Imagining and evaluating future events activates two distinct brain networks
When given quiet moments, a person’s mind often wanders to future events such as tomorrow’s plans, to-do list items, or an upcoming trip. During these instances, the brain network called the default mode network (DMN) is being activated, but researchers have never fully understood how it functioned. In a study funded by NIDA, researchers evaluated brain activity in independent regions and found that the DMN uses two complimentary networks to create the imagined event (the “constructive” function) and assess whether this constructed event is positive or negative (the “evaluative” function).
Using fMRI, the research team examined the brain activity of 24 subjects (13 female, 11 male) in response to different prompts to imagine a future event. After imagining the event with a prompt such as “Imagine you win the lottery next year”, participants quickly rated the vividness and valence of the imagined event. The vividness rating was a measure of how well participants felt that they could construct the imagined event in their mind while the valence measured the emotional response that participants felt when imagining the event.
Based on fMRI data, researchers found that the dorsal DMN was more influenced by emotional valence in that it was more active for positive events and less active for negative events. The ventral DMN, conversely, was more active if an imagined event was deemed highly vivid compared to less vivid imagined events. Neither network was influenced by the other factor, which demonstrates that these networks have distinct “constructive” and “evaluative” roles when imagining future events.
Lee S, Parthasarathi T, Kable JW. 2021. The ventral and dorsal default mode networks are dissociably modulated by the vividness and valence of imagined events, Journal of Neuroscience. JN-RM-1273-20. doi: 10.1523/JNEUROSCI.1273-20.2021.
Disparities in COVID-19 testing and positivity rates found in rural Black communities and areas with food insecurity
The novel coronavirus, SARS-CoV-2, has had a major impact on vulnerable communities. While measures to mitigate risk of infection have been undertaken, many communities still face higher risk of poor outcomes. Higher risk in minority communities is associated with lower socioeconomic status and inability to practice mitigation efforts due to societal constraints. Location can pose even more risk with rural communities facing unique challenges due to historically lower access to healthcare. Research funded by the NIGMS sought to identify factors related to low levels of COVID-19 testing among rural communities to provide testing insights and pinpoint where testing needs can be improved in rural minority populations.
Researchers examined data collected from March to September 2020 obtained from West Virginia Health and Human Resources. To perform the granular spatial model, coronavirus positive testing data at the zip code level were utilized, and Bayesian hierarchical models were used to identify associations. Key outcomes were COVID-19 testing rates and positivity rates. Researchers found higher positive test rates in communities with higher food insecurity and rural areas with a high density of Black populations. Overall COVID-19 testing rates were also found to be lower in these communities.
Findings indicate a need to improve COVID-19 testing rates and to identify barriers to testing in rural communities, particularly those with denser minority populations. Historically, rural minorities interact less with the healthcare system compared to White rural populations. A better understanding of potential barriers to COVID-19 testing is needed to expand testing services and improve testing disparities in rural populations.
Hendricks, Brian, et al. "Coronavirus testing disparities associated with community level deprivation, racial inequalities, and food insecurity in West Virginia." Annals of Epidemiology 59 (2021). doi.org/10.1016/j.annepidem.2021.03.009
Computational Methods to Measure Patterns of Gaze in Toddlers with Autism Spectrum Disorder
Atypical eye gazing is a well-studied early symptom of toddlers/children with Autism Spectrum Disorder (ASD). However, current eye-tracking methods require special equipment and the process can be cost prohibitive. Is it possible to develop a low-cost software application ('app') that can be used on mobile phones or tablets to help diagnose abnormal eye gazing patterns of toddlers with ASD? If so, can this software produce reliable results that can be used by clinicians in a cost-effective manner? A study sponsored by the NICHD and the NIMH pursued these questions by assessing the effectiveness of an app that analyzes vision on an iPhone or iPad to discern and quantify differences in eye gaze patters of toddlers with ASD versus typical development.
Data was collected from children that were referred by their pediatrician from December 2018-220 based on results of the Modified Checklist for Autism in Toddlers-Revised. Caregivers of 1564 toddlers were invited to participate during a well-child visit. The study enrolled a total of 993 toddlers ages 16-38 months (mean age 21.1 months, 50.6% were boys, 59.8% White individuals, 16.5% Black individuals, 23.7% other race, and 16.9% Hispanic/Latino individuals). Of those assessed, 40 were diagnosed with ASD.
The children were observed watching videos of people smiling and making eye contact or engaging in conversation. Researchers recorded the children's gaze patterns with the device's camera and measured them using computer vision and machine learning analysis. Distinctive eye-gaze patterns were detected in toddlers diagnosed with ASD, characterized by reduced gaze to social stimuli and to social moments during the movies. Children with ASD were much less likely than typically developing children to focus on social cues and visually track the conversations in the videos.
In summary, the researchers provide promising evidence that it is possible to develop a cost-effective app that can reliably measure both known and new gaze biomarkers that distinguish toddlers with ASD vs typical development. These novel results point to the potential for developing scalable autism screening tools. With confirmation by larger studies, the authors conclude that this eye-tracking app could be a viable method for identifying young children with ASD.
Chang Z, Di Martino JM, Aiello R, et al. Computational Methods to Measure Patterns of Gaze in Toddlers With Autism Spectrum Disorder. JAMA Pediatr. Published online April 26, 2021. doi:10.1001/jamapediatrics.2021.0530