Alzheimer’s Disease is a complex, multifactorial, and comorbid condition. The asymptomatic behavior in the early stages makes the identification of the disease onset particularly challenging. Mild cognitive impairment (MCI) is an intermediary stage between the expected decline of normal aging and the pathological decline associated with dementia. The identification of risk factors for MCI is thus sorely needed. Self-reported personal information such as age, education, income level, sleep, diet, physical exercise, etc. is called to play a key role not only in the early identification of MCI but also in the design of personalized interventions and the promotion of patients empowerment. In this study, we leverage a large longitudinal study on healthy aging in Spain, to identify the most important self-reported features for future conversion to MCI. Using machine learning (random forest) and permutation-based methods we select the set of most important self-reported variables for MCI conversion which includes among others, subjective cognitive decline, educational level, working experience, social life, and diet. Subjective cognitive decline stands as the most important feature for future conversion to MCI across different feature selection techniques.

By 2050 it is estimated that the number of worldwide Alzheimer’s (AD) patients will quadruple from the current number of 36 million people. To date, no single test, prior to postmortem examination, can confirm that a person suffers from AD. Therefore, there is a strong need for accurate and sensitive tools for the early diagnoses of AD. The complex etiology and multiple pathogenesis of AD calls for a system-level understanding of the currently available biomarkers and the study of new biomarkers via network-based modeling of heterogeneous data types. In this review, we summarize recent research on the study of AD as a connectivity syndrome. We argue that a network-based approach in biomarker discovery, will provide key insights to fully understand the network degeneration hypothesis (disease starts in specific network areas and progressively spreads to connected areas of the initial loci-networks) with a potential impact for early diagnosis and disease-modifyig treatments. We introduce a new framework for the quantitative study of biomarkers that can help shorten the transition between academic research and clinical diagnosis in AD.

Background and Objectives To determine the relative importance of global or regional MRI and blood markers of neurodegeneration and neuroaxonal injury in predicting cognitive performance for recently diagnosed patients with multiple sclerosis (MS). Methods Thirty-five newly diagnosed patients with relapsing-remitting MS (RRMS) and 23 healthy controls (HCs) simultaneously completed a full clinical and neuropsychological assessment, structural brain MRI, and serum neurofilament light chain (sNfL) level test. Linear regression analyses were performed to determine which global or regional measures of gray matter (GM) atrophy and cortical thickness (CT), in combination with sNfL levels and clinical scores, are most strongly related to neuropsychological impairment. Results Compared with HCs, patients with MS showed bilateral thalamic GM atrophy (left, p = 0.033; right, p = 0.047) and diminished CT, particularly in the right superior and transverse temporal gyri (p = 0.045; p = 0.037). Regional atrophy failed to add predictive variance, whereas anxiety symptoms, sNfL, and global CT were the best predictors (R² = 0.404; p < 0.001) of cognitive outcomes, with temporal thickness accounting for greater variance in cognitive deficits than global CT. Discussion Thalamic GM atrophy and thinning in temporal regions represent a distinctive MRI trait in the early stages of MS. Although sNfL levels alone do not clearly differentiate HCs and patients with RRMS, in combination with global and regional CT, sNfL levels can better explain the presence of underlying cognitive deficits. Hence, cortical thinning and sNfL increases can be considered 2 parallel neurodegenerative markers in the pathogenesis of progression in newly diagnosed patients with MS.