Most recent paper

Front Behav Neurosci. 2026 Jan 9;19:1690499. doi: 10.3389/fnbeh.2025.1690499. eCollection 2025.

ABSTRACT

INTRODUCTION: Flow is characterized by complete immersion and optimal engagement in a task, striking a balance between challenge and skill. Recent neuroimaging studies suggest that flow involves dynamic interactions among large-scale brain networks, particularly the default mode network (DMN) and the executive control network (ECN). This review aims to synthesize current findings on how flow-related DMN-ECN connectivity supports creativity and emotional regulation (ER).

METHODOLOGY: Following PRISMA guidelines, we searched PubMed, PsycINFO, and Google Scholar for peer-reviewed neuroimaging studies that experimentally induced or measured flow states. Inclusion criteria encompassed task-based and resting-state fMRI, PET, or EEG designs focusing on DMN, ECN, or related networks (e.g., salience, reward), and studies explicitly reporting on creativity or ER outcomes. We extracted data on sample characteristics, flow induction methods, neuroimaging modalities, and main findings regarding DMN/ECN activation and connectivity. Risk of bias was assessed in the domains of selection, performance, detection, attrition, and reporting.

RESULTS: Nine studies met the inclusion criteria. Across diverse tasks-ranging from video games to jazz improvisation-flow was consistently associated with (1) down-regulation of core DMN regions (e.g., medial prefrontal cortex, posterior cingulate cortex) linked to diminished self-referential thought, (2) increased activity in lateral prefrontal and parietal areas underpinning attentional control, and (3) functional connectivity between networks often considered anti-correlated (e.g., DMN and ECN). This integrated network state appears to facilitate simultaneous idea generation (DMN) and goal-directed processing (ECN), supporting creativity. Additionally, reduced amygdala activity and insula-reward network coupling during flow suggest potential benefits for emotional regulation, allowing high focus and low anxiety.

CONCLUSION: Flow emerges as a unique neurocognitive phenomenon marked by selective DMN suppression and enhanced ECN engagement. Such network reconfiguration fosters creativity through DMN-ECN synergy while providing emotional stability via reduced self-monitoring and negative affect. Although these findings are promising, further research should employ larger, more diverse samples, incorporate causal and longitudinal designs, and explicitly measure ER outcomes. Elucidating the neurochemical underpinnings of flow (e.g., dopamine release) and individual differences in "flow-proneness" remains an important future direction.

PMID:41583727 | PMC:PMC12827708 | DOI:10.3389/fnbeh.2025.1690499

Neuroimage. 2026 Jan 23:121751. doi: 10.1016/j.neuroimage.2026.121751. Online ahead of print.

ABSTRACT

BACKGROUND: Repetitive transcranial magnetic stimulation (rTMS) shows therapeutic potential for obsessive-compulsive disorder (OCD). Brain entropy has recently emerged as a candidate biomarker in neuropsychiatry, yet its modulation by rTMS in OCD remains unclear. Given EEG's superior temporal resolution for capturing rapid fluctuations in neural complexity, it was used to evaluate the effects of fMRI-neuronavigated rTMS on frontal entropy and its potential as an objective treatment marker.

METHODS: Resting-state EEG was recorded from 44 OCD patients and 24 healthy controls (HCs) to compute frontal entropy- and complexity-based measures, including approximate entropy (ApEn), sample entropy (SampEn), and Lempel-Ziv complexity (LZC). Patients were randomized to an active (n = 22) or sham (n = 22) rTMS group, with the active group receiving individualized 1 Hz stimulation over the right pre-supplementary motor area for 14 consecutive days. EEG was repeated post-intervention.

RESULTS: At baseline, OCD patients exhibited higher frontal complexity than healthy controls across all three measures. Linear mixed-effects models consistently revealed significant main effects of time and stimulation, as well as their interaction. Bayesian and FDR-corrected analyses confirmed significant reductions in all three measures following active stimulation. Post-treatment, frontal complexity remained elevated in the sham group relative to healthy controls, whereas no such difference was observed in the active stimulation group.

CONCLUSION: OCD is characterized by increased frontal neural complexity as indexed by multiple entropy- and complexity-based EEG measures. Individualized rTMS modulated these abnormalities, supporting frontal EEG complexity as a promising objective biomarker of neuromodulatory effects.

PMID:41581677 | DOI:10.1016/j.neuroimage.2026.121751

Prog Neuropsychopharmacol Biol Psychiatry. 2026 Jan 23:111622. doi: 10.1016/j.pnpbp.2026.111622. Online ahead of print.

ABSTRACT

BACKGROUND: Major depressive disorder (MDD) is frequently accompanied by suicidal ideation (SI), which has been linked to functional disruptions in brain regions involved in emotion regulation, including the key amygdala. However, the temporal dynamics of amygdala-based functional connectivity in MDD patients with SI remain unclear.

METHODS: First-episode MDD patients with SI (n = 53), without SI (NSI, n = 27), and healthy controls (HCs, n = 58) who underwent resting-state functional magnetic resonance imaging were included. Co-activation pattern (CAP) analysis was employed to characterize amygdala connectivity dynamics. Five distinct network states were identified and corresponding dynamic metrics were analyzed with respect to clinical characteristics, and support vector machine (SVM) classification was applied to classify individuals based on aberrant CAP features.

RESULTS: Both SI and NSI groups showed increased dominance of the affective network (AN) co-activated with the amygdala compared to HCs, with a positive correlation to anxiety symptoms. Notably, SI group exhibited decreased occurrence rate and resilience of a transient network state, predominantly involving the default mode network (DMN) and reward network (RN), which was negatively correlated with SI severity. SVM classification achieved robust performance in distinguishing SI individuals from NSI and HC based on dynamic CAP metrics.

CONCLUSION: These findings highlight heightened temporal instability of the AN and associated excessive anxious mood in MDD, while the diminished dominance of the amygdala-DMN-RN coupling indicates impairments in self-referential and reward processing in MDD patients with SI. Overall, disruptions in amygdala-based network dynamics may implicate neurobiological substrates underlying emotional dysregulation and elevated suicide risk in MDD.

TRIAL REGISTRATION: The registration number is ChiCTR2000031931 and date of registration is April 15th 2020.

PMID:41581546 | DOI:10.1016/j.pnpbp.2026.111622

Schizophrenia (Heidelb). 2026 Jan 24. doi: 10.1038/s41537-025-00708-9. Online ahead of print.

ABSTRACT

Few neuroimaging studies have examined other specified schizophrenia spectrum and other psychotic disorder (OSSO). We sought to identify features differentiating patients with OSSO from those with schizophrenia spectrum disorders (SSD) and healthy controls (HC) using auditory seed-based functional connectivity (FC) analysis. Patients with OSSO (n = 88), patients with SSD (n = 81), and HC (n = 85), matched for age, sex, and education, underwent resting-state functional magnetic resonance imaging (rs-fMRI) and clinical evaluation. To reduce heterogeneity of OSSO, individuals with specific subtypes of OSSO, i.e., pure delusion and delusion with attenuated auditory hallucinations (AHs) were only included. Using five auditory seeds, we conducted seed-to-voxel and seed-to-region of interest (ROI) analyses. We also conducted between- and within-network connectivity analyses of 13 networks, and correlations of altered FC with symptomatology were explored. The SSD group showed significantly greater connectivity between the superior temporal gyrus (STG) and precuneus, and between the temporal pole cortex (TP) and precuneus, compared to the OSSO group. Overall auditory seed-based hypoconnectivity and middle temporal gyrus-based hyperconnectivity were observed in both groups compared to HC. In OSSO, hallucination severity was positively associated with insula-putamen connectivity, whereas delusional and negative symptoms showed inverse correlations with TP-insula and STG-Heschl's gyrus connectivity, respectively. In SSD, hallucination severity correlated positively with STG-Heschl's gyrus and TP-insula connectivity whereas negative symptoms correlated negatively with STG-insula connectivity. These findings suggest that there are distinct differences in FC between patients with OSSO and patients with SSD, which supports the proposal that OSSO should be treated as a separate clinical syndrome with distinct neural connectomes. Future research may explore whether interventions targeting these altered connectivity patterns could help reduce the risk of progression from OSSO to SSD.

PMID:41580400 | DOI:10.1038/s41537-025-00708-9

Brain Topogr. 2026 Jan 24;39(2):19. doi: 10.1007/s10548-026-01174-x.

ABSTRACT

Cigarette smoking is known to be associated with altered static functional connectivity in the brain. However, investigating its dynamics may offer novel and insightful perspectives for elucidating the neural mechanisms underlying smoking addiction. The aim of this study was to explore the characteristics of dynamic functional network connectivity in heavy smokers. This study is a secondary analysis of a previously acquired dataset, leveraging novel dynamic functional network connectivity methodologies to investigate distinct research questions. Resting-state functional magnetic resonance imaging data were collected from 34 heavy smokers and 36 non-smokers. Forty-two meaningful independent components were selected after the group independent component analysis. Four distinct brain states were identified based on a sliding window approach and k-means clustering analysis. The temporal properties of these states were compared between the two groups, and correlations between these differences and smoking-related factors were examined in heavy smokers. Compared with non-smokers, heavy smokers exhibited a lower occurrence rate and mean dwell time in state 2 characterized by synchrony within the default mode network and anticorrelation with other domains, and a reduced mean dwell time in state 3 marked by high connectivity within the sensory domains. Network-based statistics revealed that cognitive control and cerebellar domains played important roles in the altered subnetworks. In heavy smokers, the occurrence rate showed negative relationships with the duration of smoking in state 2. These findings advance our understanding of the temporal and network-level dysfunctions associated with smoking addiction, offering a new framework for future studies aimed at developing targeted treatments and preventive strategies.

PMID:41579218 | DOI:10.1007/s10548-026-01174-x

Neurol Sci. 2026 Jan 24;47(2):194. doi: 10.1007/s10072-025-08637-2.

ABSTRACT

BACKGROUND AND PURPOSE: In recent years, resting-state functional magnetic resonance imaging (rs-fMRI) has been widely used in the study of Parkinson's disease (PD), but the findings have not yet reached consensus. Besides, no studies have been conducted to standardize meta-analyses by combining the amplitude of low-frequency fluctuation (ALFF), fractional amplitude of low-frequency fluctuation (fALFF), and regional homogeneity (ReHo) functional indicators of PD.

METHODS: A whole-brain voxel-wise meta-analysis was performed on resting-state functional imaging studies that explored differences in spontaneous functional brain activity between individuals with PD and healthy controls (HCs) using the Seed-based d Mapping with Permutation of Subject Images (SDM-PSI) software.

RESULTS: A total of 59 independent functional imaging studies (88 datasets) with 2591 individuals with PD and 1804 HCs were included. The results of the main meta-analysis revealed decreased resting-state regional functional activity in the left lenticular nucleus, the putamen, and the left supplementary motor area in PD patients compared to HCs, and no brain regions with significantly increased functional activity were identified. In the subsequent jackknife sensitivity analyses, these results showed high robustness and no significant heterogeneity or publication bias was observed.

CONCLUSION: This functional meta-analysis not only revealed robust and consistent brain regions with altered spontaneous functional activity in PD, but also helped to deepen our understanding of the complex neuropathological mechanisms of PD.

PMID:41579202 | DOI:10.1007/s10072-025-08637-2

Hum Brain Mapp. 2026 Feb 1;47(2):e70453. doi: 10.1002/hbm.70453.

ABSTRACT

Psychomotor disturbances like agitation and retardation are key symptoms of major depressive disorder (MDD). Despite their clinical significance, the underlying neural mechanisms, for example, motor or psychomotor, remain yet elusive. This study aimed to investigate whether psychomotor agitation and retardation in MDD are associated with alterations in brain dynamics. A total of 119 patients with MDD and 94 HCs were recruited and undertaken fMRI testing. Brain dynamics was measured by the time delays, the lag propagation of global to somatomotor network (SMN) resting state functional connectivity (FC, e.g., lag propagation). Lag propagation of global to SMN FC was delayed in retarded MDD compared to both agitated MDD (t = 3.256, pFDR = 0.006) and HC (t = 2.493, pFDR = 0.041). Further, we observed a significant correlation of the severity of agitation and retardation, measured by the Hamilton depression scale, with global to local SMN's time delays, respectively (agitation: r = -0.19, p = 0.04; retardation: r = 0.32, p = 0.03). Finally, early global to SMN delays predicted a close association of agitation and anxiety levels (F = 5.18, p = 0.025). In contrast to these results in global-to-SMN dynamics, no significant delay changes were observed in the local intra-network SMN dynamics. Together, our findings show distinct neural dynamics in MDD psychomotor retardation, for example, delayed, and agitation, for example, early in global to local SMN functional connectivity. This supports the psychomotor over the motor model of psychomotor retardation which carries major implications for clinical diagnosis and therapy.

PMID:41578838 | DOI:10.1002/hbm.70453

Psychiatry Res Neuroimaging. 2026 Jan 14;357:112133. doi: 10.1016/j.pscychresns.2025.112133. Online ahead of print.

ABSTRACT

In this paper, a new multiclass Alzheimer diagnosis system is proposed using Broad Learning (BL) and the combination of Local Coherence (LCOR) and Intrinsic Connectivity Contrast (ICC) parameters. A public resting state fMRI database; including healthy elderly subjects (HC), Alzheimer Disease (AD) and Mild Cognitive Impairment (MCI) patients; was chosen in this study. All rs-fMRI pre-processing and analysis were performed by CONN toolbox. Three contrast cases of AD, MCI and HC were implemented within the group-level analysis, then both LCOR and ICC parameters of the effected brain clusters were combined and collected. For diagnosis system, Broad Learning (BL) classifier is trained to classify three stages of AD, MCI and HC, respectively. Referring to the experimental results and compared with other current studies, the proposed system achieved high average accuracy of 99.6% with low training time of 2 s. Furthermore, a mapping between effected brain regions and their functions is given to interprets the common symptoms for AD and MCI patients.

PMID:41576905 | DOI:10.1016/j.pscychresns.2025.112133

Epilepsy Behav. 2026 Jan 22;176:110873. doi: 10.1016/j.yebeh.2025.110873. Online ahead of print.

ABSTRACT

OBJECTIVE: Depression is the most common comorbidity in epilepsy. Currently, the diagnosis of comorbid depression in epilepsy primarily relies on medical history and scales. However, this approach is highly subjective and heavily dependent on the physician's experience, and prone to missed or misdiagnosis. The primary objective of this study was to evaluate the effectiveness of network homogeneity (NH) measurements analyzed via support vector machine (SVM) in diagnosing MRI-negative temporal lobe epilepsy with depression (MRI-negative TLED).

METHODS: The study included a total of 217 participants, comprising 90 healthy controls, 45 patients with MRI-negative temporal lobe epilepsy (MRI-negative TLE) and 82 patients with MRI-negative TLED. All subjects underwent resting-state fMRI scans for data collection. For analytical purposes, NH were computed and combined with SVM techniques for comprehensive data analysis.

RESULTS: Compared to healthy control individuals, MRI-negative TLED patients demonstrated significantly increased NH values in the right mid-cingulum, right precuneus and right supramarginal, accompanied by decreased NH in the bilateral inferior temporal gyrus, left parahippocampal gyrus (PHG) and the right medial superior frontal gyrus (mSFG). Compared to MRI-negative TLE patients, MRI-negative TLED patients demonstrated significantly decreased NH values in the left parahippocampal gyrus (PHG) and the left mid temporalpole (MTP). SVM was used to differentiate patients with MRI-negative TLED from healthy control individuals based on rs-fMRI data, and the decreased NH in the left PHG showed highe diagnostic accuracy (71.56%).

SIGNIFICANCE: According to the results, decreased NH values in the left PHG could serve as neuroimaging marker for MRI-negative TLED, offering objective guidance for its diagnosis.

PMID:41576839 | DOI:10.1016/j.yebeh.2025.110873

Neural Netw. 2026 Jan 18;198:108621. doi: 10.1016/j.neunet.2026.108621. Online ahead of print.

ABSTRACT

Resting-state functional magnetic resonance imaging (rs-fMRI) provides critical biomarkers for diagnosing neuropsychiatric disorders such as autism spectrum disorder (ASD) and major depressive disorder (MDD). However, existing deep learning models heavily rely on labeled data, limiting their clinical applicability. This study proposes a GIN-Transformer-based pairwise graph contrastive learning framework (GITrans-PairCL) that integrates a Graph Isomorphism Network (GIN) and Transformer to address data scarcity through unsupervised graph contrastive learning. The framework comprises two key components: a Dual-modal Contrastive Learning (DCL) module and a Task-Driven Fine-tuning (TDF) module. DCL employs sliding-window augmented rs-fMRI time series, combining GIN for modeling local spatial connectivity and Transformer for capturing global temporal dynamics, enabling multi-scale feature extraction via cross-view contrastive learning. TDF adapts the pre-trained model to downstream classification tasks. We conducted single-site and cross-site evaluation on two publicly available datasets, and the experimental results showed that GITrans-PairCL outperforms both traditional machine learning and deep learning baseline methods in automatic diagnosis of brain diseases. The model combines local and global features, and uses pre-trained contrast learning to reduce the dependence on labeling information and improve generalization.

PMID:41576557 | DOI:10.1016/j.neunet.2026.108621

Geroscience. 2026 Jan 23. doi: 10.1007/s11357-025-02094-7. Online ahead of print.

ABSTRACT

The lateral prefrontal cortex (LPFC) plays a pivotal role in executive functions and exhibits a hierarchical rostro-caudal organization critical for higher-order cognition. Using connectome gradient mapping of resting-state fMRI data across young, middle-aged, and older adults (N = 478), we found preserved global gradient structure but significant compression of the principal gradient in older adults relative to middle-aged adults, particularly in dorsolateral (DLPFC) and frontopolar (FPC) regions. This reduced functional differentiation corresponded to lower spatial separation between LPFC subdivisions. Meta-analytic decoding linked these changes to attenuated engagement of executive functions. Crucially, in an independent cohort of older adults (N = 99), individuals with better executive function exhibited greater gradient range and variation at the global level, along with higher gradient values in the DLPFC and ventrolateral prefrontal cortex (VLPFC) and lower values in the premotor cortex at the regional level. These findings suggest that age-related disruption of LPFC gradient organization may reflect neural dedifferentiation and is closely related to executive decline. Gradient compression in the LPFC may serve as a novel biomarker of cognitive aging, offering insights into the hierarchical reorganization of brain networks in late life.

PMID:41575684 | DOI:10.1007/s11357-025-02094-7

CNS Neurosci Ther. 2026 Jan;32(1):e70759. doi: 10.1002/cns.70759.

ABSTRACT

BACKGROUND: Supratentorial focal lesions following ischemic stroke can lead to crossed cerebellar diaschisis (CCD). However, it remains unclear how CCD affects the functional connectivity between the cerebellum and the rest of the brain in ischemic stroke patients.

METHODS: This case-control study involved resting-state fMRI data from 65 patients with basal ganglia ischemic stroke (Stroke) and 72 healthy controls (HC). Cerebral, cerebellar, and cerebrocerebellar inter-module functional connectivity in both 7-module and 17-module conditions were calculated and compared between the Stroke and HC groups. Spearman correlation analyses were further conducted to examine the relationships between connectivity alterations and both stroke severity and lesion size in Stroke patients.

RESULTS: The Stroke patients exhibited disrupted inter-module functional connectivity, characterized by increased intra-hemispheric and decreased inter-hemispheric connectivity between cerebral modules, increased inter-module connectivity in the cerebellum, and reduced connectivity between ipsilesional cerebral modules and cerebellar modules while increasing connectivity between contralesional cerebral modules and cerebellar modules. Moreover, these connectivity changes, particularly disruptions in the cerebellar connectome, may be associated with lesion size and stroke severity in Stroke patients.

CONCLUSIONS: These findings highlight the importance of cerebellar connectome disruptions in ischemic stroke, which may provide valuable insights into the disease's underlying brain mechanisms.

PMID:41574670 | DOI:10.1002/cns.70759

bioRxiv [Preprint]. 2025 Dec 10:2025.12.07.692856. doi: 10.64898/2025.12.07.692856.

ABSTRACT

Resting-state fMRI studies increasingly emphasize the dynamic nature of brain networks. While most approaches examine temporal fluctuations in connectivity, we focus on the spatial dynamics and complexity at voxel level - how networks expand and contract, and change their structural complexity over time. Using dynamic independent component analysis (ICA), we investigate the hierarchical structure of the resulting time-varying spatial networks, from their broad periphery to their most active core. We combine this with fractal dimension (FrD) as a measure of a network's spatial complexity and analyze temporal changes (dynamic flexibility) in a network and synchronized fluctuations between network pairs (fractal dimension coupling, FrDC). We refer to this approach as "dynamic spatial network complexity and connectivity (dSNCC)". Using a combined cohort of 508 subjects (315 healthy controls, 193 schizophrenia patients), we found that schizophrenia is associated with higher mean FrD in several networks, suggesting more irregular patterns/boundaries and a disorganized network structure. Critically, patients showed significantly reduced dynamic flexibility, indicating their networks are "stuck" in a less adaptable state. This robust finding is evidenced by a synergistic loss of temporal standard deviations in both network volume and FrD across multiple networks and activity thresholds. This maladaptive complexity was associated with cognitive impairment, with several dSNCC measures showing significant associations with subject scores for processing speed, visual learning, and verbal learning. Higher complexity in these networks and more significantly, their reduced dynamic flexibility as seen in patients, were particularly associated with impaired performance. Furthermore, we found aberrant connectivity (FrDC) in schizophrenia, with certain network pairs exhibiting overly synchronized complexity changes. Our results demonstrate that dSNCC is a powerful tool for characterizing network dynamics and may potentially provide a measurable mechanism for maladaptation in schizophrenia, where the brain's inability to fluidly change its complexity may contribute to cognitive deficits and symptoms like disorganized thought. These findings highlight the importance of studying the intrinsic spatial dynamic properties to reveal the fundamental principles of brain network organization in health and disease. Our work represents a significant leap in complex systems neuroscience and provides a novel, quantifiable biomarker framework highly relevant for understanding and targeting other complex disorders characterized by network dysfunction, such as Alzheimer's disease, autism, or other mental health conditions.

PMID:41573947 | PMC:PMC12822663 | DOI:10.64898/2025.12.07.692856

bioRxiv [Preprint]. 2025 Dec 4:2025.12.02.691788. doi: 10.64898/2025.12.02.691788.

ABSTRACT

Intrinsic brain activity is often described as wandering within a continuous multivariate space, yet the organizing principles that constrain these dynamics remain unclear. Here we show that spontaneous human brain activity during rest is structured by a deformable attractor manifold. Using large-scale fMRI datasets and a latent dynamical model, we find that cortical activity occupies two reproducible regimes: a low-coherence state with a unimodal latent distribution and a high-coherence state that exhibits bimodality, consistent with transient bistability across association networks. A compact two-parameter energy landscape explains these dynamics, revealing that transitions arise not from switching between discrete states, but from continuous deformation of the manifold that reshapes attractor geometry. Excursions into the bistable regime occur as rapid "jumps", whereas returns follow slow drifts along the manifold, reflecting network-specific timescales. Individuals with greater expression of the bistable regime show higher cognitive fluidity, and manifold parameters differentiate mild cognitive impairment from matched controls. These findings identify an organizing geometric and dynamical principle of resting activity, linking large-scale cortical coordination, cognitive variability, and vulnerability to pathology.

PMID:41573819 | PMC:PMC12822801 | DOI:10.64898/2025.12.02.691788

Imaging Neurosci (Camb). 2026 Jan 20;4:IMAG.a.1099. doi: 10.1162/IMAG.a.1099. eCollection 2026.

ABSTRACT

A known issue with multi-site studies is the presence of site-specific effects that may confound effects of interest. These effects may be additive, multiplicative, or both. Numerous strategies have been developed and tested on microarray data from multiple batches, structural magnetic resonance imaging (MRI), diffusion tensor imaging (DTI), and functional MRI (fMRI). Multi-site magnetoencephalography (MEG) data represent a unique problem, however. The major MEG platforms differ substantially in sensor geometry, sensor layout, and noise-cancellation strategy, all of which may affect the distribution of the data. Another factor to consider in harmonization is retention of the relationship between the data and any covariates of interest. These relationships may be nonlinear, and individual sites may differ in the distribution of covariates. In this report, we test several previously developed methods for harmonization on a set of 16 open access datasets. We investigated ComBat, which uses empirical Bayes to improve model estimation; GAM-ComBat (Neuroharmonize), which extends ComBat to incorporate generalized additive modeling of the covariates of interest; CovBat (with the GAM extension), which performs a second round of ComBat harmonization to harmonize the covariance; and RELIEF, a matrix factorization technique. We found that overall, GAM-ComBat was the best choice for harmonizing the data while retaining the nonlinear dependence of the data on covariates of interest such as age. We demonstrate that harmonization of MEG data is possible and should be an integral part of any multi-site study.

PMID:41573591 | PMC:PMC12820802 | DOI:10.1162/IMAG.a.1099

Front Aging Neurosci. 2026 Jan 7;17:1716076. doi: 10.3389/fnagi.2025.1716076. eCollection 2025.

ABSTRACT

BACKGROUND AND PURPOSE: Alzheimer's disease (AD), the most common form of dementia worldwide, is characterized by progressive cognitive decline. Extensive evidence from dynamic functional connectivity (dFC) studies has demonstrated unstable functional states, reduced network flexibility, and impaired transitions between large-scale neurocognitive networks across the AD continuum. However, how these temporal abnormalities are embedded within the hierarchical spatial organization of brain networks, as captured by functional gradients (FG), and whether combined FG-dFC metrics can provide mechanistically interpretable and potentially sensitive imaging biomarkers, remain to be elucidated.

METHODS: This study enrolled 46 AD patients who were diagnosed according to the Amyloid/Tau/Neurodegeneration (ATN) biological diagnostic framework and 37 age- and sex-matched healthy controls (HC). All participants underwent resting-state fMRI. Functional gradients were derived using connectivity similarity matrices and diffusion embedding (aligned and standardized), while dFC was estimated with a sliding window approach and clustered into four recurrent states. Group differences were assessed with two-sample t-tests with Gaussian Random Field (GRF) correction. Correlation analyses included ATN biomarkers and cognitive scores. A linear support vector machine (SVM) with leave-one-out cross-validation evaluated classification performance based on significant FG features.

RESULTS: Compared to the healthy controls, AD patients exhibited widespread FG alterations between regions of the Default Mode Network (DMN) and the Sensorimotor Network (SMN). In the first gradient DMN, the left precuneus showed reduced gradient scores, whereas the right medial superior frontal gyrus and bilateral angular gyri were increased. In the first gradient of the SMN, the right supplementary motor area increased while bilateral superior temporal gyri decreased. Second-gradient reductions were confined to two regions: the left postcentral gyrus (SMN) and left middle occipital gyrus (visual network, VIS). The right medial superior frontal gyrus first-gradient score correlated negatively with T-Tau (r = -0.50, P = 0.006) and age (r = -0.36, P = 0.02); the right angular gyrus correlated negatively with age (r = -0.29, P = 0.04); the left precuneus correlated positively with age (r = 0.38, P = 0.009). dFC revealed four recurrent states (27.59, 17.67, 28.27, 26.47% of total occurrences). Relative to HC, AD showed higher FT and MDT in states 1-2 and lower scores in state 3, with NT unchanged, alongside state-dependent bidirectional connectivity changes (fronto-insular-sensorimotor increases; DMN-temporal and visuo-auditory decreases). The SVM achieved an AUC of 0.776, sensitivity 78.26%, specificity 67.57%, and accuracy 73.49%, with the right superior temporal gyrus within SMN first-gradient contributing most.

CONCLUSION: AD is characterized by macro-scale hierarchical disorganization centered on the principal functional gradient, accompanied by reduced cross-state flexibility and state-dependent connectivity abnormalities. The combined functional gradient-dynamic functional connectivity (FG-dFC) analysis provides complementary spatiotemporal insights and reveals imaging features associated with T-Tau levels and age, offering new perspectives on the neuropathological mechanisms of AD and potential imaging biomarkers. Moreover, these network topology and dynamic connectivity metrics may prove useful for monitoring disease progression, evaluating treatment effects, and stratifying patients in future clinical and interventional studies.

PMID:41573383 | PMC:PMC12819802 | DOI:10.3389/fnagi.2025.1716076

Int J Ophthalmol. 2026 Feb 18;19(2):291-301. doi: 10.18240/ijo.2026.02.11. eCollection 2026.

ABSTRACT

AIM: To study the relationships between amplitude of low-frequency fluctuations (ALFF) changes and clinical ophthalmic parameters in patients with primary open angle glaucoma (POAG) and analyze the diagnostic value of ALFF.

METHODS: Twenty-four POAG patients and 24 healthy controls (HCs) underwent resting-state functional magnetic resonance imaging (rs-fMRI). Nonparametric rank-sum tests were used to compare the ALFF values in the slow-4 and slow-5 bands, and Spearman or Pearson correlation analysis was used to assess the correlation between ALFF changes and clinical ophthalmic parameters in POAG patients. Receiver operating characteristic (ROC) curves were used to evaluate the diagnostic performance of the ALFF.

RESULTS: There were 16 males in POAG patients (median age 48y) and 12 males in HCs (median age 39y). Compared with HCs, POAG patients presented increased or decreased ALFF values in different brain regions, and similar changes were observed in mild POAG patients. The ALFF values were correlated with retinal nerve fiber layer (RNFL) thickness, inner limiting membrane-retinal pigment epithelium thickness changes and the degree of visual field defects. Analysis of the diagnostic value of the ALFF via ROC curves revealed that the right medial frontal gyrus [area under the curve (AUC)=0.9063] and superior frontal gyrus (AUC=0.9097) had better diagnostic value than did the optic disc area (AUC=0.8019), visual field index (VFI%, AUC=0.8988) and macular parameters.

CONCLUSION: POAG patients present altered cortical function that is significantly correlated with the optic nerve and retinal thickness and had good diagnostic value, which may reflect the underlying neuropathological mechanism of POAG.

PMID:41573011 | PMC:PMC12820637 | DOI:10.18240/ijo.2026.02.11

Int J Ophthalmol. 2026 Feb 18;19(2):273-280. doi: 10.18240/ijo.2026.02.09. eCollection 2026.

ABSTRACT

AIM: To compare spontaneous brain regional activities between diabetic vitreous hemorrhage patients (DVHs) and healthy controls (HCs).

METHODS: Thirty-two DVHs and 32 HCs were enrolled in this study. Baseline demographic and vision data were compared between groups using an independent sample t-test. Resting-state functional magnetic resonance imaging (rs-fMRI) was used in all participants. fMRI data was obtained and analyzed using MRIcro and SPM8 software. Fractional amplitude of low-frequency fluctuation (fALFF) technology was used to measure regional spontaneous brain activity, and sensitivity was tested using receiver operating characteristic curves (ROCs). The fALFF values were analyzed using REST software and two-sample t-tests were used to compare values between groups. Hospital anxiety and depression scale (HADS) score was assessed in DVHs and Pearson's correlation was used to test relationships between mean fALFF value and both HADS score and duration of DVH.

RESULTS: Except for the best-corrected visual acuity (BCVA) in both eyes, which showed a statistically significant difference (P<0.05), there were no statistically significant differences in the other indicators (P>0.05) between the HCs and DVHs group. Compared with controls, fALFF value was higher in DVH in cerebellum posterior lobe (CPL) and lower in right anterior cingulate cortex (ACC) and right medial orbitofrontal cortex (OFC). In DVH patients, mean fALFF value of CPL was positively correlated with HADS score and duration of diabetes. However, no such correlation was found, for right ACC or right medial OFC. DVH may lead to abnormal activities in certain brain regions related to visual control and mood.

CONCLUSION: Visual impairment caused by DVH may lead to adjustment in regional visual brain activities and may be related to depression or reward system processing in some brain regions.

PMID:41573000 | PMC:PMC12820647 | DOI:10.18240/ijo.2026.02.09

Curr Comput Aided Drug Des. 2026 Jan 12. doi: 10.2174/0115734099400072251022043532. Online ahead of print.

ABSTRACT

INTRODUCTION: There has been increasing interest in neuroimaging studies in recent years, and computer-aided approaches have gained prominence in improving diagnostic accuracy. Attention Deficit Hyperactivity Disorder (ADHD) is a prevalent neurodevelopmental disorder characterized by inattention, impulsivity, and hyperactivity. Traditional diagnostic approaches often rely on subjective assessments, highlighting the need for more objective, datadriven methods. This study aims to classify ADHD subtypes and assess medication effects by converting resting-state fMRI images into one-dimensional (1D) signals and extracting statistical features using Variational Mode Decomposition (VMD).

METHODS: Resting-state fMRI data from the ADHD-200 dataset, including 41 healthy controls (HC), 41 medicated ADHD-Combined (ADHD-C) individuals, and 41 non-medicated ADHD-C individuals, were analyzed. The 1D fMRI signals were decomposed into nine sub-bands using VMD. Statistical features were extracted from each sub-band and classified using Support Vector Machines (SVM), Linear Discriminant Analysis (LDA), and Artificial Neural Networks (ANN).

RESULTS: VMD-derived features substantially improved classification performance. The highest binary classification accuracy was achieved by LDA: 96.34% distinguishing non-medicated ADHD from controls and 88.41% for medicated ADHD versus controls. The classification between medicated and non-medicated ADHD yielded 79.63% accuracy. Ternary classification across all groups reached 69.51% accuracy.

DISCUSSION: These findings show that the VMD-based approach improves the classification of ADHD subtypes and helps evaluate medication effects. However, the lower performance in multi-class classification reflects the complexity of ADHD neuroimaging data.

CONCLUSION: The VMD-based approach improves classification accuracy, especially in distinguishing ADHD subtypes and medication effects, supporting its potential as an objective tool for diagnosis and treatment planning.

PMID:41572717 | DOI:10.2174/0115734099400072251022043532

Sci Rep. 2026 Jan 22. doi: 10.1038/s41598-026-36384-7. Online ahead of print.

ABSTRACT

Increased signal intensity (ISI) on T2-weighted cervical MR is common in patients with degenerative cervical myelopathy (DCM). However, the subtype-specific brain mechanisms for different ISI types remain unclear. The current study aimed to investigate the subtype-specific brain mechanisms associated with different types of ISI and their impact on the prognosis of DCM patients. ISI types were identified according to axial images for cervical compressive myelopathy (CCM) (Ax-CCM system). 54 CSM patients and 50 healthy controls were analyzed using resting-state fMRI data to derive the voxel-wise amplitude of low frequency fluctuation (ALFF). We conducted one-way ANOVA to compare the discrepancy in ALFF among patients of DCM with type 2 ISI and patients with other types of ISI and normal controls (NCs). The clusters surviving ANOVA were entered into pairwise two-sample t tests to disclose the pairwise ALFF differences among three groups. Pearson correlation coefficients were computed separately for each patient group in brain regions that exhibited significant between-group differences. In addition, we tested the utility of ALFF within brain regions identified by ANOVA for predicting preoperative symptom severity and prognosis of DCM via support vector regression (SVR). DCM patients with type 2 ISI identified by the axial images for cervical compressive myelopathy system (Ax-CCM) exhibited significantly lower ALFF within the right posterior cerebellum, which positively correlated with the prognosis in patients. Additionally, DCM patients with other types of ISI showed significantly lower ALFF within the left precentral gyrus. Moreover, the addition of functional imaging metrics to the set improved the SVR model's prediction accuracy for predicting symptom severity and prognosis in DCM patients. DCM patients can display distinct functional alterations in cerebral/cerebellar regions, which correspond to specific structural lesions in the spinal cord, as indicated by ISI subtypes. Including these functional alterations in the prognostic prediction model of DCM patients undergoing decompression surgery can be valuable in predicting their prognosis.

PMID:41571825 | DOI:10.1038/s41598-026-36384-7