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1. 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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2. Computational models of adaptive behavior and prefrontal cortex

   https://doi.org/10.1038/s41386-021-01123-1  

   Soltani, Alireza; Koechlin, Etienne (August 2021, Neuropsychopharmacology)

   Abstract The real world is uncertain, and while ever changing, it constantly presents itself in terms of new sets of behavioral options. To attain the flexibility required to tackle these challenges successfully, most mammalian brains are equipped with certain computational abilities that rely on the prefrontal cortex (PFC). By examining learning in terms of internal models associating stimuli, actions, and outcomes, we argue here that adaptive behavior relies on specific interactions between multiple systems including: (1) selective models learning stimulus–action associations through rewards; (2) predictive models learning stimulus- and/or action–outcome associations through statistical inferences anticipating behavioral outcomes; and (3) contextual models learning external cues associated with latent states of the environment. Critically, the PFC combines these internal models by forming task sets to drive behavior and, moreover, constantly evaluates the reliability of actor task sets in predicting external contingencies to switch between task sets or create new ones. We review different models of adaptive behavior to demonstrate how their components map onto this unifying framework and specific PFC regions. Finally, we discuss how our framework may help to better understand the neural computations and the cognitive architecture of PFC regions guiding adaptive behavior. 

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3. Examining relations between performance on non‐verbal executive function and verbal self‐regulation tasks in demographically‐diverse populations

   https://doi.org/10.1111/desc.13228  

   Dutra, Natália B.; Chen, Lydia; Anum, Adote; Burger, Oskar; Davis, Helen E.; Dzokoto, Vivian A.; Fong, Frankie T. K.; Ghelardi, Sabrina; Mendez, Kimberly; Messer, Emily J. E.; et al (January 2022, Developmental Science)

   Abstract Self‐regulation is a widely studied construct, generally assumed to be cognitively supported by executive functions (EFs). There is a lack of clarity and consensus over the roles of specific components of EFs in self‐regulation. The current study examines the relations between performance on (a) a self‐regulation task (Heads, Toes, Knees Shoulders Task) and (b) two EF tasks (Knox Cube and Beads Tasks) that measure different components of updating: working memory and short‐term memory, respectively. We compared 107 8‐ to 13‐year‐old children (64 females) across demographically‐diverse populations in four low and middle‐income countries, including: Tanna, Vanuatu; Keningau, Malaysia; Saltpond, Ghana; and Natal, Brazil. The communities we studied vary in market integration/urbanicity as well as level of access, structure, and quality of schooling. We found that performance on the visuospatial working memory task (Knox Cube) and the visuospatial short‐term memory task (Beads) are each independently associated with performance on the self‐regulation task, even when controlling for schooling and location effects. These effects were robust across demographically‐diverse populations of children in low‐and middle‐income countries. We conclude that this study found evidence supporting visuospatial working memory and visuospatial short‐term memory as distinct cognitive processes which each support the development of self‐regulation. 

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4. Modeling naturalistic face processing in humans with deep convolutional neural networks

   https://doi.org/10.1073/pnas.2304085120  

   Jiahui, Guo; Feilong, Ma; Visconti di Oleggio Castello, Matteo; Nastase, Samuel A.; Haxby, James V.; Gobbini, M. Ida (October 2023, Proceedings of the National Academy of Sciences)

   Deep convolutional neural networks (DCNNs) trained for face identification can rival and even exceed human-level performance. The ways in which the internal face representations in DCNNs relate to human cognitive representations and brain activity are not well understood. Nearly all previous studies focused on static face image processing with rapid display times and ignored the processing of naturalistic, dynamic information. To address this gap, we developed the largest naturalistic dynamic face stimulus set in human neuroimaging research (700+ naturalistic video clips of unfamiliar faces). We used this naturalistic dataset to compare representational geometries estimated from DCNNs, behavioral responses, and brain responses. We found that DCNN representational geometries were consistent across architectures, cognitive representational geometries were consistent across raters in a behavioral arrangement task, and neural representational geometries in face areas were consistent across brains. Representational geometries in late, fully connected DCNN layers, which are optimized for individuation, were much more weakly correlated with cognitive and neural geometries than were geometries in late-intermediate layers. The late-intermediate face-DCNN layers successfully matched cognitive representational geometries, as measured with a behavioral arrangement task that primarily reflected categorical attributes, and correlated with neural representational geometries in known face-selective topographies. Our study suggests that current DCNNs successfully capture neural cognitive processes for categorical attributes of faces but less accurately capture individuation and dynamic features. 

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5. The Contribution of Bilingualism to Cognitive Functioning and Regional Brain Volume in Normal and Abnormal Aging

   https://doi.org/10.1017/S1366728921000705  

   Torres, Valeria L.; Rosselli, Mónica; Loewenstein, David A.; Lang, Merike; Vélez-Uribe, Idaly; Arruda, Fernanda; Conniff, Joshua; Curiel, Rosie E.; Greig, Maria T.; Barker, Warren W.; et al (March 2022, Bilingualism: Language and Cognition)

   Abstract We examined the association between bilingualism, executive function (EF), and brain volume in older monolinguals and bilinguals who spoke English, Spanish, or both, and were cognitively normal (CN) or diagnosed with Mild Cognitive Impairment (MCI) or dementia. Gray matter volume (GMV) was higher in language and EF brain regions among bilinguals, but no differences were found in memory regions. Neuropsychological performance did not vary across language groups over time; however, bilinguals exhibited reduced Stroop interference and lower scores on Digit Span Backwards and category fluency. Higher scores on Digit Span Backwards were associated with a younger age of English acquisition, and a greater degree of balanced bilingualism was associated with lower scores in category fluency. The initial age of cognitive decline did not differ between language groups. The influence of bilingualism appears to be reflected in increased GMV in language and EF regions, and to a lesser degree, in EF. 

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