Threshold-free cluster enhancement (TFCE) is widely used for cluster-based inference in neuroimaging, but existing implementations typically rely on discretized approximations that may introduce numerical variability. We present eTFCE, an efficient framework that provides a numerically exact evaluation of the TFCE integral using an optimized cluster retrieval algorithm. Across multiple datasets, eTFCE and the standard implementation produce highly consistent inference results. Voxel-wise comparisons reveal a systematic asymmetry: the standard method yields smaller p-values for more voxels, while eTFCE concentrates stronger statistical evidence within a smaller subset. These differences are primarily confined to voxels near the inference boundary and have minimal impact on overall inference. This pattern is consistent with discretization effects in standard implementations, where the TFCE integral is approximated using a finite set of threshold levels, introducing subtle biases in statistical evidence accumulation across thresholds. Furthermore, eTFCE improves computational efficiency (71.3

Two permutation-based methods for simultaneous inference on the proportion of active voxels in cluster-wise brain imaging analysis have recently been published: Notip (Blain et al. 2022) and pARI (Andreella et al. 2023). Both rely on the definition of a critical vector of ordered p-values, chosen from a family of candidate vectors, but differ in how the family is defined: computed from randomization of external data for Notip and determined a priori for pARI. These procedures were compared to other proposals in the literature, but an extensive comparison between the two methods is missing due to their parallel publication. We provide such a comparison and find that pARI outperforms Notip if both methods are applied under their recommended settings. However, each method carries different advantages and drawbacks.

We propose a permutation-based method for testing a large collection of hypotheses simultaneously. Our method provides lower bounds for the number of true discoveries in any selected subset of hypotheses. These bounds are simultaneously valid with high confidence. The methodology is particularly useful in functional Magnetic Resonance Imaging cluster analysis, where it provides a confidence statement on the percentage of truly activated voxels within clusters of voxels, avoiding the well-known spatial specificity paradox. We offer a user-friendly tool to estimate the percentage of true discoveries for each cluster while controlling the family-wise error rate for multiple testing and taking into account that the cluster was chosen in a data-driven way. The method adapts to the spatial correlation structure that characterizes functional Magnetic Resonance Imaging data, gaining power over parametric approaches.

Cluster inference based on spatial extent thresholding is a popular analysis method multiple testing in spatial data, and is frequently used for finding activated brain areas in neuroimaging. However, the method has several well-known issues. While powerful for finding regions with some activation, the method as currently defined does not allow any further quantification or localisation of signal. In this paper, we repair this gap. We show that cluster-extent inference can be used (1) to infer the presence of signal in any region of interest and (2) to quantify the percentage of activation in such regions. These additional inferences come for free, i.e. they do not require any further adjustment of the alpha-level of tests, while retaining full family-wise error control. We achieve this extension of the possibilities of cluster inference by embedding the method into a closed testing procedure, and solving the graph-theoretic k-separator problem that results from this embedding. We demonstrate the usefulness of the improved method in a large-scale application to neuroimaging data from the Neurovault database.

Classical cluster inference is hampered by the spatial specificity paradox. Given the null-hypothesis of no active voxels, the alternative hypothesis states that there is at least one active voxel in a cluster. Hence, the larger the cluster the less we know about where activation in the cluster is. Rosenblatt et al. (2018) proposed a post-hoc inference method, All-resolutions Inference (ARI), that addresses this paradox by estimating the number of active voxels of any brain region. ARI allows users to choose arbitrary brain regions and returns a simultaneous lower confidence bound of the true discovery proportion (TDP) for each of them, retaining control of the family-wise error rate. ARI does not, however, guide users to regions with high enough TDP. In this paper, we propose an efficient algorithm that outputs all maximal supra-threshold clusters, for which ARI gives a TDP lower confidence bound that is at least a chosen threshold, for any number of thresholds that need not be chosen a priori nor all at once. After a preprocessing step in linearithmic time, the algorithm only takes linear time in the size of its output. We demonstrate the algorithm with an application to two fMRI datasets. For both datasets, we found several clusters whose TDP confidently meets or exceeds a given threshold in less than a second.

Frontal midline (FM) theta (4–8 Hz) reactivity to unexpected social rejection seems to be an important correlate of a neural threat-detection system. Neurovisceral integration theory proposes that the functioning of such systems is indexed by heart rate variability (HRV). Here, we tested this by examining whether baseline HRV predicts FM-theta reactivity to unexpected rejection feedback. Additionally, we examined whether this alleged heart-brain connection differs based on individual differences in personality and behavioral constructs relevant to social threat sensitivity. Female undergraduates (n = 149; mean age = 19.7 years) performed the social-judgment paradigm, where they communicated their expectations about being liked/disliked by unfamiliar peers who had allegedly evaluated them, and received peer-feedback indicating social acceptance/rejection. We used community structure analysis to subtract subgroups, based on self-esteem, social feedback expectations, and response speed of providing expectations. Results provided evidence of two distinct subgroups: optimistic vs. pessimistic in light of social threat. Baseline HRV did not predict FM-theta reactivity to unexpected rejection, and this relationship was not modulated by the subgroups. Both subgroups showed a significant FM-theta power increase following unexpected rejection. Additionally, the optimistic subgroup was uniquely characterized by a FM-theta power increase following rejection (as against acceptance) feedback. Supporting prior studies, our results suggest that enhanced FM-theta signals the need for cognitive control when faced with unexpected outcomes, and extend this by suggesting that differences in social threat sensitivity may determine whether an outcome is deemed meaningful enough to signal the need for cognitive control.

Capturing developmental and learning-induced brain dynamics is extremely challenging as changes occur interactively across multiple levels and emerging functions. Different levels include the (social) environment, cognitive and behavioral levels, structural and functional brain changes, and genetics, while functions include domains such as math, reading, and executive function. Here, we report the insights that emerged from the workshop "Capturing Developmental Brain Dynamics", organized to bring together multidisciplinary approaches to integrate data on development and learning across different levels, functions, and time points. During the workshop, current main gaps in our knowledge and tools were identified including the need for: (1) common frameworks, (2) longitudinal, large-scale, multisite studies using representative participant samples, (3) understanding interindividual variability, (4) explicit distinction of understanding versus predicting, and (5) reproducible research. After illustrating interactions across levels and functions during development, we discuss the identified gaps and provide solutions to advance the capturing of developmental brain dynamics.

As proposed in a prominent developmental model, social anxiety has different manifestations: social fear, shy temperament. anxious cognitions, and avoidance of social situations. Drawing from this model, we used the network approach to psychopathology to gain a detailed understanding of specific social anxiety components and their associations. The current article investigated (a) how social anxiety components are interconnected within a network, and (b) the consistency of the network over time, in a community sample of children and adolescents. Data from 3 waves of a longitudinal study were used. At Time 1 (T1) the total sample comprised 331 participants (M-age = 13.34 years); at Time 3 (T3) there were 236 participants (M-age = 17.48 years). Social anxiety components were assessed with self-report questionnaires. Networks of 15 nodes (i.e., components) were estimated. Network analysis of T1 components revealed 4 communities: cognitive, social-emotional. avoidance of performance, and avoidance of interaction situations. There were no direct connections between the cognitive and behavioral communities; social-emotional nodes appeared to act as bridge components between the 2 communities. A similar pattern of component associations and communities was found in the T2 and T3 networks. and the longitudinal network incorporating node change trajectories. Networks were estimated on group-level observational data and conclusions about cause-effect relationships are tentative. Although the sample size decreased across the 3 waves, the reliability of parameter estimates were minimally affected. Findings attest to the potential value of applying the network approach to investigate the pattern of associations among social anxiety components in youth.

Cross-validation is a statistical procedure that every psychologist should know. Most are possibly familiar with the procedure in a global way but have not used it for the analysis of their own data. We introduce cross-validation for the purpose of model selection in a general sense, as well as an R package we have developed for this kind of analysis, and we present examples illustrating the use of this package for types of research problems that are often encountered in the social sciences. Cross-validation can be an easy-to-use alternative to null-hypothesis testing, and it has the benefit that it does not make as many assumptions.

Data analysis workflows in many scientific domains have become increasingly complex and flexible. Here we assess the effect of this flexibility on the results of functional magnetic resonance imaging by asking 70 independent teams to analyse the same dataset, testing the same 9 ex-ante hypotheses1. The flexibility of analytical approaches is exemplified by the fact that no two teams chose identical workflows to analyse the data. This flexibility resulted in sizeable variation in the results of hypothesis tests, even for teams whose statistical maps were highly correlated at intermediate stages of the analysis pipeline. Variation in reported results was related to several aspects of analysis methodology. Notably, a meta-analytical approach that aggregated information across teams yielded a significant consensus in activated regions. Furthermore, prediction markets of researchers in the field revealed an overestimation of the likelihood of significant findings, even by researchers with direct knowledge of the dataset2–5. Our findings show that analytical flexibility can have substantial effects on scientific conclusions, and identify factors that may be related to variability in the analysis of functional magnetic resonance imaging. The results emphasize the importance of validating and sharing complex analysis workflows, and demonstrate the need for performing and reporting multiple analyses of the same data. Potential approaches that could be used to mitigate issues related to analytical variability are discussed. The results obtained by seventy different teams analysing the same functional magnetic resonance imaging dataset show substantial variation, highlighting the influence of analytical choices and the importance of sharing workflows publicly and performing multiple analyses.

Adolescents with attention-deficit/hyperactivity disorder (ADHD) are at increased risk of developing substance use disorders (SUDs) and nicotine dependence (ND). It remains unclear whether and how stimulant treatment may affect this risk. We aimed to investigate how stimulant use profiles influence the risk of SUDs and ND, using a novel data-driven community detection analysis to construct different stimulant use profiles. Comprehensive lifetime stimulant prescription data and data on SUDs and ND were available for 303 subjects with ADHD and 219 controls, with a mean age 16.3 years. Community detection was used to define subgroups based on multiple indicators of treatment history, start age, treatment duration, total dose, maximum dose, variability, stop age. In stimulant-treated participants, three subgroups with distinct medication trajectories were distinguished (late-and-moderately dosed, n = 91; early-and-moderately dosed, n = 51; early-and-intensely dosed, n = 103). Compared to stimulant-naïve participants (n = 58), the early-and-intense treatment group had a significantly lower risk of SUDs and ND (HR = 0.28, and HR = 0.29, respectively), while the early-and-moderate group had a significantly lower risk of ND only (HR = 0.30). The late-and-moderate group was at a significantly higher risk of ND compared to the other two treatment groups (HR = 2.66 for early-and-moderate, HR = 2.78 for early-and-intense). Our findings show that in stimulant-treated adolescents with ADHD, long-term outcomes are associated with treatment characteristics, something that is often ignored when treated individuals are compared to untreated individuals.

BACKGROUND:The assumption is that executive dysfunctions (EF), associated with frontal lobe injury, are responsible for behavioral disturbances. Some studies do not find a relationship between EF and behavior following frontal lobe lesions. Our main goal of this study was to use a novel statistical method, graph theory, to analyze this relationship in different brain injury groups; frontal lobe damage, non-frontal lobe damage, and controls. Within the frontal group, we expect to find a pattern of executive nodes that are highly interconnected. METHODS:For each group, we modeled the relationship between executive functions and behavior as a network of interdependent variables. The cognitive tests and the behavioral questionnaire are the "nodes" in the network, while the relationships between the nodes were modeled as the correlations between two nodes corrected for the correlation with all other nodes in the network. Sparse networks were estimated within each group using graphical LASSO. We analyzed the relative importance of the nodes within a network (centrality) and the clustering (modularity) of the different nodes. RESULTS:Network analysis showed distinct patterns of relationships between EF and behavior in the three subgroups. The performance on the verbal learning test is the most central node in all the networks. In the frontal group, verbal memory forms a community with working memory and fluency. The behavioral nodes do not differentiate between groups or form clusters with cognitive nodes. No other communities were found for cognitive and behavioral nodes. CONCLUSION:The cognitive phenotype of the frontal lobe damaged group, with its stability and proportion, might be theoretically interpreted as a potential "buffer" for possible cognitive executive deficits. This might explain some of the ambiguity found in the literature. This alternative approach on cognitive test scores provides a different and possibly complimentary perspective of the neuropsychology of brain-injured patients.

In cognitive neuroscience there is a growing interest in individual differences. We propose the Multiple Indicators Multiple Causes (MIMIC) model of combined behavioral and fMRI data to determine whether such differences are quantitative or qualitative in nature. A simulation study revealed the MIMIC model to have adequate power for this goal, and parameter recovery to be satisfactory. The MIMIC model was illustrated with a re-analysis of Van Duijvenvoorde et al. (2016) and Blankenstein et al. (2018) decision making data. This showed individual differences in Van Duijvenvoorde et al. (2016) to originate in qualitative differences in decision strategies. Parameters indicated some individuals to use an expected value decision strategy, while others used a loss minimizing strategy, distinguished by individual differences in vmPFC activity. Individual differences in Blankenstein et al. (2018) were explained by quantitative differences in risk aversion. Parameters showed that more risk averse individuals preferred safe over risky choices, as predicted by heightened vmPFC activity. We advocate using the MIMIC model to empirically determine, rather than assume, the nature of individual differences in combined behavioral and fMRI datasets.

Monitoring social threat is essential for maintaining healthy social relationships, and recent studies suggest a neural alarm system that governs our response to social rejection. Frontal-midline theta (4–8 Hz) oscillatory power might act as a neural correlate of this system by being sensitive to unexpected social rejection. Here, we examined whether frontal-midline theta is modulated by individual differences in personality constructs sensitive to social disconnection. In addition, we examined the sensitivity of feedback-related brain potentials (i.e., the feedback-related negativity and P3) to social feedback. Sixty-five undergraduate female participants (mean age = 19.69 years) participated in the Social Judgment Paradigm, a fictitious peer-evaluation task in which participants provided expectancies about being liked/disliked by peer strangers. Thereafter, they received feedback signaling social acceptance/rejection. A community structure analysis was employed to delineate personality profiles in our data. Results provided evidence of two subgroups: one group scored high on attachment-related anxiety and fear of negative evaluation, whereas the other group scored high on attachment-related avoidance and low on fear of negative evaluation. In both groups, unexpected rejection feedback yielded a significant increase in theta power. The feedback-related negativity was sensitive to unexpected feedback, regardless of valence, and was largest for unexpected rejection feedback. The feedback-related P3 was significantly enhanced in response to expected social acceptance feedback. Together, these findings confirm the sensitivity of frontal midline theta oscillations to the processing of social threat, and suggest that this alleged neural alarm system behaves similarly in individuals that differ in personality constructs relevant to social evaluation.

OBJECTIVES:To adequately monitor the course of cognitive functioning in persons with moderate to severe dementia, relevant cognitive tests for the advanced dementia stages are needed. We examined the ability of a test developed for the advanced dementia stages, the Severe Impairment Battery Short version (SIB-S), to measure cognitive change over time. Second, we examined type of memory impairment measured with the SIB-S in different dementia stages.METHODS:Participants were institutionalized persons with moderate to severe dementia (N = 217). The SIB-S was administered at 6-month intervals during a 2-year period. Dementia severity at baseline was classified according to Global Deterioration Scale criteria. We used mixed models to evaluate the course of SIB-S total and domain scores, and whether dementia stage at baseline affected these courses.RESULTS:SIB-S total scores declined significantly over time, and the course of decline differed significantly between dementia stages at baseline. Persons with moderately severe dementia declined faster in mean SIB-S total scores than persons with moderate or severe dementia. Between persons with moderate and moderately severe dementia, there was only a difference in the rate of decline of semantic items, but not episodic and non-semantic items.CONCLUSIONS:Although modest floor and slight ceiling effects were noted in severe and milder cases, respectively, the SIB-S proved to be one of few available adequate measures of cognitive change in institutionalized persons with moderate to severe dementia. (JINS, 2019, 25, 204-214).

This investigation aims to further our understanding of the brain mechanisms underlying the awareness of one's erroneous actions. While all errors are registered as such in the rostral cingulate zone, errors enter awareness only when the anterior insula cortex is activated. Aware but not unaware errors elicit autonomic nervous system reactivity. Our aim is to investigate the hypothesis that activation in the insula during error awareness is related to autonomic arousal and to inter-regional interactions with other areas of the brain. To examine the role of the anterior insula in error awareness, we assessed its functional connectivity to other brain regions along with autonomic nervous system reactivity in young healthy participants who underwent simultaneous pupil-diameter and functional magnetic resonance imaging measurements while performing a complex and error-prone task. Error blindness was associated with failures to engage sufficient autonomic reactivity. During aware errors increased pupil-diameter along with increased task-related activation within, and increased connectivity between anterior insula and task-related networks suggested an increased capacity for action-control information transfer. Increased pupil-diameter during aware errors was furthermore associated with decreased activation of the default-mode network along with decreased insular connectivity with regions of the default mode system, possibly reflecting decreased task-irrelevant information processing. This shifting mechanism may be relevant to a better understanding of how the brain and the autonomic nervous system interact to enable efficient adaptive behavior during cognitive challenge.

Adolescence is characterized by considerable changes in cognitive and socio-emotional skills. There are considerable differences between adolescents with regards to the development of these skills. However, most studies examine adolescents’ average functioning, without taking into account this heterogeneity. The current study applies network analysis in order to examine heterogeneity of cognitive and socio-emotional functioning in adolescents on-track or delayed in their school progression. Data was collected at two time-points for on-track (n = 320) and delayed (n = 69) adolescents (Mage = 13.30 years, SDage = 0.77). Repeated measures ANOVA showed no significant differences between the groups in cognitive and socio-emotional functioning (p’s > 0.05). Network analysis revealed that executive functions play a key role in the network of cognitive, social, and emotional functioning. This is especially the case in the delayed group where executive functions are even more central, both at T1 (inhibition and shifting) and T2 (shifting). Subsequent community analysis revealed three profiles in both groups: a well-adapted and well-balanced group, a group with high levels of need for arousal and risk-taking, and a group with regulation problems. Compared to on-track adolescents, delayed adolescents showed even higher levels of risk-taking in the second profile and higher levels of executive function problems in the third profile at T1. These differences were leveled out at T2, indicating adolescents in the delayed group catch up with their peers. This study highlights the intricate balance between cognitive, social and emotional functioning in adolescents in relation to school performance and provides preliminary evidence of the importance of taking individual differences within groups into account.

The most prevalent approach to activation localization in neuroimaging is to identify brain regions as contiguous supra-threshold clusters, check their significance using random field theory, and correct for the multiple clusters being tested. Besides recent criticism on the validity of the random field assumption, a spatial specificity paradox remains: the larger the detected cluster, the less we know about the location of activation within that cluster. This is because cluster inference implies "there exists at least one voxel with an evoked response in the cluster", and not that "all the voxels in the cluster have an evoked response". Inference on voxels within selected clusters is considered bad practice, due to the voxel-wise false positive rate inflation associated with this circular inference. Here, we propose a remedy to the spatial specificity paradox. By applying recent results from the multiple testing statistical literature, we are able to quantify the proportion of truly active voxels within selected clusters, an approach we call All-Resolutions Inference (ARI). If this proportion is high, the paradox vanishes. If it is low, we can further "drill down" from the cluster level to sub-regions, and even to individual voxels, in order to pinpoint the origin of the activation. In fact, ARI allows inference on the proportion of activation in all voxel sets, no matter how large or small, however these have been selected, all from the same data. We use two fMRI datasets to demonstrate the non-triviality of the spatial specificity paradox, and its resolution using ARI. We verify that the endless circularity permitted by ARI does not render its estimates overly conservative using both simulation, and a data split.

Adolescence is a period characterised by increases in risk-taking. This behaviour has been associated with an imbalance in the integration of the networks involved in cognitive control and motivational processes. We examined whether the influence of emotional cues on cognitive control differs between adolescents who show high or low levels of risk-taking behaviour. Participants who scored especially high or low on a risky decision task were subsequently administered an emotional go/no-go fMRI task comprising angry, happy and calm faces. Both groups showed decreased cognitive control when confronted with appetitive and aversive emotional cues. Activation in the inferior frontal gyrus (IFG) increased in line with the cognitive control demands of the task. Though the risk taking groups did not differ in their behavioural performance, functional connectivity analyses revealed the dorsal striatum plays a more central role in the processing of cognitive control in high than low risk-takers. Overall, these findings suggest that variance in fronto-striatal circuitry may underlie individual differences in risk-taking behaviour.

OBJECTIVE:The past decades have seen a surge in stimulant prescriptions for the treatment of attention-deficit/hyperactivity disorder (ADHD). Stimulants acutely alleviate symptoms and cognitive deficits associated with ADHD by modulating striatal dopamine neurotransmission and induce therapeutic changes in brain activation patterns. Long-term functional changes after treatment are unknown, as long-term studies are scarce and have focused on brain structure. In this observational study (2009-2012), we investigated associations between lifetime stimulant treatment history and neural activity during reward processing.METHODS:Participants fulfilling DSM-5 criteria for ADHD (N = 269) were classified according to stimulant treatment trajectory. Of those, 124 performed a monetary incentive delay task during magnetic resonance imaging, all in their nonmedicated state (nEARLY&INTENSE = 51; nLATE&MODERATE = 49; nEARLY&MODERATE = 9; nNAIVE = 15; mean age = 17.4 years; range, 10-26 years). Whole-brain analyses were performed with additional focus on the striatum, concentrating on the 2 largest treatment groups.RESULTS:Compared to the late-and-moderate treatment group, the early-and-intense treatment group showed more activation in the supplementary motor area and dorsal anterior cingulate cortex (SMA/dACC) during reward outcome (cluster size = 8,696 mm³; PCLUSTER < .001). SMA/dACC activation of the control group fell in between the 2 treatment groups. Treatment history was not associated with striatal activation during reward processing.CONCLUSIONS:Our findings are compatible with previous reports of acute increases of SMA/dACC activity in individuals with ADHD after stimulant administration. Higher SMA/dACC activity may indicate that patients with a history of intensive stimulant treatment, but currently off medication, recruit brain regions for cognitive control and/or decision-making upon being rewarded. No striatal or structural changes were found.