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Sci Rep. 2025 Nov 18;15(1):40421. doi: 10.1038/s41598-025-18146-z.

ABSTRACT

About 30% of temporal lobe epilepsy (TLE) cases are negative on MRI, so quantitative diagnosis based on clinical symptoms becomes challenging. There is an urgent need for an accurate and reliable method to differentiate patients with MRI-negative TLE from healthy individuals. This study aimed to explore the use of machine learning methods to diagnose MRI-negative TLE patients based on single and combined resting-state fMRI (rs-fMRI) metrics. This study investigates the diagnostic implications of using both singular and composite resting-state fMRI (rs-fMRI) indices in patients with MRI-negative TLE. We carried out a retrospective analysis of the clinical data and rs-fMRI data of 90 patients with MRI-negative TLE and 90 healthy controls (HCs). Next, the participants were divided into a training set and a test set at 8:2. Functional indices extracted from each brain region included degree centrality (DC), voxel-mirrored homotopic connectivity (VMHC), regional homogeneity (ReHo), fractional amplitude of low-frequency fluctuations (fALFF), and amplitude of low-frequency fluctuations (ALFF). A two-sample t-test was utilized to select significant voxels. After this, classification models based on individual rs-fMRI indices and combined rs-fMRI indices were constructed using ML algorithms such as support vector machines (SVM), random forests (RF), and logistic regression (LR) on the training set. Model performance was evaluated using metrics such as specificity, the area under the receiver operating characteristic curve (AUC), sensitivity, and accuracy, and validations were performed on the test set. Lastly, the feature contribution was assessed using Shapley Additive explanations (SHAP) values. The SVM model employing a combination of rs-fMRI functional indices had optimal performance. On the test set, this model achieved an AUC of 0.89, with an accuracy rate of 82%, where the ALFF values from the cerebellum contributed most significantly to the model. In contrast, ML models based on individual rs-fMRI indices demonstrated inferior classification performance, whereas the RF model using the DC index had the lowest accuracy of 47% on the test set. The SVM model combining the fMRI indices has the greatest potential to distinguish between MRI-negative temporal lobe epilepsy patients and healthy individuals, suggesting a complementary role for the classification of resting-state fMRI indices.

PMID:41253869 | DOI:10.1038/s41598-025-18146-z

NPJ Parkinsons Dis. 2025 Nov 18;11(1):319. doi: 10.1038/s41531-025-01168-9.

ABSTRACT

Apathy is a prevalent non-motor symptom in Parkinson's disease (PD) that negatively impacts quality of life. Impaired self-awareness of apathy (ISA-a) further impacts patient care by limiting engagement. While apathy has been associated with reduced fronto-striatal functional connectivity (FC), the neural basis of ISA-a remains unclear. We examined ISA-a in 52 individuals and the neural basis of ISA-a in 35 individuals with PD using a dimensional approach (i.e., initiation, executive, and emotional apathy) and resting-state fMRI (3T scanner). Apathetic PD patients (42%) showed poorer self-awareness than non-apathetic peers. Apathetic PD patients showed a trend towards reduced FC between the left anterior cingulate cortex (ACC) and the left nucleus accumbens (NAcc). A trend for ISA-a in the emotional domain showed altered FC between the left NAcc and orbitofrontal cortices, and the right ACC and right anterior insular cortex. These findings suggest potential neural mechanisms underlying apathy and ISA-a to be studied in larger populations.

PMID:41253797 | DOI:10.1038/s41531-025-01168-9

Transl Psychiatry. 2025 Nov 18;15(1):484. doi: 10.1038/s41398-025-03670-3.

ABSTRACT

Narcolepsy type 1 (NT1) is characterized by sleep-onset rapid eye movement periods (SOREMPs), reflecting dysregulated rapid eye movement (REM) sleep control. Treatment response variability in SOREMP persistence remains poorly understood, particularly regarding hypothalamic functional connectivity (FC) and depressive symptoms. This study investigated clinical, polysomnographic, and neuroimaging differences between NT1 patients with low (0-1) versus high (≥2) post-treatment SOREMPs, and explored whether hypothalamic FC mediates the relationship between depressive symptoms and SOREMPs outcomes. One hundred ten NT1 patients were categorized into low (n = 62) and high (n = 48) post-treatment SOREMPs groups. Demographic, clinical variables (symptoms and questionnaires), and polysomnography (PSG)/multiple sleep latency test (MSLT) parameters were compared. Resting-state fMRI assessed hypothalamic FC with whole-brain regions. LASSO regression modeled associations between FC, sleep latency, and clinical variables, while mediation analysis tested hypothalamic pathways as mediators of depression-SOREMP relationships. High post-treatment SOREMPs patients exhibited shorter pre/post-treatment REM sleep latency, lower post-treatment wakefulness index, and higher depressive symptom prevalence compared to low SOREMPs patients. Hypothalamic FC differed significantly between groups: low SOREMPs patients showed enhanced connectivity in right medial hypothalamus-right thalamus/left precuneus, left medial hypothalamus-left inferior parietal lobule (IPL), and right lateral hypothalamus-left IPL pathways, but reduced connectivity in left lateral hypothalamus-right insula/left anterior cingulate cortex pathways (p < 0.05, GRF-corrected). LASSO regression identified left medial hypothalamus-left IPL FC as a significant predictor of post-treatment MSLT mean sleep latency (β = 0.272, p = 0.001), alongside age (β = -0.256, p = 0.002) and pre-treatment sleep latency (β = 0.392, p < 0.001). Mediation analysis revealed complete mediation by two hypothalamic pathways: depressive symptoms predicted reduced right lateral hypothalamus-left IPL FC (indirect effect: 0.15-1.05), associated with fewer SOREMPs, and increased left lateral hypothalamus-right insula FC (indirect effect: 0.08-1.14), associated with more SOREMPs. Hypothalamic-parietal/insular FC abnormalities link depressive symptoms to post-treatment SOREMP variability in NT1, with specific pathways mediating opposing effects on REM sleep regulation. These findings highlight hypothalamic connectivity as a critical neural substrate for treatment response, integrating sleep-wake and emotional processing networks. Targeting these pathways may improve personalized management for NT1 patients with comorbid depression and treatment-resistant SOREMPs.

PMID:41253778 | DOI:10.1038/s41398-025-03670-3

Schizophr Res. 2025 Nov 17;287:37-45. doi: 10.1016/j.schres.2025.11.007. Online ahead of print.

ABSTRACT

Schizophrenia is a complex neurodevelopmental disorder characterized by widespread functional dysconnectivities across the brain. While disturbed temporal dynamics have been reported in schizophrenia, the information flow involving both temporal and spatial dynamics remains unclear. To capture spatio-temporal transition of brain information and to investigate these processes from a neurodevelopmental perspective, we collected resting-state functional MRI (rs-fMRI) data from 86 early-onset schizophrenia (EOS) patients (onset before age 18) and 91 demographically matched typically developing (TD) controls. We employed a non-homogeneous Markov model (NHMM) on dynamic functional connectivities derived from fMRI data. By means of transition probabilities, we modeled the switching of information flow in brain functional networks over time. Stationary probability vectors were used to describe the information convergence distribution of each network, while optimal reachable steps were used to characterize inter-network transmission efficiency. Compared to controls, EOS patients showed significantly increased stationary transition probabilities in the ventral attention network (VAN) and the dorsal attention network (DAN) but decreased probabilities in the default mode network (DMN). In terms of the dynamic interaction characteristics between networks, patients showed increased optimal reachable steps relative to controls, particularly in the VAN-DMN pathway. By integrating NHMM with neuroimaging data, this study revealed VAN- and DMN-involved information transition abnormalities in the early stage of schizophrenia spatio-temporal dynamics, offering novel insights into the developmental pathophysiology of the disorder. Our approach thus provides a novel analytical framework for quantifying spatio-temporal brain dynamics in neurodevelopmental disorders.

PMID:41253019 | DOI:10.1016/j.schres.2025.11.007

Cephalalgia. 2025 Nov;45(11):3331024251396102. doi: 10.1177/03331024251396102. Epub 2025 Nov 18.

ABSTRACT

BackgroundMenstrually-related migraine (MRM) is a subtype of migraine associated with the ovarian cycle that imposes a significant burden on female patients. Although MRM and non-menstrual migraine (NMM) differ in clinical presentation and treatment response, their distinct neural mechanisms remain unclear. Emerging evidence suggests that alterations in intrinsic functional connectivity (FC) within and between large-scale brain networks may underlie the phenotypic heterogeneity of migraine subtypes. This study investigated FC alterations between patients with MRM and NMM, explored their correlations with clinical characteristics, and assessed the preliminary utility of FC in subtype differentiation.MethodsResting-state functional magnetic resonance imaging (MRI) with independent component analysis was used to examine whole-brain FC in 50 patients with MRM, 50 with NMM and 50 age-balanced healthy controls (HC). We analyzed within- and between-network connectivity across major resting-state networks, including the frontoparietal, default mode, salience and dorsal attention networks, and applied logistic regression to test whether FC values could classify migraine subtypes. Correlation analyses were further performed between FC measures and clinical indices, including disease duration, headache frequency, visual analog scale scores and Headache Impact Test (HIT-6) scores.ResultsBoth MRM and NMM groups showed weaker within-network connectivity compared to HCs, primarily in the right frontoparietal, default mode and salience networks. Compared with NMM, the MRM group exhibited significantly stronger connectivity in the left frontoparietal network and weaker between-network connectivity between the dorsal attention and default mode networks. In the women with migraine, FC within the dorsal attention network (DAN) was negatively correlated with disease duration (r = -0.200, p = 0.046) and HIT-6 score (r = -0.183, p = 0.049). Furthermore, FC between the DAN and auditory network was inversely associated with disease duration (r = -0.225, p = 0.025). The logistic regression model achieved an area under the receiver operating characteristic curve of 0.73 (sensitivity = 0.70; specificity = 0.64) in distinguishing MRM from NMM.ConclusionsOur findings reveal both shared and distinct alterations in large-scale brain networks in MRM and NMM, potentially explaining differences in clinical presentation and treatment response. This enhanced understanding of migraine pathophysiology supports the development of subtype-specific diagnostic tools and targeted therapies and underscores the value of resting-state fMRI as a non-invasive tool for migraine phenotyping and personalized care.Registration NumberChiCTR2200065586.

PMID:41252278 | DOI:10.1177/03331024251396102

J Neurosci. 2025 Nov 17:e0577252025. doi: 10.1523/JNEUROSCI.0577-25.2025. Online ahead of print.

ABSTRACT

Sensorimotor and cognitive abilities undergo substantial changes throughout the human lifespan, but the corresponding changes in the functional properties of cortical networks remain poorly understood. This can be studied using temporal and spatial scales of functional magnetic resonance imaging (fMRI) signals, which provide a robust description of the topological structure and temporal dynamics of neural activity. For example, timescales of resting-state fMRI signals parsimoniously predict a significant amount of the individual variability in functional connectivity networks identified in adult human brains. In the present study, we quantified and compared temporal and spatial scales in resting-state fMRI data collected from 2,352 subjects of either sex between the ages of 5 and 100 in Developmental, Young Adult, and Aging datasets from the Human Connectome Project. For most cortical regions, we found that both temporal and spatial scales decreased with age throughout the lifespan, with the visual cortex and the limbic network consistently showing the largest and smallest scales, respectively. For some prefrontal regions, however, these two scales displayed non-monotonic trajectories and peaked around the same time during adolescence and decreased throughout the rest of the lifespan. We also found that cortical myelination increased monotonically throughout the lifespan, and its rate of change was significantly correlated with the changes in both temporal and spatial scales across different cortical regions in adulthood. These findings suggest that temporal and spatial scales in fMRI signals, as well as cortical myelination, are closely coordinated during both development and aging.Significance Statement Temporal and spatial scales of resting-state cortical activity in humans measured by fMRI largely decreased throughout the lifespan, except that for some regions in the prefrontal cortex they peaked similarly during adolescence. In addition, whereas cortical myelination consistently increased throughout the lifespan, its variation across different cortical networks and the rate of age-related changes were correlated with the dynamics of temporal and spatial scales of rs-fMRI activity, suggesting that the spatio-temporal scales of cortical activity and cortical myelination might be co-regulated during development and aging.

PMID:41249059 | DOI:10.1523/JNEUROSCI.0577-25.2025

Am J Physiol Regul Integr Comp Physiol. 2025 Nov 17. doi: 10.1152/ajpregu.00211.2025. Online ahead of print.

ABSTRACT

In preclinical models, the organum vasculosum of the lamina terminalis (OVLT) and subfornical organ (SFO) sense changes in serum sodium chloride (NaCl) concentration and mediate NaCl-induced changes in sympathetic nerve activity, vasopressin (AVP), thirst, and blood pressure (BP). In humans, brain imaging studies have shown that acute hypernatremia alters the activity or functional connectivity of the SFO and OVLT. However, no studies have investigated whether there are sex differences in central NaCl sensing in humans, which could underlie sex differences in neurohumoral responses to hypernatremia. Therefore, the purpose of this study was to test the hypothesis that acute relative hypernatremia would increase resting-state functional connectivity between NaCl-sensing brain regions and that these responses would be greater in men. Thirty-two young adults (17 men/15 women) underwent resting-state functional magnetic resonance imaging (fMRI) at baseline and during a 30-minute intravenous hypertonic saline infusion. We performed a seed-to-seed functional connectivity analysis. Despite similar increases in serum sodium, thirst, systolic BP, and plasma AVP between the sexes, there was a time*sex interaction (p<0.001) on SFO-OVLT functional connectivity, as SFO-OVLT functional connectivity increased in men during the late phase (15-30 minutes) of the hypertonic saline infusion (z-scores: baseline=0.21±0.20, late phase=0.29±0.21; p=0.04), but decreased in women (z-scores: baseline=0.27±0.17, late phase=0.15±0.18; p=0.004). Collectively, these results suggest that the functional coupling of the SFO and OVLT, which regulate sympathoexcitation and BP during acute hypernatremia, may be modulated by sex.

PMID:41247769 | DOI:10.1152/ajpregu.00211.2025

J Neurol. 2025 Nov 17;272(12):765. doi: 10.1007/s00415-025-13506-1.

ABSTRACT

OBJECTIVES: Cognitive decline in Parkinson's disease (PD) is closely associated with degeneration of the cholinergic system; however, the stage-dependent reorganization of cholinergic networks remains poorly understood. This study aimed to delineate alterations in cholinergic connectivity across the spectrum of cognitive impairment in PD patients.

METHODS: We enrolled 211 PD patients-classified as PD with normal cognition (PD-NC, n = 91), mild cognitive impairment (PD-MCI, n = 79), or dementia (PDD, n = 41)-and 71 healthy controls (HCs). Cholinergic functional networks were reconstructed by mapping predefined cholinergic subnetwork maps onto individual resting-state functional MRI data to derive subject-specific functional connectivity matrices. Graph theoretical measures were applied to quantify global and local topological characteristics. In addition, voxel-based morphometry (VBM) was used to assess group differences in cholinergic nuclei volumes. Furthermore, correlation and mediation analyses were conducted to explore the relationship between network disruption and cognitive performance.

RESULTS: PD patients showed stage-dependent alterations in cholinergic network topology, with increased shortest path length (Lp) and global efficiency in the Ch1-3 pathway and reduced clustering coefficient, gamma, Lp, and sigma in the medial Ch4 pathway (p < 0.05). Regionally, right hippocampal nodal centrality (Ch1-3) and inferior occipital gyrus/local efficiency (Ch4 lateral capsular division) were reduced in PDD, while posterior orbital part of the right medial superior frontal gyrus (medial Ch4) degree centrality increased. Medial Ch4 topological brain metrics correlated with global cognition and key domains, whereas metrics of Ch4 lateral capsular division pathway related to visuospatial and language performance. Structurally, compared to HCs, Ch4 volume loss occurred in PD-NC and PD-MCI groups, while Ch5-6 atrophy was specific in PDD group. Mediation analysis confirmed that medial Ch4 Lp mediated the effect of disease stage on global cognition.

CONCLUSIONS: This study provides new insights into the stage-specific disruption of cholinergic network topology and structural atrophy in PD, demonstrating that Ch4 nucleus degeneration is critically associated with stage-dependent network dysfunction and domain-specific cognitive impairment, thereby offering cholinergic network biomarkers as potential tools for stratifying cognitive stages.

PMID:41247531 | DOI:10.1007/s00415-025-13506-1

Front Neurol. 2025 Oct 30;16:1660039. doi: 10.3389/fneur.2025.1660039. eCollection 2025.

ABSTRACT

BACKGROUND: Blepharospasm, characterized by involuntary contractions of the orbicularis oculi muscles, significantly impairs the quality of life. Its pathophysiology remains unclear. Inter-hemispheric cooperation is a prominent feature of the human brain. This study utilizes resting-state functional magnetic resonance imaging (rs-fMRI) to explore inter-hemispheric functional cooperation in blepharospasm patients by examining connectivity between functionally homotopic voxels (CFH), aiming to identify neural disruptions associated with the disorder.

METHODS: We recruited 30 patients with blepharospasm and 30 age-, sex-, and education-matched healthy controls. All participants underwent rs-fMRI scanning. CFH maps were generated for each participant to quantify inter-hemispheric connectivity at the voxel level. Group differences were assessed, and partial correlation analyses were performed in the patient group to examine the relationship between aberrant CFH values and clinical variables.

RESULTS: Compared to healthy controls, patients with blepharospasm showed significantly increased CFH in the left putamen and left precentral gyrus. However, these aberrant CFH values did not significantly correlate with clinical variables, including disease duration or total Jankovic Rating Scale (JRS) scores and its subscales.

CONCLUSIONS: This study identifies increased inter-hemispheric functional connectivity (FC) within key motor-related brain regions in blepharospasm. The observed hyperconnectivity in the putamen and precentral gyrus may reflect a compensatory neural mechanism to counteract motor dysfunction. These findings provide novel insights into the pathophysiology of blepharospasm and suggest that modulating inter-hemispheric communication may be a potential therapeutic target.

PMID:41245859 | PMC:PMC12611749 | DOI:10.3389/fneur.2025.1660039

Front Aging Neurosci. 2025 Oct 31;17:1682996. doi: 10.3389/fnagi.2025.1682996. eCollection 2025.

ABSTRACT

INTRODUCTION: Cognitive frailty, defined by the coexistence of mild cognitive impairment and physical frailty, imposes greater risk of negative health consequences than either condition alone. Cognitive intraindividual variability (IIV), which reflects the extent of fluctuation in cognitive performance, is an early indicator of impaired cognition and mobility. To extend current understanding of the underlying neural mechanisms of increased IIV due to cognitive frailty, this study investigated the association between brain networks, IIV, and mobility.

METHODS: A total of 38 community-dwelling cognitively frail/non-cognitively frail older adults (CF and non-CF; n = 17 and n = 21, respectively) underwent clinical assessments including the Trail Making Test, Stroop Test, Timed Up and Go test (TUG), and resting-state functional magnetic resonance imaging. Dispersion across executive tests was computed to ascertain IIV (IIV-dispersion). Analysis of covariance was used to determine group differences in IIV-dispersion and functional network connectivity adjusted for functional comorbidities. Moderation models were constructed to investigate the role of functional neural networks in the association between IIV-dispersion and TUG performance.

RESULTS: Compared to non-CF group, CF group exhibited greater IIV-dispersion (p = 0.042), lower within sensorimotor network (SMN) connectivity, and lower connectivity between the default mode network (DMN), fronto-executive network (FEN), and SMN (all p < 0.050). Further, regional DMN-FEN connectivity moderated the relationship between IIV-dispersion and TUG performance (R-sq = 0.427, p = 0.001) only among the CF.

DISCUSSION: Greater IIV-dispersion due to cognitive frailty may be underpinned by large-scale altered functional connectivity across networks. However, localized reconfiguration of DMN-FEN connectivity may uniquely represent adaptive compensatory processes by which mobility is protected against the detrimental impact of greater IIV-dispersion secondary to cognitive frailty.

PMID:41245136 | PMC:PMC12615459 | DOI:10.3389/fnagi.2025.1682996

Hum Brain Mapp. 2025 Nov;46(16):e70413. doi: 10.1002/hbm.70413.

ABSTRACT

Macroscale brain modeling using neural mass models (NMMs) offers a framework for simulating human whole-brain dynamics. These models are pivotal for investigating the brain as a complex dynamic system, exploring phenomena like bifurcations, oscillatory patterns, and responses to stimuli. While connectome-based NMMs allow for the creation of personalized NMMs, their utility in capturing individual-specific neural characteristics remains underexplored, with current studies constrained by small sample sizes and computational inefficiencies. To address these limitations, we employed an algorithmically differentiable version of the reduced Wong Wang (RWW) model, enabling efficient optimization for large datasets. Applying this to resting-state fMRI data from 1444 samples, we optimized models with varying parameter complexities (n = 4, 658, and 23,875), which were derived from creating biologically plausible model variants. The optimized models achieved 4%, 19%, and 56% variance explanation in empirical functional connectivity (FC), respectively. Subject identification accuracy, based on simulated FC patterns, improved from < 1% (n = 4) to almost 100% (n = 23,875). Despite this precision, individual-level correlations between model parameters and attributes like age, gender, or intelligence quotient were small (effect sizes: η partial 2 ≤ 0.03 $$ {\eta}_{\mathrm{partial}}^2\le 0.03 $$ , standardized β ≤ 0.234 $$ \beta \le 0.234 $$ ). Machine learning analyses confirmed that these parameters lack the granularity to encode personal traits effectively. These findings suggest that, while current implementations of the RWW NMM can robustly replicate resting-state dynamics, the resulting parameters may lack the granularity required to map onto individual-specific behavioral metrics. This highlights a critical alignment problem: neural patterns and behavioral constructs such as intelligence may not correspond in a one-to-one fashion but instead represent higher-level abstractions. Bridging this gap will require the development of new tools capable of uncovering the underlying mapping manifolds, likely situated at the level of functional dynamics rather than isolated parameters. Future efforts should build on individual-level mechanistic modeling by exploring more expressive model classes and integrating richer sources of data, such as multimodal imaging or task-based paradigms, to better capture individual variability in both neural dynamics and behavioral traits. Such approaches may ultimately help to bridge the gap between model-based neural similarity and clinically meaningful personalization.

PMID:41243355 | DOI:10.1002/hbm.70413

J Affect Disord. 2025 Nov 13:120670. doi: 10.1016/j.jad.2025.120670. Online ahead of print.

ABSTRACT

BACKGROUND: Bipolar disorder (BD) carries high suicidality risk. While suicidal ideation (SI) correlates with evening chronotype, their joint neuroimaging mechanisms remain unclear. Unimodal MRI lacks sensitivity to detect coupled structural-functional abnormalities. We hypothesized BD patients with SI (BD-SI) would exhibit altered structural connectivity-functional connectivity (SC-FC) coupling versus BD patients without SI (BD-nSI) and healthy controls (HC), potentially correlating with chronotype.

METHODS: We recruited 138 BD-SI, 46 BD-nSI, and 280 HC. Resting-state functional MRI (rs-fMRI) and diffusion tensor imaging (DTI) data were acquired, and chronotype was assessed by Morningness-Eveningness Questionnaire (MEQ). Structural/functional connectivity and SC-FC coupling were compared across groups. Associations between SI, chronotype, and altered SC-FC coupling were examined in BD-SI group.

RESULTS: We found an altered functional connectivity (FC) network between 3 groups, involving the caudate nucleus, putamen, supplementary motor area, postcentral gyrus, inferior temporal gyrus and fusiform gyrus as important nodes. BD-SI patients demonstrated the most pronounced evening chronotype shift in MEQ total scores. BD-SI patients showed reduced SC-FC coupling in the triangular part of right inferior frontal gyrus (IFGtriang.R) compared to BD-nSI and HC, while both BD subgroups exhibited decreased coupling in the right olfactory cortex (OLF.R) relative to HC. The right amygdala (AMYG.R) displayed increased coupling in BD-SI versus BD-nSI, however, its nominal association with evening chronotype in BD-SI patients did not survive multiple comparisons correction.

CONCLUSION: Our study reveals changes in SC-FC coupling and a significant eveningness chronotype in BD-SI patients. This conjunction of physiological and clinical features warrants further investigation into chronotherapeutic strategies.

PMID:41241069 | DOI:10.1016/j.jad.2025.120670

Neuroimage Clin. 2025 Nov 9;48:103907. doi: 10.1016/j.nicl.2025.103907. Online ahead of print.

ABSTRACT

BACKGROUND: Previous studies indicated that motor stroke is characterized by changes in the motor system's resting-state functional connectivity and functional topology. Furthermore, recent reports have shown that time-varying connectivity among motor areas is crucial for motor impairment and its recovery. Yet, it is unknown to what extent the topological organization of the motor network exhibits temporal dynamics that might be clinically relevant for the motor deficit after motor stroke.

OBJECTIVE: We combined a graph-theoretic approach and dynamic functional connectivity MRI to identify dynamic central motor nodes, that is motor areas whose time-varying topological properties are associated with motor impairment, and to characterize the dynamic interactions among regions of the motor system after stroke.

METHODS: Resting-state fMRI data were collected from a cohort of twenty acute right-hemispheric stroke patients (17 ischemic/3 hemorrhagic) exhibiting NIHSS scores ranging from 1 to 22 (mean=10.05; SD=5.58). Dynamic functional connectivity was estimated using a sliding window approach applied to regions of the motor network. Next, time-varying nodal betweenness centrality, defined as the portion of all shortest paths in the network involving such a node, was computed at each sliding window. Then, dynamic central motor nodes were characterized by correlating the amount of time that a given node exhibited high centrality (i.e., high centrality mode) with the degree of the upper limb impairment. Finally, the time-varying topological interactions within the motor network were investigated by characterizing its shortest paths.

RESULTS: A dynamic central motor node was identified in a region located within the ipsilesional primary cortex, namely the anterior wall of the ventral central sulcus (vCS). Specifically, severely impaired patients exhibited shorter stays in high centrality mode than less affected patients. Furthermore, upper limb impairment was associated with a dynamic network profile characterized by low functional connections among such a dynamic central motor node and a set of regions located in the central sulcus and supplementary motor area of the left hemisphere, as well as in the right cerebellum.

CONCLUSIONS: The current results indicate that acute motor stroke with upper limb impairment affects the time-varying topological properties of functional interactions within the motor network. Therefore, these findings may contribute to understanding motor deficits after stroke.

PMID:41240754 | DOI:10.1016/j.nicl.2025.103907

Alzheimers Res Ther. 2025 Nov 14;17(1):247. doi: 10.1186/s13195-025-01898-1.

ABSTRACT

BACKGROUND: Alzheimer's Disease Spectrum (ADS) progresses from preclinical stages to dementia, with dynamic functional connectivity (dFC) changes emerging early. This study aimed to investigate the dynamic changes in brain networks across different stages of ADS and their underlying molecular mechanisms.

METHODS: This cross-sectional study included 239 participants: 69 Healthy Controls (HC), 83 with Subjective Cognitive Decline (SCD), 56 with Mild Cognitive Impairment (MCI), and 31 with Alzheimer's disease (AD). All participants underwent neuropsychological testing and resting-state functional magnetic resonance imaging (rs-fMRI). Leading Eigenvector Dynamics Analysis (LEiDA), a data-driven method that captures time-resolved whole-brain dFC, was applied to identify transient brain states and calculated their occupancy rate, dwell time, and transition probabilities. Group differences in these dynamic metrics were assessed using a General Linear Model (GLM), and their correlations with cognitive performance were examined. To explore the molecular basis of significant dFC alterations, we performed gene-category enrichment analysis. This analysis integrated the spatial maps of altered brain states with regional gene expression data from the Allen Human Brain Atlas (AHBA), using spin permutations to ensure statistical robustness.

RESULTS: We identified ten recurring brain states and characterized how their transition patterns, stability, and frequency differed as a function of disease severity. Specifically, early disruptions appeared as altered transition probabilities between states, while later stages showed pronounced changes in the dwell time and occurrence rates of specific states, closely associated with cognitive decline. Notably, one brain state marked by synchronized activity in attention, salience, and default mode networks emerged as a critical hub linked to both cognitive deterioration and excitatory-inhibitory imbalance. Genes associated with this state were enriched in glycine-mediated synaptic pathways and expressed in both excitatory and inhibitory neurons, showing spatial and temporal patterns that extended from early development into late disease stages.

CONCLUSIONS: Our study uncovered the stage-dependent dFC changes and their molecular underpinnings of brain network dysfunction across the ADS.

PMID:41239516 | DOI:10.1186/s13195-025-01898-1

Commun Biol. 2025 Nov 14;8(1):1572. doi: 10.1038/s42003-025-09158-6.

ABSTRACT

Task-based functional magnetic resonance imaging (fMRI) reveals individual differences in neural correlates of cognition but faces scalability challenges due to cognitive demands, protocol variability, and limited task coverage in large datasets. Here, we propose DeepTaskGen, a deep-learning approach that synthesizes non-acquired task-based contrast maps from resting-state (rs-) fMRI. We validate this approach using the Human Connectome Project lifespan data, then generate 47 contrast maps from 7 different cognitive tasks for over 20,000 individuals from UK Biobank. DeepTaskGen outperforms several benchmarks in generating synthetic task-contrast maps, achieving superior reconstruction performance while retaining inter-individual variation essential for biomarker development. We further show comparable or superior predictive performance of synthetic maps relative to actual maps and rs-connectomes across diverse demographic, cognitive, and clinical variables. This approach facilitates the study of individual differences and the generation of task-related biomarkers by enabling the generation of arbitrary functional cognitive tasks from readily available rs-fMRI data.

PMID:41238730 | DOI:10.1038/s42003-025-09158-6

PLoS One. 2025 Nov 14;20(11):e0334165. doi: 10.1371/journal.pone.0334165. eCollection 2025.

ABSTRACT

While respiration is known to rhythmically modulate brain activity, how different breathing modes (nasal vs. oral) affect frequency-specific large-scale neural connectivity in humans remains unexplored. We used resting-state functional magnetic resonance imaging (fMRI) to examine how nasal and oral breathing modulate functional brain connectivity, focusing on blood oxygenation level-dependent (BOLD) fluctuations in the intermediate frequency band of 0.1-0.2 Hz in 20 healthy male participants. A fully data-driven ROI-based inference approach across 133 whole-brain ROIs revealed that nasal and oral breathing significantly activated the olfactory region and brainstem, respectively. Seed-based connectivity (SBC) analysis, using nonparametric permutation testing (10,000 iterations) and cluster-wise false discovery rate (FDR) thresholding (p-FDR < 0.05), based on these seeds, revealed distinct patterns of network engagement depending on breathing mode. Nasal breathing was associated with greater functional connectivity within higher-order brain networks, including the salience, somatosensory, default mode, and frontoparietal networks. Conversely, oral breathing increased connectivity centered on the brainstem, engaging subcortical regions involved in autonomic regulation and survival functions. Despite these differences, both conditions recruited stable respiratory core regions comprising the hippocampus, amygdala, and insula. These findings suggest a novel framework, the respiration-entrained brain oscillation network (REBON), defined by three operational criteria: (1) it is frequency-specific to the 0.1-0.2 Hz band (centered around ~0.16 Hz); (2) the activity of its principal regions, the olfactory region and brainstem, alternates in dominance depending on the mode of breathing; and (3) it includes a stable core of limbic and interoceptive structures, such as the hippocampus, amygdala, and insula. Understanding this network may have implications for future therapeutic strategies aimed at supporting cognitive functions, emotion regulation, and the integrity of large-scale brain networks in both clinical and wellness contexts; however, these translational implications require validation in future experimental studies.

PMID:41237075 | DOI:10.1371/journal.pone.0334165

Elife. 2025 Nov 14;14:RP105537. doi: 10.7554/eLife.105537.

ABSTRACT

Cognitive abilities are closely tied to mental health from early childhood. This study explores how neurobiological units of analysis of cognitive abilities-multimodal neuroimaging and polygenic scores (PGS)-represent this connection. Using data from over 11,000 children (ages 9-10) in the Adolescent Brain Cognitive Development (ABCD) Study, we applied multivariate models to predict cognitive abilities from mental health, neuroimaging, PGS, and environmental factors. Neuroimaging included 45 MRI-derived features (e.g. task/resting-state fMRI, structural MRI, diffusion imaging). Environmental factors encompassed socio-demographics (e.g. parental income/education), lifestyle (e.g. sleep, extracurricular activities), and developmental adverse events (e.g. parental use of alcohol/tobacco, pregnancy complications). Cognitive abilities were predicted by mental health (r = 0.36), neuroimaging (r = 0.54), PGS (r = 0.25), and environmental factors (r = 0.49). Commonality analyses showed that neuroimaging (66%) and PGS (21%) explained most of the cognitive-mental health link. Environmental factors accounted for 63% of the cognitive-mental health link, with neuroimaging and PGS explaining 58% and 21% of this environmental contribution, respectively. These patterns remained consistent over two years. Findings highlight the importance of neurobiological units of analysis for cognitive abilities in understanding the cognitive-mental health connection and its overlap with environmental factors.

PMID:41236810 | DOI:10.7554/eLife.105537

Brain Res Bull. 2025 Nov;232:111603. doi: 10.1016/j.brainresbull.2025.111603. Epub 2025 Oct 25.

ABSTRACT

BACKGROUND: Social anxiety disorder (SAD) is a prevalent psychiatric disorder, yet its underlying neural mechanisms remain unclear. Patients with SAD often show cognitive impairments associated with brain dysfunction. However, no study has examined the relationship between frequency-dependent brain activity and cognitive performance in SAD using resting-state functional magnetic resonance imaging (rs-fMRI) and neuropsychological assessments. In this study, we examined this association in patients with SAD using rs-fMRI.

METHODS: rs-fMRI data were collected from 27 patients with SAD and 40 healthy controls (HCs). Frequency-dependent alterations in fractional amplitude of low-frequency fluctuations (fALFF) were examined across typical (0.01-0.08 Hz), slow-5 (0.01-0.027 Hz), and slow-4 (0.027-0.073 Hz) bands to identify regions with abnormal spontaneous brain activity. Cognitive function was assessed using the Cambridge Neuropsychological Test Automated Battery. Correlations among abnormal brain activity, clinical symptoms, and cognitive functions were analyzed.

RESULTS: Compared with HCs, patients with SAD showed lower mean fALFF (mfALFF) in the bilateral postcentral gyrus across the typical and slow-5 bands, but only in the left postcentral gyrus for the slow-4 band. While mfALFF was not significantly associated with clinical symptom severity, significant correlations were observed between mfALFF and cognitive functioning.

CONCLUSIONS: Our findings indicate that cognitive function in patients with SAD is associated with frequency-dependent abnormalities in spontaneous brain activity, particularly reduced mfALFF in the postcentral gyrus. Additionally, frequency-dependent neural markers may help identify and target cognitive dysfunction in SAD, with abnormal postcentral gyrus activity potentially contributing to understanding of its underlying neural mechanisms.

PMID:41236075 | DOI:10.1016/j.brainresbull.2025.111603

Brain Res Bull. 2025 Nov;232:111600. doi: 10.1016/j.brainresbull.2025.111600. Epub 2025 Oct 25.

ABSTRACT

BACKGROUND: Resting-state functional MRI (rs-fMRI), reflecting the functional connectivity in the brain, is a useful tool for investigating the functional alteration induced by neurological diseases. In animal studies, anesthesia is essential for the acquisition of rs-fMRI data to eliminate motion artifacts. However, the optimal anesthesia protocol for adolescent rats is rarely discussed. The aim of the current study is to propose a feasible anesthesia protocol combining isoflurane and dexmedetomidine for use in adolescent rats.

MATERIAL AND METHODS: The rs-fMRI data were acquired in ten adolescent rats (postnatal day 40) at 60 and 90 min after the bolus injection of the dexmedetomidine, respectively, to assess the different patterns of the resting-state networks. The subject-level independent component analysis (sICA) was performed to demonstrate the resting-state networks in the adolescent rats. The Z-scores, transformed from Pearson's correlation coefficients, were calculated to compare the difference of within-region functional connectivity between two time points. The functional connectivity matrix was demonstrated to show the interregional functional connectivity over the anesthesia protocol.

RESULTS: The respiratory rate of the adolescent rats returned to the baseline at around 35 min after the bolus injection, becoming stable at around 60 min. In the adolescent rats, the typical resting-state networks including the default mode network (DMN), sensory, and motor networks were observed, similar to that in adult ones under the same anesthesia protocol. The within-region functional connectivity was lower at 60 min compared to that at 90 min. The interregional functional connectivity showed the more specific network pattern at 90 min.

CONCLUSIONS: Our results demonstrated the feasibility of the anesthesia protocol with the combination of isoflurane and dexmedetomidine and highlighted its time-dependent and dosage effect in the adolescent rats.

PMID:41236072 | DOI:10.1016/j.brainresbull.2025.111600

Behav Brain Res. 2026 Feb 4;497:115895. doi: 10.1016/j.bbr.2025.115895. Epub 2025 Oct 24.

ABSTRACT

BACKGROUND AND OBJECTIVE: Attention-Deficit/Hyperactivity Disorder (ADHD) is a common neurodevelopmental condition characterized by inattention, hyperactivity, and impulsivity. Emerging evidence suggests that ADHD is linked to hypofunction of the prefrontal-parietal attention network, accompanied by compensatory hyperactivation in the cerebellum and brainstem. However, the underlying neural mechanisms remain insufficiently understood. In recent years, computerized cognitive training has gained attention as a promising non-pharmacological intervention for alleviating ADHD symptoms, though its mechanisms of action and effects on neural plasticity remain contentious. This study utilized longitudinal neuroimaging to investigate abnormal brain function in individuals with ADHD, assess the effects of personalized computerized cognitive training (PCCT) on core symptoms, and examine the relationship between functional brain changes and behavioral improvements.

MATERIALS AND METHODS: Sixteen children with ADHD (ADHD group) and sixteen age- and sex-matched healthy controls (HC group) were recruited. All participants underwent resting-state functional magnetic resonance imaging (rs-fMRI) within three days of clinical assessment. The fractional amplitude of low-frequency fluctuations (fALFF) was calculated to evaluate spontaneous neural activity. The ADHD group received a 16-week PCCT intervention consisting of interference inhibition, sustained inhibition, and dominant inhibition training, administered once per week for 60 min per session. Baseline differences in fALFF between the groups were examined, along with pre- and post-intervention changes in clinical scores and fALFF values within the ADHD group. Correlation analyses were conducted between changes in fALFF and behavioral measures.

RESULTS: 1.At baseline, the ADHD group showed significantly higher scores than the healthy control (HC) group in SNAP (hyperactivity/inattention rating scale), CPT (Continuous Performance Test), and MMFT (Memory Function Test) (P < 0.05). fALFF analysis revealed decreased fALFF values in the precuneus, angular gyrus, and postcentral gyrus, and increased fALFF values in the cerebellum and brainstem in the ADHD group (P < 0.05, GRF-corrected).2.Effects of PCCT: Following the intervention, the ADHD group demonstrated significant reductions in SNAP-IV, CPT, and MMFT scores (P < 0.05), along with improved performance in all three inhibitory control tasks. fALFF values increased in the precuneus and lingual gyrus and decreased in the cerebellum and hippocampus, indicating modulation of abnormal neural activity.3.Correlation Analysis: The fALFF value in the right cerebellar lobule IX was positively correlated with CPT scores (r = 0.715), and the fALFF value in the left hippocampus was also positively correlated with CPT scores (r = 0.642). In contrast, the fALFF value in the right superior temporal gyrus was negatively correlated with MMFT scores (r = -0.721). These findings suggest that the cerebellar-prefrontal circuit, hippocampus, and superior temporal gyrus play important roles in the regulation of cognitive functions in children with ADHD.

CONCLUSION: This study revealed widespread functional abnormalities in the brains of children with ADHD, characterized by inefficiencies in the prefrontal-parietal network and compensatory hyperactivation in the cerebellum and brainstem. PCCT effectively improved core ADHD symptoms and induced neuroplastic changes in specific brain regions, including reduced activity in the cerebellum and hippocampus and increased activity in the precuneus and lingual gyrus. Furthermore, fALFF changes in the cerebellar lobule IX, hippocampus, and superior temporal gyrus were closely associated with cognitive improvements, supporting the central role of the cerebellar-prefrontal circuitry in modulating executive functions in ADHD. These findings provide new evidence for neural compensation mechanisms and non-pharmacological treatment strategies for ADHD. Future studies may explore precision interventions targeting the cerebellar-prefrontal network, such as combining neuromodulation with cognitive training, to optimize long-term outcomes in ADHD.

PMID:41235972 | DOI:10.1016/j.bbr.2025.115895