In-vivo brain neuroimaging provides a gateway for integrating biological and clinical biomarkers of Alzheimer's disease.

PURPOSE OF REVIEW: Only 5% of the Alzheimer's cases are explained by genetic mutations, whereas the remaining 95% are sporadic. The pathophysiological mechanisms underlying sporadic Alzheimer's disease are not well understood, suggesting a complex multifactorial cause. This review summarizes the recent findings on research aiming to show how biomarkers can be used for revealing the underlying mechanisms of preclinical stage Alzheimer's disease and help in their diagnosis. RECENT FINDINGS: The undisputed successful publicly accessible repositories provide longitudinal brain images, clinical, genetic and proteomic information of Alzheimer's disease. By combining with increasingly sophisticated data analysis methods, it is a great opportunity for searching new biomarkers. Innovative studies validated theoretical models of disease progression demonstrating the sequential ordering of well-established biomarkers. Novel observations shed light on the interaction between biomarkers to confirm that disease progression is related to multiple pathological factors. A typical example is the tau-associated neuronal toxicity that can be additionally potentiated by amyloid ß peptides. To increase further the complexity, studies report specific impact of common genetic variants that can be traced from childhood through middle age up to the symptomatic onset of Alzheimer's disease. SUMMARY: The discovery of efficient therapies to prevent the disease or modify the progression of disease requires a more thorough understanding of the underlying biological processes. Neuroimaging, genetic and proteomic biomarkers for Alzheimer's disease are critically discussed and proposed to be included in clinical descriptions and diagnostic guidelines.

The effect of gamified robot-enhanced training on motor performance in chronic stroke survivors.

Task-specific training constitutes a core element for evidence-based rehabilitation strategies targeted at improving upper extremity activity after stroke. Its combination with additional treatment strategies and neurotechnology-based solutions could further improve patients' outcomes. Here, we studied the effect of gamified robot-assisted upper limb motor training on motor performance, skill learning, and transfer with respect to a non-gamified control condition with a group of chronic stroke survivors. The results suggest that a gamified training strategy results in more controlled motor performance during the training phase, which is characterized by a higher accuracy (lower deviance), higher smoothness (lower jerk), but slower speed. The responder analyses indicated that mildly impaired patients benefited most from the gamification approach. In conclusion, gamified robot-assisted motor training, which is personalized to the individual capabilities of a patient, constitutes a promising investigational strategy for further improving motor performance after a stroke.

Toward individualized medicine in stroke-The TiMeS project: Protocol of longitudinal, multi-modal, multi-domain study in stroke.

Despite recent improvements, complete motor recovery occurs in <15% of stroke patients. To improve the therapeutic outcomes, there is a strong need to tailor treatments to each individual patient. However, there is a lack of knowledge concerning the precise neuronal mechanisms underlying the degree and course of motor recovery and its individual differences, especially in the view of brain network properties despite the fact that it became more and more clear that stroke is a network disorder. The TiMeS project is a longitudinal exploratory study aiming at characterizing stroke phenotypes of a large, representative stroke cohort through an extensive, multi-modal and multi-domain evaluation. The ultimate goal of the study is to identify prognostic biomarkers allowing to predict the individual degree and course of motor recovery and its underlying neuronal mechanisms paving the way for novel interventions and treatment stratification for the individual patients. A total of up to 100 patients will be assessed at 4 timepoints over the first year after the stroke: during the first (T1) and third (T2) week, then three (T3) and twelve (T4) months after stroke onset. To assess underlying mechanisms of recovery with a focus on network analyses and brain connectivity, we will apply synergistic state-of-the-art systems neuroscience methods including functional, diffusion, and structural magnetic resonance imaging (MRI), and electrophysiological evaluation based on transcranial magnetic stimulation (TMS) coupled with electroencephalography (EEG) and electromyography (EMG). In addition, an extensive, multi-domain neuropsychological evaluation will be performed at each timepoint, covering all sensorimotor and cognitive domains. This project will significantly add to the understanding of underlying mechanisms of motor recovery with a strong focus on the interactions between the motor and other cognitive domains and multimodal network analyses. The population-based, multi-dimensional dataset will serve as a basis to develop biomarkers to predict outcome and promote personalized stratification toward individually tailored treatment concepts using neuro-technologies, thus paving the way toward personalized precision medicine approaches in stroke rehabilitation.

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.

Neuroticism, depression, and anxiety traits exacerbate the state of cognitive impairment and hippocampal vulnerability to Alzheimer's disease.

INTRODUCTION: Certain personality traits are associated with higher risk of Alzheimer's disease, similar to cognitive impairment. The identification of biological markers associated with personality in mild cognitive impairment could advance the early detection of Alzheimer's disease. METHODS: We used hierarchical multivariate linear models to quantify the interaction between personality traits, state of cognitive impairment, and MRI biomarkers (gray matter brain volume, gray matter mean water diffusion) in the medial temporal lobe (MTL). RESULTS: Over and above a main effect of cognitive state, the multivariate linear model showed significant interaction between cognitive state and personality traits predicting MTL abnormality. The interaction effect was mainly driven by neuroticism and its facets (anxiety, depression, and stress) and was associated with right-left asymmetry and an anterior to posterior gradient in the MTL. DISCUSSION: Our results support the hypothesis that personality traits can alter the vulnerability and pathoplasticity of disease and therefore modulate related biomarker expression.

Maladaptive exploratory behavior and neuropathology of the PS-1 P117L Alzheimer transgenic mice.

Patients with the early-onset Alzheimer's disease P117L mutation in the presenilin-1 gene (PS-1) present pathological hallmarks in the hippocampus, the frontal cortex and the basal ganglia. In the present work we determined by immunohistochemistry which brain regions were injured in the transgenic PS-1 P117L mice, in comparison to their littermates, the B6D2 mice. Furthermore, as these regions are involved in novelty detection, we investigated the behavior of these mice in tests for object and place novelty recognition. Limited numbers of senile plaques and neurofibrillary tangles were detected in aged PS-1 P117L mice in the CA1 only, indicating that the disease is restrained to an initial neuropathological stage. Western blots showed a change in PSD-95 expression (p=0.03), not in NR2A subunit, NR2B subunit and synaptophysin expressions in the frontal cortex, suggesting specific synaptic alterations. The behavioral tests repeatedly revealed, despite a non-significant preference for object or place novelty, maladaptive exploratory behavior of the PS-1 P117L mice in novel environmental conditions, not due to locomotor problems. These mice, unlike the B6D2 mice, were less inhibited to visit the center of the cages (p=0.01) and they continued to move excessively in the presence of a displaced object (p=0.021). Overall, the PS-1 P117L mice appear to be in an initial Alzheimer's disease-like neuropathological stage, and they showed a lack of reaction toward novel environmental conditions.