Data fusion of complementary information from parietal and occipital event related potentials for early diagnosis of Alzheimer\u27s disease

The number of the elderly population affected by Alzheimer\u27s disease is rapidly rising. The need to find an accurate, inexpensive, and non-intrusive procedure that can be made available to community healthcare providers for the early diagnosis of Alzheimer\u27s disease is becoming an increasingly urgent public health concern. Several recent studies have looked at analyzing electroencephalogram signals through the use of many signal processing techniques. While their methods show great promise, the final outcome of these studies has been largely inconclusive. The inherent difficulty of the problem may be the cause of this outcome, but most likely it is due to the inefficient use of the available information, as many of these studies have used only a single EEG source for the analysis. In this contribution, data from the event related potentials of 19 available electrodes of the EEG are analyzed. These signals are decomposed into different frequency bands using multiresolution wavelet analysis. Two data fusion approaches are then investigated: i.) concatenating features before presenting them to a classification algorithm with the expectation of creating a more informative feature space, and ii.) generating multiple classifiers each trained with a different combination of features obtained from various stimuli, electrode, and frequency bands. The classifiers are then combined through the weighted majority vote, product and sum rule combination schemes. The results indicate that a correct diagnosis performance of over 80% can be obtained by combining data primarily from parietal and occipital lobe electrodes. The performance significantly exceeds that reported from community clinic physicians, despite their access to the outcomes of longitudinal monitoring of the patients

Ensemble of classifiers based data fusion of EEG and MRI for diagnosis of neurodegenerative disorders

The prevalence of Alzheimer\u27s disease (AD), Parkinson\u27s disease (PD), and mild cognitive impairment (MCI) are rising at an alarming rate as the average age of the population increases, especially in developing nations. The efficacy of the new medical treatments critically depends on the ability to diagnose these diseases at the earliest stages. To facilitate the availability of early diagnosis in community hospitals, an accurate, inexpensive, and noninvasive diagnostic tool must be made available. As biomarkers, the event related potentials (ERP) of the electroencephalogram (EEG) - which has previously shown promise in automated diagnosis - in addition to volumetric magnetic resonance imaging (MRI), are relatively low cost and readily available tools that can be used as an automated diagnosis tool. 16-electrode EEG data were collected from 175 subjects afflicted with Alzheimer\u27s disease, Parkinson\u27s disease, mild cognitive impairment, as well as non-disease (normal control) subjects. T2 weighted MRI volumetric data were also collected from 161 of these subjects. Feature extraction methods were used to separate diagnostic information from the raw data. The EEG signals were decomposed using the discrete wavelet transform in order to isolate informative frequency bands. The MR images were processed through segmentation software to provide volumetric data of various brain regions in order to quantize potential brain tissue atrophy. Both of these data sources were utilized in a pattern recognition based classification algorithm to serve as a diagnostic tool for Alzheimer\u27s and Parkinson\u27s disease. Support vector machine and multilayer perceptron classifiers were used to create a classification algorithm trained with the EEG and MRI data. Extracted features were used to train individual classifiers, each learning a particular subset of the training data, whose decisions were combined using decision level fusion. Additionally, a severity analysis was performed to diagnose between various stages of AD as well as a cognitively normal state. The study found that EEG and MRI data hold complimentary information for the diagnosis of AD as well as PD. The use of both data types with a decision level fusion improves diagnostic accuracy over the diagnostic accuracy of each individual data source. In the case of AD only diagnosis, ERP data only provided a 78% diagnostic performance, MRI alone was 89% and ERP and MRI combined was 94%. For PD only diagnosis, ERP only performance was 67%, MRI only was 70%, and combined performance was 78%. MCI only diagnosis exhibited a similar effect with a 71% ERP performance, 82% MRI performance, and 85% combined performance. Diagnosis among three subject groups showed the same trend. For PD, AD, and normal diagnosis ERP only performance was 43%, MRI only was 66%, and combined performance was 71%. The severity analysis for mild AD, severe AD, and normal subjects showed the same combined effect

Decision-based data fusion of complementary features for the early diagnosis of Alzheimer\u27s disease

As the average life expectancy increases, particularly in developing countries, the prevalence of Alzheimer\u27s disease (AD), which is the most common form of dementia worldwide, has increased dramatically. As there is no cure to stop or reverse the effects of AD, the early diagnosis and detection is of utmost concern. Recent pharmacological advances have shown the ability to slow the progression of AD; however, the efficacy of these treatments is dependent on the ability to detect the disease at the earliest stage possible. Many patients are limited to small community clinics, by geographic and/or financial constraints. Making diagnosis possible at these clinics through an accurate, inexpensive, and noninvasive tool is of great interest. Many tools have been shown to be effective at the early diagnosis of AD. Three in particular are focused upon in this study: event-related potentials (ERPs) in electroencephalogram (EEG) recordings, magnetic resonance imaging (MRI), as well as positron emission tomography (PET). These biomarkers have been shown to contain diagnostically useful information regarding the development of AD in an individual. The combination of these biomarkers, if they provide complementary information, can boost overall diagnostic accuracy of an automated system. EEG data acquired from an auditory oddball paradigm, along with volumetric T2 weighted MRI data and PET imagery representative of metabolic glucose activity in the brain was collected from a cohort of 447 patients, along with other biomarkers and metrics relating to neurodegenerative disease. This study in particular focuses on AD versus control diagnostic ability from the cohort, in addition to AD severity analysis. An assortment of feature extraction methods were employed to extract diagnostically relevant information from raw data. EEG signals were decomposed into frequency bands of interest hrough the discrete wavelet transform (DWT). MRI images were reprocessed to provide volumetric representations of specific regions of interest in the cranium. The PET imagery was segmented into regions of interest representing glucose metabolic rates within the brain. Multi-layer perceptron neural networks were used as the base classifier for the augmented stacked generalization algorithm, creating three overall biomarker experts for AD diagnosis. The features extracted from each biomarker were used to train classifiers on various subsets of the cohort data; the decisions from these classifiers were then combined to achieve decision-based data fusion. This study found that EEG, MRI and PET data each hold complementary information for the diagnosis of AD. The use of all three in tandem provides greater diagnostic accuracy than using any single biomarker alone. The highest accuracy obtained through the EEG expert was 86.1 ±3.2%, with MRI and PET reaching 91.1 +3.2% and 91.2 ±3.9%, respectively. The maximum diagnostic accuracy of these systems averaged 95.0 ±3.1% when all three biomarkers were combined through the decision fusion algorithm described in this study. The severity analysis for AD showed similar results, with combination performance exceeding that of any biomarker expert alone

Place cell physiology in a transgenic mouse model of Alzheimer's disease

Alzheimer's disease (AD) is a multifactorial neurodegenerative disorder characterized by progressive cognitive impairments (Selkoe, 2001). Hippocampal place cells are a well understood candidate for the neural basis of one type of memory in rodents; these cells identify the animal's location in an environment and are crucial for spatial memory and navigation. This PhD project aims to clarify the mechanisms responsible for the cognitive deficits in AD at the hippocampal network level, by examining place cell physiology in a transgenic mouse model of AD. I have recorded place cells in tg2576 mice, and found that aged (16 months) but not young (3 months) transgenic mice show degraded neuronal representations of the environment. The level of place cell degradation correlates with the animals' (poorer) spatial memory as tested in a forced-choice spatial alternation T-maze task and with hippocampal, but not neocortical, amyloid plaque burden. Additionally, pilot data show that physiological changes of the hippocampus in tg2576 mice seem to start as early as 3 months, when no pathological and behavioural deficits are present. However, these changes are not obvious at the neuronal level, but only at the hippocampal network level, which represent hippocampal responses to environmental changes. Place cell recording provides a sensitive assay for measuring the amount and rate of functional deterioration in animal models of dementia as well as providing a quantifiable physiological indication of the beneficial effects of potential therapies

Quantitative Electroencephalography and genetics as biomarkers of dementia in Parkinson’s disease

The importance of cognitive decline in Parkinson’s disease (PD), which eventually progresses to dementia (PD-D) in the majority of surviving patients, has been widely recognised during the last decade. PD-D is associated with a twofold increase in mortality, increased caregiver strain and increased healthcare costs. Thus, early and correct identification of the PD patients with a risk of dementia is a challenging problem of neurology, which has led to the suggestion of various markers of cognitive decline in PD. If validated, these markers would offer the opportunity for disease modification and therapeutic intervention at a critical early stage of the illness, when the viable neuronal population is greater. The focus of this thesis was to assess how various factors - quantitative electroencephalography (qEEG) changes, genetics, deep brain stimulation (DBS), olfactory function, etc. – may be related with the risk of cognitive decline in PD patients. We performed four clinical studies with various design. These studies included PD patients who were dementia-free on inclusion, and control participants. Principal findings are the following: (1) increase of global median relative power theta (4–8 Hz), executive and working memory dysfunction are independent prognostic markers of severe cognitive decline in PD patients over a period of 3 years. (2) DBS of the subthalamic nuclei in a group of PD patients with mean age 63.2 years, in comparison with a group of younger patients (52.9 years), causes higher incidence of psychiatric events over 2 years of observation. However, these events were transient and did not outweigh the benefits of surgery. (3) Worsening of verbal fluency performance is an early cognitive outcome of DBS of the subthalamic nuclei in PD patients. (4) Among early appearing non-motor signs of Parkinson’s disease, alteration of olfaction but not EEG spectrum correlates with motor function. (5) A composite score approach seems to be a realistic goal in the search for biomarkers of severe cognitive decline

Investigations at the Crossroads of Down Syndrome and Alzheimer’s Disease

People with Down syndrome (DS) have elevated neuroinflammation early in life and develop neuropathology by the age of twenty. Most individuals with DS go on to develop abnormal dementia and Alzheimer’s disease (AD). This dissertation is focused on biological pathways involved in DS-AD and includes studies in humans with DS and DS mouse models. Locus coeruleus (LC) noradrenergic (NE) neurons decline before other transmitter systems on the path to DS-AD, which leads to increased neuropathology and accelerated memory loss. To investigate the specific roles of LC-NE in DS-AD, designer receptors exclusively activated by designer drugs (DREADDs) were utilized in the Ts65Dn mouse model of DS to selectively stimulate or inhibit LC-NE activity. LC-NE activity modulated neuroinflammation, memory performance, and AD pathology in this mouse model. Altogether these findings implicate the importance of LC-NE function in the context of DS-AD. LC-NE dysfunction may also affect resolution response to neuroinflammation. Insufficient resolution activity was already known to correlate with AD neuropathology in humans and mouse models, but specialized pro-resolving factors have not been evaluated as a therapeutics in DS-AD. In the next portion of my thesis, I developed a novel therapeutic approach to enhance resolution activity in Ts65Dn mice with a pro-resolving mediator, resolvin E1 (RvE1). RvE1 treatment significantly reduced glial activation in the brain and pro-inflammatory cytokines in the periphery of Ts65Dn mice. RvE1 therapy reversed Ts65Dn deficits in memory and cognitive flexibility, which correlated with significant proteomic measures of the inflammatory resolution process. Finally, I investigated blood biomarkers that are relevant to AD including neuron-derived exosome levels of amyloid-beta peptides and phosphorylated-Tau (P-Tau) and serum BDNF levels. These AD biomarkers were already significantly elevated early in childhood with unique trajectories associated with dementia in humans with DS. Serum BDNF levels correlated with exosome P-Tau levels, suggesting an interaction between these two pathways in the development of DS-AD in humans. These data provide novel hope for meaningful therapeutics, to be implemented in early childhood in those with DS and inform both research and clinical perspectives at the crossroads of DS and AD

Role of Anterior Cingulate Cortex in Saccade Control

Cognitive control is referred to the guidance of behavior based on internal goals rather than external stimuli. It has been postulated that prefrontal cortex is mainly involved in higher order cognitive functions. Specifically, anterior cingulate cortex (ACC), which is part of the prefrontal cortex, is suggested to be involved in performance monitoring and conflict monitoring that are considered to be cognitive control functions. Saccades are the fast eye movements that align the fovea on the objects of interest in the environment. In this thesis, I have explored the role of ACC in control of saccadic eye movements. First, I performed a resting-state fMRI study to identify areas within the ACC that are functionally connected to the frontal eye fields (FEF). It has been shown that FEF is involved in saccade generation. Therefore, the ACC areas that are functionally connected to FEF could be hypothesized to have a role in saccade control. Then, I performed simultaneous electrophysiological recordings in the ACC and FEF. Furthermore, I explored whether ACC exerts control over FEF. My results show that ACC is involved in cognitive control of saccades. Furthermore, the ACC and FEF neurons communicate through synchronized theta and beta band activity in these areas. The results of this thesis shine light on the mechanisms by which these brain areas communicate. Moreover, my findings support the notion that ACC and FEF have a unique oscillatory property, and more specifically ACC has a prominent theta band, and to a lesser extent beta band activity

Electrophysiological premotor processing in Huntington's disease: an issue of functional connectivity

Huntington’s disease is a neurodegenerative disease which presents with cognitive, motor and emotional-behavioural changes. Neural degeneration begins up to 20 years prior to symptom onset, arising initially in the striatum. Motor symptoms are a hallmark; disturbances occur due to disruption of crucial motor pathways through atrophy. However, these are not seen until much later in the disease. Such findings raise questions about the role internal processes play in maintenance of function, but little is known about functional motor connectivity during early stages. Electroencephalography is a sensitive measure of the integrity of neural process occurring prior to, and at, motor execution. This thesis explores the relationship between electrophysiological premotor/motor activation and structural integrity of critical neuroanatomy providing intra-hemispheric and inter-hemispheric connectivity – the striatum and corpus callosum. In the principal study, presymptomatic persons showed abnormal premotor activation (Contingent Negative Variation); greater relative activation across the premotor period, accompanied by normal motor potentials (Readiness Potential) and execution (response time). Aberrant premotor activation likely reflects disruption of critical inter-hemispheric circuitry such as fronto-striatal networks and the corpus callosum. Results implicated compensation in a context of early atrophy and/or an inability to regulate responses. Extending this hypothesis, Study Two examines the relationship between premotor electrophysiological activity and morphology of the striatum using magnetic resonance imaging. Structural integrity of the caudate and putamen was theorised to determine the fronto-striatal neuroanatomical circuits subserving electrophysiological responses. Quantification of volume/shape yielded structure and function associations in our combined sample: timing (latency) and consistency (relative activation/slope) of the premotor response was determined by degeneration, with greater atrophy predicting later and less consistent activation. This suggests compromise to motor pathways is progressive, and may first emerge as delayed, inefficient, and inconsistent electrophysiology. In Study Three, investigation was extended to the corpus callosum, which provides inter-hemispheric connectivity between primary and supplementary motor regions distinct from fronto-striatal pathways. It was proposed that atrophy to the corpus callosum (thinning) would disrupt both homotopic (e.g. parietal to frontal lobe) and heterotopic (e.g. left and right frontal lobe) circuits supporting premotor/motor connectivity. Raw correlations suggested compromise to mid- posterior (motor) and mid-anterior (frontal cortex/premotor/supplementary motor) affects premotor performance (extent and consistency of response). While results did not survive stringent FDR error corrections, they followed known anatomical relationships, suggesting functional motor connectivity and premotor processing are also determined by structural integrity of the corpus callosum. These findings are important in showing early, disease-related morphological changes to the striatum and corpus callosum do disrupt critical fronto-striatal and inter-hemispheric networks. Morphological changes accompanied by abnormal electrophysiological premotor activation support hypotheses of dysfunctional connectivity arising from atrophy to anatomical landmarks. Progression of circuit derangement may be mediated by secondary activation (e.g. supplementary motor areas, executive circuits) and typically indirect subcortical structures, which preserve function as connectivity with the primary motor area declines. Future studies using technology such as transcranial magnetic stimulation and diffusion tensor imaging may allow identification and stimulation of vulnerable and robust circuits respectively, potential intervention targets to preserve function and quality of life for longer

Investigation of neuronal activity in a murine model of Alzheimer’s disease using in vivo two-photon calcium imaging

Alzheimer’s disease (AD) is one of the biggest challenges for biomedical research nowadays as with the growth of life span more and more people are affected by this disorder. Etiology of AD is unknown, yet growing evidence identifies alterations in neuronal activity as of the great importance for pathology. Although several significant studies of neuronal activity alteration in AD were done during the last decade, none of them addressed the question of the time course of these changes over the disease progression. Alzheimer’s disease (AD) is characterized by impairments of brain neurons that are responsible for the storage and processing of information. Studies have revealed decrease in the activity of neurons (Silverman et al., 2001; Prvulovic et al., 2005) and it was proposed that generalized hypoactivity and silencing of brain circuits takes place as formulated in the synaptic failure hypothesis (Selkoe, 2002). However, more recent studies also reported opposite effects – hyperexcitability and hyperactivity of neurons in the AD models (Busche et al., 2008; Sanchez et al., 2012; Liebscher et al., 2016). It still remains unclear if these are two sides of the same coin or if these are two stages, that follow each other. Moreover, it is not clear if observed neuronal activity alterations are caused by the dysfunction of individual neurons or if overall circuitry is disturbed because the crucial “activity controllers” (most probably - inhibitory neurons) alter their activity. This project aimed to examine spontaneous neuronal activity in the murine model of AD at the early stages of disease progression using chronic in vivo imaging to address the character and the stability of neuronal activity alterations as well relation of the activity alterations to amyloid plaque proximity. Compared to earlier studies the approach of in vivo awake calcium imaging used in the current study has many benefits for brain research. The main advantage is that brain activity can be measured without artifacts generated by anesthesia, which can exaggerate or mitigate experimental readouts. In this project, I used genetically encoded calcium indicator GCaMP6 that enables prolonged repetitive imaging of the same neurons in an intact environment. Recording of calcium transients in cell bodies of neurons was accompanied by in vivo imaging of Aβ plaques and followed by immunohistochemical staining of GCaMP6-expressing neurons to investigate how activity changes are correlated with proximity to the plaque. All the experiments were done in awake mice to ensure the absence of anesthesia-derived impact on spontaneous neuronal activity. My results support previously published reports of the increased proportion of hyperactive excitatory neurons in the AD mouse model. Importantly, my results also demonstrate that this increased activity is present in the awake state, is stable over a longer period of time (one month) and does not depend on the distance to the closest plaque. These findings support the hypothesis of permanent network alterations driving aberrant activity patterns that appear early in the disease progression, resulting in a chronic excitation/inhibition disbalance. Another important finding of my project is that individual neurons do not stay in the silent state and most of them remain functional demonstrating normal activity at the later time points. This finding requires further research as it has important implication for the development of the AD treatment, as in case many neurons remain functional and their normal neuronal activity can be recovered by addressing the cause of the circuit dysfunction with treatment. To summarize, the study presented in this PhD thesis is the first longitudinal study of neuronal activity changes in an AD mouse model, and while it provides important insight into pathology, it also emphasizes the importance of chronic in vivo studies to investigate neuronal activity and its role in the disease progression