Effective cognitive performance often requires the allocation of additional neural resources (i.e. blood-oxygen-level-dependent [BOLD] activation) as task demands increase, and this demand-related modulation is affected by amyloid-beta deposition and normal aging. The present study investigated these complex relationships between amyloid, modulation, and cognitive function (i.e. fluid ability). Participants from the Dallas Lifespan Brain Study (DLBS, n = 252, ages 50-89) completed a semantic judgment task during functional magnetic resonance imaging (fMRI) where the judgments differed in classification difficulty. Amyloid burden was assessed via positron emission tomography (PET) using 18F-florbetapir. A quadratic relationship between amyloid standardized value uptake ratios (SUVRs) and BOLD modulation was observed such that modulation was weaker in those with moderately elevated SUVRs (e.g. just reaching amyloid-positivity), whereas those with very high SUVRs (e.g. SUVR > 1.5) showed strong modulation. Greater modulation was related to better fluid ability, and this relationship was strongest in younger participants and those with lower amyloid burden. These results support the theory that effective demand-related modulation contributes to healthy cognitive aging, especially in the transition from middle age to older adulthood, whereas high modulation may be dysfunctional in those with substantial amyloid deposition.

Multiple studies have explored the factor structure of working memory (WM) tasks, yet few have done so controlling for both the domain and category of the memory items in a single study. In the current pre-registered study, we conducted a large-scale latent variable analysis using variant forms of n-back and backward recall tasks to test whether they measured a single underlying construct, or were distinguished by stimuli-, domain-, or paradigm-specific factors. Exploratory analyses investigated how the resulting WM factor(s) were linked to fluid intelligence. Participants (N = 703) completed a fluid reasoning test and multiple n-back and backward recall tasks containing memoranda that varied across (spatial or verbal material) and within (verbal digits or letters) domain, allowing the variance specific to task content and paradigm to be assessed. Two distinct but related backward recall and n-back constructs best captured the data, in comparison to other plausible model constructions (single WM factor, two-factor domain, and three-factor materials models). Common variance associated with WM was a stronger predictor of fluid reasoning than a residual n-back factor, but the backward recall factor predicted fluid reasoning as strongly as the common WM factor. These data emphasise the distinctiveness between backward recall and n-back tasks.

The quantification of cognitive powers rests on identifying a behavioural task that depends on them. Such dependence cannot be assured, for the powers a task invokes cannot be experimentally controlled or constrained a priori, resulting in unknown vulnerability to failure of specificity and generalisability. Evaluating a compact version of Raven\'s Advanced Progressive Matrices (RAPM), a widely used clinical test of fluid intelligence, we show that LaMa, a self-supervised artificial neural network trained solely on the completion of partially masked images of natural environmental scenes, achieves representative human-level test scores a prima vista, without any task-specific inductive bias or training. Compared with cohorts of healthy and focally lesioned participants, LaMa exhibits human-like variation with item difficulty, and produces errors characteristic of right frontal lobe damage under degradation of its ability to integrate global spatial patterns. LaMa\'s narrow training and limited capacity suggest matrix-style tests may be open to computationally simple solutions that need not necessarily invoke the substrates of reasoning.

The first therapeutical goal followed by neurooncological surgeons dealing with prefrontal gliomas is attempting supramarginal tumor resection preserving relevant neurological function. Therefore, advanced knowledge of the frontal aslant tract (FAT) functional neuroanatomy in high-order cognitive domains beyond language and speech processing would help refine neurosurgeries, predicting possible relevant cognitive adverse events and maximizing the surgical efficacy. To this aim we performed the recently developed correlational tractography analyses to evaluate the possible relationship between FAT\'s microstructural properties and cognitive functions in 27 healthy subjects having ultra-high-field (7-Tesla) diffusion MRI. We independently assessed FAT segments innervating the dorsolateral prefrontal cortices (dlPFC-FAT) and the supplementary motor area (SMA-FAT). FAT microstructural robustness, measured by the tract\'s quantitative anisotropy (QA), was associated with a better performance in episodic memory, visuospatial orientation, cognitive processing speed and fluid intelligence but not sustained selective attention tests. Overall, the percentual tract volume showing an association between QA-index and improved cognitive scores (pQACV) was higher in the SMA-FAT compared to the dlPFC-FAT segment. This effect was right-lateralized for verbal episodic memory and fluid intelligence and bilateralized for visuospatial orientation and cognitive processing speed. Our results provide novel evidence for a functional specialization of the FAT beyond the known in language and speech processing, particularly its involvement in several higher-order cognitive domains. In light of these findings, further research should be encouraged to focus on neurocognitive deficits and their impact on patient outcomes after FAT damage, especially in the context of glioma surgery.

The timing of structural changes in executive functions (EFs) across development is a matter of controversy; whereas some studies suggest a uniform structure of EFs in early childhood, findings in middle and late childhood are mixed. There are results indicating uniformity of EFs as well as several studies suggesting multidimensionality of the construct. In addition, studies demonstrate an age-related differentiation of the relation between EFs and intelligence. We conducted a comparative analysis of the EF structure and relations with fluid intelligence in two distinct age groups. A sample of n = 145 preschool children (5.2-6.7 years of age) and n = 109 elementary school children (8.8-11.8 years) completed measures of working memory, inhibition, cognitive flexibility, and fluid intelligence. Confirmatory factor analysis (CFA) revealed that a single-factor model best represented performance on EF tasks in both preschool and elementary school children. Multi-group CFA indicated equivalent and strong relations between EFs and intelligence across both age groups (r = .64 in preschool and elementary school children). Our results confirm that EFs are significantly related to fluid intelligence but might not underlie a uniform pattern of successive differentiation into multiple EF components in childhood. We discuss how methodological artifacts such as simultaneous interference might have contributed to previous findings on differentiation in middle and late childhood.

UNASSIGNED: The aim of this study was to develop a predictive model for uncorrected/actual fluid intelligence scores in 9-10 year old children using magnetic resonance T1-weighted imaging. Explore the predictive performance of an autoencoder model based on reconstruction regularization for fluid intelligence in adolescents.
UNASSIGNED: We collected actual fluid intelligence scores and T1-weighted MRIs of 11,534 adolescents who completed baseline tasks from ABCD Data Release 3.0. A total of 148 ROIs were selected and 604 features were proposed by FreeSurfer segmentation. The training and testing sets were divided in a ratio of 7:3. To predict fluid intelligence scores, we used AE, MLP and classic machine learning models, and compared their performance on the test set. In addition, we explored their performance across gender subpopulations. Moreover, we evaluated the importance of features using the SHapley Additive Explain method. Results: The proposed model achieves optimal performance on the test set for predicting actual fluid intelligence scores (PCC = 0.209 ± 0.02, MSE = 105.212 ± 2.53). Results show that autoencoders with refactoring regularization are significantly more effective than MLPs and classical machine learning models. In addition, all models performed better on female adolescents than on male adolescents. Further analysis of relevant characteristics in different populations revealed that this may be related to gender differences in underlying fluid intelligence mechanisms.
UNASSIGNED: We construct a weak but stable correlation between brain structural features and raw fluid intelligence using autoencoders. Future research may need to explore ensemble regression strategies utilizing multiple machine learning algorithms on multimodal data in order to improve the predictive performance of fluid intelligence based on neuroimaging features.

R4Alz is utilized for the early detection of minor neurocognitive disorders. It was designed to assess three main dimensions of cognitive-control abilities: working-memory capacity, attentional control, and executive functioning.
OBJECTIVE: To reveal the cognitive-control dimensions that can differentiate between adults and older adults with healthy cognition, people with subjective cognitive impairment, and people diagnosed with mild cognitive impairment by examining the factorial structure of the R4Alz tool.
METHODS: The study comprised 404 participants: (a) healthy adults (n = 192), (b) healthy older adults (n = 29), (c) people with SCI (n = 74), and (d) people diagnosed with MCI (n = 109). The R4Alz battery was administered to all participants, including tests that assess short-term memory storage, information processing, information updating in working memory, and selective, sustained and divided attention), task/rule-switching, inhibitory control, and cognitive flexibility.
RESULTS: A two-factorial structural model was confirmed for R4Alz, with the first factor representing \"fluid intelligence (FI)\" and the second factor reflecting \"executive functions (EF)\". Both FI and EFs discriminate among all groups.
CONCLUSIONS: The R4Alz battery presents sound construct validity, evaluating abilities in FI and EF. Both abilities can differentiate very early cognitive impairment (SCI) from healthy cognitive aging and MCI.

This study aims to reveal the association between sleep quality and crystallized intelligence (Gc), fluid intelligence (Gf), and the underlying brain structural basis. Using the data from the Human Connectome Project (N = 1087), we performed mediation analysis to explore whether regional brain structure related to sleep quality mediate the association between sleep quality and intellectual abilities, and further examined whether socioeconomic status (i.e., income and education level) moderate the mediation effect. Results showed that poorer sleep quality was associated with lower Gc rather than Gf, and worse sleep quality was associated with smaller volume and surface area in temporal lobe, including inferior temporal gyrus and middle temporal gyrus. Notably, temporal lobe structures mediated the association between sleep quality and Gc rather than Gf. Furthermore, socioeconomic status (i.e., income and education level) moderated the mediating effect, showing low socioeconomic status has a more significant mediating effect with stronger association between sleep quality and Gc as well as stronger association between temporal lobe structure and Gc in low socioeconomic status group. These findings suggest that individuals with higher socioeconomic status are less susceptible to the effect of sleep quality on Gc.

BACKGROUND: Global brain connectivity (GBC) enables measuring brain regions\' functional connectivity strength at rest by computing the average correlation between each brain voxel\'s time-series and that of all other voxels.
METHODS: We used resting-state fMRI (rs-fMRI) data of young adult participants from the Human Connectome Project (HCP) dataset to explore the test-retest stability of GBC, the brain regions with higher or lower GBC, as well as the associations of this measure with age, sex, and fluid intelligence. GBC was computed by considering separately the positive and negative correlation coefficients (positive GBC and negative GBC).
RESULTS: Test-retest stability was higher for positive compared to negative GBC. Areas with higher GBC were located in the default mode network, insula, and visual areas, while regions with lower GBC were in subcortical regions, temporal cortex, and cerebellum. Higher age was related to global reduction of positive GBC. Males displayed higher positive GBC in the whole brain. Fluid intelligence was associated to increased positive GBC in fronto-parietal, occipital and temporal regions.
CONCLUSIONS: Compared to previous works, this study adopted a larger sample size and tested GBC stability using data from different rs-fMRI sessions. Moreover, these associations were examined by testing positive and negative GBC separately.
CONCLUSIONS: Lower stability for negative compared to positive GBC suggests that negative correlations may reflect less stable couplings between brain regions. Our findings indicate a greater importance of positive compared to negative GBC for the associations of functional connectivity strength with biological and neurocognitive variables.

BACKGROUND: Mild Cognitive Impairment (MCI) is a preclinical condition between healthy and pathological aging, which is characterized by impairments in executive functions (EFs), including cognitive flexibility. According to Diamond\'s model, cognitive flexibility is a core executive function, along with working memory and inhibition, but it requires the development of these last EFs to reach its full potential. In this model, planning and fluid intelligence are considered higher-level EFs. Given their central role in enabling individuals to adapt their daily life behavior efficiently, the goal is to gain valuable insight into the functionality of cognitive flexibility in a preclinical form of cognitive decline. This study aims to investigate the role of cognitive flexibility and its components, set-shifting and switching, in MCI. The hypotheses are as follows: (I) healthy participants are expected to perform better than those with MCI on cognitive flexibility and higher-level EFs tasks, taking into account the mediating role of global cognitive functioning; (II) cognitive flexibility can predict performance on higher-level EFs (i.e., planning and fluid intelligence) tasks differently in healthy individuals and those diagnosed with MCI.
METHODS: Ninety participants were selected and divided into a healthy control group (N = 45; mean age 64.1 ± 6.80; 66.6% female) and an MCI group (N = 45; mean age 65.2 ± 8.14; 40% female). Cognitive flexibility, fluid intelligence, planning, and global cognitive functioning of all participants were assessed using standardized tasks.
RESULTS: Results indicated that individuals with MCI showed greater impairment in global cognitive functioning and EFs performance. Furthermore, the study confirms the predictive role of cognitive flexibility for higher EFs in individuals with MCI and only partially in healthy older adults.