Olfactory adaptation, the progressive reduction of neural responses to repeated odor stimulation, is closely linked to cognitive function and is altered in Alzheimer's disease (AD). However, its evolution across biomarker-defined stages and relationship with plasma p-tau217 remain unclear. We studied 168 participants classified by plasma p-tau217: cognitively normal (p-tau217-NC, n = 37; p-tau217+NC, n = 8), subjective cognitive decline (p-tau217-SCD, n = 57; p-tau217+SCD, n = 16), and mild cognitive impairment (p-tau217-MCI, n = 40; p-tau217+MCI, n = 10). Odor-induced fMRI with four concentrations (0.032%, 0.1%, 0.32%, 1.0%) presented in a fixed ascending order, although concentration effects could not be fully separated from time-related factors and other confounders, to assess activation in the primary olfactory cortex (POC), hippocampus (HP), and parietal lobe (PL). Receiver operating characteristic (ROC) analyses were performed using logistic regression models. In NC groups, adaptation emerged at 0.1% and 0.32% odor conditions. In p-tau217-SCD, POC adaptation was delayed to 1.0% odor condition, HP was largely preserved, and PL was dysregulated. p-tau217+SCD and MCI groups showed delayed and dysregulated adaptation across all regions. Odor adptations were associated with plasma p-tau217 levels and olfactory memory (p_unc 〈 0.05, p_FDR 〉 0.05). Furthermore, plasma p-tau217 partially mediated the relationship between adaptation-related alterations and olfactory memory. ROC analyses indicated that olfactory adaptation may distinguished individuals across disease stages, require further confirmation in independent cohorts. These findings reveal that impaired olfactory adaptation may represent an early signature associated with AD continuum, particularly in SCD and p-tau217-positive stages.

AIMS:Metabolic dysfunction-associated steatotic liver disease (MASLD) is increasingly linked to cognitive decline, yet the hepatic factors associated with imaging-derived neurovascular coupling (NVC) remain unclear. This study aimed to investigate whether liver stiffness or liver fat content was more closely associated with resting-state CBF-ReHo surrogate measures. MATERIALS AND METHODS:A total of 130 participants including 98 MASLD patients and 32 age- and education-matched healthy controls (HCs) underwent clinical assessment, neuropsychological testing, and multi-modal MRI. Liver stiffness and fat content were quantified using MR elastography (MRE) and MRI-proton density fat fraction (PDFF). Imaging-derived NVC was assessed using global cerebral blood flow (CBF)-regional homogeneity (ReHo) coupling and voxel-wise CBF/ReHo ratios, interpreted as resting-state surrogates rather than direct stimulus-evoked NVC. Multivariable regression and exploratory mediation analyses were employed. RESULTS:Compared to HCs and patients with lower liver stiffness (MASLD_low), those with higher liver stiffness (MASLD_high) showed reduced global CBF-ReHo coupling and altered CBF/ReHo ratios, primarily localized to the bilateral superior temporal pole/superior temporal gyrus (TPOsup). In multivariable regression, liver stiffness remained independently associated with TPOsup CBF/ReHo ratios (p < 0.001). Exploratory mediation analysis showed a statistically significant indirect association between hepatocellular injury markers and TPOsup CBF/ReHo ratios involving MRE-derived liver stiffness (95% CI: 0.0795-0.2790). CONCLUSIONS:Within this cohort and the observed PDFF range, MRE-derived liver stiffness was more closely associated with imaging-derived NVC surrogate measures than liver fat content in MASLD. These findings are hypothesis-generating and require validation in longitudinal studies.

Background:White matter hyperintensity (WMH) has been reported to be associated with brain structure changes and Alzheimer's disease (AD) pathology in the aging process. This study sought to explore the underlying mechanisms linking cerebrovascular pathology, structural brain changes, and AD pathology in the aging process. Methods:The routine magnetic resonance images of 218 cognitively normal elderly individuals who underwent venous blood sampling were retrospectively collected. The Fazekas score was used to stratify the cohort into mild (Fazekas scores of 0-1, n=113) and severe (Fazekas scores of 2-3, n=105) WMH groups. All the three-dimensional (3D) T1-weighted (T1W) images, including the original 3D T1W images and the 3D T1W images reconstructed from two-dimensional (2D) diagnostic images, were processed with FreeSurfer to determine the cortical thickness and subcortical nucleus volumes. The plasma amyloid-beta (Aβ)42 and phosphorylated tau (p-Tau) 181 levels were measured by enzyme-linked immunosorbent assay (ELISA). The cerebral small vessel disease (CSVD)-related imaging markers were assessed manually. Group comparisons of brain structures were performed using general linear models (GLMs). Partial correlation analyses were conducted to assess the associations between plasma Aβ42/p-Tau 181 and the subcortical volumes. A mediation analysis was conducted to evaluate the mediating role of the WMH burden in the relationship between plasma biomarker levels and brain structure. Results:The participants with severe WMH were older (P<0.001) and exhibited higher plasma p-Tau 181 (P<0.001) than those in the mild WMH group, but no significant difference in plasma Aβ42 was found (P=0.065). Based on the original 3D T1W images only, the left caudate nucleus (P=0.042) was enlarged in the participants with severe WMH. Based on all the 3D T1W images, the plasma p-Tau 181 levels were found to be positively correlated with the Fazekas scores (r=0.165, P=0.015). A significant interaction was observed between age and groups in terms of the left caudate volume (β=1.288, P=0.047). More importantly, the Fazekas scores were found to partially mediate the relationship between the p-Tau 181 levels and left caudate volumes (indirect effect =1.761, P=0.035), accounting for 23.0% of the total effect. Conclusions:Severe WMH is associated with caudate nucleus enlargement. WMH may partially mediate the association between elevated plasma p-Tau 181 and caudate nucleus enlargement, suggesting a mixed pathology in the aging process of the brain, and highlighting the importance of early vascular risk control.

Spatial and temporal autocorrelation, as low-dimensional statistical properties, account for a substantial portion of variance in complex functional brain network topology, thereby providing a method for understanding the architecture of brain functional organization in specific populations. In this study, we investigated lifelong premature ejaculation (LPE) by analyzing spatiotemporal autocorrelation and observed increased temporal autocorrelation in the right thalamus, left prefrontal cortex, and left somatomotor cortex, which may reflect abnormal neural signal persistence. Seed-based functional connectivity (FC) analysis further indicated increased FC in the right inferior parietal lobule, left anterior cingulate cortex, and right precuneus, alongside decreased FC in the right superior occipital gyrus. These FC alterations showed significant correlations with spatial autocorrelation metrics. FC in the right precuneus was positively associated with ejaculatory latency and patient-reported control, and negatively correlated with clinical scores. These disrupted spatiotemporal dynamics within prefrontal-somatosensory and occipital-limbic networks suggest that autocorrelation patterns may represent a promising neural correlate of LPE, offering new insights into its underlying neural mechanisms.

Plasma p-tau217/Aβ42 accurately captures the systemic molecular risk of Alzheimer’s disease (AD) but lacks the spatial resolution necessary to predict individualized clinical trajectories. Here, we combined plasma biomarker stratification with normative connectome mapping to identify macroscale neurodegenerative epicenters and developed a personalized prognostic tool, the Network Vulnerability Index (NVI). Across the Alzheimer’s Disease Neuroimaging Initiative and independent China ADNI cohorts, plasma p-tau217/Aβ42-positive individuals exhibited highly reproducible epicenters tightly anchored to the default mode network and limbic axis. Multiscale analyses revealed that this spatial vulnerability aligned with transcriptomic signatures of synaptic and mitochondrial dysfunction, monoaminergic receptor density gradients, and memory-related cognitive domains. Longitudinally, baseline epicenter centrality strictly dictated future localized atrophy rates. To translate these group-level topological constraints into a personalized prognostic metric, we utilized least absolute shrinkage and selection operator regression to formulate the NVI. Cross-sectionally, the NVI robustly tracked progressive tau-positron emission tomography accumulation (meta-temporal r = 0.547) and hippocampal atrophy (r = −0.455). Crucially, the NVI demonstrated robust, stage-dependent prognostic utility. When evaluated across the continuous disease spectrum, incorporating the NVI into a fully adjusted baseline model comprising plasma p-tau217/Aβ42 and APOE-ε4, and clinical scores significantly improved the prediction of conversion from mild cognitive impairment to dementia (hazard ratio = 1.47, P = 0.004), providing essential incremental prognostic value. Collectively, our spatially contextualized framework demonstrates that mapping systemic molecular risk onto structural network vulnerability supports a highly scalable “plasma pre-screen plus standard MRI” triage pathway for precision staging in Alzheimer’s disease.

Neuroinflammation is a key factor contributing to cognitive decline in Alzheimer’s disease (AD). This study aims to investigate the mechanistic associations among neuroinflammation, glymphatic dysfunction, tau pathology, and cognitive decline in AD spectrum. The study included 355 participants from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) and a supportive cohort of 59 individuals from Wuhan Union Hospital (WHUH). Tau pathology was quantified using 18F-AV1451 positron emission tomography (PET). Glymphatic function was estimated through diffusion tensor image analysis along the perivascular space (DTI-ALPS). Neuroinflammation was assessed via plasma glial fibrillary acidic protein (GFAP) in two cohorts and translocator protein (TSPO) PET imaging with 18F-DPA-714 in supportive cohort. Correlation analyses and mediation models were employed to evaluate the directional relationships among tau deposition, inflammation, glymphatic function, and cognition. Higher levels of inflammation were significantly associated with lower DTI-ALPS index (β = −0.171, P = 0.046), which in turn was associated with higher tau burden (β = 0.162, P = 0.010). Path analysis revealed significant indirect associations linking neuroinflammation to cognitive performance through glymphatic dysfunction and tau pathology, with total indirect effects of − 0.165 (95

Recent expansion of metabolomic coverage opens unparalleled avenues to unveil new biomarkers of Alzheimer's disease (AD). We included 635 participants from the Alzheimer's Disease Neuroimaging Initiative (ADNI) with baseline clinical diagnoses of CN (23.15%), MCI (56.69%) and AD dementia (20.16%). All included participants not only underwent comprehensive CSF metabolomic assays but also had complete CSF Aβ42. 419 A+ (including 58 preclinical AD, 241 MCI due to AD and 120 dementia due to AD) participants were biologically defined as AD. 172 CN participants from the Parkinson's Progression Markers Initiative (PPMI) cohort were also enrolled. Among 348 cerebrospinal fluid (CSF) metabolite analysed from the ADNI database, the combination of N-acetylthreonine and choline performed best in diagnosing both biologically (AUC = 0.961) and clinically (AUC = 0.833) defined AD. Four- ( N -acetylthreonine, choline, N-acetylserine and 2-O-methylascorbic acid) and four- ( N -acetylthreonine, choline, O-sulfo-L-tyrosine and 3-amino-2-piperidone) metabolite panels greatly improved the accuracy to 0.987 and 0.871, respectively. Their superior performance was validated in an independent external cohort. Moreover, they effectively predicted the clinical progression to AD dementia and were strongly associated with AD core biomarkers and cognitive decline. Our findings revealed promising high-performance biomarkers for AD diagnosis and prediction.

Migraine is increasingly acknowledged as a disorder of large-scale brain network hierarchy rather than a merely focal dysfunction. However, the neural substrates driving the transition from episodic migraine (EM) to chronic migraine (CM)-a key pathological process in migraine chronification, remain poorly delineated. Using 5.0 T MRI, we examined 122 participants (30 healthy controls [HC], 66 EM, and 16 CM). Morphometric INverse Divergence (MIND)-based structural similarity networks and resting-state functional connectivity (FC) matrices were constructed from 3D T1 and rs-fMRI data parcellated with the Schaefer-400 atlas. Diffusion map embedding was applied to derive low-dimensional gradients indexing macroscale cortical hierarchy. Group differences in the principal gradient (G1) were assessed using one-way ANOVA with FDR-corrected post hoc tests. Partial Spearman correlations were used to link regional G1 values with attack frequency, disease duration, and anxiety (HAMA), depression (HAMD), and sleep quality (PSQI) scores. Both MIND- and FC-derived gradients revealed a progressive flattening of the G1 along the HC-EM-CM continuum. These alterations were most prominent in the visual and somatomotor networks. Across the top overlapping regions, lower G1 values, reflecting diminished hierarchical segregation, were consistently associated with higher HAMA and HAMD scores and poorer sleep quality on the PSQI after FDR correction, particularly within visual–somatomotor network. 5.0 T MRI demonstrated a consistent, cross-modal disruption of the cortical hierarchy linked to migraine chronification, emphasizing the reduced hierarchical differentiation of visual-sensorimotor integration and its coupling with affective and sleep dysfunction. Gradient-based metrics may provide promising imaging markers for tracking disease progression.

Olfactory impairment was assessed in akinetic-rigid (PDAR) and tremor-predominant (PDT) subtypes of Parkinson’s disease (PD), classified based on motor symptoms. Seventeen PDAR, fifteen PDT, and twenty-four cognitively normal (CN) participants completed the University of Pennsylvania Smell Identification Test (UPSIT). Groups were well-matched for age and demographic variables, with cognitive performance statistically controlled. Resting-state fMRI (rs-fMRI) and seed-based functional connectivity (FC) analyses were conducted to characterize olfactory network (ON) connectivity across groups. UPSIT scores were significantly lower in PDAR compared to PDT. Consistently, ON FC values were reduced in PDAR relative to both PDT and CN. FC of the primary olfactory cortex (POC) significantly differed between CN and the PD subtypes. Furthermore, connectivity in the orbitofrontal cortex and insula showed significant differences between PDAR and PDT, as well as between PDAR and CN. Notably, ON FC between the left hippocampus and the posterior cingulate cortex (PCC) also differed significantly between PDAR and PDT. These findings reveal distinct ON FC patterns across PDAR and PDT subtypes. Variations in UPSIT scores suggest that motor symptom subtype is associated with olfactory performance. Moreover, ON connectivity closely paralleled the UPSIT scores, reinforcing a neural basis for olfactory deficits in PD. Given the accelerated motor and cognitive decline often observed in the PDAR, these results support the potential of olfactory impairment as a clinical marker for disease severity.

Glioma is the most common primary malignant brain tumor and preoperative genetic profiling is essential for the management of glioma patients. Our study focused on tumor regions segmentation and predicting the World Health Organization (WHO) grade, isocitrate dehydrogenase (IDH) mutation, and 1p/19q codeletion status using deep learning models on preoperative MRI. To achieve accurate tumor segmentation, we developed an optimal mass transport (OMT) approach to transform irregular MRI brain images into tensors. In addition, we proposed an algebraic preclassification (APC) model utilizing multimode OMT tensor singular value decomposition (SVD) to estimate preclassification probabilities. The fully automated deep learning model named OMT-APC was used for multitask classification. Our study incorporated preoperative brain MRI data from 3,565 glioma patients across 16 datasets spanning Asia, Europe, and America. Among these, 2,551 patients from 5 datasets were used for training and internal validation. In comparison, 1,014 patients from 11 datasets, including 242 patients from The Cancer Genome Atlas (TCGA), were used as independent external test. The OMT segmentation model achieved mean lesion-wise Dice scores of 0.880. The OMT-APC model was evaluated on the TCGA dataset, achieving accuracies of 0.855, 0.917, and 0.809, with AUC scores of 0.845, 0.908, and 0.769 for WHO grade, IDH mutation, and 1p/19q codeletion, respectively, which outperformed the four radiologists in all tasks. These results highlighted the effectiveness of our OMT and tensor SVD-based methods in brain tumor genetic profiling, suggesting promising applications for algebraic and geometric methods in medical image analysis.

Background/Objectives: Identifying pathological distinctions among mild cognitive impairment (MCI) subtypes is important for differentiating dementia. The purpose of this study is to investigate subtype-specific structural alterations in amnestic MCI (aMCI) and non-amnestic MCI (naMCI) and evaluate their potential as imaging biomarkers for subtype classification. Methods: T1 and DTI MRI data from two independent cohorts were analyzed, including a discovery dataset (58 aMCI, 35 naMCI, and 95 NC) and a replication dataset (61 aMCI, 39 naMCI, and 67 NC). Surface-based morphometry and automated fiber quantification (AFQ) were used to examine cortical thickness and white matter microstructure. Mediation models were used to explore the links between brain structure and cognitive outcomes. A logistic regression model was applied to evaluate classification performance. Results: The aMCI exhibited right hippocampal atrophy. In the naMCI, reduced cortical thickness was observed in the right anterior cingulate cortex (rACC) and opercular inferior frontal gyrus, along with increased fractional anisotropy (FA) in the right inferior fronto-occipital fasciculus (IFOF). These alterations were linked to domain-specific cognitive deficits. Moreover, partial mediation effects of IFOF FA values were observed in the link between rACC thickness and cognitive outcomes. Furthermore, these structural alterations effectively distinguished between aMCI and naMCI, showing stable performance across independent datasets (Accuracy = 0.821, AUC = 0.904). Conclusions: Our findings reveal distinct structural alterations across MCI subtypes, providing deeper insight into the heterogeneous mechanisms of dementia and supporting the potential of imaging markers for the diagnosis of MCI subtypes.

We present a novel multigrid solver framework that significantly advances the efficiency of physical simulation for unstructured meshes. While multi-grid methods theoretically offer linear scaling, their practical implementation for deformable body simulations faces substantial challenges, particularly on GPUs. Our framework achieves up to 6.9× speedup over traditional methods through an innovative combination of matrix-free vertex block Jacobi smoothing with a Full Approximation Scheme (FAS), enabling both piecewise constant and linear Galerkin formulations without the computational burden of dense coarse matrices. Our approach demonstrates superior performance across varying mesh resolutions and material stiffness values, maintaining consistent convergence even under extreme deformations and challenging initial configurations. Comprehensive evaluations against state-of-the-art methods confirm our approach achieves lower simulation error with reduced computational cost, enabling simulation of tetrahedral meshes with over one million vertices at approximately one frame per second on modern GPUs.

Background Tau pathology is closely associated with brain atrophy and cognitive decline, but how it specifically influences local and distant gray matter volume (GMV) and cognitive function remains unclear. Objective This study aims to explore the spatial relationships between tau pathology, GMV and cognition using hybrid positron emission tomography/magnetic resonance imaging (PET/MRI). Methods Twenty amyloid-β (Aβ)-positive Alzheimer's disease (AD) patients, 14 mild cognitive impairment (MCI) patients, and 22 Aβ-negative normal controls (NC) underwent standardized neuropsychological assessments and 18 F-fortaucipir PET/MRI scans. We investigated the associations between regional tau standardized uptake value ratio (SUVR) and GMV in AD signature regions. Mediation analyses were conducted to explore the potential mediating effects of local and distant GMV in the relationship between tau pathology and cognition. Results The study indicated that increased 18 F-fortaucipir SUVR and decreased GMV were related to cognitive performance in MCI and AD patients. Compared to NC group, the number of brain regions with local and distant correlations between GMV and SUVR was greater in AD/MCI group. Mediation analysis revealed that GMV served as a significant mediator between tau pathology and cognition in local regions. Furthermore, distant effects were also observed, with hippocampal atrophy partially mediated the relationship between entorhinal cortex tau pathology and cognition. Meanwhile, medial parietal lobe atrophy partially mediated the relationship between medial temporal lobe tau deposition and cognition. Conclusions Our findings provide an anatomically detailed insight into relationships between tau, GMV and cognition, especially in entorhinal cortex-hippocampus, temporal-parietal lobe cortical circuits.

ABSTRACT Aim This study aimed to examine microvascular lesions and neurodegenerative changes in diabetic retinopathy (DR) compared to type 2 diabetes mellitus (T2DM) without DR (NDR) using structural MRI and to explore their associations with DR. Methods 243 patients with NDR and 122 patients with DR were included. Participants underwent conventional brain MRI scans, clinical measurements, and fundus examinations. Cerebral small vessel disease (CSVD) imaging parameters were obtained using AI‐based software, manually verified, and corrected for accuracy. Volumes of major cortical and subcortical regions representing neurodegeneration were assessed using automated brain segmentation and quantitative techniques. Statistical analysis included T‐test, chi‐square test, Mann–Whitney U test, multivariate analysis of variance (MANCOVA), multivariate logistic regression, area under the receiver operating characteristic curve (AUC), and Delong test. Results DR group exhibited significant differences in 11 CSVD features. Meanwhile, DR showed an atrophy trend in the frontal cortex, occipital cortex, and subcortical gray matter (GM) compared to NDR. After adjustment, DR patients exhibited greater perivascular spaces (PVS) numbers in the parietal lobe (OR = 1.394) and deep brain regions (OR = 1.066), greater dilated perivascular spaces (DPVS) numbers in the left basal ganglia (OR = 2.006), greater small subcortical infarcts (SSI) numbers in the right hemisphere (OR = 3.104), and decreased left frontal PVS (OR = 0.824), total left DPVS (OR = 0.714), and frontal cortex volume (OR = 0.959) compared to NDR. Further, the CSVD model showed a larger AUC (0.823, 95% CI: 0.781–0.866) than the brain atrophy model (AUC = 0.757, 95% CI: 0.706–0.808). Conclusion Microvascular and neurodegeneration are significantly associated with DR. CSVD is a better imaging biomarker for DR than brain atrophy.

BACKGROUND:Brain glymphatic system is thought to play a critical role in the pathogenesis of Alzheimer's disease (AD). OBJECTIVE:To investigate the relationships between glymphatic function and AD-signature region volumes, plasma biomarkers and disease progression in cognitively unimpaired older adults. METHODS:Two datasets comprising a total of 229 cognitively unimpaired older adults were enrolled. Brain glymphatic function was assessed using diffusion tensor imaging along the perivascular space (DTI-ALPS). The associations between the DTI-ALPS index and volumes in AD-signature regions, including the basal forebrain, entorhinal cortex and hippocampus, were evaluated, along with white matter hyperintensity (WMH) volumes. In dataset 1 with plasma biomarkers, the mediation effects of DTI-ALPS index on plasma biomarkers and cognition were examined. In dataset 2 with follow-up data, the baseline DTI-ALPS index was correlated with the annual percent change in volumes of AD-signature regions and WMH. RESULTS:The DTI-ALPS index showed positive correlations with volumes in the basal forebrain, entorhinal cortex and hippocampus, and negative correlations with WMH volumes in both datasets. The DTI-ALPS index negatively associated with plasma phosphorylated tau (ptau) and mediated the relationship between ptau and cognition. The baseline DTI-ALPS index was negatively associated with WMH progression at follow-up. CONCLUSION:Worse glymphatic system function indicates decreased AD-signature region volumes, severe WMH lesions, elevated plasma ptau, and accelerated WMH progression before the occurrence of objective cognitive impairment. Therapeutic methods targeting the glymphatic system may prevent cognitive decline through the clearance of AD pathological proteins and the deceleration of WMH lesions.

Background Individuals with subjective cognitive decline (SCD) are at high risk of preclinical Alzheimer's disease (AD). While olfactory dysfunction is evident in AD and mild cognitive impairment (MCI), its presence and neural mechanism in SCD remain unclear. Objective This study examined functional connectivity (FC) alterations across olfactory networks and their mediating role between olfactory and cognitive functions in SCD and MCI. Methods Eighty SCD, 51 MCI, and 80 normal controls underwent cognitive and olfactory tests and resting-state functional magnetic resonance imaging scanning. Two olfactory networks (primary and advanced), each with six selected spherical regions, were defined. We compared FCs within and between networks, examined correlations with olfactory and cognitive functions, performed mediation analysis, and evaluated classification via receiver operating characteristic curves. Results SCD participants presented increased FCs in key regions with normal olfactory scores, while MCI patients exhibited reduced FCs and olfactory scores. Altered FCs correlated with olfactory performance and mediated the relationship between olfactory and cognitive functions. FC features effectively distinguished SCD from normal controls. Conclusions Increased FCs in SCD indicated a significant compensatory neural mechanism for the disrupted FCs in MCI, leading to an apparent normal olfactory function in SCD participants. Moreover, the findings suggest that olfactory deficits may be associated with cognitive decline rather than solely impaired olfactory sensory processing. As such olfactory deficits could be a proxy for cognitive decline in AD and the altered FCs could aid in the early detection of individuals at high risk for preclinical AD.

Aims This study investigates the relationship between multisensory (visual, somatosensory, and olfactory) dysfunction and cognitive decline in Type 2 diabetes (T2D), with a particular focus on the mediating role of olfactory dysfunction.Methods We used resting-state fMRI to assess seed-based functional connectivity from the primary sensory cortices (visual, somatosensory, and olfactory) and whole-brain regional activity metrics in 152 patients with T2D and 50 controls. A Multisensory Dysfunction Index (MSDI) was constructed to quantify integrated sensory dysfunction, and moderated mediation analysis was performed to examine the impact of sensory complications on cognitive function.Results The MSDI was correlated with sensory complication burden and associated with worse global cognitive performance (Montreal Cognitive Assessment, MoCA). Mediation analysis showed that odour identification mediated the relationship between MSDI and MoCA in T2D. This indirect effect was absent in diabetic peripheral neuropathy (DPN)+ individuals but remained significant in DPN- individuals. Additionally, olfactory dysfunction had both direct and indirect effects on cognition in DPN- patients.Conclusions Our findings highlight the central role of olfactory dysfunction in linking multisensory impairment to cognitive decline in T2D. The results emphasize the need for personalized management strategies based on sensory complications and suggest that preserving sensory network integrity may help maintain olfactory and cognitive health in T2D.

Background:Mild cognitive impairment (MCI) is associated with an increased risk of dementia in older adults. Olfactory impairment may indicate prodromal dementia, yet its underlying mechanisms are not fully understood. This study aimed to investigate the alterations in functional connectivity (FC) of odor-induced olfactory neural circuits in MCI patients. Methods:The study included 39 MCI patients and 42 normal controls (NCs). All subjects underwent cognitive assessments, olfactory behavior tests, and odor-based functional magnetic resonance imaging (fMRI). Differences in FC within olfactory circuits were analyzed using the generalized psychophysiological interaction (gPPI) method. Results:Mild cognitive impairment patients showed significant cognitive deficits, including lower scores on the Mini-Mental State Examination (MMSE) and Montreal Cognitive Assessment (MoCA), alongside impairments in episodic memory, visuospatial memory, executive function, language, attention, olfactory threshold, and total olfactory function. Compared to NCs, MCI patients exhibited reduced activation in the bilateral primary olfactory cortex (bPOC) during olfactory stimulation. Odor-induced bPOC activation correlated with olfactory thresholds across the cohort. During odor stimulation, MCI patients showed increased FC from the bPOC to the right anterior frontal lobe, particularly the middle frontal gyrus (MFG) and superior frontal gyrus (SFG). Conversely, FC from the right anterior frontal lobe to the medial temporal cortex, including the fusiform and parahippocampal gyri, was reduced in MCI patients. Increased FC from the bPOC to the right SFG/MFG negatively correlated with episodic memory, while decreased FC to the right fusiform/parahippocampal gyri positively correlated with attention, language ability, and olfactory identification. Conclusion:This study indicates that impaired FC within the primary olfactory cortex (POC)-anterior frontal cortex-medial temporal cortex circuit is a sensitive neuroimaging marker for early MCI identification. The primary dysfunction appears in the POC, suggesting that FC alterations from this region may provide novel diagnostic and therapeutic avenues for early intervention.

Introduction:Abnormal spontaneous neural activity has been detected in the brain of anejaculation patients. It has been confirmed that anejaculation may be associated with altered regional activation in the brain. Aim:This study aimed to explore the changes of grey matter in the brain of anejaculation patients. Methods:Structural magnetic resonance imaging data were collected from 20 primary intravaginal anejaculation (PIAJ) patients and 16 matched healthy controls (HCs). The 3D high resolution T1 weighted images were processed to calculate the grey matter volume and density by the method of voxel-based morphometry analysis. Outcomes:Differences of grey matter volume and density were compared between groups, and receiver operating characteristic curve was performed to evaluate the values of altered brain regions in distinguishing PIAJ from HCs. Results:PIAJ patients showed increased grey matter volume in the right supplementary motor area, right inferior temporal gyrus, right superior and inferior occipital gyrus, part of the left precuneus and decreased grey matter grey matter in another part of the left precuneus. In addition, increased grey matter density of the right supplementary motor area, right postcentral gyrus, right inferior temporal gyrus, left middle frontal gyrus, left inferior temporal gyrus and decreased grey matter density of the left precuneus were revealed in PIAJ patients. Both abnormal grey matter volume and altered grey matter density exhibited satisfactory performance in distinguishing PIAJ from HCs. Clinical Implications:These findings suggested that increased microstructural changes of grey matter might be associated with the increased inhibiting effect of the brain on ejaculation. Strengths & Limitations:This study provided new insights into the pathological mechanism underlying PIAJ, These findings are exploratory and that future longitudinal or comparative studies (eg, PIAJ vs. premature ejaculation) will be necessary to clarify whether these changes are predisposing, consequential, or potentially modifiable. Conclusion:These findings indicated that patients suffering from PIAJ might exhibit abnormal grey matter volume and density in some brain regions, which might be linked to the inability to ejaculate intravaginally. PIAJ patient showed more increased indicators of grey matter when compared with premature ejaculation patients, which often had decreased brain function.