INTRODUCTION: Interpretation and analysis of magnetic resonance imaging (MRI) scans in clinical settings comprise time-consuming visual ratings and complex neuroimage processing that require trained professionals. To combat these challenges, artificial intelligence (AI) techniques can aid clinicians in interpreting brain MRI for accurate diagnosis of neurodegenerative diseases but they require extensive validation. Thus, the aim of this study was to validate the use of AI-based AQUA (Neurophet Inc., Seoul, Republic of Korea) segmentation software in a Southeast Asian community-based cohort with normal cognition, mild cognitive impairment (MCI) and dementia. METHOD: Study participants belonged to the community-based Biomarker and Cognition Study in Singapore. Participants aged between 30 and 95 years, having cognitive concerns, with no diagnosis of major psychiatric, neurological or systemic disorders who were recruited consecutively between April 2022 and July 2023 were included. Participants underwent neuropsychological assessments and structural MRI, and were classified as cognitively normal, with MCI or with dementia. MRI pre-processing using automated pipelines, along with human-based visual ratings, were compared against AI-based automated AQUA output. Default mode network grey matter (GM) volumes were compared between cognitively normal, MCI and dementia groups. RESULTS: A total of 90 participants (mean age at visit was 63.32±10.96 years) were included in the study (30 cognitively normal, 40 MCI and 20 dementia). Non-parametric Spearman correlation analysis indicated that AQUA-based and human-based visual ratings were correlated with total (ρ=0.66; P<0.0001), periventricular (ρ=0.50; P<0.0001) and deep (ρ=0.57; P<0.0001) white matter hyperintensities (WMH). Additionally, volumetric WMH obtained from AQUA and automated pipelines was also strongly correlated (ρ=0.84; P<0.0001) and these correlations remained after controlling for age at visit, sex and diagnosis. Linear regression analyses illustrated significantly different AQUA-derived default mode network GM volumes between cognitively normal, MCI and dementia groups. Dementia participants had significant atrophy in the posterior cingulate cortex compared to cognitively normal participants (P=0.021; 95% confidence interval [CI] -1.25 to -0.08) and in the hippocampus compared to cognitively normal (P=0.0049; 95% CI -1.05 to -0.16) and MCI participants (P=0.0036; 95% CI -1.02 to -0.17). CONCLUSION Our findings demonstrate high concordance between human-based visual ratings and AQUA-based ratings of WMH. Additionally, the AQUA GM segmentation pipeline showed good differentiation in key regions between cognitively normal, MCI and dementia participants. Based on these findings, the automated AQUA software could aid clinicians in examining MRI scans of patients with cognitive impairment.
Humans ; Cognitive Dysfunction/pathology* ; Magnetic Resonance Imaging/methods* ; Male ; Middle Aged ; Female ; Aged ; Artificial Intelligence ; Software ; Dementia/diagnostic imaging* ; Aged, 80 and over ; Adult ; Singapore ; Neuropsychological Tests ; Brain/pathology* ; Cohort Studies ; Gray Matter/pathology* ; Southeast Asian People

Gray Matter/diagnostic imaging* ; Cerebral Cortex ; Brain ; Magnetic Resonance Imaging

OBJECTIVES: Prediabetes is associated with an increased risk of cognitive impairment and neurodegenerative diseases. However, the exact mechanism of prediabetes-related brain diseases has not been fully elucidated. The brain structure of patients with prediabetes has been damaged to varying degrees, and these changes may affect the topological characteristics of large-scale brain networks. The structural covariance of connected gray matter has been demonstrated valuable in inferring large-scale structural brain networks. The alterations of gray matter structural covariance networks in prediabetes remain unclear. This study aims to examine the topological features and robustness of gray matter structural covariance networks in prediabetes. METHODS: A total of 48 subjects were enrolled in this study, including 23 patients with prediabetes (the PD group) and 25 age-and sex-matched healthy controls (the Ctr group). All subjects' high-resolution 3D T₁ images of the brain were collected by a 3.0 Tesla MR machine. Mini-mental state examination was used to evaluate the cognitive status of each subject. We calculated the gray matter volume of 116 brain regions with automated anatomical labeling (AAL) template, and constructed gray matter structural covariance networks by thresholding interregional structural correlation matrices as well as graph theoretical analysis. The area under the curve (AUC) in conjunction with permutation testing was employed for testing the differences in network measures, which included small world parameter (Sigma), normalized clustering coefficient (Gamma), normalized path length (Lambda), global efficiency, characteristic path length, local efficiency, mean clustering coefficient, and network robustness parameters. RESULTS: The network in both groups followed small-world characteristics, showing that Sigma was greater than 1, the Lambda was much higher than 1, and Gamma was close to 1. Compared with the Ctr group, the network of the PD group showed increased Sigma, Lambda, and Gamma across a range of network sparsity. The Gamma of the PD group was significantly higher than that in the Ctr group in the network sparsity range of 0.12-0.16, but there was no difference between the 2 groups (all P>0.05). The grey matter network showed an increased characteristic path length and a decreased global efficiency in the PD group, but AUC analysis showed that there was no significant difference between groups (all P>0.05). For the network separation measures, the local efficiency and mean clustering coefficient of the gray matter network in the PD group were significantly increased and AUC analysis also confirmed it (P=0.001 and P=0.004, respectively). In addition, network robustness analysis showed that the grey matter network of the PD group was more vulnerable to random damage (P=0.001). CONCLUSIONS The prediabetic gray matter network shows an increased average clustering coefficient and local efficiency, and is more vulnerable to random damage than the healthy control, suggesting that the topological characteristics of the prediabetes grey matter covariant network have changed (network separation enhanced and network robustness reduced), which may provide new insights into the brain damage relevant to the disease.
Humans ; Gray Matter/diagnostic imaging* ; Prediabetic State ; Magnetic Resonance Imaging ; Cerebral Cortex ; Brain

Objective: To analyze the morphological changes of gray matter in clefts lip and palate (CLP) children using voxel-based morphometric (VBM) and surface-based morphometric (SBM) methods. So as to provide a reference for the neural mechanism of cleft palate speech disorder (CPSD) in CLP population. Methods: A total of 29 CLP children with CPSD who were admitted to Department of Oral & Maxillofacial Plastic & Tramuma Surgery, Capital Medical University School of Stomatology from January 2017 to January 2022 were selected (CLP group), including 9 males and 20 females, aged (9.6±3.2) years (5-17 years old). During the same period, 33 healthy children (healthy control group) were recruited in Beijing Children's Hospital, including 9 males and 24 females, aged (9.6±2.4) years (5-16 years). Results: There were no significant differences in age and gender between the healthy control group and the CLP group (t=-0.07, P=0.944; χ²=0.11, P=0.745). Compared with the healthy control group, the left inferior temporal gyrus, right inferior parietal angular gyrus, and left upper cerebellar gray matter volume in the CLP group were significantly decreased (P<0.05), and the volume of the right medial superior frontal gyrus was significantly increased (P<0.05). The left inferior frontal gyrus insula and the right insula cortical thickness of the subjects in the CLP group were significantly thinner [family-wise error rate (FWER) correction, P=0.035]. The right hemisphere cingulate gyrus isthmus sulcus index increased in the CLP group (FWER correction, P=0.040). Conclusions: CLP children with speech disorder have multiple language-related brain regions with reduced gray matter (GM) volume, decreased cortical thickness, and increased gyrificaition index. The abnormal changes in GM morphology in these brain regions may be related to the characteristics of cleft palate speech disorder in the CLP population.
Adolescent ; Brain ; Child ; Child, Preschool ; Cleft Palate ; Female ; Gray Matter/diagnostic imaging* ; Humans ; Magnetic Resonance Imaging/methods* ; Male ; Speech

OBJECTIVE: To investigate the intra- and inter-scanner reproducibility of quantitative susceptibility mapping (QSM) of cerebral subcortical nuclei in healthy adults. METHODS: QSM was performed in 21 healthy adults on two different 3.0T MR scanners, and the region of interest (ROI) method was used to measure the magnetic susceptibility value of the left subcortical nuclei (the head of the caudate, putamen, globus pallidus, thalamus, substantia nigra and red nucleus). The intraclass correlation coefficient (ICC) and Bland-Altman method were used to evaluate the inter-scanner and intra-scanner reliability. RESULTS: The ICCs of the susceptibility value ranged from 0.90 to 0.99 for all the subcortical gray nuclei except for the head of the caudate nucleus measured on the same MR scanner by the same observer. Bland-Altman analysis revealed that the points with susceptibility differences for all the subcortical gray nuclei except for substantia nigra located in the 95% CI of limits of agreement for the same MR scanner. The ICCs of the susceptibility value for the inter-scanner was 0.49 (0.08-0.75) for the head of the caudate nuleus, 0.80 (0.57-0.91) for the putamen, 0.77 (0.51-0.90) for the globus pallidus, 0.78 (0.54-0.91) for the thalamus, 0.80 (0.56-0.91) for the substantia nigra and 0.93 (0.83-0.97) for the red nucleus. The points with susceptibility difference (95.2%, 20/21) located in the 95% CI of limits of agreement for the putamen and the thalamus measured on two different MR scanners. CONCLUSIONS The intra-scanner reproducibility of QSM of the subcortical gray nuclei is superior to the inter-scanner reproducibility in healthy adults.
Adult ; Brain/diagnostic imaging* ; Gray Matter ; Humans ; Iron ; Magnetic Resonance Imaging ; Reproducibility of Results ; Substantia Nigra/diagnostic imaging*

Cross-sectional and longitudinal studies have identified widespread and progressive grey matter volume (GMV) reductions in schizophrenia, especially in the frontal lobe. In this study, we found a progressive GMV decrease in the rostral medial frontal cortex (rMFC, including the anterior cingulate cortex) in the patient group during a 6-week follow-up of 40 patients with schizophrenia and 31 healthy controls well-matched for age, gender, and education. The higher baseline GMV in the rMFC predicted better improvement in the positive score on the Positive and Negative Syndrome Scale (PANSS), and this might be related to the improved reality-monitoring. Besides, a higher baseline GMV in the posterior rMFC predicted better remission of general symptoms, and a lesser GMV reduction in this region was correlated with better remission of negative symptoms, probably associated with ameliorated self-referential processing and social cognition. Besides, a shorter disease course and higher educational level contributed to better improvement in the general psychopathological PANSS score, and a family history was negatively associated with improvement of the negative and total PANSS scores. These phenomena might be important for understanding the neuropathological mechanisms underlying the symptoms of schizophrenia and for making clinical decisions.
Adult ; Antipsychotic Agents ; therapeutic use ; Female ; Frontal Lobe ; diagnostic imaging ; drug effects ; pathology ; Genetic Predisposition to Disease ; Gray Matter ; diagnostic imaging ; drug effects ; pathology ; Humans ; Image Processing, Computer-Assisted ; Longitudinal Studies ; Magnetic Resonance Imaging ; Male ; Organ Size ; Psychiatric Status Rating Scales ; Regression Analysis ; Schizophrenia ; diagnostic imaging ; drug therapy ; genetics ; pathology ; Time Factors ; Treatment Outcome

OBJECTIVETo explore the morphometric abnormalities of brain gray matter (GM) in patients with chronic low back pain (CLBP).

METHODSThirty patients with CLBP and 30 healthy individuals were enrolled and examined with a 3.0 T magnetic resonance (MR) scanner. High-resolution T1 structural MR data were acquired and data analysis was performed using voxel-based morphometry (VBM) in FMRIB Software Library. The morphological differences were compared between the two groups.

RESULTSs Compared with the healthy control subjects, patients with CLBP showed decreased GM volumes in several brain cortical areas including the bilateral superior frontal gyrus, right frontal pole, left insular cortex, left middle and left inferior temporal gyrus (P<0.05, after TFCE correction). Increased GM volumes were found in the patients in the subcortical structures including the left thalamus, bilateral putamen, bilateral nucleus accumben and right caudate nucleus (P<0.05, after TFCE correction).

CONCLUSIONPatients with CLBP have different patterns of GM abnormalities in different brain regions, characterized by reduced GM volume in cerebral cortical regions and increased GM volume in the subcortical nuclei. Such changes might be associated with the maladaptation of the brain in chronic pain state.

Cerebral Cortex ; Frontal Lobe ; Gray Matter ; diagnostic imaging ; pathology ; Humans ; Low Back Pain ; physiopathology ; Magnetic Resonance Imaging ; Temporal Lobe ; Thalamus