Integrated Classification of Amnestic Mild Cognitive Impairment Using. Functional and Structural MRI. Jing Ming

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1 Integrated Classification of Amnestic Mild Cognitive Impairment Using Functional and Structural MRI By Jing Ming B.S., Zhejiang University, China, 2002 M.S., University of Illinois at Chicago, Chicago, 2009 Defense Committee: THESIS Submitted as partial fulfillment of the requirements for the degree of Doctor of Philosophy in Bioengineering in the Graduate College of the University of Illinois at Chicago, 2013 Chicago, Illinois Dr. Richard Magin (Chair) Dr. John Hetling Dr. Glenn Stebbins (Advisor), Rush University Dr. Lei Wang, Northwestern University Dr. Minjie Wu, Psychiatry

2 This dissertation is dedicated to my husband, Yirong, without whom it would never been accomplished. ii

3 Acknowledgements I would like to sincerely thank all members of my thesis committee: advisor Dr. Glenn Stebbins for his guidance in all areas of this project and help me accomplish my dissertation; Dr. Lei Wang for his great help in developing integrated classifier; Dr. Minjie Wu for her valuable suggestion about functional connectivity; Dr. Richard Magin and Dr. John Hetling for their unwavering support from Bioengineering Department. I also would like to thank Vy Dinh of Rush Medical Center for her great help in data collection and organization and Dr. Mehul Trivedi for paradigm demonstration. iii

4 Table of Contents Chapter Page Part I Background Chapter 1 Statement of the Purpose..1 Chapter 2 Background Alzheimer s Disease Mild Cognitive Impairment and amnestic MCI Neuroimaging use in the diagnosis of AD and amci Structural Neuroimaging studies Functional Neuroimaging studies Multi-modality studies of AD and amci Machine Learning Logistic Regression Support Vector Machine Classification studies using SVM and dimensionality reduction...33 Chapter 3 The present study The rationale of the present study The summary of method procedure and organization...41 Part II Method Chapter 4 Study Settings Participants fmri paradigms imaging acquisition...47 Chapter 5 Functional and Structural MRI data analysis Functional data preprocess GLM-based functional contrasts Contrasts for encoding paradigms Contrasts for recognition paradigms Functional connectivity Structural data analysis.54 Chapter 6 Classification Data dimension reduction Stepwise Logistical Regression Support Vector Machine Combine stepwise logistical regression and support vector machine Classification using integrated models...58 Chapter 7 Visualization Group comparison T map based on reconstructed image Global amci index..62 iv

5 Table of Contents (continued) Chapter Page Part III Result and Discussion Chapter 8 Classification results Classifier performance based on individual measure GLM-based functional contrasts Functional connectivity maps Structural measures Integrated classifier performance Functional measure combinations Structural measure combinations smri and fmri measure combinations Comparison of optimal measure combinations 72 Chapter 9 Visualization result GLM-based functional contrast Functional connectivity...77 Chapter 10 Discussion Individual measurements The integrated model Comparison of SVM and SLR Limitation Future direction Conclusion..88 Reference...91 VITA v

6 Table List of Tables Page 1. Demographic information for all participants Classifier performance of individual GLM-based functional contrast Classifier performance of individual functional connectivity map Classifier performance of individual cortical structural measure Classifier performance of integrated models with GLM-based functional contrasts combinations Classifier performance of integrated models with functional connectivity combinations Classifier performance of integrated models with GLM-based functional contrast and functional connectivity map combinations during encoding condition Classifier performance of integrated models with GLM-based functional contrast and functional connectivity map combinations during recognition condition Classifier performance of integrated models with cortical structural measure combinations Classifier performance of integrated models with cortical structural measure and functional measure combinations SVM+SLR 5 fold CV AUC and classification margin of Global amci index for six optimal integrated models 74 vi

7 List of Figures Figure Page 1. Machine learning demonstration A complete separation case with multiple decision surfaces Inter-subject variability of brain activation pattern for four visualspatial paradigms Classification and visualization flow chart of the present study fmri paradigm demonstration for encoding and recognition conditions Global amci index classification plots for six optimal combinations T map of group comparison for Hits Vs. Push (enc02) contrast during encoding condition T map of group comparison for Hits Vs. Push (enc02) contrast reconstructed from all eigenvectors during encoding condition with threshold T= T map of group comparison for functional connectivity of hippocampus during encoding paradigm T map of group comparison for functional connectivity of hippocampus reconstructed from all eigenvectors during encoding condition with threshold T= T map of group comparison for functional connectivity map of posterior cingulate during encoding paradigm T map of group comparison for functional connectivity of posterior cingulate reconstructed from all eigenvectors during encoding condition with threshold T= vii

8 List of Abbreviations AD amci AUC BOLD CV DMN DTI GLM IFP LOOCV LR MCI MTL PC PCA PET ROC SLR SVM VBM Alzheimer s Disease amnestic Mild Cognitive Impairment Area Under Curve Blood Oxygen Level Dependent Cross Validation Default Mode Network Diffusion Tensor Imaging General Linear Model Inferior Parietal Leave one out cross validation Logistic Regression Mild Cognitive Impairment Medial Temporal Lobe Posterior Cingulate Principal Component Analysis Positron emission tomography Receiver Operating Characteristic Stepwise Logistic Regression Support Vector Machine Voxel Based Morphometry viii

9 Summary As a prodromal phase of Alzheimer Disease (AD), amnestic Mild Cognitive Impairment (amci) may be the appropriate stage for clinical trials of early therapeutic intervention delaying AD progress. Blood Oxygen Level Dependent (BOLD) fmri, as a non-invasive functional neuroimaging technique, has the potential to reflect the early neural network change associated with AD pathology at amci stage, and can be used to identify amci patients so as to enrich amci population for clinical trials. In the present study, we investigated how fmri data could be used to differentiate potential amci patients from age-matched healthy controls. We explored the predictive power of multiple high dimensional fmri measures (include seven General Linear Model-based (GLM) functional contrasts and functional connectivity maps of hippocampus, parahippocampus, posterior cingulate cortex and inferior parietal cortex), acquired while subjects performed episodic memory encoding and recognition tasks, through the use of Support Vector Machine and Logistic Regression classifier. We also investigated if integrating different fmri measures could improve the classifier performance. Furthermore, we compare the fmri measure s predicting power to the surface-based cortical structural measures (including cortical thickness, local sulcal depth, local curvature and metric distortion) and investigated if integrating fmri measures with structural MRI measures could improve the classifier performance. Our result demonstrate functional connectivity maps of hippocampus and inferior parietal cortex during encoding tasks achieved highest discriminative power (0.88), ix

10 Summary (Continued) which is comparable to the accuracy achieved by the surface-based structural measure cortical thickness (0.83) and white surface sulcal depth (0.88). Integrating two measures across different modalities (fmri and smri) or from the same modality greatly increased the classification accuracy from 0.88 to over 0.96 (Leave-one-out-cross-validation). The best classification performance was achieved by integrating functional connectivity map of hippocampus with GLM based functional contrast Hits Vs. Push during encoding task (100% 5 fold cross validation accuracy with AUC=0.9). These results indicate combining high dimensional fmri measures with dimensionality control method (Such as Principal Component Analysis) and machine-learning methods (such as Logistic Regression and Support Vector Machine) can possibly differentiate amci patients from control subjects with a high degree of accuracy. Furthermore, the functional and structural brain change of amci subjects reflected by different types of fmri measures (i.e. functional contrasts and functional connectivity) and smri measures may be induced by asymmetrical Alzheimer s pathological process. Integrating multiple measures can provide complementary information to classifiers and greatly increase classification accuracy. Furthermore, the most homogenous neural network feature for patients at amci stage may be the disconnection between hippocampus and prefrontal cortex. This hippo-frontal disconnection causes amci subjects difficulty in the formation of new memory and triggered extensive and more individual-specific compensational brain activation. x

11 Part I Introduction Chapter 1 Statement of the Purpose The main objective of present study is to investigate how fmri data can be used to identify potential patients with amnestic mild cognitive impairment (amci) and differentiate them from age-matched healthy controls. We explored the predictive power of multiple high dimensional fmri measurements and measurement combinations assessed while subjects performed episodic memory tasks. From these analyses, we propose an integrated amci index, in order to build a most efficient classification system for amci diagnosis. Amnestic mild cognitive impairment (amci) is a subgroup of MCI with memory impairment. Patients with amci show a decline predominantly in memory function, but they do not meet criteria for dementia (Peterson et al., 2004). Typically, amci patients have impairment either in memory only or in memory and other cognitive domains, such as executive function, language, and visuospatial skills. However, their daily functional activities are mostly intact, except for some mild interference (Peterson 2011). amci patients carry a very high risk of progression to dementia, and more than 90% of those progress to dementia have clinical signs of Alzheimer's disease (AD) (Peterson 2011; Peterson et al., 2005). The conversion rate from amci to AD is in the range of percent per year, which indicate that after 10 years almost all amci patients progress to AD (Peterson et al., 2001; Tierney et al., 1996; Bowen et al., 1997; Ahmed et al., 2008; Kidd 2008). Therefore, amci is usually treated as a prodromal stage of AD. 1

12 AD is one of major threats to the older population in the developed world. The people who have AD suffer from severe memory loss and have difficulty in engaging in daily activities. Although the symptoms of severe memory loss and impaired activities of daily living are hallmarks of advanced AD, the neuropathological changes in the brain of AD patients appears to be present decades preceding the development of cognitive dysfunction (Braak & Braak 1991; Delacourt et al., 1999). Although there is no absolute cure for AD so far, some therapeutic and pharmaceutical interventions for delaying the onset of AD are under development. Indeed, the Food and Drug Administration (FDA) has already approved four meidcations with this indication. It has been proposed that these interventions could achieve maximum effect when introduced at the very early stage of the disease process (Tariot & Federoff 2003; Petersen et al., 2005). Hence, patients with amci, who have mild to moderate AD could serve as appropriate clinical trial subjects to validate the effect of those interventions. The diagnosis of amci is challenging. According to the MCI diagnosis criteria (Peterson et al., 2001; Peterson et al., 2005; Albert et al., 2011), amci patients should have the following four features: (1) Concern regarding a change in memory function; (2) Impairment in memory or memory and other cognitive domains; (3) Preservation of independence in functional abilities; (4) Not demented. Although clinical criteria guidelines are available, there are still neither standardized methods to implement them, nor cut-off values of certain measures to assure clinician s judgment. The amci diagnosis requires information of intraindividual changes and heavily depends on clinician s expertise and subjective judgment. When used in clinical, these criteria had diagnostic sensitivities of 46% to 88% and 2

13 specificities of 37 to 90% (Visser et al., 2005). Therefore, other supplementary measures are desired to assist the process of identifying potential amci patients more objectively. Along with developments in neuroimaging, biomarkers that reflect different aspect of AD pathology demonstrate promising effects in the diagnosis of amci and identification of subjects who will convert to AD subsequently. These biomarkers include: 1) medial temporal lobe atrophy identified by structural MRI; 2) Temporoparietal/precunues hypometabolism or hypoperfusion identified by positron emission tomography (PET); 3) Amyloid 42 deposit identified by CSF marker or PET amyloid imaging. These established biomarkers are incorporated into the latest research and clinical criteria guidelines to serve as supplementary supportive features for amci diagnosis (Albert et al., 2011), although the relation between, and importance of, different biomarkers is still unclear. Among these neuroimaging biomarkers, medial temporal lobe atrophy may be the best established. However, the macroscopic brain matter atrophy could be the result of long time accumulative neural network change in the brain. Therefore, functional neuroimaging data may be more sensitive than structural atrophy data in terms of recognizing patients at early stages of the disease. Widely used PET functional imaging technique need to inject radioactive tracer into the imaging subjects and also has some constraints, such as low temporal resolution and rigid paradigm design (only block design can be used). Blood Oxygen Level Dependent (BOLD) functional MRI, which also measures brain metabolism indirectly by blood oxygen coupling effect, may serve as an alternative technique to measure functional brain changes for amci diagnosis. Compared to PET, BOLD functional 3

14 MRI has it specific advantage of non-invasiveness, flexible paradigm scheme, and higher spatial and temporal resolution (Devlin et al., 2000). However, it also suffers from the disadvantage of low signal to noise ratio, paradigm dependent result and large amount of inter-subject variability (Cabeza et al., 2000; Ming et al., 2012a). A number of studies have investigated fmri activation involved in memory function for amci patients. These studies reported both increased (Dickerson et al., 2004; Dickerson et al., 2005; Hamalainen et al., 2007; Heun et al., 2007; Kircher et al., 2007) and decreased (Johnson et al., 2006; Johnson et al.,2004; Mandzia et al., 2009; Xu et al., 2007) brain activation within medial temporal lobe, especially in the region of the hippocampus. This discrepancy may due to different experimental setting, statistical procedures and the heterogeneity of amci patient samples. Indeed, Celone and colleagues (Celone et al., 2006) demonstrated that the functional activation change for amci is a non-linear projection. Less impaired amci subjects show increased BOLD response in the hippocampus compared to control subjects whereas more impaired amci subjects show reduced BOLD response comparable to the levels observed in mild AD subjects. Furthermore, reorganization of sensorimotor and visualspatial processing network for amci patients have also been reported (Agosta et al., 2009; Alichniewicz et al., 2012). In addition to regional brain activation change, some studies also explored the neural network or functional connectivity change among multiple brain regions for amci patients (Bai et al., 2008; Bai et al., 2009; Bai et al., 2011; Jin et al., 2011; Qi et al., 2009; Sorg et al., 2007; Das et al., 2012). These researchers hypothesized regional neural activity changes were due to the altered cognitive networking or Default Mode Network (DMN) change. Functional 4

15 connectivity studies for amci, indeed, demonstrated reduced hippocampus connectivity to other brain regions, especially to regions in the DMN, such as posterior cingulate cortex and prefrontal cortex, either during resting-state or task-state (Bai et al., 2009; Bai et al., 2011). Furthermore, diffused compensationally stronger functional connectivity was also revealed for hippocampus, especially hippocampus to those regions within medial temporal lobe (Bai et al., 2009; Das et al., 2012). Overall, these functional MRI studies mainly focused on identifying different patterns of activation between amci patients and control subjects, and have yielded variable results. In order to take advantage of BOLD-fMRI and identify potential fmri biomarkers for diagnosing amci, we need to find the most reliable and sensitive fmri measures. The heterogeneity of amc cohorts must be considered, and for these diverse targeted subjects, we must assess which brain functional change index is most homogenous and has highest predicting power. Besides identifying group differences in the spatial pattern of function/structure, we also need to treat these differences as classification tools (or we should not seek to use the findings to inform diagnostic considerations). Indeed, the most significant p value of group differences in function/structure doesn t guarantee highest classification performance (Odwyer et al, 2012). Comparing the predictive power of multiple fmri measures based on different memory processes, components and/or regions using the same subjects can inform us as to the most homogenous and therefore the most intrinsic brain functional change for heterogeneous amci patients. Furthermore, several integrated amci classification studies, which use measures 5

16 from structural MRI and PET, indicate measures from different modalities or different measures within same modality can provide complementary information so as to improve the final classification accuracy (Desikan et al., 2009, Fan et al., 2008, Park et al., 2012, Hinriches et al., 2011, Zhang et al., 2011; Ming et al., 2012b). Therefore, fmri measures, which reflect dynamic metabolic state of the brain, should also be complementary to structural MRI measures (Kim et al.,2012). Furthermore, the fmri measures based on different aspects of neural activation change, for example, different memory processes or on both regional activation and functional connectivity, can also provide complementary information to the classifier, and finally increase the classification accuracy. A search of the literature reveals that, to date, no study has integrated multiple fmri measures to identify potential amci patients. Finding the most complementary measure combinations can not only help us build a highly effective diagnosis approach, but also can show us which functional brain activation change measures are relatively independent from each other. These independent fmri measures may be induced by asymmetric pathological origin of AD. One difficulty with using neuroimaging for biomarker development is the nature of the data. Since the majority of raw neuroimaging data is high-dimensional by nature, it would first be important to identify approaches that can effectively control the data dimensionality while simultaneously preserving the spatial distribution information to facilitate multi-measures classification. Such an approach is particularly important when working from studies with relatively small sample sizes. (Duin, et al., 2000, Ramirez, et al., 2010, Park, et al., 2012). Another difficulty in the usage of neuroimaging data for diagnostic biomarker development is the lack of a universal cut off value for individual measure. Here, we propose 6

17 an integrated amci likelihood index that depends on T 2 statistical comparisons of multiple measures. This index separates amci subjects and controls by threshold value 0. To begin to address these approaches and explore their applicability, the present study had the following aims: 1) Evaluate the predicting power of multiple BOLD fmri measures (including General Linear Model based contrasts and functional connectivity maps) through encoding and recognition conditions of an episodic memory paradigm, which are specifically intended to activate those brain regions associated with amci pathology. 2) Evaluate the predictive power of fmri and smri measure combinations in order to find the most discriminative combinations and explore the complementarity between different brain measures which may reflect the relationship between different aspects of amci pathological progress, such as gray matter atrophy, white matter integrity damage and gray matter hypometabolism. 3) Propose an effective machine learning classification procedure that can handle high dimension input of multiple whole-brain measures of function and structure that is efficient at feature selection and avoiding overfitting. From the application of this classification procedure we propose an integrated amci likelihood index to assist quick and accurate diagnosis. 7

18 Chapter 2 Background In this chapter, background information of AD, amci, neuroimaging diagnosis and machine learning method is introduced to give context for the importance and design of the present study. 2.1 Alzheimer Disease The present study is closely related with a well known neural disease among the older population: Alzheimer disease (AD). The people who have AD suffer from severe memory loss and have difficulty in finishing daily routines. AD already affected more than 35 million people in the world. Currently, every 100 seconds one person in the United States is diagnosed with AD. The majority of AD occurs in the older population, especially people aged over 85. By 2050, the number of AD cases is expected to double because of longer life expectancy (Finder 2010). AD s main symptom is degeneration in memory and learning domains. The known pathology of AD is the progressive accumulation of abnormal protein (amyloid-beta and hyperphosphorylated tau) in the brain. These abnormal proteins lead to progressive synaptic, neuronal and axonal damage and finally lead to large amount of neuron death and brain atrophy (Braak & Braak, 1991; Delacoourt et al., 1999). The cause of AD is complex. Both genetic and environmental factors affect the progress of AD (Peterson, 2003). The progression to AD is a gradually process. Braak characterize the AD in six neuropathological stages as neurofibrillary tangles and neuropil threads spread from the 8

19 transentorhinal layer Pre- to proper entorhinal cortex and first Ammon s horn sector, and finally to virtually all isocortical association areas(braak & Braak, 1991). End stages AD (Stages V-VI)can be easily recognized by neurophathological examination, however, cases of mild neurofibrillary affection usually do not meet conventional diagnostic criteria of dementia. Although there is no cure for AD so far, early interventions are under development. The FDA has already approved four drugs that are designed to delay AD progression. Therapeutic interventions, like computer-based cognitive training, also show merit to the AD patients (Yu et al., 2009). As studies suggest these interventions yield their greatest effect only when conducted at the very early AD stage (Tariot&Federoff 2003; Petersen et al., 2005), it is therefore crucial for researchers to identify those individuals with very mild AD affection. The current research diagnosis criteria for probable AD include: 1) Gradual and progressive change in memory function at disease onset reported by patients or informants for a period greater than 6 months; 2) Objective evidence of significantly impaired episodic memory; 3) The episodic memory impairment can be isolated or associated with other cognitive changes at onset of AD or as AD advances (Dubois et al., 2007). Compared to the previous National Institute of Neurological and Communicative Disorders and Stroke-Alzheimer s Disease and Related Disorders Association (NINCDS-ADRDA) criteria, these more recent research criteria remove the requirement that patients must be dementia. In stead, several well-established biomarker have been proposed for use in diagnosing early AD and predicting its progress. 9

20 These supportive biomarkers include: 1) medial temporal lobe atrophy, specifically the volume loss of hippocampus, entorhinal cortex and amygdala identified by structural MRI; 2) Abnormal cerebrospinal fluid biomakers, include Low amyloid 42 concentrations, increased total tau concentrations, or increased phosphor-tau concentrations; 3) Reduced glucose metabolism in bilateral temporal parietal regions identified by PET (Dubois et al., 2007). Furthermore Previous studies show some gene mutation strongly indicate future AD pathology, which include amyloid precursor protein, presenilin 1 and presenilin 2 (Bird, 2005). Positive family history also adds risk to developing AD, especially for individuals within the immediate family who have the above autosomal dominant gene mutations (Bird, 2005). In addition, apolipoprotein E epsilon-4 (APOE4) is a genetic susceptibility marker of AD and could predict AD progression when used conjunctionally with memory test score (Peterson et al., 1995; Tierney et al., 1996). Researches also indicate APOE4 gene is a relatively weak predictor of disease process (Peterson, 2003). The APOE4 alone only predicts less than 40% of AD cases, so it needs to be combined with other measures to increase the predictive power. For example, cognitively normal subjects with positive APOE4 and who also show a decreased medial temporal lobe or posterior cingulate cortex hypometabolism are at very high risk to develop AD. Overall, AD is a neurodegenerative disease that is attracting intense research experiments and clinical trials to reveal the underpinning of its pathology and develop effective treatments. In addition to clinical diagnostic criteria, some biomarkers are helping clinicians and researchers identify potential AD patients when their cognitive impairment is not sever enough to cause disability (i.e. at the Mild Cognitive Impairment stage). The detailed usage of 10

21 these established biomarkers is still under further investigation and validation. 2.2 Mild Cognitive Impairment and Amnestic Mild Cognitive Impairment Mild cognitive impairment (MCI) is traditionally defined as a transitional state between healthy aging and dementia. It often presents first as memory related cognitive impairment and later extends to mild impairment in other domains. Current MCI can be classified into four categories: 1) single domain amnestic MCI, which only affect memory function; 2) multi domain amnestic MCI, which affect memory and other cognitive functions, such as language, visuospatial abilities etc; 3) single domain non-amnestic MCI, which affect only one cognitive function, and this function is not memory; 4) multi domain non amnestic MCI, which affect several cognitive functions, and these functions don t include memory. Amnestic MCI (amci) is an overall name for the first and the second categories (Peterson, 2011). The estimated prevalence of MCI ranges from 10 to 20% in persons older than 65 years of age (Busse et al., 2006; Di Carlo et al., 2007; Plassman et al., 2008; Manly et al., 2008; Lopez et al., 2003). According to Mayo clinic study of aging, among 70 to 89 years old MCIs, around 70% are amci (Peterson et al, 2010). And Peterson also point out in his 2011 review of clinic practice of MCI that Nonamnestic type of MCI is less common than the amnestic type and may be the forerunner of dementia that are not related to Alzheimer s disease, such as frontotemporal lobar degeneration or dementia with Lewy bodies. (Peterson, 2011, Page 2227) Since the dominant symptom of AD is memory impairment, it is much more likely that 11

22 amci will proceed to AD than any other type of dementia. And current clinical data show two-thirds of amci patients indeed develop pathological features of AD and proceed to the clinical syndrome of AD dementia within 5 years (Frisoni et al., 2010). Hence, diagnosis of amci is also treated as a strong predictive factor of AD. While completely reversing the progress to AD appears to be currently impossible, early pharmaceutical and therapeutic interventions are under development with the intention to slow down or halt the progress to AD. Therefore, how to identify these amci patients which tend to proceed to AD is particular meaningful. According to the latest clinical diagnosis criteria guideline for MCI (Albert et al., 2011; Dubois et al., 2007), amnestic type of MCI diagnosis consists of four components: (1) Concern regarding a change in memory function. This concern can be from patient or from an informant who is familiar with patient, or from a clinician observing the patient. (2) Impairment in memory or memory and more cognitive domains. There should be poorer performance in memory or memory and more cognitive domains than would be expected for the patient s age and educational background. Impairment in episodic memory and other domains usually is measured by cognitive tests. (3) Preservation of independence in functional abilities. That means, patients may have mild problems performing complex functional tasks, but they generally preserve their independence of function in daily living. The application of this criterion is challenging, because it needs knowledge of the current daily life functional level for each individual patient. (4) Not demented. The cognitive changes experienced by the patients should be sufficiently mild to not interfere with social or occupational functioning. The judgment of dementia requires intraindividual change, but this 12

23 kind of information may not be available for every case. Overall, the latest amci clinical diagnosis criteria is very similar to the one previously described by Peterson et al (Peterson et al., 1999; Peterson et al., 2004). From the beginning, the operationalization of these criteria has been faced wtih difficulties, such as lacking standardized methods and cutoff point of cognitive tests. The clinical judgments are heavily depending on patients subjective complaint and clinicians expertise. So it is not surprising that the clinical diagnosis process doesn t produce reliable outcome. A study indicated that when these clinical criteria are applied to a cohort of memory clinic patients in an observational study, the amci diagnostic sensitivities are ranging from 46% 88%, and specificities are ranging from 37% - 90%. (Visser et al., 2005). More diagnostic markers that can reflect AD associated amci pathology are desired to assist the amci diagnosis procedure. 2.3 Neuroimaging application to the diagnosis of amci Recent developments in neuroimaging techniques enable researchers to look directly at brain structure and dynamic brain functioning to diagnose AD and amci as well as evaluate disease progression. In an effort to diagnose early AD or amci patients, a promising direction of ongoing research is focused on exploiting advanced imaging-based techniques to characterize prominent neurodegenerative patterns during the prodromal stages. Several well established biomarkers have demonstrated promising diagnostic accuracy (all over 80% sensitivity and specificity), and are incorporated in the latest MCI research diagnostic criteria guideline (Albert et al., 2011). They include structural medial temporal lobe changes visible on MRI, temporoparietal area hypometabolism or hypoperfusion seen with PET and changes 13

24 seen in cerebrospinal fluid. In addition to these established biomarkers, researchers are also investigating measures coming from other less validated neuroimaging modalities, such as functional MRI, functional connectivity, DTI and MR spectroscopy. Besides the individual biomarker, the complementarity between different biomarkers is also attracted extensive attention. In the following part, I would like to review some important findings and research trends in structural, functional neuroimaging fields, as well as some integrated classification studies in terms of amnestic MCI diagnosis Structural neuroimaging studies Among all neuroimaging modalities biomarkers, the best established may be atrophy of hippocampus (Modrego et al., 2006). From high-resolution T1-weighted brain anatomic image, researchers observed significant gray matter atrophy in hippocampal, entorhnial and parahippocampal areas (Shcheltens et al., 1992; Korf et al., 2004; Decarli et al., 2007; Duara et al., 2008). Hippocampal atrophy can be evaluated directly by visual inspection of coronal T1 image of the brain (Frisoni et al., 2010). Studies have demonstrated that visual rating scales provide approximately 80%~85% sensitivity and specificity in distinguishing patients with AD from normal aging people (Duara et al., 2008). Such ratings achieved only slightly lower sensitivity and specificity level for diagnosing amci patients (Korf et al., 2004; Decarli et al., 2007). Along with the development of quantitative analysis of neuroimaging data, more accurate methods have been used to assess brain structural change, such as region of interest (ROI) analysis and voxel-based morphometry (VBM) analysis (Dickerson et al., 2001., Du et al 14

25 2001, Krasuski et al., 2002, Csernansky et al., 2000). These studies further demonstrated that brain volume atrophy and longitudinal atrophy rates in temporal lobe, especially in hippocampus and entorhinal cortex are significant imaging markers of AD and can be identified at the amci stage. In addition to gray matter atrophy, disruption of white matter integrity and decreases in white matter volume are also observed in AD patients around the temporal lobe, corpus callosum, and inferior longitudinal fasciculus (Guo et al, 2010), and in amci patients predominantly around bilateral parahippocampus area and temporal gyrus (Stoub et al., 2006; Xie et al., 2006; Rogalski et al.,2009). Studies also show AD pathology affect white matter and gray matter asymmetrically (Stoub et al., 2006; Zhuang et al., 2012). In addition to volumetric data, surface-based cortical geometry features, which represent gray matter atrophy and white matter atrophy, may provide more detailed and complementary information of AD progress. Thanks to the recent development of surface-based modeling (SBM) (Apostolova and Thompson, 2008,Dickerson, et al., 2011), we are able to capture subtle changes of geometry features of the cortical mantle. As an alternative to widely used Region of Interest (ROI) analysis (Jack, et al., 1999) and Voxel Based Morphometry (VBM) (Hamalainen, et al., 2007), these automated surface-based methods model the cortical gray matter mantle and its interfaces with white matter or CSF as geometrical mesh structures. SBM achieves accurate and reliable inter-subject registration of individual cortical mantle surfaces to a template based on high-dimensional diffeomorphic maps (Apostolova and Thompson, 2008; Fischl, et al., 1999; Miller, 2004), thereby providing gray matter thickness 15

26 and area measures sensitive to sub-millimeter changes in neuropsychiatric diseases (Im, et al., 2008a). Previous surface-based studies show extensive cortical thinning in frontal, parietal and medial temporal lobe for patients with very mild AD (Im et al, 2008b; Julkunen et al, 2009; Lehmann et al, 2009). In addition to cortical thickness, studies focusing on cortical geometry changes associated with AD also report wider and shallower sulcal shape due to loss of cortical thickness and white matter volume (Im et al, 2008a, Park et al, 2012). In surface-based modeling, a common approach to register brain surfaces across subjects is to compute an intermediate mapping to a canonical space, such as a sphere (Fischl et al, 1999). However, because of the complex branching topology of some subcortical structures, it generally requires substantial distortions of the native image to map these structures to a sphere. The resulting deformation maps encode the local shape variation of each subject relative to the template. These deformation maps include not only the volume change, but also the direction of the volume change. Recently, this deformation-based morphometry was shown to be more sensitive for detecting group differences than other standard statistics in AD studies (Wang et al., 2011, Xue et al., 2008). Overall, at the amci stage, patients have both gray and white matter atrophy in distributed brain regions. This atrophy may affect multiple morphometry features that describe the pial surface and white matter surface. Examining the predicting power among different structural features and their complementarity with functional MRI data could help to 16

27 understand the underlying pathologic process of amci and detect most sensitive imaging biomarker for amci diagnosis. Therefore, in present study, we use surface-based gray matter thickness, local sulcal depth, local mean curvature and vertex-based metric distortion as four structural MRI features to classify amci subjects from healthy Control subjects Functional neuroimaging studies Although structural neuroimaging techniques are widely used, evidence shows that functional brain change may happen earlier than structural change and thereby suggests that functional neuroimaging offers considerable promise as a technique for detecting early and subtle functional brain changes that occur in subjects at asymptotic stage. The most widely used functional neuroimaging technique used to diagnose amci or AD so far is Positron Emission Tomography (PET). Research shows a progressive reduction in glucose metabolism, as measured by FDG-PET, that has been reported to occur years in advance of clinical symptoms in pathologically verified AD patients for temporo-parietal, frontal and posterior cingulate cortices (Masconi et al., 2009; Nordberg et al., 2010). These decreases in metabolism are correlated with the severity of dementia (Masconi et al., 2005). Patients with AD also show similar reduction in cerebral blood flow using different imaging tracers (Devous et al., 2002; Dougall et al., 2004; Ishii et al., 2005). Furthermore, the similar hypometabolism pattern was seen in asymptomatic adults carrying of either amyloid-beta protein precursor gene or presenilin gene mutations (Mosconi, 2005) and ApoE 4 allele (Small et al., 1995; Reiman et al., 2004). 17

28 Using FDG-PET, patients were diagnosed with AD in the early stages or amci stages with up to 90% sensitivity, although the specificity of differentiating AD from other causes of dementias are lower (Mosconi, 2005; Rinne et al.,2010). In longitudinal studies, FDG-PET studies were able to predict whether a healthy elderly individual would develop MCI and whether a patient with MCI would convert to AD (Mosconi et al., 2005; Mosconi et al., 2006). Medial temporal lobe glucose hypometabolism was identified to be the most sensitive marker for predicting MCI (Mosconi et al., 2005), whereas regional hypometabolism in the posterior cingulate cortex was found to be the earliest and most sensitive marker for predicting the conversion of MCI to AD (Drzezga et al., 2003; Drzezga et al., 2005; Minoshima et al., 1997). Recently, PET has also been used to image Amyloid protein deposit in vivo. There are several ligands available to bind to Amyloid. Among them,[ 11 C]PIB (Pittsburgh compound-b) has been used by most PET imaging studies of amyloid deposits. The landmark study showed a clear retention of PIB in the affected cortices of mild AD patients (Klunk et al., 2004), the signal increase for AD patients could reach to 104% compared to control subjects using PIB as tracer (Lopresti et al., 2005; Ono et al., 2003). Later studies have also demonstrated a widespread increase in [ 11 C]PIB uptake in AD patients(kemppainen et al., 2006; Rowe et al., 2007). Since Amyloid protein accumulation is the fundamental pathology of AD, PET amyloid imaging could provide strongest prediction for amci subjects or even asymptomatic preclinical AD patients. Although PET is very useful for diagnosing potential AD and amci patients, it is an invasive neuroimaging technique requiring the exposure to radioactive ligands. Non-invasive 18

29 imaging techniques, such as BOLD fmri, provide specific advantages such as the ability to repeatedly assess a given patient with risk of radioactivity exposure and flexible stimulation scheme. Because of these advantages, fmri could be used to track the outcome of potential therapeutic intervention at lower risk than that posed by PET imaging. BOLD fmri, characterized by high spatial resolution and noninvasiveness, is a powerful tool to investigate human brain function across distributed networks. In particular, fmri is used to assess the cortical reorganization and brain plasticity of patients with different neural diseases (Thulborn et al., 1996; Detre et al., 2001; Powell et al., 2005; Price et al., 2005; Mitterschiffthaler et al., 2006). A number of fmri studies have already been conducted to find the group difference of brain activation between AD, amci and healthy controls. Episodic memory measures have been proven to be more effective for differentiating between groups than measures of other cognitive domains such as executive functions, attention, constructional ability, or psychomotor speed (Bondi et al., 1999; Lange et al., 2002). fmri studies about AD generic risk factor (e.g., APOE 4 allele carriers) found non-demented adults with APOE 4 allele show increased BOLD brain responses in the medial temporal lobe during episodic learning tasks compared to non-demented adults without APOE 4 allele (Bondi et al., 2005; Bookheimer et al., 2000; Dickerson et al., 2004, 2005; Ham et al., 2006). Furthermore, the increased activity was not restricted to the medial temporal lobe (MTL) but appeared to extend to other cortical regions, such as frontal regions (Bookheimer et al., 2000; Filbey et al., 2006) and parietal regions (Wishart et al., 2006). However, decreases of brain activity in cortical regions, including the inferior parietal cortex, and 19

30 bilateral anterior cingulated regions during a semantic categorization task, have been reported as probably due to the compensation mechanism of the brain (Lind et al., 2006). Although diagnosis of amci is another predictive factor of AD (12% to 15% conversion rate to AD for single domain amci), relatively few studies have investigated functional change in amci using fmri. Using memory process associated paradigms, investigations of MTL generally demonstrate that less impaired MCI subjects show increased BOLD response in the hippocampus compared to control groups, whereas more impaired MCI subjects demonstrated decreased BOLD response similar to the levels observed in mild AD patients (Celone et al., 2006; Dickerson et al., 2004, 2005; Hamalainen et al., 2007; Johnson et al., 2006; Machulda et al., 2003). However, increase in BOLD response appears to be regionally specific to the MTL in MCI patients. Research results indicate greater memory-related deactivation in medial and lateral parietal regions in less impaired MCI and a loss of activation in more impaired MCI and mild AD adults (Celone et al., 2006). Furthermore, reorganization of sensorimotor and visualspatial processing network for amci patients has also been reported (Agosta et al., 2009; Karolina et al., 2012). More specifically, a study done by Machulda and colleagues (Machulda et al, 2009) evaluated the brain activation for amci during encoding and recognition block design tasks. Their result showed controls had greater activation than amci in bilateral temporo-parietal and frontal regions during encoding task, and controls had greater activation than amci in predominantly temporo-parietal regions bilaterally during recognition task. 20

31 And another study done by Trivedi and colleagues (Trivedi et al, 2009) use event-related design to examine multiple functional contrasts based on subjects performance during encoding and recognition condition of episodic memory task. Their result showed that the amci group displayed significantly less activation in the right hippocampus gyrus, right inferior parietal and some regions in inferior frontal cortex during encoding condition. But using contrast based on behavioral performance (i.e. Hits Vs. Misses), results showed that amci patients displayed significantly greater activation in the right hippocampus. During recognition condition, amci patients displayed significant greater activation in the left inferior frontal cortex, but less activation in the medial prefrontal cortex and the left parahippocampal cortex based on the contrasts of successfully recognition (i.e. Hits Vs. Misses). In addition to regional brain activation change, some studies have also explored the neural network or functional connectivity change among multiple brain regions for amci patients (Bai et al., 2008; Bai et al., 2009; Bai et al., 2011; Jin et al., 2011; Qi et al., 2009; Sorg et al., 2007; Das et al., 2012). These researchers hypothesize regional brain neural activity changes are due to the altered cognitive networking or Default Mode Network change. Functional connectivity studies for amci indeed demonstrated reduced hippocampus connectivity to other brain regions, especially regions in the Default Mode Network (DMN), such as posterior cingulate cortex, prefrontal cortex, either in resting-state or task-state (Bai et al., 2009; Bai et al., 2011). Furthermore, diffuse compensational stronger functional connectivity was also revealed for hippocampus, especially within medial temporal lobe (Bai et al., 2009; Das et al., 2012). 21

32 From the above review, we can see there is a large amount of variability in functional MRI results related to amci patients. A number of potential sources can cause this variability. Variability may due to the different types of paradigms, different baseline condition for contrast calculating, different brain registration and different statistical procedures used by different research group. Another more intrinsic reason this variability may be the nature of BOLD signal that arises from the local cerebral hemodynamic response to the increased neuronal activity associated with performing a task (Ogawa et al., 1990; Kwong et al., 1992; Bandettini et al., 1992). BOLD contrast is small (1-5% signal change), delayed from the stimulus, and requires signal averaging with statistical methods for detection. BOLD contrast is also subject to psychological factors, including mood, attention, strategy and learning, as well as to non-psychological factors, including differences in scanner equipment, head motion and physiological condition (Ming et al, 2012a). More importantly, the variability of fmri study results may demonstrate the great heterogeneity of BOLD signal among amci subjects and also the heterogeneity of amci population itself. Although the term amci has been proposed and used in the literature for over a decade, many controversies regarding to its characterization, definition, and application in clinical practice still remain unresolved. According to one previous fmri study associated with visual spatial processing, the inter-subject variability of whole brain activation pattern among healthy controls is striking even for very basic visual motor task (Ming et al., 2012a). To use fmri data as a possible biomarker for early diagnosis of MCI or AD, we need to 22

33 control the data variability to find the most homogenous and robust brain functional change for amci patients. However, direct comparison of the existing fmri studies results on amci is difficult due to different experimental setting and statistical procedures. It is also difficult for us to identify which region and which type of fmri measurement is most suitable to be used as a classifier based on the results in the literature because most studies only pay attention to the spatial pattern of group difference, but do not treat it as a classification task. The most significant P value doesn t guarantee highest classification performance (Odwyer et al, 2012). Comparing the predicting power of multiple fmri measurements based on the same subjects, same paradigm setting, but different statistical procedures, could help to shed light on the usage of fmri as potential biomarker for early diagnosis of amci or AD. In this study, we detect the BOLD signal difference between amci subjects and age-matched normal control subjects, and also treat it as a classification task. We use an event-related episodic memory task to arouse brain activation within the brain regions most affected by AD progress. The event-related design enables us to examine different contrasts based on subject behavioral performance, such as successful trails versus unsuccessful trails. Since the brain compensation network is forming gradually and needs to go through intensive self-training, these subtle brain activation contrasts may reflect the earliest brain network change of episodic memory task for amci patients. In addition, we generate functional connectivity maps for five key regions for two task conditions. DTI and VBM research indicate the white matter integrity damage may also happen to the early AD or MCI patients. The extensive gray matter atrophy in medial 23

34 temporal lobe and altered brain activation pattern for episodic memory may be due to this interrupted connectivity across brain regions. There are also fmri studies measuring the default mode network that indicate alternations for patients with AD or MCI. So functional connectivity of selected seed regions during episodic memory task may be more sensitive and more homogeneous among amci subjects, and hence, could serve a better biomarker for amci diagnosis multi-modality studies of MCI and AD. So far, this introduction only touched the researches using only structural MRI or only functional MRI to classify amci subjects from controls. However, because of the complicated pathologic process of AD, multiple morphometry and functional changes may happen simultaneously in the brain. So different measures in structural MRI and functional MRI data may provide complementary information of the brain change, integrating them could further increase the classification accuracy. Recently, integrated classification has become the topics of many AD or MCI studies (Desikan et al., 2009, Fan et al., 2008, Park et al., 2012, Hinriches et al., 2011, Kim et al., 2012, Zhang et al., 2011). Most of these integrated studies are based on the Alzheimer s Disease Neuroimaging Initiative (ADNI) data (Jack et al., 2008), which specifically focuses on integrating structural MRI data with the FDG-PET images. Despite the different classification algorithm used, the majority of the studies report improved classification performance compared to using single modality data, indicating multi-modality/measure classification will be a promising direction for find neuroimaging biomarker of early AD. 24

35 In addition, some studies also pay attention to integrating multiple features of structural modality. For example, Park et al (Park et al., 2012) report a study integrating cortical thickness, sulcal depth and find improved classification performance using this within modality integration. Another study which integrates cortical thickness, white surface sulcal depth and metric distortion, also show improved classification performance for 207 subjects, among whom patients with CDR=0.5 and normal controls with CDR=0 (Ming et al., 2012b) One study which utilized features from most modalities was conducted by Hinrichs and colleagues (Hinrichs et al., 2011). This study integrated VBM, Tensor-based Morphometry (TBM) of structural MRI (Jacobian determinants which represent shape distortion relate to a template), FDG-PET, biological measures (including Tau, Amyloid-beta, P-Tau, T-Tau and APOE level), and cognitive performance scores to differentiate AD from control subject and separate MCI subjects who progress to AD from other, non-progressing MCI subjects. Using all modalities and a multi-kernel learning classification method, they achieved ROC Area Under Curve (AUC) value for the differentiation of AD from control and AUC for separating progressing MCI from non-progressing. Despite the evidence that fmri could serve as a promising non-invasive functional neuroimaging biomarker, very few studies try to treat fmri as an imaging modality and integrate it with other structural MRI-based MRI measures or cognitive measures. One such study was conducted by Kim and colleagues (Kim et al., 2012). They used a visual-motor event-relate paradigm. They did not use a paradigm specially addressing the episodic memory 25

36 circuit. However, even this visual-motor task demonstrated decent classification performance, which is consistent with the study done by Alichniewicz and the colleagues (Alichniewicz et al., 2012). After using a Support Vector Machine classifier with an automatic voxel selection procedure, the Kim group demonstrated around 14% error rate using a fmri-biomarker compared to approximately 40% error rate using a structural based biomarker. In addition, integrating fmri biomarker and structural MRI biomarker together generate around a 6% error rate. This study demonstrates fmri is more sensitive than structural MRI data and could be very promising as neuroimaging biomarkers to identify early MCI subjects. And in the present study, the prediction powers of multiple functional MRI measures were explored. We also investigated the combination of different fmri measures and integrating them with smri measures. The unique advantages of the present study include: 1) we used even-related episodic memory paradigm which can specifically arouse brain activation in those brain regions most associated with AD progression; 2) we are not just using a single GLM-based contrast, we also developed functional connectivity, and compared the discriminative power of integrating different functional MRI measures with structural MRI measures; 3) instead of regional volume change (cortical thickness), we used surface based cortical morphemory measures: cortical thickness, sulcal depth, metric distortion and local mean curvature, which reflect both gray matter and white matter atrophy; 4) When using the SVM algorithm, dimensionality reduction procedure was applied to boost classification reliability. This fourth point of advantages will be introduced more detailed in the next part of introduction: machine learning. 26

37 2.4 Machine Learning Machine learning is a terminology to describe computer learning with experience. Machine learning technology is widely used for many applications, such as search algorithms, machine translation, speech recognition, face recognition and robot control. In terms of clinical use, machine learning also can help develop efficient diagnostic systems to facilitate the identification of specific diseases. The basic flow of machine learning classification is demonstrated in figure 1. The classifier first builds a model between outcome (Y) and independent variables (X). Training data include N independent observations of X and corresponding outcome Y. The classifier uses these known pairs to train the model weights (w) through certain learning rules, such as maximum likelihood. The trained weights (w) are used to predict Y for new or test observations of X. (X,Y) pairs (train time) Learning (X,?) (test time) Inference Predict Y w Figure 1. Machine learning demonstration. In this study, we mainly used two machine learning approaches: logistic regression and support vector machine to perform classification. Logistic regression is a standard statistical procedure of classification in clinical use. On the other hand, SVM is supposed to be a more advanced algorithm because of its ability to prevent overfitting and handling of non-linear 27

38 separation. We compared the classifier performance between these two methods and also integrated the two methods to boost classification accuracy. The following is brief introduction for each of them respectively Logistic regression Logistic regression is a standard statistical classification method in the clinical area. It has binary output, as 0 or 1, and it models the log odds of an event as the linear regression of multiple independent features. For one dimension linear logistic regression, decision function is stated in equation 2.1. And the extended d-dimension decision function is stated in equation 2.2. PY ( wx, ) 1 wx w0 1 e (2.1) 1 PY ( wx, ) e ( wx 1 1 w2x2... wdxd w0) 1 (2.2) Here, if we are given the independent features (X), and model parameterization (w), we are going to infer the disease status of a patient (Y). During learning process, the algorithm is trying to find the best w given (X,y) pairs. The best w is achieved by maximizing the log likelihood for both Y=1 and Y=0 as in equation 2.3. The decision surface of logistic regression is linear, as we can see from the decision function: if P = 0.5 as the threshold of classification, then decision surface is represented by equation

39 j j j j j j lw ( ) ( Y ln PY ( 1 X, w) (1 Y)ln PY ( 0 X, w)) j (2.3) wx wx... wx w n n 0 (2.4) The main constraints of logistic regression are that: 1) it intends to build a classification model that fits a set of patients (training set) optimally, so it often results in a model that fits the training data too well, but is not capable of making good prediction in another data set. This is called over fitting the data. 2) The decision surface of logistic regression is linear, so it can t handle non-linear data distribution Support Vector Machine Support Vector Machine is a more advanced algorithm developed by Vanpik (Vanpik, 1995) and has been used in quite a number of neuroimaging studies to classify MCI or AD patients from controls, demonstrating promising results (O Dwyer et al., 2012; Kloppel et al., 2008; Magnin et al., 2009; Fan et al., 2008; Plant et al., 2010; Lerch et al., 2006; Davatzikos et al., 2008; Wee et al., 2011). Unlike logistic regression, which maximizes post-estimate likelihood of data, support vector machine directly maximizes the accuracy (margin). Consider the classification problem in figure 2. It is a complete separate case, there is no maximum likelihood, and a searching algorithm that doesn t consider the capacity problem can generate several spaces that meet the separation criteria. But SVM can generate the optimal separating surface. 29

40 Figure 2. A complete separation case with multiple decision surfaces. Let s model this problem using formulas. For this general simple two-class pattern classification problem: Given l ( X1, Y1 ),...,( Xl, Y l) data point,where X i, for i 1,..., l is a feature vector of length d and Y { 1, 1} i is the class label for data point X i. We need to find a classifier with the decision function, such as Y f( x). The decision function of the classifier is f ( x, ) sgn( w x b). If the training data is linearly separable, then a set of { wb, } pairs can be found such that the constraint y ( x w b) 1 0 i satisfied. Notice that there is ambiguity in the magnitude of w and b. They can be arbitrarly scaled so that f( xp,{ w, b}) 1, where x p is the training data nearest to the decision plane (also called support vector). This means the distance from one point on the boundary to the decision plan is 1/ w. i i As we can see from figure 2, the classifier with the largest margin will give lower expected risk and better generalization. Comparing the two different decision planes in Figure 2, the classifier with the smaller margin will have higher expected risk. The margin for this 30

41 linear classifier is 2/ w. In order to maximize the margin, one needs to minimize the w with constraints y ( x w b) 1 0 i. In short, the training of this classifier is achieved by i i solving a linearly constraint optimization problem. For the situation that complete separation is not possible, SVM algorithm is revised by adding a slack variable to the constraint and another variable C to balance the misclassification cost and margin maximization. And the revised SVM optimizing algorithm (also called soft margin SVM) can be represent by the following primal/dual problem (equation ). Primal: w min w, 2 2 C (2.5) i i T s.t. y ( w x b) 1 i (2.6) i i i 0 i (2.7) i Dual: max yy( x x) (2.8) i i i j i j i j i, j s.t. 0 i C i (2.9) iyi 0 i (2.10) i w iyixi 0 i (2.11) i In the primal problem, the slack variable i represent the distance from misclassified or within unit distance point x to the margin (boundary surface). If 0 1, the point does i i not have the maximum margin but still is correctly classified. But if i 1, then the point is 31

42 misclassified. Variable i constrained by cost C, serves as the penalization for misclassification. A higher C value will give a larger penalty for classification error, and this means is allowed to have a larger value; hence, each misclassification data can assert a stronger influence on the boundary. Note that in the dual problem, the optimal hyperplane parameter w can be represented as a linear combination of the { x, y } pair: w iyixi, and the coefficients of the such a i linear combination is the dual variable i. Variable i decides the contribution of each point to the optimal hyperplance surface. The dual problem can be solved by quadratic programming, since w is convex and all constraints are linear. For this particular problem, the Karush-Kuhn-Tucker (KKT) conditions are formed. From these KKT conditions, the following conclusion can be made as in equation if i 0, then yi( w xi b) 1 if i 0, then yi( w xi b) 1 (2.12) Now we can see the significance of the value for each training point. Those points with nonzero values will fall on the +1 or -1 plane. These are the points that contribute to defining the decision boundary. These data points are called Support Vectors (SV). SVs with larger are more important, because they have stronger influence on the decision boundary. Solve the maximization of dual problem will give the value for all. Another big advantage of SVM is that it is able to deal with non-linear classification. SVM achieve this by mapping the training data from n to some higher Euclidean space H, which possibly has infinite dimensions. In this high dimension space, the data are linearly 32

43 separable; hence the linear SVM formulation above can be applied to these data. In the above SVM formulation, the training points only appear in the form of dot products, i.e. x i x j. These can be replaced by dot product in the Euclidean space, i.e. ( x ) ( x ) where i j : N. The three widely used SVM kernels are: 1) Gaussian radial based function (RBF), kernel function: 2 x y K( x, y) exp( ) ; 2) Polynomial of degree d, kernel function: 2 2 K( x, y) ( x y 1) d ; 3) Multi layer Perceptron, kernel function: K( x, y) tanh( x y ). In the present study, we use RBF and Polynomial as the nonlinear SVM kernels for SVM based classifier Classification studies using SVM and dimensionality reduction In sum, SVM has the advantage of automatic multivariate, maximizing the margin and determining nonlinear decision surfaces, so it can separate the nonlinear distribution data in very high dimensional space. Currently, a number of studies are integrating this advanced machine learning method into the early diagnose of AD and MCI (O Dwyer et al., 2012; Kloppel et al., 2008; Magnin et al., 2009; Fan et al., 2008; Plant et al., 2010; Lerch et al., 2006; Davatzikos et al., 2008; Wee et al., 2011;Kim et al, 2012; Park et al, 2012; Hinriches et al, 2011; Oliveira et al, 2010), and the results are promising. In the most studies using a SVM classifier, the voxels (vertices) within certain ROIs or voxels (vertices) across the whole brain are used as features. Usually, the number of features used in such methods is very large, especially for surface based measures; the vertices number 33

44 across the whole brain is in the tens of thousands. Based on the common procedure, one may link the feature values into a long vector and use it as one feature vector. To study N patients, one would have feature vectors whose dimensionality is in the tens of thousands, that means there going to be more than independent variables in models if using traditional statistical method, which make direct usage of logistic regression impossible. Typically, one would have less than one hundred patients in a fmri study. Thus, researchers are left with a situation where the number of observation (less than 100) is far less than the dimensionality of the features, which leads to unreliable classifier performance. This problem is represents a small sample size problem. When we try to integrate multiple features from different modality, this problem is more apparent, because adding one more feature might mean adding more than 10,000 dimensions into the feature vector. So integrating multiple features becomes impractical using this simple voxel concatenating method. One way to overcome this problem is to reduce the dimensionality of the features vector. Hinriches et al (Hinriches et al., 2011) report a study using multi-kernel learning, which introduce weight vectors to zero for lots of non-discriminative vertices on the surface. Hence, SVM classifier could classify data only based on those non-zero vertices. This method is not only reduce the dimension of the data, but also integrates brain region selection into the scheme, i.e. classifier only keep those voxels which show group difference. However, the dimension of the feature vector in this study is still around 10000, may still too large for a small sample size study. O Dwyer and colleagues (O Dwyer et al., 2012) used a different SVM feature selection method termed Relief (Robnik-Sikonja & Kononenko, 2003) to reduce the feature input to the SVM classifier. They selected 500 to 3000 of the most discriminative 34

45 voxels through whole brain and concluded 500 to 1000 voxel had given optimal classification results. For pure dimensionality reduction, the most straight-forward method is to use manifold learning methods. Principal component analysis is a well-known manifold learning method. Through it, the dimension of the feature vectors will soon become same as the number of observations so as to enable multi-modality classification on relatively small samples. For paradigm dependent fmri (not resting state fmri), this method may be specifically useful, because the subject number of paradigm fmri were usually below 50. PCA-based classification has been used to separate patients with schizophrenic (Cobia et al., 2011) from non-schizophrenic controls. Specifically, one study conducted by Park and colleagues (Park et al., 2012) used PCA to reduce the dimensionality of cortical features: cortical thickness and sulcal depth, to differentiate MCIs from controls. They achieved 0.79 accuracy based on dimension-reduced data, and 0.69 using long feature vector. However, this study used all voxels from whole brain but change the representation of this whole-brain information. That means, they input all eigenvectors into the classifier, including those represented non-discriminative regions of the brain. Selection of brain regions that are truely different between groups may further improve classifier performance. Hinrichs and colleagues also report improved classifier performance when comparing results from weighted multi-kernel SVM to long vector-based SVM. This indicates dimensionality reduction and voxel selection are very important for the usage of SVM classifier. In the present study, we not only used PCA to reduce data dimension, but also use stepwise logistic regression to select those eigenvectors that showed significant correlation 35

46 with subject status. By doing this, we get rid of the affect of large amounts of non-discriminative voxels or vertices, and we only use the information within those AD-associated brain area that are informative. We hypothesize that this approach should further improve the classifier performance. 36

47 Chapter 3 The present study The in this chapter, I summarized the rationale of the present study, demonstrate the related primary study results, stated specific aim and hypothesis of the present study and briefly introduced the method procedure and organization of the present study. 3.1 The rationale of the present study As introduced in the background, fmri, with it specific advantage of non-invasiveness, high sensitivity and flexible paradigm scheme, should be a promising neuroimaging biomarker for early diagnosis of amci. But due to the complexity of fmri data, appropriate paradigm selection, statistical analysis scheme, as well as the measure selection should be investigated carefully before using it in the real clinical setting. fmri data are very noisy by nature (Ogawa et al., 1990; Kwong et al., 1992; Bandettini et al., 1992). The brain activation patterns for same task with similar performance level may include striking inter-subject variability (Ming et al., 2012a; Cabeza et al., 2000). Our preliminary result of fmri study from a series of simple visuospatial processing tasks (include dot saccade, letter saccade, 3-letter-word recognition and 6-letter-word recognition), indicates that healthy control subjects with similar performance level show distinctive and disparate brain activation patterns (Ming et al., 2012a). In this study, ten normally-sighted subjects were asked to perform four tasks three times while being scanned with the interval at least four weeks between sessions. The brain activation pattern of each paradigm for each subject in each session was summarized by a pie chart that represented the distributions of activation across the frontal, parietal, temporal and occipital brain lobes (Figure 3). While the 37

48 brain activation patterns across subjects were highly different, it was interesting to note that these patterns were relatively consistent across different sessions for the same subject. This observation is consistent with the previous fmri studies that show inter-subject variability is larger than intra-subject variability (Fletcher et al., 1999; Little et al., 2008). General, these results demonstrate that fmri is reliable in terms of repeated measuring for same subject over time. However, the revealed group difference by normal statistical comparison method may Figure 3. Inter-subject variability of brain activation pattern for four visualspatial paradigms. Pie charts for control subjects ( ) illustrating the percentage activation across the 4 lobes of the cerebrum: frontal lobe = blue, parietal lobe = green, temporal lobe = yellow and occipital lobe = magenta for all control subjects across 3 sessions for the 4 paradigms. The missing pie charts indicate where data were not calculated due to excessive head movement. 38

49 be compromised by the inter-subject variability within each group. Therefore, finding fmri measures with minimum within group inter-subject variability is crucial for developing highly sensitive and specific classifiers. In this study, we explored the inter-subject variability via the classifier performance using various fmri measures. These measures include the 7 functional contrasts from both encoding and recognition paradigms of a episodic memory task, as well as the functional connectivity maps of the four key regions close associated with AD pathology as identified in the scientific literature. We used an event-related episodic memory task to arouse brain activation within the brain regions most affected by AD progress. Event-related design enables us to examine different contrasts based on subject behavioral performance, such as successful trails versus. unsuccessful trail and successful trails versus push baseline. Since the brain activation patterns usually are correlated with subjects behavioral performance, separating successful trails from failed trails could reduce the brain activation variability across trails and increase the statistical power during regression. In addition, we generated functional connectivity maps for four key regions for two task conditions. DTI and VBM research indicated the white matter integrity damage also happened to the early AD or MCI patients (Zhuang et al., 2012; Stoub et al., 2006; Rogalski et al., 2009). The extensive gray matter atrophy in medial temporal lobe and altered brain activation pattern for episodic memory may both be due to this interrupted connectivity across brain regions. There are also fmri studies related with functional connectivity (Bai et al., 2011; Bai et al., 2009a; Bai et al., 2009b; Das et al., 2013) and default mode network (Jin et al., 2012; Qi et al., 39

50 2009; Sorg et al., 2007) that indicate alternations in patients with amci. So, functional connectivity of selected seed regions during episodic memory task may be a very sensitive and homogeneous marker among amci subjects, and could serve as a better biomarker for amci diagnosis. PET data reveales that the regions with hypometabolism in potential AD subjects are located in posterio-temporal brain regions. These regions are not completely overlapping with the areas of gray matter atrophy, i.e. hippocampus and parahippocampus (Villian et al., 2008). This discrepancy indicates the AD progress may have multiple pathological circuits and that white matter integrity changes may affect the gray matter atrophy around the network associated with that part of the white matter. So, structural MRI and functional MRI may carry complementary information to diagnosis MCI patients. Furthermore, different fmri measure also could be complementary. Integrating them should further increase the accuracy of classifier. For structural data, in addition to widely used cortical thickness, white matter volume change may also change the geometry of the white surface, or the sulcal depth, curvature, and the Jacobian distortion. Surface-based data has high-resolution spatial distribution information compared to single brain volume values within one region. Since gray matter atrophy is highly correlated with white matter damage, but goes through different processes, these geometry data may be complementary among themselves. This within-modality complementarity reflects the asymmetry of AD associated change in gray matter and white matter respectively. 40

51 Furthermore, in present study, we use the advanced SVM classifier to increase the cross validation value and compare the result to that from standard logistic regression methods. In order to achieve optimal performance, we reduce the data dimensionality of the feature vector to the level of the subject due to our specific small sample size. And stepwise logistic regression was used to select those eigenvalues that have significant correlations with the patient status. This indirect voxel selection should further increase the classifier performance. Additionally, to display the results of the classification scheme, we use a visualization method based on Hotelling's T 2 statistic. This method enables us to develop a global amci likelihood index that integrats discriminative information from multiple features. The single value amci likelihood index allows for quickly determining if a subject belongs to the patient or control group, and how close he/she lies to the disease threshold. Since there is no universal cut off value for certain cognitive test to diagnosis amci, the global amci likelihood index based on patients and controls biomarker value distribution provides a cut off value from multi-measure statistical comparison, so as to use for individual subject s diagnosis. 3.2 The summary of the method procedures and organization In this part, I briefly introduced the method procedure and organization to make the thesis easier for reading. The ultimate aim of current study was to propose a highly efficient classifier and the corresponding visualization tool that can assist early diagnosis of amci. In order to construct 41

52 the classifier, multiple features with possible discriminative powers were investigated and combined together. In this study, we primarily used three types of brain measures: 1) functional brain activation contrasts based on General Linear Model (GLM) during the above stated paradigms (two contrasts for encoding paradigm and four contrasts for recognition paradigm); 2) functional connectivity map based on four predefined seed regions (hippocampus, parahippocampus, posterior cingulate, inferior parietal cortex); 3) cortical structure measures generated by freesurfer which include cortical thickness, cortical average convexity, cortical metric distortion and curvature, which reflect the cortex geometry pattern change associated with AD progress. Two machine learning methods were used to construct the classifier: logistical regression and Support Vector Machine. These two methods were applied individually and also conjunctively. Furthermore, the classifier was constructed based on single measures, as well as measures from different modality integrated together. Principle component analysis (PCA) was used to reduce the data dimensions for the classifier. Classification performance under each condition was analyzed and compared using Leave One Out Cross Validation (LOOCV). For the most accurate models, global amci likelihood indexes and reconstructed group difference maps were generated for visualization. Furthermore, ROC analysis was applied using 5 fold Cross Validation, and AUC was calculated for further differentiation among optimal models. The global amci index was constructed as a single numeric value using 42

53 Hotelling's T 2 statistic, integrating the most discriminative features. Zero was the separate point for two groups. This index could serve as integrated diagnostic index with cut off value of 0 for identifying amci. The reconstructed group difference maps reflected brain regions distribution associated with different aspect of amci pathologic process. The general step of current study was demonstrated in Figure 4. Functional Scan Structural Scan Functional contrast Functional Connectivity Cortical measurements Principal Component Analysis for reducing data dimension Stepwise Logistic Regression classification Support Vector Machine classification Stepwise Logistic Regression + Support Vector Machine classification Classifier performance comparison Reconstructed T map using selected eigenvectors Global amci index scatter plot Figure 4. Classification and visualization flow chart of the present study. 43

54 Part II Method Chapter 4 Study settings 4.1 Participants There are 24 subjects included in the study. Nine of them were amci subjects and 15 of them were Normal aging subjects. All participants were right-handed as determined by the Edingburgh Inventory and provided informed consent as approved by the Rush University Medical Center Institutional Review Board. Each participant in this investigation received a detailed clinical evaluation including medical history, neuropsychological examinations, informant interviews and laboratory tests. Basically, these evaluations examine a variety of cognitive domains including semantic memory, working memory/attention, perceptual speed and visual-spatial ability. Normal neurological examination is used to determine the subjects labeled as Old Normal Controls (ONC). The criteria for this classification are: normal cognition relative to the normative data for each of the neuropsychological test measures and a score of >=27 on the Mini-Mental State Examination. The diagnostic criteria for amci included: (1) presence of memory complaints by the patients or informants during interview; (2) neuropsychological examination demonstrating normal cognition relative to test normative data in all cognitive domains except episodic memory; (3) essentially intact activities of daily living, and (4) no diagnosis of dementia. The exclusion criteria for all participants were evidence of any other neurological, psychiatric or systemic disorder that could cause cognitive impairment. The 44

55 demographic information of all subjects is listed in table 1 (table 1). There is no significant difference of gender, age and education level between two groups. amci ONC Comparison (P-value) Number of subjects 9 15 Male/Female 3/6 9/ P<0.2 Age 77.22± ±5.921 P<0.217 Education 15.55± ±3.020 P<0.649 Table 1. Demographic information for all participants 4.2 fmri paradigms The fmri paradigms used in the study consisted of serial presentations of black and white line drawings of nameable objects from the Snodgrass and Vanderwart training set (Figure 5) (Snodgrass & Vanderwart., 1980). Visual stimuli were presented to the participants using a magnet-compatible projector (Resonance Technology, Inc., Van Nuys, Calif., USA), which back-projected the visual images onto a screen that was mounted in the bore of the magnet. The participants viewed the projected image via a mirror-mounted head coil and responded to the images via magnet-compatible button-press device. Figure 5. fmri paradigm demonstration for encoding and recognition conditions. 45

56 Two experimental phase paradigms were presented: 1) encoding; 2) recognition. Each phase consisted of 1 scanning series. During the encoding portion of the fmri paradigm, the participants were asked to determine whether the image presented represented a man-made or a naturally occurring object and respond via pushing the magnet-compatible pushbuttons as quickly as possible (man-made objects required button push with the left hand, and naturally occurring objects required button push with the right hand). Each picture displayed for 5000-ms then change to another picture. Therefore, 5000-ms inter-stimulus interval was used in this event-related design paradigm. Naturally occurring pictures (n=50) were randomly intermixed with man-made pictures (n=50), with the rule that no event type could be repeated more than 3 times consecutively. Null events were the presence of word Push (n=50) on the screen, and subjects need to respond them via pushing the buttons using both hands. Push were also randomly intermixed with the natural and man-made objects with the same display time and interstimulus interval. There were a total 150 events during the encoding paradigm. Furthermore, no 2 subjects received identical forms of the encoding task. During recognition phase of fmri paradigm, the participants were asked to remember the items that they would distinguish between items that had been previously presented during the encoding portion of the experiment and novel items that had not been shown. The participants were asked to try and respond to every item as quickly as possible. The participants were not instructed to encode (remember) the novel items that were presented during recognition paradigm. The previously presented and novel items were randomly intermixed with null events of the word push. This random setting let every subject received a different from of the recognition task. 46

57 During the recognition paradigm, an average of 52 novel items intermixed with an average of 104 previously presented items (all 104 presented during encoding) and 44 null events of the word push for every subject, with the same rule that no more than 3 same event type could be presented consecutively. For the recognition phase of the experiment, each of the stimuli was presented for 4500 ms with a 500-ms interstimulus interval. Subjects behavioral responses to previously presented, novel and null events were recorded by button presses in the left, right or both hands, respectively. Behavioral data were recorded to assure that the all subjects understood the task instructions correctly and the amci subjects were able to perform the task adequately. 4.3 imaging acquisition Imaging was performed on a 1.5-tesla General Electric scanner with an LX Horizon high-speed gradient upgrade (General Electric Medical Systems Signa, Waukesha, Wisc., USA) with a standard quadrature head coil for signal acquisition. Head movement was minimized using foam pillows around the participant s head, as well as a securing tape across the forehead. Magnet-compatible vision correction lenses were used when appropriate. Functional images (repetition time = 2,250 ms; echo time = 40 ms; 24-cm field of view; 84 flip angle; slice thickness = 6 mm with 0-mm gap; inplane resolution = 3.75 mm) were obtained using a T 2 * -weighted 2-dimensional gradient-echo spiral pulse sequence with higher order shimming (Glover et al., 1998), which is relatively insensitive to motion artifacts (Glover et al., 1995). 47

58 A total of 300 functional volumes were acquired for each participant for the encoding paradigm and 400 functional volumes for the recognition paradigm. 2 volumes that were acquired at the beginning of each run were discarded to allow for stabilization of the magnetic field. The total scanning time was 11 min and 20 s for the encoding paradigm and 15 min and 5 s for the recognition paradigm including 2 discarded functional volumes at the beginning. A 3-dimensional Fourier transform spoiled gradient recalled pulse sequence scan (repetition time =34 ms; echo time = 7 ms; 22-cm field of view; 35 flip angle; slice thickness = 1.6 mm; inplane resolution = mm) was acquired for all sections that received functional scans. These images were used to correlate functional activation with anatomical structures, i.e. voxels that were found to be significantly activated during the functional scan were overlaid on these structural images. 48

59 Chapter 5 functional and Structural data analysis 5.1 functional data preprocess Image reconstruction was performed off-line by transferring the data to a Sun SparcStation. A gridding algorithm was employed to resample raw data into a Cartestian matrix. Once individual images had been reconstructed, all T2* weighted images were realigned to correct for within-scan motion using SPM8. The data were visually inspected and examined for signal and excessive signal intensity (bigger than 3SD above the overall whole-brain mean signal intensity) were also excluded from each subjects functional volumes To facilitate group comparison, the structural T1-weighted 3 dimensional spoiled gradient recalled volumes were spatially normalized to a standard brain template provided by SPM8 using a 12-parameter affine normalization and nonlinear adjustments with 7x8x7 basis functions. The spatial transformation parameters derived from normalizing and structural volume were applied to the realigned T2*-weighted images. The resultant realigned normalized T2* weighted volumes were then smoothed with a 8-mm full width at half maximum isotropic Gaussian kernel to compensate for residual between-subject variability after spatial normalization. The time series at each voxel were regressed on a reference waveform. The time series at each voxel were regressed on a reference waveform, and the significance of this regression was assessed with a t-statistic at each voxel to construct a SPM T map. The reference waveform was calculated by convolving a square wave representing the event with an estimated hemodynamic response function template. Low-frequency drifts in the fmri signal were removed from the data set by using a 49

60 high-pass filter with an upper cutoff period 128. Statistical analyses of the time series data were performed using the GLM in SPM8. An external mask was added to insure that signals from all voxels were represented within each participant functional volumes obtained for both the encoding and recognition phases of the fmri experiment. 5.2 GLM-based functional contrast Contrasts for encoding paradigms: We generated 3 types of contrast for subject s brain activation during encoding paradigms. All trials during encoding condition were classified into 3 types according to visual stimulus property and subject s behavioral response during recognition condition. Type 1) Hits, represent the trials that items seen by subjects were successfully recognized during the later recognition paradigm. 2) Misses, represent the trials that items seen by subjects were not successfully recognized during the later recognition paradigm. 3) Push, represent trails that only showed word Push. And three contrasts were generated based on different contrast coefficient setting for the regressors of these three types of trials sequence. Contrast 1 (Enc01): Hits Vs. Misses. The contrast parameter setting is [1-1 0] for hits, misses, and push regressors respectively during first level statistical analysis. This contrast is aimed to reveal the brain regions involved in successfully and efficient encoding. This contrast is identical to the contrast used in numerous previous similar studies and corresponding to the examination of subsequent memory. Contrast 2 (Enc02): Hits Vs. Push. The contrast parameter setting is [1 0-1] for hits, 50

61 misses, and push regressors respectively during first level statistical analysis. This contrast is aimed to reveal overall brain activation during successful encoding including high-level cognitive domains and low-level visual processing areas in spite of the subsequent memory success. Contrast 3 (Enc03): Misses Vs. Push. The contrast parameter setting is [0 1-1] for hits, misses, and push regressors respectively during first level statistical analysis. This contrast is aimed to reveal overall brain activation during unsuccessful encoding including high-level cognitive domains and low-level visual processing areas in spite of the subsequent memory success Contrasts for recognition paradigms All trials during recognition paradigms were separated into 5 types. 1) Hits, represent the trials that subjects successfully recognized items previously seen during encoding paradigm. 2) Misses, represent the trials that subjects failed to recognize items previously seen during encoding paradigm. 3) Correct Rejection, represent the trials that subjects correctly identify novel items as novel. 4) False Alarm, represent the trials that subjects mistakenly identify the novel items as previously seen during encoding paradigms. 5) Push. Represent the trails that only displayed word Push. And four contrasts were generated based on different setting for the regressors of these five types of trials sequence. 51

62 Contrast 1 (recog01): Hits Vs. Misses. The contrast parameter setting is [ ] for hits, misses, correct rejection, false alarms and push regressors respectively during first level statistical analysis. Contrast 2 (recog02): Correct rejection Vs. False Alarms. The contrast parameter setting is [ ] for hits, misses, correct rejection, false alarms and push regressors respectively during first level statistical analysis. Contrast 3 (recog03): Hits Vs. Push. The contrast parameter setting is [ ] for hits, misses, correct rejection, false alarms and push regressors respectively during first level statistical analysis. Contrast 4 (recog04): Misses Vs. Push. The contrast parameter setting is [ ] for hits, misses, correct rejection, false alarms and push regressors respectively during first level statistical analysis. 5.3 Functional Connectivity In addition to GLM-based functional contrast map, functional connectivity based on five seed regions were generated for preprocessed time series during encoding and recognition paradigms. Previous research indicates Alzheimer Disease mostly affect four cortical regions, include: hippocampus, parahippocampus, posterior cingulate, and inferior parietal cortex. These four ROI masks were generated using Wake Forest Pickatlas toolbox integrated in SPM8. Talariach Daemon labels atlas (Lancaster et al., 1997; Lancaster et al., 2000) was used 52

63 and 2D dilation was employed to increase ROI area so as to reduce warping distortion affect. For each subject, functional time series were preprocessed through volume align, head movement correction, normalization and smoothing as described in the fmri preprocessing system. An external mask used in the GLM-based analysis was applied to remove non-brain area so as to reduce noise. Then ROI masks were applied and the average time-series within each ROI were calculated. Correlation coefficient between this averaged ROI time series and time series for each voxel across the whole brain was calculated by the equation 5.1: r X X Y XY N ( X) ( Y) )( Y ) N N (5.1) Here, X represents the time-series of one voxel within the brain and Y represents the average tiem-serises from one ROI. N represents the time points, which is 300 for encoding paradigm and 400 for recognition paradigm. Due to the NAN voxels existed within the different smoothed functional volume, NULL value was obtained for correlation coefficients sometimes. These NULL values were arbitrarily set to -2. Since the range of correlation coefficient is from -1 to could be identified later as abnormal correlation value obtained. All functional connectivity calculation was realized by customized MATLAB program. 53

64 5.4 structural data analysis SPGR high resolution anatomy image of each subject was processed using the FreeSurfer v5.0. The detailed material about Freesurfer is available online at In summary, Freesurfer is a powerful brain image processing tool, and its surface-based functions include: 1) Identify the gray/white surface and gray/csf surface (Pial surface) based on local signal intensity gradient. 2) Inflate gray/white surface and then register the inflated surface into a sphere coordinate system. In this study, we used four cortical morphometric parameters:1) Cortical thickness, 2) The average convexity, 3) Cortical Curvature, and 4) local metric distortion, to distinguish amci patients. 1) Cortical thickness: Cortical thickness is a parameter which closely correlates with gray matter volume. It is calculated as the shortest distance between the pial surface and white surface at each vertex. 2) The average cortical convexity or concavity (sulc) Average cortical convexity is a parameter reflecting the primary folding pattern of a surface. It represents the movement distance to flattened surface as each vertex during inflation. C captures large-scale geometric features and. is insensitive to small wrinkle noise (Fischl et al, 1999). 3) Mean Curvature(curv) Mean (radial) curvature was used to measure pattern of the small secondary and tertiary 54

65 folds in the surface. These folds would not be captured by the average convexity measure reflecting large-scale geometric characteristics. 4) Metric distortion (Jacobian): Metric distortion represents the integral displacement of the individual gray/white surface relative to the average template, and is calculated as the following formula (Wisco et al, 2007). Jacobian = (area of a triangle on registered sphere) / (area of triangle on original gray / white interfaces surface) Here k= (total surface area of original gray / white interface surface) / (total surface area of individual sphere) The above four parameters of each subject were registered into freesurfer s fsaverage surface template, which is an average surface of 40 healthy subjects and has nodes for each hemisphere. Smoothing with a 20 mm FWHM kernel was applied to reduce warping distortion during registering. 55

66 Chapter 6 Classification 6.1 Data dimension reduction Principle Component Analysis (PCA) was applied to reduce the data dimensions of each whole brain measures. PCA was implemented by Singular Value Decomposition (SVD). SVD is an approach to decompose a non-square matrix. Let X represent the vector field to be T decomposed and its mean is subtracted, then X USQ, where U T U 1, Q T Q 1 and S is a diagonal matrix consisting of singular values of X. Comparing it to eigen decomposition of covariance matrix of X ( XX T USQ T QSU T US 2 U T ), we know U is equivalent to the eigenvectors, and S is proportional to the eigen value of each eigenvector. And the T coefficient for the eigenvector can be got asc diag( S) Q. The eigenvectors covering 95% of variance across the entire sample for each parameter matrix of each hemisphere were chosen as dominant eigenvectors and their coefficients were afterwards used as independent regressors for stepwise logistic regression and Support Vector Machine algorithm. For each coefficient vector, we put gender vector as the covariate using general regression model and get the residue as the gender-removed coefficient vector to conduct the following statistical analysis. 6.2 Stepwise Logistical Regression Stepwise logistic regression was then used to select statistical significant eigenvectors that could discriminate the subject groups. Due to our relatively small sample size, the significant level of selection and elimination were both set to 0.1. This procedure was 56

67 repeated for the eigenvectors of each GLM based brain activation contrast, and also for the eigenvectors of functional connectivity during encoding and recognition conditions, as well as the eigenvectors of individual structural parameter. In logistic proc, SAS uses leave one out cross validation (LOOCV) to represent the model s predicting power. We set cut-off threshold of p as 0.38 to get single accuracy, sensitivity and specificity of each model. This threshold is chosen based on the proportion of the patients among all subjects (Teh et al., 2010). Furthermore, in order to get more unbiased index, area under Receiver Operating Characteristics curves (AUC) were generated for all models and compared. All stepwise logistical regression analysis were conducted via SAS Support Vector Machine SVM was also used to classify amci patients from controls based on eigenvectors from all individual measurements including seven GLM contrasts, functional connectivity of four seed regions and four cortical structural parameters. Both linear SVM and SVM with non-linear kernels were applied. SVM classifier was tuned for kernel parameters and misclassification cost. Two widely used SVM kernels; 1) Radial Basis Function (RBF) : 2 xi xj K( xi, xj) exp( ) ; 2) polynomial function: K( x, ) ( 1) d 2 i xj xi xj ; 2 were selected to investigate the sample data property and classifier performance. And for RBF kernel, sigma value is tuned from 0.1 to 20 with 0.2 width step. For polynomial kernel, we tried 5 orders from 1 to 5. Each kernel and linear SVM was applied under six misclassification cost: As in logistic regression, we also applied LOOCV procedure to generate predicting power of each model. The maximum accuracy was 57

68 selected to compare with the result of stepwise logistic regression. Note here, all eigenvector coefficients of each measurement were input to SVM classifier, the feature number is around Combine stepwise logistical regression and Support vector machine An integrated classifier which combined stepwise logistic regression and support vector machine algorithm was applied. In this classifier, we firstly use stepwise logistic regression to select most discriminative eigenvectors of each feature and then use support vector machine on these discriminative eigenvectors only. The significant level of selection and elimination for stepwise logistic regression were both set to 0.1. And SVM procedure was applied as specified above. LOOCV accuracy, sensitivity and specificity of each model were generated for comparison. After censoring data via stepwise logistic regression, we further reduced data dimensions for SVM classifier. We hypothesized that this dimension-reduced data includes much more discriminative information than discriminative noise, hence the ultimate accuracy should be increased. 6.5 Classification using integrated models In addition to classification based on individual measurement, integrating multiple measurements may provide complementary information to separate the two groups. In this study, we integrate measurements from same modality as well as from different modality. Integrated model were separated into six groups: 1) Integrated model based on GLM-based contrasts in two paradigms 58

69 There were three contrasts in encoding paradigm and four contrasts in recognition condition. Selecting two different contrasts formed 7*6/2=21 feature combinations. 2) Integrated model based on functional connectivity in two paradigms There were four functional connectivity maps based on different ROIs in either encoding paradigm or recognition paradigm. Selecting two different connectivity maps formed 8*7/2=28 measurement combinations. 3) Integrated model based on functional connectivity and GLM-based contrast Encoding paradigm: selecting one GLM-based contrast and one functional connectivity map during encoding paradigm formed 3*4=12 measurement combinations. Recognition paradigm: selecting one GLM-based contrast and one functional connectivity map during recognition paradigm formed 4*4=16 measurement combinations. 4) Integrated model based on GLM-based contrasts in two paradigms and cortical measurements There are 7 functional contrasts from encoding and recognition together and 4 cortical measurements. Selecting one functional contrast and one cortical measurement formed 7*4=28 two measurement combinations. 5) Integrated model based on functional connectivity in two paradigms and cortical measurements 59

70 There are 8 functional connectivity maps from encoding and recognition together and 4 cortical measurements. Selecting one functional connectivity map and one cortical measurement formed 8*4=32 two measurement combinations. 6) Integrated model based on multiple structural parameters There are 4 different cortical measurement, therefore forming 4*3/2=6 two measurement combinations. The integrated models were examined using only SLR+SVM integrated method. In addition to LOOCV, we also conduct 5 fold cross validation to the most discriminative models. Furthermore, since single accuracy, sensitivity and specificity may be biased for different tuned kernel or cost parameter, ROCs using fixed cost value but changing kernel parameter value for different integrated model were generated and AUC were calculated for each model to compare with each other. 60

71 Chapter 7 Visualization For the most discriminative individual features and integrated model, we would like to generate more detailed visualization picture to let us know why this models have high discriminative power and which brain regions relate with features change associated with amci. For the individual feature based model, reconstructed brain images using discriminative eigenvaluess were generated to reflect brain regions associated with feature change. For integrated models, global amci index integrating information from multiple features was generated to serve as a convenient marker for distinguish amci patients. In addition, integrated amci log likelihood maps were generated to combine spatial information from multiple features to find the regions most associated with amci progress. 7.1 Reconstructed image from discriminative eigenvectors and group comparison T map After selecting the most discriminating subset of basis functions (i.e. eigenvector) and coefficients for each measure, we reconstructed the discriminating part of the measure s value by summing up these selected subsets of eigenvectors weighted by their coefficients. For the j ith subject and jth measure type, we have reconstructed U ( x ) calculated as in equation 7.1. i N j j j j (7.1) U(x)= α (x) i ik ik k=1 Where x represent one vertex on surface, j (x) is the kth orthonormal eigenvector of ik measure type j for subject i, and j ik is the coefficients corresponding to this eigenvector for the ith subject. N j is the total number of eigenvectors selected by logistical regression for measure type j. 61

72 After reconstructing the discriminating part of each measure, student T comparison was applied to demonstrate the group difference. And group difference T maps for each measure were all projected to freesurfer s brain surface template fsaverage. An uncorrected universal threshold T=2.5 was used to highlight those brain regions with significantly group difference. 7.2 global amci index In order to achieve a global AD index for the whole hemishpere, we omit the component dependent on single vertex x and construct one vector only by coefficients. Here we separate the subjects into two groups, 1 for mild AD, 2 for control subjects. For ith subject in group g, this coefficient vector g Z i was calculated as in equation 7.2. And the sample mean of Z in group g can be represented as in equation 7.3. And the pooled sample covariance of Z from both groups are given by equation 7.4. V V V V V n n [,,, 1 i1 in i1 in 1 2 n g V Z i i in,,,,,, ] (7.2) N ˆ 1 g g g Zi (7.3) N g i 1 N 1 g ˆ ˆ ˆ ( Z Z )( Z Z) i i N 2 g g g i 1 g g g T (7.4) Based on the above definition, we calculate a likelihood ratio index for each subject using the equation 7.5. ˆ ctrl T 1 ˆ ctrl ˆ amci T 1 ˆ ˆ ˆ amci Z i i i i ( ) ( ) ( ) ( ) (7.5) 62

73 This log-likelihood ratio index integrates the information from functional measures and cortical structural measures and reflects probability of a subject belongs to the amci population. If a subject has very similar coefficient pattern as group mean of amci group, he tend to have a high and positive, otherwise, a healthy subject tends to have a low and negative. If = 0, it means a subject lies in the middle of two groups. Scatter plot of of two groups were generated from different integrated models and compared. 63

74 Part III Result and Discussion Chapter 8 Classification result In this chapter, I report the classification results of classifier based on both individual cortical measurement and measurement combinations. Classifier s performances from three type methods, stepwise logistic regression (SLR), support vector machine (SVM) and integrated approach (SLR+SVM), were compared. Predicting power of individual fmri measures and measure combinations were explored and compared to smri measures. The measures that composed the best models were visualized in Chapter 10 to see the detailed group difference pattern. 8.1 Classifier performance based on individual measures GLM-based functional contrasts Table 2 summarized classifier performance of individual GLM-based functional contrast for both encoding and recognition condition. From Table 2, we can see contrast Hits Vs. Push during both encoding and recognition condition achieved highest accuracy (83% accuracy with over 0.9 AUC of SLR). There is no significant difference between encoding condition and recognition condition in terms of classifier performance. SLR achieved better accuracy then SVM, and SVM+SLR method achieved significant increased accuracy than both SVM and SLR individually. Using normalized (z-scored) contrasts, SVM classifier generally reach optimal performance at cost value (C) equals to 10 and radial base function sigma below

75 Contrast accuracy AUC of sensitivity specificity SLR SVM Kernel SVM Cost SLR SVM RBF(0.1) 10 Hits Vs. Misses SVM+LR RBF(0.9) 10 encoding SLR SVM RBF(0.1) 10 Hits Vs. Push SVM+LR RBF(2.3) 10 SLR SVM RBF(3.9) 10 Misses Vs. Push SVM+LR RBF(17.1) 10 SLR enchits Vs. SVM Poly(2) 10 encmisses SVM+LR RBF(2.1) 10 recognition SLR novelhits Vs. SVM RBF(0.1) 10 novelmisses SVM+LR RBF(3.9) 100 SLR SVM RBF(0.1) 10 Hits Vs. Push SVM+LR RBF(1.3) 10 SLR SVM RBF(0.1) 10 Misses Vs. Push SVM+LR RBF(0.9) 10 Table 2. Classifier performance of individual GLM-based functional contrast Functional connectivity maps Table 3 summarized classifier performance of individual functional connectivity map for both encoding and recognition condition. From Table 3, we see functional connectivity of hippocampus and inferior parietal cortex during encoding condition achieved highest accuracy (88%). Functional connectivity during encoding condition shows significantly greater predicting power than those during recognition condition (P<0.001 using wolcox test). 65

76 SLR achieved better accuracy than SVM, and SVM+SLR method achieved significant increased accuracy than both SVM and SLR individually. Seed Region accuracy sensitivity specificity AUC of LR SLR SVM Kernel SVM Cost SVM RBF(0.1) 10 hippocampus SVM+LR RBF(4.7) 10 SLR SVM RBF(0.1) 10 encoding parahippocampus SVM+LR RBF(3.7) 10 SLR SVM RBF(0.1) 10 posterior cingulate SVM+LR RBF(0.7) 0.1 SLR SVM RBF(3.5) 10 inferial parietal SVM+LR RBF(3.5) 100 SLR SVM RBF(3.5) 10 hippocampus SVM+LR RBF(0.5) 10 SLR recognition SVM RBF(0.1) 10 parahippocampus SVM+LR RBF(7.1) 10 LR SVM RBF(0.1) 10 posterior cingulate SVM+LR RBF(0.9) 100 SLR SVM RBF(0.1) 10 inferial parietal SVM+LR RBF(1.5) 10 Table 3. Classifier performance of individual functional connectivity maps Structural MRI measures Table 4 summarized classifier performance of individual cortical structural measures. From Table 4, we see local sulcal depth (sulc) achieved highest accuracy (0.88), even higher 66

77 than cortical thickness (0.83). However, AUC, which is the unbiased index of classification ability, indicated thickness had slightly better predicting power than sulc, curv and jacobian_white. This result is consistent with the few studies which indicate AD progress affect brain s folding pattern, probably because AD-associated white matter atrophy also change the geometry characteristics of white matter surface of amci patients. Sulcal depth has comparable classification power as cortical thickness. Similarly, SLR achieved better accuracy then SVM, and SVM+SLR method achieved significant increased accuracy than both SVM and SLR individually. accuracy sensitivity specificity AUC SVM Kernel Cost LR SVM RBF(0.1) 10 thickness SVM+LR LR SVM RBF(0.1) 10 sulc SVM+LR LR SVM RBF(0.1) 10 curv SVM+LR LR SVM RBF(0.1) 10 jacobian_white SVM+LR poly(2) 0.1 Table 4. Classifier performance of individual cortical structural measures In sum, at the amci stage, the brain changed both functionally and structurally. There was no significant difference of predicting power between fmri measurements and smri measurements. This indicates that at the amci stage, macro-scale brain atrophy of both gray matter and white matter are found. The interruption of hippocampus and inferior parietal connection during encoding phase may be the most homogenous and universal characteristic 67

78 of amci, which may subsequently cause extended brain volume atrophy and compensational brain activation for amci or AD subjects. amci-associated brain activation changes are not limited to high-order cognitive level, but also include low-order visual-spatial information processing, and even motor function. So that may explain the functional contrasts using Push condition as the baseline demonstrated better predicting power than using more high-order baseline, i.e. Misses. Overall, our results are consistent with the theory that hippocampus atrophy and posterior-cortex area hypo-metabolism are the most sensitive biomarkers of AD-associated amci. One study even found that the posterior-cortex area hypo-metabolism is highly correlated with damaged hippocampus region signal input (Villain et al., 2008). 8.2 Integrated classifier performance In addition to individual measurements, we also investigate the complementarity of different brain measures, both within single modality or cross modalities. The following tables showed the optimal classifier performance when integrating two measurements together. All these measurement combinations are examined using integrated method (SVM+SLR). SVM classifier was tuned for optimal performance. The highest LOOCV accuracies and corresponding SVM kernel parameter and cost value were listed in Table 5 to Table fmri measure combinations Table 5 to Table 8 are the classifier performance using within fmri modality integration. While 47 out of 77 combinations (61% labeled bold and italic) increased classifier performance, there are 7 out of 77 combination (9.1% labeled underline) actually decrease the 68

79 classifier performance. It indicates if there is no complementary classifying information, adding more features could only add disturbance but not predicting power. GLM contrast enc02 enc03 recog01 recog02 recog03 recog04 enc enc enc recog recog Recog Table 5. Classifier performance using integrated model with GLM-based functional contrasts combinations. enc01-enc03 represent the first to third contrast during encoding condition. Recog01-recog04 represent the first to fourth contrast during recognition condition. Bold italic number indicated increased accuracy for integrated classifier. enc recog recog functional connectivityparahippocampusencpcencifphippocampusparahippocampus recogpc recogifp enchippocampus encparahippocampus encpc encifp recog hippocampus recogparahippocampus recogpc 0.96 Table 6. Classifier performance of integrated model with functional connectivity combinations. PC represent posterior cingulate cortex. IFP represent inferior parietal cortex. Bold italic number indicated increased accuracy, and underlined number indicated decreased accuracy for integrated classifier. Among all within fmri modality combinations, four combinations achieved 100% LOOCV accuracy. There are: 1) enchippocampus+encifp; 2) encpc+encifp; 3) enchippocampus+enc02; 4) encpc+enc02. Here we can see functional connectivity of critical brain regions (such as hippocampus, inferior parietal and posterior cingulate) during encoding condition has great predicting power and are complementary to GLM-based functional 69

80 contrasts using push as the baseline. enc01 enc02 enc03 enchippocampus encparahippocampus encpc encifp Table 7. Classifier performance of integrated model with GLM-based functional contrast and functional connectivity map combinations during encoding condition. PC represent posterior cingulate cortex. IFP represent inferior parietal cortex. enc01-enc03 represent the first to third functional contrasts in encoding condition. Bold italic number indicated increased accuracy, and underlined number indicated decreased accuracy for integrated classifier. recog01 recog02 recog03 recog04 recog_hippocampus recog_parahippocampus recog_pc recog_ifp Table 8. Classifier performance of integrated model with GLM-based functional contrast and functional connectivity map combinations during recognition condition. PC represent posterior cingulate cortex. IFP represent inferior parietal cortex. recog01-04 represent the first to fourth functional contrast during recognition condition. Bold italic number indicated increased accuracy Structural measure combinations Table 9 is the classifier performance summary using within smri modality integration. 5 out of 6 combinations increased classifier performance, but 1 combination decreased classifier performance. There is no combination achieved 100% accuracy. 70

81 sulc curv jacobian_white thickness sulc curv 0.92 Table 9. Classifier performance of integrated models with cortical structural measure combinations. Bold italic number indicated increased accuracy, and underlined number indicated decreased accuracy for integrated classifier smri and fmri measuret combination Table 10 is the summary of classifier accuracy using cross modality integration. 48 out of 60 combination (80% labeled red) increased classifier accuracy, only 1 combination (1.7% labeled blue) decreased classifier accuracy. This indicates the fmri and smri data have great complementarity. Among all combination, there are 2 combinations achieved 100% accuracy. They are: 1) enc01+curv; 2) recog03+curv. thickness sulc curv jacobian_white enc enc enc recog recog recog recog enc_hippocampus enc_parahippocampus enc_pc enc_ifp recog_hippocampus recog_parahippocampus recog_pc recog_ifp Table 10. Classifier performance of integrated models with cortical structural measure and functional measure combinations. PC represent posterior cingulate cortex. IFP represent inferior parietal cortex. enc01-enc03 represent the first to third contrast during encoding condition. recog01-04 represent the first to fourth functional contrast during recognition condition. Bold italic number indicated increased accuracy, and underlined number indicated decreased accuracy for integrated classifier. 71

82 8.3 Comparison of optimal measure combinations Part 8.2 demonstrated integrating two measurements could improve the classifier s performance most of the time. Among all measurement combinations, six achieved 100% LOOCV accuracy, including 1) enchippocampus+encpc, 2) enchippocampus+encifp, 3) enchippocampus+enc02, 4) encpc+enc02, 5) enc01+curv, 6) recog03+curv. Because our small sample size, the classifier performance is easy to get saturated. Single highest accuracy (all 100% for the six optimal combinations) can t differentiate the predicting power of those 100% LOOCV accuracy combinations. Here, we use the un-biased classification standard: Area Under Curve of ROC to compare these six combinations. The corresponding global amci index scatter plot was also created for each combination. The classification margins based on global amci index (decision surface is y=0) were calculated to reflect its relationship with AUC. As stated in the method part, 5 fold cross validation procedure was used here to generate ROC curve for each measurement combinations. SVM classifier used RBF kernel and cost value equals to 10. The change variable is the sigma ( ) of RBF kernel, griding from 0.1 to 10 with 0.1 width. Because training data and testing data settings will affect the result greatly, for each combination we ran 5 fold cross validation for 20 times. Mean AUC, standard deviation of AUC and classification margin of global amci index are listed in table

83 Mean AUC (20) Std Margin of Global amci index enchippocampus+encifp encpc+encifp enc01+curv Recog03+curv enchippocampus+enc encpc+enc Table 11. SVM+SLR 5 fold CV AUC and classification margin of Global amci index for six optimal integrated models. PC represent posterior cingulate cortex. IFP represent inferior parietal cortex. enc01-enc02 represent the first (Hits Vs. Misses) and second (Hits Vs. Push) functional contrast during encoding condition. Recog03 represent the third contrast (Hits Vs. Push) to fourth functional contrast during recognition condition. From table 11 and figure 6, we find that the global amci index class margin is closely correlated with mean AUC (r = ), which demonstrated AUC is very effective in terms of reflecting the unbiased classifier performance. Among the various combinations, combination enchippocampus+enc02 and encpc+enc02 achieved highest AUC. This result is consistent with our hypothesis that amci pathology have caused both gray matter and white matter change, and change in these two domain could provide complimentary information for differentiate amci patient from normal age-matched controls. The micro-scale damage of white matter integrity could cause functional connectivity change but may not induce measurable white matter surface change. Similarly, loss of usual signal input or hypometabolism of the cortex will cause GLM-based brain activation change but may not induce measurable gray matter atrophy. So, integrating two fmri measurements reflecting earliest gray and white matter change respectively, achieving superior classification power is somehow expected. The group difference of functional measurement enc02, enchippocampus and encpc would be visualized in the next visualization chapter. 73

84 40 enchippocampus+encifp Integrated Model 40 encpc+encifp Integrated Model Global amci Index Global amci Index Control Patient -40 Control Patient 40 enc Hits Vs. Misses+curv Integrated Model 40 recog Hits Vs. Push+curv Integrated Model Global amci Index Global amci Index Control Patient -40 Control Patient 40 enchippocampus+enc Hits Vs. Push Integrated Model 40 encpc+enc Hits Vs Push Integrated Model Global amci Index Global amci Index Control Patient -40 Control Patient Figure 6.Global amci index classification plots for six optimal combinations. 74

85 Chapter 9 Visualization results In this chapter, we take a closer look at spatial change pattern of those measurements with high predicting power. According to classification performance, we select three measurements to visualize, including enc02 (Hits Vs. Misses) for GLM-based functional contrast, enchippocampus and encpc for functional connectivity. 9.1 GLM-based functional contrast Figure 7 demonstrate the T comparison map between control group and patient group for contrast Hits Vs. push (enc02) in encoding paradigm. The A parts of the pictures represent the comparison map using contrast value reconstructed from selected discriminative eigenvectors, and the B parts of the pictures represent the comparison map using contrast value reconstructed from all eigenvectors. From figure 7, we can see the patterns of difference are very similar between the right and left parts, indicating selected discriminative eigenvectors (3 rd and 18 th eigenvector) capture the major group difference of functional contrast enc02. However, we reduced the data dimension from 79*85*96 to 2. Figure 8 is T comparison map reconstructed from all eigenvectors with threshold T=2.5. From figure 7 and 8, we can see amci patients have significantly reduced brain activation in left frontal lobe, left insula, left pastriangularis, left inferior temporal, right frontal lobe, right insula, right fusiform, right occipital and right anterior cingulate, right isthumus cingulate. And amci patients tend to have compensational stronger activation in left rostralanteriorcingulat, left supermarginal, left paracentral areas (motor area), right supermarginal, right superior-parietal, right paracentral area (motor area), right precuneus. 75

86 GLM-based functional contrast Hits Vs. Push in encoding condition A B Figure 7. T map of group comparison for Hits Vs. Push (enc02) contrast during encoding condition. A part represents T comparison map (Control Vs. Patients) using contrast value reconstructed from selected discriminative eigenvectors, B part represents T comparison map (Control Vs. Patients) using contrast value reconstructed from all eigenvectors. GLM-based functional contrast Hits Vs. Push during encoding condition Figure 8. T map of group comparison for Hits Vs. Push (enc02) contrast reconstructed from all eigenvectors during encoding condition with threshold T=2.5. The T value is calculated as Control Vs. Patients. 9.2 Functional connectivity Figure 9, Figure 11 shows the T comparison map between control group and patient group for functional connectivity using hippocampus as seed region in encoding condition 76

87 (enchippocampus) and functional connectivity using posterior cingulate as seed region in encoding condition (encpc). The A parts of the pictures represent the comparison map using connectivity value reconstructed from selected discriminative eigenvectors, and the B parts of the pictures represent the comparison map using connectivity value reconstructed from all eigenvectors,. From figure 6 and figure 8, we can see the patterns of difference are very similar between the A and B parts, indicating selected discriminative eigenvectors capture the major group difference of functional connectivity enchippocampus and encpc. Figure 10 and figure 12 are T comparison maps reconstructed from all eigenvectors with threshold T=2.5 for enchippocampus and encpc respectively. From figure 9 and figure10, we can see amci patients have significantly reduced hippocampus connectivity in large region of left frontal lobe, right frontal lobe. amci patients have compensational stronger connectivity in left paracentral (motor area), left superior temporal, right paracentral area(motor area, include small part of precuneus) and right fusiform gyrus. Overall, amci patients tend to have damage in the hippocampal-frontal connection, and have compensation in hippo-motor connection. From figure 11 and figure 12, we see that amci patients have significant reduced posterior cingulate connectivity in left frontal lobe, left superior temporal, left middle temporal, very small region in right frontal lobe, right superior temporal, right inferior temporal. amci patients tend to have stronger compensational posterior cingulate connectivity to left occipital lobe, small region of left motor area, all close to posterior-region, large region of right occipital lobe, large region of right motor area, right parietal lobe and 77

88 right fusiform gyrus. Overall, amci patients tend to have decreased left hemisphere posterior cingulate connectivity but stronger right hemisphere posterior cingulate connectivity. Functional connectivity of hippocampus during encoding condition A B Figure 9. T map of group comparison for functional connectivity of hippocampus during encoding paradigm. A part represents T comparison map (Control Vs. Patients) using functional connectivity value reconstructed from selected discriminative eigenvectors. B part represents T comparison map (Control Vs. Patients) using functional connectivity value reconstructed from all eigenvectors. Functional connectivity of hippocampus during encoding condition Figure 10. T map of group comparison for functional connectivity of hippocampus reconstructed from all eigenvectors during encoding condition with threshold T=2.5. The T value is calculated as Control Vs. Patients. 78

89 Functional connectivity of posterior cingulate during encoding condition A B Figure 11. T map of group comparison for functional connectivity map of posterior cingulate during encoding paradigm. A part represents T comparison map (Control Vs. Patients) using functional connectivity value reconstructed from selected discriminative eigenvectors. B part represents T comparison map (Control Vs. Patients) using functional connectivity value reconstructed from all eigenvectors. Functional connectivity of posterior cingulate during encoding condition Figure 12. T map of group comparison for functional connectivity of posterior cingulate reconstructed from all eigenvectors during encoding condition with threshold T=2.5. The T value is calculated as Control Vs. Patients. 79