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1. Creative Connections: Computational Semantic Distance Captures Individual Creativity and Resting-State Functional Connectivity

   https://doi.org/10.1162/jocn_a_01658  

   Orwig, William; Diez, Ibai; Vannini, Patrizia; Beaty, Roger; Sepulcre, Jorge (March 2021, Journal of Cognitive Neuroscience)

   null (Ed.)

   Recent studies of creative cognition have revealed interactions between functional brain networks involved in the generation of novel ideas; however, the neural basis of creativity is highly complex and presents a great challenge in the field of cognitive neuroscience, partly because of ambiguity around how to assess creativity. We applied a novel computational method of verbal creativity assessment—semantic distance—and performed weighted degree functional connectivity analyses to explore how individual differences in assembly of resting-state networks are associated with this objective creativity assessment. To measure creative performance, a sample of healthy adults ( n = 175) completed a battery of divergent thinking (DT) tasks, in which they were asked to think of unusual uses for everyday objects. Computational semantic models were applied to calculate the semantic distance between objects and responses to obtain an objective measure of DT performance. All participants underwent resting-state imaging, from which we computed voxel-wise connectivity matrices between all gray matter voxels. A linear regression analysis was applied between DT and weighted degree of the connectivity matrices. Our analysis revealed a significant connectivity decrease in the visual-temporal and parietal regions, in relation to increased levels of DT. Link-level analyses showed higher local connectivity within visual regions was associated with lower DT, whereas projections from the precuneus to the right inferior occipital and temporal cortex were positively associated with DT. Our results demonstrate differential patterns of resting-state connectivity associated with individual creative thinking ability, extending past work using a new application to automatically assess creativity via semantic distance. 

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2. 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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3. A multicohort geometric deep learning study of age dependent cortical and subcortical morphologic interactions for fluid intelligence prediction

   https://doi.org/10.1038/s41598-022-22313-x  

   Wu, Yunan; Besson, Pierre; Azcona, Emanuel A.; Bandt, S. Kathleen; Parrish, Todd B.; Breiter, Hans C.; Katsaggelos, Aggelos K. (December 2022, Scientific Reports)

   Abstract The relationship of human brain structure to cognitive function is complex, and how this relationship differs between childhood and adulthood is poorly understood. One strong hypothesis suggests the cognitive function of Fluid Intelligence (Gf) is dependent on prefrontal cortex and parietal cortex. In this work, we developed a novel graph convolutional neural networks (gCNNs) for the analysis of localized anatomic shape and prediction of Gf. Morphologic information of the cortical ribbons and subcortical structures was extracted from T1-weighted MRIs within two independent cohorts, the Adolescent Brain Cognitive Development Study (ABCD; age: 9.93 ± 0.62 years) of children and the Human Connectome Project (HCP; age: 28.81 ± 3.70 years). Prediction combining cortical and subcortical surfaces together yielded the highest accuracy of Gf for both ABCD (R = 0.314) and HCP datasets (R = 0.454), outperforming the state-of-the-art prediction of Gf from any other brain measures in the literature. Across both datasets, the morphology of the amygdala, hippocampus, and nucleus accumbens, along with temporal, parietal and cingulate cortex consistently drove the prediction of Gf, suggesting a significant reframing of the relationship between brain morphology and Gf to include systems involved with reward/aversion processing, judgment and decision-making, motivation, and emotion. 

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4. Shorter Duration and Lower Quality Sleep Have Widespread Detrimental Effects on Developing Functional Brain Networks in Early Adolescence

   https://doi.org/10.1093/texcom/tgab062  

   Brooks, Skylar J; Katz, Eliot S; Stamoulis, Catherine (January 2022, Cerebral Cortex Communications)

   Abstract Sleep is critical for cognitive health, especially during complex developmental periods such as adolescence. However, its effects on maturating brain networks that support cognitive function are only partially understood. We investigated the impact of shorter duration and reduced quality sleep, common stressors during development, on functional network properties in early adolescence—a period of significant neural maturation, using resting-state functional magnetic resonance imaging from 5566 children (median age = 120.0 months; 52.1% females) in the Adolescent Brain Cognitive Development cohort. Decreased sleep duration, increased sleep latency, frequent waking up at night, and sleep-disordered breathing symptoms were associated with lower topological efficiency, flexibility, and robustness of visual, sensorimotor, attention, fronto-parietal control, default-mode and/or limbic networks, and with aberrant changes in the thalamus, basal ganglia, hippocampus, and cerebellum (P < 0.05). These widespread effects, many of which were body mass index-independent, suggest that unhealthy sleep in early adolescence may impair neural information processing and integration across incompletely developed networks, potentially leading to deficits in their cognitive correlates, including attention, reward, emotion processing and regulation, memory, and executive control. Shorter sleep duration, frequent snoring, difficulty waking up, and daytime sleepiness had additional detrimental network effects in nonwhite participants, indicating racial disparities in the influence of sleep metrics. 

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5. Mapping Causal Brain Interactions in fMRI Decoded Neurofeedback

   Arab, Fahimeh; Ghassami, AmirEmad; Peters, Megan_A K; Nozari, Erfan (August 2024, Proceedings of the 7th Annual Conference on Cognitive Computational Neuroscience (CCN))

   Neurofeedback (NF), including its specialized form De- coded Neurofeedback (DecNef), holds great promise for improving mental health and cognitive function by al- lowing individuals to voluntarily control their brain ac- tivity. However, there exists vast subject-to-subject and region-to-region variability in neurofeedback outcomes and the causal mechanisms involved in successful neu- rofeedback are largely unknown. In this paper, we inves- tigate the neural mechanisms behind this variability us- ing whole-brain causal connectomes derived from func- tional Magnetic Resonance Imaging (fMRI) data from a DecNef study aimed at reducing common fears via sub- conscious induction of feared images. During NF, we found strongest causal connections among regions of the attention and somatomotor subnetworks. Addition- ally, the net strength of causal effects between most pairs of subnetworks was significantly correlated with mean NF score, though with different signs. Specifically, we found most connections among visual, subcortical, de- fault mode, and dorsal attention subnetworks to support NF success, while most connections among ventral at- tention, somatomotor, limbic, and control subnetworks correlated negatively with NF scores. 

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