Biology subjects

Iturria-Medina, Y.

Publications and source records attributed to Iturria-Medina, Y..

30 records · Page 2Linked to original sources

bioRxiv2023

Improving fMRI-based Autism Spectrum Disorder Classification with Random Walks-informed Feature Extraction and Selection

Functional magnetic resonance imaging (fMRI) is a non-invasive technique measuring brain activity by detecting blood flow changes, enabling the study of cognitive processes and brain states. However, the high dimensionality of resting-state (rs) fMRI data poses challenges for machine learning applications. Feature extraction (FE) and feature selection (FS) are critical for developing efficient machine learning models. Transforming raw data into meaningful features and selecting the most relevant ones, allows models to achieve improved generalization, accuracy, and robustness. Previous studies demonstrated the effectiveness of FE and FS methods for analyzing rs-fMRI data for Autism Spectrum Disorder (ASD) classification. In this study, we apply a random walks technique for correlation-based brain networks to extract features from rs-fMRI data, specifically the number of random walkers on each brain area. We then select significant features, i.e., brain areas with a statistically significant difference in the number of random walkers between neurotypical and ASD subjects. Our random walks-based FE and FS approach reduces the number of brain areas used in the classification and converts the functional connectivity matrix into a manageable vector, enabling faster computation. We examined 16 pipelines and tested support vector machines (SVM) and logistic regression for classification, identifying the optimal pipeline to consist of no filtering, no global signal regression (GSR), and FS, achieving a 76.54% classification accuracy with SVM. Our findings suggest that random walks capture a wide range of interactions and dynamics in brain networks, providing a deeper characterization of their structure and function, ultimately enhancing classification performance. CCS CONCEPTSComputing methodologies[->]Machine learning

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bioRxiv2023

Revealing the combined roles of Abeta and tau in Alzheimer's disease via a pathophysiological activity decoder

Neuronal dysfunction and cognitive deterioration in Alzheimers disease (AD) are likely caused by multiple pathophysiological factors. However, evidence in humans remains scarce, necessitating improved non-invasive techniques and integrative mechanistic models. Here, we introduce personalized brain activity models incorporating functional MRI, amyloid-{beta} (A{beta}) and tau-PET from AD-related participants (N=132). Within the model assumptions, electrophysiological activity is mediated by toxic protein deposition. Our integrative subject-specific approach uncovers key patho-mechanistic interactions, including synergistic A{beta} and tau effects on cognitive impairment and neuronal excitability increases with disease progression. The data-derived neuronal excitability values strongly predict clinically relevant AD plasma biomarker concentrations (p-tau217, p-tau231, p-tau181, GFAP). Furthermore, our results reproduce hallmark AD electrophysiological alterations (theta band activity enhancement and alpha reductions) which occur with A{beta}-positivity and after limbic tau involvement. Microglial activation influences on neuronal activity are less definitive, potentially due to neuroimaging limitations in mapping neuroprotective vs detrimental phenotypes. Mechanistic brain activity models can further clarify intricate neurodegenerative processes and accelerate preventive/treatment interventions.

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bioRxiv2022

Resolving inter-regional communication capacity in the human connectome

Applications of graph theory to the connectome have inspired several models of how neural signaling unfolds atop its structure. Analytic measures derived from these communication models have mainly been used to extract global characteristics of brain networks, obscuring potentially informative interregional relationships. Here we develop a simple standardization method to investigate polysynaptic communication pathways between pairs of cortical regions. This procedure allows us to determine which pairs of nodes are topologically closer and which are further than expected on the basis of their degree. We find that communication pathways delineate canonical functional systems. Relating nodal communication capacity to meta-analytic probabilistic patterns of functional specialization, we also show that areas that are most closely integrated within the network are associated with higher-order cognitive functions. We find that these regions proclivity towards functional integration could naturally arise from the brains anatomical configuration through evenly distributed connections among multiple specialized communities. Throughout, we consider two increasingly constrained null models to disentangle the effects of the networks topology from those passively endowed by spatial embedding. Altogether, the present findings uncover relationships between polysynaptic communication pathways and the brains functional organization across multiple topological levels of analysis and demonstrate that network integration facilitates cognitive integration.

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bioRxiv2021

Social isolation is linked to classical risk factors of Alzheimer's disease-related dementias

Alzheimers disease and related dementias is a major public health burden - compounding over upcoming years due to longevity. Recently, clinical evidence hinted at the experience of social isolation in expediting dementia onset. In 502,506 UK Biobank participants and 30,097 participants from the Canadian Longitudinal Study of Aging, we revisited traditional risk factors for developing dementia in the context of loneliness and lacking social support. Across these measures of subjective and objective social deprivation, we have identified strong links between individuals social capital and various indicators of Alzheimers disease and related dementias risk, which replicated across both population cohorts. The quality and quantity of daily social encounters had deep connections with key aetiopathological factors, which represent 1) personal habits and lifestyle factors, 2) physical health, 3) mental health, and 4) societal and external factors. Our population-scale assessment suggest that social lifestyle determinants are linked to most neurodegeneration risk factors, highlighting them promising targets for preventive clinical action.

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bioRxiv2021

Differentiating amyloid beta spread in autosomal dominant and sporadic Alzheimer's disease

Amyloid-beta (A{beta}) deposition is one of the hallmark pathologies in both sporadic Alzheimers disease (sAD) and autosomal dominant Alzheimers disease (ADAD), the latter of which is caused by mutations in genes involved in A{beta} processing. Despite A{beta} deposition being a centerpiece to both sAD and ADAD, some differences between these AD subtypes have been observed with respect to the spatial pattern of A{beta}. Previous work has shown that the spatial pattern of A{beta} in individuals spanning the sAD spectrum can be reproduced with high accuracy using an epidemic spreading model (ESM), which simulates the diffusion of A{beta} across neuronal connections and is constrained by individual rates of A{beta} production and clearance. However, it has not been investigated whether A{beta} deposition in the rarer ADAD can be modeled in the same way, and if so, how congruent the spreading patterns of A{beta} across sAD and ADAD are. We leverage the ESM as a data-driven approach to probe individual-level variation in the spreading patterns of A{beta} across three different large-scale imaging datasets (2 SAD, 1 ADAD). We applied the ESM separately to the Alzheimers Disease Neuroimaging initiative (N=737), the Open Access Series of Imaging Studies (N=510), and the Dominantly Inherited Alzheimers Network (N=249), the latter two of which were processed using an identical pipeline. We assessed inter- and intra-individual model performance in each dataset separately, and further identified the most likely epicenter of A{beta} spread for each individual. Using epicenters defined in previous work in sAD, the ESM provided moderate prediction of the regional pattern of A{beta} deposition across all three datasets. We further find that, while the most likely epicenter for most A{beta}-positive subjects overlaps with the default mode network, 13% of ADAD individuals were best characterized by a striatal origin of A{beta} spread. These subjects were also distinguished by being younger than ADAD subjects with a DMN A{beta} origin, despite having a similar estimated age of symptom onset. Together, our results suggest that most ADAD patients express A{beta} spreading patters similar to those of sAD, but that there may be a subset of ADAD patients with a separate, striatal phenotype.

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bioRxiv2020

Impact of long- and short-range fiber depletion on the cognitive deficits of fronto-temporal dementia

Fronto-temporal dementia (FTD) is a neurodegenerative disease characterized by focal atrophy of the gray matter (GM), especially in the frontal and temporal regions. Recent studies suggest a framework where white matter (WM) atrophy plays an important role in FTD pathophysiology. However, these studies often overlook the fact that WM tracts bridging different brain regions may have different vulnerabilities to the disease and the relative contribution of GM atrophy to this WM model, resulting in a less comprehensive understanding of the relationship between clinical symptoms and pathology. Here, by leveraging the sensitivity of advanced diffusion MRI modelling and metrics to precise white matter microstructural properties, we aim to clarify the relative contributions of WM fibers and GM atrophy to the cognitive symptoms typically found in FTD. A total of 155 participant from the Frontotemporal Lobar Degeneration Neuroimaging Initiative (FTLDNI) were analysed, including 68 normal elderly controls (CN), 28 behavioral variants (BV), 26 sematic variants (SV) and 30 progressive non fluent aphasia variants (PNFA) of FTD. Diffusion MRI analysis was performed using two complementary techniques: whole brain fixel-based analysis (FBA) and structural connectivity based on probabilistic tractography. Whole brain GM atrophy was assessed using voxel-based morphometry (VBM). Using a common factor analysis to extract a semantic and an executive factor, we aim to test the relative contribution of WM and GM of specific tracts in predicting cognition. We found that semantic symptoms were mainly dependent on short-range WM fiber disruption, while damage to long-range WM fibers was preferentially associated to executive dysfunction with the GM contribution to cognition being predominant for local processing. Our results support the importance of the disruption of specific WM tracts to the core cognitive symptoms associated with FTD. As large-scale WM tracts, which are particularly vulnerable to vascular disease, were highly associated with executive dysfunction, our findings highlight the importance of controlling for risk factors associated with deep white matter disease, such as vascular risk factors, in patients with FTD in order not to potentiate underlying executive dysfunction.

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bioRxiv2020

Creation of an Open Science Dataset from PREVENT-AD, a Longitudinal Cohort Study of Pre-symptomatic Alzheimer's Disease.

To move Alzheimer Disease (AD) research forward it is essential to collect data from large cohorts, but also make such data available to the global research community. We describe the creation of an open science dataset from the PREVENT-AD (PResymptomatic EValuation of Experimental or Novel Treatments for AD) cohort, composed of cognitively unimpaired older individuals with a parental or multiple-sibling history of AD. From 2011 to 2017, 386 participants were enrolled (mean age 63 years old {+/-} 5) for sustained investigation among whom 349 have retrospectively agreed to share their data openly. Repositories are findable through the unified interface of the Canadian Open Neuroscience Platform (https://portal.conp.ca/) and contain up to five years of longitudinal imaging data, cerebral fluid biochemistry, neurosensory capacities, cognitive, genetic, and medical information. Imaging data can be accessed openly at https://openpreventad.loris.ca while most of the other information, sensitive by nature, is accessible by qualified researchers at https://registeredpreventad.loris.ca. In addition to being a living resource for continued data acquisition, PREVENT-AD offers opportunities to facilitate understanding of AD pathogenesis.

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bioRxiv2019

A molecular gradient along the longitudinal axis of the human hippocampus informs large-scale behavioral systems

The functional organization of the hippocampus is distributed as a gradient along its longitudinal axis that explains its differential interaction with diverse brain systems. We show that the location of human tissue samples extracted along the longitudinal axis of the hippocampus can be predicted within 2mm using the expression pattern of less than 100 genes. When variation in this specific gene expression pattern was observed across the whole brain, a distinct anterioventral-posteriodorsal gradient was observed. Frontal, anterior temporal and brainstem regions involved in social and motivational behaviors, selectively vulnerable to frontotemporal dementia and more functionally connected to the anterior hippocampus could be clearly differentiated from posterior parieto-occipital and cerebellar regions involved in spatial cognition, selectively vulnerable to Alzheimers disease, and more functionally connected to the posterior hippocampus. These findings place the human hippocampus at the interface of two major brain systems defined by a single distinct molecular gradient. (148/150)

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bioRxiv2019

Pathological tau spreads through communicating brain regions in human Alzheimer's disease

Tau is one of the two pathological hallmarks of Alzheimers disease, and bears a much closer relationship to local neurodegeneration and cognitive impairment than the other hallmark, {beta}-amyloid. Cell and rodent models have shown evidence that tau spreads from cell to cell through anatomical neuronal connections, and that this process is facilitated by the presence of {beta}-amyloid. We test this hypothesis in humans by using an epidemic spreading model (ESM) to simulate the spread of tau over human neuronal connections, and we compare the simulated pattern of progression to the observed pattern measured in the brains of 312 individuals on the Alzheimers disease spectrum, using PET. Fitting our model, we found that the majority of variance in the overall pattern of tau progression could be explained by diffusion of an agent through the human connectome, measured using either functional connectivity or diffusion tractography. These models far exceeded chance, and outperformed models testing the extracellular spread of tau over Euclidian space. Surprisingly, the ESM predicted the spatial patterns of tau irrespective of whether subjects demonstrated evidence for brain {beta}-amyloid. In addition, in {beta}-amyloid-positive subjects only, regions with greater amyloid burden showed greater tau than predicted by connectivity patterns, suggesting a role of amyloid in accelerating the spread of tau in certain isocortical regions. Altogether, our results provide strong evidence that tau spreads through neuronal communication pathways even in normal aging, and that this process is accelerated by the presence of brain {beta}-amyloid.

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bioRxiv2019

Blood and Brain Gene Expression Trajectories Underlying Neuropathology and Cognitive Impairment in Neurodegeneration

Neurodegenerative disorders take decades to develop and their early detection is challenged by confounding non-pathological aging processes. For all neurodegenerative conditions, we lack longitudinal gene expression (GE) data covering their large temporal evolution, which hinders the fully understanding of the underlying dynamic molecular mechanisms. Here, we aimed to overcome this limitation by introducing a novel GE contrastive trajectory inference (GE-cTI) method that reveals enriched temporal patterns in a diseased population. Evaluated on 1969 subjects in the spectrum of late-onset Alzheimers and Huntingtons diseases (from ROSMAP, HBTRC and ADNI studies), this unsupervised machine learning algorithm strongly predicts neuropathological severity (e.g. Braak, Amyloid and Vonsattel stages). Furthermore, when applied to in-vivo blood samples (ADNI), it predicts 97% of the variance in memory deterioration and its future declining rate, supporting the identification of a powerful and minimally invasive (blood-based) tool for early clinical screening and disease prevention. This technique also allows the discovery of genes and molecular pathways, in both peripheral and brain tissues, that are highly predictive of disease evolution. Eighty percent of the most predictive molecular pathways identified in the brain were also top predictors in the blood. The GE-cTI is a promising tool for revealing complex neuropathological mechanisms, with direct implications for implementing personalized dynamic treatments in neurology. HIGHLIGHTS- Unsupervised learning detects enriched gene expression (GE) trajectories in disease - These plasma and brain GE trajectories predict neuropathology and future cognitive impairment - Most predictive molecular functions/pathways in the brain are also top predictors in the plasma - By identifying plasma GE trajectories, patients can be easily screened and follow dynamic treatments

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bioRxiv2018

A signature of cognitive deficits and brain atrophy that is highly predictive of progression to Alzheimer’s dementia

Patients with mild cognitive impairment (MCI) are at risk of progressing to Alzheimers dementia, yet only a fraction of them do. We explore here whether a very high-risk MCI subgroup can be identified using cognitive assessments and structural neuroimaging. A multimodal signature of Alzheimers dementia was first extracted using machine learning tools in the ADNI1 sample, and was comprised of cognitive deficits across multiple domains as well as atrophy in temporal, parietal and occipital regions. We then validated the predictive value of this signature on two MCI cohorts. In ADNI1 (N=235), the presence of the signature predicted progression to dementia over three years with 80.4% positive predictive value, adjusted for a \"typical\" MCI baseline rate of 33% (95.6% specificity, 55.1% sensitivity). These results were replicated in ADNI2 (N=235), with 87.8% adjusted positive predictive value (96.7% specificity, 47.3% sensitivity). Our results demonstrate that, even for widely used markers, marked improvement in positive predictive value over the literature can be achieved by focusing on a subgroup of individuals with similar brain characteristics. The signature can be readily applied for the enrichment of clinical trials.

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bioRxiv2018

Data-driven approaches improve Tau-PET biomarkers in Alzheimer’s disease

Previous positron emission tomography (PET) studies have quantified filamentous tau pathology using regions-of-interest (ROIs) based on observations of the topographical distribution of neurofibrillary tangles in post-mortem tissue. However, such approaches may not take full advantage of information contained in neuroimaging data. The present study employs an unsupervised data-driven method to identify spatial patterns of tau-PET distribution, and to compare these patterns to previously published \"pathology-driven\" ROIs. Tau-PET patterns were identified from a discovery sample comprised of 123 normal controls and patients with mild cognitive impairment or Alzheimers disease (AD) dementia from the Swedish BioFINDER cohort, who underwent [18F]AV1451 PET scanning. Associations with cognition were tested in a separate sample of 90 individuals from ADNI. BioFINDER [18F]AV1451 images were entered into a robust voxelwise stable clustering algorithm, which resulted in five clusters. Mean [18F]AV1451 uptake in the data-driven clusters, and in 35 previously published pathology-driven ROIs, was extracted from ADNI [18F]AV1451 scans. We performed linear models comparing [18F]AV1451 signal across all 40 ROIs to several tests of global cognition, adjusting for age, sex and education. Two data-driven ROIs consistently demonstrated the strongest or near-strongest effect sizes across all cognitive tests. Inputting all regions plus demographics into a feature selection routine resulted in selection of two ROIs (one data-driven, one pathology-driven) and education, which together explained 28% of the variance of a global cognitive composite score. Our findings suggest that [18F]AV1451-PET data naturally clusters into spatial patterns that are biologically meaningful and that may offer advantages as clinical tools.

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