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Soc Cogn Affect Neurosci. 2026 Jan 31:nsag004. doi: 10.1093/scan/nsag004. Online ahead of print.

ABSTRACT

Understanding the neural mechanisms underlying the impact of psychedelics on social perception and cognition may be instrumental to unravel their therapeutic potential. We conducted a pharmacoimaging study to examine ayahuasca's effects on a key theory of mind region, at the core of the third visual pathway (TVP) - the posterior superior temporal sulcus (pSTS), which is involved in facial emotion recognition and social perception. Twelve healthy participants (mean age: 40 ± 6.6 years; 4female) completed a crossover design with three conditions: 0.5 mg/kg DMT, 0.8 mg/kg DMT, and placebo, with 1-2 months washout intervals. Resting-state fMRI was used to assess pSTS functional and effective connectivity. The highest dose significantly increased right pSTS connectivity and directed modulation from visual (primary and extrastriate cortices) and mirror-neuron regions (supplementary motor cortex; SMC). Subjectively, this enhanced social cognitive states, with a strong positive correlation between pSTS-SMC connectivity and perspective-taking experiences. Additionally, ayahuasca produced positive psychological effects, including improved perceived social relationships, at one-week follow-up despite minimal acute effects. Our findings reveal a novel mechanism of action of psychedelics at early stages of social information processing, with enhanced integration of the TVP and mirror-neuron systems. The pSTS emerged as a critical hub supported by top-down and bottom-up evidence, providing a basis for understanding ayahuasca's prosocial therapeutic effects.

PMID:41619760 | DOI:10.1093/scan/nsag004

Cortex. 2026 Jan 14;196:90-100. doi: 10.1016/j.cortex.2026.01.002. Online ahead of print.

ABSTRACT

Older adults have difficulty switching between competing goals with increasing age due to declines in executive function (EF) and changes in brain network connectivity, including the Cognitive Control Network (CCN). Prior research shows that greater global functional connectivity (GFC) in the CCN supports cognitive flexibility. However, it is unclear whether CCN GFC is associated with task-switching in older adults. Task-switching performance relies on both switching and working memory. Mixing cost reflects the ability to maintain and coordinate multiple task rules in working memory and is sensitive to age-related declines in EF, whereas switching cost is more closely linked to age-related general slowing in processing speed. This study investigates how CCN GFC relates to task-switching performance in older adults using two task versions. Participants aged 55-80 years old performed the Separate and Overlap versions for behavioral analyses (n = 118). Six 8-min resting-state fMRI sessions were collected over two days for brain behavior analyses (n = 112). Whole grey-matter GFC was calculated, followed by average GFC extraction from the CCN, Default Mode Network (DMN), and Somatomotor Network (SMN). Results showed that older adults were slower and less accurate in the Overlap version. Greater CCN, DMN, and SMN GFC were associated with smaller mixing costs in the Overlap version. SMN GFC was linked to larger mixing costs and smaller switching costs in the Separate version. Our findings suggest that greater integration of the CCN, DMN, and SMN, as measured by GFC, is associated with better task-switch performance under increasing working memory demands.

PMID:41619616 | DOI:10.1016/j.cortex.2026.01.002

Parkinsonism Relat Disord. 2026 Jan 27;144:108216. doi: 10.1016/j.parkreldis.2026.108216. Online ahead of print.

ABSTRACT

INTRODUCTION: This study assessed the changes in functional connectivity from resting functional magnetic resonance imaging (fMRI) in patients with Parkinson's disease (PD) given N-Acetylcysteine (NAC), the prodrug to L-cysteine and a precursor to the natural biological antioxidant glutathione (GSH). The aim of this study was to determine whether NAC is associated with changes in functional connectivity, particularly in the basal ganglia, and improvements in Parkinson's symptoms.

METHODS: Forty-four patients with PD were randomized to either weekly intravenous infusions of NAC (50 mg/kg) plus oral doses (500 mg twice per day) for six months plus standard of care, or standard of care only. Participants received pre and post brain imaging with resting Blood Oxygen Level Dependent (BOLD) MRI to measure functional connectivity between key brain regions involved with PD. These findings were compared to changes in PD symptoms as measured by the Unified Parkinson's Disease Rating Scale (UPDRS).

RESULTS: There were significant differences in the NAC group compared to the control group in functional connectivity measures after NAC. Specifically, there was significantly different functional connectivity between basal ganglia structures and the precuneus, precentral gyrus, postcentral gyrus, and particularly the Rolandic operculum. Changes in the precuneus also correlated with changes in UPDRS scores.

CONCLUSION: The results suggest that NAC may positively affect brain functional connectivity in PD patients, with corresponding positive clinical effects. Larger scale studies are warranted.

PMID:41619526 | DOI:10.1016/j.parkreldis.2026.108216

Biol Sex Differ. 2026 Jan 31. doi: 10.1186/s13293-026-00835-7. Online ahead of print.

ABSTRACT

Problematic smartphone use has been associated with altered reward and executive control network activity, yet potential sex differences in the underlying neural mechanisms remain insufficiently understood. We investigated sex-specific neural correlates of smartphone cue reactivity (CR) in 69 healthy young adult smartphone users (age range 18-30 years, female/male n = 45/24). Participants completed the Smartphone Addiction Inventory (SPAI) and underwent functional MRI during a smartphone CR paradigm. In addition, resting-state data were acquired to ensure that neural differences between female and male participants could be attributed to the CR paradigm rather than to sex differences in intrinsic neural activity. Whole-brain analyses revealed stronger activation in males compared to females in response to the presentation of smartphone cues within the right middle frontal gyrus (MFG), thalamus, cortical sensorimotor, parietal and occipital regions, whereas females showed no suprathreshold clusters compared to males. No overlap with resting-state amplitude of low-frequency fluctuation maps was observed with CR results, confirming task specificity. In males, right MFG correlated positively with SPAI-I total score, craving, and sleep interference scores, while in females, right parietal cortex activity correlated negatively with SPAI-I total score, daily life interference, and craving. Complementary cross-modal analyses showed that CR-related activation patterns were associated with several cortical excitatory and inhibitory neuronal and cellular markers, revealing subtle sex differences. These findings suggest sex-specific frontoparietal mechanisms underlying smartphone CR and highlight neurochemical pathways potentially linking excessive smartphone use to differential motivational and cognitive control processes in males compared to females.

PMID:41618473 | DOI:10.1186/s13293-026-00835-7

Neuropsychologia. 2026 Jan 28:109379. doi: 10.1016/j.neuropsychologia.2026.109379. Online ahead of print.

ABSTRACT

Externalizing behaviors such as aggression, defiance, and hyperactivity are common in autistic and non-autistic children. Research suggests that externalizing behaviors are not associated with intellectual functioning (FSIQ), gender, language, or autism symptom severity. Instead, recent studies suggest externalizing behaviors are more related to and are often linked to difficulties in executive functioning (EF). The current study examined behavioral and neural predictors of externalizing behaviors in a transdiagnostic sample of school-age children (N = 90; ages 7-13 years; 48 autistic, 42 non-autistic). Parents completed measures of EF (Behavior Rating Inventory of Executive Function, Second Edition; BRIEF-2) and externalizing behaviors (Behavior Assessment System for Children, Third Edition; BASC-3). Children completed resting-state fMRI scans. After controlling for age and FSIQ, the BRIEF-2 composite index scores (Behavioral, Emotional, and Cognitive Regulation) significantly predicted externalizing behaviors. Seed-to-seed analyses revealed positive associations between externalizing behaviors and connectivity among the left superior parietal lobule, left inferior parietal lobule, anterior insula, and lateral frontal ECN nodes. Seed-to-voxel analyses showed widespread alterations, including increased connectivity within frontoparietal executive regions alongside reduced connectivity in salience-related areas, such as cingulate and insula. This dual connectivity profile suggests a neural mechanism involving compensatory executive engagement paired with diminished salience processing that may contribute to behavioral dysregulation. These results suggest that executive dysfunction, at both the behavioral and neural levels, is associated with externalizing behaviors in children regardless of diagnostic status. Findings underscore the potential utility of EF-based interventions for mitigating externalizing problems in both autistic and non-autistic populations.

PMID:41617079 | DOI:10.1016/j.neuropsychologia.2026.109379

J Int Neuropsychol Soc. 2026 Jan 30:1-9. doi: 10.1017/S1355617725101781. Online ahead of print.

ABSTRACT

OBJECTIVE: This study investigated functional connectivity in the default mode, central executive, dorsal attention, and salience networks (SN) and its relation to executive function in youth with traumatic brain injury.

METHODS: Twenty-three youth with traumatic brain injury (11 with moderate-to-severe injury (6 male, mage = 11.78 ± 2.68 years, mtimesinceinjury = 3.71 ± 2.43 years) and 12 with complicated-mild injury (9 male, mage = 12.59 ± 1.99 years, mtimesinceinjury = 4.55 ± 1.59 years) and 17 youth with orthopedic injury (11 male, mage = 11.75 ± 2.12 years, mtimesinceinjury = 3.95 ± 1.79 years)) completed resting-state functional magnetic resonance imaging and a parent rated their child's executive function.

RESULTS: We found group differences in the strength of connectivity among four regions in the default mode network (DMN) and two regions of the SN, ps < .05, Eta2 = .151-.229. The orthopedic injury group demonstrated significant negative between-network connectivity, while brain injury groups had negligible negative or, in some cases, positive between-network associations. Groups did not differ on parent ratings of executive function, as all groups fell above the normative mean, reflecting poorer than expected everyday executive behavior. Attenuation of typical negative between-network association between the posterior cingulate in the DMN and two regions of the salience network was associated with worse parent-rated executive behavior (rs = .291-.317, ps < .05).

CONCLUSIONS: Findings illustrate the implications of disrupted downregulation of the default mode network by the SN following pediatric brain injury. They also demonstrate how disruption in functional connectivity may underlie poor executive function after childhood traumatic brain injury.

PMID:41614312 | DOI:10.1017/S1355617725101781

Front Artif Intell. 2026 Jan 12;8:1679218. doi: 10.3389/frai.2025.1679218. eCollection 2025.

ABSTRACT

INTRODUCTION: Detecting epileptic seizures remains a major challenge in clinical neurology due to the complex, heterogeneous, and non-stationary characteristics of electroencephalogram (EEG) signals. Although recent machine learning (ML) and deep learning (DL) approaches have improved detection performance, most methods still struggle with limited interpretability, inadequate spatial-temporal modeling, and suboptimal generalization. To address these limitations, this study proposes an enhanced hybrid parallel convolutional-GhostNet framework (HPG-ESD) for robust seizure detection using multimodal EEG and functional Magnetic Resonance Imaging (fMRI) data.

METHODS: The experimental data consist of pediatric scalp EEG recordings from 24 subjects in the CHB-MIT dataset (22-channel 10-20 system, 256 Hz sampling, continuous multi-hour recordings) and resting-state 3T fMRI scans from 52 participants in the UNAM TLE dataset (26 epilepsy patients and 26 healthy controls). EEG data underwent Gauss-based median filtering, while fMRI images were denoised using an adaptive weight-based Wiener filter. Spatial, temporal, and spectral EEG features were extracted alongside an enhanced common spatial pattern (E-CSP) representation, whereas fMRI features were obtained using deep 3D CNN embeddings combined with a smoothened pyramid histogram of oriented gradients (S-PHOG) descriptor. These multimodal features were fused within a soft voting hybrid parallel convolutional-GhostNet (S-HPCGN) model, integrating an improved attention based parallel convolutional network (IAPCNet) and GhostNet to capture complementary spatial-temporal patterns.

RESULTS: The proposed HPG-ESD framework achieved an accuracy of 0.941, precision of 0.939, and sensitivity of 0.944, outperforming conventional unimodal and state-of-the-art methods.

DISCUSSION: These results demonstrate the potential of multi-modal learning and lightweight attention-enhanced architectures for reliable and clinically relevant seizure detection.

PMID:41613820 | PMC:PMC12850516 | DOI:10.3389/frai.2025.1679218

Cogn Neurodyn. 2026 Dec;20(1):29. doi: 10.1007/s11571-025-10401-3. Epub 2026 Jan 27.

ABSTRACT

Autism Spectrum Disorder (ASD) is a complex neurodevelopmental disorder, and the existing clinical diagnosis mainly relies on subjective behavioral assessment and lacks objective biomarkers. This paper proposes a hierarchical deep learning architecture, VaeTF, incorporating community-aware mechanisms based on resting-state functional magnetic resonance imaging (rs-fMRI) data. VaeTF introduces a priori knowledge of the functional community, extracts localized features through a variational auto-encoder (VAE), captures global dependencies across brain regions using the Transformer module, and incorporates an improved pooling mechanism to enhance the expressive power and model generalization performance. Experimental results on the ABIDE database show that VaeTF achieves 71.4% accuracy in ASD and typically performs well in group classification tasks. Further feature weighting analysis reveals that VaeTF is capable of identifying local functional abnormalities and cross-network functional synergistic dysfunctions closely related to ASD, thereby uncovering the underlying neurobiological mechanisms. VaeTF not only improves the classification performance of ASD but also provides a new method and theoretical support for objective assessment and early diagnosis based on fMRI.

PMID:41613420 | PMC:PMC12847550 | DOI:10.1007/s11571-025-10401-3

Front Comput Neurosci. 2026 Jan 14;19:1742563. doi: 10.3389/fncom.2025.1742563. eCollection 2025.

ABSTRACT

Extracting features from abnormal brain regions in schizophrenia patients' brain images holds significant importance for aiding diagnosis. However, existing methods remained limited in simultaneously capturing spatiotemporal information. Dynamic mode decomposition (DMD) effectively extracts spatiotemporal features from dynamic systems, making it suitable for time-series signals such as functional magnetic resonance imaging (fMRI) and electrocorticography (ECoG). This study utilized resting-state fMRI data from 68 healthy subjects and 68 schizophrenia patients. The DMD method was employed to extract the mean amplitude of dynamic patterns as features, with feature selection conducted via Least Absolute Shrinkage and Selection Operator (LASSO) regression. A support vector machine (SVM) was further employed to validate the predictive capability of the selected features across subject groups. Based on the LASSO screening, we identified brain regions exhibiting significant inter-group differences in mean amplitude, designated these as abnormal regions, and subsequently analyzed their functional deviations. The DMD method not only provided explicit temporal dynamic representations of brain activity but also supported signal reconstruction and prediction, thereby enhancing feature interpretability. Results demonstrated that DMD effectively extracted mean amplitude features from fMRI data. Combined with LASSO and SVM, it enabled the identification of abnormal brain regions and functional abnormalities in schizophrenia patients. Furthermore, this method captured frequency-dependent signal patterns, with extracted features correlating with both regional activation intensity and functional connectivity. This approach provides novel insights for exploring potential biomarkers of psychiatric disorders.

PMID:41613385 | PMC:PMC12847263 | DOI:10.3389/fncom.2025.1742563

Front Neurosci. 2026 Jan 14;19:1745334. doi: 10.3389/fnins.2025.1745334. eCollection 2025.

ABSTRACT

BACKGROUND: Deep brain stimulation (DBS) of the subthalamic nucleus (STN) is an effective treatment for Parkinson's disease (PD) patients. However, postoperative outcomes vary with no reliable predictive method.

METHODS: Our study involves 43 PD patients undergoing STN-DBS. Preoperative resting-state functional magnetic resonance imagings (rs-fMRI) were collected. The volume of tissue activated (VTA) was defined based on contact points and stimulation parameters. A model of the cortico-basal ganglia network was established using dynamic causal modeling. The correlation between the UPDRS-III and the network edges was determined through Pearson correlation analysis. Furthermore, a generalized linear model was employed to predict the post-DBS motor improvement.

RESULTS: Individual STN-VTA intersections were found to be important to UPDRS-III improvement induced by DBS (R = 0.59, P = 0.001). STN-VTA intersections were related to the thalamic-primary motor cortex (M1) (R = 0.47, P = 0.005), and M1-STN (R = 0.40, P = 0.006) coupling strength. The coupling strength of Thal-M1 (R = 0.442, P = 0.009) and M1-STN (R = 0.481 P = 0.004) resulted in DBS-induced movement enhancement, particularly rigidity. The strength of effective connections within the STN-Thal-M1 pathway was found to predict improvements in UPDRS-III scores (P = 0.003).

CONCLUSION: Our study confirmed the relationship between clinical improvements in STN-DBS and target location as well as the stimulation parameters. By constructing personalized cortical-basal ganglia network models based on target location as well as the stimulation parameters, we discovered that the effective connection strength in STN-THA-M1 can predict motor improvement in PD patients undergoing STN-DBS.

PMID:41613265 | PMC:PMC12847302 | DOI:10.3389/fnins.2025.1745334

Alzheimers Dement. 2026 Feb;22(2):e71025. doi: 10.1002/alz.71025.

ABSTRACT

INTRODUCTION: We investigated stage-specific alterations in functional connectivity (FC) of the default mode network (DMN) across the Alzheimer's disease (AD) continuum and tested whether early amyloid beta (Aβ)-related changes in within-DMN FC (DMN-FCwithin) predicted longitudinal alterations in DMN between-network connectivity (DMN-FCbetween).

METHODS: Resting-state functional magnetic resonance imaging (fMRI) data were analyzed from 396 older adults: Aβ-negative cognitively normal (CN-, n = 213), Aβ-positive CN (CN+, n = 37), Aβ-positive mild cognitive impairment (MCI+, n = 72), and Aβ-positive dementia (dementia+, n = 74). Cross-sectional analyses compared DMN-FC across groups and examined associations with continuous Aβ burden at baseline. Longitudinal analyses in 171 CN participants with 2-year follow-up (CN-, n = 147; CN+, n = 24) tested whether baseline DMN-FCwithin predicted changes in DMN-FCbetween.

RESULTS: CN+ individuals showed elevated DMN-FCwithin and reduced DMN-FCbetween relative to other groups. In CN, Aβ burden was associated with FC, and baseline DMN-FCwithin predicted longitudinal increases in DMN-FCbetween only in CN+.

DISCUSSION: Aβ-related hyperconnectivity characterizes preclinical AD and may drive progressive network-level vulnerability.

HIGHLIGHTS: Cognitively normal amyloid beta (Aβ)-positive (CN+) individuals showed stronger connectivity within the default mode network (DMN). CN+ individuals also showed weaker links between the DMN and other brain networks. Amyloid was not linked to connectivity changes in cognitively impaired adults. Higher DMN connectivity predicted broader network changes in CN+ individuals.

PMID:41612924 | DOI:10.1002/alz.71025

Magn Reson Med. 2026 Jan 29. doi: 10.1002/mrm.70265. Online ahead of print.

ABSTRACT

PURPOSE: To achieve high resolution (≤ 1 mm isotropic) whole-brain perfusion imaging at 7 T with next generation ASL pulse sequence, reconstruction algorithm, and MRI hardware.

METHODS: We capitalized on three major innovations: (1) FLASH-based pseudo-Continuous ASL (pCASL) sequence with rotated golden-angle stack-of-spirals (rGA-SoS) sampling; (2) dynamic compressed sensing (CS) reconstruction with high spatiotemporal resolution and motion-resolved self-navigation; and (3) high density array coil and high-performance Impulse gradient of the NexGen 7 T scanner. Whole-brain laminar perfusion imaging was validated by correlation with histological data of microvascular and cell body density, as well as through finger-tapping (FT) and working memory (WM) fMRI tasks.

RESULTS: The proposed rGA-SoS sequence achieved a 3.3-fold SNR and 2-fold higher intraclass correlation coefficient (ICC) compared to matched Cartesian sampling at 7 T, enabling up to 0.8 mm isotropic spatial resolution and/or a temporal resolution of 14 s at 1 mm isotropic. Resting-state perfusion showed strong correlations with microvascular and cell body density. Laminar perfusion fMRI revealed a two-peak activation in the primary motor cortex induced by FT, and distinct laminar profiles for task-positive and task-negative networks during WM task.

CONCLUSION: This method offers a noninvasive imaging tool to bridge the gap between mesoscopic MRI with microscopic cellular imaging, as well as to investigate neural excitation and inhibition underlying positive and negative fMRI activations.

PMID:41612124 | DOI:10.1002/mrm.70265

Sci Rep. 2026 Jan 29. doi: 10.1038/s41598-026-37646-0. Online ahead of print.

ABSTRACT

To comprehensively examine static and dynamic functional connectivity density (FCD) in left temporal lobe epilepsy (LTLE) and right temporal lobe epilepsy (RTLE). For 46 LTLE patients, 43 RTLE patients, and 53 healthy controls (HCs), T1-weighted structural images and resting-state functional images were collected. Static FCD and corresponding temporal dynamic FCD (dFCD) obtained via a sliding window approach were measured and compared. Additionally, seed-based functional connectivity (FC) analysis was executed. Relationships between cognitive scores and FCD or dFCD values were analyzed. Compared with HCs, LTLE and RTLE patients presented reduced static FCD values in the ipsilateral lateral temporal lobe. However, for LTLE, the FCD in the lateral temporal lobe contralateral to the epileptic focus also decreased. In RTLE patients, the FCD and dFCD in the left occipital lobe increased, whereas the dFCD in the left cerebellum decreased. FC analysis (seed: left middle temporal gyrus) demonstrated that LTLE patients had a wider range of aberrant brain regions than RTLE patients did. However, the left occipital lobe-based FC reductions were detected only in the RTLE patients. FCD and dFCD revealed moderate discrimination abilities between LTLE and RTLE. This study reveals that while both LTLE and RTLE share reduced static FCD in the ipsilateral temporal lobe, indicating common global network impairment, they exhibit distinct disruption profiles: LTLE presents more bilateral and widespread alterations, whereas RTLE specifically involves the occipital lobe and cerebellum. These findings consolidate TLE as a system-level disorder with lateralized network phenotypes. Integrating static and dynamic FCD provides a comprehensive framework for elucidating whole-brain network abnormalities and the associated pathophysiological and compensatory mechanisms in TLE.

PMID:41611827 | DOI:10.1038/s41598-026-37646-0

J Integr Neurosci. 2026 Jan 21;25(1):46820. doi: 10.31083/JIN46820.

ABSTRACT

BACKGROUND: Executive dysfunction is the most prominent feature of cognitive impairment in patients with end-stage renal disease (ESRD). The dorsolateral prefrontal cortex (DLPFC) is a central region for the regulation of executive functions. The aim of our study was to examine alterations in neural activity and functional connectivity (FC) of the DLPFC in relation to cognitive assessments and clinical indicators in patients with ESRD using the resting-state functional magnetic resonance imaging (rs-fMRI) technique, and to further predict cognitive-related brain damage in this population.

METHODS: A total of 37 ESRD patients and 35 normal controls received MRI scans and neuropsychological assessments. Inter-group differences in fractional amplitude of low-frequency fluctuations (fALFF) and FC of the DLPFC were compared. Additionally, the relationships between DLPFC abnormalities and cognitive function were analyzed in ESRD patients, along with the clinical characteristics. Finally, we ascertained the potential of DLPFC abnormalities to predict cognitive-related brain damage using receiver operating characteristic (ROC) curve analysis.

RESULTS: ESRD patients exhibited decreased fALFF in the bilateral DLPFC (p < 0.05, false discovery rate [FDR] corrected). These also showed abnormal FC with the frontoparietal cortex, cingulate cortex, cerebellar posterior lobe, inferior temporal gyrus, and rolandic operculum (p < 0.05, FDR corrected). Several alterations in the DLPFC were associated with cognitive assessments (p < 0.05) in ESRD patients, and were also correlated with the levels of uric acid and hemoglobin (p < 0.05). Importantly, ROC curve analysis showed the fALFF value of left DLPFC, and FC between right DLPFC and right middle frontal gyrus effectively predicted cognitive-related brain damage in patients with ESRD.

CONCLUSIONS: This study demonstrated that the DLPFC is an important pathological brain region associated with the cognitive impairment of ESRD patients. Our results provide neuroimaging insights to further understand neural mechanisms of cognitive decline in this population.

PMID:41609046 | DOI:10.31083/JIN46820

Front Psychol. 2026 Jan 13;16:1728241. doi: 10.3389/fpsyg.2025.1728241. eCollection 2025.

ABSTRACT

INTRODUCTION: Previous studies on white matter (WM) in patients with obsessive-compulsive disorder (OCD) have focused primarily on its structural aspects. This study aimed to investigate any abnormal spontaneous WM neural activity in patients with OCD.

METHODS: The study was based on resting-state functional magnetic resonance imaging (fMRI) data from 27 patients with OCD and 24 matched healthy controls (HC). Regional homogeneity (ReHo) and the amplitude of low-frequency fluctuation (ALFF) were used to explore spontaneous neural activity changes in the subjects' WM regions. A two-sample Student's t-test was performed, and correlations between the Yale-Brown Obsessive-Compulsive Scale (Y-BOCS), Hamilton Anxiety Scale (HAMA), and Hamilton Depression Scale (HAMD) scores were analyzed.

RESULTS: The ReHo in the left posterior limb of the internal capsule (LPLIC) and right superior corona radiata (RSCR) of the OCD group was significantly higher than those in the HCs (pTFCE-FWE < 0.001). The ALFFs in the right superior longitudinal fasciculus (RSLF) and right cerebral peduncle (RCP) of the OCD group, by contrast, were significantly lower than those in the HCs (pTFCE-FWE < 0.05). There was no correlation between the clinical symptoms of patients with OCD and their abnormal amplitude of low-frequency fluctuation (ALFF) and ReHo values.

DISCUSSION: Abnormal spontaneous WM activity was observed in several brain regions in patients with OCD. This activity may help explain the cognitive inflexibility often observed in this patient group.

PMID:41608182 | PMC:PMC12834757 | DOI:10.3389/fpsyg.2025.1728241

Front Aging Neurosci. 2026 Jan 13;17:1744413. doi: 10.3389/fnagi.2025.1744413. eCollection 2025.

ABSTRACT

INTRODUCTION: Olfactory dysfunction is common in the Alzheimer's Disease continuum, and olfaction may be altered before clinical syndrome onset. The present study aimed at investigating the functional connectivity of the olfactory cortex and its correlation with olfaction performance in a group of patients with Mild Cognitive Impairment (MCI) who subsequently converted or not converted to Alzheimer's Disease (AD) dementia.

METHODS: At baseline, 30 MCI patients were evaluated with the Sniffin' Sticks (threshold, discrimination, and identification) to assess olfactory capacities, and they were followed up over time to identify converter and stable patients. Resting-state fMRI data acquired at baseline were analyzed to assess functional connectivity of left and right olfactory cortex. Beta values were extracted from the stable versus converter contrasts and correlated with olfactory scores.

RESULTS: Functional connectivity of the olfactory cortex was significantly increased with the posterior cingulate cortex, and significantly decreased with middle cingulate cortex, supplementary motor area, and left pre- and postcentral gyri, in converter compared to stable patients. Reduced negative functional connectivity between olfactory cortex and left angular gyrus emerged in converter patients, and a negative correlation was found between angular gyrus and discrimination scores.

DISCUSSION: Our findings indicate alterations of functional connectivity of the olfactory cortex in subjects with MCI at risk of conversion to AD dementia, even at the early stages of the disease. Additionally, the negative correlation between olfactory ability and the angular gyrus functional connectivity, a cerebral region known to be involved in multisensory integration processing, may be considered as a marker of disease progression.

PMID:41607501 | PMC:PMC12835387 | DOI:10.3389/fnagi.2025.1744413

Front Neurosci. 2026 Jan 13;19:1744783. doi: 10.3389/fnins.2025.1744783. eCollection 2025.

ABSTRACT

OBJECTIVE: Postherpetic neuralgia (PHN), a refractory neuropathic pain following herpes zoster reactivation, lacks clear central mechanisms for emerging therapies like short-term spinal cord stimulation (stSCS). This longitudinal study used multimodal neuroimaging to examine the effects of 14-day stSCS on brain function and white matter microstructure in PHN patients, and to identify neural correlates of clinical improvements.

METHODS: In this longitudinal, single-arm, pre-post study, 17 PHN patients received 14 days of continuous stSCS. Clinical outcomes including pain intensity (Numeric Rating Scale, NRS), anxiety and depression (Hospital Anxiety and Depression Scale, HADS), and sleep quality (Pittsburgh Sleep Quality Index, PSQI), were assessed pre-stSCS and 3 days post-stSCS. Resting-state functional MRI (rs-fMRI) and Diffusion Tensor Imaging (DTI) data were acquired at both time points. Longitudinal changes in amplitude of low-frequency fluctuations (ALFF) and fractional ALFF (fALFF) were analyzed, alongside white matter integrity via TBSS and ROI analysis of key tracts.

RESULTS: Post-stSCS, significant improvements occurred in all clinical outcomes (Wilcoxon signed-rank, all p < 0.001). Neuroimaging showed no DTI microstructural changes but significant fALFF increases in regions including the dorsal striatum. Notably, right medial orbitofrontal cortex (mOFC) fALFF increases correlated with NRS reductions (Spearman's r = 0.71, FDR-corrected p = 0.036). Baseline cingulum integrity (lower FA, higher MD/RD) predicted greater striatal fALFF changes (r = ±0.75, FDR-corrected p < 0.02).

CONCLUSION: These findings suggest that stSCS's early clinical benefits in PHN are mediated by rapid functional reorganization rather than immediate microstructural changes. This reorganization appears prominent within fronto-striatal circuits: specifically, mOFC functional changes correlate with analgesia, while baseline cingulum integrity predicts subsequent striatal plasticity. This provides initial mechanistic insights into stSCS and suggest that baseline brain structure could be explored as a potential biomarker for treatment response, warranting validation in larger, controlled cohorts.

PMID:41607406 | PMC:PMC12835296 | DOI:10.3389/fnins.2025.1744783

Front Neurosci. 2026 Jan 13;19:1695556. doi: 10.3389/fnins.2025.1695556. eCollection 2025.

ABSTRACT

OBJECTIVE: This study aimed to investigate alterations in brain activity due to acute carbon monoxide poisoning (ACOP) and their relationship with clinical manifestations using static and dynamic amplitude of low-frequency fluctuation (ALFF) analyses.

METHODS: Resting-state functional magnetic resonance imaging (fMRI) and clinical data were obtained from 31 ACOP patients and 28 healthy controls. The static ALFF value and dynamic ALFF variability were measured and compared between groups. Partial correlation analysis explored the relationships between changes in ALFF and clinical features in ACOP patients.

RESULTS: ACOP patients exhibited increased dynamic ALFF in the bilateral superior frontal gyrus (SFG) and left middle frontal gyrus (MFG) and decreased static ALFF in the left middle occipital gyrus (MOG) compared to controls. Aberrant dynamic ALFF in the left SFG and MFG was negatively correlated with MoCA-B scores (r = -0.430, p = 0.036; r = -0.439, p = 0.032).

CONCLUSION: ACOP patients exhibited abnormal instability in intrinsic brain activity, particularly in prefrontal areas, where temporal variability in local brain activity correlates with cognitive performance. This study highlights the value of combined static and dynamic ALFF approaches in understanding brain disturbances caused by CO exposure, providing new insights into the neuropathological mechanisms of ACOP.

PMID:41607402 | PMC:PMC12835238 | DOI:10.3389/fnins.2025.1695556

J Oral Facial Pain Headache. 2026 Jan;40(1):61-70. doi: 10.22514/jofph.2026.006. Epub 2026 Jan 12.

ABSTRACT

BACKGROUND: Temporomandibular disorders (TMD), particularly pain-related TMD (TMDp), are closely associated with social and psychological factors. However, the neuromechanisms of pain of TMDp are still currently unclear. This study aimed to investigate the altered topological properties of the brain network in the TMDp patients using resting-state functional magnetic resonance imaging (rs-fMRI), and to explore the association between these parameters and emotional and clinical variables of TMDp.

METHODS: A total of 41 TMDp patients and 33 age- and gender-matched healthy controls (NCs) were recruited, and rs-fMRI data were obtained from a 3.0T MR scanner. The topological properties of brain functional networks were calculated based on the rs-fMRI data and were compared between two groups to investigate the altered topological characteristics in TMDp. The correlation analysis was also performed between the abnormal topological characteristics and the clinical variables in TMDp patients.

RESULTS: TMDp patients presented significantly decreased clustering coefficient (Cp) and decreased local efficiency (Eloc) when sparsity threshold was 0.05 and 0.06 compared with NCs (p < 0.05), and the Eloc values when sparsity threshold was 0.06 were positively correlated with depressive (r = 0.319, p = 0.042) and anxious (r = 0.348, p = 0.026) variables in TMDp patients.

CONCLUSIONS: The current study demonstrated the abnormal topological changes of the brain network were observed in TMDp, which could be helpful in understanding the neuromechanisms of pain of TMDp. The topological properties of the brain network based on rs-fMRI could be considered as a new simple tool to monitor the dysfunction network of the brain in TMDp.

PMID:41607323 | DOI:10.22514/jofph.2026.006

Cereb Cortex. 2026 Jan 6;36(1):bhag002. doi: 10.1093/cercor/bhag002.

ABSTRACT

This study introduces a novel oscillatory network model to simulate simultaneous electroencephalography-functional magnetic resonance (EEG-fMRI) data, addressing the reconstruction challenge that arises due to their contrasting spatiotemporal scales. Here, each brain region is modeled by 2 oscillator clusters-a cluster of low-frequency (LFO) and high-frequency Hopf oscillators (HFO) coupled with an innovative power-coupling rule, facilitating cross-frequency interactions. The model is trained in 2 stages: learning oscillators' frequencies and phase relations using a biologically plausible complex-Hebbian rule in the first stage and, followed by a modified complex backpropagation for amplitude approximation, overcoming limitations of poor accuracy and computational complexity in existing models. This framework outperforms current methods in replicating empirical functional connectivity (FC), FC dynamics (FCD), and modularity over disparate spatio-temporal scales. The correlation between the FC of fMRI and the FCs of various EEG frequency bands is reflected in the strengths of the LFO-HFO coupling. Furthermore, in silico structural perturbation studies quantified the effect of pruning of the anatomical connectivity on spatiotemporal dynamics in terms of FC, FCD, modularity, and integration level integrated state of occurrence rate. The model's ability to reconstruct simultaneous EEG-fMRI data showcases significant advancement in understanding the resting-state brain's functionality from multimodal settings and deciphering neurological disorders in diverse spatiotemporal scales.

PMID:41606834 | DOI:10.1093/cercor/bhag002