The Parieto-Frontal Integration Theory (P-FIT) of intelligence: converging neuroimaging evidence

Functional divergence between the two cerebral hemispheres contributes to human fluid intelligence

Hemispheric lateralization is linked to potential cognitive advantages. It is considered a driving force behind the generation of human intelligence. However, establishing quantitative links between the degree of lateralization and intelligence in humans remains elusive. In this study, we propose a framework that utilizes the functional aligned multidimensional representation space derived from hemispheric functional gradients to compute between-hemisphere distances within this space. Applying this framework to a large cohort (N = 777), we identified high functional divergence across the two hemispheres within the frontoparietal network. We found that both global divergence between the cerebral hemispheres and regional divergence within the multiple demand network were positively associated with fluid composite score and partially mediated the relationship between brain size and individual differences in fluid intelligence. Together, these findings deepen our understanding of hemispheric lateralization as a fundamental organizational principle of the human brain, providing empirical evidence for its role in supporting fluid intelligence.

Intelligence, brain structure, dendrites, and genes: Genetic, epigenetic and the underlying of the quadruple helix complexity

Intelligence can be referred to as the mental ability to learn, comprehend abstract concepts, and solve complex problems. Twin and adoption studies have provided insights into the influence of the familial environment and highlighted the importance of heritability in the development of cognition. Detecting the relative contribution of brain areas, neuronal structures, and connectomes has brought some understanding on how various brain areas, white/gray matter structures and neuronal connectivity process information and contribute to intelligence. Using histological, anatomical, electrophysiological, neuropsychological, neuro-imaging and molecular biology methods, several key concepts have emerged: 1) the parietofrontal-hippocampal integrations probably constitute a substrate for smart behavior, 2) neuronal activity results in structural plasticity of dendritic branches responsible for information transfer, critical for learning and memory, 3) intelligent people process information efficiently, 4) the environment triggers mnemonic epigenomic programs (via dynamic regulation of chromatin accessibility, DNA methylation, loop interruption/formation and histone modification) conferring cognitive phenotypes throughout life, and 5) single/double DNA breaks are prominent in human brain disorders associated with cognitive impairment including Alzheimer's disease and schizophrenia. Along with these observations, molecular/cellular/biological studies have identified sets of specific genes associated with higher scores on intelligence tests. Interestingly, many of these genes are associated with dendritogenesis. Because dendrite structure/function is involved in cognition, the control of dendrite genesis/maintenance may be critical for understanding the landscape of general/specific cognitive ability and new pathways for therapeutic approaches.

To enhance or not to enhance: A debate about cognitive enhancement from a psychological and neuroscientific perspective

The enhancement of humans' core cognitive abilities-such as intelligence-is a frequently debated topic in scientific and public discourse. Different enhancement methods such as cognitive trainings, smart drugs, and brain stimulation techniques have been proposed and tested to enhance human's cognition. In this narrative review, we summarize the main psychological and neuroscientific findings regarding those cognitive enhancement methods. We thereby distinguish passive (e.g., smart drugs) and active enhancement methods (e.g., working memory training)-which require different levels of agency. While for both forms of enhancement there is no (or only little) empirical evidence on their effectiveness to improve overall cognitive abilities, passive methods entail severe risks. Thus, we criticize promoting an overly optimistic view on especially passive enhancement. Furthermore, we highlight which individuals might be willing to enhance themselves, related motivational aspects of cognitive enhancement, and ethical considerations thereof. To raise awareness for the (in)effectiveness and risks of passive and active enhancement, we propose a category framework that distinguishes cognitive enhancement from clinical methods to treat disorders or diagnosed deficits and introduces important dimensions thereof. Finally, we present open questions for psychological and neuroscientific research which should become part of enhancement debates. Taken together, our narrative review provides a broad overview and critical assessment of enhancement-related topics such as effectiveness and risks of enhancement methods, motivational aspects to apply enhancement, and societal implications of cognitive enhancement.

A right frontal network for analogical and deductive reasoning

Two of the most well-studied types of reasoning are analogical reasoning (AR) and deductive reasoning (DR). Yet, our understanding of the relationship between reasoning abilities and their neuroanatomical basis remains surprisingly limited. We aimed to conduct fine-grained anatomical mapping of performance on tests of AR, DR and fluid intelligence (Gf), in a large sample of patients with unilateral focal frontal or posterior lesions and healthy controls. We assessed 247 prospectively recruited patients using two new tests: the Analogical Reasoning Test (ART) and the Deductive Reasoning Test (DRT); and the best-established measure of Gf: Raven's Advanced Progressive Matrices (RAPM). Non-parametric Bayesian stochastic block modelling was used to reveal the community structure of lesion deficit networks, disentangling functional from confounding pathological distributed effects. ART and DRT performance was significantly impaired in patients with frontal lesions [ART: F(2,238) = 18.93; P < 0.001; Frontal group worse than Posterior group and healthy controls, both P < 0.001; DRT: F(2,387) = 18.491; P < 0.001; Frontal group worse than healthy controls, P < 0.01]. Right frontal effects were evident on both tests. Thus, on the ART, right frontal patients were more impaired than left (P < 0.05). On the DRT, right frontal patients were more impaired than left frontal patients on questions with indeterminate solutions (P < 0.05) but not on questions with determinate ones. Non-parametric Bayesian stochastic block modelling implicated a right frontal network in ART and DRT performance. Strikingly, we found that this network was also implicated in performance on RAPM. Our study represents the most robust investigation of AR and DR in the focally injured brain. Our findings imply that a right frontal network is critical. The ART and DRT appear to be promising new clinical tests, capable of evaluating reasoning abilities and identifying right frontal lobe dysfunction.

Thinking as Analogy-Making: Toward a Neural Process Account of General Intelligence

What is the secret of human intelligence? A key discovery in psychology is that performance correlations across diverse cognitive tasks are explained by a few broad abilities and one overarching general factor, which is also predictive of real-life achievements. Whether these factors correspond to biological processes is a century-old debate. While previous research focused on localizing their correlates in brain structure, connectivity, and activation levels, the mechanisms of neural information processing related to intelligence are still unexplored. I outline a new approach integrating psychometrics with neuroscientific advances in identifying the computations underlying single tasks from their representational geometry to provide a novel perspective on this topic. In particular, I propose a neural process account of the general factor that builds on the central role of structure mapping-the process of abstracting and reasoning based on relational knowledge-in human cognition. Neural coding properties in the hippocampal and prefrontal-parietal systems that enable inferential leaps through structural abstraction might contribute to the general factor. In general, integrating neuro-representational and psychometric research has the potential to uncover core principles of natural intelligence.

Intrinsic brain mapping of cognitive abilities: A multiple-dataset study on intelligence and its components

This study investigates how functional brain network features contribute to general intelligence and its cognitive components by analyzing three independent cohorts of healthy participants. Cognitive scores were derived from 1) the Wechsler Adult Intelligence Scale (WAIS-IV), 2) the Raven Standard Progressive Matrices (RPM), and 3) the NIH and Penn cognitive batteries from the Human Connectome Project. Factor analysis on the NIH and Penn cognitive batteries yielded latent variables that closely resembled the content of the WAIS-IV indices and RPM. We employed graph theory and a multi-resolution network analysis by varying the modularity parameter (γ) to investigate hierarchical brain-behavior relationships across different scales of brain organization. Brain-behavior associations were quantified using multi-level robust regression analyses to accommodate variability and confounds at the subject-level, node-level, and resolution-level. Our findings reveal consistent brain-behavior relationships across the datasets. Nodal efficiency in fronto-parietal sensorimotor regions consistently played a pivotal role in fluid reasoning, whereas efficiency in visual networks was linked to executive functions and memory. A broad, low-resolution 'task-positive' network emerged as predictive of full-scale IQ scores, indicating a hierarchical brain-behavior coding. Conversely, increased cross-network connections involving default mode and subcortical-limbic networks were associated with reductions in both general and specific cognitive performance. These outcomes highlight the relevance of network efficiency and integration, as well as of the hierarchical organization in supporting specific aspects of intelligence, while recognizing the inherent complexity of these relationships. Our multi-resolution network approach offers new insights into the interplay between multilayer network properties and the structure of cognitive abilities, advancing the understanding of the neural substrates of the intelligence construct.

Parsing the heterogeneity of brain structure and function in male children with autism spectrum disorder: a multimodal MRI study

Autism spectrum disorder (ASD) is a neurodevelopmental condition with high structural and functional heterogeneity. Multimodal fusion of structural and functional magnetic resonance imaging (MRI) allows better integration of ASD features from multiple perspectives. This study aimed to uncover the potential ASD subtypes by fusing the features of brain structure and function. An unsupervised learning method, similarity network fusion (SNF), was used. Resting-state functional MRI and structural MRI from the Autism Brain Imaging Data Exchange database of 207 male children were included in this study (105 ASD; 102 healthy controls (HC)). Gray matter volume (GMV) and amplitude of low-frequency fluctuation (ALFF) were utilized to represent structural and functional features separately. Structural and functional distance networks were constructed and fused by SNF. Then spectral clustering was carried out on the fused network. At last, the multivariate support vector regression analysis was used to investigate the relationship between the multimodal alterations and symptom severity of ASD subtypes. Two ASD subtypes were identified. Compared to HC, the two ASD subtypes demonstrated opposite GMV changes and distinct ALFF alterations. Furthermore, the alterations of ALFF predicted the severity of social communication impairments in ASD subtype 1. However, no significant associations were found between the multimodal alterations and symptoms in ASD subtype 2. These findings demonstrate the existence of heterogeneity with distinct structural and functional patterns in ASD and highlight the crucial role of combining multimodal features in investigating the neural mechanism underlying ASD.

Computational memory capacity predicts aging and cognitive decline

Memory is a crucial cognitive function that deteriorates with age. However, this ability is normally assessed using cognitive tests instead of the architecture of brain networks. Here, we use reservoir computing, a recurrent neural network computing paradigm, to assess the linear memory capacities of neural-network reservoirs extracted from brain anatomical connectivity data in a lifespan cohort of 636 individuals. The computational memory capacity emerges as a robust marker of aging, being associated with resting-state functional activity, white matter integrity, locus coeruleus signal intensity, and cognitive performance. We replicate our findings in an independent cohort of 154 young and 72 old individuals. By linking the computational memory capacity of the brain network with cognition, brain function and integrity, our findings open new pathways to employ reservoir computing to investigate aging and age-related disorders.

Study on Intermittent Theta Burst Stimulation Improves Expression Function and Mechanism in Patients With Aphasia After Stroke

OBJECTIVE

To explore the effects of Intermittent Theta Burst Stimulation (iTBS) on the posterior inferior frontal gyrus of the left hemisphere on the expression function of patients with aphasia after stroke, and to explore the specific mechanism of fractional amplitude of low-frequency fluctuation (fALFF) analysis and degree centrality (DC) analysis of resting-state functional MRI.

METHODS

According to the inclusion and exclusion criteria, 40 patients with poststroke aphasia were randomized into a treatment group (iTBS group) and a control group (S-iTBS group). Patients in the iTBS group received iTBS +speech training, and patients in the S-iTBS group received sham iTBS + speech training. The Western aphasia test (Chinese version) was used to assess spontaneous language, naming, retelling, and aphasia quotient before and after treatment; resting-state fMRI scans were performed before and after treatment, and the scanned image data were analyzed to explore specific activated or suppressed brain regions.

RESULTS

Compared with before and after treatment, the scores of spontaneous language, naming, retelling, and aphasia quotient of the patients in iTBS group improved significantly, and the spontaneous language, naming, retelling, and aphasia quotient of the patients in S-iTBS group also improved. After the treatment, the scores of naming, retelling and aphasia quotient of the patients in the iTBS group improved significantly compared with that of the patients in the S-iTBS group. The resting-state fMRI results of the 2 groups before and after treatment were fALFF analysis found that the fALFF value increased in multiple brain regions in the left frontal and temporal lobes of the patients in iTBS group. Meanwhile, DC analysis also found increased DC values in multiple frontotemporal brain regions of the left hemisphere of patients in the iTBS group, indicating that the improved activation of the above brain regions of the patients in the iTBS group was significantly compared with that of the patients in the S-iTBS group.

CONCLUSIONS

iTBS combined with conventional speech training significantly improved the expression function of patients with aphasia after stroke. After iTBS action on the left hemisphere, increased activation of multiple brain regions in the left hemisphere may be one of the important mechanisms by which iTBS improves expression function in poststroke aphasia patients.

Differences in Anatomical Structures and Resting-State Brain Networks Between Elite Wrestlers and Handball Athletes

BACKGROUND/OBJECTIVES

Advancements in biomedical imaging technologies over the past few decades have made it increasingly possible to measure the long-term effects of exercise on the central nervous system. This study aims to compare the brain morphology and functional connectivity of wrestlers and handball players, exploring sport-specific neural adaptations.

METHODS

Here, we examined 26 elite male athletes (13 wrestlers and 13 handball players) using anatomical and resting-state functional magnetic resonance imaging (fMRI) measurements. Connectivity maps are derived using the seed-based correlation analysis of resting-state fMRI, while voxel-based morphometry (VBM) is employed to identify anatomical differences. Additionally, the cortical thickness and global volumetric values of the segmented images are examined to determine the distinctions between elite wrestlers and handball players using non-parametric statistical tests.

RESULTS

Wrestlers exhibited greater grey matter volume (GMV) in the right middle temporal gyrus, left middle frontal gyrus, and right posterior cingulate gyrus (uncorr., p < 0.001). On the other hand, wrestlers showed increased functional connectivity in the left superior temporal gyrus, left parahippocampal gyrus, the left anterior orbital gyrus, and right superior frontal gyrus-medial frontal region (P(FWE) < 0.05). In addition, wrestlers showed greater cortical thickness in several brain regions.

CONCLUSIONS

The increased GMV, cortical thickness, and functional connectivity observed in wrestlers highlight the presence of sport-specific neural adaptations. While this research provides valuable insights into the neuroplastic effects of various athletic disciplines, further studies involving additional sports and control groups are needed for a more comprehensive understanding.

Improving auditory alarm sensitivity during simulated aeronautical decision-making: the effect of transcranial direct current stimulation combined with computerized working memory training

Auditory alarm deafness is a failure to notice a salient auditory signal in a high-load context, which is one of the major causes of flight accidents. Therefore, it is of great practical significance for aviation safety to explore ways to avoid auditory alarm deafness under a high-load scenario. One potential reason for its occurrence could be the fact that cognitive resources are limited. Working memory (WM) capacity is important for the availability of cognitive resources. The present study investigated the effects of different types of WM ability and transcranial direct current stimulation (tDCS) combined with WM training on auditory alarm sensitivity in a simulated high-load aeronautical decision-making task in two experiments, with participants who were not trained pilots. The results showed that different types of WM storage capacity did not predict auditory alarm deafness. However, individuals with high executive function of WM were more sensitive to the auditory alarm than those with low executive function. During WM training, tDCS over the right dorsolateral prefrontal cortex not only improved WM executive function but also improved auditory alarm sensitivity under high-load conditions. These findings suggest that the storage and executive function of WM have different roles in auditory alarm sensitivity. WM training based on brain stimulation technology can provide empirical evidence for the enhancement of auditory alarm alertness and cognitive function in the human-machine context.

Individualized prediction of multi-domain intelligence quotient in bipolar disorder patients using resting-state functional connectivity

BACKGROUND

Although accumulating studies have explored the neural underpinnings of intelligence quotient (IQ) in patients with bipolar disorder (BD), these studies utilized a classification/comparison scheme that emphasized differences between BD and healthy controls at a group level. The present study aimed to infer BD patients' IQ scores at the individual level using a prediction model.

METHODS

We applied a cross-validated Connectome-based Predictive Modeling (CPM) framework using resting-state fMRI functional connectivity (FCs) to predict BD patients' IQ scores, including verbal IQ (VIQ), performance IQ (PIQ), and full-scale IQ (FSIQ). For each IQ domain, we selected the FCs that contributed to the predictions and described their distribution across eight widely-recognized functional networks. Moreover, we further explored the overlapping patterns of the contributed FCs for different IQ domains.

RESULTS

The CPM achieved statistically significant prediction performance for three IQ domains in BD patients. Regarding the contributed FCs, we observed a widespread distribution of internetwork FCs across somatomotor, visual, dorsal attention, and ventral attention networks, demonstrating their correspondence with aberrant FCs correlated to cognition deficits in BD patients. A convergent pattern in terms of contributed FCs for different IQ domains was observed, as evidenced by the shared-FCs with a leftward hemispheric dominance.

CONCLUSIONS

The present study preliminarily explored the feasibility of inferring individual IQ scores in BD patients using the FCs-based CPM framework. It is a step toward the development of applicable techniques for quantitative and objective cognitive assessment in BD patients and contributes novel insights into understanding the complex neural mechanisms underlying different IQ domains.

Virtues as protective factors for adolescent mental health

This paper explores the decline in adolescent mental health and the weakening of traditional moral frameworks, positing education in the virtues as protective of mental health due to the intrinsic link between moral/existential wellbeing and psychological health. By integrating character education into school curricula, a continuous "dosage" of moral guidance may be an optimal way to ensure a gradual and ever-clearer articulation of a life worth living and how to live well. The paper critiques popular clinical and positive psychological approaches to promoting wellbeing, which often miss the existential and moral dimensions of adolescent growth. The conclusion emphasizes the need for integrating moral education into mental health interventions to address the comprehensive existential and moral dimensions of adolescent development. This paper advocates for a proactive character developmental model that nurtures moral and existential growth, recognizing challenges with virtue and meta-virtue development as integral to personal and moral evolution, and enhancing the moral and psychological fortitude of adolescents.

Understanding the Link Between Functional Profiles and Intelligence Through Dimensionality Reduction and Graph Analysis

There is a growing interest in neuroscience for how individual-specific structural and functional features of the cortex relate to cognitive traits. This work builds on previous research which, by using classical high-dimensional approaches, has proven that the interindividual variability of functional connectivity (FC) profiles reflects differences in fluid intelligence. To provide an additional perspective into this relationship, the present study uses a recent framework for investigating cortical organization: functional gradients. This approach places local connectivity profiles within a common low-dimensional space whose axes are functionally interpretable dimensions. Specifically, this study uses a data-driven approach to model the association between FC variability and interindividual differences in intelligence. For one of these loci, in the right ventral-lateral prefrontal cortex (vlPFC), we describe an association between fluid intelligence and the relative functional distance of this area from sensory and high-cognition systems. Furthermore, the topological properties of this region indicate that, with decreasing functional affinity with high-cognition systems, vlPFC functional connections are more evenly distributed across all networks. Participating in multiple functional networks may reflect a better ability to coordinate sensory and high-order cognitive systems.

Temporal Interference Stimulation Boosts Working Memory Performance in the Frontoparietal Network

Temporal interference (TI) stimulation is a novel neuromodulation technique that overcomes the depth limitations of traditional transcranial electrical stimulation while avoiding the invasiveness of deep brain stimulation. Our previous behavioral research has demonstrated the effects of multi-target TI stimulation in enhancing working memory (WM) performance, however, the neural mechanisms of this special form of envelope modulation remain unclear. To address this issue, here we designed this randomized, double-blind, crossover study, which consisted of a task-based functional magnetic resonance imaging (fMRI) experiment, to explore how offline TI stimulation modulated brain activity and behavioral performance in healthy adults. We conducted a 2 × 2 within-subjects design with two factors: stimulation (TI vs. Sham) and time (pre vs. post). Participants received two stimulation protocols in a random order: TI (beat frequency: 6 Hz, targeting middle frontal gyrus [MFG] and inferior parietal lobule [IPL]) and sham stimulation. Neuroimaging data of a WM task with different cognitive loads were acquisited immediately before and after stimulation. We found TI stimulation significantly improved d' in the high-demand WM task. Whole-brain analysis showed the significant time-by-stimulation interactions in two main clusters in IPL and precuneus with lower activation after TI stimulation. The generalized psychophysiological interaction (gPPI) analysis revealed a significant interaction in task-modulated connectivity between MFG and IPL, with improvement observed after TI stimulation. Notably, this increasing functional connectivity induced by TI stimulation was positively correlated with better behavioral performance. Overall, our findings show specific effects of TI stimulation on brain activation and functional connectivity in the frontoparietal network and may contribute to provide new perspectives for future neuromodulation applications.

Anchoring functional connectivity to individual sulcal morphology yields insights in a pediatric study of reasoning

A salient neuroanatomical feature of the human brain is its pronounced cortical folding, and there is mounting evidence that sulcal morphology is relevant to functional brain architecture and cognition. However, our understanding of the relationships between sulcal anatomy, brain activity, and behavior is still in its infancy. We previously found the depth of three small, shallow sulci in lateral prefrontal cortex (LPFC) was linked to reasoning performance in childhood and adolescence (Voorhies et al., 2021). These findings beg the question: what is the linking mechanism between sulcal morphology and cognition? To shed light on this question, we investigated functional connectivity among sulci in LPFC and lateral parietal cortex (LPC). We leveraged manual parcellations (21 sulci/hemisphere, total of 1806) and functional magnetic resonance (fMRI) data from a reasoning task from 43 participants aged 7-18 years (20 female). We conducted clustering and classification analyses of individual-level functional connectivity among sulci. Broadly, we found that 1) the connectivity patterns of individual sulci could be differentiated - and more accurately than rotated sulcal labels equated for size and shape; 2) sulcal connectivity did not consistently correspond with that of probabilistic labels or large-scale networks; 3) sulci clustered together into groups with similar patterns, not dictated by spatial proximity; and 4) across individuals, greater depth was associated with higher network centrality for several sulci under investigation. These results highlight that functional connectivity can be meaningfully anchored to individual sulcal anatomy, and demonstrate that functional network centrality can vary as a function of sulcal depth.

Relational Integration Training Modulated the Frontoparietal Network for Fluid Intelligence: An EEG Microstates Study

Relational integration is a key subcomponent of working memory and a strong predictor of fluid intelligence. Both relational integration and fluid intelligence share a common neural foundation, particularly involving the frontoparietal network. This study utilized a randomized controlled experiment to examine the effect of relational integration training on brain networks using electroencephalogram (EEG) and microstate analysis. Participants were randomly assigned to either a relational integration training group (n = 29) or an active control group (n = 28) for one month. The Sandia matrices task assessed fluid intelligence, while rest-EEG was recorded during pre- and post-tests. Microstate analysis revealed that, for microstate D, the training group demonstrated a significant increase in occurrence and contribution following the intervention compared to the control group. Additionally, microstate D occurrence was negatively correlated with reaction times (RTs). Post-training, the training group showed a lower occurrence and contribution of microstate C compared to the control group. Regarding transfer probability, the training group exhibited a decrease between microstates A and B, and an increase between microstates C and D. In contrast, the control group showed increased transfer probability between microstates A, B, and C, and a decrease between microstate D and other microstates (B and A). These findings indicate that relational integration training influences frontoparietal networks associated with fluid intelligence. The current study suggests that relational integration training is an effective intervention for enhancing fluid intelligence.

Brain maps of general cognitive function and spatial correlations with neurobiological cortical profiles

In this paper, we attempt to answer two questions: 1) which regions of the human brain, in terms of morphometry, are most strongly related to individual differences in domain-general cognitive functioning (g)? and 2) what are the underlying neurobiological properties of those regions? We meta-analyse vertex-wise g-cortical morphometry (volume, surface area, thickness, curvature and sulcal depth) associations using data from 3 cohorts: the UK Biobank (UKB), Generation Scotland (GenScot), and the Lothian Birth Cohort 1936 (LBC1936), with the meta-analytic N = 38,379 (age range = 44 to 84 years old). These g-morphometry associations vary in magnitude and direction across the cortex (|β| range = -0.12 to 0.17 across morphometry measures) and show good cross-cohort agreement (mean spatial correlation r = 0.57, SD = 0.18). Then, to address (2), we bring together existing - and derive new - cortical maps of 33 neurobiological characteristics from multiple modalities (including neurotransmitter receptor densities, gene expression, functional connectivity, metabolism, and cytoarchitectural similarity). We discover that these 33 profiles spatially covary along four major dimensions of cortical organisation (accounting for 65.9% of the variance) and denote aspects of neurobiological scaffolding that underpin the spatial patterning of MRI-cognitive associations we observe (significant |r| range = 0.21 to 0.56). Alongside the cortical maps from these analyses, which we make openly accessible, we provide a compendium of cortex-wide and within-region spatial correlations among general and specific facets of brain cortical organisation and higher order cognitive functioning, which we hope will serve as a framework for analysing other aspects of behaviour-brain MRI associations.

Where in the brain is human intelligence?✰

We still know relatively little about how the human brain supports intelligence. I this personal view I argue that adopting the framework of neurocognitive component processes (NCP) might advance the current state of knowledge. Integration of information processing across distributed brain regions is proposed as a potential NCP, and some possible clinical implications of adopting the NCP framework are outlined.

Choosing explanation over performance: Insights from machine learning-based prediction of human intelligence from brain connectivity

A growing body of research predicts individual cognitive ability levels from brain characteristics including functional brain connectivity. The majority of this research achieves statistically significant prediction performance but provides limited insight into neurobiological processes underlying the predicted concepts. The insufficient identification of predictive brain characteristics may present an important factor critically contributing to this constraint. Here, we encourage to design predictive modeling studies with an emphasis on interpretability to enhance our conceptual understanding of human cognition. As an example, we investigated in a preregistered study which functional brain connections successfully predict general, crystallized, and fluid intelligence in a sample of 806 healthy adults (replication: N = 322). The choice of the predicted intelligence component as well as the task during which connectivity was measured proved crucial for better understanding intelligence at the neural level. Further, intelligence could be predicted not solely from one specific set of brain connections, but from various combinations of connections with system-wide locations. Such partially redundant, brain-wide functional connectivity characteristics complement intelligence-relevant connectivity of brain regions proposed by established intelligence theories. In sum, our study showcases how future prediction studies on human cognition can enhance explanatory value by prioritizing a systematic evaluation of predictive brain characteristics over maximizing prediction performance.

Individual differences in baseline eye movement indices: Examining the relationships between baseline pupil size, inhibitory control, and fixation stability

The relationship among baseline pupil size, fixation stability, and inhibitory control were examined in this study. Participants performed a baseline eye measure in which they were instructed to stare at a fixation dot on screen for 2 min. Following the baseline eye measure, participants completed an antisaccade task to measure inhibitory control ability. We found a correlation between baseline pupil size variability and inhibitory control, as well as between fixation stability and inhibitory control. We showed that participants with better inhibitory control exhibited larger variability in pupil size, and those with better fixation stability showed superior inhibitory control ability. Overall, our results indicate that there are significant correlations between inhibitory control and baseline pupil size, as well as between inhibitory control and fixation stability.

Human microbiome in post-acute COVID-19 syndrome (PACS)

The global COVID-19 pandemic, which began in 2019, is still ongoing. SARS-CoV-2, also known as the severe acute respiratory syndrome coronavirus 2, is the causative agent. Diarrhea, nausea, and vomiting are common GI symptoms observed in a significant number of COVID-19 patients. Additionally, the respiratory and GI tracts express high level of transmembrane protease serine 2 (TMPRSS2) and angiotensin-converting enzyme-2 (ACE2), making them primary sites for human microbiota and targets for SARS-CoV-2 infection. A growing body of research indicates that individuals with COVID-19 and post-acute COVID-19 syndrome (PACS) exhibit considerable alterations in their microbiome. In various human disorders, including diabetes, obesity, cancer, ulcerative colitis, Crohn's disease, and several viral infections, the microbiota play a significant immunomodulatory role. In this review, we investigate the potential therapeutic implications of the interactions between host microbiota and COVID-19. Microbiota-derived metabolites and components serve as primary mediators of microbiota-host interactions, influencing host immunity. We discuss the various mechanisms through which these metabolites or components produced by the microbiota impact the host's immune response to SARS-CoV-2 infection. Additionally, we address confounding factors in microbiome studies. Finally, we examine and discuss about a range of potential microbiota-based prophylactic measures and treatments for COVID-19 and PACS, as well as their effects on clinical outcomes and disease severity.

Inferring neurocognition using artificial intelligence on brain MRIs

Brain magnetic resonance imaging (MRI) offers a unique lens to study neuroanatomic support of human neurocognition. A core mystery is the MRI explanation of individual differences in neurocognition and its manifestation in intelligence. The past four decades have seen great advancement in studying this century-long mystery, but the sample size and population-level studies limit the explanation at the individual level. The recent rise of big data and artificial intelligence offers novel opportunities. Yet, data sources, harmonization, study design, and interpretation must be carefully considered. This review aims to summarize past work, discuss rising opportunities and challenges, and facilitate further investigations on artificial intelligence inferring human neurocognition.

Wait, Where's the Flynn Effect on the WAIS-5?

The recent release of the WAIS-5, a decade and a half after its predecessor, the WAIS-IV, raises immediate questions about the Flynn effect (FE). Does the traditional FE of points per decade in the U.S. for children and adults, identified for the Full Scale IQs of all Wechsler scales and for other global IQ scores as well, persist into the 2020s? The WAIS-5 Technical and Interpretive Manual provides two counterbalanced validity studies that address the Flynn effect directly-N = 186 adolescents and adults (16-90 years, mean age = 47.8) tested on the WAIS-IV and WAIS-5; and N = 98 16-year-olds tested on the WISC-V and WAIS-5. The FE is incorporated into the diagnostic criteria for intellectual disabilities by the American Association on Intellectual and Developmental Disabilities (AAIDD), by DSM-5-TR, and in capital punishment cases. The unexpected result of the two counterbalanced studies was a reduction in the Flynn effect from the expected value of 3 IQ points to 1.2 points. These findings raise interesting questions regarding whether the three point adjustment to FSIQs should be continued for intellectual disability diagnosis and whether the federal courts should rethink its guidelines for capital punishment cases and other instances of high stakes decision-making. Limitations include a lack of generalization to children, the impact of the practice effects, and a small sample size.

Deep learning of structural MRI predicts fluid, crystallized, and general intelligence

Can brain structure predict human intelligence? T1-weighted structural brain magnetic resonance images (sMRI) have been correlated with intelligence. However, the population-level association does not fully account for individual variability in intelligence. To address this, studies have emerged recently to predict individual subject's intelligence or neurocognitive scores. However, they are mostly on predicting fluid intelligence (the ability to solve new problems). Studies are lacking to predict crystallized intelligence (the ability to accumulate knowledge) or general intelligence (fluid and crystallized intelligence combined). This study tests whether deep learning of sMRI can predict an individual subject's verbal, comprehensive, and full-scale intelligence quotients (VIQ, PIQ, and FSIQ), which reflect fluid and crystallized intelligence. We performed a comprehensive set of 432 experiments, using different input image channels, six deep learning models, and two outcome settings, in 850 healthy and autistic subjects 6-64 years of age. Our findings indicate a statistically significant potential of T1-weighted sMRI in predicting intelligence, with a Pearson correlation exceeding 0.21 (p < 0.001). Interestingly, we observed that an increase in the complexity of deep learning models does not necessarily translate to higher accuracy in intelligence prediction. The interpretations of our 2D and 3D CNNs, based on GradCAM, align well with the Parieto-Frontal Integration Theory (P-FIT), reinforcing the theory's suggestion that human intelligence is a result of interactions among various brain regions, including the occipital, temporal, parietal, and frontal lobes. These promising results invite further studies and open new questions in the field.

Evidence for a general cognitive structure in pigeons (Columba livia)

A well replicated result in humans is that performance, whether good or bad, is consistent across a wide variety of cognitive tasks. Factor analysis extracts one factor that can account for approximately half of the variance in performance. This factor is termed g and almost all cognitive tasks positively load onto this factor. While some neurobiological correlates of g have been identified in humans, causal experiments are only feasible in animals. When mice and some avian species are assessed with cognitive test batteries, performance positively correlates, and the first component extracted has similar properties to g. There are some limitations to the species tested thus far, including comparability in the cognitive domains assessed. The pigeon is an ideal subject to overcome these issues since pigeons, humans, and other primates are frequently given similar tasks and many neural correlates of performance have been identified in the pigeon. We created a test battery that assessed different domains, including associative learning, memory, cognitive flexibility, and reaction time. When all tasks were included, there was evidence for a two-component structure that was influenced by subjects' age. When the reaction time task was excluded, there was a g-like component. The implications for these results when constructing future test batteries and comparing across species are discussed.

Prediction of individual performance and verbal intelligence scores from resting-state fMRI in children and adolescents

The neuroimaging basis of intelligence remains elusive; however, there is a growing body of research employing connectome-based predictive modeling to estimate individual intelligence scores, aiming to identify the optimal set of neuroimaging features for accurately predicting an individual's cognitive abilities. Compared to adults, the disparities in cognitive performance among children and adolescents are more likely to captivate individuals' interest and attention. Limited research has been dedicated to exploring neuroimaging markers of intelligence specifically in the pediatric population. In this study, we utilized resting-state functional magnetic resonance imaging (fMRI) and intelligence quotient (IQ) scores of 170 healthy children and adolescents obtained from a public database to identify brain functional connectivity markers associated with individual intellectual behavior. Initially, we extracted and summarized relevant resting-state features from whole-brain or functional network connectivity that were most pertinent to IQ scores. Subsequently, these features were employed to establish prediction models for both performance and verbal IQ scores. Within a 10-fold cross-validation framework, our findings revealed that prediction models based on whole-brain functional connectivity effectively predicted performance IQ scores( R = 0.35 , P = 2.2 × 10 - 4 ) but not verbal IQ scores( R = 0.12 , P = 0.20 ). Results of prediction models based on brain functional network connectivity further demonstrated the exceptional predictive ability of the default mode network (DMN) and fronto-parietal task control network (FTPN) for performance IQ scores ( R = 0.71 , P = 2.2 × 10 - 18 ). The above findings have also been validated using an independent dataset. Our findings suggest that the performance IQ of children and adolescents primarily relies on the connectivity of brain regions associated with DMN and FTPN. Moreover, variations in intellectual performance during childhood and adolescences are closely linked to alterations in brain functional network connectivity.

Exploring crystallized and fluid intelligence in down syndrome using graph theory

This cross-sectional study examined the cognitive performance of crystallized intelligence (Gc) and fluid intelligence (Gf) in 340 individuals, comparing adults (aged 22-45) to adolescents (aged 16-21) in two groups of etiologies. Down syndrome (DS) and non-specific intellectual disability (NSID). The aim was to estimate whether their cognitive performance reflected accelerated, stable, or continuous trajectories. Participants were assessed using the Vocabulary, Similarities, Block Design, and Raven Matrix tests. ANOVA analysis indicated that adults exhibited higher scores than adolescents on three of the crystallized and fluid intelligence tests, with similar trends observed in the Raven Matrix test, thus supporting the Compensation Age Theory. Participants with NSID exhibited higher scores in Vocabulary than participants with DS. Participants with DS exhibited higher scores in Block Design and Raven than participants with NSID. There was no difference between the groups in Similarities, suggesting that the verbal ability of individuals with DS is not so impaired relative to participants with NSID. Graph analysis demonstrated divergent Gc-Gf networks between the two groups of etiologies. The DS etiology revealed more coherent connections between crystallized and fluid intelligence, especially in adulthood, compared to the diffuse and absent connections seen in adults with NSID. Thus, the relative strength in Similarities and the more coherent Gc-Gf interconnections in the DS etiology suggested a more coherent and not-so-impaired profile in a clear diagnostic etiology such as DS, especially in adulthood, compared to unclear genetic etiologies such as NSID. The findings hold educational implications for adults with ID with and without Down syndrome at least until their 40's as a time for growth and development, perhaps serving as a protective factor against possible cognitive decline in the future.

Interplay between preclinical indices of obesity and neural signatures of fluid intelligence in youth

Pediatric obesity rates have quadrupled in the United States, and deficits in higher-order cognition have been linked to obesity, though it remains poorly understood how deviations from normal body mass are related to the neural dynamics serving cognition in youth. Herein, we determine how age- and sex-adjusted measures of body mass index (zBMI) scale with neural activity in brain regions underlying fluid intelligence. Seventy-two youth aged 9-16 years underwent high-density magnetoencephalography while performing an abstract reasoning task. The resulting data were transformed into the time-frequency domain and significant oscillatory responses were imaged using a beamformer. Whole-brain correlations with zBMI were subsequently conducted to quantify relationships between zBMI and neural activity serving abstract reasoning. Our results reveal that participants with higher zBMI exhibit attenuated theta (4-8 Hz) responses in both the left dorsolateral prefrontal cortex and left temporoparietal junction, and that weaker temporoparietal responses scale with slower reaction times. These findings suggest that higher zBMI values are associated with weaker theta oscillations in key brain regions and altered performance during an abstract reasoning task. Thus, future investigations should evaluate neurobehavioral function during abstract reasoning in youth with more severe obesity to identify the potential impact.

Individual cognitive traits can be predicted from task-based dynamic functional connectivity with a deep convolutional-recurrent model

There has been increased interest in understanding the neural substrates of intelligence and several human traits from neuroimaging data. Deep learning can be used to predict different cognitive measures, such as general and fluid intelligence, from different functional magnetic resonance imaging experiments providing information about the main brain areas involved in these predictions. Using neuroimaging and behavioral data from 874 subjects provided by the Human Connectome Project, we predicted various cognitive scores using dynamic functional connectivity derived from language and working memory functional magnetic resonance imaging task states, using a 360-region multimodal atlas. The deep model joins multiscale convolutional and long short-term memory layers and was trained under a 10-fold stratified cross-validation. We removed the confounding effects of gender, age, total brain volume, motion and the multiband reconstruction algorithm using multiple linear regression. We can explain 17.1% and 16% of general intelligence variance for working memory and language tasks, respectively. We showed that task-based dynamic functional connectivity has more predictive power than resting-state dynamic functional connectivity when compared to the literature and that removing confounders significantly reduces the prediction performance. No specific cortical network showed significant relevance in the prediction of general and fluid intelligence, suggesting a spatial homogeneous distribution of the intelligence construct in the brain.

Abnormal microstructure of corpus callosum in children with primary nocturnal enuresis: a DTI study

Primary nocturnal enuresis (PNE) is a common childhood disorder with abnormal sleep or arousal. The corpus callosum (CC) continues to develop into adulthood and plays an important role in sleep arousal. This study aimed to evaluate the microstructure of the CC in children with PNE. Diffusion tensor imaging (DTI) indices were extracted throughout the CC and its seven subregions were compared between the children with PNE and healthy children (HC). The correlation between abnormal DTI indices of the CC and cognitive condition was also tested. Compared to HC, decreased fiber number (NF) (F = 8.492, PFDR = 0.032) and fractional anisotropy (FA) value (F = 8.442, PFDR = 0.040) were found in the posterior midbody of the CC, increased RD was found in the posterior midbody (F = 6.888, PFDR = 0.040) and isthmus (F = 7.967, PFDR = 0.040) in children with PNE. The reduction of FA value was more obvious in boys than girls with PNE. In children with PNE, there was a significant positive correlation between the NF of the posterior midbody and full IQ (r = 0.322, P = 0.025) and between the FA value and the general knowledge memory (r = 0.293, P = 0.043). This study provides imaging evidence for abnormalities in the microstructure of the CC in children with PNE, especially in male PNE, which might affect the children's cognitive performance.

Brain white matter microstructural alterations in Behcet's syndrome correlate with cognitive impairment and disease severity: A diffusion tensor imaging study

OBJECTIVES

To evaluate the microstructural integrity of brain white matter tracts in patients with Neuro-Behcet's syndrome (NBS) and Behcet's syndrome (BS) without neurological manifestations using diffusion tensor imaging (DTI) and to investigate potential utility of DTI as a surrogate biomarker of neurocognitive functioning and disease severity.

METHODS

This cross-sectional study comprised 34 NBS patients and 32 BS patients without neurological involvement, identified based on the International Study Group of the Behcet's disease (ISGBD) and the International Consensus Recommendation (ICR) criteria, as well as 33 healthy controls. Cognitive functions, including attention, memory, language, abstraction, executive control, visuospatial skills, and sensorimotor performance were assessed using standardized questionnaires. DTI data were analyzed using tract-based spatial statistics (TBSS) and automated probabilistic tractography to investigate inter-group differences. Subsequently, correlations between tensor-derived parameters of white matter tracts, neurocognitive test scores, and disease severity measures were examined.

RESULTS

DTI revealed decreased fractional anisotropy and increased radial diffusivity, mean diffusivity, and axial diffusivity in both supratentorial and infratentorial white matter in NBS patients, indicating widespread loss of microstructural integrity. Moreover, this loss of integrity was also observed in BS patients without neurological manifestations, albeit to a lesser extent. In NBS patients, certain white matter tracts, including cingulum bundle, were associated with poor cognitive performance across multiple domains and disease severity.

DISCUSSION

DTI findings might potentially serve as a neuroimaging marker to predict the extent of neurocognitive impairment and disease severity associated with central nervous system involvement in BS.

Functional brain hubs are related to age: A primer study with rs-fMRI

Background/Objective

Research on the ontogenetic development of brain networks using resting state has shown to be useful for understanding age-associated changes in brain connectivity. This work aimed to analyze the relationship between brain connectivity, age and intelligence.

Methods

A sample of 26 children and adolescents between 6 and 18 years of both sexes underwent a resting-state functional magnetic resonance imaging study. We estimated the values of fractional Amplitude low-frequency fluctuations (fALFF) and the values of Regional homogeneity (ReHo) in a voxelwise analysis to later correlate them with age and intelligence quotient (IQ).

Results

No significant correlations were found with IQ, but it was found that the fALFF values of the left precentral cortex (premotor cortex and supplementary motor area), as well as the ReHo values of the medial frontal gyrus, and the precentral cortex of the left hemisphere, correlate with age. Conclusions: Hubs related to various "task positive" networks closely related to cognitive functioning would present a development more related to age and relatively independent of individual differences in intelligence. These findings suggest that the premotor cortex and supplementary motor cortex could be a cortical hub that develops earlier than previously reported and that it would be more related to age than to intelligence level.

Biomarkers of preschool children with autism spectrum disorder: quantitative analysis of whole-brain tissue component volumes, intelligence scores, ADOS-CSS, and ages of first-word production and walking onset

BACKGROUND

Preschooling is a critical time for intervention in children with autism spectrum disorder (ASD); thus, we analyzed brain tissue component volumes (BTCVs) and clinical indicators in preschool children with ASD to identify new biomarkers for early screening.

METHODS

Eighty preschool children (3-6 years) with ASD were retrospectively included. The whole-brain myelin content (MyC), white matter (WM), gray matter (GM), cerebrospinal fluid (CSF), and non-WM/GM/MyC/CSF brain component volumes were obtained using synthetic magnetic resonance imaging (SyMRI). Clinical data, such as intelligence scores, autism diagnostic observation schedule-calibrated severity scores, age at first production of single words (AFSW), age at first production of phrases (AFP), and age at walking onset (AWO), were also collected. The correlation between the BTCV and clinical data was evaluated, and the effect of BTCVs on clinical data was assessed by a regression model.

RESULTS

WM and GM volumes were positively correlated with intelligence scores (both P < 0.001), but WM and GM did not affect intelligence scores (P = 0.116, P = 0.290). AWO was positively correlated with AFSW and AFP (both P < 0.001). The multivariate linear regression analysis revealed that MyC, AFSW, AFP, and AWO were significantly different (P = 0.005, P < 0.001, P < 0.001).

CONCLUSIONS

This study revealed positive correlations between WM and GM volumes and intelligence scores. Whole-brain MyC affected AFSW, AFP, and AWO in preschool children with ASD. Noninvasive quantification of BTCVs via SyMRI revealed a new visualizable and quantifiable biomarker (abnormal MyC) for early ASD screening in preschool children.

Dynamic transient brain states in preschoolers mirror parental report of behavior and emotion regulation

The temporal dynamics of resting-state networks may represent an intrinsic functional repertoire supporting cognitive control performance across the lifespan. However, little is known about brain dynamics during the preschool period, which is a sensitive time window for cognitive control development. The fast timescale of synchronization and switching characterizing cortical network functional organization gives rise to quasi-stable patterns (i.e., brain states) that recur over time. These can be inferred at the whole-brain level using hidden Markov models (HMMs), an unsupervised machine learning technique that allows the identification of rapid oscillatory patterns at the macroscale of cortical networks. The present study used an HMM technique to investigate dynamic neural reconfigurations and their associations with behavioral (i.e., parental questionnaires) and cognitive (i.e., neuropsychological tests) measures in typically developing preschoolers (4-6 years old). We used high-density EEG to better capture the fast reconfiguration patterns of the HMM-derived metrics (i.e., switching rates, entropy rates, transition probabilities and fractional occupancies). Our results revealed that the HMM-derived metrics were reliable indices of individual neural variability and differed between boys and girls. However, only brain state transition patterns toward prefrontal and default-mode brain states, predicted differences on parental-report questionnaire scores. Overall, these findings support the importance of resting-state brain dynamics as functional scaffolds for behavior and cognition. Brain state transitions may be crucial markers of individual differences in cognitive control development in preschoolers.

GABAergic inhibition in human hMT+ predicts visuo-spatial intelligence mediated through the frontal cortex

The prevailing opinion emphasizes fronto-parietal network (FPN) is key in mediating general fluid intelligence (gF). Meanwhile, recent studies show that human MT complex (hMT+), located at the occipito-temporal border and involved in 3D perception processing, also plays a key role in gF. However, the underlying mechanism is not clear, yet. To investigate this issue, our study targets visuo-spatial intelligence, which is considered to have high loading on gF. We use ultra-high field magnetic resonance spectroscopy (MRS) to measure GABA/Glu concentrations in hMT+ combining resting-state fMRI functional connectivity (FC), behavioral examinations including hMT+ perception suppression test and gF subtest in visuo-spatial component. Our findings show that both GABA in hMT+ and frontal-hMT+ functional connectivity significantly correlate with the performance of visuo-spatial intelligence. Further, serial mediation model demonstrates that the effect of hMT+ GABA on visuo-spatial gF is fully mediated by the hMT+ frontal FC. Together our findings highlight the importance in integrating sensory and frontal cortices in mediating the visuo-spatial component of general fluid intelligence.

The factorial structure of executive functions in preschool and elementary school children and relations with intelligence

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.

BrainSuite BIDS App: Containerized Workflows for MRI Analysis

There has been a concerted effort by the neuroimaging community to establish standards for computational methods for data analysis that promote reproducibility and portability. In particular, the Brain Imaging Data Structure (BIDS) specifies a standard for storing imaging data, and the related BIDS App methodology provides a standard for implementing containerized processing environments that include all necessary dependencies to process BIDS datasets using image processing workflows. We present the BrainSuite BIDS App, which encapsulates the core MRI processing functionality of BrainSuite within the BIDS App framework. Specifically, the BrainSuite BIDS App implements a participant-level workflow comprising three pipelines and a corresponding set of group-level analysis workflows for processing the participant-level outputs. The Anatomical Pipeline extracts cortical surface models from a T1-weighted (T1w) MRI. It then performs surface-constrained volumetric registration to align the T1w MRI to a labeled anatomical atlas, which is used to delineate anatomical regions of interest in the MRI brain volume and on the cortical surface models. The Diffusion Pipeline processes diffusion-weighted imaging (DWI) data, with steps that include coregistering the DWI data to the T1w scan, correcting for susceptibility-induced geometric image distortion, and fitting diffusion models to the DWI data. The Functional Pipeline performs fMRI processing using a combination of FSL, AFNI, and BrainSuite tools. It coregisters the fMRI data to the T1w image, then transforms the data to the anatomical atlas space and to the Human Connectome Project's grayordinate space. The outputs of each pipeline can then be processed during group-level analysis. The outputs of the Anatomical Pipeline and the Diffusion Pipeline are analyzed using the BrainSuite Statistics Toolbox in R (bstr), which provides functionality for hypothesis testing and statistical modeling. The outputs of the Functional Pipeline can be analyzed using atlas-based or atlas-free statistical methods during group-level processing. These analyses include the application of BrainSync, which synchronizes the time-series data temporally and enables comparison of resting-state or task-based fMRI data across scans. We also present the BrainSuite Dashboard quality control system, which provides a browser-based interface for reviewing the outputs of individual modules of the participant-level pipelines across a study in real-time as they are generated. BrainSuite Dashboard facilitates rapid review of intermediate results, enabling users to identify processing errors and make adjustments to processing parameters if necessary. The comprehensive functionality included in the BrainSuite BIDS App provides a mechanism for rapidly deploying the BrainSuite workflows into new environments to perform large-scale studies. We demonstrate the capabilities of the BrainSuite BIDS App using structural, diffusion, and functional MRI data from the Amsterdam Open MRI Collection's Population Imaging of Psychology dataset.

The frontal association area: exercise-induced brain plasticity in children and adolescents and implications for cognitive intervention practice

Objective

Explore the plasticity of the frontal associative areas in children and adolescents induced by exercise and potential moderating variables.

Methods

Computer searches of CNKI, WOS, PubMed and EBSCO databases were conducted, and statistical analyses were performed based on SPSS 25.0, Stata 12.0 and Ginger ALE 2.3 software after literature screening, data extraction and quality assessment were performed independently by two researchers.

Results

A total of 13 articles, including 425 participants aged 8.9∼16.8 years, were included. Frequency analysis revealed that exercise induced enhanced activation in frontal, parietal, occipital, limbic system and cerebellum (P < 0.01). Activation Likelihood Estimation (ALE) meta-analysis revealed that exercise altered the activation status of the frontal association (medial frontal gyrus, middle frontal gyrus, inferior frontal gyrus and precentral gyrus), cuneus, lingual gyrus, cingulate gyrus, parahippocampal gyrus, caudate nucleus and cerebellar apex, with the volume of activation in the frontal association accounting for 61.81% of the total activation cluster volume and an enhanced activation effect. Additionally, the study design, age, gender, nationality, cognitive tasks, as well as exercise intensity, intervention time, and type of exercise may be potential moderating variables. Particularly, sustained exercise induced a decrease in activation in the left parahippocampal gyrus, culmen, and lingual gyrus, while variable exercise induced an increase in activation in the left middle frontal gyrus.

Conclusion

Exercise-induced activation increase in the frontal associative areas of children and adolescents is dominant, especially longer periods of moderate-intensity variable exercise can induce more brain region activation. However, some of the included studies are cross-sectional, and the accuracy of the results still requires further verification.

Systematic review registration

https://www.crd.york.ac.uk/prospero/, identifier PROSPERO, CRD42022348781.

Stable individual differences from dynamic patterns of function: brain network flexibility predicts openness/intellect, intelligence, and psychoticism

A growing understanding of the nature of brain function has led to increased interest in interpreting the properties of large-scale brain networks. Methodological advances in network neuroscience provide means to decompose these networks into smaller functional communities and measure how they reconfigure over time as an index of their dynamic and flexible properties. Recent evidence has identified associations between flexibility and a variety of traits pertaining to complex cognition including creativity and working memory. The present study used measures of dynamic resting-state functional connectivity in data from the Human Connectome Project (n = 994) to test associations with Openness/Intellect, general intelligence, and psychoticism, three traits that involve flexible cognition. Using a machine-learning cross-validation approach, we identified reliable associations of intelligence with cohesive flexibility of parcels in large communities across the cortex, of psychoticism with disjoint flexibility, and of Openness/Intellect with overall flexibility among parcels in smaller communities. These findings are reasonably consistent with previous theories of the neural correlates of these traits and help to expand on previous associations of behavior with dynamic functional connectivity, in the context of broad personality dimensions.

Global brain connectivity: Test-retest stability and association with biological and neurocognitive variables

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.

NEW METHOD

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.

COMPARISON WITH EXISTING METHOD

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.

Examining the neurostructural architecture of intelligence: The Lothian Birth Cohort 1936 study

Examining underlying neurostructural correlates of specific cognitive abilities is practically and theoretically complicated by the existence of the positive manifold (all cognitive tests positively correlate): if a brain structure is associated with a cognitive task, how much of this is uniquely related to the cognitive domain, and how much is due to covariance with all other tests across domains (captured by general cognitive functioning, also known as general intelligence, or 'g')? We quantitatively address this question by examining associations between brain structural and diffusion MRI measures (global tissue volumes, white matter hyperintensities, global white matter diffusion fractional anisotropy and mean diffusivity, and FreeSurfer processed vertex-wise cortical volumes, smoothed at 20mm fwhm) with g and cognitive domains (processing speed, crystallised ability, memory, visuospatial ability). The cognitive domains were modelled using confirmatory factor analysis to derive both hierarchical and bifactor solutions using 13 cognitive tests in 697 participants from the Lothian Birth Cohort 1936 study (mean age 72.5 years; SD = .7). Associations between the extracted cognitive factor scores for each domain and g were computed for each brain measure covarying for age, sex and intracranial volume, and corrected for false discovery rate. There were a range of significant associations between cognitive domains and global MRI brain structural measures (r range .008 to .269, p < .05). Regions implicated by vertex-wise regional cortical volume included a widespread number of medial and lateral areas of the frontal, temporal and parietal lobes. However, at both global and regional level, much of the domain-MRI associations were shared (statistically accounted for by g). Removing g-related variance from cognitive domains attenuated association magnitudes with global brain MRI measures by 27.9-59.7% (M = 46.2%), with only processing speed retaining all significant associations. At the regional cortical level, g appeared to account for the majority (range 22.1-88.4%; M = 52.8% across cognitive domains) of regional domain-specific associations. Crystallised and memory domains had almost no unique cortical correlates, whereas processing speed and visuospatial ability retained limited cortical volumetric associations. The greatest spatial overlaps across cognitive domains (as denoted by g) were present in the medial and lateral temporal, lateral parietal and lateral frontal areas.

Anodal transcranial direct-current stimulation and non-verbal intelligence in autism spectrum disorder: A randomized controlled trial

AIM

To understand the impact of anodal transcranial direct-current stimulation (tDCS) on non-verbal intelligence in high-functioning young adults with autism spectrum disorder (ASD).

METHOD

Thirty individuals with ASD were randomly divided into three groups receiving 2 mA, 20 minutes daily anodal tDCS for 10 sessions. Group A received 10 sham tDCS sessions, group B five real followed by five sham sessions, and group C received 10 real tDCS sessions. The total score of non-verbal intelligence was measured using the Test of Nonverbal Intelligence, Fourth Edition. The left dorsolateral prefrontal cortex (LDLPFC) was targeted using the International 10-20 electroencephalography system, and concurrent cognitive training was avoided.

RESULTS

Group C demonstrated a mean difference of 4.10 (95% confidence interval 1.41-6.79; p = 0.005) in Test of Nonverbal Intelligence scores compared with group A, with an effect size of 0.47. No significant differences were observed between groups A and B (p = 0.296), or between groups B and C (p = 0.140).

INTERPRETATION

Ten sessions of anodal tDCS to the LDLPFC led to improved non-verbal intelligence among individuals with ASD. These results emphasize the potential of tDCS as a discrete method for boosting cognitive abilities in the high-functioning population with ASD. Future studies with larger groups of participants and extended observation periods are necessary to validate these findings.

Prevalence and characteristics of vascular cognitive impairment in a European cohort of adult patients with Moyamoya angiopathy

INTRODUCTION

Moyamoya angiopathy (MMA) is associated with a high risk of stroke, but it is also increasingly recognized as leading to cognitive impairment. The aim of this study was to determine the prevalence, nature, and severity of vascular cognitive impairment no dementia (VCIND) in adults with MMA and to identify clinical and imaging factors associated with VCIND.

METHODS

We conducted a retrospective study of consecutive adult patients with MMA followed in two tertiary hospitals (Toulouse and Paris Lariboisiere). All patients underwent neuropsychological assessment and brain magnetic resonance imaging (MRI). VCIND was defined as at least two variables of the same cognitive process with z-scores of < 2 standard deviations, regardless of the cognitive domain, that do not interfere in everyday life. Baseline demographic, clinical, and imaging data were compared between patients with and without VCIND.

RESULTS

A total of 102 patients (mean age 43 years; 65% women) were included. Thirty-four patients (33.3%) had VCIND. VCIND was mild in 20/34 (59%), moderate in 8/34 (23%), and severe in 6/34 (18%) patients. Executive function was the most widely affected (25.5%), followed by attention and processing speed (24.8%). In univariable analyses, VCIND was associated with ischemic stroke at diagnosis and the presence of ischemic lesions on MRI.

CONCLUSIONS

VCIND is highly prevalent in adults with MMA. Executive functions and processing speed are predominantly affected. These findings may guide clinicians in their evaluation of patients with MMA. Further research should assess the effect of revascularization therapies on cognitive functions.

Neural basis of reward expectancy inducing proactive aggression

Proactive aggression refers to deliberate and unprovoked behavior, typically motivated by personal gain or expected reward. Reward expectancy is generally recognized as a critical factor that may influence proactive aggression, but its neural mechanisms remain unknown. We conducted a task-based functional magnetic resonance imaging (fMRI) experiment to investigate the relationship between reward expectancy and proactive aggression. 37 participants (20 females, mean age = 20.8 ± 1.42, age range = 18-23 years) completed a reward-harm task. In the experiment, reward valence expectancy and reward possibility expectancy were manipulated respectively by varying amounts (low: 0.5-1.5 yuan; high: 10.5-11.5 yuan) and possibilities (low: 10%-30%; high: 70%-90%) of money that participants could obtain by choosing to aggress. Participants received fMRI scans throughout the experiment. Brain activation regions associated with reward expectancy mainly involve the middle frontal gyrus, lingual gyrus, inferior temporal gyrus, anterior cuneus, caudate nucleus, inferior frontal gyrus, cingulate gyrus, anterior central gyrus, and posterior central gyrus. Associations between brain activation and reward expectancy in the left insula, left middle frontal gyrus, left thalamus, and right middle frontal gyrus were found to be related to proactive aggression. Furthermore, the brain activation regions primarily involved in proactive aggression induced by reward expectancy were the insula, inferior frontal gyrus, inferior temporal gyrus, pallidum, and caudate nucleus. Under conditions of high reward expectancy, participants engage in more proactive aggressive behavior. Reward expectancy involves the activation of reward- and social-cognition-related brain regions, and these associations are instrumental in proactive aggressive decisions.

Frontoparietal Response to Working Memory Load Mediates the Association between Sleep Duration and Cognitive Function in Children

Lack of sleep has been found to be associated with cognitive impairment in children, yet the neural mechanism underlying this relationship remains poorly understood. To address this issue, this study utilized the data from the Adolescent Brain Cognitive Development (ABCD) study (n = 4930, aged 9-10), involving their sleep assessments, cognitive measures, and functional magnetic resonance imaging (fMRI) during an emotional n-back task. Using partial correlations analysis, we found that the out-of-scanner cognitive performance was positively correlated with sleep duration. Additionally, the activation of regions of interest (ROIs) in frontal and parietal cortices for the 2-back versus 0-back contrast was positively correlated with both sleep duration and cognitive performance. Mediation analysis revealed that this activation significantly mediated the relationship between sleep duration and cognitive function at both individual ROI level and network level. After performing analyses separately for different sexes, it was revealed that the mediation effect of the task-related activation was present in girls (n = 2546). These findings suggest that short sleep duration may lead to deficit in cognitive function of children, particularly in girls, through the modulation of frontoparietal activation during working memory load.

Learning Spatiotemporal Brain Dynamics in Adolescents via Multimodal MEG and fMRI Data Fusion Using Joint Tensor/Matrix Decomposition

OBJECTIVE

Brain function is understood to be regulated by complex spatiotemporal dynamics, and can be characterized by a combination of observed brain response patterns in time and space. Magnetoencephalography (MEG), with its high temporal resolution, and functional magnetic resonance imaging (fMRI), with its high spatial resolution, are complementary imaging techniques with great potential to reveal information about spatiotemporal brain dynamics. Hence, the complementary nature of these imaging techniques holds much promise to study brain function in time and space, especially when the two data types are allowed to fully interact.

METHODS

We employed coupled tensor/matrix factorization (CMTF) to extract joint latent components in the form of unique spatiotemporal brain patterns that can be used to study brain development and function on a millisecond scale.

RESULTS

Using the CMTF model, we extracted distinct brain patterns that revealed fine-grained spatiotemporal brain dynamics and typical sensory processing pathways informative of high-level cognitive functions in healthy adolescents. The components extracted from multimodal tensor fusion possessed better discriminative ability between high- and low-performance subjects than single-modality data-driven models.

CONCLUSION

Multimodal tensor fusion successfully identified spatiotemporal brain dynamics of brain function and produced unique components with high discriminatory power.

SIGNIFICANCE

The CMTF model is a promising tool for high-order, multimodal data fusion that exploits the functional resolution of MEG and fMRI, and provides a comprehensive picture of the developing brain in time and space.

Neuromodulatory effects of parietal high-definition transcranial direct-current stimulation on network-level activity serving fluid intelligence

Fluid intelligence (Gf) involves rational thinking skills and requires the integration of information from different cortical regions to resolve novel complex problems. The effects of non-invasive brain stimulation on Gf have been studied in attempts to improve Gf, but such studies are rare and the few existing have reached conflicting conclusions. The parieto-frontal integration theory of intelligence (P-FIT) postulates that the parietal and frontal lobes play a critical role in Gf. To investigate the suggested role of parietal cortices, we applied high-definition transcranial direct current stimulation (HD-tDCS) to the left and right parietal cortices of 39 healthy adults (age 19-33 years) for 20 min in three separate sessions (left active, right active and sham). After completing the stimulation session, the participants completed a logical reasoning task based on Raven's Progressive Matrices during magnetoencephalography. Significant neural responses at the sensor level across all stimulation conditions were imaged using a beamformer. Whole-brain, spectrally constrained functional connectivity was then computed to examine the network-level activity. Behaviourally, we found that participants were significantly more accurate following left compared to right parietal stimulation. Regarding neural findings, we found significant HD-tDCS montage-related effects in brain networks thought to be critical for P-FIT, including parieto-occipital, fronto-occipital, fronto-parietal and occipito-cerebellar connectivity during task performance. In conclusion, our findings showed that left parietal stimulation improved abstract reasoning abilities relative to right parietal stimulation and support both P-FIT and the neural efficiency hypothesis. KEY POINTS: Abstract reasoning is a critical component of fluid intelligence and is known to be served by multispectral oscillatory activity in the fronto-parietal cortices. Recent studies have aimed to improve abstract reasoning abilities and fluid intelligence overall through behavioural training, but the results have been mixed. High-definition transcranial direct-current stimulation (HD-tDCS) applied to the parietal cortices modulated task performance and neural oscillations during abstract reasoning. Left parietal stimulation resulted in increased accuracy and decreased functional connectivity between occipital regions and frontal, parietal, and cerebellar regions. Future studies should investigate whether HD-tDCS alters abstract reasoning abilities in those who exhibit declines in performance, such as healthy ageing populations.

COVID-19-associated encephalopathy: connection between neuroinflammation and microbiota-gut-brain axis

While neurological complications of COVID-19, such as encephalopathy, are relatively rare, their potential significant impact on long-term morbidity is substantial, especially given the large number of infected patients. Two proposed hypotheses for the pathogenesis of this condition are hypoxia and the uncontrolled release of proinflammatory cytokines. The gut microbiota plays an important role in regulating immune homeostasis and overall gut health, including its effects on brain health through various pathways collectively termed the gut-brain axis. Recent studies have shown that COVID-19 patients exhibit gut dysbiosis, but how this dysbiosis can affect inflammation in the central nervous system (CNS) remains unclear. In this context, we discuss how dysbiosis could contribute to neuroinflammation and provide recent data on the features of neuroinflammation in COVID-19 patients.

Differential neural correlates underlying visuospatial versus semantic reasoning in autistic children

While fronto-posterior underconnectivity has often been reported in autism, it was shown that different contexts may modulate between-group differences in functional connectivity. Here, we assessed how different task paradigms modulate functional connectivity differences in a young autistic sample relative to typically developing children. Twenty-three autistic and 23 typically developing children aged 6 to 15 years underwent functional magnetic resonance imaging (fMRI) scanning while completing a reasoning task with visuospatial versus semantic content. We observed distinct connectivity patterns in autistic versus typical children as a function of task type (visuospatial vs. semantic) and problem complexity (visual matching vs. reasoning), despite similar performance. For semantic reasoning problems, there was no significant between-group differences in connectivity. However, during visuospatial reasoning problems, we observed occipital-occipital, occipital-temporal, and occipital-frontal over-connectivity in autistic children relative to typical children. Also, increasing the complexity of visuospatial problems resulted in increased functional connectivity between occipital, posterior (temporal), and anterior (frontal) brain regions in autistic participants, more so than in typical children. Our results add to several studies now demonstrating that the connectivity alterations in autistic relative to neurotypical individuals are much more complex than previously thought and depend on both task type and task complexity and their respective underlying cognitive processes.