovascular hospitalization savings offset increased outpatient and primary care costs. The cost per QALY gain was €1104 and the intervention has an 88% probability of being cost-effective at a willingness to pay threshold of €30 000.

CONCLUSION: Among patients with cardiovascular risk factors, natriuretic peptide-based screening and collaborative care reduced LVD, HF, and MACE, and has a high probability of being cost-effective.

TRIAL REGISTRATION: NCT00921960.

Natriuretic peptide-based screening and collaborative care for heart failure:the STOP-HF randomized trial

IMPORTANCE: Prevention strategies for heart failure are needed.

OBJECTIVE: To determine the efficacy of a screening program using brain-type natriuretic peptide (BNP) and collaborative care in an at-risk population in reducing newly diagnosed heart failure and prevalence of significant left ventricular (LV) systolic and/or diastolic dysfunction.

DESIGN, SETTING, AND PARTICIPANTS: The St Vincent's Screening to Prevent Heart Failure Study, a parallel-group randomized trial involving 1374 participants with cardiovascular risk factors (mean age, 64.8 [SD, 10.2] years) recruited from 39 primary care practices in Ireland between January 2005 and December 2009 and followed up until December 2011 (mean follow-up, 4.2 [SD, 1.2] years).

INTERVENTION: Patients were randomly assigned to receive usual primary care (control condition; n=677) or screening with BNP testing (n=697). Intervention-group participants with BNP levels of 50 pg/mL or higher underwent echocardiography and collaborative care between their primary care physician and specialist cardiovascular service.

MAIN OUTCOMES AND MEASURES: The primary end point was prevalence of asymptomatic LV dysfunction with or without newly diagnosed heart failure. Secondary end points included emergency hospitalization for arrhythmia, transient ischemic attack, stroke, myocardial infarction, peripheral or pulmonary thrombosis/embolus, or heart failure.

RESULTS: A total of 263 patients (41.6%) in the intervention group had at least 1 BNP reading of 50 pg/mL or higher. The intervention group underwent more cardiovascular investigations (control, 496 per 1000 patient-years vs intervention, 850 per 1000 patient-years; incidence rate ratio, 1.71; 95% CI, 1.61-1.83; P<.001) and received more renin-angiotensin-aldosterone system-based therapy at follow-up (control, 49.6%; intervention, 56.5%; P=.01). The primary end point of LV dysfunction with or without heart failure was met in 59 (8.7%) of 677 in the control group and 37 (5.3%) of 697 in the intervention group (odds ratio [OR], 0.55; 95% CI, 0.37-0.82; P = .003). Asymptomatic LV dysfunction was found in 45 (6.6%) of 677 control-group patients and 30 (4.3%) of 697 intervention-group patients (OR, 0.57; 95% CI, 0.37-0.88; P = .01). Heart failure occurred in 14 (2.1%) of 677 control-group patients and 7 (1.0%) of 697 intervention-group patients (OR, 0.48; 95% CI, 0.20-1.20; P = .12). The incidence rates of emergency hospitalization for major cardiovascular events were 40.4 per 1000 patient-years in the control group vs 22.3 per 1000 patient-years in the intervention group (incidence rate ratio, 0.60; 95% CI, 0.45-0.81; P = .002).

CONCLUSION AND RELEVANCE: Among patients at risk of heart failure, BNP-based screening and collaborative care reduced the combined rates of LV systolic dysfunction, diastolic dysfunction, and heart failure.

TRIAL REGISTRATION: clinicaltrials.gov Identifier: NCT00921960.

Classifying Child Maltreatment by Brain Imaging Data

Thesis (Master's)--University of Washington, 2016-08

Sviluppo e test di un sistema BCI SSVEP-based

Una Brain Computer Interface (BCI) è un dispositivo che permette la misura e l’utilizzo di segnali cerebrali al fine di comandare software e/o periferiche di vario tipo, da semplici videogiochi a complesse protesi robotizzate. Tra i segnali attualmente più utilizzati vi sono i Potenziali Evocati Visivi Steady State (SSVEP), variazioni ritmiche di potenziale elettrico registrabili sulla corteccia visiva primaria con un elettroencefalogramma (EEG) non invasivo; essi sono evocabili attraverso una stimolazione luminosa periodica, e sono caratterizzati da una frequenza di oscillazione pari a quella di stimolazione. Avendo un rapporto segnale rumore (SNR) particolarmente favorevole ed una caratteristica facilmente studiabile, gli SSVEP sono alla base delle più veloci ed immediate BCI attualmente disponibili. All’utente vengono proposte una serie di scelte ciascuna associata ad una stimolazione visiva a diversa frequenza, fra le quali la selezionata si ripresenterà nelle caratteristiche del suo tracciato EEG estratto in tempo reale. L’obiettivo della tesi svolta è stato realizzare un sistema integrato, sviluppato in LabView che implementasse il paradigma BCI SSVEP-based appena descritto, consentendo di: 1. Configurare la generazione di due stimoli luminosi attraverso l’utilizzo di LED esterni; 2. Sincronizzare l’acquisizione del segnale EEG con tale stimolazione; 3. Estrarre features (attributi caratteristici di ciascuna classe) dal suddetto segnale ed utilizzarle per addestrare un classificatore SVM; 4. Utilizzare il classificatore per realizzare un’interfaccia BCI realtime con feedback per l’utente. Il sistema è stato progettato con alcune delle tecniche più avanzate per l’elaborazione spaziale e temporale del segnale ed il suo funzionamento è stato testato su 4 soggetti sani e comparato alle più moderne BCI SSVEP-based confrontabili rinvenute in letteratura.