Machine-learning based exploration of determinants of gray matter volume in the KORA-MRI study

To identify the most important factors that impact brain volume, while accounting for potential collinearity, we used a data-driven machine-learning approach. Gray Matter Volume (GMV) was derived from magnetic resonance imaging (3T, FLAIR) and adjusted for intracranial volume (ICV). 93 potential determinants of GMV from the categories sociodemographics, anthropometric measurements, cardio-metabolic variables, lifestyle factors, medication, sleep, and nutrition were obtained from 293 participants from a population-based cohort from Southern Germany. Elastic net regression was used to identify the most important determinants of ICV-adjusted GMV. The four variables age (selected in each of the 1000 splits), glomerular filtration rate (794 splits), diabetes (323 splits) and diabetes duration (122 splits) were identified to be most relevant predictors of GMV adjusted for intracranial volume. The elastic net model showed better performance compared to a constant linear regression (mean squared error = 1.10 vs. 1.59, p<0.001). These findings are relevant for preventive and therapeutic considerations and for neuroimaging studies, as they suggest to take information on metabolic status and renal function into account as potential confounders

Effect of data harmonization of multicentric dataset in ASD/TD classification

Machine Learning (ML) is nowadays an essential tool in the analysis of Magnetic Resonance Imaging (MRI) data, in particular in the identification of brain correlates in neurological and neurodevelopmental disorders. ML requires datasets of appropriate size for training, which in neuroimaging are typically obtained collecting data from multiple acquisition centers. However, analyzing large multicentric datasets can introduce bias due to differences between acquisition centers. ComBat harmonization is commonly used to address batch effects, but it can lead to data leakage when the entire dataset is used to estimate model parameters. In this study, structural and functional MRI data from the Autism Brain Imaging Data Exchange (ABIDE) collection were used to classify subjects with Autism Spectrum Disorders (ASD) compared to Typical Developing controls (TD). We compared the classical approach (external harmonization) in which harmonization is performed before train/test split, with an harmonization calculated only on the train set (internal harmonization), and with the dataset with no harmonization. The results showed that harmonization using the whole dataset achieved higher discrimination performance, while non-harmonized data and harmonization using only the train set showed similar results, for both structural and connectivity features. We also showed that the higher performances of the external harmonization are not due to larger size of the sample for the estimation of the model and hence these improved performance with the entire dataset may be ascribed to data leakage. In order to prevent this leakage, it is recommended to define the harmonization model solely using the train set

Reliability of Graph Measures Derived from Resting-State MEG Data Using Source Space Functional Connectivity Analysis

The reliability of global graph measures derived from neuroimaging data is an important criterion for their use as biomarkers for neurological disorders. This study examined the reliability of the global efficiency (GE), characteristic path length (CPL), transitivity, and synchronizability of functional whole-brain and intra-hemispheric networks based on resting-state magnetoencephalography. Brain sources were reconstructed using atlas-based beamforming, and functional connectivity in six frequency bands was estimated using the debiased weighted phase lag index. An optimal threshold of 100% was chosen based on test-retest reliability of the measures. At this threshold, test-retest reliability of the GE, CPL, and transitivity was mostly fair to excellent except for in the delta band. However, test-retest reliability of the synchronizability was mostly poor to fair. There was no significant effect of gender on any graph measure. Overall, these results indicate that the GE, CPL, and transitivity in most of the frequency bands may be useful biomarkers

Functional Imaging Connectome of the Human Brain and its Associations with Biological and Behavioral Characteristics

Functional connectome of the human brain explores the temporal associations of different brain regions. Functional connectivity (FC) measures derived from resting state functional magnetic resonance imaging (rfMRI) characterize the brain network at rest and studies have shown that rfMRI FC is closely related to individual subject\u27s biological and behavioral measures. In this thesis we investigate a large rfMRI dataset from the Human Connectome Project (HCP) and utilize statistical methods to facilitate the understanding of fundamental FC-behavior associations of the human brain. Our studies include reliability analysis of FC statistics, demonstration of FC spatial patterns, and predictive analysis of individual biological and behavioral measures using FC features. Covering both static and dynamic FC (sFC and dFC) characterizations, the baseline FC patterns in healthy young adults are illustrated. Predictive analyses demonstrate that individual biological and behavioral measures, such as gender, age, fluid intelligence and language scores, can be predicted using FC. While dFC by itself performs worse than sFC in prediction accuracy, if appropriate parameters and models are utilized, adding dFC features to sFC can significantly increase the predictive power. Results of this thesis contribute to the understanding of the neural underpinnings of individual biological and behavioral differences in the human brain

Sexually divergent development of depression-related brain networks during healthy human adolescence

Sexual differences in human brain development could be relevant to sex differences in the incidence of depression during adolescence. We tested for sex differences in parameters of normative brain network development using fMRI data on N = 298 healthy adolescents, aged 14 to 26 years, each scanned one to three times. Sexually divergent development of functional connectivity was located in the default mode network, limbic cortex, and subcortical nuclei. Females had a more “disruptive” pattern of development, where weak functional connectivity at age 14 became stronger during adolescence. This fMRI-derived map of sexually divergent brain network development was robustly colocated with i prior loci of reward-related brain activation ii a map of functional dysconnectivity in major depressive disorder (MDD), and iii an adult brain gene transcriptional pattern enriched for genes on the X chromosome, neurodevelopmental genes, and risk genes for MDD. We found normative sexual divergence in adolescent development of a cortico-subcortical brain functional network that is relevant to depression

Developmental and sex modulated neurological alterations in autism spectrum disorder

Autism Spectrum Disorder (ASD) was first described in 1943 by Dr. Leo Kranner in a case study published in The Nervous Child. It is a neurodevelopment disorder, with a range of clinical symptoms. According to the Diagnostic and Statistical Manual of Mental Disorders (DSM-5), used by clinicians to diagnose mental disorders, a child needs to have persistent social deficits, language impairments, and repetitive behaviors, that cannot be explained by neurological damage or intellectual disability. It is known that children diagnosed with ASD are often are developmentally delayed therefore alterations in the typical developmental trajectory should be a major factor in consideration when studying ASD. As of 2016, 1 in 68 children in the USA is diagnosed with ASD, of those diagnosed young males are four times more likely to be diagnosed than their female peers. Although genetic and behavioral theories exist to explain these differences, the cause for the disparity is still unknown. This Dissertation presents a unique opportunity to understand the intersection of altered neurodevelopment and the alarming sex disparities in patients with ASD from a neuroimaging perspective. The hypothesis is that there exist differences due to development and sex in with ASD. Access to ABIDE (Autism Brain Imaging Data Exchange), a open source large scale data sharing consortium of functional and anatomical MR data. Analyzing MR data for alterations due to ASD, developmental trajectory, and sex as well as the intersection of these factors. Theses modulations are observed in three Project Aims that employ various analytical approaches: (1) Structural Morphology, (2) Resting-state Functional Connectivity, and (3) Graph Theory. The major findings lie at the interaction of these three factors; developmental stage-by-diagnosis-by-sex. Structural Morphological Analyses of anatomical data show differences in cortical thickness, on the left rostral middle frontal gyrus and surface area in along the sensory motor strip, of the left paracentral gyrus and right precentral gyrus. Resting-state Functional Connectivity analyzed in multiple data driven approaches, and altered resting state connectivity patterns between the left frontal parietal network and the left parahippcampal gyrus are reported. The regions found in the Morphological Analyses are used as seeds for a priori connectivity analysis, connectivity between the left rostral middle frontal cortex and bilateral superior temporal gyrus as well as the right precentral gyrus and right middle frontal gyrus and left inferior frontal gyrus are described. Finally using Graph Theory analysis, which quantifies a whole brain connectivity matrix to calculate metrics such as path length, cluster coefficient, local efficiency, and betweeness centrality all of which are altered by the interaction of all three factors. The last investigation is an attempt to correlate the behavioral assessments, conducted by clinicians with theses neuroimaging findings to determine if there exist a relationship between them. Significant interaction effects of sex and development on ASD diagnosis are observed. The goal of the Study is to provide more information on the disorder that is by nature highly heterogeneous in symptomatology. Studying these interactions, may be key to better understand a disorder that was introduced into the medical literature 75 years ago

Multimodal and multiscale brain networks : understanding aging, Alzheimer’s disease, and other neurodegenerative disorders

The human brain can be modeled as a complex network, often referred to as the connectome, where structural and functional connections govern its organization. Several neuroimaging studies have focused on understanding the architecture of healthy brain networks and have shed light on how these networks evolve with age and in the presence of neurodegenerative disorders. Many studies have explored the brain networks in Alzheimer’s disease (AD), the most common type of dementia, using various neuroimaging modalities independently. However, most of these studies ignored the complex and multifactorial nature of AD. The aim of this thesis was to investigate and analyze the brain’s multimodal and multiscale network organization in aging and in AD by using different multilayer brain network analyses and different types of data. Additionally, this research extended its scope to incorporate other dementias, such as Lewy body dementias, allowing for a comparison of these disorders with AD and normal aging. These comparisons were made possible through the application of protein co-expression networks. In Study I, we investigated sex differences in healthy individuals using multimodal brain networks. To do this we used resting-state functional magnetic resonance imaging (rs-fMRI) and diffusion-weighted imaging (DWI) data from the Human Connectome Project (HCP) to perform multilayer and deep learning analyses. These analyses identified differences between men's and women's underlying brain network organization, showing that the deep-learning analysis with multilayer network metrics (area under the curve, AUC, of 0.81) outperforms the classification using single-layer network measures (AUC of 0.72 for functional networks and 0.70 for anatomical networks). Furthermore, we integrated the multilayer brain networks methodology and neural network models into a software package that is easy to use by researchers with different backgrounds and is also easily expandable for researchers with different levels of programming experience. Then, we used the multilayer brain networks methodology to study the interaction between sex and age on the functional network topology using a large group of people from the UK Biobank (Study II). By incorporating multilayer brain network analyses, we analyzed both positive and negative connections derived from functional correlations, and we obtained important insights into how cognitive abilities, physical health, and even genetic factors differ between men and women as they age. Age and sex were strongly associated with multiplex and multilayer measures such as the multiplex participation coefficient, multilayer clustering, and multilayer global efficiency, accounting for up to 89.1%, 79.9%, and 79.5% of the variance related to age, respectively. These results indicate that incorporating separate layers for positive and negative connections within a complex network framework reveals sensitive insights into age- and sex-related variations that are not detected by traditional metrics. Furthermore, our functional metrics exhibited associations with genes that have previously been linked to processes related to aging. In Study III, we assessed whether multilayer connectome analyses could offer new perspectives on the relationship between amyloid pathology and gray matter atrophy across the AD continuum. Subjects from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) were divided into four groups based on cerebrospinal fluid (CSF) amyloid-β (Aβ) biomarker levels and clinical diagnosis. We compared the different groups using weighted and binary multilayer measures that assess the strength of the connections, the modularity, as well as the multiplex segregation and integration of the brain connectomes. Across Aβ-positive (Aβ+) groups, we found widespread increases in the overlapping connectivity strength and decreases in the number of identical connections in both layers. Moreover, the brain modules were reorganized in the mild cognitive impairment (MCI) Aβ+ group and an imbalance in the quantity of couplings between the two layers was found in patients with MCI Aβ+ and AD Aβ+. Using a subsample from the same database, ADNI, we analyzed rs-fMRI data from individuals at preclinical and clinical stages of AD (Study IV). By dividing the time series into different time windows, we built temporal multilayer networks and studied the modular organization across time. We were able to capture the dynamic changes across different AD stages using this temporal multilayer network approach, obtaining outstanding areas under the curve of 0.90, 0.92 and 0.99 in the distinction of controls from preclinical, prodromal, and clinical AD stages, respectively, on top and beyond common risk factors. Our results not only improved the discrimination between various disease stages but, importantly, they also showed that dynamic multilayer functional measures are associated with memory and global cognition in addition to amyloid and tau load derived from positron emission tomography. These results highlight the potential of dynamic multilayer functional connectivity measures as functional biomarkers of AD progression. In Study V, we used in-depth quantitative proteomics to compare post-mortem brains from three key brain regions (prefrontal cortex, cingulate cortex, and the parietal cortex) directly related to the disease mechanisms of AD, Parkinson’s disease with dementia (PDD), dementia with Lewy bodies (DLB) in prospectively followed patients and older adults without dementia. We used covariance weighted networks to find modules of protein sets to further understand altered pathways in these dementias and their implications for prognostic and diagnostic purposes. In conclusion, this thesis explored the complex world of brain networks and offered insightful information about how age, sex, and AD influence these networks. We have improved our understanding of how the brain is organized in different imaging modalities and different time scales, as well as developing software tools to make this methodology available to more researchers. Additionally, we assessed the connections among various proteins in different areas of the brain in relation to health, Alzheimer's disease, and Lewy body dementias. This work contributes to the collective effort of unraveling the mysteries of the human brain organization and offers a foundation for future research to understand brain networks in health and disease

Effects of vascular risk factors, Framingham stroke risk profile, amyloid, and tau within functionally connected networks in relatively young healthy adults

In adult humans, the aging process is marked by gradual declines in most bodily functions. Such decline is thought to begin sometime in early middle age. Declines in cognition and other functions have been well studied primarily in those over the age of 65. Cerebrovascular factors, amyloid deposition, and tau deposition have all been linked to declines in the brain’s ability to function in this older aged group. Less is known about the impact of these factors in a relatively younger group of aged adults. The series of studies in my dissertation were designed to fill this gap in our knowledge. I was fortunate in my doctoral work to have been given access to data that was being collected by the Framingham Heart Study. This is a single-site, longitudinal community-based cohort study that was initiated in 1948 with a recruitment of 5209 participants. Since the inception of the study, three generations of participants have been enrolled. The Offspring cohort, recruited between 1971 and 1975, is comprised of a total of 5,124 participants. The generation 3 cohort recruited between 2002 and 2005 consists of 4,095 participants. They have been evaluated approximately every 4 years since, for a variety of factors including cardiovascular, socio-demographic, and cognition. For the purposes of this investigation, I was able to work with data from the Offspring and the generation 3 cohort participants who attended the 9th examination cycle (2011-2014), and the third examination cycle (2016-2019) respectively. Participants underwent an MRI scan, C-Pittsburgh Compound-B (PiB)-PET scan and F-Flortaucipir PET scan between 2016 and 2019. They were free of stroke, dementia and other neurological condition at the time of assessment. All the participants included in my work were cognitively normal. To assess brain function, we examined functional connectivity with a focus on the default mode network (DMN). The goal of our first study was to determine whether vascular factors, expressed as the Framingham Stroke Risk Profile (FSRP), impact brain connectivity within and between various functional brain networks i.e. the default mode, frontoparietal, dorsal attention, ventral attention, somatomotor, limbic, and visual networks. Both T1 and resting state fMRI scans were acquired and processed using Freesurfer version 6.0 and FSL. Functional brain networks were constructed using the Yeo 7 network atlas. The FSRP score is a composite based on cardiovascular risk factors which were developed to quantify stroke risk. The FSRP is comprised of measures of systolic blood pressure, antihypertensive therapy, diabetes, cigarette smoking status, history of cardiovascular disease, and atrial fibrillation. In addition to FSRP, we examined the isolated effects of age, sex, total cholesterol to hdl cholesterol ratio, hypertention, and body mass index (BMI). In our first study of 388 participants, we examined whether isolated vascular factors expressed separately or as a composite score using FSRP age, sex, total cholesterol to hdl cholesterol ratio, hypertention, body mass index (BMI), and/or ApoE status are associated with functional brain network connectivity with a primary focus on the DMN and a secondary focus on frontoparietal, dorsal attention, and ventral attention networks. We found that FSRP and ApoE status were not associated with functional connectivity within or between any of the functional brain networks. However, individual factors such as age, sex, total cholesterol to hdl cholesterol ratio, hypertention, and BMI had an effect on functional connectivity. In our second study, we examined the relationship between global amyloid deposition and functional connectivity. This study included 305 out of the 388 individuals who participated in study one above and underwent PET imaging with the PiB compound. The primary analysis was focused on functional connectivity within the DMN as components of this network have been described as having a susceptiblety to amyloid plaque deposition. Secondary analyses were focused on functional connectivity within the remaining functional networks such as frontoparietal, dorsal attention, ventral attention, somatomotor, limbic, and visual networks. Interestingly, global amyloid deposition and ApoE status were not related to functional connectivity within any of the networks. Rather, connectivity within the DMN, frontoparietal and limbic networks were related to age and sex. Connectivity within the visual network was only related to age. No relationships were found for functional connectivity within the dorsal attention, ventral attention or somatosensory networks. In our third study, we investigated the relationship between regional tau (hippocampus, parahippocampal gyrus, entorhinal cortex, precuneus, inferior temporal gyrus and rhinal cortex) and functional connectivity. This study included 247 out of the 388 individuals who participated in study one above and underwent PET imaging with the F-Flortaucipir (FTP) compound. The primary analyses here were focused on functional connectivity within the DMN and limbic networks. As with amyloid deposition, we did not find any relationship between regional tau or APOE status and functional connectivity in any of the functional networks. Age and sex were related to functional connectivity within the DMN, frontoparietal and limbic networks. Age alone was related to functional connectivity within the visual network. No relationships were found with functional connectivity within the dorsal attention, ventral attention or somatosensory networks. The findings from our studies differ from what has been reported in an older populations (>65 years) where amyloid and tau accumulation along with vascular factors have a significant effect on functional network connectivity especially in the DMN in healthy older adults

Exploring sex differences: insights into gene expression, neuroanatomy, neurochemistry, cognition, and pathology

Increased knowledge about sex differences is important for development of individualized treatments against many diseases as well as understanding behavioral and pathological differences. This review summarizes sex chromosome effects on gene expression, epigenetics, and hormones in relation to the brain. We explore neuroanatomy, neurochemistry, cognition, and brain pathology aiming to explain the current state of the art. While some domains exhibit strong differences, others reveal subtle differences whose overall significance warrants clarification. We hope that the current review increases awareness and serves as a basis for the planning of future studies that consider both sexes equally regarding similarities and differences