A Long Skip Connection for Enhanced Color Selectivity in CNN Architectures.

Some recent studies show that filters in convolutional neural networks (CNNs) have low color selectivity in datasets of natural scenes such as Imagenet. CNNs, bio-inspired by the visual cortex, are characterized by their hierarchical learning structure which appears to gradually transform the representation space. Inspired by the direct connection between the LGN and V4, which allows V4 to handle low-level information closer to the trichromatic input in addition to processed information that comes from V2/V3, we propose the addition of a long skip connection (LSC) between the first and last blocks of the feature extraction stage to allow deeper parts of the network to receive information from shallower layers.

New Trends in Cognitive Aging and Mild Cognitive Impairment.

In this editorial, we aim to highlight some lessons learned in our field and to discuss some open questions regarding the continuum between healthy cognitive aging and dementia [...

Relevance of Complaint Severity in Predicting the Progression of Subjective Cognitive Decline and Mild Cognitive Impairment: A Machine Learning Approach.

Background: The presence of subjective cognitive complaints (SCCs) is a core criterion for diagnosis of subjective cognitive decline (SCD); however, no standard procedure for distinguishing normative and non-normative SCCs has yet been established.

Objective: To determine whether differentiation of participants with SCD according to SCC severity improves the validity of the prediction of progression in SCD and MCI and to explore validity metrics for two extreme thresholds of the distribution in scores in a questionnaire on SCCs.

Methods: Two hundred and fifty-three older adults with SCCs participating in the Compostela Aging Study (CompAS) were classified as MCI or SCD at baseline.

Smart conversational agents for the detection of neuropsychiatric disorders: A systematic review.

Objective: To determine whether smart conversational agents can be used for detection of neuropsychiatric disorders. Therefore, we reviewed the technologies used, targeted mental disorders and validation procedures of relevant proposals in this field.

Methods: We searched Scopus, PubMed, Pro-Quest, IEEE Xplore, Web of Science, CINAHL and the Cochrane Library using a predefined search strategy.

Neuropsychiatric symptoms as predictors of conversion from MCI to dementia: a machine learning approach.

Objectives: To use a Machine Learning (ML) approach to compare Neuropsychiatric Symptoms (NPS) in participants of a longitudinal study who developed dementia and those who did not.

Design: Mann-Whitney U and ML analysis. Nine ML algorithms were evaluated using a 10-fold stratified validation procedure.

Touchscreen games to detect cognitive impairment in senior adults. A user-interaction pilot study.

Introduction: Alzheimer's disease is a degenerative brain disease and the most common cause of dementia. Today, 47 million people live with dementia worldwide. This number is projected to increase to more than 131 million by 2050, as populations age.

Machine learning approaches to studying the role of cognitive reserve in conversion from mild cognitive impairment to dementia.

Objectives: The overall aim of the present study was to explore the role of cognitive reserve (CR) in the conversion from mild cognitive impairment (MCI) to dementia. We used traditional and machine learning (ML) techniques to compare converter and nonconverter participants. We also discuss the predictive value of CR proxies in relation to the ML model performance.

Learning to Detect Cognitive Impairment through Digital Games and Machine Learning Techniques.

Objective: Alzheimer's disease (AD) is one of the most prevalent diseases among the adult population. The early detection of Mild Cognitive Impairment (MCI), which may trigger AD, is essential to slow down the cognitive decline process.

Methods: This paper presents a suit of serious games that aims at detecting AD and MCI overcoming the limitations of traditional tests, as they are time-consuming, affected by confounding factors that distort the result and usually administered when symptoms are evident and it is too late for preventive measures.

Episodix: a serious game to detect cognitive impairment in senior adults. A psychometric study.

Introduction: Assessment of episodic memory is traditionally used to evaluate potential cognitive impairments in senior adults. The present article discusses the capabilities of Episodix, a game to assess the aforementioned cognitive area, as a valid tool to discriminate among mild cognitive impairment (MCI), Alzheimer's disease (AD) and healthy individuals (HC); that is, it studies the game's psychometric validity study to assess cognitive impairment.

Materials And Methods: After a preliminary study, a new pilot study, statistically significant for the Galician population, was carried out from a cross-sectional sample of senior adults as target users.

Design process and preliminary psychometric study of a video game to detect cognitive impairment in senior adults.

Introduction: Assessment of episodic memory has been traditionally used to evaluate potential cognitive impairments in senior adults. Typically, episodic memory evaluation is based on personal interviews and pen-and-paper tests. This article presents the design, development and a preliminary validation of a novel digital game to assess episodic memory intended to overcome the limitations of traditional methods, such as the cost of its administration, its intrusive character, the lack of early detection capabilities, the lack of ecological validity, the learning effect and the existence of confounding factors.

The acceptability of TV-based game platforms as an instrument to support the cognitive evaluation of senior adults at home.

Introduction: The computing capabilities of state-of-the-art television sets and media centres may facilitate the introduction of computer-assisted evaluation at home. This approach would help to overcome the drawbacks of traditional pen-and-paper evaluations administered in clinical facilities, as they could be performed in a more comfortable environment, the subject's home, and they would be more flexible for designing complex environments for the evaluation of neuropsychological constructs that are difficult to assess through traditional testing. The objective of this work was to obtain some initial evidence about the technical acceptance by senior adults of serious games played at home on the TV set and therefore about the convenience of further investigating such an approach to cognitive assesment.

Trends on the application of serious games to neuropsychological evaluation: A scoping review.

Background: The dramatic technological advances witnessed in recent years have resulted in a great opportunity for changing the way neuropsychological evaluations may be performed in clinical practice. Particularly, serious games have been posed as the cornerstone of this still incipient paradigm-shift, as they have characteristics that make them especially advantageous in trying to overcome limitations associated with traditional pen-and-paper based neuropsychological tests: they can be easily administered and they can feature complex environments for the evaluation of neuropsychological constructs that are difficult to evaluate through traditional tests. The objective of this study was to conduct a scoping literature review in order to map rapidly the key concepts underpinning this research area during the last 25years on the use of serious games for neuropsychological evaluation.