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Brain Sci. 2025 Dec 23;16(1):16. doi: 10.3390/brainsci16010016.

ABSTRACT

Objectives: With the development of advanced neuroimaging techniques, including resting-state functional magnetic resonance imaging and diffusion tensor imaging, Parkinson's disease (PD) has increasingly been recognized as a complex brain network disorder. In this review, we summarized research on brain networks in PD to elucidate the network abnormalities underlying its four major motor symptoms and to identify the networks modulated by deep brain stimulation (DBS). Materials and Methods: We searched PubMed and Web of Science for the most recent literature on brain network alterations in PD. Eligible studies included those investigating the general PD network (n = 10), symptom-specific networks-tremor-dominant (n = 13), postural instability and gait disorder (n = 9), freezing of gait (n = 9), akinetic-rigidity (n = 3)-as well as DBS-modulated networks (n = 14). Based on these studies, we integrated the findings and used BrainNet Viewer to generate schematic network visualizations. Results: The symptom-specific networks exhibited common abnormalities within the sensorimotor network. Evidence from DBS studies suggested that therapeutic effects were associated with modulation of the motor cortex through both functional and structural connectivity. Moreover, the four motor symptoms each demonstrated distinct network features. Specifically, the tremor network was characterized by widespread alterations in the cortico-thalamic-cerebellar circuitry; the postural instability and gait disorder network showed more severe disruptions within the striatum and visual cortex; the freezing of gait network exhibited disruptions in midbrain regions, notably the pedunculopontine nucleus; and the akinetic-rigidity network involved changes in cognition-related networks, particularly the default mode network. Conclusions: PD motor symptoms exhibit both distinct network features and shared alterations within the sensorimotor network. DBS modulates large-scale brain networks, especially motor-related networks, contributing to the alleviation of motor symptoms. Characterizing symptom-specific networks may support precision DBS target selection and parameter optimization.

PMID:41594737 | DOI:10.3390/brainsci16010016

Entropy (Basel). 2026 Jan 10;28(1):83. doi: 10.3390/e28010083.

ABSTRACT

This work investigates the mechanisms of information transfer underlying causal relationships between brain regions during resting-state conditions in patients with schizophrenia (SCZ). A large fMRI dataset including healthy controls and SCZ patients was analyzed to estimate directed information flow using local Transfer Entropy (TE). Four functional interaction patterns-referred to as rules-were identified between brain regions: activation in the same state (ActS), activation in the opposite state (ActO), turn-off in the same state (TfS), and turn-off in the opposite state (TfO), indicating a dynamics toward converging (Acts/Tfs = S) and diverging (ActO/TfO = O) states of brain regions. These interactions were integrated within a multiplex network framework, in which each rule was represented as a directed network layer. Our results reveal widespread alterations in the functional architecture of SCZ brain networks, particularly affecting schizophrenia-related systems such as bottom-up sensory pathways and associative cortical dynamics. An imbalance between S and O rules was observed, leading to reduced network stability. This shift results in a more randomized functional network organization. These findings provide a mechanistic link between excitation/inhibition (E/I) imbalance and mesoscopic network dysconnectivity, in agreement with previous dynamic functional connectivity and Dynamic Causal Modeling (DCM) studies. Overall, our approach offers an integrated framework for characterizing directed brain communication patterns and psychiatric phenotypes. Future work will focus on systematic comparisons with DCM and other functional connectivity methods.

PMID:41593990 | DOI:10.3390/e28010083

Tomography. 2025 Dec 23;12(1):2. doi: 10.3390/tomography12010002.

ABSTRACT

Objective: This study aimed to investigate the functional connectivity (FC) of three amygdala subregions-the laterobasal amygdala (LBA), centromedial amygdala (CMA), and superficial amygdala (SFA)-with large-scale brain networks in individuals with schizophrenia (SCZ) compared to healthy controls (HC). Methodology: Resting-state functional magnetic resonance imaging (rs-fMRI) data were obtained from 100 participants (50 SCZ, 50 HC) with balanced age and gender distributions. FC between amygdala subregions and target functional networks was assessed using a region-of-interest (ROI)-to-ROI approach implemented in the CONN toolbox. Result: Connectivity patterns of the LBA, CMA, and SFA differed between SCZ and HC groups. After false discovery rate (FDR) correction (p < 0.05), SCZ patients exhibited significantly increased FC between the left CMA and both the default mode network (DMN) and the visual network (VN). In contrast, decreased FC was observed between the right LBA and the sensorimotor network (SMN) in SCZ compared with HC. Conclusions: These findings reveal novel FC alterations linking amygdala subregions with large-scale networks in schizophrenia. The results underscore the importance of examining the amygdala as distinct functional subregions rather than as a single structure, offering new insights into the neural mechanisms underlying SCZ.

PMID:41591135 | DOI:10.3390/tomography12010002

J Alzheimers Dis. 2026 Jan 27:13872877251411478. doi: 10.1177/13872877251411478. Online ahead of print.

ABSTRACT

BackgroundIndividuals with mild cognitive impairment (MCI) are at an increased risk of developing Alzheimer's disease. Anatomical and functional brain alterations associated with this condition are still elusive.ObjectiveThis study explored the cognitive correlates of cortical thickness, white matter (WM) volume, and resting-state connectivity among people with MCI.MethodsA total of 56 older participants (aged 51 to 92 years) with amnestic MCI were recruited. Cognitive abilities were measured using the Trail Making Task, the Stroop Color-Word Test, the Forward and Backward Digit Span test, and computerized n-back tasks. Morphometry was used to measure cortical thickness and WM volume from 3 T MR images, while functional connectivity was measured using resting-state fMRI and calculated using Independent Component Analysis. Voxel-wise regressions were used to test associations between cognitive scores and brain measures.ResultsWorse working memory updating (n-back) performance was associated with lower cortical thickness of the left middle temporal gyrus. Additionally, at a lower demand, working memory performance was linked to frontoparietal network (FPN) intrinsic connectivity, while WM volume within the anterior segment of the left arcuate fasciculus and default mode network (DMN) resting-state connectivity were relevant when the demand was higher. Lower DMN connectivity was also associated with worse conflict monitoring (Stroop) performance (all cluster-corrected ps < 0.05).ConclusionsThe findings highlight the relevance of the perisylvian region to working memory updating and conflict monitoring in people with MCI.

PMID:41589476 | DOI:10.1177/13872877251411478

J Affect Disord. 2026 Jan 24:121229. doi: 10.1016/j.jad.2026.121229. Online ahead of print.

ABSTRACT

BACKGROUND: Visual cortical dysfunction has been implicated in the early stages of psychosis and associated with psychotic symptoms. However, the functional connectivity (FC) of visual regions in early-onset bipolar disorder (EOBD) remains poorly understood. This study aimed to examine visual-related FC alterations in EOBD and explore their associations with psychiatric symptoms and clinical characteristics.

METHODS: Eighty-eight EOBD individuals and 56 healthy controls (HCs) underwent resting-state functional MRI. Visual cortical regions were selected by seeds from a probabilistic visual atlas. Twenty-five seed-based FC were compared between groups. Partial correlations with multiple comparison corrections were conducted to examine associations between altered FC and psychiatric symptoms in EOBD.

RESULTS: Compared to HCs, 14 visual cortical seeds showed higher FC with frontal and posterior temporal areas, while 5 visual cortical seeds showed lower FC with temporal and sensorimotor related regions. Higher psychotic positive, negative, and general scores were significantly associated with higher FC in visual and frontal regions, particularly involving the right lateral occipital complex area 1 (LO1), LO2, human middle temporal visual area (hMT/V5), intraparietal sulcus (IPS5), precentral gyri, and superior frontal gyri.

CONCLUSION: Individuals with EOBD exhibited altered visual system connectivity, characterized by higher FC with frontal regions and lower FC with sensorimotor and emotion regulation related regions. These findings suggest that visual-related FC abnormalities may emerge in the early course of BD and are associated with psychotic symptoms, potentially serving as early markers of vulnerability to psychosis.

PMID:41587694 | DOI:10.1016/j.jad.2026.121229

Front Syst Neurosci. 2026 Jan 8;19:1713434. doi: 10.3389/fnsys.2025.1713434. eCollection 2025.

ABSTRACT

OBJECTIVES: Although neuroticism is a major risk factor for adverse health outcomes, its neural basis is obscured by inconsistent findings from studies of regional gray matter volume (GMV) correlates. This study sought to identify convergent functional brain networks underlying these heterogeneous GMV correlates using functional connectivity network mapping (FCNM), and to explore their neurochemical basis.

METHODS: We systematically identified 10 voxel-based morphometry (VBM) studies (N = 1,595) reporting neuroticism-associated GMV coordinates. Using resting-state fMRI data from 1,093 healthy Human Connectome Project participants, FCNM was applied to map functional connectivity patterns associated with these coordinates. Overlap with canonical networks was assessed. The Juspace toolbox explored spatial relationships between identified networks and major neurotransmitter receptor distributions.

RESULTS: Despite spatial heterogeneity, neuroticism-related GMV changes consistently mapped onto three principal functional networks: the default mode network (DMN), frontoparietal network (FPN), and ventral attention network (VAN). These mappings were robust across varied analytical parameters. Moreover, the implicated networks demonstrated significant spatial correlation with the distributions of 5-hydroxytryptamine receptor 2A (5-HT2A), cannabinoid receptor type 1 (CB1), and metabotropic glutamate receptor 5 (mGluR5).

CONCLUSION: Despite regional variability, GMV correlates of neuroticism converge on common large-scale brain networks involved in self-referential processing, cognitive control, and salience processing. Their significant spatial coupling with 5-HT2A, CB1, and mGluR5 receptor distributions suggests serotonergic, endocannabinoid, and glutamatergic modulatory mechanisms contributing to network-level alterations. This cross-modal and network-based approach provides a unified framework for understanding the biological substrates of neuroticism, reconciling prior inconsistencies, and identifying key targets for prevention or biomarker development.

PMID:41586162 | PMC:PMC12823854 | DOI:10.3389/fnsys.2025.1713434

Imaging Neurosci (Camb). 2026 Jan 22;4:IMAG.a.1108. doi: 10.1162/IMAG.a.1108. eCollection 2026.

ABSTRACT

We have previously shown that autocorrelation analyses of BOLD signal applied to single voxels in healthy controls can identify behaviorally-relevant gradients of temporal dynamics throughout the hippocampal long-axis. A question that remains is how changes in the brain's functional and structural integrity affect single-voxel autocorrelation. In this study, we investigate how hippocampal autocorrelation is affected by hippocampal dysfunction by investigating a population of patients with unilateral temporal lobe epilepsy (TLE). Many patients with TLE have mesial temporal sclerosis (MTS), characterized by scarring and neuronal loss, particularly in the anterior hippocampus. Here, we compared patients with left and right TLE, some with and without MTS, to healthy controls. We applied our single-voxel autocorrelation method and data-driven clustering approach to segment the hippocampus based on the autocorrelation of resting-state fMRI. We found that patients with left TLE had longer intrinsic timescales (i.e., higher autocorrelation) compared to controls, particularly in the anterior-medial portion of the hippocampus. This was true for both the epileptogenic and non-epileptogenic hemispheres. We also evaluated the extent of cluster preservation (i.e., spatial overlap with controls) of patient autocorrelation clusters and the relationship to verbal and visuospatial memory. We found that patients with greater cluster preservation in the anterior-medial hippocampus had better memory performance. Surprisingly, we did not find any effect of MTS on single-voxel autocorrelation, despite the structural changes associated with the condition. These results suggest that spatiotemporal dynamics of activity can be informative regarding the functional integrity of the hippocampus in TLE.

PMID:41585470 | PMC:PMC12828354 | DOI:10.1162/IMAG.a.1108

Front Psychiatry. 2026 Jan 8;16:1725066. doi: 10.3389/fpsyt.2025.1725066. eCollection 2025.

ABSTRACT

BACKGROUND: Generalized Anxiety Disorder (GAD), characterized by excessive worry and somatic symptoms. Although neuroimaging studies have identified alterations in functional connectivity (FC), structural integrity, and neural activation in GAD, most include medicated or psychotherapy-treated patients, limiting insights into the neurobiology of the untreated state. This study investigated resting-state FC (rsFC) abnormalities in medication-naïve GAD patients using a whole-brain, data-driven approach.

METHODS: In this cross-sectional study, medication-naïve GAD patients (n = 85) and HCs (n = 82) underwent rs-fMRI at Guang'anmen Hospital on a Siemens 3.0T scanner. Data were analyzed using CONN toolbox (v22.v2407). After preprocessing, cluster-based rsFC was examined across 9, 453 connections in 138 ROIs (FSL Harvard-Oxford atlas, excl. cerebellum). Clusters correlated with HAMA scores; rsFC for 10 ROI pairs extracted via MATLAB; key ROIs seeded voxel-wise maps in SBC, controlling gender.

RESULTS: Significant group differences emerged in rsFC clusters, centered on connections between the posterior cingulate cortex (PCC) and right supramarginal gyrus (SMG). Compared to HCs, GAD patients exhibited hyper-connectivity in 5 connections and hypo-connectivity in 5 others within these clusters. Four connections showed positive correlations with HAMA scores.

LIMITATIONS: The analysis of 9, 354 connections may have reduced statistical power, possibly obscuring additional relevant findings.

CONCLUSION: This study demonstrates aberrant resting-state functional connectivity in medication-naïve GAD patients, particularly enhanced PCC-SMG rsFC correlated with anxiety severity, suggesting a potential role for interoceptive hypersensitivity in GAD pathophysiology. These findings support the hypothesis of SMG-driven vigilance engaging PCC and mPFC to perpetuate anxiety cycles, warranting future validation with direct interoceptive measures and highlighting neural targets for interventions.

PMID:41584758 | PMC:PMC12823798 | DOI:10.3389/fpsyt.2025.1725066

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

Med Image Anal. 2026 Jan 17;110:103949. doi: 10.1016/j.media.2026.103949. Online ahead of print.

ABSTRACT

Functional magnetic resonance imaging (fMRI) allows real-time observation of brain activity through blood oxygen level-dependent (BOLD) signals and is extensively used in studies related to sex classification, age estimation, behavioral measurements prediction, and mental disorder diagnosis. However, the application of deep learning techniques to brain fMRI analysis is hindered by the small sample size of fMRI datasets. Transfer learning offers a solution to this problem, but most existing approaches are designed for large-scale 2D natural images. The heterogeneity between 4D fMRI data and 2D natural images makes direct model transfer infeasible. This study proposes a novel geometric mapping-based fMRI transfer learning method that enables transfer learning from 2D natural images to 4D fMRI brain images, bridging the transfer learning gap between fMRI data and natural images. The proposed Multi-scale Multi-domain Feature Aggregation (MMFA) module extracts effective aggregated features and reduces the dimensionality of fMRI data to 3D space. By treating the cerebral cortex as a folded Riemannian manifold in 3D space and mapping it into 2D space using surface geometric mapping, we make the transfer learning from 2D natural images to 4D brain images possible. Moreover, the topological relationships of the cerebral cortex are maintained with our method, and calculations are performed along the Riemannian manifold of the brain, effectively addressing signal interference problems. The experimental results based on the Human Connectome Project (HCP) dataset demonstrate the effectiveness of the proposed method. Our method achieved state-of-the-art performance in sex classification, age estimation, and behavioral measurement prediction tasks. Moreover, we propose a cascaded transfer learning approach for depression diagnosis, and proved its effectiveness on 23 depression datasets. In summary, the proposed fMRI transfer learning method, which accounts for the structural characteristics of the brain, is promising for applying transfer learning from natural images to brain fMRI images, significantly enhancing the performance in various fMRI analysis tasks.

PMID:41576824 | DOI:10.1016/j.media.2026.103949

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