Most recent paper

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

Acad Radiol. 2026 Jan 21:S1076-6332(25)01158-4. doi: 10.1016/j.acra.2025.12.025. Online ahead of print.

ABSTRACT

PURPOSE: Leveraging hypergraph theory and spatio-temporal graph convolutional network (ST-GCN), this study uses multimodal MRI to elucidate thalamus-mediated high-order network dyscoordination of motor impairment in Parkinson's disease (PD).

MATERIALS AND METHODS: 64 PD patients and 64 age- and sex-matched healthy controls (HC) underwent resting-state functional MRI (rs-fMRI) and T1-weighted anatomical imaging (T1WI). Functional hypergraphs were constructed using dynamic thresholds on Pearson correlations; structural hypergraphs were generated from gray matter volume (GMV) via k-nearest neighbors (KNN). ST-GCN was employed to fuse the multimodal hypergraph features, and discriminative features were identified via stratified five-fold cross-validation. Group differences and clinical correlations were assessed using t-tests/Mann-Whitney U and Spearman's correlation (P<0.05), respectively.

RESULTS: Compared to HC, eight key brain regions exhibited abnormalities in PD: left precentral gyrus (PreCG.L), left middle frontal gyrus (MFG.L), right superior occipital gyrus (SOG.R), left thalamus (THA.L), left hippocampus (HIP.L), right caudate nucleus (CAU.R), right supplementary motor area (SMA.R), and right paracentral lobule (PCL.R). Three significant hyperedges were identified: left putamen-left thalamus-right supplementary motor area (PUT.L-THA.L-SMA.R), right globus pallidus-right thalamus-right cerebellar Crus II (PAL.R-THA.R-Crus II.R), and left thalamus-left hippocampus-right angular gyrus (THA.L-HIP.L-ANG.R). Hyperedge strengths revealed a modest increase in PUT.L-THA.L-SMA.R, a significant increase in THA.L-HIP.L-ANG.R (P<0.05), and a reduction in PAL.R-THA.R-Crus II.R. These hyperedges were all positively correlated with UPDRS-III scores (P<0.05).

CONCLUSION: Multimodal hypergraph analysis reveals high-order network dysregulation of motor impairment in PD, involving the cerebellum, limbic system, and cortical-basal ganglia circuits, mediated by the thalamus. Furthermore, hyperedges may serve as potential biomarkers for motor dysfunction.

PMID:41571558 | DOI:10.1016/j.acra.2025.12.025

Neuroimage. 2026 Jan 20:121743. doi: 10.1016/j.neuroimage.2026.121743. Online ahead of print.

ABSTRACT

Personalized repetitive transcranial magnetic stimulation (rTMS) increasingly relies on resting-state functional magnetic resonance imaging (fMRI) to select stimulation sites, yet most pipelines depend on user-defined thresholds and atlas masks, which can shift individualized targets. We propose a watershed-based approach, implemented in a graphical user interface, that performs threshold-independent segmentation of functional images to support rTMS target localization. As a proof-of-concept, we focused on Alzheimer's disease-related circuits within the default mode network, designating the posterior cingulate cortex (PCC) as the deep effective region and the inferior parietal lobule (IPL) as the superficial stimulation target. In a cohort of 21 healthy participants, quantitative comparison with a conventional threshold-based, mask-constrained peak strategy revealed high concordance for PCC peaks but a median spatial displacement of 6.0 mm (95% CI: 0.0-12.7 mm) for IPL targets. Qualitative examples further illustrate that watershed segmentation reduces bias from neighboring functional clusters, truncation by atlas boundaries, and ambiguity among multiple local peaks. By decoupling target definition from user-chosen thresholds and packaging the method in an accessible toolbox, this framework offers a generalizable tool for individualized fMRI-guided rTMS.

PMID:41570954 | DOI:10.1016/j.neuroimage.2026.121743

Front Neurosci. 2026 Jan 6;19:1731143. doi: 10.3389/fnins.2025.1731143. eCollection 2025.

ABSTRACT

Resting-state fMRI functional connectivity analysis is usually performed with seed-based methods that strongly rely on user-dependent definitions of regions of interest. Data-driven methods like independent component analysis (ICA) can mitigate this need. However, the number of components that should be expected in an fMRI acquisition, which determines the model order of the ICA, is not defined, and it is not uniformly chosen across studies. This variability is further complicated by the dependence of component number on field strength, with higher field strengths typically yielding more detectable components. Therefore, relying on a predetermined number may influence the results. Here, we compare functional maps obtained through ICA analysis at different magnetic field strengths and at various levels of spatial detail. Our results confirm the presence of the most frequently reported resting-state networks across field strengths and demonstrate that higher magnetic field strength enables more robust detection of functional networks with greater spatial detail. We also show that: (1) fixing the number of components, although improving interpretability of group results, may provide an incomplete picture of brain function; (2) a greater number of components is consistently identified at higher field strength, suggesting that the model order should be adapted according to both field strength and spatial detail.

PMID:41567512 | PMC:PMC12816346 | DOI:10.3389/fnins.2025.1731143

iScience. 2025 Dec 23;29(2):114523. doi: 10.1016/j.isci.2025.114523. eCollection 2026 Feb 20.

ABSTRACT

Motor skills learned in one hand generalize to the other hand via plastic changes in motor systems. Such "intermanual transfer" may arise during complex tool-use learning, but its neural underpinnings remain unknown. Using resting-state fMRI, we explored neurobehavioral effects occurring while right-handed participants were trained to use a novel complex tool with their left hand. Behaviorally, training improved tool-use performance equally for both hands, demonstrating a robust effect of intermanual transfer. For both hands, this behavioral effect correlated with functional connectivity changes between right dorsal premotor cortex (PMd) and intraparietal sulcus (IPS). For the untrained right hand, additional change emerged in right basal ganglia (BG), which showed increased behavior-connectivity correlation with bilateral PMd. Thus, tool-use skill learned by left-hand training is represented in the right PMd-IPS network and transferred to left PMd responsible for right-hand performance, pointing to a pivotal role of BG in generalizing complex tool-use skill across hands.

PMID:41567245 | PMC:PMC12818111 | DOI:10.1016/j.isci.2025.114523

J Clin Exp Neuropsychol. 2026 Jan 21:1-23. doi: 10.1080/13803395.2026.2617353. Online ahead of print.

ABSTRACT

INTRODUCTION: Breast cancer patients undergoing adjuvant hormone therapy commonly report adverse effects that can lead to lower quality of life and treatment nonadherence. How hormone therapy, independent of other systemic therapies, may impact patient functioning is a relatively new area of research with few neuroimaging studies delineating the effects. Prior nonspecific neuropsychological findings and the multifaceted role of estrogen in the brain suggest potentially diffuse effects of hormone therapy. The current study examined intrinsic neural functional organization and cognitive correlates unique to breast cancer patients undergoing hormone therapy.

METHOD: Resting state functional magnetic resonance imaging was acquired from 24 breast cancer patients undergoing hormone therapy and 32 healthy controls. Resting-state functional connectivity (rsFC) was calculated between brain regions. Fractional amplitude of low frequency fluctuations (fALFF) was computed within a rsFC-derived mask to describe the regional properties within sites of dysconnectivity. Objective measures of cognition were obtained using neuropsychological tests and correlated with rsFC.

RESULTS: Patients demonstrated extensive dysconnectivity relative to controls, largely characterized by parietal-occipital hypoconnectivity. Reduced rsFC occurred primarily between regions with increased fALFF. A modest relationship between rsFC and visual working memory was observed in breast cancer patients but not in controls.

CONCLUSIONS: This study is the first to examine whole-brain rsFC in breast cancer patients undergoing hormone therapy. We found robust hypoconnectivity in patients, which demonstrated modest relationships with cognition. Identifying the pattern by which breast cancer and hormone therapy affect brain networks may aid in the development of therapeutic options for patients experiencing negative effects of hormone therapy, thus improving quality of life for cancer survivors. Further, the detection of abnormal brain function may help characterize treatment-associated neural changes that are not captured by standard cognitive measures.

PMID:41566912 | DOI:10.1080/13803395.2026.2617353

Sci Rep. 2026 Jan 21. doi: 10.1038/s41598-026-36953-w. Online ahead of print.

ABSTRACT

To tackle the disease-related process in early pre-dementia Lewy Body Dementia, we investigated the changes of functional brain networks and their cognitive relevance. A cohort of 38 Mild Cognitive Impairment with Lewy Bodies (MCI-LB) subjects and one of 24 healthy controls (HC) underwent neuropsychological assessment and resting state (RS) functional and structural MRI. Functional connectivity (FC) between ROIs belonging to a set of RS networks, including the Salience Network (SN), Fronto-Parietal (FPN), Default Mode (DMN), Dorsal and Ventral Attention (DAN and VAN), Somato-Motor (SMN), Visual (VN) and Language (LN) was estimated and compared between cohorts. Finally, neuropsychological scores were correlated with FC of MCI-LB and HC separately. Compared to HC, MCI-LB exhibited lower FC between DAN, FPN and LN. Higher inter-network FC was found between FPN and SN, FPN and DMN, SN and SMN and DAN and SMN. In MCI-LB the correlational analysis revealed significant positive and negative associations between cognitive performance and FC values between nodes. In conclusion, we found a possible compensation mechanism between nodes in SN and FPN, and FPN and DMN following disconnection between the control system of the FPN and the top down attention system. The complex compensatory mechanisms involving multiple networks may not be efficient to counteract the cognitive impairment in MCI-LB. Overall, in MCI-LB we found an aberrant engagement of the networks that are not primarily involved in the performance of specific tasks.

PMID:41565763 | DOI:10.1038/s41598-026-36953-w