Development of Gaussian Learning Algorithms for Early Detection of Alzheimer\u27s Disease

Alzheimer’s disease (AD) is the most common form of dementia affecting 10% of the population over the age of 65 and the growing costs in managing AD are estimated to be $259 billion, according to data reported in the 2017 by the Alzheimer\u27s Association. Moreover, with cognitive decline, daily life of the affected persons and their families are severely impacted. Taking advantage of the diagnosis of AD and its prodromal stage of mild cognitive impairment (MCI), an early treatment may help patients preserve the quality of life and slow the progression of the disease, even though the underlying disease cannot be reversed or stopped. This research aims to develop Gaussian learning algorithms, natural language processing (NLP) techniques, and mathematical models to effectively delineate the MCI participants from the cognitively normal (CN) group, and identify the most significant brain regions and patterns of changes associated with the progression of AD. The focus will be placed on the earliest manifestations of the disease (early MCI or EMCI) to plan for effective curative/therapeutic interventions and protocols. Multiple modalities of biomarkers have been found to be significantly sensitive in assessing the progression of AD. In this work, several novel multimodal classification frameworks based on proposed Gaussian Learning algorithms are created and applied to neuroimaging data. Classification based on the combination of structural magnetic resonance imaging (MRI), positron emission tomography (PET), and cerebrospinal fluid (CSF) biomarkers is seen as the most reliable approach for high-accuracy classification. Additionally, changes in linguistic complexity may provide complementary information for the diagnosis and prognosis of AD. For this research endeavor, an NLP-oriented neuropsychological assessment is developed to automatically analyze the distinguishing characteristics of text data in MCI group versus those in CN group. Early findings suggest significant linguistic differences between CN and MCI subjects in terms of word usage, vocabulary, recall, fragmented sentences. In summary, the results obtained indicate a high potential of the neuroimaging-based classification and NLP-oriented assessment to be utilized as a practically computer aided diagnosis system for classification and prediction of AD and its prodromal stages. Future work will ultimately focus on early signs of AD that could help in the planning of curative and therapeutic intervention to slow the progression of the disease

Multidimensional computational modeling of Potent BACE1 (β-Secretase) inhibitors towards Alzheimer’s disease treatment.

Doctoral Degree. University of KwaZulu-Natal, Durban.Alzheimer’s disease (AD), as a progressive multifactorial neurodegenerative abnormality of the brain, is often connected with loss or death of neurons as its primary pathogenesis. Another kind of dementia is associated with memory loss and unstable and irrational behaviors, especially among the elderly above 60 years. In South Africa, there are over four million people above the age of 60 years, with an approximation of one hundred and eighty-seven thousand living with dementia. The two distinguishing features (hallmarks) of AD are neurofibrillary tangles and β-amyloid plaques. The β-amyloid plaques result when amyloid precursor protein (APP) is cleaved by β-amyloid precursor protein cleaving enzyme1 (BACE1), otherwise known as β-secretase. Since 1999 the first BACE1 was discovered, it has become a major interest in attempting to develop drugs for the inhibition or reduction of the β-amyloid aggregates in the brain. Reducing or inhibiting the accumulation of β-amyloid has long been the target in the design of drugs for AD treatment. Having a good knowledge of the characteristic properties (BACE1) would assist in the design of potent selective BACE1 inhibitors with fewer or no side effects. Hitherto, only five drugs have been approved by the Food and Drug Administration (FDA) for the remediation of Alzheimer’s disease, and none of the approved drugs targets BACE1. In about twenty years of its discovery, several past and ongoing studies have focused on BACE1 therapeutic roles as a target in managing AD. Several attempts have previously beenmade in designing some small drugmolecules capable of good BACE1 inhibition. Some of the initially discovered BACE1 inhibitors include verubecestat, lanabecestat, atabecestat, and umibecestat (CNP-520). Although these inhibitors significantly lowered β-amyloid plaques in persons having neurological Alzheimer’s at its clinical trials (phase 3), they were suddenly terminated for some health concerns. The termination contributed to the reasons why there are insufficient BACE-targeted drugs for AD treatment. Lately, a novel potent, orally effective, and highly selective AM-6494 BACE1 inhibitor was discovered. This novel BACE1 inhibitor exhibited no fur coloration and common skin alteration, as observed with some initial BACE1 inhibitors. AM-6494 with an IC50 value of 0.4 nM in vivo is presently selected and at the preclinical phase trials. Before this study, the inhibition properties of this novel BACE1 inhibitor at the atomistic and molecular level of BACE1 inhibition remained very unclear. The first manuscript (chapter two) is a literature review on Alzheimer's disease and β-secretase inhibition: An update focusing on computer-aided inhibitor design. We provide an introductory background of the subject with a brief discussion on Alzheimer’s pathology. The review features computational methods involved in designing BACE1 inhibitors including the discontinued drugs. Using the topical keywords BACE1, inhibitor design, and computational/theoretical study in theWeb of Science and Scopus database, we retrieved over 49 relevant articles. The search years are from 2010 and 2020, with analysis conducted from May 2020 to March 2021. Our second manuscript (chapter three) reviewed BACE1 exosite-binding antibody and allosteric inhibition as an alternative therapeutic development. We studied BACE1 biological functions, the pathogenesis of the associated diseases, and the enzymatic properties of the APP site cleavage. We suggested an extensive application of advanced computational simulations in the investigation of anti-BACE1 body and allosteric exosites. It is believed that this investigation will further help in reducing the associated challenges with designing BACE1 inhibitors while exploring the opportunities in the design of allosteric antibodies. The review also revealed that some molecules exhibited dual binding sites at the active site and allosteric site. As a result, we recommend an extensive investigation of the binding free energy beyond molecular docking (such as advanced molecular dynamic simulations) as this promises to reveal the actual binding site for the compounds under investigation. Chapter four contains the detailed computational science techniques which cover the application of the vitally essential methods of molecular mechanics (MM), quantum mechanics (QM), hybrid of QM/MM, basis sets, and other computational instruments employed in this study. In the third manuscript (chapter five), we carried out computational simulations of AM-6494 and CNP- 520.CNP520 was one of the earliest BACE1 drugs that were terminated, chosen in this study forcomparative reasons. This simulation was to elucidate and understand the binding affinities of these two inhibitors at the atomistic level. We explored the quantum mechanics (QM) density functional theory (DFT) and hybrid QM/MM of Our Own N-layered Integrated molecular Orbital and Molecular Mechanics (ONIOM) in these simulations. These computational approaches helped in predicting the electronic properties of AM-6494 and CNP-520, including their binding energies when in complex with BACE1. Considering the debates on which protonated forms of Asp 32 and Asp 288 gives a more favorable binding energy, we analysed the two forms which involved the protonation and un-protonation of Asp 32 and Asp 228.The ONIOM protonated model calculation gave binding free energy of -33.463 kcal/mol (CNP-520)and 62.849 kcal/mol (AM-6494) while the binding free energy of -59.758 kcal/mol was observed for the unprotonated AM-6494 model. These results show the protonated model as a more favourable binding free energy when compared with the un-protonation AM-6494 model. Further thermochemistry processes coupled with molecular interaction plots indicate that AM-6494 has better inhibition properties thanCNP-520.However, it was observed that the protonation and the un-protonation of Asp 32 and Asp 228 modelscould adequately illustrate the interatomic binding of the ligands-BACE1 complex. To further explicate the binding mechanism, conformational and structural dynamism of AM-6494 relative to CNP-520 in complex with BACE1, we carried out advanced computational simulations in the fourth manuscript (chapter six). The extensive application of accelerated molecular dynamics simulations, as well as principal component analysis, were involved. From the results, AM-6494 further exhibited higher binding affinity with van der Waals as the predominant contributing energy relative to CNP-520. Furthermore, conformational analysis of the β-hairpin (flap) within the BACE1 active site exhibited efficient closed flap conformations in complex withAM-6494 relative to CNP-520, whichmostly alternated between closed and semi-open conformational dynamics. These observations further elucidate that AM- 6494 shows higher inhibitory potential towards BACE1. The catalytic dyad (Asp32/228), Tyr14, Leu30, Tyr71, and Gly230 constitute essential residues in both AM-6494 potencies CNP-520 at the BACE1 binding interface. The results from these extensive computational simulations and analysis undoubtedly elucidate AM-6494 higher inhibition potentials that will further help develop new molecules with improved potency and selectivity for BACE1. Besides, grasping the comprehensive molecular mechanisms of the selected inhibitors would also help in fundamental pharmacophore investigation when designing BACE1 inhibitors. Finally, the implementation of computational techniques in the designing of BACE1 inhibitors has been quite interesting. Nevertheless, the designing of potent BACE1 inhibitors through the computational application of the QM method such as the density functional theory (DFT), MM, and a hybrid QM/MM method should be extensively explored. We highly recommend that experimentalists should always collaborate with computational chemists to save time and other resources. ISIZULU ABSTRACT Iqoqa Isifo se-Alzheimer (AD), njengoba siqhubeka siyinhlanganisela yezimbangela ze- neurodegenerative engajwayelekile ebuchosheni, isikhathi esiningi kuxhumana nokulahleka noma ukufa kwama-neurons njengongqaphambili we-pathogenesis. Kungolunye uhlobo lwedementia oluhambisana nokulahlekelwa ukukhumbula kanyenokuxenga kanye nokuphanjanelwa ingqondo, ikakhulukazi kubantu abadala esebeneminyaka engaphezulu kuka-60. ENingizimu Afrikha, kunabantu abangaphezulu kwezigidi ezine abangephezulu kweminyaka ewu-60, ngokuhlawumbisela nje abayinkulungwane namashumi ayisishayangolombili nesikhombisa baphila nedemetia. Zimbili izimpawu ezihlukanisekayo ze-AD ziba-ama-neurofibrillary tangles kanye ne-B-amyloid plaques. I-B-amyloid plaques ingumphumela ngesikhathi i-amyloid eyiprotheni egijimayo iqhwakele oketshezini i-enzyme1 (BACEI), ngale kwalokho yaziwanjenge B-secretase. Kusukela ngo 1999 i-BAC1 yatholakala, isiphenduke ungqaphambili emizamweni yokwakha isidakamizwa sokwehlisa i-B-amyloid ngokwezinga lengqondo. Ngokunciphisa ukwanda kwe-B-amyloid isiphenduke okuqondiwe mayelana nokuqopha isidakamizwa ukuze kwelashwe i-AD. Ukuba nolwazi oluhle oluthinta isici sezakhi ze-BACE1 kuzosiza ekubazeni amandla akhethiwe i-BACE1 ukuvimbela imiphumela engaqondiwe. Kuze kube manje mihlanu imithi esiphasisiwe ngabezokuphatha ukudla kanye nezidakamizwa (FDA) ukwelapha isifo se-Alzheimer kanye nokuthi azikho kulezi eziphasisiwe izidakamizwa ebhekana ngqo ne-BACE1. Emva kokuba selitholakele lapho nje eminyakeni engu 20, sekunezinye esikhathini esedlule kanye nezifundo ezisaqhubeka zigxile ngokubheka kakhulu iqhaza lokwelapha i-BAC1 njengokuqondiswe ekungameleni u-AD. Imizamo eminingana yenziwa esikhathini esedlule ukuqopha uketshezi lwezidakamizwa olukwazi ukuvimba kahle i-BACE1. i-B-amyloid plaques kumuntu one-neurological ye-Alzheimer’s kumzamo (isigaba 3), kwabuye kwanqanyulwa ngenxa yokukhathazeka ngokwezempilo. Ukunqanyulwa kwanikela kuzizathu zokusilele kwezidakamizwa okuqondene nokulashwa kwe-AD. Kamuva, i-novel enamandla, ngisho ngawo umlomo kanye neyakhethwa ngezinga eliphezulu i-AM-6494 BACE1 evikelayo yatholakala. Le noveli i-BACE1 evimbayo yabukisa hhayi ukushintsha kombala woboya kanye nokushintsha kwesikhumba okujwayelekile, njengoba kubukwa nezivimbo zokuqala ze-BACE1. I-AM-6494 ne-IC50 enobumqoka buka 0.4nM kuyo i-vivo ekhethwa ngokwamanje kanye nesigaba sembulambethe yemizamo. Ngaphambi kwalesi sifundo, izakhi zesivimbela zale noveli i-BACE1zivimba ngokwe-atomistic kanye neqophelo le-molecular ye-B ACE1evimbayo kusale nje kungacacile. Umqulu wokuqala (isahluko sesibili) ukubuyekezwa kwesifo se-Alzheimer’s kanye no-B-secretase ovimbayo: ezikhumbuzayo ezigxile ngokusizwa yikhompuyutha eyisivimbo ngokwakhiwa. Sethula isendlalelo sesifundo kanye nengxoxo kafushane nezimbangela nemiphumela ye-Alzheimer. Ukubukezwa kwezimpawu zendlela zobukhompuyutha kufaka ekuqopheni isivimbo se-BACE1 nokuqhutshekiswa kwesidakamizwa. Ngokusebenzisa ofeleba begama BACE1, kusho ukwakha isivimbo, kanye nesifundo senjulalwazi kulwembu lobuchwepheshe kanye ne-Scopus sesizindalwazi. Sathola amaphepha acwaningiwe anokuhlobana angaphezulu kuka 49. Unyaka wokuthungatha usukela ku2010 kuya ku2020, nohlaziyo lwenziwa kusukela kuNhlaba 2020 kuya kuNdasa 2021. Umqulu wethu wesibili (isahluko sesithathu) sabuyekeza i-BACE ehlanganisa i-exosite antibody kanye ne-allosteric yokuthuthukisa ukwelashwa. Sakufunda ukusebenza kwesayensi yokuphila ye-BACE1, i-pathogenesis ehambisana nezifo kanye nezakhi zama-enzymatic esizinda sokuhlukana se-APP. Saphakamisa ukufakwa okunzulu nokucokeme kokulinganisa ngobuchwepheshe bekhompuyutha ekuphenyeni ama-anti-BACE1 omzimba kanye ne-allosteric ye-exosites. Kuyakholeka ukuthi uphenyo luzoqhubeka nokusiza ekwehliseni izinselelo ezihambisana nokwakha isithiyo se-BACE1 ngesikhathi kuhlolwa amathuba okwakheka kwe-allosteric yama-antibodies. Ubuyekezo luphinde lwaveza uketshezi olubukisa isizinda sokuhlanganisa kabili kusizinda esikhuthele kanye nesizinda se-allosteric. Umphumela, kube ukwenza isincomo mayelana nocwaningo olunzulu oluzohlanganisa umfutho okhululekile odlulele ku-molecular docking (njengesicokeme se-molecular yokuhlukahlukana kokulinganisa) njengoba lokhu kuthembisa ukuveza isiza esibopha ngempela ama-compounds angaphansi Isahluko sesine siqukethe imininingwane ngamaqhinga e-computational sayensi efaka isicelo esibalulekile sezindlela ezibalulekile ze-molecular mechanics (MM), i-quantum mechanics (QM), i-hybrid ye-QM/MM, ngesisekelo samasethi kanye namanye amathuluzi ekhompuyutha akhethwa kulesi sifundo. Kumqulu wesithathu (isahluko sesihlanu), siqhube isilinganiso se-computational ye-AM-6494 kanye CNP-520.I-CNP-520 kwakungenye yezidakamizwa zokuqala zeBACE1 ezashatshalaliswa, zakhethwa kulesisifundo ngezizathu zokuqhathanisa. Ukulinganisa kwakuchaza kanye nokuqonda ukusondelana ngokuhlanganiswa kwezithiyo ezimbili kusigaba se-atomistic. Kwahlolwa i-quatum mechanics (QM) yesisindo yokusebenza kwenjulalwazi (DFT) kanye ne-hybrid QM/MM yokwethu okuno-N oluwugqinsi lwe-molecular Orbital kanye ne-Molecular Mechanics (ONIOM) kulolu linganiso. Lezi zindlelakwenza ze-computational zasiza ekuqageleni kwezakhiwo zama-electronic e-AM-6494 kanye CNP-520, kungena namandla okuhlanganisa ngesikhathi kuba lukhuni ne-BACE1. Ngokucabanga izinkulumo mpikiswano mayelana nokuma kwe-protonated ye-Asp32 kanye Asp288 kunika ukuvumelana namandla okuhlanganisa, nokuhlaziya izimo ezimbili ezifaka i-protonation kanye ne-unprotonation ye-Asp32 kanye Asp228. I-ONIOM ye-protonated yomfanekiso wokubala wanikeza amandla akhululekile okuhlanganisa -33,463kcal/mol (NP-520) kanye 62.849 kcal /mol kwavela i-unprotonate ye-AM6494. Imiphumela itshengisa ukuthi i-protonated iyisifanekiso njengoba kuyisona esivumela ukuhlanganiswa ngokukhululeka ngesikhathi lapho bekuqhathanisa ne-unprotonation yomfanekiso u-AM-649. Kuqhutshelwa phambili nemisebenzi ye-thermochemistry kuhlangana nokudlelana ne-molecular plots kutshengisa ukuthi i-AM-649 inezakhiwo ezinhle zokuvimba kune CNP-520. Yize kunjalo kwabonakala ukuthi i-protonation kanye ne-unprotonation ye-Asp32 kanye neyomfanekiso owu- Asp228 bekungatshengisa ngokwenele ukuhlanganisa ngokwe-interatomic yama-ligands EBACE1 ebilukhuni. be-BACE1 ngokwedlulele isilinganiso se-computational. Ukwenza ngokujulile kuphangiswa isilinganiso se-molecular ngokuhlukana, kwakakwa nohlaziyo olusemqoka lwezingxenyana. Imiphumela ye-AM-6494 yaqhubeka yatshengisa ukusondelana kokuhlanganiswayo no-van der Waals njengohamba phambili ekunikeleni amandla ahlobene ne-CNP-520. Ukuvuma kohlaziyo lwe-B-hairpin ngaphakathi ku-BACE1 kutshengiswa esizeni esiphilayo esivala ngendlela umnyakazo wokuvuma kobunkimbinkimbi be-AM-6494 ehlobene neCNP-520, ngokuvamile eshitshashintshayo phakathi kwevalekile kanye nezishaya sakuvuleka kokuvuma okunhlobonhlobo. Lokhu kuhlolwa kuqhubeke kwachazwa ngokuthi i-AM-6494 itshengisa ukuvimba okukhulu nokunethemba mayelana ne-BACE1. Isikhuthazizinguquko se-dyad (Asp32/228), Tyr14, Leu 30, Tyr 71, kanye ne-Gly230 kwakha izinsalela ezibalulekile nxazombili kuAM-6494ne-potencies yeCNP-520 kuBACE1 nesixhumanisi esihlanganisayo. Imiphumela ivela kulama-computational anzulu ayisilinganiso kanye nohlaziyo olucacisa ngokungangabazi i-AM-6494 enesivimbelo esiphakeme esingakwazi ukuqhubeka nokusiza intuthuko yama-molecules amasha anamandla athuthukile kanye nakhethelwe i-BACE1. Ngaphandle kwalokhu, ukucosha izinkambiso ezibanzi ze-moleculor mayelana nezivimbo ezikhethiwe kuzosiza mayelana nophenyo olubalulekile lwe- pharmacophore ngesikhathi kuqoshwa izivimbo se-BACE1. Ekugcineni, ukwenziwa kwe-computational ngokwamacebo ekubazeni izivimbo ze-BACE1 kube into ehlaba umxhwele. Nokho ukubaza izivimbo ezinamandla ze-BACE1 ngokusebenzisa i-computational yendlela ye-QM njengenjulalwazi yesisindo esisebenzayo (DFT), MM, kanye nendlela ye-hybrid QM/MM kufanele iphenywe kanzulu. Sincoma kakhulu ukuthi ongoti abenza izibonisi kufanele njalo bahlangane nama-computational chemists ukonga isikhathi kanye nezinye izinsiza

Understanding Cognitive Variability in Alzheimer’s Disease

Alzheimer’s Disease (AD) is highly heterogenous, both clinically and biologically. This variability is exacerbated by the ways within which, the clinical presentation is assessed with cognitive measures. This inhibits clinical trial success and earlier diagnosis of individuals. Marrying the clinical presentation to the pathology of the disease has so far proved troublesome. This thesis will look at how cognitive measures can best capture the clinical presentation of AD and how these measures can link to the underlying pathology using machine learning methods. This thesis studied this problem across four analyses and two cohorts. Each study looked at a different aspect of cognitive testing within AD. This was done with the overarching aim to interrogate the cognitive variability across the spectrum of AD. Study 1 showed a novel discrepancy score is different to memory measures at screening for AD. It also showed it tracks with AD severity, in the same way memory recall does. Studies 2 & 3 uncovered broad psychometric variance within amnestic measurement of impairment due to AD. This was done in two different populations across two different constructs of amnestic measurement, story recall and verbal list learning. These tests are frequently used interchangeably. These two studies show they should not be. Finally, Study 4 built models from cognitive measures to predict AD pathology. The performance of these models was moderate showing that even with novel cognitive measures, further work is needed to link the clinical and amyloid related biological presentations of AD. Bridging the gap between clinical presentation and pathology of AD using clinical and cognitive markers alone is not possible. Even when using a novel measure of discrepancy score. The discrepancy measure shows promise but was limited due to the inability of the MMSE to measure verbal ability. Conceptually a discrepancy score remains a promising avenue of research for screening, but broader language measures, as well as other AD biomarkers are needed to further test the construct validity of this measure

Pattern recognition and machine learning for magnetic resonance images with kernel methods

The aim of this thesis is to apply a particular category of machine learning and pattern recognition algorithms, namely the kernel methods, to both functional and anatomical magnetic resonance images (MRI). This work specifically focused on supervised learning methods. Both methodological and practical aspects are described in this thesis. Kernel methods have the computational advantage for high dimensional data, therefore they are idea for imaging data. The procedures can be broadly divided into two components: the construction of the kernels and the actual kernel algorithms themselves. Pre-processed functional or anatomical images can be computed into a linear kernel or a non-linear kernel. We introduce both kernel regression and kernel classification algorithms in two main categories: probabilistic methods and non-probabilistic methods. For practical applications, kernel classification methods were applied to decode the cognitive or sensory states of the subject from the fMRI signal and were also applied to discriminate patients with neurological diseases from normal people using anatomical MRI. Kernel regression methods were used to predict the regressors in the design of fMRI experiments, and clinical ratings from the anatomical scans

The Significance of Machine Learning in Clinical Disease Diagnosis: A Review

The global need for effective disease diagnosis remains substantial, given the complexities of various disease mechanisms and diverse patient symptoms. To tackle these challenges, researchers, physicians, and patients are turning to machine learning (ML), an artificial intelligence (AI) discipline, to develop solutions. By leveraging sophisticated ML and AI methods, healthcare stakeholders gain enhanced diagnostic and treatment capabilities. However, there is a scarcity of research focused on ML algorithms for enhancing the accuracy and computational efficiency. This research investigates the capacity of machine learning algorithms to improve the transmission of heart rate data in time series healthcare metrics, concentrating particularly on optimizing accuracy and efficiency. By exploring various ML algorithms used in healthcare applications, the review presents the latest trends and approaches in ML-based disease diagnosis (MLBDD). The factors under consideration include the algorithm utilized, the types of diseases targeted, the data types employed, the applications, and the evaluation metrics. This review aims to shed light on the prospects of ML in healthcare, particularly in disease diagnosis. By analyzing the current literature, the study provides insights into state-of-the-art methodologies and their performance metrics.Comment: 8 page

Machine Learning for Multiclass Classification and Prediction of Alzheimer\u27s Disease

Alzheimer\u27s disease (AD) is an irreversible neurodegenerative disorder and a common form of dementia. This research aims to develop machine learning algorithms that diagnose and predict the progression of AD from multimodal heterogonous biomarkers with a focus placed on the early diagnosis. To meet this goal, several machine learning-based methods with their unique characteristics for feature extraction and automated classification, prediction, and visualization have been developed to discern subtle progression trends and predict the trajectory of disease progression. The methodology envisioned aims to enhance both the multiclass classification accuracy and prediction outcomes by effectively modeling the interplay between the multimodal biomarkers, handle the missing data challenge, and adequately extract all the relevant features that will be fed into the machine learning framework, all in order to understand the subtle changes that happen in the different stages of the disease. This research will also investigate the notion of multitasking to discover how the two processes of multiclass classification and prediction relate to one another in terms of the features they share and whether they could learn from one another for optimizing multiclass classification and prediction accuracy. This research work also delves into predicting cognitive scores of specific tests over time, using multimodal longitudinal data. The intent is to augment our prospects for analyzing the interplay between the different multimodal features used in the input space to the predicted cognitive scores. Moreover, the power of modality fusion, kernelization, and tensorization have also been investigated to efficiently extract important features hidden in the lower-dimensional feature space without being distracted by those deemed as irrelevant. With the adage that a picture is worth a thousand words, this dissertation introduces a unique color-coded visualization system with a fully integrated machine learning model for the enhanced diagnosis and prognosis of Alzheimer\u27s disease. The incentive here is to show that through visualization, the challenges imposed by both the variability and interrelatedness of the multimodal features could be overcome. Ultimately, this form of visualization via machine learning informs on the challenges faced with multiclass classification and adds insight into the decision-making process for a diagnosis and prognosis

Computer aided diagnosis in temporal lobe epilepsy and Alzheimer's dementia

Computer aided diagnosis within neuroimaging must rely on advanced image processing techniques to detect and quantify subtle signal changes that may be surrogate indicators of disease state. This thesis proposes two such novel methodologies that are both based on large volumes of interest, are data driven, and use cross-sectional scans: appearance-based classification (ABC) and voxel-based classification (VBC).The concept of appearance in ABC represents the union of intensity and shape information extracted from magnetic resonance images (MRI). The classification method relies on a linear modeling of appearance features via principal components analysis, and comparison of the distribution of projection coordinates for the populations under study within a reference multidimensional appearance eigenspace. Classification is achieved using forward, stepwise linear discriminant analyses, in multiple cross-validated trials. In this work, the ABC methodology is shown to accurately lateralize the seizure focus in temporal lobe epilepsy (TLE), differentiate normal aging individuals from patients with either Alzheimer's dementia (AD) or Mild Cognitive Impairment (MCI), and finally predict the progression of MCI patients to AD. These applications demonstrated that the ABC technique is robust to different signal changes due to two distinct pathologies, to low resolution data and motion artifacts, and to possible differences inherent to multi-site acquisition.The VBC technique relies on voxel-based morphometry to identify regions of grey and white matter concentration differences between co-registered cohorts of individuals, and then on linear modeling of variables extracted from these regions. Classification is achieved using linear discriminant analyses within a multivariate space composed of voxel-based morphometry measures related to grey and white matter concentration, along with clinical variables of interest. VBC is shown to increase the accuracy of prediction of one-year clinical status from three to four out of five TLE patients having undergone selective amygdalo-hippocampectomy. These two techniques are shown to have the necessary potential to solve current problems in neurological research, assist clinical physicians with their decision-making process and influence positively patient management

Evaluation of the potentials for optical coherence tomography (OCT) to detect early signs of retinal neurodegeneration

Among neuroretinal degenerations, glaucoma and age-related macular degeneration (AMD) have become the most frequent reasons for irreversible blindness globally. Among the causes of the elderly and senile dementia, Alzheimer’s disease (AD) has the leading position, the early ocular symptoms of which can potentially be a prognostic factor. The aim of this thesis was the early in vivo ligand-free detection of degenerative changes in the inner and outer retinal layers, which was possible using high-resolution optical coherence tomography (OCT) with the machine learning (ML) algorithms: support vector machine (SVM) and principal component analysis (PCA). Prior to the application of SVM and PCA for the classification of human OCT images, evaluation of the classifiers was performed in the classification of optical phantoms, the accuracy of which was in the range of 82-100%. This was the first attempt to measure the textural properties of various polystyrene and silica beads optical phantoms. To identify optical changes that characterise early apoptosis, OCT imaging of axotomised retinal ganglion cells (RGCs) in ex vivo retinal murine explants was performed. Substantial optical alterations in RGC dendrites in the early stages of apoptosis (up to 2 hours) were detected. ML algorithms correctly classified the retinal texture of the inner plexiform layer (IPL) of transgenic AD mice in all cases, indicating the potential for further investigation in in vivo animal and human studies. Not only the optical signature but also the transparency of the dissected murine retinal explants was investigated. Moreover, ML classification of 3xTg mice IPL layer was studied in terms of optical changes due to the RGD dendritic atrophy. ML classifiers’ accuracy in the detection of early and neovascular AMD was 93-100% for the texture of retinal pigment epithelium, 69-67% for the outer nuclear layer, 70% for the inner segment and 60-90% for the outer segment of photoreceptors. Classification of AMD stages and comparison with the age-matched healthy controls was carried out in the outer retina and RPE. Grey-level co-occurrence, run-length matrices, local binary patterns features were extracted from the IPL of the macula to classify glaucoma OCT images. The accuracy of linear and non-linear SVMs, linear and quadratic discriminant analyses, decision tree and logistic regression was between 55-70%. Based on the classifiers’ precision, recall and F1-score, Gaussian SVM outperformed other ML techniques. In this study, the observation of early glaucomatous subtle optical changes of human IPL was conducted. Also, the significance of various supervised ML algorithms was investigated. Understanding the optical signature of cumulative inherent speckle of OCT scans arising from apoptotic retinal ganglion cells and photoreceptors may provide vital information for the prevention of retinal neurodegeneration