Biology subjects

Hilger, K.

Publications and source records attributed to Hilger, K..

18 recordsLinked to original sources

bioRxiv2026

Mismatch Negativity and General Cognitive Ability - A Meta-Analysis

Intelligence or general cognitive ability (GCA) is fundamental to human behavior and cognition. It impacts important life outcomes like educational success and even health and longevity have been related to differences in GCA. Understanding the biological basis of such individual variations presents a crucial goal of neuroscience. The mismatch negativity (MMN) is an event-related brain potential that can be measured when, within a series of frequent standard stimuli, rare deviants are presented, and is suggested to reflect conscious (active) or unconscious (passive) detection processes of the brain. Importantly, variations in MMN amplitude and latency have been linked to differences in GCA. Yet findings vary considerably. This preregistered meta-analysis provides a comprehensive and structured overview of the current state of research. Following the study selection process in accordance with the PRISMA guidelines, and the rating of study design quality with the Study Design and Implementation Assessment Device for Individual Difference Research (DIAD-ID), the association between GCA and MMN amplitude and latency was examined in 695 healthy adults across 13 included studies. The estimated across-sample associations between MMN and GCA were small, but significant (MMN amplitude GCA: r = -0.08, MMN latency GCA: r = -0.13; p < 0.05) and moderators were identified. Between-sample heterogeneity was moderate, with no evidence of publication bias. Our findings suggest that higher cognitive ability is associated with slightly stronger and faster MMN responses. However, the low estimated across-sample effect sizes and the small number of included studies also highlight the need for more research.

neuroscience↗

bioRxiv2026

Human Cognitive Ability and the P300 Event-Related Brain Potential

Human intelligence is essential to understand complex ideas, to engage in various forms of reasoning, to learn from experience, and to adapt to new situations by taking thought. The P300 event-related brain potential has been related to intelligence scores and thus represents as a promising biomarker of general cognitive ability. However, empirical results are characterized by enormous heterogeneity, and a quantitative assessment of this literature is lacking. This preregistered meta-analysis provides the first systematic overview of neuroscientific studies that have examined associations between general cognitive ability and the P300 in healthy adult participants. Out of 5641 articles screened, 49 studies with up to 381 effects were eligible for PRISMA-based meta-analytic comparison. Study quality was evaluated using a novel Study Design and Implementation Assessment Device, which we developed particularly for Individual Difference Research (DIAD-ID) and provide together with our analysis code as free online resources to support future meta-analyses. Confirming our hypotheses, a small but significant positive across-study association was observed for general cognitive ability and P300 amplitudes (r = .13; 95%-CI [.06, .19]), while a significant negative across-study association was revealed for P300 latencies (r = -.18; 95%-CI [-.24, -.13]). Study heterogeneity was substantial, and sub-analyses highlighted potential moderators such as type of the task during EEG recording. We discuss limitations, open questions, and provide concrete guidelines for future research on the neurobiological underpinnings of individual differences in cognitive ability.

neuroscience↗

bioRxiv2026

Individual Dynamics in Stress-Related Pain Responses

BackgroundStress and pain are adaptive, bidirectionally related systems, modulated by shared factors. However, how stress reactivity relates to individual differences in pain-related distress and pain sensitivity remains unclear. This preregistered study therefore investigates how physiological reactivity to acute stress relates to trait pain-related distress and stress-induced changes in pain sensitivity, and whether pain-related distress acts as moderator. MethodsTrait pain-related distress was assessed in 148 healthy males using questionnaires. Baseline blood pressure, pulse rate, alpha-amylase, and cortisol were obtained as well as initial heat pain thresholds and tolerances. One group underwent the Maastricht Acute Stress Task, while another group performed the placebo version. Consecutively all stress- and experimental pain indicators were examined again. ResultsTrait pain-related distress was not associated with stress reactivity (MAST-induced changes in physiological stress indicators), while stress-induced changes in pain sensitivity showed high individual variability, but were not associated with stress reactivity. Finally, we found preliminary evidence that in individuals with a higher tendency to catastrophize and to fear pain, stronger alpha-amylase increases were associated with larger stress-induced increases in pain threshold (p-FDR = .07). ConclusionOur study illustrates the idiosyncrasy of the complex interplay between trait pain-related distress, physiological stress reactivity, and stress-induced changes in pain sensitivity. While our preliminary results require replication, they suggest that stable individual differences influence the link between stress and pain beyond physiology. This underscores the importance of considering trait differences in future research on stress-pain interactions to, ultimately, better tailoring preventions and treatments for patients with chronic pain.

neuroscience↗

bioRxiv2026

The Neural Code of Neuroticism

High neuroticism is a risk factor for mental disorders. Understanding whether individuals share a common foundation in brain function underlying neuroticism is therefore an essential goal of neuroscience. We applied Inter-Subject Representational Similarity Analysis on data from 174 Human Connectome Project participants to investigate if their similarity in neuroticism is reflected in their similarity of fMRI-recorded brain activity during movie watching. To test behavioral personality theories that consider trait expression as dependent on situational context, we examined whether brain-trait representational similarity varied between trait-relevant and trait-irrelevant movie scenes (independently rated, N = 86). Higher neuroticism was associated with greater heterogeneity in brain responses, a pattern that was particularly pronounced during trait-relevant scenes. Our study informs dimensional conceptualizations of psychopathology viewing neuroticism as risk factor and extends behavioral personality theories to the neural level. Broadly, it highlights the value of naturalistic imaging and underscores the importance of stimulus selection when investigating brain-behavior associations.

neuroscience↗

bioRxiv2025

Trait-Relevant Tasks Improve Personality Prediction from Structural-Functional Brain Network Coupling

Personality traits capture stable patterns of behavior and thought, and neurobiological correlates were identified in structural and functional brain networks. Here, we investigate whether the coupling between structural and functional brain networks (SC-FC coupling), during resting state and seven tasks of varying trait-relevance, is associated with individual differences in the Big Five personality traits. We used diffusion-weighted and functional magnetic resonance imaging from 764 participants of the Human Connectome Project and modelled individual differences in SC-FC coupling with similarity and communication measures. These measures approximate functional interactions unfolding on top of the structural connectome and were set in relation to individual variations in personality traits. Significant associations were only observed during trait-relevant tasks: for agreeableness during social cognition, and conscientiousness could be predicted from task-general coupling patterns. We conclude that optimizing trait-relevance of tasks during neuroscientific measurements presents a promising means to increase effect sizes in studies on brain-behavior associations.

neuroscience↗

bioRxiv2025

Network Neuroscience of Human Multitasking: Local Features Matter

The neural basis of multitasking costs is subject to continuing debate. Cognitive theories assume that overlap of task representations may lead to between-task crosstalk in concurrent task processing and thus requires cognitive control. Recent research suggests that modality-based crosstalk contributes to multitasking costs, involving central overlap of modality-specific representations. Consistently increased costs for specific modality pairings (visual-vocal and auditory-manual vs. visual-manual and auditory-vocal) were demonstrated (modality-compatibility effect), which were recently linked to representational overlap in the auditory cortex. However, it remains unclear whether modality-based crosstalk emerges from overlapping patterns of global brain connectivity and whether resolving it requires additional involvement of cognitive control as reflected in the fronto-parietal control network. This preregistered functional imaging study investigates these questions in 64 healthy, young human adults. Specifically, we focus on the modality-compatibility effect in multitasking by employing functional connectivity (FC) analysis. First, we tested the FC similarity between the single-task networks. Second, we compared the strength of the control network in whole-brain FC between dual tasks. We found no evidence for different FC similarities of single-task networks between modality pairings and no additional involvement of the control network during dual tasks. However, post-hoc connectivity analysis revealed a brain-behavior correlation for the modality-compatibility effect in dual tasks. This effect was locally restricted to FC between lateral frontal and sensory auditory regions, providing evidence for the modality-based crosstalk theory. More generally, the findings suggest that robust behavioral differences in multitasking are not necessarily related to global functional connectivity differences but to local connectivity changes. SignificanceOur lives are dominated by multitasking. Understanding the neurocognitive mechanisms of multitasking and its associated limitations is relevant for safety-relevant consequences. Here, we investigate functional brain-connectivity patterns associated with modality-based crosstalk in multitasking, describing unintentional exchange of information between two tasks, leading to robust dual-task costs. Our network analyses revealed no significant difference between single or dual tasks with varying degrees of modality overlap on the whole-brain level. However, specific connections between cognitive control-related frontal and sensory-related regions were associated with individual multitasking performance, emphasizing that behavioral differences can arise from specific neural interactions rather than from widespread network reconfigurations. These findings advance our understanding of the underlying mechanisms responsible for modality-based crosstalk, providing practical implications for human-machine interactions.

neuroscience↗

bioRxiv2025

Decoding the Human Brain during Intelligence Testing

Understanding the brain mechanisms underlying complex human cognition is a major objective in neuroscience. Previous studies have identified neural correlates of intelligence at different temporal and spatial scales using functional magnetic resonance imaging (fMRI) and electroencephalography (EEG) separately. This study treats intelligence as a multilayer phenomenon across temporal and spatial scales and examines how the connectedness of brain regions as well as the complexity of multilayer brain dynamics relates to performance in an established intelligence test. Graph-theoretical analyses of fMRI-derived functional connectivity (N=67) revealed that the connectedness of frontal and parietal regions was associated with individual performance. Further, multiscale entropy analyses of EEG signals (N=131) disclosed that higher test scores were linked to more complex long-range processes and, at a trend level, to less complex short-range processes. These findings support the Multilayer Processing Theory, proposing the interplay between flexible long-range and modular short-range processes as the neural bases of intelligence.

neuroscience↗

bioRxiv2024

Structural-Functional Brain Network Coupling During Task Performance Reveals Intelligence-Relevant Communication Strategies

Intelligence is a broad mental capability influencing human performance across tasks. Individual differences in intelligence have been linked to characteristics of structural and functional brain networks. Here, we consider their alignment, the structural-functional brain network coupling (SC-FC coupling) during resting state and during active cognition, to predict general intelligence. Using DWI and fMRI data from 764 participants of the Human Connectome Project (Replication: N1=126, N2=180), we modelled SC-FC coupling with similarity and communication measures that capture functional interactions unfolding on top of structural brain networks. By accounting for variations in brain region-specific neural signaling strategies, we show that individual differences in SC-FC coupling patterns predict individual intelligence scores. Most accurate predictions resulted from cognitively demanding tasks and task combinations. Our study suggests the existence of an intrinsic SC-FC coupling organization enabling fine-drawn intelligence-relevant adaptations that support efficient information processing by facilitating brain region-specific adjustment to external task demands.

neuroscience↗

bioRxiv2024

Individual Differences in the Correspondence Between Psychological and Physiological Stress Indicators

Stress threatens physical and mental health. Reactions to acute stress comprise multiple levels, including negative thoughts, bodily symptoms and behaviors. Individuals differ in their reaction to acute stress, and importantly, also in the extent to which these levels align, with a closer correspondence between psychological and physiological stress indicators being beneficial for mental health and well-being. This preregistered study investigates such individual differences systematically by inducing psychological (social-evaluative) and physiological (cold water) stress with the Maastricht Acute Stress Test (MAST) in 149 healthy adults. Participants indicated their perceived stress and four physiological stress indicators (blood pressure, heart rate, salivary cortisol, alpha-amylase) were obtained. Finally, multiple personality traits were assessed as potential moderators, including the Big Five, trait anxiety, and general cognitive ability. In line with previous research, psychological and physiological stress indicators were only weakly correlated and Bayesian analyses provided evidence favoring the absence of close psychophysiological correspondence. Considering individual differences in personality, especially conscientiousness and openness emerged as potential moderators. We propose individual differences in interoceptive abilities as another critical moderator, which deserves further investigation, and discuss how future research on individual differences in psycho-physiological correspondence can contribute to further our understanding of mental and physical diseases.

physiology↗

bioRxiv2024

Association between Adolescent Brain Morphometry and Cognitive Function: Insights from a Cross-Sectional Analysis of ABCD Data from 9 to 15 Years Old

Significant changes occur in brain structure and cognitive abilities during adolescence. Investigating their association can provide insight into brain-based cognitive development, yet previous studies were limited by narrow brain measures, small samples, and lacking focus on age-related variation. Here, we analyzed a large cohort (N = 8,534, age 9-15) from the Adolescent Brain Cognitive Development dataset. Using structural MRI and diffusion imaging, we derived 16 regional structural measures and integrated them via morphometric similarity networks to characterize 16,563 regional, connectivity, and hub features. We applied large-scale computational models to investigate their associations with performance on seven cognitive subtests and general intelligence (g), as well as age-related changes. Brain areas most strongly associated with cognitive ability also showed the greatest age-related variability in these associations, located primarily in the frontal, temporal, and occipital lobes. Structural MRI measures exhibited stronger associations with cognition and greater age-related variability than diffusion-derived metrics, while global hub measures showed stronger and more variable associations than local measures. Overall, our study provides a comprehensive and reliable understanding of brain structure-cognition associations during adolescence.

neuroscience↗

bioRxiv2023

Can machine learning-based predictive modelling improve our understanding of human cognition?

A growing body of research predicts individual cognitive ability levels from brain characteristics including functional brain connectivity. The majority of this research achieves good prediction performance but provides limited insight into neurobiological processes underlying the predicted concepts. The insufficient identification of predictive characteristics may present an important factor critically contributing to this constraint. Here, we encourage to design predictive modelling 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, system-wide functional characteristics complement intelligence-relevant connectivity of brain regions proposed by established intelligence theories. In sum, our study showcases how future predictive studies on human cognition can enhance explanatory value by prioritizing a systematic evaluation of predictive characteristics over maximizing prediction performance. Significance StatementIntelligence represents a hallmark of human behavior, and a surge number of studies predicted individual scores from functional brain connectivity. However, actual understanding about its neural basis remains limited. We demonstrate how predictive modelling can be applied strategically to improve tracing predictive functional brain connections to enhance our understanding of intelligence. Our study unveils crucial findings about intelligence: differences in the neural code of distinct intelligence facets not detectable on a behavioral level and a brain-wide distribution of functional brain characteristics relevant to intelligence that extends those proposed by major intelligence theories. In a broader context, it offers a framework for future prediction studies that prioritize meaningful insights into the neural basis of complex human traits over predictive performance.

neuroscience↗

bioRxiv2023

Modulation of Human Frontal Midline Theta by Neurofeedback: A Systematic Review and Quantitative Meta-Analysis

Human brain activity consists of different frequency bands associated with varying functions. Oscillatory activity of frontal brain regions in the theta range (4-8Hz) is linked to cognitive processing and can be modulated by neurofeedback - a technique where participants receive real-time feedback about their brain activity and learn to modulate it. However, criticism of this technique evolved, and high heterogeneity of study designs complicates a valid evaluation of its effectiveness. This meta-analysis provides the first systematic overview over studies attempting to modulate frontal midline theta with neurofeedback in healthy human participants. Out of 1431 articles screened, 14 studies were eligible for systematic review and 11 for quantitative meta-analyses. Studies were evaluated following the DIAD model and the PRISMA guidelines. A significant across-study effect of medium size (Hedges g = .66; 95%-CI [-0.62, 1.73]) with substantial between-study heterogeneity (Q(16) = 167.43, p < .0001) was observed and subanalysis revealed effective frontal midline theta upregulation. We discuss moderators of effect sizes and provide guidelines for future research in this dynamic field.

neuroscience↗

bioRxiv2023

Structural-Functional Brain Network Coupling Predicts Human Cognitive Ability

Individual differences in general cognitive ability (GCA) have a biological basis within the structure and function of the human brain. Network neuroscience investigations revealed neural correlates of GCA in structural as well as in functional brain networks. However, whether the relationship between structural and functional networks, the structural-functional brain network coupling (SC-FC coupling), is related to individual differences in GCA remains an open question. We used data from 1030 adults of the Human Connectome Project, derived structural connectivity from diffusion weighted imaging, functional connectivity from resting-state fMRI, and assessed GCA as a latent g-factor from 12 cognitive tasks. Two similarity measures and six communication measures were used to model possible functional interactions arising from structural brain networks. SC-FC coupling was estimated as the degree to which these measures align with the actual functional connectivity, providing insights into different neural communication strategies. At the whole-brain level, higher GCA was associated with higher SC-FC coupling, but only when considering path transitivity as neural communication strategy. Taking region-specific variations in the SC-FC coupling strategy into account and differentiating between positive and negative associations with GCA, allows for prediction of individual cognitive ability scores in a cross-validated prediction framework (correlation between predicted and observed scores: r = .25, p < .001). The same model also predicts GCA scores in a completely independent sample (N = 567, r = .19, p < .001). Our results propose structural-functional brain network coupling as a neurobiological correlate of GCA and suggest brain region-specific coupling strategies as neural basis of efficient information processing predictive of cognitive ability.

neuroscience↗

bioRxiv2022

How much data do we need? Lower bounds of brain activation states to predict human cognitive ability

Human functional brain connectivity can be temporally decomposed into states of high and low cofluctuation, defined as coactivation of brain regions over time. Rare states of particularly high cofluctuation have been shown to reflect fundamentals of intrinsic functional network architecture and to be highly subject-specific. However, it is unclear whether such network-defining states also contribute to individual variations in cognitive abilities - which strongly rely on the interactions among distributed brain regions. By introducing CMEP, a new eigenvector-based prediction framework, we show that as few as 16 temporally separated time frames (< 1.5% of 10min resting-state fMRI) can significantly predict individual differences in intelligence (N = 263, p < .001). Against previous expectations, individuals network-defining time frames of particularly high cofluctuation do not predict intelligence. Multiple functional brain networks contribute to the prediction, and all results replicate in an independent sample (N = 831). Our results suggest that although fundamentals of person-specific functional connectomes can be derived from few time frames of highest connectivity, temporally distributed information is necessary to extract information about cognitive abilities. This information is not restricted to specific connectivity states, like network-defining high-cofluctuation states, but rather reflected across the entire length of the brain connectivity time series.

neuroscience↗

bioRxiv2022

Multimodal Brain Signal Complexity Predicts Human Intelligence

Spontaneous brain activity builds the foundation for human cognitive processing during external demands. Neuroimaging studies based on functional magnetic resonance imaging (fMRI) identified specific characteristics of spontaneous (intrinsic) brain dynamics to be associated with individual differences in general cognitive ability, i.e., intelligence. However, fMRI research is inherently limited by low temporal resolution, thus, preventing conclusions about neural fluctuations within the range of milliseconds. Here, we used resting-state electroencephalographical (EEG) recordings from 144 healthy adults to test whether individual differences in intelligence (Ravens Advanced Progressive Matrices scores) can be predicted from the complexity of temporally highly resolved intrinsic brain signals. We compared different operationalizations of brain signal complexity (multiscale entropy, Shannon entropy, Fuzzy entropy, and specific characteristics of microstates) regarding their relation to intelligence. The results indicate that associations between brain signal complexity measures and intelligence are of small effect sizes (r ~ .20) and vary across different spatial and temporal scales. Specifically, higher intelligence scores were associated with lower complexity in local aspects of neural processing, and less activity in task-negative brain regions belonging to the defaultmode network. Finally, we combined multiple measures of brain signal complexity to show that individual intelligence scores can be significantly predicted with a multimodal model within the sample (10-fold cross-validation) as well as in an independent sample (external replication, N = 57). In sum, our results highlight the temporal and spatial dependency of associations between intelligence and intrinsic brain dynamics, proposing multimodal approaches as promising means for future neuroscientific research on complex human traits. Significance StatementSpontaneous brain activity builds the foundation for intelligent processing - the ability of humans to adapt to various cognitive demands. Using resting-state EEG, we extracted multiple aspects of temporally highly resolved intrinsic brain dynamics to investigate their relationship with individual differences in intelligence. Single associations were of small effect sizes and varied critically across spatial and temporal scales. However, combining multiple measures in a multimodal cross-validated prediction model, allows to significantly predict individual intelligence scores in unseen participants. Our study adds to a growing body of research suggesting that observable associations between complex human traits and neural parameters might be rather small and proposes multimodal prediction approaches as promising tool to derive robust brain-behavior relations despite limited sample sizes.

neuroscience↗

bioRxiv2022

Intelligence and Visual Mismatch Negativity: Is Pre-Attentive Visual Discrimination Related to General Cognitive Ability?

Electroencephalography (EEG) has been used for decades to identify neurocognitive processes related to intelligence. Evidence is accumulating for associations with neural markers of higher-order cognitive processes (e.g., working memory); however, whether associations are specific to complex processes or also relate to earlier processing stages remains unclear. Addressing these issues has implications for improving our understanding of intelligence and its neural correlates. The mismatch negativity (MMN) is an event-related brain potential (ERP) that is elicited when, within a series of frequent standard stimuli, rare deviant stimuli are presented. As stimuli are typically presented outside the focus of attention, the MMN is suggested to capture automatic pre-attentive discrimination processes. However, the MMN and its relation to intelligence has largely only been studied in the auditory domain, thus preventing conclusions about the involvement of automatic discrimination processes in humans dominant sensory modality vision. Electroencephalography was recorded from 50 healthy participants during a passive visual oddball task that presented simple sequence violations as well as deviations within a more complex hidden pattern. Signed area amplitudes and fractional area latencies of the visual mismatch negativity (vMMN) were calculated with and without Laplacian transformation. Correlations between vMMN and intelligence (Ravens Advanced Progressive Matrices) were of negligible to small effect sizes, differed critically between measurement approaches, and Bayes Factors provided anecdotal to substantial evidence for the absence of an association. We discuss differences between the auditory and visual MMN, the implications of different measurement approaches, and offer recommendations for further research in this evolving field. HIGHLIGHTS- Testing whether intelligence is related to automatic visual discrimination - Visual mismatch negativity (vMMN) as a neural indicator of pre-attentive processing - No association between intelligence and vMMN amplitudes or latencies - Critical differences between auditory and visual MMN? - Results partly depend on different measurement approaches

neuroscience↗

bioRxiv2021

Multi-Task Brain Network Reconfiguration is Inversely Associated with Human Intelligence

Intelligence describes the general cognitive ability level of a person. It is one of the most fundamental concepts in psychological science and is crucial for effective adaption of behavior to varying environmental demands. Changing external task demands have been shown to induce reconfiguration of functional brain networks. However, whether neural reconfiguration between different tasks is associated with intelligence has not yet been investigated. We used fMRI data from 812 subjects to show that higher scores of general intelligence are related to less brain network reconfiguration between resting state and seven different task states as well as to network reconfiguration between tasks. This association holds for all functional brain networks except the motor system, and replicates in two independent samples (N = 138, N = 184). Our findings suggest that the intrinsic network architecture of individuals with higher intelligence scores is closer to the network architecture as required by various cognitive demands. Multi-task brain network reconfiguration may, therefore, represent a neural reflection of the behavioral positive manifold - the essence of the concept of general intelligence. Finally, our results support neural efficiency theories of cognitive ability and reveal insights into human intelligence as an emergent property from a distributed multi-task brain network.

neuroscience↗

bioRxiv2018

ADHD Symptoms are Associated with the Modular Structure of Intrinsic Brain Networks in a Representative Sample of Healthy Adults

Attention-deficit/hyperactivity disorder (ADHD) is one of the most common neurodevelopmental disorders with significant and often lifelong effects on social, emotional, and cognitive functioning. Influential neurocognitive models of ADHD link behavioral symptoms to altered connections between and within functional brain networks. Here, we investigate whether network-based theories of ADHD can be generalized to understanding variations in ADHD-related behaviors within the normal (i.e., clinically unaffected) adult population. In a large and representative sample, self-rated presence of ADHD symptoms varied widely; only eight out of 291 participants scored in the clinical range. Subject-specific brain-network graphs were modeled from functional MRI resting-state data and revealed significant associations between (non-clinical) ADHD symptoms and region-specific profiles of between-module and within-module connectivity. Effects were located in brain regions associated with multiple neuronal systems including the default-mode network, the salience network, and the central executive system. Our results are consistent with network perspectives of ADHD and provide further evidence for the relevance of an appropriate information transfer between task-negative (default-mode) and task-positive brain regions. More generally, our findings support a dimensional conceptualization of ADHD and contribute to a growing understanding of cognition as an emerging property of functional brain networks. Author SummaryNeurocognitive models of ADHD link behavioral symptoms to altered connections between and within functional brain networks. We investigate whether these network-based theories of ADHD can be generalized to ADHD-related behaviors within the normal adult population. Subject-specific brain graphs were modeled from functional MRI resting-state data of a large and representative sample (N = 291). Significant associations between ADHD-related behaviors and region-specific profiles of between-module and within-module connectivity were observed in brain regions associated with multiple functional systems including the default-mode network, the salience network, and the central executive system. Our results support a dimensional conceptualization of ADHD and enforce network-based models of ADHD by providing further evidence for the relevance of an appropriate information transfer between task-negative (default-mode) and task-positive brain regions.

neuroscience↗