Remote Work: What’s the Impact on Team Collaboration?

Post by Ifrah Khanyaree

What's the science?

The COVID-19 pandemic has accelerated digital transformation across many industries and organizations. Within a matter of weeks of the onset of the pandemic, many office-based working adults shifted to working remotely full time. This week in Nature Human Behaviour, Yang and colleagues analyzed communication and working hours data from a large US tech company to find out the impact of remote work on employee collaboration and communication.                  

How did they do it?

The authors used anonymized email, instant message (IM), calendar, video/audio call, working hour data of 61,182 US Microsoft employees from December 2019 - June 2020, collected using Microsoft’s Workplace Analytics product. The authors then analyzed this data using a modified version of the traditional Difference-in-Difference model (DiD), which is a technique used in econometrics that measures causal effect between at least two sets of longitudinal data, where one group receives a ‘treatment’ and the other does not (the control group). This works because many of Microsoft’s employees were remote even before the pandemic hit; that group acts as the control group that also experiences the effects of working during COVID, but not the treatment (switching to remote work). 

They used a modified version of DiD both because COVID affected both the treatment and control groups and because their model measured the effects of changes in two different treatment variables instead of one - an employee’s remote work status and also their colleague’s remote work status.                            

What did they find?

The authors found that the shift to remote work for all employees caused the communication network to become siloed: a decrease in cross-group communication but an increase in the connectedness of one’s own group. Remote work led to a substantial increase in unscheduled calls, emails, and instant messages, but a decrease in meeting hours, and total video/audio call hours. Synchronous collaboration, where more complex information can be conveyed, such as video calls, was decreased overall in favour of asynchronous communication, like emails or messages. Further, the total hours worked were increased. These changes were particularly enhanced for managers. Finally, connections between employees became more static, with fewer social connections being added or lost over time.

What's the impact?

The authors suggest that the increase in asynchronous communication and more siloed networks could negatively affect workers’ productivity and innovation because of the difficulty in collaboration and sharing of information. They propose that firms carry on more qualitative and quantitative research before finalizing any remote work policies. Based on their analyses, firms that want to continue with full-time remote work need to be intentional about strengthening cross-group ties in their organizations. The sudden shift to remote work has brought about a much-needed acceleration and transformation to support working remotely, and it is likely that some version of remote work will continue to prevail even after the pandemic is over. Therefore more research needs to be done to understand the long-term effects of remote work on team communication and collaboration and what the downstream impact might be.

Yang et al. The effects of remote work on collaboration among information workers (2021). Access the original scientific publication here.

Testing Domain Selectivity in the Human Brain Using Artificial Neural Networks

Post by Lina Teichmann

What's the science?

Several brain areas that are part of the human visual system have been shown to respond to some images more than others. For example, the fusiform face area (FFA) responds strongly to images of faces while the parahippocampal place area (PPA) responds more strongly to scenes. A prominent idea is that certain parts of the brain’s cortex are domain-selective, specializing in different types of visual content. One challenge of putting category-selectiveness in the brain to the test is deciding how to define what counts as an image of a given category. Additionally, we can only test a limited number of stimuli in each experiment, meaning that many potential images are untested. Thus, there is always a possibility that we have not tested the “right” images to put the idea of category-selectiveness to the test. This week in Nature Communications, Ratan Murty and colleagues address these challenges by showing that we can use artificial neural networks to predict the brain response in apparent category-selective areas. 

How did they do it?

Four healthy participants viewed a variety of natural images while their brain activity was recorded with functional magnetic resonance imaging (fMRI). Using the neural responses to a subset of the images, the authors then used artificial neural networks to predict the neural response of held-out images (not seen by participants) in areas FFA, PPA, and the extrastriate body areas (EBA). In addition, they used data recorded from a subset of participants to predict the neural response in other participants. To put the model’s ability to predict neural responses into perspective, the authors asked experts in the field to predict the neural responses they would expect for the given images. Additionally, they screened millions of images to identify images that would evoke a strong response in FFA, PPA, and EBA and also used a specific type of deep learning model to synthesize new images that were predicted to evoke strong neural responses in FFA, PPA, EBA. Finally, the model was used to identify features in the images that would drive the responses in each brain area.

What did they find?

First, the authors demonstrated that the artificial neural network could predict neural responses in FFA, PPA, and EBA, using only pixel-based information as input. The results even showed that the model outperformed the predictions of experts in the field. Based on these findings, the authors showed that the artificial neural network could be used to look at a huge number of images and make image-based predictions about the brain response in different brain areas. When assessing which images would evoke a strong response in FFA, PPA, and EBA, the authors found that images within the hypothesized preferred category (i.e., faces, scenes, and bodies, respectively) were predicted to have the strongest response. Thus, the findings support the hypothesis of category-selectively within areas of the cortex involved in vision.

What's the impact?

Overall, the authors have used artificial neural networks in an elegant way to enhance our understanding of human vision. The results lend further support to the domain-specificity hypothesis in the human brain, as several million images were predicted to align with category-selective responses in FFA, PPA, and EBA.

Ratan Murty et al. Computational models of category-selective brain regions enable high-throughput tests of selectivity (2021). Access the original scientific publication here.

Medial Parietal Tau Deposition is Associated with Hippocampal-Retrosplenial Functional Connectivity

Post by Shireen Parimoo

What's the science?

One of the hallmarks of Alzheimer’s disease pathology is the accumulation of misfolded tau protein, which begins in the transentorhinal region of the medial temporal lobe (MTL). Tau is thought to spread trans-synaptically between regions that are anatomically connected with the anterolateral entorhinal cortex (alERC), before spreading to the rest of the neocortex. Recent work suggests that tau might propagate from the MTL to functionally connected regions like the medial parietal cortex, which is part of the posteromedial memory network through its connectivity with the posteromedial entorhinal cortex (pmERC). It is important to better understand whether the functional connectivity between MTL regions and medial parietal cortex is associated with the spread of tau and episodic memory decline. This week in The Journal of Neuroscience, Ziontz and colleagues investigated the relationship between medial parietal tau accumulation and functional connectivity of MTL regions with the medial parietal lobe.

How did they do it?

Ninety-seven cognitively normal older adults (60–93 years old) were recruited from the Berkeley Aging Cohort Study and completed tests of verbal and visuospatial episodic memory. Participants also underwent a resting-state functional magnetic resonance imaging scan and a positron emission tomography scan, which allowed the authors to examine functional connectivity and amyloid/tau deposition in the brain, respectively. Functional connectivity was assessed between the hippocampus, alERC, and pmERC in the MTL and the retrosplenial cortex in the medial parietal lobe. The authors examined tau pathology in the entorhinal and inferior temporal cortices of the MTL, and in the medial parietal lobe, which included the retrosplenial cortex, posterior cingulate cortex, and precuneus regions. Specifically, they quantified tau deposition based on the signal magnitude of flortaucipir, a tracer that binds to tau protein in the brain. Lastly, they used the PiB tracer to examine amyloid-beta deposition in the medial parietal lobe and in the whole brain.

What did they find?

The retrosplenial cortex was functionally connected with the hippocampus and pmERC, but not with the alERC. Functional connectivity between these regions was not related to episodic memory. On the other hand, higher connectivity of the retrosplenial cortex with the hippocampus – but not with the alERC or pmERC – was associated with greater tau deposition in the medial parietal lobe. Medial parietal tau was not associated with functional connectivity between other regions, such as the hippocampus and the superior frontal gyrus. They also showed that the relationship between tau deposition in the medial parietal lobe and hippocampal-retrosplenial functional connectivity was unique to these regions.

Higher medial parietal tau was related to greater tau deposition in the MTL as well as increased global amyloid-beta levels. Individuals with greater functional connectivity between the hippocampus and retrosplenial cortex showed stronger correlations between tau levels in the MTL and medial parietal lobe. Interestingly, these individuals were also likely to have worse visuospatial episodic memory, which is in line with the role of the medial parietal lobe in representing visuospatial information. Thus, visuospatial episodic memory suffered when tau levels and functional connectivity between the MTL and medial parietal lobe were both high.

What's the impact?

The results of this study suggest that tau might spread between regions that are functionally connected to each other. Tau pathology in cognitively healthy individuals might be a potential biomarker for the development of Alzheimer’s disease, a notion that is supported by the current finding that visuospatial memory was lower only in individuals who showed a stronger association between tau accumulation and functional connectivity. Overall, these findings provide an exciting avenue for future research to use tau and functional connectivity in conjunction to track and predict the trajectory of cognitive decline.

Ziontz et al. Hippocampal connectivity with retrosplenial cortex is linked to neocortical tau accumulation and memory function. The Journal of Neuroscience (2021). Access the original scientific publication here.