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1. Temporal sequences of brain activity at rest are constrained by white matter structure and modulated by cognitive demands

   https://doi.org/10.1038/s42003-020-0961-x  

   Cornblath, Eli J.; Ashourvan, Arian; Kim, Jason Z.; Betzel, Richard F.; Ciric, Rastko; Adebimpe, Azeez; Baum, Graham L.; He, Xiaosong; Ruparel, Kosha; Moore, Tyler M.; et al (December 2020, Communications Biology)

   null (Ed.)

   Abstract A diverse set of white matter connections supports seamless transitions between cognitive states. However, it remains unclear how these connections guide the temporal progression of large-scale brain activity patterns in different cognitive states. Here, we analyze the brain’s trajectories across a set of single time point activity patterns from functional magnetic resonance imaging data acquired during the resting state and an n-back working memory task. We find that specific temporal sequences of brain activity are modulated by cognitive load, associated with age, and related to task performance. Using diffusion-weighted imaging acquired from the same subjects, we apply tools from network control theory to show that linear spread of activity along white matter connections constrains the probabilities of these sequences at rest, while stimulus-driven visual inputs explain the sequences observed during the n-back task. Overall, these results elucidate the structural underpinnings of cognitively and developmentally relevant spatiotemporal brain dynamics. 

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2. Functional brain connectivity predicts sleep duration in youth and adults

   https://doi.org/10.1002/hbm.26488  

   Mummaneni, Anurima; Kardan, Omid; Stier, Andrew J; Chamberlain, Taylor A; Chao, Alfred F; Berman, Marc G; Rosenberg, Monica D (December 2023, Human Brain Mapping)

   Abstract Sleep is critical to a variety of cognitive functions and insufficient sleep can have negative consequences for mood and behavior across the lifespan. An important open question is how sleep duration is related to functional brain organization which may in turn impact cognition. To characterize the functional brain networks related to sleep across youth and young adulthood, we analyzed data from the publicly available Human Connectome Project (HCP) dataset, which includesn‐back task‐based and resting‐state fMRI data from adults aged 22–35 years (taskn = 896; restn = 898). We applied connectome‐based predictive modeling (CPM) to predict participants' mean sleep duration from their functional connectivity patterns. Models trained and tested using 10‐fold cross‐validation predicted self‐reported average sleep duration for the past month fromn‐back task and resting‐state connectivity patterns. We replicated this finding in data from the 2‐year follow‐up study session of the Adolescent Brain Cognitive Development (ABCD) Study, which also includesn‐back task and resting‐state fMRI for adolescents aged 11–12 years (taskn = 786; restn = 1274) as well as Fitbit data reflecting average sleep duration per night over an average duration of 23.97 days. CPMs trained and tested with 10‐fold cross‐validation again predicted sleep duration fromn‐back task and resting‐state functional connectivity patterns. Furthermore, demonstrating that predictive models are robust across independent datasets, CPMs trained on rest data from the HCP sample successfully generalized to predict sleep duration in the ABCD Study sample and vice versa. Thus, common resting‐state functional brain connectivity patterns reflect sleep duration in youth and young adults. 

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3. Predicting children’s math skills from task-based and resting-state functional brain connectivity

   https://doi.org/10.1093/cercor/bhab476  

   Lynn, Andrew; Wilkey, Eric D; Price, Gavin R (December 2021, Cerebral Cortex)

   Abstract A critical goal of cognitive neuroscience is to predict behavior from neural structure and function, thereby providing crucial insights into who might benefit from clinical and/or educational interventions. Across development, the strength of functional connectivity among a distributed set of brain regions is associated with children’s math skills. Therefore, in the present study we use connectome-based predictive modeling to investigate whether functional connectivity during numerical processing and at rest “predicts” children’s math skills (N = 31, Mage = 9.21 years, 14 Female). Overall, we found that functional connectivity during symbolic number comparison and rest, but not during nonsymbolic number comparison, predicts children’s math skills. Each task revealed a largely distinct set of predictive connections distributed across canonical brain networks and major brain lobes. Most of these predictive connections were negatively correlated with children’s math skills so that weaker connectivity predicted better math skills. Notably, these predictive connections were largely nonoverlapping across task states, suggesting children’s math abilities may depend on state-dependent patterns of network segregation and/or regional specialization. Furthermore, the current predictive modeling approach moves beyond brain–behavior correlations and toward building models of brain connectivity that may eventually aid in predicting future math skills. 

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4. Resting-state functional network segregation of the default mode network predicts valence bias across the lifespan

   https://doi.org/10.1162/imag_a_00403  

   Pierce, Jordan E; Wig, Gagan S; Harp, Nicholas R; Neta, Maital (January 2024, Imaging Neuroscience)

   Abstract The brain is organized into intrinsically connected functional networks that can be reliably identified during resting-state functional magnetic resonance imaging (fMRI). Healthy aging is marked by decreased network segregation, which is linked to worse cognitive functioning, but aging-related changes in emotion are less well characterized. Valence bias, which represents the tendency to interpret emotionally ambiguous information as positive or negative, is more positive in older than younger adults and is associated with differences in task-based fMRI activation in the amygdala, prefrontal cortex, and a cingulo-opercular (CO) network. Here, we examined valence bias, age, and resting-state network segregation of 12 brain networks in a sample of 221 healthy individuals from 6 to 80 years old. Resting-state network segregation decreased linearly with increasing age, extending prior reports of de-differentiation across the lifespan. Critically, a more positive valence bias was related to lower segregation of the default mode network (DMN), due to stronger functional connectivity of the DMN with CO and, to a lesser extent, the ventral attention network (VAN) in all participants. In contrast to this overall segregation effect, in participants over 39 years old (who tend to show a positive valence bias), bias was also related to weaker connectivity between the DMN and Reward networks. The present findings indicate that specific interactions between the DMN, a task control network (CO), an emotion processing network (Reward), and, to a weaker extent, an attention network (VAN), support a more positive valence bias, perhaps through regulatory control of self-referential processing and reduced emotional reactivity in aging. The current work offers further insight into the functional brain network alterations that may contribute to affective well-being and dysfunction across the lifespan. 

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5. Predicting Affective Cognitions in the Resting Adult Brain

   Bush, Keith A.; Privratsky, Anthony A.; Kilts, Clinton D. (January 2018, Proceedings of the Conference on Cognitive Computational Neuroscience)

   Patterns of estimated neural activity derived from resting state functional magnetic resonance imaging (rs-fMRI) have been shown to predict a wide range of cognitive and behavioral outcomes in both normative and clinical populations. Yet, without links to established cognitive processes, the functional brain states associated with the resting brain will remain unexplained, and potentially confounded, markers of individual differences. In this work we demonstrate the application of multivoxel pattern classifiers (MVPCs) to predict the valence and arousal properties of spontaneous affect processing in the task-non-engaged resting state. rs-fMRI data were acquired from subjects that were held out from a subject set that underwent image-based affect induction concurrent with fMRI to train the MVPCs. We also validated these affective predictions against a well-established, independent measure of autonomic arousal, skin conductance response. These findings suggest a new neuroimaging methodology for resting state analysis in which models, trained on cognition-specific task-based fMRI acquired from well-matched cohorts, capably predict hidden cognitive processes operating within the resting brain. 

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