Machine Learning for Alzheimer’s Disease and Related Dementias

Dementia denotes the condition that affects people suffering from cognitive and behavioral impairments due to brain damage. Common causes of dementia include Alzheimer’s disease, vascular dementia, or frontotemporal dementia, among others. The onset of these pathologies often occurs at least a decade before any clinical symptoms are perceived. Several biomarkers have been developed to gain a better insight into disease progression, both in the prodromal and the symptomatic phases. Those markers are commonly derived from genetic information, biofluid, medical images, or clinical and cognitive assessments. Information is nowadays also captured using smart devices to further understand how patients are affected. In the last two to three decades, the research community has made a great effort to capture and share for research a large amount of data from many sources. As a result, many approaches using machine learning have been proposed in the scientific literature. Those include dedicated tools for data harmonization, extraction of biomarkers that act as disease progression proxy, classification tools, or creation of focused modeling tools that mimic and help predict disease progression. To date, however, very few methods have been translated to clinical care, and many challenges still need addressing

Multimodal mechanisms of human socially reinforced learning across neurodegenerative diseases

Social feedback can selectively enhance learning in diverse domains. Relevant neurocognitive mechanisms have been studied mainly in healthy persons, yielding correlational findings. Neurodegenerative lesion models, coupled with multimodal brain measures, can complement standard approaches by revealing direct multidimensional correlates of the phenomenon. To this end, we assessed socially reinforced and non-socially reinforced learning in 40 healthy participants as well as persons with behavioural variant frontotemporal dementia (n = 21), Parkinson's disease (n = 31) and Alzheimer's disease (n = 20). These conditions are typified by predominant deficits in social cognition, feedback-based learning and associative learning, respectively, although all three domains may be partly compromised in the other conditions. We combined a validated behavioural task with ongoing EEG signatures of implicit learning (medial frontal negativity) and offline MRI measures (voxel-based morphometry). In healthy participants, learning was facilitated by social feedback relative to non-social feedback. In comparison with controls, this effect was specifically impaired in behavioural variant frontotemporal dementia and Parkinson's disease, while unspecific learning deficits (across social and non-social conditions) were observed in Alzheimer's disease. EEG results showed increased medial frontal negativity in healthy controls during social feedback and learning. Such a modulation was selectively disrupted in behavioural variant frontotemporal dementia. Neuroanatomical results revealed extended temporo-parietal and fronto-limbic correlates of socially reinforced learning, with specific temporo-parietal associations in behavioural variant frontotemporal dementia and predominantly fronto-limbic regions in Alzheimer's disease. In contrast, non-socially reinforced learning was consistently linked to medial temporal/hippocampal regions. No associations with cortical volume were found in Parkinson's disease. Results are consistent with core social deficits in behavioural variant frontotemporal dementia, subtle disruptions in ongoing feedback-mechanisms and social processes in Parkinson's disease and generalized learning alterations in Alzheimer's disease. This multimodal approach highlights the impact of different neurodegenerative profiles on learning and social feedback. Our findings inform a promising theoretical and clinical agenda in the fields of social learning, socially reinforced learning and neurodegeneration.Fil: Legaz, Agustina. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Houssay. Instituto de Neurociencia Cognitiva. Fundación Favaloro. Instituto de Neurociencia Cognitiva; Argentina. Universidad Nacional de Córdoba; ArgentinaFil: Abrevaya, Sofia. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Houssay. Instituto de Neurociencia Cognitiva. Fundación Favaloro. Instituto de Neurociencia Cognitiva; ArgentinaFil: Dottori, Martín. Universidad de San Andrés; ArgentinaFil: Campo, Cecilia González. Universidad de San Andrés; ArgentinaFil: Birba, Agustina. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Universidad de San Andrés; ArgentinaFil: Martorell Caro, Miguel Angel. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Houssay. Instituto de Neurociencia Cognitiva. Fundación Favaloro. Instituto de Neurociencia Cognitiva; ArgentinaFil: Aguirre, María Julieta. Universidad Nacional de Córdoba. Instituto de Investigaciones Psicológicas. - Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Córdoba. Instituto de Investigaciones Psicológicas; ArgentinaFil: Slachevsky, Andrea. Universidad de Chile.; ChileFil: Aranguiz, Rafael. Instituto Nacional de Geriatría; ChileFil: Serrano, Cecilia Mariela. Unidad Asistencial "Dr. César Milstein"; ArgentinaFil: Gillan, Claire M.. University of California; Estados UnidosFil: Leroi, Iracema. University of California; Estados UnidosFil: García, Adolfo Martín. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Universidad de San Andrés; Argentina. University of California; Estados Unidos. Trinity College; Estados Unidos. Universidad Nacional de Cuyo; Argentina. Universidad de Santiago de Chile; ChileFil: Fittipaldi, Sol. Universidad de San Andrés; ArgentinaFil: Ibañez, Agustin Mariano. Universidad de San Andrés; Argentina. University of California; Estados Unidos. Trinity College; Estados Unidos. Universidad Adolfo Ibañez; Chile. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentin

Doctor of Philosophy

dissertationNeurodegenerative diseases are an increasing health care problem in the United States. Quantitative neuroimaging provides a noninvasive method to illuminate individual variations in brain structure to better understand and diagnose these disorders. The overall objective of this research is to develop novel clinical tools that summarize and quantify changes in brain shape to not only help better understand age-appropriate changes but also, in the future, to dissociate structural changes associated with aging from those caused by dementing neurodegenerative disorders. Because the tools we will develop can be applied for individual assessment, achieving our goals could have a significant clinical impact. An accurate, practical objective summary measure of the brain pathology would augment current subjective visual interpretation of structural magnetic resonance images. Fractal dimension is a novel approach to image analysis that provides a quantitative measure of shape complexity describing the multiscale folding of the human cerebral cortex. Cerebral cortical folding reflects the complex underlying architectural features that evolve during brain development and degeneration including neuronal density, synaptic proliferation and loss, and gliosis. Building upon existing technology, we have developed innovative tools to compute global and local (voxel-wise and regional) cerebral cortical fractal dimensions and voxel-wise cortico-fractal surfaces from high-contrast MR images. Our previous research has shown that fractal dimension correlates with cognitive function and changes during the course of normal aging. We will now apply unbiased diffeomorphic atlasing methodology to dramatically improve the alignment of complex cortical surfaces. Our novel methods will create more accurate, detailed geometrically averaged images to take into account the intragroup differences and make statistical inferences about spatiotemporal changes in shape of the cerebral cortex across the adult human lifespan

A MEG study of the neural substrates of semantic processing in semantic variant primary progressive aphasia

Despite a well-documented pattern of semantic memory (SM) impairment, the patterns of brain activation during semantic processing in svPPA still remain poorly understood. The current study aimed to investigate the neural substrates of residual semantic processing in the context of this significant but selective SM impairment, through the case study of one svPPA patient. One svPPA patient (EC) and six elderly controls carried out a general-level semantic categorization task (biological and manufactured objects) while their brain activity was recorded using magnetoencephalography (MEG). Despite similar behavioral performance, EC showed hyperactivation of the left inferior temporal gyrus (ITG) and right anterior temporal lobe (ATL) relative to controls. This suggests that periatrophic regions within the ATL region may support preserved semantic abilities in svPPA. These results thus contribute to our understanding of the brain regions which are recruited to compensate for bilateral atrophy of the ATL and ensure residual semantic processing in svPPA

Network connectivity and structural correlates of survival in progressive supranuclear palsy and corticobasal syndrome

There is a pressing need to understand the factors that predict prognosis in progressive supranuclear palsy (PSP) and corticobasal syndrome (CBS), with high heterogeneity over the poor average survival. We test the hypothesis that the magnitude and distribution of connectivity changes in PSP and CBS predict the rate of progression and survival time, using datasets from the Cambridge Centre for Parkinson-plus and the UK National PSP Research Network (PROSPECT-MR). Resting-state functional MRI images were available from 146 participants with PSP, 82 participants with CBS, and 90 healthy controls. Large-scale networks were identified through independent component analyses, with correlations taken between component time series. Independent component analysis was also used to select between-network connectivity components to compare with baseline clinical severity, longitudinal rate of change in severity, and survival. Transdiagnostic survival predictors were identified using partial least squares regression for Cox models, with connectivity compared to patients' demographics, structural imaging, and clinical scores using five-fold cross-validation. In PSP and CBS, between-network connectivity components were identified that differed from controls, were associated with disease severity, and were related to survival and rate of change in clinical severity. A transdiagnostic component predicted survival beyond demographic and motion metrics but with lower accuracy than an optimal model that included the clinical and structural imaging measures. Cortical atrophy enhanced the connectivity changes that were most predictive of survival. Between-network connectivity is associated with variability in prognosis in PSP and CBS but does not improve predictive accuracy beyond clinical and structural imaging metrics

Machine Learning for Alzheimer’s Disease and Related Dementias

Copyright © The Author(s) 2023. Dementia denotes the condition that affects people suffering from cognitive and behavioral impairments due to brain damage. Common causes of dementia include Alzheimer’s disease, vascular dementia, or frontotemporal dementia, among others. The onset of these pathologies often occurs at least a decade before any clinical symptoms are perceived. Several biomarkers have been developed to gain a better insight into disease progression, both in the prodromal and the symptomatic phases. Those markers are commonly derived from genetic information, biofluid, medical images, or clinical and cognitive assessments. Information is nowadays also captured using smart devices to further understand how patients are affected. In the last two to three decades, the research community has made a great effort to capture and share for research a large amount of data from many sources. As a result, many approaches using machine learning have been proposed in the scientific literature. Those include dedicated tools for data harmonization, extraction of biomarkers that act as disease progression proxy, classification tools, or creation of focused modeling tools that mimic and help predict disease progression. To date, however, very few methods have been translated to clinical care, and many challenges still need addressing.MM, LC, and SO research is supported by the Wellcome EPSRC Centre for Medical Engineering at King’s College London (WT 203148/Z/16/Z), EPSRC (EP/T022205/1), the Wellcome Trust (WT 215010/Z/18/Z), the UK Research and Innovation London Medical Imaging and Artificial Intelligence Centre for Value-Based Healthcare, Medical Research Council (MRC), NIHR, Alzheimer’s Society, and the European Union. DMC is supported by the UK Dementia Research Institute which receives its funding from DRI Ltd, funded by the UK Medical Research Council, Alzheimer’s Society and Alzheimer’s Research UK (ARUK-PG2017-1946), the UCL/UCLH NIHR Biomedical Research Centre, and the UKRI Innovation Scholars: Data Science Training in Health and Bioscience (MR/V03863X/1). MB is supported by a Fellowship award from the Alzheimer’s Society, UK (AS-JF-19a-004-517). MB’s work was also supported by the UK Dementia Research Institute which receives its funding from DRI Ltd, funded by the UK Medical Research Council, Alzheimer’s Society, and Alzheimer’s Research UK

HIV-associated structural brain changes as related to cognition

Nearly half of all HIV-positive individuals present with some form of HIV-associated neurocognitive disorder (HAND). The experiments described in this thesis examined the structural changes that occur in the brain as a result of HIV infection. While previous work has established that HIV targets the basal ganglia and fronto-striatal systems and impacts cortical and white matter pathways, it was unknown whether these changes occur in the absence of HAND. The studies described here focused on cognitively asymptomatic HIV+ individuals (CAHIV+) without HAND as determined by widely accepted neuropsychological performance guidelines. Experiment 1 utilized diffusion tensor imaging (DTI) to examine HIV-associated alterations in white matter (WM) fractional anisotropy (FA) in the absence of HAND in 23 HIV+ individuals and 17 control participants (HIV-) matched for age, education, and verbal IQ. The hypothesis was that CAHIV+ participants would show lower FA values than HIV- in the corpus callosum, frontotemporal, and parietal regions of interest (ROIs). CAHIV+ individuals demonstrated higher FA in the frontotemporal region and posterior corpus callosum, but lower FA in parietal WM relative to HIV- individuals. Experiment 2 utilized structural MRI to compare cortical thickness in 22 CAHIV+ individuals and 19 control participants (HIV-) matched for age, education, and verbal IQ. The hypothesis was that CAHIV+ participants would have thinner frontal, temporal, and parietal regions than HIV- participants. Reduced cortical thickness measures were identified in the cingulate and superior temporal gyri, with increased cortical thickness measures in the inferior occipital gyrus, for HIV+ participants compared to HIV-. Experiment 3 examined the relationship between the structural alterations identified in Experiments 1 and 2, neuropsychological performance on tests sensitive to HAND identification, and immunological characteristics in 30 HIV+ participants and 28 HIV- control participants. As hypothesized, regional FA values, cortical thickness, and viral load were related to neuropsychological composite scores for CAHIV+, but not HIV-. Together, results from these three studies suggest that regional FA and cortical alterations identified in CAHIV+ patients may contribute to the cognitive deficits often seen in later stages of HIV disease

A comparison of FreeSurfer-generated data with and without manual intervention

This paper examined whether FreeSurfer - generated data differed between a fully – automated, unedited pipeline and an edited pipeline that included the application of control points to correct errors in white matter segmentation. In a sample of 30 individuals, we compared the summary statistics of surface area, white matter volumes, and cortical thickness derived from edited and unedited datasets for the 34 regions of interest (ROIs) that FreeSurfer (FS) generates. To determine whether applying control points would alter the detection of significant differences between patient and typical groups, effect sizes between edited and unedited conditions in individuals with the genetic disorder, 22q11.2 deletion syndrome (22q11DS) were compared to neurotypical controls. Analyses were conducted with data that were generated from both a 1.5 tesla and a 3 tesla scanner. For 1.5 tesla data, mean area, volume, and thickness measures did not differ significantly between edited and unedited regions, with the exception of rostral anterior cingulate thickness, lateral orbitofrontal white matter, superior parietal white matter, and precentral gyral thickness. Results were similar for surface area and white matter volumes generated from the 3 tesla scanner. For cortical thickness measures however, seven edited ROI measures, primarily in frontal and temporal regions, differed significantly from their unedited counterparts, and three additional ROI measures approached significance. Mean effect sizes for edited ROIs did not differ from most unedited ROIs for either 1.5 or 3 tesla data. Taken together, these results suggest that although the application of control points may increase the validity of intensity normalization and, ultimately, segmentation, it may not affect the final, extracted metrics that FS generates. Potential exceptions to and limitations of these conclusions are discussed