ObjectiveTo explore the relationship between placental oxygen transport on MRI and fetal birth weight, and to identify associated transcriptional pathways.Study DesignWe conducted a prospective cohort study of six monochorionic twin pairs. For each pair, the twin with the higher placental oxygen time-to-plateau (TTP) was designated as Group A and the co-twin with the lower TTP as Group B. RNA sequencing of cord blood was performed to assess differential gene expression. Analyses were performed using paired methods to account for within-pair comparisons.ResultsOn descriptive analysis, higher TTP twins (Group A) had lower mean birth weight than their lower TTP co-twins (Group B) (2121 ± 256 g vs 2397 ± 283 g), but this difference was not statistically significant on paired analysis (p = 0.51). TTP values were also not significantly different within pairs (p = 0.17). Across all twins, higher TTP was associated with lower birth weight (p = 0.02). No differentially expressed genes or pathways were identified.ConclusionIn this cohort of monochorionic twin pairs, the twin with slower placental oxygen transport tended to have lower birth compared with its co-twin, although this difference was not statistically significant on paired analysis. Higher TTP was correlated with lower birth weight across twins, but no transcriptomic differences were identified. Larger paired datasets are needed to further explore these associations.
Intraplaque T2* values help to identify symptomatic carotid plaques and correlate with intraplaque iron deposits in plaque progression. However, intracranial T2* mapping in vivo at 3T MRI is challenging due to limited resolution and signal-to-noise ratio. This study aimed to quantitatively measure T2* value of middle cerebral artery (MCA) atherosclerotic plaques using 7T MRI and to assess its correlation with cerebrovascular symptoms. Phantom studies were performed to evaluate the accuracy of T2* mapping obtained with the proposed sequence by comparison with the ground truth acquisition. In the in vivo study, intraplaque T2* values obtained from multi-echo T2* mapping and plaque characteristics from T1-weighted 3D sampling perfection with application-optimized contrast using different flip angle evolutions sequence on 7T MRI were analyzed and compared between patients with symptomatic and asymptomatic MCA plaques. Multivariate logistic regression was used to determine the odds ratios of T2* values and plaque characteristics in discriminating symptomatic from asymptomatic plaques. Diagnostic performance was evaluated using area under the curve (AUC) values. Correlation analyses were performed between T2* values and intraplaque hemorrhage (IPH). Phantom T2* measurements using the proposed sequence showed excellent agreement with the ground truth sequence (ICC=0.998, p<0.001), with a mean percentage error of 3.97 ± 3.11%. The clinical cohort of this prospective cross-sectional study included 39 symptomatic patients with MCA plaques and 21 age-, sex-, and stenosis degree-matched asymptomatic patients. Scan-rescan reproducibility of T2* mapping was excellent (p < 0.001). Symptomatic plaques had significantly lower T2* values than asymptomatic plaques (22.24±5.31 vs. 30.24±7.00 ms, p<0.001). In multivariate analysis, intraplaque T2* values (OR: 0.162, 95% CI: 0.053-0.497, p=0.001) and normalized wall index (NWI) (OR: 2.150, 95% CI: 1.041-4.443, p=0.039) were independently associated with symptomatic plaques. The optimal combination of T2* values and NWI showed the best diagnostic performance (AUC=0.861, 95% CI:0.747-0.937), with 94.9% sensitivity and 66.7% specificity. T2* values were negatively correlated with and IPH (r=-0.290, p=0.027) after age- and sex- adjustments. The feasibility of intracranial T2*mapping in vivo on 7T MRI has been proven, indicating its potential as a sensitive tool for characterizing intracranial symptomatic plaques.
Obesity and binge eating disorder (BED) are global health concerns that share overlapping neural mechanisms. These include alterations in the brain's reward and control systems leading to heightened sensitivity to food cues and impaired self-regulation, which underpin overeating. Identifying neuroimaging-based biomarkers that index these mechanisms could advance individualised treatments. This scoping review examined evidence on fMRI food cue reactivity as a potential approach for developing predictive and response biomarkers relevant to the treatment of obesity and BED. A systematic search of MEDLINE, Scopus, PsycINFO, and Embase (to July 2025) identified 57 eligible studies incorporating fMRI cue reactivity measures in the context of pharmacological, surgical, psychological, and lifestyle interventions. Of these, 7 reported predictive outcomes only (6 for adults with obesity and 1 for children and adolescents with obesity), 41 reported response outcomes only (36 for adults with obesity, 3 for children and adolescents with obesity and 2 for adults with binge eating), and 9 reported both predictive and response outcomes (8 for adults with obesity and 1 for adults with binge eating). Across paradigms and intervention modalities, there was consistent involvement of reward (striatum, insula, orbitofrontal and ventromedial prefrontal cortex) and cognitive control regions (dorsolateral and dorsomedial prefrontal cortex) as response outcomes from successful treatment. Reductions in reward-system reactivity following interventions were consistently associated with improved clinical outcomes, supporting the potential of fMRI food cue reactivity as a candidate biomarker of treatment response. However, this finding is highly skewed towards obesity, given the limited number of studies that report results for BED (3 studies). Furthermore, consistent evidence for reliable predictive biomarkers was also limited, likely due to methodological variability and small sample sizes. Overall, this review supports the potential of response outcomes from fMRI food cue reactivity as an indicator of treatment efficacy in obesity and highlights the limited evidence in BED. We also emphasise the need for further standardisation of paradigms and biomarker validation efforts.
Segmentation of spinal nerve rootlets is relevant for spinal level estimation, lesion classification, neuromodulation therapy, and group-level analyses. The aim of this study was to develop a deep learning method for the automatic segmentation of C2-T1 dorsal and ventral spinal nerve rootlets on various MRI scans. The study included MRI scans from two open-access and one private dataset, consisting of 3D isotropic 3T turbo spin echo T2-weighted (T2w) and 7T MP2RAGE (T1-weighted [T1w] INV1 and INV2, and UNIT1) MRI scans. A deep learning model, RootletSeg, was developed on 93 MRI scans from 50 healthy adults (mean age, 28.70 years ± 6.53 [SD]; 28 [56%] males, 22 [44%] females) and achieved a mean ± SD Dice score of 0.67 ± 0.09 for T1w-INV2, 0.65 ± 0.11 for UNIT1, 0.64 ± 0.08 for T2w, and 0.62 ± 0.10 for T1w-INV1 contrasts. RootletSeg accurately segmented C2-T1 spinal rootlets across MRI contrasts, enabling the determination of spinal levels directly from MRI scans. The method is open-source and can be used for a variety of downstream analyses.
One of the main objectives of cognitive neuroscience is to investigate brain processes that underlie narrative comprehension. Furthermore, earlier studies that used naturalistic functional magnetic resonance imaging (fMRI) datasets, like Narratives, has advanced our knowledge of large-scale language and narrative networks, most studies have relied on correlation-based analyses or single-region importance measures, overlooking the dynamic and structural properties of brain networks. In this work, we present a new graph-based framework to identify important regions in narrative comprehension by combining a composite node importance scoring method with multiple node embedding algorithms. We first used controlled simulations with stochastic block models (SBM) with different hub nodes and community strengths to validate the framework. This made it possible to systematically assess seven embedding algorithms for node influence attribution, link prediction, and community detection. Applying the same framework to fMRI data, we analyzed two parcellation schemes, the Harvard-Oxford and Schaefer (100-parcel) atlases, to identify influential cortical regions. Our findings reveal consistent engagement of the default mode, salience, and limbic networks across stories and atlases, emphasizing their central role in narrative processing. Overall, this work offers a reliable, comprehensible method for identifying key brain regions, bridging the gap between graph representation learning and cognitive neuroscience. The framework provides a scalable basis for further research that connects naturalistic cognition, dynamic brain connectivity, and linguistic features.
Pupillometry has been used as a biomarker of activity in the Locus Coeruleus (LC), a noradrenergic nucleus crucial for arousal. As the LC has been implicated in modulating cerebrospinal fluid (CSF) flow, here we investigated the associations of pupil size with CSF T2-dependent signal in the ventricles, BOLD signal in cortical and subcortical brain regions (including LC), and peripheral physiology (heart rate and respiration) as a function of arousal. We hypothesized that arousal changes indexed by pupil size would be associated with changes in CSF signal intensity and volume in opposition to brain BOLD signal. Analyses of fMRI data from healthy controls (HCP 7T) showed that changes in pupil size were linked with changes in LC, cortical and subcortical BOLD in opposition to signal changes in the lateral ventricles. CSF signal in the ventricles increased as the activity in LC, thalamus and cortical regions declined concomitant with decreases in pupil size. The coupling between pupil size and LC emerged earlier than for other ascending arousal nuclei and Granger causality analysis corroborated strong LC-pupil coupling. While physiological measures had stronger correlations with each other and with pupil size in the drowsy state, pupil-BOLD and CSF associations were stronger during the vigilant state. The weakened pupil-CSF association in the drowsy state suggests a segregation of central and peripheral signal modulations of CSF during low arousal states. Mental states that promote arousal might be beneficial in neuropsychiatric disorders with glymphatic system impairment and pupillometry could serve as a biomarker to monitor glymphatic function.
Stress engages coordinated psychological, neuroendocrine, autonomic, and neural processes that enable adaptation to environmental demands but may contribute to vulnerability when stress is prolonged, uncontrollable, or socially evaluative. Functional neuroimaging has become central to psychoneuroendocrinology by enabling direct investigation of how acute stress shapes brain activation and connectivity and how these neural responses interact with hypothalamic-pituitary-adrenal axis regulation. This editorial introduces the Special Issue "Effects of stress on brain activation changes: Recent developments" and outlines key conceptual and methodological advances in the field. We highlight progress from endocrine-marker-based stress research toward brain-based models of stress, emphasizing evidence from scanner-based paradigms such as the Montreal Imaging Stress Task and ScanSTRESS, as well as emerging multimodal approaches including fNIRS, PET, EEG, and harmonized large-scale analyses. We discuss recent developments concerning exposure-time effects, network-level models of stress processing, and the importance of functional connectivity. We further emphasize the need to account for individual and contextual variability, including sex, gender, developmental stage, clinical vulnerability, and real-world stress relevance. This Special Issue invites contributions that use neuroimaging to advance mechanistic, translational, and reproducible models of stress-related brain function.
Fatigue is a common and disabling non-motor symptom in Parkinson's disease (PD), significantly affecting patients' quality of life. However, it is often underdiagnosed due to its subjective nature and the lack of a clear definition, hindering the development of effective treatments. This study aims to investigate the prevalence of fatigue and its associations with sociodemographic factors, disease severity, levodopa equivalent daily dosage (LEDD) and motor, non-motor, and cognitive symptoms in an Italian cohort of patients with PD. An observational cross-sectional study was carried out in three Italian centers from January to May 2024. One hundred PD patients (H&Y ≤ 4) were assessed using validated tools: Parkinson Fatigue Scale (PFS), Fatigue Severity Scale, and Modified Fatigue Impact Scale. Motor and non-motor signs and symptoms, cognitive status, and LEDD were analyzed using non-parametric tests and Spearman's correlations. Fatigue prevalence was determined based on PFS score ≥ 3.09. Fatigue was present in 36% of patients, more prevalent in women and more severe in those with H&Y > 2. Fatigue correlated strongly with non-motor symptoms (MDS-UPDRS Part I; ρ > 0.6, p < 0.001) and moderately with motor complications (0.4 < ρ < 0.5, p < 0.001), but weakly with disease duration, LEDD and age (ρ < 0.3, 0.002 < p < 0.05). Significant intercorrelations among fatigue scales supported their ability to consistently measure the fatigue construct. Fatigue in PD is a multidimensional phenomenon influenced mainly by non-motor symptoms. Gender-specific differences and the association with disease progression underscore the need of comprehensive and integrated management strategies to address this challenging symptom.
Quantification using the Centiloid (CL) scale has become a valuable information to consider when interpreting amyloid-PET images and is now implemented in several software packages. This work aims to assess the comparability of CL from [18F]flutemetamol scans derived using several research and commercial quantification pipelines. This analysis relies on three datasets: a test-retest cohort, a group of clinically relevant patients with amnestic mild cognitive impairment (aMCI) and a subgroup from the BioFINDER-1 cohort enriched with scans with amyloid loads around potential clinical decision thresholds (0-50CL). Images from the Test-Retest and aMCI cohorts were processed across seven quantification pipelines: three commercial software platforms and four research tools, including the standard SPM8 workflow. The statistical analysis was based on three steps: 1) a repeatability analysis using the test-retest data; 2) a reproducibility analysis across all pipelines using the aMCI cohort; 3) an inter-software reliability analysis around three clinically relevant thresholds: 11, 25 and 37 CL using the aMCI and the BioFINDER-1 data. In the Test-Retest dataset composed of 10 Alzheimer's Disease (AD) patients, high test-retest repeatability and reliability were observed with an absolute bias of less than 5 CL. Within-individual coefficients of variation ranged from 2.6 to 4.4% and repeatability coefficients from ∼8 to ∼16 CL. CL quantification was generally reproducible across pipelines in a dataset of 80 aMCI individuals (R2 in [0.94-0.99], slope in [0.98-1.03], intercept in [-4, 4], but the 95% limits of agreement (LoAs) ranged between ∼±12 and ∼±21 CL. Agreement between software around the three clinically relevant thresholds was 92-100% (kappa 0.83-1) in the aMCI data (N = 80) and 75-99% (kappa 0.48-0.96) in the BioFINDER-1 subgroup (N = 110). In this study, CL quantification was shown to be robust across a range of currently available software platforms. Uncertainty estimates should always be considered when interpreting results. In clinical practice, the choice of quantification software should not impact patient management decisions.
The glymphatic system plays a critical role in brain waste clearance and has been implicated in neurodegenerative diseases. Primary angle-closure glaucoma (PACG) is increasingly recognized as a neurodegenerative disorder extending beyond the eye. This study aimed to investigate glymphatic system function in patients with PACG using diffusion tensor imaging analysis along the perivascular space (DTI-ALPS). This cross-sectional study included patients with PACG and age- and sex-matched healthy controls. All participants underwent magnetic resonance imaging, including diffusion tensor imaging. The DTI-ALPS index was calculated to assess glymphatic function. Group differences and correlations with disease severity were analyzed. Patients with PACG showed a significantly lower whole-brain DTI-ALPS index compared with healthy controls (1.50 ± 0.14 vs 1.60 ± 0.15, P = .030; Cohen d = 0.63). Lower DTI-ALPS values were significantly correlated with worse visual field mean deviation and thinner retinal nerve fiber layer thickness (P < .05). Glymphatic system function appears to be impaired in PACG and is associated with disease severity. These findings provide further evidence supporting the concept of glaucoma as a neurodegenerative disease involving the central nervous system.
Phakomatoses, also known as neurocutaneous syndromes are rare disorders characterized by multisystem involvement with variable neurological manifestations in children, including intracranial vascular malformations. Cavernous malformations may present with acute haemorrhage and stroke-like symptoms. Diagnostic difficulty arises when radiologic findings suggest a benign lesion, yet histopathology reveals discordant malignant pathology. An 8-year-old female presented with sudden-onset left hemiparesis and recurrent seizures. Physical examination revealed multiple cutaneous naevi, raising suspicion of a syndromic association. Brain magnetic resonance imaging demonstrated a well-circumscribed right parietal intra-axial lesion with a "popcorn" appearance and hypointense susceptibility blooming, highly suggestive of a cavernous malformation. Cranial computed tomography scan subsequently showed an associated large intracerebral haematoma. The patient underwent right parietal craniotomy with haematoma evacuation and excision of the lesion. The immediate postoperative course was initially satisfactory with neurological improvement. Histopathological examination of the excised specimen, however, revealed a malignant neoplasm, establishing a significant radiologic-histologic discordance which fundamentally altered the diagnostic interpretation. The patient had a relapse of symptoms two months after surgery, with repeat neuroimaging showing multicentric tumour recurrence, necessitating referral for adjuvant neuro-oncologic management. This case illustrates a rare diagnostic pitfall and challenge in paediatric neurosurgery, where a malignant intracranial tumour mimicked a cavernous malformation in the context of cutaneous stigmata. The report emphasizes the limitations of neuroimaging alone and underscores the importance of careful clinicoradiologic correlation, histopathological confirmation, and multidisciplinary evaluation when managing presumed vascular lesions in children, particularly in resource-limited settings.
Posttraumatic stress disorder (PTSD) is linked with impairments of fear learning, particularly context and safety cue discrimination. However, it remains unclear whether alterations in distinguishing threat and safety stem from trauma exposure itself or from anxiety-related pathology more generally. This study used multiple control groups to disentangle the impact of PTSD on behavioral and neural indices of fear learning. Four groups of participants were recruited: PTSD related to interpersonal violence (n = 21), trauma exposed- (n = 46), anxiety- (n = 19), and healthy-controls (n = 34). Participants completed a contextual fear conditioning task while undergoing fMRI. Multivariate pattern analysis was used to decode the anticipatory neural representation of the unconditioned stimulus (i.e., electric shock) when participants were presented with conditioned stimuli. Behaviorally, PTSD participants showed poorer discrimination between high- and low-threat conditioned stimuli in trial-by-trial shock predictions relative to all control groups, with a convergent pattern in shock likelihood estimates. Neural shock reinstatement in the salience network discriminated between high- and low-threat stimuli to a greater extent among healthy-controls compared to the PTSD group. Shock representations in the medial prefrontal cortex and inferior frontal networks were more strongly coupled with shock likelihood estimates among healthy-controls and anxiety-controls, respectively, compared to the PTSD group. PTSD is uniquely associated with poorer threat discrimination during fear learning. Neural networks that encode differential threat expectation for conditioned stimuli among healthy participants do not discriminate threat magnitude among PTSD participants.
Childhood trauma can lead to lasting psychological and physiological effects, including altered brainwave patterns. This review examines the relationship between childhood trauma and brainwave activity, exploring the potential use of quantitative electroencephalography (qEEG) as an indicator for trauma-related disorders. This narrative review examined six studies that were published from 1997-2025, which investigated the neural differences in the population who have experienced traumatic event(s) during their childhood. Altered brainwave patterns, particularly the delta, theta, alpha, and beta waves, were found in trauma-affected individuals. These changes are linked to emotional regulation, sensory processing, and cognitive control disruptions. The current review utilizes the polyvagal theory as a potential physiological framework to link childhood trauma to the specific brainwave alterations observed in EEG and qEEG findings. While the theory remains a subject of ongoing debate, it offers a useful perspective for understanding the relationship between trauma and neural changes. Additionally, this study suggests that qEEG could serve as a reliable tool for early trauma detection, although further research is needed to validate these findings across diverse populations.
Agenesis of the corpus callosum (ACC) presents with highly heterogeneous clinical features. Common methods rarely achieve accurate prenatal or early postnatal diagnosis and prognosis. We aimed to develop and test an interpretable deep neural network (DNN) that combines multimodal clinical data to improve diagnostic accuracy and neurodevelopmental outcome prediction. We collected data from 205 pediatric patients with ACC at Wuhan Children's Hospital between 2016 and 2024. A total of 27 clinical features were extracted, including neuroimaging findings, perinatal risk factors, and follow-up developmental quotients (Gesell Developmental Schedules and Gross Motor Function scores). Five-fold cross-validation was adopted. We built an eight-layer fully connected DNN with ReLU activation in the hidden layers. For categorical endpoints, a sigmoid output layer with binary cross-entropy loss was used. For continuous endpoints, a linear output layer with mean squared error loss was used. SHAP (Shapley Additive Explanations) values were used to quantify the contribution of individual features to model predictions. Performance was compared with a support vector machine (SVM) baseline and across hyperparameter settings. Area under the receiver-operating-characteristic curve (AUC), F1 score, precision, recall, mean absolute error (MAE), mean squared error (MSE), and coefficient of determination (R2) served as primary metrics. Across 12 neurodevelopmental disorders, the model reached an average AUC of 0.97. AUCs for intellectual disability, autism spectrum disorder (ASD), attention deficit hyperactivity disorder (ADHD), specific learning disorder and developmental coordination disorder ranged from 0.98 to 1.00. Prediction remained moderate for cerebral palsy (AUC = 0.74) and epilepsy (AUC = 0.67). MAE for both Gesell and Gross Motor Function scores was 0.10, with corresponding R2 values of 0.62 and 0.63. SHAP analysis identified extracranial malformation (clinical type III), facial dysmorphism and birth weight as the most influential features for developmental outcome. The DNN model outperformed the SVM baseline, with an AUC improvement of 0.16 for communication disorder and an R2 increase of 0.19 for Gesell score (p < 0.001). Ablation experiments confirmed eight layers, sixteen neurons per layer, a learning rate of 0.01 and ten training epochs as the optimal configuration. Additional layers or higher learning rates caused overfitting. The proposed interpretable DNN framework outperforms traditional classifiers in early ACC diagnosis and developmental outcome prediction. It provides a potential tool for clinical decision support. Larger samples and integration of raw imaging data are needed to enhance prediction of complex phenotypes such as cerebral palsy and epilepsy.
Primary angle-closure glaucoma (PACG) has traditionally been regarded as an ocular disorder, but accumulating evidence suggests broader central nervous system involvement. Although previous neuroimaging studies have identified static functional abnormalities, the dynamic properties of large-scale brain networks and their associated molecular signatures in PACG remain insufficiently understood. We applied Leading Eigenvector Dynamics Analysis to resting-state functional MRI data from 44 patients with PACG and 57 healthy controls to characterize recurrent whole-brain dynamic states. State-specific temporal metrics and spatial patterns were further evaluated using multiple machine learning models. To explore potential biological correlates, imaging-derived spatial patterns were linked to cortical gene expression profiles from the Allen Human Brain Atlas using partial least squares regression, followed by pathway enrichment, cell-type enrichment, and neurotransmitter receptor/transporter mapping analyses. Compared with healthy controls, PACG patients showed prolonged dwell time in one recurrent dynamic state, suggesting reduced flexibility of large-scale brain dynamics. Machine learning models showed promising classification performance within the current dataset, with the most informative features primarily located in default mode network regions. Transcriptomic decoding revealed enrichment of genes related to synaptic signaling, ion channel activity, neurotransmitter transport, and neuronal communication. Cell-type enrichment analyses further implicated excitatory neurons, inhibitory neurons, and astrocytes. In addition, a significant spatial association with VMAT2 suggested that monoaminergic systems may be relevant to the observed imaging phenotype. PACG is associated with altered large-scale brain dynamics, particularly involving default mode network-related state instability. These imaging abnormalities show spatial associations with molecular, cellular, and neurotransmitter-related signatures.
This study aimed to systematically review functional neuroimaging literature on the neural substrates underlying contextual modulation of pain in healthy individuals. A search was conducted in PubMed-Medline, Cochrane, and Web-of-Science databases (PROSPERO-CRD42024586392). Studies on chronic pain were excluded, and the risk of bias was assessed with Cochrane RoB2. Spatial coordinates of brain regions undergoing activity changes were included in a meta-analysis using both Activation Likelihood Estimation and frequency estimation of activated or deactivated regions in individual studies (convergence analysis). From 224 full texts reviewed and n=100 articles retained (2735 individuals), three broad activity patterns were identified. One involved activation of prefrontal cortical areas, together with a modification of the sensory message in nociceptive cortical areas (deactivated during hypoalgesia and hyperactivated in hyperalgesia), consistent with top-down regulation via descending controls. A second configuration also involved prefrontal mobilisation, but without activity changes in nociceptive areas and was consistent with a 'perceptual decision bias'. A third configuration was associated with irregular ventromedial prefrontal involvement and significant deactivation in dorsolateral and ventrolateral prefrontal areas. While the first two patterns were observed in a range of attentional or expectation manipulations, including placebo/nocebo, the third pattern was essentially observed during tasks involving introspection and self-referential procedures such as meditation, religious prayer or nostalgia. Similar subjective pain changes can coexist with different brain activation patterns, reflecting diverse neural strategies. Whereas prefrontal cortex-driven descending modulation is one mechanism, introspective approaches can alter perception without involving prefrontal activity. This mechanistic diversity supports multiple avenues for behavioural or neuromodulation-based pain control.
Theta burst stimulation (TBS) is a promising form of repetitive transcranial magnetic stimulation (rTMS) capable of modulating cortical excitability and intracortical processes, offering therapeutic potential for neurological and psychiatric disorders. However, clinical translation remains limited by high inter-subject and inter-session variability in stimulation effects. To more directly capture cortical responses, there is increased interest in combining TMS with electroencephalography (EEG) to assess TMS-evoked potentials (TEPs) before and after stimulation. As an individual's neurophysiological state influences stimulation outcomes, this study explores whether pre-stimulation resting-state EEG can predict changes in TEP component amplitudes following intermittent (iTBS) and continuous (cTBS) protocols applied to the left primary motor cortex, using data from fifteen healthy male participants in a randomized, single-blind crossover design. Linear (Lasso regression) and nonlinear (CatBoost regression) models were designed to predict changes in six TEP components (N15, P30, N45, P60, N100, P180). Both models consistently achieved lower mean absolute errors than the random guessing baseline, demonstrating their ability to capture meaningful predictive patterns in cortical responses. The best performing model varied by TEP component and TBS protocol. Incorporating feature deltas (post- vs. pre-stimulation feature difference) did not significantly enhance predictive performance. Feature importance analysis revealed the predictive value of spectral power and connectivity measures. For instance, connectivity between the stimulation site and frontal/parietal regions, together with oscillatory power in frontal and motor areas, often emerged as the top predictors. Given the limited sample size, these findings should be interpreted as exploratory and hypothesis-generating, requiring validation in larger and independent cohorts. The study establishes a framework for predicting individual TEP responses to TBS using machine learning, paving the way for personalized neuromodulation.
Adolescence is a critical developmental period during which parenting practices interact with temperament and sociocultural context to shape mental health and adaptation. Most parenting models are derived from Western settings, with limited evidence from India. This simultaneous mixed methods study drew on cross sectional data from the Indian Consortium on Vulnerability to Externalizing Disorders and Addictions (cVEDA) cohort, including adolescents aged 12-17 years (parent report n = 931; child report n = 836). Exploratory factor analysis was conducted on parent and child versions of the Alabama Parenting Questionnaire. Qualitative data were obtained through in-depth interviews with 31 adolescents and their parents and analysed using thematic analysis. Findings were integrated at the interpretation stage. The original APQ structure did not replicate. Parent reports yielded three dimensions-Involvement/Positive Parenting, Poor Monitoring, and Corporal Punishment-while child reports yielded five, distinguishing father's and mother's involvement. Inconsistent disciplining did not emerge as a distinct construct. Qualitative findings indicated high involvement and behavioural and psychological control, largely driven by academic goals. Adolescents experienced these practices as both supportive and restrictive, with parental openness shaping communication. Contextual pressures, including resource constraints and urban stressors, contributed to a competency-control paradox. Parenting of adolescents in India must be understood within its relational and sociocultural ecology. While involvement and control function as primary supports, excessive control may constrain broader competency development. Integrating parent and adolescent perspectives is essential for culturally grounded research and intervention.
Iron plays a central role in neurodevelopment and dopaminergic regulation, yet its relationship with schizophrenia remains conceptually unresolved. Research conducted across the life span, from prenatal exposure to postmortem brain tissue, has produced fragmented findings without an integrating physiological framework. We aimed to systematically synthesize this literature and reinterpret prior findings through core principles of iron regulation, with particular attention to the distinction between absolute iron depletion and inflammation-driven functional restriction. We conducted a PROSPERO-registered systematic review (CRD42022382842) following PRISMA guidelines and included 51 studies spanning genetic risk, gestational exposure, peripheral biomarkers, neuroimaging, and postmortem analyses. Genetic studies do not implicate core iron-regulatory pathways in inherited schizophrenia risk. Large population-based cohorts consistently associate maternal iron deficiency during pregnancy with increased schizophrenia risk in offspring. Sixteen studies assessed adult peripheral iron markers; most report lower serum iron, although heterogeneity remains substantial. Few investigations evaluated regulatory markers such as hepcidin, and only one examined the hepcidin-ferroportin pathway directly. Neuroimaging studies in early psychosis report reduced subcortical iron alongside increased dopaminergic activity, whereas postmortem investigations describe cortical iron-ferritin decoupling in chronic stages. Across levels of analysis, apparent inconsistencies converge when iron physiology is considered. Absolute iron depletion and inflammation-driven functional iron restriction represent biologically distinct states that share reduced circulating iron but arise from different mechanisms. Stratifying schizophrenia according to iron phenotype offers a coherent framework to reinterpret prior evidence and guide future mechanistic research.
Differentiating progressive supranuclear palsy (PSP) from Parkinson's disease (PD) can be clinically challenging. In the neuroimaging field, radiomics has emerged as a promising approach to capture subtle microstructural and textural image alterations, improving differential diagnoses. To assess the diagnostic value of brainstem radiomic features from T1-weighted magnetic resonance imaging (MRI) in distinguishing PSP from PD patients. This study included 433 participants from two independent cohorts: an Italian training cohort (84 PSP and 177 PD) and an international validation cohort (68 PSP and 104 PD). Radiomic features including first-order, shape, and texture descriptors were extracted with PyRadiomics from brainstem segmentations generated by the automated deep-learning-based AssemblyNet pipeline. Classification models (Decision Tree, Support Vector Machine, Random Forest, and XGBoost) were trained using nested cross-validation and tested on the independent cohort. Model interpretability was examined with SHapley Additive exPlanations. Radiomics-based models yielded high and consistent performance in distinguishing PSP from PD, higher than brainstem volume. In the validation cohort, Random Forest and XGBoost achieved the best performance (area under the curve [AUC]: 0.93 and 0.94, respectively). Texture- and intensity-based radiomic features emerged as the most informative predictors, while shape descriptors showed lower relevance in discrimination between PSP and PD. Brainstem radiomics extracted from routine T1-weighted MRI demonstrated excellent classification performance in distinguishing PSP from PD patients and generalized robustly across independent datasets. Texture-based features captured microstructural disorganization not reflected by automated volumetry, underscoring the added value of radiomics for differential diagnosis in atypical parkinsonism and for integration in future multimodal biomarker frameworks. © 2026 The Author(s). Movement Disorders published by Wiley Periodicals LLC on behalf of International Parkinson and Movement Disorder Society.