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. This type of connection improves classification accuracy by combining simple-visual and complex-abstract features to create more color-selective ones. We have applied this strategy to classic CNN architectures and quantitatively and qualitatively analyzed the improvement in accuracy while focusing on color selectivity. The results show that, in general, skip connections improve accuracy, but LSC improves it even more and enhances the color selectivity of the original CNN architectures. As a side result, we propose a new color representation procedure for organizing and filtering feature maps, making their visualization more manageable for qualitative color selectivity analysis.

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. The participants underwent two follow-up assessments and were classified as cognitively stable or worsened. Severity of SCCs (low and high) in SCD was established by using two different percentiles of the questionnaire score distribution as cut-off points. The validity of these cut-off points for predicting progression using socio-demographic, health, and neuropsychological variables was tested by machine learning (ML) analysis. RESULTS: Severity of SCCs in SCD established considering the 5th percentile as a cut-off point proved to be the best metric for predicting progression. The variables with the main role in conforming the predictive algorithm were those related to memory, cognitive reserve, general health, and the stability of diagnosis over time. CONCLUSION: Moderate to high complainers showed an increased probability of progression in cognitive decline, suggesting the clinical relevance of standard procedures to determine SCC severity. Our findings highlight the important role of the multimodal ML approach in predicting progression.

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. Studies were included if they focused on neuropsychiatric disorders and involved conversational data for detection and diagnosis. They were assessed for eligibility by independent reviewers and ultimately included if a consensus was reached about their relevance. RESULTS: 2356 references were initially retrieved. Eventually, 17 articles - referring 9 smart conversational agents - met the inclusion criteria. Out of the selected studies, 7 are targeted at neurocognitive disorders, 7 at depression and 3 at other conditions. They apply diverse technological solutions and analysis techniques (82.4% use Artificial Intelligence), and they usually rely on gold standard tests for criterion validity assessment. Acceptability, reliability and other aspects of validity were rarely addressed. CONCLUSION: The use of smart conversational agents for the detection of neuropsychiatric disorders is an emerging and promising field of research, with a broad coverage of mental disorders and extended use of AI. However, the few published studies did not undergo robust psychometric validation processes. Future research in this field would benefit from more rigorous validation mechanisms and standardized software and hardware platforms.

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. Performance metrics (accuracy, recall, F-1 score, and Cohen's kappa) were computed for each algorithm, and graphic metrics (ROC and precision-recall curves) and features analysis were computed for the best-performing algorithm. SETTING: Primary care health centers. PARTICIPANTS: 128 participants: 78 cognitively unimpaired and 50 with MCI. MEASUREMENTS: Diagnosis at baseline, months from the baseline assessment until the 3rd follow-up or development of dementia, gender, age, Charlson Comorbidity Index, Neuropsychiatric Inventory-Questionnaire (NPI-Q) individual items, NPI-Q total severity, and total stress score and Geriatric Depression Scale-15 items (GDS-15) total score. RESULTS: 30 participants developed dementia, while 98 did not. Most of the participants who developed dementia were diagnosed at baseline with amnestic multidomain MCI. The Random Forest Plot model provided the metrics that best predicted conversion to dementia (e.g. accuracy=.88, F1=.67, and Cohen's kappa=.63). The algorithm indicated the importance of the metrics, in the following (decreasing) order: months from first assessment, age, the diagnostic group at baseline, total NPI-Q severity score, total NPI-Q stress score, and GDS-15 total score. CONCLUSIONS: ML is a valuable technique for detecting the risk of conversion to dementia in MCI patients. Some NPS proxies, including NPI-Q total severity score, NPI-Q total stress score, and GDS-15 total score, were deemed as the most important variables for predicting conversion, adding further support to the hypothesis that some NPS are associated with a higher risk of dementia in MCI.

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. Therefore, the World Health Organization considers serious cognitive deterioration a public health priority. OBJECTIVES: Advanced cognitive evaluation mechanisms are needed to help make an early diagnosis. These new mechanisms should overcome the limitations of current neuropsychological tests, including delayed detection; being perceived as intrusive; being non-ecological; being dependent on confounding factors; or their administration being expensive, among others. A promising novel approach consists of the introduction of serious games based on virtual reality and machine learning able to assess cognitive traits relevant to the diagnosis of mild cognitive impairment and Alzheimer's disease. METHODS: As a result of a preliminary pilot experiment, promising evidence was obtained about the predictive power of this solution. However, for these new serious games to be effective, evidence has to be gathered on the player experience by senior adults, avoiding the limitations of traditional tests at the same time. This study addresses these aspects with the participation of 74 senior users and 15 test administrators. RESULTS: Main findings confirm the usability and playability of Panoramix, a game battery designed according to the principles discussed above, its technological acceptability and its accessibility. For example, in relation to acceptability, the game battery was scored 4.39 in a 5-point scale, while its average usability score was 4.45 regardless of socio-cultural level or previous experience with digital technologies. In addition, health professionals confirm both, usability and playability, levels with an average score of 6.5 in a 7-point scale. Participants' willingness of using this kind of systems for cognitive evaluation was also confirmed. CONCLUSION: Promising results obtained pave the way for additional work to confirm the diagnostic validity according to clinical standards of these new cognitive assessment tools.

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. METHODS: In total, 169 participants completed the longitudinal study. Participants were divided into a control group and three MCI subgroups, according to the Petersen criteria for diagnosis. Information about the participants was compared using nine ML classification techniques. Seven relevant performance metrics were computed in order to evaluate the accuracy of prediction regarding converter and nonconverter participants. RESULTS: ML algorithms applied to socio-demographic, basic health, and CR proxy data enabled prediction of conversion to dementia. The best performing models were the gradient boosting classifier (accuracy (ACC) = 0.93; F1 = 0.86, and Cohen κ = 0.82) and random forest classifier (ACC = 0.92; F1 = 0.79, and Cohen κ = 0.71). Use of ML techniques corroborated the protective role of CR as a mediator of conversion to dementia, whereby participants with more years of education and higher vocabulary scores survived longer without developing dementia. CONCLUSIONS: We used ML approaches to explore the role of CR in conversion from MCI to dementia. The findings indicate the potential value of ML algorithms for detecting risk of conversion to dementia in cognitive aging and CR studies. Further research is required to develop an ML-based procedure that can be used to make robust predictions.

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. A total of 64 individuals (28 HC, 16 MCI, 20 AD) completed the experiment from an initial sample of 74. Participants were administered a collection of classical pen-and-paper tests and interacted with the games developed. A total of six machine learning classification techniques were applied and four relevant performance metrics were computed to assess the classification power of the tool according to participants' cognitive status. RESULTS: According to the classification performance metrics computed, the best classification result is obtained using the Extra Trees Classifier (F1 = 0.97 and Cohen's kappa coefficient = 0.97). Precision and recall values are also high, above 0.9 for all cognitive groups. Moreover, according to the standard interpretation of Cohen's kappa index, classification is almost perfect (i.e., 0.81-1.00) for the complete dataset for all algorithms. LIMITATIONS: Weaknesses (e.g., accessibility, sample size or speed of stimuli) detected during the preliminary study were addressed and solved. Nevertheless, additional research is needed to improve the resolution of the game for the identification of specific cognitive impairments, as well as to achieve a complete validation of the psychometric properties of the digital game. CONCLUSION: Promising results obtained about psychometric validity of Episodix, represent a relevant step ahead towards the introduction of serious games and machine learning in regular clinical practice for detecting MCI or AD. However, more research is needed to explore the introduction of item response theory in this game and to obtain the required normative data for clinical validity.

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. The battery, named Panoramix, assesses the main early cognitive markers (i.e., memory, executive functions, attention and gnosias). Regarding its validation, it has been tested with a cohort study of 16 seniors, including AD, MCI and healthy individuals. RESULTS: This first pilot study offered initial evidence about psychometric validity, and more specifically about construct, criterion and external validity. After an analysis using machine learning techniques, findings show a promising 100% rate of success in classification abilities using a subset of three games in the battery. Thus, results are encouraging as all healthy subjects were correctly discriminated from those already suffering AD or MCI. CONCLUSIONS: The solid potential of digital serious games and machine learning for the early detection of dementia processes is demonstrated. Such a promising performance encourages further research to eventually introduce this technique for the clinical diagnosis of cognitive impairment.

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. MATERIALS AND METHODS: Our proposal is based on the gamification of the California Verbal Learning Test (CVLT) and it has been designed to comply with the psychometric characteristics of reliability and validity. Two qualitative focus groups and a first pilot experiment were carried out to validate the proposal. RESULTS: A more ecological, non-intrusive and better administrable tool to perform cognitive assessment was developed. Initial evidence from the focus groups and pilot experiment confirmed the developed game's usability and offered promising results insofar its psychometric validity is concerned. Moreover, the potential of this game for the cognitive classification of senior adults was confirmed, and administration time is dramatically reduced with respect to pen-and-paper tests. LIMITATIONS: Additional research is needed to improve the resolution of the game for the identification of specific cognitive impairments, as well as to achieve a complete validation of the psychometric properties of the digital game. CONCLUSION: Initial evidence show that serious games can be used as an instrument to assess the cognitive status of senior adults, and even to predict the onset of mild cognitive impairments or Alzheimer's disease.

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. MATERIALS AND METHODS: We developed a collection of games to be deployed on a TV environment. These games were tried by a group of senior adults at their homes. The Technology Acceptance Model (TAM) was used to validate this approach. Surveys were performed to study the perceived usefulness and perceived ease of use of such technical setting as an instrument for their cognitive evaluation; that is, its technical acceptance. Subjective information collected from participants was correlated with actual interaction data captured. An additional survey was performed 36 months after pilot testing to have an indication about the long-term perceptions about usefulness and ease of use. RESULTS: More than 90% of participating subjects perceived cognitive games on TV as useful or very useful. The majority of participants selected the TV set as their preferred option to interact with serious games at home, when compared to other devices such as smartphones, tablets or PCs. This result correlates with the number of participants perceiving them as easily usable or very easy to use, and also with automatically captured interaction data. Three out of four seniors expressed their interest in keeping the system at home after the pilot. Besides, these perceptions are fairly stable in time as shown by the survey performed 36 months after pilot testing. LIMITATIONS: Although participating users are a representative sample of the Galician population, which in turn is comparable to the population of most rural areas in Europe, a larger and more diverse user sample would be needed to obtain significant results for a wider population profile. CONCLUSION: The study confirmed the technical acceptance, that is, the perceived usefulness and perceived ease of use, of the TV-based home technical setting introduced as a means of cognitive evaluation. This study provides initial evidence on the viability of a TV-based serious games approach for cognitive longitudinal screening at home with little intervention of clinical professionals, thus contributing to the early detection of cognitive impairments in the senior population.

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. METHODS: MEDLINE, PsycINFO, Scopus and IEEE Xplore databases were systematically searched. The main eligibility criteria were to select studies published in a peer-reviewed journal; written in English; published in the last 25years; focused on the human population, and classified in the neuropsychological field. Moreover, to avoid risk of bias, studies were selected by consensus of experts, focusing primarily in psychometric properties. Therefore, selected studies were analyzed in accordance with a set of dimensions of analysis commonly used for evaluating neuropsychological tests. RESULTS: After applying the selected search strategy, 57 studies -including 54 serious games- met our selection criteria. The selected studies deal with visuospatial capabilities, memory, attention, executive functions, and complex neuropsychological constructs such as Mild Cognitive Impairment (MCI). Results show that the implementation of serious games for neuropsychological evaluation is tackled in several different ways in the selected studies, and that studies have so far been mainly exploratory, just aiming at testing the feasibility of the proposed approaches. DISCUSSION: It may be argued that the limited number of databases used might compromise this study. However, we think that the finally included sample is representative, in spite of how difficult is to achieve an optimum and maximum scope. Indeed, this review identifies other research issues related to the development of serious games beyond their reliability and validity. The main conclusion of this review is that there is a great interest in the research community in the use of serious games for neuropsychological evaluation. This scoping review is pertinent, in accordance with the increasing number of studies published in the last three years, they demonstrate its potential as a serious alternative to classic neuropsychological tests. Nevertheless, more research is needed in order to implement serious games that are reliable, valid, and ready to be used in the everyday clinical practice.