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

Abnormal reactivity of resting-state EEG alpha rhythms during eyes open in patients with Alzheimer's and Lewy body diseases

Previous studies suggest that resting-state electroencephalographic (rsEEG) rhythms recorded in old patients with dementia due to different neurodegenerative diseases have a significant heuristic and clinical potential in identifying peculiar abnormalities of the ascending activating systems and reciprocal thalamocortical circuits in which oscillatory (de)synchronizing signals dynamically underpin cortical arousal in the regulation of quiet vigilance. In the present PhD program, a new methodological approach based on rsEEG cortical source estimation and individually-based frequency bands was used to test the hypothesis of significant abnormalities in the neurophysiological oscillatory mechanisms underlying the regulation of the quiet vigilance during the transition from an eyes-closed to an eyes-open condition in patients with the most prevalent neurodegenerative dementing disorders such as Alzheimer’s disease and Lewy Body and Parkinson’s diseases and initial abnormalities in the prodromal stage of ADD, characterized by mild cognitive impairment. Three rsEEG studies were performed for that purpose. In the first study, we tested if the reactivity of posterior rsEEG alpha rhythms from the eye- closed to the eyes-open condition may differ in patients with dementia due to Lewy Bodies (DLB) and Alzheimer’s disease (ADD) as a functional probe of the dominant neural synchronization mechanisms regulating the vigilance in posterior visual systems. We used clinical, demographical, and rsEEG datasets in 28 healthy elderly (Healthy) seniors, 42 DLB, and 48 ADD participants. The eLORETA freeware estimated rsEEG cortical sources at individual delta, theta, and alpha frequencies. Results showed a substantial (> -10%) reduction in the posterior alpha activities during the eyes-open condition in 24 Healthy, 26 ADD, and 22 DLB subjects. There were lower reductions in the posterior alpha activities in the ADD and DLB groups than in the Healthy group. The reduction in the occipital region was lower in the DLB than in the ADD group. These results suggest that DLB patients may suffer a greater alteration in the neural synchronization mechanisms regulating vigilance in occipital cortical systems compared to ADD patients. In the second study, we hypothesized that the vigilance dysregulation seen in PDD patients might be reflected by altered reactivity of posterior rsEEG alpha rhythms during the vigilance transition from an eyes-closed to an eyes-open condition. We used clinical, demographical, and rsEEG datasets in 28 healthy elderly (Healthy), 73 PDD, and 35 ADD participants. We have applied the same methodology used for the first study. Results showed substantial (> -10%) reduction (reactivity) in the posterior alpha source activities from the eyes-closed to the eyes-open condition in 88% of the Healthy seniors, 57% of the ADD patients, and only 35% of the PDD patients. In these alpha-reactive participants, there was lower reactivity in the parietal alpha source activities in the PDD group than in the Healthy and the ADD groups. These results suggest that PDD is characterized by poor reactivity of mechanisms desynchronizing posterior rsEEG alpha rhythms in response to visual inputs. This finding could be an interesting biomarker of impaired vigilance regulation in quiet wakefulness in PDD patients. Indeed, such biomarkers may provide endpoints for pharmacological intervention and brain electromagnetic stimulations to improve the PDD patients’ general ability to regulate vigilance and primary visual consciousness in the activities of daily living. In the third study, we tested the exploratory hypothesis that rsEEG alpha rhythms may predict and be sensitive to mild cognitive impairment due to AD (ADMCI) progression at a 6-month follow- up (a relevant feature for intervention clinical trials). Clinical, neuroimaging, and rsEEG datasets in 52 ADMCI and 60 Healthy seniors were used. We applied the same methodology used for the first and the second studies. Results showed a substantial (> -10%) reduction in the posterior alpha source activities during the eyes-open condition in about 90% and 70% of the Healthy and ADMCI participants, respectively. In the younger ADMCI patients (mean age of 64.3±1.1) with “reactive” rsEEG alpha source activities, posterior alpha source activities during the eyes closed condition predicted the global cognitive status at the 6-month follow-up. In all ADMCI participants with “reactive” rsEEG alpha source activities, posterior alpha source activities during the eyes-closed condition reduced in magnitude at that follow-up. These effects could not be explained by neuroimaging and neuropsychological biomarkers of AD. These results suggest that in ADMCI patients, the true (“reactive”) posterior rsEEG alpha rhythms, when present, predict (in relation to younger age) and are quite sensitive to the effects of the disease progression on neurophysiological mechanisms underpinning vigilance regulation. The results of the three studies unveiled the significant extent to which the well-known impairments in the cholinergic and dopaminergic neuromodulatory ascending systems could affect the brain neurophysiological oscillatory mechanisms underpinning the reactivity of rsEEG alpha rhythms during eyes open and, then, the regulation of quiet vigilance in ADD, PDD, and DLB patients, thus enriching the neurophysiological model underlying their known difficulties to remain awake in quiet environmental conditions during daytime

Making it count : novel behavioural tasks to quantify symptoms of dementia with Lewy bodies

Dementia with Lewy bodies (DLB) is a neurodegenerative disease and a common cause of dementia in the elderly. The primary pathology of DLB is the mis-folding of the α-synuclein protein, classifying DLB as a synucleinopathy. However, concomitant pathologies are commonly found in post-mortem examination of DLB patients that may complicate diagnosis. Furthermore, DLB is a relatively new disease, first discovered in 1976, while the first official diagnostic criteria released in 1996. Consequently, the diagnostic criteria for DLB have evolved as more is learnt about the clinical and neuropathological profile. Synucleinopathies are also known to be heterogeneous, with no single symptom or biomarker present in all DLB cases. Instead, combinations of common symptoms lead to a diagnosis of probable DLB. Two of the most prominent and debilitating symptoms of DLB are visual hallucinations and cognitive fluctuations. Visual hallucinations (VH) in DLB patients are typically vivid, well-formed percepts and are a major cause of patient and caregiver stress as well as a risk factor for the patient being placed into professional care. Cognitive fluctuations (CF) involve a cycling change in attention and alertness and may occur on a daily or monthly basis, while drops in awareness may last seconds or hours. Currently, the only tools to measure cognitive fluctuations or visual hallucinations are scales or questionnaires that rely on responses from the patient or informant. Furthermore, severity of the symptom is then ranked on an arbitrary ranking system. While this method has advantages in a clinical setting, the subjective nature of the scales combined with the ranking of scores results in a loss of sensitivity. In a research setting, especially imaging or clinical trials, objective measures that are sensitive to changes in symptom severity are highly valued. This allows researchers to assess the relationship between behavioural and fMRI data and clinicians to observe subtle changes in severity. Furthermore, the measures need to be easy to conduct as patients are often severely impaired. The aim of this thesis is to test cognitive function using three paradigms that are novel to DLB patients: Sustained Attention Response Task (SART), the Mental Rotation (MR) task and the Bistable Percept Paradigm (BPP). Overall, this thesis provided the groundwork needed before these three tasks can be utilised in a clinical or research setting. Moreover, as each task was accessible to DLB patients and provided a measure associated with VH or CF, they may prove useful for future neuroimaging/neuropsychological studies

A Knowledge-based Integrative Modeling Approach for <em>In-Silico</em> Identification of Mechanistic Targets in Neurodegeneration with Focus on Alzheimer’s Disease

Dementia is the progressive decline in cognitive function due to damage or disease in the body beyond what might be expected from normal aging. Based on neuropathological and clinical criteria, dementia includes a spectrum of diseases, namely Alzheimer's dementia, Parkinson's dementia, Lewy Body disease, Alzheimer's dementia with Parkinson's, Pick's disease, Semantic dementia, and large and small vessel disease. It is thought that these disorders result from a combination of genetic and environmental risk factors. Despite accumulating knowledge that has been gained about pathophysiological and clinical characteristics of the disease, no coherent and integrative picture of molecular mechanisms underlying neurodegeneration in Alzheimer’s disease is available. Existing drugs only offer symptomatic relief to the patients and lack any efficient disease-modifying effects. The present research proposes a knowledge-based rationale towards integrative modeling of disease mechanism for identifying potential candidate targets and biomarkers in Alzheimer’s disease. Integrative disease modeling is an emerging knowledge-based paradigm in translational research that exploits the power of computational methods to collect, store, integrate, model and interpret accumulated disease information across different biological scales from molecules to phenotypes. It prepares the ground for transitioning from ‘descriptive’ to “mechanistic” representation of disease processes. The proposed approach was used to introduce an integrative framework, which integrates, on one hand, extracted knowledge from the literature using semantically supported text-mining technologies and, on the other hand, primary experimental data such as gene/protein expression or imaging readouts. The aim of such a hybrid integrative modeling approach was not only to provide a consolidated systems view on the disease mechanism as a whole but also to increase specificity and sensitivity of the mechanistic model by providing disease-specific context. This approach was successfully used for correlating clinical manifestations of the disease to their corresponding molecular events and led to the identification and modeling of three important mechanistic components underlying Alzheimer’s dementia, namely the CNS, the immune system and the endocrine components. These models were validated using a novel in-silico validation method, namely biomarker-guided pathway analysis and a pathway-based target identification approach was introduced, which resulted in the identification of the MAPK signaling pathway as a potential candidate target at the crossroad of the triad components underlying disease mechanism in Alzheimer’s dementia

Lewy Body Dementia and the Role of Inflammation

Background: Lewy body dementia (LBD), consisting of Parkinson’s disease dementia (PDD) and dementia with Lewy bodies (DLB), is known to make up more than 15% of dementia cases at autopsy, however the clinical prevalence rate is reported to be much lower at around 5-6%. Difficulties with diagnosis and/or lack of specific treatments may contribute to this difference. This study investigated the diagnosis and management pathways of LBD and whether inflammation could play a role in the pathophysiology and hence provide a route for future diagnostic and treatment pathways. Methods: Clinical diagnostic rates of LBD in clinics across several NHS trusts in East Anglia were reviewed, followed by an in-depth notes review of patients identified with LBD together with age and gender matched controls. A literature review of the current evidence for inflammation in LBD, preceded a case control study to investigate further. Nineteen DLB patients together with 16 age and gender matched healthy controls underwent [11C]PK11195 PET imaging, and the same cohorts, plus an additional 10 matched control subjects underwent peripheral cytokine analysis. Results: The clinical prevalence rate of LBD was low compared to the known pathology rates, with delays identified in the diagnosis of DLB compared to other dementia subtypes. Delays were also seen between the onset of dementia symptoms and the clinical diagnosis of dementia in Parkinson’s disease (PD). The literature review identified studies providing evidence of inflammation in PD but few studies had been carried out in DLB. PET imaging revealed microglial activation negatively correlated with disease severity in DLB, suggesting inflammation occurs early in the disease. DLB patients also showed evidence of differences in cytokine levels compared to healthy controls. Conclusion: The study showed evidence of inflammatory changes in DLB, providing a potential target for treatment and/or biomarkers, that could assist in increasing clinical diagnostic rates.Some of the work (chapters 2 & 3) was carried out as part of the Diamond Lewy study, funded by the NIHR (Grant Reference Number DTC-RP-PG-0311-12001). The work described in Chapter 6 was primarily funded by the National Institute for Health Research Cambridge Biomedical Research Centre (NIHR, RG64473), Alzheimer’s Research UK and the Bury Free Press (Liffe Media Publishing Limited)

Multimodal and multiscale brain networks : understanding aging, Alzheimer’s disease, and other neurodegenerative disorders

The human brain can be modeled as a complex network, often referred to as the connectome, where structural and functional connections govern its organization. Several neuroimaging studies have focused on understanding the architecture of healthy brain networks and have shed light on how these networks evolve with age and in the presence of neurodegenerative disorders. Many studies have explored the brain networks in Alzheimer’s disease (AD), the most common type of dementia, using various neuroimaging modalities independently. However, most of these studies ignored the complex and multifactorial nature of AD. The aim of this thesis was to investigate and analyze the brain’s multimodal and multiscale network organization in aging and in AD by using different multilayer brain network analyses and different types of data. Additionally, this research extended its scope to incorporate other dementias, such as Lewy body dementias, allowing for a comparison of these disorders with AD and normal aging. These comparisons were made possible through the application of protein co-expression networks. In Study I, we investigated sex differences in healthy individuals using multimodal brain networks. To do this we used resting-state functional magnetic resonance imaging (rs-fMRI) and diffusion-weighted imaging (DWI) data from the Human Connectome Project (HCP) to perform multilayer and deep learning analyses. These analyses identified differences between men's and women's underlying brain network organization, showing that the deep-learning analysis with multilayer network metrics (area under the curve, AUC, of 0.81) outperforms the classification using single-layer network measures (AUC of 0.72 for functional networks and 0.70 for anatomical networks). Furthermore, we integrated the multilayer brain networks methodology and neural network models into a software package that is easy to use by researchers with different backgrounds and is also easily expandable for researchers with different levels of programming experience. Then, we used the multilayer brain networks methodology to study the interaction between sex and age on the functional network topology using a large group of people from the UK Biobank (Study II). By incorporating multilayer brain network analyses, we analyzed both positive and negative connections derived from functional correlations, and we obtained important insights into how cognitive abilities, physical health, and even genetic factors differ between men and women as they age. Age and sex were strongly associated with multiplex and multilayer measures such as the multiplex participation coefficient, multilayer clustering, and multilayer global efficiency, accounting for up to 89.1%, 79.9%, and 79.5% of the variance related to age, respectively. These results indicate that incorporating separate layers for positive and negative connections within a complex network framework reveals sensitive insights into age- and sex-related variations that are not detected by traditional metrics. Furthermore, our functional metrics exhibited associations with genes that have previously been linked to processes related to aging. In Study III, we assessed whether multilayer connectome analyses could offer new perspectives on the relationship between amyloid pathology and gray matter atrophy across the AD continuum. Subjects from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) were divided into four groups based on cerebrospinal fluid (CSF) amyloid-β (Aβ) biomarker levels and clinical diagnosis. We compared the different groups using weighted and binary multilayer measures that assess the strength of the connections, the modularity, as well as the multiplex segregation and integration of the brain connectomes. Across Aβ-positive (Aβ+) groups, we found widespread increases in the overlapping connectivity strength and decreases in the number of identical connections in both layers. Moreover, the brain modules were reorganized in the mild cognitive impairment (MCI) Aβ+ group and an imbalance in the quantity of couplings between the two layers was found in patients with MCI Aβ+ and AD Aβ+. Using a subsample from the same database, ADNI, we analyzed rs-fMRI data from individuals at preclinical and clinical stages of AD (Study IV). By dividing the time series into different time windows, we built temporal multilayer networks and studied the modular organization across time. We were able to capture the dynamic changes across different AD stages using this temporal multilayer network approach, obtaining outstanding areas under the curve of 0.90, 0.92 and 0.99 in the distinction of controls from preclinical, prodromal, and clinical AD stages, respectively, on top and beyond common risk factors. Our results not only improved the discrimination between various disease stages but, importantly, they also showed that dynamic multilayer functional measures are associated with memory and global cognition in addition to amyloid and tau load derived from positron emission tomography. These results highlight the potential of dynamic multilayer functional connectivity measures as functional biomarkers of AD progression. In Study V, we used in-depth quantitative proteomics to compare post-mortem brains from three key brain regions (prefrontal cortex, cingulate cortex, and the parietal cortex) directly related to the disease mechanisms of AD, Parkinson’s disease with dementia (PDD), dementia with Lewy bodies (DLB) in prospectively followed patients and older adults without dementia. We used covariance weighted networks to find modules of protein sets to further understand altered pathways in these dementias and their implications for prognostic and diagnostic purposes. In conclusion, this thesis explored the complex world of brain networks and offered insightful information about how age, sex, and AD influence these networks. We have improved our understanding of how the brain is organized in different imaging modalities and different time scales, as well as developing software tools to make this methodology available to more researchers. Additionally, we assessed the connections among various proteins in different areas of the brain in relation to health, Alzheimer's disease, and Lewy body dementias. This work contributes to the collective effort of unraveling the mysteries of the human brain organization and offers a foundation for future research to understand brain networks in health and disease

APOE AS A METABOLIC REGULATOR IN HUMANS, MICE, AND ASTROCYTES

Altered metabolic pathways appear to play central roles in the pathophysiology of late-onset Alzheimer’s disease (AD). Carrier status of the E4 allele of the APOE gene is the strongest genetic risk factor for late-onset AD, and increasing evidence suggests that E4 carriers may be at an increased risk for neurodegeneration based on inherent metabolic impairments. A new appreciation is forming for the role of APOE in cerebral metabolism, and how nutritional factors may impact this role. In chapter 1, the literature on nutritional interventions in E4 carriers aimed at mitigating disease risk is reviewed. Studies investigating the mechanism by which E4 increases disease risk have focused primarily on the association of E4 with the neuropathological hallmarks. While these studies have aided in our understanding of the role of E4 in late-disease pathology, investigating metabolic signatures of E4 carriers who have not yet developed neuropathology gives insight into the potential earlier mechanisms of E4 as a risk factor for AD. For example, an early and consistent biological hallmark of AD is cerebral glucose hypometabolism as observed by fluorodeoxyglucose positron emission tomography (FDG-PET). Interestingly, E4 carriers also display an AD-like pattern of decreased glucose metabolism by FDG-PET far before clinical symptomology. Since glucose hypometabolism occurs early in AD and early in E4 carriers, it may represent a critical prodromal phase of AD. Beyond this brain imaging finding, it is unclear if APOE has any other discernable metabolic effects in cognitively unimpaired young people. In chapter 2 we bridge this knowledge gap in the field. We utilized indirect calorimetry (IC) as a method for assessing metabolism in young and middle aged volunteers with and without the E4 allele. While IC is commonly used in clinical settings to assess nutritional status, it has never been used to assess risk for cognitive decline. Thus, repurposing IC to study the metabolic effects of an AD risk factor such as E4 represents a simple, cost-effective, and innovative new approach. We found that young female E4 carriers show a lower resting energy expenditure compared to non-carriers. We also tested how E4 carriage affects response to a glucose challenge by administering a glucose rich beverage in conjunction with IC measurements and plasma metabolomics. We found that female E4 carriers were unable to increase oxygen consumption relative to non-carriers, reflecting an impairment in glucose oxidation. Additionally, the plasma metabolome of E4 carriers showed increased lactate and an overall metabolic profile consistent with aerobic glycolysis. We translated these findings to mice expressing the human alleles of APOE. We found that E4 mice on a normal chow diet have lower energy expenditure than E3 mice, a difference further exacerbated by high carbohydrate diet feeding. Stable isotope tracing in mice whole brains and astrocytes implicate increased utilization of aerobic glycolysis as a mechanism for altered glucose handling in E4 carriers. Another pathological feature of the Alzheimer’s brain is glial lipid accumulation. The mechanism for this is largely unknown. In chapter 3, the literature pertaining to lipid droplets (LD) in the brain is reviewed. We show that LDs are much more than simple fat depots, playing critical roles in metabolism, inflammation, and various neurodegenerative diseases. In chapter 4, the effect of the E4 allele on astrocyte LD accumulation and turnover is assessed. Using an in vitro model of APOE we probed the storage and oxidation capacity of fatty acids in E3 and E4 astrocytes. We observed that E4 astrocytes exhibit greater storage of fatty acids as LDs under control and lipid loaded conditions compared to E3 astrocytes. Furthermore, we found that E4 astrocytes rely on these LDs as a source of fuel for oxidation. Therefore, APOE appears to regulate whole body energy expenditure, cerebral glucose oxidation, astrocyte LD metabolism, and risk for a host of metabolic diseases. In chapter 5, the evolutionary history of APOE is presented to posit a hypothesis for why E4 may be disadvantageous in modern times compared to its prior advantages in the pre-historic era. These results point toward a larger role for APOE in the regulation of metabolism than previously understood and advocates for alternative nutritional approaches including calorie reduction and intermittent fasting as plausible interventions to mitigate disease risk in E4 carriers

Metabolic profiling of Parkinson's disease: evidence of biomarker from gene expression analysis and rapid neural network detection

<p>Abstract</p> <p>Background</p> <p>Parkinson's disease (PD) is a neurodegenerative disorder. The diagnosis of Parkinsonism is challenging because currently none of the clinical tests have been proven to help in diagnosis. PD may produce characteristic perturbations in the metabolome and such variations can be used as the marker for detection of disease. To test this hypothesis, we used proton NMR and multivariate analysis followed by neural network pattern detection.</p> <p>Methods & Results</p> <p>¹H nuclear magnetic resonance spectroscopy analysis was carried out on plasma samples of 37 healthy controls and 43 drug-naive patients with PD. Focus on 22 targeted metabolites, 17 were decreased and 5 were elevated in PD patients (p < 0.05). Partial least squares discriminant analysis (PLS-DA) showed that pyruvate is the key metabolite, which contributes to the separation of PD from control samples. Furthermore, gene expression analysis shows significant (p < 0.05) change in expression of <it>PDHB </it>and <it>NPFF </it>genes leading to increased pyruvate concentration in blood plasma. Moreover, the implementation of ¹H- NMR spectral pattern in neural network algorithm shows 97.14% accuracy in the detection of disease progression.</p> <p>Conclusion</p> <p>The results increase the prospect of a robust molecular definition in detection of PD through the early symptomatic phase of the disease. This is an ultimate opening for therapeutic intervention. If validated in a genuinely prospective fashion in larger samples, the biomarker trajectories described here will go a long way to facilitate the development of useful therapies. Moreover, implementation of neural network will be a breakthrough in clinical screening and rapid detection of PD.</p

The Phenomenology, Pathophysiology and Progression of the Core Features of Lewy Body Dementia

Lewy body dementias – Dementia with Lewy bodies (DLB) and Parkinson’s disease dementia (PDD) - are disabling neurodegenerative conditions defined pathologically by the presence of intraneuronal α-synuclein rich aggregates (‘Lewy bodies’ and ‘Lewy neurites’). These disorders are characterized by a set of ‘core’ clinical features, namely cognitive fluctuations, visual hallucinations, motor parkinsonism, and most recently added, REM sleep behaviour disorder. These features are central to the diagnosis of Lewy bodies dementias (especially DLB) and discriminate them from other neurodegenerative disorders. Despite decades of research, the etiopathogenesis underlying Lewy body disorders is poorly understood. This accounts for the relative lack of objective biomarkers and both symptomatic and disease modifying therapies. The present thesis comprises a series of investigations that seeks to understand the phenomenology, pathophysiology, and clinical progression of Lewy body dementias through focus on each of the core clinical features. Systematic review and empiric studies are organized under the respective headings of cognitive fluctuations, visual hallucinations, REM sleep behaviour disorder, motor features, interrelationships, and clinical progression of the core features. Novel clinical and pathophysiological insights are obtained which have implications for the prediction and diagnosis of core features, the development of new objective biomarkers, and clinical endpoints of disease progression. From these studies, a shared pathophysiological basis for the core features is postulated and potential avenues for future directions are highlighted, focusing on replication and validation of new biomarkers and clinical measures, discovery of new biomarkers and mechanisms, and translation to prodromal and patient cohorts