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Brain Behav. 2025 Oct;15(10):e70899. doi: 10.1002/brb3.70899.

ABSTRACT

BACKGROUND: To explore the long-term efficacy and resting-state functional magnetic resonance imaging (fMRI) changes of lateral habenula nucleus (LHb) deep brain stimulation (DBS; LHb-DBS) for treatment-resistant bipolar disorder (TRBD).

METHODS: An 18-year-old woman with TRBD received bilateral LHb-DBS. We assessed changes in Hamilton Depression Scale-17 (HDRS-17), Bech-Rafaelsen Melancholia Scale (BRMS), Hamilton Anxiety Scale (HAMA), and Pittsburgh Sleep Quality Scale (PSQI) scores from preoperative baseline to postoperative continuous 24-month follow-up. Brain activity and resting-state functional connectivity (rsFC) were examined off-stimulation at 0.6 and 15 months post-LHb-DBS. Overall improvement and adverse events were analyzed.

RESULTS: Continuous 24-month follow-up showed average improvements from baseline of 65.33%, 54.90%, 63.33%, and 48.72% for HDRS-17, BRMS, HAMA, and PSQI scores, respectively. At the final follow-up, improvement was 96.00%, 88.24%, 84.85%, and 69.23%, respectively. Resting-state fMRI results revealed an increase in fractional amplitude of low-frequency fluctuations (fALFF) within the putamen, ventral tegmental area (VTA), and substantia nigra pars compacta (SNc) over 15 months of continuous bilateral LHb stimulation when DBS was off. From baseline to 15 months, fALFF in the putamen, VTA, and SNc increased by 1.68%, 6.36%, and 1.10%, respectively. Consistently reduction in rsFC was observed between the left nucleus accumbens (NAcc) and left hippocampus. Over the 15 months of continuous stimulation, rsFC decreased by 72% from baseline.

CONCLUSIONS: Long-term LHb-DBS can control symptoms and improve the quality of life in patients with TRBD. This may be attributed to an increase in fALFF in the putamen, VTA, and SNc, and a reduction in rsFC between the left NAcc and left hippocampus.

PMID:41030103 | PMC:PMC12484712 | DOI:10.1002/brb3.70899

BMC Psychiatry. 2025 Sep 30;25(1):895. doi: 10.1186/s12888-025-07331-z.

ABSTRACT

BACKGROUND: Non-suicidal self-injury (NSSI) addiction is prevalent among adolescents, but its underlying neural mechanisms remain unclear. This study aims to investigate the neural activity and functional connectivity characteristics associated with NSSI addiction using resting-state functional magnetic resonance imaging (rs-fMRI).

METHODS: A prospective collection of 62 adolescents was completed for this study, including 33 adolescents with self-injury behaviors and 29 age-, gender-, and education-matched healthy controls. The addiction component of the Ottawa Self-Injury Inventory (OSI) was used to assess the degree of NSSI addiction. Amplitude of low-frequency fluctuation (ALFF) analysis was employed to detect changes in local neural activity. Differential brain regions were considered regions of interests (ROIs). Whole-brain functional connectivity (FC) analysis based on ALFF was used to further explore potential changes in functional connections between ROIs and other brain areas in the NSSI group, and to analyze the relationship between these neural changes and addiction characteristics.

RESULTS: ALFF analysis revealed decreased ALFF values in the bilateral hippocampus and increased ALFF values in the right supplementary motor area of NSSI adolescents compared to healthy controls. Significantly reduced FC values was observed between the left hippocampus and the left precuneus, right middle temporal gyrus, and right inferior temporal gyrus, and between the right hippocampus and the right middle temporal gyrus. Additionally, increased FC values was observed between the left hippocampus and the left thalamus. Furthermore, ALFF values in the bilateral hippocampus were negatively correlated with the total score of addiction characteristics in NSSI adolescents.

CONCLUSIONS: This study highlights reduced local neural activity and functional connectivity in the bilateral hippocampus of NSSI adolescents, and demonstrates that these alterations are associated with heightened addictive features in self-injuring individuals.

TRIAL REGISTRATION: A study of positive psychological group interventions in adolescents with non-suicidal self-injury (registration date: 03/01/2024; registration number: ChiCTR2400079412).

PMID:41029257 | PMC:PMC12486709 | DOI:10.1186/s12888-025-07331-z

Addict Behav. 2025 Sep 12;172:108494. doi: 10.1016/j.addbeh.2025.108494. Online ahead of print.

ABSTRACT

BACKGROUND: Patients with internet gaming disorder (IGD) suffer from an imbalance of over-integrated and weakly dissociated functional brain networks. Approach bias modification training (ApBMt) has been used to correct patients' automatic approach biases to addictive stimuli; however, research exploring changes in brain network topology is limited.

METHODS: Seventy subjects were randomly assigned to the approach-avoidance task (AAT) group or the sham-AAT group, and 57 subjects (AAT, 30; sham-AAT, 27) completed the entire procedure, which included pretests, AAT/sham-AAT interventions, and posttests. Behavioral and resting-state fMRI data were collected before and after the tests. This study aimed to investigate the effects of ApBMt on topological changes in resting functional brain networks in patients with IGD and explore the relationship between these network changes and behavioral indicators of addiction severity.

RESULTS: Repeated-measures ANOVA of the behavioral data showed that the AAT group had significant score reductions after ApBMt. Imaging data revealed significant decreases in brain network over-integration and increases in segregation of the fronto-parietal network (FPN) and the cingulo-opercular network (CON). Additionally, a positive correlation was found between the post-pre difference in DSM-5 scores and the post-pre difference in nodal efficiency (Ne) in the anterior prefrontal cortex (aPFC).

CONCLUSIONS: The findings of this study demonstrate that ApBMt effectively reduces the severity of IGD, along with associated changes in brain network topology, such as enhanced segregation and decreased over-integration. However, it is important to highlight that the neurobiological changes observed are correlated with the reduction in IGD severity, but causality cannot be established. Further research is necessary to better understand the clinical potential of ApBMt in treating IGD, either as a stand-alone intervention or in combination with other therapeutic approaches.

PMID:41027144 | DOI:10.1016/j.addbeh.2025.108494

J Sick Cell Dis. 2025 Sep 18;2(1):yoaf031. doi: 10.1093/jscdis/yoaf031. eCollection 2025.

ABSTRACT

BACKGROUND: Adults with sickle cell disease (SCD) often experience cognitive deficits and chronic pain, but the cerebral mechanisms underlying these symptoms remain unclear. Elevated cerebral blood flow (CBF) is a compensatory response to anemia, yet its impact on brain function and perception is not well understood.

OBJECTIVE: To examine alterations in cerebral perfusion and resting-state brain function in adults with SCD and their associations with cognition and pain sensitivity.

METHODS: Seven adults with SCD and 3 healthy controls underwent arterial spin labeling (ASL) and resting-state functional MRI (rs-fMRI). Metrics included global/regional CBF, resting-state functional connectivity (rsFC), and amplitude of low-frequency fluctuations (ALFF). Participants completed NIH Toolbox fluid cognition tests and the Pain Sensitivity Questionnaire (PSQ).

RESULTS: SCD patients exhibited significantly higher global CBF (72.1 vs. 47.2 mL/100g/min; P = .04), reduced cortical zALFF (P = .0013), and elevated white-matter zALFF (P = .0023). They also showed resting-state network hyperconnectivity, with diminished anti-correlations between the default mode and salience networks. SCD participants scored lower on processing speed (P = .02) and reported higher pain sensitivity (PSQ total, P = .0040). Higher CBF was associated with slower cognitive performance but not directly with pain sensitivity. Exploratory mediation models suggested that altered brain activity may partially mediate this relationship.

CONCLUSIONS: Adults with SCD demonstrate cerebral hyperperfusion, disrupted functional connectivity, and altered spontaneous brain activity, which may contribute to cognitive slowing and heightened pain sensitivity. These findings highlight the need for further research into brain-targeted therapies in SCD.

PMID:41024864 | PMC:PMC12476913 | DOI:10.1093/jscdis/yoaf031

Nat Commun. 2025 Sep 29;16(1):8614. doi: 10.1038/s41467-025-63661-2.

ABSTRACT

In-scanner head motion introduces systematic bias to resting-state fMRI functional connectivity (FC) not completely removed by denoising algorithms. Researchers studying traits associated with motion (e.g. psychiatric disorders) need to know if their trait-FC relationships are impacted by residual motion to avoid reporting false positive results. We devised Split Half Analysis of Motion Associated Networks (SHAMAN) to assign a motion impact score to specific trait-FC relationships. SHAMAN distinguishes between motion causing overestimation or underestimation of trait-FC effects. We assessed 45 traits from n = 7270 participants in the Adolescent Brain Cognitive Development (ABCD) Study. After standard denoising with ABCD-BIDS and without motion censoring, 42% (19/45) of traits had significant (p < 0.05) motion overestimation scores and 38% (17/45) had significant underestimation scores. Censoring at framewise displacement (FD) < 0.2 mm reduced significant overestimation to 2% (1/45) of traits but did not decrease the number of traits with significant motion underestimation scores.

PMID:41022827 | PMC:PMC12479937 | DOI:10.1038/s41467-025-63661-2

Braz J Psychiatry. 2025 Sep 29. doi: 10.47626/1516-4446-2025-4202. Online ahead of print.

ABSTRACT

OBJECTIVE: Functional magnetic resonance imaging (fMRI) techniques were conducted to investigate the immediate and sustained effects of acupuncture, as well as its impact on the functional connectivity (FC) within the default mode network (DMN) and the external FC.

METHODS: Thirty healthy participants received acupuncture needle stimulation at Baihui (GV20) and Yintang (GV29), and underwent resting-state fMRI scans in three phases: pre-needle insertion, during needle retention, and post-needle removal. Each phase lasted for 20 minutes.

RESULTS: In terms of the within-network connectivity of the DMN, post-needle removal scans showed a decrease in FC between the medial prefrontal cortex (mPFC) and the angular gyrus (ANG) compared to pre-needle insertion scans. The FC analysis from seed points to whole-brain voxels showed the following changes: compared to pre-needle insertion scans, acupuncture needle insertion increased the FC between the posterior cingulate cortex/precuneus (PCC/PCU) and Cerebellum_8_L, acupuncture needle withdrawal increased the FC between the PCC/PCU and Cerebellum_8_L; acupuncture needle insertion decreased the FC between ANG and Frontal_Sup_Medial_L, and acupuncture needle withdrawal decreased the FC between ANG and Frontal_Sup_Medial_L.

CONCLUSIONS: These results suggested that acupuncture has an impact on the DMN, and acupuncture exhibits sustained effects, not just immediate effects.

PMID:41022570 | DOI:10.47626/1516-4446-2025-4202

Brain Struct Funct. 2025 Sep 29;230(8):151. doi: 10.1007/s00429-025-03014-3.

ABSTRACT

To investigate the potential bidirectional causal relationships between resting-state functional brain activity and major kidney diseases. We accessed genome-wide association study (GWAS) summary data of 191 resting-state fMRI (rs-fMRI) phenotypes. Summary-level GWAS data for seven kidney diseases-diabetic nephropathy, chronic kidney disease, glomerulonephritis, nephrotic syndrome, cystic kidney disease, IgA nephropathy, and kidney cyst-were obtained from the FinnGen consortium or the Kiryluk Lab, all based on European ancestry (sample sizes ranging up to 11,265 cases and 436,208 controls). We employed inverse variance weighted (IVW) analysis as the primary MR approach, supplemented by MR-Egger, Weighted Median, Weighted Mode, and Robust Adjusted Profile Score (RAPS) to evaluate pleiotropy and heterogeneity. Forward MR demonstrated that certain brain networks, such as the central executive network, default mode network, limbic network, and other interconnected circuits, appear to influence susceptibility to various kidney diseases. Reverse MR indicated that disrupted kidney function, particularly CKD, may adversely affect key brain functional networks, including those responsible for sensory-motor processing and cognitive integration. Although the observed effect sizes were modest, our results provide evidence that kidney diseases and brain functional activity may be interlinked, aligning with clinical observations of neurological-urinary system correlations and emerging data on cortical structural changes in chronic kidney disease. The "kidney-brain axis" could be relevant to both renal and neurological pathophysiology.

PMID:41020910 | DOI:10.1007/s00429-025-03014-3

Hum Brain Mapp. 2025 Oct 1;46(14):e70370. doi: 10.1002/hbm.70370.

ABSTRACT

We previously reported that a unilateral dorsal column lesion (DCL) at the cervical C4 level primarily reduces inter-horn resting-state functional connectivity (rsFC) measured by functional Magnetic Resonance Imaging (fMRI) in segments below the lesion. This study compares changes in rsFC from fMRI with changes in local field potential (LFP) coherence over an extended post-injury period. High-resolution fMRI and LFP data were acquired bilaterally in healthy monkeys and at 3- and 6-months post-lesion. At 3 months post-injury, tactile-stimulus-evoked LFP power in the dorsal horn was significantly weaker than in the healthy cord and non-lesion side. LFP coherences increased on the lesion side for the dorsal-to-intermediate zone (D-IGM) and dorsal-to-ventral (D-V) pairs but decreased for the non-lesion side D-IGM. By 6 months, stimulus-evoked LFP power on the lesion side remained low. LFP coherences between dorsal-to-dorsal (D-D), ventral-to-ventral (V-V), and D-V pairs on both the lesion and non-lesion sides were significantly reduced relative to the healthy cord. Low-frequency (delta, theta, and alpha) D-IGM coherences on the lesion side, and high-frequency (beta and gamma) coherences on the non-lesion side, were also significantly weakened. Across specific inter-horn pairs and time points, changes in LFP coherences and rsFC measures were weakly correlated. Measurements of inter-horn correlations two segments caudal to the lesion level at C7 revealed distance-dependent intraspinal connectivity changes following DCL. Post-mortem histology confirmed a complete DCL in most animals (7/9). The extent of the disruption of ascending sensory afferents, as assessed histologically, did not appear to correlate with the degree of LFP power reduction or rsFC changes at post-injury time points. In summary, we observed temporally and spatially heterogeneous changes of fMRI correlations and LFP coherences within intraspinal circuits. fMRI rsFC and LFP coherences were not always concordant, with discrepancies depending on specific gray-matter horns and intermediate-zone pairs.

PMID:41020550 | PMC:PMC12477704 | DOI:10.1002/hbm.70370

Imaging Neurosci (Camb). 2025 Sep 24;3:IMAG.a.162. doi: 10.1162/IMAG.a.162. eCollection 2025.

ABSTRACT

Functional connectivity (FC) has been invaluable for understanding the brain's communication network, with strong potential for enhanced FC approaches to yield additional insights. Unlike with the fMRI field-standard method of pairwise correlation, theory suggests that partial correlation can estimate FC without confounded and indirect connections. However, partial correlation FC can also display low repeat reliability, impairing the accuracy of individual estimates. We hypothesized that reliability would be increased by adding regularization, which can reduce overfitting to noise in regression-based approaches like partial correlation. We therefore tested several regularized alternatives-graphical lasso, graphical ridge, and principal component regression-against unregularized partial and pairwise correlation, applying them to empirical resting-state fMRI and simulated data. As hypothesized, regularization vastly improved reliability, quantified using between-session similarity and intraclass correlation. This enhanced reliability then granted substantially more accurate individual FC estimates when validated against structural connectivity (empirical data) and ground truth networks (simulations). Graphical lasso showed especially high accuracy among regularized approaches, seemingly by maintaining more valid underlying network structures. We additionally found graphical lasso to be robust to noise levels, data quantity, and subject motion-common fMRI error sources. Lastly, we demonstrated that resting-state graphical lasso FC can effectively predict fMRI task activations and individual differences in behavior, further establishing its reliability, external validity, and ability to characterize task-related functionality. We recommend graphical lasso or similar regularized methods for calculating FC, as they can yield more valid estimates of unconfounded connectivity than field-standard pairwise correlation, while overcoming the poor reliability of unregularized partial correlation.

PMID:41019970 | PMC:PMC12461088 | DOI:10.1162/IMAG.a.162

Neurooncol Adv. 2025 Sep 2;7(1):vdaf192. doi: 10.1093/noajnl/vdaf192. eCollection 2025 Jan-Dec.

ABSTRACT

BACKGROUND: Neurocognitive decline in patients with primary brain tumors is associated with alterations in the functional connectome and reduced independence in daily living. This study explores postoperative connectomic changes associated with functional independence outcomes in patients with eloquent glioma, and how these associations differ from neurocognitive-connetcomic relationships.

METHODS: Fifteen patients with left perisylvian glioma underwent resting-state functional magnetic resonance imaging (fMRI) and neuropsychological evaluation within 2 weeks before and on average 1 month after resection. Functional independence was measured with the Physical Self-Maintenance Scale (PSMS) and the Instrumental Activities of Daily Living scale (IADL). Graph theoretical analysis quantified functional brain network properties.

RESULTS: Postoperative need for assistance in at least 1 activity on the IADL increased in 80% of patients with Total scores significantly increasing at the group level (Mdn change = 4.0, P = .006). In contrast, need for assistance on the PSMS increased in less than 30% of patients and Total scores were unchanged. Connectomic changes in Local Efficiency, Clustering Coefficient, Path Length, and Betweenness Centrality showed significant associations with need for assistance on the IADL (ρ = 0.63 to.72, all P < .01) but few activities on the PSMS. Functional independence ratings were not associated with Karnofsky Performance Status, manual dexterity, tumor volume, or extent of resection.

CONCLUSIONS: Alterations in functional connectomic properties after eloquent glioma resection are associated with early postoperative need for assistance in instrumental activities. Changes in connectomics are also associated with cognitive outcome in this population, though properties most involved appear to differ from those underlying changes in independence.

PMID:41019665 | PMC:PMC12461250 | DOI:10.1093/noajnl/vdaf192

Front Neurosci. 2025 Sep 12;19:1627269. doi: 10.3389/fnins.2025.1627269. eCollection 2025.

ABSTRACT

OBJECTIVE: An early and accurate diagnosis of myelin oligodendrocyte glycoprotein antibody seropositive optic neuritis (MOG-ON) versus seronegative-ON is critical for optimal management. We aimed to explore alterations in static and dynamic functional networks for differentiation by resting-state functional magnetic resonance imaging (RS-fMRI) with the graph theory method.

METHODS: RS-fMRI was performed on 53 patients (23 with MOG-ON and 30 with seronegative-ON) and 26 healthy controls (HCs). Graph theory analysis was used to investigate the topological properties of the functional networks. Receiver operating characteristic (ROC) curve analysis was also performed to determine their effectiveness in differential diagnosis.

RESULTS: With respect to static properties, the MOG-ON and seronegative-ON groups presented a spectrum of abnormalities in global and nodal properties compared with the HC group. Furthermore, compared with the seronegative-ON group, the MOG-ON group also presented with abnormal properties mostly located in the visual network (VN). With respect to dynamic properties, the MOG-ON and seronegative-ON groups presented with greater variances of global and nodal properties compared with the HC group. Importantly, the variances in several global and nodal properties were greater in the MOG-ON group. Compared with that in HCs, the subnetwork (24 nodes and 28 edges) in the MOG-ON patients was enhanced. For ROC analysis, the optimal diagnostic performance was obtained by combining static and dynamic approaches.

CONCLUSION: In conclusion, abnormal topological organization of static and dynamic brain functional networks may help explore the neural mechanisms of ON in different phenotypes and serve as biomarkers for differentiation.

PMID:41017978 | PMC:PMC12463965 | DOI:10.3389/fnins.2025.1627269

Epilepsy Behav. 2025 Sep 27;172:110725. doi: 10.1016/j.yebeh.2025.110725. Online ahead of print.

ABSTRACT

OBJECTIVE: To investigate the impact of the SCN1A rs3812718 polymorphism on gray matter volume (GMV) and resting-state functional network topology in temporal lobe epilepsy (TLE) patients.

METHODS: 60 TLE patients and 28 healthy controls (HCs) underwent genotyping and MRI (3D-T1, rs-fMRI). Participants were grouped by genotype (AA/AGvs.GG) and disease status (TLEvs.HC). Voxel-based morphometry assessed GMV; graph theory analyzed functional network topology. 2x2 ANCOVA tested genotype and disease main effects and their interaction.

RESULTS: AA/AG genotype frequency was higher in (TLE vs.HCs). GMV: Significant genotype main effect (AA/AGvs.GG): reduced GMV in right temporal regions/hippocampus/left SMG; increased in left MTG/right precuneus. Significant disease main effect (TLEvs.HC): widespread GMV reductions, especially in mesiotemporal/neocortical areas. Significant genotype-by-disease interaction: TLE patients with AA/AG genotype showed the most extensive GMV reductions (bilateral ITG, fusiform gyri, right hippocampus/precuneus/occipital, left caudate/rectus).

FUNCTIONAL NETWORKS: Significant disease main effect: reduced degree centrality in left dorsolateral prefrontal cortex (SFGdor/MFG) in TLEvs.HC. No significant interaction effects on global/nodal topology.

CORRELATIONS: In AA/AG TLE patients, left MTG GMV negatively correlated with epilepsy duration.

CONCLUSION: The SCN1A rs3812718AA/AG genotype is a TLE risk factor. It independently and interactively (with disease status) is associated with structural brain alterations (GMV) in TLE and is linked to disease-related functional network changes (DC) in cognitive regions. These genetic-neuroimaging signatures offer potential biomarkers for TLE precision medicine.

PMID:41016120 | DOI:10.1016/j.yebeh.2025.110725

Brain Behav. 2025 Oct;15(10):e70868. doi: 10.1002/brb3.70868.

ABSTRACT

BACKGROUND: The interplay between the ventral attention network (VAN) and dorsal attention network (DAN) is crucial for attentional control, particularly in disengagement processes. While task-based fMRI studies have extensively characterized their roles, less is known about whether intrinsic connectivity patterns during rest within these networks predict individual differences in attentional disengagement. This study investigates the relationship between resting-state functional VAN-DAN connectivity and disengagement efficiency.

METHODS: An initial sample of 85 healthy participants completed a spatial cueing task, assessing attentional disengagement through reaction time differences between valid and invalid cue trials. Resting-state fMRI data were collected and analyzed using seed-based connectivity methods. Functional connectivity between key VAN and DAN regions-frontal eye fields (FEF), intraparietal sulcus (IPS), inferior frontal gyrus (IFG), and supramarginal gyrus (SMG)-was examined in relation to a disengagement index, representing cue validity effects.

RESULTS: Participants exhibited slower responses to invalidly cued targets, with a greater disengagement cost in the left visual field. Functional connectivity analyses revealed that VAN regions, particularly the right IFG and SMG, showed stronger associations with attentional disengagement than DAN regions. Increased FC with occipito-temporal areas correlated with heightened validity effects in the left hemifield, while greater connectivity with medial parietal and cingulate regions was linked to reduced disengagement asymmetry. Interhemispheric connectivity also played a modulatory role in attentional control.

CONCLUSION: These findings underscore the role of VAN over DAN in attentional disengagement, emphasizing the right IFG and SMG in reorienting attention. Greater connectivity with occipito-temporal regions may hinder disengagement, while enhanced functional connectivity with medial cortical areas facilitates adaptive shifts in attention. This study highlights the importance of intrinsic VAN-DAN interactions in shaping attentional control and provides insights into the neural mechanisms underlying disengagement efficiency.

PMID:41016017 | PMC:PMC12476866 | DOI:10.1002/brb3.70868

Geroscience. 2025 Sep 27. doi: 10.1007/s11357-025-01888-z. Online ahead of print.

ABSTRACT

Repetitive transcranial magnetic stimulation (rTMS) improves cognition in Alzheimer's disease (AD), yet its biomarker and neuroplasticity effects remain unclear. Cognitive scale scores, plasma biomarker levels, and resting-state fMRI brain network in AD patients were analyzed before and after a 14-day 20 Hz rTMS intervention. The results demonstrated that rTMS intervention significantly improved cognitive performance (MMSE: Z = - 2.863, q = 0.017; MoCA: t = - 6.137, q < 0.001; RAVLT_I: t = - 3.436, q = 0.011) and reduced neuropsychiatric symptoms (NPI: Z = - 2.547, q = 0.037; HAMD: Z = - 3.472, q = 0.009). A 9.4% reduction in neurofilament light chain levels was demonstrated (Z = - 2.371, P = 0.018), with baseline p-Tau181 levels being inversely correlated to Aβ42 changes (R = - 0.428, P = 0.033). Enhanced global efficiency (GE: t = - 1.865, P = 0.081, r = 0.423) and increased connection density (CD: Z = - 1.823, P = 0.068, r = 0.442) were identified in neural networks. Notably, GE improvements positively correlated with elevated Aβ42/40 (R = 0.596, P = 0.025), while cognitive gains measured by the MoCA were significantly associated with network reorganization metrics (GE: R = 0.486, P = 0.048; CD: R = 0.514, P = 0.035). rTMS demonstrates potential in mitigating neurodegeneration by enhancing brain network integration and modulating Aβ metabolism. This effect was particularly pronounced in early-stage AD patients who exhibit preserved neural integrity. These findings advance therapeutic assessment frameworks and decode TMS neuromodulation mechanisms. The trial was prospectively registered (ChiCTR2400080657, ClinicalTrials.gov; 2024-02-04).

PMID:41015617 | DOI:10.1007/s11357-025-01888-z

Neuron. 2025 Sep 26:S0896-6273(25)00664-6. doi: 10.1016/j.neuron.2025.08.029. Online ahead of print.

ABSTRACT

Precision mapping of brain networks within individuals prevailingly relies on functional connectivity analysis of resting-state data. Here, we explored whether networks can be estimated using only task data. Correlation matrices estimated from task data were similar to those derived from resting-state data. The largest factor affecting similarity was the amount of data. Precision networks estimated from task data showed strong spatial overlap with those derived from resting-state data and predicted the same triple functional dissociation in independent data. To illustrate novel possibilities enabled by the present methods, we mapped the detailed organization of thalamic association zones within individuals by pooling extensive resting-state and task data. We also demonstrated how task data can be used to estimate networks while simultaneously extracting task responses. Broadly, these findings suggest that there is an underlying, stable network architecture that is idiosyncratic to the individual and persists across task states.

PMID:41015029 | DOI:10.1016/j.neuron.2025.08.029

Brain Imaging Behav. 2025 Sep 27. doi: 10.1007/s11682-025-01058-x. Online ahead of print.

ABSTRACT

Pre-surgical planning often involves task-based functional magnetic resonance imaging (fMRI) in the context of intractable epilepsy or brain tumors. Resting-state fMRI can be used for the same goal, with the advantage of being a simpler technique that does not require the patient to cooperate in complex cognitive tasks. However, the methods for resting-state fMRI analysis are not yet robust or of practical usage. This work proposes an algorithm for sorting components resulting from independent component analysis (ICA) that emphasizes the language resting-state network. We recruited 20 healthy volunteers and acquired resting-state and task-based fMRI using three linguistic tasks. Task data was processed using general linear model analysis, while resting-state networks were extracted using ICA. An automated IC sorting procedure was developed based on three characteristics: spatial similarity with a probability map, low/high frequency ratio, and IC reliability over several bootstrapping folds. Task-related activation consistent with the language network was identified at the subject-specific level. The algorithm is shown to sort ICs with the resting-state language maps appearing among the first three with an accuracy of 74%. Overall, the Dice coefficient showed a good overlap between the sorted ICs of relevance and the task language maps. Results showed that resting-state networks were more specific and less sensitive than task-based maps. We expect that the proposed algorithm for optimal sorting will contribute towards making ICA usage viable in the clinical context and become a reliable alternative method for pre-surgical planning.

PMID:41014464 | DOI:10.1007/s11682-025-01058-x

Brain Imaging Behav. 2025 Sep 27. doi: 10.1007/s11682-025-01050-5. Online ahead of print.

ABSTRACT

Prosocial decisions in daily life are often influenced by cognitive constraints, such as time pressure and cognitive load, which can impact how we process information and make decisions that benefit others. Understanding how these constraints interact with our brain's intrinsic connectivity patterns and contribute to individual differences is crucial. This study investigates the neural mechanisms underlying the effects of cognitive constraints on prosocial decision-making. We developed a resting-state functional connectivity (rsFC) network model using machine learning regression to predict how cognitive constraints influence prosocial choices, while accounting for individual variability through intersubject representational similarity analysis (IS-RSA). Our findings reveal that the rsFC network-including regions involved in affective processing (insula, INS; amygdala, AMYG), empathy (temporo-parietal junction, TPJ; medial cingulate gyrus, MCG), and valuation (ventral striatum, VS; ventral prefrontal cortex, vmPFC)-predicts the impact of cognitive constraints on decision-making. Notably, rsFC between MCG and TPJ and bilateral TPJ connectivity showed intersubject variability that aligned with behavioral responses. These findings elucidate how cognitive constraints shape prosocial decision-making at the neural level, uncovering individual variability that advances theoretical understanding and offers practical implications for fostering prosociality in cognitively demanding contexts.

PMID:41014463 | DOI:10.1007/s11682-025-01050-5

Hum Brain Mapp. 2025 Oct 1;46(14):e70364. doi: 10.1002/hbm.70364.

ABSTRACT

Modeling dynamic neuronal activity within brain networks enables the precise tracking of rapid temporal fluctuations across different brain regions. However, current approaches in computational neuroscience fall short of capturing and representing the spatiotemporal dynamics within each brain network. We developed a novel weakly supervised spatiotemporal dense prediction model capable of generating personalized 4D dynamic brain networks from fMRI data, providing a more granular representation of brain activity over time. We developed a model that leverages the vision transformer (ViT) as its backbone, jointly encoding spatial and temporal information from fMRI inputs using two different configurations: space-time and sequential encoders. The model generates 4D brain network maps that evolve over time, capturing dynamic changes in both spatial and temporal dimensions. In the absence of ground-truth data, we used spatially constrained windowed independent component analysis (ICA) components derived from fMRI data as weak supervision to guide the training process. The model was evaluated using large-scale resting-state fMRI datasets, and statistical analyses were conducted to assess the effectiveness of the generated dynamic maps using various metrics. Our model effectively produced 4D brain maps that captured both inter-subject and temporal variations, offering a dynamic representation of evolving brain networks. Notably, the model demonstrated the ability to produce smooth maps from noisy priors, effectively denoising the resulting brain dynamics. Additionally, statistically significant differences were observed in the temporally averaged brain maps, as well as in the summation of absolute temporal gradient maps, between patients with schizophrenia and healthy controls. For example, within the Default Mode Network (DMN), significant differences emerged in the temporally averaged space-time configurations, particularly in the thalamus, where healthy controls exhibited higher activity levels compared to subjects with schizophrenia. These findings highlight the model's potential for differentiating between clinical populations. The proposed spatiotemporal dense prediction model offers an effective approach for generating dynamic brain maps by capturing significant spatiotemporal variations in brain activity. Leveraging weak supervision through ICA components enables the model to learn dynamic patterns without direct ground-truth data, making it a robust and efficient tool for brain mapping. Significance: This work presents an important new approach for dynamic brain mapping, potentially opening up new opportunities for studying brain dynamics within specific networks. By framing the problem as a spatiotemporal dense prediction task in computer vision, we leverage the spatiotemporal ViT architecture combined with weakly supervised learning techniques to efficiently and effectively estimate these maps.

PMID:41014302 | PMC:PMC12476115 | DOI:10.1002/hbm.70364

Arthritis Res Ther. 2025 Sep 26;27(1):181. doi: 10.1186/s13075-025-03646-0.

ABSTRACT

OBJECTIVES: Knee osteoarthritis (KOA), a degenerative joint disease marked by chronic pain, is associated with systemic inflammation that may extend to neurocognitive dysfunction. While chronic low-grade inflammation in KOA has been implicated in mild cognitive impairment (MCI), a prodromal stage of dementia, the mediating role of inflammation in brain functional reorganization remains unclear.

METHODS: This study integrated neuroimaging, inflammatory biomarkers, and machine learning to investigate inflammation-mediated brain functional alterations in 63 KOA patients with/without MCI. Serum levels of pro-inflammatory cytokines (IL-6, TNF-α) and resting-state fMRI data were analyzed using voxel-wise Regional Homogeneity (ReHo) and Amplitude of Low-Frequency Fluctuation (ALFF).

RESULTS: Comparisons across healthy controls, KOA-MCI, and KOA-non-MCI groups identified MCI-linked functional alterations in the medial prefrontal cortex (mPFC), precuneus, and superior temporal gyrus. Mediation analysis revealed that mPFC ReHo significantly mediated the relationship between elevated IL-6 and cognitive decline. Machine learning models incorporating ReHo features from mPFC demonstrated robust classification of MCI status (AUC: 0.87), validated in an external dataset.

CONCLUSION: Our findings suggest that IL-6-driven mPFC dysfunction is a potential pathway linking KOA-related inflammation to MCI, while highlighting the combined utility of ReHo/ALFF metrics in mPFC, precuneus, and temporal regions as potential neuroimaging biomarkers. This multimodal approach advances understanding of neuroinflammatory mechanisms in osteoarthritis and provides a framework for early detection of cognitive vulnerability in KOA populations.

PMID:41013635 | PMC:PMC12465908 | DOI:10.1186/s13075-025-03646-0

BMC Psychiatry. 2025 Sep 26;25(1):877. doi: 10.1186/s12888-025-07379-x.

ABSTRACT

BACKGROUND: Sleep disorders are common among adolescents with depression, yet lack reliable neuroimaging diagnostic techniques. This study aimed to predict sleep disorders in depressed adolescents using brain network features, including betweenness centrality (BC) and functional connectivity (FC).

METHODS: 117 adolescents diagnosed with depression underwent resting-state fMRI. Whole-brain FC (reflecting inter-regional relationships) and BC (quantifying a node's importance for network information flow) were analyzed. Differences in FC and BC between depressed adolescents with sleep disorders and depressed adolescents without sleep disorders were compared using two-sample t-tests in a discovery dataset (n = 86). A support vector machine (SVM) classifier was trained to differentiate these groups. Validation employed leave-one-out cross-validation (LOOCV) internally and an independent dataset (n = 31).

RESULTS: Depressed adolescents with sleep disorders showed elevated BC in the right middle temporal gyrus (MTG.R) and decreased BC in the left median cingulate and paracingulate gyri (DCG.L) and left caudate nucleus (CAU.L), indicating altered information flow hubs. Alterations in FC were observed across several regions, with the most pronounced changes occurring between the left middle occipital gyrus and MTG.R (MOG.L-MTG.R). The SVM model, using combined whole-brain BC and FC features, achieved 81.40% accuracy during LOOCV and identified discriminative features. Predictive performance was validated externally, yielding 74.19% accuracy.

CONCLUSIONS: Significant functional brain network alterations occur in depressed adolescents with sleep disorders. Integrating brain network analysis(BC and FC analysis) with machine learning techniques offers a promising approach to identifying neuroimaging markers for diagnosing sleep disorders in depressed adolescents.

PMID:41013389 | PMC:PMC12465739 | DOI:10.1186/s12888-025-07379-x