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Brain mechanisms of error processing have often been investigated using response interference tasks and focusing on the posterior medial frontal cortex, which is also implicated in resolving response conflict in general. Thereby, the role other brain regions may play has remained undervalued. Here, activation likelihood estimation meta-analyses were used to synthesize the neuroimaging literature on brain activity related to committing errors versus responding successfully in interference tasks and to test for commonalities and differences. The salience network and the temporoparietal junction were commonly recruited irrespective of whether responses were correct or incorrect, pointing towards a general involvement in coping with situations that call for increased cognitive control. The dorsal posterior cingulate cortex, posterior thalamus, and left superior frontal gyrus showed error-specific convergence, which underscores their consistent involvement when performance goals are not met. In contrast, successful responding revealed stronger convergence in the dorsal attention network and lateral prefrontal regions. Underrecruiting these regions in error trials may reflect failures in activating the task-appropriate stimulus-response contingencies necessary for successful response execution.

Current major efforts in human neuroimaging research aim to understand individual differences and identify biomarkers for clinical applications. One particularly promising approach is the prediction of individual-level behavioural phenotypes (e.g. treatment response, cognition) from brain imaging data. An essential prerequisite to identify replicable brain-behaviour prediction models is sufficient measurement reliability. By attenuating the relationship between two variables, low reliability increases the sample size necessary to identify an effect, making large datasets a necessity rather than an advantage. While previous work has evaluated the reliability of brain-based measures, the impact of the reliability of behavioural phenotypes has been largely neglected, as target selection for prediction is often guided by scientific interest or data availability. Here we demonstrate the impact of low phenotypic reliability on individual-level prediction performance. Using simulated and empirical data from the Human Connectome Projects, we found that even moderate reliability levels of commonly used behavioural phenotypes can markedly limit the ability to link brain and behaviour when underlying relations are weak. Next, using 5000 subjects from the UK Biobank, we show that highly reliable data in smaller samples outperform large amounts of moderately reliable data. These findings suggest that research programmes focused on identifying generalizable brain–behaviour associations must seriously consider the reliability of outcome measures. Ultimately, a stronger focus on reliability will help reduce the financial and societal costs incurred in acquiring large-scale datasets with unreliable “markers” of behaviour.

The human brain operates in large-scale functional networks. These networks are an expression of temporally correlated activity across brain regions, but how global network properties relate to the neural dynamics of individual regions remains incompletely understood. Here, we show that the brain’s network architecture is tightly linked to critical episodes of neural regularity, visible as spontaneous “complexity drops” in functional magnetic resonance imaging signals. These episodes closely explain functional connectivity strength between regions, subserve the propagation of neural activity patterns, and reflect interindividual differences in age and behavior. Furthermore, complexity drops define neural activity states that dynamically shape the connectivity strength, topological configuration, and hierarchy of brain networks and comprehensively explain known structure-function relationships within the brain. These findings delineate a principled complexity architecture of neural activity—a human “complexome” that underpins the brain’s functional network organization.

Delay discounting is a measure of impulsive choice relevant in adolescence as it predicts many real-life outcomes, including substance use disorders, obesity, and academic achievement. However, the functional networks underlying individual differences in delay discounting during youth remain incompletely described. Here we investigate the association between multivariate patterns of functional connectivity and individual differences in impulsive choice in a large sample of youth. A total of 293 youth (9-23 years) completed a delay discounting task and underwent resting-state fMRI at 3T. A connectome-wide analysis using multivariate distance-based matrix regression was used to examine whole-brain relationships between delay discounting and functional connectivity was then performed. These analyses revealed that individual differences in delay discounting were associated with patterns of connectivity emanating from the left dorsal prefrontal cortex, a hub of the default mode network. Delay discounting was associated with greater functional connectivity between the dorsal prefrontal cortex and other parts of the default mode network, and reduced connectivity with regions in the dorsal and ventral attention networks. These results suggest that delay discounting in youth is associated with individual differences in relationships both within the default mode network and between the default mode and networks involved in attentional and cognitive control.

Healthy aging is associated with altered executive functioning (EF). Earlier studies found age-related differences in EF performance to be partially accounted for by changes in resting-state functional connectivity (RSFC) within brain networks associated with EF. However, it remains unclear which role RSFC in EF-associated networks plays as a marker for individual differences in EF performance. Here, we investigated to what degree individual abilities across 3 different EF tasks can be predicted from RSFC within EF-related, perceptuo-motor, whole-brain, and random networks separately in young and old adults. Specifically, we were interested if (i) young and old adults differ in predictability depending on network or EF demand level (high vs. low), (ii) an EF-related network outperforms EF-unspecific networks when predicting EF abilities, and (iii) this pattern changes with demand level. Both our uni- and multivariate analysis frameworks analyzing interactions between age × demand level × networks revealed overall low prediction accuracies and a general lack of specificity regarding neurobiological networks for predicting EF abilities. This questions the idea of finding markers for individual EF performance in RSFC patterns and calls for future research replicating the current approach in different task states, brain modalities, different, larger samples, and with more comprehensive behavioral measures.

The neural underpinnings of individual identity reflected in cognition, behavior, and disease remain elusive. Functional connectivity profiles have been used as a "fingerprint" with which an individual can be identified in a dataset. These established functional connectivity fingerprints generally show high accuracy but are still sensitive to mental states. A truly unique, and especially state-independent, neural fingerprint will shed light on fundamental intra-individual brain organization. Moreover, a fingerprint that also captures inter-individual differences in brain-behavior associations will provide the necessary ingredients for the development of biomarkers for precision medicine. With resting-state and task fMRI-data of the Human Connectome Project and the enhanced Nathan Kline Institute sample, we show that the local functional fingerprint, and especially regional homogeneity (ReHo), is 1) a highly accurate neural fingerprint, 2) more stable within an individual regardless of their mental state (compared to the baseline functional connectome fingerprint), and 3) captures specific inter-individual differences. Our findings are replicable across parcellations as well as resilient to confounding effects. Further analyses showed that the attention networks and the Default Mode Network contributed most to individual "uniqueness". Moreover, with the OpenNeuro.ds000115 sample, we show that ReHo is also stable in individuals with schizophrenia and that its stability relates to intelligence subtest scores. Altogether, our findings show the potential of the application of local functional fingerprints in precision medicine.

The human brain operates in large-scale functional networks, collectively subsumed as the functional connectome1-13. Recent work has begun to unravel the organization of the connectome, including the temporal dynamics of brain states14-20, the trade-off between segregation and integration9,15,21-23, and a functional hierarchy from lower-order unimodal to higher-order transmodal processing systems24-27. However, it remains unknown how these network properties are embedded in the brain and if they emerge from a common neural foundation. Here we apply time-resolved estimation of brain signal complexity to uncover a unifying principle of brain organization, linking the connectome to neural variability6,28-31. Using functional magnetic resonance imaging (fMRI), we show that neural activity is marked by spontaneous "complexity drops" that reflect episodes of increased pattern regularity in the brain, and that functional connections among brain regions are an expression of their simultaneous engagement in such episodes. Moreover, these complexity drops ubiquitously propagate along cortical hierarchies, suggesting that the brain intrinsically reiterates its own functional architecture. Globally, neural activity clusters into temporal complexity states that dynamically shape the coupling strength and configuration of the connectome, implementing a continuous re-negotiation between cost-efficient segregation and communication-enhancing integration9,15,21,23. Furthermore, complexity states resolve the recently discovered association between anatomical and functional network hierarchies comprehensively25-27,32. Finally, brain signal complexity is highly sensitive to age and reflects inter-individual differences in cognition and motor function. In sum, we identify a spatiotemporal complexity architecture of neural activity - a functional "complexome" that gives rise to the network organization of the human brain.

Uncertainty is often inevitable in everyday life and can be both stressful and exciting.\ud Given its relevance to psychopathology and wellbeing, recent research has begun to\ud address the brain basis of uncertainty. In the current review we examined whether\ud there are discrete and shared neural signatures for different uncertain contexts.\ud From the literature we identified three broad categories of uncertainty currently\ud empirically studied using functional MRI (fMRI): basic threat and reward uncertainty,\ud decision-making under uncertainty, and associative learning under uncertainty. We\ud examined the neural basis of each category by using a coordinate based metaanalysis, where brain activation foci from previously published fMRI experiments\ud were drawn together (1998-2017; 87 studies). The analyses revealed shared and\ud discrete patterns of neural activation for uncertainty, such as the insula and\ud amygdala, depending on the category. Such findings will have relevance for\ud researchers attempting to conceptualise uncertainty, as well as clinical researchers\ud examining the neural basis of uncertainty in relation to psychopathology.