Most recent paper

Eur J Neurosci. 2026 Feb;63(3):e70417. doi: 10.1111/ejn.70417.

ABSTRACT

Subjective tinnitus (ST) has been hypothesized to arise from large-scale network reorganization, but the affected circuits and their symptom scaling remain unclear. In a normal-hearing cohort (N = 114; 57 ST, 57 matched controls), we combined resting-state fMRI graph topology, ROI-to-ROI connectivity, voxel-based morphometry (VBM), and multivariate modelling using a harmonized 50-ROI parcellation from the CONN atlas, spanning 15 functional networks. Node-wise analyses (covarying age, sex, education, and motion; multiple-comparison control) showed selective, not global, reconfiguration: reduced integration centered on the left posterior parahippocampal gyrus (lower global efficiency and degree), increased segregation/clustering in inferior frontal and anterior insular hubs, longer path length in parahippocampal and frontal regions, and elevated local efficiency in the right amygdala. Network-based revealed hyperconnectivity in fronto-salience-language-cerebellar circuits and hypoconnectivity across default mode and dorsal attention/temporo-parietal pathways. Symptom coupling was convergent and dissociable: Higher tinnitus severity/duration tracked reduced integration in medial visual/limbic regions with increased integration/degree in right frontal-temporal opercular nodes, whereas higher anxiety related to increased integration/clustering in subcallosal, cerebellar, and occipito-limbic territories alongside decreases in putamen. VBM demonstrated widespread white-matter reductions (inferior frontal, temporal pole, insula, inferior temporal gyrus, and putamen) with more focal gray-matter effects, and a multivariate GLM confirmed a robust omnibus group difference. These multimodal, symptom-linked signatures provide strong evidence that ST reflects targeted network reorganization reduced medial temporal/visual-limbic integration accompanied by increased local specialization within salience and control hubs and yield actionable circuit markers for patient stratification and mechanism-guided treatment targeting.

PMID:41630178 | DOI:10.1111/ejn.70417

BMC Med. 2026 Feb 2. doi: 10.1186/s12916-026-04668-4. Online ahead of print.

ABSTRACT

BACKGROUND: Household cannabis use is a risk factor for adolescents' mental health problems. However, little is known about the association of the cessation and psychological impairments in affected adolescents. This study examined the associations of household cannabis cessation and adolescents' mental health outcomes and potential pathways.

METHODS: This cohort study used data from the Adolescent Brain Cognitive Development study and included adolescents aged 10-13 years with household cannabis use within 12 months at wave 2. Household cannabis cessation was defined as the absence of cannabis use by household members (excluding the adolescent participant) at wave 3 among households that reported use at wave 2. Internalizing and externalizing problems were assessed using the Child Behavior Checklist, and psychotic-like experiences (PLEs) were evaluated using the Prodromal Questionnaire-Brief Child Version. Family conflict and sleep problems were assessed using the Family Environment subscale and the Sleep Disturbance Scale for Children, respectively. Demographic and psychometric confounders were balanced with propensity score matching (PSM). Linear regression was applied to investigate the associations between cessation and mental health outcomes. Mediation analyses of family conflict and adolescent sleep problems were performed. We further considered the influence of genetic predisposition to cannabis use disorder (CUD) and examined whether brain connectivity patterns, measured by resting-state fMRI, modified the relationships.

RESULTS: Of the 1426 adolescents exposed to household cannabis within 12 months, 438 (30.7%) were no longer exposed by wave 3. After PSM, cessation was associated with lower levels of internalizing and externalizing problems, and PLEs (mean ratios, 0.84-0.86, all P < 0.02), adjusting for baseline scores. The associations persisted after additionally adjusting for the adolescents' polygenic risk for CUD among White participants. Family conflict and sleep problems mediated the associations of cessation with internalizing (proportion mediated, 6.8% and 25.8%, respectively) and externalizing symptoms (14.3% and 24.8%, respectively). Adolescents with weaker connections between cingulo-parietal and dorsal attention networks showed stronger associations between cessation and PLEs.

CONCLUSIONS: Household cannabis cessation was linked to a lower level of adolescent mental health problems at follow-up. These findings suggest that interventions aimed at reducing or eliminating household cannabis exposure may be beneficial for youth well-being.

PMID:41629925 | DOI:10.1186/s12916-026-04668-4

PLoS One. 2026 Feb 2;21(2):e0340899. doi: 10.1371/journal.pone.0340899. eCollection 2026.

ABSTRACT

The clinical diagnosis of fibromyalgia (FM), a syndrome characterized by generalized pain, is challenging due to its unknown etiology and frequent comorbidity with other diseases. As a noninvasive modality, functional magnetic resonance imaging has been extensively employed in investigating the pathogenesis of FM. This study proposes a novel diagnostic approach utilizing resting-state functional magnetic resonance imaging (rs-fMRI) and diffusion tensor imaging (DTI) combined with a machine learning algorithm with the objective of enhancing the clinical diagnostic efficiency of FM. Two-sample t tests revealed differences between FM patients and healthy controls in rs-fMRI and DTI corresponding to brain image indices, mainly in the temporal lobe and frontal lobe. In addition, an effective diagnostic classification model was developed based on the single variable feature selection method by applying a support vector and random forest classifier combined with different brain image indicators. Our study demonstrated that the integration of DTI features with a support vector machine model yields superior diagnostic outcomes.

PMID:41628150 | DOI:10.1371/journal.pone.0340899

IBRO Neurosci Rep. 2026 Jan 15;20:139-147. doi: 10.1016/j.ibneur.2026.01.005. eCollection 2026 Jun.

ABSTRACT

BACKGROUND: An increasing number of neuroimaging studies have consistently indicated that the locus coeruleus is associated with cognitive impairment in the early stages of Alzheimer's disease, and the locus coeruleus plays a critical role in cognition, including memory encoding, consolidation, and retrieval.

OBJECTIVE: To investigate whether and how acupuncture modulates the functional connectivity patterns of the locus coeruleus, and offer a new perspective on the mechanism through which acupuncture exerts its efficacy.

METHODS: Resting-state functional magnetic resonance imaging (fMRI) data were collected from 50 patients with amnestic cognitive impairment (aMCI) before and after verum or sham acupuncture. Seed-based whole-brain functional connectivity (FC) was calculated and compared to explore the changing patterns of the locus coeruleus in aMCI patients following acupuncture.

RESULTS: Increased FCs were observed between the left locus coeruleus and the left inferior parietal lobule, and between the right locus coeruleus and the right posterior cerebellum in aMCI patients after verum acupuncture. Further analyses revealed a correlation between FC of the left locus coeruleus and the left inferior parietal lobule before acupuncture and improvement in immediate recall in aMCI patients.

CONCLUSIONS: These results suggest that acupuncture could enhance FC between the locus coeruleus and the inferior parietal lobule/the posterior cerebellum. These functional alterations appear to be linked to the efficacy of acupuncture, particularly in ameliorating memory deficits.

PMID:41626073 | PMC:PMC12856631 | DOI:10.1016/j.ibneur.2026.01.005

Front Psychiatry. 2026 Jan 16;16:1722172. doi: 10.3389/fpsyt.2025.1722172. eCollection 2025.

ABSTRACT

BACKGROUND: Mild cognitive impairment (MCI) is a precursor state of Alzheimer's disease (AD) and has attracted attention, but why amnestic mild cognitive impairment (aMCI) is more likely to progress to AD than non-amnestic mild cognitive impairment (naMCI) is unclear. The present study of aMCI compares differences in intra- and inter-network functional connectivity (FC) across multiple networks in naMCI and further correlates FC with cognitive assessment scores to assess their ability to predict AD progression.

METHODS: Resting-state functional magnetic resonance imaging (rs-fMRI) was performed in 30 naMCI and 40 aMCI cases, and 12 resting-state networks (RSNs) were identified by independent component analysis (ICA). Two-sample t-tests were performed to detect intra-network FC differences, and functional network connectivity (FNC) was calculated to compare inter-network FC differences. Subsequently, Pearson or Spearman correlation analyses were used to explore the correlation between altered FC and cognitive assessment scores.

RESULTS: The aMCI compared to the naMCI differed within the (Default mode network) DMN, (Dorsal attention network) DAN, (Sensorimotor system) SMN, and (Salience network) SN networks (corrected for FWEc, P< 0.05), and inter-network differences in DAN-DMN, DMN-SN, SN-SMN (corrected for FWEc, P<0.05).

CONCLUSION: aMCI contrasts naMCI with widespread intra- and inter-static FNC differences, mainly involving the DMN, DAN, SMN, and SN. these network interactions provide a powerful method for assessing and predicting why aMCI is more likely to progress to AD, and contribute to our understanding of the neurological mechanisms underlying the pathological process of AD.

PMID:41625622 | PMC:PMC12855511 | DOI:10.3389/fpsyt.2025.1722172

Front Psychiatry. 2026 Jan 15;16:1704170. doi: 10.3389/fpsyt.2025.1704170. eCollection 2025.

ABSTRACT

BACKGROUND: Autism spectrum disorder (ASD) and attention deficit hyperactivity disorder (ADHD) are highly comorbid. The neural basis of this comorbidity remains unclear. We compared brain structural-functional coupling (SC-FC coupling) across ASD subgroups and typically developing (TD) controls to parse the neurobiological heterogeneity of ASD.

METHODS: We analyzed T1-weighted and resting-state fMRI data from 331 participants from ABIDE II (130 ASD [39 ASD+ADHD, 91 ASD-only] and 201 TD). For each participant, we extracted multivariate structural features from T1-weighted images to construct an individual structural covariance network. SC-FC coupling for each brain region was quantified by correlating its observed functional connectivity profile with the profile predicted from individual structural features via linear regression.

RESULTS: Compared to TD individuals, the ASD group showed altered SC-FC coupling in networks critical for social cognition, emotion, sensory processing, and cognitive control: the default mode network (DMN), limbic system (LimN), somatomotor network (SMN), and frontoparietal network (FPN). Crucially, distinct patterns emerged between ASD subgroups. The ASD-only group had stronger coupling in the left inferior temporal gyrus (ITG.L). The ASD+ADHD group showed increased coupling in specific cerebellar regions: the right cerebellar lobule IX (Cerebellum_9_R) and right cerebellum Crus II (Cerebellum_Crus2_R).

CONCLUSIONS: Our findings demonstrate both shared and subtype-specific alterations in SC-FC coupling in ASD. Comparing ASD subgroups clarifies that comorbid ADHD is associated with unique neural pathways, particularly involving cerebellar integration for attentional processes. Measuring SC-FC coupling offers a valuable approach for disentangling the heterogeneity in ASD and may aid in developing targeted interventions.

PMID:41625611 | PMC:PMC12852334 | DOI:10.3389/fpsyt.2025.1704170

Neuroimage Rep. 2026 Jan 20;6(1):100321. doi: 10.1016/j.ynirp.2026.100321. eCollection 2026 Mar.

ABSTRACT

OBJECTIVE: SNAP-25, a synaptic vesicle docking protein, carries a polymorphism (rs3746544) in its 3'-UTR region that is associated with ADHD, yet its functional mechanism remains unknown. The purpose of this study is to evaluate the impact of synaptosomal-associated protein 25 (SNAP-25) gene MnlI polymorphism (rs3746544) on spontaneous brain activity in children with attention deficit hyperactivity disorder (ADHD), employing the fractional amplitude of low-frequency fluctuation (fALFF) analysis of resting-state functional Magnetic Resonance Imaging (rs-fMRI) data, to explore its potential neurobiological mechanisms and neuroimaging biomarkers.

METHODS: This study enrolled 56 boys with ADHD (aged 8-10 years) and 21 age-matched healthy boys as healthy controls (HCs). According to the SNAP-25 MnlI genotype, ADHD patients were divided into two groups: the TT homozygote group (TT group, n = 36) and the G-allele carrier group (TG group, n = 20). Rs-fMRI data were acquired and analyzed using fALFF to measure spontaneous brain activity.One-sample t-tests were performed to calculate fALFF maps for each group, setting the threshold as a cluster greater than 20 voxels, with P < 0.01 after AlphaSim correction. Two-sample t-tests were performed to calculate the differences in fALFF values among the TT, TG, and HCs groups, with age as a covariate. A cluster of greater than 20 voxels, with P < 0.01 after AlphaSim correction, was considered to have statistically significant differences. Assessed the Working Memory Index (WMI) using the Wechsler Intelligence Scale for Children-IV (WISC-IV) in children with ADHD from the TT and TG groups.

RESULTS: One-sample t-tests revealed that children with ADHD group (both TT and TG group) exhibited significantly lower fALFF values in the default mode network (DMN) and parieto-occipital cortex compared to HCs, while showing increased fALFF located in the posterior cerebellar lobe; Two-sample t-tests demonstrated that: (a) Compared to HCs, the ADHD group (both TT and TG group) showed widespread reductions of fALFF values across multiple brain regions, including the posterior cingulate cortex and precuneus. The TG group showed more pronounced decreases when compared with the TT group. (b) In comparison to the TG group, the TT group exhibited higher fALFF values in higher-order cognitive regions, such as the right superior frontal gyrus and left medial frontal gyrus, but lower fALFF values in the posterior cerebellar lobe and posterior cingulate cortex. The TT group had significantly higher WMI compared to the TG group (t = 2.098, P < 0.05).

CONCLUSIONS: The SNAP-25 gene MnlI polymorphism has an impact on spontaneous brain activity in children with ADHD, as measured by fALFF. This study reveals the potential mechanisms from the perspective of brain networks, demonstrates how ADHD genotypes affect neural function, and provides a new approach for clinical decision-making and efficacy monitoring.

PMID:41624630 | PMC:PMC12856436 | DOI:10.1016/j.ynirp.2026.100321

Front Neurosci. 2026 Jan 16;19:1667360. doi: 10.3389/fnins.2025.1667360. eCollection 2025.

ABSTRACT

BACKGROUND: Impaired attention is a key feature of HIV-associated brain damage, and people living with HIV (PLWH) often have potential visual-auditory perceptual deficits. This study aimed to explore functional alterations in divided attention in PLWH using a parallel audio-visual spatiotemporal task with multimodal functional magnetic resonance imaging (fMRI) and to explore candidate neuroimaging markers of HIV-related attention impairment.

METHODS: Thirty-one cognitively unimpaired PLWH and 34 healthy controls (HC) completed a divided attention task during fMRI via a modified Posner paradigm. Behavioral performance and task-related brain activation were compared between the two groups. Seed-based whole-brain functional connectivity (FC) maps were computed in resting-state fMRI (rs-fMRI) using a priori anatomical regions of interest (ROIs) from the audiovisual attention network, defined based on previous independent fMRI studies employing similar spatial-temporal attention paradigms.

RESULTS: The PLWH showed lower accuracy than HC. Task-related brain activation was more extensive in PLWH, including increased activation in occipital/temporal lobes, plus frontal/parietal lobes, insula, and limbic system. Using a priori anatomical regions of interest from the audiovisual attention network as seeds, PLWH exhibited increased resting-state FC between these frontal-parietal-temporal-insular regions and bilateral posterior cerebellar lobules VIII-IX, as well as with multimodal associative cortices. Within the PLWH group, percent BOLD signal change showed significant positive correlations with HIV infection duration in a subset of task-difference ROIs-7 regions identified under spatial cueing and 13 regions identified under temporal cueing.

CONCLUSION: The HIV impairs audio-visual divided attention, with fMRI revealing neural alterations in cognitively unimpaired PLWH. These findings suggest that task-related activation patterns and resting-state connectivity measures may serve as sensitive candidate markers of HIV-related brain involvement and help identify individuals at increased risk of cognitive decline, although longitudinal studies are needed to establish their prognostic value.

PMID:41624135 | PMC:PMC12855078 | DOI:10.3389/fnins.2025.1667360

Front Netw Physiol. 2026 Jan 16;5:1681597. doi: 10.3389/fnetp.2025.1681597. eCollection 2025.

ABSTRACT

Structural balance theory, widely used in social network research, has recently been applied to brain network studies to explore how higher-order interactions relate to neural function and dysfunction. The theory is founded on the core assumption that balanced triads, representing internally consistent relationships, are intrinsically stable, while imbalanced triads, which introduce structural tension, are unstable and tend to reconfigure toward balance. Despite its promising application, these foundational assumptions have not been empirically validated in the brain. Here, we address this gap using resting-state fMRI data from the Human Connectome Project to analyze the temporal dynamics of triadic configurations. We defined two metrics: triad lifetime, the duration a triad persists, and absolute peak energy, the maximum triadic interaction strength during that time. Balanced triads showed significantly longer lifetimes and higher peak energy than imbalanced ones, consistent with their theorized stability. Imbalanced triads were more transient and weaker, reflecting structural conflict. Comparison with surrogate null models confirmed that these patterns were not random, but reflected meaningful higher-order neural organization. The joint distribution of lifetime and energy revealed two clusters of triads aligning with strong, not weak, structural balance theory. Additionally, specific transition patterns between triadic configurations, combined with lifetime profiles, shaped the non-uniform prevalence of triadic states in brain networks. Our findings provide empirical validation of structural balance theory in brain networks and introduce dynamic measures for characterizing triadic brain interactions, together offering a framework for studying the dynamics of higher-order interactions and the stability of brain networks in health and disease.

PMID:41623735 | PMC:PMC12855509 | DOI:10.3389/fnetp.2025.1681597

Curr Opin Behav Sci. 2021 Aug;40:189-200. doi: 10.1016/j.cobeha.2021.06.002. Epub 2021 Jun 30.

ABSTRACT

Layer fMRI, requiring 7T scanners, sophisticated pulse sequences, and advanced post processing methods, has emerged and matured as a field in the past decade. In the past two years, the rate of layer fMRI papers published has grown sharply as the noninvasive delineation of mesoscopic scale functional organization promises to uncover new insight into human brain processing. Understanding laminar activity and connectivity throughout the brain will take fMRI beyond being able to simply identify where and when activation is taking place as delineating laminar location will provide detailed directional feedforward and feedback activity which will add to the sophistication of circuit and network models, thus providing a bridge between invasive measures and those typically carried out on humans. This review outlines the methods used to achieve layer fMRI as well as the challenges. It emphasizes the value and necessity of deep imaging of individuals rather than relying on pooled multi-subject databases to boost sensitivity. It highlights recent studies that have explored layer fMRI in cognitive tasks as well as resting state connectivity studies that have revealed cortical hierarchy. Finally, it concludes with a perspective on what the field promises, the challenges that lay ahead, and the direction that the field may evolve.

PMID:41623662 | PMC:PMC12858290 | DOI:10.1016/j.cobeha.2021.06.002

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