Cognitive training in MCI may stimulate pre-existing neural reserves or recruit neural circuitry as “compensatory scaffolding” prompting neuroplastic reorganization to meet task demands (Reuter-Lorenz & Park, 2014). However, existing systematic reviews and meta-analytic studies exploring the benefits of cognitive interventions in MCI have been mixed. An updated examination regarding the efficacy of cognitive intervention in MCI is needed given improvements in adherence to MCI diagnostic criteria in subject selection, better defined interventions and strategies applied, increased use of neuropsychological measures pre- and post-intervention, as well as identification of moderator variables which may influence treatment. As such, this meta-analytic review was conducted to examine the efficacy of cognitive intervention in individuals diagnosed with mild cognitive impairment (MCI) versus MCI controls based on performance of neuropsychological outcome measures in randomized controlled trials (RCT). RCT studies published from January 1995 to June 2017 were obtained through source databases of MEDLINE-R, PubMed, Healthstar, Global Health, PSYCH-INFO, and Health and Psychological Instruments using search parameters for MCI diagnostic category (mild cognitive impairment, MCI, pre-Alzheimer’s disease, early cognitive decline, early onset Alzheimer’s disease, and preclinical Alzheimer’s disease) and the intervention or training conducted (intervention, training, stimulation, rehabilitation, or treatment). Other inclusion and exclusion criteria included subject selection based on established MCI criteria, RCT design in an outpatient setting, MCI controls (active or passive), and outcomes based on objective neuropsychological measures. From the 1199 abstracts identified, 26 articles met inclusion criteria for the meta-analyses completed across eleven (11) countries; 92.31% of which have been published within the past 7 years. A series of meta-analyses were performed to examine the effects of cognitive intervention by cognitive domain, type of training, and intervention content (cognitive domain targeted). We found significant, moderate effects for multicomponent training (Hedges’ g observed = 0.398; CI [0.164, 0.631]; Z = 3.337; p = 0.001; Q = 55.511; df = 15; p = 0.000; I ² = 72.978%; τ ² = 0.146) as well as multidomain-focused strategies (Hedges’ g = 0.230; 95% CI [0.108, 0.352]; Z = 3.692; p < 0.001; Q = 12.713; df = 12; p = 0.390; I ² = 5.612; τ ² = 0.003). The effects for other interventions explored by cognitive domain, training type, or intervention content were indeterminate due to concerns for heterogeneity, bias, and small cell sizes. In addition, subgroup and meta-regression analyses were conducted with the moderators of MCI category, mode of intervention, training type, intervention content, program duration (total hours), type of control group (active or passive), post-intervention follow-up assessment period, and control for repeat administration. We found significant overall effects for intervention content with memory focused interventions appearing to be more effective than multidomain approaches. There was no evidence of an influence on outcomes for the other covariates examined. Overall, these findings suggest individuals with MCI who received multicomponent training or interventions targeting multiple domains (including lifestyle changes) were apt to display an improvement on outcome measures of cognition post-intervention. As such, multicomponent and multidomain forms of intervention may prompt recruitment of alternate neural processes as well as support primary networks to meet task demands simultaneously. In addition, interventions with memory and multidomain forms of content appear to be particularly helpful, with memory-based approaches possibly being more effective than multidomain methods. Other factors, such as program duration, appear to have less of an influence on intervention outcomes. Given this, although the creation of new primary network paths appears strained in MCI, interventions with memory-based or multidomain forms of content may facilitate partial activation of compensatory scaffolding and neuroplastic reorganization. The positive benefit of memory-based strategies may also reflect transfer effects indicative of compensatory network activation and the multiple-pathways involved in memory processes. Limitations of this review are similar to other meta-analysis in MCI, including a modest number studies, small sample sizes, multiple forms of interventions and types of training applied (some overlapping), and, while greatly improved in our view, a large diversity of instruments used to measure outcome. This is apt to have contributed to the presence of heterogeneity and publication bias precluding a more definitive determination of the outcomes observed.

Spontaneous intracranial hypotension (SIH) is an important cause of headaches. For the initial evaluation of patients with suspected SIH, post-myelography computed tomography (CT myelography or CTM) has long been considered the gold standard for the detection of cerebrospinal fluid (CSF) leaks,¹ but spine magnetic resonance imaging including heavily T2-weighted magnetic resonance myelography (MRM) may be a noninvasive alternative.^2-4 We compared the diagnostic accuracy of MRM with CTM for the identification of spinal CSF leaks.

We fit an SARS-CoV-2 model to US data of COVID-19 cases and deaths. We conclude that the model is not structurally identifiable. We make the model identifiable by prefixing some of the parameters from external information. Practical identifiability of the model through Monte Carlo simulations reveals that two of the parameters may not be practically identifiable. With thus identified parameters, we set up an optimal control problem with social distancing and isolation as control variables. We investigate two scenarios: the controls are applied for the entire duration and the controls are applied only for the period of time. Our results show that if the controls are applied early in the epidemic, the reduction in the infected classes is at least an order of magnitude higher compared to when controls are applied with 2-week delay. Further, removing the controls before the pandemic ends leads to rebound of the infected classes.

Introduction: The rise in global temperatures due to climate change has escalated the frequency and intensity of wildfires worldwide. Beyond their direct impact on physical health, these wildfires can significantly impact mental health. Conventional mental health studies predominantly rely on surveys, often constrained by limited sample sizes, high costs, and time constraints. As a result, there is an increasing interest in accessing social media data to study the effects of wildfires on mental health.Methods: In this study, we focused on Twitter users affected by the California Tubbs Fire in 2017 to extract data signals related to emotional well-being and mental health. Our analysis aimed to investigate tweets posted during the Tubbs Fire disaster to gain deeper insights into their impact on individuals. Data were collected from October 8 to October 31, 2017, encompassing the peak activity period. Various analytical methods were employed to explore word usage, sentiment, temporal patterns of word occurrence, and emerging topics associated with the unfolding crisis.Results: The findings show increased user engagement on wildfire-related Tweets, particularly during nighttime and early morning, especially at the onset of wildfire incidents. Subsequent exploration of emotional categories using Linguistic Inquiry and Word Count (LIWC) revealed a substantial presence of negative emotions at 43.0%, juxtaposed with simultaneous positivity in 23.1% of tweets. This dual emotional expression suggests a nuanced and complex landscape, unveiling concerns and community support within conversations. Stress concerns were notably expressed in 36.3% of the tweets. The main discussion topics were air quality, emotional exhaustion, and criticism of the president's response to the wildfire emergency.Discussion: Social media data, particularly the data collected from Twitter during wildfires, provides an opportunity to evaluate the psychological impact on affected communities immediately. This data can be used by public health authorities to launch targeted media campaigns in areas and hours where users are more active. Such campaigns can raise awareness about mental health during disasters and connect individuals with relevant resources. The effectiveness of these campaigns can be enhanced by tailoring outreach efforts based on prevalent issues highlighted by users. This ensures that individuals receive prompt support and mitigates the psychological impacts of wildfire disasters.