Electronic Clinical Decision Support System for allergic rhinitis management: MASK e-CDSS.

BACKGROUND: Allergic rhinitis (AR) management has changed in recent years following the switch from the concept of disease severity to the concept of disease control, publication of the AR clinical decision support system (CDSS) and development of mobile health (m-health) tools for patients (eg Allergy Diary). The Allergy Diary Companion app for healthcare providers is currently being developed and will be launched in 2018. It incorporates the AR CDSS to provide evidence-based treatment recommendations, linking all key stakeholders in AR management. OBJECTIVE: To produce an electronic version of the AR CDSS (e-CDSS) for incorporation into the Allergy Diary Companion, to describe the app interfaces used to collect information necessary to inform the e-CDSS and to summarize some key features of the Allergy Diary Companion. METHODS: The steps involved in producing the e-CDSS and incorporating it into the Allergy Diary Companion were (a) generation of treatment management scenarios; (b) expert consensus on treatment recommendations; (c) generation of electronic decisional algorithms to describe all AR CDSS scenarios; (d) digitization of these algorithms to form the e-CDSS; and (e) embedding the e-CDSS into the app to permit easy user e-CDSS interfacing. RESULTS: Key experts in the AR field agreed on the AR CDSS approach to AR management and on specific treatment recommendations provided by Allergy Diary Companion. Based on this consensus, decision processes were developed and programmed into the Allergy Diary Companion using Titanium Appcelerator (JavaScript) for IOS tablets. To our knowledge, this is the first time the development of any m-health tool has been described in this transparent and detailed way, providing confidence, not only in the app, but also in the provided management recommendations. CONCLUSION: The Allergy Diary Companion for providers provides guideline and expert-endorsed AR management recommendations. [MASK paper No 32].

MACVIA clinical decision algorithm in adolescents and adults with allergic rhinitis.

The selection of pharmacotherapy for patients with allergic rhinitis (AR) depends on several factors, including age, prominent symptoms, symptom severity, control of AR, patient preferences, and cost. Allergen exposure and the resulting symptoms vary, and treatment adjustment is required. Clinical decision support systems (CDSSs) might be beneficial for the assessment of disease control. CDSSs should be based on the best evidence and algorithms to aid patients and health care professionals to jointly determine treatment and its step-up or step-down strategy depending on AR control. Contre les MAladies Chroniques pour un VIeillissement Actif en Languedoc-Roussillon (MACVIA-LR [fighting chronic diseases for active and healthy ageing]), one of the reference sites of the European Innovation Partnership on Active and Healthy Ageing, has initiated an allergy sentinel network (the MACVIA-ARIA Sentinel Network). A CDSS is currently being developed to optimize AR control. An algorithm developed by consensus is presented in this article. This algorithm should be confirmed by appropriate trials.

Multimodal Outcome at 7 Years of Age after Neonatal Arterial Ischemic Stroke.

OBJECTIVES: To evaluate the epileptic, academic, and developmental status at age 7 years in a large population of term-born children who sustained neonatal arterial ischemic stroke (NAIS), and to assess the co-occurrence of these outcomes. STUDY DESIGN: A cohort study including 100 term newborns with NAIS was designed. Two infants died during the neonatal period, 13 families were lost to follow-up, and 5 families declined to participate in this evaluation. Thus, 80 families completed the 7-year clinical assessment. Epileptic status, schooling, motor abilities, global intellectual functioning, spoken language, and parental opinions were recorded. Principal component analysis was applied. RESULTS: Rates of impaired language, cerebral palsy, low academic skills, active epilepsy, and global intellectual deficiency were 49%, 32%, 28%, 11%, and 8%, respectively. All were highly correlated. Eventually, 59% of children were affected by at least 1 of the aforementioned conditions. In 30% of cases, the viewpoints of health practitioners and parents did not match. CONCLUSION: The prevalence of severe disabilities at 7 years after NAIS is low, but most children exhibit some impairment in developmental profile. TRIAL REGISTRATION: ClinicalTrials.gov (NCT02511249), Programme Hospitalier de Recherche Clinique Régional (0308052), Programme Hospitalier de Recherche Clinique Interrégional (1008026), and EudraCT (2010-A00329-30).

Stable decoding of working memory load through frequency bands.

Numerous studies have shown that working memory modulates every frequency band's power in the human brain. Yet, the question of how the highly distributed working memory adapts to external demands remains unresolved. Here, we explored frequency band modulations underlying working memory load, taking executive control under account. We hypothesized that synchronizations underlying various cognitive functions may be sequenced in time to avoid interference and that transient modulation of decoding accuracy of task difficulty would vary with increasing difficulty. We recorded whole scalp EEG data from 12 healthy participants, while they performed a visuo-spatial n-back task with three conditions of increasing difficulty, after an initial learning phase. We analyzed evoked spectral perturbations and time-resolved decoding of individual synchronization. Surprisingly, our results provide evidence for persistent decoding above the level-of-chance (83.17% AUC) for combined frequency bands. In fact, the decoding accuracy was higher for the combined than for isolated frequency bands (AUC from 65.93% to 74.30%). However, in line with our hypothesis, frequency band clusters transiently emerged in parieto-occipital regions within two separate time windows for alpha-/beta-band (relative synchronization from approximately 200 to 600 ms) and for the delta-/theta-band (relative desynchronization from approximately 600 to 1000 ms). Overall, these findings highlight concurrent sustained and transient measurable features of working memory load. This could reflect the emergence of stability within and between functional networks of the complex working memory system. In turn, this process allows energy savings to cope with external demands.

Using shallow neural networks with functional connectivity from EEG signals for early diagnosis of Alzheimer's and frontotemporal dementia.

Introduction: Dementia is a neurological disorder associated with aging that can cause a loss of cognitive functions, impacting daily life. Alzheimer's disease (AD) is the most common cause of dementia, accounting for 50-70% of cases, while frontotemporal dementia (FTD) affects social skills and personality. Electroencephalography (EEG) provides an effective tool to study the effects of AD on the brain. Methods: In this study, we propose to use shallow neural networks applied to two sets of features: spectral-temporal and functional connectivity using four methods. We compare three supervised machine learning techniques to the CNN models to classify EEG signals of AD / FTD and control cases. We also evaluate different measures of functional connectivity from common EEG frequency bands considering multiple thresholds. Results and discussion: Results showed that the shallow CNN-based models achieved the highest accuracy of 94.54% with AEC in test dataset when considering all connections, outperforming conventional methods and providing potentially an additional early dementia diagnosis tool.

Mental arithmetic task classification with convolutional neural network based on spectral-temporal features from EEG.

In recent years, neuroscientists have been interested to the development of brain-computer interface (BCI) devices. Patients with motor disorders may benefit from BCIs as a means of communication and for the restoration of motor functions. Electroencephalography (EEG) is one of most used for evaluating the neuronal activity. In many computer vision applications, deep neural networks (DNN) show significant advantages. Towards to ultimate usage of DNN, we present here a shallow neural network that uses mainly two convolutional neural network (CNN) layers, with relatively few parameters and fast to learn spectral-temporal features from EEG. We compared this models to three other neural network models with different depths applied to a mental arithmetic task using eye-closed state adapted for patients suffering from motor disorders and a decline in visual functions. Experimental results showed that the shallow CNN model outperformed all the other models and achieved the highest classification accuracy of 90.68%. It's also more robust to deal with cross-subject classification issues: only 3% standard deviation of accuracy instead of 15.6% from conventional method.

Cerebral Oxygenation Responses to Aerobatic Flight.

BACKGROUND: Aerobatic pilots must withstand high and sudden acceleration forces (Gz) up to 10 Gz. The physiological consequences of such a succession of high and abrupt positive and negative Gzon the human body over time remain mostly unknown. This case report emphasizes changes in physiological factors such as cerebral oxygenation and heart rate dynamics collected in real aerobatic flights. CASE REPORT: A 37-yr-old man, experienced in aerobatic flying, voluntarily took part in this study. During two flight runs (1520 min), the pilot performed aerobatic maneuvers with multiple high (10 Gz) positive and negative accelerations. During the flights he wore a Polar heart rate sensor while cerebral oxygenation was measured continuously over his prefrontal cortex via near-infrared spectroscopy (NIRS). NIRS allows for measurement of the relative concentration changes of oxygenated hemoglobin (O2Hb) and deoxygenated hemoglobin (HHb), making it possible to determine cerebral oxygenation and hemodynamic status. DISCUSSION: The continuous in-flight monitoring of O2Hb and HHb revealed the large effects of successive positive and negative Gz exposures on cerebral hemodynamics alterations. The results showed a significant and positive correlation between changes in Gz exposures and O2Hb concentration. This case report highlights that NIRS provides some valuable and sensitive indicators for the monitoring of cerebral hemodynamics during aerobatic flights exposed to multiple and high acceleration forces. To our knowledge, this first study quantifying cerebral oxygenation changes in aerobatics opens the way for the assessment of individual physiological responses and tolerance in pilots to repeated high Gz during real flights. Fresnel E, Dray G, Pla S, Jean P, Belda G, Perrey S. Cerebral oxygenation responses to aerobatic flight. Aerosp Med Hum Perform. 2021; 92(10):838842.

Recovering arm function in chronic stroke patients using combined anodal HD-tDCS and virtual reality therapy (ReArm): a study protocol for a randomized controlled trial.

BACKGROUND: After a stroke, 80% of the chronic patients have difficulties to use their paretic upper limb (UL) in activities of daily life (ADL) even after rehabilitation. Virtual reality therapy (VRT) and anodal transcranial direct current stimulation (tDCS) are two innovative methods that have shown independently to positively impact functional recovery of the paretic UL when combined with conventional therapy. The objective of the project will be to evaluate the impact of adding anodal high-definition (HD)-tDCS during an intensive 3-week UL VRT and conventional therapy program on paretic UL function in chronic stroke. METHODS: The ReArm project is a quadruple-blinded, randomized, sham-controlled, bi-centre, two-arm parallel, and interventional study design. Fifty-eight chronic (> 3 months) stroke patients will be recruited from the Montpellier and Nimes University Hospitals. Patients will follow a standard 3-week in-patient rehabilitation program, which includes 13 days of VRT (Armeo Spring, 1 × 30 min session/day) and conventional therapy (3 × 30 min sessions/day). Twenty-nine patients will receive real stimulation (4x1 anodal HD-tDCS montage, 2 mA, 20 min) to the ipsilesional primary motor cortex during the VRT session and the other 29 patients will receive active sham stimulation (2 mA, 30 s). All outcome measures will be assessed at baseline, at the end of rehabilitation and again 3 months later. The primary outcome measure will be the wolf motor function test. Secondary outcomes will include measures of UL function (Box and Block Test), impairment (Fugl Meyer Upper Extremity), compensation (Proximal Arm Non-Use), ADL (Actimetry, Barthel Index). Other/exploratory outcomes will include pain, fatigue, effort and performance, kinematics, and motor cortical region activation during functional motor tasks. DISCUSSION: This will be the first trial to determine the impact of adding HD-tDCS during UL VRT and conventional therapy in chronic stroke patients. We hypothesize that improvements in UL function will be greater and longer-lasting with real stimulation than in those receiving sham. TRIAL REGISTRATION: The ReArm project was approved by The French Research Ethics Committee, (Comité de Protection des Personnes-CPP SUD-EST II, N°ID-RCB: 2019-A00506-51, http://www.cppsudest2.fr/ ). The ReArm project was registered on ClinicalTrials.gov ( NCT04291573 , 2nd March 2020.

Concurrent anodal transcranial direct-current stimulation and motor task to influence sensorimotor cortex activation.

Functional targeting with anodal high-definition transcranial direct current stimulation (HD-atDCS) of involved brain areas during performance of a motor task (online) may facilitate sensorimotor cortex neuroplasticity compared to performing the motor task after HD-atDCS (offline). The aim of this study was to employ functional near-infrared spectroscopy to compare the time course of motor task-related changes in sensorimotor cortex activation between online and offline HD-atDCS. We hypothesized that online HD-atDCS would have a greater effect on task-related sensorimotor cortex activation than offline HD-atDCS. In a within-subject sham controlled and randomized study design, 9 healthy participants underwent 3 HD-atDCS sessions (online, offline and sham) targeting the left sensorimotor cortex separated by 1 week. Functional near-infrared spectroscopy hemodynamic changes were measured from the left sensorimotor cortex during a simple finger opposition motor task before (Pre), immediately (T1) and 30 min after (T2) each session. The movement rates were not different between (online, offline, sham) or within (Pre, T1, T2) sessions. At T2, online HD-atDCS was associated with a significant increase (large effect size) in sensorimotor cortex activation (Hedges g = 1.01, p < 0.001) when compared to sham; there was a nonsignificant trend to increase activation between offline and sham (Hedges g = 0.52, p = 0.05) and between online and offline (Hedges g = 0.53, p = 0.06). Concurrent application of HD-atDCS during a motor task may produce larger sensorimotor cortex activation than sequential application.

How Knowledge Emerges From Artificial Intelligence Algorithm and Data Visualization for Diabetes Management.

BACKGROUND: Self-monitoring blood glucose (SMBG) is facilitated by application available to analyze these data. They are mainly based on descriptive statistical analyses. In this study, we are proposing a method inspired by artificial intelligence algorithm for displaying glycemic data in an intelligible way with high-level information that is compatible with the short duration allocated to medical visits. METHOD: We propose a display method based on a numerical glycemic data conversion using a qualitative color scale that exhibits the patient's overall glycemic state. Moreover, a machine learning algorithm inputs these displays to exhibit recurrent glycemic pattern over configurable extended time period. RESULTS: A demonstrator of our method, output as a glycemic map, could be used by the physician during quarterly patient consultations. We have tested this methodology retrospectively on a database in order to observe the behavior of our algorithm. In some data files we were able to highlight some of the glycemic patterns characteristics that remain invisible on the tabular representations or through the use of descriptive statistic. In a next step the interpretation will have to be done by physicians to confirm they underlie knowledge. CONCLUSIONS: Our approach with artificial intelligence algorithm paired up with graphical color display allow a large database fast analysis to provide insights on diabetes knowledge. The next steps are first to set up a clinical trial to validate this methodology with dedicated patients and physicians then we will adapt our methodology for the huge data sets generated by continuous glycemic measurement (CGM) devices.

Towards a near infrared spectroscopy-based estimation of operator attentional state.

Given the critical risks to public health and safety that can involve lapses in attention (e.g., through implication in workplace accidents), researchers have sought to develop cognitive-state tracking technologies, capable of alerting individuals engaged in cognitively demanding tasks of potentially dangerous decrements in their levels of attention. The purpose of the present study was to address this issue through an investigation of the reliability of optical measures of cortical correlates of attention in conjunction with machine learning techniques to distinguish between states of full attention and states characterized by reduced attention capacity during a sustained attention task. Seven subjects engaged in a 30 minutes duration sustained attention reaction time task with near infrared spectroscopy (NIRS) monitoring over the prefrontal and the right parietal areas. NIRS signals from the first 10 minutes of the task were considered as characterizing the 'full attention' class, while the NIRS signals from the last 10 minutes of the task were considered as characterizing the 'attention decrement' class. A two-class support vector machine algorithm was exploited to distinguish between the two levels of attention using appropriate NIRS-derived signal features. Attention decrement occurred during the task as revealed by the significant increase in reaction time in the last 10 compared to the first 10 minutes of the task (p<.05). The results demonstrate relatively good classification accuracy, ranging from 65 to 90%. The highest classification accuracy results were obtained when exploiting the oxyhemoglobin signals (i.e., from 77 to 89%, depending on the cortical area considered) rather than the deoxyhemoglobin signals (i.e., from 65 to 66%). Moreover, the classification accuracy increased to 90% when using signals from the right parietal area rather than from the prefrontal cortex. The results support the feasibility of developing cognitive tracking technologies using NIRS and machine learning techniques.

Prefrontal cortex activity during motor tasks with additional mental load requiring attentional demand: a near-infrared spectroscopy study.

Functional near-infrared spectroscopy (fNIRS) is suitable for investigating cerebral oxygenation changes during motor and/or mental tasks. In the present study, we investigated how an additional mental load during a motor task at two submaximal loadings affects the fNIRS-measured brain activation over the right prefrontal cortex (PFC). Fifteen healthy males performed isometric grasping contractions at 15% and 30% of the maximal voluntary contraction (MVC) with or without an additional mental (i.e., arithmetic) task. Mental performance, force variability, fNIRS and subjective perception responses were measured in each condition. The performance of the mental task decreased significantly while the force variability increased significantly at 30% MVC as compared to 15% MVC, suggesting that performance of dual-task required more attentional resources. PFC activity increased significantly as the effort increased from 15% to 30% MVC (p<.001). Although a larger change in the deoxyhemoglobin was observed in dual-task conditions (p=.051), PFC activity did not change significantly as compared to the motor tasks alone. In summary, participants were unable to invest more attention and effort in performing the more difficult levels in order to maintain adequate mental performance.