Most recent paper

Imaging Neurosci (Camb). 2026 Sep 17;4:IMAG.a.1362. doi: 10.1162/IMAG.a.1362. eCollection 2026.

ABSTRACT

Ultra-high-field magnetic resonance imaging (MRI) promises major gains in blood oxygen level dependent (BOLD) sensitivity but introduces severe challenges such as RF field inhomogeneity, B 0 field inhomogeneity, motion and vibration-induced field variations. We report the first whole-brain resting-state and task-based fMRI experiments at 11.7T. A 3D-EPI sequence was optimized using tailored parallel-transmit RF pulses, gradient reshaping to suppress vibration-induced B 0 fluctuations, and prospective motion correction using servo navigation combined with retrospective phase equalization (PEERS). Four participants were scanned at 11.7T with whole-brain 1.2 mm isotropic resolution with servo navigation and PEERS that considerably reduced residual motion and increased temporal signal-to-noise ratio (SNR). The Default Mode Network in resting state could be detected and a physiological-noise-dominated regime was identified. Four additional but different participants were scanned at 7T for tSNR comparison and validation of the protocol, data quality, and processing pipeline. Task-based fMRI likewise was conducted at 11.7T and yielded robust, spatially specific activations across motor, visual, mathematical, and language networks, again with improved sensitivity and cleaner BOLD responses when using servo navigation and PEERS. These results demonstrate the feasibility and reliability of whole-brain human fMRI at 11.7T and constitute a first-quality control milestone to further increase resolution at that field strength.

PMID:42761695 | PMC:PMC13588317 | DOI:10.1162/IMAG.a.1362

Am J Alzheimers Dis Other Demen. 2026 Jan-Dec;41:15333175261491804. doi: 10.1177/15333175261491804. Epub 2026 Sep 18.

ABSTRACT

We present a resting-state fMRI-based protocol for quantifying anatomical distance-dependent functional connectivity strength (FCS) and demonstrate its application in vascular cognitive impairment, no dementia (VCIND). Resting-state fMRI data from VCIND patients and demographically matched normal controls were analyzed using voxel-wise FCS across six anatomical distance intervals to characterize short-, medium-, and long-range functional connectivity. Network-level analyses were also performed to identify large-scale network involvement. The results reveal a hierarchical distance-dependent organization of functional connectivity, with short-range connectivity concentrated in primary sensory systems and long-range connectivity increasingly involving higher-order association networks. In VCIND, significant alterations were observed particularly within the frontoparietal network, suggesting compensatory network reorganization associated with cognitive performance. These distance-dependent FCS alterations may serve as potential neuroimaging markers for early identification and risk stratification of VCIND and may also support longitudinal monitoring of functional network changes.

PMID:42760808 | DOI:10.1177/15333175261491804

BMC Psychiatry. 2026 Sep 18;26(1):733. doi: 10.1186/s12888-026-08360-y.

ABSTRACT

BACKGROUND: Parental education is associated with body mass index (BMI), mental health, and neurodevelopment in young adults, yet the neurobiological mechanisms remain unclear. This study investigated whether parental education is related to BMI and depressive scores through brain structural-functional connectivity (SC-FC) coupling.

METHODS: This study included 490 healthy young adults who underwent resting-state functional magnetic resonance imaging (rs-fMRI) and diffusion tensor imaging (DTI) scans. Parental education was measured as years of schooling, BMI by weight/height2, and depressive scores by the Beck Depression Inventory-II (BDI). SC and FC were derived from DTI and rs-fMRI, respectively. SC-FC coupling was quantified using Spearman's correlation between SC and FC matrices. Associations among variables were examined using Pearson correlation and mediation analyses.

RESULTS: Paternal education was significantly correlated with BMI (r = 0.10, P < 0.05) and depressive scores (r = -0.09, P < 0.05). Additionally, paternal education, BMI, and depressive scores were positively associated with SC-FC coupling in the right inferior parietal lobule (IPL, r = 0.19, corrected P < 0.001; r = 0.12, corrected P < 0.05; r = 0.10, corrected P < 0.05, respectively). Mediation analysis showed that the right IPL statistically mediated the associations of paternal education with both BMI (indirect effect = 0.01, 95% confidence interval (CI): 0.001-0.04) and depressive scores (indirect effect = 0.01, 95% CI: 0.001-0.03).

CONCLUSION: In this cross-sectional study, paternal education was associated with BMI and depressive scores, with SC-FC coupling in the right IPL statistically mediating these associations. No corresponding effects were observed for maternal education. These findings suggest a potential neural pathway linking socioeconomic factors to physical and mental health outcomes.

CLINICAL TRIAL NUMBER: Not applicable.

PMID:42760514 | DOI:10.1186/s12888-026-08360-y

Transl Psychiatry. 2026 Sep 2;16(1):473. doi: 10.1038/s41398-026-04401-y.

ABSTRACT

Individuals with methamphetamine use disorder (MUD) exhibited functional and structural brain recovery during abstinence. However, longitudinal neural changes of large-scale brain networks after prolonged abstinence remain unclear. Here, energy landscape analysis was applied to resting-state fMRI to characterize dominant activity patterns in 41 healthy controls and 40 individuals with MUD at baseline and after prolonged abstinence. We compared the dynamic characteristics of these patterns across groups and stages. Support vector regression model was constructed using baseline dynamic indices to predict craving changes after prolonged abstinence. We found that individuals with MUD exhibited rigid brain dynamics, which were correlated with abnormal brain network coordination and behaviors. Following prolonged abstinence, neural flexibility showed partial improvement, and dynamic indices could predict longitudinal craving changes. We revealed atypical brain network dynamics and their partial recovery during prolonged abstinence in MUD and suggested that dynamic indices could serve as biomarkers for predicting craving changes.

PMID:42760282 | DOI:10.1038/s41398-026-04401-y

Neuroimage. 2026 Sep 18:122246. doi: 10.1016/j.neuroimage.2026.122246. Online ahead of print.

ABSTRACT

Functional connectomics has advanced our understanding of child development, neurodiversity, and brain organization. Most studies assume that collected fMRI data approximate the stable, trait-like connectome of interest. Here, we asked how stable a young child's functional connectome is, how stability is affected by scan paradigm (rest vs. passive viewing) and age, and how much data are needed to estimate a "stable connectome." We addressed these questions using a densely sampled single-subject dataset and a multi-subject pediatric cohort. We first collected 25 hours of resting-state and passive-viewing data from a single child, ages 5 to 9. Split-half connectome stability was near perfect (r = 0.98), even when data were split chronologically (r = 0.97). Connectome similarity differed across scan paradigms and collection years. Consistent patterns were observed in the multi-subject cohort, where individual-specific effects substantially outweighed movie-specific effects. A linear mixed-effects model showed that greater head motion and longer scan intervals were associated with lower session-to-session stability, whereas older age was associated with greater stability. A whole-connectome similarity threshold of r ≥ 0.8 was reached with 13.5 minutes of raw passive-viewing data, although many high-motion recordings failed to reach this threshold within the available recording duration. Similarly, ∼15.4 minutes of post-censor data were required for 70% of participants in the multi-subject cohort to reach r ≥ 0.7, while most high-motion scans failed to reach this threshold. Allowing for typical motion-related data loss (∼22% of volumes censored), these findings support approximately 20-minute acquisitions to obtain about 15 minutes of usable data, with longer recordings or multiple sessions for children with elevated head motion.

PMID:42759782 | DOI:10.1016/j.neuroimage.2026.122246

J Psychoactive Drugs. 2026 Sep 18:1-11. doi: 10.1080/02791072.2026.2729619. Online ahead of print.

ABSTRACT

Due to the psychotropic properties of kratom (Mitragyna speciosa), further studies are needed to measure its neurobiological impacts. This exploratory study employed resting-state functional magnetic resonance imaging (rs-fMRI) to examine alterations in brain activity and functional connectivity (FC) among kratom users. A total of 43 respondents were recruited (22 regular kratom users and 21 healthy controls). RS-fMRI data were acquired using a 1.5T GE Signa HDxt scanner. Preprocessing and analysis were performed with Statistical Parametric Mapping 12 (SPM 12) and CONN toolbox. Group differences in the low-frequency fluctuations (LFFs) and FC were examined. Kratom users exhibited reduced LFF in brain regions critical to the default mode network (DMN), cognitive control, and self-referential processing, including the precuneus, superior frontal gyrus, and cerebellar and cingulate cortices. They also showed increased FC between salience and cerebellar networks, specifically in the posterior cerebellum, and left rostral prefrontal cortex (RPFC). Chronic kratom consumption may alter brain activity, especially in areas linked to the DMN, cognition, and reward processing. Enhanced FC between salience and cerebellar networks may reflect compensatory mechanisms or adaptations to kratom's effects. Further research is crucial to elucidate the underlying mechanisms and potential implications for cognitive function and addictive behaviors.

PMID:42757738 | DOI:10.1080/02791072.2026.2729619

Rhinology. 2026 Sep 18. doi: 10.4193/Rhin25.519. Online ahead of print.

ABSTRACT

BACKGROUND: Olfactory training (OT) has emerged as a first-line non-pharmacological treatment for olfactory dysfunction. This study aimed to determine whether multisensory stimulation during OT improves olfactory recovery and to explore the underlying neurobiological mechanisms.

METHODOLOGY: Individuals aged 18-80 years with post-viral or idiopathic olfactory dysfunction lasting for at least one month were recruited. Participants were allocated to either multisensory OT (MOT, odor-congruent video clips during OT) or conventional OT (COT) for three months. A subset of participants underwent resting-state fMRI at baseline and post-training to evaluate changes in olfactory network connectivity, using the piriform cortex and orbitofrontal cortex as the predefined seed regions. Statistical analyses were performed using repeated-measures ANOVA to evaluate changes over time and between groups.

RESULTS: Ninety-five patients (MOT=51, COT=44) were included. Significant improvements in odor discrimination and composite TDI scores were observed across participants without significant session x group interaction. In the MOT group, pre-post gains were observed in odor discrimination, identification, and TDI. In the MRI subgroup (MOT=21, COT=18), MOT was associated with reduced functional connectivity between the right orbitofrontal cortex and the superior occipital, superior temporal, and postcentral gyri.

CONCLUSIONS: MOT was associated with olfactory improvement over the training period. Although between-group differences were not statistically significant, exploratory neuroimaging findings suggest post-MOT functional reorganization between olfactory and visual, auditory, and somatosensory regions. Incorporating multisensory stimulation into OT warrants further investigation as a potential strategy for olfactory rehabilitation.

PMID:42757552 | DOI:10.4193/Rhin25.519

Brain Behav Immun Health. 2026 Sep 10;57:101351. doi: 10.1016/j.bbih.2026.101351. eCollection 2026 Nov.

ABSTRACT

Prenatal inflammation is a recognized risk factor for autism spectrum disorder (ASD), but the neural pathways linking in utero immune exposure to later outcomes remain unclear. We examined whether chronic placental inflammation (CPI), a sustained immune response within the placenta, is associated with fetal brain functional connectivity differences and later ASD traits. A cohort of 105 pregnant individuals (85.7% African American/Black, 1.0% Asian American, 4.8% Bi-racial, 4.8% White, 1.0% Other, 2.9% Missing) underwent fetal resting-state fMRI between 24 and 38 weeks' gestation. After delivery, placental tissues were evaluated by expert pathologists for CPI lesions. Fetuses exposed to CPI (n = 44) showed significant differences in resting-state functional connectivity (RSFC) compared to non-exposed fetuses (n = 61) across ten network pairs, with the greatest effects in prefrontal, cerebellar, visual, and motor regions. These connectivity differences were associated with behavioral outcomes: cerebellar-medial visual RSFC was the strongest prenatal predictor of ASD traits at age 3. An exploratory structural equation modeling indicated that CPI was associated with reduced cerebellum-medial visual RSFC, which was associated with higher ASD trait scores, though the indirect pathway did not reach statistical significance. This study provides the first human evidence that chronic placental inflammation is associated with large-scale fetal brain network differences that are also associated with autism traits, suggesting that prenatal immune activity may be relevant to neurodevelopmental trajectories.

PMID:42756825 | PMC:PMC13583612 | DOI:10.1016/j.bbih.2026.101351

Front Neurol. 2026 Sep 3;17:1796739. doi: 10.3389/fneur.2026.1796739. eCollection 2026.

ABSTRACT

BACKGROUND: Migraine is a prevalent and disabling neurological disorder. Resting-state functional magnetic resonance imaging (rs-fMRI) studies have investigated regional intrinsic neural activity alterations in migraine but have often yielded inconsistent findings. Emerging network perspectives suggest that brain disorders may be better understood through disruptions in large-scale networks.

METHODS: This systematic review included 31 rs-fMRI studies (1,238 migraine patients; 1,005 controls; 302 altered ALFF/ReHo coordinates) to clarify these discrepancies. Using functional connectivity network mapping (FCNM) with Human Connectome Project data (n = 1,093), we identified that heterogeneous regional intrinsic neural activity alterations in migraine converge onto common brain functional networks. Spatial relationships between the identified brain networks and the distribution of major neurotransmitter receptors/transporters were subsequently characterized using the Juspace toolbox.

RESULTS: FCNM analysis revealed that heterogeneous regional intrinsic neural activity alterations reported in migraine studies mapped to common brain functional networks, particularly within visual, somatomotor, and attention systems. The FCNM-identified migraine network exhibited significant spatial correlations with normative distributions of metabotropic glutamate receptor 5 (mGluR5), 5-hydroxytryptamine receptor 2A (5-HT2A), and noradrenaline transporter (NAT).

CONCLUSION: These findings suggest that intrinsic neural dysfunctions in migraine map to large-scale networks with multi-neurochemical susceptibility. This framework suggests that heterogeneous regional intrinsic activity alterations in migraine are preferentially connected to visual, somatomotor, and attentional systems and are spatially aligned with normative neurochemical maps. These findings are hypothesis-generating and should not be interpreted as evidence of causality or patient-specific receptor alterations.

PMID:42755892 | PMC:PMC13581937 | DOI:10.3389/fneur.2026.1796739

Front Nutr. 2026 Sep 3;13:1917977. doi: 10.3389/fnut.2026.1917977. eCollection 2026.

ABSTRACT

BACKGROUND: Obesity affects the brain, but most neuroimaging studies examine a single modality, leaving it unclear whether obesity-related functional, microstructural, and perfusion abnormalities co-localize. We combined three modalities across the normal-weight-overweight-obese spectrum.

METHODS: Of 80 adults enrolled, 10 were excluded (sleep, incomplete acquisition, artifacts), leaving 70 (normal-weight, n = 27; overweight, n = 21; obese, n = 22), who did not differ in age, sex, or education. All underwent resting-state functional MRI, diffusion tensor imaging, and 3D arterial spin labeling at 3.0 T, yielding amplitude of low-frequency fluctuation (ALFF), regional homogeneity (ReHo), functional connectivity (FC), fractional anisotropy (FA), mean (MD), axial, and radial diffusivity (RD), and cerebral blood flow (CBF). Group differences were assessed with covariate-adjusted models; diffusion metrics were tested across all 48 atlas regions with false-discovery-rate (FDR) correction, and body mass index (BMI) relationships by whole-sample regression without prior region selection. Spatial convergence was tested by minimum-statistic conjunction and random-displacement permutation.

RESULTS: Obese participants showed lower ALFF than controls in the cerebellar posterior lobe (vermis, left lobule VIII, right Crus II) and left superior frontal gyrus, higher ALFF in the bilateral parahippocampal gyri, lower ReHo in the left superior frontal gyrus, and lower ReHo than overweight participants in the left middle cingulate gyrus. No regional diffusion difference survived FDR correction, but BMI predicted FA, MD, and RD in the right inferior cerebellar peduncle in whole-sample regression (all q < 0.015), the only associations to survive. CBF was reduced in the cerebellar posterior lobe and increased in the left inferior frontal and right postcentral gyri. ALFF-CBF overlap there exceeded chance at every threshold, reaching 43.1-fold at P < 0.001, confirmed by conjunction and permutation (P = 0.0002-0.0008); peak-level in the left lobule VIII and right Crus II, but atlas-level only at the vermis. FC showed no group differences.

CONCLUSION: Overweight and obesity are associated with spatially convergent functional, microstructural, and perfusion abnormalities preferentially involving cerebellar and cortico-limbic regions, scaling continuously with BMI in the right inferior cerebellar peduncle. Because cognition was assessed only by the MMSE, these roles suggest hypotheses rather than conclusions about cognition.

PMID:42755846 | PMC:PMC13581818 | DOI:10.3389/fnut.2026.1917977

Front Neurol. 2026 Sep 3;17:1865496. doi: 10.3389/fneur.2026.1865496. eCollection 2026.

ABSTRACT

INTRODUCTION: This study investigated the immediate effects of LiuZiJue breathing practice on executive control and its associated neural correlates using a multimodal approach combining behavioral testing, functional near-infrared spectroscopy (fNIRS), resting-state functional MRI (rs-fMRI), and transcranial magnetic stimulation (TMS).

METHODS: A randomized crossover design was conducted in 20 healthy young adults. Participants completed Stroop task performance and TMS assessments under LiuZiJue and control conditions (eyes-closed rest). Resting-state fMRI and task-based fNIRS recordings were additionally acquired to examine brain activity and functional connectivity changes.

RESULTS: LiuZiJue significantly reduced Stroop reaction time compared with both baseline and control conditions (p < 0.05), without affecting accuracy. fNIRS results showed decreased oxygenated hemoglobin (HBO) signals in the prefrontal cortex, premotor area, supplementary motor area, and primary motor cortex during the LiuZiJue task (p < 0.05). Resting-state fMRI revealed altered regional brain activity and functional connectivity (FDR-corrected, p < 0.05), including decreased zALFF in precentral gyrus, middle temporal gyrus, postcentral gyrus, and insula and increased zALFF in cerebellar vermis lobule IX. Functional connectivity analysis indicated significant alterations in interregional connectivity (FDR-corrected, p < 0.05). TMS results indicated a significant prolongation of cortical silent period (CSP, p < 0.01), while short-interval intracortical inhibition (SICI) remained unchanged.

CONCLUSION: LiuZiJue was associated with changes in behavioral performance, cortical excitability, and large-scale brain activity in healthy young adults. These findings suggest potential modulation of executive-related neural processes; however, causal interpretations and underlying mechanisms should be considered cautiously given the exploratory nature of the study and limited sample size.

CLINICAL TRIAL REGISTRATION: https://www.chictr.org.cn/searchprojEN.html, identifier: ChiCTR2400087145.

PMID:42755788 | PMC:PMC13581813 | DOI:10.3389/fneur.2026.1865496

Hum Brain Mapp. 2026 Oct 1;47(14):e70641. doi: 10.1002/hbm.70641.

ABSTRACT

Functional magnetic resonance imaging (fMRI) time series are known to exhibit long-range temporal dependencies that challenge traditional modeling approaches. In this study, we propose a novel computational pipeline to characterize and interpret these dependencies using a long-memory (LM) framework, which captures the slow, power-law decay of autocorrelation in resting-state fMRI (rs-fMRI) signals. The pipeline involves voxelwise estimation of LM parameters via a wavelet-based Bayesian method, yielding spatial maps that reflect temporal dependence across the brain. These maps are then projected onto a lower-dimensional space via a composite basis and are then related to individual-level covariates through group-level regression. We applied this framework to the ADHD-200 dataset. Analysis identified a significant positive association between age and the long-memory parameter in the hippocampus after adjusting for ADHD symptom severity and medication status. After accounting for acquisition site, the estimated effect sizes were attenuated and no longer reached statistical significance, although the spatial pattern was largely preserved, with the strongest estimated effects remaining localized to the same anatomical regions. These findings suggest a relationship between long-range temporal dependence and developmental changes in memory-related brain regions while highlighting the influence of acquisition-site variability on effect magnitude and statistical significance. Overall, the proposed methodology enables detailed mapping of intrinsic temporal dynamics in rs-fMRI and offers new insights into the relationship between functional signal memory and brain development.

PMID:42754916 | DOI:10.1002/hbm.70641

Int J Neural Syst. 2027 Jan;37(1):2750008. doi: 10.1142/S0129065727500080.

ABSTRACT

The global signal (GS) represents the broad variations in neural activity throughout the brain. Recent research has identified changes in the GS of functional magnetic resonance imaging (fMRI) in schizophrenia, challenging the traditional view of GS as mere noise that is typically discarded during data preprocessing. However, there has been no comprehensive characteristic analysis of GS in schizophrenia. This study recruited 100 patients with schizophrenia and 113 healthy subjects to perform resting-state fMRI. The averaged gray matter fMRI signal is denoted as GS. The GS topography was constructed by calculating Pearson correlation (i.e. GSCORR) between the GS and time series of each gray matter voxel. Furthermore, the relevance between GS topography and the function network features was constructed according to graph theory. Finally, we implemented an integrated analytical framework combining independent component analysis and multiple linear regression to quantify the contributions of resting-state brain networks to the spatiotemporal characteristics of the GS. The GSCORR of schizophrenia decreased in the bilateral insula and exhibited a significant negative association with disease duration. The increased GSCORR in the thalamus and default mode network (DMN) showed a positive correlation with the scale scores. We found that global properties are represented by GS topography in schizophrenia. In addition, the linear characterization results of GS showed that the component contribution decreased at the primary sensory network but increased at the high-order associated network. Our results further demonstrated that GS contains brain features associated with schizophrenia, which would help to understand the neural mechanisms underlying the psychopathological symptoms of schizophrenia.

PMID:42754391 | DOI:10.1142/S0129065727500080

Neuroimage Clin. 2026 Sep 12;52:104061. doi: 10.1016/j.nicl.2026.104061. Online ahead of print.

ABSTRACT

The two hemispheres of the brain are strongly connected both structurally and functionally, and disruptions to interhemispheric communication can have significant cognitive and behavioral consequences. While alterations to interhemispheric structural and/or functional connectivity have been reported in many neurological conditions, this issue has been scarcely addressed in Primary Progressive Aphasia (PPA). PPA is a neurogenerative syndrome primarily involving (left-)lateralized gray matter atrophy and typically categorized into three variants - logopenic, non-fluent, and semantic. Here we examined interhemispheric, homotopic structural and functional connectivity in 72 individuals from the three variants and age-matched controls. Structural connectivity (SC) was calculated from diffusion-weighted MRI and functional connectivity (FC) from resting-state fMRI. Both resulting connectomes were based on the same atlas of homotopic parcels. We asked: 1) Are patterns of abnormal homotopic SC and/or FC shared across PPA variants? 2) How are the FC changes related to underlying structural changes to white and gray matter? We found, first, that average homotopic SC was reduced in all variants and was related to greater behavioral disease severity. By contrast, for homotopic FC, the three variants displayed distinct patterns. Second, direct FC-SC comparisons established that, across variants, the FC and SC changes were distinct from one another and were largely dissociated from gray matter atrophy. In sum, the structural connectivity between the two hemispheres weakens in PPA with disease progression. However, the uncoupling of FC from underlying structural changes indicates that these changes reflect complex patterns of functional re-organization of interhemispheric communication in response to the disease.

PMID:42753639 | DOI:10.1016/j.nicl.2026.104061

Psychiatry Res Neuroimaging. 2026 Sep 14;364:112334. doi: 10.1016/j.pscychresns.2026.112334. Online ahead of print.

ABSTRACT

Panic disorder (PD) is a psychiatric condition marked by recurring, unexpected panic attacks characterized by intense fear and rapid-onset physiological symptoms. Functional magnetic resonance imaging (fMRI) in individuals with PD has revealed abnormalities in several brain regions associated with fear processing, emotional regulation, and interoception. A systematic search was conducted in PubMed and Embase to review the fMRI changes associated with PD published until April 2024. Eighteen studies were included, primarily employing resting-state fMRI and examining functional connectivity. Consistent findings included amygdala and insula hyperactivity and reduced connectivity within the default mode network, particularly in the posterior cingulate cortex and precuneus. Altered connectivity patterns were also observed in the prefrontal cortex, anterior cingulate cortex, and hippocampus. Task-based fMRI studies revealed increased activity in fear-related regions that correlated with clinical measures of PD severity. This review consolidates the evidence of neural network disruptions in PD, emphasizing dysregulated fear processing, emotional regulation, and self-referential thinking. These findings highlight the potential of fMRI to elucidate the pathophysiology of PD and inform targeted interventions. However, the limited number of available studies, small sample sizes, and methodological heterogeneity, particularly among task-based fMRI studies, warrant cautious interpretation.

PMID:42753522 | DOI:10.1016/j.pscychresns.2026.112334

Philos Trans R Soc Lond B Biol Sci. 2026 Sep 17;381(1958):2024-0472.1. doi: 10.1098/rstb.2024.0472.

ABSTRACT

Recent advances in neuroimaging have significantly reshaped our understanding of functional neurological disorder (FND), shifting the focus from structural damage to dysfunctional brain networks. Building on Charcot's early intuition of a 'functional lesion', this review traces the evolution of neuroimaging research in FND, from its historical roots to current developments. We synthesize findings from structural and functional imaging studies, highlighting alterations in brain regions involved in motor control, self-agency and emotion regulation. Task-based fMRI studies have shown atypical activation in areas such as the supplementary motor area, dorsolateral prefrontal cortex and temporo-parietal junction, supporting the notion of impaired voluntary motor control. Additionally, changes in the amygdala and periaqueductal grey point to the role of emotional dysregulation. Structural imaging reveals grey and white matter alterations, while machine learning applied to resting-state connectivity shows promise for biomarker development. Emerging evidence also suggests that genetic and epigenetic factors-particularly within oxytocinergic and serotonergic systems-may contribute to individual vulnerability. We argue for a multi-modal approach integrating neuroimaging, genetics and computational methods to advance both the diagnosis and treatment of FND. This article is part of the discussion meeting issue 'Digital healthcare for the management of functional neurological disorders'.

PMID:42750448 | DOI:10.1098/rstb.2024.0472

J Affect Disord. 2026 Sep 16:122515. doi: 10.1016/j.jad.2026.122515. Online ahead of print.

ABSTRACT

Accelerated intermittent theta burst stimulation (aiTBS) is an effective neuromodulatory intervention for treatment resistant depression (TRD). aiTBS delivers repetitive transcranial magnetic stimulation sessions over a compressed timeframe and can be individualized to specific locations using resting-state functional magnetic resonance imaging (rs-fMRI) to target pathological cortical networks or individualized in time using electroencephalography (EEG) to determine optimal participant-specific stimulation frequencies. In this randomized, placebo-controlled trial, we tested these forms of personalization against sham stimulation. Fifty-one patients with TRD were randomized to one of three arms; 43 completed the protocol per pre-specified criteria and assigned to one of three arms: (1) fMRI-connectivity-individualized aiTBS; (2) frequency- and connectivity-individualized aiTBS; or (3) sham aiTBS. Participants received 10 aiTBS sessions per day for five consecutive days with clinical assessments at baseline, and 1 and 4 weeks post-treatment. The primary outcome was change in depression severity on the Montgomery-Åsberg Depression Rating Scale from baseline to 1 week post-treatment. Anhedonia severity measured with the Dimensional Anhedonia Rating Scale was a secondary outcome. No significant differences were observed between active and sham arms on primary MADRS or secondary DARS endpoints for either the intent-to-treat or per-protocol sample. Remission and response rates also did not differ between arms. aiTBS individualized using either rs-fMRI or EEG and rs-fMRI failed to yield significant antidepressant benefits over sham stimulation, with relatively muted overall therapeutic benefits, underscoring the need for further clinical trials evaluating the efficacy of personalized aiTBS in TRD.

PMID:42749167 | DOI:10.1016/j.jad.2026.122515

Neuroscience. 2026 Sep 16:S0306-4522(26)00626-3. doi: 10.1016/j.neuroscience.2026.09.015. Online ahead of print.

ABSTRACT

Mild cognitive impairment (MCI) is an important prodromal stage of Alzheimer's disease, and its early identification is critical for risk assessment and timely intervention. Resting-state functional magnetic resonance imaging (rs-fMRI) can noninvasively characterize brain functional activity and connectivity. However, most existing methods rely on static second-order functional connectivity, limiting their ability to capture dynamic coordination and high-order interactions among multiple brain regions. To address these limitations, we propose a brain functional network analysis framework integrating multi-scale temporal feature fusion with adaptive high-order hypergraph convolution. Specifically, the multi-scale temporal fusion module captures functional dynamics at different temporal resolutions, while the adaptive hypergraph convolutional network learns high-order associations among brain regions in an end-to-end manner. Spatiotemporal feature encoding is further incorporated to facilitate automatic MCI identification. The proposed framework was evaluated using rs-fMRI data from the ADNI dataset, with regional BOLD time series extracted based on the AAL atlas. Under five-fold cross-validation, the proposed method achieved an accuracy of 82.66%, a sensitivity of 82.07%, and a specificity of 83.07% on ADNI-2, while maintaining an accuracy of 81.50% on ADNI-3, outperforming existing methods on both datasets. Further analysis identified abnormal high-order coordination patterns involving the default mode network, limbic system, and hippocampus-related memory circuits in patients with MCI. These findings provide methodological support for the early identification of MCI and the discovery of potential neuroimaging biomarkers.

PMID:42749078 | DOI:10.1016/j.neuroscience.2026.09.015

Neural Netw. 2026 Sep 12;205(Pt C):109616. doi: 10.1016/j.neunet.2026.109616. Online ahead of print.

ABSTRACT

Major depressive disorder (MDD) is a brain disorder characterized by substantial individual heterogeneity, and its accurate diagnosis remains challenging in clinical practice. Graph neural networks applied to functional brain networks (FBNs) constructed from resting-state functional magnetic resonance imaging (rs-fMRI) data have shown promise for MDD diagnosis. However, in multicenter settings, site differences are often entangled with disease-related information, which not only interferes with the model's learning of disease patterns but also compromises the reliable modeling of topological patterns in individual FBNs, thereby limiting identification performance. To address this problem, we propose a disentangled population-guided graph neural network with dynamic refinement (DPGR-Net) for MDD identification. Specifically, DPGR-Net first constructs individual FBN from rs-fMRI data and learns discriminative subject-level brain graph representations. To mitigate the interference of site effects, we disentangle disease-related associations from site-related variations at the population level and impose an independence constraint to learn more stable cross-site disease-discriminative representations. On this basis, we innovatively design a population-guided dynamic optimization mechanism for individual brain graphs, which feeds the stable disease-discriminative information learned at the population level back into the structural updating of individual brain graphs, thereby enhancing disease-related connections while suppressing site-related noisy connections. Experimental results on the public REST-meta-MDD dataset show that the proposed method achieves an ACC of 92.37% and outperforms existing state-of-the-art methods. Furthermore, DPGR-Net identifies discriminative brain regions and aberrant functional connectivity patterns associated with MDD, thereby advancing the interpretability and clinical utility of rs-fMRI for MDD diagnosis.

PMID:42748805 | DOI:10.1016/j.neunet.2026.109616

Psychiatry Res Neuroimaging. 2026 Sep 10;364:112327. doi: 10.1016/j.pscychresns.2026.112327. Online ahead of print.

ABSTRACT

BACKGROUND: Generalized anxiety disorder (GAD) and major depressive disorder (MDD) show substantial clinical overlap, yet their shared and disorder-specific intrinsic functional abnormalities remain unclear. This study examined regional brain activity and whole-brain functional integration in GAD and MDD using resting-state fMRI.

METHODS: Forty-two GAD patients, 43 MDD patients, and 83 healthy controls underwent resting-state fMRI. ALFF and FCS were calculated to assess regional spontaneous activity and whole-brain functional integration, respectively. Group differences were tested using voxel-wise ANCOVA controlling for age, sex, education, and frame-wise displacement. Partial correlations examined associations with clinical symptoms.

RESULTS: Both patient groups showed decreased ALFF in a cluster spanning the left precuneus and right cuneus, with lower ALFF in GAD than in MDD. Both groups also showed decreased FCS in the bilateral lingual gyri, right middle occipital gyrus, and left postcentral gyrus, and increased FCS in the right inferior frontal gyrus. GAD showed lower FCS in the left lingual gyrus than MDD.

CONCLUSIONS: GAD and MDD showed shared functional alterations in posterior cortical, visual, somatosensory, and frontal regions, while GAD exhibited lower ALFF and FCS in specific posterior cortical regions than MDD.

PMID:42748768 | DOI:10.1016/j.pscychresns.2026.112327