Grand challenges in dementia 2010

High-Order Laplacian Regularized Low-Rank Representation for Multimodal Dementia Diagnosis

Multimodal heterogeneous data, such as structural magnetic resonance imaging (MRI), positron emission tomography (PET), and cerebrospinal fluid (CSF), are effective in improving the performance of automated dementia diagnosis by providing complementary information on degenerated brain disorders, such as Alzheimer's prodromal stage, i.e., mild cognitive impairment. Effectively integrating multimodal data has remained a challenging problem, especially when these heterogeneous data are incomplete due to poor data quality and patient dropout. Besides, multimodal data usually contain noise information caused by different scanners or imaging protocols. The existing methods usually fail to well handle these heterogeneous and noisy multimodal data for automated brain dementia diagnosis. To this end, we propose a high-order Laplacian regularized low-rank representation method for dementia diagnosis using block-wise missing multimodal data. The proposed method was evaluated on 805 subjects (with incomplete MRI, PET, and CSF data) from the real Alzheimer's Disease Neuroimaging Initiative (ADNI) cohort. Experimental results suggest the effectiveness of our method in three tasks of brain disease classification, compared with the state-of-the-art methods.

Health Outcome Prioritization in Alzheimer's Disease: Understanding the Ethical Landscape

BACKGROUND

Dementia has been described as the greatest global health challenge in the 21st Century on account of longevity gains increasing its incidence, escalating health and social care pressures. These pressures highlight ethical, social, and political challenges about healthcare resource allocation, what health improvements matter to patients, and how they are measured. This study highlights the complexity of the ethical landscape, relating particularly to the balances that need to be struck when allocating resources; when measuring and prioritizing outcomes; and when individual preferences are sought.

OBJECTIVE

Health outcome prioritization is the ranking in order of desirability or importance of a set of disease-related objectives and their associated cost or risk. We analyze the complex ethical landscape in which this takes place in the most common dementia, Alzheimer's disease.

METHODS

Narrative review of literature published since 2007, incorporating snowball sampling where necessary. We identified, thematized, and discussed key issues of ethical salience.

RESULTS

Eight areas of ethical salience for outcome prioritization emerged: 1) Public health and distributive justice, 2) Scarcity of resources, 3) Heterogeneity and changing circumstances, 4) Knowledge of treatment, 5) Values and circumstances, 6) Conflicting priorities, 7) Communication, autonomy and caregiver issues, and 8) Disclosure of risk.

CONCLUSION

These areas highlight the difficult balance to be struck when allocating resources, when measuring and prioritizing outcomes, and when individual preferences are sought. We conclude by reflecting on how tools in social sciences and ethics can help address challenges posed by resource allocation, measuring and prioritizing outcomes, and eliciting stakeholder preferences.

Biological basis for cerebral dysfunction in schizophrenia in contrast with Alzheimer's disease

Schizophrenia and Alzheimer's disease are two disorders that, while conceptualized as pathophysiologically and clinically distinct, cause substantial cognitive and behavioral impairment worldwide, and target apparently similar - or nearby - circuitry in regions such as the temporal and frontal lobes. We review the salient differences and similarities from selected historical, nosological, and putative mechanistic viewpoints, as a means to help both clinicians and researchers gain a better insight into these intriguing disorders, for which over a century of research and decades of translational development was needed to begin yielding treatments that are objectively effective, but still very far from entirely satisfactory. Ongoing comparison and "cross-pollination" among these approaches to disorders that produce similar deficits is likely to continue improving both our insight into the mechanisms at play, and the development of biotechnological approaches to tackle both conditions - and related disorders - more rapidly and efficaciously.

Multi-source feature learning for joint analysis of incomplete multiple heterogeneous neuroimaging data

Analysis of incomplete data is a big challenge when integrating large-scale brain imaging datasets from different imaging modalities. In the Alzheimer's Disease Neuroimaging Initiative (ADNI), for example, over half of the subjects lack cerebrospinal fluid (CSF) measurements; an independent half of the subjects do not have fluorodeoxyglucose positron emission tomography (FDG-PET) scans; many lack proteomics measurements. Traditionally, subjects with missing measures are discarded, resulting in a severe loss of available information. In this paper, we address this problem by proposing an incomplete Multi-Source Feature (iMSF) learning method where all the samples (with at least one available data source) can be used. To illustrate the proposed approach, we classify patients from the ADNI study into groups with Alzheimer's disease (AD), mild cognitive impairment (MCI) and normal controls, based on the multi-modality data. At baseline, ADNI's 780 participants (172AD, 397 MCI, 211 NC), have at least one of four data types: magnetic resonance imaging (MRI), FDG-PET, CSF and proteomics. These data are used to test our algorithm. Depending on the problem being solved, we divide our samples according to the availability of data sources, and we learn shared sets of features with state-of-the-art sparse learning methods. To build a practical and robust system, we construct a classifier ensemble by combining our method with four other methods for missing value estimation. Comprehensive experiments with various parameters show that our proposed iMSF method and the ensemble model yield stable and promising results.

Multi-Source Learning for Joint Analysis of Incomplete Multi-Modality Neuroimaging Data

Incomplete data present serious problems when integrating largescale brain imaging data sets from different imaging modalities. In the Alzheimer's Disease Neuroimaging Initiative (ADNI), for example, over half of the subjects lack cerebrospinal fluid (CSF) measurements; an independent half of the subjects do not have fluorodeoxyglucose positron emission tomography (FDG-PET) scans; many lack proteomics measurements. Traditionally, subjects with missing measures are discarded, resulting in a severe loss of available information. We address this problem by proposing two novel learning methods where all the samples (with at least one available data source) can be used. In the first method, we divide our samples according to the availability of data sources, and we learn shared sets of features with state-of-the-art sparse learning methods. Our second method learns a base classifier for each data source independently, based on which we represent each source using a single column of prediction scores; we then estimate the missing prediction scores, which, combined with the existing prediction scores, are used to build a multi-source fusion model. To illustrate the proposed approaches, we classify patients from the ADNI study into groups with Alzheimer's disease (AD), mild cognitive impairment (MCI) and normal controls, based on the multi-modality data. At baseline, ADNI's 780 participants (172 AD, 397 MCI, 211 Normal), have at least one of four data types: magnetic resonance imaging (MRI), FDG-PET, CSF and proteomics. These data are used to test our algorithms. Comprehensive experiments show that our proposed methods yield stable and promising results.

Exercise-induced cognitive plasticity, implications for mild cognitive impairment and Alzheimer's disease

Lifestyle factors such as intellectual stimulation, cognitive and social engagement, nutrition, and various types of exercise appear to reduce the risk for common age-associated disorders such as Alzheimer’s disease (AD) and vascular dementia. In fact, many studies have suggested that promoting physical activity can have a protective effect against cognitive deterioration later in life. Slowing or a deterioration of walking speed is associated with a poor performance in tests assessing psychomotor speed and verbal fluency in elderly individuals. Fitness training influences a wide range of cognitive processes, and the largest positive impact observed is for executive (a.k.a. frontal lobe) functions. Studies show that exercise improves additional cognitive functions such as tasks mediated by the hippocampus, and result in major changes in plasticity in the hippocampus. Interestingly, this exercise-induced plasticity is also pronounced in APOE ε4 carriers who express a risk factor for late-onset AD that may modulate the effect of treatments. Based on AD staging by Braak and Braak (1991) and Braak et al. (1993) we propose that the effects of exercise occur in two temporo-spatial continua of events. The “inward” continuum from isocortex (neocortex) to entorhinal cortex/hippocampus for amyloidosis and a reciprocal “outward” continuum for neurofibrillary alterations. The exercise-induced hypertrophy of the hippocampus at the core of these continua is evaluated in terms of potential for prevention to stave off neuronal degeneration. Exercise-induced production of growth factors such as the brain-derived neurotrophic factor (BDNF) has been shown to enhance neurogenesis and to play a key role in positive cognitive effects. Insulin-like growth factor (IGF-1) may mediate the exercise-induced response to exercise on BDNF, neurogenesis, and cognitive performance. It is also postulated to regulate brain amyloid β (Aβ) levels by increased clearance via the choroid plexus. Growth factors, specifically fibroblast growth factor and IGF-1 receptors and/or their downstream signaling pathways may interact with the Klotho gene which functions as an aging suppressor gene. Neurons may not be the only cells affected by exercise. Glia (astrocytes and microglia), neurovascular units and the Fourth Element may also be affected in a differential fashion by the AD process. Analyses of these factors, as suggested by the multi-dimensional matrix approach, are needed to improve our understanding of this complex multi-factorial process, which is increasingly relevant to conquering the escalating and intersecting world-wide epidemics of dementia, diabetes, and sarcopenia that threaten the global healthcare system. Physical activity and interventions aimed at enhancing and/or mimicking the effects of exercise are likely to play a significant role in mitigating these epidemics, together with the embryonic efforts to develop cognitive rehabilitation for neurodegenerative disorders.

The fourth element targeting hypothesis of Alzheimer's disease pathogenesis and pathophysiology

Despite well over a century of research on all forms of the disorder known as Alzheimer's disease (AD), it is still not known whether the condition targets initially neurons, glial cells, other cellular elements in the brain, or components of cells, such as synapses, or molecules independently of their cellular compartmentalization, or otherwise (e.g., specific neuronal circuits). Multiple lines of highly suggestive but as yet insufficient experimental evidence are discussed here to formulate the hypothesis that AD results from primary (i.e., direct and initial) or secondary targeting of what we designate as the Fourth Element Cell (4EC): a relatively recently identified type of brain cell that exhibits features in common with neurons (e.g., synapses, participation in glutamatergic, and GABAergic neurotransmission), astrocytes, oligodendrocytes, and their precursors, but is in other respects clearly distinct from all of them. The 4EC is proposed to be the main target of both: (1) converging insults (i.e., not true "causes") that over time cause sporadic forms of AD as postulated by the Danger Signal Hypothesis - which was not formulated with 4EC in mind - as well as (2) the causes of inherited (i.e., familial) forms of neurodegeneration that resemble certain aspects of the clinical manifestations of sporadic AD.

Integrative Understanding of Emergent Brain Properties, Quantum Brain Hypotheses, and Connectome Alterations in Dementia are Key Challenges to Conquer Alzheimer's Disease

The biological substrate for cognition remains a challenge as much as defining this function of living beings. Here, we examine some of the difficulties to understand normal and disordered cognition in humans. We use aspects of Alzheimer's disease and related disorders to illustrate how the wealth of information at many conceptually separate, even intellectually decoupled, physical scales - in particular at the Molecular Neuroscience versus Systems Neuroscience/Neuropsychology levels - presents a challenge in terms of true interdisciplinary integration towards a coherent understanding. These unresolved dilemmas include critically the as yet untested quantum brain hypothesis, and the embryonic attempts to develop and define the so-called connectome in humans and in non-human models of disease. To mitigate these challenges, we propose a scheme incorporating the vast array of scales of the space and time (space-time) manifold from at least the subatomic through cognitive-behavioral dimensions of inquiry, to achieve a new understanding of both normal and disordered cognition, that is essential for a new era of progress in the Generative Sciences and its application to translational efforts for disease prevention and treatment.