Most recent paper

Sci Rep. 2026 Feb 10. doi: 10.1038/s41598-026-38116-3. Online ahead of print.

ABSTRACT

This study aimed to explore abnormal patterns of functional connectivity density (FCD) and functional connectivity (FC) in patients with vestibular migraine (VM) and their associations with clinical symptoms. Resting-state functional magnetic resonance imaging (rs-fMRI) data from 49 VM patients and 61 healthy controls (HCs) were analyzed using Global FCD (GFCD), long-range FCD (LRFCD), and seed-based FC. Compared with HCs, VM patients demonstrated decreased GFCD and LRFCD in the bilateral medial prefrontal cortex (mPFC), along with increased GFCD in the right lingual gyrus (LING), right middle occipital cortex (MOC), left precuneus (preCUN), and elevated LRFCD in the middle cingulate cortex (MCC) and bilateral MOC. Seed-based FC analysis revealed significantly reduced connectivity between the mPFC and multiple regions, including the right cuneus/precuneus (CUN/preCUN), bilateral posterior cingulate cortex (PCC), bilateral hippocampus/parahippocampus (HIPP/ParaHIPP), and left calcarine cortex (CAL) in VM patients. Correlation analysis identified a positive association between GFCD in the left preCUN and Dizziness Handicap Inventory (DHI) scores (r = 0.370, p = 0.011). These findings highlight disrupted prefrontal-limbic-visual network integration in VM, with precuneus dysfunction potentially linked to dizziness severity. This study provides novel insights into the neural mechanisms underlying VM, highlighting the role of altered functional integration in symptom manifestation.

PMID:41667547 | DOI:10.1038/s41598-026-38116-3

Geroscience. 2026 Feb 10. doi: 10.1007/s11357-026-02141-x. Online ahead of print.

ABSTRACT

Exercise improves cognition, mental wellbeing, and protects against neurodegeneration. However, most prior neuroscience studies have focused on localized brain changes without quantifying their impact on brain ageing. To quantify the effect of resistance training on brain health using longitudinal assessments. Using resting-state functional magnetic resonance imaging (rs-fMRI) data from 2,433 healthy adults, we trained models to predict brain age and applied them to 309 participants from the Live Active Successful Aging (LISA) randomized trial. Participants in this trial were assigned to one of three groups: heavy-resistance training, moderate-intensity training, or a non-exercise control group. They underwent repeated rs-fMRI and physical fitness assessments at baseline, with follow-up assessments at 1 and 2 years. First, we examined changes in local connectivity between groups. Second, we assessed the impact of resistance training on brain ageing using brain clock models trained on the independent dataset of 2,433 adults. Local analyses revealed increased prefrontal functional connectivity following heavy training, while moderate- and heavy-resistance training significantly reduced brain age (-1.4 to -2.3 years, pFDR < 0.05). These effects emerged at the whole-brain level, rather than within isolated networks such as the default mode, motor, or cerebellar systems. These findings suggest a hierarchical organization of brain aging, driven by distributed network-level changes and expressed through focal regional patterns. Resistance exercise training decelerates brain ageing, as indexed by brain clocks, reinforcing its role as a preventive strategy for brain health.

PMID:41665740 | DOI:10.1007/s11357-026-02141-x

bioRxiv [Preprint]. 2026 Jan 26:2026.01.23.701360. doi: 10.64898/2026.01.23.701360.

ABSTRACT

Alzheimer's disease (AD) is characterized by early tau pathology in subcortical neuromodulatory nuclei, followed by progressive cortical tau accumulation; however, the mechanisms linking subcortical dysfunction to cortical tau pathology remain unclear. Using multimodal neuroimaging data from the ADNI cohort, we examined how infra-slow (< 0.1 Hz) global brain (i.e., gBOLD) activity is related to the volume of the nucleus basalis of Meynert (NbM) and cortical tau accumulations in the early stages of AD. NbM degeneration was associated with reduced gBOLD activity and spatially co-localized tau accumulation, appearing in early Braak regions during the preclinical stage, i.e., cognitively unimpaired participants with abnormal CSF markers, and extending to more advanced Braak areas during the prodromal stage, i.e., mild cognitive impairment (MCI) subjects. Our findings suggest that infra-slow gBOLD activity serves as a functional neural mediator linking subcortical degeneration to cortical tau pathology, highlighting a potential functional pathway linking subcortical and cortical pathology in early AD.

PMID:41659428 | PMC:PMC12873826 | DOI:10.64898/2026.01.23.701360

Commun Biol. 2026 Feb 9. doi: 10.1038/s42003-026-09688-7. Online ahead of print.

ABSTRACT

Human working memory is supported by a broadly distributed set of brain networks. Content-specific networks communicate with a domain-general, supramodal network that is recruited regardless of the type of content. Here, we contrasted visual and auditory working memory tasks to examine interactions between the supramodal network and two content-specific networks. Functional connectivity among visual-biased, auditory-biased, and supramodal working memory networks was assayed by collecting task and resting-state fMRI data from 24 human participants (age 18-43; 11 men and 13 women). At rest, as found previously, the supramodal network exhibited stronger functional connectivity with the visual-biased network than with the auditory-biased network. This asymmetry raises questions about how networks communicate to support robust performance across modalities. However, during auditory task performance, dynamic changes increased auditory network connectivity with supramodal and visual-biased frontal regions, while decreasing connectivity from posterior visual areas to supramodal and frontal visual regions. In contrast, the visual task produced weak changes. Across individuals, auditory working memory precision correlated with the strength of auditory network connectivity changes, while no such brain-behavior link was observed for visual working memory. These results demonstrate an asymmetry in working memory network organization and reveal that dynamic reorganization accompanies performance of working memory tasks.

PMID:41663792 | DOI:10.1038/s42003-026-09688-7

AJNR Am J Neuroradiol. 2026 Feb 9:ajnr.A9212. doi: 10.3174/ajnr.A9212. Online ahead of print.

ABSTRACT

BACKGROUND: Radiologically Isolated Syndrome (RIS) entails incidental Multiple Sclerosis (MS)-like MRI lesions. Longitudinal fMRI could clarify brain-symptom links; however, no longitudinal resting-state fMRI studies in RIS existed until now.

OBJECTIVES: Compare 14-month clinical, neuropsychological, and resting-state functional connectivity (FC) trajectories in RIS, MS, and healthy controls (HC), and relate FC change to fatigue.

METHODS: Nineteen RIS, 20 MS, and 22 HC completed baseline and 14-month assessments (fatigue, neuropsychology) and 3T MRI (rs-fMRI, 3D T1, FLAIR). FC within canonical networks and the ventral attention network (VAN) seed-to-voxel (CONN) connections were tested with a repeated-measures ANOVA (FWE-corrected). Regression analysis related to FC to fatigue; ROC curves evaluated discrimination.

RESULTS: Fatigue rose in MS but was stable in RIS. VAN connectivity showed opposing trajectories (group × time, p < 0.001): RIS increased within-VAN (and within-DAN vs. HC), whereas MS decreased within-VAN. In MS, VAN connectivity increased with orbitofrontal and striatal regions and decreased with thalamus/caudate (FWE p<0.05). Greater increases in within-VAN and VAN-thalamus/caudate connectivity were predicted to lead to fatigue reduction. A composite VAN metric differentiated RIS from MS (AUC=0.919). Lesion volumes were unchanged.

CONCLUSIONS: RIS and MS exhibit divergent, VAN-centered FC trajectories paralleling fatigue evolution. VAN-based longitudinal FC metrics may provide sensitive, noninvasive biomarkers that complement lesion measures in early MS.

PMID:41663204 | DOI:10.3174/ajnr.A9212

PLOS Ment Health. 2024 Jun 4;1(1):e0000022. doi: 10.1371/journal.pmen.0000022. eCollection 2024.

ABSTRACT

Internet usage has seen a stark global rise over the last few decades, particularly among adolescents and young people, who have also been diagnosed increasingly with internet addiction (IA). IA impacts several neural networks that influence an adolescent's behaviour and development. This article issued a literature review on the resting-state and task-based functional magnetic resonance imaging (fMRI) studies to inspect the consequences of IA on the functional connectivity (FC) in the adolescent brain and its subsequent effects on their behaviour and development. A systematic search was conducted from two databases, PubMed and PsycINFO, to select eligible articles according to the inclusion and exclusion criteria. Eligibility criteria was especially stringent regarding the adolescent age range (10-19) and formal diagnosis of IA. Bias and quality of individual studies were evaluated. The fMRI results from 12 articles demonstrated that the effects of IA were seen throughout multiple neural networks: a mix of increases/decreases in FC in the default mode network; an overall decrease in FC in the executive control network; and no clear increase or decrease in FC within the salience network and reward pathway. The FC changes led to addictive behaviour and tendencies in adolescents. The subsequent behavioural changes are associated with the mechanisms relating to the areas of cognitive control, reward valuation, motor coordination, and the developing adolescent brain. Our results presented the FC alterations in numerous brain regions of adolescents with IA leading to the behavioural and developmental changes. Research on this topic had a low frequency with adolescent samples and were primarily produced in Asian countries. Future research studies of comparing results from Western adolescent samples provide more insight on therapeutic intervention.

PMID:41661825 | DOI:10.1371/journal.pmen.0000022

Neurophotonics. 2026 Jan;13(1):015010. doi: 10.1117/1.NPh.13.1.015010. Epub 2026 Feb 6.

ABSTRACT

SIGNIFICANCE: Adapting optical imaging technology to avian models can overcome many limitations imposed by functional magnetic resonance imaging (fMRI), which currently restricts the number of species used to study functional connectivity. Developing advanced technology to expand the diversity of species that can be effectively imaged is crucial for addressing significant questions that are currently unreachable, such as understanding the evolution of cognition from a comparative perspective.

AIM: We assessed the potential of optical imaging technology to measure functional connectivity in birds, utilizing pigeons as an avian model. We evaluated whether we could partition the dorsal surface of the pigeon brain into units that correspond to known anatomical regions. Finally, we compared our results with those obtained from a separate dataset acquired using fMRI.

APPROACH: Using optical intrinsic signal imaging, a widefield optical imaging method, we imaged resting state functional connectivity in scalp-retracted anesthetized pigeons. We then used iterative parcellation and hierarchical clustering to create functional connectivity maps of correlation between parcels at two spatial scales. We recorded a second independent dataset of ten pigeons using a single-shot multi-slice gradient echo EPI sequence fMRI and applied the same parcellation method to compare functional connectivity patterns between the two methodologies.

RESULTS: We successfully partitioned signal activity into clusters of parcels that exhibit left-right symmetry between hemispheres and which align well with known anatomical regions of the dorsal surface of the pigeon brain. Moreover, functional connectivity matrices reveal positive correlations between homotopic regions. These cluster partitions and functional connectivity maps display similar patterns across and within individuals. Finally, WOI imaging results were comparable to resting state data acquired using fMRI.

CONCLUSIONS: Taken together, these results demonstrate the potential of optical imaging technology for the reliable and cost-effective characterization of functional connectivity in birds. In addition, they position optical imaging methods as a valuable tool for large-scale comparative and network-level studies in this taxon.

PMID:41660356 | PMC:PMC12879446 | DOI:10.1117/1.NPh.13.1.015010

bioRxiv [Preprint]. 2026 Jan 27:2026.01.25.701594. doi: 10.64898/2026.01.25.701594.

ABSTRACT

People differ remarkably from one another, yet isolating individual differences in their brain activity remains challenging. Non-invasive whole-brain recordings of human brain activity, such as those from resting state fMRI (rs-fMRI), are complex and noisy, making it difficult to isolate stable dimensions of individual differences. Ideally, we want to find a few core dimensions that vary across people but have high test-retest reliability, giving the same value each time they are measured in the same person. However, it is still unknown whether any such reliable dimensions exist, and if they do, what could drive this reliability. Here, we show that there is a low-dimensional linear subspace of highly-reliable rs-fMRI activity. These dimensions form personal fingerprints, allowing participants to be identified with high accuracy despite fingerprints explaining only a fraction of the total variance. Many of these dimensions inherit their reliability from a single morphological, demographic, or behavioral property, and most dimensions can be predicted from the anatomical layout of cortical regions. These dimensions were identified using reliability component analysis (RCA), a new dimensionality reduction technique similar to principal component analysis (PCA) but which maximizes reliability instead of explained variance. Together, our findings suggest that stable individual signatures can be isolated from rs-fMRI. These signatures reflect persistent anatomical and physiological differences, and provide a principled low-dimensional basis for biomarker discovery.

PMID:41659684 | PMC:PMC12873822 | DOI:10.64898/2026.01.25.701594

Front Neurosci. 2026 Jan 22;20:1745862. doi: 10.3389/fnins.2026.1745862. eCollection 2026.

ABSTRACT

BACKGROUND: Migraine is a prevalent neurological disorder that is frequently observed in clinical practice and is commonly comorbid with insomnia. Insomnia can exacerbate and precipitate migraine attacks, with both conditions exerting a reciprocal influence on one another. The cerebellar crus is significantly associated with the pathophysiology of migraine and insomnia. The relationship between cerebellar crus functional alterations and migraine-associated insomnia remains unclear. This study utilizes resting-state functional magnetic resonance imaging (rs-fMRI) to examine functional alterations in the cerebellar crus of patients with migraine and concurrent insomnia.

METHODS: Participants underwent resting-state functional magnetic resonance imaging. Subsequently, the disparity in amplitude of low-frequency fluctuations (ALFF) values among groups was analyzed, followed by functional connectivity (FC) investigations employing the cerebellum crus as seed regions.

RESULTS: Migraine patients frequently experience neuropsychological disorders and insomnia, which are interconnected. Both migraine with insomnia (MwI) and migraine without insomnia (MwoI) groups demonstrated elevated amplitude of low-frequency fluctuations (ALFF) in the left Crus I and II compared to the healthy controls (HC) group, with the MwI group exhibiting more pronounced alterations. Additionally, both patient groups showed decreased FC between the left Crus I and the right middle temporal gyrus (MTG) and inferior temporal gyrus (ITG) relative to the HC group. The MwoI group showed significantly lower FC compared to both the HC and MwI groups. A significant negative correlation was observed between ALFF in the left Crus I/II and Pittsburgh Sleep Quality Index (PSQI) scores in the MwoI group. Conversely, in the combined migraine cohort, FC between the left Crus I and the right MTG/ITG showed a positive correlation with PSQI scores.

CONCLUSION: This study identified a correlation between aberrant functional activity in the left Crus I/II and migraine comorbidity with insomnia. These findings provide fresh perspectives on the neural mechanisms underlying the migraine-insomnia relationship, thereby facilitating the identification of potential neuroimaging biomarkers and the exploration of targeted interventions for this patient subgroup.

PMID:41658941 | PMC:PMC12872798 | DOI:10.3389/fnins.2026.1745862

Imaging Neurosci (Camb). 2026 Feb 5;4:IMAG.a.1126. doi: 10.1162/IMAG.a.1126. eCollection 2026.

ABSTRACT

Resting-state fMRI time series are punctuated by spontaneous moments of high-amplitude activity lasting mere seconds. Previous research has demonstrated that such moments may contain a disproportionate amount of information and can be used to recapitulate maps of distributed brain activity or to recreate spatial functional connectivity patterns. Ultimately, this body of work has established that modeling neurovascular activity as a succession of spontaneous, punctuated moments is an effective approach for understanding cortex-wide brain activity. Here, we expand on this line of work by focusing our attention on the spatiotemporal properties of such punctuated moments, particularly on their duration. For this, we turn to an edge time series approach to resolve the dynamics of functional connectivity, identify moments of prominent synchrony, and record their duration. This procedure allows us to differentiate such punctuated moments by the time scales at which they unfold. By mapping moment duration to the cortex, we find that connectivity emanating from brain's primary sensory areas transpires with the longest durations. We further construct spatial patterns of connectivity unfolding over distinct durations, demonstrating how time scales differentially relate to traditionally constructed functional connectivity. Finally, we show how the longest connectivity moments could convey information about fluctuations in subjects' vigilance. Overall, the information that we have gleaned about prominent connectivity moments and their duration would otherwise be largely obscured when using other prevalent methods. Here we highlight an additional feature of functional connectivity to further our characterization of the brain's spatiotemporal organization.

PMID:41658341 | PMC:PMC12878659 | DOI:10.1162/IMAG.a.1126

Front Neurol. 2026 Jan 22;17:1683526. doi: 10.3389/fneur.2026.1683526. eCollection 2026.

ABSTRACT

The intrinsic brain activity measured by resting-state fMRI (rs-fMRI) consists of synchronized neural oscillations across a broad range of low frequencies. Although previous studies have linked frequency-specific changes to cognitive function and impairment, the alterations of these frequency-specific spatiotemporal patterns in chronic occlusive cerebrovascular disease remain unclear. In this study, we investigated the cross-frequency structure underlying cognitive impairment in patients with severe asymptomatic carotid stenosis (SACS) using wavelet-transformed amplitude of low-frequency fluctuation (wavelet-ALFF) of rs-fMRI. We found that, in healthy controls, frequency-specific wavelet-ALFF exhibited a spatial distribution from lower to higher frequencies, aligned with the functional hierarchy extending from the default mode network (DMN) to primary somatomotor and subcortical regions. In contrast, SACS patients exhibited frequency-dependent changes, including significantly decreased wavelet-ALFF in the anteromedial DMN at lower frequencies and the posteromedial DMN at higher frequencies. Further spatiotemporal decomposition analysis revealed that SACS patients exhibited abnormal cross-frequency coupling in the DMN. Our findings suggest that frequency-specific changes underlying cognitive impairment in SACS arise from spatiotemporally abnormal cross-frequency interplay within the DMN. These insights may contribute to a better understanding of other major brain diseases.

PMID:41657414 | PMC:PMC12872527 | DOI:10.3389/fneur.2026.1683526

Neurosci Lett. 2026 Feb 6:138537. doi: 10.1016/j.neulet.2026.138537. Online ahead of print.

ABSTRACT

BACKGROUND: Schizotypal personality (SP) is characterized by cognitive-perceptual disturbances, interpersonal difficulties, and disorganized behavior. We examined associations between SP traits and affect, and insula-centered neural mechanisms underlying this link.

METHODS: One hundred sixty-one university students completed the Schizotypal Personality Questionnaire-Brief and the Positive and Negative Affect Schedule and underwent resting-state fMRI. Seed-based whole-brain functional connectivity (FC) analyses used bilateral ventral anterior, dorsal anterior, and posterior insula seeds. Pearson correlations and mediation analyses tested associations among SP traits, Negative Affect, and FC.

RESULTS: Cognitive-Perceptual traits correlated positively with Negative Affect (r = 0.36, p < 0.001). FC between the right inferior parietal lobule (IPL.R) and the left ventral anterior insula (vAI.L) was positively correlated with Cognitive-Perceptual traits (r = 0.33, p < 0.001), whereas FC between the right cerebellar Crus I and the vAI.L was negatively correlated (r = -0.37, p < 0.001). FC between the right ventral anterior insula (vAI.R) and the Left Calcarine Gyrus (CAL.L) was also negative (r = -0.30, p < 0.001). vAI.L-IPL.R FC partially mediated the Cognitive-Perceptual traits-Negative Affect association (indirect effect = 0.1883, 95% bootstrap CI [0.0246, 0.4022]).

CONCLUSION: vAI.L-IPL.R FC partially accounts for the link between Cognitive-Perceptual traits and Negative Affect, highlighting a potential neural pathway underlying affective vulnerability in SP.

PMID:41655807 | DOI:10.1016/j.neulet.2026.138537

Comput Biol Med. 2026 Feb 7;204:111533. doi: 10.1016/j.compbiomed.2026.111533. Online ahead of print.

ABSTRACT

Brain networks display hierarchical organization, a complexity that is challenging for deep learning models that are often flat classifiers and lack interpretability. To address this, we propose a novel architecture called the Emergent Language Symbolic Autoencoder (ELSA), a hierarchical symbolic autoencoder informed by weak supervision and an Emergent Language framework that learns to represent brain networks as interpretable symbolic sentences while simultaneously reconstructing the original data. Our framework's primary innovations are a set of hierarchically-aware loss functions and their application to modeling resting-state fMRI networks. By combining weak supervision from Independent Component Analysis (ICA) order with novel Progressive, Strict, and Containing Bias losses, we explicitly enforce a coarse-to-fine structure on the emergent language without requiring extensive manual labeling. We evaluated ELSA on data from the publicly available 1000 Functional Connectomes Project. The model generated sentences with clear hierarchical organization, where early symbols corresponded to broad parent networks and later symbols specified finer sub-networks. With the use of our proposed Progressive Strict loss function and containing bias penalty, the model's hierarchical consistency drastically improves compared to baseline, achieving near-perfect consistency at higher ICA orders and 43.5% at the challenging lowest order. The model also produces qualitatively superior visual progressions of the network reconstructions. By replacing opaque feature vectors with an interpretable symbolic language, ELSA provides a transparent, multi-level description of functional brain organization and offers a general framework for studying other hierarchically structured biomedical data.

PMID:41655479 | DOI:10.1016/j.compbiomed.2026.111533

J Prev Alzheimers Dis. 2026 Feb 6;13(4):100501. doi: 10.1016/j.tjpad.2026.100501. Online ahead of print.

ABSTRACT

BACKGROUND: Brain age gap (BAG)-the difference between predicted and chronological age-captures neurobiological aging, but MRI-only models insufficiently reflect Alzheimer's disease (AD) pathology. Whether incorporating regional amyloid-β (Aβ) positron emission tomography (PET) improves sensitivity to early AD processes remains unknown.

OBJECTIVES: To develop an amyloid-informed multimodal BAG model and examine its associations with cognition, plasma biomarkers, and functional connectivity across the AD continuum.

DESIGN: Cross-sectional analysis using integrated machine-learning models.

SETTING: Chinese Preclinical Alzheimer's Disease Study (CPAS), a cohort recruited from community settings and memory clinics.

PARTICIPANTS: Nine hundred ninety community-dwelling adults spanning normal cognition, subjective cognitive decline (SCD), mild cognitive impairment (MCI), and dementia.

MEASUREMENTS: Regional Aβ-PET and structural MRI informed BAG estimation. Cognitive tests, plasma biomarkers (p-tau217, p-tau181, neurofilament light [NfL], glial fibrillary acidic protein [GFAP], Aβ42/40), and hippocampus-default mode network (DMN) connectivity from resting-state fMRI were assessed.

RESULTS: Higher BAG was associated with greater odds of SCD, MCI, or dementia across the cohort, with stronger effects in Aβ-positive individuals. BAG explained more cognitive variance than global Aβ burden and was linked to multidomain cognitive deficits. Elevated BAG corresponded to higher p-tau217, p-tau181, NfL, and GFAP and lower Aβ42/40, indicating early biomarker alterations. BAG was also associated with reduced hippocampus-DMN connectivity.

CONCLUSIONS: An amyloid-informed multimodal BAG model captures convergent AD-related pathology, biomarker alterations, and cognitive vulnerability beyond amyloid burden alone, supporting its value for individualized risk s2tratification and prevention-focused assessment.

PMID:41653882 | DOI:10.1016/j.tjpad.2026.100501

Neuroimage Clin. 2026 Jan 26;49:103956. doi: 10.1016/j.nicl.2026.103956. Online ahead of print.

ABSTRACT

Mild traumatic brain injury (mTBI) is a prevalent condition with symptoms spanning physical, psychological, cognitive, and sleep domains. Altered functional brain networks have been implicated in mTBI, but the relationship between these network changes and post-concussive symptoms remains poorly understood. This study is a systematic scoping review, adhering to PRISMA-ScR guidelines, assessing current literature on the association between brain network dysfunction and mTBI-related symptoms. Searches across ProQuest, Web of Science, and PubMed yielded 41 studies for full review, with most (n = 39) employing resting-state functional magnetic resonance imaging (rs-fMRI) to examine brain networks. The default mode network (DMN) was a primary focus, with studies reporting heterogeneous findings of increased and decreased connectivity both within and outside this network. Over 85% of studies used mTBI-specific symptom measures, and 50% employed detailed questionnaires for emotional and physical symptom assessment. Of these, 23 studies identified significant correlations between symptom scores and network connectivity. However, methodological inconsistencies, including variable analytic approaches, highlight the need for standardization in this field. Key areas for future research include incorporating multimodal imaging techniques, conducting longitudinal studies or extending recruitment time points, and stratifying analyses by sex to optimise identification of connectivity changes. Addressing these gaps is crucial for advancing our understanding of functional network alterations in mTBI and their clinical implications, ultimately supporting improved diagnostic and therapeutic strategies.

PMID:41653507 | DOI:10.1016/j.nicl.2026.103956

J Neurooncol. 2026 Feb 7;176(3):198. doi: 10.1007/s11060-026-05443-4.

NO ABSTRACT

PMID:41653232 | DOI:10.1007/s11060-026-05443-4

Brain Imaging Behav. 2026 Feb 7;20(1):5. doi: 10.1007/s11682-026-01076-3.

ABSTRACT

The present study aimed to investigate the characteristics of abnormal resting-state brain-network connectivity and the reorganization effects of antidepressant drug escitalopram oxalate treatment in patients with major depressive disorder (MDD), and to explore spatial correlations between brain network alterations and gene expression profiles. We employed a longitudinal study design to recruit 113 patients with MDD and 114 healthy controls (HCs) between November 2020 and October 2022. Clinical symptoms were assessed using the 17-item Hamilton Depression Scale (HAMD-17). Resting-state functional magnetic resonance imaging (rs-fMRI) data were acquired using a Siemens 3.0 T MRI scanner. At baseline, patients with MDD exhibited significantly reduced functional connectivity (FC) within the default mode network (DMN) compared to HCs, along with significantly increased FC between the sensorimotor network (SMN) and both the frontoparietal network (FPN) and the salience network (SN) (False Discovery Rate, FDR-corrected, p < 0.05). Following treatment with escitalopram oxalate, MDD patients showed a significant enhancement in intra-DMN connectivity, as well as a significant reduction in SMN-FPN and SMN-SN connectivity (FDR-corrected, p < 0.05). Notably, the degree of increase in intra-DMN connectivity was significantly and negatively correlated with improvement in core depressive symptoms (r = - 0.305, p = 0.026), while the reduction in SMN-DMN connectivity was positively correlated with the alleviation of somatic symptoms (r = 0.362, p = 0.008). Further neuroimaging-guided transcriptomics analysis indicated that these alterations in brain network connectivity were linked to biological pathways, such as the Wnt signaling. In conclusion, our findings demonstrate a multidimensional imbalance in brain network connectivity in MDD and show that antidepressant treatment can partially ameliorate aberrant connectivity patterns. These neural changes are closely associated with symptomatic improvements, offering valuable imaging-based evidence for understanding the neurobiological mechanisms of MDD and informing the development of personalized treatment strategies.

PMID:41653205 | DOI:10.1007/s11682-026-01076-3

Res Sq [Preprint]. 2026 Jan 26:rs.3.rs-8321466. doi: 10.21203/rs.3.rs-8321466/v2.

ABSTRACT

BACKGROUND: Transcranial magnetic stimulation (TMS) is a non-invasive neuromodulation therapy that is applied across psychiatric conditions to modulate specific neural circuits and improve clinical symptoms. While functional magnetic resonance imaging (fMRI)-guided personalized TMS targets are increasingly used, there are critical unresolved methodological, neurobiological, and clinical questions. Addressing topographic variability, stability, and associations with clinical outcomes is essential for advancing clinical development and scalable precision neuromodulation.

METHODS: A precision neurocircuitry-based fMRI-guided TMS approach was developed to treat disorders of the amygdala. In a randomized clinical trial for posttraumatic stress disorder (PTSD; n=50), topographic variability and stability of patient-specific right dorsolateral prefrontal cortex (rDLPFC) targets with the strongest functional connectivity to the right amygdala were analyzed.

RESULTS: There was significant target variability between participants and between targeting methods, but target stability was observed after engaging the amygdala circuitry with behavioral threat-related tasks. Target topography did not change after 20 sessions of sham TMS. However, after active TMS (1Hz, 36,000 pulses) target topography was significantly different. A larger change in the medial-anterior direction correlated with greater PTSD symptom improvement.

CONCLUSIONS: Target variability and stability for fMRI-guided TMS of the amygdala circuitry is demonstrated, supporting the use of patient-specific targeting strategies for TMS. A clinical change in PTSD symptoms was associated with greater change in target topography, which suggests neuroplastic adaptations in the targeted networks and a possible treatment-dependent shift towards more medial prefrontal control over amygdala regulation. These findings are important for fMRI-guided precision neuromodulation therapy development, particularly for the amygdala circuitry.

PMID:41646285 | PMC:PMC12869549 | DOI:10.21203/rs.3.rs-8321466/v2

NPJ Aging. 2026 Feb 6. doi: 10.1038/s41514-026-00345-1. Online ahead of print.

ABSTRACT

As individuals age, cortical alterations in brain structure contribute to cognitive decline. However, the specific patterns of age-related changes and their impact on cognition remain poorly understood. This study assessed the effects of aging on individual gray matter similarity networks and compared them to anatomical and functional connectivity networks derived from diffusion-weighted imaging and resting-state fMRI, respectively. Our results showed that gray matter similarity networks outperformed anatomical and functional connectivity in predicting age and cognition, showing the earliest age-related changes across the adult lifespan. These networks also demonstrated greater robustness to individual differences in cognition, behavior, and sex. Notably, age-related changes in gray matter similarity were associated with the brain's underlying cytoarchitecture, being strongest in brain regions from cortical layers II and III. These findings provide a new biological insight into the neural mechanisms of cognitive aging and highlight the potential of individual morphological similarity for capturing complex brain changes across the lifespan.

PMID:41651845 | DOI:10.1038/s41514-026-00345-1

J Affect Disord. 2026 Feb 4:121346. doi: 10.1016/j.jad.2026.121346. Online ahead of print.

ABSTRACT

BACKGROUND: Major depressive disorder (MDD) and Internet addiction (IA) are common and cause significant impairment, yet their relationship remains unclear. This study aims to explore the neurobiological mechanisms of comorbid MDD and IA and to inform clinical interventions.

METHODS: This study recruited 141 first-episode, drug-naïve MDD patients (72 with IA, 69 without) and 61 healthy controls (HC). Clinical assessments included the Hamilton Depression Rating Scale (HAMD) and Internet Addiction Test (IAT). Resting-state fMRI data were acquired using a 3 T Siemens scanner, and fractional amplitude of low-frequency fluctuations (fALFF) was computed with the Data Processing Assistant for Resting-State fMRI (DPARSF) software. Statistical analyses involved ANOVA, MANCOVA, and partial correlation, with multiple comparisons corrected using the FDR and Bonferroni methods.

RESULTS: Compared to HC group, both MDD + IA and MDD groups exhibited common elevations in fALFF within the left superior medial frontal gyrus and right superior frontal gyrus, alongside reductions in the right middle occipital gyrus. Concurrently, group-specific alterations were identified: MDD + IA had higher fALFF in the right inferior frontal gyrus triangular region, while MDD exhibited lower fALFF in the right postcentral gyrus and left inferior temporal gyrus. MDD + IA had significantly higher fALFF in the left inferior parietal lobule than MDD. Furthermore, fALFF in this region was positively correlated with IAT scores.

CONCLUSIONS: MDD with IA is associated with distinct neurological alterations in frontal and parietal regions. The left inferior parietal lobule may serve as a potential neurobiological marker for MDD comorbid with IA, providing a target for future interventions.

PMID:41651243 | DOI:10.1016/j.jad.2026.121346