The complex interplay between brain regions that support emotional experience and their link to individual differences is a topic of active research. Additionally, there has been growing interest in using functional gradients to investigate human cortical organization during both rest and film fMRI. Among these, several studies demonstrated improved brain fingerprinting performance, reflecting greater neural identification capability of film fMRI against rest fMRI despite higher subject synchronization during film-watching than in rest. Comparably, in this work we study the relation between individual differences, in particular, state anxiety and openness scores, and brain activity during the processing of emotional scenes in films, through functional gradients. In addition to including subcortical regions, we propose a new exploratory framework for computing functional gradients based on a subset of frames selected using emotional annotation data of films, resulting in emotion-informed functional gradients. Then we evaluate the variance in emotion-informed gradients across subjects and employ these same gradients in the prediction of individual differences. For emotion-informed functional gradients, the highest predictability of state anxiety was found for scenes of negative valence and medium-high arousal, corresponding to the typical location of anxiety within the valence-arousal-power emotional space. Additionally, predictability of state anxiety tended to be negatively correlated to inter-subject variability. In contrast, predictability of openness was found to be highest during scenes with low arousal and positively correlated to inter-subject variability. Although preliminary, these findings suggest that macroscale brain organization is affected by emotional experience, and that frame selection based on the latter could be useful to remove non-subject-specific variability while extracting subject-specific information related to the emotion experience. It also suggests that frame selection increases inter-subject variability allowing the extraction of more subject-specific information. Thus, expanding on the idea of brain fingerprint in film fMRI, our results provide initial evidence that emotional experiences enhance disentanglement of various domains of individual differences. Moreover, depending on the individual difference of interest, fMRI acquired during more or less constrained paradigms would be more suitable to reveal different properties of brain function.

Graph theory provides a promising technique to investigate Alzheimer's disease (AD)-related alterations in brain network properties. However, there are discrepancies in the reported disruptions that occur to network topology across the AD continuum. In this study, we examined whether diagnostic group differences in graph metrics are attributed to differences in denoising approach used in fMRI processing. Resting state data from 60 cognitively normal (CN), 55 Mild Cognitive Impairment (MCI), and 38 AD participants from the Alzheimer's Disease Neuroimaging Initiative (ADNI) database were denoised using 11 pipelines including combinations of confound regression (head motion parameters, white matter [WM], cerebrospinal fluid [CSF], global signal), volume censoring (scrubbing, spike regression), and component-based noise removal (Independent Component Analysis-based Automatic Removal of Motion Artifacts [ICA-AROMA], anatomical and temporal component correction). Graph metrics representing network segregation (clustering coefficient, modularity, local efficiency), network integration (largest connected component, path length, global efficiency), and small-worldness were calculated. The results revealed that diagnostic group differences in modularity and local efficiency were dependent on denoising approach, especially in high-parameter regression models in combination with censoring methods (36 parameters and spike regressor or volume censoring). Independent of denoising approach, CN exhibited more segregated (clustering coefficient) but less integrated (largest component, path length, global efficiency) networks than MCI and AD. Independent of diagnosis, denoising strategy significantly affected the magnitude of all metrics, particularly models including global signal regression. Collectively, these results suggest that the directionality of the diagnostic differences in network topology, particularly in global metrics of network segregation, can vary based upon the denoising approach employed, although the effect size is small. Transparent reporting of preprocessing decisions is critical for the accurate interpretation of graph theoretical findings in the context of AD and a better understanding of the mechanisms underlying pathological aging.

Observing another person's action recruits the action observation network (AON). Evidence suggests that distinct AON pathways are engaged depending on action features, such as their transitivity or the movement's target; however, the spatiotemporal dynamics of these circuitries remain unclear and debated. In the present study, to further explore this aspect, healthy participants completed two experiments involving the passive observation of distinct grasping actions: intransitive (isolated grasp), object-directed (hand grasping a bottle), and socially-directed (hand grasping another person's hand). In Experiment 1, using electroencephalography (EEG), we examined whole-brain temporal patterns of AON activation during the observation of these stimuli through event-related potentials and microstate analyses. In Experiment 2, informed by EEG results, we used transcranial magnetic stimulation (TMS) to assess the temporal dynamics of motor resonance, a corticospinal marker of AON recruitment. Experiment 1 showed that observing transitive and intransitive grasping activates the same core circuit within the AON, albeit with distinct spatiotemporal dynamics emerging in two separate time windows, peaking at approximately 200 and 350 ms after action onset. Specifically, observation of object-directed grasping relies more on the engagement of posterior brain regions. In contrast, intransitive and socially-directed movements recruit anterior areas more quickly, with the activation pattern of the latter class of stimuli likely reflecting the additional visuo-tactile processing associated with the observed action. Experiment 2 showed muscle-specific motor resonance at 200 ms only for intransitive and object-directed grasping, but no corticospinal facilitation at later latencies, where inhibitory patterns instead emerged, suggesting that EEG and TMS capture complementary dynamics underlying action observation and understanding. Overall, our findings suggest that observing transitive and intransitive movements modulates AON activity through shared pathways expressed in specific spatiotemporal gradients, corresponding to distinct cortical and corticospinal signatures that encode the features of the observed action.

Individual brains are unique in structure and function. Functional differences are captured by neural fingerprints, which reflect individual differences in behavior and cognition as well as group-level changes related to neurodegenerative diseases. Most research efforts so far have focused on fingerprints comprising full functional connectomes. However, the high dimensionality of the connectomes can increase computational load and impede performance of machine learning methods in potential applications. A low-dimensional alternative that retains individual features of the full connectomes would thus be beneficial. The present study employed latent-noise Bayesian Reduced Rank Regression (lnBRRR) to learn low-dimensional latent spaces that capture individual features in functional connectivity and power spectral density data derived from MEG recordings. LnBRRR performance was assessed with low training set sizes (N = 20-44), and against principal component analysis and linear discriminant analysis. Model performance was also assessed with task data, and the solutions were compared across task conditions with cosine similarity to establish whether individual features are altered by different cognitive processes. LnBRRR captured generalizable individual patterns already at N = 20 but N = 30-35 was needed to reach optimal test accuracies and to prevent potential overfitting. The model also achieved comparable performance to the alternative models. Latent fingerprints derived from task data attained comparable performance to resting-state latent fingerprints, and lnBRRR solutions were shown to generalize across conditions. Additionally, the model solutions for power spectral density data were discovered to be notably similar, yet differently rotated, over task conditions, suggesting that similar patterns of individual features were captured by the model regardless of the task condition. Altogether, the present results highlight lnBRRR as a potential tool for neuroimaging data analysis and demonstrate that individual differences in power spectral density are largely intrinsic and unaffected by varying cognitive processes.

Conventional BOLD-fMRI relies on hemodynamic responses that are temporally and spatially indirect markers of neural activity. Developing alternative contrasts, sensitive to neuroelectrical phenomena, is a critical challenge in brain imaging. Spin-lock (SL) fMRI has shown promise in phantom studies for detecting magnetic field changes associated with neuronal activity, but its in-vivo sensitivity and practicality remain unclear. This study evaluated whether SL contrast can effectively detect and localize human neuronal activation, benchmarked against complementary functional modalities, magnetoencephalography (MEG) and 3T BOLD-fMRI, to assess the sensitivity of MR-based neuronal current imaging. Thirteen healthy young volunteers underwent SL-based imaging during 8 Hz visual stimulation, along with BOLD and MEG acquisitions. Subjects viewed quadrant-checkerboard stimuli to elicit localized cortical responses. Two balanced SL contrast mechanisms, rotary excitation (REX) and stimulus-induced rotary saturation (SIRS), were employed. Postprocessing targeted stimulus-locked signal fluctuations using a regression-filtering-rectification strategy. Phantom experiments tested sensitivity and analysis pipeline performance. MEG revealed robust stimulus-locked responses in the occipital cortex, with estimated local magnetic field amplitudes of ~0.07 nT. Conventional BOLD-fMRI confirmed reliable hemodynamic activation. In contrast, neither balanced REX nor balanced SIRS produced consistent stimulus-related activation in vivo. Phantom experiments subsequently yielded detection thresholds of 0.2 nT for REX and 0.6 nT for SIRS, exceeding the MEG-estimated physiological field amplitudes. Under the present experimental conditions, the tested spin-lock fMRI implementations did not achieve sufficient sensitivity for reliable in vivo detection of neuronal magnetic fields at 3T. Phantom and MEG-based estimates indicate that physiological field amplitudes in the visual cortex lie below current detection limits. These findings establish quantitative constraints on direct neuronal current imaging with MRI and provide a benchmark for future methodological developments aimed at bridging electrophysiology and functional MRI.

Current transcranial magnetic stimulation (TMS) practice uses fixed-percentage motor threshold dosing, conventionally 120% rMT, for depression treatment. Individual anatomical variability may result in substantially different cortical electric-Field (E-Field) strengths across patients. We used prospective real-time E-Field modeling to characterize individual TMS intensity requirements and examine associations with clinical outcomes. Twenty-eight subjects with major depressive disorder received single-day accelerated intermittent theta-burst stimulation (10 sessions, 1800 pulses/session) with real-time E-Field-guided dosing targeting M1-equivalent stimulation at left dorsolateral prefrontal cortex (DLPFC). Required %rMT for motor-equivalent DLPFC E-Field ranged from 49.7%-150.4% rMT (mean = 99.7% ± 18.9%), with 53.6% of subjects requiring less than 100% rMT. Real-time E-Field-guided dosing achieved 48.1% better precision than conventional 120% rMT in approximating motor-equivalent E-Field delivery (t(27) = 2.45, p = 0.021). Three dosing frameworks were tested against change in QIDS-SR16 scores: delivered %rMT was not significantly associated with outcomes (ρ = -0.15, p = 0.436), while both the ratio of DLPFC to M1 E-Field strength (ρ = -0.49, p = 0.008) and absolute DLPFC E-Field strength (ρ = -0.49, p = 0.008) were significantly negatively correlated with greater symptom reduction. These findings demonstrate substantial interindividual variability in cortical E-Field delivery under fixed-percentage dosing, and that real-time E-Field guidance more precisely approximates motor-equivalent stimulation than conventional 120% rMT. The association of lower absolute DLPFC E-Field strength with greater symptom reduction challenges the assumption that higher stimulation intensity produces better clinical outcomes, and warrants systematic investigation in larger trials.

Perinatal stroke affects millions of individuals worldwide, often leading to lifelong complications for those who survive and who require targeted rehabilitation to limit disability. Although biological brain age prediction may be a valuable biomarker to analyze neurodevelopment following perinatal stroke and guide rehabilitation, there have been no prior studies exploring its utility. Therefore, in this work, we analyzed neurodevelopment in children with two forms of perinatal stroke, namely arterial ischemic stroke (AIS) and periventricular venous infarction (PVI), by using T1-weighted neuroimaging data and machine learning-based biological brain age prediction at a global and voxel level. Specifically, we analyzed trends in the brain age gap (BAG) at both global and voxel-wise levels in the contralesional hemisphere, alongside correlation analyses with motor scores in stroke cohorts. Global and voxel-wise biological brain age prediction machine learning models were developed and trained using 5969 T1-weighted MRI scans of typically-developing children (mean age: 12.11 ± 2.72 years). These trained models were then applied to T1-weighted MRI data from N = 105 subjects with perinatal stroke (mean age: 11.41 ± 3.27 years) and N = 105 age- and sex-matched controls. The Wilcoxon signed-rank test and Mann-Whitney U-test were used to identify differences in BAGs in the stroke vs. controls subgroups, and AIS vs. PVI subgroups, respectively. Spearman's correlation coefficient was used to identify relationships between BAGs and motor scores. Lastly, sex-specific analyses were performed to identify sexually dimorphic characteristics in the data. Children with perinatal stroke exhibited, on average, more positive global and voxel-wise BAGs, particularly those with AIS. Motor scores were negatively correlated with global and voxel-wise BAGs in the contralesional hemisphere. The correlations between BAGs and motor scores differed between the sexes. This is the first study to propose and explore voxel-wise brain age prediction for a pediatric cohort and the first to utilize brain age prediction to study perinatal stroke. Further development of these methods may reveal biomarkers that are valuable to study other pediatric diseases and promote personalized rehabilitation for affected individuals.

Understanding how multiple languages are represented in the multilingual brain is critical to theories of multilingual assimilation and accommodation. Whether a second language (L2) recruits native-like networks (assimilation) or engages additional neural resources (accommodation) depends partly on the linguistic distance between L1 and L2. However, it remains unclear whether multilinguals can flexibly modulate assimilation and accommodation mechanisms when dealing with different L2 systems. Using 7 T-fMRI, we investigated brain activation patterns during a covert picture-naming task involving one-back phonological retrieval in 15 native Chinese speakers highly proficient in Japanese and English. Whole-brain univariate analysis revealed a common fronto-parieto-occipital network across all three languages (Chinese, Japanese, and English), supporting a shared core language system. However, the Japanese task elicited greater activation in the left lingual and middle occipital gyri, suggesting increased implicit visual-lexical demands. Representational Similarity Analysis (RSA) showed greater pattern similarity between Chinese and English than between Chinese and Japanese, indicating differential neural representation across L2 languages. VOI-based RSA showed a trend toward correlations between Japanese proficiency and representational similarity in the executive regions (pars triangularis and anterior cingulate gyrus), although these correlations did not reach statistical significance. These findings demonstrated that the trilingual brain relied on both assimilation and accommodation, with the level of each modulated by differences in linguistic structure and language experience. Language with a distinct writing system (English) seemed to be assimilated into the dominant L1 Chinese neural framework, whereas orthographically related language (Japanese) rather drove neural accommodation, probably due to script-driven interference. This study highlighted multi-level neural adaptation processes underlying multilingual lexical processing and provided a neurocognitive basis for tailor-made educational strategies considering each student's linguistic background.

Lesion-symptom mapping results can vary substantially as a function of specific analysis parameters, but the extent to which individual methodological choices interact to modulate the sensitivity and specificity of results is not clear. Here, we employed a large-scale simulation approach to inform practical recommendations for lesion-symptom mapping studies. Routine clinical imaging from 959 stroke survivors (mean age = 72.5, 49.3% female) was used to conduct 384,780 lesion-symptom mapping analyses based on simulated behavioural data. Each simulated analysis used different combinations of plausible sample inclusion criteria, analysis parameters (e.g., correction factors), analysis types (e.g., univariate vs. multivariate), and underlying target correlates. Simulated analysis accuracy (percent coverage of target correlates, Dice similarity coefficient, and false positives) was compared across designs. Overall, analysis accuracy varied widely and was substantially modulated by the specific design used. Analyses that maximised lesion coverage by including large and diverse samples reliably outperformed analyses using more restricted samples. Analyses using direct total lesion volume controls outperformed analyses using other (or no) volume corrections across all accuracy measures. False discovery rate corrections yielded the best performance in terms of target coverage, while permutation corrections yielded the best Dice coefficients. While multivariate approaches were more accurate than univariate analyses in terms of Dice coefficient, univariate analyses generated higher target hit rates and percent target coverage. These results identify specific analysis designs suitable for studies aiming to maximise their sensitivity and/or specificity to underlying critical correlates, while highlighting the inferential strengths and weaknesses of these complementary approaches.

Being able to remember the origin of a particular memory is for times even more important than the memory itself. Yet, although memory for the information and memory for the source are intrinsically connected, the particular neural underpinnings for the source-memory monitoring remains unclear. We run an fMRI meta-analysis to help to clarify the brain structures necessary for source memory monitoring. Source monitoring (SM) relates to distinguishing the origin of the information of two external sources, whereas reality monitoring (RM) refers to distinguishing between one internal (e.g., thoughts) and one external source, or two internal sources. We used PRISMA to select the manuscripts and Ginger ALE to analyze the data of the final 51 eligible selected articles. Most of the articles referred to external SM paradigms (34), but the number of articles on RM (17) allowed us to run a separate analysis to identify brain regions active during this specific process. The SM and RM contrasts showed concordance in 10 clusters, being the largest cluster with the highest ALE value found in the left claustrum and middle Frontal Gyrus (BA 46, 9 and 10). SM contrasts show concordance in seven clusters that include the largest cluster centered in the left middle frontal (BA 9) and inferior frontal gyrus (BA 45). Five suprathreshold clusters were detected for RM contrasts. The largest one was detected in the parietal lobe, centered in the left inferior parietal lobule (BA 40) and left angular gyrus (BA 39). The results support the neural differentiation, as well as a partial overlapping, for the two different types of monitoring. Moreover, they help to understand the divergent results in fMRI studies most likely due to the stimuli characteristics.

By studying changes in gene expression that occur without altering the DNA sequence, epigenetics offers a promising lead in unraveling how biological factors interact with the environment to shape brain development. However, prior developmental studies have focused on candidate brain or epigenetic regions, limiting our understanding of brain- and epigenome-wide links during childhood. This study explored associations between brain-wide structural imaging (T1-weighted, diffusion-weighted) and epigenome-wide DNA methylation in 144 children with and without ADHD, using parallel Independent Component Analysis (pICA) to identify linked patterns. We identified five significantly linked brain-epigenome components, in which methylation explains a significant proportion of variance in associated brain components, and further pinpointed 908 CpGs as part of the strongest epigenetic-brain associations using multivariate random forest analysis. Genes linked to these CpGs were enriched in neuronal development and associated with psychiatric/neurodevelopmental disorders, underscoring their functional relevance. Exploratory analyses suggested that ADHD diagnosis and demographic variables may modulate these brain-epigenome associations. Our study links brain anatomy to DNA methylation patterns in functionally relevant genes involved in neuronal development and psychiatric disorders, highlighting potential biological insights through which genetic and environmental factors may influence brain development. We further provide epigenetic to brain cluster mappings and a companion script to extract epigenetic-brain links, offering resources for future neurodevelopmental studies.

Understanding neurovascular coupling (NVC) is essential for interpreting functional MRI (fMRI) data, particularly in task-based paradigms. BOLD-ASL coupling studies, where simultaneously collected blood oxygenation dependent (BOLD) and arterial spin labeling (ASL) signals are correlated, have shown widespread coupling during the resting state. While prior work has demonstrated widespread BOLD-ASL coupling at rest, less is known about how this coupling behaves during tasks and whether nonlinear dependencies contribute to NVC. This study investigates BOLD-ASL coupling during a visual checkerboard task and a finger-tapping motor task. Coupling was evaluated at zero lag and at the lag of maximum dependence. Spatial correspondence with task activation was quantified using Dice coefficients within Yeo 17-network regions. Coupling was evaluated using traditional Pearson correlation (corr) and mutual information (MI), a model-free approach capable of detecting both linear and nonlinear dependencies. Results showed strong spatial correspondence between task activation and BOLD-ASL coupling for both tasks, particularly in expected visual, sensory, and motor regions. Notably, MI showed more overlap with traditional GLM-based task activation models compared to corr. This suggests that MI may provide additional insights into the complexity of NVC beyond traditional linear methods. These findings reinforce the importance of using multimodal fMRI approaches to characterize NVC more comprehensively.

Major depressive disorder (MDD) has been increasingly characterized as a network dysconnectivity syndrome. Although single-subject morphological networks are advantageous in studying the brain connectome, extant research on MDD is limited by either small samples or a lack of integration of multi-feature across different morphological features. We used the largest structural MRI data from 1442 MDD patients and 1277 controls to construct individual-level cortical morphological networks based on cortical thickness (CT), cortical volume (CV), surface area (SA), and sulcal depth (SD). Group comparisons in interregional morphological connectivity (MC) and graph-theoretical nodal properties were performed. Furthermore, support vector machine (SVM) was applied to evaluate whether the network alterations could distinguish patients from controls. As a result, MDD patients presented widespread alterations in MC, with distinct alteration patterns observed across four morphological networks. Specifically, CT-based networks exhibited reduced MC primarily within and between higher-order networks involving the default mode and frontoparietal networks, whereas CV-based networks showed increased MC predominantly within the default mode network. By contrast, both SA- and SD-based networks demonstrated enhanced MC mainly within and between lower-order networks implicating the somatomotor and visual networks. Similar patterns of MC alterations were observed in first-episode, drug-naive MDD patients. Concurrently, nodal property analysis revealed increased betweenness centrality in multiple cortical regions in MDD. Moreover, SVM models based on the altered MC achieved moderate-to-good classification performance in distinguishing patients from controls. Overall, our findings of individual-level morphological network alterations in depressed patients may corroborate the dysconnectivity hypothesis of MDD and could further inform its more accurate diagnosis.

Distinguishing atypical parkinsonian disorders (APS) from Parkinson's disease (PD) remains challenging due to overlapping clinical features, yet accurate differentiation is critical for prognosis and treatment. Here, we employed multimodel diffusion MRI (dMRI) analysis to characterize microstructural alterations across corticobasal syndrome (CBS), progressive supranuclear palsy-Richardson syndrome (PSP-RS), and PD, with the aim of identifying which dMRI model and regional metrics show the strongest group-level separation. We analyzed 25 CBS, 42 PSP-RS, and 21 PD participants compared to 35 age and sex-matched controls. Using a clinically feasible 3-shell high angular resolution diffusion imaging (HARDI) protocol, we applied 11 metrics from five complementary dMRI models-diffusion tensor imaging (DTI), free-water-eliminated model of DTI (FWE), neurite orientation dispersion and density imaging (NODDI), tissue-weighted NODDI, and fixel density (FD) in fixel-based analysis (FBA)-to comprehensively assess regional white and gray matter integrity. Group differentiation was quantified using covariate-adjusted Cohen's d effect sizes and spearman correlations were assessed between dMRI metrics and clinical scales. Distinct microstructural signatures were observed across disorders, and the sensitivity of the dMRI models differed. In group contrasts, DTI and NODDI-derived metrics consistently captured the strongest effects in midbrain and peduncular pathways for PSP-RS, whereas precentral and corticospinal alterations in CBS were most prominent using NODDI and FBA measures. Free-water-corrected metrics showed attenuated group differences. Across clinical-diffusion analyses, NODDI metrics exhibited the most robust associations with disease severity, while DTI and FWE measures detected more limited, regionally constrained effects. Together, these findings highlight complementary yet distinct sensitivities of tensor, free-water, multicompartment, and fixel-based models to APS-related neurodegeneration.

Transcutaneous auricular vagus nerve stimulation (taVNS) has shown promise in enhancing cognitive and emotional functions, yet its neural mechanisms remain unclear largely because existing analytical methods cannot characterize multiscale functional connectivity nor reliably infer causal interactions between brain regions in the presence of hemodynamic delays in fMRI signals. To address these limitations, we propose a Multiscale Spatiotemporal Causal Mapping (MSTCM) algorithm that integrates community-aware multiscale functional connectivity with delay-compensated causal inference. This design enables MSTCM to characterize multiscale connectivity structure and infer directed information flow with enhanced robustness. In evaluations using simulated fMRI data, MSTCM significantly outperformed seven existing causal inference algorithms across multiple evaluation metrics, including precision, sensitivity, Matthews correlation coefficient (MCC), and area under the receiver operating characteristic curve (AUC). Applied to resting-state fMRI across four predefined large-scale cortical networks before and after taVNS, MSTCM revealed that taVNS reduced functional coupling between the left lateral sensorimotor cortex (L-LSMC) and the right intraparietal sulcus (R-IPS), increased global efficiency, enhanced causal integration within the salience network (SN), weakened causal connectivity within the dorsal attention network (DAN), and strengthened information flow from DAN to SN. These findings suggest that taVNS may enhance cognitive flexibility and emotional regulation by shifting information processing from exteroceptive toward interoceptive pathways and improving large-scale network efficiency. Consequently, this study provides not only a novel methodological approach but also new neuroimaging evidence supporting the clinical potential of taVNS.