Most recent paper

Sci Rep. 2026 Jun 29. doi: 10.1038/s41598-026-58431-z. Online ahead of print.

ABSTRACT

Predicting behavioral and personality traits from neuroimaging data requires effective modeling of complex and high-dimensional brain dynamics. Although recent advances in neurocomputations have paved the way for trait prediction models, the attempts remain at a nascent stage, as reflected in the moderate prediction accuracies. To this end, in this work, we propose a novel unified framework that leverages both spatial and temporal information derived from resting-state functional MRI (rs-fMRI) for trait prediction. Task-activation maps are first estimated directly from rs-fMRI, providing spatial representations of task-evoked brain activity without requiring explicit task paradigms. To capture temporal dynamics, MultiRocket, an efficient time-series feature-extraction method, is employed, encoding trait-specific patterns in rs-fMRI signals. The spatial and temporal features are then fused to form a unique unified spatio-temporal representation that is subsequently provided to an ensemble framework to predict cognitive traits, viz., reading ability, fluid intelligence, and processing speed, and personality traits, viz., openness to experience and extraversion. Experimental validation of the proposed framework on the HCP dataset demonstrates superior predictive performance, achieving state-of-the-art correlations of up to 0.5284, thereby highlighting the importance of the proposed spatio-temporal modeling for understanding brain-behaviour relationships.

PMID:42374074 | DOI:10.1038/s41598-026-58431-z

Prog Neuropsychopharmacol Biol Psychiatry. 2026 Jun 29:111798. doi: 10.1016/j.pnpbp.2026.111798. Online ahead of print.

ABSTRACT

Chronic pain is increasingly conceptualized within a stress-related framework. However, it remains unclear whether chronic pain and prototypical stress-related conditions-such as post-traumatic stress disorder (PTSD)-show convergence in their morphometric alterations and underlying normative functional connectivity profiles. To this end, we conducted a pre-registered transdiagnostic meta-analytic study of gray matter volume alterations in chronic pain (60 studies) and PTSD (20 studies), testing convergence at two complementary levels: direct anatomical overlap and network-level convergence within normative resting-state functional systems. Disorder-specific meta-analyses revealed that chronic pain was associated with distributed volume reductions across ventromedial prefrontal, middle cingulate, and insular cortices, whereas PTSD exhibited a single cluster of reduced volume in the anterior cingulate/dorsomedial prefrontal cortices. A direct conjunction analysis identified a spatially focal overlapping cluster of reduced volume in the bilateral medial orbitofrontal/anterior cingulate area. Importantly, using normative resting-state fMRI data (HCP 7 T dataset), we found that the disorder-specific structural abnormalities were embedded within partially overlapping large-scale systems. Specifically, chronic pain abnormalities were embedded within a distributed architecture of large-scale circuits encompassing mesocorticolimbic/reward, default mode, salience, frontoparietal, dorsal attention, and somatosensory networks. On the other hand, the PTSD focal neuroanatomical alteration was embedded in a single large-scale circuit mapping onto the mesocorticolimbic/reward, default mode, salience, and visual networks. In both conditions, the mesocorticolimbic/reward circuit emerged as the most robustly involved large-scale network. Notably, the shared cluster of reduced volume showed functional integration within the mesocorticolimbic/reward and default mode networks, with neurochemical fingerprinting revealing robust spatial correspondence with dopaminergic, serotonergic, opioid, and endocannabinoid receptor/transporter maps. Overall, these findings indicate that brain morphological alterations in chronic pain and PTSD converge in a focal medial prefrontal/anterior cingulate region and that disorder-specific abnormalities map onto partially overlapping normative functional networks, particularly involving the mesocorticolimbic/reward-related system, the default mode network, and the salience network. This network-level convergence is consistent with the hypothesis that chronic pain may, at least in part, be conceptualized within a stress-related framework.

PMID:42373038 | DOI:10.1016/j.pnpbp.2026.111798

Res Sq [Preprint]. 2026 Jun 1:rs.3.rs-9613537. doi: 10.21203/rs.3.rs-9613537/v1.

ABSTRACT

Social recognition, the ability to distinguish between individuals, is essential for cognitively demanding social behaviors. The anterior olfactory nucleus (AON), a primary olfactory cortical region, is implicated in this process, but the underlying neurocircuitry remains poorly understood. Here, we generated a novel mouse line to enable genetic access to AON pyramidal neurons and mapped their whole-brain synaptic inputs and outputs. The medial prefrontal cortex (mPFC), a crucial hub for social cognition, is the primary neocortical target of AON neurons, which form monosynaptic excitatory connections with a substantial fraction of mPFC neurons. The AON→mPFC pathway is activated during social investigation, and chemogenetic inhibition of this pathway impairs social recognition. Moreover, an analogous AON-prefrontal pathway is present in humans, as supported by resting-state functional magnetic resonance imaging (fMRI) functional connectivity analyses. Taken together, these findings reveal a conserved olfactory-prefrontal circuit spanning mice to humans, potentially linking olfactory dysfunction to neuropsychiatric disorders.

PMID:42370285 | PMC:PMC13308722 | DOI:10.21203/rs.3.rs-9613537/v1

Front Neurol. 2026 Jun 11;17:1831863. doi: 10.3389/fneur.2026.1831863. eCollection 2026.

ABSTRACT

OBJECTIVE: This study aimed to characterize resting-state functional differences across clinically defined bothersome tinnitus, non-bothersome tinnitus, and hospital-based non-tinnitus control groups, focusing on imaging differences related to tinnitus phenotype.

METHODS: This case-control study included 61 patients with bothersome tinnitus (BT), 52 with non-bothersome tinnitus (NBT), and 50 hospital-based non-tinnitus controls. Resting-state fMRI scans were acquired, and the data were analyzed using fractional amplitude of low-frequency fluctuations (fALFF), regional homogeneity (ReHo), and seed-based functional connectivity (FC) using broad bilateral temporal lobe regions of interest to assess temporal-related network connectivity, including temporal-limbic interactions. Group comparisons were performed using ANCOVA controlling for demographic, audiological, physiological, sleep-related, and emotional covariates, cluster-level FDR-corrected p < 0.05. Exploratory correlation analyses were further conducted to examine associations between extracted imaging indices and clinical measures, as well as potential relationships among significant imaging findings.

RESULTS: Resting-state fMRI revealed significant group differences in functional connectivity between the bilateral temporal lobe seed and the left ACC/mOFC cluster. Post hoc analyses showed stronger connectivity in BT than in NBT, whereas BT did not differ significantly from hospital-based non-tinnitus controls. A similar pattern was observed for ReHo in the medial superior frontal gyrus, with lower values in NBT than in both BT and controls. fALFF analyses showed region-specific differences across temporal, frontal, insular, occipital, supramarginal, and postcentral regions, with the most consistent differences observed between BT and NBT. Exploratory analyses showed no significant FC-ReHo correlation within the BT group, and no associations between imaging indices and clinical or audiological measures survived FDR correction. In sensitivity analyses retaining the original covariate structure and additionally adjusting for tinnitus loudness VAS, most BT-NBT differences were attenuated, suggesting that these imaging differences may partly reflect tinnitus loudness or related clinical burden.

CONCLUSION: Resting-state FC, fALFF, and ReHo measures revealed phenotype-related functional differences between BT and NBT. Because several key measures did not differ between BT and hospital-based non-tinnitus controls, these findings should be interpreted as group-level imaging features across tinnitus phenotypes, not as BT-specific abnormalities.

PMID:42369362 | PMC:PMC13293808 | DOI:10.3389/fneur.2026.1831863

Front Psychiatry. 2026 Jun 12;17:1833948. doi: 10.3389/fpsyt.2026.1833948. eCollection 2026.

ABSTRACT

BACKGROUND: Aberrant interhemispheric functional connectivity has been implicated in the pathophysiology of schizophrenia. Voxel-mirrored homotopic connectivity (VMHC) provides a reliable measure of interhemispheric synchronization, yet the frequency-specific characteristics of VMHC alterations in schizophrenia remain poorly understood.

METHODS: Resting-state functional magnetic resonance imaging data were analyzed in patients with schizophrenia and matched healthy controls. VMHC was computed across frequency bands, with particular focus on slow-4 and slow-5 oscillations. Group differences, as well as group-by-frequency interactions, were assessed to identify frequency-specific disruption.

RESULT: Patients with schizophrenia exhibited significantly reduced VMHC within key sensory networks, including primary visual and sensorimotor regions. Frequency-specific analyses revealed higher VMHC at slow-5 compared with slow-4 in visual gyrus and subcortical regions. Significant group-by-frequency interaction effects were observed in the middle occipital gyrus and postcentral gyrus, with post-hoc analyses indicating selectively reduced slow-4 VMHC in patients with schizophrenia.

CONCLUSION: This study demonstrates frequency-dependent reductions of interhemispheric connectivity in schizophrenia, particularly within sensory systems. The findings highlight the disrupted integration of primary perceptual and motor-related processes as a core feature of schizophrenia and emphasize the utility of frequency-resolved VMHC analyses for refining our understanding of network dysfunction. Future longitudinal studies are warranted to determine the clinical significance of these frequency-specific alterations in illness progression and treatment response.

PMID:42368798 | PMC:PMC13303831 | DOI:10.3389/fpsyt.2026.1833948

Front Behav Neurosci. 2026 Jun 12;20:1821101. doi: 10.3389/fnbeh.2026.1821101. eCollection 2026.

ABSTRACT

INTRODUCTION: Transcranial alternating current stimulation (tACS) has emerged as a promising tool to modulate cognitive functions by entraining endogenous neural oscillations. This study investigated the behavioral and neurophysiological after-effects of theta-frequency tACS individualized to each participant's intrinsic theta frequency (ITF).

METHODS: Twenty-two healthy participants were randomly assigned to either a real stimulation group (tACS group) or a sham group. Cognitive assessments and resting-state EEG/fMRI data were collected pre- and post-stimulation.

RESULTS: Participants in the real tACS group showed significant post-stimulation improvements in short-term memory, verbal fluency and category fluency scores compared to the sham group. Functional connectivity analyses revealed increased activity in the dorsal attention and frontoparietal networks only in the stimulation group, suggesting network-specific modulation.

DISCUSSION: These effects align with the theta-gamma coupling theory and provide evidence for long-lasting neuroplastic changes following personalized tACS. This study contributes to our understanding of brain stimulation and supports the use of tACS for cognitive enhancement in healthy individuals.

PMID:42368749 | PMC:PMC13303487 | DOI:10.3389/fnbeh.2026.1821101

bioRxiv [Preprint]. 2026 Jun 18:2026.06.14.732139. doi: 10.64898/2026.06.14.732139.

ABSTRACT

Low back pain is a prevalent issue with few reliable treatments. Although there is great variation in clinical presentation within the low back pain population, little is known about the neurobiological mechanisms underlying these differences. In this study, we sought to stratify chronic low back pain patients (N = 275) into phenotypes characterized by correlated patterns of resting-state brain activity and sensory abnormalities (pain, numbness, and pins and needles) indicated on hand-drawn body maps. Our cross-decomposition analysis yielded phenotypes that resemble previously documented mechanistic pain types, revealing distinct brain connectivity patterns associated with different clinical presentations. Our model was then used to predict pain body maps from fMRI data in a small novel dataset of chronic pain subjects, suggesting that these relationships may generalize to other chronic pain conditions. Our results support the utility of resting-state fMRI in understanding the heterogeneity of chronic pain, which may be leveraged to develop more targeted pain treatments.

PMID:42368013 | PMC:PMC13307932 | DOI:10.64898/2026.06.14.732139

bioRxiv [Preprint]. 2026 Jun 16:2026.06.11.731643. doi: 10.64898/2026.06.11.731643.

ABSTRACT

Individual-specific cortical parcellations enable the characterization of brain network organization that is often obscured by population-level atlases, with broad implications for both basic neuroscience and translational applications. However, existing methods rely primarily on resting-state fMRI and underutilize task-evoked data, which provide complementary information about functional specialization. This limitation partly reflects the challenge of integrating heterogeneous datasets that differ in task design, sample size, and cortical coverage. Here, we present mRBM-HBP, a scalable hierarchical Bayesian framework that incorporates a multinomial restricted Boltzmann machine to model spatial dependencies, enabling efficient and flexible integration of resting-state and task fMRI across diverse datasets and inference of both group-level and individual-level cortical parcellations. We show that mRBM-HBP achieves performance comparable to state-of-the-art resting-state-based parcellation methods while substantially reducing computational cost. By integrating large-scale task-fMRI datasets, we derive a task-based parcellation and demonstrate that resting-state and task conditions reveal largely consistent macroscopic networks, while task data provide state-specific refinements of functional boundaries. Moreover, a fused rest-task group-level atlas improves the accuracy, reliability, and individual specificity of inferred parcellations, particularly when individual-level data are limited. These results indicate that integrating resting-state and task fMRI enhances precision mapping of functional brain organization.

PMID:42367978 | PMC:PMC13308068 | DOI:10.64898/2026.06.11.731643

Int J Dev Neurosci. 2026 Aug;86(5):e70145. doi: 10.1002/jdn.70145.

ABSTRACT

Autism Spectrum Disorder (ASD) is a neurodevelopmental disorder characterized by social communication deficits and repetitive behaviours. Diagnosing ASD early is difficult for healthcare professionals due to its diverse and intricate presentation. However, early detection is vital for enhancing outcomes and enabling the children to access targeted therapies that support the development of social and communication skills. Moreover, Classical models were time-consuming and resource-intensive, and they required lengthy assessments and specialized training. To bridge these complications, this research proposes a Sales Training-Based Optimization enabled Deep High-Order Principal Component Network (STBO_DHPCNet) for ASD classification using resting-state fMRI (rs-fMRI) brain images from 1114 subjects in the ABIDE dataset. First, gamma correction is applied to enhance the quality of the autism brain image. Next, the Region of Interest (ROI) extraction is performed. Afterwards, the nub region extraction is performed based on Sales Training Based Optimization (STBO). On the other hand, feature extraction is done based on an enhanced brain image. Finally, the classification of ASD is done by using DHPCNet, and it is trained using STBO. Here, DHPCNet is developed by incorporating the Deep High-Order Attention Neural Network (DHA-Net) and Principal Component Analysis Network (PCA-Net). Moreover, the evaluation results show that the DHPCNet gained an increased range of accuracy, sensitivity and specificity as 95.62%, 94.79%, and 95.86%.

PMID:42366641 | DOI:10.1002/jdn.70145

Nat Commun. 2026 Jun 27. doi: 10.1038/s41467-026-74953-6. Online ahead of print.

ABSTRACT

Resting-state fMRI (rsfMRI) scans-acquired in the absence of experimentally controlled stimuli or task demands-are widely used to identify aberrant patterns of functional connectivity (FC) in clinical populations. To minimize interpretational uncertainty, researchers routinely control for across-cohort disparities in age, gender, comorbidities, and head motion. Yet, studies rarely consider the possibility that systematic differences in inner experience (i.e., how subjects think and feel during the scan) directly affect FC measures. Here, using an rsfMRI dataset comprising 469 scans with retrospective experiential annotations, we show that summary descriptors of in-scanner experience are reproducible across visits and subject-specific, consistent with trait-like characteristics. We further show that widespread significant differences in FC are observed between scans that are associated with different reported experiential profiles, and that FC can predict specific experiential dimensions with performance comparable to that reported for demographic, cognitive, and clinical variables. Together, these findings highlight the key role that in-scanner experience should play when interpreting FC in the context of rsfMRI. Given that the available experiential measures are retrospective summaries, these results speak to stable experiential tendencies rather than potential moment-to-moment, state-dependent relationships between ongoing experience and concurrent brain activity.

PMID:42365014 | DOI:10.1038/s41467-026-74953-6

BMC Psychiatry. 2026 Jun 27. doi: 10.1186/s12888-026-08332-2. Online ahead of print.

ABSTRACT

BACKGROUND: Major depressive disorder (MDD) is a highly prevalent psychiatric condition that frequently emerges during adolescence, a critical developmental stage characterized by heightened vulnerability to emotional dysregulation. Despite increasing evidence of large-scale brain network dysfunction in adult MDD, the static and dynamic connectivity alterations underlying adolescent MDD remain poorly understood.

METHODS: We recruited 29 first-episode, treatment-naïve adolescents with MDD and 29 age- and sex-matched healthy controls (HCs). Resting-state functional magnetic resonance imaging (rs-fMRI) data were analyzed using group independent component analysis (ICA) combined with sliding-window clustering to evaluate both static functional network connectivity (sFNC) and dynamic functional network connectivity (dFNC) across the default mode network (DMN), salience network (SN), central executive network (CEN), and dorsal attention network (DAN). Correlation analyses were performed between connectivity metrics and clinical severity assessed by the 17-item Hamilton Depression Rating Scale (HAMD-17).

RESULTS: Compared with HCs, adolescents with MDD exhibited significantly reduced intra- and inter-network connectivity within the DMN, SN, and CEN, alongside a trend toward increased DAN-SN connectivity. Dynamic analyses revealed reduced state transition frequency, shorter dwell time in low-connectivity states (e.g., DMN-CEN-SN interactions), and longer dwell time in high-connectivity states (e.g., DAN-SN coupling). Clinical analyses demonstrated that weaker intra-DMN and intra-DAN connectivity, as well as reduced DMN-CEN and DMN-SN connectivity, were negatively correlated with HAMD-17 scores. Conversely, prolonged dwell time in hyperconnected states positively correlated with greater symptom severity.

CONCLUSION: Our findings highlight distinct static and dynamic network abnormalities in adolescent MDD, including disrupted DMN-CEN competitive balance and maladaptive DAN-SN hyperconnectivity. These alterations may hint at developmental-stage-specific neuropathological mechanisms that could differ from adult depression, although direct comparison with adult cohorts is lacking. Integrating static and dynamic FNC analyses may provide preliminary insights into candidate neuroimaging markers for early detection and intervention strategies in adolescent MDD, though these findings require validation in independent, larger cohorts.

PMID:42365265 | DOI:10.1186/s12888-026-08332-2

J Affect Disord. 2026 Jun 27:122181. doi: 10.1016/j.jad.2026.122181. Online ahead of print.

ABSTRACT

BACKGROUND: Bipolar Disorder (BD) is characterized by severe emotional instability. While traditionally viewed through the lens of anatomical dysconnectivity, it remains unclear how the brain's anatomical scaffold abnormally constrains its functional dynamics to produce such volatile mood states.

METHODS: We investigated the multi-modal connectome in 41 BD patients and 40 healthy controls (N=81). A high-fidelity pipeline was employed to robustly reconstruct structural networks. We then applied Graph Signal Processing (GSP) and functional gradient analysis to resting-state fMRI data to quantify structure-function alignment and hierarchical network dynamics.

RESULTS: Network-based statistics revealed no NBS-detected macroscale structural disruptions in BD. However, GSP analysis uncovered a profound pathological shift toward "functional rigidity". BD patients exhibited significantly elevated structure-function alignment (hyper-coupling), indicating that functional dynamics are excessively restricted by the underlying anatomical backbone. Furthermore, a multimodal connectomic model demonstrated good performance in distinguishing BD from healthy controls at the individual level (accuracy=74.1%, AUC=0.888, sensitivity=70.7%, specificity=77.5%, permutation p=0.001). SHAP analysis localized the dominant predictors to the prefrontal-limbic emotion-regulation circuit.

CONCLUSION: BD is characterized by profound "functional rigidity" rather than sheer anatomical degradation. This excessive structure-function tethering may constitute a candidate inter-episode connectomic signature with preliminary individual-level discrimination potential, offering new insights into the connectomic basis of inter-episode functional dysregulation in BD and motivating prospective validation in independent longitudinal cohorts.

PMID:42364661 | DOI:10.1016/j.jad.2026.122181

Mol Psychiatry. 2026 Jun 26. doi: 10.1038/s41380-026-03694-1. Online ahead of print.

ABSTRACT

Theta-band oscillation is integral to fronto-parietal connectivity in the executive control network and its top-down regulation on subcortical areas. External frontoparietal synchronization using theta-frequency transcranial alternating current (tACS) is a technology to potentially engage this network. In this pre-registered, triple-blind, sham-controlled trial (NCT03907644), we tested this intervention targeting the right frontoparietal network in people with opioid use disorder (OUD) to measure network engagement and behavioral outcomes. Sixty male participants with OUD were randomized to receive 20 min of active or sham 6 Hz tACS (HD electrodes over F4 and P4). Structural, resting-state, task-based fMRI drug cue reactivity, and repeated cue-induced craving assessments were collected immediately before and after stimulation. Pre-registered outcome measures were analyzed using time × group interaction models to examine (1) modulation of drug cue-related brain activity, (2) changes in craving, (3) alterations in functional connectivity, and (4) relationship between electric field, neural responses, and craving behavior. (1) A significant Time × Group interaction revealed decreased post-stimulation opioid cue-related activity in the active group relative to sham, involving key nodes in reward processing (ventral striatum, amygdala and ventral tegmental area) (FWE corrected α = 0.05) (2) subjective craving did not differ significantly between groups (3) Group by time generalized psychophysiological interaction analyses showed increased right frontoparietal network engagement (β = 2.63, p= 0.0308) following stimulation, and increased top-down inhibitory regulation of frontoparietal network on right ventral striatum (β = 1.99, p= 0.037) and left medial amygdala (β = 1.97, p= 0.039) (4) Electric field strength in the right frontal/parietal node predicted frontoparietal network engagement in the active group (r = 0.43, p= 0.02). Together, these findings demonstrate that theta-band frontoparietal tACS can modulate activity and task-dependent coupling within cortical-subcortical circuits in OUD, supporting network-targeted neuromodulation as a potential intervention for addiction.

PMID:42362768 | DOI:10.1038/s41380-026-03694-1

J Affect Disord. 2026 Jun 26:122179. doi: 10.1016/j.jad.2026.122179. Online ahead of print.

ABSTRACT

Late-life depression (LLD) is characterized by high treatment resistance, particularly in patients with significant cerebral amyloid-beta (Aβ) deposition. However, the underlying mechanisms through which Aβ compromises clinical recovery remain poorly understood. We investigated whether Aβ burden drives treatment resistance by inducing hierarchical rigidity in the brain's macroscale functional organization. We analyzed longitudinal resting-state fMRI and amyloid-PET data from 93 LLD patients (44 Aβ+, 49 Aβ-) over a one-year treatment interval. Functional connectivity (FC) gradients were constructed using diffusion map embedding to characterize the sensory-to-transmodal cortical hierarchy. At baseline, Aβ + patients exhibited widespread FC gradient aberrations, primarily within the frontoparietal control and default mode networks, despite equivalent depression severity to Aβ- patients. Following treatment, the Aβ + group showed significantly poorer clinical remission (p < 0.001). Longitudinal analysis revealed a lack of significant group × time interaction, indicating that gradient disorganization in Aβ + LLD remained unresolved. Cerebral Aβ acts as a physiological constraint on the hierarchical plasticity required for antidepressant response. This "hierarchical rigidity" suggests that Aβ anchors the brain in a dysfunctional, compressed state, precluding the adaptive reorganization necessary for recovery. Baseline functional gradients may serve as a potent prognostic marker for identifying amyloid-associated treatment resistance in LLD.

PMID:42362062 | DOI:10.1016/j.jad.2026.122179

Neurosci Biobehav Rev. 2026 Jun 26:106837. doi: 10.1016/j.neubiorev.2026.106837. Online ahead of print.

ABSTRACT

The prognostic value of spontaneous brain activity in patients with Disorders of Consciousness (DoC) remains questionable due to methodological heterogeneity, small sample sizes, and the difficulty in conducting longitudinal studies on this clinical population. We performed a coordinate-based meta-analysis of the studies adopting resting state brain imaging techniques to identify whether the spontaneous activity of specific brain areas has a prognostic value for DoC patients. We included studies published until 2025 providing the peak coordinates deriving from contrasting brain activations of patients showing full consciousness recovery (Good Outcome;GO) and patients showing either no consciousness recovery or death (Bad Outcome; BO) with voxel-wise whole-brain analyses. Twelve studies were included in the meta-analysis containing data from 332 DoC patients (n= 192 BO) in the post-acute phase, assessed through resting state functional Magnetic Resonance Imaging. Compared to BO, GO patients showed increased spontaneous activity in sensory and associative areas, including visual areas, precuneus, temporo-parietal, and, marginally, premotor regions. Taken together, the results suggest preservation of posterior brain areas is pivotal in assisting prognosis, with a marginal role played by the premotor areas. However, both low-level sensory and high-level associative areas contribute to outcome prediction of DoC patients, possibly due to the need to integrate low-level sensory information to enable functional interaction with the environment.

PMID:42361934 | DOI:10.1016/j.neubiorev.2026.106837

Eur J Radiol. 2026 Jun 17;203:113018. doi: 10.1016/j.ejrad.2026.113018. Online ahead of print.

ABSTRACT

BACKGROUND: Heart failure (HF) is frequently accompanied by cognitive and affective impairments, yet the underlying neural mechanisms of altered brain information processing in HF remain poorly defined.

METHODS: We applied an integrated information decomposition framework to resting-state fMRI from 48 HF patients (including HFrEF and HFmrEF, with LVEF < 50 %) and 33 matched healthy controls, quantifying synergistic and redundant information across 246 brain regions. Group differences were assessed at global and regional levels and correlated with cardiac function, cognitive performance, and affective symptoms. Neuromaps, neurotransmitter and neuropeptide receptor maps and cognition-related meta-analytic data from NeuroSynth were used to contextualize findings.

RESULTS: HF patients exhibited significantly greater global brain synergy, with regional elevations in the thalamus, basal ganglia, and limbic-temporal areas. Altered synergistic interactions correlated with lower ejection fraction, poorer cognition, and greater depression. Spatial mapping showed negative associations between synergy and cerebral blood flow, glucose and oxygen metabolism, and microstructural integrity. Synergy patterns overlapped with the distribution of dopamine, histamine, and opioid receptors, and neuropeptides including somatostatin, vasopressin, and kinin/tensin. NeuroSynth decoding linked these patterns to memory, emotion, pain, and motor control domains.

CONCLUSIONS: HF is characterized by distinct alterations in synergistic brain information processing that relate to cognitive-emotional impairment and show exploratory spatial correspondence with normative molecular architecture. These findings provide multimodal evidence for altered heart-brain interactions in HF.

PMID:42361709 | DOI:10.1016/j.ejrad.2026.113018

J Neurol. 2026 Jun 26;273(7):425. doi: 10.1007/s00415-026-13930-x.

ABSTRACT

BACKGROUND AND PURPOSE: Altered cerebellar resting-state functional connectivity (FC) may contribute to hand motor impairment in People with Multiple Sclerosis (PwMS). We aimed to assess whether: 1. FC abnormalities of the sensorimotor and cognitive cerebellum (smCb, cCb) are associated with impaired hand dexterity; 2. these alterations contribute to impaired hand dexterity beyond structural brain and spinal cord damage.

METHODS: Six hundred and sixty-seven subjects from the Italian Neuroimaging Network Initiative database were included: two hundred forty-eight PwMS with impaired (iPwMS) and two hundred forty-eight with preserved hand dexterity (pPwMS), and one hundred seventy-one healthy controls (HC). We obtained measures of white matter lesion load, cortical, deep gray matter and cerebellar volumes, and C2-C3 spinal cord area. Cerebellar FC of the smCb and cCb was evaluated using voxel-wise seed-based analyses. Group comparisons were performed using ANCOVA models, with and without adjusting for global and regional structural measures.

RESULTS: Compared to HC and pPwMS, iPwMS showed widespread FC reductions of both smCb and cCb in several cortical, subcortical, and cerebellar regions (p < 0.05, FDR-corrected), whereas pPwMS did not differ significantly from HC. After adjusting for structural measures, FC reductions in iPwMS persisted, although to a lesser extent, particularly in cortical and cerebellar regions.

CONCLUSION: Cerebellar FC reductions involving both sensorimotor and cognitive domains are robustly associated with impaired hand dexterity in PwMS and remain significant even after controlling for brain and spinal cord structural damage. This suggests that cerebellar FC alterations are a key, only partly structure-dependent contributor to dexterity impairment, highlighting cerebellar connectivity as a promising target for motor rehabilitation in MS.

PMID:42360506 | DOI:10.1007/s00415-026-13930-x

Ann Neurosci. 2026 Jun 23:09727531261452860. doi: 10.1177/09727531261452860. Online ahead of print.

ABSTRACT

BACKGROUND: Traumatic brain injury (TBI) is a significant global health concern resulting in persistent physical, cognitive, and social impairments that affect daily functioning and quality of life. Resting-state functional magnetic resonance imaging (rs-fMRI) has emerged as a valuable neuroimaging modality offering novel insights into brain function at rest.

SUMMARY: This integrative review synthesised evidence from studies indexed in PubMed, ScienceDirect, and Scopus on resting-state network (RSN) alterations and their association with cognitive dysfunction in TBI. Consistent alterations in functional connectivity were identified across multiple RSNs in TBI population, particularly within the default mode network (DMN), frontoparietal network (FPN), and salience network. DMN connectivity showed strong associations with attention, processing speed, and memory. FPN connectivity changes corresponded with executive function deficits, while sensorimotor network disruptions were linked to attentional impairments. Dynamic longitudinal changes in network connectivity further suggested underlying neuroplasticity mechanisms.

KEY MESSAGE: Resting-state functional connectivity demonstrates as a potential sensitive biomarker for TBI diagnosis, prognosis, and rehabilitation monitoring. With implications for individualised rehabilitation, these findings highlight the need for a network oriented approach for understanding, diagnosing, and treating TBI. Future research should explore longitudinal changes in RSN connectivity and their implications for neuro-rehabilitation strategies.

PMID:42359244 | PMC:PMC13290743 | DOI:10.1177/09727531261452860

Front Neurol. 2026 Jun 10;17:1839830. doi: 10.3389/fneur.2026.1839830. eCollection 2026.

ABSTRACT

OBJECTIVE: Focusing on patients with epilepsy and left-sided epileptogenic foci, this study aimed to clarify language lateralization differences from healthy individuals, explore Broca's/Wernicke's area lateralization and dominant hemisphere shift, and construct a laterality index-activation map combined scheme. It provides a theoretical basis for optimizing epilepsy surgery and reducing postoperative language impairment risk.

METHODS: We retrospectively studied 36 patients with left-sided epileptogenic foci and 45 healthy controls (2018-2023, Hebei Medical University Second Hospital). After preprocessing rs-fMRI data, 12 bilateral language-related seed points were selected to calculate functional connectivity and generate activation maps. We further computed two types of laterality indexes: the global laterality index (LI) reflecting overall whole-brain language lateralization, and the regional LI assessing independent lateralization of Broca's area (6 frontal seed points) and Wernicke's area (6 temporal seed points). The Kappa coefficient was used to analyze the consistency between different methods, and SEEG cortical stimulation (only performed in surgical candidates) combined with postoperative follow-up was applied to verify clinical reliability.

RESULTS: Non-classical language dominance was higher in epilepsy (69.4% vs. 45.5%, p = 0.024), especially in Wernicke's area (81.8% vs. Broca's 50.0%). Laterality index-activation map consistency was 83.3% (Kappa = 0.586); regional laterality index-activation map consistency was 75.0% (Kappa = 0.40). SEEG stimulation and surgical verification confirmed that the left Broca's area retained core language function, whereas the left Wernicke's area showed significant functional impairment (only 10.0% positive stimulation rate, and no language deficits occurred after partial resection in 6 patients).

CONCLUSION: Left-sided epilepsy patients show language lateralization remodeling, with Wernicke's area more prone to shift. Bilateral language dominance does not equate to equal functional contribution; the left Broca's area retains core motor language function and requires priority protection in all epilepsy surgeries involving the left frontotemporal lobe, especially left frontal lobe epileptogenic focus resection. The combined laterality index-activation map scheme reliably supports preoperative language lateralization, optimizes surgery, and reduces language impairment risk, with clinical value.

PMID:42358935 | PMC:PMC13290606 | DOI:10.3389/fneur.2026.1839830

Brain Sci. 2026 Jun 4;16(6):610. doi: 10.3390/brainsci16060610.

ABSTRACT

Neuroticism, a Big Five trait characterized by emotional instability and susceptibility to negative affect, is a robust transdiagnostic predictor for the onset, severity, and persistence of anxiety disorders, major depressive disorder (MDD), and other affective conditions. Recent advances in functional magnetic resonance imaging (fMRI) techniques-including resting-state fMRI, multimodal neuroimaging, and their integration with machine learning-have enabled multi-perspective investigations into the neural substrates of neuroticism. Current research in this field primarily follows three complementary approaches: cross-sectional studies identifying key brain regions for emotional processing and cognitive control (e.g., amygdala (AMG), prefrontal cortex); longitudinal studies capturing neural mechanisms evolution across adolescence, middle age, and old age to elucidate relationships between neuroticism and brain plasticity; and intervention studies exploring plastic pathways for reshaping the neural representations of neuroticism, challenging the classic "trait stability" paradigm. This review synthesizes recent progress in the cognitive neuroscience of neuroticism across these three approaches, proposes a unified emotion-cognition neural model centered on the AMG-prefrontal-default mode network circuit, and outlines a hypothesized lifespan trajectory of Limbic Sensitivity → Regulatory Strain → Prefrontal Decline. While accumulated evidence broadly supports the cross-sectional and interventional pillars of this framework, the lifespan trajectory remains a theoretically informed working model requiring further longitudinal validation. The field still faces critical limitations, including small effect sizes, methodological heterogeneity, and unresolved questions regarding causality and circuit specificity. This review aims to provide a conceptual integration of existing findings, identify key uncertainties, and propose evidence-based future directions. We further link the proposed neural model to clinical phenotypic characteristics of high neuroticism and discuss its implications for targeted neural interventions, thereby advancing our understanding of the biological basis of neuroticism and providing a theoretical framework for prevention and intervention in neuroticism-related affective disorders.

PMID:42352621 | DOI:10.3390/brainsci16060610