Left hemisphere (LH) strokes often disrupt the language network (LN), causing aphasia. The right hemisphere (RH) has been proposed to contribute to post-stroke aphasia outcomes, but existing findings on RH functional connectivity (FC) changes after LH stroke are inconsistent. This study systematically investigates whether and how the RH FC differs in chronic stroke survivors compared to healthy controls, and whether these differences relate to lesion characteristics and language outcomes. Using a naturalistic movie-watching fMRI paradigm, we compared the RH FC in a large sample of chronic LH stroke survivors (n = 85) to neurologically healthy controls (n = 71). We first used a validated semantic decision fMRI task to identify a core LN and a non-canonical LN (labeled Semantic Network (SN)). We then mapped group FC differences within and between these LH networks and their RH homotopes. Compared to controls, stroke survivors had higher overall within-RH FC and lower within-LH and interhemispheric connectivity. These differences were greater with larger lesions. However, few of the RH FC differences were observed within the RH LN. Rather, most changes were between networks and were distributed widely in the RH. Greater RH connectivity was not associated with lesions or reduced FC in homotopic LH regions, although lesions of LH superior and middle temporal gyri were associated with increased FC between RH SN and LN. RH FC was not associated with behavioral outcomes. These findings show that LH lesions lead to increased RH FC, but the pattern of results suggests that these changes may not reflect meaningful functional reorganization that contributes to language function after stroke. TRIAL REGISTRATION: clinicaltrials.gov NCT04991519.
The postpartum period involves substantial brain changes, but whether these represent pathological aging or adaptive plasticity remains unclear. We applied machine learning-based brain age prediction to quantify postpartum brain aging trajectories in a controlled longitudinal study. Brain age gap, the difference between MRI-predicted and chronological age, was assessed in 55 women (25 postpartum and 30 nulliparous controls). Postpartum women were assessed at approximately 3 and 12 months postpartum; controls were assessed at matched intervals. Postpartum women showed elevated brain age gap at 3 months (2.5 ± 2.7 years) compared with controls (0.3 ± 3.6 years; p = 0.01, Cohen's d = 0.69), which normalized by 12 months. Within-group analysis revealed significant brain age gap decrease in postpartum women (-3.3 ± 3.5 years; p = 0.008), while controls showed minimal change; the between-group difference in longitudinal change did not reach statistical significance (p = 0.12, Cohen's d = -0.58). Across all participants, brain age gap correlated with working memory performance (ρ = -0.30; p = 0.03), depressive symptoms (ρ = 0.38; p = 0.004), and subjective cognitive complaints (ρ = 0.28; p = 0.04) at baseline, providing preliminary evidence of clinical relevance. Explainability analysis using Shapley additive explanations identified subcortical structures as the network most selectively associated with the postpartum elevation in brain age gap at 3 months (p = 0.006, Cohen's d = 0.82), with normalization by 12 months. Together, these findings indicate transient brain age gap elevation in postpartum women without statistical confirmation of a trajectory differing from controls. With brain age gap providing a quantitative index of postpartum brain structural change, these preliminary findings are consistent with adaptive neuroplasticity, suggesting that such change may be temporary rather than reflecting permanent impairment.
Understanding the cortical architecture underlying individual differences in general cognitive ability (GCA) remains a central question in cognitive neuroscience. Prior work has established associations between global brain size and GCA, yet the regional effects and directionality of these relationships remain debated. Using a genetically informed cortical parcellation in 11,289 UK Biobank participants, we examined associations between cortical surface area (SA), cortical thickness (CT), and GCA measured via verbal-numerical reasoning. Total SA showed a robust positive association with GCA. At the regional level, dorsolateral prefrontal and superior temporal SA exhibited the strongest positive associations, which persisted after adjustment for global SA. In contrast, CT showed comparatively modest associations. Using Mendelian randomization (MR) with genome-wide significant genetic instruments, we observed evidence consistent with a bidirectional relationship between total SA and GCA. At the regional level, dorsolateral prefrontal and temporal SA demonstrated evidence of MR-inferred directional effects on GCA, while GCA showed evidence of MR-inferred directional effects on total SA and perisylvian thickness. These findings support a polyregional SA architecture underlying GCA, with prominent contributions from prefrontal and temporal association cortices. Our results refine global brain-GCA models and highlight the value of genetically informed parcellation for identifying regional cortical contributions.
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, 18F-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 18F-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.
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.
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.
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.
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.
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.
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.
Major life transitions have been proposed to influence trajectories of brain aging, yet the timing and cognitive correlates of such divergence remain unclear. Motherhood has been associated with younger brain-predicted age in midlife, but whether similar signatures are already observable in early adulthood-and whether such signatures coincide with differences in cognitive performance-remains unknown. In over 1000 young adults (ages 22-37), brain-predicted age was estimated from structural MRI scans and cognition was assessed across fluid and crystallized domains. Mothers exhibited significantly younger brain-predicted age than non-mothers after adjustment for chronological age, intracranial volume (ICV), socioeconomic status and body mass index (BMI); no comparable association was observed between fathers and non-fathers. Regional analyses revealed a spatially distributed signature, with directionally consistent reductions across all 10 lobar regions, of which bilateral frontal, left parietal, and bilateral subcortical/cerebellar regions survived false discovery rate correction. The younger brain-age profile in mothers did not exhibit a graded association with number of children or the duration of parental experience, providing no support for a strictly linear cumulative exposure pattern in early adulthood. Despite younger-appearing brain anatomy, mothers did not exhibit superior cognitive performance. Instead, crystallized cognition was modestly lower in mothers after covariate adjustment, and a parallel pattern was observed in fathers, whereas fluid cognition did not significantly differ from non-parents in either sex. This cross-sex convergence in crystallized cognition, combined with the brain age effect observed only in mothers, suggests that structural and cognitive correlates of parenthood may have partially distinct origins. Collectively, these findings indicate that motherhood-related differences in brain predicted age are already observable within years of childbirth, albeit without concomitant cognitive advantages.
Pediatric "mild" traumatic brain injury (pmTBI), or concussion, is increasingly associated with persisting and often undiagnosed cognitive difficulties, particularly, in adolescents. Emerging evidence suggests that these deficits are most pronounced during tasks requiring competing attentional demands (i.e., reactive control). However, it remains unclear how injury severity, indexed by loss of consciousness and/or posttraumatic amnesia (LOC/PTA) interacts with neurodevelopment to shape long-term, modality-specific alterations in cognitive control. In this longitudinal fMRI study, we examined reactive cognitive control using a multimodal attention task in a large cohort of pmTBI (N = 236) and age- and sex-matched healthy controls (N = 212), across three timepoints (~1 week, ~4 months, and ~1 year) postinjury. Participants with LOC/PTA+ exhibited reduced task performance across both visual and auditory modalities, accompanied by persistent neural disruptions, indicating that greater injury severity is associated with more widespread, cross-modal impairments in attentional control. In contrast, participants without LOC/PTA- demonstrated increased neural activation early after injury, potentially reflecting compensatory recruitment in the auditory domain to maintain performance. However, this was accompanied by greater clinical burden (i.e., sleep disturbance, depression) and was followed by evidence of dysregulated control by ~1 year postinjury, suggesting that short-term neural compensation may carry longer-term consequences. Notably, neural differences across groups were primarily observed in the auditory domain, with alterations in frontotemporal and cerebellar regions. This modality-specific effect may reflect developmental shifts toward visual dominance during adolescence, which could increase processing demands for auditory information and render it more vulnerable to disruption following injury. Together these findings indicate that pmTBI is associated with persistent alterations in cognitive control processes that evolve over time as a function of injury severity.
To investigate the characteristics of brain region activation associated with explicit outcome expectation effects in treatment contexts, and to identify core brain regions consistently involved across diverse experimental paradigms. A systematic search was conducted for studies published from January 1, 2014, to December 31, 2025. Functional magnetic resonance imaging (fMRI) and positron emission tomography (PET) studies investigating treatment expectation effects were included. In neuroimaging paradigms, these effects are commonly induced through explicit outcome expectations via verbal instructions or contextual cues. Risk of bias was assessed using the ROB 2.0 and ROBINS-I tools. Activation likelihood estimation (ALE) meta-analysis was performed using GingerALE 3.0.2 based on whole-brain activation coordinates. Results were visualized using Mango software. Sensitivity analyses were performed by excluding the study with the highest risk of bias and using a leave-one-study-out approach. Additionally, a qualitative synthesis of activation foci was conducted across four domains to address paradigm heterogeneity. A total of 21 articles with 855 subjects were included in this study, consisting of 17 randomized controlled trials and 4 non-randomized studies. The risk of bias assessment indicated that only five RCTs were rated as low risk, while approximately three-quarters of studies showed some concerns or high risk. Most studies modulated expectations through interventions such as verbal instruction and pain stimulation. Convergent neuroimaging studies implicated the insula, frontal lobe, striatum, and additional relevant brain regions. The ALE meta-analysis identified two significant activation clusters located in the caudate nucleus/lentiform nucleus and the red nucleus. Sensitivity analyses indicated that the majority of findings remained stable. Qualitative domain-based synthesis further revealed consistent striatal activation across all domains, together with distinct domain-specific activation patterns. The findings of this study suggest that explicit outcome expectation effects, as a core operationalized component of treatment expectation, do not rely on a single brain region but emerge from distributed and dynamically interacting neural systems that depend on reward expectation and information integration. The coordinated activity of multiple brain regions collectively supports the generation of explicit outcome expectations. The striatum serves as a shared core substrate across different expectancy domains, while domain-specific activation reflects functional specialization for distinct contexts. These findings provide important evidence for elucidating the neural correlates of explicit outcome expectation effects and establish a foundation for further brain mechanism-based intervention strategies.Trial Registration: PROSPERO: CRD420251074894
