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Objectives: This study aimed to comprehensively investigate the neuroanatomical alterations associated with idiopathic Ménière's disease (MD) using voxel-based morphometry and surface-based morphometry techniques. The primary objective was to explore nuanced changes in gray matter volume, cortical thickness, fractal dimension, gyrification index, and sulcal depth in MD patients compared with healthy controls (HC). Additionally, we sought to develop a machine learning classification model utilizing these neuroimaging features to effectively discriminate between MD patients and HC., Design: A total of 55 patients diagnosed with unilateral MD and 70 HC were enrolled in this study. Voxel-based morphometry and surface-based morphometry were employed to analyze neuroimaging data and identify structural differences between the two groups. The selected neuroimaging features were used to build a machine learning classification model for distinguishing MD patients from HC., Results: Our analysis revealed significant reductions in gray matter volume in MD patients, particularly in frontal and cingulate gyri. Distinctive patterns of alterations in cortical thickness were observed in brain regions associated with emotional processing and sensory integration. Notably, the machine learning classification model achieved an impressive accuracy of 84% in distinguishing MD patients from HC. The model's precision and recall for MD and HC demonstrated robust performance, resulting in balanced F1-scores. Receiver operating characteristic curve analysis further confirmed the discriminative power of the model, supported by an area under the curve value of 0.92., Conclusions: This comprehensive investigation sheds light on the intricate neuroanatomical alterations in MD. The observed gray matter volume reductions and distinct cortical thickness patterns emphasize the disease's impact on neural structure. The high accuracy of our machine learning classification model underscores its diagnostic potential, providing a promising avenue for identifying MD patients. These findings contribute to our understanding of MD's neural underpinnings and offer insights for further research exploring the functional implications of structural changes., Competing Interests: This work was supported by the National Natural Science Foundation of China (Grant No. 82101231, 81701673, 81670930), and the Hubei Provincial Natural Science Foundation of China (Grant No. 2021CFB547, 2019CFB497). There are no conflicts of interest, financial, or otherwise., (Copyright © 2024 Wolters Kluwer Health, Inc. All rights reserved.)

Olfactory dysfunction is a common non-motor symptom of Parkinson's disease(PD) and may hold valuable insights into the disease's underlying pathophysiology. This study aimed to investigate cortical morphometry alterations in PD patients with severe hyposmia(PD-SH) and mild hyposmia(PD-MH) using surface-based morphometry(SBM) methods. Participants included 36 PD-SH patients, 38 PD-MH patients, and 40 healthy controls(HCs). SBM analysis revealed distinct patterns of cortical alterations in PD-SH and PD-MH patients. PD-MH patients exhibited reduced cortical thickness in the right supramarginal gyrus, while PD-SH patients showed widespread cortical thinning in regions including the bilateral pericalcarine cortex, bilateral lingual gyrus, left inferior parietal cortex, left lateral occipital cortex, right pars triangularis, right cuneus, and right superior parietal cortex. Moreover, PD-SH patients displayed reduced cortical thickness in the right precuneus compared to PD-MH patients. Fractal dimension analysis indicated increased cortical complexity in PD-MH patients' right superior temporal cortex and right supramarginal gyrus, as well as decreased complexity in the bilateral postcentral cortex, left superior parietal cortex, and right precentral cortex. Similarly, cortical gyrification index and cortical sulcal depth exhibited heterogeneous patterns of changes in PD-SH and PD-MH patients compared to HCs. These findings underscore the multifaceted nature of olfactory impairment in PD, with distinct patterns of cortical morphometry alterations associated with different degrees of hyposmia. The observed discrepancies in brain regions showing alterations reflect the complexity of PD's pathophysiology. These insights contribute to a deeper understanding of olfactory dysfunction in PD and provide potential avenues for early diagnosis and targeted interventions., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier B.V. All rights reserved.)

Background: Patients with delayed endolymphatic hydrops (DEH) often show caloric hypofunction and endolymphatic hydrops (ELH) on gadolinium (Gd) enhanced magnetic resonance imaging (MRI) of the inner ear., Objectives: We aimed to investigate the relationship between the ELH in vivo and caloric results in ipsilateral DEH., Material and Methods: Twelve patients with ipsilateral DEH were included, who underwent delayed MRI following intratympanic Gd application, pure-tone audiometry, caloric test, and video head impulse test (vHIT)., Results: For the affected ears, the overall prevalence of inner ear hydrops was 91.7%, including 75% in the cochlear and 50% in vestibular compartment. For the non-affected ears, the overall prevalence of inner ear hydrops was 25%, including 25% in cochlear and 16.7% in vestibular region. Caloric hypofunction was demonstrated in 75% of the affected ears. No pathologic vHIT were found. Caloric results were in agreement with the radiological evidence of inner ear hydrops on affected and non-affected ears in 9 cases. There was fair concordance between inner ear hydrops and canal paresis abnormality on the affected side., Conclusions and Significance: MRI provides auxiliary evidence of ELH in vivo in the ipsilateral DEH-affected ears. The association between morphological alterations and caloric hypofunction warrants further investigation.

Objectives: To investigate the cerebral structural changes related to venous erectile dysfunction (VED) and the relationship of these changes to clinical symptoms and disorder duration and distinguish patients with VED from healthy controls using a machine learning classification., Methods: 45 VED patients and 50 healthy controls were included. Voxel-based morphometry (VBM), tract-based spatial statistics (TBSS) and correlation analyses of VED patients and clinical variables were performed. The machine learning classification method was adopted to confirm its effectiveness in distinguishing VED patients from healthy controls., Results: Compared to healthy control subjects, VED patients showed significantly decreased cortical volumes in the left postcentral gyrus and precentral gyrus, while only the right middle temporal gyrus showed a significant increase in cortical volume. Increased axial diffusivity (AD), radial diffusivity (RD) and mean diffusivity (MD) values were observed in widespread brain regions. Certain regions of these alterations related to VED patients showed significant correlations with clinical symptoms and disorder durations. Machine learning analyses discriminated patients from controls with overall accuracy 96.7%, sensitivity 93.3% and specificity 99.0%., Conclusions: Cortical volume and white matter (WM) microstructural changes were observed in VED patients, and showed significant correlations with clinical symptoms and dysfunction durations. Various DTI-derived indices of some brain regions could be regarded as reliable discriminating features between VED patients and healthy control subjects, as shown by machine learning analyses., Key Points: • Multimodal magnetic resonance imaging helps clinicians to assess patients with VED. • VED patients show cerebral structural alterations related to their clinical symptoms. • Machine learning analyses discriminated VED patients from controls with an excellent performance. • Machine learning classification provided a preliminary demonstration of DTI's clinical use.

In the clinical treatment of Alzheimer's disease, one of the most important tasks is evaluating its severity for diagnosis and therapy. However, traditional testing methods are deficient, such as their susceptibility to subjective factors, incomplete evaluation, low accuracy, or insufficient granularity, resulting in unreliable evaluation scores. To address these issues, we propose an objective dementia severity scale based on MRI (ODSS-MRI) using contrastive learning to automatically evaluate the neurological function of patients. The approach utilizes a deep learning framework and a contrastive learning strategy to mine relevant information from structural magnetic resonance images to obtain the patient's neurological function level score. Given that the model is driven by the patient's whole brain imaging data, but without any possible biased manual intervention or instruction from the physician or patient, it provides a comprehensive and objective evaluation of the patient's neurological function. We conducted experiments on the Alzheimer's disease Neuroimaging Initiative (ADNI) dataset, and the results showed that the proposed ODSS-MRI was correlated with the stages of AD 88.55% better than all existing methods. This demonstrates its efficacy to describe the neurological function changes of patients during AD progression. It also outperformed traditional psychiatric rating scales in discriminating different stages of AD, which is indicative of its superiority for neurological function evaluation. [ABSTRACT FROM AUTHOR]

Objective: To evaluate the predictive value of gadoxetic acid-enhanced MRI features (focused on Liver Imaging Reporting and Data System (LI-RADS) v2018 features and non-LI-RADS imaging features) for microvascular invasion (MVI) of hepatocellular carcinoma (HCC). Methods: From October 2018 to December 2020, 134 patients who underwent gadoxetic acid-enhanced MRI with a pathological diagnosis of HCC after hepatectomy were enrolled in this retrospective study. Two radiologists assessed the pre-hepatectomy LI-RADS v2018 imaging features and non-LI-RADS features to identify independent predictors of MVI of HCC with a logistic regression model. Results: Four MRI features were found to be independent predictors of MVI: corona enhancement [odds ratio (OR) 5.787; 95% confidence interval (CI) 1.180, 28.369; p = 0.030], mosaic architecture (OR 7.097; 95% CI 1.299, 38.783; p = 0.024), nonsmooth tumor margin (OR 13.131; 95% CI 3.950, 43.649; p < 0.001), and peritumoral hypointensity on hepatobiliary phase (HBP) (OR 33.123; 95% CI 2.897, 378.688; p = 0.005). When one of four imaging features was present, the sensitivity was 93.2% (41/44), and the specificity was 71.1% (64/90). Conclusion: The four imaging features including corona enhancement, mosaic architecture, nonsmooth tumor margin, and peritumoral hypointensity on HBP can be used as preoperative imaging biomarkers for predicting MVI in patients at high risk for HCC. When one of the four imaging features is present, MVI can be predicted with a sensitivity > 90%. [ABSTRACT FROM AUTHOR]

The aim of our study was to investigate differences in whole brain connectivity at different levels between drug-naïve individuals with early Parkinson's disease (PD) and healthy controls (HCs). Resting-state functional magnetic resonance imaging data were collected from 47 patients with early-stage, drug-naïve PD and 50 HCs. Functional brain connectivity was analyzed at the integrity, network, and edge levels; UPDRS-III, MMSE, MOCA, HAMA, and HAMD scores, reflecting the symptoms of PD, were collected for further regression analysis. Compared with age-matched HCs, reduced functional connectivity were mainly observed in the visual (VSN), somatomotor (SMN), limbic (LBN), and deep gray matter networks (DGN) at integrity level [ p < 0.05, false discovery rate (FDR) corrected]. Intra-network analysis indicated decreased functional connectivity in DGN, SMN, LBN, and ventral attention networks (VAN). Inter-network analysis indicated reduced functional connectivity in nine pairs of resting-state networks. At the edge level, the LBN was the center of abnormal functional connectivity (p < 0.05, FDR corrected). MOCA score was associated with the intra-network functional connectivity strength (FC) of the DGN, and inter-network FC of the DGN-VAN. HAMA and HAMD scores were associated with the FC of the SMN and DGN, and either the LBN or VAN, respectively. We demonstrated variations in whole brain connections of drug-naïve patients with early PD. Major changes involved the SMN, DGN, LBN, and VSN, which may be relevant to symptoms of early PD. Additionally, our results support PD as a disconnection syndrome. [ABSTRACT FROM AUTHOR]

To assess gadoxetic acid (Gd-EOB-DTPA) and diffusion-weighted imaging (DWI) value in classification (benign vs. malignant) and characterization of solid focal liver lesions (SFLLs) and impact on confidence in diagnosis. A total of 195 lesions (46 hepatocellular carcinomas [HCCs], 45 metastases, 32 adenomas, 37 focal nodular hyperplasias [FNHs] and 35 hemangiomas) were retrospectively evaluated in 93 patients. Three imaging datasets were compared: DWI/ apparent diffusion coefficient (ADC) (set A), Gd-EOB-DTPA (set B) and combination of both (set C). Two radiologists (R) independently classified (on a five-point ordinal scale) and characterized each lesion. The accuracy in classification and characterization was compared, and the diagnostic confidence was assessed. The classification accuracy on set A, B and C was 86.2%, 91.3% and 91.8% (R1), and 84.6%, 91.8% and 93.3% (R2); and characterization accuracy was 67.2%, 88.2% and 87.7% (R1), and 60.5%, 88.2% and 85.6% (R2). Classification by reader 1 showed no significant difference between set A and B (p=.09). For both readers, there was a significant difference between set A and C in both classification and characterization (all p <.05), but no significant difference between set B and C in neither classification nor characterization. No significant difference between the three datasets in classification and characterization of hemangiomas (all p >.05). The diagnostic confidence of the readers has increased progressively from set A to Set C (all p <.01). DWI may suggest benignity or malignancy of solid liver lesions, while Gd-EOB-DTPA-enhanced imaging remains superior in lesions characterization and the combination of both increases the diagnostic confidence. DWI is very helpful in the diagnosis of hepatic hemangiomas. [ABSTRACT FROM AUTHOR]

The brain is a complex high-order system. Body movements or mental activities are both dependent on the transmission of information among billions of neurons. However, potential patterns are hardly discoverable due to the high dimensionality in neural signals. Previous studies have identified rotary trajectories in rhythm and nonrhythm movements when projecting the neural electrical signals into a two-dimensional space. However, it is unclear how well this analogy holds at the resting state. Given the low-frequency fluctuations noted during spontaneous neural activities using functional magnetic resonance imaging (fMRI), it is natural to hypothesize that the neural response at resting state also shows a periodic trajectory. In this study, we explored the potential patterns in resting state fMRI data at four frequency bands (slow 2–slow 5) on two cohorts, one of which consisted of young and elderly adults and the other of patients with Alzheimer’s disease and normal controls (NC). The jPCA algorithm was applied to reduce the high-dimensional BOLD signal into a two-dimensional space for visualization of the trajectory. The results indicated that the “resting state” is a basic state showing an inherent dynamic pattern with a low frequency and long period during normal aging, with changes appearing in the rotary period at the slow 4 frequency band (0.027–0.073 Hz) during the pathological process of Alzheimer’s disease (AD). These findings expand the original understanding that neural signals can rotate themselves and that motor executive signals consist of neural signals. Meanwhile, the rotary period at band slow 4 may be a physiological marker for AD, and studies of this frequency band may be useful for understanding the potential pathophysiology of AD and ultimately facilitate characterization and auxiliary diagnosis of AD. [ABSTRACT FROM AUTHOR]

Medial temporal lobe epilepsy (mTLE) is a system-level disease characterized by aberrant neuronal synchronization and widespread alterations in function. Previous studies have focused on the amplitude analysis of the blood oxygenation level-dependent (BOLD) signals to reveal the aberrant alterations in mTLE. However, these methods did not work well in the cases where the amplitudes of two oscillations are correlated but the underlying oscillations are neither phase coherent nor frequency consistent. To address this problem, we investigated the differences of frequency specificity between patients with mTLE and healthy controls using the extreme-point symmetric mode decomposition (ESMD) method. In this method, the BOLD signals were decomposed into a set of intrinsic mode functions (IMFs) and the instantaneous frequency of each IMF was calculated using the direct interpolation strategy. The intrinsic frequency (denoted as Freq) for every voxel was obtained by the weighted sum of the instantaneous frequencies of all the IMFs. The Freq was used as an index to evaluate the altered frequency specificity of 41 patients with mTLE (17 right-side, 24 left-side) and 24 healthy control subjects. The results show that the peak of frequency distribution curve for the patients moves towards the higher frequency than that for the healthy controls. Compared with the healthy control group, the patients with left mTLE demonstrate higher Freq in the default mode network, middle frontal gyrus, insula, middle temporal gyrus and calcarine gyrus; the patients with right mTLE demonstrate higher Freq in the precuneus and occipital lobe. For the three groups, the distinct frequency distribution appears in the left and right hippocampus due to the hippocampal structural and functional asymmetries. The preliminary results imply that the frequency-specific correlated oscillations in the distributed brain regions can provide information about the nature of diseases affecting the brain and the alterations of frequency specificity are associated with the pathological characteristics of mTLE. [ABSTRACT FROM AUTHOR]

The key components of insight include breaking mental sets and forming the novel, task-related associations. The majority of researchers have agreed that the anterior cingulate cortex may mediate processes of breaking one’s mental set, while the exact neural correlates of forming novel associations are still debatable. In the present study, we used a paradigm of answer selection to explore brain activations of insight by using event-related functional magnetic resonance imaging during solving Chinese ‘chengyu’ (in Chinese pinyin) riddles. Based on the participant’s choice, the trials were classified into the insight and non-insight conditions. Both stimulus-locked and response-locked analyses are conducted to detect the neural activity corresponding to the early and late periods of insight solution, respectively. Our data indicate that the early period of insight solution shows more activation in the middle temporal gyrus, the middle frontal gyrus and the anterior cingulate cortex. These activities might be associated to the extensive semantic processing, as well as detecting and resolving cognitive conflicts. In contrast, the late period of insight solution produced increased activities in the hippocampus and the amygdala, possibly reflecting the forming of novel association and the concomitant “Aha” feeling. Our study supports the key role of hippocampus in forming novel associations, and indicates a dynamic neural network during insight solution. [ABSTRACT FROM AUTHOR]

Background: Resting-state functional magnetic resonance imaging has been widely used for the assessment of brain functional network, yet with inconsistent results. The present study aimed to investigate intranetwork and internetwork connectivity differences between patients with major depressive disorder (MDD) and healthy controls at the integrity, network and edge levels of 8 well-defined resting state networks.Methods: Thirty patients with MDD and sixty-three healthy control subjects were recruited in this study.Results: Compared with healthy controls, patients with MDD showed increased node degree in the right amygdala and putamen, increased connectivity strength in the deep gray matter network (DGN) and increased functional connectivity in intranetwork and internetwork. Meanwhile, MDD showed decreased connectivity strength in visual network-DGN pair.Limitations: The sample size was small, and all patients in this study were of Asian ethnicity, especially Han individuals.Conclusions: These findings demonstrate that MDD cases and healthy controls may have divergent intranetwork and internetwork connectivity at an early stage without confounding influence of medication. These differences may underlie cognitive and behavioral alterations in patients with MDD. And these differences may help with the discrimination of patients and healthy people at an early stage of MDD. More studies in the future are warranted to assist in the diagnosis of this burdensome disease. [ABSTRACT FROM AUTHOR]