Bereavement is a near-universal experience in late life, yet only some older adults develop prolonged grief disorder (PGD). While most transition from acute grief (AG) to integrated (adaptive) grief, the neurobiological substrates underlying divergent trajectories are unclear. Emotion regulation dysfunction is hypothesized to play a central role in PGD pathogenesis, but longitudinal neuroimaging data in bereaved adults are lacking. This study aims to identify functional brain circuit measures of emotional regulation that predict pathological versus adaptive grief trajectories, aligning with the Research Domain Criteria (RDoc) framework for the Negative Valence System (Loss) construct. This single-site, 1-year longitudinal study aims to enroll 170 adults aged 50-89 years: 115 with AG and 55 age- and gender-equated non-bereaved participants. Participants will complete comprehensive psychiatric, neuropsychological, and psychosocial assessments, alongside neuroimaging both at study baseline and after 12 months. Functional neuroimaging includes resting-state fMRI, a face-shape matching task probing emotion processing, and a stop-signal task probing inhibitory control. Functional neuroimaging data are acquired using a harmonized Human Connectome Project protocol on a GE Signa Premier 3T MRI scanner. We present a comprehensive overview of the eligibility criteria, clinical study procedures, and neuroimaging protocol. Baseline findings from 103 AG and 40 non-bereaved participants thus far enrolled show that the groups are demographically matched and provide high-quality neuroimaging data and robust task performance. This study is among the first longitudinal neuroimaging investigations of AG in older adults and may identify early biomarkers of PGD risk, potentially guiding precision prevention and intervention strategies for bereaved older adults.
This review summarizes neuroimaging studies designed to investigate cortical and subcortical brain changes in older adults with bipolar disorder (OABD). The imaging modalities discussed in the review include structural MRI, functional MRI (fMRI), diffusion tensor imaging (DTI) addressing white-matter microstructure, and functional and molecular neuroimaging with PET (18-F-FDG-PET, amyloid-PET, and tau-PET). Although limited, the neuroimaging evidence in OABD points to cortical thinning, reduced gray matter volume, subcortical alterations, and decreased fractional anisotropy in white-matter tracts. Studies also report a 'brain age gap' in bipolar disorder, in which predicted brain age (as per neuroimaging scans) exceeds chronological age. Neuroimaging research in OABD remains limited by methodological heterogeneity, including variable imaging protocols, differences in episode severity and frequency, the effects of pharmacological treatment and neurobiological changes associated with disease chronicity. These factors restrict comparability and generalizability across studies.
Genomic, transcriptomic, and proteomic studies suggest that the complement system contributes to the pathophysiology of various psychiatric disorders partly through neurodevelopmental effects linked to C4A protein levels variations. We conducted a systematic review to characterize how brain micro- and macrostructure and connectivity vary with proxies of in vivo brain C4A protein levels in both psychiatric and general-population cohorts. We used Medline, Web of Science, and Embase, and included all studies published before April 14, 2025. Inclusion criteria were: (1) inclusion of healthy controls and/or individuals with psychiatric disorders assessed according to recognized diagnostic manuals (DSM or ICD); (2) use of MRI-based neuroimaging; and (3) use of genomic, transcriptomic and/or proteomic approaches as proxies of in vivo brain C4A proteins levels. From 317 identified articles, 11 were included. Associations between C4A levels and brain structure were heterogeneous across regions. Only the mOFC, dlPFC, and entorhinal cortex were implicated in more than one study. Findings for the mOFC and dlPFC varied by the type of metrics and clinical status, whereas higher C4A levels were more consistently associated with smaller entorhinal cortex surface area and cortical thickness in pediatric, middle-aged, and older general-population cohorts. In addition, one study found higher genetically predicted C4A expression to be associated with higher TSPO levels. The limited number of available studies and their methodological heterogeneity make synthesis challenging. However, biological hypotheses such as excessive synaptic pruning or broader inflammatory effects on the brain may provide plausible explanatory frameworks for the reported associations.
Alzheimer's disease (AD) is a progressive neurodegenerative disorder characterized primarily by a gradual decline in cognitive function and specific pathological changes in the brain. In recent years, although various neuroelectrophysiological and neuroimaging techniques have greatly advanced the mechanistic study of abnormal brain function in AD, an integrative discussion of these technologies remains fragmented. This paper primarily summarizes and interactively analyzes the research progress of several non-invasive neuroimaging and neuroelectrophysiological techniques-event-related potential (ERP), electroencephalogram (EEG), transcranial magnetic stimulation-electroencephalogram (TMS-EEG), functional near-Infrared spectroscopy (fNIRS), magnetoencephalography (MEG), structural magnetic resonance imaging (structural MRI) and functional magnetic resonance imaging (fMRI)-to depict a panoramic view of AD pathology from a microscopic to a macroscopic scale from a multimodal perspective. It further compares the advantages and limitations of various technologies for detecting early AD biomarkers, emphasizing the synergistic value of multimodal integration in capturing changes in dynamic functional and structural brain networks. Additionally, we explore the potential of these technologies in clinical translation, particularly when combined with machine learning and deep learning approaches, to enhance the accuracy of early diagnosis and the depth of mechanism analysis. Through the above discussion, this review aims to provide new insights for the early identification of AD and advance our understanding of the neural mechanisms underlying AD.
Hormonal contraceptives (HCs) contain synthetic gonadal hormones that act on receptors widely distributed throughout the brain, thereby altering the body's endogenous hormonal milieu in ways that may influence brain and behavior. Although HCs are among the most commonly prescribed medications for female adolescents, their effects on the developing brain and mental health remain poorly understood. This gap is concerning given that adolescence is marked by substantial hormonal change, neurodevelopment, and a sharp rise in depression risk among female youth. In this review, we synthesize current evidence on associations between adolescent HC use, depression risk, and brain structure and function. Epidemiological studies have consistently reported associations between HC use during adolescence and increased depression risk, but causal interpretation is limited by residual confounding. Neuroimaging research remains scarce, particularly in adolescents, and rarely accounts for heterogeneity in HC formulations and characteristics of use or for endogenous hormonal variation related to puberty or the menstrual cycle. We outline 3 considerations to guide future research: accounting for HC heterogeneity, incorporating developmental features of adolescent menstrual cycles, and situating HC use within its broader developmental and sociocultural context. We conclude by emphasizing the need for rigorous developmentally sensitive research to counter misinformation and better support adolescents' reproductive and mental health care needs. Hormonal contraceptives (HCs) are widely used by adolescent girls, but their potential effects on the developing brain and mental health are not well understood. This matters because puberty brings major hormonal and brain changes, and depression risk rises for girls during adolescence. Many population studies link adolescent HC use to higher depression risk, but these findings may reflect other differences between users and nonusers. In this review, we evaluate evidence linking adolescent HC use with depression risk and with brain structure and function. We highlight key research gaps and priorities to reduce misinformation about HCs and better support adolescents’ reproductive and mental health.
Sleep is critical for brain function and cognitive development, especially in children and adolescents. Sleep disturbances or insufficient sleep may pose a risk for poor long-term health outcomes, including major depressive disorder (MDD). This systematic review sought to the neurobiological relationship between sleep and risk for future depressive disorders or symptoms. A pre-registered, systematic search was conducted using MEDLINE, PsychInfo, and Embase databases from January 2000 to February 2025. Studies included investigated neurobiological changes related to sleep in predicting subsequent risk for first episode MDD or depressive symptoms in children, adolescents, and young adults (mean age < 30 years). We narratively synthesized the results and completed a quality assessment using a modified Newcastle-Ottawa scale. The search yielded four cross-sectional studies comparing high and low MDD risk individuals, and seven longitudinal studies predicting risk of future depressive symptoms. Sleep neurophysiological markers like rapid eye movement latency and sleep spindle density, white matter integrity in the cingulum bundle and superior longitudinal fasciculus, and activity within the salience network and dorsomedial prefrontal cortex associated with sleep quality and differed in individuals with an increased risk for MDD. A low-to-moderate risk of bias was detected among all studies. Due to the limited number of studies and methodological heterogeneity, the highlighted evidence remains preliminary. This review emphasizes the importance of addressing sleep problems in youth before they potentially contribute to lasting changes in emotional and mental health, as well as the need to further study the relationship between sleep disturbances and risk for MDD.
Histopathological assessment has served as the gold standard for diagnosing Alzheimer's disease (AD). Emerging technological advancements, including the development of amyloid positron emission tomography (PET), have enabled early detection of amyloid pathology, one of the neuropathological hallmarks of AD. Genome-wide association study (GWAS) across cohorts of aging and AD, leveraging different measurements of amyloid burden, may facilitate the identification of novel genetic variants that drive the earliest neuropathological changes in AD. This study presents the largest GWAS of brain amyloidosis to date, leveraging amyloid β (Aβ) measured by in vivo amyloid PET and postmortem histopathology from 13,555 individuals of European ancestry. Amyloid positivity was defined as moderate or frequent neuritic plaques according to the Consortium to Establish a Registry for Alzheimer's Disease (CERAD) staging scores for each postmortem cohort. A Gaussian mixture model (GMM) was applied to each amyloid PET cohort to identify the cohort and tracer-specific cut-offs that differentiate amyloid positive and negative populations. In silico and ex vivo analyses further characterized implicated loci, including interrogating the association between bulk and single-nucleus gene expression profiles and AD-related traits. Genetic covariance analysis assessed the extent amyloid PET and postmortem measures reflect the shared genetic architecture of brain amyloidosis. Our combined amyloidosis GWAS identified three established AD risk loci: BIN1 (rs6733839, OR = 1.20, 95% CI 1.14-1.26, P = 1.32 × 10-11), CR1 (rs4844610, OR = 1.24, 95% CI = 1.16-1.32, P = 4.21 × 10-10), APOE (rs429358, OR = 4.01, 95% CI = 3.66-4.38, P = 4.54 × 10-201), and a newly identified brain amyloidosis-associated variant on chromosome 17 (rs35635959, OR = 1.18, 95% CI = 1.12-1.25, P = 1.47 × 10-8). SuSiE fine-mapping identified a single credible set of 15 putative causal variants with rs35635959 as the lead variant. Subsequent eQTL and SuSiE-based colocalization analyses prioritized rs35635959 as a strong eQTL for TUBG2, encoding tubulin gamma 2, which is involved in microtubule organization and synaptic plasticity. Further cell-type-specific characterization of this gene in neurons from dorsolateral prefrontal cortex tissue indicated that decreased TUBG2 expression was associated with increased Aβ burden and AD case status (PFDR < 0.045). Furthermore, our study is the first to report a modest genetic covariance (covariance=0.17, P < 6.54 × 10-8) between the genetic architecture of amyloid burden captured by different modalities. While APOE showed a strong association with both amyloid endophenotypes, the observed genetic covariance was not substantially attenuated after excluding variants within the APOE region (covariance=0.16, P < 1.32 × 10-7). Our results highlight the benefits of leveraging compatible, harmonized AD endophenotypes to increase power to uncover new molecular insights into the etiology of AD neuropathology. Wang et al. present the largest GWAS of brain amyloidosis to date, analysing 13,555 individuals using amyloid PET and postmortem Aβ measures. They identify a novel amyloidosis-associated variant on chromosome 17, demonstrate genetic covariance between modalities, and highlight complex traits sharing genetic architecture with Aβ burden.
Functional magnetic resonance imaging (fMRI) provides a crucial window for understanding brain functional connectivity (FC) in psychiatric disorders, yet its complex spatiotemporal dynamics pose substantial challenges for modeling. Existing methods often rely on static FC, making it difficult to capture the dynamic plasticity of brain, while generally ignoring structural differences across functional networks or discarding informative weak connections due to excessive sparsification. Here, we propose SPSGL, a biologically inspired deep learning framework designed to construct novel brain connectivity patterns from fMRI signals. SPSGL transforms voxel-wise time series into frequency-domain, feature-driven functional brain graphs and employs a biologically inspired gated edge-update mechanism to capture dynamic changes in connectivity strength. On this basis, core functional networks and whole-brain patterns are mapped as structural priors to explicitly guide multi-head attention in forming complementary subspace foci that emphasize neurobiologically meaningful connections. Further combined with Orthonormal Clustering Readout (OCRead), our model achieves adaptive learning of multi-scale brain graph representations and functional parcellations. Across five psychiatry-related computational tasks, SPSGL demonstrates superior performance compared with existing approaches. Moreover, it identifies task-relevant functional connections and hub regions associated with aberrant coupling among the default mode, sensorimotor, and subcortical networks, highlighting potential neuroimaging biomarkers and uncovering shared brain network factors shared across diverse psychiatric conditions. Overall, SPSGL provides a unified, interpretable, and high-performing framework for fMRI-based brain connectivity analysis, advancing mechanistic understanding and potential clinical translation in mental health research. Our code is publicly available on https://github.com/zhaoqi106/SPSGL. The online version contains supplementary material available at 10.1007/s13755-026-00467-6.
The United States Supreme Court's proportionality decisions in Atkins v. Virginia and Roper v. Simmons rest on a single constitutional principle: individuals who lack the behavioral and cognitive capacities necessary for full culpability cannot be sentenced to death. Contemporary neuroscience now provides the ability to measure these capacities directly. Research demonstrates that the neural systems supporting judgment, behavioral inhibition, emotional regulation, and future-oriented reasoning mature heterogeneously, vary substantially across individuals, and can be impaired by developmental deviation, psychiatric illness, traumatic injury, or neurodegenerative disease. Quantitative neuroimaging enables these impairments to be identified through norm-referenced structural and functional metrics, revealing when an individual's neural functioning falls below statistically defined thresholds. Behavioral and cognitive equivalence (BACE) operationalizes the Court's constitutional requirement by determining whether an individual's measurable functioning is equivalent to that of categorically exempt groups. Using validated neuroimaging techniques, normative modeling, and network-level analysis, BACE represents a transparent, reproducible method for assessing diminished capacity consistent with Hall v. Florida and Moore v. Texas. Integrating contemporary neuroscience with constitutional proportionality therefore supports extending categorical protection to individuals whose measurable impairments render them functionally incapable of the culpability required for capital punishment.
The convergence of digital technology and behavioral medicine has catalyzed the development of novel therapeutic modalities. This scoping review systematically synthesizes the literature regarding the integration of Virtual Reality (VR) and Music Therapy (MT) to delineate current integration paradigms, clinical efficacy, and underlying neurobiological mechanisms. A systematic search was conducted across major databases for literature published between 2001 and 2024. A total of 34 peer-reviewed articles met the inclusion criteria and were analyzed to identify technical frameworks and clinical outcomes across diverse patient populations. The synthesis reveals that the integration of VR and MT primarily follows two complementary modalities: Immersive Scene Construction and Task-Oriented Intervention. The former utilizes multisensory immersion to facilitate rapid emotional regulation and stress reduction, while the latter employs goal-driven protocols to enhance functional rehabilitation. Clinical evidence indicates that these combined interventions significantly alleviate symptoms of anxiety, chronic pain, and cognitive impairment. The therapeutic efficacy is driven by a dynamic interplay of autonomic nervous system regulation, enhanced neuroplasticity, multisensory integration, and emotional resonance. While the synergistic application of VR and MT shows substantial promise, the field is currently limited by non-standardized intervention parameters, high equipment costs, and an incomplete understanding of how individual variability influences outcomes. Future research must focus on the standardization of treatment protocols, the development of cost-effective technologies, and the implementation of personalized interventions guided by neuroimaging. Advancing these areas is critical to transitioning from experimental frameworks to widespread clinical adoption, ultimately providing innovative solutions for global mental and physical health challenges.
Accelerated brain aging is increasingly recognized as a transdiagnostic risk factor for neuropsychiatric and neurodegenerative disorders, yet its metabolic underpinnings remain poorly understood. Here we integrated multimodal neuroimaging (MRI), plasma metabolomics, and genomic data from the UK Biobank to identify metabolic markers of brain aging and evaluate their causal relevance. Using 1079 imaging-derived phenotypes (IDPs) from 4333 healthy participants, we trained and validated machine learning models for brain age prediction, with a least absolute shrinkage and selection operator (LASSO) regression model achieving the best performance (mean absolute error = 3.26 years, R² = 0.68). Brain age gap (BAG) was then estimated in 37,458 participants. Association analyses in 21,780 individuals identified nine plasma metabolites significantly linked to BAG after Bonferroni correction, with glucose showing the strongest effect (β = 0.32, P = 9.90 × 10⁻¹²). Genome-wide association studies (GWAS) identified 392 BAG-associated single-nucleotide polymorphisms (SNPs) (P < 5 × 10⁻⁸), and two-sample Mendelian randomization (MR) provided evidence supporting a potential causal role of glucose in accelerating brain aging. Clinically, elevated plasma glucose was positively associated with seven brain disorders, including all-cause dementia, Alzheimer's disease, vascular dementia, Parkinson's disease, stroke, depression, and anxiety, and negatively associated with cognitive performance, movement function, and mental health outcomes. Higher glucose concentrations were also associated with reduced regional brain volumes across 80 cortical, subcortical, and cerebellar regions. These findings implicate glucose metabolism as a modifiable pathway in brain aging, with implications for early intervention strategies aimed at preserving brain health across the lifespan.
Hemodialysis (HD) is the predominant treatment for end-stage renal disease (ESRD). Despite the efficacy of HD, the neurobiological underpinnings underlying high-risk complications remain unclear. In this study, using unsupervised fusion of functional and structural MRI, we identified a longitudinally altered default mode network (DMN)-insula pattern in ESRD receiving HD over 1-year follow-up (n = 39). This pattern was associated with cognition, and its related genes were enriched in biological processes involving DNA damage and repair, energy metabolism, and cellular activation. The baseline DMN-insula pattern demonstrated potential predictive value for follow-up cognition in ESRD. More importantly, these brain-cognition associations were validated in independent high-risk complications cohorts, including major depressive disorder (n = 60), mild cognitive impairment (n = 291), and Alzheimer's disease (n = 77) by extracting the corresponding brain features and assessing their correlations with cognition. Collectively, this study may help researchers better understand the underlying mechanisms of ESRD receiving HD from a multimodal neuroimaging and molecular perspective.
Developing clinically useful brain-based biomarkers remains a central challenge in translational psychiatry and neurology. Traditional approaches focusing on disorder-specific signals have shown limited clinical utility. EEG, a scalable and non-invasive measure of brain function, illustrates the value of an alternative perspective: transdiagnostic and dimensional biomarker development. Here, we use low-frequency activity (LFA) as an illustrative example to demonstrate this framework. We synthesize evidence from 176 EEG studies across chronic pain, migraine, fatigue, and depression and identify increased low-frequency activity (LFA) as the most consistent alteration across studies. Crucially, this absence of disorder specificity does not diminish its clinical value. Instead, it points to shared neural dysfunction, consistent with frameworks of thalamo-cortical dysrhythmia and excitation-inhibition imbalance. These processes may underlie shared symptom dimensions, such as negative affect, cognitive dysfunction, and somatic manifestations. Accordingly, such transdiagnostic, dimensional markers could support prevention, monitoring, stratification, and neuromodulation across disorders, exemplifying precision neuroscience via mechanistically grounded, clinically actionable biomarkers.
Acetylcholinesterase inhibitors (AChEIs) provide symptomatic relief in Alzheimer's disease (AD), whereas lecanemab may modify disease progression; however, real-world evidence on its safety and clinical impact remains limited. Therefore, this study aimed to compare the safety and effectiveness of initiating lecanemab versus AChEIs in patients with mild cognitive impairment (MCI) or AD. Using the TriNetX US electronic health record network, we conducted a retrospective cohort study including individuals diagnosed with MCI or AD between July 2023 and September 2025. A target trial emulation with 1:1 propensity score matching and Cox models estimated comparative risks. Lecanemab was associated with a fivefold higher incidence of neuroimaging abnormalities than AChEIs, while 1-year treatment persistence was similar (53.4% vs 52.5%). After matching, 589 patients were included in each cohort. Compared with AChEIs, lecanemab was associated with significantly lower risks of behavioral and psychological symptoms of dementia (BPSD) (HR, 0.52; 95% CI, 0.36-0.77) and emergency visits (HR, 0.66; 95% CI, 0.51-0.85), but a higher risk of hospitalization (HR, 1.31; 95% CI, 1.03-1.67). Lecanemab was also associated with lower use of antipsychotics (HR, 0.47; 95% CI, 0.32-0.70), antidepressants (HR, 0.60; 95% CI, 0.43-0.85), melatonin/orexin antagonists (HR, 0.61; 95% CI, 0.42-0.88), antibiotics (HR, 0.61; 95% CI, 0.44-0.86), and antifungals (HR, 0.57; 95% CI, 0.37-0.88), whereas steroid use was higher among lecanemab users (HR, 2.19; 95% CI, 1.55-3.10). Compared with an AChEI-based conventional care strategy, lecanemab initiation was associated with comparable treatment persistence and lower observed risks of BPSD, emergency visit as well as reduced use of psychotropic and infection-related medications in exploratory analyses. However, the higher incidence of neuroimaging abnormalities associated with lecanemab, along with increased risks of hospitalization and corticosteroid use, likely reflects proactive clinical monitoring and management of amyloid-related imaging abnormalities (ARIA). While residual confounding cannot be excluded and results warrant cautious interpretation, these exploratory findings warrant further validation in biomarker-confirmed cohorts and head-to-head randomized trials.
Anti-amyloid therapies such as lecanemab have demonstrated statistically significant slowing of decline on the Clinical Dementia Rating-Sum of Boxes (CDR-SB) in patients with early Alzheimer's disease (AD) in pivotal trials. Converting treatment differences on CDR-SB into time saved from disease progression may help convey clinical relevance for patients and caregivers more effectively. Disease progression models were developed using Alzheimer's Disease Neuroimaging Initiative and National Alzheimer's Coordinating Center data. A 37% treatment-related time delay, derived from the Clarity AD trial, was applied to estimate long-term efficacy of lecanemab. Natural progression models estimated 11.5 to 13.7 years from mild cognitive impairment due to AD to severe AD. When starting treatment at CDR-SB 3.2, lecanemab delayed progression to severe AD by 2.5 to 3.7 years when assuming patients remained on treatment and 2.0 to 3.0 years when accounting for treatment discontinuation. Results were consistent across different datasets. Projections suggest lecanemab substantially delays clinical progression of AD, preserving patients' time in earlier stages of AD.
Aberrant resting-state functional connectivity (rsFC) within neurocognitive networks is a hallmark of alcohol use disorder (AUD). However, the spatial architecture of the central autonomic network (CAN)-the vital neural substrate governing cardiovascular and homeostatic control-and its association with heart rate variability (HRV) remain poorly characterised. We investigated group differences in CAN-related rsFC and its statistical relationship with HRV metrics in 112 young adults (54 AUD, 58 healthy controls [HC]) using resting-state fMRI and separate, HRV assessments. Seed-based analyses targeted core cortical CAN nodes. Young adults with AUD exhibited a profound pattern of functional connectivity alterations within the CAN compared with HCs. Specifically, the AUD group demonstrated attenuated long-range connectivity between CAN seeds and temporo-frontal regions, alongside heightened, pathologically constrained intra-network connectivity involving the insula and brainstem. In HCs, resting HRV indices correlated positively with rsFC across widely distributed cortical and cerebellar systems, reflecting a robust baseline association between central CAN integrity and autonomic output. Conversely, the AUD group exhibited an attenuated and highly segregated pattern of relationships, where the expected association between CAN network architecture and resting HRV features was markedly diminished. These findings demonstrate that early-stage AUD is characterised by a fundamental spatial reorganization of the CAN and a significant blunting of brain-autonomic relationships. This neuro-autonomic alteration may represent a distinctive neuroimaging trait marker of chronic autonomic dysregulation, highlighting the critical need for early detection and targeted management of AUD in young adults.
A key challenge in neuroscience is inferring relationships between brain structure and function from high-dimensional, multimodal neuroimaging data. While conventional multivariate approaches often simplify statistical assumptions and estimate one-dimensional independent sources shared across modalities, the true relationships between latent sources are likely more complex-statistical dependence may exist both within and between modalities and span more than one dimension per modality. Here, we introduce Multimodal Subspace Independent Vector Analysis (MSIVA), a method for capturing both joint and unique vector sources from multiple data modalities by defining cross-modal and unimodal subspaces with variable dimensions. MSIVA enables flexible estimation of varying-size independent subspaces within modalities and their one-to-one linkage to corresponding subspaces across modalities. Crucially, it captures subject-level variability at the voxel level within independent subspaces, in contrast to traditional methods that share identical independent components across subjects. We evaluated three initialization workflows with five candidate subspace structures in multiple synthetic datasets and two large multimodal neuroimaging datasets, including structural MRI (sMRI) and functional MRI (fMRI). After confirming that MSIVA successfully recovered ground-truth subspace structures in synthetic data, we applied MSIVA to identify latent subspace structures in neuroimaging data. Subsequent subspace-specific canonical correlation analysis, brain-phenotype prediction, and voxelwise brain-age delta analysis revealed that MSIVA sources were strongly associated with multiple phenotype variables, including age, sex, schizophrenia, lifestyle factors, and cognitive functions. Further, we identified modality- and group-specific brain regions related to age (for example, cerebellum, precentral gyrus, and cingulate gyrus in sMRI; occipital lobe and superior frontal gyrus in fMRI), sex (for example, cerebellum in sMRI, frontal lobe in fMRI, and precuneus in both sMRI and fMRI), and schizophrenia (for example, cerebellar, frontal, and insular cortices in sMRI; occipital pole, lingual gyrus, and precuneus in fMRI), shedding light on linked phenotypic and neuropsychiatric biomarkers of brain structure and function.
Ultra-low-field (ULF) MRI facilitates neuroimaging access, yet its application in early infancy is constrained by low resolution and contrast, and the limited suitability of existing segmentation tools. In this work we introduce and validate miniMORPH, an open-source pipeline for automated brain volumetry from 0.064T T2-weighted MRI acquired across infancy and toddlerhood. ULF scans were acquired from infants aged 2 to 27 months across two cohorts in South Africa and Uganda. Age-specific templates and priors were used to segment major brain tissues and substructures. Validation used two high-field (HF) references: (i) expert manual HF segmentations for key ROIs across ages, and (ii) automated HF segmentations from SuperSynth on paired HF-ULF scans. We quantified (a) between-subject ordering across modalities using Pearson's correlation (r) and (b) systematic scaling differences using percentage error (PE) and time-corrected percentage error (CPE), stratifying performance by cohort and age. Face validity was also tested via mixed-effects models of age, sex, and birthweight. miniMORPH generated anatomically plausible segmentations of major brain regions across infancy. In paired HF-ULF comparisons, between-subject ordering was generally preserved across many ROIs, with stronger correspondence in the South African cohort than in the Ugandan cohort at 12 months. Systematic scaling offsets were most evident in CSF-rich or boundary-sensitive compartments, with consistently negative CPE for ventricles and cerebellum. Performance varied with age, showing the greatest variability at 3 months. miniMORPH successfully captured regional age-related growth trajectories. Sex-dependent volumetric differences were widespread but attenuated after intracranial volume correction. Low birthweight infants exhibited reduced regional volumes and altered growth trajectories. Taken together, these findings indicate that miniMORPH enables volumetric analysis of ULF infant MRI and preserves between-subject variation suitable for developmental and group analyses. ROI- and cohort-specific offsets, particularly in CSF-rich regions, may require calibration when absolute volumes are needed. The pipeline is openly available at https://github.com/UNITY-Physics/fw-minimorph.
In recent years, the prevalence of electronic cigarettes (e-cigs) use has continued to rise, particularly among youth populations. However, the long-term effects of e-cigs use on brain functional activity remain unclear. Resting-state functional brain controllability metrics, including average controllability and modal controllability, were computed and compared at the network level between 93 nicotine-dependent e-cigs users and 96 demographically-matched healthy controls. Spearman correlation analyses were implemented to assess the relationships between controllability and various e-cigs use characteristics, including duration of use, frequency patterns, and dependence severity measures. Compared with healthy controls, e-cigs users exhibited increased average controllability in the right middle frontal gyrus, inferior frontal gyrus, insula, and thalamus (P < .001, false discovery rate corrected), along with increased modal controllability in the left medioventral occipital cortex (P < .001, false discovery rate corrected). Additionally, controllability in subcortical and visual network regions showed a negative correlation with e-cigs use characteristics and sleep quality. This study is the first to apply network control theory and reveals significant alterations in the average controllability of the right middle frontal gyrus, middle frontal gyrus, inferior frontal gyrus, insula, thalamus and the left insula, as well as in the modal controllability of the left medioventral occipital cortex among nicotine-dependent e-cigs users, indicating impaired brain state transition function. Furthermore, these alterations in brain network properties show negative correlations with both the duration of e-cigs use and sleep quality, suggesting potential behavioral and clinical relevance. These changes may serve as potential biomarkers for future research exploring related intervention strategies.This trial is registered with clinicaltrials.gov (NCT05788068). Our study identified brain regions that may represent potential neuroimaging features of nicotine-dependent e-cigs users. Furthermore, our findings suggest a new hypothesis for targeted intervention. Key nodes identified-such as the right IFG and INS-warrant further investigation as potential targets for neuromodulation techniques. Future interventional trials are needed to verify whether modulating activity in these brain areas can restore normal control functions and aid smoking cessation, which may pave the way for personalized treatment strategies.