Neural activity data can be associated with behavioral and physiological variables by analyzing their changes in the temporal domain. However, such relationships are often difficult to quantify and test, requiring advanced computational modeling approaches. Here, we provide a protocol for the statistical analysis of brain dynamics and for testing their associations with behavioral, physiological and other non-imaging variables. The protocol is based on an open-source Python package built on a generalization of the hidden Markov model (HMM)—the Gaussian-linear HMM—and supports multiple experimental modalities, including task-based and resting-state studies, often used to explore a wide range of questions in neuroscience and mental health. Our toolbox is available as both a Python library and a graphical interface, so it can be used by researchers with or without programming experience. Statistical inference is performed by using permutation-based methods and structured Monte Carlo resampling, and the framework can easily handle confounding variables, multiple testing corrections and hierarchical relationships within the data, among other features. The package includes tools developed to facilitate the intuitive visualization of statistical results, along with comprehensive documentation and step-by-step tutorials for data interpretation. Overall, the protocol covers the full workflow for the statistical analysis of functional neural data and their temporal dynamics. This protocol covers a package for running statistical testing on the temporal dynamics of neural activity data obtained from various functional neural recording modalities.

Behaviors arise from coordinated neural activity across diverse spatial and temporal scales. Prior work has linked better task performance and cognitive functioning to patterns of global network connectivity requiring minimal reconfiguration when switching between task demands. This metric indexing similarity in functional connectivity across task and rest has been termed “neural efficiency.” Here we assess stability of neural efficiency over approximately 3 years in adolescence, specificity across two task-rest combinations and associations with anxiety. At approximately age 16 and/or 19, 95 participants completed a resting state scan alongside a cognitive control and/or threat task. Neural efficiency was quantified as partial correlations between intrinsic and task-related functional connectivity patterns across the whole brain. We tested temporal stability across the three-year interval, as well as associations with task performance and anxiety across the two task-rest combinations at the two time points. Neural efficiency values remained relatively stable from mid to late adolescence (ICC[3,1] = 0.51–0.58). The cognitive control task showed higher values than the threat task. Across tasks, neural efficiency was associated with better performance (i.e., reduced interference), although not consistently (r = −0.19, p = 0.26 – r = −0.37, p = 0.021). These effects did not survive correction for multiple testing. No associations were found between neural efficiency and self/parent-reported anxiety. In sum, the metric shows moderate developmental stability and associations with task performance. Task features impact neural efficiency. Given small sample sizes, findings need to be interpreted cautiously.

BACKGROUND:Because pediatric anxiety disorders precede the onset of many other problems, successful prediction of response to the first-line treatment, cognitive-behavioral therapy (CBT), could have a major impact. This study evaluates whether structural and resting-state functional magnetic resonance imaging can predict post-CBT anxiety symptoms. METHODS:Two datasets were studied: (A) one consisted of n = 54 subjects with an anxiety diagnosis, who received 12 weeks of CBT, and (B) one consisted of n = 15 subjects treated for 8 weeks. Connectome predictive modeling (CPM) was used to predict treatment response, as assessed with the PARS. The main analysis included network edges positively correlated with treatment outcome and age, sex, and baseline anxiety severity as predictors. Results from alternative models and analyses are also presented. Model assessments utilized 1000 bootstraps, resulting in a 95% CI for R2, r, and mean absolute error (MAE). RESULTS:The main model showed a MAE of approximately 3.5 (95% CI: [3.1-3.8]) points, an R2 of 0.08 [-0.14-0.26], and an r of 0.38 [0.24-0.511]. When testing this model in the left-out sample (B), the results were similar, with an MAE of 3.4 [2.8-4.7], R2-0.65 [-2.29-0.16], and r of 0.4 [0.24-0.54]. The anatomical metrics showed a similar pattern, where models rendered overall low R2. CONCLUSIONS:The analysis showed that models based on earlier promising results failed to predict clinical outcomes. Despite the small sample size, this study does not support the extensive use of CPM to predict outcomes in pediatric anxiety.

Objective: Adolescents face significant changes in many domains of their daily lives that require them to flexibly adapt to changing environmental demands. To shift efficiently among various goals, adolescents must reconfigure their brains, disengaging from previous tasks and engaging in new activities. Method: To examine this reconfiguration, we obtained resting-state and task-based functional magnetic resonance imaging (fMRI) scans in a community sample of 164 youths. We assessed the similarity of functional connectivity (FC) of the reward network between resting state and a reward- processing state, indexing the degree of reward network reconfiguration required to meet task demands. Given research documenting relations among reward network function, early life stress (ELS), and adolescent depression, we examined the association of reconfiguration efficiency with age across adolescence, the moderating effect of ELS on this association, and the relation between reconfiguration efficiency and depressive symptoms. Results: We found that older adolescents showed greater reconfiguration efficiency than younger adolescents and, furthermore, that this age-related association was moderated by the experience of ELS. Conclusion: These findings suggest that reconfiguration efficiency of the reward network increases over adolescence, a developmental pattern that is attenuated in adolescents exposed to severe ELS. In addition, even after controlling for the effects of age and exposure to ELS, adolescents with higher levels of depressive symptoms exhibited greater reconfiguration efficiency, suggesting that they have brain states at rest that are more strongly optimized for reward processing than do asymptomatic youth. Plain language summary: Adolescents face significant changes in many domains of their lives which requires them to flexibly adapt and reconfigure their brains to disengage from previous tasks and engage in new activities. In this study of a sample of 164 youth aged 9 to 20, the authors found an age- related increase in the reconfiguration efficiency of the reward network, which was pronounced in older adolescents exposed to severe early life stress. In addition, the study findings indicate that adolescents with higher levels of depressive symptoms showed greater reconfiguration efficiency, suggesting that their brains may be more optimized for processing rewards even at rest compared to their peers without any symptoms. Diversity & Inclusion Statement: We worked to ensure race, ethnic, and/or other types of diversity in the recruitment of human participants. We worked to ensure sex and gender balance in the recruitment of human participants. We worked to ensure that the study questionnaires were prepared in an inclusive way. One or more of the authors of this paper self-identifies as a member of one or more historically underrepresented racial and/or ethnic groups in science.

Importance:Anxiety disorders are highly prevalent and associated with heightened error monitoring, the detection of one's mistakes. However, error monitoring, anxiety, and their associations change throughout adolescence, limiting the ability to estimate future anxiety trajectories during this period. Objective:To ascertain whether measures of error monitoring obtained via the integration of electroencephalogram (EEG) and functional magnetic resonance imaging (fMRI) improve estimations of future anxiety compared with EEG or fMRI alone, in adolescents with or without a history of behaviorally inhibited temperament. Design, Setting, and Participants:This longitudinal cohort study was conducted at a university research laboratory and government research hospital. Study assessments took place between January 2014 and July 2019, and data analyses were completed in January 2025. A community sample of infants completed a laboratory screening at age 4 months. A subset of these infants was oversampled to maximize variability of early temperament and followed up throughout adolescence. At ages 13 and 15 years, participants completed a flanker task during an EEG session and separate fMRI session. Brain activity at age 13 years and its change from 13 to 15 years of age were evaluated as potential risk factors for anxiety. Main Outcomes and Measures:Change in anxiety from age 13 to 15 years as measured using clinical interviews and questionnaires. Hypotheses were formulated after data collection. Results:Analyses included 176 adolescents with neuroimaging data (92 females at birth [52.3%]; 5 Asian individuals [2.8%], 21 Black or African American individuals [11.9%], 11 Hispanic or Latino individuals [6.3%], 133 White individuals [75.6%], and 6 individuals of other [3.4%] race and ethnicity). Among neural variables (EEG-only, fMRI-only, and EEG-fMRI fusion), only the EEG-fMRI fusion scores explained additional variance in anxiety change scores (change in R2 = 0.25; P = .001) beyond demographics (sex, racial and ethnic minority status) and anxiety at age 13 years. Planned follow-up analyses revealed that early temperament interacted with dorsal anterior cingulate activity at age 13 years (β = 0.40; B = 8.77; 95% CI, 0.74-16.79; P = .03) and with changes in posterior cingulate activity (β = -0.42; B = -16.89; 95% CI, -28.21 to -5.57; P = .003). Conclusions and Relevance:This cohort study found that integrating multimodal neuroimaging measures of error monitoring was associated with improved estimations of future anxiety in youths over and above each modality separately. Early temperament interacted with error monitoring to further differentiate anxiety trajectories; yet, these interactions differed across brain regions, highlighting the value of incorporating the complementary temporal and spatial information of EEG and fMRI.

The role of precise timing in episodic memory remains obscure. We showed 139 participants episodes consisting of objects, and tested subsequent memory for the precise timing and order of the objects and episodes. Temporal compression of the episode enhanced memory for relative but not absolute timing of the objects' presentation and their order. Conversely, temporal expansion between neighboring episodes was associated with successful memory for episode order. fMRI in 36 participants revealed that temporal compression of the episode was associated with more similar activation patterns within episodes in several brain regions including the posterior hippocampus. However, the activation pattern in the anterior hippocampus and other brain regions became more unique with temporal expansion between neighboring episodes. We propose that human episodic memory relies on two fundamentally opposite processes: pattern integration helps strengthen the relationship between the items that make up an episode and pattern differentiation keeps different episodes apart.

BACKGROUND:Anxiety disorders may partly stem from altered neurodevelopment of attention-related networks. Neonatal alterations in resting-state functional connectivity (rsFC) among the dorsal attention network (DAN), frontoparietal network (FPN), salience network (SN), and default mode network (DMN) relate to fearful temperament, a risk marker for anxiety. Nevertheless, few studies have examined the development of these networks beyond the first months of life, particularly in fearful infants. In this study, we examined how changes in these networks during the first 2 years of life relate to fearful temperament. METHODS:Using data from the Baby Connectome Project (from 180 infants across 396 sessions), we conducted independent component analysis to extract rsFC among the DMN, SN, DAN, and FPN. Longitudinal modeling characterized 1) age-related changes (slope) in rsFC through age 2 years, 2) the relationship between rsFC change (slope) and fearfulness at age 2 years, and 3) the relationship between rsFC and fearfulness trajectories (slope and intercept) during the first 2 years of life. RESULTS:Age-related decreases occurred in DAN-FPN and DMN-SN rsFCs. Smaller decreases in DAN-FPN rsFC over time related to greater fear at age 2 and to increases in fearfulness over time. High initial DAN-FPN rsFC and low initial DAN-SN rsFC also related to increasing fearfulness over time. CONCLUSIONS:This study provides the first evidence that changes in attention-related brain networks are related to early-life fearfulness, a robust early-life risk marker of anxiety.

OBJECTIVE:Pediatric anxiety disorders are common and predict adult psychopathology, yet current treatments, such as cognitive-behavioral therapy (CBT), produce lasting remission in less than 50% of affected youths. To support the search for improved, mechanistically grounded interventions, this study evaluated neural efficiency, defined as similarity in functional connectivity between a threat task and rest, as a potential biomarker. The study evaluated neural efficiency in relation to anxiety diagnosis and treatment response. METHODS:The authors compared 103 youths with an anxiety disorder diagnosis (mean age, 12.5 years [SD=2.91], 62% female) to 103 youths with no psychiatric diagnosis (mean age, 13.4 years [SD=2.58], 53% female). Participants completed functional MRI while resting and during a dot-probe task with threatening faces. Neural efficiency was calculated as partial correlations between intrinsic and task-related functional connectivity patterns across the whole brain. Four-month test-retest reliability as well as relationships with anxiety and response to exposure-based CBT were examined. RESULTS:Neural efficiency demonstrated satisfactory test-retest reliability (intraclass correlation coefficient=0.65) in healthy youths over a period of 11 to 18 weeks. Neural efficiency was significantly negatively related to anxiety as both a diagnostic category (t=2.62, d=0.29) and a symptom dimension (r=-0.18). Although it did not change after CBT, lower neural efficiency at baseline was significantly associated with poorer treatment response in a subset of 80 anxious youths who underwent CBT (β=-11.88, χ2=9.20). CONCLUSIONS:Neural efficiency, measured as network reconfiguration between rest and task, holds promise as a biomarker in pediatric anxiety. Its association with CBT response suggests that it might aid in patient stratification and offer a target for interventions aimed at enhancing CBT efficacy.

Individual differences in how the brain responds to novelty are present from infancy. A common method of studying novelty processing is through event-related potentials (ERPs). While ERPs possess millisecond precision, spatial resolution remains poor, especially in infancy. This study aimed to balance spatial and temporal precision by combining ERP data with functional magnetic resonance imaging (fMRI) data. Twenty-nine infants (15 female) underwent resting-state fMRI (M-age = 4.73 months) and electroencephalography (EEG) during a three-stimulus auditory oddball task (M-age = 5.19 months). The mismatch response (MMR) and P3 were computed from ERP data, and resting-state functional connectivity (rs-FC) was computed from fMRI data. We first source localized the MMR and P3 responses to five regions-of-interest (ROIs), based on prior literature. We then performed network-level enrichment analyses to identify associations between rs-FC and MMR and P3, at each of the five ROIs. In line with prior work, source-localized EEG analyses implicated the bilateral auditory cortices, posterior cingulate cortex, and superior parietal cortex in the generation of MMR and P3 responses. The MMR and P3 related to functional connectivity within the somatomotor network as well as between the somatomotor and the dorsal and ventral attention networks (DAN/VAN). This was especially true for novelty response ERPs recorded at superior parietal lobule, known for its implications in initial reorienting to novel stimuli. The DAN, known for its implication in initial reorienting to support novelty detection, was implicated for the MMR. In contrast, the VAN, known for its support of later-stage, complex adjustments in attention, related to the later P3. This work further solidifies our understanding of the underlying networks implicated in the development of immediate responses to stimuli. Altered configurations of such networks may increase the risk for heightened sensitivity to novelty in certain individuals, which could have behavioral and clinical significance.

OBJECTIVE:Specific phobia is a common anxiety disorder, but the literature on associated brain structure alterations exhibits substantial gaps. The ENIGMA Anxiety Working Group examined brain structure differences between individuals with specific phobias and healthy control subjects as well as between the animal and blood-injection-injury (BII) subtypes of specific phobia. Additionally, the authors investigated associations of brain structure with symptom severity and age (youths vs. adults).METHODS:Data sets from 31 original studies were combined to create a final sample with 1,452 participants with phobia and 2,991 healthy participants (62.7% female; ages 5-90). Imaging processing and quality control were performed using established ENIGMA protocols. Subcortical volumes as well as cortical surface area and thickness were examined in a preregistered analysis.RESULTS:Compared with the healthy control group, the phobia group showed mostly smaller subcortical volumes, mixed surface differences, and larger cortical thickness across a substantial number of regions. The phobia subgroups also showed differences, including, as hypothesized, larger medial orbitofrontal cortex thickness in BII phobia (N=182) compared with animal phobia (N=739). All findings were driven by adult participants; no significant results were observed in children and adolescents.CONCLUSIONS:Brain alterations associated with specific phobia exceeded those of other anxiety disorders in comparable analyses in extent and effect size and were not limited to reductions in brain structure. Moreover, phenomenological differences between phobia subgroups were reflected in diverging neural underpinnings, including brain areas related to fear processing and higher cognitive processes. The findings implicate brain structure alterations in specific phobia, although subcortical alterations in particular may also relate to broader internalizing psychopathology.

This overview critically appraises the literature on the treatment of pediatric anxiety disorders. The two established treatments for these conditions comprise cognitive-behavioral therapy (CBT) and antidepressant medications. Many youths receiving these treatments fail to achieve remission, which creates a need for new treatments. After summarizing the literature on CBT and currently available medications, the authors describe research that lays a foundation for improvements in the treatment of pediatric anxiety disorders. This foundation leverages neuroscientific investigations, also described in the overview, which provide insights on mechanisms of successful treatment.

Neuroimaging studies point to neurostructural abnormalities in youth with anxiety disorders. Yet, findings are based on small-scale studies, often with small effect sizes, and have limited generalizability and clinical relevance. These issues have prompted a paradigm shift in the field towards highly powered (i.e., big data) individual-level inferences, which are data-driven, transdiagnostic, and neurobiologically informed. Here, we built and validated neurostructural machine learning (ML) models for individual-level inferences based on the largest-ever multi-site neuroimaging sample of youth with anxiety disorders (age: 10-25 years, N=3,343 individuals from 32 global sites), as compiled by three ENIGMA Anxiety Working Groups: Panic Disorder (PD), Generalized Anxiety Disorder (GAD), and Social Anxiety Disorder (SAD). ML classifiers were trained on MRI-derived regional measures of cortical thickness, surface area, and subcortical volumes to classify patients and healthy controls (HC) for each anxiety disorder separately and across disorders (transdiagnostic classification). Modest, yet robust, classification performance was achieved for PD vs. HC (AUC=0.62), but other disorder-specific and transdiagnostic classifications were not significantly different from chance. However, above chance-level transdiagnostic classifications were obtained in exploratory subgroup analyses of male patients vs. male HC, unmedicated patients vs. HC, and patients with low anxiety severity vs. HC (AUC 0.59-0.63). The above chance-level classifications were based on plausible and specific neuroanatomical features in fronto-striato-limbic and temporo-parietal regions. This study provides a realistic estimate of classification performance in a large, ecologically valid, multi-site sample of youth with anxiety disorders, and may as such serve as a benchmark.

Longitudinal studies using resting-state functional magnetic resonance imaging (rs-fMRI) focused on adolescent internalizing psychopathology are scarce and have mostly investigated standardized treatment effects on functional connectivity (FC) of the full amygdala. The role of amygdala subregions and large resting-state networks had yet to be elucidated, and treatment is in practice often personalized. Here, longitudinal FC development of amygdala subregions and whole-brain networks are investigated in a clinically representative sample.Treatment-naïve adolescents with clinical depression and comorbid anxiety who started care-as-usual (n = 23; INT) and healthy controls (n = 24; HC) participated in rs-fMRI scans and questionnaires at baseline (before treatment) and after three months. Changes between and within groups over time in FC of the laterobasal amygdala (LBA), centromedial amygdala (CMA) and whole-brain networks derived from independent component analysis (ICA) were investigated.Groups differed significantly in FC development of the right LBA to the postcentral gyrus and the left LBA to the frontal pole. Within INT, FC to the frontal pole and postcentral gyrus changed over time while changes in FC of the right LBA were also linked to symptom change. No significant interactions were observed when considering FC from CMA bilateral seeds or within ICA-derived networks.Results in this cohort suggest divergent longitudinal development of FC from bilateral LBA subregions in adolescents with internalizing disorders compared to healthy peers, possibly reflecting nonspecific treatment effects. Moreover, associations were found with symptom change. These results highlight the importance of differentiation of amygdala subregions in neuroimaging research in adolescents.

Functional magnetic resonance imaging (fMRI) studies most commonly use cluster-based inference to detect local changes in brain activity. Insufficient statistical power and disproportionate false-positive rates reportedly hinder optimal inference. We propose a structural connectivity-guided clustering framework, called topological cluster statistic (TCS), that enhances sensitivity by leveraging white matter anatomical connectivity information. TCS harnesses multimodal information from diffusion tractography and functional imaging to improve task fMRI activation inference. Compared to conventional approaches, TCS consistently improves power over a wide range of effects. This improvement results in a 10%-50% increase in local sensitivity with the greatest gains for medium-sized effects. TCS additionally enables inspection of underlying anatomical networks and thus uncovers knowledge regarding the anatomical underpinnings of brain activation. This novel approach is made available in the PALM software to facilitate usability. Given the increasing recognition that activation reflects widespread, coordinated processes, TCS provides a way to integrate the known structure underlying widespread activations into neuroimaging analyses moving forward. Neuroimaging studies often encounter challenges in reliable inference of statistical maps due to limited statistical power. This article introduces TCS, a novel method that integrates anatomical connectivity data from diffusion tractography into cluster-based inference techniques. Our findings demonstrate that TCS enhances statistical power, improves the detection of spatially disjoint localized activations, and identifies the underlying network linking distant inferred active regions. By elucidating the coordinated network supporting inferred effects, TCS enables data-driven interpretation of inference results. The availability of TCS as a publicly accessible tool offers a promising avenue for future neuroimaging research to leverage anatomical connectivity for enhanced inference and interpretation.

False discovery rate (FDR) is commonly used for correction for multiple testing in neuroimaging studies. However, when using two-tailed tests, making directional inferences about the results can lead to vastly inflated error rate, even approaching 100\% in some cases. This happens because FDR only provides weak control over the error rate, meaning that the proportion of error is guaranteed only globally over all tests, not within subsets, such as among those in only one or another direction. Here we consider and evaluate different strategies for FDR control with two-tailed tests, using both synthetic and real imaging data. Approaches that separate the tests by direction of the hypothesis test, or by the direction of the resulting test statistic, more properly control the directional error rate and preserve FDR benefits, albeit with a doubled risk of errors under complete absence of signal. Strategies that combine tests in both directions, or that use simple two-tailed p-values, can lead to invalid directional conclusions, even if these tests remain globally valid. To enable valid thresholding for directional inference, we suggest that imaging software should allow the possibility that the user sets asymmetrical thresholds for the two sides of the statistical map. While FDR continues to be a valid, powerful procedure for multiple testing correction, care is needed when making directional inferences for two-tailed tests, or more broadly, when making any localized inference.

We have previously identified a network of higher-order brain regions particularly vulnerable to the ageing process, schizophrenia and Alzheimer’s disease. However, it remains unknown what the genetic influences on this fragile brain network are, and whether it can be altered by the most common modifiable risk factors for dementia. Here, in ~40,000 UK Biobank participants, we first show significant genome-wide associations between this brain network and seven genetic clusters implicated in cardiovascular deaths, schizophrenia, Alzheimer’s and Parkinson’s disease, and with the two antigens of the XG blood group located in the pseudoautosomal region of the sex chromosomes. We further reveal that the most deleterious modifiable risk factors for this vulnerable brain network are diabetes, nitrogen dioxide – a proxy for traffic-related air pollution – and alcohol intake frequency. The extent of these associations was uncovered by examining these modifiable risk factors in a single model to assess the unique contribution of each on the vulnerable brain network, above and beyond the dominating effects of age and sex. These results provide a comprehensive picture of the role played by genetic and modifiable risk factors on these fragile parts of the brain.

BACKGROUND: Observational assessments of infant temperament have provided unparalleled insight into prediction of risk for social anxiety. However, it is challenging to administer and score these assessments alongside highquality infant neuroimaging data. In the current study, we aimed to identify infant resting-state functional connectivity associated with both parent report and observed behavioral estimates of infant novelty-evoked distress. METHODS: Using data from the OIT (Origins of Infant Temperament) study, which includes deep phenotyping of infant temperament, we identified parent-report measures that were associated with observed novelty-evoked distress. These parent-report measures were then summarized into a composite score used for imaging analysis. Our infant magnetic resonance imaging sample was a synthetic cohort, harmonizing data from 2 functional magnetic resonance imaging studies of 4-month-old infants (OIT and BCP [Baby Connectome Project]; n = 101), both of which included measures of parent-reported temperament. Brain-behavior associations were evaluated using enrichment, a statistical approach that quantifies the clustering of brain-behavior associations within network pairs. RESULTS: Results demonstrated that parent-report composites of novelty-evoked distress were significantly associated with 3 network pairs: dorsal attention-salience/ventral attention, dorsal attention-default mode, and dorsal attention-control. These network pairs demonstrated negative associations with novelty-evoked distress, indicating that less connectivity between these network pairs was associated with greater novelty-evoked distress. Additional analyses demonstrated that dorsal attention-control network connectivity was associated with observed novelty-evoked distress in the OIT sample (n = 38). CONCLUSIONS: Overall, this work is broadly consistent with existing work and implicates dorsal attention network connectivity in novelty-evoked distress. This study provides novel data on the neural basis of infant novelty-evoked distress.

The study of brain connectivity, both functional and structural, can inform us on the development of psychopathology. The use of multimodal MRI methods allows us to study associations between structural and functional connectivity, and how this relates to psychopathology. This may be especially useful during childhood and adolescence, a period where most forms of psychopathology manifest for the first time. The current paper explores structure-function coupling, measured through diffusion and resting-state functional MRI, and quantified as the correlation between structural and functional connectivity matrices. We investigate associations between psychopathology and coupling in a transdiagnostic group of adolescents, including many treatment-seeking youth with relatively high levels of symptoms (n = 72, Mage = 13.3). We used a bifactor model to extract our main outcome measure, Negative Affectivity, from anxiety and irritability ratings. This provided the principal measure of psychopathology. Supplementary analyses investigated ‘domain-specific’ factors of anxiety and irritability. Findings indicate a positive association between negative affectivity and structure-function coupling between the default mode and the fronto-parietal control networks. Higher structure-function coupling may indicate heightened structural constraints on function, which limit functional network reorganization during adolescence required for healthy psychological outcomes.