Most recent paper

Neuroimage Clin. 2025 Aug 22:103869. doi: 10.1016/j.nicl.2025.103869. Online ahead of print.

NO ABSTRACT

PMID:40849253 | DOI:10.1016/j.nicl.2025.103869

J Neuroimaging. 2025 Jul-Aug;35(4):e70075. doi: 10.1111/jon.70075.

ABSTRACT

BACKGROUND AND PURPOSE: Resting-state fMRI (rs-fMRI) is increasingly used to map brain networks in patients under anesthesia, but technical factors can affect its utility. We evaluated the effects of sevoflurane, multiband acceleration, and scan duration on rs-fMRI signal quality and within-subject reliability under anesthesia.

METHODS: We retrospectively analyzed 64 clinical rs-fMRI scans acquired under anesthesia, with or without sevoflurane and multiband factor 5 acceleration. Temporal signal-to-noise ratio (tSNR) was used as a measure of signal quality. For each patient, the scan was split in half, and seed-based connectivity maps were generated for the primary motor cortex (M1), posterior cingulate cortex (PCC), and subgenual anterior cingulate cortex (sgACC). Split-half spatial correlations were used to assess within-subject reliability. Group comparisons examined differences in tSNR and reliability across conditions, and correlations with scan duration were tested.

RESULTS: Multiband acceleration was associated with significantly lower tSNR (U = 652.0, p = 8.9 × 10─6) and reduced split-half reliability for M1 (p = 0.019), PCC (p = 0.010), and sgACC (p = 0.0064). Sevoflurane showed no significant effect on tSNR or reliability. Longer scan duration correlated with improved reliability for M1 (r = 0.38, p = 0.003) but not for PCC or sgACC. No correlation was found between tSNR and reliability.

CONCLUSION: Hight multiband acceleration reduces both signal quality and reliability of rs-fMRI under anesthesia. Sevoflurane had no measurable effect. The lack of correlation between tSNR and reliability underscores the need for more robust metrics when evaluating scan quality.

PMID:40848013 | DOI:10.1111/jon.70075

Brain Behav. 2025 Aug;15(8):e70751. doi: 10.1002/brb3.70751.

ABSTRACT

OBJECTIVE: The perception of pain is thought to arise from the integration of information between multiple brain regions. Data from observational studies indicates that dysfunction of brain resting-state functional networks is present in a wide range of peripheral neuropathic pain (pNP). The present study thus sought to investigate whether a causal relationship exists and to determine the potential mediating role of circulating inflammatory proteins in this association.

METHODS: The resting-state functional magnetic imaging phenotype is defined as a stable feature that quantifies the pattern of functional connectivity (i.e., synchronized activity) between different regions of the brain in the resting state of an individual. We gathered publicly available genome-wide association study (GWAS) summary statistics for brain functional networks, including 191 rsfMRI phenotypes and postherpetic neuralgia (PHN) and trigeminal neuralgia (TN) in the FinnGen biobank. Furthermore, data were collected on genetic variation related to inflammation, including 91 circulating inflammatory proteins. We performed two-sample MR analysis to investigate the causal effects of functional brain networks on PHN and TN. To explore the possible mediation of inflammatory factor changes between rsfMRI phenotypes and PHN and TN.

RESULTS: The forward MR approach identifies five rs-fMRI phenotypes that are causally associated with the risk of developing PHN. For instance, enhanced motor network connectivity was found to be associated with a reduced risk of PHN. Six rsfMRI phenotypes were identified as causally associated with TN risk. These brain network phenotypes mainly involve the default mode network (DMN), the sensory-motor network (SMN), and the motor network, etc. Two-step MR-mediated analysis revealed that the inflammatory protein interleukin 20 receptor alpha (IL-20RA) is a mediator of the pathway from the phenotype Pheno 12 of the brain motor network to PHN.

CONCLUSION: The findings provide valuable insights into potential targets for disease intervention and treatment at the level of functional brain networks.

PMID:40847468 | PMC:PMC12373714 | DOI:10.1002/brb3.70751

Hear Res. 2025 Aug 18;466:109406. doi: 10.1016/j.heares.2025.109406. Online ahead of print.

ABSTRACT

Tinnitus, the perception of sound without an external source, is increasingly recognized as a disorder of large-scale brain networks rather than isolated auditory dysfunction. Aging introduces widespread neurobiological changes including cortical atrophy, reduced inhibitory control, and altered connectivity that may distinctly shape the neural basis of tinnitus across the adult lifespan. This study investigates age-specific brain network alterations in chronic subjective tinnitus using resting-state functional magnetic resonance imaging (rs-fMRI) and diffusion tensor imaging (DTI). Sixty tinnitus patients and sixty age-matched controls were stratified into younger (19-35 years) and older (45-65 years) cohorts. We assessed spontaneous activity via amplitude of low-frequency fluctuations (ALFF), local functional connectivity using local correlation (LCOR), and structural-functional coupling through voxel-wise correlations between ALFF and fractional anisotropy (FA). Younger tinnitus patients exhibited hyperactivity in auditory and limbic regions, alongside reduced prefrontal and occipital regulation, suggesting heightened sensory salience and impaired habituation. In contrast, older patients showed increased activation in frontal and cerebellar regions but diminished integration in default mode and attention-related cortices, indicating compensatory recruitment constrained by age-related decline. Structure-function coupling revealed more diffuse and adaptive correlations in younger patients, while older adults exhibited focal and limited coupling, particularly in temporal and occipital regions. Network alterations were differentially modulated by tinnitus severity and anxiety across age groups. These findings reveal distinct, age-contingent neural signatures of tinnitus, emphasizing the role of aging in modulating brain network plasticity and underscoring the need for lifespan-informed models in tinnitus research.

PMID:40845546 | DOI:10.1016/j.heares.2025.109406

Biol Psychiatry Cogn Neurosci Neuroimaging. 2025 Aug 19:S2451-9022(25)00250-2. doi: 10.1016/j.bpsc.2025.08.002. Online ahead of print.

ABSTRACT

BACKGROUND: Posttraumatic stress disorder (PTSD) and mild traumatic brain injury (mTBI) are associated with alterations in the functional connectome, specifically in canonical resting state networks including the default mode (DMN), central executive (CEN), and salience networks (SN). Comorbid PTSD+mTBI is linked to worse functional outcomes, but little is known about effects on the functional connectome.

METHODS: We investigated brain phenotypes from resting-state fMRI associated with PTSD (n=326), mTBI (n=448), and comorbid PTSD+mTBI (n=289) in military veterans and civilians (n=1526) from ENIGMA-TBI and -PTSD. We examined static functional connectivity (SFC) and dynamic functional connectivity (DFC), quantified both as variability in FC (VFC) over time and as dwell time in recurring FC states identified through clustering. ANCOVA was followed by post-hoc linear regression to test main and interaction effects of diagnosis on FC metrics.

RESULTS: We found a significant (pFDR<0.05) interaction of diagnosis by age on VFC. Older comorbid subjects had greater VFC within SN, between SN-to-CEN and SN-to-DMN than older controls. Comorbid relative to control subjects had significantly greater dwell time in an externally focused state. Comorbid and mTBI groups, relative to control subjects, had greater dwell time in a moderate connectivity transition state.

CONCLUSIONS: DFC related to the SN revealed distinct brain network patterns across diagnostic groups, with comorbid PTSD+mTBI showing age- and anxiety-related effects. Older comorbid subjects had heightened hypervigilance and reduced network segregation. PTSD and anxiety may synergistically worsen network instability, while mTBI reflects more rigid, disconnected states, highlighting DFC as a sensitive marker of neuropsychiatric comorbidity.

PMID:40840859 | DOI:10.1016/j.bpsc.2025.08.002

Brain Res. 2025 Aug 19;1865:149896. doi: 10.1016/j.brainres.2025.149896. Online ahead of print.

ABSTRACT

BACKGROUND: Obesity significantly increases not only the incidence and mortality rates of cardiovascular diseases, diabetes, and other metabolic disorders, but also elevates the risk of cognitive impairment-related conditions such as Alzheimer's disease by 3 to 5 times. Based on some brain regions related to reward drive, this study combined brain entropy (BEN) and resting state functional connectivity (RSFC) to explore the neural basis of obesity-induced memory impairment.

METHODS: Based on resting-state functional magnetic resonance imaging (rs-fMRI) data from 23 obese individuals and 36 healthy controls, the BEN values of some brain regions related to reward drive (Nucleus accumbens, Pallidum, Caudate, and Anterior cingulate cortex) were calculated. Mediation analysis was performed to examine whether body mass index (BMI) mediates the relationship between BEN and memory quotient (MQ). Additionally, whole-brain functional connectivity analysis was conducted based on regions showing significant BEN differences.

RESULTS: The BEN in left pallidum (lPAL) was significantly reduced in the obese group compared to controls (p = 0.005). Moreover, BMI mediated the relationship between lPAL entropy and MQ (Indirect effect: 0.2227, 95 % CI [0.0866, 0.3658]). Functional connectivity analysis revealed decreased connectivity between the lPAL and the right superior parietal gyrus, cerebellar Crus II, and cerebellar VIIB in the obese group, along with increased connectivity between the lPAL and the right pallidum.

CONCLUSION: BMI fully mediates the reduced brain complexity of the left pallidum in obese individuals, which is associated with memory impairment and is accompanied by changes in specific functional connectivity patterns. These findings provide new insights into the neural substrates of obesity-related cognitive decline.

PMID:40840855 | DOI:10.1016/j.brainres.2025.149896

Transl Psychiatry. 2025 Aug 21;15(1):303. doi: 10.1038/s41398-025-03500-6.

ABSTRACT

Depression is associated with reduced functional connectivity within the brain's salience network and its strengthened interactions with the default mode network (DMN). Modification of this clinical pattern is challenging. Leveraging the direct neural pathways from olfactory processing regions to the salience network, we explored the effects of electrical stimulation of the olfactory mucosa on brain connectivity. In a randomized, blinded within-subject design, 45 healthy individuals received olfactory or trigeminal nerve stimulation followed by resting-state fMRI. Olfactory stimulation resulted in a significant increase in functional connectivity between the salience network and the piriform cortex - a primary olfactory structure. Importantly, this stimulation increased functional connectivity within the salience network and weakened connectivity between the salience network and the DMN. These findings suggest that olfactory stimulation may modulate connectivity patterns implicated in depression, offering a novel potential minimal invasive therapeutic strategy. However, as these results were obtained from a healthy cohort, further studies are required to evaluate the efficacy in individuals with depression.

PMID:40841359 | PMC:PMC12370952 | DOI:10.1038/s41398-025-03500-6

Comput Biol Med. 2025 Aug 20;196(Pt C):110946. doi: 10.1016/j.compbiomed.2025.110946. Online ahead of print.

ABSTRACT

Understanding the neurobiology of opioid use disorder (OUD) using resting-state functional magnetic resonance imaging (rs-fMRI) may help inform treatment strategies to improve patient outcomes. Recent literature suggests time-frequency characteristics of rs-fMRI blood oxygenation level-dependent (BOLD) signals may offer complementary information to traditional analysis techniques. However, existing studies of OUD analyze BOLD signals using measures computed across all time points. This study, for the first time in the literature, employs data-driven machine learning (ML) for time-frequency analysis of local neural activity within key functional networks to differentiate OUD subjects from healthy controls (HC). We obtain time-frequency features based on rs-fMRI BOLD signals from the default mode network (DMN), salience network (SN), and executive control network (ECN) for 31 OUD and 45 HC subjects. Then, we perform 5-fold cross-validation classification (OUD vs. HC) experiments to study the discriminative power of functional network features while taking into consideration significant demographic features. ML-based time-frequency analysis of DMN, SN, and ECN significantly (p < 0.05) outperforms chance baselines for discriminative power with mean F1 scores of 0.6675, 0.7090, and 0.6810, respectively, and mean AUCs of 0.7302, 0.7603, and 0.7103, respectively. Follow-up Boruta ML analysis of selected time-frequency (wavelet) features reveals significant (p < 0.05) detail coefficients for all three functional networks, underscoring the need for ML and time-frequency analysis of rs-fMRI BOLD signals in the study of OUD.

PMID:40839932 | DOI:10.1016/j.compbiomed.2025.110946

Hum Brain Mapp. 2025 Aug 15;46(12):e70326. doi: 10.1002/hbm.70326.

ABSTRACT

Major depressive disorder (MDD) is a globally prevalent psychiatric disorder that significantly impairs quality of life and increases suicide risk. Accurate identification of MDD is critical for clinically assisted diagnosis. Although substantial progress has been made in MDD identification, extracting region of interest (ROI) features from functional brain networks remains underexplored. Furthermore, most studies rely on small-scale resting-state functional magnetic resonance imaging (rs-fMRI) datasets, which limits the generalizability of their findings to large-scale brain networks. To address these issues, we propose a novel graph embedding-based feature selection classification framework (GEF-FSC) to identify MDD through multi-site rs-fMRI data. The framework employs the node2vec algorithm to learn local and global functional connectivity (FC) features of ROIs via flexible random walks, capturing structural information in functional brain networks. Random Forest is then applied for feature selection on the learned embedding features, followed by classification using an ensemble classifier. This approach captures complex, higher-order structural information between ROIs and retains important features, enhancing classification accuracy by minimizing redundancy in high-dimensional FC features. Evaluated on the REST-meta-MDD dataset, our framework achieved 81.65% accuracy under the Dosenbach template and 75.30% under the AAL atlas. Comparative experiments with eight benchmark methods and six state-of-the-art classifiers demonstrated superior accuracy, sensitivity, specificity, and F1-score. Interpretability analysis highlighted key brain regions and networks consistent with previous findings. The GEF-FSC framework effectively classifies MDD and identifies key brain regions and networks associated with the disorder, emphasizing the importance of higher-order structural information in improving diagnostic accuracy.

PMID:40838619 | PMC:PMC12368899 | DOI:10.1002/hbm.70326

J Psychopharmacol. 2025 Aug 21:2698811251360745. doi: 10.1177/02698811251360745. Online ahead of print.

ABSTRACT

BACKGROUND: Cannabis constituents, including Δ9-tetrahydrocannabinol (THC) and cannabidiol (CBD), show distinct pharmacological profiles with therapeutic relevance for neurological and psychiatric conditions. THC exerts euphoric effects primarily via CB1 receptor activation, while CBD displays non-euphoric properties affecting various pathways.

AIMS: This study evaluated the effects of THC, CBD, and their combination on brain functional connectivity (FC) and cerebral blood flow (CBF) using multimodal neuroimaging.

METHODS: Adult male Sprague Dawley rats received intraperitoneal doses of 10 mg/kg THC, 150 mg/kg CBD, 10.8:10 mg/kg THC:CBD, or vehicle. Resting-state blood oxygenation level dependent magnetic resonance imaging and arterial spin labelling assessed FC and CBF, approximately 2 h after drug administration. Graph-theory metrics and seed-based analyses identified connectivity and perfusion alterations, while plasma analyses determined cannabinoid concentrations.

RESULTS: THC increased whole-brain FC and clustering coefficient, with elevated CBF in cortical and subcortical regions. CBD decreased FC metrics without affecting CBF, while THC:CBD induced moderate increases in both. Seed-based analysis revealed THC-driven increases in cortical-hippocampal and cortical-striatal connectivity, attenuated in the THC:CBD group. A multivariate combined analysis of FC and CBF revealed a divergent pattern of changes induced by each drug.

CONCLUSIONS: In conclusion, we show that THC and CBD induce distinct neurophysiological profiles in rats, with THC increasing both connectivity and perfusion, moderated by CBD when combined. These findings corroborate existing knowledge about the effects of cannabinoids on the brain, while also supporting the potential of preclinical functional neuroimaging to delineate cannabinoid-induced endophenotypes, offering insights for therapeutic development.

PMID:40838351 | DOI:10.1177/02698811251360745

Hum Brain Mapp. 2025 Aug 15;46(12):e70304. doi: 10.1002/hbm.70304.

ABSTRACT

Numerous studies have emphasized the time-varying modular architecture of functional brain networks and its relevance to cognitive functions in healthy participants. However, how modular dynamics of resting-state functional networks change in schizophrenia and how these alterations relate to neurotransmitter and transcriptomic signatures have not been well elucidated. We harmonized resting-state fMRI data from a multi-site sample including 223 patients and 279 healthy controls and applied the multilayer network method to estimate the regional module switching rate (flexibility) of functional brain connectomes. We examined aberrant flexibility in patients relative to controls and explored its relations to neurotransmitter systems and postmortem gene expression. Compared with controls, patients with schizophrenia had significantly higher flexibility in the somatomotor and right visual regions, and lower flexibility in the left parahippocampal gyrus, right supramarginal gyrus, right frontal-operculum-insula, bilateral precuneus, posterior cingulate cortex, and bilateral inferior parietal gyrus. These alterations were associated with multiple neurotransmitter systems and weighted gene transcriptomic profiles. The most relevant genes were preferentially enriched for biological processes of transmembrane transport and brain development, specific cell types, and previously identified schizophrenia-related genes. This study reveals aberrant modular dynamics in schizophrenia and its relations to neurotransmitter systems and schizophrenia-related transcriptomic profiles, providing insights into the understanding of the pathophysiology underlying schizophrenia.

PMID:40838333 | PMC:PMC12368516 | DOI:10.1002/hbm.70304

Front Physiol. 2025 Aug 5;16:1633028. doi: 10.3389/fphys.2025.1633028. eCollection 2025.

ABSTRACT

INTRODUCTION: Trigeminal neuralgia (TN) is a chronic neuropathic pain disorder characterized by spontaneous or triggered electric shock-like facial pain. Microvascular decompression (MVD) is the most effective surgical intervention for classical TN that is refractory to medication. Recent advances in neuroimaging have enhanced visualization of the trigeminal nerve's vascular anatomy, deepening insights into TN pathophysiology and paving the way for improved diagnostics and therapies. Resting-state functional magnetic resonance imaging (rs-fMRI) has been extensively applied in studies of TN, uncovering alterations in brain activity, functional connectivity, cortical thickness and neural networks.

METHODS: Independent component analysis (ICA) presents a powerful alternative for analyzing fMRI data, offering several advantages over traditional region of interests (ROIs) approaches. The sensorimotor network playing a key role in pain modulation, identifying neuroimaging differences in the sensorimotor network is crucial for detecting and intervening in TN, Forty TN patients underwent MVD surgery, with follow-up assessments conducted 6 months postoperatively and twenty-five healthy controls (HC) were recruited and scanned with resting state fMRI (rs-fMRI). Group ICA was used to identify ROIs and assessed inter-group differences in neural activity using false discovery rate (FDR) correction.

RESULTS: Compared to the HC, increased activity was observed in the right frontal operculum cortex, right insular cortex, right inferior frontal gyrus (pars opercularis), and right frontal pole in TN patients. Conversely, decreased activity was found in the right cerebellum (lobule IX) and left cerebellum (lobules VIII and IX). Compared to the pre-surgery, increased activity was found in the right precentral gyrus in the post-surgery group. Compared to the HC, long-term increased activity was still present in the right frontal operculum cortex, right insular cortex, right inferior frontal gyrus (pars opercularis), and right frontal pole despite the effectiveness of MVD surgery. In contrast, MVD significantly reduced the area of aberrant activation regions, particularly in the operculo-insular cortex, and also normalized cerebellar abnormalities.

DISCUSSION: Our study demonstrates that ICA can effectively identify distinct patterns of functional connectivity in the sensorimotor network associated with TN and MVD surgery. These regions are involved in altered pain processing, including nociceptive stimulus integration, subjective pain perception, pain chronification, and pain-related empathy. Our findings suggest promising biomarkers for TN and provide insights for developing targeted treatments.

PMID:40837096 | PMC:PMC12361157 | DOI:10.3389/fphys.2025.1633028

Sci Rep. 2025 Aug 20;15(1):30537. doi: 10.1038/s41598-025-15842-8.

ABSTRACT

Recent studies have highlighted the intricate relationship between cerebrospinal fluid (CSF) dynamics and global brain activity, suggesting a role in neurovascular coupling and brain waste clearance. The lateral ventricles are believed to play a key role in linking global BOLD (gBOLD) signals to CSF inflow (CSFin) to the fourth ventricle. In this study, we developed a method to reliably quantify lateral ventricle volume (LVV) in fMRI data. Using three independent datasets, including resting-state and task-based fMRI, we assessed dynamic changes in LVV and their associations with gBOLD and CSFin. Our findings reveal a strong anti-correlation between LVV and gBOLD across all datasets, with an average gBOLD lag of approximately 1 s. The derivative of the LVV time series were positively correlated with CSFin, with CSFin lagging LVV changes by 1.4-2.4 s. A moderate negative correlation was also observed between CSFin and gBOLD, consistent with prior research. These results support the hypothesis that LVV fluctuations, driven by global cerebral blood volume oscillations, regulate CSF movement into and out of the fourth ventricle. Our findings provide a foundation for further investigations into the role of LVV dynamics in aging and neurological disorders.

PMID:40835878 | PMC:PMC12368167 | DOI:10.1038/s41598-025-15842-8

Cogn Affect Behav Neurosci. 2025 Aug 20. doi: 10.3758/s13415-025-01328-7. Online ahead of print.

ABSTRACT

Impaired breathing-related interoceptive abilities have been associated with adverse outcomes, including higher levels of anxiety. However, brain connectivity patterns related to poor interoception, and how these may be modulated by anxiety, are poorly understood. This exploratory study investigated connectivity profiles associated with breathing-related interoceptive abilities in 65 volunteers who underwent ultrahigh-field (7 Tesla) "resting-state" magnetic resonance imaging (rs-fMRI), as well as completed a breathing-related interoceptive task and an anxiety questionnaire. The breathing task measured four aspects of interoceptive ability (sensitivity, decision bias, metacognitive bias, and insight), which served alongside anxiety to explain amygdala connectivity in the rs-fMRI data. We observed that connectivity between bilateral amygdala and insula cortex was linked to the level of confidence ascribed to respiratory-related interoceptive judgements (metacognitive bias), while left-lateralised connectivity between amygdala and insula cortex was associated with a worsened ability to detect inspiratory resistances (interoceptive sensitivity). Both reductions in confidence and sensitivity correlated weakly with heightened anxiety levels at a behavioural level. By contrast, the connectivity differences across levels of metacognitive bias and interoceptive sensitivity were not accounted for by anxiety. Our findings could suggest that, in the general population, connectivity between amygdala and insula cortex is linked to breathing-related interoceptive processes in a manner that is largely independent of anxiety.

PMID:40835808 | DOI:10.3758/s13415-025-01328-7

J Neurovirol. 2025 Aug 20. doi: 10.1007/s13365-025-01270-x. Online ahead of print.

ABSTRACT

Sleep disturbances are frequently reported in persons with HIV and have been associated with the use of certain integrase strand transfer inhibitors (INSTIs), such as dolutegravir. This exploratory study assessed changes in cerebral function parameters in individuals with insomnia switching INSTIs. Individuals with an insomnia severity index (ISI) above 8 and virologically suppressed on a dolutegravir-containing ART regimen (DTG-ART) were randomised 1:1 to either continue DTG-ART or switch to bictegravir/emtricitabine/tenofovir alafenamide (BIC-ART) for 120 days. Cerebral function parameters were measured longitudinally at baseline (D0) and day 120 (D120) and included: (1) patient-reported outcomes (PROs) assessing sleep, quality of life (QoL) and symptoms related to ART, (2) resting-state functional cerebral MRI (fMRI), examining functional connectivity networks previously associated with DTG use or sleep and (3) plasma soluble inflammatory biomarkers associated with neuroinflammation or HIV disease progression (Neopterin, CXCL10 and IL-6). Functional connectivity analyses were performed using Seed-Based Correlations (SBC), and correlations between connectivity changes, PRO measures and biomarker concentrations determined. Of 19 individuals (12 DTG-ART, 7 BIC-ART), median age was 55 years (range 28-83), all were male and 17 of white ethnicity. Over 120 days, improvements in sleep and QoL in those randomised to BIC-ART vs. DTG-ART were observed. Median change in Insomnia Severity Index (ISI) score - 9 (-14 to -2) vs. -1 (-10 to -4), p = 0.030, Epworth Sleepiness Scale (ESS) -3.0 (-6 to -1) vs. 2 (-3 to 6), p = 0.007 and Short Form-36 Physical Function (SF36-PF) -5 (-40 to 5) vs. 0 (-5 to 15), p = 0.026) for BIC- vs. DTG- ART, respectively. BIC-ART was also associated with increased functional connectivity in the Default Mode and Salience Networks (both p < 0.05), which correlated with improvements in PRO measures (ESS and SF36-PF, both p < 0.05). No significant changes in soluble biomarkers were observed. Individuals with insomnia switching to BIC-ART had improvements in self-reported sleep, QoL and resting state fMRI networks associated with sleep, when compared to those continued on DTG-ART.

PMID:40835806 | DOI:10.1007/s13365-025-01270-x

J Affect Disord. 2025 Aug 18;391:120074. doi: 10.1016/j.jad.2025.120074. Online ahead of print.

ABSTRACT

Suicide, which involves a decision-making process biased toward a lethal option that may result in the loss of one's own life, remains a major public health concern, particularly among young adults with major depressive disorder (MDD). This study investigates whether impaired decision-making in the context of loss distinguishes young adults with MDD and suicidal ideation (MDSI) from those without suicidal ideation (MDNSI) and healthy controls (HC), and explores the underlying neurocomputational mechanisms. A total of 110 young adults (23 MDSI, 31 MDNSI, and 56 HC) underwent resting-state functional magnetic resonance imaging (fMRI) and completed a two-armed bandit decision-making task designed to separate loss and reward contexts. Accuracy and computational parameters reflecting decision impulsivity were compared among groups using analysis of covariance. Logistic regression was performed to identify features predicting MDSI among MDD patients. Response time modeling was conducted to differentiate loss-related impulsivity from indecisiveness. Functional connectivity analyses focused on the ventral tegmental area (VTA) and habenula networks to identify alterations mediating loss-decision impulsivity in MDSI. MDSI patients uniquely exhibited premature, value-insensitive impulsive decisions in the loss context, distinguishing them from MDNSI patients independent of depression severity. These decision abnormalities were not attributable to indecisiveness. In contrast, reward-based decision impairments were shared across both MDD subgroups. Disruptions in resting-state functional connectivity within the VTA-orbitofrontal and habenula-default mode networks in MDSI fully mediated their loss-specific impulsivity. These findings highlight loss-specific decision impulsivity and associated neural dysconnectivity as potential early markers of suicide risk, offering novel insights into targeted intervention strategies.

PMID:40835190 | DOI:10.1016/j.jad.2025.120074

Brain Lang. 2025 Aug 19;270:105634. doi: 10.1016/j.bandl.2025.105634. Online ahead of print.

ABSTRACT

Previous studies have revealed the involvement of the bilateral ventral occipitotemporal cortex (vOT) in word reading, especially in Chinese character reading. However, the interhemispheric communication mechanisms of the bilateral vOT and how they work in Chinese character reading have not been fully investigated. Two experiments were conducted in this study to address those questions using resting-state and task-based fMRI. Experiment 1 revealed stronger interhemispheric resting-state functional connectivity (rsFC) in the posterior vOT subregion compared to the middle and anterior subregions and a significant positive correlation with Chinese reading efficiency in the posterior subregion. Experiment 2 further explored the effective connectivity in the Chinese rhythm and semantic judgment tasks using dynamic causal model analysis. Results showed significant interhemispheric intrinsic connections similar to those in the resting state in the posterior subregion and right-to-left modulatory connections in the middle and anterior subregions. In addition, stronger right-to-left modulatory connectivity in the anterior subregion was associated with better behavioral performance in the semantic judgment task. These convergent findings highlight the importance of interhemispheric communication of the bilateral vOT in Chinese character reading.

PMID:40834679 | DOI:10.1016/j.bandl.2025.105634

medRxiv [Preprint]. 2025 Aug 15:2025.08.13.25333244. doi: 10.1101/2025.08.13.25333244.

ABSTRACT

OBJECTIVE: Emotional dysfunction is a common consequence of severe traumatic brain injury (TBI), yet the mechanisms underlying these symptoms remain poorly understood. This study investigated whether brain network and autonomic mechanisms involved in emotional processing are abnormal in TBI.

METHODS: We conducted a cross-sectional study of chronic severe TBI (n=26) and healthy control participants (n=15). We analysed functional MRI (fMRI) data to assess brain processing of emotionally salient music (joyful and fearful stimuli; n=15 TBI, n=15 controls), and resting-state fMRI (rsfMRI) to measure the functional connectivity of relevant intrinsic brain networks (limbic, salience, and default mode networks; n=16 TBI, n=15 controls). We additionally measured the pupillary light reflect (PLR) to assess parasympathetic and sympathetic function (n=14 TBI, n=11 controls).

RESULTS: Individuals with severe TBI did not demonstrate the left insula activation elicited by joyful versus fearful musical stimuli seen in healthy controls. rsfMRI revealed decreased connectivity between the salience network, caudate and hippocampus in severe TBI compared to controls. Exploratory analyses identified reduced connectivity between default mode (bilateral medial prefrontal cortex) and limbic (bilateral amygdala) nodes in TBI compared to controls. PLR measurements revealed blunted dark-adaptation responses in individuals with severe TBI compared to controls (F(1,24)=27.4, p<0.001).

INTERPRETATION: Individuals with chronic severe TBI show reduced insula activation during emotional stimuli processing, resting connectivity abnormalities in salience, limbic and default mode networks, and evidence of sympathetic dysfunction. Brain network and autonomic alterations may be potential neural mechanisms of post-TBI emotional dysregulation.

PMID:40832426 | PMC:PMC12363739 | DOI:10.1101/2025.08.13.25333244

bioRxiv [Preprint]. 2025 Aug 11:2025.08.07.669216. doi: 10.1101/2025.08.07.669216.

ABSTRACT

The amplitude of low-frequency fluctuations (ALFF) and its related measure, fractional ALFF (fALFF), are widely used resting-state fMRI techniques for quantifying spontaneous neural activity within specific frequency bands. However, inconsistencies in the definition and implementation of ALFF have led to confusion in the field. In this study, we provide a mathematical clarification of ALFF and fALFF by introducing two variants: the arithmetic mean-defined ALFF/fALFF (amALFF/amfALFF) and the quadratic mean-defined ALFF/fALFF (qmALFF/qmfALFF). We examine the relationships between mean BOLD intensity (MBI), amALFF, and qmALFF across both subjects and voxels using two independent datasets mapped onto different brain templates. Additionally, we investigate the impact of z -scoring the original BOLD signal on ALFF and fALFF metrics. Our key findings include: (1) MBI is positively correlated with both amALFF and qmALFF, highlighting the need for normalization to subject-level means; (2) normalized qmALFF and qmfALFF are highly correlated with normalized amALFF and amfALFF, respectively, at both the subject and voxel levels; (3) z -scoring the BOLD signal does not affect amfALFF or qmfALFF, but it substantially alters amALFF and qmALFF. Based on these findings, we present a comprehensive flowchart of the (f)ALFF algorithm implemented in the temporal domain. The full procedure is implemented in R, and the corresponding script is available at: https://github.com/lejianhuang/ALFF .

PMID:40832169 | PMC:PMC12363799 | DOI:10.1101/2025.08.07.669216

Imaging Neurosci (Camb). 2025 Aug 14;3:IMAG.a.112. doi: 10.1162/IMAG.a.112. eCollection 2025.

ABSTRACT

Mental transformations, such as mental rotation, rely on motor representations and engage neural processes similarly to physical actions. Neuroimaging studies reveal that mental rotation activates the occipito-parietal cortex and motor-related areas, with differences based on whether stimuli are bodily or non-bodily. These findings emphasize the role of frontoparietal networks in mental rotation, similar to those used in motor planning. This study investigated whether resting-state functional connectivity of the left lateral prefrontal cortex (lPFC), a region linked to motor planning, and other functional brain networks predicts mental rotation performance. Fifty-nine healthy individuals underwent functional magnetic resonance imaging (fMRI) to capture resting-state blood oxygenation level dependent (BOLD) activity and completed mental rotation tasks using bodily (hands) and non-bodily (letters) stimuli. Performance in both mental rotation tasks exhibited the expected peak of difficulty with completely inverted stimuli, which require a mental transformation of 180 degrees. At the functional level, mental rotation error rates correlated with lPFC connectivity to the default mode network (DMN). However, this relationship was negative and much stronger for the hands task, indicating that lPFC-DMN interactions were associated with poorer mental rotation performance. These results indicate that effective mental rotation relies on the functional disconnection of the DMN from motor planning networks. The findings highlight the significance of studying resting-state functional connectivity to understand how brain networks contribute to cognitive functions and how their interactions can enhance or impair performance. This work advances our understanding of the neural mechanisms underlying mental rotation, emphasizing the interplay between motor cognition and resting-state dynamics.

PMID:40831904 | PMC:PMC12358948 | DOI:10.1162/IMAG.a.112