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- Vascular cognitive disorder (VCD) – Paranoid hallucinatory syndrome – Late-onset trauma-related symptoms – Depression – Assertive community treatment (ACT) (1)
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Spontaneous brain activity builds the foundation for human cognitive processing during external demands. Neuroimaging studies based on functional magnetic resonance imaging (fMRI) identified specific characteristics of spontaneous (intrinsic) brain dynamics to be associated with individual differences in general cognitive ability, i.e., intelligence. However, fMRI research is inherently limited by low temporal resolution, thus, preventing conclusions about neural fluctuations within the range of milliseconds. Here, we used resting-state electroencephalographical (EEG) recordings from 144 healthy adults to test whether individual differences in intelligence (Raven’s Advanced Progressive Matrices scores) can be predicted from the complexity of temporally highly resolved intrinsic brain signals. We compared different operationalizations of brain signal complexity (multiscale entropy, Shannon entropy, Fuzzy entropy, and specific characteristics of microstates) regarding their relation to intelligence. The results indicate that associations between brain signal complexity measures and intelligence are of small effect sizes (r ∼ 0.20) and vary across different spatial and temporal scales. Specifically, higher intelligence scores were associated with lower complexity in local aspects of neural processing, and less activity in task-negative brain regions belonging to the default-mode network. Finally, we combined multiple measures of brain signal complexity to show that individual intelligence scores can be significantly predicted with a multimodal model within the sample (10-fold cross-validation) as well as in an independent sample (external replication, N = 57). In sum, our results highlight the temporal and spatial dependency of associations between intelligence and intrinsic brain dynamics, proposing multimodal approaches as promising means for future neuroscientific research on complex human traits.
Behavioral and psychological syndromes such as depression and psychosis often occur along with cognitive (esp. executive) deficits in vascular cognitive disorder (VCD) in the elderly. We present the case of an 85-year-old woman with deficits in executive functions as well as a persistent and clearly circumscribed paranoid hallucinatory syndrome (most probably due to VCD) which could not be adequately treated with antipsychotic medication. The patient also suffered from severe depression (independent of psychotic symptoms). Both psychosis and depression were successfully managed in a home treatment based on Flexible Assertive Community Treatment (FACT). Interestingly, a thematic association between the delusional contents and early childhood traumata could be reconstructed, and late-onset trauma-related symptoms could be successfully treated with cognitive-behavioral therapy (CBT) as well. In sum, behavioral management of psychotic syndromes is possible in the absence of adequate pharmacological treatment options, and multiprofessional and person-centered home treatment may be successful in the elderly, even in severe and complex disorders.
Recent findings indicate that visual feedback derived from episodic memory can be traced down to the earliest stages of visual processing, whereas feedback stemming from schema-related memories only reach intermediate levels in the visual processing hierarchy. In this opinion piece, we examine these differences in light of the 'what' and 'where' streams of visual perception. We build upon this new framework to propose that the memory deficits observed in aphantasics might be better understood as a difference in high-level feedback processing along the ‘what’ stream, rather than an episodic memory impairment.
Human functional brain connectivity can be temporally decomposed into states of high and low cofluctuation, defined as coactivation of brain regions over time. Rare states of particularly high cofluctuation have been shown to reflect fundamentals of intrinsic functional network architecture and to be highly subject-specific. However, it is unclear whether such network-defining states also contribute to individual variations in cognitive abilities – which strongly rely on the interactions among distributed brain regions. By introducing CMEP, a new eigenvector-based prediction framework, we show that as few as 16 temporally separated time frames (< 1.5% of 10min resting-state fMRI) can significantly predict individual differences in intelligence (N = 263, p < .001). Against previous expectations, individual’s network-defining time frames of particularly high cofluctuation do not predict intelligence. Multiple functional brain networks contribute to the prediction, and all results replicate in an independent sample (N = 831). Our results suggest that although fundamentals of person-specific functional connectomes can be derived from few time frames of highest connectivity, temporally distributed information is necessary to extract information about cognitive abilities. This information is not restricted to specific connectivity states, like network-defining high-cofluctuation states, but rather reflected across the entire length of the brain connectivity time series.