Research on Teaching Method of Children’s Image Cognition Based on AR Technology

In this paper,the research on the teaching method of children’s image cognition based on AR technology is carried out.By analyzing the principle ofARtechnology to recognize images,we understand thatARtechnology can promote children’s image cognition,and this teaching method is in line with the Tower of Experience theory.It further analyzes the current situation of children’s image cognition teaching with simple teaching methods,backward AR teaching tools,and poor perception of teaching objects.Teachers’traditional image teaching methods cannot effectively and efficiently improve children’s image cognition.Therefore,based on AR technology,two commonly used image cognition teaching methods are proposed:AR interactive picture book education andARinteractive game education.Both of these educational methods can improve children’s ability to recognize images.

Individual identification using multi-metric of DTI in Alzheimer's disease and mild cognitive impairment

Accurate identification of Alzheimer＇s disease （AD） and mild cognitive impairment （MCI） is crucial so as to improve diagnosis techniques and to better understand the neurodegenerative process. In this work, we aim to apply the machine learning method to individual identification and identify the discriminate features associated with AD and MCI. Diffusion tensor imaging scans of 48 patients with AD, 39 patients with late MCI, 75 patients with early MCI, and 51 age-matched healthy controls （HCs） are acquired from the Alzheimer＇s Disease Neuroimaging Initiative database. In addition to the common fractional anisotropy, mean diffusivity, axial diffusivity, and radial diffusivity metrics, there are two novel metrics, named local diffusion homogeneity that used Spearman＇s rank correlation coefficient and Kendall＇s coefficient concordance, which are taken as classification metrics. The recursive feature elimination method for support vector machine （SVM） and logistic regression （LR） combined with leave-one-out cross validation are applied to determine the optimal feature dimensions. Then the SVM and LR methods perform the classification process and compare the classification performance. The results show that not only can the multi-type combined metrics obtain higher accuracy than the single metric, but also the SVM classifier with multi-type combined metrics has better classification performance than the LR classifier. Statistically, the average accuracy of the combined metric is more than 92% for all between-group comparisons of SVM classifier. In addition to the high recognition rate, significant differences are found in the statistical analysis of cognitive scores between groups. We further execute the permutation test, receiver operating characteristic curves, and area under the curve to validate the robustness of the classifiers, and indicate that the SVM classifier is more stable and efficient than the LR classifier. Finally, the uncinated fasciculus, cingulum, corpus callosum, corona radiate, external capsule, and internal capsule have been regarded as the most important white matter tracts to identify AD, MCI, and HC. Our findings reveal a guidance role for machine-learning based image analysis on clinical diagnosis.

Altered functional connectivity networks of hippocampal subregions in remitted late-onset depression:a longitudinal resting-state study

The regional specifi city of hippocampal abnormalities in late-life depression（LLD） has been demonstrated in previous studies. In this study,we sought to examine the functional connectivity（FC） patterns of hippocampal subregions in remitted late-onset depression（r LOD）,a special subtype of LLD. Fourteen r LOD patients and 18 healthy controls underwent clinical and cognitive evaluations as well as resting-state functional magnetic resonance imaging scans at baseline and at ~21 months of follow-up. Each hippocampus was divided into three parts,the cornu ammonis（CA）,the dentate gyrus,and the subicular complex,and then six seed-based hippocampal subregional networks were established.Longitudinal changes of the six networks over time were directly compared between the rL OD and control groups. From baseline to follow-up,the r LOD group showed a greater decline in connectivity of the left CA to the bilateral posterior cingulate cortex/precuneus（PCC/PCUN）,but showed increased connectivity of the right hippocampal subregional networks with the frontal cortex（bilateral medial prefrontal cortex/anterior cingulate cortex and supplementary motor area）. Further correlative analyses revealed thatthe longitudinal changes in FC between the left CA and PCC/PCUN were positively correlated with longitudinal changes in the Symbol Digit Modalities Test（r = 0.624,P = 0.017） and the Digit Span Test（r = 0.545,P = 0.044） scores in the r LOD group. These results may provide insights into the neurobiological mechanism underlying the cognitive dysfunction in r LOD patients.

Application of functional MRI in epilepsy

Objective To review the recent development of functional MRI application in epilepsy. Data sources Both Chinese and English language literatures were researched using MEDLINE/ CD ROM (1996-2005) and the Chinese Biomedical Literature Disk (1996-2005). Study selection Published articles about functional MRI application and epilepsy were selected.Data extraction Data were mainly extracted from 38 articles which are listed in the reference section of this review.Results fMRI can be used to localize seizure foci through detecting these cerebral hemodynamic changes produced by epileptiform discharges. EEG-triggered fMRI, which has higher spatial and temporal resolution, helps to detect the spatiotemporal pattern of spike origin and propagation, and define localization of the epileptogenic focus. fMRI is also useful in language and memory cognitive function assessment and presurgical assessment of refractory epilepsy. Atypically distributed cognitive function areas can be detected by fMRI, because of cortical language and memory areas reorganization during long-term epileptic activity in patients with epilepsy. Conclusions fMRI technique plays a very important role in cognitive function and presurgical assessment of patients with epilepsy. It is meaningful for understanding pathogenesis of epilepsy.

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.

Continuous representation of human portraits and natural scenery in human ventral temporal cortex: evidence from functional magnetic resonance imaging

Background Functional magnetic resonance imaging (fMRI) has become a powerful tool for tracking human brain activity in vivo. This technique is mainly based on blood oxygenation level dependence (BOLD) contrast. In the present study, we employed this newly developed technique to characterize the neural representations of human portraits and natural sceneries in the human brain.Methods Nine subjects were scanned with a 1.5 T magnetic resonance imaging (MRI) scanner using gradient-recalled echo and echo-planar imaging (GRE-EPI) pulse sequence while they were visually presented with 3 types of white-black photographs: natural scenery, human portraits, and scrambled nonsense pictures. Multiple linear regression was used to identify brain regions responding preferentially to each type of stimulus and common regions for both human portraits and natural scenery. The relative contributions of each type of stimulus to activation in these regions were examined using linear combinations of a general linear test.Results Multiple linear regression analysis revealed two distinct but adjacent regions in both sides of the ventral temporal cortex. The medial region preferentially responded to natural scenery, whereas the lateral one preferentially responded to the human portraits. The general linear test further revealed a distribution gradient such that a change from portraits to scenes shifted areas of activation from lateral to medial.Conclusions The boundary between portrait-associated and scenery-associated areas is not as clear as previously demonstrated. The representations of portraits and scenes in ventral temporal cortex appear to be continuous and overlap.