Neurofeedback (NFB) and Brain-Computer Interface (BCI) research seldom present within-session individual learning dynamics. This is even though a large proportion of NFB and BCI users cannot learn the neural self-regulation required to control the feedback. Understanding the time course and learning dynamics between subjects will enable us to design more effective NFB and BCI protocols that promote the learning of neural self-regulation. In this study, we aimed to analyze individual learning trajectories of self-regulation of four different cortical rhythms, in terms of both frequency and spatial selectivity. Twenty healthy subjects performed four sessions of NFB training, each session with feedback reflecting a different cortical rhythm as measured with an electroencephalogram. We specifically tested frontal midline (fm) Theta, occipital Alpha, unilateral centrotemporal sensorimotor rhythms (SMR), and central Beta. We show that all subjects were able to self-regulate at least two of these features; however, with varied specificity in the spatial and frequency domains. Unexpectedly, we show that none of the subjects succeeded in regulating fm Theta. Using a clustering approach, we identified two different learning dynamics among the learners across features: a linear increase/decrease and a non-linear plateau-like trajectory. This is the first NFB study employing an intra-subject cross-over experimental design, enabling the direct comparison of neural self-regulation between multiple features. Our results provide important insights into the "non-learner" problem, showing that it is not a feature-universal personal trait. We further show feature-specific spatial and frequency selectivity of neural self-regulation, providing important considerations for future NFB protocols.

Two decades of research have provided evidence for gray matter volume (GMV) differences in developmental dyslexia (or reading disability, RD) in the left perisylvian cortex. However, there are concerns about result inconsistencies, likely attributable to small sample sizes, lenient statistical thresholds, and insufficient accounting for demographic variables and global GMV. To address these concerns, we conducted a Discovery and Replication Study (N = 262) using data from the Adolescent Brain Cognitive Development[SM] Study. We found GMV differences between the RD and Control Groups did not replicate across the Discovery and Replication Studies using voxel-based morphometry (VBM) in Statistical Parametric Mapping (SPM), and that a more conservative threshold yielded few results. We then conducted Reproducibility Studies and first found that when using surface-based morphometry in FreeSurfer instead of VBM, the Discovery and the Replication Study results again failed to converge. Second, we combined all groups in a factorial VBM/SPM analysis and the interaction analysis provided quantitative confirmation for diverging between-group difference results across the two studies. Third, we tested for the role of covariates of no interest and found that when total GMV is not controlled for, this divergence dissipates and group differences in RD (main effect of Reading Ability) are amplified. In conclusion, replication of GMV differences in RD is low, even when using large, well-matched groups, and analyses approaches play a modulating role. As such, results from prior studies using lenient statistical thresholds and not accounting for total GMV should be viewed with caution.

Altered affective state dynamics are a characteristic feature of depression and can persist beyond symptomatic remission. Individuals with remitted major depressive disorder (rMDD) often show heightened reactivity to negative mood states and reduced efficiency in recovering from them, consistent with changes in affective dynamics after remission. These patterns may reflect alterations in the brain's capacity to flexibly shift between neural states that support distinct affective modes. Characterizing the dynamical mechanisms that govern transitions into and out of experimentally induced affective states is therefore essential for understanding vulnerability to recurrence and informing mechanistic interventions. We developed a data-driven framework combining dynamical system reconstruction (DSR) with model-based control to infer optimal control policies for transitions between resting and sad mood brain states using functional magnetic resonance imaging (fMRI) data. Nonlinear DSR models trained on individuals with rMDD and healthy controls (HC) yielded region-specific, state-dependent control strategies. Small regions (e.g., sgACC, NAcc) showed higher controllability, requiring less energy for state transitions. Notably, rMDD participants required less control energy than HC to shift both into and, to a more spatially restricted extent, out of sad mood states. Despite reaching the resting state target with similar proximity, however, they remained closer to the sad mood distribution when returning to rest, reflecting a residual bias toward the sad mood distribution. Elevated coupling in rMDD, especially toward the DLPFC, was linked to lower control energy, suggesting that stronger network coupling facilitates transitions. These findings indicate rMDD dynamics that ease entry into sad mood states but impede full disengagement. More broadly, they demonstrate how closed-loop control applied to data-driven dynamical models can provide mechanistic insight into brain state transitions and inform future hypotheses about cognitive vulnerability or compensatory processes.

Addictions are commonly classified as either substance-use disorders (SUD) or behavioral addictions (BA). SUDs are well established in taxonomies of mental-health disorders. BAs are only recently recognized as mental-health disorders, with limited and conflicting categorizations. Within each category, individual diagnoses are based on objects of addiction. Despite addiction prevalence, no comprehensive coordinate-based meta-analysis (CBMA) has examined both SUD and BA. The overall goal of this study was to identify GM alterations associated with SUD and BA, both independently and jointly. To this end, a total of 108 peer-reviewed VBM studies reporting case-control contrasts in addiction disorders were identified: 83 reports across five SUD diagnoses (8206 participants); and 25 BA reports across two diagnoses (1572 participants). On these datasets, eight transdiagnostic alteration likelihood estimation (ALE) CBMAs were performed. Three primary ALEs computed cross-study neuroanatomical convergence for: (1) SUD; (2) BA; and (3) Pooled Addictions (SUD + BA). These three primary ALEs were supplemented by five ALE-to-ALE comparison analyses: interaction (SUD + BA to SUD and BA), overlap (SUD on BA), conjunction (SUD on BA), and contrast (SUD > BA; BA > SUD). The independently performed ALEs demonstrated largely distinct anatomical-alteration patterns, overlapping only in the caudate nucleus. The pooled ALE, however, yielded many (n = 24/34) alteration foci not identified in either independent ALE. All newly emerging foci were contributed to by both conditions (SUD and BA) and thus termed "interactive." Functional decoding of all three ALEs consistently identified reward and reward-related cognitive operations as being performed by the brain-regions exhibiting structural alterations in addiction disorders.

While the field of imaging transcriptomics is evolving rapidly, several methodological challenges persist in functional enrichment analysis. Here, we introduce BrainEnrich, an R package that integrates whole-brain gene expression profiles from the Allen Human Brain Atlas (AHBA) with in vivo imaging-derived phenotypes (IDPs). By offering a suite of flexible association methods, aggregation strategies, comprehensive lists of predefined gene sets, and both competitive and self-contained null models, the package enables researchers to examine the spatial coupling between molecular profiles and IDPs at both group and individual levels. A novel feature of BrainEnrich is its individual-level enrichment analysis, which mapped individual IDPs onto a molecular coordinate framework, capturing the molecular signature of individual IDPs and enabling a deeper exploration of inter-individual variability. Its statistical power was examined through extensive simulation studies based on linear regression with different combinations of test statistics and null models. The results suggest that with appropriate null models, this approach effectively controlled Type 1 error while retaining sensitivity to detect associations between molecular profiles and phenotypic data. Two case studies were performed to demonstrate the utility of the package. In the group-level enrichment analysis, the effect size map of case-control comparison in cortical thickness of major depressive disorder patients was associated with molecular pathways such as synaptic signaling, lipid regulation, and steroid hormone balance, providing candidate molecular annotations for group-level IDPs. A separate case study found that synaptic gene set scores showed nominal associations with multiple cognitive measures, demonstrating its utility for individual-level molecular annotations to explore associations with phenotypic variables. Collectively, BrainEnrich provides a flexible framework for integrating macro-level IDPs with micro-level transcriptomic profiles for molecular contextualization of IDPs.

Adolescence is a critical period for brain development, impacting social, cognitive, and emotional functions. While hypothesis-driven studies have linked multiple person characteristics to brain development, the driving force behind differential brain development remains unclear. We applied unsupervised clustering to multimodal neuroimaging data from early adolescents in the Adolescent Brain Cognitive Development (ABCD) study. Clustering analyses were conducted separately for resting-state functional MRI (rs-fMRI), structural MRI (sMRI), and diffusion MRI (dMRI), and replicated in two independent samples of 2666 individuals each. Longitudinal trajectories over a two-year follow-up period were examined. Associations with sociodemographic and family-related factors were assessed. Two clusters were identified in rs-fMRI data across both independent samples. One cluster, comprising approximately 9%-10% of individuals, showed functional brain differences at baseline, along with altered neurodevelopmental trajectories over 2 years. These functional differences were associated with lower socioeconomic status, family instability, and stronger cultural/family values. In contrast, no significant clustering emerged from structural MRI and diffusion MRI data. Our findings suggest that sociodemographic factors are closely associated with early adolescent brain function and development, underscoring the need to consider social environment in neurodevelopmental models and prevention strategies.

The integration of EEG and fMRI offers a powerful method for exploring the brain's spatial and temporal dynamics. However, existing approaches typically summarize both EEG and fMRI, often removing temporal information before combining the modalities. Our novel approach brings together group ICA of fMRI, group ICA of EEG, and joint ICA to propose a multimodal data fusion approach, named group joint ICA (gjICA), that links simultaneous EEG-fMRI data from multiple subjects. The proposed framework enables group-level mapping and provides single-subject estimates via back-reconstruction, facilitating a comprehensive picture of brain networks. The gjICA also introduces joint functional network connectivity (jFNC), which provides connectivity between fMRI networks as well as between EEG components, hence linking temporal and spatial information. When applied to concurrent resting EEG-fMRI data from 121 participants, we identified 63 multimodal brain components. The statistical analyses of these components further revealed that depression is characterized by widespread multimodal connectivity, with visual and higher cognition networks acting as hubs in these alterations. Further, joint histogram analysis revealed that depression is broadly associated with reduced EEG component expression with increased fMRI component expression in cerebellar, visual, subcortical, and sensorimotor networks, suggesting altered neurovascular coupling and modality-specific dysfunctions. Overall, the proposed gjICA approach provides a robust framework for fusing EEG and fMRI data while preserving the spatiotemporal information in both modalities, enabling the identification of a more comprehensive picture of multimodal neural relationships, enhancing our understanding of brain dynamics and providing new insights into complex brain disorders such as depression.

Post-stroke pain (PSP) affects nearly half of stroke survivors, severely compromising quality of life. The causes of PSP remain underexplored, although there is likely a complex interplay of lesion effects, psychological factors, and mobility that play a role in its development. The aim of the study was to investigate clinical characteristics associated with PSP, as well as structural and functional correlates of PSP using lesion symptom (LSM) and network mapping (LNM). We analyzed data from the INSPiRE-TMS cohort, encompassing 1022 minor ischemic stroke patients. Pain severity and psychological factors (EQUATION 5D-3L questionnaire) were assessed annually for up to 3 years. In a sub-group of 391 patients with available imaging data, LSM and LNM analyses were conducted to identify neural correlates of PSP. Overall, 47% of patients reported pain 1-year post-stroke. Multivariable regression analyses identified baseline anxiety as associated with PSP assessed at 1-year post-stroke (OR 2.90, 95% CI 1.17-7.17, p = 0.021). LSM did not identify any voxels associated with new severe pain. LNM identified a network involving the anterior cingulate cortex, thalamus, and insular cortex. Adjusting for anxiety highlighted distinct network contributions, suggesting interactive effects of psychological states on pain perception. Automated comparison to large metanalytical findings using Neurosynth associated the terms 'pain' and 'nociception' most strongly to the identified network. PSP is closely associated with psychological factors such as anxiety. LNM of PSP revealed disruptions in a pain-related neural network consistent with prior pain research. These results warrant external validation and could guide future network-targeted neuromodulation therapies. Trial Registration: ClinicalTrials.gov identifier: NCT01586702.

Many daily activities involve series of interrelated movements and thus the capacity to learn new motor sequences is vital for everyday functioning. Although learning new skills is especially prominent throughout childhood, remarkably few studies have examined the neural underpinnings of motor sequence learning (MSL) and memory consolidation in children. Twenty-two children (7-11 years) and 23 adults (18-30 years) underwent functional magnetic resonance imaging while completing two sessions of a MSL task, separated by a 5-h offline period of wakefulness. Analyses of the behavioral data revealed comparable initial learning in children and adults. However, and consistent with previous research, children exhibited superior motor memory consolidation over the 5-h offline epoch. Neuroimaging analyses revealed that children exhibited smaller modulations in brain activity between task and rest epochs in a widespread network, including the sensorimotor cortex, supplementary motor area, cerebellum, putamen, and regions associated with the default mode network. Similar levels of activity during task and rest epochs in the hippocampus, dorsolateral prefrontal cortex and somatosensory cortex were associated with better motor memory consolidation in children. Results potentially suggest that the continued engagement of the developing brain during interleaved rest contributes to the childhood advantage in motor memory consolidation.

Resting-state fMRI (rs-fMRI) is widely used to assess intrinsic brain activity, yet concerns about its test-retest reliability and reproducibility persist. Circadian rhythms strongly influence brain physiology, but their impact on rs-fMRI reliability remains poorly understood. In this study, we scanned 39 healthy young adults six times within a single day (08:00-20:00) under standardized conditions. For each session, we computed four common rs-fMRI metrics, including amplitude of low-frequency fluctuations (ALFF), wavelet-transformed ALFF (wALFF), fractional ALFF (fALFF), and regional homogeneity (ReHo), and assessed reliability using intraclass correlation coefficients (ICCs). ReHo showed relatively higher and more stable reliability across sessions, whereas amplitude-based metrics, particularly fALFF, exhibited greater diurnal variation. Both network-level and region-specific analyses revealed low reliability in the limbic and subcortical structures, with a mid-morning dip at 10:00. Moreover, ICCs for ALFF, wALFF, and fALFF declined with increasing inter-scan intervals, whereas ReHo remained robust. These findings demonstrate diurnal fluctuations in rs-fMRI reliability, with different metrics exhibiting distinct temporal stability profiles. We recommend that scan timing and circadian influences should be explicitly considered in the design, analysis, and interpretation of future rs-fMRI studies.

Specific phobia (SPH) is a prevalent anxiety disorder and may involve advanced biological aging. However, limited brain age research has been conducted in anxiety disorders. This mega-analysis investigated brain aging in SPH participants within the ENIGMA-Anxiety Working Group. 3D brain structural MRI scans from 17 international samples (600 SPH individuals, of whom 504 formally diagnosed and 96 questionnaire-based cases; 1134 controls; age range: 22-75 years) were processed with FreeSurfer. Brain age was estimated from 77 subcortical and cortical regions with a publicly available ENIGMA brain age model. The brain-predicted age difference (brain-PAD) was calculated as brain age minus chronological age. Linear mixed-effects models examined group differences in brain-PAD and moderation by age. No significant group difference in brain-PAD manifested (βdiagnosis [SE] = 0.37 years [0.43], p = 0.39). A negative diagnosis-by-age interaction was identified, which was most pronounced in formally diagnosed SPH (βdiagnosis-by-age = -0.08 [0.03], pFDR = 0.02). This interaction remained significant when excluding participants with anxiety comorbidities, depressive comorbidities, and medication use. Post hoc analyses revealed a group difference for formal SPH diagnosis in younger participants (22-35 years; βdiagnosis = 1.20 [0.60], p < 0.05, mixed-effects d [95% confidence interval] = 0.14 [0.00-0.28]), but not older participants (36-75 years; βdiagnosis = 0.07 [0.65], p = 0.91). Brain aging did not relate to SPH in the full sample. However, a diagnosis-by-age interaction was observed across analyses, and was strongest in formally diagnosed SPH. Post hoc analyses showed subtle advanced brain aging in young adults with formally diagnosed SPH. Taken together, these findings indicate the importance of clinical severity, impairment, and persistence, and may suggest a slightly earlier end to maturational processes or subtle decline of brain structure in SPH.

Utilizing naturalistic stimuli while maintaining experimental rigor presents a significant challenge in neuroimaging research, primarily due to the complexities of annotation and high-dimensional analysis. To address this, we introduce a novel paradigm that leverages multimodal large language models (LLMs) as high-throughput, automated annotation tools to extract human-understandable semantic features. This "talking to the brain" approach employs a Visual Question Answering (VQA) strategy to transform complex visual scenes into structured semantic feature vectors, which are then used to predict voxel-wise brain activity. As a critical methodological validation, our model successfully replicated established neural correlates for categories such as face and building. Expanding beyond these benchmarks, we mapped the cortical activation patterns of 80 diverse semantic labels and constructed a data-driven brain semantic similarity space. This space revealed robust clusters reflecting both functional categories and real-world contextual associations. This innovative methodology offers a scalable solution for investigating brain semantic organization, overcoming the limitations of manual annotation and paving the way for more ecologically valid explorations of human cognition.

The processing of discrete stimuli is modulated by the emotional context in which they appear. According to the limited data available, this modulation could occur from the initial levels of processing. Our aim was to find the first trace of emotional context modulation in the neural response to discrete stimuli presented within that context. We focused on the somatosensory domain, characterized by faster and better localized responses within the ascending pathway than the more widely studied visual modality. Early components of somatosensory evoked potentials (SEPs), particularly those reflecting the thalamic and primary somatosensory cortex activity occurring within the first 20 milliseconds from stimulus onset, were analyzed to test whether they are already affected by the emotional context. Participants (n = 37) received somatosensory stimulation via electrical pulses over the median nerve of the right arm while viewing 12 stereoscopic (3D) images of animals categorized as emotionally positive, negative, or neutral to generate different affective contexts. SEPs elicited by somatosensory stimuli-which were identical in all contexts- were recorded through a bipolar CP3-CP4 channel, revealing a P17 (originating in the thalamus) and an N20 (primary somatosensory cortex or S1) component. The negative contexts evoked significantly smaller P17-N20 peak-to-peak amplitudes than positive and neutral contexts, an attenuation of somatosensory perception probably reflecting a rapid defensive mechanism. These results reveal the earliest emotion-modulated brain response described to date in humans (17-20 milliseconds and thalamic/S1 origin) and point to the somatosensory ascending pathway as an active emotional processor.

Benzodiazepines act as positive allosteric modulators of the GABAA receptor and affect several motor and cognitive functions. By engaging perceptual-motor as well as inhibitory control processes, the antisaccade task was used in previous studies to investigate effects of benzodiazepines on behavioral performance. Using a randomized, double-blind, placebo-controlled within-subjects design, this study combined eye-tracking with BOLD fMRI in order to examine the neural correlates of these effects for the first time. N = 39 healthy participants completed an antisaccade task after administration of either 1 mg lorazepam or placebo. On a behavioral level, lorazepam led to reduced (anti-)saccadic peak velocity as well as increased (anti-)saccadic latency. On a neural level, drug-induced reduction of BOLD was found in a fronto-parietal-occipital network, including key oculomotor regions. This result was further supported by our finding of increased GABAA receptor density in the affected network. On an individual level, decline in peak velocity under lorazepam was associated with decreased neural activation in several cortical regions, including medial frontal eye fields. No interactions between drug and saccade condition (prosaccade, antisaccade) were found. Our results therefore suggest GABAergic modulation of a more general saccade-related network rather than of specific components for inhibitory control processes. Future studies may rely on BOLD signal as a sensitive marker for benzodiazepine activity during saccadic eye movements.