Discrimination of Mild Cognitive Impairment and Alzheimer\u27s Disease Using Transfer Entropy Measures of Scalp EEG

Mild cognitive impairment (MCI) is a neurological condition related to early stages of dementia including Alzheimer\u27s disease (AD). This study investigates the potential of measures of transfer entropy in scalp EEG for effectively discriminating between normal aging, MCI, and AD participants. Resting EEG records from 48 age-matched participants (mean age 75.7 years)-15 normal controls, 16 MCI, and 17 early AD-are examined. The mean temporal delays corresponding to peaks in inter-regional transfer entropy are computed and used as features to discriminate between the three groups of participants. Three-way classification schemes based on binary support vector machine models demonstrate overall discrimination accuracies of 91.7- 93.8%, depending on the protocol condition. These results demonstrate the potential for EEG transfer entropy measures as biomarkers in identifying early MCI and AD. Moreover, the analyses based on short data segments (two minutes) render the method practical for a primary care setting

Dynamic Complexity and Causality Analysis of Scalp EEG for Detection of Cognitive Deficits

This dissertation explores the potential of scalp electroencephalography (EEG) for the detection and evaluation of neurological deficits due to moderate/severe traumatic brain injury (TBI), mild cognitive impairment (MCI), and early Alzheimer’s disease (AD). Neurological disorders often cannot be accurately diagnosed without the use of advanced imaging modalities such as computed tomography (CT), magnetic resonance imaging (MRI), and positron emission tomography (PET). Non-quantitative task-based examinations are also used. None of these techniques, however, are typically performed in the primary care setting. Furthermore, the time and expense involved often deters physicians from performing them, leading to potential worse prognoses for patients. If feasible, screening for cognitive deficits using scalp EEG would provide a fast, inexpensive, and less invasive alternative for evaluation of TBI post injury and detection of MCI and early AD. In this work various measures of EEG complexity and causality are explored as means of detecting cognitive deficits. Complexity measures include eventrelated Tsallis entropy, multiscale entropy, inter-regional transfer entropy delays, and regional variation in common spectral features, and graphical analysis of EEG inter-channel coherence. Causality analysis based on nonlinear state space reconstruction is explored in case studies of intensive care unit (ICU) signal reconstruction and detection of cognitive deficits via EEG reconstruction models. Significant contributions in this work include: (1) innovative entropy-based methods for analyzing event-related EEG data; (2) recommendations regarding differences in MCI/AD of common spectral and complexity features for different scalp regions and protocol conditions; (3) development of novel artificial neural network techniques for multivariate signal reconstruction; and (4) novel EEG biomarkers for detection of dementia

Intrinsic functional brain networks in health and disease

6 Introduction   6  6.1   Imaging  cognitive  processes  with  functional  magnetic  resonance  imaging   7  6.2   Imaging  the  brain’s  resting  state   8  6.3   Intrinsic  connectivity  networks  in  the  resting  state   9  6.4   Investigating  modulations  and  plasticity  of  intrinsic  connectivity  networks   12 7 Paper  1:   Towards  discovery  science  of  human  brain  function  (PNAS  2010)   14 8 Paper  2:   Repeated  pain  induces  adaptations  of  intrinsic  brain  activity  to  reflect  past  and  predict future pain  (Neuroimage  2011)   30 9 Paper  3:   Intrinsic  network  connectivity  reflects  consistency  of  synesthetic  experience

Brain Dynamics as Confirmatory Biomarker of Dementia with Lewy Bodies Versus Alzheimer’s Disease - an Electrophysiological Study

PhD ThesisIntroduction Dementia with Lewy bodies (DLB), Parkinson’s disease dementia (PDD) and Alzheimer’s disease dementia (AD) are associated with different pathologies. Nevertheless, symptomatic overlap between these conditions may lead to misdiagnosis. Resting-state functional connectivity features in DLB as assessed with electroencephalography (EEG) are emerging as diagnostic biomarkers. However, their pathological significance is still questioned. This study aims to further investigate this aspect and to infer functional and structural sources of EEG abnormalities in DLB. Methods Graph theory analysis was first performed to assess EEG network differences between healthy controls (HC) and dementia groups. Source localisation and Network Based Statistics (NBS) were used to infer EEG cortical network and dominant frequency (DF) alterations in DLB compared with AD. Further analysis aimed to assess the subnetwork associated with visual hallucination (VH) symptom in DLB and PDD, i.e. LBD, compared with not-hallucinating (NVH) patients. Finally, probabilistic tractography was performed on diffusion tensor imaging (DTI) data between cortical regions, thalamus, and basal forebrain (NBM). Correlation between structural and functional connectivity was tested. Results EEG α-band (7-13.5 Hz) network features were affected in LBD compared with HC, whilst DLB β-band network (14-20.5 Hz) was weaker and more segregated when compared with AD. This scenario replicated in the source domain. DF was significantly lower in DLB compared with AD, and positively correlated with structural connectivity strength between NBM and the cortex. Functional visual ventral network connectivity and cholinergic projections towards the cortex were affected in VH compared with NVH, and significantly correlated in NVH. Conclusions Functional connectivity as assessed with EEG is more affected in DLB compared with AD. Moreover, the visual ventral network is functionally altered in VH compared with NVH. Results from structural analysis provide empirical evidence on the role of cholinergic dysfunctions in DLB and PDD pathology and corresponding functional correlates

Morphological alterations in frontotemporal dementia:

The present thesis explores alterations in brain morphology in the neurodegenerative disorder of frontotemporal dementia (FTD). With the aim to improve the clinical diagnostics of FTD, we explored the diagnostic potential of measuring morphological alterations in the white matter by diffusion tensor imaging (DTI)- MRI, compared with the more commonly used assessment of grey matter thickness and volume. DTI-MRI was better at separating FTD cases from controls than grey matter parameters, and may thus be a promising supplementary imaging tool for the diagnostic work in FTD. We used DTI in combination with grey matter imaging to explore the morphological underpinnings of one of the central behavioural symptoms in FTD, disinhibition. Our results show that this symptom appears related to the integrity of an orbitofrontal-temporal network, as opposed to the prevailing view of a degeneration of the orbitofrontal cortex. An important question in FTD is what constitutes the morphological link between the molecular pathologies and the characteristic frontotemporal pattern of cortical degeneration. The von Economo neurons (VENs), are a particular type of neurons that are proposed to constitute this link. We confirm results from others, showing that these neurons are selectively degenerated in FTD. In addition we show that these neurons are more afflicted than pyramidal neurons in the superficial cortical layers, previously thought to be the most selectively degenerated in the cortex of FTD. The findings presented in this thesis will hopefully contribute both to improved diagnostics, understanding of clinico-pathological relationships, and of the pathophysiology of this condition

Detecting and tracking early neurodegeneration in familial Alzheimer’s disease

Alzheimer’s disease (AD) is recognized to have a long presymptomatic period, with initial deposition of extracellular amyloid and intracellular tau, followed by downstream neurodegeneration and cognitive decline. There is great interest in testing potential disease-modifying treatments for AD prior to the onset of symptoms, when minimal neuronal loss has occurred. To facilitate this, robust and sensitive methods are needed to identify at-risk individuals, stage their disease, and track progression. Familial Alzheimer’s disease (FAD) shares many features, clinically, radiologically, and neurophysiologically, with the more common sporadic form of disease. Carriers of autosomal dominantly inherited mutations in the presenilin 1, presenilin 2, and amyloid precursor protein genes have relatively predictable ages at symptom onset, based on family history. Study of FAD mutation carriers therefore provides the opportunity for the prospective study of asymptomatic individuals with known underlying AD pathology prior to the onset of clinical disease. The studies presented herein aim to improve the identification and characterization of early FAD neurodegenerative change and its earliest downstream cognitive effects. A multimodal approach is taken, with both presymptomatic and mildly symptomatic individuals included. Chapter one provides an introduction to AD and methods for measuring early neurodegeneration. Chapter two then outlines the general methodological approach across the different studies. Chapters three and four present results of magnetic resonance imaging studies of macrostructural (cortical thickness) and microstructural (diffusion-weighted imaging) cortical change. Chapter five reports results for a new blood-based biomarker of neurodegeneration – serum neurofilamentlight. Chapter six investigates a novel approach to presymptomatic cognitive testing – 6 assessing accelerated long-term forgetting. In all studies, significant differences between mutation carriers and non-carrier controls are detectable during the presymptomatic period. The thesis draws together these different approaches and discusses how they advance our understanding of the neurobiology of AD and their potential utility in both clinical assessment and presymptomatic therapeutic trials