OBJECTIVE:To characterise white matter microstructural differences within very preterm (VPT; <33 weeks' gestational age) infants that develop motor impairment in the first 2 years of life. DESIGN:Cohort study, VPT infants (Cincinnati Infant Neurodevelopment Early Prediction Study) recruited from five level III/IV neonatal intensive care units in the greater Cincinnati area between September 2016 and November 2019. SETTING:Multicentre study; participants received MRI at term-equivalent age at Cincinnati Children's Hospital Medical Center and were followed-up at 2 years corrected age to assess their motor performance. PATIENTS:Infants born before 33 weeks were eligible. MAIN OUTCOMES AND MEASURES:Composite motor scores at 2 years corrected age (CA) on the Bayley Scales of Infant and Toddler Development, III. Fractional anisotropy (FA) along the white matter tracts was used to measure white matter microstructure. Motor impairment was defined as Bayley-III motor score <85. RESULTS:247 controls and 84 impaired infants were included in the study. Compared with the controls, infants with motor impairment were characterised by location-dependent credible group differences and significantly lower FA in both sensorimotor and non-sensorimotor tracts. A location-specific significant positive association between FA and Bayley scores in the impaired group was observed in multiple sensorimotor tracts and non-sensorimotor tracts. No significant associations were found between FA and Bayley scores in the controls. CONCLUSION:VPT infants developing motor impairments show altered inter and intrahemispheric connectivity, with early indications of impaired visual-motor, sensorimotor and thalamo-cortical connectivity, extending beyond sensorimotor tracts.

The insula serves as an integrative hub across sensory, affective, and cognitive domains, yet its heterogeneous connectivity architecture has been largely overlooked in obsessive-compulsive disorder (OCD). Using both discrete parcellation and continuous gradient approaches, we comprehensively characterized insula functional connectivity (FC) alterations in 92 medication-free, noncomorbid OCD patients and 90 healthy controls. Data-driven clustering divided the insula into anterior and posterior subregions, followed by a diagnosis-by-subregion factorial analysis of variance (ANOVA) to assess group differences. OCD patients showed unbalanced subregional FC alterations with the supplementary motor area, precentral gyrus, and dorsolateral prefrontal cortex, exhibiting increased anterior and decreased posterior insula FC, with a significant effect in the anterior subregion. Within OCD patients, anterior insula-dorsolateral prefrontal FC was negatively correlated with obsession severity. Gradient analysis further revealed reduced explanation ratio, gradient range, and variation in the left insula, but these effects did not survive correction for multiple comparisons. Together, these results indicate that insula FC alterations in OCD are more localized to specific subregions rather than reflecting a large-scale gradient shift, with anterior insula dysconnectivity emerging as a key feature linked to obsessive symptoms.

BACKGROUND:Insomnia is common in major depressive disorder (MDD), and varying severity of insomnia may be associated with distinct neural alterations in MDD. Dynamic functional connectivity can capture time-varying brain network interactions and may help disentangle insomnia-related mechanisms in MDD. METHODS:We recruited 203 drug-naïve adult MDD patients, divided into high- (HI-MDD, n = 133) and low-insomnia (LI-MDD, n = 70) groups using the Hamilton Depression Rating Scale insomnia subscale, along with 122 health controls (HCs). Independent component analysis and a sliding-window approach were applied to explore static and dynamic functional network connectivity (FNC). RESULTS:While static FNC revealed no significant group difference, dynamic analysis identified distinct connectivity states between two groups. Compared to HCs, HI-MDD displayed more frequent occurrence of state I and less of state IV, a pattern was absent in LI-MDD. Both MDD groups showed increased default mode network (DMN)-lateral ventral attention network (VAN) connectivity in states I and II, accompanied by decreased dorsal attention network-cerebellar/DMN connectivity in state I relative to HCs. Compared with LI-MDD, HI-MDD exhibited enhanced DMN-medial VAN connectivity in state II, along with increased DMN connectivity with visual and sensorimotor networks in state I, suggesting insomnia-related changes. In addition, we identified insomnia-related memory deficits and depression-related processing speed impairment in MDD. Insomnia significantly moderated the association between altered DMN-lateral VAN connectivity in state I and logical memory impairment in MDD. CONCLUSIONS:These findings suggest that insomnia severity in MDD is associated with distinct temporal patterns of brain network alterations beyond shared depression-related changes and moderate cognitive functions in MDD.

OBJECTIVE:To evaluate diffuse white matter abnormality (DWMA) volume at term-equivalent age as an independent predictor of neurodevelopmental outcomes in preterm infants. STUDY DESIGN:In this multicentre prospective cohort study, 392 preterm infants (≤32 weeks' gestation) underwent term-equivalent MRI with automated DWMA quantification. The primary outcome was cognitive function at 3 years corrected age using the Differential Ability Scales-II General Conceptual Ability (GCA) score. Secondary outcomes included motor function (Bayley-III) and cerebral palsy (CP) at 2 years. Multivariable regression analysed DWMA's prognostic value. RESULTS:Follow-up was available for 89% (GCA) and 87% (Bayley-III/CP) of participants (mean gestational age 29 (SD: 2.5 weeks). Mean GCA was 94 (20.2); Bayley motor composite was 93 (14.5). CP was diagnosed in 12% of children (28 Gross Motor Function Classification System level I, six level II and III and five level IV and V). Higher DWMA volume independently predicted lower cognitive (β=-1.9; 95% CI -3.7 to -0.1), though only marginally over existing predictors (p=0.04), and motor scores (β=-2.0; 95% CI -3.3 to -0.7; p=0.003) and increased CP risk (adjusted OR=1.7; 95% CI 1.2 to 2.4; p=0.003) after controlling for clinical and socioeconomic factors. Socioeconomic disadvantages have amplified DWMA's adverse effects. CONCLUSIONS:This first external validation study demonstrates that objective DWMA quantification independently predicts multiple developmental outcomes through age 3 in preterm infants. The findings validate DWMA's pathological significance and support its utility as an early biomarker for risk stratification and targeted intervention.

IntroductionWhite matter tract segmentation is critical for mapping brain connectivity in both clinical and research settings. Recent deep learning methods have enabled direct voxel-wise segmentation from diffusion MRI (dMRI), bypassing tractography. However, most approaches rely on a limited number of peaks extracted from the fiber orientation distribution function (fODF) at each voxel, which discards important orientation information, particularly in problematic regions with complex fiber configurations such as crossing fibers and bottlenecks.MethodsIn this work, we introduce FODSeg, a voxel-based segmentation method that utilizes the complete fODF representation for each voxel, capturing the full angular structure of white matter orientation. Additionally, we reformulate tract segmentation as a singleclass problem, training one model per tract to reduce label conflicts inherent in multi-class approaches. This combination allows FODSeg to better distinguish tracts with similar local orientations and improves robustness in regions with structural ambiguity. We evaluate FODSeg on the Human Connectome Project dataset across all 72 white matter tracts using six segmentation accuracy metrics.ResultsFODSeg achieves higher Dice scores and lower volumetric overreach values in 70% of the tracts while maintaining high specificity. Our results demonstrate the superior performance of FODSeg over existing segmentation approaches. Notably, our method shows significant improvements in anatomically challenging bottleneck regions, reducing false positives and improving tract-specific precision.DiscussionOverall, FODSeg advances white matter tract segmentation by leveraging the full richness of the fODF signal while improving accuracy, specificity, and anatomical consistency.

Obsessive-compulsive disorder (OCD) is signified by altered functional network connectivity (FNC), particularly within the default mode network (DMN), salience network (SAL), and fronto-parietal network (FPN). While previous studies suggest disruptions within triple networks, dynamic causal interactions across networks remain unaddressed. This study seeks to validate previous findings of static dysconnectivity between triple networks and further delineate the time-varying interactions and causal relationships among these networks in OCD. A resting-state functional magnetic resonance imaging study was performed on a relatively large and well-characterized clinical sample, comprising 88 medication-free OCD patients and 93 healthy controls (HC). Group independent component analysis, combined with a sliding window approach and k-means clustering analysis, was used to assess static and dynamic time-varying FNC within triple networks. Spectral dynamic causal modelling and parametric empirical Bayes framework were utilized to explore the abnormal effective connectivity among these networks in OCD patients. Our results proposed a novel dysregulated connectivity configuration of the triple-network model for OCD. With the self-inhibition increase in the left FPN, the excitatory effect onto the right FPN decrease, resulting in a weakened static connectivity between the left and right FPNs. Concurrently, time-varying hypoconnectivity patterns are observed between the left FPN and DMN, as well as the right FPN and SAL in OCD. Additionally, the excitatory influence from the DMN to the SAL suggests an atypical modulation within OCD’s network pathology. These findings advance our understanding of the dysregulated information transfer and the complex interplay of brain networks in OCD, potentially guiding future therapeutic strategies.

The integration of artificial intelligence (AI) with radiology signifies a transformative era in medicine. Vision foundation models have been adopted to enhance radiologic imaging analysis. However, the inherent complexities of 2D and 3D radiologic data present unique challenges that existing models, which are typically pretrained on general nonmedical images, do not adequately address. To bridge this gap and harness the diagnostic precision required in radiologic imaging, we introduce radiologic contrastive language-image pretraining (RadCLIP): a cross-modal vision-language foundational model that utilizes a vision-language pretraining (VLP) framework to improve radiologic image analysis. Building on the contrastive language-image pretraining (CLIP) approach, RadCLIP incorporates a slice pooling mechanism designed for volumetric image analysis and is pretrained using a large, diverse dataset of radiologic image-text pairs. This pretraining effectively aligns radiologic images with their corresponding text annotations, resulting in a robust vision backbone for radiologic imaging. Extensive experiments demonstrate RadCLIP's superior performance in both unimodal radiologic image classification and cross-modal image-text matching, underscoring its significant promise for enhancing diagnostic accuracy and efficiency in clinical settings. Our key contributions include curating a large dataset featuring diverse radiologic 2D/3D image-text pairs, pretraining RadCLIP as a vision-language foundation model on this dataset, developing a slice pooling adapter with an attention mechanism for integrating 2D images, and conducting comprehensive evaluations of RadCLIP on various radiologic downstream tasks.

AIM:Noninvasive neuromodulation techniques, including transcranial magnetic stimulation (TMS), transcranial direct current stimulation (tDCS), and transcranial focused ultrasound stimulation (tFUS), are promising interventions for acute treatment of depressive episodes. However, the comparative efficacy and acceptability of stimulation protocols remain unclear. This network meta-analysis (NMA) aimed to compare the efficacy and tolerability of various noninvasive neuromodulation strategies. METHODS:We conducted a systematic review and NMA of randomized controlled trials (RCTs) enrolling patients with major depressive disorder or bipolar depression, including nine repetitive TMS (rTMS) protocols, three theta burst stimulation (TBS) protocols, as well as tDCS and tFUS. Primary outcomes were response and all-cause discontinuation rates. Subgroup analyses examined treatment-resistant depression (TRD) and monotherapy versus add-on therapy. RESULTS:A total of 129 RCTs (7667 patients; 272 treatment arms) were included. All protocols except low-frequency rTMS over the left dorsolateral prefrontal cortex (DLPFC) showed higher response rates than sham. tFUS demonstrated the highest response rate (OR: 7.24, 95% CI: 1.35-38.47), followed by bilateral rTMS (OR: 5.75, 95% CI: 3.29-10.07) and bilateral TBS (OR: 5.37, 95% CI: 2.51-11.36), both effective for general depression and TRD. Bilateral TBS showed the highest response rate when administered as monotherapy, whereas bilateral rTMS was most effective as add-on therapy. Most studies (87.6%) were rated as having low or unclear risk of bias. CONCLUSIONS:Our findings provide preliminary evidence that bilateral stimulation over DLPFC is more beneficial than unilateral stimulation for treating depressive episodes. Nonetheless, tFUS may represent a highly promising novel intervention warranting further investigation.

The hypothalamus, which consists of histologically and functionally distinct subunits, primarily modulates vegetative symptoms in major depressive disorder (MDD). Sex differences in MDD have been well-documented in terms of illness incidence rates and symptom profiles. However, few studies have explored subunit-level and sex-specific anatomic differences in the hypothalamus in MDD compared to healthy controls (HCs). High-resolution 3D T1-weighted images were obtained from 133 treatment-naïve patients with MDD and 130 age-, sex-, education years-, and handedness-matched HCs. MRI data were preprocessed and segmented into ten bilateral hypothalamic subunits with FreeSurfer software. We tested for both common and sex-specific patterns of hypothalamic anatomic differences in MDD. Regardless of sex, patients with MDD showed significantly smaller volumes in the left anterior-inferior subunit (a-iHyp) and larger volumes in the right posterior subunit (posHyp). The volumes of the left a-iHyp were negatively correlated with sleep disturbance scores in the MDD group. A significant sex-by-diagnosis interaction was observed in the right whole hypothalamus, and subsequent post-hoc analyses revealed that males with MDD showed significantly larger volumes, while females with MDD showed significantly smaller volumes relative to their sex-matched HCs. Common differences in MDD were found in the left anterior-inferior and right posterior hypothalamus that are involved in regulating circadian rhythms and reward, while sex-specific differences in MDD were observed in the right whole hypothalamus. These findings enhance our understanding of distinct hypothalamic subunit related to MDD and shed light on the neurobiology underlying sex-related variations in MDD.

Objective:To analyze changes in sleep, mood state, and brain function in healthy populations living in near-sea-level environments before and after exposure to high-altitude environment, and to explore the correlations between regional brain functional changes and variations in sleep and mood states. Methods:A total of 45 healthy volunteers were enrolled. The participants came from regions of near-sea-level altitudes and were exposed to the high-altitude environment for a short period of time. The Pittsburgh Sleep Quality Index (PSQI), Zung Self-Rating Depression Scale (SDS), Patient Health Questionnaire-9 (PHQ-9), Zung Self-Rating Anxiety Scale (SAS), and Generalized Anxiety Disorder-7 (GAD-7) were administered to assess sleep quality as well as depressive and anxiety symptoms at 4 time points-prior to high-altitude exposure, immediately after exposure, one month after returning to low-altitude regions, and three months after returning to low-altitude regions. Resting-state functional magnetic resonance imaging (rs-fMRI) data were collected before and after high-altitude exposure, and regional brain functional parameters, including the amplitude of low-frequency fluctuations (ALFF) and functional connectivity strength, were analyzed. Statistical analyses were performed, including a linear mixed-effects model to evaluate longitudinal changes in scale scores, paired-sample t-tests to compare brain function differences before and after exposure, and Pearson correlation analyses to examine the relationship between brain functional changes and alterations in sleep and mood states. Results:Compared with the pre-exposure findings, the participants exhibited significantly increased PSQI scores (8.89 ± 4.41 vs. 5.08 ± 2.69, P < 0.05) and PHQ-9 scores (3.60 ± 4.19 vs.1.54 ± 2.30, P < 0.05) immediately after high-altitude exposure. One month after returning to the low-altitude environment, both sleep and depression scores decreased relative to the findings immediately after exposure (PSQI: 3.88 ± 2.13 vs. 8.89 ± 4.41, P < 0.05; PHQ-9: 1.50 ± 2.25 vs. 3.60 ± 4.19, P < 0.05) and showed no statistically significant difference compared with the pre-exposure findings (P > 0.05). Three months after returning to near-sea-level environment, sleep, depression, and anxiety scores were all reduced compared with the findings immediately after exposure (PSQI: 3.76 ± 2.31 vs. 8.89 ± 4.41, P < 0.05; PHQ-9: 1.24 ± 2.13 vs. 3.60 ± 4.19, P < 0.05; SAS: 23.84 ± 5.93 vs. 27.93 ± 7.05, P < 0.05), also showing no significant difference compared with the pre-exposure levels (P > 0.05). Brain function analysis revealed that, relative to the pre-exposure levels, ALFF in the bilateral superior temporal gyrus, insula, and dorsolateral prefrontal cortex (DLPFC) increased after high-altitude exposure (P < 0.05), and that functional connectivity strength in the DLPFC was also elevated (P < 0.05). Furthermore, changes in DLPFC functional connectivity strength were positively correlated with changes in sleep and mood scores (P < 0.05). Conclusion:High-altitude exposure has a significant impact on the sleep, mood states, and brain function of populations from near-sea-level regions, and DLPFC, in particular, is closely associated with changes in sleep and mood states. The findings of this study provide a theoretical basis for health management and intervention strategies in high-altitude environments.

The amygdala-centered functional networks are pivotal to the neuropathology of many psychiatric disorders, yet transdiagnostic abnormalities across disorders remain unclear. This neuroimaging meta-analysis examined convergence in whole and subregion-specific amygdala functional connectivity alterations across major psychiatric disorders. We included 96 amygdala functional connectivity studies comprising 8730 individuals across seven diverse diagnostic groups (depression, bipolar disorder, anxiety, post-traumatic stress disorder, obsessive-compulsive disorder, schizophrenia, and addiction). Our findings revealed that amygdala functional connectivity alterations converged in the regions associated with sensorimotor, emotional, and cognitive processes identified by behavioral decoding analyses, including the medial prefrontal cortex, hippocampus, insula, inferior and middle temporal gyrus, cuneus, postcentral gyrus, and thalamus. More refined disorder-specific analyses suggested that these overall patterns were shared to varying degrees, with notable differences between psychotic disorders versus nonpsychotic disorders, as well as between anxiety-related disorders versus mood disorders. Follow-up meta-analyses of 27 studies on amygdala subregional functional connectivity further indicated lower connectivity of the subregions with their typical target brain regions across disorders. These findings suggest that amygdala connectivity disruptions may represent a transdiagnostic vulnerability factor in psychopathology and a potential target for therapeutic interventions.

INTRODUCTION:Rhabdomyosarcomas are the most common soft tissue sarcoma in children. While treatment outcomes have improved, risk-based therapy classification relies on staging and tumor subtypes for therapeutic planning. OBJECTIVE:This study investigated the utility of T2-weighted MR radiomics features and machine learning models in identifying the presence of distant metastasis and alveolar histological subtypes at baseline imaging in children diagnosed with rhabdomyosarcoma. MATERIALS AND METHODS:This retrospective cross-sectional study utilized MRIs from 86 patients, 49 (median age (IQR) 59 months (37-161), alveolar subtype=15, distant metastasis=9) of whom had been imaged at outside imaging centers (training set); and 37 (median age 52 months (24-164), alveolar subtype=14, distant metastasis=8) of whom were imaged at our institution (holdout validation set). Radiomic features were extracted from T2-weighted images. We selected features that demonstrated intra-scan repeatability and used maximum relevance and minimum redundancy supervised feature selection to identify the 50 most important features. Lasso logistic regression and support vector machine (SVM) classifiers were trained to predict binary outcomes. The median of all predictions for a given patient was used as patient-level predictions. DeLong's test compared the area under the receiver operating characteristic curves (AUC). Cut-offs obtained by maximizing the Youden index were evaluated on an external validation set, and accuracy metrics were reported. RESULTS:On the validation set, the Lasso and SVM classifiers obtained patient level AUCs of 0.76 (95% CI 0.59-0.94) and 0.73 (0.54-0.92), respectively, in predicting alveolar subtype, with the Lasso regressor obtaining 71.4% (41.9-91.6) sensitivity and 60.9% (38.5-80.3) specificity. When predicting the presence of distant metastasis, the Lasso and SVM classifier had AUCs of 0.81 (0.67-0.95) and 0.77 (0.58-0.97), respectively. There were no differences between model performance (P>0.05). A total of 12 and 18 features had nonzero coefficients in the Lasso regressors for predicting alveolar subtype and tumor metastasis, respectively. CONCLUSION:MRI radiomics from baseline T2-weighted MRI demonstrated potential in predicting alveolar subtype and distant metastatic disease at presentation. Larger studies are needed to explore multinomial multiclass models for better prognostication of pediatric rhabdomyosarcomas.

Major depressive disorder (MDD) demonstrates significant alterations in intrinsic functional connectivity (FC) of the subcortical networks (SCN). However, the neurobiological mechanisms underlying these changes in adolescents with MDD and their association with stressors and sleep disturbances remain poorly understood. Using seed-based functional connectivity analysis, we investigated changes in intrinsic connectivity of the SCN in 83 first-episode medication-naïve adolescents with MDD (aMDD) and 59 healthy controls (HC). We then examined the correlation between the SCN connectivity alterations with sleep disturbances and stress factors, measured by the Pittsburgh Sleep Quality Index (PSQI), the Adolescent Life Events Checklist (ASLEC), and the Family Environment Scale-Chinese Version (FES-CV). Compared with HC, aMDD patients showed decreased connectivity between multiple subcortical regions (amygdala, hippocampus, nucleus accumbens (NAc), and thalamus) and cortical regions of the limbic (orbital frontal cortex, inferior temporal gyrus, temporal pole), visual (middle occipital gyrus) and sensorimotor (precentral gyrus) networks. Moreover, we observed that reduced amygdala- and NAc-based FC was correlated with more stress from life events, while reduced thalamus-based FC was associated with more severe sleep disturbances. Our study was the first to comprehensively investigate the SCN-based FC alterations in adolescents with MDD. We revealed disruptions in connectivity between the SCN and cortical networks in aMDD, and further identified potential environmental mechanisms associated with these SCN-based dysconnectivity patterns. These findings underscore critical role of the SCN in the neurobiological mechanisms of aMDD and suggest potential targets for developing novel treatment strategies for adolescents with MDD.

Previous studies have highlighted alterations of white matter (WM) integrity underlying mechanisms of obsessive-compulsive disorder (OCD). However, whole-brain WM abnormalities in OCD at the tract-level still remain largely unknown. In current study, we evaluated the integrity of 42 WM tracts in individuals with OCD using the novel tractography toolbox, TRActs Constrained by UnderLying Anatomy (TRACULA), and investigated the grey matter changes linked to the white matter alterations. Furthermore, we investigated the association of diffusion measures with clinical symptom severity. DTI and T1-weighted image data were collected from 54 medication-free OCD patients and 39 age- and sex-matched healthy controls (HCs). TRACULA was used to reconstruct 42 WM tracts and produce tract volume, fractional anisotropy (FA), mean diffusivity (MD), radial diffusivity (RD), and axial diffusivity (AD) of each tract, and grey matter measures were extracted from T1-weighted images. Group comparisons of these measures were evaluated with analysis of covariance. Comparing to HCs, OCD patients showed widespread decreased FA, including rostrum of the corpus callosum, uncinate fasciculus, frontal aslant tract, inferior longitudinal fasciculus, acoustic radiation and optic radiation. Additionally, we found grey matter changes linking to the detected white matter changes, including abnormalities in the medial orbital frontal cortex, superior frontal gyrus, pars opercularis, precentral gyrus, postcentral gyrus, lingual gyrus, and Heschl’s Gyrus. Our results demonstrated that OCD had structural disconnection not only within the traditional frontal-limbic networks but also extended to the visual and auditory systems.

BACKGROUND:Major depressive disorder (MDD) is a leading cause of disability worldwide. Investigating early-stage alterations in cerebral intrinsic activity among drug-naive patients may enhance our understanding of MDD's neurobiological mechanisms and contribute to early diagnosis and intervention. AIMS:To examine alterations in the amplitude of low-frequency fluctuation (ALFF) in first-episode, drug-naive MDD individuals and explore associations between ALFF changes and clinical parameters, including depression severity and illness duration. METHOD:A total of 30 first-episode, drug-naive MDD individuals (mean illness duration 14 weeks) and 52 healthy controls were included in this study. Resting-state functional magnetic resonance imaging was used to obtain whole-brain ALFF measurements. Voxel-based ALFF maps were compared between MDD and healthy control groups using a two-sample t-test. Simple regression analysis was performed to assess associations between ALFF and clinical measures, including Hamilton Rating Scale for Depression (HAMD) scores and illness duration. RESULTS:MDD individuals exhibited significantly increased ALFF in the dorsal anterior cingulate cortex and vermal subregion V3 of the cerebellum. Additionally, ALFF in the right dorsolateral prefrontal cortex was negatively correlated with HAMD scores (r = -0.591, P < 0.001). However, no significant association was found between ALFF and illness duration. CONCLUSIONS:This study demonstrates early-stage ALFF alterations in drug-naive MDD patients, particularly in brain regions implicated in cognitive and emotional regulation. These findings suggest potential neuroimaging biomarkers for the early diagnosis and intervention of MDD.

Understanding the distinct and shared neural mechanisms of generalized anxiety disorder, panic disorder, and social anxiety disorder could help address critical gaps in anxiety disorder diagnosis and treatment. This study aimed to explore common and disorder-specific brain activity and connectivity in patients with generalized anxiety disorder, panic disorder, and social anxiety disorder using resting-state functional magnetic resonance imaging. A total of 127 adults (33 with generalized anxiety disorder, 26 with panic disorder, 36 with social anxiety disorder, and 32 healthy controls) were recruited. We found that all individuals with generalized anxiety disorder, social anxiety disorder, and panic disorder showed abnormal activity in the prefrontal-limbic-cerebellar circuit and default mode network regions. Patients with panic disorder showed unique hypoconnectivity between the default mode network and sensory-motor network, whereas patients with social anxiety disorder showed unique extensive hyperconnectivity between the default mode network and other networks. In addition, increased activity in the left orbital inferior frontal gyrus was associated with depression and anxiety symptom severity, decreased activity in the left superior temporal gyrus was associated with panic symptom severity, and decreased activity in the right fusiform gyrus was correlated with social anxiety symptom severity. These findings provide valuable implications for understanding the neuropathology, diagnosing, and developing targeted therapeutic interventions for different subtypes of anxiety disorders.

Attention deficit hyperactivity disorder (ADHD) is characterized by dysconnectivity among large-scale brain functional networks. How these changes manifest across different spatial scales remains unclear. We investigated ADHD-related functional connectivity alterations at global, region-to-region and network scales using resting-state functional MRI data from 454 children and adolescents with ADHD and typically developing controls from three cohorts. At the global level, individuals with ADHD exhibited hypoconnectivity in default-mode network (DMN) hubs and visual areas. Region-to-region analysis revealed hypoconnectivity within the DMN, between DMN hubs and visual regions, and between nodes of salience (SAN) and frontoparietal networks and auditory/sensorimotor areas, alongside hyperconnectivity linking DMN with SAN, frontoparietal and auditory regions. At the network level, SAN-auditory/sensorimotor hypoconnectivity persisted whereas DMN alterations were no longer significant. These results were independent of age and sex but influenced by medication status and comorbidity. Collectively, our findings indicate a scale-dependent imbalanced interplay between higher-order cognitive and lower-order sensory networks in ADHD.

OBJECTIVES:To assess the comparative efficacy and acceptability of different delivery formats of cognitive behavior therapy (CBT) in treating generalized anxiety disorder (GAD). METHODS:We searched MEDLINE, Embase, PsycINFO, and the Web of Science from database inception to September, 2023, to identify randomized clinical trials (RCTs) of CBT for patients with GAD. Pairwise and network meta-analyses were conducted using a random-effects model. RESULTS:Finally, 52 trials that randomized 4361 patients (mean age 43 years; 69.7% women) with generalized anxiety disorder met the inclusion criteria. The most studied treatment comparisons were individual and remote CBT versus waiting list. The quality of the evidence was typically of low or unclear risk of bias (39 out of 52 trials, 75%). The network meta-analysis including 30 studies showed that individual CBT was superior to remote CBT (SMD 0.96; 95% Cl 0.13-1.79), treatment as usual (SMD 1.12; 95% Cl 0.24-2.00) and waiting list (SMD 1.62; 95% Cl 1.03-2.22) in relieving anxiety symptoms of GAD. Group CBT (SMD 1.65; 95% Cl 0.47-2.84) was more efficacious than waiting list. Remote CBT was not superior to treatment as usual or waiting list. In terms of acceptability CBT delivery formats did not differ significantly from each other. CONCLUSIONS:Our findings provide evidence for the consideration of group treatment formats as alternative to individual CBT in relieving anxiety symptoms in patients with GAD, but remote CBT may be less effective.

Cleft lip and palate (CLP) may induce alterations in functional connectivity (FC) throughout the whole brain, potentially leading to speech dysfunctions; however, the precise neurobiological mechanisms involved remain unknown. This study aimed to systematically examine the consequences of neurological impairments associated with CLP on whole-brain FC and speech functionality. A total of 33 CLP individuals and 41 control participants were included in this study. Eight meaningful brain networks were identified through independent component analysis (ICA). The intergroup differences and correlations with speech scores for both intranetwork and internetwork FC were calculated. We observed decreased FC within the sensorimotor network (SMN), default mode network (DMN), and cerebellar network (CN) and increased FC within the executive control network (ECN). Additionally, FC was enhanced between the SMN and the auditory network (AN), attention network (ATN), and salience network (SAN); between the DMN and the visual network (VN) and ECN; and between two independent components of the DMN. Furthermore, significant correlations were observed between altered FC and speech assessment scores. Our research demonstrated that brain plasticity in CLP individuals with speech deficits involves widespread changes in brain connectivity, significantly improving our understanding of the neural basis of speech impairment in CLP individuals.