Most recent paper

Brain Res Bull. 2026 Feb 24:111787. doi: 10.1016/j.brainresbull.2026.111787. Online ahead of print.

ABSTRACT

BACKGROUND: Post-stroke cognitive impairment (PSCI) is associated with abnormal dynamic functional connectivity, yet the temporal dynamic of brain activity and their underlying molecular mechanisms remain unclear.

METHODS: Participants were classified into two groups based on neuropsychological assessments: PSCI group (N=67) and post-stroke with no cognitive impairment (NPSCI) group (N=65), alongside 47 healthy controls (HCs). Dynamic brain states were analyzed using a Hidden Markov Model (HMM) with the Brainnetome Atlas, yielding metrics like fractional occupancy (FO), mean dwell time (MDT), switching rate (SR) and transition probabilities (TP) based on resting-state functional magnetic resonance imaging (rs-fMRI). Finally, we further assessed the spatial correlations between the mean activation of HMM state and neurotransmitter receptors/transporters distribution, cognitive relative term, and gene expression profiles.

RESULTS: Five HMM states were identified. Compared with HCs and NPSCI group, patients with PSCI group exhibited different dynamics, including FO, MDT, SR, and TP. Additionally, we found that the mean activation maps of HMM state were associated with the neurotransmitter receptors/transporters distribution and cognitive relative term. Furthermore, our results demonstrated a spatial correlation between the mean activation maps of state 5 and gene expression patterns. Finally, enrichment analysis indicated that PLS-positive genes were enriched in pathways related to DNA/RNA metabolism, signal transduction and regulation, and immune-disease associations, whereas, PLS-negative genes were mainly enriched in lipid metabolism and insulin response, virus-cytokine interactions, and influenza response pathways.

CONCLUSIONS: This study provides new insights into characterizing dynamic neural activity in PSCI. The brain network dynamics defined by HMM analysis may deepen our understanding of the neurobiological underpinnings of PSCI, indicating a linkage between neural configuration and gene expression in PSCI.

PMID:41747873 | DOI:10.1016/j.brainresbull.2026.111787

Prog Neuropsychopharmacol Biol Psychiatry. 2026 Feb 24:111653. doi: 10.1016/j.pnpbp.2026.111653. Online ahead of print.

ABSTRACT

BACKGROUND: Food addiction (FA) affects a significant proportion of the general population and could contribute to excess weight and its related complications. This phenomenon has been well described in terms of behavior, but little is known about its neurological determinants. The primary aim of this systematic review is to identify the neuroimaging characteristics associated with FA, using the Yale Food Addiction Scale (YFAS) as a validated assessment tool.

METHODS: A systematic search was conducted in PubMed and ScienceDirect databases from 2009 to July 2024 in accordance with the PRISMA 2020 guidelines. Studies were included if they investigated associations between the YFAS and neuroimaging outcomes. A descriptive analysis was conducted due to the methodological heterogeneity between the included articles.

RESULTS: Of the 528 records identified, 25 studies were included in the review, representing 3081 participants in total. Functional magnetic resonance imaging (fMRI, n = 18) and structural MRI (n = 9), were the most commonly used imaging techniques. Studies reported associations between YFAS scores and altered resting-state functional connectivity or brain responses to cognitive tasks, especially in caudate, putamen, amygdala, insula, nucleus accumbens, orbitofrontal cortex, thalamus and precuneus. Yet, numerous neuroimaging findings related to FA presented discrepancies across studies.

DISCUSSION: There is some evidence of altered activation and functional connectivity in brain areas involved in reward and cognitive control among individuals with FA. However, neuroimaging outcomes related to FA remain highly inconsistent across studies, partly due to heterogenous methodologies. Methodological recommendations are provided to improve consistency of future neuroimaging research in the context of FA.

PMID:41747855 | DOI:10.1016/j.pnpbp.2026.111653

Front Comput Neurosci. 2026 Feb 10;20:1731868. doi: 10.3389/fncom.2026.1731868. eCollection 2026.

ABSTRACT

Understanding the neural mechanisms underlying the transitions between different states of consciousness is a fundamental challenge in neuroscience. Thus, we investigate the underlying drivers of changes during the resting-state dynamics of the human brain, as captured by functional magnetic resonance imaging (fMRI) across varying levels of consciousness (awake, light sedation, deep sedation, and recovery). We deploy a model-based approach relying on linear time-invariant (LTI) dynamical systems under unknown inputs (UI). Our findings reveal distinct changes in the spectral profile of brain dynamics-particularly regarding the stability and frequency of the system's oscillatory modes during transitions between consciousness states. These models further enable us to identify external drivers influencing large-scale brain activity during naturalistic auditory stimulation. Our findings suggest that these identified inputs delineate how stimulus-induced co-activity propagation differs across consciousness states. Notably, our approach showcases the effectiveness of LTI models under UI in capturing large-scale brain dynamic changes and drivers in complex paradigms, such as naturalistic stimulation, which are not conducive to conventional general linear model analysis. Importantly, our findings shed light on how brain-wide dynamics and drivers evolve as the brain transitions toward conscious states, holding promise for developing more accurate biomarkers of consciousness recovery in disorders of consciousness.

PMID:41743844 | PMC:PMC12929524 | DOI:10.3389/fncom.2026.1731868

Front Pediatr. 2026 Feb 10;14:1711271. doi: 10.3389/fped.2026.1711271. eCollection 2026.

ABSTRACT

BACKGROUND: Motor coordination impairment among children with autism spectrum disorder (ASD) has recently gained increasing attention. However, the relationship between functional connectivity (FC) alterations and motor coordination impairment among ASD remains inconclusive.

METHODS: We evaluated motor coordination function using the Developmental Coordination Disorder Questionnaire (DCDQ) and acquired resting-state functional magnetic resonance imaging (rs-fMRI) scans from 23 autistic individuals and 25 typically developing (TD) controls (6-10 years old). Within- and between-network FC was estimated using group independent component analysis (ICA) and group comparison was addressed using two-sample t-tests. Relationships between abnormal FC and motor coordination among ASD were investigated with multiple linear regression, with age, gender, and intelligence quotient (IQ) considered as covariates.

RESULTS: In the ASD group, 1) FC within the right cerebellar crus II was negatively correlated to the score of general coordination (β = -.566, p = 0.035) and control during movement (β = -0.529, p = 0.026); 2) FC between the cerebellar network and frontal-temporal-parietal network was negatively correlated to the score of general coordination (β = -2.610, p = 0.006); 3) Increased FC between the cerebellar network and insular network was associated with a higher score of fine motor/handwriting (β = -0.529, p = 0.026).

CONCLUSIONS: We confirmed the role of the insular network in interoception and motor processing among ASD, which was related to impaired information integrating, relaying, and visual feedback during movement. A significant relationship between the cerebellar network and frontal-temporal-parietal network in motor coordination indicated that a deficit in the planning of movements may contribute to atypical motor skills. The study gained an understanding of neuroimaging traits among ASD children and may provide evidence for the design of the motor-related intervention.

PMID:41743223 | PMC:PMC12930269 | DOI:10.3389/fped.2026.1711271

Neuropsychopharmacology. 2026 Feb 25. doi: 10.1038/s41386-026-02375-5. Online ahead of print.

ABSTRACT

Predicting selective serotonin reuptake inhibitor (SSRI) response in obsessive-compulsive disorder (OCD) remains a clinical challenge. Converging evidence implicated that the sensorimotor circuit is linked to OCD-related sensory phenomena and repetitive motor rituals, and it is densely innervated by serotonergic projections, making it a plausible substrate of SSRI effects. We therefore hypothesized that baseline functional connectivity (FC) of this circuit could serve as a candidate neural marker of SSRI treatment response. In this exploratory single-site resting-state fMRI study, 54 drug-naïve patients with OCD and 39 matched healthy controls (HCs) underwent scanning. Patients received sertraline for 12 weeks and were classified as responders (rOCD, n = 33) or non-responders (nOCD, n = 21) based on Yale-Brown Obsessive Compulsive Scale score reductions. Seed-based FC analysis of the sensorimotor circuit was conducted across the three groups. We observed that OCD patients exhibited abnormal FC primarily within the sensorimotor circuit and in its connections with the cerebellum. The rOCD group showed generally higher FC within the sensorimotor circuit than HCs, whereas the nOCD group showed lower FC values. Cerebellar regions with altered connectivity included areas involved in sensorimotor processing and higher-level functions. In prediction analyses, the connectivity between right thalamus and cerebellar Crus I region achieved an AUC of 0.854 for distinguishing responders from non-responders under leave-one-out cross-validation. Moreover, FC-based models showed better predictive performance than clinical models. These findings suggest that baseline sensorimotor-network FC may serve as a candidate biomarker of sertraline response in OCD, pending validation in large, independent samples.

PMID:41741690 | DOI:10.1038/s41386-026-02375-5

Int J Eat Disord. 2026 Feb 25. doi: 10.1002/eat.70066. Online ahead of print.

ABSTRACT

OBJECTIVE: Numerous publications utilize data from the Adolescent Brain and Cognitive Development (ABCD) Study. This review aimed to evaluate how data from the ABCD cohort contributes to understanding the pathophysiology of incipient eating disorders.

METHOD: Searches were completed using PubMed and the ABCD Study research publications database. All available neuroimaging articles assessing prevalence and predictors of disordered eating/eating disorders were included.

RESULTS: Thirty-eight articles met inclusion criteria, 10 of which presented neuroimaging results, all analyzing baseline brain data. The majority (n = 9) assessed brain structure and function in children with binge eating (BE)/binge eating disorder (BED). Results were inconsistent across imaging modalities. Structural MRI studies included widespread increases in gray matter density and reductions in cortical thickness associated with eating pathology. Task-based fMRI studies reported conflicting findings, with frontostriatal activation during reward processing in children with BE/BED reduced, increased, or not different compared to controls. Resting-state fMRI analyses consistently identified reduced functional connectivity in key frontal circuits, although patterns differed when samples were stratified by sex or BMI. Non-imaging studies showed positive associations between eating disorders/disordered eating and several sociodemographic, cognitive, behavioral, and biological correlates.

DISCUSSION: Alterations in brain structure and function associated with binge eating are identified in neuroimaging analyses of baseline scans from the ABCD cohort, with inconsistent results. One potential pattern suggests alterations in reward system function, although the direction and exact location of such alterations are unclear. Consistency in methodological approaches is necessary to allow patterns in neural alterations to be more clearly identified. There is significant and ongoing potential for the ABCD Study dataset to quantify developmental aspects of binge eating. Recommendations for future analyses as the sample progresses through puberty and eating disorder prevalence increases are also presented.

PMID:41741359 | DOI:10.1002/eat.70066

AJNR Am J Neuroradiol. 2026 Feb 25:ajnr.A9264. doi: 10.3174/ajnr.A9264. Online ahead of print.

ABSTRACT

This case report uses magnetoencephalography (MEG), electroencephalography (EEG), diffusion kurtosis imaging (DKI), pseudo-continuous arterial spin labelling (pCASL), and resting-state functional MRI (rs-fMRI) to compare female, high-school soccer dizygotic twins who differed in recent concussion history. One twin, "Twin A", sustained her first clinically diagnosed concussion 72 hours before baseline imaging. "Twin B" was not concussed and served as a control for Twin A. Participants completed clinical, neuropsychological, and neurophysiological assessments at baseline (T1), 1 month (T2), and 3 months (T3) timepoints. Imaging and electrophysiology were acquired using a harmonized protocol across modalities. MEG was collected with a MEGIN Triux Neo whole-head system, and 64-channel EEG was acquired simultaneously. MRI was conducted on a 3T Siemens Prisma scanner following the Adolescent Brain Cognitive Development (ABCD) protocol. DKI was processed using FSL to generate fractional anisotropy and mean diffusivity maps. pCASL was analyzed using BASIL to estimate cerebral blood flow. rs-fMRI preprocessing and denoising were performed in CONN, and voxel-wise power spectral density (0.01-0.1 Hz) was computed to quantify low-frequency oscillatory activity. Twin A demonstrated acute symptoms, left frontal hypoperfusion, reduced rs-fMRI power, and increased low-frequency electrophysiological activity at T1, with gradual recovery across modalities. Twin B exhibited stable findings across all assessments. Our findings highlight the potential of multimodal brain imaging to localize sports-related concussion and to help inform return-to-play decisions.

PMID:41741217 | DOI:10.3174/ajnr.A9264

Neuroimage. 2026 Feb 23:121829. doi: 10.1016/j.neuroimage.2026.121829. Online ahead of print.

ABSTRACT

Problematic use of social media (PUSM) is a major public health concern estimated to affect 35% of adolescents. However, data-driven research to identify neural networks predictive of PUSM in adolescents remains limited. The aim of this study was to utilize connectome-based predictive modelling (CPM), a machine-learning approach that employs whole-brain functional connectivity data, to predict PUSM severity and identify underlying neural networks in adolescents. We included 2294 participants from the Adolescent Brain Cognitive Development study (Mage = 10.03, 50.6% female) who had resting-state functional magnetic resonance imaging (fMRI) data at baseline and PUSM scores at the four-year follow-up. CPM with 10-fold cross-validation was applied to resting-state fMRI data and PUSM scores. CPM successfully predicted PUSM scores and identified connectivity within and between multiple large-scale neural networks predictive of PUSM severity, which could be categorized into two key systems: (i) a cognitive control and self-regulation system consisting of the default mode, frontoparietal, and medial frontal networks, and (ii) a perceptual-motor integration system consisting of the visual area 1 and sensorimotor networks. The large-scale networks identified in the present study provide mechanistic insight into PUSM vulnerability and represent potential targets for personalized interventions. Future research should aim to replicate and extend the current results to refine prevention and treatment approaches.

PMID:41740634 | DOI:10.1016/j.neuroimage.2026.121829

Neuroimage Clin. 2026 Feb 18;49:103973. doi: 10.1016/j.nicl.2026.103973. Online ahead of print.

ABSTRACT

BACKGROUND: Patients implanted with modern deep brain stimulation (DBS) hardware can now undergo functional magnetic resonance imaging (fMRI), leading to its increased used to study DBS' mechanisms and predict optimal therapy settings. To accurately interpret fMRI data and realize its clinical potential for DBS, a better understanding of reliability is needed.

METHODS: Sixteen patients with Parkinson's disease (PD) and DBS targeting the subthalamic nucleus or pallidum underwent 3T test-retest resting-state fMRI with and without concurrent stimulation. Effects of stimulation and device-metal artifacts on reliability of fMRI brain connectivity and moment-to-moment brain variability were explored, plus factors influencing between-subject variations in reliability such as motion.

RESULTS: The brain variability fMRI metric yielded higher intra-class correlation coefficients than the connectivity metric (range across whole brain, motor, limbic, and cognitive networks: 0.36-0.85 and 0.68-0.99, respectively). Average network connectivity appeared less reproducible when DBS was ON versus OFF during fMRI, and fMRI metric reliability for brain areas affected by metal artifacts was significantly higher (brain variability) or lower (connectivity) than unaffected areas (puncorrected < 0.05). Motion and DBS target best explained between-subject variations.

CONCLUSION: DBS hardware and active stimulation may alter fMRI reliability. To develop clinically useful fMRI biomarkers for DBS and aid assessments of reproducibility across studies, the reliability of single study results need reporting.

PMID:41740214 | DOI:10.1016/j.nicl.2026.103973

Neuropsychiatr Dis Treat. 2026 Feb 18;22:566720. doi: 10.2147/NDT.S566720. eCollection 2026.

ABSTRACT

Multiple system atrophy (MSA) is a neurodegenerative disease characterized by α-synuclein pathology and pronounced clinical heterogeneity, making early diagnosis difficult. Functional magnetic resonance imaging (fMRI) has emerged as a promising tool to enhance diagnostic precision. By identifying disease- and symptom-specific network connectivity abnormalities, fMRI may reflect pathological changes in corresponding brain regions, thereby providing mechanistic insights. Recent work demonstrates that resting-state fMRI (rs-fMRI) can capture subtype-specific patterns, predominant basal ganglia-cortical disruption observed in the parkinsonian subtype of MSA (MSA-P) and cerebellar-cortical disconnection in the cerebellar subtype (MSA-C), reflecting their respective underlying pathologies of striatonigral degeneration and olivopontocerebellar atrophy. Rs-fMRI can also distinguish MSA from related parkinsonian syndromes, including Parkinson's disease (PD) and progressive supranuclear palsy (PSP), based on characteristic disruptions in cerebellar-cortical network connectivity. These patterns align with pathological features, providing important insights into disease progression. Task-based fMRI (t-fMRI), though less studied, further highlights impairments in motor network integration. Beyond diagnosis, fMRI has shown potential in evaluating treatment effects, with neuromodulatory interventions such as transcranial magnetic stimulation associated with measurable network changes. However, existing studies remain constrained by small sample sizes, single-center designs, and methodological variability. Future directions include large, multicenter trials, standardized imaging protocols, and integration with multimodal and computational approaches to establish robust fMRI-based biomarkers. Collectively, these advances position fMRI as a promising biomarker-oriented tool in MSA, supporting subtype classification, enhancing differential diagnosis from PD and PSP, elucidating symptom-specific network dysfunction, and enabling objective evaluation of therapeutic interventions in clinical and translational settings.

PMID:41738058 | PMC:PMC12927845 | DOI:10.2147/NDT.S566720

Imaging Neurosci (Camb). 2026 Feb 20;4:IMAG.a.1143. doi: 10.1162/IMAG.a.1143. eCollection 2026.

ABSTRACT

Numerous approaches have been used to denoise spinal cord functional magnetic resonance imaging (fMRI) data. Principal component analysis (PCA)-based techniques, which derive regressors from a noise region of interest (ROI), have been used in both brain (e.g., CompCor) and spinal cord fMRI. However, spinal cord fMRI denoising methods have yet to be systematically evaluated. Here, we formalize and evaluate a PCA-based technique for deriving nuisance regressors for spinal cord fMRI analysis (SpinalCompCor). In this method, regressors are derived with PCA from a noise ROI, an area defined outside of the spinal cord and cerebrospinal fluid. A parallel analysis is used to systematically determine how many components to retain as regressors for modeling; this designated a median of 9 regressors across four fMRI datasets: motor task (n = 26), breathing task (n = 27), and resting state (n = 15 and n = 10). First-level fMRI modeling demonstrated that principal component regressors did fit noise (e.g., physiological noise from blood vessels), though the effectiveness may be dependent upon the acquisition parameters. However, group-level activation maps did not show a clear benefit from including SpinalCompCor regressors. The potential for collinearity of principal component regressors with the task may be a concern, and this should be considered in future implementations for which task-correlated noise is anticipated. In general, denoising with SpinalCompCor regressors in place of physiological recording-derived regressors is only recommended when the latter are unavailable, as SpinalCompCor may not consistently reproduce recording-based denoising across datasets or acquisitions.

PMID:41738011 | PMC:PMC12926774 | DOI:10.1162/IMAG.a.1143

Front Neurol. 2026 Feb 9;17:1699988. doi: 10.3389/fneur.2026.1699988. eCollection 2026.

ABSTRACT

OBJECTIVE: To investigate the local brain functional changes after heat-sensitive moxibustion at the left ST35 (Dubi) acupoint in patients with knee osteoarthritis (KOA) based on resting-state functional magnetic resonance imaging (rs-fMRI), and to explore the possible neuroregulatory mechanisms of heat-sensitive moxibustion for pain relief using the fractional amplitude of low-frequency fluctuation (fALFF) analysis.

METHODS: A total of 30 KOA patients who were found to be insensitive to the heat of moxibustion in the non-heat-sensitive moxibustion (NHSM) group, and enrolled another 30 KOA patients with moxibustion sensation in the heat-sensitive moxibustion (HSM) group. Both groups received moxibustion at the left ST35 acupoint for 10 min (once daily for 10 consecutive days) at a distance of about 3 cm from the skin. Before the first treatment and after the tenth treatment, we assessed knee pain using visual analog scale (VAS) and performed rs-fMRI scans on the patients. The fALFF data of both groups were processed using the SPM 12 module of MATLAB software.

RESULTS: Compared with pre-moxibustion, the fALFF value of the HSM group in the frontal lobe, white matter, and left temporal lobe was significantly higher, while the occipital lobe and the right hemisphere was significantly lower. The region with the highest increase was the left temporal lobe, followed by white matter, and the region with the strongest decrease was the occipital lobe, followed by the frontal lobe and the right hemisphere. In the NHSM group, the fALFF value in the left occipital lobe, left medial frontal gyrus, left middle frontal gyrus, right superior frontal gyrus, right superior temporal gyrus, and right cerebellar posterior lobe was significantly lower, with the strongest decrease in the right cerebellar posterior lobe, followed by the right superior temporal gyrus. Compared with the NHSM group after treatment, the fALFF value of the HSM group in the external nucleus, white matter, right hemisphere, left cerebellum, and left hemisphere was significantly higher, and the frontal lobe, occipital lobe, and precentral gyrus was significantly lower. Additionally, a positive correlation was found between the fALFF changes of the left temporal lobe and the VAS score changes for each patient (pre- vs. post-treatment) in the HSM group (r = 0.764, p < 0.01), whereas a negative correlation was observed for the occipital lobe (r = -0.595, p < 0.01).

CONCLUSION: This study reveals that the superior pain relief from heat-sensitive moxibustion is underpinned by a sensation-specific, bidirectional modulation of the brain's pain-processing network. Unlike the generalized suppression observed in the NHSM group, the heat-sensitive state is characterized by a concerted increase in temporal lobe activity and decrease in occipital lobe activity, both changes being strongly predictive of individual clinical improvement. These results offer compelling neuroimaging evidence that the subjective heat-sensitive sensation reflects a more efficient and integrated brain state for analgesia.

CLINICAL TRIAL REGISTRATION: https://www.chictr.org.cn/, ChiCTR2000033075.

PMID:41738006 | PMC:PMC12926134 | DOI:10.3389/fneur.2026.1699988

Brain Behav Immun Health. 2026 Feb 9;53:101196. doi: 10.1016/j.bbih.2026.101196. eCollection 2026 May.

ABSTRACT

BACKGROUND: Persistent fatigue is one of the most common and disabling sequelae of COVID-19, yet its neurobiological mechanisms remain poorly understood. Emerging evidence implicates systemic inflammation and fronto-striatal dysfunction in fatigue across diverse clinical conditions. However, the links between early inflammatory responses, brain connectivity, and the acute-to-chronic trajectory of post-COVID fatigue are unclear.

METHODS: In a multi-center longitudinal cohort of 193 young-to-middle-aged adults with mild COVID-19, we assessed acute-phase C-reactive protein (CRP), fatigue severity (FAS) at <1 month (acute, FAS-1) and 3 months (chronic, FAS-2) post-infection, and resting-state fMRI at 3 months. Functional connectivity (FC) differences between participants with persistent (n = 48) and non-persistent fatigue (n = 145) were examined, and mediation analyses were performed to evaluate pathways linking CRP, FC alterations, and fatigue progression.

RESULTS: Acute-phase CRP levels were elevated in the persistent fatigue group and positively correlated with fatigue severity at both time points. Compared with the non-persistent group, individuals with persistent fatigue showed reduced functional connectivity (FC) between the left superior frontal gyrus (SFG L) and striatal regions (caudate L and putamen L). This SFG L-striatal FC was negatively correlated with fatigue severity. Crucially, a chain mediation model suggested that the association between CRP on chronic fatigue was statistically mediated through two sequential pathways: (1) via acute fatigue alone, and (2) via acute fatigue followed by reduced SFG L-striatal FC.

CONCLUSION: In this cohort of mild COVID-19 survivors, this study identifies acute inflammation (elevated CRP) as a significant predictor of post-COVID fatigue and suggests that reduced fronto-striatal connectivity may mediate the transition from acute to chronic fatigue. These findings highlight the fronto-striatal circuit as a potential imaging biomarker and point to the acute phase as a critical window for anti-inflammatory or neuromodulatory interventions. Further longitudinal and interventional studies are needed to validate these mechanisms and therapeutic strategies.

PMID:41737723 | PMC:PMC12926603 | DOI:10.1016/j.bbih.2026.101196

Cogn Affect Behav Neurosci. 2026 Feb 25. doi: 10.3758/s13415-025-01397-8. Online ahead of print.

ABSTRACT

Evaluating others' actions requires integrating their intentions with the outcomes they produce. Several studies have investigated the neural processes supporting this aspect of moral judgment, but findings remain heterogeneous. We conducted a pooled Activation Likelihood Estimation (ALE) meta-analysis of fMRI studies comparing evaluations of intentional and accidental harm, which is preregistered at https://doi.org/10.17605/OSF.IO/2HTFU . Following a systematic search on PubMed, Scopus, and Web of Science (last search: October 2024), eight studies met our inclusion criteria, yielding a total of 18 contrasts. Eligible studies reported whole-brain group analyses with stereotactic coordinates for direct contrasts between intentional and accidental harm. Studies were excluded if they focused on patient populations or lacked such contrasts. The meta-analysis identified two regions of consistent activation: the right amygdala and the left hippocampus. To better characterize their functional roles, we performed meta-analytic connectivity modeling and resting-state connectivity analyses. The amygdala showed reliable associations with regions involved in salience detection and affective regulation, supporting its established role in encoding harm-related signals. The hippocampus exhibited a broad connectivity profile, suggesting possible roles in interpersonal harm evaluation, such as episodic simulation, contextual reconstruction, and schema-based reasoning. These results confirm key aspects of existing models of moral judgment and offer novel insights by highlighting the involvement of the hippocampus, a region not typically emphasized in intent-based moral evaluation.

PMID:41735754 | DOI:10.3758/s13415-025-01397-8

Yonsei Med J. 2026 Mar;67(3):238-250. doi: 10.3349/ymj.2025.0012.

ABSTRACT

PURPOSE: Recently, mental health interventions through mobile applications have been increasing. This study sought to explore what changes occurred in psychometric properties and brain functional connectivity (FC) among people who practiced mindfulness meditation through a mobile application.

MATERIALS AND METHODS: Subjects underwent mindfulness-based intervention (MBI) for about 24 minutes every day for 8 weeks through a mobile application. Before and after MBI, a total of 21 adult men and women completed self-report questionnaires and functional magnetic resonance imaging (fMRI) tests. The fMRI data were acquired during an attention network test and during the resting state.

RESULTS: In self-report questionnaires, participants reported increased levels of mindfulness and decreased emotion regulation difficulties after MBI. In task-based fMRI, the time-by-intervention effect was not significant. In resting-state fMRI, FC between the right posterior insula and the left ventromedial prefrontal cortex (VMPFC) increased after MBI. FC between the default mode network-related regions and the occipital regions decreased after MBI. The decrease in FC between the VMPFC and the cuneus showed a significant correlation with the improvement in emotional awareness after MBI.

CONCLUSION: In a pre- and post-MBI comparison of a single group, subjects who underwent mobile-based MBI showed FC changes including the VMPFC. In particular, some of these FC changes were correlated with changes in emotional awareness. The results of this study suggest that further research is needed to verify whether mobile-based MBI affects improvement in emotion regulation through neural changes in functional brain networks.

PMID:41734985 | DOI:10.3349/ymj.2025.0012

Alzheimers Res Ther. 2026 Feb 24. doi: 10.1186/s13195-026-01991-z. Online ahead of print.

NO ABSTRACT

PMID:41736154 | DOI:10.1186/s13195-026-01991-z

BMC Res Notes. 2026 Feb 24. doi: 10.1186/s13104-026-07745-2. Online ahead of print.

NO ABSTRACT

PMID:41736146 | DOI:10.1186/s13104-026-07745-2

Hum Brain Mapp. 2026 Feb 15;47(3):e70482. doi: 10.1002/hbm.70482.

ABSTRACT

There is an urgent need to advance understanding of the neural underpinnings of depression, especially early in the life span. Examination of neural dynamics using resting-state functional magnetic resonance imaging (fMRI) data can provide indices of neural flexibility, which may provide important new insights for the neurobiology of pediatric depression. Here we applied Hidden Markov Modeling (HMM) to resting-state fMRI data to investigate neural flexibility in relation to depression and suicidal thinking in children. We utilized data from the Adolescent Brain Cognitive Development℠ Study (ABCD Study), and included data from 10,763 children (9-10 years) who completed two 5-min resting state fMRI scans at the baseline visit. After applying the NeuroMark framework to the data, HMM was applied with a varying number of states; a six-state model was selected from candidate models based on between-scan reliability. We applied linear mixed-effect modeling to test the relationship between two clinical predictors: current major depressive disorder (MDD) diagnosis and presence of suicidal ideation (SI) with our primary outcome for neural flexibility: the frequency of transitions between HMM-derived states ("state-switching"), while including sex, age, and other socio-demographic variables as covariates. Analyses were conducted both with and without correction for head motion. We also explored relationships with total time and dwell time in each state of the six states. Lower state-switching during rest was associated with both MDD and SI, although these findings were no longer significant after correcting for head motion. Notably, state-switching was inversely related to head motion and was higher in females than males. Exploratory analysis showed that MDD was associated with shorter dwell time in one state and longer dwell time in another, suggesting altered temporal persistence of specific neural configurations. Tentative evidence supported our hypothesis that lower state-switching in children with MDD and SI may reflect a reduction in brain flexibility, potentially contributing to a tendency to become "stuck" in negative patterns of thinking and feeling. However, the relatively low frequency of these problems in late childhood reduced statistical power after correcting for motion. Future research is needed to assess these relationships at later adolescent time points, when higher prevalence of depression and SI and lower prevalence of head motion will allow more powerful tests of these associations.

PMID:41733392 | DOI:10.1002/hbm.70482

Alzheimers Res Ther. 2026 Feb 24. doi: 10.1186/s13195-026-01993-x. Online ahead of print.

ABSTRACT

BACKGROUND: Digital speech-based assessments provide scalable tools for detecting subtle cognitive decline. Here, we investigated whether digitally derived speech-based composite score of cognition and individual speech features were associated with alterations in functional connectivity (FC) within task-related brain networks in the Alzheimer's disease spectrum, which are known to reflect cognitive performance and disease-related changes.

METHODS: Data were analyzed from 129 participants of the German PROSPECT-AD study, ranging from cognitively healthy individuals to those with mild cognitive impairment. Speech-based cognitive scores and speech features were derived from automated phone-administered semantic verbal fluency (SVF) and verbal learning tasks (VLT). Resting-state fMRI assessed FC, with intrinsic connectivity networks identified via independent component analysis and dual regression. Associations were examined using permutation-based voxel-wise regression, controlling for demographic and clinical covariates. Seed-to-voxel analyses were conducted to support network identification and complement findings.

RESULTS: Greater language network connectivity in the left middle temporal gyrus was associated with increased SVF temporal cluster switching (FWE < .05, cluster size = 12 voxels, mean T = 3.86). Exploratory analyses (uncorrected p < .01) demonstrated no significant associations between cognitive composite scores and FC. However, individual SVF and VLT speech features exhibited network-specific associations across executive, language, and default mode networks, indicating exploratory yet spatially distinct connectivity patterns.

CONCLUSION: Digital speech-based assessments may have limited current utility for detecting FC alterations in at-risk individuals. Further validation using complementary methodological approaches, shorter intervals between fMRI and speech assessments, and testing in independent cohorts, are essential to establish their reliability and clinical relevance for monitoring brain network changes.

PMID:41731608 | DOI:10.1186/s13195-026-01993-x

bioRxiv [Preprint]. 2026 Feb 10:2026.02.08.704721. doi: 10.64898/2026.02.08.704721.

ABSTRACT

The brain is never truly at rest. Even in the absence of external stimuli or cognitive tasks, the central nervous system continuously receives peripheral signals from visceral organs, such as the heart, lungs, and stomach, and sends motor commands to regulate organ physiology. This bidirectional brain-body interaction, also known as interoception, engages neural pathways via a hierarchical set of brain regions, including the nucleus of the solitary tract, hypothalamus, paraventricular nuclei of the thalamus, insular cortex, and anterior cingulate cortex, among others. However, it is largely unclear to what extent interoceptive signaling shapes ongoing fluctuations and correlations of brain activity. To address this question, we recorded resting state functional magnetic resonance imaging data from 34 anesthetized rats, examined intrinsic correlations (or functional connectivity), and tested their dependence on the body's digestive state and peripheral nerve integrity. We observed reciprocal functional connectivity among brain regions situated along established interoceptive pathways, revealing a cohesive network, which we refer to as the interoception network. This network showed stronger functional connectivity in the fed condition (digestive phase) compared to the fasted condition (inter-digestive phase), suggesting its dependence on distinct states of gastrointestinal interoception without apparent cardiac or respiratory confounds. Importantly, we found that the interoception network relied on the integrity of the vagus nerve, a primary component of the peripheral nervous system for visceral sensation and parasympathetic control. When vagal signaling was surgically severed by bilateral cervical vagotomy, functional connectivity within the interoception network was notably reduced. Given these findings, we conclude that resting state functional connectivity is not sustained by the central nervous system alone, but relies on interoceptive signaling mediated through peripheral nerves that connect the brain and viscera.

PMID:41726989 | PMC:PMC12919019 | DOI:10.64898/2026.02.08.704721