Major depressive disorder (MDD) involves network-level abnormalities that may reflect dysfunction in catecholaminergic nuclei, particularly the substantia nigra (SN) and locus coeruleus (LC), which regulate cortical gain and signal-to-noise ratio. However, whether MRI-derived SN/LC phenotypes are linked to depressive symptom severity through functional connectivity pathways in young individuals with MDD remains unclear. We studied 84 young participants (MDD, n = 46; controls, n = 38). MRI measures were extracted from the bilateral SN and LC using neuromelanin-sensitive magnetization transfer contrast (MTC) imaging and iron-sensitive quantitative susceptibility mapping (QSM). Seed-to-voxel analyses assessed associations between these phenotypes and resting-state functional connectivity. Graph-theoretical analyses extended functional connectivity findings to global network topology. Mediation analyses tested whether functional connectivity linked SN/LC MRI phenotypes to Hamilton Depression Rating Scale scores within the MDD group. Multiple comparisons were controlled using false discovery rate (FDR) at q < 0.05. Compared with controls, the MDD group exhibited reduced bilateral SN volume on QSM and decreased LC signal intensity on MTC. Neuromelanin- and iron-sensitive phenotypes showed distributed functional connectivity associations involving frontal, sensorimotor, precuneus, and cerebellar regions. Bilateral SN susceptibility showed diagnosis-dependent associations with global efficiency. Mediation analyses identified four candidate right LC functional connectivity pathways linking SN/LC qMRI phenotypes to HAMD scores, mainly involving right LC connectivity with the left precuneus, right paracentral lobule, and cerebellum.SN/LC catecholaminergic MRI phenotypes are associated with depressive severity and right LC-centered functional connectivity, supporting candidate circuit-level associations linking brainstem neuromodulatory phenotypes to clinical expression in MDD.

Surgical failure in temporal lobe epilepsy with hippocampal sclerosis (TLE-HS) may reflect insufficient disruption of epileptogenic networks. We investigated whether integrating quantitative spatial patterns of regional atrophy and hypometabolism, along with clinical features, could provide complementary prognostic information for postoperative seizure freedom. T1-weighted MRI, [18]F-FDG-PET, and postoperative CT scans of patients with TLE-HS were retrospectively analyzed against a validated healthy control cohort to compute age- and gender-adjusted W-score maps. Regional relationships of atrophy and hypometabolism were assessed using bivariate correlations and multiple regression analyses. Machine learning models integrating laterality of seizure onset, preoperative (extra-)temporal atrophy/hypometabolism extent, seizure frequency, focal to bilateral tonic-clonic seizures during the preceding year, and resection volume were developed to predict seizure freedom 1 year after surgery. Model interpretability was assessed via permutation-based variable importance analysis. The cohort comprised 101 patients with TLE-HS (48 left, LHS; 53 right, RHS), including 72 patients who underwent anterior temporal lobectomy. Distinct modality-specific patterns emerged: hypometabolism was significantly greater than local atrophy in left temporo-limbic cortex in TLE-LHS, while atrophy exceeded hypometabolism in bilateral occipital regions in TLE-RHS. Positive correlations between local atrophy and hypometabolism were more robust in ipsilateral temporo-limbic cortex in TLE-LHS. The combined model integrating preoperative atrophy and hypometabolism features (AUC: 0.56-0.64) significantly outperformed single-modality models (pfdr < 0.05). Notably, incorporating clinical factors further enhanced predictive performance (AUC: 0.63-0.70) and model calibration. This comprehensive preoperative clinical-imaging model achieved robust prognostic efficacy that was not significantly improved by the addition of actual postoperative resection volumes. Structural MRI and [18]F-FDG-PET reveal complementary facets of the epileptogenic network in TLE-HS. While integrating multimodal imaging with clinical metrics demonstrates significant synergistic potential for predicting surgical outcomes, our models remain exploratory. External validation in independent multi-center cohorts is required before translating these findings into precise clinical tools for surgical planning.

Human language often allows flexible word order in sentences with either an argument or an adjunct scrambled. This study examined brain mechanisms during the comprehension of such sentences in Japanese by conducting a functional magnetic resonance imaging (fMRI) experiment. The imaging results revealed that regardless of whether arguments or adjuncts undergo scrambling, the comprehension of noncanonical word order sentences yields a higher brain activation than their canonical counterparts. Specifically, the activation was found in the left inferior frontal gyrus and the posterior superior temporal sulcus. The current results lend neurological support for the canonical versus noncanonical word order distinction in sentences with adjuncts, suggesting a unified explanation for the brain mechanisms for the processing of word order permutation in human language.

Functional MRI (fMRI) is widely used to assess brain function, but neural-derived fMRI signals are susceptible to contamination from in-scanner head motion. Preprocessing pipelines often regress out and censor head motion artifacts using six rigid-body motion parameters. However, recent research suggests these head-motion estimates are susceptible to contamination from other sources of noise, such as respiratory rate, body size, and estimated cardiorespiratory fitness (eCRF), that correspond to apparent head motion at higher frequencies (HF-motion; > 0.1 Hz). Thus, these health variables are thought to be linked to the artifactual inflation of head motion estimates, introducing additional challenges to appropriate modeling and preprocessing procedures that reduce noise. Whether this artifactual relationship extends to changes in body size, midline abdominal fat (i.e., body composition), CRF measured with gold-standard methods, and respiratory rate measured during scanning remains unclear. The Brain EXTEND Trial acquired multiple health variables before and after an exercise intervention that changed CRF, offering a means to test the relationship between these health variables and HF-motion in a longitudinal design. Subjects (55-80 years of age) completed a 6-month chronic exercise intervention at two intensities. We analyzed baseline and longitudinal (EXTEND, baseline n = 122; longitudinal n = 84) relationships between HF-motion and health variables that included body mass index (BMI), waist circumference (WC), CRF measured with a maximal exercise test (VO2max), and resting respiratory rate. HF-motion was examined in each head-motion parameter as the proportion of HF-motion above > 0.1 Hz-the typical upper bound of fMRI signals at rest. At baseline, HF-motion for all translational axes was positively associated with body size (BMI, WC) while CRF was negatively associated with only the roll y-rotation. Additionally, longitudinally, decreased BMI related to reduced HF-motion in the z-translation. Results indicate health changes related to body size associate with the presentation of high-frequency MRI head-motion artifacts. We caution researchers against ignoring the presence of HF-motion in their analyses as their use of contaminated motion traces will negatively affect their modeling of fMRI measures. Results have implications for a wide range of investigations in health neuroscience involving body size, respiratory function, and cardiorespiratory fitness.

The temporal signal-to-noise ratio (tSNR) of functional magnetic resonance imaging (fMRI) is particularly poor in ventral anterior temporal and orbitofrontal regions because of B0 and B1 [+] magnetic field inhomogeneity, a problem that is exacerbated at higher field strengths. In this 7T-fMRI study we compared three methods of improving sensitivity in these areas: parallel transmit, which uses multiple transmit elements, controlled independently, to homogenise the flip angle experienced by the tissue; multi-echo, which entails collection of multiple volumes at different echo times following a single radiofrequency pulse; and multiband, in which multiple slices are acquired simultaneously. We found that parallel transmit and multi-echo increased the magnitude of the BOLD signal change, but only multi-echo increased BOLD magnitude in areas prone to susceptibility artefacts. Multiband and denoising of multi-echo data with independent components analysis (ICA) both improved precision of GLM fit. Exploratory results suggested that multi-echo and ICA denoising can both benefit multivariate analyses. In conclusion, a multi-echo, multiband sequence improved fMRI quality in areas prone to susceptibility artefacts while maintaining sensitivity across the whole brain. We recommend this approach for studies investigating the functional roles of ventral temporal and orbitofrontal regions with 7T fMRI.

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.