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Hum Brain Mapp. 2025 Dec 1;46(17):e70380. doi: 10.1002/hbm.70380.

ABSTRACT

The estimation of brain networks is instrumental in quantifying and evaluating brain function. Nevertheless, achieving precise estimations of subject-level networks has proven to be a formidable task. In response to this challenge, researchers have developed group-inference frameworks that leverage robust group-level estimations as a common reference point to infer corresponding subject-level networks. Generally, existing approaches either leverage the common reference as a strict, voxel-wise spatial constraint (i.e., strong constraints at the voxel level) or impose no constraints. Here, we propose a targeted approach that harnesses the topological information of group-level networks to encode a high-level representation of spatial properties to be used as constraints, which we refer to as Topologically Optimized Intrinsic Brain Networks (TOIBN). Consequently, our method inherits the significant advantages of constraint-based approaches, such as enhancing estimation efficacy in noisy data or small sample sizes. On the other hand, our method provides a softer constraint than voxel-wise penalties, which can result in the loss of individual variation, increased susceptibility to model biases, and potentially missing important subject-specific information. Our analyses show that the subject maps from our method are less noisy and true to the group networks while promoting subject variability that can be lost from strict constraints. We also find that the topological properties resulting from the TOIBN maps are more expressive of differences between individuals with schizophrenia and controls in the default mode, subcortical, and visual networks.

PMID:41272954 | DOI:10.1002/hbm.70380

BMC Psychiatry. 2025 Nov 21. doi: 10.1186/s12888-025-07586-6. Online ahead of print.

NO ABSTRACT

PMID:41272514 | DOI:10.1186/s12888-025-07586-6

J Headache Pain. 2025 Nov 21;26(1):268. doi: 10.1186/s10194-025-02207-9.

NO ABSTRACT

PMID:41272442 | DOI:10.1186/s10194-025-02207-9

Sci Rep. 2025 Nov 21;15(1):41448. doi: 10.1038/s41598-025-19860-4.

ABSTRACT

Stroke impairs limb motor function, which affects patients' quality of life and imposes economic burdens. Early prediction of motor recovery is essential for guiding treatment and rehabilitation. While the corticospinal tract is a known biomarker, the role of non-motor brain regions remains under explored. Fifty-five stroke patients with unilateral subcortical lesions and 49 healthy controls underwent resting-state functional MRI scans at 1 week, 4 weeks, and 12 weeks after stroke. Focusing on two motor and 15 non-motor networks defined by the Schaefer atlas, machine learning models were used to predict changes in motor function measured by the Fugl-Meyer assessment using functional connectivity (FC) data. The network-based statistic (NBS) method was used to identify significant FC differences between patients and controls. Among 90 predictive models tested, only the model based on FC within the Somatomotor A (SomMotA) and Control A (ContA) networks at 1 week after stroke significantly predicted motor recovery from the acute to subacute phases (p = 0.00040 after Bonferroni correction). The ContA network contributed more to the prediction than the SomMotA network did. NBS analysis revealed significant FC alterations within the SomMotA network in patients versus controls but no direct correlation between predictive FC and group differences. This study revealed acute-phase FC between the non-motor ContA and motor SomMotA networks can be used to effectively predict motor recovery in stroke patients. These findings highlight the significant role of non-motor networks in motor recovery and suggest that rehabilitation strategies incorporating non-motor interventions may improve patient outcomes.

PMID:41271861 | DOI:10.1038/s41598-025-19860-4

Transl Psychiatry. 2025 Nov 21. doi: 10.1038/s41398-025-03760-2. Online ahead of print.

ABSTRACT

A central challenge in psychiatry is the need for improved diagnostic accuracy and treatment efficacy. Recent dimensional frameworks like the Research Domain Criteria (RDoC) initiative address this by promoting a transdiagnostic approach to identify shared neural mechanisms across psychiatric disorders. Here, we conducted a transdiagnostic meta-analysis of resting-state fMRI studies that employed amplitude-based measures of spontaneous brain activity-the amplitude of low-frequency fluctuations/fractional ALFF (ALFF/fALFF) and regional homogeneity (ReHo). Our results revealed that patients, compared to healthy controls, exhibited significantly elevated ALFF/fALFF in the lateral orbitofrontal cortex, anterior insula, and caudate, as well as increased ReHo in the ventrolateral prefrontal cortex but reduced ReHo in the middle occipital gyrus. These regions were then subjected to resting-state functional connectivity and functional decoding analyses based on a dataset of 110 healthy participants, allowing for a data-driven inference on psychophysiological functions. These regions and their networks are mapped onto systems implicated in cognitive control, social functioning, emotional processing, and sensory perception. Collectively, our findings delineate a suite of transdiagnostic neural aberrations reflected in resting-state activity, thereby advancing the neurobiological validation of the dimensional frameworks and highlighting potential common targets for therapeutic intervention.

PMID:41271623 | DOI:10.1038/s41398-025-03760-2

Biol Psychiatry Cogn Neurosci Neuroimaging. 2025 Nov 19:S2451-9022(25)00359-3. doi: 10.1016/j.bpsc.2025.11.002. Online ahead of print.

ABSTRACT

BACKGROUND: Neuroimaging methods rely on models of neurovascular coupling that assume hemodynamic responses are canonical; evolving seconds after changes in neural activity. However, emerging evidence reveals noncanonical blood oxygen level dependent (BOLD) responses that are delayed under stress and aberrant in neuropsychiatric conditions.

METHODS: We simultaneously recorded EEG and fMRI in people with schizophrenia (n=57) and psychiatrically unaffected participants (n=46) during a resting-state paradigm. We focused on alpha band power to examine correlations with voxelwise, time-lagged BOLD signals as a dynamic measure of EEG-BOLD coupling.

RESULTS: We found pronounced diversity in the temporal profile of alpha-BOLD coupling across the brain. This included early coupling (0-2 seconds BOLD lag) for more posterior regions of the default mode network (DMN), thalamus and brainstem. Anterior regions of the DMN showed coupling at more canonical lags (4-6 seconds), although some participants showed greater than expected lags associated with self-reported measures of stress as well as greater lag scores in participants with schizophrenia. Overall, noncanonical alpha-BOLD coupling is widespread across the DMN and other non-cortical regions, and is delayed in people with schizophrenia.

CONCLUSIONS: These findings suggest that hemodynamic signals are dynamically coupled to ongoing neural activity across distributed networks. And further, that the hemo-neural lag may be associated with subjective arousal or stress. Our work highlights the need for more studies of neurovascular coupling in psychiatric conditions.

PMID:41271013 | DOI:10.1016/j.bpsc.2025.11.002

Phys Rev Lett. 2025 Nov 7;135(19):198401. doi: 10.1103/lvwj-hjr3.

ABSTRACT

We apply the phenomenological renormalization group to resting-state fMRI time series of brain activity in a large population. By recursively coarse graining the data, we compute scaling exponents for the series variance, log probability of silence, and largest covariance eigenvalue. The scaling exponents clearly exhibit linear interdependencies in the form of scaling relations and inherent variability of values closely related to the structure of correlations of brain activity. The scaling relations between the exponents are derived analytically. We find a significant correlation of exponents with clinical (gray matter volume) and behavioral (cognitive performance) traits. Akin to scaling relations near critical points in thermodynamics, our results suggest that this interdependency is intrinsic to brain organization, and may also exist in other complex systems.

PMID:41269946 | DOI:10.1103/lvwj-hjr3

Front Hum Neurosci. 2025 Nov 5;19:1682584. doi: 10.3389/fnhum.2025.1682584. eCollection 2025.

ABSTRACT

Minimal hepatic encephalopathy (MHE) is the initial stage of hepatic encephalopathy (HE), MHE patients have associated with widespread neuro-psychological impairment. Liver transplantation (LT) can restore metabolic abnormalities but the mechanisms are unclear. This study aimed to longitudinally evaluate brain function alteration in MHE patients one month after LT and their correlation with cognitive changes by using resting-state functional magnetic resonance imaging (rs-fMRI). Rs-fMRI data was collected from 32 healthy controls and 27 MHE before and 1 month after LT. Between-group comparisons of demographic data and neuropsychological scores were analyzed using SPSS 25.0. Functional imaging data were analyzed using RESTplus and SPM12 software based on MATLAB 2017b. Gender, age, and years of education were used as covariates to obtain low-frequency fluctuationd (ALFF) and dynamic low-frequency fluctuation (dALFF) dindices. Correlation analyses were performed to explore the relationship between the change of ALFF and dALFF with the change of clinical indexes pre- and post-LT. Compared to controls, ALFF values increased in the Left Cerebelum 8, right orbital part of the inferior frontal gyrus (ORBinf), right superior occipital gyrus (SOG) and decreased in right PreCG and left middle frontal gyrus (MFG) in patients post-LT; dALFF values increased in the right temporal pole and middle temporal gyrus (TPOmid), right ORBinf, left caudate nucleus (CAU), right SOG and decreased in left PreCG, left PCUN, left ANG, left SMA and left MFG in patients post-LT. Compared to pre-LT, ALFF values of post-LT patients increased in the right calcarine fissure and surrounding cortex (CAL), right MOG and decreased in right cerebelum 8, left PCUN; dALFF values of post-LT patients decreased in right thalamus (THA), left posterior cingulate gyrus (PCG) and left MFG. The changes of ALFF in the left PCUN, right CAL and right MOG were correlated with change of digit symbol test (DST) scores (P < 0.05). In summary, this study not only showcases the potential of ALFF/dALFF algorithms for assessing alterations in spontaneous neural activity in MHE, but also provides new insights into the altered brain functions in MHE patients 1 month after LT, which may facilitate the elucidation of elucidation of mechanisms underlying cognitive restoration post-LT in MHE patients.

PMID:41268147 | PMC:PMC12626923 | DOI:10.3389/fnhum.2025.1682584

Sci Rep. 2025 Nov 20;15(1):41056. doi: 10.1038/s41598-025-24944-2.

ABSTRACT

Identifying brain mechanisms implicated in the intergenerational transmission of major depressive disorder (MDD) is crucial for early detection and developing novel interventions. One promising mechanism involves altered intrinsic connectivity patterns in brain networks supporting emotion processing, including within the cognitive control network (CCN). The current preliminary study used resting state functional magnetic resonance imaging (fMRI) to examine whether altered CCN connectivity patterns are a brain-based mechanism of intergenerational risk for depression. We tested whether CCN connectivity patterns (1) differentiated mothers with and without recurrent MDD, (2) differentiated their high-risk (HR) and low-risk (LR) daughters, and (3) served as prospective predictors of daughters' depressive symptoms over a multi-wave follow-up. Participants were 56 mother-daughter pairs who completed a resting state fMRI scan. Mothers with, versus without, a history of MDD exhibited reduced connectivity between the CCN and other regions within the CCN, such as the middle frontal gyrus and dorsal anterior cingulate cortex (ACC). Reduced connectivity between the CCN and dorsal ACC was also observed in HR, relative to LR, daughters, correlated significantly among mothers and daughters, and associated with higher depression symptoms in daughters across 18 months. Reduced connectivity within the CCN may constitute one brain-based marker to further investigate as a target for prevention to attenuate the intergenerational transmission of depression.

PMID:41266636 | DOI:10.1038/s41598-025-24944-2

NMR Biomed. 2026 Jan;39(1):e70189. doi: 10.1002/nbm.70189.

ABSTRACT

Cerebrovascular reactivity (CVR) is a crucial physiological marker of vascular health and has been linked to aging-related cerebrovascular decline. Resting-state BOLD MRI-based relative CVR mapping (RS-rCVR) offers a noninvasive and compliance-friendly alternative to gas-challenge methods, making it suitable for lifespan studies. This study aimed to examine age-related differences in RS-rCVR among healthy adults using voxel-wise and region-of-interest (ROI) analyses. We prospectively recruited 54 healthy adults, including 27 younger (20-28 years, mean = 23.3 ± 3.4; 16 females) and 27 older (57-75 years, mean = 66.5 ± 5.3; 16 females) participants. Resting-state fMRI data were acquired using a T2*-weighted gradient-echo EPI sequence at 3T. RS-rCVR maps were generated by linear regression of the voxel-wise BOLD time series against the global BOLD signal and normalized to cerebellar gray matter. Group-level analyses included voxel-wise comparisons, histogram analyses, and ROI-based statistical tests. The distribution of RS-rCVR values significantly differed between age groups (Kolmogorov-Smirnov test: KS = 0.039, p < 0.001). Voxel-wise comparisons revealed age-related reductions in RS-rCVR in the medial frontal cortex, precuneus, and cuneus in older adults. In contrast, ROI-averaged RS-rCVR values showed no statistically significant group differences across frontal, temporal, and occipital cortices (p > 0.05). Effect size analysis indicated small to moderate differences in specific regions (e.g., occipital cortex: d = 0.441; parietal cortex: d = 0.430; frontal middle gyrus: d = 0.275), but negligible effects in others (e.g., cingulate cortex: d = 0.006). While voxel-wise RS-rCVR mapping detects spatially localized age-related reductions in cerebrovascular reactivity, ROI-based analysis may obscure these effects due to anatomical averaging. These findings underscore the spatial heterogeneity of cerebrovascular aging and support the utility of voxel-level RS-rCVR approaches in lifespan research.

PMID:41265868 | DOI:10.1002/nbm.70189

Neuroimage Clin. 2025 Nov 8;48:103906. doi: 10.1016/j.nicl.2025.103906. Online ahead of print.

ABSTRACT

Very preterm birth significantly increases the risk of lifelong cognitive and motor deficits by disrupting early brain development. We characterised functional brain organisation at six years of age in children born very preterm (VPT) compared to term-born controls (TC). Functional MRI comprised i) visually cued ∼1 Hz right- and left-hand tapping analysed with a general linear model and permutation testing, and ii) eyes-closed resting-state acquisitions assessed with ROI-to-ROI connectivity for cortical/cerebellar and subcortical networks. Seventy-one children born <31 weeks gestation were scanned; high-quality task data were available from 58 (43 VPT, 15 TC) for right-hand and 48 (36 VPT, 12 TC) for left-hand tapping, and resting-state data from 40 (28 VPT, 12 TC). Both groups activated expected motor regions, but VPT children showed greater activation and stronger left-temporal lateralisation during right-hand tapping (p < 0.032). Resting-state analyses revealed weaker connectivity in VPT children within striatal circuits and between salience, dorsal-attention, and visual networks, alongside reduced anticorrelation between default-mode and frontoparietal networks. Our findings indicate that children born very preterm exhibit a persistent reorganisation of both task-evoked motor activation and large-scale cortical and subcortical resting-state network connectivity at six years of age. These differences in functional brain organisation provide insights into long-lasting neurobiological changes associated with very preterm birth during a critical period of child development.

PMID:41265076 | DOI:10.1016/j.nicl.2025.103906

Commun Biol. 2025 Nov 20;8(1):1619. doi: 10.1038/s42003-025-08972-2.

ABSTRACT

The hippocampus and cerebellum are traditionally considered to support distinct memory systems, yet evidence from nonhuman species indicates a close relationship during spatial-mnemonic behaviour, with hippocampal projections to and from several cerebellar regions. However, little is known about this relationship in humans. To address this, we applied seed-based functional connectivity analysis to resting-state fMRI data from 479 cognitively normal participants aged 18-88 years. We identified significant functional correlations between the hippocampus and widespread areas of cerebellar cortex, particularly lobules HIV, HV, HVI, HVIIA (Crus I and II), HIX, and HX. Moreover, anterior hippocampus showed stronger connectivity with right Crus II, whereas posterior hippocampus was strongly connected to vermal lobule V. Finally, we observed age-related reductions in functional connectivity between the hippocampus and lobules HVI and HV. These findings provide insight into the topography of hippocampal-cerebellar functional organisation in humans and the influence of ageing on this system.

PMID:41266803 | DOI:10.1038/s42003-025-08972-2

Imaging Neurosci (Camb). 2025 Nov 17;3:IMAG.a.1022. doi: 10.1162/IMAG.a.1022. eCollection 2025.

ABSTRACT

Many studies utilize resting-state functional magnetic resonance imaging (rs-fMRI) metrics, such as functional connectivity (FC), to investigate the neuronal underpinnings of autism and identify functional brain networks related to autistic behaviors. However, fMRI indirectly measures neuronal activity by imaging local fluctuations in the blood oxygen level dependent (BOLD) signal, which, in turn, rely on the cerebrovascular system to efficiently direct oxygenated blood flow. Most rs-fMRI studies of autism interpret group differences in FC as autism-related changes in neuronal activity, without considering the underlying vascular function. Yet, atypical cerebrovasculature has been identified in preclinical and post-mortem studies of autism, strongly underscoring the need to characterize cerebrovascular differences to enhance our neurobiological understanding of autism. We evaluated relative cerebrovascular reactivity (rCVR) in autistic and non-autistic children using a novel measure of local brain vasodilatory capacity based on rs-fMRI. We leveraged the cross-sectional Autism Brain Imaging Data Exchange repository to quantify rCVR in 199 non-autistic (74 female) and 95 autistic (16 female) children, 9-12 years old. We identified sex-specific differences in rCVR in autism, particularly in right-frontal brain regions, where rCVR was increased in autistic females compared to non-autistic females. Then, within the same rs-fMRI scans, we demonstrated that rCVR in the right inferior frontal gyrus was positively associated with its FC to regions associated with attentional control in girls, suggesting that cerebrovascular differences may differentially affect FC findings between regions and sexes in children. Our study highlights potential sex differences in cerebrovascular function in autism that enhance our neurobiological understanding of autism and improve interpretations of rs-fMRI findings in children.

PMID:41262555 | PMC:PMC12624364 | DOI:10.1162/IMAG.a.1022

Transl Psychiatry. 2025 Nov 10. doi: 10.1038/s41398-025-03715-7. Online ahead of print.

ABSTRACT

Hemispheric lateralization, recognized as a pivotal feature in both the structural and functional organization of the human brain, may undergo alterations in specific psychiatric disorders. However, the time-varying patterns of hemispheric lateralization in individuals with major depressive disorder (MDD) and the relationship between these patterns and gene expression profiles remain largely unexplored thus far. Using a large multi-site resting-state functional magnetic resonance imaging (rs-fMRI) data encompassing 2611 participants (1660 MDD patients and 1341 healthy controls), we examined MDD-related abnormalities in dynamic laterality and its association with clinical symptoms, meta-analytic cognitive functions, and neurotransmitter receptor profiles, respectively. And the biological basis behind these changes was investigated through gene enrichment analysis and cell-specific analysis. Here we found revealed pronounced fluctuations in lateralization primarily in the regions in default mode network, attention network and control network in MDD patients when compared to healthy controls. In addition, these fluctuations exhibited significant correlations with higher-order cognition terms and the distributions of disease related neurotransmitters. Further, through gene enrichment and cell-specific analysis, we identified a molecular genetic basis for these changes, highlighting synaptic function-related genes and neuronal cells. Collectively, these results demonstrated robust altered brain lateralization patterns in MDD and its molecular genetic basis, providing new clues to understand the pathophysiology of MDD.

PMID:41257981 | DOI:10.1038/s41398-025-03715-7

Mol Psychiatry. 2025 Nov 19. doi: 10.1038/s41380-025-03367-5. Online ahead of print.

ABSTRACT

Psychotic disorders, such as schizophrenia and bipolar disorder, pose significant diagnostic challenges with major implications on mental health. The measures of resting-state fMRI spatiotemporal complexity offer a powerful tool for identifying irregularities in brain activity. To capture global brain connectivity, we employed information-theoretic metrics, overcoming the limitations of pairwise correlation analysis approaches. This enables a more comprehensive exploration of higher-order interactions and multiscale intrinsic connectivity networks (ICNs) in the psychotic brain. In this study, we provide converging evidence suggesting that the psychotic brain exhibits states of randomness across both spatial and temporal dimensions. To further investigate these disruptions, we estimated brain network connectivity using redundancy and synergy measures, aiming to assess the integration and segregation of topological information in the psychotic brain. Our findings reveal a disruption in the balance between redundant and synergistic information, a phenomenon we term brainquake in this study, which highlights the instability and disorganization of brain networks in psychosis. Moreover, our exploration of higher-order topological functional connectivity reveals profound disruptions in brain information integration. Aberrant information interactions were observed across both cortical and subcortical ICNs. We specifically identified the most easily affected irregularities in the sensorimotor, visual, temporal, default mode, and fronto-parietal networks, as well as in the hippocampal and amygdalar regions, all of which showed disruptions. These findings underscore the severe impact of psychotic states on multiscale critical brain networks, suggesting a profound alteration in the brain's complexity and organizational states.

PMID:41261142 | DOI:10.1038/s41380-025-03367-5

Behav Brain Res. 2025 Nov 17:115944. doi: 10.1016/j.bbr.2025.115944. Online ahead of print.

ABSTRACT

This study aimed to explore the neurobiology of implicit and explicit suicidal ideation (SI) in depression. Seventy-four patients with major depressive disorder (MDD) along with 74 age- and gender- matched healthy controls were enrolled. The Death/Suicide implicit association test (D/S-IAT), the explicit Beck Scale for Suicidal Ideation (BSSI), and Resting-state functional magnetic resonance imaging (rs-fMRI) scanning were administered. The amplitude of low-frequency fluctuations (ALFF) was calculated and compared between groups to identify brain regions showing spontaneous neural activity related to implicit and explicit SI, and then seed-based functional connectivity (FC) was performed among these regions to reconstruct brain networks related to SI. Behavioral analysis demonstrated higher implicit SI (D values from D/S-IAT) in MDD patients, when compared to HCs，which was also significantly correlated with explicit SI (BSSI scores). Whole brain regression analysis indicated abnormal ALFF in the right postcentral gyrus associated with implicit SI, while ALFF alterations in the left insula, postcentral gyrus, and right middle temporal gyrus (MTG) was associated with explicit SI in MDD. Furthermore, FC analysis revealed increased connectivity between the right postcentral gyrus with the right SFG, MFG, MTG, SPG, insula, and amygdala for implicit SI. Conversely, higher FC between the left insula ROI and left SFG, as well as between the right MTG and left MFG and IPL for explicit SI. These findings suggesting partly overlapped but largely distinct neural basis of the implicit and explicit SI in the brain.

PMID:41260561 | DOI:10.1016/j.bbr.2025.115944

Transl Psychiatry. 2025 Nov 10. doi: 10.1038/s41398-025-03715-7. Online ahead of print.

ABSTRACT

Hemispheric lateralization, recognized as a pivotal feature in both the structural and functional organization of the human brain, may undergo alterations in specific psychiatric disorders. However, the time-varying patterns of hemispheric lateralization in individuals with major depressive disorder (MDD) and the relationship between these patterns and gene expression profiles remain largely unexplored thus far. Using a large multi-site resting-state functional magnetic resonance imaging (rs-fMRI) data encompassing 2611 participants (1660 MDD patients and 1341 healthy controls), we examined MDD-related abnormalities in dynamic laterality and its association with clinical symptoms, meta-analytic cognitive functions, and neurotransmitter receptor profiles, respectively. And the biological basis behind these changes was investigated through gene enrichment analysis and cell-specific analysis. Here we found revealed pronounced fluctuations in lateralization primarily in the regions in default mode network, attention network and control network in MDD patients when compared to healthy controls. In addition, these fluctuations exhibited significant correlations with higher-order cognition terms and the distributions of disease related neurotransmitters. Further, through gene enrichment and cell-specific analysis, we identified a molecular genetic basis for these changes, highlighting synaptic function-related genes and neuronal cells. Collectively, these results demonstrated robust altered brain lateralization patterns in MDD and its molecular genetic basis, providing new clues to understand the pathophysiology of MDD.

PMID:41257981 | DOI:10.1038/s41398-025-03715-7

J Biomed Life Sci. 2025;5(2):122-138. doi: 10.31586/jbls.2025.6208. Epub 2025 Nov 6.

ABSTRACT

BACKGROUND: Resting-state functional MRI (rsfMRI) provides insights into large-scale brain network organization associated with cognitive control, emotion regulation, and attentional processes. The ventral attention network (VAN) is a key salience-driven network that supports attentional re-orienting to behaviorally relevant stimuli. However, little is known about how VAN resting state functional connectivity varies by demographic, socioeconomic, psychosocial, and behavioral factors during early adolescence.

OBJECTIVE: To examine associations between VAN rsfMRI connectivity and multiple demographic, socioeconomic, psychosocial, and behavioral characteristics.

METHODS: Data came from the baseline and early follow-up waves of the Adolescent Brain Cognitive Development (ABCD) Study. The analytic sample included youth with high-quality baseline rsfMRI data and complete socioeconomic and psychosocial measures. The primary outcome was mean resting-state functional connectivity within the VAN across subcortical and cortical regions of interest (ROIs). Bivariate correlations were computed between VAN connectivity and demographic (age, sex, puberty, race/ethnicity), socioeconomic (income, parental education, marital status, neighborhood income), psychosocial (trauma, discrimination, financial difficulty), trait (impulsivity), and behavioral variables (body mass index, depression, suicide, prodromal symptoms, and substance use). Unadjusted bivariate correlations and adjusted logistic regressions were used for data analysis.

RESULTS: VAN connectivity showed small but significant correlations with multiple contextual factors. Higher household income, parental education, and neighborhood affluence were associated with greater connectivity, whereas Black race and Hispanic ethnicity were related to lower connectivity. Youth reporting higher discrimination and financial difficulty exhibited weaker VAN connectivity. Greater VAN connectivity was negatively associated with impulsive reward-driven trait (drive), prodromal symptoms, BMI, and marijuana and alcohol use. Associations between VAN connectivity and suicide, depression, marijuana use, and alcohol use remained significant in age and sex adjusted models.

CONCLUSIONS: VAN connectivity reflects subtle neural correlates of socioeconomic and psychosocial context in early adolescence. Our results underscore the importance of integrating structural and contextual factors in interpreting brain-behavior associations across diverse populations. These findings are suggestive of stable socioeconomic and psychosocial correlates of network efficiency.

PMID:41257054 | PMC:PMC12622571 | DOI:10.31586/jbls.2025.6208

Front Psychiatry. 2025 Nov 3;16:1671813. doi: 10.3389/fpsyt.2025.1671813. eCollection 2025.

ABSTRACT

BACKGROUND: Non-Suicidal Self-Injury (NSSI) is a primary risk factor for suicide, but objective biomarkers to assess this risk are urgently needed. The "prefrontal-limbic dysregulation" model provides a neurobiological framework for self-injurious behaviors. This study aimed to identify resting-state neural markers of suicidal ideation severity in adolescents with NSSI and to build a predictive model for individualized risk assessment.

METHODS: We recruited 64 adolescent psychiatric inpatients with NSSI. Suicidal ideation was measured using the Beck Scale for Suicide Ideation (BSI). Resting-state functional MRI (rs-fMRI) was used to measure spontaneous brain activity via the amplitude of low-frequency fluctuation (ALFF). We performed a whole-brain correlation analysis between ALFF and BSI scores. A support vector regression (SVR) model was then developed using the identified neural feature to predict individual BSI scores.

RESULTS: A significant negative correlation was found between BSI scores and ALFF values in the left Middle Frontal Gyrus (MFG). Lower spontaneous activity in this region was associated with more severe suicidal ideation. The SVR model, based on the left MFG ALFF values, successfully predicted individual BSI scores with significant accuracy (r = 0.492, p < 0.001), a finding confirmed by permutation testing.

CONCLUSION: Diminished resting-state activity in the left MFG is a key neural correlate of suicidal ideation severity in adolescents with NSSI. The functional activity of the left MFG is a promising biomarker for suicide risk assessment and may serve as a potential target for novel neuromodulatory therapies in this high-risk population.

PMID:41256945 | PMC:PMC12620615 | DOI:10.3389/fpsyt.2025.1671813

bioRxiv [Preprint]. 2025 Sep 29:2025.09.26.678805. doi: 10.1101/2025.09.26.678805.

ABSTRACT

Accumulation of amyloid-beta plaques and disruption of intrinsic brain networks are two important characteristics of Alzheimer's disease (AD), yet the relationship between amyloid accumulation and network dysfunction remains unclear. In this study, we integrated [18F]Florbetapir PET and resting-state fMRI (rsfMRI) derived Functional Network Connectivity (FNC) from 552 temporally matched longitudinal PET-rsfMRI sessions across 395 participants spanning Cognitively Normal (CN), Mild Cognitive Impairment (MCI), and AD stages. With a model order of 11, joint Independent Component Analysis (jICA) was applied to the fused PET-FNC data, identifying 11 stable components, of which 9 PET-derived components corresponded to previously characterized brain regions or networks. The multimodal analysis revealed disease progression markers, including (1) a pattern of reduced subject loadings across clinical stages (CN > MCI > AD) in white matter and cerebellar regions, reflecting structural degeneration; (2) increased amyloid accumulation in affected individuals in grey matter regions, particularly in frontal, sensorimotor, extended hippocampal, and default mode network (DMN) regions, accompanied by functional connectivity alterations that reflected both compensatory and disruptive network dynamics. We identified PET-derived components that captured distinct stages of disease progression, with the DMN component emerging as a late-stage biomarker and a white matter component showing early-stage changes with limited progression thereafter. Additionally, several components showed significant variation in loadings between APOE ε 4 carriers and non-carriers, linking the multimodal signatures to a well-established genetic risk factor for AD.

PMID:41256650 | PMC:PMC12621773 | DOI:10.1101/2025.09.26.678805