Is brain connectome research the future frontier for subjective cognitive decline? A systematic review.

Objective: We performed a systematic literature review on Subjective Cognitive Decline (SCD) in order to examine whether the resemblance of brain connectome and functional connectivity (FC) alterations in SCD with respect to MCI, AD and HC can help us draw conclusions on the progression of SCD to more advanced stages of dementia.

Methods: We searched for studies that used any neuroimaging tool to investigate potential differences/similarities of brain connectome in SCD with respect to HC, MCI, and AD.

Results: Sixteen studies were finally included in the review.

Can a Novel High-Density EEG Approach Disentangle the Differences of Visual Event Related Potential (N170), Elicited by Negative Facial Stimuli, in People with Subjective Cognitive Impairment?

Background: Studies on subjective cognitive impairment (SCI) and neural activation report controversial results.

Objective: To evaluate the ability to disentangle the differences of visual N170 ERP, generated by facial stimuli (Anger & Fear) as well as the cognitive deterioration of SCI, mild cognitive impairment (MCI), and Alzheimer's disease (AD) compared to healthy controls (HC).

Method: 57 people took part in this study.

Building and evaluating resources for sentiment analysis in the Greek language.

Sentiment lexicons and word embeddings constitute well-established sources of information for sentiment analysis in online social media. Although their effectiveness has been demonstrated in state-of-the-art sentiment analysis and related tasks in the English language, such publicly available resources are much less developed and evaluated for the Greek language. In this paper, we tackle the problems arising when analyzing text in such an under-resourced language.

Multilabel user classification using the community structure of online networks.

We study the problem of semi-supervised, multi-label user classification of networked data in the online social platform setting. We propose a framework that combines unsupervised community extraction and supervised, community-based feature weighting before training a classifier. We introduce Approximate Regularized Commute-Time Embedding (ARCTE), an algorithm that projects the users of a social graph onto a latent space, but instead of packing the global structure into a matrix of predefined rank, as many spectral and neural representation learning methods do, it extracts local communities for all users in the graph in order to learn a sparse embedding.