A neural basis for general intelligence

Synergistic information supports modality integration and flexible learning in neural networks solving multiple tasks

Striking progress has been made in understanding cognition by analyzing how the brain is engaged in different modes of information processing. For instance, so-called synergistic information (information encoded by a set of neurons but not by any subset) plays a key role in areas of the human brain linked with complex cognition. However, two questions remain unanswered: (a) how and why a cognitive system can become highly synergistic; and (b) how informational states map onto artificial neural networks in various learning modes. Here we employ an information-decomposition framework to investigate neural networks performing cognitive tasks. Our results show that synergy increases as networks learn multiple diverse tasks, and that in tasks requiring integration of multiple sources, performance critically relies on synergistic neurons. Overall, our results suggest that synergy is used to combine information from multiple modalities-and more generally for flexible and efficient learning. These findings reveal new ways of investigating how and why learning systems employ specific information-processing strategies, and support the principle that the capacity for general-purpose learning critically relies on the system's information dynamics.

Studying the social mind: An updated summary of findings from the Vietnam Head Injury Study

Lesion mapping studies allow us to evaluate the potential causal contribution of specific brain areas to human cognition and complement other cognitive neuroscience methods, as several authors have recently pointed out. Here, we present an updated summary of the findings from the Vietnam Head Injury Study (VHIS) focusing on the studies conducted over the last decade, that examined the social mind and its intricate neural and cognitive underpinnings. The VHIS is a prospective, long-term follow-up study of Vietnam veterans with penetrating traumatic brain injury (pTBI) and healthy controls (HC). The scope of the work is to present the studies from the latest phases (3 and 4) of the VHIS, 70 studies since 2011, when the Raymont et al. paper was published (Raymont et al., 2011). These studies have contributed to our understanding of human social cognition, including political and religious beliefs, theory of mind, but also executive functions, intelligence, and personality. This work finally discusses the usefulness of lesion mapping as an approach to understanding the functions of the human brain from basic science and clinical perspectives.

Multiple group membership and executive function in a socioeconomically diverse sample

Belonging to multiple groups is an important feature of our social lives. However, it is largely unknown if it is related to individual differences in cognitive performance. Given that changing self-identities linked to each group requires cognitive operations on knowledge bases associated with each group, the extent to which people belong to multiple groups may be related to individual differences in cognitive performance. Therefore, the main objective of this study was to test if multiple group membership is related to executive function task performance. A socioeconomically diverse sample of 395 individuals in Indonesia participated in this study. Our results show that multiple group membership was positively related to the 3-back working memory performance. However, we also found that this relationship was significant only among participants with high (not median or low) SES. We also observed that Contact diversity was negatively related to working memory performance among participants with low SES. Our results show that the complexity of our social lives is related to individual differences in executive function performance, although this seems to be constrained by SES.

Improved estimation of general cognitive ability and its neural correlates with a large battery of cognitive tasks

Elucidating the neural mechanisms of general cognitive ability (GCA) is an important mission of cognitive neuroscience. Recent large-sample cohort studies measured GCA through multiple cognitive tasks and explored its neural basis, but they did not investigate how task number, factor models, and neural data type affect the estimation of GCA and its neural correlates. To address these issues, we tested 1,605 Chinese young adults with 19 cognitive tasks and Raven's Advanced Progressive Matrices (RAPM) and collected resting state and n-back task fMRI data from a subsample of 683 individuals. Results showed that GCA could be reliably estimated by multiple tasks. Increasing task number enhances both reliability and validity of GCA estimates and reliably strengthens their correlations with brain data. The Spearman model and hierarchical bifactor model yield similar GCA estimates. The bifactor model has better model fit and stronger correlation with RAPM but explains less variance and shows weaker correlations with brain data than does the Spearman model. Notably, the n-back task-based functional connectivity patterns outperform resting-state fMRI in predicting GCA. These results suggest that GCA derived from a multitude of cognitive tasks serves as a valid measure of general intelligence and that its neural correlates could be better characterized by task fMRI than resting-state fMRI data.

Connectome-based predictive modeling of fluid intelligence: evidence for a global system of functionally integrated brain networks

Cognitive neuroscience continues to advance our understanding of the neural foundations of human intelligence, with significant progress elucidating the role of the frontoparietal network in cognitive control mechanisms for flexible, intelligent behavior. Recent evidence in network neuroscience further suggests that this finding may represent the tip of the iceberg and that fluid intelligence may depend on the collective interaction of multiple brain networks. However, the global brain mechanisms underlying fluid intelligence and the nature of multi-network interactions remain to be well established. We therefore conducted a large-scale Connectome-based Predictive Modeling study, administering resting-state fMRI to 159 healthy college students and examining the contributions of seven intrinsic connectivity networks to the prediction of fluid intelligence, as measured by a state-of-the-art cognitive task (the Bochum Matrices Test). Specifically, we aimed to: (i) identify whether fluid intelligence relies on a primary brain network or instead engages multiple brain networks; and (ii) elucidate the nature of brain network interactions by assessing network allegiance (within- versus between-network connections) and network topology (strong versus weak connections) in the prediction of fluid intelligence. Our results demonstrate that whole-brain predictive models account for a large and significant proportion of variance in fluid intelligence (18%) and illustrate that the contribution of individual networks is relatively modest by comparison. In addition, we provide novel evidence that the global architecture of fluid intelligence prioritizes between-network connections and flexibility through weak ties. Our findings support a network neuroscience approach to understanding the collective role of brain networks in fluid intelligence and elucidate the system-wide network mechanisms from which flexible, adaptive behavior is constructed.

Brain-based medical education model for expert's clinical decision making

PURPOSE

The expertise of medicians in clinical decision-making is very important since it improves the quality of medical services provided to patients. This study analyzed the characteristics of the decision-making process and confirmed clinicians' electroencephalography (EEG) characteristics by measuring their cerebral activity during clinical decision-making. This study aims to present learning directions for brain-based clinical decision-making to develop medical experts.

METHODS

This study was performed in the following two projects: (1) a qualitative study of clinical decision-making in a clinical scenario and (2) an analysis of differences in cortical activity of experts and novices through EEG.

RESULTS

In the 1st study, this study found that "confirmation of the patient's chief complaints," "physical examination," and "radiography reading" steps, which showed the most prominent differences in the experts' and novices' decision making, were set as the significant steps of dentists' clinical decision making. In the 2nd study, the experts' and novices' cortical activities were measured through a 32-channel EEG. In task 6, which had the lowest accuracy of diagnoses made by the experts, the brain activities in both groups were higher than in other tasks.

CONCLUSION

This study developed and suggested a model of the decision-making process for experts and novices and suggested the basic directions for brain-based learning needed to raise experts based on brain activity.

The Involvement of the Multiple Demand and Default Mode Networks in a Trial-by-Trial Cognitive Control

Adaptive behavior in the environment requires a high level of cognitive control to bias limited processing resources to behaviorally significant stimuli. Such control has been associated with a set of brain regions located in the fronto-parietal cortex (multiple demand network), whose activity was found to increase as the control demand for a task increases. In contrast, another set of regions, default mode network regions, were found to be deactivated during top-down processing of task stimuli. Despite this dissociation in their activation amplitudes, it is possible that activation patterns of these regions commonly encode specific task features. In two independent neuroimaging datasets, involving a total of 40 human samples, we found that the performance of an attentional task evoked positive activity of the MDN and deactivation of the DMN. Consistent with previous studies, task features could be decoded from the fronto-parietal cognitive regions. Importantly, the regions of the DMN also encoded task features when the task set had to be rapidly reconfigured in a transient, trial-by-trial manner, along with the MDN regions. These results suggest that the two separate brain networks ultimately co-ordinate for the effective establishment of top-down cognitive control.

Four errors and a fallacy: pitfalls for the unwary in comparative brain analyses

Comparative analyses are the backbone of evolutionary analysis. However, their record in producing a consensus has not always been good. This is especially true of attempts to understand the factors responsible for the evolution of large brains, which have been embroiled in an increasingly polarised debate over the past three decades. We argue that most of these disputes arise from a number of conceptual errors and associated logical fallacies that are the result of a failure to adopt a biological systems-based approach to hypothesis-testing. We identify four principal classes of error: a failure to heed Tinbergen's Four Questions when testing biological hypotheses, misapplying Dobzhansky's Dictum when testing hypotheses of evolutionary adaptation, poorly chosen behavioural proxies for underlying hypotheses, and the use of inappropriate statistical methods. In the interests of progress, we urge a more careful and considered approach to comparative analyses, and the adoption of a broader, rather than a narrower, taxonomic perspective.

Brain working memory network indices as landmarks of intelligence

Identifying the neural correlates of intelligence has long been a goal in neuroscience. Recently, the field of network neuroscience has attracted researchers' attention as a means for answering this question. In network neuroscience, the brain is considered as an integrated system whose systematic properties provide profound insights into health and behavioral outcomes. However, most network studies of intelligence have used univariate methods to investigate topological network measures, with their focus limited to a few measures. Furthermore, most studies have focused on resting state networks despite the fact that brain activation during working memory tasks has been linked to intelligence. Finally, the literature is still missing an investigation of the association between network assortativity and intelligence. To address these issues, here we employ a recently developed mixed-modeling framework for analyzing multi-task brain networks to elucidate the most critical working memory task network topological properties corresponding to individuals' intelligence differences. We used a data set of 379 subjects (22-35 y/o) from the Human Connectome Project (HCP). Each subject's data included composite intelligence scores, and fMRI during resting state and a 2-back working memory task. Following comprehensive quality control and preprocessing of the minimally preprocessed fMRI data, we extracted a set of the main topological network features, including global efficiency, degree, leverage centrality, modularity, and clustering coefficient. The estimated network features and subject's confounders were then incorporated into the multi-task mixed-modeling framework to investigate how brain network changes between working memory and resting state relate to intelligence score. Our results indicate that the general intelligence score (cognitive composite score) is associated with a change in the relationship between connection strength and multiple network topological properties, including global efficiency, leverage centrality, and degree difference during working memory as it is compared to resting state. More specifically, we observed a higher increase in the positive association between global efficiency and connection strength for the high intelligence group when they switch from resting state to working memory. The strong connections might form superhighways for a more efficient global flow of information through the brain network. Furthermore, we found an increase in the negative association between degree difference and leverage centrality with connection strength during working memory tasks for the high intelligence group. These indicate higher network resilience and assortativity along with higher circuit-specific information flow during working memory for those with a higher intelligence score. Although the exact neurobiological implications of our results are speculative at this point, our results provide evidence for the significant association of intelligence with hallmark properties of brain networks during working memory.

Causal role of a neural system for separating and selecting multidimensional social cognitive information

People are multi-faceted, typically good at some things but bad at others, and a critical aspect of social judgement is the ability to focus on those traits relevant for the task at hand. However, it remains unknown how the brain supports such context-dependent social judgement. Here, we examine how people represent multidimensional individuals, and how the brain extracts relevant information and filters out irrelevant information when comparing individuals within a specific dimension. Using human fMRI, we identify distinct neural representations in dorsomedial prefrontal cortex (dmPFC) and anterior insula (AI) supporting separation and selection of information for context-dependent social judgement. Causal evaluation using non-invasive brain stimulation shows that AI disruption alters the impact of relevant information on social comparison, whereas dmPFC disruption only affects the impact of irrelevant information. This neural circuit is distinct from the one supporting integration across, as opposed to separation of, different features of a multidimensional cognitive space.

Investigating cognitive neuroscience theories of human intelligence: A connectome-based predictive modeling approach

Central to modern neuroscientific theories of human intelligence is the notion that general intelligence depends on a primary brain region or network, engaging spatially localized (rather than global) neural representations. Recent findings in network neuroscience, however, challenge this assumption, providing evidence that general intelligence may depend on system-wide network mechanisms, suggesting that local representations are necessary but not sufficient to account for the neural architecture of human intelligence. Despite the importance of this key theoretical distinction, prior research has not systematically investigated the role of local versus global neural representations in predicting general intelligence. We conducted a large-scale connectome-based predictive modeling study (N = 297), administering resting-state fMRI and a comprehensive cognitive battery to evaluate the efficacy of modern neuroscientific theories of human intelligence, including spatially localized theories (Lateral Prefrontal Cortex Theory, Parieto-Frontal Integration Theory, and Multiple Demand Theory) and recent global accounts (Process Overlap Theory and Network Neuroscience Theory). The results of our study demonstrate that general intelligence can be predicted by local functional connectivity profiles but is most robustly explained by global profiles of whole-brain connectivity. Our findings further suggest that the improved efficacy of global theories is not reducible to a greater strength or number of connections, but instead results from considering both strong and weak connections that provide the basis for intelligence (as predicted by the Network Neuroscience Theory). Our results highlight the importance of considering local neural representations in the context of a global information-processing architecture, suggesting future directions for theory-driven research on system-wide network mechanisms underlying general intelligence.

Training allows switching from limited-capacity manipulations to large-capacity perceptual processing

In contrast to perceptual tasks, which enable concurrent processing of many stimuli, working memory (WM) tasks have a very small capacity, limiting cognitive skills. Training on WM tasks often yields substantial improvement, suggesting that training might increase the general WM capacity. To understand the underlying processes, we trained a test group with a newly designed tone manipulation WM task and a control group with a challenging perceptual task of pitch pattern discrimination. Functional magnetic resonance imaging (fMRI) scans confirmed that pretraining, manipulation was associated with a dorsal fronto-parietal WM network, while pitch comparison was associated with activation of ventral auditory regions. Training induced improvement in each group, which was limited to the trained task. Analyzing the behavior of the group trained with tone manipulation revealed that participants learned to replace active manipulation with a perceptual verification of the position of a single salient tone in the sequence presented as a tentative reply. Posttraining fMRI scans revealed modifications in ventral activation of both groups. Successful WMtrained participants learned to utilize auditory regions for the trained task. These observations suggest that the huge task-specific enhancement of WM capacity stems from a task-specific switch to perceptual routines, implemented in perceptual regions.

Seeing inferences: brain dynamics and oculomotor signatures of non-verbal deduction

We often express our thoughts through words, but thinking goes well beyond language. Here we focus on an elementary but basic thinking process, disjunction elimination, elicited by elementary visual scenes deprived of linguistic content, describing its neural and oculomotor correlates. We track two main components of a nonverbal deductive process: the construction of a logical representation (A or B), and its simplification by deduction (not A, therefore B). We identify the network active in the two phases and show that in the latter, but not in the former, it overlaps with areas known to respond to verbal logical reasoning. Oculomotor markers consistently differentiate logical processing induced by the construction of a representation, its simplification by deductive inference, and its maintenance when inferences cannot be drawn. Our results reveal how integrative logical processes incorporate novel experience in the flow of thoughts induced by visual scenes.

Correlates of poor insight: A comparative fMRI and sMRI study in obsessive-compulsive disorder and schizo-obsessive disorder

BACKGROUND

Despite the several researches on the correlates of insight in psychosis, less is known regarding the specificity of disease diagnosis on the relationship between insight and the correlates. The current study sought to explore the effects of insight and disease diagnosis on those in patients with obsessive-compulsive disorder (OCD) and patients with schizo-obsessive disorder (SOD).

METHODS

We evaluated clinical symptoms and neurocognitions among 111 patients (including 41 OCD with good insight, 40 OCD with poor insight, 14 SOD with good insight and 16 SOD with poor insight. Gray matter volume and spontaneous neural activity were also examined by analyzing the voxel-based morphometry and amplitude of low frequency fluctuation (ALFF), respectively.

RESULTS

Interactive effects of insight and diagnosis was found on working memory and the gray matter volume in right superior and middle temporal gyrus. Main effect of insight was found on working and visual memory, compulsion and obsession, and ALFF in right middle and superior occipital cortex. Main effect of diagnosis was found on severity of compulsion, relative verbal IQ, executive function, verbal and visual memory, working memory and ALFF in precuneus, medial superior frontal gyrus, anterior cingulate and paracingulate gyri, and inferior parietal, postcentral gyrus, paracentral lobule.

CONCLUSIONS

As a common feature in mental disorders, insight has its own special influence on neurocognition and possible structural/functional alterations in brain, and the influence is partly dependent of disease diagnosis.

Neural Contributions to Reduced Fluid Intelligence across the Adult Lifespan

Fluid intelligence, the ability to solve novel, complex problems, declines steeply during healthy human aging. Using fMRI, fluid intelligence has been repeatedly associated with activation of a frontoparietal brain network, and impairment following focal damage to these regions suggests that fluid intelligence depends on their integrity. It is therefore possible that age-related functional differences in frontoparietal activity contribute to the reduction in fluid intelligence. This paper reports on analysis of the Cambridge Center for Ageing and Neuroscience data, a large, population-based cohort of healthy males and females across the adult lifespan. The data support a model in which age-related differences in fluid intelligence are partially mediated by the responsiveness of frontoparietal regions to novel problem-solving. We first replicate a prior finding of such mediation using an independent sample. We then precisely localize the mediating brain regions, and show that mediation is specifically associated with voxels most activated by cognitive demand, but not with voxels suppressed by cognitive demand. We quantify the robustness of this result to potential unmodeled confounders, and estimate the causal direction of the effects. Finally, exploratory analyses suggest that neural mediation of age-related differences in fluid intelligence is moderated by the variety of regular physical activities, more reliably than by their frequency or duration. An additional moderating role of the variety of nonphysical activities emerged when controlling for head motion. A better understanding of the mechanisms that link healthy aging with lower fluid intelligence may suggest strategies for mitigating such decline.SIGNIFICANCE STATEMENT Global populations are living longer, driving urgency to understand age-related cognitive declines. Fluid intelligence is of prime importance because it reflects performance across many domains, and declines especially steeply during healthy aging. Despite consensus that fluid intelligence is associated with particular frontoparietal brain regions, little research has investigated suggestions that under-responsiveness of these regions mediates age-related decline. We replicate a recent demonstration of such mediation, showing specific association with brain regions most activated by cognitive demand, and robustness to moderate confounding by unmodeled variables. By showing that this mediation model is moderated by the variety of regular physical activities, more reliably than by their frequency or duration, we identify a potential modifiable lifestyle factor that may help promote successful aging.

Graph lesion-deficit mapping of fluid intelligence

Fluid intelligence is arguably the defining feature of human cognition. Yet the nature of its relationship with the brain remains a contentious topic. Influential proposals drawing primarily on functional imaging data have implicated 'multiple demand' frontoparietal and more widely distributed cortical networks, but extant lesion-deficit studies with greater causal power are almost all small, methodologically constrained, and inconclusive. The task demands large samples of patients, comprehensive investigation of performance, fine-grained anatomical mapping, and robust lesion-deficit inference, yet to be brought to bear on it. We assessed 165 healthy controls and 227 frontal or non-frontal patients with unilateral brain lesions on the best-established test of fluid intelligence, Raven's Advanced Progressive Matrices, employing an array of lesion-deficit inferential models responsive to the potentially distributed nature of fluid intelligence. Non-parametric Bayesian stochastic block models were used to reveal the community structure of lesion deficit networks, disentangling functional from confounding pathological distributed effects. Impaired performance was confined to patients with frontal lesions [F(2,387) = 18.491; P < 0.001; frontal worse than non-frontal and healthy participants P < 0.01, P <0.001], more marked on the right than left [F(4,385) = 12.237; P < 0.001; right worse than left and healthy participants P < 0.01, P < 0.001]. Patients with non-frontal lesions were indistinguishable from controls and showed no modulation by laterality. Neither the presence nor the extent of multiple demand network involvement affected performance. Both conventional network-based statistics and non-parametric Bayesian stochastic block modelling heavily implicated the right frontal lobe. Crucially, this localization was confirmed on explicitly disentangling functional from pathology-driven effects within a layered stochastic block model, prominently highlighting a right frontal network involving middle and inferior frontal gyrus, pre- and post-central gyri, with a weak contribution from right superior parietal lobule. Similar results were obtained with standard lesion-deficit analyses. Our study represents the first large-scale investigation of the distributed neural substrates of fluid intelligence in the focally injured brain. Combining novel graph-based lesion-deficit mapping with detailed investigation of cognitive performance in a large sample of patients provides crucial information about the neural basis of intelligence. Our findings indicate that a set of predominantly right frontal regions, rather than a more widely distributed network, is critical to the high-level functions involved in fluid intelligence. Further they suggest that Raven's Advanced Progressive Matrices is a useful clinical index of fluid intelligence and a sensitive marker of right frontal lobe dysfunction.

Searching for a Relationship between Early Breastfeeding and Cognitive Development of Attention and Working Memory Capacity

Previous research consistently reported that subjects that were exclusively breastfed (eBF) vs. not-exclusively breastfed (neBF) during infancy (0-6 months) showed increased scores of general intelligence measures (e.g., the intelligence quotient). However, the existent literature largely neglected whether breastfeeding also affects specific cognitive processes, such as attention and working memory (WM) capacity. We tested whether eBF vs. neBF subjects showed performance differences in relation to these two core cognitive functions. The Attention Network Test (ANT), to measure alerting, orienting, and conflict, and the Change Colour Task (CCT), to measure visuospatial WM capacity, were administered to 144 participants divided according to age (6-, 10-, and 18-year-old participants) and breastfeeding (eBF or neBF during 0-6 months of life). Importantly, the sub-groups were homogenous in terms of maternal education, a factor potentially affecting the relation between breastfeeding and cognition. While we found increased performance as a function of participants' age in both tasks, we failed to observe effects related to breastfeeding, as evidenced by Bayesian analyses. These findings highlight for the first time that the pattern of nutrition provided during early infancy does not appear to affect the development of attention and WM capacity, at least starting from the age considered in the present study.

Improving fluid intelligence of children through working memory training: The role of inhibition control

Intelligence is strongly associated with working memory. Working memory training can improve fluid intelligence, but the underlying mechanism requires further investigation. Because inhibition control may play a key role in working memory training, this study investigated this process from an electrophysiological perspective. In total, 40 children aged 9 to 11 years were enrolled and randomly divided into a training group (n = 20) and a control group (n = 20). The training group received 20 days of working memory training, whereas the control group did not receive any training. Before and after the training period, all participants were tested using Raven's Standard Progressive Matrices (RSPM), and electrophysiological indicators were recorded while they performed go/no-go and Stroop tasks. The results revealed that relative to the control group, the training group had significantly improved RSPM scores in the test conducted after their training. For the go/no-go tasks, the training group exhibited a significant decrease in N2 amplitude, a significant increase in P3 amplitude, a significant decrease in theta band energy, and an improvement in response inhibition ability. No significant change was observed for the Stroop task. Correlation analysis revealed that an improvement in individual response inhibition can positively predict an improvement in fluid intelligence. These results suggest that working memory training enhances the fluid intelligence of children by enhancing their response inhibition ability.

Association of PPP1R1B polymorphisms with working memory in healthy Han Chinese adults

Aims

The dopamine- and cAMP-regulated phosphoprotein (DARPP-32), which is encoded by the PPP1R1B gene, plays a converging regulatory role in the central nervous system by mediating the actions of dopamine, serotonin, and glutamate. Previous studies have demonstrated that variations in genes related to the dopamine system influence working memory. The present study thus investigated whether polymorphisms in PPP1R1B gene were associated with working memory.

Materials and methods

A sample of 124 healthy Han Chinese were genotyped for three single nucleotide polymorphisms of PPP1R1B gene, namely rs12601930C/T, rs879606A/G, and rs3764352A/G, using polymerase chain reaction and restriction fragment length polymorphism analysis. Working memory performance was assessed using the Wisconsin Card Sorting Test (WCST).

Results

Significant differences were observed in the Total Correct (TC), Total Errors (TE), and Conceptual Level Responses (CLR) scores of the WCST among the three rs12601930C/T genotypes (p = 0.044, 0.044, and 0.047, respectively); in TC, TE, Non-Perseverative Errors (NPE), and CLR scores between participants with the CC and (CT + TT) rs12601930C/T polymorphism genotypes (p = 0.032, 0.032, 0.019, and 0.029, respectively); in TC, TE, Perseverative Errors (PE), NPE, and CLR scores between participants with the (CT + CC) and TT rs12601930C/T polymorphism genotypes (p = 0.001, 0.001, 0.011, 0.004, and 0.001, respectively); and in NPE and CLR scores between participants with the GG and (AG + AA) genotypes of the rs3764352A/G polymorphism (p = 0.011 and 0.010). Furthermore, for males only, there were significant differences in TC, TE, PE, NPE, and CLR scores among the rs12601930C/T genotypes (p = 0.020, 0.020, 0.037, 0.029, and 0.014, respectively) and NPE and CLR scores among the rs3764352 genotypes (p = 0.045 and 0.042).

Conclusion

PPP1R1B gene polymorphisms rs12601930C/T and rs3764352A/G might be associated with working memory assessed by the WCST in healthy Chinese adults, especially among males.

Cross-attractor repertoire provides new perspective on structure-function relationship in the brain

The brain exhibits complex intrinsic dynamics, i.e., spontaneously arising activity patterns without any external inputs or tasks. Such intrinsic dynamics and their alteration are thought to play crucial roles in typical as well as atypical cognitive functioning. Linking the ever-changing intrinsic dynamics to the rather static anatomy is a challenging endeavor. Dynamical systems models are important tools for understanding how structure and function are linked in the brain. Here, we provide a novel modeling framework to examine how functional connectivity depends on structural connectivity in the brain. Existing modeling frameworks typically focus on noise-driven (or stochastic) dynamics near a single attractor. Complementing existing approaches, we examine deterministic features of the distribution of attractors, in particular, how regional states are correlated across all attractors - cross-attractor coordination. We found that cross-attractor coordination between brain regions better predicts human functional connectivity than noise-driven single-attractor dynamics. Importantly, cross-attractor coordination better accounts for the nonlinear dependency of functional connectivity on structural connectivity. Our findings suggest that functional connectivity patterns in the brain may reflect transitions between attractors, which impose an energy cost. The framework may be used to predict transitions and energy costs associated with experimental or clinical interventions.

Optimizing the Development of Space-Temporal Orientation in Physical Education and Sports Lessons for Students Aged 8–11 Years

The purpose of this research was to analyze how we can improve the space–temporal orientation ability with the help of physical exercises in physical education and sports lessons. In total,148 children between the ages of 8 and 11 participated in this study (M = 9.70; SD = 0.79). They were subjected to three tests, which measured general intelligence (Raven Progressive Matrices) and space–temporal orientation skills (Piaget-Head test and Bender–Santucci test). The tests were carried out both in the pre-test and in the post-test period. In the case of participants in the experimental group, a specific program was applied for a period of 12 weeks. The results showed that general intelligence level was identified as a predictor of spatial–temporal orientation (beta = 0.17, t = 2.08, p = 0.03) but only for the Piaget-Head test. Similarly, no differences between children’s age groups were identified in any of the spatial–temporal orientation test scores. However, children in the “+9” age category had higher scores on the intelligence test compared to younger children (77.31 vs. 35.70). In conclusion, the intervention program had a positive effect on spatial orientation skills.

Statistical quantification of confounding bias in machine learning models

BACKGROUND

The lack of nonparametric statistical tests for confounding bias significantly hampers the development of robust, valid, and generalizable predictive models in many fields of research. Here I propose the partial confounder test, which, for a given confounder variable, probes the null hypotheses of the model being unconfounded.

RESULTS

The test provides a strict control for type I errors and high statistical power, even for nonnormally and nonlinearly dependent predictions, often seen in machine learning. Applying the proposed test on models trained on large-scale functional brain connectivity data (N= 1,865) (i) reveals previously unreported confounders and (ii) shows that state-of-the-art confound mitigation approaches may fail preventing confounder bias in several cases.

CONCLUSIONS

The proposed test (implemented in the package mlconfound; https://mlconfound.readthedocs.io) can aid the assessment and improvement of the generalizability and validity of predictive models and, thereby, fosters the development of clinically useful machine learning biomarkers.

Intelligenzdiagnostik der Zukunft

Zusammenfassung. Die menschliche Intelligenz ist eines der am besten erforschten und validierten Konstrukte innerhalb der Psychologie. Dennoch wird die Validität von Intelligenztests im gruppen- und insbesondere kulturvergleichenden Kontext regelmäßig und berechtigterweise kritisch hinterfragt. Obwohl verschiedene Alternativen und Weiterentwicklungen der Intelligenzdiagnostik vorgeschlagen wurden (z. B. kulturfaire Tests), sind fundamentale Probleme in der vergleichenden Intelligenzdiagnostik noch immer ungelöst und die Validitäten entsprechender Verfahren unklar. In dem vorliegenden Positionspapier wird diese Thematik aus der Perspektive der Kognitionspsychologie und der kognitiven Neurowissenschaften beleuchtet und eine prozessorientierte und biologisch inspirierte Form der Intelligenzdiagnostik als potentieller Lösungsansatz vorgeschlagen. Wir zeigen die Bedeutung elementarer kognitiver Prozesse auf (insbesondere Arbeitsgedächtniskapazität, Aufmerksamkeit, Verarbeitungsgeschwindigkeit), die individuellen Leistungsunterschieden zu Grunde liegen, und betonen, dass der Unterscheidung zwischen Inhalten und Prozessen eine zentrale, jedoch oft vernachlässigte Rolle in der Diagnostik allgemeiner kognitiver Leistungsunterschiede zukommt. Während aus kognitions- und neuropsychologischer Sicht davon ausgegangen werden kann, dass sich insbesondere Prozesse für interkulturelle Vergleiche eignen, sollten Inhalte als stärker kulturspezifisch verstanden werden. Darauf aufbauend diskutieren wir drei verschiedene Ansätze zur Verbesserung interkultureller Vergleichbarkeit der Intelligenzdiagnostik sowie deren Grenzen. Wir postulieren, dass sich die Intelligenzforschung im Austausch mit verschiedenen Disziplinen stärker auf die Identifikation von generellen kognitiven Prozessen fokussieren sollte und diskutieren das Potenzial zukünftiger Forschung hin zu einer prozessorientierten und biologisch inspirierten Intelligenzdiagnostik. Schließlich zeigen wir derzeitige Möglichkeiten auf, gehen aber auch auf etwaige Herausforderungen ein und beleuchten Implikationen für die zukünftige Intelligenzdiagnostik und -forschung.

Structural and functional variations in the prefrontal cortex are associated with learning in pre-adolescent common marmosets (Callithrix jacchus)

There is substantial evidence linking the prefrontal cortex (PFC) to a variety of cognitive abilities, with adolescence being a critical period in its development. In the current study, we investigated the neural basis of differences in learning in pre-adolescent common marmosets. At 8 months old, marmosets were given anatomical and resting state MRI scans (n=24). At 9 months old, association learning and inhibitory control was tested using a 'go/no go' visual discrimination (VD) task. Marmosets were grouped into 'learners' (n=12) and 'non-learners' (n=12), and associations between cognitive performance and sub-regional PFC volumes, as well as PFC connectivity patterns, were investigated. 'Learners' had significantly (p<0.05) larger volumes of areas 11, 25, 47 and 32 than 'non-learners', although 'non-learners' had significantly larger volumes of areas 24a and 8v than 'learners'. There was also a significant correlation between average % correct responses to the 'punished' stimulus and volume of area 47. Further, 'non-learners' had significantly greater global PFC connections, as well as significantly greater numbers of connections between the PFC and basal ganglia, cerebellum and hippocampus, compared to 'non-learners'. These results suggest that larger sub-regions of the orbitofrontal cortex and ventromedial PFC, as well more refined PFC connectivity patterns to other brain regions associated with learning, may be important in successful response inhibition. This study therefore offers new information on the neurodevelopment of individual differences in cognition during pre-adolescence in non-human primates.

An Investigation of Working Memory Profile and Fluid Intelligence in Children With Neurodevelopmental Difficulties

The present study aims to evaluate the distinct patterns of working memory (WM) capacity of children with Developmental Language Disorder (DLD), High-functioning children with Autism Spectrum Disorder (ASD) and children with Down syndrome (DS). More specifically, the current study investigates the complex relationship of fluid intelligence and WM between 39 children with DLD, 20 H igh-functioning children with ASD, and 15 children with DS. All children were evaluated in different measures of Phonological Working Memory, Visual-spatial Working Memory whereas Fluid Intelligence was measured with Raven Progressive Matrices. The result analysis revealed a significant difference among the three groups, both among each function separately and the correlations among them, as well. The results revealed that the DLD groups and High-functioning ASD group exhibited a common picture or an overlap of performances in all Phonological and Visuo-spatial working memory measures, except Backward Digit Recall task. As for the DS group research findings revealed different and unique working memory patterns in comparison to DLD group and High-functioning ASD. Their differences have been studied and further conclusions have been drawn about the different patterns of working memory among the three clinical groups. The implications of these findings are discussed in light of support for learning. The common profile that characterize the two developmental conditions and the distinct pattern of working memory performance in DS group underlies the need for further research in the field.

Spatio-Temporal Brain Dynamic Differences in Fluid Intelligence

Human fluid intelligence is closely linked to the sequential solving of complex problems. It has been associated with a distributed cognitive control or multiple-demand (MD) network, comprising regions of lateral frontal, insular, dorsomedial frontal, and parietal cortex. Previous neuroimaging research suggests that the MD network may orchestrate the allocation of attentional resources to individual parts of a complex task: in a complex target detection task with multiple independent rules, applied one at a time, reduced response to rule-critical events across the MD network in lower fluid intelligence was observed. This was in particular the case with increasing task complexity (i.e., larger sets of rules), and was accompanied by impairment in performance. Here, we examined the early spatiotemporal neural dynamics of this process in electroencephalography (EEG) source analyses using a similar task paradigm. Levels of fluid intelligence specifically predicted early neural responses in a left inferiorparietal MD region around 200-300 ms post stimulus onset. Evoked source amplitudes in left parietal cortex within this early time window also correlated with behavioural performance measures. Like in previous research, we observed impaired performance in lower fluid intelligence with increasing number of task rules. This links fluid intelligence to a process of attentional focus on those parts of a task that are most critical for the current behaviour. Within the MD system, our time re-resolved measures suggest that the left parietal cortex specifically impacts on early processes of attentional focus on task critical features. This is novel evidence on the neurocognitive correlates of fluid intelligence suggesting that individual differences are critically linked to an early process of attentional focus on task-relevant information, which is supported by left parietal MD regions.

Fluid Intelligence Moderates the Link Between Psychopathy and Aggression Differently for Men and Women

Research on sex differences in the association of psychopathy with fluid intelligence is limited, and it remains unknown if fluid intelligence plays a meaningful role in explaining the psychopathy-aggression link for men and women. The present study aimed to test for sex differences in the relation between the four-facet model of psychopathy and intelligence, and to assess whether fluid intelligence moderates the link between psychopathy and aggression. In a community sample of men (n = 356) and women (n = 196), we assessed psychopathy using the Psychopathy Checklist: Screening Version (PCL:SV), fluid intelligence using the Raven's Progressive Matrices, and types of aggression using the Aggression Questionnaire (AQ). Hierarchical regressions showed that the psychopathy lifestyle facet was negatively associated with intelligence and there were no sex differences. Our analyses for types of aggression revealed sex differences and similarities. For both men and women, total AQ scores were predicted by higher antisocial facet scores. Lower intelligence moderated the link between higher antisocial facet scores and aggression in men, but not for women. Physical aggression in women was associated with higher interpersonal, affective, and antisocial facet scores, whereas for men, it was only associated with higher antisocial facet scores. Verbal and indirect aggression were associated with higher intelligence in both men and women. For men only, higher antisocial facet scores were associated with verbal and indirect aggression. Higher intelligence moderated the link between the lifestyle facet and indirect aggression for women, whereas for men, it moderated the link between the affective facet and indirect aggression. This study further highlights sex differences in mechanisms of psychopathy-related aggression, which need to be considered in the development of violence interventions and risk assessment.

Comparison of factor analysis models applied to the NCANDA neuropsychological test battery

The factor structure of neuropsychological functioning among a large sample (N = 831) of American youth (ages 12-21 at baseline) was investigated in order to identify an optimal model. Candidate models were selected based on their potential to provide service to the study of adolescent development and the effects of heavy episodic alcohol consumption. Data on neuropsychological functioning were obtained from the NCANDA study. This is a longitudinal community study of the effects of alcohol exposure on neurodevelopment. Three conceptually motivated and one empirically motivated factor analysis model of neuropsychological domains were compared based on penalized-likelihood selection criteria and model fit statistics. Two conceptually-motivated models were found to have adequate fit and pattern invariance to function as a measurement model for the Penn Computerized Neurocognitive Battery (Penn CNB) anchored neuropsychological battery in NCANDA. Corroboration of previous factor analysis models was obtained, in addition to the identification of an alternative factor model that has higher discriminant capacity for neuropsychological domains hypothesized to be most sensitive to alcohol exposure in human adolescents. The findings support the use of a factor model developed originally for the Penn CNB and a model developed specifically for the NCANDA project. The NCANDA 8-Factor Model has conceptual and empirical advantages that were identified in the current and prior studies. These advantages are particularly valuable when applied in alcohol research settings.

Instruction-based learning: A review

Humans are able to learn to implement novel rules from instructions rapidly, which is termed "instruction-based learning" (IBL). This remarkable ability is very important in our daily life in both learning individually or working as a team, and almost every psychology experiment starts with instructing participants. Many recent progresses have been made in IBL research both psychologically and neuroscientifically. In this review, we discuss the role of language in IBL, the importance of the first trial performance in IBL, why IBL should be considered as a goal-directed behavior, intelligence and IBL, cognitive flexibility and IBL, how behaviorally relevant information is processed in the lateral prefrontal cortex (LPFC), how the lateral frontal cortex (LFC) networks work as a functional hierarchy during IBL, and the cortical and subcortical contributions to IBL. Finally, we develop a neural working model for IBL and provide some sensible directions for future research.

The effect of personality characteristics combined with behavioral activation system (BAS) / behavioral inhibition system (BIS) and sport emotional induction on working memory

Study aim: To investigate the interactive effects of behavioral activation or behavioral inhibition and induced pleasant or unpleasant emotions on working memory performance among young adults.

Material and methods: Eighty participants (age 18–32 years) were randomly classified into 2 groups behavioral activation or inhibition and then sub-divided further via whether they received inductions in either pleasant emotions or unpleasant emotions. A pre-test measure of the working memory test was recorded and repeated after emotional induction.

Results: The ANOVA analysis indicated significant effect of PERSONALITY factor (F1,76 = 4.760, p = 0.0322, ηp 2 = 0.059) on results of the working memory test. The memory performance of the behavioral activation group that received positive emotion was better than the other groups, and the working memory performance of the behavioral inhibition group that received positive emotion was poorer than the other groups. Furthermore, the working memory performance of the behavioral activation group (positive emotion) was better than the other groups. Also, the working memory performance of the behavioral inhibition group (positive emotion) was poorer than the other groups.

Conclusions: Finally, the findings support the different effects of the brain behavioral systems and sport emotional induction on working memory.

Neurofeedback Training Based on Motor Imagery Strategies Increases EEG Complexity in Elderly Population

Neurofeedback training (NFT) has shown promising results in recent years as a tool to address the effects of age-related cognitive decline in the elderly. Since previous studies have linked reduced complexity of electroencephalography (EEG) signal to the process of cognitive decline, we propose the use of non-linear methods to characterise changes in EEG complexity induced by NFT. In this study, we analyse the pre- and post-training EEG from 11 elderly subjects who performed an NFT based on motor imagery (MI-NFT). Spectral changes were studied using relative power (RP) from classical frequency bands (delta, theta, alpha, and beta), whilst multiscale entropy (MSE) was applied to assess EEG-induced complexity changes. Furthermore, we analysed the subject's scores from Luria tests performed before and after MI-NFT. We found that MI-NFT induced a power shift towards rapid frequencies, as well as an increase of EEG complexity in all channels, except for C3. These improvements were most evident in frontal channels. Moreover, results from cognitive tests showed significant enhancement in intellectual and memory functions. Therefore, our findings suggest the usefulness of MI-NFT to improve cognitive functions in the elderly and encourage future studies to use MSE as a metric to characterise EEG changes induced by MI-NFT.

Two Sides of the Same Coin: Fluid Intelligence and Crystallized Intelligence as Cognitive Reserve Predictors of Social Cognition and Executive Functions Among Vulnerable Elderly People

The concept of cognitive reserve -CR- postulates two forms that prevent cognitive impairment: neural reserve and neural compensation. Both have been primarily linked to the protective role played by genetic factors, educational level, occupation or socioeconomic status. Though it is true that it has been related to executive functions, so far very little attention has been paid to its predictive capacity with other variables more related to social cognition and psychosocial adaptation. Considering socially vulnerable contexts with reduced cultural capital and educational levels, the neural reserve function would be the most relevant and best predictor of aspects related to social cognition and executive functions. We suggest that variables such as fluid and crystallized intelligence influence social cognition and executive functions. This study included a sample of 27 participants over 60 years old from varied contexts of social vulnerability. The procedure included data collection using various cognitive measures. Results show that elderly people with high intelligence-mainly fluid intelligence-have better executive functions, emotional recognition and theory of mind. These results focus on cognitive reserve and its importance because they show that elderly people in vulnerable contexts who strengthen these aspects protect themselves against the deterioration of cognitive skills. This study is the first preliminary research to present a relationship between cognitive reserve and social cognition factors in elderly subjects. Fluid intelligence functions as a highly related factor to protect the performance of executive functions, along with other social-cognitive factors relevant to facilitating the conditions of social adaptation.

Fluid intelligence and the locus coeruleus-norepinephrine system

The last decade has seen significant progress identifying genetic and brain differences related to intelligence. However, there remain considerable gaps in our understanding of how cognitive mechanisms that underpin intelligence map onto various brain functions. In this article, we argue that the locus coeruleus-norepinephrine system is essential for understanding the biological basis of intelligence. We review evidence suggesting that the locus coeruleus-norepinephrine system plays a central role at all levels of brain function, from metabolic processes to the organization of large-scale brain networks. We connect this evidence with our executive attention view of working-memory capacity and fluid intelligence and present analyses on baseline pupil size, an indicator of locus coeruleus activity. Using a latent variable approach, our analyses showed that a common executive attention factor predicted baseline pupil size. Additionally, the executive attention function of disengagement--not maintenance--uniquely predicted baseline pupil size. These findings suggest that the ability to control attention may be important for understanding how cognitive mechanisms of fluid intelligence map onto the locus coeruleus-norepinephrine system. We discuss how further research is needed to better understand the relationships between fluid intelligence, the locus coeruleus-norepinephrine system, and functionally organized brain networks.

High-definition transcranial direct current stimulation modulates performance and alpha/beta parieto-frontal connectivity serving fluid intelligence

Fluid intelligence (Gƒ) includes logical reasoning abilities and is an essential component of normative cognition. Despite the broad consensus that parieto-prefrontal connectivity is critical for Gƒ (e.g. the parieto-frontal integration theory of intelligence, P-FIT), the dynamics of such functional connectivity during logical reasoning remains poorly understood. Further, given the known importance of these brain regions for Gƒ, numerous studies have targeted one or both of these areas with non-invasive stimulation with the goal of improving Gƒ, but to date there remains little consensus on the overall stimulation-related effects. To examine this, we applied high-definition direct current anodal stimulation to the left and right dorsolateral prefrontal cortex (DLPFC) of 24 healthy adults for 20 min in three separate sessions (sham, left, and right active). Following stimulation, participants completed a logical reasoning task during magnetoencephalography (MEG). Significant neural responses at the sensor-level were imaged using a beamformer, and peak task-induced activity was subjected to dynamic functional connectivity analyses to evaluate the impact of distinct stimulation montages on network activity. We found that participants responded faster following right DLPFC stimulation vs. sham. Moreover, our neural findings followed a similar trajectory of effects such that left parieto-frontal connectivity decreased following right and left DLPFC stimulation compared to sham, with connectivity following right stimulation being significantly correlated with the faster reaction times. Importantly, our findings are consistent with P-FIT, as well as the neural efficiency hypothesis (NEH) of intelligence. In sum, this study provides evidence for beneficial effects of right DLPFC stimulation on logical reasoning. KEY POINTS: Logical reasoning is an indispensable component of fluid intelligence and involves multispectral oscillatory activity in parietal and frontal regions. Parieto-frontal integration is well characterized in logical reasoning; however, its direct neural quantification and neuromodulation by brain stimulation remain poorly understood. High-definition transcranial direct current stimulation of dorsolateral prefrontal cortex (DLPFC) had modulatory effects on task performance and neural interactions serving logical reasoning, with right stimulation showing beneficial effects. Right DLPFC stimulation led to a decrease in the response time (i.e. better task performance) and left parieto-frontal connectivity with a marginal positive association between behavioural and neural metrics. Other modes of targeted stimulation of DLPFC (e.g. frequency-specific) can be employed in future studies.

Brain-wide functional connectivity patterns support general cognitive ability and mediate effects of socioeconomic status in youth

General cognitive ability (GCA) is an individual difference dimension linked to important academic, occupational, and health-related outcomes and its development is strongly linked to differences in socioeconomic status (SES). Complex abilities of the human brain are realized through interconnections among distributed brain regions, but brain-wide connectivity patterns associated with GCA in youth, and the influence of SES on these connectivity patterns, are poorly understood. The present study examined functional connectomes from 5937 9- and 10-year-olds in the Adolescent Brain Cognitive Development (ABCD) multi-site study. Using multivariate predictive modeling methods, we identified whole-brain functional connectivity patterns linked to GCA. In leave-one-site-out cross-validation, we found these connectivity patterns exhibited strong and statistically reliable generalization at 19 out of 19 held-out sites accounting for 18.0% of the variance in GCA scores (cross-validated partial η²). GCA-related connections were remarkably dispersed across brain networks: across 120 sets of connections linking pairs of large-scale networks, significantly elevated GCA-related connectivity was found in 110 of them, and differences in levels of GCA-related connectivity across brain networks were notably modest. Consistent with prior work, socioeconomic status was a strong predictor of GCA in this sample, and we found that distributed GCA-related brain connectivity patterns significantly statistically mediated this relationship (mean proportion mediated: 15.6%, p < 2 × 10^-16). These results demonstrate that socioeconomic status and GCA are related to broad and diffuse differences in functional connectivity architecture during early adolescence, potentially suggesting a mechanism through which socioeconomic status influences cognitive development.

Neuroanatomical differences in the memory systems of intellectual giftedness and typical development

INTRODUCTION

Studying neuro-structural markers of intellectual giftedness (IG) will inform scientific understanding of the processes helping children excel academically.

METHODS

Structural and diffusion-weighted MRI was used to compare regional brain shape and connectivity of 12 children with average to high average IQ and 18 IG children, defined as having IQ greater than 145.

RESULTS

IG had larger subcortical structures and more robust white matter microstructural organization between those structures in regions associated with explicit memory. TD had more connected, larger subcortical structures in regions associated with implicit memory.

CONCLUSIONS

It was found that the memory systems within brains of children with exceptional intellectual abilities are differently sized and connected compared to the brains of typically developing children. These different neurodevelopmental trajectories suggest different learning strategies. A spectrum of intelligence types is envisioned, facilitated by different ratios of implicit and explicit system, which was validated using a large external dataset.

The fronto-parietal network is not a flexible hub during naturalistic cognition

The fronto‐parietal network (FPN) is crucial for cognitively demanding tasks as it selectively represents task‐relevant information and controls other brain regions. To implement these functions, it has been argued that it is a flexible hub that reconfigures its functional connectivity with other networks. This was supported by a study in which a set of demanding tasks were presented, that varied in their sensory features, comparison rules, and response mappings, and the FPN showed greater reconfiguration of functional connectivity between tasks than any other network. However, this task set was designed to engage the FPN, and therefore it remains an open question whether the FPN is in a flexible hub in general or only for such task sets. Using two freely available datasets (Experiment 1, N = 15, Experiment 2, N = 644), we examined dynamic functional connectivity during naturalistic cognition, while participants watched a movie. Many differences in the flexibility were found across networks but the FPN was not the most flexible hub in the brain, during either movie for any of two measures, using a regression model or a correlation model and across five timescales. We, therefore, conclude that the FPN does not have the trait of being a flexible hub, although it may adopt this state for particular task sets.

Regional radiomics similarity networks (R2SNs) in the human brain: Reproducibility, small-world properties and a biological basis

A structural covariance network (SCN) has been used successfully in structural magnetic resonance imaging (sMRI) studies. However, most SCNs have been constructed by a unitary marker that is insensitive for discriminating different disease phases. The aim of this study was to devise a novel regional radiomics similarity network (R2SN) that could provide more comprehensive information in morphological network analysis. R2SNs were constructed by computing the Pearson correlations between the radiomics features extracted from any pair of regions for each subject (AAL atlas). We further assessed the small-world property of R2SNs, and we evaluated the reproducibility in different datasets and through test-retest analysis. The relationships between the R2SNs and general intelligence/interregional coexpression of genes were also explored. R2SNs could be replicated in different datasets, regardless of the use of different feature subsets. R2SNs showed high reproducibility in the test-retest analysis (intraclass correlation coefficient > 0.7). In addition, the small-word property (σ > 2) and the high correlation between gene expression (R = 0.29, p < 0.001) and general intelligence were determined for R2SNs. Furthermore, the results have also been repeated in the Brainnetome atlas. R2SNs provide a novel, reliable, and biologically plausible method to understand human morphological covariance based on sMRI.

Intergenerational transmission of the patterns of functional and structural brain networks

There is clear evidence of intergenerational transmission of life values, cognitive traits, psychiatric disorders, and even aspects of daily decision making. To investigate biological substrates of this phenomenon, the brain has received increasing attention as a measurable biomarker and potential target for intervention. However, no previous study has quantitatively and comprehensively investigated the effects of intergenerational transmission on functional and structural brain networks. Here, by employing an unusually large cohort dataset (N = 84 parent-child dyads; 45 sons, 39 daughters, 81 mothers, and 3 fathers), we show that patterns of functional and structural brain networks are preserved over a generation. We also demonstrate that several demographic factors and behavioral/physiological phenotypes have a relationship with brain similarity. Collectively, our results provide a comprehensive picture of neurobiological substrates of intergenerational transmission and demonstrate the usability of our dataset for investigating the neurobiological substrates of intergenerational transmission.

Multiview Diffusion Map Improves Prediction of Fluid Intelligence With Two Paradigms of fMRI Analysis

OBJECTIVE

To understand the association between brain networks and behaviors of an individual, most studies build predictive models based on functional connectivity (FC) from a single dataset with linear analysis techniques. Such approaches may fail to capture the nonlinear structure of brain networks and neglect the complementary information contained in FC networks (FCNs) from multiple datasets. To address this challenging issue, we use multiview dimensionality reduction to extract a coherent low-dimensional representation of the FCNs from resting-state and emotion identification task-based functional magnetic resonance imaging (fMRI) datasets.

METHODS

We propose a scheme based on multiview diffusion map to extract intrinsic features while preserving the underlying geometric structure of high dimensional datasets. This method is robust to noise and small variations in the data.

RESULTS

After validation on the Philadelphia Neurodevelopmental Cohort data, the predictive model built from both resting-state and emotion identification task-based fMRI datasets outperforms the one using each individual fMRI dataset. In addition, the proposed model achieves better prediction performance than principal component analysis (PCA) and three other competing data fusion methods.

CONCLUSION

Our framework for combing multiple FCNs in one predictive model exhibits improved prediction performance.

SIGNIFICANCE

To our knowledge, we demonstrate a first application of multiview diffusion map to successfully fuse different types of fMRI data for predicting fluid intelligence (gF).

Distinct Regionalization Patterns of Cortical Morphology are Associated with Cognitive Performance Across Different Domains

Cognitive performance in children is predictive of academic and social outcomes; therefore, understanding neurobiological mechanisms underlying individual differences in cognition during development may be important for improving quality of life. The belief that a single, psychological construct underlies many cognitive processes is pervasive throughout society. However, it is unclear if there is a consistent neural substrate underlying many cognitive processes. Here, we show that a distributed configuration of cortical surface area and apparent thickness, when controlling for global imaging measures, is differentially associated with cognitive performance on different types of tasks in a large sample (N = 10 145) of 9-11-year-old children from the Adolescent Brain and Cognitive DevelopmentSM (ABCD) study. The minimal overlap in these regionalization patterns of association has implications for competing theories about developing intellectual functions. Surprisingly, not controlling for sociodemographic factors increased the similarity between these regionalization patterns. This highlights the importance of understanding the shared variance between sociodemographic factors, cognition and brain structure, particularly with a population-based sample such as ABCD.

A Cortical Surface-Based Meta-Analysis of Human Reasoning

Recent advances in neuroimaging have augmented numerous findings in the human reasoning process but have yielded varying results. One possibility for this inconsistency is that reasoning is such an intricate cognitive process, involving attention, memory, executive functions, symbolic processing, and fluid intelligence, whereby various brain regions are inevitably implicated in orchestrating the process. Therefore, researchers have used meta-analyses for a better understanding of neural mechanisms of reasoning. However, previous meta-analysis techniques include weaknesses such as an inadequate representation of the cortical surface’s highly folded geometry. Accordingly, we developed a new meta-analysis method called Bayesian meta-analysis of the cortical surface (BMACS). BMACS offers a fast, accurate, and accessible inference of the spatial patterns of cognitive processes from peak brain activations across studies by applying spatial point processes to the cortical surface. Using BMACS, we found that the common pattern of activations from inductive and deductive reasoning was colocalized with the multiple-demand system, indicating that reasoning is a high-level convergence of complex cognitive processes. We hope surface-based meta-analysis will be facilitated by BMACS, bringing more profound knowledge of various cognitive processes.

New insights into human prefrontal cortex aging with a lipidomics approach

INTRODUCTION

Human prefrontal cortex (hPFC) is a recent evolutionarily developed brain region involved in cognitive functions. Human cognitive functions decline during aging. Yet the molecular mechanisms underlying the functional deterioration of the neural cells of this brain region still remain to be fully described.

AREAS COVERED

In this review, we explore the role of lipids in hPFC aging. Firstly, we briefly consider the approaches used to identify lipid species in brain tissue with special attention paid to a lipidomics analysis. Then, as the evolution process has conferred a specific lipid profile on the hPFC, we consider the lipidome of hPFC. In addition, the role of lipids in hPFC aging, and in particular, the cognitive decline associated with aging, is discussed. Finally, nutritional and pharmacological interventions designed to modulate this process are examined. It is suggested that the dysfunction of key cellular processes secondarily to the damage of lipid membrane underlies the cognitive decline of hPFC during aging.

EXPERT OPINION

Lipidomics methods are and will continue to be key tools in the effort to gain additional insights into the aging of the human brain.

Frontoparietal theta tACS nonselectively enhances encoding, maintenance, and retrieval stages in visuospatial working memory

Neurobiological and cognitive evidence suggests that working memory is processed through three distinctive and well-characterized phases: encoding, maintenance, and retrieval. Several studies have reported that applying theta transcranial alternating current stimulation (tACS) to the right prefrontal and parietal cortices can significantly improve visual working memory performance. However, it remains unclear whether the facilitative effect of tACS on visual working memory is due to a domain-general or stage-specific process. In this study, we combined pre-task right frontoparietal theta tACS (6 Hz, 15 min) with a stage-specific change detection paradigm that provided retro-cues during various stages of working memory. This stage-specific tagging via the use of retro-cues enabled us to probe whether theta tACS would create a nonspecific/additive effect that is equal in magnitude across all cognitive stages or would create a stage-specific effect that is interactive with the retro-cue in a particular stage (e.g., maintenance, retrieval). We observed significant retro-cue and theta tACS effects on visual working memory performance, but no interaction between them. This finding suggests that the aforementioned two factors can facilitate visual working memory processing independently in an additive manner. Furthermore, low-performers benefited more from tACS, and their VWM deficit seemed to have originated from the second half of the memory retention stage, which possibly suggests faster memory decay as the key to poor VWM performance. Together, we conclude that frontoparietal theta tACS likely creates a domain-general boost in visual attention, which in turn benefits overall visual working memory processes that are not specific to the information maintenance or retrieval stages.

Neuroimaging evidence for a network sampling theory of individual differences in human intelligence test performance

Despite a century of research, it remains unclear whether human intelligence should be studied as one dominant, several major, or many distinct abilities, and how such abilities relate to the functional organisation of the brain. Here, we combine psychometric and machine learning methods to examine in a data-driven manner how factor structure and individual variability in cognitive-task performance relate to dynamic-network connectomics. We report that 12 sub-tasks from an established intelligence test can be accurately multi-way classified (74%, chance 8.3%) based on the network states that they evoke. The proximities of the tasks in behavioural-psychometric space correlate with the similarities of their network states. Furthermore, the network states were more accurately classified for higher relative to lower performing individuals. These results suggest that the human brain uses a high-dimensional network-sampling mechanism to flexibly code for diverse cognitive tasks. Population variability in intelligence test performance relates to the fidelity of expression of these task-optimised network states.

Overlapping and dissociable brain activations for fluid intelligence and executive functions

Cognitive enhancement interventions aimed at boosting human fluid intelligence (gf) have targeted executive functions (EFs), such as updating, inhibition, and switching, in the context of transfer-inducing cognitive training. However, even though the link between EFs and gf has been demonstrated at the psychometric level, their neurofunctional overlap has not been quantitatively investigated. Identifying whether and how EFs and gf might share neural activation patterns could provide important insights into the overall hierarchical organization of human higher-order cognition, as well as suggest specific targets for interventions aimed at maximizing cognitive transfer. We present the results of a quantitative meta-analysis of the available fMRI and PET literature on EFs and gf in humans, showing the similarity between gf and (i) the overall global EF network, as well as (ii) specific maps for updating, switching, and inhibition. Results highlight a higher degree of similarity between gf and updating (80% overlap) compared with gf and inhibition (34%), and gf and switching (17%). Moreover, three brain regions activated for both gf and each of the three EFs also were identified, located in the left middle frontal gyrus, left inferior parietal lobule, and anterior cingulate cortex. Finally, resting-state functional connectivity analysis on two independent fMRI datasets showed the preferential behavioural correlation and anatomical overlap between updating and gf. These findings confirm a close link between gf and EFs, with implications for brain stimulation and cognitive training interventions.

Brain Entropy During Aging Through a Free Energy Principle Approach

Neural complexity and brain entropy (BEN) have gained greater interest in recent years. The dynamics of neural signals and their relations with information processing continue to be investigated through different measures in a variety of noteworthy studies. The BEN of spontaneous neural activity decreases during states of reduced consciousness. This evidence has been showed in primary consciousness states, such as psychedelic states, under the name of "the entropic brain hypothesis." In this manuscript we propose an extension of this hypothesis to physiological and pathological aging. We review this particular facet of the complexity of the brain, mentioning studies that have investigated BEN in primary consciousness states, and extending this view to the field of neuroaging with a focus on resting-state functional Magnetic Resonance Imaging. We first introduce historic and conceptual ideas about entropy and neural complexity, treating the mindbrain as a complex nonlinear dynamic adaptive system, in light of the free energy principle. Then, we review the studies in this field, analyzing the idea that the aim of the neurocognitive system is to maintain a dynamic state of balance between order and chaos, both in terms of dynamics of neural signals and functional connectivity. In our exploration we will review studies both on acute psychedelic states and more chronic psychotic states and traits, such as those in schizophrenia, in order to show the increase of entropy in those states. Then we extend our exploration to physiological and pathological aging, where BEN is reduced. Finally, we propose an interpretation of these results, defining a general trend of BEN in primary states and cognitive aging.

Sleep and self-regulation in early childhood

What is the role of sleep in children's behavioral, emotional, and cognitive regulation? This chapter considers theoretical and conceptual links between sleep and self-regulation, with special attention to sleep and self-regulation in early childhood. We selectively review the growing body of research on associations between sleep and self-regulation, mentioning some methodological issues. We also consider how child characteristics and sociocontextual factors may interact with sleep in the development of self-regulation in early childhood. We provide some relevant empirical examples from our own research.

Towards an interdisciplinary framework about intelligence

In recent years, advances in science, technology, and the way in which we view our world have led to an increasingly broad use of the term "intelligence". As we learn more about biological systems, we find more and more examples of complex and precise adaptive behavior in animals and plants. Similarly, as we build more complex computational systems, we recognize the emergence of highly sophisticated structures capable of solving increasingly complex problems. These behaviors show characteristics in common with the sort of complex behaviors and learning capabilities we find in humans, and therefore it is common to see them referred to as "intelligent". These analogies are problematic as the term intelligence is inextricably associated with human-like capabilities. While these issues have been discussed by leading researchers of AI and renowned psychologists and biologists highlighting the commonalities and differences between AI and biological intelligence, there have been few rigorous attempts to create an interdisciplinary approach to the modern problem of intelligence. This article proposes a comparative framework to discuss what we call "purposeful behavior", a characteristic shared by systems capable of gathering and processing information from their surroundings and modifying their actions in order to fulfill a series of implicit or explicit goals. Our aim is twofold: on the one hand, the term purposeful behavior allows us to describe the behavior of these systems without using the term "intelligence", avoiding the comparison with human capabilities. On the other hand, we hope that our framework encourages interdisciplinary discussion to help advance our understanding of the relationships among different systems and their capabilities.