The effect of masks on cognitive performance.

The use of face masks has been a key response to the COVID-19 pandemic in almost every country. However, despite widespread use of masks in classrooms and offices around the world, almost nothing is known about their effects on cognitive performance. Using a natural experiment, I show that mandatory mask wearing has a negative causal effect on the cognitive performance of competitive chess players. I analyzed the quality of almost 3 million chess moves played by 8,531 individuals (ages 5-98 y) in 18 countries before and during the pandemic. Wearing a mask decreased the quality of players' decisions-a measure of their cognitive performance-by approximately one-third of an SD. However, the disruptive effect of masks is relatively short-lived, gradually weakening such that there is no measurable disadvantage from wearing a mask after roughly 4 h of play. The mask effect is driven by a large, negative effect for experts, with minimal change in performance at lower levels, and is stronger in high-incentive competitions. I provide support for a distraction mechanism whereby masks interfere with performance when working memory load is high.

Changes in Brain Activation Patterns During Working Memory Tasks in People With Post-COVID Condition and Persistent Neuropsychiatric Symptoms.

BACKGROUND AND OBJECTIVES: Post-COVID condition (PCC) is common and often involves neuropsychiatric symptoms. This study aimed to use blood oxygenation level-dependent fMRI (BOLD-fMRI) to assess whether participants with PCC had abnormal brain activation during working memory (WM) and whether the abnormal brain activation could predict cognitive performance, motor function, or psychiatric symptoms. METHODS: The participants with PCC had documented coronavirus disease 2019 (COVID-19) at least 6 weeks before enrollment. Healthy control participants had no prior history of COVID-19 and negative tests for severe acute respiratory syndrome coronavirus 2. Participants were assessed using 3 NIH Toolbox (NIHTB) batteries for Cognition (NIHTB-CB), Emotion (NIHTB-EB), and Motor function (NIHTB-MB) and selected tests from the Patient-Reported Outcomes Measurement Information System (PROMIS). Each had BOLD-fMRI at 3T, during WM (N-back) tasks with increasing attentional/WM load. RESULTS: One hundred sixty-nine participants were screened; 50 fulfilled the study criteria and had complete and usable data sets for this cross-sectional cohort study. Twenty-nine participants with PCC were diagnosed with COVID-19 242 ± 156 days earlier; they had similar ages (42 ± 12 vs 41 ± 12 years), gender proportion (65% vs 57%), racial/ethnic distribution, handedness, education, and socioeconomic status, as the 21 uninfected healthy controls. Despite the high prevalence of memory (79%) and concentration (93%) complaints, the PCC group had similar performance on the NIHTB-CB as the controls. However, participants with PCC had greater brain activation than the controls across the network (false discovery rate-corrected p = 0.003, Tmax = 4.17), with greater activation in the right superior frontal gyrus (p = 0.009, Cohen d = 0.81, 95% CI 0.15-1.46) but lesser deactivation in the default mode regions (p = 0.001, d = 1.03, 95% CI 0.61-1.99). Compared with controls, participants with PCC also had poorer dexterity and endurance on the NIHTB-MB, higher T scores for negative affect and perceived stress, but lower T scores for psychological well-being on the NIHTB-EB, as well as more pain symptoms and poorer mental and physical health on measures from the PROMIS. Greater brain activation predicted poorer scores on measures that were abnormal on the NIHTB-EB. DISCUSSION: Participants with PCC and neuropsychiatric symptoms demonstrated compensatory neural processes with greater usage of alternate brain regions, and reorganized networks, to maintain normal performance during WM tasks. BOLD-fMRI was sensitive for detecting brain abnormalities that correlated with various quantitative neuropsychiatric symptoms.

Attention and memory after COVID-19 as measured by neuropsychological tests: Systematic review and meta-analysis.

COVID-19 is associated with a range of sequelae, including cognitive dysfunctions as long-standing symptoms. Considering that the number of people infected worldwide keeps growing, it is important to understand specific domains of impairments to further organize appropriate rehabilitation procedures. In this study we conducted a meta-analysis to investigate specific cognitive functions impacted by COVID-19. A literature search was conducted in Web of Science, Scopus, PubMed, Academic Search Premier, Health Source: Nursing/Academic Edition, and preprint databases (OSF and PsyArXiv via OSF Preprints, medRxiv, bioRxiv, Research Square). We included the studies that compared cognitive functioning in COVID-19 reconvalescents and healthy controls, and used at least one validated neuropsychological test. Our findings show that short-term memory in the verbal domain, and possibly, visual short-term memory and attention, are at risk in COVID-19 reconvalescents. The impact of COVID-19 on cognitive functioning has yet to be studied in detail. In the future more controlled studies with validated computerized tests might help deepen our understanding of the issue. PSYCINFO CLASSIFICATION: 3360 Health Psychology & Medicine.

The negative impact of COVID-19 on working memory revealed using a rapid online quiz.

Although coronavirus disease 2019 (COVID-19) affects the respiratory system, it can also have neurological consequences leading to cognitive deficits such as memory problems. The aim of our study was to assess the impact of COVID-19 on working memory function. We developed and implemented an online anonymous survey with a working memory quiz incorporating aspects of gamification to engage participants. 5428 participants successfully completed the survey and memory quiz between 8th December 2020 and 5th July 2021 (68.6% non-COVID-19 and 31.4% COVID-19). Most participants (93.3%) completed the survey and memory quiz relatively rapidly (mean time of 8.84 minutes). Categorical regression was used to assess the contribution of COVID status, age, time post-COVID (number of months elapsed since having had COVID), symptoms, ongoing symptoms and gender, followed by non-parametric statistics. A principal component analysis explored the relationship between subjective ratings and objective memory scores. The objective memory scores were significantly correlated with participants' own assessment of their cognitive function. The factors significantly affecting memory scores were COVID status, age, time post-COVID and ongoing symptoms. Our main finding was a significant reduction in memory scores in all COVID groups (self-reported, positive-tested and hospitalized) compared to the non-COVID group. Memory scores for all COVID groups combined were significantly reduced compared to the non-COVID group in every age category 25 years and over, but not for the youngest age category (18-24 years old). We found that memory scores gradually increased over a period of 17 months post-COVID-19. However, those with ongoing COVID-19 symptoms continued to show a reduction in memory scores. Our findings demonstrate that COVID-19 negatively impacts working memory function, but only in adults aged 25 years and over. Moreover, our results suggest that working memory deficits with COVID-19 can recover over time, although impairments may persist in those with ongoing symptoms.

Long-short-term memory machine learning of longitudinal clinical data accurately predicts acute kidney injury onset in COVID-19: a two-center study.

OBJECTIVES: This study used the long-short-term memory (LSTM) artificial intelligence method to model multiple time points of clinical laboratory data, along with demographics and comorbidities, to predict hospital-acquired acute kidney injury (AKI) onset in patients with COVID-19. METHODS: Montefiore Health System data consisted of 1982 AKI and 2857 non-AKI (NAKI) hospitalized patients with COVID-19, and Stony Brook Hospital validation data consisted of 308 AKI and 721 NAKI hospitalized patients with COVID-19. Demographic, comorbidities, and longitudinal (3 days before AKI onset) laboratory tests were analyzed. LSTM was used to predict AKI with fivefold cross-validation (80%/20% for training/validation). RESULTS: The top predictors of AKI onset were glomerular filtration rate, lactate dehydrogenase, alanine aminotransferase, aspartate aminotransferase, and C-reactive protein. Longitudinal data yielded marked improvement in prediction accuracy over individual time points. The inclusion of comorbidities and demographics further improves prediction accuracy. The best model yielded an area under the curve, accuracy, sensitivity, and specificity to be 0.965 ± 0.003, 89.57 ± 1.64%, 0.95 ± 0.03, and 0.84 ± 0.05, respectively, for the Montefiore validation dataset, and 0.86 ± 0.01, 83.66 ± 2.53%, 0.66 ± 0.10, 0.89 ± 0.03, respectively, for the Stony Brook Hospital validation dataset. CONCLUSION: LSTM model of longitudinal clinical data accurately predicted AKI onset in patients with COVID-19. This approach could help heighten awareness of AKI complications and identify patients for early interventions to prevent long-term renal complications.

Linguistic Pattern-Infused Dual-Channel Bidirectional Long Short-term Memory With Attention for Dengue Case Summary Generation From the Program for Monitoring Emerging Diseases-Mail Database: Algorithm Development Study.

BACKGROUND: Globalization and environmental changes have intensified the emergence or re-emergence of infectious diseases worldwide, such as outbreaks of dengue fever in Southeast Asia. Collaboration on region-wide infectious disease surveillance systems is therefore critical but difficult to achieve because of the different transparency levels of health information systems in different countries. Although the Program for Monitoring Emerging Diseases (ProMED)-mail is the most comprehensive international expert-curated platform providing rich disease outbreak information on humans, animals, and plants, the unstructured text content of the reports makes analysis for further application difficult. OBJECTIVE: To make monitoring the epidemic situation in Southeast Asia more efficient, this study aims to develop an automatic summary of the alert articles from ProMED-mail, a huge textual data source. In this paper, we proposed a text summarization method that uses natural language processing technology to automatically extract important sentences from alert articles in ProMED-mail emails to generate summaries. Using our method, we can quickly capture crucial information to help make important decisions regarding epidemic surveillance. METHODS: Our data, which span a period from 1994 to 2019, come from the ProMED-mail website. We analyzed the collected data to establish a unique Taiwan dengue corpus that was validated with professionals' annotations to achieve almost perfect agreement (Cohen κ=90%). To generate a ProMED-mail summary, we developed a dual-channel bidirectional long short-term memory with attention mechanism with infused latent syntactic features to identify key sentences from the alerting article. RESULTS: Our method is superior to many well-known machine learning and neural network approaches in identifying important sentences, achieving a macroaverage F1 score of 93%. Moreover, it can successfully extract the relevant correct information on dengue fever from a ProMED-mail alerting article, which can help researchers or general users to quickly understand the essence of the alerting article at first glance. In addition to verifying the model, we also recruited 3 professional experts and 2 students from related fields to participate in a satisfaction survey on the generated summaries, and the results show that 84% (63/75) of the summaries received high satisfaction ratings. CONCLUSIONS: The proposed approach successfully fuses latent syntactic features into a deep neural network to analyze the syntactic, semantic, and contextual information in the text. It then exploits the derived information to identify crucial sentences in the ProMED-mail alerting article. The experiment results show that the proposed method is not only effective but also outperforms the compared methods. Our approach also demonstrates the potential for case summary generation from ProMED-mail alerting articles. In terms of practical application, when a new alerting article arrives, our method can quickly identify the relevant case information, which is the most critical part, to use as a reference or for further analysis.

Impact of active and latent concerns about COVID-19 on attention.

The interactions between emotion and attention are complex due to the multifaceted nature of attention. Adding to this complexity, the COVID-19 pandemic has altered the emotional landscape, broadly heightening health and financial concerns. Can the heightened concerns about COVID-19 impair one or more of the components of attention? To explore the connection between heightened concerns about COVID-19 and attention, in a preregistered study, we collected survey responses from 234 participants assessing levels of concerns surrounding COVID-19, followed by four psychophysics tasks hypothesized to tap into different aspects of attention: visual search, working memory, sustained attention, and cognitive control. We also measured task-unrelated thoughts. Results showed that task-unrelated thoughts, but not survey reports of concern levels, negatively correlated with sustained attention and cognitive control, while visual search and working memory remained robust to task-unrelated thoughts and survey-indicated concern levels. As a whole, these findings suggest that being concerned about COVID-19 does not interfere with cognitive function unless the concerns are active in the form of task-unrelated thoughts.

Assessment of a digital game as a neuropsychological test for postoperative cognitive dysfunction.

OBJECTIVE: Postoperative cognitive dysfunction may result from worsening in a condition of previous impairment. It causes greater difficulty in recovery, longer hospital stays, and consequent delay in returning to work activities. Digital games have a potential neuromodulatory and rehabilitation effect. In this study, a digital game was used as a neuropsychological test to assess postoperative cognitive dysfunction, with preoperative patient performance as control. METHODS: It was a non-controlled study, with patients selected among candidates for elective non-cardiac surgery, evaluated in the pre- and postoperative periods. The digital game used has six phases developed to evaluate selective attention, alternating attention, visuoperception, inhibitory control, short-term memory, and long-term memory. The digital game takes about 25 minutes. Scores are the sum of correct answers in each cognitive domain. Statistical analysis compared these cognitive functions pre- and post-surgery using a generalized linear mixed model (ANCOVA). RESULTS: Sixty patients were evaluated, 40% male and 60% female, with a mean age of 52.7 ± 13.5 years. Except for visuoperception, a reduction in post-surgery scores was found in all phases of the digital game. CONCLUSION: The digital game was able to detect decline in several cognitive functions postoperatively. As its completion is faster than in conventional tests on paper, this digital game may be a potentially recommended tool for assessing patients, especially the elderly and in the early postoperative period.

A Long Short-Term Memory Biomarker-Based Prediction Framework for Alzheimer's Disease.

The early prediction of Alzheimer's disease (AD) can be vital for the endurance of patients and establishes as an accommodating and facilitative factor for specialists. The proposed work presents a robotized predictive structure, dependent on machine learning (ML) methods for the forecast of AD. Neuropsychological measures (NM) and magnetic resonance imaging (MRI) biomarkers are deduced and passed on to a recurrent neural network (RNN). In the RNN, we have used long short-term memory (LSTM), and the proposed model will predict the biomarkers (feature vectors) of patients after 6, 12, 21 18, 24, and 36 months. These predicted biomarkers will go through fully connected neural network layers. The NN layers will then predict whether these RNN-predicted biomarkers belong to an AD patient or a patient with a mild cognitive impairment (MCI). The developed methodology has been tried on an openly available informational dataset (ADNI) and accomplished an accuracy of 88.24%, which is superior to the next-best available algorithms.

Intermittent theta burst stimulation over the parietal cortex has a significant neural effect on working memory.

The crucial role of the parietal cortex in working memory (WM) storage has been identified by fMRI studies. However, it remains unknown whether repeated parietal intermittent theta-burst stimulation (iTBS) can improve WM. In this within-subject randomized controlled study, under the guidance of fMRI-identified parietal activation in the left hemisphere, 22 healthy adults received real and sham iTBS sessions (five consecutive days, 600 pulses per day for each session) with an interval of 9 months between the two sessions. Electroencephalography signals of each subject before and after both iTBS sessions were collected during a change detection task. Changes in contralateral delay activity (CDA) and K-score were then calculated to reflect neural and behavioral WM improvement. Repeated-measures ANOVA suggested that real iTBS increased CDA more than the sham one (p = .011 for iTBS effect). Further analysis showed that this effect was more significant in the left hemisphere than in the right hemisphere (p = .029 for the hemisphere-by-iTBS interaction effect). Pearson correlation analyses showed significant correlations for two conditions between CDA changes in the left hemisphere and K score changes (ps <.05). In terms of the behavioral results, significant K score changes after real iTBS were observed for two conditions, but a repeated-measures ANOVA showed a nonsignificant main effect of iTBS (p = .826). These results indicate that the current iTBS protocol is a promising way to improve WM capability based on the neural indicator (CDA) but further optimization is needed to produce a behavioral effect.

The Influence of Noise in the Neurofeedback Training Sessions in Student Athletes.

Considering that athletes constantly practice and compete in noisy environments, the aim was to investigate if performing neurofeedback training in these conditions would yield better results in performance than in silent ones. A total of forty-five student athletes aged from 18 to 35 years old and divided equally into three groups participated in the experiment (mean ± SD for age: 22.02 ± 3.05 years). The total neurofeedback session time for each subject was 300 min and were performed twice a week. The environment in which the neurofeedback sessions were conducted did not seem to have a significant impact on the training's success in terms of alpha relative amplitude changes (0.04 ± 0.08 for silent room versus 0.07 ± 0.28 for noisy room, p = 0.740). However, the group exposed to intermittent noise appears to have favourable results in all performance assessments (p = 0.005 for working memory and p = 0.003 for reaction time). The results of the study suggested that performing neurofeedback training in an environment with intermittent noise can be interesting to athletes. Nevertheless, it is imperative to perform a replicated crossover design.

Remote Assessment of Verbal Memory in Youth With Cochlear Implants During the COVID-19 Pandemic.

Purpose Youth with cochlear implants (CIs) are at risk for delays in verbal short-term memory (STM)/working memory (WM), which adversely affect language, neurocognitive, and behavioral outcomes. Assessment of verbal STM/WM is critical for identifying and addressing these delays, but standard assessment procedures require face-to-face (FTF) administration. The purpose of this study was to determine the feasibility and validity of remote testing methods (teleassessment) of verbal STM/WM in youth with CIs as a method of addressing COVID-19-related restrictions on FTF test administration. Method Tests of verbal STM/WM for nonwords, digit spans, letter-number sequences, sentences, and stories were individually administered by speech-language pathologists over a teleassessment platform to 28 youth (aged 9-22 years) with CIs and 36 same-aged normal-hearing peers. Examiners, parents, and participants completed quality and satisfaction ratings with the teleassessment procedure. Teleassessment scores were compared to results of tests obtained at FTF visits an average of 1.6 years earlier. Results Quality and satisfaction ratings for teleassessment were high and in almost all cases did not differ between the CI and normal-hearing samples. Youth with CIs scored lower than normal-hearing peers on measures of verbal STM/WM, and scores for digit span and letter-number sequencing did not differbetween teleassessment and FTF methods. Correlations across teleassessment and FTF visits were strong for digit span, letter-number sequencing, and sentence memory, but were more modest for nonword repetition. Conclusion With some caveats, teleassessment of verbal STM/WM was feasible and valid for youth with CIs.

Network-based rTMS to modulate working memory: The difficult choice of effective parameters for online interventions.

BACKGROUND: Online repetitive transcranialmagnetic stimulation (rTMS) has been shown to modulate working memory (WM) performance in a site-specific manner, with behavioral improvements due to stimulation of the dorsolateral prefrontal cortex (DLPFC), and impairment from stimulation to the lateral parietal cortex (LPC). Neurobehavioral studies have demonstrated that subprocesses of WM allowing for the maintenance and manipulation of information in the mind involve unique cortical networks. Despite promising evidence of modulatory effects of rTMS on WM, no studies have yet demonstrated distinct modulatory control of these two subprocesses. The current study therefore sought to explore this possibility through site-specific stimulation during an online task invoking both skills. METHODS: Twenty-nine subjects completed a 4-day protocol, in which active or sham 5Hz rTMS was applied over the DLPFC and LPC in separate blocks of trials while participants performed tasks that required either maintenance alone, or both maintenance and manipulation (alphabetization) of information. Stimulation targets were defined individually based on fMRI activation and structural network properties. Stimulation amplitude was adjusted using electric field modeling to equate induced current in the target region across participants. RESULTS: Despite the use of advanced techniques, no significant differences or interactions between active and sham stimulation were found. Exploratory analyses testing stimulation amplitude, fMRI activation, and modal controllability showed nonsignificant but interesting trends with rTMS effects. CONCLUSION: While this study did not reveal any significant behavioral changes in WM, the results may point to parameters that contribute to positive effects, such as stimulation amplitude and functional activation.

Social Robots for Evaluating Attention State in Older Adults.

Sustained attention is essential for older adults to maintain an active lifestyle, and the deficiency of this function is often associated with health-related risks such as falling and frailty. The present study examined whether the well-established age-effect on reducing mind-wandering, the drift to internal thoughts that are seen to be detrimental to attentional control, could be replicated by using a robotic experimenter for older adults who are not as familiar with online technologies. A total of 28 younger and 22 older adults performed a Sustained Attention to Response Task (SART) by answering thought probes regarding their attention states and providing confidence ratings for their own task performances. The indices from the modified SART suggested a well-documented conservative response strategy endorsed by older adults, which were represented by slower responses and increased omission errors. Moreover, the slower responses and increased omissions were found to be associated with less self-reported mind-wandering, thus showing consistency with their higher subjective ratings of attentional control. Overall, this study demonstrates the potential of constructing age-related cognitive profiles with attention evaluation instruction based on a social companion robot for older adults at home.

Computer-assisted instruction versus inquiry-based learning: The importance of working memory capacity.

The Covid-19 pandemic has led millions of students worldwide to intensify their use of digital education. This massive change is not reflected by the scant scientific research on the effectiveness of methods relying on digital learning compared to other innovative and more popular methods involving face-to-face interactions. Here, we tested the effectiveness of computer-assisted instruction (CAI) in Science and Technology compared to inquiry-based learning (IBL), another modern method which, however, requires students to interact with each other in the classroom. Our research also considered socio-cognitive factors-working memory (WM), socioeconomic status (SES), and academic self-concept (ASC)-known to predict academic performance but usually ignored in research on IBL and CAI. Five hundred and nine middle-school students, a fairly high sample size compared with relevant studies, received either IBL or CAI for a period varying from four to ten weeks prior to the Covid-19 events. After controlling for students' prior knowledge and socio-cognitive factors, multilevel modelling showed that CAI was more effective than IBL. Although CAI-related benefits were stable across students' SES and ASC, they were particularly pronounced for those with higher WM capacity. While indicating the need to adapt CAI for students with poorer WM, these findings further justify the use of CAI both in normal times (without excluding other methods) and during pandemic episodes.

Spatio-temporal prediction of the COVID-19 pandemic in US counties: modeling with a deep LSTM neural network.

Prediction of complex epidemiological systems such as COVID-19 is challenging on many grounds. Commonly used compartmental models struggle to handle an epidemiological process that evolves rapidly and is spatially heterogeneous. On the other hand, machine learning methods are limited at the beginning of the pandemics due to small data size for training. We propose a deep learning approach to predict future COVID-19 infection cases and deaths 1 to 4 weeks ahead at the fine granularity of US counties. The multi-variate Long Short-term Memory (LSTM) recurrent neural network is trained on multiple time series samples at the same time, including a mobility series. Results show that adding mobility as a variable and using multiple samples to train the network improve predictive performance both in terms of bias and of variance of the forecasts. We also show that the predicted results have similar accuracy and spatial patterns with a standard ensemble model used as benchmark. The model is attractive in many respects, including the fine geographic granularity of predictions and great predictive performance several weeks ahead. Furthermore, data requirement and computational intensity are reduced by substituting a single model to multiple models folded in an ensemble model.

Long-term consequences of COVID-19 on cognitive functioning up to 6 months after discharge: role of depression and impact on quality of life.

Neurologic and psychiatric symptoms have been reported in the months following the infection with COVID-19. A low-grade inflammation has been associated both with depression and cognitive symptoms, suggesting a link between these disorders. The aim of the study is to investigate cognitive functioning 6 months following hospital discharge for COVID-19, the impact of depression, and the consequences on quality of life. Ninety-two COVID-19 survivors evaluated at 1-month follow-up, 122 evaluated at 3 months and 98 evaluated at 6 months performed neuropsychological and psychiatric evaluations and were compared with a healthy comparison group (HC) of 165 subjects and 165 patients with major depression (MDD). Cognitive performances were adjusted for age, sex, and education. Seventy-nine percent of COVID-19 survivors at 1 month and 75% at 3- and 6-month follow-up showed cognitive impairment in at least one cognitive function. No significant difference in cognitive performances was observed between 1-, 3-, and 6-months follow-up. COVID-19 patients performed worse than HC but better than MDD in psychomotor coordination and speed of information processing. No difference between COVID-19 survivors and MDD was observed for verbal fluency, and executive functions, which were lower than in HC. Finally, COVID-19 survivors performed the same as HC in working memory and verbal memory. The factor that most affected cognitive performance was depressive psychopathology which, in turn, interact with cognitive functions in determining quality of life. Our results confirm that COVID-19 sequelae include signs of cognitive impairment which persist up to 6 months after hospital discharge and affect quality of life.

The Positive Modulation Effect of a 6-Week Consumption of an Anthocyanin-Rich Mulberry Milk on Working Memory, Cholinergic, and Monoaminergic Functions in Healthy Working-Age Adults.

Due to the increase of stress-related memory impairment accompanying with the COVID-19 pandemic and financial crisis, the prevention of cognitive decline induced by stress has gained much attention. Based on the evidence that an anthocyanin-rich mulberry milk demonstrated the cognitive enhancing effect, we hypothesized that it should be able to enhance memory in working-age volunteers who are exposed to working stress. This study is an open-label, two-arm randomized study. Both men and women volunteers at age between 18 and 60 years old were randomly assigned to consume the tested product either 1 or 2 servings daily for 6 weeks. All subjects were assessed for cortisol, acetylcholinesterase (AChE), monoamine oxidase (MAO), monoamine oxidase type A (MAO-A), and monoamine oxidase type B (MAO-B) in saliva, and their working memory was determined both at baseline and at a 6-week period. The results showed that the working memory of subjects in both groups was enhanced at the end of the study period together with the reduction of saliva cortisol. The suppression of AChE, MAO, and MAO-A was also observed in subjects who consumed the tested product 2 servings daily. Therefore, we suggest the memory enhancing effect of an anthocyanin-rich mulberry milk. The possible mechanism may occur primarily via the suppression of cortisol. In addition, the high dose of mulberry milk also suppresses AChE, MAO, and MAO-A.

The Influence of Media Exposure on Anxiety and Working Memory during Lockdown Period in Italy.

The rapid spread of the coronavirus pandemic has caused anxiety around the world. During lockdown, the media became a point of reference for people seeking information. However, little is known on the relationships between anxiety resulting from persistent media exposure to coronavirus-related programs and the effects produced on working memory. In this work, a total of 101 Italian citizens (53.7% female) aged between 18 and 45 years old, who were from 14 provinces in Italy, participated in an online survey. Participants were presented with media exposure and anxiety questionnaires and they were instructed to carry out working memory tasks (visual and auditory n-back). The results showed that media exposure is related to anxiety. It was also found that high levels of anxiety have a negative influence on the performance of both visual and auditory working memory tasks in terms of increased reaction times of responses and decreased accuracy. The results were critically discussed in the light of the Social Compensation Hypothesis.