Current Search:  discriminant function analysis (x)

View All Items

  • CSV Spreadsheet
(1 - 3 of 3)
DETERMINING SEX AND ANCESTRY OF THE HYOID FROM THE ROBERT J. TERRY ANATOMICAL COLLECTION
Title
DETERMINING SEX AND ANCESTRY OF THE HYOID FROM THE ROBERT J. TERRY ANATOMICAL COLLECTION.
Creator
Kindschuh, Sarah, Dupras, Tosha, University of Central Florida
Abstract / Description
One of the basic goals of the physical anthropologist is to create a biological profile, consisting of sex, ancestry, age, and stature, from the skeletal material that they are presented with. This thesis seeks to explore size and shape differences related to sex and ancestry from the hyoid bones of the Robert J. Terry Anatomical Collection in order to gauge its usefulness in the process of developing a biological profile. A series of measurements were taken from 398 hyoids and analysis was...
Show moreOne of the basic goals of the physical anthropologist is to create a biological profile, consisting of sex, ancestry, age, and stature, from the skeletal material that they are presented with. This thesis seeks to explore size and shape differences related to sex and ancestry from the hyoid bones of the Robert J. Terry Anatomical Collection in order to gauge its usefulness in the process of developing a biological profile. A series of measurements were taken from 398 hyoids and analysis was conducted using a number of statistical methods. Independent samples t-tests were used to examine size differences between sexes and ancestries, while linear regression analysis and principle component analysis were used to examine shape differences. Discriminant function analysis was employed to test the ability of the hyoids to be classified by sex or ancestry. The ultimate goal of the thesis is to provide physical anthropologists with a series of discriminant function equations that can be used to estimate the sex and ancestry of a hyoid. Five equations ranging in accuracy from 83-88% were developed to determine sex of a hyoid, while four equations ranging in accuracy from 70-89% can be used to determine ancestry. In addition, the t-tests, regression analyses, and principle component analysis have identified several variations in size and shape between sexes and ancestries. These analyses have provided further knowledge as to the morphological form of the hyoid, as well as a method that can be easily used by physical anthropologists to assess sex and ancestry.
Show less
Date Issued
2009
Identifier
CFE0002599, ucf:48282
Format
Document (PDF)
PURL
http://purl.flvc.org/ucf/fd/CFE0002599
SEX DETERMINATION USING DISCRIMINANT FUNCTION ANALYSIS OF CARPALS FROM MAYA SITES IN BELIZE FROM PRE-CLASSIC TO SPANISH COLONIAL PERIOD
Title
SEX DETERMINATION USING DISCRIMINANT FUNCTION ANALYSIS OF CARPALS FROM MAYA SITES IN BELIZE FROM PRE-CLASSIC TO SPANISH COLONIAL PERIOD.
Creator
Labbe, Michelle D, Williams, Lana, University of Central Florida
Abstract / Description
The sexing of human skeletal remains is important for identification and demographic purposes. It is made more difficult when elements such as the skull and pelvis are not recovered or are in too poor of a condition to assess. Previous studies have used carpal (wrist) bones of contemporary populations to assess the viability of these skeletal elements exhibiting sexual dimorphism, as these bones are small, compact elements that are usually recovered in good condition. This study evaluates the...
Show moreThe sexing of human skeletal remains is important for identification and demographic purposes. It is made more difficult when elements such as the skull and pelvis are not recovered or are in too poor of a condition to assess. Previous studies have used carpal (wrist) bones of contemporary populations to assess the viability of these skeletal elements exhibiting sexual dimorphism, as these bones are small, compact elements that are usually recovered in good condition. This study evaluates the use of carpal bones recovered from an ancient Maya population from Belize to determine the biological sex of individuals. The study sample is part of the Maya Archaeological Skeletal Collection (MASC), which contains individuals from the sites of Lamanai, San Pedro, Altun Ha, and Marco Gonzalez and dates from the Late Maya Pre-Classic (400 BC-AD 250) to the Spanish Colonial period (AD 1521-1821). Multiple measurements were taken on 36 capitate, 34 lunate, 34 scaphoid, 27 trapezium, 24 hamate, 22 triquetral, 22 trapezoid, and 16 pisiform bones from several individuals. Discriminant function analysis was used to determine if sexual dimorphism is measurable in this population using these elements. Previous studies used populations with known identities, assessing individuals from crypts, graveyards, or medical collections from the last few centuries. This study varies from previous studies as it utilizes archaeological remains, making this study one of the first to evaluate non-contemporary remains with unknown sex. Results of this study demonstrate that this population exhibits sexual dimorphism and discriminant function analysis can be used to distinguish between two groups. This demonstrates that carpals could be used to help determine biological sex of archaeological populations as well as a tool to help with identification in forensic cases.
Show less
Date Issued
2019
Identifier
CFH2000562, ucf:45645
Format
Document (PDF)
PURL
http://purl.flvc.org/ucf/fd/CFH2000562
Automatic Detection of Brain Functional Disorder Using Imaging Data
Title
Automatic Detection of Brain Functional Disorder Using Imaging Data.
Creator
Dey, Soumyabrata, Shah, Mubarak, Jha, Sumit, Hu, Haiyan, Weeks, Arthur, Rao, Ravishankar, University of Central Florida
Abstract / Description
Recently, Attention Deficit Hyperactive Disorder (ADHD) is getting a lot of attention mainly for two reasons. First, it is one of the most commonly found childhood behavioral disorders. Around 5-10% of the children all over the world are diagnosed with ADHD. Second, the root cause of the problem is still unknown and therefore no biological measure exists to diagnose ADHD. Instead, doctors need to diagnose it based on the clinical symptoms, such as inattention, impulsivity and hyperactivity,...
Show moreRecently, Attention Deficit Hyperactive Disorder (ADHD) is getting a lot of attention mainly for two reasons. First, it is one of the most commonly found childhood behavioral disorders. Around 5-10% of the children all over the world are diagnosed with ADHD. Second, the root cause of the problem is still unknown and therefore no biological measure exists to diagnose ADHD. Instead, doctors need to diagnose it based on the clinical symptoms, such as inattention, impulsivity and hyperactivity, which are all subjective.Functional Magnetic Resonance Imaging (fMRI) data has become a popular tool to understand the functioning of the brain such as identifying the brain regions responsible for different cognitive tasks or analyzing the statistical differences of the brain functioning between the diseased and control subjects. ADHD is also being studied using the fMRI data. In this dissertation we aim to solve the problem of automatic diagnosis of the ADHD subjects using their resting state fMRI (rs-fMRI) data.As a core step of our approach, we model the functions of a brain as a connectivity network, which is expected to capture the information about how synchronous different brain regions are in terms of their functional activities. The network is constructed by representing different brain regions as the nodes where any two nodes of the network are connected by an edge if the correlation of the activity patterns of the two nodes is higher than some threshold. The brain regions, represented as the nodes of the network, can be selected at different granularities e.g. single voxels or cluster of functionally homogeneous voxels. The topological differences of the constructed networks of the ADHD and control group of subjects are then exploited in the classification approach.We have developed a simple method employing the Bag-of-Words (BoW) framework for the classification of the ADHD subjects. We represent each node in the network by a 4-D feature vector: node degree and 3-D location. The 4-D vectors of all the network nodes of the training data are then grouped in a number of clusters using K-means; where each such cluster is termed as a word. Finally, each subject is represented by a histogram (bag) of such words. The Support Vector Machine (SVM) classifier is used for the detection of the ADHD subjects using their histogram representation. The method is able to achieve 64% classification accuracy.The above simple approach has several shortcomings. First, there is a loss of spatial information while constructing the histogram because it only counts the occurrences of words ignoring the spatial positions. Second, features from the whole brain are used for classification, but some of the brain regions may not contain any useful information and may only increase the feature dimensions and noise of the system. Third, in our study we used only one network feature, the degree of a node which measures the connectivity of the node, while other complex network features may be useful for solving the proposed problem.In order to address the above shortcomings, we hypothesize that only a subset of the nodes of the network possesses important information for the classification of the ADHD subjects. To identify the important nodes of the network we have developed a novel algorithm. The algorithm generates different random subset of nodes each time extracting the features from a subset to compute the feature vector and perform classification. The subsets are then ranked based on the classification accuracy and the occurrences of each node in the top ranked subsets are measured. Our algorithm selects the highly occurring nodes for the final classification. Furthermore, along with the node degree, we employ three more node features: network cycles, the varying distance degree and the edge weight sum. We concatenate the features of the selected nodes in a fixed order to preserve the relative spatial information. Experimental validation suggests that the use of the features from the nodes selected using our algorithm indeed help to improve the classification accuracy. Also, our finding is in concordance with the existing literature as the brain regions identified by our algorithms are independently found by many other studies on the ADHD. We achieved a classification accuracy of 69.59% using this approach. However, since this method represents each voxel as a node of the network which makes the number of nodes of the network several thousands. As a result, the network construction step becomes computationally very expensive. Another limitation of the approach is that the network features, which are computed for each node of the network, captures only the local structures while ignore the global structure of the network.Next, in order to capture the global structure of the networks, we use the Multi-Dimensional Scaling (MDS) technique to project all the subjects from an unknown network-space to a low dimensional space based on their inter-network distance measures. For the purpose of computing distance between two networks, we represent each node by a set of attributes such as the node degree, the average power, the physical location, the neighbor node degrees, and the average powers of the neighbor nodes. The nodes of the two networks are then mapped in such a way that for all pair of nodes, the sum of the attribute distances, which is the inter-network distance, is minimized. To reduce the network computation cost, we enforce that the maximum relevant information is preserved with minimum redundancy. To achieve this, the nodes of the network are constructed with clusters of highly active voxels while the activity levels of the voxels are measured based on the average power of their corresponding fMRI time-series. Our method shows promise as we achieve impressive classification accuracies (73.55%) on the ADHD-200 data set. Our results also reveal that the detection rates are higher when classification is performed separately on the male and female groups of subjects.So far, we have only used the fMRI data for solving the ADHD diagnosis problem. Finally, we investigated the answers of the following questions. Do the structural brain images contain useful information related to the ADHD diagnosis problem? Can the classification accuracy of the automatic diagnosis system be improved combining the information of the structural and functional brain data? Towards that end, we developed a new method to combine the information of structural and functional brain images in a late fusion framework. For structural data we input the gray matter (GM) brain images to a Convolutional Neural Network (CNN). The output of the CNN is a feature vector per subject which is used to train the SVM classifier. For the functional data we compute the average power of each voxel based on its fMRI time series. The average power of the fMRI time series of a voxel measures the activity level of the voxel. We found significant differences in the voxel power distribution patterns of the ADHD and control groups of subjects. The Local binary pattern (LBP) texture feature is used on the voxel power map to capture these differences. We achieved 74.23% accuracy using GM features, 77.30% using LBP features and 79.14% using combined information.In summary this dissertation demonstrated that the structural and functional brain imaging data are useful for the automatic detection of the ADHD subjects as we achieve impressive classification accuracies on the ADHD-200 data set. Our study also helps to identify the brain regions which are useful for ADHD subject classification. These findings can help in understanding the pathophysiology of the problem. Finally, we expect that our approaches will contribute towards the development of a biological measure for the diagnosis of the ADHD subjects.
Show less
Date Issued
2014
Identifier
CFE0005786, ucf:50060
Format
Document (PDF)
PURL
http://purl.flvc.org/ucf/fd/CFE0005786