Most recent paper

J Prev Alzheimers Dis. 2026 Feb 6;13(4):100501. doi: 10.1016/j.tjpad.2026.100501. Online ahead of print.

ABSTRACT

BACKGROUND: Brain age gap (BAG)-the difference between predicted and chronological age-captures neurobiological aging, but MRI-only models insufficiently reflect Alzheimer's disease (AD) pathology. Whether incorporating regional amyloid-β (Aβ) positron emission tomography (PET) improves sensitivity to early AD processes remains unknown.

OBJECTIVES: To develop an amyloid-informed multimodal BAG model and examine its associations with cognition, plasma biomarkers, and functional connectivity across the AD continuum.

DESIGN: Cross-sectional analysis using integrated machine-learning models.

SETTING: Chinese Preclinical Alzheimer's Disease Study (CPAS), a cohort recruited from community settings and memory clinics.

PARTICIPANTS: Nine hundred ninety community-dwelling adults spanning normal cognition, subjective cognitive decline (SCD), mild cognitive impairment (MCI), and dementia.

MEASUREMENTS: Regional Aβ-PET and structural MRI informed BAG estimation. Cognitive tests, plasma biomarkers (p-tau217, p-tau181, neurofilament light [NfL], glial fibrillary acidic protein [GFAP], Aβ42/40), and hippocampus-default mode network (DMN) connectivity from resting-state fMRI were assessed.

RESULTS: Higher BAG was associated with greater odds of SCD, MCI, or dementia across the cohort, with stronger effects in Aβ-positive individuals. BAG explained more cognitive variance than global Aβ burden and was linked to multidomain cognitive deficits. Elevated BAG corresponded to higher p-tau217, p-tau181, NfL, and GFAP and lower Aβ42/40, indicating early biomarker alterations. BAG was also associated with reduced hippocampus-DMN connectivity.

CONCLUSIONS: An amyloid-informed multimodal BAG model captures convergent AD-related pathology, biomarker alterations, and cognitive vulnerability beyond amyloid burden alone, supporting its value for individualized risk s2tratification and prevention-focused assessment.

PMID:41653882 | DOI:10.1016/j.tjpad.2026.100501

Neuroimage Clin. 2026 Jan 26;49:103956. doi: 10.1016/j.nicl.2026.103956. Online ahead of print.

ABSTRACT

Mild traumatic brain injury (mTBI) is a prevalent condition with symptoms spanning physical, psychological, cognitive, and sleep domains. Altered functional brain networks have been implicated in mTBI, but the relationship between these network changes and post-concussive symptoms remains poorly understood. This study is a systematic scoping review, adhering to PRISMA-ScR guidelines, assessing current literature on the association between brain network dysfunction and mTBI-related symptoms. Searches across ProQuest, Web of Science, and PubMed yielded 41 studies for full review, with most (n = 39) employing resting-state functional magnetic resonance imaging (rs-fMRI) to examine brain networks. The default mode network (DMN) was a primary focus, with studies reporting heterogeneous findings of increased and decreased connectivity both within and outside this network. Over 85% of studies used mTBI-specific symptom measures, and 50% employed detailed questionnaires for emotional and physical symptom assessment. Of these, 23 studies identified significant correlations between symptom scores and network connectivity. However, methodological inconsistencies, including variable analytic approaches, highlight the need for standardization in this field. Key areas for future research include incorporating multimodal imaging techniques, conducting longitudinal studies or extending recruitment time points, and stratifying analyses by sex to optimise identification of connectivity changes. Addressing these gaps is crucial for advancing our understanding of functional network alterations in mTBI and their clinical implications, ultimately supporting improved diagnostic and therapeutic strategies.

PMID:41653507 | DOI:10.1016/j.nicl.2026.103956

J Neurooncol. 2026 Feb 7;176(3):198. doi: 10.1007/s11060-026-05443-4.

NO ABSTRACT

PMID:41653232 | DOI:10.1007/s11060-026-05443-4

Brain Imaging Behav. 2026 Feb 7;20(1):5. doi: 10.1007/s11682-026-01076-3.

ABSTRACT

The present study aimed to investigate the characteristics of abnormal resting-state brain-network connectivity and the reorganization effects of antidepressant drug escitalopram oxalate treatment in patients with major depressive disorder (MDD), and to explore spatial correlations between brain network alterations and gene expression profiles. We employed a longitudinal study design to recruit 113 patients with MDD and 114 healthy controls (HCs) between November 2020 and October 2022. Clinical symptoms were assessed using the 17-item Hamilton Depression Scale (HAMD-17). Resting-state functional magnetic resonance imaging (rs-fMRI) data were acquired using a Siemens 3.0 T MRI scanner. At baseline, patients with MDD exhibited significantly reduced functional connectivity (FC) within the default mode network (DMN) compared to HCs, along with significantly increased FC between the sensorimotor network (SMN) and both the frontoparietal network (FPN) and the salience network (SN) (False Discovery Rate, FDR-corrected, p < 0.05). Following treatment with escitalopram oxalate, MDD patients showed a significant enhancement in intra-DMN connectivity, as well as a significant reduction in SMN-FPN and SMN-SN connectivity (FDR-corrected, p < 0.05). Notably, the degree of increase in intra-DMN connectivity was significantly and negatively correlated with improvement in core depressive symptoms (r = - 0.305, p = 0.026), while the reduction in SMN-DMN connectivity was positively correlated with the alleviation of somatic symptoms (r = 0.362, p = 0.008). Further neuroimaging-guided transcriptomics analysis indicated that these alterations in brain network connectivity were linked to biological pathways, such as the Wnt signaling. In conclusion, our findings demonstrate a multidimensional imbalance in brain network connectivity in MDD and show that antidepressant treatment can partially ameliorate aberrant connectivity patterns. These neural changes are closely associated with symptomatic improvements, offering valuable imaging-based evidence for understanding the neurobiological mechanisms of MDD and informing the development of personalized treatment strategies.

PMID:41653205 | DOI:10.1007/s11682-026-01076-3

Res Sq [Preprint]. 2026 Jan 26:rs.3.rs-8321466. doi: 10.21203/rs.3.rs-8321466/v2.

ABSTRACT

BACKGROUND: Transcranial magnetic stimulation (TMS) is a non-invasive neuromodulation therapy that is applied across psychiatric conditions to modulate specific neural circuits and improve clinical symptoms. While functional magnetic resonance imaging (fMRI)-guided personalized TMS targets are increasingly used, there are critical unresolved methodological, neurobiological, and clinical questions. Addressing topographic variability, stability, and associations with clinical outcomes is essential for advancing clinical development and scalable precision neuromodulation.

METHODS: A precision neurocircuitry-based fMRI-guided TMS approach was developed to treat disorders of the amygdala. In a randomized clinical trial for posttraumatic stress disorder (PTSD; n=50), topographic variability and stability of patient-specific right dorsolateral prefrontal cortex (rDLPFC) targets with the strongest functional connectivity to the right amygdala were analyzed.

RESULTS: There was significant target variability between participants and between targeting methods, but target stability was observed after engaging the amygdala circuitry with behavioral threat-related tasks. Target topography did not change after 20 sessions of sham TMS. However, after active TMS (1Hz, 36,000 pulses) target topography was significantly different. A larger change in the medial-anterior direction correlated with greater PTSD symptom improvement.

CONCLUSIONS: Target variability and stability for fMRI-guided TMS of the amygdala circuitry is demonstrated, supporting the use of patient-specific targeting strategies for TMS. A clinical change in PTSD symptoms was associated with greater change in target topography, which suggests neuroplastic adaptations in the targeted networks and a possible treatment-dependent shift towards more medial prefrontal control over amygdala regulation. These findings are important for fMRI-guided precision neuromodulation therapy development, particularly for the amygdala circuitry.

PMID:41646285 | PMC:PMC12869549 | DOI:10.21203/rs.3.rs-8321466/v2

NPJ Aging. 2026 Feb 6. doi: 10.1038/s41514-026-00345-1. Online ahead of print.

ABSTRACT

As individuals age, cortical alterations in brain structure contribute to cognitive decline. However, the specific patterns of age-related changes and their impact on cognition remain poorly understood. This study assessed the effects of aging on individual gray matter similarity networks and compared them to anatomical and functional connectivity networks derived from diffusion-weighted imaging and resting-state fMRI, respectively. Our results showed that gray matter similarity networks outperformed anatomical and functional connectivity in predicting age and cognition, showing the earliest age-related changes across the adult lifespan. These networks also demonstrated greater robustness to individual differences in cognition, behavior, and sex. Notably, age-related changes in gray matter similarity were associated with the brain's underlying cytoarchitecture, being strongest in brain regions from cortical layers II and III. These findings provide a new biological insight into the neural mechanisms of cognitive aging and highlight the potential of individual morphological similarity for capturing complex brain changes across the lifespan.

PMID:41651845 | DOI:10.1038/s41514-026-00345-1

J Affect Disord. 2026 Feb 4:121346. doi: 10.1016/j.jad.2026.121346. Online ahead of print.

ABSTRACT

BACKGROUND: Major depressive disorder (MDD) and Internet addiction (IA) are common and cause significant impairment, yet their relationship remains unclear. This study aims to explore the neurobiological mechanisms of comorbid MDD and IA and to inform clinical interventions.

METHODS: This study recruited 141 first-episode, drug-naïve MDD patients (72 with IA, 69 without) and 61 healthy controls (HC). Clinical assessments included the Hamilton Depression Rating Scale (HAMD) and Internet Addiction Test (IAT). Resting-state fMRI data were acquired using a 3 T Siemens scanner, and fractional amplitude of low-frequency fluctuations (fALFF) was computed with the Data Processing Assistant for Resting-State fMRI (DPARSF) software. Statistical analyses involved ANOVA, MANCOVA, and partial correlation, with multiple comparisons corrected using the FDR and Bonferroni methods.

RESULTS: Compared to HC group, both MDD + IA and MDD groups exhibited common elevations in fALFF within the left superior medial frontal gyrus and right superior frontal gyrus, alongside reductions in the right middle occipital gyrus. Concurrently, group-specific alterations were identified: MDD + IA had higher fALFF in the right inferior frontal gyrus triangular region, while MDD exhibited lower fALFF in the right postcentral gyrus and left inferior temporal gyrus. MDD + IA had significantly higher fALFF in the left inferior parietal lobule than MDD. Furthermore, fALFF in this region was positively correlated with IAT scores.

CONCLUSIONS: MDD with IA is associated with distinct neurological alterations in frontal and parietal regions. The left inferior parietal lobule may serve as a potential neurobiological marker for MDD comorbid with IA, providing a target for future interventions.

PMID:41651243 | DOI:10.1016/j.jad.2026.121346

Biol Psychiatry Cogn Neurosci Neuroimaging. 2026 Feb 4:S2451-9022(26)00027-3. doi: 10.1016/j.bpsc.2026.01.009. Online ahead of print.

ABSTRACT

BACKGROUND: Cognitive symptoms are among the core features of schizophrenia, but their underlying mechanisms remain unclear. Current hypotheses suggest that alterations in the frontal cortex cause network dysfunction, contributing to cognitive symptoms. Growing evidence links reactive astrocytes with cognitive function and the pathophysiology of schizophrenia. We aimed to investigate in vivo reactive astrocyte signals in the dysconnected networks underlying cognitive symptoms in patients with schizophrenia.

METHODS: [18F]THK5351 positron emission tomography (PET) and resting-state functional MRI data were obtained from 32 patients with schizophrenia and 32 age- and sex-matched healthy controls. [18F]THK5351 PET was used to measure monoamine oxidase B, a marker of reactive astrocytes. We performed network analysis to identify dysconnected subnetworks related to cognitive symptoms and examined reactive astrocyte signals in these subnetwork regions.

RESULTS: Patients showed impaired verbal learning (F = 18.97, p < 0.001) and memory (F = 24.31, p <0.001). In patients, reduced left medial orbitofrontal cortex (mOFC)-left dorsolateral prefrontal cortex and left mOFC-right dorsal anterior cingulate cortex connectivity predicted impaired verbal learning (β = 0.45, p = 0.011) and memory (β = 0.56, p = 0.001), respectively. The PET standardized uptake value ratio was greater in the left mOFC in patients than in controls (t = -2.61, p = 0.011).

CONCLUSIONS: We found evidence of increased reactive astrocyte activity in the key region of the dysconnected network underlying cognitive impairments in schizophrenia. These results suggest a potential link between reactive astrocytes in the mOFC and the pathophysiology underlying cognitive symptoms in schizophrenia.

PMID:41651218 | DOI:10.1016/j.bpsc.2026.01.009

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