Platonic model of mind as an approximation to neurodynamics

Hierarchy of approximations involved in simplification of microscopic theories, from sub-cellural to the whole brain level, is presented. A new approximation to neural dynamics is described, leading to a Platonic-like model of mind based on psychological spaces. Objects and events in these spaces correspond to quasi-stable states of brain dynamics and may be interpreted from psychological point of view. Platonic model bridges the gap between neurosciences and psychological sciences. Static and dynamic versions of this model are outlined and Feature Space Mapping, a neurofuzzy realization of the static version of Platonic model, described. Categorization experiments with human subjects are analyzed from the neurodynamical and Platonic model points of view

Genetic architecture of the white matter connectome of the human brain

White matter tracts form the structural basis of large-scale functional networks in the human brain. We applied brain-wide tractography to diffusion images from 30,810 adult participants (UK Biobank), and found significant heritability for 90 regional connectivity measures and 851 tract-wise connectivity measures. Multivariate genome- wide association analyses identified 355 independently associated lead SNPs across the genome, of which 77% had not been previously associated with human brain metrics. Enrichment analyses implicated neurodevelopmental processes including neurogenesis, neural differentiation, neural migration, neural projection guidance, and axon development, as well as prenatal brain expression especially in stem cells, astrocytes, microglia and neurons. We used the multivariate association profiles of lead SNPs to identify 26 genomic loci implicated in structural connectivity between core regions of the left-hemisphere language network, and also identified 6 loci associated with hemispheric left-right asymmetry of structural connectivity. Polygenic scores for schizophrenia, bipolar disorder, autism spectrum disorder, attention-deficit hyperactivity disorder, left-handedness, Alzheimer’s disease, amyotrophic lateral sclerosis, and epilepsy showed significant multivariate associations with structural connectivity, each implicating distinct sets of brain regions with trait-relevant functional profiles. This large-scale mapping study revealed common genetic contributions to the structural connectome of the human brain in the general adult population, highlighting links with polygenic disposition to brain disorders and behavioural traits

Multi-view machine learning methods to uncover brain-behaviour associations

The heterogeneity of neurological and mental disorders has been a key confound in disease understanding and treatment outcome prediction, as the study of patient populations typically includes multiple subgroups that do not align with the diagnostic categories. The aim of this thesis is to investigate and extend classical multivariate methods, such as Canonical Correlation Analysis (CCA), and latent variable models, e.g., Group Factor Analysis (GFA), to uncover associations between brain and behaviour that may characterize patient populations and subgroups of patients. In the first contribution of this thesis, we applied CCA to investigate brain-behaviour associations in a sample of healthy and depressed adolescents and young adults. We found two positive-negative brain-behaviour modes of covariation, capturing externalisation/ internalisation symptoms and well-being/distress. In the second contribution of the thesis, I applied sparse CCA to the same dataset to present a regularised approach to investigate brain-behaviour associations in high dimensional datasets. Here, I compared two approaches to optimise the regularisation parameters of sparse CCA and showed that the choice of the optimisation strategy might have an impact on the results. In the third contribution, I extended the GFA model to mitigate some limitations of CCA, such as handling missing data. I applied the extended GFA model to investigate links between high dimensional brain imaging and non-imaging data from the Human Connectome Project, and predict non-imaging measures from brain functional connectivity. The results were consistent between complete and incomplete data, and replicated previously reported findings. In the final contribution of this thesis, I proposed two extensions of GFA to uncover brain behaviour associations that characterize subgroups of subjects in an unsupervised and supervised way, as well as explore within-group variability at the individual level. These extensions were demonstrated using a dataset of patients with genetic frontotemporal dementia. In summary, this thesis presents multi-view methods that can be used to deepen our understanding about the latent dimensions of disease in mental/neurological disorders and potentially enable patient stratification

Early human brain development:insights into macroscale connectome wiring

BACKGROUND: Early brain development is closely dictated by distinct neurobiological principles. Here, we aimed to map early trajectories of structural brain wiring in the neonatal brain. METHODS: We investigated structural connectome development in 44 newborns, including 23 preterm infants and 21 full-term neonates scanned between 29 and 45 postmenstrual weeks. Diffusion-weighted imaging data were combined with cortical segmentations derived from T2 data to construct neonatal connectome maps. RESULTS: Projection fibers interconnecting primary cortices and deep gray matter structures were noted to mature faster than connections between higher-order association cortices (fractional anisotropy (FA) F = 58.9, p < 0.001, radial diffusivity (RD) F = 28.8, p < 0.001). Neonatal FA-values resembled adult FA-values more than RD, while RD approximated the adult brain faster (F = 358.4, p < 0.001). Maturational trajectories of RD in neonatal white matter pathways revealed substantial overlap with what is known about the sequence of subcortical white matter myelination from histopathological mappings as recorded by early neuroanatomists (mean RD 68 regions r = 0.45, p = 0.008). CONCLUSION: Employing postnatal neuroimaging we reveal that early maturational trajectories of white matter pathways display discriminative developmental features of the neonatal brain network. These findings provide valuable insight into the early stages of structural connectome development

Notch Signaling Pathway in Tooth Shape Variations throughout Evolution

Evolutionary changes in vertebrates are linked to genetic alterations that often affect tooth crown shape, which is a criterion of speciation events. The Notch pathway is highly conserved between species and controls morphogenetic processes in most developing organs, including teeth. Epithelial loss of the Notch-ligand Jagged1 in developing mouse molars affects the location, size and interconnections of their cusps that lead to minor tooth crown shape modifications convergent to those observed along Muridae evolution. RNA sequencing analysis revealed that these alterations are due to the modulation of more than 2000 genes and that Notch signaling is a hub for significant morphogenetic networks, such as Wnts and Fibroblast Growth Factors. The modeling of these tooth crown changes in mutant mice, via a three-dimensional metamorphosis approach, allowed prediction of how Jagged1-associated mutations in humans could affect the morphology of their teeth. These results shed new light on Notch/Jagged1-mediated signaling as one of the crucial components for dental variations in evolution

A quantitative study of the neuropathology of 32 sporadic and familial cases of frontotemporal lobar degeneration with TDP-43 proteinopathy (FTLD-TDP)

To further characterize the neuropathology of the heterogeneous molecular disorder frontotemporal lobar degeneration (FTLD) with transactive response (TAR) DNA-binding protein of 43 kDa (TDP-43) proteinopathy (FTLD-TDP)

CLASSIFICATION OF NEUROANATOMICAL STRUCTURES BASED ON NON-EUCLIDEAN GEOMETRIC OBJECT PROPERTIES

Studying the observed morphological differences in neuroanatomical structures between individuals with neurodevelopmental disorders and a control group of typically developing individuals has been an important objective. Researchers study the differences with two goals: to assist an accurate diagnosis of the disease and to gain insights into underlying mechanisms of the disease that cause such changes. Shape classification is commonly utilized in such studies. An effective classification is difficult because it requires 1) a choice of an object model that can provide rich geometric object properties (GOPs) relevant for a given classification task, and 2) a choice of a statistical classification method that accounts for the non-Euclidean nature of GOPs. I lay out my methodological contributions to address the aforementioned challenges in the context of early diagnosis and detection of Autism Spectrum Disorder (ASD) in infants based on shapes of hippocampi and caudate nuclei; morphological deviations in these structures between individuals with ASD and typically developing individuals have been reported in the literature. These contributions respectively lead to 1) an effective modeling of shapes of objects of interest and 2) an effective classification. As the first contribution for modeling shapes of objects, I propose a method to obtain a set of skeletal models called s-reps from a set of 3D objects. First, the method iteratively deforms the object surface via Mean Curvature Flow (MCF) until the deformed surface is approximately ellipsoidal. Then, an s-rep of the approximate ellipsoid is obtained analytically. Finally, the ellipsoid s-rep is deformed via a series of inverse MCF transformations. The method has two important properties: 1) it is fully automatic, and 2) it yields a set of s-reps with good correspondence across the set. The method is shown effective in generating a set of s-reps for a few neuroanatomical structures. As the second contribution with respect to modeling shapes of objects, I introduce an extension to the current s-rep for representing an object with a narrowing sharp tail. This includes a spoke interpolation method for interpolating a discrete s-rep of an object with a narrowing sharp tail into a continuous object. This extension is necessary for representing surface geometry of objects whose boundary has a singular point. I demonstrate that this extension allows appropriate surface modeling of a narrowing sharp tail region of the caudate nucleus. In addition, I show that the extension is beneficial in classifying autistic and non-autistic infants at high risk of ASD based on shapes of caudate nuclei. As the first contribution with respect to statistical methods, I propose a novel shape classification framework that uses the s-rep to capture rich localized geometric descriptions of an object, a statistical method called Principal Nested Spheres (PNS) analysis to handle the non-Euclidean s-rep GOPs, and a classification method called Distance Weighted Discrimination (DWD). I evaluate the effectiveness of the proposed method in classifying autistic and non-autistic infants based on either hippocampal shapes or caudate shapes in terms of the Area Under the ROC curve (AUC). In addition, I show that the proposed method is superior to commonly used shape classification methods in the literature. As my final methodological contribution, I extend the proposed shape classification method to perform the classifcation task based on temporal shape differences. DWD learns a class separation direction based on the temporal shape differences that are obtained by taking differences of the temporal pair of Euclideanized s-reps. In the context of early diagnosis and detection of ASD in young infants, the proposed temporal shape difference classification produces some interesting results; the temporal differences in shapes of hippocampi and caudate nuclei do not seem to be as predictive as the cross-sectional shape of these structures alone.Doctor of Philosoph