Altered mean apparent propagator-based microstructure and the corresponding functional connectivity of the parahippocampus and thalamus in Crohn's disease.

Crohn's disease (CD) is a chronic and relapsing inflammatory bowel disorder that has been shown to generate neurological impairments, which has the potential to signify disease activity in an underlying neurological manner. The objective of this study was to investigate the abnormalities of brain microstructure and the corresponding functional connectivity (FC) in patients with CD, as well as their associations with disease condition. Twenty-two patients with CD and 22 age-, gender-, and education-matched healthy controls (HCs) were enrolled in this study. All subjects underwent mean apparent propagator (MAP)-MRI and resting-state functional magnetic resonance imaging (MRI) (rs-fMRI) data collection. Each patient was evaluated clinically for the condition and duration of the disease. The MAP metrics were extracted and compared between two groups. Pearson's correlation analysis was conducted to determine the relationship between disease characteristics and significantly abnormal MAP metrics in the CD group. Regions of interest (ROIs) for ROI-wise FC analysis were selected based on their correlation with MAP metrics. Results showed that multiple brain regions, including the parahippocampus and thalamus, exhibited statistically significant differences in MAP metrics between CD patients and HCs. Additionally, CD patients exhibited decreased FC between the left parahippocampus and bilateral thalamus, as well as the right parahippocampus and bilateral thalamus. The findings of this work provide preliminary evidence that structural abnormalities in the parahippocampal gyrus (PHG) and thalamus, as well as decreased FC between them, may reflect the degree of inflammatory of the disease and serve as brain biomarkers for evaluating CD activity.

Characterization of white matter over 1-2 years in small vessel disease using MR-based quantitative susceptibility mapping and free-water mapping.

Purpose: The aim of this study was to investigate alterations in white matter lesions (WMLs) and normal-appearing white matter (NAWM) with small vessel disease (SVD) over 1-2 years using quantitative susceptibility mapping (QSM) and free-water (FW) mapping. Methods: Fifty-one SVD patients underwent MRI brain scans and neuropsychological testing both at baseline and follow-up. The main approach for treating these patients is the management of risk factors. Quantitative susceptibility (QS), fractional anisotropy (FA), mean diffusivity (MD), FW, FW-corrected FA (FAT), and FW-corrected MD (MDT) maps within WMLs and NAWM were generated. Furthermore, the JHU-ICBM-DTI label atlas was used as an anatomic guide, and the measurements of the segmented NAWMs were calculated. The average regional values were extracted, and a paired t-test was used to analyze the longitudinal change. Partial correlations were used to assess the relationship between the MRI indices changes (e.g., ΔQSfollowup - baseline/QSbaseline) and the cognitive function changes (e.g., ΔMoCAfollowup - baseline/MoCAbaseline). Results: After SVD risk factor control, no gradual cognitive decline occurred during 1-2 years. However, we still found that the QS values (index of demyelination) increased in the NAWM at follow-up, especially in the NAWM part of the left superior frontal blade (SF), left occipital blade, right uncinate fasciculus, and right corticospinal tract (CST). FW (index of neuroinflammation/edema) analysis revealed that the follow-up group differed from the baseline group in the NAWM part of the right CST and inferior frontal blade (IF). Decreased FAT (index of axonal loss) was observed in the NAWM part of the right SF and IF at follow-up. In addition, the FAT changes in the NAWM part of the right IF were associated with overall cognitive performance changes. In contrast, no significant differences were found in the WMLs. Conclusion: The NAWM was still in the progressive injury process over time, while WMLs remained relatively stable, which supports the notion that SVD is a chronic progressive disease. The process of axonal loss in the NAWM part of the prefrontal lobe might be a biomarker of cognitive changes in the evolution of SVD.

Altered Intra- and Inter-Network Functional Connectivity in Patients With Crohn's Disease: An Independent Component Analysis-Based Resting-State Functional Magnetic Resonance Imaging Study.

Background: Many studies have reported changes in the structure and function of several brain areas in patients with Crohn's disease (CD). However, little is known about whether the possible functional connectivity of resting-state networks (RSNs) is altered in CD patients. Purpose: Aim to investigate the intra- and inter-network alterations between related RSNs in patients with CD and the potential relationships between altered neuroimaging and CD clinical indices. Materials and Methods: In this study, 20 CD patients and 22 age- and sex-matched healthy controls were included. All participants underwent functional magnetic resonance imaging examination. We used independent component analysis (ICA) to explore the changes in RSNs and evaluated functional connectivity between different RSNs using functional network connectivity (FNC) analysis, and Pearson correlation analysis was performed between altered intra- and inter-network functional connectivity and CD clinical index. Results: Six CD-related RSNs were identified via ICA, namely the high visual, prime visual, language, dorsal default mode, posterior insula, and precuneus networks. Compared to healthy controls, patients with CD showed significant changes in prime visual and language networks. Additionally, the functional connectivity (FC) values of the left calcarine within the prime visual network were negatively correlated with CD duration. The inter-alterations showed that a significantly increased FNC existed between the language and dorsal default mode networks. Conclusion: The results showed CD-related changes in brain functional networks. This evidence provides more insights into the pathophysiological mechanisms of brain plasticity in CD.

Decline of anterior cingulate functional network efficiency in first-episode, medication-naïve somatic symptom disorder and its relationship with catastrophizing.

The high prevalence of somatic symptom disorder (SSD) led to cumulative burdens to the medical system. However, the pathogenesis of this disease still remains unclear. Graph theoretical analysis discovered altered network topology across various psychiatric disorders, yet alteration in the topological structure of brain functional network in SSD patients is still unexplored. Catastrophizing is a common cognitive distortion in SSD. We hypothesize that the network topological metrics of SSD should be altered, and should correlate with catastrophizing scales. 32 medication-naïve, first-episode SSD patients and 30 age, gender matched HCs were recruited. The 17-item Hamilton Depression Rating Scale (HAMD-17), Hamilton Anxiety Rating Scale (HAMA) and Cognitive Emotion Regulation Questionnaire (CERQ) were accessed. Functional MRI were scanned and brain functional networks were constructed based on 166 anatomically cerebrum regions from the automated anatomical labeling 3 (AAL3) template. Network topological metrics were calculated and compared between the two groups. Correlation between these metrics and clinical scales were also calculated. Network global efficiency of SSD was significantly lower than that of HC. Nodal global efficiency of the left subgenual anterior cingulate cortex (sgACC) of SSD was significantly lower than that of HC. FCs between the left sgACC and other 21 seed nodes were significantly declined in SSD in comparison with HC. In SSD group, HAMD total score was significantly negatively correlated with the connection between the left medial superior frontal gyrus and the left sgACC. CERQ catastrophizing score was significantly negatively correlated with nodal global efficiency of left sgACC and with the FCs between the left sgACC and other 13 seed nodes. Catastrophizing could reflect the specific sgACC-centered dysfunction of brain network global efficiency of SSD. The left sgACC may be a future treatment target of dealing with catastrophizing, which is a core cognitive distortion of SSD.

The Contribution of White Matter Diffusion and Cortical Perfusion Pathology to Vascular Cognitive Impairment: A Multimode Imaging-Based Machine Learning Study.

Widespread impairments in white matter and cerebrovascular integrity have been consistently implicated in the pathophysiology of patients with small vessel disease (SVD). However, the neural circuit mechanisms that underlie the developing progress of clinical cognitive symptoms remain largely elusive. Here, we conducted cross-modal MRI scanning including diffusion tensor imaging and arterial spin labeling in a cohort of 113 patients with SVD, which included 74 patients with vascular mild cognitive impairment (vMCI) and 39 patients without vMCI symptoms, and hence developed multimode imaging-based machine learning models to identify markers that discriminated SVD subtypes. Diffusion and perfusion features, respectively, extracted from individual white matter and gray matter regions were used to train three sets of classifiers in a nested 10-fold fashion: diffusion-based, perfusion-based, and combined diffusion-perfusion-based classifiers. We found that the diffusion-perfusion combined classifier achieved the highest accuracy of 72.57% with leave-one-out cross-validation, with the diffusion features largely spanning the capsular lateral pathway of the cholinergic tracts, and the perfusion features mainly distributed in the frontal-subcortical-limbic areas. Furthermore, diffusion-based features within vMCI group were associated with performance on executive function tests. We demonstrated the superior accuracy of using diffusion-perfusion combined multimode imaging features for classifying vMCI subtype out of a cohort of patients with SVD. Disruption of white matter integrity might play a critical role in the progression of cognitive impairment in patients with SVD, while malregulation of coritcal perfusion needs further study.

Loss of Integrity of Corpus Callosum White Matter Hyperintensity Penumbra Predicts Cognitive Decline in Patients With Subcortical Vascular Mild Cognitive Impairment.

Loss of white matter (WM) integrity contributes to subcortical vascular mild cognitive impairment (svMCI). Diffusion tensor imaging (DTI) has revealed damage beyond the area of WM hyperintensity (WMH) including in normal-appearing WM (NAWM); however, the functional significance of this observation is unclear. To answer this question, in this study we investigated the relationship between microstructural changes in the WMH penumbra (WMH-P) and cognitive function in patients with svMCI by regional tract-based analysis. A total of 111 patients with svMCI and 72 patients with subcortical ischemic vascular disease (SIVD) without cognitive impairment (controls) underwent DTI and neuropsychological assessment. WMH burden was determined before computing mean values of fractional anisotropy (FA) and mean diffusivity (MD) within WMHs and WMH-Ps. Pearson's partial correlations were used to assess the relationship between measurements showing significant intergroup differences and composite Z-scores representing global cognitive function. Multiple linear regression analysis was carried out to determine the best model for predicting composite Z-scores. We found that WMH burden in the genu, body, and splenium of the corpus callosum (GCC, BCC, and SCC respectively); bilateral anterior, superior, and posterior corona radiata; left sagittal stratum was significantly higher in the svMCI group than in the control group (p < 0.05). The WMH burden of the GCC, BCC, SCC, and bilateral anterior corona radiata was negatively correlated with composite Z-scores. Among diffusion parameters showing significant differences across the 10 WM regions, mean FA values of WMH and WMH-P of the BCC were correlated with composite Z-scores in svMCI patients. The results of the multiple linear regression analysis showed that the FA of WMH-P of the BCC and WMH burden of the SCC and GCC were independent predictors of composite Z-score, with the FA of WMH-P of the BCC making the largest contribution. These findings indicate that disruption of the CC microstructure-especially the WMH-P of the BCC-may contribute to the cognitive deficits associated with SIVD.

Multi-Dimensional Diffusion Tensor Imaging Biomarkers for Cognitive Decline From the Preclinical Stage: A Study of Post-stroke Small Vessel Disease.

Objectives: We aim to investigate whether multi-dimensional diffusion tensor imaging (DTI) measures can sensitively identify different cognitive status of cerebral small vessel disease (CSVD) and to explore the underlying pattern of white matter disruption in CSVD. Methods: Two hundred and two participants were recruited, composed of 99 CSVD patients with mild cognitive impairment (VaMCI) and 60 with no cognitive impairment (NCI) and 43 healthy subjects as normal controls (NC). Full domain neuropsychological tests and diffusion-weighted imaging were performed on each subject. DTI metrics such as fractional anisotropy (FA), mean diffusivity (MD), the skeletonized mean diffusivity (PSMD), and structural brain network measures including network strength, global efficiency (EGlobal), and local efficiency (ELocal) were calculated. Region of interest (ROI) analysis of 42 white matter tracts was performed to examine the regional anatomical white matter disruption for each group. Results: Significant differences of multiple cognitive test scores across all cognitive domains especially processing and executive function existed among the three groups. DTI measures (FA, MD, and PSMD) showed significant group difference with the cognitive status changing. FA and EGlobal showed significant correlation with processing speed, executive function, and memory. ROI analysis found that white matter integrity impairment occurred from the preclinical stage of vascular cognitive impairment (VCI) due to CSVD. These lesions in the NCI group mainly involved some longitudinal fibers such as right superior longitudinal fasciculus (SLF-R), right superior fronto-occipital fasciculus (SFO-R), and right uncinate fasciculus (UNC-R), which might be more vulnerable to the cerebrovascular aging and disease process. Conclusions: DTI measures are sensitive neuroimaging markers in detecting the early cognitive impairment and able to differentiate the different cognitive status due to CSVD. Subtle changes of some vulnerable white matter tracts may be observed from the preclinical stage of VCI and have a local to general spreading pattern during the disease progression.

A Deep Learning-Based Model for Classification of Different Subtypes of Subcortical Vascular Cognitive Impairment With FLAIR.

Deep learning methods have shown their great capability of extracting high-level features from image and have been used for effective medical imaging classification recently. However, training samples of medical images are restricted by the amount of patients as well as medical ethics issues, making it hard to train the neural networks. In this paper, we propose a novel end-to-end three-dimensional (3D) attention-based residual neural network (ResNet) architecture to classify different subtypes of subcortical vascular cognitive impairment (SVCI) with single-shot T2-weighted fluid-attenuated inversion recovery (FLAIR) sequence. Our aim is to develop a convolutional neural network to provide a convenient and effective way to assist doctors in the diagnosis and early treatment of the different subtypes of SVCI. The experiment data in this paper are collected from 242 patients from the Neurology Department of Renji Hospital, including 78 amnestic mild cognitive impairment (a-MCI), 70 nonamnestic MCI (na-MCI), and 94 no cognitive impairment (NCI). The accuracy of our proposed model has reached 98.6% on a training set and 97.3% on a validation set. The test accuracy on an untrained testing set reaches 93.8% with robustness. Our proposed method can provide a convenient and effective way to assist doctors in the diagnosis and early treatment.

Structural Brain Network Disruption at Preclinical Stage of Cognitive Impairment Due to Cerebral Small Vessel Disease.

Cerebral small vessel disease (CSVD) is a common disease among elderly individuals and recognized as a major cause of vascular cognitive impairment. Recent studies demonstrated that CSVD is a disconnection syndrome. However, due to the covert neurological symptoms and subtle changes in clinical performance, the connection alterations during the stage of preclinical cognitive impairment (PCI) and mild cognitive impairment (MCI) are usually neglected and still largely unknown. Using diffusion tensor imaging (DTI), we investigated the early structural network changes in PCI and MCI patients. The PCI group demonstrated well preserved rich-club organization, less nodal strength loss, and disruption of connections centered in the feeder and local connections. Nevertheless, the MCI group manifested a disruption in the rich-club organization, a worse nodal strength loss especially in hub nodes, and an overall disturbance in rich-club, feeder and local connections. Moreover, in all CSVD patients, the strength of the rich-club, feeder and local connections was significantly correlated with multiple cognitive scores, especially in attention, executive, and memory domains; while in MCI patients, only the strength of the rich-club connections was significantly correlated with cognition. Furthermore, based on the network-based statistic analysis, we also identified distinct network component disruption pattern between the PCI group and the MCI group, validating the results described above. These results suggest a disruption pattern from peripheral to central connections with the change of cognitive status, shedding light on the early identification and the underlying disruption of CSVD.