Une nouvelle approche pour l’identification des états dynamiques de la parcellisation fonctionnelle cérébrale individuelle

Les parcellations cérébrales sont appliquées en neuroimagerie pour aider les chercheurs à ré- duire la haute dimensionnalité des données d’IRM fonctionnelle. L’objectif principal est une meilleure compréhension de l’organisation fonctionnelle du cerveau tant chez les sujets sains que chez les sujets souffrant de troubles neurologiques, dont la maladie d’Alzheimer. Malgré la vague d’approches de parcellations précédentes, les mesures de performance doivent en- core être améliorées pour générer des parcellations fiables, même avec de longues acquisitions. Autrement dit, une reproductibilité plus élevée qui permet aux chercheurs de reproduire des parcellations et de comparer leurs études. Il est également important de minimiser la perte d’informations entre les données compressées et les données brutes pour représenter avec précision l’organisation d’un cerveau individuel. Dans cette thèse, j’ai développé une nou- velle approche pour parcellaire le cerveau en reconfigurations spatiales distinctes appelées «états dynamiques de parcellations». J’ai utilisé une méthode d’agrégation de cluster simple DYPAC1.0 de parcelles basées sur des semences sur plusieurs fenêtres de temps. J’ai émis l’hypothèse que cette nouvelle façon de formaliser le problème de parcellisation améliorera les mesures de performance par rapport aux parcellations statiques. Le premier chapitre de ce document est une introduction générale au contexte des réseaux à grande échelle du cerveau humain. Je montre également l’importance des parcellations pour une meilleure compréhension du cerveau humain à l’aide de connectomes fonctionnels afin de prédire les schémas de progression de la maladie. Ensuite, j’explique pourquoi le problème de parcelli- sation cérébrale est difficile et les différentes questions de recherche ouvertes associées à ce domaine. Mes contributions à la recherche sont subdivisées en deux articles. Les deuxième et troisième chapitres sont consacrés au premier article principal et à son supplément publié dans Network Neuroscience Journal. Le quatrième chapitre représente le deuxième document en préparation. Le cinquième chapitre conclut mes contributions et ses implications dans le domaine de la neuroimagerie, ainsi que des orientations de recherche ouvertes. En un mot, la principale conclusion de ce travail est l’existence de reconfigurations spatiales distinctes dans tout le cerveau avec des scores de reproductibilité presque parfaits sur les données de test-retest (jusqu’à 0,9 coefficient de corrélation de Pearson). Un algorithme d’agrégation de cluster simple et évolutif appelé DYPAC 1.0 est expliqué pour identifier ces reconfigu- rations ou «états dynamiques de parcellations» pour des sous-réseaux de départ spécifiques (deuxième chapitre). L’analyse de ces états a montré l’existence d’un répertoire plus riche «d’états dynamiques» dans le cas des cortex hétéromodaux (ex: cortex cingulaire posté- rieur et cortex cingulaire antérieur dorsal) par rapport aux cortex unimodaux (ex: cortex visuel). En outre, les résultats de l’analyse de reproductibilité ont montré que DYPAC 1.0 a de meilleurs résultats de reproductibilité (en termes de corrélation de Pearson) par rapport aux parcelles statiques (deuxième chapitre). Plusieurs analyses démontrent que DYPAC 1.0 est robuste au choix de ses paramètres (troisième chapitre). Ces résultats et l’évolutivité de DYPAC 1.0 ont motivé une analyse complète du niveau cérébral. Je présente DYPAC 2.0 comme une approche au niveau cérébral complet pour fragmenter le cerveau en «états dynamiques de parcellations». Des reconfigurations spatiales distinctes et se chevauchant ou «états dynamiques» sont identifiées pour différentes régions du cerveau (quatrième chapitre). Ces états ont des scores de compression prometteurs qui montrent une faible perte d’infor- mations entre les cartes de stabilité d’état réduit et les données d’origine dans les cortex cérébraux, c’est-à-dire jusqu’à seulement 20% de perte de la variance expliquée. Cette thèse présente ainsi de nouvelles contributions dans le domaine de la parcellisation fonctionnelle qui pourraient avoir un impact sur la manière dont les chercheurs modélisent les interactions riches et dynamiques entre les réseaux cérébraux dans la santé et la maladie.Brain parcellations are applied in neuroimaging to help researchers reduce the high dimen- sionality of the functional MRI data. The main objective is a better understanding of the brain functional organization in both healthy subjects and subjects having neurological dis- orders, including Alzheimer disease. Despite the flurry of previous parcellation approaches, the performance measures still need improvement to generate reliable parcellations even with long acquisitions. That is, a higher reproducibility that allows researchers to replicate par- cellations and compare their studies. It is also important to minimize the information loss between the compressed data and the raw data to accurately represent the organization of an individual brain. In this thesis, I developed a new approach to parcellate the brain into distinct spatial reconfigurations called “dynamic states of parcellations”. I used a simple cluster aggregation method DYPAC1.0 of seed based parcels over multiple time windows. I hypothesized this new way to formalize the parcellation problem will improve performance measures over static parcellations. The first chapter of this document is a general context introduction to the human brain large scale networks. I also show the importance of par- cellations for a better understanding of the human brain using functional connectomes in order to predict patterns of disease progression. Then, I explain why the brain parcellation problem is hard and the different open research questions associated with this field. My research contributions are subdivided into two papers. The second and the third chapters are dedicated to the first main paper and its supplementary published in Network Neuro- science Journal. The fourth chapter represents the second paper under preparation. The fifth chapter concludes my contributions and its implications in the neuroimaging field, along with open research directions. In a nutshell, the main finding of this work is the existence of distinct spatial reconfigurations throughout the brain with near perfect reproducibility scores across test-retest data (up to .9 Pearson correlation coefficient). A simple and scalable clus- ter aggregation algorithm called DYPAC 1.0 is explained to identify these reconfigurations or “dynamic states of parcellations” for specific seed subnetworks (second chapter). The analysis of these states showed the existence of a richer repertoire of “dynamic states” in the case of heteromodal cortices (e.g., posterior cingulate cortex and the dorsal anterior cingulate cortex) compared to unimodal cortices (e.g., visual cortex). Also, the reproducibility analysis results showed that DYPAC 1.0 has better reproducibility results (in terms of Pearson corre- lation) compared to static parcels (second chapter). Several analyses demonstrate DYPAC 1.0 is robust to the choice of its parameters (third chapter). These findings and the scalabil- ity of DYPAC 1.0 motivated a full brain level analysis. I present DYPAC 2.0 as the full brain level approach to parcellate the brain into “dynamic states of parcellations”. Distinct and overlapping spatial reconfigurations or “dynamic states” are identified for different regions throughout the brain (fourth chapter). These states have promising compression scores that show low information loss between the reduced state stability maps and the original data throughout the cerebral cortices, i.e. up to only 20% loss in explained variance. This thesis thus presents new contributions in the functional parcellation field that may impact how researchers model the rich and dynamic interactions between brain networks in health and disease

Brain decoding of the Human Connectome Project Tasks in a Dense Individual fMRI Dataset

Les études de décodage cérébral visent à entrainer un modèle d'activité cérébrale qui reflète l'état cognitif du participant. Des variations interindividuelles substantielles dans l'organisation fonctionnelle du cerveau représentent un défi pour un décodage cérébral précis. Dans cette thèse, nous évaluons si des modèles de décodage cérébral précis peuvent être entrainés avec succès entièrement au niveau individuel. Nous avons utilisé un ensemble de données individuel dense d'imagerie par résonance magnétique fonctionnelle (IRMf) pour lequel six participants ont terminé l'ensemble de la batterie de tâches du “Human Connectome Project” > 13 fois sur dix sessions d'IRMf distinctes. Nous avons implémenté plusieurs méthodes de décodage, des simples machines à vecteurs de support aux réseaux complexes de neurones à convolution de graphes. Tous les décodeurs spécifiques à l'individu ont été entrainés pour classifier simultanément les volumes d'IRMf simples (TR = 1,49) entre 21 conditions expérimentales, en utilisant environ sept heures de données d'IRMf par participant. Les meilleurs résultats de prédiction ont été obtenus avec notre modèle de machine à vecteurs de support avec une précision de test allant de 64 à 79 % (niveau de la chance environ 7%). Les perceptrons multiniveaux et les réseaux convolutionnels de graphes ont également obtenu de très bons résultats (63-78% et 63-77%, respectivement). Les cartes d'importance des caractéristiques dérivées du meilleur modèle (SVM) ont révélé que la classification utilise des régions pertinentes pour des domaines cognitifs particuliers, sur la base d’a priori neuro-anatomique. En appliquant un modèle individuel aux données d’un autre sujet (classification inter-sujets), on observe une précision nettement inférieure à celle des modèles spécifiques au sujet, ce qui indique que les décodeurs cérébraux individuels ont appris des caractéristiques spécifiques à chaque individu. Nos résultats indiquent que des ensembles de données de neuroimagerie profonde peuvent être utilisés pour former des modèles de décodage cérébral précis au niveau individuel. Les données de cette étude sont partagées librement avec la communauté (https://cneuromod.ca), et pourront servir de benchmark de référence, pour l’entrainement de modèles de décodage cérébral individuel, ou bien des études de “transfert learning” à partir de l’échantillon collecté par le human connectome project.Brain decoding studies aim to train a pattern of brain activity that reflects the cognitive state of the participant. Substantial inter-individual variations in functional organization represent a challenge to accurate brain decoding. In this thesis, we assess whether accurate brain decoding models can be successfully trained entirely at the individual level. We used a dense individual functional magnetic resonance imaging (fMRI) dataset for which six participants completed the entire Human Connectome Project (HCP) task battery>13 times across ten separate fMRI sessions. We assessed several decoding methods, from simple support vector machines to complex graph convolution neural networks. All individual-specific decoders were trained to classify single fMRI volumes (TR = 1.49) between 21 experimental conditions simultaneously, using around seven hours of fMRI data per participant. The best prediction accuracy results were achieved with our support vector machine model with test accuracy ranging from 64 to 79% (chance level of about 7%). Multilevel perceptrons and graph convolutional networks also performed very well (63-78% and 63-77%, respectively). Best Model Derived Feature Importance Maps (SVM) revealed that the classification uses regions relevant to particular cognitive domains, based on neuroanatomical priors. Applying an individual model to another subject's data (across-subject classification) yields significantly lower accuracy than subject-specific models, indicating that individual brain decoders have learned characteristics specific to each individual. Our results indicate that deep neuroimaging datasets can be used to train accurate brain decoding models at the individual level. The data from this study is shared freely with the community (https://cneuromod.ca) and can be used as a reference benchmark, for training individual brain decoding models, or for “transfer learning” studies from the sample collected by the human connectome project

The INCF Digital Atlasing Program: Report on Digital Atlasing Standards in the Rodent Brain

The goal of the INCF Digital Atlasing Program is to provide the vision and direction necessary to make the rapidly growing collection of multidimensional data of the rodent brain (images, gene expression, etc.) widely accessible and usable to the international research community. This Digital Brain Atlasing Standards Task Force was formed in May 2008 to investigate the state of rodent brain digital atlasing, and formulate standards, guidelines, and policy recommendations.

Our first objective has been the preparation of a detailed document that includes the vision and specific description of an infrastructure, systems and methods capable of serving the scientific goals of the community, as well as practical issues for achieving
the goals. This report builds on the 1st INCF Workshop on Mouse and Rat Brain Digital Atlasing Systems (Boline et al., 2007, _Nature Preceedings_, doi:10.1038/npre.2007.1046.1) and includes a more detailed analysis of both the current state and desired state of digital atlasing along with specific recommendations for achieving these goals

Functional connectivity alterations between default mode network and occipital cortex in patients with obsessive-compulsive disorder (OCD)

Altered brain network connectivity is a potential biomarker for obsessive-compulsive disorder (OCD). A meta-analysis of resting-state MRI studies by Gürsel et al. (2018) described altered functional connectivity in OCD patients within and between the default mode network (DMN), the salience network (SN), and the frontoparietal network (FPN), as well as evidence for aberrant fronto-striatal circuitry. Here, we tested the replicability of these meta-analytic rsfMRI findings by measuring functional connectivity during resting-state fMRI in a new sample of OCD patients (n = 24) and matched controls (n = 33). We performed seed-to-voxel analyses using 30 seed regions from the prior meta-analysis. OCD patients showed reduced functional connectivity between the SN and the DMN compared to controls, replicating previous findings. We did not observe significant group differences of functional connectivity within the DMN, SN, nor FPN. Additionally, we observed reduced connectivity between the visual network to both the DMN and SN in OCD patients, in particular reduced functional connectivity between lateral parietal seeds and the left inferior lateral occipital pole. Furthermore, the right lateral parietal seed (associated with the DMN) was more strongly correlated with a cluster in the right lateral occipital cortex and precuneus (a region partly overlapping with the Dorsal Attentional Network (DAN)) in patients. Importantly, this latter finding was positively correlated to OCD symptom severity. Overall, our study partly replicated prior meta-analytic findings, highlighting hypoconnectivity between SN and DMN as a potential biomarker for OCD. Furthermore, we identified changes between the SN and the DMN with the visual network. This suggests that abnormal connectivity between cortex regions associated with abstract functions (transmodal regions such as the DMN), and cortex regions associated with constrained neural processing (unimodal regions such as the visual cortex), may be important in OCD

The openneuro resource for sharing of neuroscience data

The sharing of research data is essential to ensure reproducibility and maximize the impact of public investments in scientific research. Here, we describe OpenNeuro, a BRAIN Initiative data archive that provides the ability to openly share data from a broad range of brain imaging data types following the FAIR principles for data sharing. We highlight the importance of the Brain Imaging Data Structure standard for enabling effective curation, sharing, and reuse of data. The archive presently shares more than 600 datasets including data from more than 20,000 participants, comprising multiple species and measurement modalities and a broad range of phenotypes. The impact of the shared data is evident in a growing number of published reuses, currently totalling more than 150 publications. We conclude by describing plans for future development and integration with other ongoing open science efforts

Group-ICA Model Order Highlights Patterns of Functional Brain Connectivity

Resting-state networks (RSNs) can be reliably and reproducibly detected using independent component analysis (ICA) at both individual subject and group levels. Altering ICA dimensionality (model order) estimation can have a significant impact on the spatial characteristics of the RSNs as well as their parcellation into sub-networks. Recent evidence from several neuroimaging studies suggests that the human brain has a modular hierarchical organization which resembles the hierarchy depicted by different ICA model orders. We hypothesized that functional connectivity between-group differences measured with ICA might be affected by model order selection. We investigated differences in functional connectivity using so-called dual regression as a function of ICA model order in a group of unmedicated seasonal affective disorder (SAD) patients compared to normal healthy controls. The results showed that the detected disease-related differences in functional connectivity alter as a function of ICA model order. The volume of between-group differences altered significantly as a function of ICA model order reaching maximum at model order 70 (which seems to be an optimal point that conveys the largest between-group difference) then stabilized afterwards. Our results show that fine-grained RSNs enable better detection of detailed disease-related functional connectivity changes. However, high model orders show an increased risk of false positives that needs to be overcome. Our findings suggest that multilevel ICA exploration of functional connectivity enables optimization of sensitivity to brain disorders

Probabilistic Logic for Coordinate-Based Meta-Analysis of Functional Segregation in the Brain

International audienc

Comparison between gradients and parcellations for functional connectivity prediction of behavior

Resting-state functional connectivity (RSFC) is widely used to predict behavioral measures. To predict behavioral measures, representing RSFC with parcellations and gradients are the two most popular approaches. Here, we compare parcellation and gradient approaches for RSFC-based prediction of a broad range of behavioral measures in the Human Connectome Project (HCP) and Adolescent Brain Cognitive Development (ABCD) datasets. Among the parcellation approaches, we consider group-average “hard” parcellations (Schaefer et al., 2018), individual-specific “hard” parcellations (Kong et al., 2021a), and an individual-specific “soft” parcellation (spatial independent component analysis with dual regression; Beckmann et al., 2009). For gradient approaches, we consider the well-known principal gradients (Margulies et al., 2016) and the local gradient approach that detects local RSFC changes (Laumann et al., 2015). Across two regression algorithms, individual-specific hard-parcellation performs the best in the HCP dataset, while the principal gradients, spatial independent component analysis and group-average “hard” parcellations exhibit similar performance. On the other hand, principal gradients and all parcellation approaches perform similarly in the ABCD dataset. Across both datasets, local gradients perform the worst. Finally, we find that the principal gradient approach requires at least 40 to 60 gradients to perform as well as parcellation approaches. While most principal gradient studies utilize a single gradient, our results suggest that incorporating higher order gradients can provide significant behaviorally relevant information. Future work will consider the inclusion of additional parcellation and gradient approaches for comparison

Towards Patient-Specific Brain Networks Using Functional Magnetic Resonance Imaging

fMRI applications are rare in translational medicine and clinical practice. What can be inferred from a single fMRI scan is often unreliable due to the relative low signal-to-noise ratio compared to other neuroimaging modalities. However, the potential of fMRI is promising. It is one of the few neuroimaging modalities to obtain functional brain organisation of an individual during task engagement and rest. This work extends on current fMRI image processing approaches to obtain robust estimates of functional brain organisation in two resting-state fMRI cohorts. The first cohort comprises of young adults who were born at extremely low gestations and age-matched healthy controls. Group analysis between term- and preterm-born adults revealed differences in functional organisation, which were discovered to be predominantly caused by underlying structural and physiological differences. The second cohort comprises of elderly adults with young onset Alzheimer’s disease and age-matched controls. Their corresponding resting-state fMRI scans are short in scanning time resulting in unreliable spatial estimates with conventional dual regression analysis. This problem was addressed by the development of an ensemble averaging of matrix factorisations approach to compute single subject spatial maps characterised by improved spatial reproducibility compared to maps obtained by dual regression. The approach was extended with a haemodynamic forward model to obtain surrogate neural activations to examine the subject’s task behaviour. This approach applied to two task-fMRI cohorts showed that these surrogate neural activations matched with original task timings in most of the examined fMRI scans but also revealed subjects with task behaviour different than intended by the researcher. It is hoped that both the findings in this work and the novel matrix factorisation approach itself will benefit the fMRI community. To this end, the derived tools are made available online to aid development and validation of methods for resting-state and task fMRI experiments