Trajectories from Mild Cognitive Impairment to Alzheimer’s Disease: A machine learning approach in the context of Precision Medicine

Mild Kognitv Svikt (MKS) er en diagnostisk kategori som beskriver en heterogen gruppe pasienter. For noen representerer MKS et tidlig tegn på en nevrodegenerativ sykdom, mens andre forbli stabile eller forbedrer seg over tid. Tidlig identifisering av nevrodegenerasjon er svært viktig for å kunne påbegynne behandling før sykdommen allerede har forårsaket store skader i hjernen. Dette motiverte den aktuelle studien, der longitudinelle data fra Alzheimer’s Disease Neuroimaging Initiative (ADNI) benyttes for å undersøke to grupper av pasienter som ved baseline viste MKS av den amnestiske typen (aMKS): en gruppe som forble stabile over tid (sMKS) og en gruppe som etterhvert fikk diagnosen Alzheimer’s sykdom (cMKS). Det ble valgt ut variabler som gjerne inngår i en klinisk undersøkelse av pasienter med aMKS. Disse omfatter mål på hukommelses- og eksekutiv funksjon, depresive symptomer, intellektuell funksjon, hippocampusvolum og genotype (ApoE). Resultatene viste bedre resultater på tester av hukommelse og eksekutiv funksjon, større hippocampusvolum, og færre individer med ApoE-ε4 i sMKS enn cMKS gruppen. Vi undersøkte deretter hvor godt et utviklingsforløp mot AD kunne predikeres basert på de utvalgte variablene ved å benytte en Random Forest (RF) modell. Evaluering av modellens nøyaktighet i et testset viste en nøyaktighet på 68.3%. Beregninger av de ulike variablenes betydning for klassifikasjonen viste at den var sterkest for mål på hukommelse, hippocampusvolum og eksekutiv funksjon. Partial dependency plots viste terskelverdier som øker sannsynligheten for å klassifiseres i cMKS gruppen. Resultatene diskuteres fra et klinisk, teoretisk og analytisk perspektiv, med vekt på studiens relevans for en fremtidsrettet presisjonsmedisin.Masteroppgave i psykologiMAPSYK360MAPS-PSYKINTL-HFINTL-MEDINTL-JUSINTL-SVINTL-MNINTL-KMDINTL-PSY

Neural correlates of post-traumatic brain injury (TBI) attention deficits in children

Traumatic brain injury (TBI) in children is a major public health concern worldwide. Attention deficits are among the most common neurocognitive and behavioral consequences in children post-TBI which have significant negative impacts on their educational and social outcomes and compromise the quality of their lives. However, there is a paucity of evidence to guide the optimal treatment strategies of attention deficit related symptoms in children post-TBI due to the lack of understanding regarding its neurobiological substrate. Thus, it is critical to understand the neural mechanisms associated with TBI-induced attention deficits in children so that more refined and tailored strategies can be developed for diagnoses and long-term treatments and interventions. This dissertation is the first study to investigate neurobiological substrates associated with post-TBI attention deficits in children using both anatomical and functional neuroimaging data. The goals of this project are to discover the quantitatively measurable markers utilizing diffusion tensor imaging (DTI), structural magnetic resonance imaging (MRI), and functional MRI (fMRI) techniques, and to further identify the most robust neuroimaging features in predicting severe post-TBI attention deficits in children, by utilizing machine learning and deep learning techniques. A total of 53 children with TBI and 55 controls from age 9 to 17 are recruited. The results show that the systems-level topological properties in left frontal regions, parietal regions, and medial occipitotemporal regions in structural and functional brain network are significantly associated with inattentive and/or hyperactive/impulsive symptoms in children post-TBI. Semi-supervised deep learning modeling further confirms the significant contributions of these brain features in the prediction of elevated attention deficits in children post-TBI. The findings of this project provide valuable foundations for future research on developing neural markers for TBI-induced attention deficits in children, which may significantly assist the development of more effective and individualized diagnostic and treatment strategies

Evaluation of Cerebral Lateral Ventricular Enlargement Derived from Magnetic Resonance Imaging: A Candidate Biomarker of Alzheimer Disease Progression in Vivo

Alzheimer disease (AD) is the most common form of dementia and has grievous mortality rates. Measuring brain volumes from structural magnetic resonance images (MRI) may be useful for illuminating disease progression. The goal of this thesis was to (1) help refine a novel technique used to segment the lateral cerebral ventricles from MRI, (2) validate this tool, and determine group-wise differences between normal elderly controls (NEC) and subjects with mild cognitive impairment (MCI) and AD and (3) determine the number of subjects necessary to detect a 20 percent change from the natural history of ventricular enlargement with respect to genotype. Three dimensional Ti-weighted MRI and cognitive measures were acquired from 504 subjects (NEC n = 152, MCI n = 247 and AD n = 105) participating in the multi-centre Alzheimer\u27s Disease Neuroimaging Initiative. Cerebral ventricular volume was quantified at baseline and after six months. For secondary analyses, all groups were dichotomized for Apolipoprotein E genotype based on the presence of an e4 polymorphism. The AD group had greater ventricular enlargement compared to both subjects with MCI (P = 0.0004) and NEC (P \u3c 0.0001), and subjects with MCI had a greater rate of ventricular enlargement compared to NEC (P =0.0001). MCI subjects that progressed to clinical AD after six months had greater ventricular enlargement than stable MCI subjects (P = 0.0270). Ventricular enlargement was different between apolipoprotein E genotypes within the AD group (P = 0.010). The number of subjects required to demonstrate a 20% change in ventricular enlargement (AD: N=342, MCI: N=1180) was substantially lower than that required to demonstrate a 20% change in cognitive scores (MMSE) (AD: N=7056, MCI: N=7712). Therefore, ventricular enlargement represents a feasible short-term marker of disease progression in subjects with MCI and subjects with AD for multi-centre studie

Multivariate neuroanatomical classification of cognitive subtypes in schizophrenia: A support vector machine learning approach

AbstractHeterogeneity in the structural brain abnormalities associated with schizophrenia has made identification of reliable neuroanatomical markers of the disease difficult. The use of more homogenous clinical phenotypes may improve the accuracy of predicting psychotic disorder/s on the basis of observable brain disturbances. Here we investigate the utility of cognitive subtypes of schizophrenia – ‘cognitive deficit’ and ‘cognitively spared’ – in determining whether multivariate patterns of volumetric brain differences can accurately discriminate these clinical subtypes from healthy controls, and from each other. We applied support vector machine classification to grey- and white-matter volume data from 126 schizophrenia patients previously allocated to the cognitive spared subtype, 74 cognitive deficit schizophrenia patients, and 134 healthy controls. Using this method, cognitive subtypes were distinguished from healthy controls with up to 72% accuracy. Cross-validation analyses between subtypes achieved an accuracy of 71%, suggesting that some common neuroanatomical patterns distinguish both subtypes from healthy controls. Notably, cognitive subtypes were best distinguished from one another when the sample was stratified by sex prior to classification analysis: cognitive subtype classification accuracy was relatively low (<60%) without stratification, and increased to 83% for females with sex stratification. Distinct neuroanatomical patterns predicted cognitive subtype status in each sex: sex-specific multivariate patterns did not predict cognitive subtype status in the other sex above chance, and weight map analyses demonstrated negative correlations between the spatial patterns of weights underlying classification for each sex. These results suggest that in typical mixed-sex samples of schizophrenia patients, the volumetric brain differences between cognitive subtypes are relatively minor in contrast to the large common disease-associated changes. Volumetric differences that distinguish between cognitive subtypes on a case-by-case basis appear to occur in a sex-specific manner that is consistent with previous evidence of disrupted relationships between brain structure and cognition in male, but not female, schizophrenia patients. Consideration of sex-specific differences in brain organization is thus likely to assist future attempts to distinguish subgroups of schizophrenia patients on the basis of neuroanatomical features

DIAGNOSTICS OF DEMENTIA FROM STRUCTURAL AND FUNCTIONAL MARKERS OF BRAIN ATROPHY WITH MACHINE LEARNING

Dementia is a condition in which higher mental functions are disrupted. It currently affects an estimated 57 million people throughout the world. A dementia diagnosis is difficult since neither anatomical indicators nor functional testing is currently sufficiently sensitive or specific. There remains a long list of outstanding issues that must be addressed. First, multimodal diagnosis has yet to be introduced into the early stages of dementia screening. Second, there is no accurate instrument for predicting the progression of pre-dementia. Third, non-invasive testing cannot be used to provide differential diagnoses. By creating ML models of normal and accelerated brain aging, we intend to better understand brain development. The combined analysis of distinct imaging and functional modalities will improve diagnostics of accelerated decline with advanced data science techniques, which is the main objective of our study. Hypothetically, an association between brain structural changes and cognitive performance differs between normal and accelerated aging. We propose using brain MRI scans to estimate the cognitive status of the cognitively preserved examinee and develop a structure-function model with machine learning (ML). Accelerated ageing is suspected when a scanned individual’s findings do not align with the usual paradigm. We calculate the deviation from the model of normal ageing (DMNA) as the error of cognitive score prediction. Then the obtained data may be compared with the results of conducted cognitive tests. The greater the difference between the expected and observed values, the greater the risk of dementia. DMNA can discern between cognitively normal and mild cognitive impairment (MCI) patients. The model was proven to perform well in the MCI-versus-Alzheimer’s disease (AD) categorization. DMNA is a potential diagnostic marker of dementia and its types

Neurobiological Impact of HIV Infection and Chronic Cannabis Use

Neuroimaging research has identified brain alterations linked with the human immunodeficiency virus (HIV) that contribute to cognitive declines characterizing the disease. Given cannabis’s (CB’s) anti-inflammatory properties, use prevalence among people living with HIV (PLWH), and impact on neurocognition, my dissertation utilizes a between-groups study design to interrogate separate and interactive effects of HIV and CB on fMRI measures of brain activity. We investigate (1) task-based brain activity at the regional-level, (2) insular resting-state functional connectivity (rsFC) at the circuit-level, and (3) large-scale brain network interactions at the systems-level. Participants (N=114) were stratified into four groups (HIV+/CB+; HIV+/CB-; HIV-/CB+; HIV-/CB-) and underwent fMRI scanning while completing an Error Awareness Task (EAT) and while at rest. Participants also completed a battery of instruments including subjective reports of cognitive failures, and objective measures of cognition and medication management abilities. Blood samples quantified disease severity (viral load) and inflammation (tumor necrosis factor alpha [TNF-α]). Regarding task-based brain activity, PLWH displayed a lack of error-related deactivation in two default mode network (DMN) regions (posterior cingulate cortex [PCC], medial prefrontal cortex [mPFC]). Across all participants, reduced error-related PCC deactivation correlated with reduced medication management abilities and mediated the effect of HIV on such abilities. Regarding insular circuitry, we observed interactive HIVxCB effects on rsFC between two anterior insula (aI) subregions and sensorimotor cortices such that, CB use normalized altered rsFC that was observed among non-using PLWH and correlated with decreased somatic complaints and increased inflammation. Finally, regarding large-scale network interactions, PLWH displayed increased salience network (SN)-DMN rsFC that was associated with diminished error-awareness. These results demonstrate that insufficient error-related DMN suppression and heightened SN-DMN rsFC are linked with HIV and have consequences for error-processing and medication management. Additionally, these outcomes suggest a potential normalizing effect of CB on altered insula-sensorimotor neurocircuitries among PLWH and begin to elucidate inflammatory mechanisms through which CB use may impact brain function in the context of HIV

ACTIVATED CARBON NANOFIBERS FROM RENEWABLE (LIGNIN) AND WASTE RESOURCES (RECYCLED PET) AND THEIR ADSORPTION CAPACITY OF REFRACTORY SULFUR COMPOUNDS FROM FOSSIL FUELS

Dementia is a condition in which higher mental functions are disrupted. It currently affects an estimated 57 million people throughout the world. Dementia diagnosis is difficult since neither anatomical indicator nor functional testing are currently sufficiently sensitive or specific. There remains a long list of outstanding issues that must be addressed. First, multimodal diagnosis has yet to be introduced into the early stages of dementia screening. Second, there is no accurate instrument for predicting the progression of pre-dementia. Third, non-invasive testing cannot be used to provide differential diagnoses. By creating ML models of normal and accelerated brain aging, we intend to better understand brain development. The combined analysis of distinct imaging and functional modalities will improve diagnostics of accelerated decline with advanced data science techniques, which is the main objective of our study. Hypothetically, an association between brain structural changes and cognitive performance differs between normal and accelerated aging. We propose using brain MRI scans to estimate the cognitive status of the cognitively preserved examinee and develop a structure-function model with machine learning (ML). Accelerated aging is suspected when a scanned individual’s findings do not align with the usual paradigm. We calculate the deviation from the model of normal aging (DMNA) as the error of cognitive score prediction. Then the obtained data may be compared with the results of conducted cognitive tests. The greater the difference between the expected and observed values, the greater the risk of dementia. DMNA can discern between cognitively normal and mild cognitive impairment (MCI) patients. The model was proven to perform well in the MCI-versus-Alzheimer’s disease (AD) categorization. DMNA is a potential diagnostic marker of dementia and its types