Most recent paper

Neuroimage. 2026 Feb 4:121782. doi: 10.1016/j.neuroimage.2026.121782. Online ahead of print.

ABSTRACT

Systematic investigations into the lateralized human brain have revealed a bivariate functional architecture that underpins distinct cognitive processes. This architecture manifests through inter- and intra-hemispheric lateralization, captured respectively by neural integration and segregation. In this study, we conducted a comprehensive evaluation of multiple quantitative laterality metrics in resting-state fMRI connectivity, using conceptual models to illustrate how inter- and intra-hemispheric correlations shape functional lateralization. We further highlight the critical influence of factors such as correlation sign, correlation coefficient distribution, and statistical thresholding methodology on the interpretation of functional connectivity-based laterality indices. Our findings show that, in our dataset, laterality metrics based on positive-only functional connectivity with a lenient connection-level threshold most consistently capture established relationships between functional brain lateralization and performance in language and visuospatial domains.

PMID:41651090 | DOI:10.1016/j.neuroimage.2026.121782

Rofo. 2026 Feb 6. doi: 10.1055/a-2779-7718. Online ahead of print.

ABSTRACT

Brain tumors, especially glioblastomas, remain among the tumor diseases with the worst prognosis. Recent findings in brain tumor research show that neuronal and glial integration of tumors, as well as the formation of glioma cell networks, promote tumor progression and therapy resistance. This highlights the need for innovative imaging techniques that conceptualize brain tumors as systemic central nervous system (CNS) diseases that are deeply integrated in the brain's network architecture.This review presents current imaging methods for analyzing tumor-associated functional and structural connectivity with a focus on resting-state functional MRI (rs-fMRI) and diffusion tensor imaging (DTI).Functional connectivity changes in glioma patients can be detected and quantified using fMRI. These changes are associated with tumor biology, as well as prognosis and cognitive performance. Rs-fMRI parameters may support prognostic assessment and the development of new therapeutic strategies. Quantitative structural connectivity analysis at the individual patient level can provide further insight into tumor integration in the brain's connectional architecture. DTI-based tractography is especially relevant in neurosurgical planning, as it maps the spatial relationship between the tumor and white matter tracts.Imaging analysis of tumor-associated network alterations provides deeper insight into brain tumor biology and may support the development of network-targeted therapeutic approaches. Connectivity-based imaging methods, particularly rs-fMRI and DTI, hold great potential to further enhance preoperative planning, prognostic assessment, and personalized treatment strategies for patients with brain tumors. · Glioma cells form networks beyond macroscopic tumor boundaries and promote therapy resistance.. · Glioma cells form synapses with neurons and exploit neural signals for growth.. · Network alterations can be visualized and quantified using rs-fMRI and DTI.. · Tumor-associated network alterations in imaging correlate with tumor biology and prognosis.. · Imaging markers optimize patient management and support development of new therapeutic strategies.. · Suvak S, Wunderlich S, Stoecklein V et al. Imaging of Brain Tumor Connectivity. Rofo 2026; DOI 10.1055/a-2779-7718.

PMID:41650981 | DOI:10.1055/a-2779-7718

Psychiatry Res Neuroimaging. 2026 Jan 15;358:112143. doi: 10.1016/j.pscychresns.2026.112143. Online ahead of print.

ABSTRACT

BACKGROUND: This resting-state functional magnetic resonance imaging (rs-fMRI) study investigated alterations in voxel-mirrored homotopic connectivity between schizophrenia patients and healthy controls. It further explored the associations between these neural alterations and clinical profiles. The findings aim to enhance the understanding of interhemispheric dysconnectivity in schizophrenia and may offer clues for identifying potential neurobiological substrates of the disorder.

METHODS: A total of 38 schizophrenic individuals who attended the psychiatric department were recruited as the experimental group, and 35 healthy volunteers from the medical examination centre were enrolled as the control group during the same time period. Scanning of the subject's entire brain using 3.0T MRI. we finally analysed the correlation between voxel-mirrored homotopic connectivity (VMHC) values and disease severity, disease duration and cognitive function.

RESULTS: (1) VMHC values were significantly lower in the bilateral lingual gyrus in the case group compared to the control group(p<0.05). (2)After applying rigorous False Discovery Rate (FDR) correction for multiple comparisons, the reduction in lingual gyrus VMHC remained specifically and positively correlated with poorer performance in delayed memory (p<0.05,Cohen's d = -1.09). Nominal associations with illness duration and overall symptom severity did not survive this statistical correction. (3) The VMHC values were positively correlated with the total cognitive scale score and the delayed memory factor score (p<0.05, q< 0.015).

CONCLUSIONS: This study identifies a robust reduction in interhemispheric functional connectivity within the lingual gyrus of chronic, medicated schizophrenia patients. Critically, the extent of this reduction is specifically linked to the severity of memory impairment, rather than to general symptom profiles. These findings highlight the role of aberrant homotopic connectivity in visual association cortex in the cognitive pathophysiology of schizophrenia and provide a focused neurobiological correlate for future mechanistic and longitudinal investigations.

PMID:41650581 | DOI:10.1016/j.pscychresns.2026.112143

Neurology. 2026 Mar 10;106(5):e214656. doi: 10.1212/WNL.0000000000214656. Epub 2026 Feb 6.

ABSTRACT

BACKGROUND AND OBJECTIVES: fMRI has proven useful in dissecting the neurobiological underpinnings of migraine. However, the existing evidence is limited by small samples, use of suboptimal statistical thresholds, and different methods of clinical data acquisition. Given these limitations, we hypothesized that a large, well-characterized sample would allow a clearer distinction between resting-state functional connectivity (rs-FC) alterations specific to migraine and those related to migraine subtypes.

METHODS: Adults with migraine and age-matched and sex-matched healthy controls (HCs) underwent a single 3T rs-fMRI scan. We compared rs-FC between migraine and HCs, and across migraine subtypes, using multi-voxel pattern and seed-based analysis. General linear models and analysis of covariance tests with Bonferroni-adjusted cluster-wise family-wise error correction (pFWE-Bonferroni ≤0.001) were applied. rs-FC measures, expressed as Z scores, were also compared across migraine subtypes using general linear models (pBonferroni < 0.05).

RESULTS: We analyzed rs-fMRI data from 264 participants with migraine (mean age 42 ± 12 years, 234 women) and 151 HCs (mean age 41 ± 11 years, 130 women). The multi-voxel pattern analysis identified significant rs-FC differences in a cluster within the bilateral middle cingulate cortex when comparing participants with migraine to HCs (pFWE-Bonferroni <0.001). The seed-based analysis revealed that participants with migraine had increased rs-FC between the cluster in the bilateral middle cingulate cortex and both the right lateral occipital cortex and bilateral occipital pole (both pFWE-Bonferroni <0.001), compared with HCs. Furthermore, increased rs-FC was identified between the limbic lobe and the right occipital pole (pFWE-Bonferroni = 0.0014) and precuneus (pFWE-Bonferroni <0.001). The cingulate-occipital rs-FC was consistently increased in participants with migraine, irrespective of the migraine subtype (pBonferroni <0.001). In addition, ictal participants who were scanned during attacks exhibited an increased hypothalamic rs-FC with the bilateral precuneus, compared with HCs (pBonferroni <0.001). No significant associations emerged between rs-FC and clinical features in migraine.

DISCUSSION: The identified rs-FC alterations between the middle cingulate cortex and occipital regions might represent a migraine-specific trait, suggesting an integration of nociceptive and visual processing. This discovery provides novel insights into the neurobiological underpinnings of migraine and proposes that altered cingulate-occipital rs-FC might serve as a potential biomarker for migraine.

PMID:41650361 | DOI:10.1212/WNL.0000000000214656

bioRxiv [Preprint]. 2026 Jan 16:2026.01.15.699580. doi: 10.64898/2026.01.15.699580.

ABSTRACT

Large-scale lifespan neuroimaging studies increasingly integrate data across distinct cohorts to characterize trajectories of brain development and aging. However, systematic differences in acquisition protocols and hardware across cohorts can alter signal characteristics in ways that bias downstream analyses. Here we examine three cohorts from the Human Connectome Project (HCP), spanning development (HCP-D), young adulthood (HCP-YA) and aging samples (HCP-A), to illustrate this issue and evaluate existing strategies to mitigate it. HCP has set standards for open, deeply phenotyped, high-resolution human neuroimaging, which are frequently used as high-quality reference datasets in tool validation, replication studies, and cross-cohort meta-analyses. Because of HCP's widespread usage, even modest protocol differences between cohorts-and their downstream effects-can have outsized impacts on the field of neuroscience research. Our analysis reveals that the HCP-YA cohort exhibits systematically weaker temporal signal-to-noise-ratio (tSNR) relative to HCP-D/A. These signal quality discrepancies propagate to downstream analyses, leading to differences in overall resting-state functional correlations, and whole-brain and node-level measures of resting-state network organization (e.g., system segregation, modularity, participation coefficient). Consistent with protocol-driven signal differences, resting-state network measures derived from HCP-YA depart from expected lifespan trajectories, as confirmed by examination of two other lifespan datasets. Harmonization approaches accounting for protocol and scanner-model differences alleviate some of the artifactual differences in brain network measurement. Our findings underscore that signal differences do not merely introduce noise, but can qualitatively alter estimated lifespan trajectories of functional network organization, including partially inverting expected lifespan patterns. Without appropriate harmonization, analyses that combine HCP cohorts can therefore result in biologically misleading inferences about development and aging. We demonstrate how small acquisition differences bias resting-state-derived network metrics, and how these effects can be mitigated. This work advances best practices for valid inferences in multi-cohort lifespan neuroscience research.

PMID:41648495 | PMC:PMC12871149 | DOI:10.64898/2026.01.15.699580

bioRxiv [Preprint]. 2026 Jan 24:2026.01.22.701118. doi: 10.64898/2026.01.22.701118.

ABSTRACT

Δ 9 -tetrahydrocannabinol (THC)-containing products are widely used recreationally, partly due to THC's ability to enhance the appetitive (i.e., rewarding) properties of diverse stimuli. However, the neural mechanisms through which THC modulates reward-related processing remain poorly understood. Here, we used a Pavlovian paradigm in adult squirrel monkeys (3males, 1female) to associate a visual conditioned stimulus (CS + ) with appetitive food delivery. The modulatory effects of acute THC (1-10μg/kg, i.m.) on behavioral and brain responses to CS + were evaluated. Event-related functional MRI (fMRI) was employed to characterize the neural correlates of conditioned responding to the CS + , both in the absence and presence of THC treatment, with preconditioning scans serving as control. Behaviorally, THC (3μg/kg) selectively enhanced conditioned responding to the CS + without altering responses to the control stimulus (CS - ) or increasing baseline consummatory responding, underscoring the specificity of THC's action on reward-associated processes. Consistently, fMRI analyses revealed that THC amplified CS + -evoked activation within mesocorticolimbic regions, including the anterior cingulate cortex (ACC), striatum, hippocampus, and substantia nigra-ventral tegmental area (SN-VTA), without affecting activity in visual and motor cortices. This finding underscores the selectivity of THC's neuromodulatory effects on reward-related circuitry. Independent of CS exposure, resting-state functional connectivity analyses indicate that THC enhanced mesocorticolimbic network integration, as evident in strengthened SN-VTA-centered connectivity with the ACC, striatum, and hippocampus. Collectively, these findings demonstrate that THC enhances the responses to appetitive stimuli, through selective modulation of mesocorticolimbic circuitry, highlighting the SN-VTA as a pivotal hub for cannabinoid-mediated regulation of incentive salience and motivational drive toward reward-associated stimuli.

ONE-SENTENCE SUMMARIES: THC enhances behavioral and neural responses to rewards through mesocorticolimbic modulation centered on the SN-VTA.

PMID:41648305 | PMC:PMC12871707 | DOI:10.64898/2026.01.22.701118

bioRxiv [Preprint]. 2026 Jan 20:2026.01.18.700221. doi: 10.64898/2026.01.18.700221.

ABSTRACT

High dimensionality of dynamic functional connectivity (dFNC) data representation complicates clinical interpretation and biomarker discovery. We propose a new complementary analytical framework based on dynamic inter-network connectivity entropy (DICE) and its potential use as a biomarker of mental illness. Our framework shows that DICE features extend beyond patient-control discrimination, revealing distinct pathophysiological signatures and differential associations with symptom dimensions. Using resting-state fMRI data from 311 participants, 160 controls, 151 schizophrenia (SZ) patients, we identified 53 intrinsic networks, computed DICE and derived three families of DICE-based metrics: (i) entropy level and range, (ii) distributional shape and temporal organization, (iii) entropy-state repertoire and occupancy. These measures revealed a multidimensional signature of altered entropy dynamics in SZ: (1) elevated baseline entropy with reduced fluctuation magnitude and reduced entropy acceleration; (2) reduced temporal persistence of entropy excursions and entropy distributions closer to Gaussian; and (3) a narrowed repertoire of entropy states, prolonged time in near-baseline entropy configurations. The DICE-based metrics within the SZ group show different associations with symptom dimensions. Reduced fluctuation magnitude and acceleration were associated with greater PANSS general symptom severity (disturbance of volition and preoccupation). Reduced deviation from Gaussianity was associated with higher PANSS positive severity (delusions and hallucinations). Reduced temporal persistence was associated with multiple PANSS positive, negative, and general symptoms. Reduced entropy-state diversity and prolonged dwell time in near-baseline states were associated with depression and PANSS positive/general severity, respectively. The multidimensional pathophysiology revealed through the different entropy patterns may potentially guide biomarker development and personalized treatments.

PMID:41648253 | PMC:PMC12871582 | DOI:10.64898/2026.01.18.700221

bioRxiv [Preprint]. 2026 Jan 24:2026.01.22.701132. doi: 10.64898/2026.01.22.701132.

ABSTRACT

Dynamic connectivity is central to understanding time-varying interactions between brain regions. Despite decades of methodological development, approaches to measuring dynamic connectivity remain fragmented, leading to inconsistent findings, limited comparability across studies, and difficulty attributing observed effects to computational choices. Here we introduce dynamic co-modulation (DyCoM), a compact operator-level framework that expresses dynamic connectivity estimators as compositions of a small set of fundamental signal processing operations. Using simulations and resting-state fMRI data, we show that DyCoM disentangles previously conflated findings by revealing that lower-order sensory and higher-order executive control neurobiological signatures, state-transition sensitivity, and medication-linked clinical associations arise from distinct operator choices within a single unified framework. Together, these results establish DyCoM as a unifying foundation for dynamic interaction analysis, revealing how differences in estimator design give rise to divergent biological interpretations and offering a principled, domain-agnostic framework for coherence, interpretability, and estimator development.

PMID:41648203 | PMC:PMC12871704 | DOI:10.64898/2026.01.22.701132

bioRxiv [Preprint]. 2026 Jan 12:2026.01.11.698919. doi: 10.64898/2026.01.11.698919.

ABSTRACT

Memory athletes can achieve superior performance (e.g., memorizing 339 digits in 5 minutes) with extensive daily training, by converting abstract information into vivid scenes, and placing them along a mental path, that is then retraced at recall (Method of Loci). Understanding the brain mechanisms underlying such training-dependent performance could suggest novel brain-based approaches to improve learning and cognitive performance in other domains. As each memory athlete uses individual-specific, personalized training techniques, it has been challenging to study them at the group level. Fortunately, precision functional mapping (PFM) which uses repeated sampling of resting state functional connectivity and task fMRI, enables detailed study of individual brains. Here, we map the brain organization of a 6-time U.S. Memory Champion (>13 hours fMRI) and compare it to control data. We observe focal functional connectivity differences in the memory champion's retrosplenial, extrastriate visual, and dorsal frontal cortex (area 55b), as well as in the caudate. These suggest additional recruitment of scene and semantic processing in the athlete, alongside a stronger integration of the caudate with memory-related networks. A control rote memorization task elicits typical activation patterns in the athlete, but when using the Method of Loci, his pattern on activation differs: his hippocampal activation was larger during recall than encoding and he recruited regions showing connectivity differences compared to controls. His unique circuit for Method of Loci, incorporates regions typically used for navigation, scene processing, language and associative learning.

PMID:41648109 | PMC:PMC12871203 | DOI:10.64898/2026.01.11.698919

Imaging Neurosci (Camb). 2026 Feb 3;4:IMAG.a.1121. doi: 10.1162/IMAG.a.1121. eCollection 2026.

ABSTRACT

Research on biomarkers for predicting psychiatric disorders from resting-state functional connectivity (FC) is advancing. While the focus has primarily been on the discriminative performance of biomarkers by machine learning, identification of abnormal FCs in psychiatric disorders has often been treated as a secondary goal. However, it is crucial to investigate the effect size and robustness of the selected FCs because they can be used as potential targets of neurofeedback training or transcranial magnetic stimulation therapy. Here, we incorporated approximately 5,000 runs of resting-state functional magnetic resonance imaging from six datasets, including individuals with three different psychiatric disorders (major depressive disorder [MDD], schizophrenia [SCZ], and autism spectrum disorder [ASD]). We demonstrated that a PCA-based feature selection method can robustly extract FCs related to psychiatric disorders compared with other conventional supervised feature selection methods. We found that our proposed method robustly extracted FCs with larger effect sizes from the validation dataset compared with different types of feature selection methods based on supervised learning for MDD (Cohen's d = 0.40 vs. 0.25), SCZ (0.37 vs. 0.28), and ASD (0.17 vs. 0.16). We found 78, 69, and 81 essential FCs for MDD, SCZ, and ASD, respectively, and these FCs were mainly thalamic and motor network FCs. The current study showed that the PCA-based feature selection method robustly identified abnormal FCs in psychiatric disorders consistently across datasets. The discovery of such robust FCs will contribute to understanding neural mechanisms as abnormal brain signatures in psychiatric disorders.

PMID:41647267 | PMC:PMC12869322 | DOI:10.1162/IMAG.a.1121

Front Endocrinol (Lausanne). 2026 Jan 21;16:1629157. doi: 10.3389/fendo.2025.1629157. eCollection 2025.

ABSTRACT

INTRODUCTION: Patients with concurrent obesity, type 2 diabetes, and depression experience high disease severity and prevalence. This triad of conditions compromises quality of life and treatment adherence, further exacerbating disease progression. Therapeutic strategies for such patients must address both glycemic control and psychological well-being. Liraglutide, a glucagon-like peptide-1 receptor agonist (GLP-1RA), offers benefits beyond glucose-lowering and weight reduction, with emerging evidence suggesting it may also alleviate depressive symptoms. Therefore, liraglutide represents a promising intervention for managing depression in patients with obesity and diabetes.

OBJECTIVES: This study aims to assess the therapeutic efficacy of liraglutide in overweight or obese patients with type 2 diabetes and comorbid depression, with a specific focus on its antidepressant effects.

METHODS: This is a randomized, double-blind, placebo-controlled pilot trial. Sixty eligible participants will be randomly assigned (1:1) to receive either liraglutide (initiated at 0.6 mg/day, titrated weekly to a maximum of 1.8 mg/day) or a matched placebo, as an adjunct to standard care for 12 weeks. The primary endpoints include blood glucose levels, glycated hemoglobin, body mass index, Hamilton Depression Rating Scale score, and metrics derived from resting-state functional magnetic resonance imaging (resting-state fMRI). Secondary endpoints will assess changes in inflammatory biomarkers (tumor necrosis factor-α, interleukin-6), oxidative stress indicators (superoxide dismutase, malondialdehyde), homeostasis model assessment of insulin resistance, insulin sensitivity index, and homeostasis model assessment of β-cell function.

CONCLUSIONS: This trial will provide preliminary data on the effects of liraglutide on depressive symptoms in overweight/obese patients with type 2 diabetes. The findings are expected to provide a basis and reference for subsequent large-scale clinical research.

PMID:41647109 | PMC:PMC12867914 | DOI:10.3389/fendo.2025.1629157

medRxiv [Preprint]. 2026 Jan 30:2026.01.27.26344860. doi: 10.64898/2026.01.27.26344860.

ABSTRACT

BACKGROUND: Clinical trajectories in functional neurological disorder (FND) are variable, and the mechanisms underlying this heterogeneity remain poorly understood.

OBJECTIVE: This longitudinal study examined resting-state functional connectivity predictors and mechanisms of symptom change in FND.

METHODS: Thirty-two adults with FND (motor and/or seizure phenotypes) completed baseline questionnaires and a functional MRI (fMRI) session, followed by naturalistic treatment for 6.8±0.8 months. All participants completed follow-up questionnaires; 28 individuals completed a follow-up fMRI. At each timepoint, three graph-theory network metrics of functional connectivity were computed: weighted-degree (centrality), integration ( between-network connectivity), and segregation ( within-network connectivity). Analyses adjusted for age, sex, anti-depressants, head motion, time between sessions, and baseline score-of-interest, with cluster-wise correction. Results were contextualized against 50 age-, sex-, and head motion-matched healthy controls (HCs).

RESULTS: Based on patient-reported Clinical Global Impression of Improvement, 59.4% improved, 31.3% were unchanged, and 9.3% worsened. Psychometric scores of core FND symptoms and non-core physical symptoms showed variable trajectories, with no group-level changes. Greater improvement in core FND symptoms was associated with higher baseline between-network integrated connectivity and reduced integration longitudinally within salience, frontoparietal, and default mode network regions. Right anterior insula integration emerged as a prognostic marker and mechanistic site of reorganization, with the most improved participants showing elevated baseline integration compared to HCs. Increased baseline within-network segregated connectivity in dorsal attention network regions correlated with non-core physical symptom improvement. Findings remained significant adjusting for FND phenotype.

CONCLUSIONS: This study identified large-scale network interactions as potential prognostic and mechanistically-relevant sites of reorganization related to symptom change in FND.

PMID:41646801 | PMC:PMC12870675 | DOI:10.64898/2026.01.27.26344860

medRxiv [Preprint]. 2026 Jan 15:2026.01.13.26344039. doi: 10.64898/2026.01.13.26344039.

ABSTRACT

Sleep problems and sensory over-responsivity (SOR) are common, co-occurring, and early-emerging features of Autism Spectrum Disorder (ASD). Yet, the early neural mechanisms underlying this relationship remain unclear. Here, we used resting-state fMRI data from the Infant Brain Imaging Study (IBIS) to examine how brain connectivity at 6 months may relate to parent-reported measures of sleep-onset problems and SOR in infants at varying familial likelihood for ASD. The right anterior insula was used in seed-based analyses to investigate Salience Network (SN) connectivity to cortical and cerebellar regions of interest previously implicated in sleep disruption, sensory processing challenges, and ASD. Infants at high (HL) and low (LL) likelihood for ASD displayed divergent patterns of SN connectivity with sensorimotor cortex, as well as cerebellar regions involved in sensorimotor processing and higher-order functions. Furthermore, stronger SN connectivity with sensorimotor cortices and cerebellar regions was associated with worse sleep-onset problems and SOR in HL infants. In contrast, stronger SN-cerebellar connectivity was related to fewer sleep-onset problems and SOR in LL infants. Our findings indicate that altered SN connectivity may result in over-attribution of attention to sensory stimuli and highlight aberrant sensory prediction learning, which may underlie worse sleep problems and higher SOR in HL infants.

PMID:41646728 | PMC:PMC12870681 | DOI:10.64898/2026.01.13.26344039

medRxiv [Preprint]. 2026 Jan 30:2026.01.28.26345061. doi: 10.64898/2026.01.28.26345061.

ABSTRACT

Exposure is considered the most active element of cognitive behavioral therapy (CBT) for pediatric anxiety, and its efficacy is theorized to depend on cognitive control and its supporting neural substrates (e.g., central executive [CEN], salience [SN], and default mode networks [DMN]). However, little work has identified how CBT, or exposure specifically, modulates intrinsic connectivity of these networks. Progress may be limited by heterogeneity in network connectivity in anxiety, which may obscure treatment-related effects in group-averaged analyses. This randomized clinical trial (RCT) leverages person-specific network modeling to test how exposure-focused CBT (EF-CBT) influences resting-state connectivity of cognitive control networks in pediatric anxiety, relative to an active control (relaxation mentorship training; RMT). Youth aged 7-18 years with an anxiety disorder (N = 104) or low/no anxiety (L/NA; N = 37) completed resting-state fMRI scans before being randomized to EF-CBT or RMT. Resting-state connectivity was reassessed following treatment (or commensurate time L/NA youth) in 113 participants. Changes in within-CEN, CEN-SN, and CEN-DMN density were examined using Group Iterative Multiple Model Estimation, which yields sparse, person-specific networks capturing both shared and individual connectivity structure. At baseline, anxious youth exhibited lower density within-CEN, between CEN-SN, and between CEN-DMN than L/NA youth. Treatment effects differed by intervention: EF-CBT selectively increased (i.e., normalized) CEN-SN density, whereas RMT increased within-CEN density. These findings demonstrate dissociable effects of exposure and relaxation on cognitive control network organization in pediatric anxiety. Exposure-focused CBT uniquely strengthens coordination between control and salience systems, consistent with a mechanism supporting top-down control of threat-related signals during exposure. Network-based measures of cognitive control may help identify mechanistic targets for optimizing and personalizing treatment.

CLINICAL TRIAL NUMBER: NCT02810171.

PMID:41646716 | PMC:PMC12870638 | DOI:10.64898/2026.01.28.26345061

medRxiv [Preprint]. 2026 Jan 13:2026.01.10.26343837. doi: 10.64898/2026.01.10.26343837.

ABSTRACT

BACKGROUND: Auditory hallucinations are among the most disabling symptoms in individuals with schizophrenia (SZ) and are linked to aberrant signaling within deep-striatal circuits, such as the nucleus accumbens (NAc) and caudate head (CH). However, causal tests of striatal involvement have been limited by the inaccessibility of these structures using noninvasive neuromodulatory techniques. Low-intensity focused ultrasound (LIFU) provides millimeter-scale precision capable of modulating deep-brain circuits, but its feasibility and impact on hallucinations in SZ remain unknown.

METHODS: SZ participated in a within-subject cross-over feasibility trial including two active LIFU sessions (NAc, CH) and one sham control (unfocused sonication), spaced one-week apart. Resting-state fMRI and hallucination symptoms were acquired at baseline and immediately post-sonication.

RESULTS: LIFU was delivered safely and well-tolerated in all patients. Acoustic simulations show consistent engagement of both striatal targets across subjects. Clinically, SZ demonstrated significant reductions in hallucination severity following active LIFU to NAc and CH, relative to baseline. Mechanistically, SZ exhibited abnormally high striatal-superior temporal cortex (STC) connectivity at baseline. Immediately after sonication, active LIFU to NAc and CH produced robust reductions in striatal-STC coupling in SZ.

CONCLUSIONS: This first-in-human study demonstrates that deep striatal LIFU is safe, feasible, and produces functional-connectivity changes accompanied by hallucination severity reductions in SZ. The convergence of symptom improvement with reduced striatal-STC coupling provides mechanistic proof-of-concept evidence that this circuit provides a promising biomarker and therapeutic LIFU target in psychosis and motivates larger sham-controlled trials to test the causal role of striatal circuitry in hallucination generation in SZ.

PMID:41646697 | PMC:PMC12870488 | DOI:10.64898/2026.01.10.26343837

Childs Nerv Syst. 2026 Feb 5;42(1):61. doi: 10.1007/s00381-026-07150-x.

ABSTRACT

BACKGROUND: Accurate localization and lateralization of language areas are essential in the preoperative evaluation of children with drug-resistant epilepsy (DRE) to minimize postoperative neurological deficits. Traditional invasive methods such as the Wada test and electrocortical stimulation remain gold standards but present significant limitations, especially in pediatric populations. Noninvasive techniques leveraging blood oxygen level-dependent (BOLD) signal fluctuations, such as functional magnetic resonance imaging (fMRI) and functional near-infrared spectroscopy (fNIRS), offer interesting and convenient alternatives, but clinical evidence is limited. This narrative review aims to synthesize current knowledge on noninvasive BOLD-based imaging techniques, specifically task-based and resting-state fMRI and fNIRS, for language mapping in children with epilepsy.

METHODS: A comprehensive literature search was conducted using PubMed, focusing on studies employing fMRI and fNIRS for language mapping in pediatric epilepsy and cross-referencing. Special consideration was given to higher-impact studies, frequently cited publications, and works by leading experts in the field.

RESULTS: Task-based fMRI remains the clinical standard for language mapping but is frequently compromised by poor task compliance in children. Resting-state fMRI provides a task-free alternative with high sensitivity but often yields broader, bilateral networks that complicate precise lateralization. fNIRS offers a portable and child-friendly option with excellent tolerability but is limited by its spatial resolution and depth penetration. Further standardization of the various data-processing methods used for these modalities is required.

CONCLUSION: BOLD-based noninvasive imaging techniques represent promising advancements in the preoperative evaluation of pediatric epilepsy surgery candidates. Future multicenter studies and the development of pediatric-specific tools are essential to establish standardized clinical use.

PMID:41644798 | DOI:10.1007/s00381-026-07150-x

Sci Rep. 2026 Feb 5. doi: 10.1038/s41598-026-38807-x. Online ahead of print.

ABSTRACT

End-stage renal disease (ESRD) is associated with an increased risk. This study investigates associations between cognitive decline, resting-state networks (RSNs), and biochemical indicators in ESRD patients pre-/post-hemodialysis. 20 hemodialysis (HD) patients and 22 healthy controls underwent resting-state fMRI (rs-fMRI) and neuropsychological assessments. Resting-state networks (RSNs) were extracted via independent component analysis (ICA), with functional connectivity strength compared between pre-/post-HD patients and controls. Correlations between connectivity strength and biochemical indicators were analyzed. Compared to HCs, the post-HD group exhibited significantly decreased FC between the auditory-somatomotor network (t = - 5.120, P < 0.001) and the visual-somatomotor network (t = - 4.199, P < 0.001). In contrast, FC between the default mode and dorsal attention networks was significantly increased (t = 2.908, P = 0.006). While serum electrolytes and iron metabolism markers remained stable post-HD (all P > 0.05), phosphorus levels decreased (P = 0.046), with significant improvements in renal function: eGFR increased from 4.560 ± 1.650 to 16.980 ± 6.428 mL/min, urea, creatinine, and PTH levels decreased (all P < 0.001). Elevated baseline chloride levels were associated with reduced post-HD attention network connectivity (r = -0.758, P < 0.001), while cognitive improvement correlated inversely with baseline connectivity (r = -0.619, P = 0.004) and positively with connectivity plasticity (r = 0.513, P = 0.021). Immediate post-HD changes in resting-state network connectivity were associated with biochemical status and cognitive performance, suggesting potential neural substrates of cognitive dysfunction in ESRD.

PMID:41644743 | DOI:10.1038/s41598-026-38807-x

Psychiatry Res Neuroimaging. 2026 Jan 30;358:112156. doi: 10.1016/j.pscychresns.2026.112156. Online ahead of print.

ABSTRACT

BACKGROUND: Functional connectivity (FC) has been used to identify brain disorders. The present study aimed to identify brain disorders by FC across multiple timescales.

METHODS: We first segmented the resting-state fMRI signals to construct multiple timescale functional connectivity (mFC) between brain regions. Next, we developed a deep multiple instance learning (MIL) approach, namely the Two-stage Multi-stream Network (TMN), to capture spatio-temporal patterns from the mFC. We evaluated the TMN in the ABIDE I dataset and the REST-Meta-MDD dataset. Furthermore, we proposed using the inputXgrad to explain the important features in the model.

RESULTS: We achieved the best performance using the TMN model with mFC. Our findings indicated that mFC outperformed both static FC and the combination of static and dynamic FC in identification tasks. The model's explanation revealed that FC across all timescales contributed to the identification of brain disorders and highlighted the important FC that are strongly associated with these conditions.

LIMITATIONS: The techniques used for data preprocessing can influence the model's performance, and this study requires further validation with a larger patient cohort and a broader range of brain disorders.

CONCLUSIONS: The experimental results demonstrate that brain disorders can be effectively identified using the proposed TMN with mFC.

PMID:41643285 | DOI:10.1016/j.pscychresns.2026.112156

Proc Natl Acad Sci U S A. 2026 Feb 10;123(6):e2526799123. doi: 10.1073/pnas.2526799123. Epub 2026 Feb 5.

ABSTRACT

Deficits in dopamine function cause alterations in episodic memory. Converging evidence implicates dopamine in postencoding hippocampal mechanisms inferred to support long-term memory, though there is a lack of direct evidence in humans. We address this gap using pharmacological functional MRI (fMRI) and positron emission tomography (PET). Using a motivated reward encoding task on and off oral methylphenidate, we tested whether individual differences in baseline dopamine ([11C]raclopride PET D2/3 receptor density) relate to drug-induced changes in hippocampal postencoding processes. Our study focused on healthy older adults, who are among those most vulnerable to memory decline and may benefit from pharmacologically enhancing dopamine. We found that methylphenidate administration was associated with improved memory performance relative to placebo for both high and low reward conditions. Older adults with high receptor density showed greater persistence of hippocampal multivoxel patterns into postencoding rest and stronger hippocampus-midbrain resting-state connectivity following encoding while on methylphenidate. These findings support the view that enhanced dopaminergic tone, verified through PET, directly modulates hippocampal postencoding dynamics in humans. Substantial variation in neurobiological effects was associated with individual differences in baseline dopamine function as older adults with high dopamine receptor density profiles showed preferential benefit of drug on hippocampal function, though these insights are qualified by null associations between memory performance and postencoding hippocampal activity. Individuals with lower dopamine receptor profiles showed preferential benefit of reward incentives suggesting altered sensitivity to extrinsic motivational factors depending on endogenous dopamine function.

PMID:41642987 | DOI:10.1073/pnas.2526799123

Hum Brain Mapp. 2026 Feb 1;47(2):e70465. doi: 10.1002/hbm.70465.

ABSTRACT

Non-primary motor areas, including dorsal premotor cortex (PMd), ventral premotor cortex (PMv), and posterior parietal cortex (PPC), contribute to movement planning, but how these regions differentially shape kinematic features of goal-directed movements, and how this specialization is associated with functional connectivity within the frontoparietal network, remains of interest, particularly in relation to recovery after stroke. We used functional magnetic resonance imaging (fMRI), transcranial magnetic stimulation (TMS), and kinematic assessments to explore how these areas influence reaching performance in neurologically intact adults. Participants performed a goal-directed planar reaching task using the KINARM exoskeleton robot. Brief TMS pulse trains were initiated before movement onset to perturb cortical activity at subthreshold and suprathreshold intensities targeting bilateral PMd, PMv, and dorsomedial superior parietal lobule (SPL) within PPC. Resting-state fMRI quantified functional connectivity among these regions to assess whether connectivity explains stimulation-induced kinematic changes. Relative to the control target within the postcentral sulcus (PCS), subthreshold stimulation of contralateral PMd and PMv reduced reach efficiency and smoothness, while suprathreshold stimulation of contralateral PPC increased deviation error and reduced smoothness. Among ipsilateral targets, PMd showed consistent TMS-induced effects, and was the only target where resting-state connectivity predicted behavioral response. Stronger interhemispheric connectivity in the primary motor cortex and weaker interhemispheric PPC connectivity were associated with greater reductions in straightness and smoothness during subthreshold ipsilateral PMd stimulation. We found that perturbation of premotor and parietal targets led to distinct kinematic effects that varied by site, intensity, and laterality, with premotor stimulation showing the most consistent disruptions at subthreshold intensity and bilateral effects, whereas parietal effects were observed primarily for contralateral stimulation at suprathreshold intensity, and differences in network organization explain variability in behavioral response. Identifying contributions of cortical areas and connectivity patterns may help personalize interventions after stroke. Trial Registration: This study was registered at ClinicalTrials.gov under ID NCT04286516.

PMID:41641924 | DOI:10.1002/hbm.70465