Most recent paper

Hum Brain Mapp. 2025 Oct 15;46(15):e70388. doi: 10.1002/hbm.70388.

ABSTRACT

It is increasingly established that the organization of the brain into functional resting-state networks allows efficient integration and processing of information. Functional hubs anchoring such networks are characterized by a high degree of communication, which relies on efficient utilization of glucose. Alzheimer's disease (AD) disrupts the balance between glucose metabolism and intrinsic functional connectivity (FC). We hypothesized that this critical coupling would also be weakened in frontotemporal dementia (FTD), particularly within the salience network, given its association with the disease. Towards this goal, behavioral variant FTD (bvFTD) patients (n = 21) and healthy participants (n = 18) underwent simultaneous FDG-PET and functional MRI imaging in a hybrid PET/MR system, with an additional cohort completing the MRI component only. PET images were converted into standardized uptake value ratios (SUVr), and local FC was quantified using regional homogeneity (ReHo) and fractional amplitude of low-frequency fluctuations (fALFF), two metrics that have been demonstrated to be related to FDG-PET uptake. The interplay between FC and glucose metabolism was investigated within the salience and default mode networks. The bvFTD group showed network-level functional breakdown and significantly weakened metabolism/FC coupling, especially in the dorsal anterior insula and posterior cingulate cortex. Importantly, reduced coupling in the posterior cingulate cortex was associated with cognitive and behavioral symptoms in patients. Though significant, the reduction in whole-brain metabolic/FC coupling in bvFTD was not as strong as reported previously for AD. These results highlight the vulnerability of functional hubs to neurodegenerative disease. Aberrant regional disruptions in the coupling between metabolism and neuronal activity may drive network-level dysfunction and contribute to functional impairments characteristic of the disease.

PMID:41128402 | DOI:10.1002/hbm.70388

Obesity (Silver Spring). 2025 Oct 23. doi: 10.1002/oby.70040. Online ahead of print.

ABSTRACT

OBJECTIVE: This study investigated the neural mechanisms underlying appetite dysregulation in female subjects with abdominal obesity (AO) by identifying functional connectivity (FC) and network-level differences between moderate appetite (MA) and strong appetite (SA) subtypes.

METHODS: A total of 60 women with AO (30 MA, 30 SA) and 30 healthy controls (HCs) underwent resting-state fMRI. Independent component analysis was used to identify and examine FC within and functional network connectivity (FNC) between key resting-state networks, including those involved in cognitive and visual processing. Network alterations and correlations with obesity-related indicators were evaluated.

RESULTS: Compared to HCs, both groups showed reduced FC in the default mode network (DMN) and visual network (VN), with SA additionally exhibiting decreased FC in the frontoparietal network (FPN) and lower angular gyrus FC than MA (p < 0.05, FDR-corrected). MA displayed increased DMN-left FPN (FPN_L) FNC (p < 0.001), while SA showed negative correlations between FC and BMI/appetite visual analog scale (VAS) scores in FPN and with body weight/BMI/appetite VAS in VN (p < 0.05). In HCs, DMN-FPN_L FNC positively correlated with BMI, a pattern that was not observed in MA.

CONCLUSION: Distinct brain network patterns characterize appetite subtypes in AO. SA showed more pronounced FC reductions in networks previously linked to self-regulation and visual processing, which may contribute to appetite dysregulation based on correlations with obesity indicators. In contrast, MA exhibited increased DMN-FPN_L FNC, potentially reflecting adaptive internetwork interactions.

PMID:41128009 | DOI:10.1002/oby.70040

Eur J Neurosci. 2025 Oct;62(8):e70276. doi: 10.1111/ejn.70276.

ABSTRACT

Resting-state fMRI (rsfMRI) is a widely used neuroimaging technique that measures spontaneous fluctuations in brain activity in the absence of specific external cognitive, motor, emotional, and sensory tasks or stimuli, based on the blood-oxygen-level-dependent (BOLD) signal. Functional connectivity (FC) is a popular rsfMRI analysis examining BOLD signal correlations between brain regions. Nevertheless, there are alternative analyses that provide different but collectively informative characteristics of the BOLD signal and, thus, brain activity. This narrative review aimed to provide a comprehensive conceptual, mathematical, and significance investigation of common rsfMRI analyses in addition to FC. To achieve this, a narrative review was conducted on studies using the most common rsfMRI analysis to investigate global and local brain activity. Five rsfMRI analyses were described, summarizing the common initial steps of rsfMRI data processing and explaining the main characteristics and how each metric is calculated. The rsfMRI analyses described are (1) FC, reflecting BOLD global connectivity; (2) the amplitude of low-frequency fluctuations (ALFF) and fractional ALFF (fALFF), representing the intensity of the BOLD signal; (3) regional homogeneity (ReHo), which reflects BOLD local connectivity; (4) Hurst exponent (H), depicting autocorrelation of the BOLD signal; and (5) entropy, depicting the BOLD signal predictability. As rsfMRI is a vital tool for exploring brain function, selecting an analysis that aligns with the research question is essential. This review offers an initial catalog of standard rsfMRI analyses, highlighting their key features, concepts, and considerations to support informed decisions by researchers and clinicians.

PMID:41127941 | DOI:10.1111/ejn.70276

Clin Ophthalmol. 2025 Oct 17;19:3837-3849. doi: 10.2147/OPTH.S543962. eCollection 2025.

ABSTRACT

BACKGROUND: High myopia (HM) is strongly linked to emotional disorders like anxiety and depression. While prior neuroimaging findings in HM are varied, the role of the amygdala-the brain's core emotion center-remains critically under-explored. Given evidence of limbic system changes in other ophthalmic disorders (eg, glaucoma), we investigated amygdala-specific functional connectivity (FC) in HM.

METHODS: We acquired resting-state fMRI data from 82 HM patients and 59 healthy controls (HCs). Using a seed-based approach with the bilateral amygdalae, whole-brain FC was compared between groups. A support vector machine (SVM) then evaluated the classification power of the identified FC alterations.

RESULTS: Compared to HCs, HM patients showed significantly increased FC between the amygdala and key regions in the visual, default mode, and executive control networks, including the calcarine fissure, precuneus, and middle frontal gyrus. SVM models achieved robust classification performance, with an area under the curve (AUC) up to 0.765.

CONCLUSION: This study provides the first report on amygdala-centric network reorganization in HM. These FC patterns show potential as neuroimaging biomarkers. Our findings offer preliminary evidence for a neural substrate underlying the emotional and cognitive dysregulation in HM, highlighting the need to address mental health in these patients.

PMID:41127888 | PMC:PMC12539358 | DOI:10.2147/OPTH.S543962

Brain Behav Immun Health. 2025 Sep 22;49:101111. doi: 10.1016/j.bbih.2025.101111. eCollection 2025 Nov.

ABSTRACT

INTRODUCTION: In NMDA-R encephalitis, which is typically accompanied by psychotic symptoms, conventional magnetic resonance imaging (MRI) is often normal, despite widespread alterations in functional connectivity. This is the first functional connectivity study in psychiatric patients with suspected autoimmune psychosis (AP) spectrum syndromes.

METHODS: Twenty-eight patients with suspected AP spectrum syndromes who were selected according to the concept of autoimmune psychiatric syndromes (APS) and 28 matched healthy controls (HCs) were examined with ultrafast functional MRI using magnetic resonance encephalography (MREG). Patients were positive for either well-characterized or novel central nervous system antibodies or well-characterized systemic antibodies with autoimmune brain involvement. MREG data were processed using "Analysis of Functional NeuroImages" (AFNI) with the "Functional And Tractographic Connectivity Analysis AFNI toolbox" to analyze connectivity across 170 regions, yielding an analysis of 5995 evaluable connectivities.

RESULTS: After correction for multiple testing, functional connectivity between the left middle cingulate/paracingulate gyri and the right insula (padj = 0.025) was significantly reduced in the patient group compared to HCs. Exploratory analyses revealed widespread global functional connectivity alterations in 226 of all connections (corresponding to 3.8 %). Notably, of these altered connections, 99 % showed reduced connectivity, while 1 % showed hyperconnectivity. The medial pulvinar of the left thalamus emerged as the most disconnected hub with altered connectivity to 33 other regions. Overall, 46 % of all analyzed regions exhibited at least one altered functional connectivity, with 19 % of hubs located in the cerebellum, 11 % in the frontal brain, and 9 % in the thalami. After correction for multiple comparisons, increased connectivity between the left insula and the left superior temporal gyrus correlated with the Beck Depression Inventory scores (padj = 0.043).

DISCUSSION: Patients with suspected AP spectrum syndromes exhibit altered insular functional connectivity associated with the severity of depressive symptoms. Broader changes identified via hypothesis-generating analyses highlighted major hubs in the cerebellum, frontal brain, and thalamus. These findings suggest that functional MRI may serve as an additional tool for detecting patients with AP/APS. Future studies in more homogeneous autoimmune-mediated patient groups may help delineate specific connectivity signatures in functional networks.

PMID:41127869 | PMC:PMC12538468 | DOI:10.1016/j.bbih.2025.101111

J Neurol. 2025 Oct 22;272(11):722. doi: 10.1007/s00415-025-13466-6.

ABSTRACT

Posterior cortical atrophy (PCA) is associated with visual attention, episodic memory, and working memory deficits, in addition to the typical visual dysfunction. The dorsal attention network (DAN) plays a critical role in modulating these functions. However, little is known about the relationship of DAN with other core networks (visual and default mode networks (DMN)) and its relationships to volume loss and memory function in PCA. Fifty-seven PCA patients were compared to 60 cognitively unimpaired (CU) individuals. Within-network connectivity was calculated within the frontal eye field (FEF) and intraparietal sulcus (IPS) and the entire DAN. Between-network connectivity was calculated with default mode network (DMN), frontoparietal, and visual networks. Models were fit to compare network connectivity between both groups and assess relationships between connectivity, gray matter volumes, and clinical test scores in PCA. PCA showed reduced within-network connectivity in DAN, specifically within the IPS, compared to CU individuals. The DAN, particularly the FEF, showed an increase in between-network connectivity with the frontoparietal network but no relationship to the DMN and visual networks. Lower DAN connectivity was associated with a trend for smaller volumes in the entire network and significantly lower scores on the auditory verbal learning test-recognition percent correct and Wechsler Memory Scale III-digit span backward in PCA patients. Our results showed disruptions in DAN connectivity, particularly in the posterior regions, which could be contributing to episodic and working memory deficits in PCA. Heightened connectivity between the DAN and the frontoparietal network suggests a compensatory mechanism to preserve attention function.

PMID:41125855 | DOI:10.1007/s00415-025-13466-6

J Biomed Phys Eng. 2025 Oct 1;15(5):467-478. doi: 10.31661/jbpe.v0i0.2306-1627. eCollection 2025 Oct.

ABSTRACT

BACKGROUND: Psychogenic Non-Epileptic Seizures (PNES), is a type of seizure that is caused by emotional factors. Symptoms of PNES are similar to epileptic seizures including disturbance in involuntary movement. Previous studies showed that neural activity altered in PNES detected through the resting-state functional Magnetic Resonance Imaging (rs-fMRI) thus this study was designed for a better understanding of PNES pathophysiology using the rs-fMRI technique.

OBJECTIVE: This study was conducted to examine dynamic Functional Connectivity (dFC) in the brain networks between PNES and healthy control subjects.

MATERIAL AND METHODS: In this experimental study, the rs-fMRI was collected from 16 PNES subjects and 16 healthy subjects. After surrogating data, the sliding window technique was used for dFC detection in nine brain networks which chosen from Stanford Findlab.

RESULTS: Our results indicate that there were no differences in the presence or absence of dFC between the PNES group and the control group in the ventral Default Mode Network (vDMN), Language Network (LN), and Visuospatial Network (VSN). However, dFC was elevated in the PNES group in comparison to the normal control group within the Sensorimotor Network (SMN), Posterior Salience Network (PSN), and Anterior Salience Network (ASN).

CONCLUSION: The findings suggest that dFC analyses hold significant potential for uncovering abnormal patterns of brain network connections in the PNES. This offers a promising finding for a better comprehension of PNES.

PMID:41122330 | PMC:PMC12536907 | DOI:10.31661/jbpe.v0i0.2306-1627

Brain Behav. 2025 Oct;15(10):e70921. doi: 10.1002/brb3.70921.

ABSTRACT

INTRODUCTION: The brain network correlates of personality traits in major depressive disorder (MDD) have not yet been investigated. Furthermore, it is still unclear whether personality traits relate to the depressive episode.

METHODS: This study assessed network properties, depression severity, and personality traits in patients with MDD (n = 25) compared with age- and sex-matched healthy controls (n = 22). We performed TCI questionnaire which assesses novelty seeking (NS, an urge to explore new experiences with heightened emotional responses), harm avoidance (HA, the tendency to hold back when faced with unpleasant situations), reward dependence (RD, a tendency to seek and value rewards rooted in social recognition), persistence (P, an individual's ability to remain focused and driven toward goals despite encountering challenges), self-directness (SD, an expression of willpower that enables individuals to adapt their behavior to situational demands while remaining focused on their personal goals and values), cooperativeness (C, a behavioral trait reflecting a person's general approach to others; ranging from friendly and cooperative to hostile), and self-transcendence (ST, lessening of self-centeredness, allowing for expanded empathy) traits of participants.

RESULTS: MDD patients with distinctive character traits exhibited significant differences in terms of depression diagnosis and severity of Hamilton Depression Rating Scale scores compared to the controls. The MDD patients also exhibited reduced resting-state network activity between the posterior default mode network, right putamen, and right frontal pole, while SD was significantly less frequently diagnosed in MDD patients. In evaluating the network correlates, differences in the SD traits were significantly associated with critical brain network alterations that were not evident in other traits.

DISCUSSION: To the best of our knowledge, this is the first study to provide preliminary evidence of an abnormal connectome in the SD trait in MDD, thus providing convincing evidence for personalized antidepressant treatment strategies in MDD. A small sample size and our depression group being not drug-naive were our limitation for this research.

PMID:41116659 | DOI:10.1002/brb3.70921

Sci Rep. 2025 Oct 20;15(1):36500. doi: 10.1038/s41598-025-21102-6.

ABSTRACT

Multiple sclerosis (MS) often leads to mobility impairments, yet the neural mechanisms underlying these deficits remain poorly understood. This study examined whether resting-state functional connectivity (rs-FC) differs between people with MS (PwMS) and healthy controls in relation to spatiotemporal mobility performance. We hypothesized that group differences within the default mode (DMN), frontoparietal (FPN), somatomotor (SN), and visual (VIS) networks would be associated with gait and turning metrics. Twenty-nine PwMS and 28 matched controls completed a two-minute walk test, 180° walking turns, and 360° in-place turns at natural and fast speeds. fMRI data were analyzed using multivariate pattern analysis (MVPA) and post-hoc seed-to-voxel analyses for gait speed, cadence, double support time, stride length, turn duration, peak velocity, and turn angle. PwMS exhibited slower gait speed, shorter stride length, and impaired 360° turning, but no group differences in cadence, double support, or 180° turn metrics. MVPA revealed rs-FC differences across DMN, FPN, SN, and VIS networks. While rs-FC differences were evident for walking metrics, within-group associations were not significant. In contrast, 360° turn angle showed distinct within-group rs-FC associations, particularly involving VAN and DAN networks. These findings highlight turning as a sensitive task for capturing functional neural differences in MS.

PMID:41115952 | PMC:PMC12537983 | DOI:10.1038/s41598-025-21102-6

Psychother Psychosom. 2025 Oct 20:1-32. doi: 10.1159/000548961. Online ahead of print.

ABSTRACT

INTRODUCTION: Obsessive-compulsive disorder (OCD) is a chronic condition where many patients remain symptomatic despite first-line treatments such as Cognitive Behavioural Therapy and selective serotonin reuptake inhibitors. This randomised controlled trial evaluated Mindfulness-Based Cognitive Therapy (MBCT) efficacy as an augmentation strategy and its impact on brain functional connectivity.

METHODS: Sixty-eight participants with moderately symptomatic OCD were randomised into MBCT or Treatment as Usual (TAU). Clinical outcomes were evaluated using the Yale-Brown Obsessive-Compulsive Scale (Y-BOCS) and the Obsessive-Compulsive Inventory, alongside other relevant secondary outcomes. Data were analysed using repeated measures ANOVA to assess time * group effects. Neuroimaging functional measures (resting-state network-connectivity), were collected before and after the intervention and analysed using independent component analysis.

RESULTS: Primary outcome: MBCT significantly reduced OCD symptoms compared to TAU (31.73% vs. 8.07% Y-BOCS reduction).

SECONDARY OUTCOMES: MBCT group also experienced reductions in depressive symptoms, rumination, perceived stress and quality of life. No significant post-treatment changes were observed in resting-state connectivity. However, baseline connectivity demonstrated significant predictive value, with lower connectivity in pre-selected networks of interest, including the fronto-striatal, salience, and default-mode networks, associated with greater reductions in Y-BOCS scores.

CONCLUSION: MBCT is an effective strategy for individuals with moderately symptomatic OCD who continue to experience symptoms despite prior gold-standard treatments. While no post-treatment changes in brain functional connectivity were observed, baseline connectivity patterns predicted symptom reduction, suggesting a neural basis for MBCT response. Trial name: Mindfulness-Based Cognitive Therapy: Efficacy and fMRI-based Response Predictors in a Group of OCD Patients. ID number: NCT03128749.

PMID:41115123 | DOI:10.1159/000548961

Imaging Neurosci (Camb). 2025 Oct 15;3:IMAG.a.936. doi: 10.1162/IMAG.a.936. eCollection 2025.

ABSTRACT

Ketamine produces fast-acting antidepressant effects in treatment-resistant depression (TRD). Prior studies have shown altered functional dynamics between brain networks in major depression. We thus sought to determine whether functional brain network dynamics are modulated by ketamine therapy in TRD. Participants with TRD (n = 58, mean age = 40.7 years, female = 48.3%) completed resting-state fMRI scans and clinical assessments (mood and rumination) at baseline and 24 h after receiving 4 ketamine infusions (0.5 mg/kg) over 2 weeks. Healthy controls (HC) (n = 56, mean age = 32.8 years, female = 57.1%) received the same assessments at baseline and after 2 weeks in a subsample without treatment. A co-activation pattern (CAP) analysis identified recurring patterns of brain activity across all subjects using k-means clustering. Statistical analyses compared CAP metrics including the fraction of time (FT) spent in a brain state, and the transition probability (TP) from one state to another over time and associations with clinical improvement. Follow-up analyses compared HC and TRD at baseline. Six brain state clusters were identified, including patterns resembling the salience (SN), central executive (CEN), visual (VN), default mode (DMN), and somatomotor (SMN) networks. Following ketamine treatment, TRD patients showed decreased FT for the VN (p = 7.4E-04) and increased FT for the CEN state (p = 1.9E-03). For TP metrics, SN-CEN increased (p = 5.8E-04) and SN-VN decreased (p = 3.6E-03). Decreased FT for the SN associated with improved rumination (p = 1.9E-03). At baseline, lower FT for CEN (p = 5.70E-04) and TP for SN-CEN (p = 0.016) and higher TP for SN-VN (p = 2.60E-03) distinguished TRD from HCs. CAP metrics remained stable over time in a subsample of HCs (n = 18). These findings suggest ketamine modulates brain network dynamics between SN, CEN, and VN in TRD, which may normalize dynamic patterns seen in TRD at baseline toward patterns seen in controls. Changes in SN state dynamics may correspond to improvements in ruminative symptoms following ketamine therapy.

PMID:41113939 | PMC:PMC12529346 | DOI:10.1162/IMAG.a.936

Nat Sci Sleep. 2025 Oct 14;17:2681-2698. doi: 10.2147/NSS.S550127. eCollection 2025.

ABSTRACT

PURPOSE: Poor sleep quality is prevalent across the population and may significantly impact both physical and mental health. However, our understanding of the complex mechanisms underlying poor sleep quality is still incomplete, particularly regarding the various contributing factors. To address this, we utilized a data-driven causal discovery analysis (CDA) approach to explore causal pathways of sleep quality.

PATIENTS AND METHODS: We relied on a large sample of healthy young adults from the Human Connectome Project (HCP; n = 1206 [54% female, 56% unmarried/non-cohabiting]) to explore causal pathways of sleep quality. We first used exploratory factor analysis to cluster 122 broad phenotypic variables into 21 factors and computed the functional connectivity of 13 resting-state brain networks. Then, using Greedy Fast Causal Inference (GFCI), we simultaneously integrated the obtained phenotypic factors, brain network connectivity, and sleep quality into the causal discovery analysis and ultimately constructed a causal model.

RESULTS: The model proposes a hierarchical structure with causal effects propagating through complex interactions across multiple domains, ultimately linked to changes in sleep quality. Our causal model identified three phenotypic factors (negative affect, somaticism, and delay discounting) as directly linked to sleep quality. In addition, we examined causal models of sleep quality across gender (male and female) and relationship status (unmarried/non-cohabiting and married/cohabiting) and found some demographic-specific pathways.

CONCLUSION: Our data-driven model reveals complex mechanisms by which factors from different domains influence sleep quality and highlights several key factors that influence sleep quality, which may have important implications for the development of sleep theories and the improvement of sleep quality.

PMID:41113905 | PMC:PMC12535245 | DOI:10.2147/NSS.S550127

World J Psychiatry. 2025 Oct 19;15(10):108861. doi: 10.5498/wjp.v15.i10.108861. eCollection 2025 Oct 19.

ABSTRACT

BACKGROUND: Sepsis is a life-threatening condition defined by organ dysfunction, triggered by a dysregulated host response to infection. there is limited published literature combining cognitive impairment with topological property alterations in brain networks in sepsis survivors. Therefore, we employed graph theory and Granger causality analysis (GCA) methods to analyze resting-state functional magnetic resonance imaging (rs-fMRI) data, aiming to explore the topological alterations in the brain networks of intensive care unit (ICU) sepsis survivors. Using correlation analysis, the interplay between topological property alterations and cognitive impairment was also investigated.

AIM: To explore the topological alterations of the brain networks of sepsis survivors and their correlation with cognitive impairment.

METHODS: Sixteen sepsis survivors and nineteen healthy controls from the community were recruited. Within one month after discharge, neurocognitive tests were administered to assess cognitive performance. Rs-fMRI was acquired and the topological properties of brain networks were measured based on graph theory approaches. GCA was conducted to quantify effective connectivity (EC) between brain regions showing positive topological alterations and other regions in the brain. The correlations between topological properties and cognitive were analyzed.

RESULTS: Sepsis survivors exhibited significant cognitive impairment. At the global level, sepsis survivors showed lower normalized clustering coefficient (γ) and small-worldness (σ) than healthy controls. At the local level, degree centrality (DC) and nodal efficiency (NE) decreased in the right orbital part of inferior frontal gyrus (ORBinf.R), NE decreased in the left temporal pole of superior temporal gyrus (TPOsup.L) whereas DC and NE increased in the right cerebellum Crus 2 (CRBLCrus2.R). Regarding directional connection alterations, EC from left cerebellum 6 (CRBL6.L) to ORBinf.R and EC from TPOsup.L to right cerebellum 1 (CRBLCrus1.R) decreased, whereas EC from right lingual gyrus (LING.R) to TPOsup.L increased. The implementation of correlation analysis revealed a negative correlation between DC in CRBLCrus2.R and both Mini-mental state examination (r = -0.572, P = 0.041) and Montreal cognitive assessment (MoCA) scores (r = -0.629, P = 0.021) at the local level. In the CRBLCrus2.R cohort, a negative correlation was identified between NE and MoCA scores, with a statistically significant result of r = -0.633 and P = 0.020.

CONCLUSION: Frontal, temporal and cerebellar topological property alterations are possibly associated with cognitive impairment of ICU sepsis survivors and may serve as biomarkers for early diagnosis.

PMID:41112595 | PMC:PMC12531965 | DOI:10.5498/wjp.v15.i10.108861

Neuroimage. 2025 Oct 17:121533. doi: 10.1016/j.neuroimage.2025.121533. Online ahead of print.

ABSTRACT

BACKGROUND: Transcranial magnetic stimulation (TMS) activates neural circuits in targeted and connected areas, yet it remains unclear how "TMS-dose" relates to local or network responses, particularly outside the primary motor hand area.

METHODS: Eighteen healthy volunteers received TMS pulse-pairs (pp-TMS) of the left dorsal premotor cortex (PMd) during functional magnetic resonance imaging (fMRI) at 3 Tesla, using an inter-pulse interval of 33 ms. We mapped stimulus-response profiles across the premotor-motor network, applying pp-TMS as slow-frequency trains of pulse-pairs (i.e., blocked design) or single pulse-pairs (i.e., event-related design). Pulse-pairs were delivered at intensities of 65, 80, 95, or 110% of resting motor threshold (RMT).

RESULTS: Pooling the effects of pp-TMS across intensities revealed that both, single pulse-pairs and pp-TMS trains produced a bilateral reduction in regional activity within the targeted dorsal premotor-motor network. Dose-dependent analysis revealed a non-linear dose-response pattern, with maximal suppression of left and right PMd activity at 80% of RMT. The event-related and blocked fMRI design yielded similar results with net suppressive effects being more consistent during trains and dose-response dependencies being more consistent in response to single pulse-pairs. Auditory co-stimulation triggered bilateral increases in the auditory cortices.

CONCLUSION: Focal ppTMS may exert a net suppressive effect on neural activity within the targeted network. Network effects may follow a non-linear response pattern and may peak at relatively low stimulation intensities. These findings highlight the value of interleaved TMS-fMRI in elucidating the impact of stimulation parameters on regional and network-level brain dynamics.

PMID:41110651 | DOI:10.1016/j.neuroimage.2025.121533

Neuroimage. 2025 Oct 17:121544. doi: 10.1016/j.neuroimage.2025.121544. Online ahead of print.

ABSTRACT

AIM: Type 2 diabetes mellitus (T2DM) is a metabolic disorder associated with cognitive decline. This study investigated the interplay between altered effective connectivity (EC) in the Papez circuit, regional atrophy, and choroid plexus volume (CPV) in relation to T2DM-related cognitive deficits.

METHODS: Eighty T2DM patients and 75 healthy controls underwent multimodal MRI. Resting-state fMRI data were analyzed using spectral dynamic causal modeling (spDCM) to quantify EC between Papez circuit regions. Structural 3D-T1 images were processed to measure regional volumes and the CPV, which might serve as a proxy for glymphatic activity. Participants underwent standardized cognitive assessments to evaluate neurocognitive function.

RESULTS: T2DM patients exhibited significant EC reductions in three Papez circuit pathways: from the hippocampus (HPC) to the mammillary body (MB) (p = 0.001), from the anterior thalamic nucleus (AT) to the anterior cingulate cortex (ACC) (p = 0.005), and from the entorhinal cortex (ERC) to the HPC (p < 0.001). Structural atrophy was observed in the left hippocampus (p = 0.001) and left thalamic (p = 0.006), accompanied by CPV enlargement (p = 0.027). Notably, the HPC to MB connectivity was partially mediated by the CPV, with a significant indirect effect of 0.1456 (95% CI: 0.0335, 0.2665). The Complex Figure Test delay (CFT-delay) score was positively correlated with the reduced EC in Papez circuit pathways; however, there was a negative correlation between the CFT-delay score and the CPV (r = -0.344, p = 0.002). Furthermore, reduced EC in the Papez circuit exhibited a negative correlation with the Homeostatic Model Assessment for Insulin Resistance (HOMA-IR).

CONCLUSION: Our study highlights the complex interplay between Papez circuit functional disruptions, structural atrophy within the circuit, as well as the CPV alterations in T2DM-related cognitive decline. These findings offer potential biomarkers for early detection of cognitive impairments and novel therapeutic targets for intervention in T2DM.

PMID:41110647 | DOI:10.1016/j.neuroimage.2025.121544

Psychiatry Res Neuroimaging. 2025 Oct 10;354:112077. doi: 10.1016/j.pscychresns.2025.112077. Online ahead of print.

ABSTRACT

BACKGROUND: Previous studies have demonstrated the efficacy of transcranial magnetic stimulation (TMS) on treating nicotine addiction. However, the underlying mechanisms remain unclear. This study aims to investigate the effects of repetitive TMS (rTMS) on nicotine addiction using resting-state functional magnetic resonance imaging (rs-fMRI).

MATERIALS AND METHODS: In this study, adult male participants with nicotine addiction were prospectively recruited, and assigned to either the rTMS group (n = 19) or Sham group (n = 12). Two groups underwent ten sessions of either real or sham rTMS treatment over a two-week period. Clinical assessments related to smoking craving and rs-fMRI scans were conducted before and after treatment. The regional homogeneity (ReHo) was utilized to explore differences in local neural synchronization between the rTMS and Sham groups.

RESULTS: After treatment, the smoking cravings were significantly reduced in two groups. Significant group-by-time interaction effects were observed in the left orbital part of the inferior frontal gyrus, left middle frontal gyrus (MFG), and right angular gyrus (AG). Post-hoc analyses revealed that, compared with pre-treatment, the rTMS group exhibited increased ReHo values after treatment in these areas, while the Sham group exhibited decreased values. Furthermore, within the rTMS group, post-treatment ReHo values in the left MFG were negatively correlated with post-treatment short Tobacco Craving Questionnaire (sTCQ)-Impulse scores. Similarly, the changes in ReHo values of the left MFG from pre- to post-treatment within the rTMS group were negatively correlated with changes in sTCQ-Impulse scores.

CONCLUSION: Our findings demonstrated that rTMS treatment may improve nicotine-related dependence by modulating local neural synchronization in the prefrontal cortex (PFC) and AG. Furthermore, ReHo values in the left MFG may serve as a promising neuroimaging biomarker for predicting nicotine addiction cessation.

PMID:41110183 | DOI:10.1016/j.pscychresns.2025.112077

Psychiatry Res Neuroimaging. 2025 Oct 16;354:112078. doi: 10.1016/j.pscychresns.2025.112078. Online ahead of print.

ABSTRACT

BACKGROUND: We tested the hypothesis that Schizophrenia (SCZ) involves a systematic breakdown in brain network organization across different levels of graph-theoretical hierarchy.

METHODS: Using resting-state fMRI from 43 SCZ patients and 63 matched healthy controls, we implemented an analytical multi-level framework. This integrated: global graph theory metrics to assess overall network topology; macronetwork metrics to measure functional specialization of large-scale systems; network-based statistics (NBS) to identify specific, altered pathways at the local level; a multigraph model to visualize hub reorganization between networks.

RESULTS: We revealed a coherent pattern of multi-level dysfunction. Globally, SCZ networks showed increased local clustering and connection density, indicating a shift toward a less efficient, overly segregated architecture. At the macroscale, sensory and salience networks displayed elevated local connectivity, while higher-order cognitive networks (e.g., DMN, DAN) showed reduced specialization and increased cross-talk. Locally, NBS identified a core subnetwork of weakened connectivity within temporal-orbitofrontal-cingulate circuits. The multigraph model synthesized these findings, showing a widespread reduction in the integrative role of key cognitive hubs.

CONCLUSIONS: Our findings establish a model of SCZ as a disorder of disintegrated brain network hierarchy, where disruptions at the level of local circuits and functional specializations collectively lead to global topological inefficiency.

PMID:41110182 | DOI:10.1016/j.pscychresns.2025.112078

Biol Psychiatry Cogn Neurosci Neuroimaging. 2025 Oct 16:S2451-9022(25)00303-9. doi: 10.1016/j.bpsc.2025.10.002. Online ahead of print.

ABSTRACT

BACKGROUND: It is unknown whether brain-based predictive models derived from sleep features are useful for the clinical diagnosis of Major Depressive Disorder (MDD).

METHODS: Using resting-state fMRI data from ABCD (Curated Data Release 3.0), we trained a connectome-based predictive model (CPM) on 35,778 pairwise connections (Pearson's r) from 2349 (234 participants with at least 1 psychiatric disorder, 2112 controls) participants aged 11-12 to predict sleep duration (measured from FitBit). Linear regression models were used to compare the predicted values from these CPMs with self-reported sleep duration and diagnostic group status in an independent cohort of 78 participants (57 MDD, 21 controls) aged 14-18.

RESULTS: The ABCD-based CPM predicted self-reported sleep duration in the independent cohort of MDD participants (partial r=0.332, p=0.009). Even though self-reported sleep duration did not significantly differ between diagnostic groups (t=0.13, p=0.90), the ABCD-based CPM successfully distinguished between diagnostic groups (partial r=0.334, p<0.001), and CPM-predicted sleep durations correlated with depression symptom severity (partial r=0.294, p<0.001). These diagnostic group differences were driven primarily by patterns of hypoconnectivity between various resting-state networks (including the default mode, frontoparietal, motor, subcortical, and visual associative networks).

CONCLUSIONS: CPMs trained to predict objective sleep duration are robust and generalizable. Intrinsic functional connectivity differences between clinically depressed and psychiatrically healthy adolescents are detectible by CPMs optimized for sleep prediction, underscoring the shared neural bases between sleep health and depression. Future work will test whether sleep-based CPMs are predictive of clinical course and if they generalize to other disorders beyond depression.

PMID:41109569 | DOI:10.1016/j.bpsc.2025.10.002

Dev Cogn Neurosci. 2025 Oct 12;76:101629. doi: 10.1016/j.dcn.2025.101629. Online ahead of print.

ABSTRACT

Preterm birth is a known risk factor for neurodevelopmental disabilities, but early neurobehavioral assessments and structural imaging often fail to predict long-term outcomes. This limitation underscores the need for alternative biomarkers that reflect early brain organization. Resting-state functional connectivity offers a powerful tool to track functional brain organization by characterizing resting-state networks (RSNs), potentially offering more sensitive biomarkers. However, most fMRI studies in infant populations use group-level analyses that average subject-specific data across several weeks of development, reducing sensitivity to subtle, time-sensitive deviations from typical brain trajectories, particularly in higher-order association networks. Using a recently introduced precision mapping approach, we estimated individual resting-state networks (RSNs) in a large cohort of term and preterm neonates from the developing Human Connectome Project. RSN connectivity strength increased linearly with age at scan, with primary sensory networks maturing earlier and higher-order association networks, including the default mode network (DMN), showing more gradual but pronounced changes, evolving from an immature organization in preterm infants to a more adult-like pattern in term-born infants. Longitudinal data from a subset of preterm infants confirmed ongoing network development shortly after birth. Despite this maturation, preterm infants did not reach the connectivity levels of term-born infants by term-equivalent age. These findings demonstrate that individualized RSN mapping captures heterogeneous developmental trajectories in the neonatal brain and highlights higher-order association networks, particularly the DMN, as promising early markers for monitoring neurodevelopmental outcomes in neonates.

PMID:41109198 | DOI:10.1016/j.dcn.2025.101629

Mov Disord Clin Pract. 2025 Oct 18. doi: 10.1002/mdc3.70391. Online ahead of print.

ABSTRACT

BACKGROUND: Apathy is a common early symptom of Parkinson's disease (PD), often co-occurring with cognitive decline and associated with fronto-striatal and mesocortico-limbic dysfunctions. Discrepancies between self- and caregiver-reported apathy have been preliminarily associated with cognitive impairments affecting patients' awareness and self-report accuracy.

OBJECTIVES: This study investigates discrepancies between PD patient- and informant-reported apathy in relation to the cognitive status (unimpaired-CU vs. impaired-CI), and explores neural correlates of apathy using magnetic resonance imaging (MRI).

METHODS: Apathy was assessed in 23 PD participants using self-report (AES-S) and informant (AES-I) versions of the Italian Apathy Evaluation Scale. Discrepancy scores (ΔAES) were compared between groups and correlated with cognitive performance. Resting-state fMRI examined associations between AES indices and connectivity from the bilateral nucleus accumbens, while whole-brain structural analyses assessed associations with gray matter (GM) volume.

RESULTS: PD-CI participants showed higher ΔAES, underestimating their apathy compared to PD-CU. ΔAES values correlated with attentional and visuospatial functioning. Higher AES-I scores were associated with hyperconnectivity between right nucleus accumbens, paracingulate, and medial frontal cortices. Structural analyses revealed associations between both AES-I and ΔAES values and GM volume in the cingulate gyrus.

DISCUSSION: These findings highlight the impact of cognitive dysfunction on apathy evaluation in PD, emphasizing the importance of caregiver perspective. Neuroimaging results further validated caregiver ratings, showing an association between fronto-striatal network changes and apathy. Further research is needed to clarify the role of such discrepancy in apathy assessment in predicting disease progression.

PMID:41108660 | DOI:10.1002/mdc3.70391