Federating and Integrating What We Know About the Brain at All Scales: Computer Science Meets the Clinical Neurosciences

A Multidimensional, Person-Centered Framework for Functional Assessment in Dementia: Insights from the 'What', 'How', 'To Whom', and 'How Much' Questions

Dementia is a syndrome characterized by cognitive and neuropsychiatric symptoms associated with progressive functional decline (FD). FD is a core diagnostic criterion for dementia, setting the threshold between its prodromal stages and the full-blown disease. The operationalization of FD continues to generate a great deal of controversy. For instance, the threshold of FD for the diagnosis of dementia varies across diagnostic criteria, supporting the need for standardization of this construct. Moreover, there is a need to reconsider how we are measuring FD to set boundaries between normal aging, mild cognitive impairment, and dementia. In this paper, we propose a multidimensional framework that addresses outstanding issues in the assessment of FD: i) What activities of daily living (ADLs) are necessary to sustain an independent living in aging? ii) How to assess FD in individuals with suspected neurocognitive disorders? iii) To whom is the assessment directed? and iv) How much does FD differentiate healthy aging from mild and major neurocognitive disorders? Importantly, the To Whom Question introduces a person-centered approach that regards patients and caregivers as active agents in the assessment process of FD. Thus, once impaired ADLs have been identified, patients can indicate how significant such impairments are for them in daily life. We envisage that this new framework will guide future strategies to enhance functional assessment and treatment of patients with dementia and their caregivers.

Interactions between Personality, Depression, Anxiety and Cognition to Understand Early Stage of Alzheimer's Disease

The multifaceted nature of Alzheimer's disease (AD) and Mild cognitive impairment (MCI) can lead to wide inter-individual differences in disease manifestation in terms of brain pathology and cognition. The lack of understanding of phenotypic diversity in AD arises from a difficulty in understanding the integration of different levels of network organization (i.e. genes, neurons, synapses, anatomical regions, functions) and in inclusion of other information such as neuropsychiatric characteristics, personal history, information regarding general health or subjective cognitive complaints in a coherent model. Non-cognitive factors, such as personality traits and behavioral and psychiatric symptoms, can be informative markers of early disease stage. It is known that personality can affect cognition and behavioral symptoms. The aim of the paper is to review the different types of interactions existing between personality, depression/anxiety, and cognition and cognitive disorders at behavioral and brain/genetic levels.

Medical Informatics Platform (MIP): A Pilot Study Across Clinical Italian Cohorts

Introduction: With the shift of research focus to personalized medicine in Alzheimer's Dementia (AD), there is an urgent need for tools that are capable of quantifying a patient's risk using diagnostic biomarkers. The Medical Informatics Platform (MIP) is a distributed e-infrastructure federating large amounts of data coupled with machine-learning (ML) algorithms and statistical models to define the biological signature of the disease. The present study assessed (i) the accuracy of two ML algorithms, i.e., supervised Gradient Boosting (GB) and semi-unsupervised 3C strategy (Categorize, Cluster, Classify-CCC) implemented in the MIP and (ii) their contribution over the standard diagnostic workup. Methods: We examined individuals coming from the MIP installed across 3 Italian memory clinics, including subjects with Normal Cognition (CN, n = 432), Mild Cognitive Impairment (MCI, n = 456), and AD (n = 451). The GB classifier was applied to best discriminate the three diagnostic classes in 1,339 subjects, and the CCC strategy was used to refine the classical disease categories. Four dementia experts provided their diagnostic confidence (DC) of MCI conversion on an independent cohort of 38 patients. DC was based on clinical, neuropsychological, CSF, and structural MRI information and again with addition of the outcome from the MIP tools. Results: The GB algorithm provided a classification accuracy of 85% in a nested 10-fold cross-validation for CN vs. MCI vs. AD discrimination. Accuracy increased to 95% in the holdout validation, with the omission of each Italian clinical cohort out in turn. CCC identified five homogeneous clusters of subjects and 36 biomarkers that represented the disease fingerprint. In the DC assessment, CCC defined six clusters in the MCI population used to train the algorithm and 29 biomarkers to improve patients staging. GB and CCC showed a significant impact, evaluated as +5.99% of increment on physicians' DC. The influence of MIP on DC was rated from "slight" to "significant" in 80% of the cases. Discussion: GB provided fair results in classification of CN, MCI, and AD. CCC identified homogeneous and promising classes of subjects via its semi-unsupervised approach. We measured the effect of the MIP on the physician's DC. Our results pave the way for the establishment of a new paradigm for ML discrimination of patients who will or will not convert to AD, a clinical priority for neurology.