The difference of gray matter volume and its correlation with clinical characteristics in patients with anxious depression / 中华行为医学与脑科学杂志

Objective:To explore the difference of gray matter volume between anxious depression(AD)and non anxious depression(NAD) patients, and its correlation with clinical characteristics.Methods:One hundred and fifty patients with depression were included from September 2014 to October 2018, meanwhile 62 healthy controls with matching demographic characteristic were recruited. The severity of the patients was assessed by Hamilton depression scale-17(HAMD-17). Patients were divided into anxious depression group(AD group, n=80)and non-anxious depression group (NAD group, n=70) according to whether anxiety/somatization factor scored 7. All subjects were scanned with 3.0 T underwent structural MRI scan. The structural magnetic resonance data were preprocessed by voxel-based morphometry (VBM). The rest toolkit was used to calculate the difference of gray matter volume among the three groups. By SPSS 19.0, post-hoc t test was used for pairwise comparison and Pearson correlation analysis was performed between gray matter volume and clinical factors in patients with anxious depression. Results:Compared to the NAD group, the gray matter volume of the right middle frontal gyrus(MNI: x=28.5, y=21.0, z=48.0, t=-4.83, Bonferroni multiple comparison adjustment, P<0.05/3) and left dorsolateral superior frontal gyrus(MNI: x=-18.0, y=27.0, z=43.5, t=-6.08, Bonferroni multiple comparison correction, P<0.05/3)were significantly decreased in AD group. Correlation analysis found that the gray matter volume of the right middle frontal gyrus in patients with anxious depression was negatively correlated with the insight of anxiety/somatization factor score ( r=-0.36, P=0.001). Conclusion:The volume of prefrontal lobe in patients with anxiety depression is lower than that in patients with non anxiety depression, which may be related to the serious clinical symptoms in patients with anxiety depression.The decrease of right middle frontal gyrus volume can be used as a potential biological marker for the severity of impaired insight.

Changes in Gray Matter Volume in Chronic Nonfluent Aphasia after Cortical Cerebral Infarction / 中国康复理论与实践

Objective:To investigate the changes of gray matter volume in patients with chronic nonfluent aphasia after cortical cerebral infarction and the relationship between gray matter volume and language function. Methods:From June, 2016 to June, 2019, 19 patients with chronic nonfluent aphasia after cortical cerebral infarction from the First Affiliated Hospital of Ji'nan University and 28 healthy subjects (controls) were scanned with structural magnetic resonance imaging. The data were analyzed using voxel-based morphological measurement to measure the gray matter volumes of the brain regions, and the differences between patients and controls were compared. The correlation between volumes of brain regions with difference and scores of items of Aphasia Battery of Chinese (ABC) was analyzed. Results:The gray matter volumes increased in the brain regions of right inferior frontal gyrus triangle, right inferior frontal gyrus island cover, right angular gyrus, the right medial frontal gyrus, left insula, left medial frontal gyrus in the patients; while decreased in right globus pallidus. The volumes of left insular lobe correlated with the scores of repeating (r = 0.665, P = 0.001) and naming (r = 0.638, P = 0.003); and the volumes of right inferior frontal gyrus triangle correlated with the scores of hearing comprehension (r = 0.493, P = 0.031), repeating (r = 0.576, P = 0.009) and naming (r = 0.674, P = 0.001) in the patients. Conclusion:The cortex volumes of left insula and right inferior frontal gyrus triangle increase in patients with chronic nonfluent aphasia after cerebral infarction, which may play a role in the language dysfunction.

Association between the entorhinal cortex and cognitive function in traumatic brain injury based on structural magnetic resonance imaging / 西安交通大学学报(医学版)

【Objective】 To explore the relationship between changes in the entorhinal cortex （EC） of traumatic brain injury （TBI） and cognitive function based on structural magnetic resonance imaging. 【Methods】 MRI was performed in 26 patients with clinically confirmed TBI after admission， and the Mini-mental State Examination （MMSE） was followed up 6 months later. The TBI patients were classified as mild TBI and moderate to severe TBI according to the post-traumatic Glasgow coma scale （GCS）. We compared the differences in age， gender， education level， hypertension， diabetes， TBI operation history， and follow-up MMSE between the two groups. Then the morphology， surface area， volume and thickness of the patient’s EC were evaluated using the visual score and Freesurfer software， and finally the correlation between EC parameters and MMSE was analyzed. 【Results】 The study included 12 cases of mild TBI and 14 cases of moderate to severe TBI. There were no statistical differences in age， gender， years of education， hypertension， diabetes or TBI operation history. However， the two groups differed significantly in follow-up MMSE. Visual evaluation showed statistical difference in the left EC scores. Structural MRI showed that the volume and thickness of left EC were statistically different between the two groups. The correlation analysis showed that there was a positive correlation between the thickness of left EC and MMSE （r=0.430， P<0.05）. 【Conclusion】 Entorhinal cortex atrophy after TBI is related to the severity of trauma， and it can reflect the long-term cognitive level of patients， which can be used as a noninvasive and reliable imaging marker for evaluating cognitive impairment after TBI.

Structural magnetic resonance imaging and diffusion tensor imaging study on white matter in left-and right-handed adults / 中华行为医学与脑科学杂志

Objective To explore the differences of macrostructural and microstructural and their correlations in brain white matter (WM) between left-and right-handed adults.Methods Structural magnetic resonance imaging (sMRI) and diffusion tensor imaging (DTI) were performed on twenty-three left-handed (LH) and thirty-two right-handed (RH) healthy subjects.The WM volume,fractional anisotropy (FA) and mean diffusivity (MD) were mearsured and compared between the two groups by using the voxel-based morphometry (VBM) and voxel-based analysis (VBA) methods.Results (1) LH adults showed lower WM volume than RH adults in bilateral splenium of corpus callosum (SC) (Left:x=-15,y=-57,z =13.5,t=-5.160;Right:x=18,y=-42,z=12,t=-3.654;P＜0.001).Compared with the RH adults,the FA values in WM of the left postcentral gyrus (PoCG) (x =-24,y =-46,z =54) and the above left insula (INS) (x =-36,y =-12,z =20) increased (P＜ 0.001),as well as the average FA values,the average length and number of streamlines in WM tracts increased (P＜0.05) in LH adults.Compared with the RH adults,the MD values in the right HIP (x=24,y=-34,z=-2) decreased(P＜0.001),as well as the average MD values decreased,and the average length in WM tracts increased (P＜0.05) in LH adults.(2)There was positive correlation between FA and the volume of right splenium of corpus callosum in LH and RH adults (LH:r=0.716,RH:r=0.471,P＜0.05).There was negative correlation between FA and MD in the left PoCG (LH:r=-0.769,RH:r=-0.841),left INS (LH:r=-0.775,RH:r=-0.744) and right HIP (LH:r=-0.842,RH:r=-0.742) in LH and RH adults (all P＜0.05).Conclusion There are differences in both macrostructure and microstructure of white matter in several brain regions and WM tracts between left-handed and right-handed people,and correlations between these measures were also observed.

Application of deep learning to mild cognitive impairment conversion and classification / 医疗卫生装备

Mild cognitive impairment (MCI) is a prodromal stage of dementia.Predicting MCI's conversion to Alzheimer's disease (AD) plays critical roles in preventing the progression of AD.Alzheimer's disease neuroimaging initiative (ADNI) was introduced briefly,which was a widely used neuroimaging database for the study on AD related diseases,and the application of machine learning algorithm was reviewed in MCI classification.Deep learning network,which transforms the original data into a higher level and more abstract expression,has shown great promise in MCI conversion and classification.Two main kinds of deep learning approaches were described,including supervised learning and unsupervised learning,and their new application was discussed in MCI conversion and classification based on structural magnetic resonance imaging (sMRI).Finally,the current limitations and future trends of deep learning in this area were explored.

Application of deep learning to mild cognitive impairment conversion and classification / 医疗卫生装备

Mild cognitive impairment (MCI) is a prodromal stage of dementia.Predicting MCI's conversion to Alzheimer's disease (AD) plays critical roles in preventing the progression of AD.Alzheimer's disease neuroimaging initiative (ADNI) was introduced briefly,which was a widely used neuroimaging database for the study on AD related diseases,and the application of machine learning algorithm was reviewed in MCI classification.Deep learning network,which transforms the original data into a higher level and more abstract expression,has shown great promise in MCI conversion and classification.Two main kinds of deep learning approaches were described,including supervised learning and unsupervised learning,and their new application was discussed in MCI conversion and classification based on structural magnetic resonance imaging (sMRI).Finally,the current limitations and future trends of deep learning in this area were explored.

The computer-aided diagnosis of attention-deficit hyperactivity disorder and autism spectrum disorder based on structural magnetic resonance imaging / 中华行为医学与脑科学杂志

Child and adolescent mental disorders are common disorders with various symptoms,and attracting more attention due to the increasing prevalence.Mental disorders,especially the attention-deficit hyperactivity disorder (ADHD) and the autism spectrum disorder (ASD),have great influence on the development of children and adolescents.Nowadays,the biomarkers from neuroimaging such as magnetic resonance imaging (MRI) have a great importance on the diagnosis of mental disorders,and machine learning has been proved to be very powerful in the processing for neuroimages.Nowadays,many researchers are focusing on the studies of computer-aided diagnosis (CAD) based on machine learning and neuroimaging.In this review,the technical details of machine learning based CAD of child and adolescent mental disorders are briefly introduced,and the research progress in CAD of ADHD and ASD based on machine learning and structural MRI are summarized.These studies showed that many machine learning methods have been used in the diagnosis of child and adolescent mental disorders,but the relevant methods cannot be applied to clinical diagnosis.Further studies should be conducted to improve the diagnostic ability of machine learning methods from multiple perspectives,and provide an objective and reliable tool for the clinical diagnosis of child and adolescent mental disorders.