| Amyotrophic lateral sclerosis(ALS)is a progressive neurodegenerative disease involving both upper motor neuron and lower motor neuron,with a mean survival time between 3 and 5 years.About 5 to 10 percent of ALS is familial;the other 90 to 95 percent of ALS is sporadic,meaning it occurs without a family history(sporadic ALS,sALS),and the core etiology of the disease remains elusive.As there is no definitive diagnostic test for sALS,the diagnosis of sALS mainly relies on experienced doctors,through detailed medical history inquiry and physical examination,to search for evidence of the joint involvement and progressive aggravation of upper and lower motor neurons.For this rapidly progressive disease,there is an average delay of one year from symptom appearance to diagnosis,which seriously impedes the early intervention of sALS.The establishment of objective and reliable biomarkers for upper motor neuron degeneration will represent a significant advance in clinical studies of sALS.Modern magnetic resonance imaging(MRI)technology has made some progress in the research of sALS,but the research content is scattered,and there is no systematic study on the brain structural and functional features of the sALS.Some of the research results are even contrary,and most of the studies just find the changes of the brain structure or functional mode,without forming imaging markers.So,this dissertation studies the features of brain structure and brain function in patients with sALS characteristics systematically based on modern system of MRI technology.Besides,this dissertation also trains a series of pattern recognition classifiers based on support vector machine(SVM)technology,so as to provide objective imaging markers for the diagnosis of sALS and the individual diagnostic tool.The main work of this dissertation is summarized as below:1.Research on structural features of patients with sALS: this dissertation studied features of gray matter volume,cortical thickness,sulcal depth,surface area and average curvature of patients with sALS based on both voxel-based morphometry(VBM)method and surface-based morphometry(SBM)method.The dissertation also built the whole brain structural network based on gray matter volume and cortical thickness respectively to study the alterations of global and local characters of the brain network based on graph theroy.Finally,correlation between these significantly altered brain structural characters and clinical variables was explored.The results showed that the volume and cortical thickness of gray matter in the primary motor area of patients with sALS were significantly lower than those in the healthy control group,which was consistent with the characteristics of sALS as motor neuron disease.In addition,the gray matter volume in subparietal lobule,the cortical thickness in superior temporal gyrus and superior frontal gyrus also decreased significantly compared with the healthy control group,indicating that the damage of sALS to the cortex was not limited to the motor area,but involved multiple brain areas such as the frontal lobe,temporal lobe and parietal lobe.As the healthy control group,sALS patients showed that both the gray matter volume network and the cortical thickness network followed a small-world organization.But with the increase of network density,small-world organization of cortical thickness network would be lost.In addition,both the gray matter volume network and the cortical thickness network in patients with sALS showed a significant decrease in the local efficiency of the network,suggesting that the local information transmission ability of the brain structure network in patients with sALS was weakened.Correlation analysis showed that the change of gray matter volume in the primary motor area was significantly negatively correlated with the disease duration of sALS,and the cortical thickness in the primary motor area was significantly positively correlated with the ALSFRS_R score,making the primary motor area a potential structural imaging marker for the diagnosis of sALS.2.Research on functional features of patients with sALS: this dissertation studied functional features of sALS from local character(including amplitude of low frequency fluctuation),local functional connection character(including regional homogeneity,voxel-mirrored homotopic connectivity,short-range and long-range functional connectivity density)and whole brain functional conneciton character(including degree centrality and brain functional connectivity)systematically based on the review of newly resting state fMRI feature extraction method.Additionally,whole brain functional connection network was constructed and changes of both global network measures and local network measures were studied.Finally,the correlation between these significantly altered brain functional characteristics and clinical variables was explored.The results showed that besides the primary motor area,medial cingulate gyrus was also the brain area with the most differences in functional features between sALS patients and the healthy control group.The involved brain areas also included parietal lobe,occipital lobe,marginal lobe,subcortical nucleus and cerebellum.Structure is the basis of brain function.Brain regions with significant difference in brain structural features also had significant difference in brain functional features.Moreover,some brain regions(i.e.median cingulate and paracingulate gyri)showed altered functional features even before they showed altered structural features,which suggested that early diagnosis of sALS should pay attention not only to the motor brain area,but also to the functional characteristics of the non-motor brain area.The whole brain functional connection network of sALS patients also show small-world characterstics,but there was no significant difference in global or regional network measures between sALS patients and healthy control group.The change of functional features in primary motor area and other brain areas of sALS patients provided potential functional imaging markers for the diagnosis of sALS.3.Individual diagnosis of sALS based on support vector machine(SVM): this dissertation trained series of classifiers to diagnose sALS individually based on brain structural and functional features mentioned previously and support vector machine(SVM)model.A repeated nested cross-validation method was used to strictly separate the train process from the evaluation of the classification model's generalization capacity.The classification models included 9 single-mode classifiers based on single brain structural or functional feature,11 multi-mode classifiers based on several brain structural or functional features and 1 multi-mode ROI(regions of interest)classifier based on multi-mode brain regions with significantly different brain structural or functional features.Single-mode classifier with the best classification performance was the cortical thickness classifier(accuracy: 83.1%,sensitivity: 82.4%,specificity: 83.9%,the area under the ROC curve: 0.9).Multi-mode classifier with the best classification performance was fused by cortical thickness classifier and VMHC classifier(accuracy: 86.2%,sensitivity: 91.2%,specificity: 80.6%,the area under the ROC curve: 0.93).And the ROI classifier had the best classification performance of all trained classifiers with accuracy of 98.5%,sensitivity of 100%,specificity of 96.8% and the area under the ROC curve of 0.99,but the ROI classifier had a risk of overfitting.Primary motor area was the brain region that contributed significantly(or contribution rate greater than 95%)for the classification results in nearly all singlemode classifiers.It makes this brain region a potential bio-marker for the diagnosis of sALS.This part of work proved the feasibility of establishing individualized diagnosis tools for sALS by using the brain mapping features,and provide objective basis for clinical diagnosis of sALS.In general,this dissertation systematically studied the changes of brain structural and functional features in patients with sALS based on modern magnetic resonance imaging technology and data analysis methods,and established a new individual level diagnosis tool of sALS by using these features.Through the work of this dissertation,the following preliminary conclusions could be obtained:(1)structural and functional features of the primary motor area should be the focus in the diagnosis of sALS,and the functional features of the non-motor brain areas(e.g.median cingulate and paracingulate gyri)were also important indicators;(2)cortical thickness might be the most representative feature in the diagnosis of sALS by comparing the classification effect of single mode classifier;(3)With the favorable results,this dissertation suggested that the individualized sALS diagnosis strategy based on image characteristics and machine learning might achieve better results than the current EI Escorial standard in the nearly future. |
PDF Full Text Request