Diagnostic value of amygdala volume on structural magnetic resonance imaging in Alzheimer’s disease

BACKGROUND The main clinical manifestation of Alzheimer’s disease(AD)is memory loss,which can be accompanied by neuropsychiatric symptoms at different stages of the disease.Amygdala is closely related to emotion and memory.AIM To evaluate the diagnostic value of amygdala on structural magnetic resonance imaging(sMRI)for AD.METHODS In this study,22 patients with AD and 26 controls were enrolled.Their amygdala volumes were measured by sMRI and analyzed using an automatic analysis software.RESULTS The bilateral amygdala volumes of AD patients were significantly lower than those of the controls and were positively correlated with the hippocampal volumes.Receiver operating characteristic curve analyses showed that the sensitivity of the left and right amygdala volumes in diagnosing AD was 80.8%and 88.5%,respectively.Subgroup analyses showed that amygdala atrophy was more serious in AD patients with neuropsychiatric symptoms,which mainly included irritability(22.73%),sleep difficulties(22.73%),apathy(18.18%),and hallucination(13.64%).CONCLUSION Amygdala volumes measured by sMRI can be used to diagnose AD,and amygdala atrophy is more serious in patients with neuropsychiatric symptoms.

Application of modern neuroimaging technology in the diagnosis and study of Alzheimer’s disease

Neurological abnormalities identified via neuroimaging are common in patients with Alzheimer’s disease.However,it is not yet possible to easily detect these abnormalities using head computed tomography in the early stages of the disease.In this review,we evaluated the ways in which modern imaging techniques such as positron emission computed tomography,single photon emission tomography,magnetic resonance spectrum imaging,structural magnetic resonance imaging,magnetic resonance diffusion tensor imaging,magnetic resonance perfusion weighted imaging,magnetic resonance sensitive weighted imaging,and functional magnetic resonance imaging have revealed specific changes not only in brain structure,but also in brain function in Alzheimer’s disease patients.The reviewed literature indicated that decreased fluorodeoxyglucose metabolism in the temporal and parietal lobes of Alzheimer’s disease patients is frequently observed via positron emission computed tomography.Furthermore,patients with Alzheimer’s disease often show a decreased N-acetylaspartic acid/creatine ratio and an increased myoinositol/creatine ratio revealed via magnetic resonance imaging.Atrophy of the entorhinal cortex,hippocampus,and posterior cingulate gyrus can be detected early using structural magnetic resonance imaging.Magnetic resonance sensitive weighted imaging can show small bleeds and abnormal iron metabolism.Task-related functional magnetic resonance imaging can display brain function activity through cerebral blood oxygenation.Resting functional magnetic resonance imaging can display the functional connection between brain neural networks.These are helpful for the differential diagnosis and experimental study of Alzheimer’s disease,and are valuable for exploring the pathogenesis of Alzheimer’s disease.

Two-dimensional horizontal visibility graph analysis of human brain aging on gray matter

Characterizing the trajectory of the healthy aging brain and exploring age-related structural changes in the brain can help deepen our understanding of the mechanism of brain aging.Currently,most structural magnetic resonance imaging literature explores brain aging merely from the perspective of morphological features,which cannot fully utilize the grayscale values containing important intrinsic information about brain structure.In this study,we propose the construction of two-dimensional horizontal visibility graphs based on the pixel intensity values of the gray matter slices directly.Normalized network structure entropy(NNSE)is then introduced to quantify the overall heterogeneities of these graphs.The results demonstrate a decrease in the NNSEs of gray matter with age.Compared with the middle-aged and the elderly,the larger values of the NNSE in the younger group may indicate more homogeneous network structures,smaller differences in importance between nodes and thus a more powerful ability to tolerate intrusion.In addition,the hub nodes of different adult age groups are primarily located in the precuneus,cingulate gyrus,superior temporal gyrus,inferior temporal gyrus,parahippocampal gyrus,insula,precentral gyrus and postcentral gyrus.Our study can provide a new perspective for understanding and exploring the structural mechanism of brain aging.

Exploring Brain Age Calculation Models Available for Alzheimer's Disease

The advantages of structural magnetic resonance imaging(sMRI)-based multidimensional tensor morphological features in brain disease research are the high sensitivity and resolution of sMRI to comprehensively capture the key structural information and quantify the structural deformation.However,its direct application to regression analysis of high-dimensional small-sample data for brain age prediction may cause“dimensional catastrophe”.Therefore,this paper develops a brain age prediction method for high-dimensional small-sample data based on sMRI multidimensional morphological features and constructs brain age gap estimation(BrainAGE)biomarkers to quantify abnormal aging of key subcortical structures by extracting subcortical structural features for brain age prediction,which can then establish statistical analysis models to help diagnose Alzheimer’s disease and monitor health conditions,intervening at the preclinical stage.

Machine learning for detecting mesial temporal lobe epilepsy by structural and functional neuroimaging

Mesial temporal lobe epilepsy(mTLE),the most common type of focal epilepsy,is associated with functional and structural brain alterations.Machine learning(ML)techniques have been successfully used in discriminating mTLE from healthy controls.However,either functional or structural neuroimaging data are mostly used separately as input,and the opportunity to combine both has not been exploited yet.We conducted a multimodal ML study based on functional and structural neuroimaging measures.We enrolled 37 patients with left mTLE,37 patients with right mTLE,and 74 healthy controls and trained a support vector ML model to distinguish them by using each measure and the combinations of the measures.For each single measure,we obtained a mean accuracy of 74%and 69%for discriminating left mTLE and right mTLE from controls,respectively,and 64%when all patients were combined.We achieved an accuracy of 78%by integrating functional data and 79%by integrating structural data for left mTLE,and the highest accuracy of 84%was obtained when all functional and structural measures were combined.These findings suggest that combining multimodal measures within a single model is a promising direction for improving the classification of individual patients with mTLE.

Decoding fear of negative evaluation from brain morphology:A machine-learning study on structural neuroimaging data

Background:Fear of negative evaluation(FNE),referring to negative expectation and feelings toward other people’s social evaluation,is closely associated with social anxiety that plays an important role in our social life.Exploring the neural markers of FNE may be of theoretical and practical significance to psychiatry research(e.g.,studies on social anxiety).Methods:To search for potentially relevant biomarkers of FNE in human brain,the current study applied multivariate relevance vector regression,a machine-learning and data-driven approach,on brain morphological features(e.g.,cortical thickness)derived from structural imaging data;further,we used these features as indexes to predict self-reported FNE score in each participant.Results:Our results confirm the predictive power of multiple brain regions,including those engaged in negative emotional experience(e.g.,amygdala,insula),regulation and inhibition of emotional feeling(e.g.,frontal gyrus,anterior cingulate gyrus),and encoding and retrieval of emotional memory(e.g.,posterior cingulate cortex,parahippocampal gyrus).Conclusions:The current findings suggest that anxiety represents a complicated construct that engages multiple brain systems,from primitive subcortical mechanisms to sophisticated cortical processes.

Gut bless you:The microbiota-gut-brain axis in irritable bowel syndrome

Irritable bowel syndrome(IBS)is a common clinical label for medically unexplained gastrointestinal symptoms,recently described as a disturbance of the microbiota-gut-brain axis.Despite decades of research,the pathophysiology of this highly heterogeneous disorder remains elusive.However,a dramatic change in the understanding of the underlying pathophysiological mechanisms surfaced when the importance of gut microbiota protruded the scientific picture.Are we getting any closer to understanding IBS’etiology,or are we drowning in unspecific,conflicting data because we possess limited tools to unravel the cluster of secrets our gut microbiota is concealing?In this comprehensive review we are discussing some of the major important features of IBS and their interaction with gut microbiota,clinical microbiota-altering treatment such as the low FODMAP diet and fecal microbiota transplantation,neuroimaging and methods in microbiota analyses,and current and future challenges with big data analysis in IBS.

Can multi-modal neuroimaging evidence from hippocampus provide biomarkers for the progression of amnestic mild cognitive impairment?

Impaired structure and function of the hippocampus is a valuable predictor of progression from amnestic mild cognitive impairment（a MCI） to Alzheimer＇s disease（AD）. As a part of the medial temporal lobe memory system,the hippocampus is one of the brain regions affected earliest by AD neuropathology,and shows progressive degeneration as a MCI progresses to AD. Currently,no validated biomarkers can precisely predict the conversion from a MCI to AD. Therefore,there is a great need of sensitive tools for the early detection of AD progression. In this review,we summarize the specifi c structural and functional changes in the hippocampus from recent a MCI studies using neurophysiological and neuroimaging data. We suggest that a combination of advanced multi-modal neuroimaging measures in discovering biomarkers will provide more precise and sensitive measures of hippocampal changes than using only one of them. These will potentially affect early diagnosis and disease-modifying treatments. We propose a new sequential and progressive framework in which the impairment spreads from the integrity of fibers to volume and then to function in hippocampal subregions. Meanwhile,this is likely to be accompanied by progressive impairment of behavioral and neuropsychological performance in the progression of a MCI to AD.

Advances in neuroimaging studies of alcohol use disorder(AUD)

Alcohol use disorder(AUD)is a worldwide problem and themost common substance use disorder.Chronic alcohol consumptionmay have negative effects on the body,the mind,the family,and even society.With the progress of current neuroimaging methods,an increasing number of imaging techniques are being used to objectively detect brain impairment induced by alcoholism and serve a vital role in the diagnosis,prognosis,and treatment assessment of AUD.This article organizes and analyzes the research on alcohol dependence concerning the main noninvasive neuroimaging methods,structural magnetic resonance imaging,functional magnetic resonance imaging,and electroencephalography,as well as the most common noninvasive brain stimulation-transcranial magnetic stimulation,and intersperses the article with joint intra-and intergroup studies,providing an outlook on future research directions.

Brain structure underlying the empathizing–systemizing difference in children with autism spectrum disorder

Background Behavioral research has shown that children with autism spectrum disorder(ASD)have a higher empathizing–systemizing difference(D score)than normal children.However,there is no research about the neuroanatomical mechanisms of the empathizing–systemizing difference in children with ASD.Methods Participants comprised 41 children with ASD and 39 typically developing(TD)children aged 6‒12 years.Empathizing–systemizing difference was estimated using the D score from the Chinese version of Children’s Empathy Quotient and Systemizing Quotient.We quantified brain morphometry,including global and regional brain volumes and surface-based cortical measures(cortical thickness,surface area,and gyrification)via structural magnetic resonance imaging.Results We found that the D score was significantly negatively associated with amygdala gray matter volume[β=−0.16;95%confidence interval(CI):−0.30,−0.02;P value=0.030]in children with ASD.There was a significantly negative association between D score and gyrification in the left lateral occipital cortex(LOC)in children with ASD(B=−0.10;SE=0.03;cluster-wise P value=0.006)and a significantly positive association between D score and gyrification in the right fusiform in TD children(B=0.10;SE=0.03;cluster-wise P value=0.022).Moderation analyses demonstrated significant interactions between D score and diagnosed group in amygdala gray matter volume(β=0.19;95%CI 0.04,0.35;P value=0.013)and left LOC gyrification(β=0.11;95%CI 0.05,0.17;P value=0.001)but not in right fusiform gyrification(β=0.08;95%CI−0.02,0.17;P value=0.105).Conclusions Neuroanatomical variation in amygdala volume and gyrification of LOC could be potential biomarkers for the empathizing–systemizing difference in children with ASD but not in TD children.Large-scale neuroimaging studies are necessary to test the replicability of our findings.

Candidate Biomarkers in Children with Autism Spectrum Disorder: A Review of MRI Studies

Searching for effective biomarkers is one of the most challenging tasks in the research ？eld of Autism Spectrum Disorder（ASD）. Magnetic resonance imaging（MRI） provides a non-invasive and powerful tool for investigating changes in the structure, function, maturation,connectivity, and metabolism of the brain of children with ASD. Here, we review the more recent MRI studies in young children with ASD, aiming to provide candidate biomarkers for the diagnosis of childhood ASD. The review covers structural imaging methods, diffusion tensor imaging, resting-state functional MRI, and magnetic resonance spectroscopy. Future advances in neuroimaging techniques, as well as cross-disciplinary studies and largescale collaborations will be needed for an integrated approach linking neuroimaging, genetics, and phenotypic data to allow the discovery of new, effective biomarkers.