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Bergström, F.

Publications and source records attributed to Bergström, F..

3 recordsLinked to original sources

Subliminal beauty engages the brain's valuation circuits

Neuroeconomic models propose that the anterior ventral striatum (aVS) and ventromedial prefrontal cortex (vmPFC) are key regions for computing subjective value (SV) signals that guide choice. However, the role of conscious awareness in this process remains debated. Here we examined whether SV can be automatically computed in these regions without conscious awareness. In an fMRI experiment, participants viewed faces that varied in attractiveness under three conditions: (i) suppressed from awareness via continuous flash suppression (CFS), (ii) clearly visible without suppression, or (iii) absent (background only) with CFS. Participants reported trial-wise facial identity (objective) and visibility (subjective) measures of facial awareness. In a post-fMRI session, they rated the attractiveness of each face as a measure of SV. When faces were seen, task performance (d) exceeded chance and responses were faster than in absent trials. When faces were unseen, performance was at chance but responses were slower than absent trials. Neurally, seen and unseen faces elicited greater neural signal than absent trials and showed similar neural patterns in the fusiform face area (FFA). Critically, neural signal in vmPFC correlated with SV for both seen and unseen faces, with similar neural patterns. In aVS, SV-related signal was only observed for unseen faces. Furthermore, mean face-related signal in FFA correlated with SV-related signals in aVS and vmPFC for unseen faces. These findings demonstrate that SV can be automatically computed in aVS and vmPFC without conscious awareness, suggesting a neural pathway by which subliminal information can influence value-based choice.

neuroscience↗

A structural MRI marker predicts individual differences in impulsivity and classifies patients with behavioral-variant frontotemporal dementia from matched controls

Impulsive decision-making is a symptom across many neuropsychiatric and neurological disorders. Yet, it is still unknown whether impulsivity can be predicted based on individual differences in brain structure. Here, we used machine-learning to develop a structural MRI signature of impulsivity, tested its validity in several independent samples, and assessed its diagnostic value in patients with behavioral variant frontotemporal dementia (bvFTD)--a neurodegenerative disease characterized by high impulsivity. The resulting whole-brain grey matter density pattern--the Structural Impulsivity Signature (SIS)--showed a good prediction-outcome correlation of r=0.35 (out-of-sample, p=0.0028) in the training sample of healthy adults (N=117) and significantly predicted individual differences in impulsivity in four other, independent studies (total N=626), including healthy and clinical participants with neuropsychiatric and neurological conditions. Further, the SIS separated bvFTD patients from controls with high accuracy (81% correct, p=0.002) and predicted impulsivity-related symptom severity among patients. The spatial distribution of weights in the SIS brain pattern highlights the key role of brain regions associated with affective processing in impulsivity. Together, these results provide evidence for a new structural neuromarker of individual differences in impulsivity that can be easily applied to new samples. Future studies can further assess its predictive value towards better prevention, diagnosis and treatment of mental disorders associated with impulsivity symptoms.

neuroscience↗

Neural and behavioral signatures of the multidimensionality of manipulable object processing.

Understanding how we recognize everyday objects requires unravelling the variables that govern the way we think about objects and the way in which our representations are organized neurally. A major hypothesis is that the organization of object knowledge follows key object-related dimensions, analogously to how sensory information is organized in the brain. Here, we explored, behaviorally and neurally, the multidimensionality of object processing. We focused on within-domain object information as a proxy for the kinds of object decision tasks we typically engage in our daily lives - e.g., identifying a knife from other types of manipulable objects such as spoons, axes or screwdrivers. To do so, we extracted object-related dimensions from subjective human judgments on a set of objects from a particular object domain - i.e., manipulable objects. We demonstrated that the extracted dimensions are cognitively interpretable - i.e., participants are able to label them; are cognitively relevant for manipulable object processing - i.e., categorization decisions are guided by these dimensions; and are important for the neural organization of knowledge - i.e., they are good predictors of the neural signals elicited by manipulable objects. This shows that multidimensionality is a hallmark of the organization of object knowledge in the brain.

neuroscience↗