High-Dimensional ICA Analysis Detects Within-Network Functional Connectivity Damage of Default-Mode and Sensory-Motor Networks in Alzheimer’s Disease

High-dimensional independent component analysis (ICA), compared to low-dimensional ICA, allows to conduct a detailed parcellation of the resting-state networks. The purpose of this study was to give further insight into functional connectivity (FC) in Alzheimer's disease (AD) using high-dimensional ICA. For this reason, we performed both low- and high-dimensional ICA analyses of resting-state fMRI data of 20 healthy controls and 21 patients with AD, focusing on the primarily altered default-mode network (DMN) and exploring the sensory-motor network. As expected, results obtained at low dimensionality were in line with previous literature. Moreover, high-dimensional results allowed us to observe either the presence of within-network disconnections and FC damage confined to some of the resting-state subnetworks. Due to the higher sensitivity of the high-dimensional ICA analysis, our results suggest that high-dimensional decomposition in subnetworks is very promising to better localize FC alterations in AD and that FC damage is not confined to the DMN

Brain connectivity analysis: a short survey

This short survey the reviews recent literature on brain connectivity studies. It encompasses all forms of static and dynamic connectivity whether anatomical, functional, or effective. The last decade has seen an ever increasing number of studies devoted to deduce functional or effective connectivity, mostly from functional neuroimaging experiments. Resting state conditions have become a dominant experimental paradigm, and a number of resting state networks, among them the prominent default mode network, have been identified. Graphical models represent a convenient vehicle to formalize experimental findings and to closely and quantitatively characterize the various networks identified. Underlying these abstract concepts are anatomical networks, the so-called connectome, which can be investigated by functional imaging techniques as well. Future studies have to bridge the gap between anatomical neuronal connections and related functional or effective connectivities

Graph Independent Component Analysis Reveals Repertoires of Intrinsic Network Components in the Human Brain

Does each cognitive task elicit a new cognitive network each time in the brain? Recent data suggest that pre-existing repertoires of a much smaller number of canonical network components are selectively and dynamically used to compute new cognitive tasks. To this end, we propose a novel method (graph-ICA) that seeks to extract these canonical network components from a limited number of resting state spontaneous networks. Graph-ICA decomposes a weighted mixture of source edge-sharing subnetworks with different weighted edges by applying an independent component analysis on cross-sectional brain networks represented as graphs. We evaluated the plausibility in our simulation study and identified 49 intrinsic subnetworks by applying it in the resting state fMRI data. Using the derived subnetwork repertories, we decomposed brain networks during specific tasks including motor activity, working memory exercises, and verb generation, and identified subnetworks associated with performance on these tasks. We also analyzed sex differences in utilization of subnetworks, which was useful in characterizing group networks. These results suggest that this method can effectively be utilized to identify task-specific as well as sex-specific functional subnetworks. Moreover, graph-ICA can provide more direct information on the edge weights among brain regions working together as a network, which cannot be directly obtained through voxel-level spatial ICA.ope

High-dimensional ICA analysis detects wthin-network functional connectivity damage of default-mode and sensory-motor networks in Alzheimer's disease

High-dimensional independent component analysis (ICA), compared to low-dimensional ICA, allows to conduct a detailed parcellation of the resting-state networks. The purpose of this study was to give further insight into functional connectivity (FC) in Alzheimer's disease (AD) using high-dimensional ICA. For this reason, we performed both low- and high-dimensional ICA analyses of resting-state fMRI data of 20 healthy controls and 21 patients with AD, focusing on the primarily altered default-mode network (DMN) and exploring the sensory-motor network. As expected, results obtained at low dimensionality were in line with previous literature. Moreover, high-dimensional results allowed us to observe either the presence of within-network disconnections and FC damage confined to some of the resting-state subnetworks. Due to the higher sensitivity of the high-dimensional ICA analysis, our results suggest that high-dimensional decomposition in subnetworks is very promising to better localize FC alterations in AD and that FC damage is not confined to the DMN

The topology of structural brain connectivity in diseases and spatio-temporal connectomics

The brain is a complex system, composed of multiple neural units interconnected at different spatial and temporal scales. Diffusion MRI allows probing in vivo the anatomical connectivity between different cortical areas through white matter tracts. In parallel, functional MRI records neural-related signals of brain activity. Particularly, during rest (in absence of specific external task) reproducible dynamical patterns of functional synchronization have been shown across different brain areas. This rich information can be conveniently represented in the form of a graph, a mathematical object where nodes correspond to cortical regions and are connected by edges representing anatomical connections. On the top of this structural network, or brain connectome, individual nodes are associated to functional signals representing neural activity over observation periods. Network science has fundamentally contributed to the characterization of the human connectome. The brain is a small-world network, able to combine segregation and integration aspects. These properties allow functional specialization on the one side, and efficient communication between distant brain areas on the other side, supporting complex cognitive and executive functions. Graph theoretical methods quantify brain topological properties, and allow their comparison between different populations and conditions. In fact, brain connectivity patterns and interdependences between anatomical substrate and functional synchronization have been proved to be impaired in a variety of brain disorders, and to change across human development and aging. Despite these important advancements in the understanding of the brain structure and functioning, many questions are currently unanswered. It is not clear for instance how structural connectivity features are related to individual cognitive capabilities and deficits, and if they have the concrete potential to distinguish pathological subgroups for early diagnosis of brain diseases. Most importantly, it is not yet understood how the connectome topology relates to specific brain functions, and how the transmission of information happens on the top of the structural connectivity infrastructure in order to generate observed functional dynamics. This thesis was motivated by these interdisciplinary inputs, and is the result of a strong interaction between biological and clinical questions on the one hand, and methodological development needs on the other hand. First, we have contributed to the characterization of the human connectome in health and pathologies by adapting and developing network measures for the description of the brain architecture at different scales. Particularly, we have focused on the topological characterization of subnetworks role within the overall brain network. Importantly, we have shown that the topological alteration of distinct brain subsystems may be a biomarker for different brain disorders. Second, we have proposed an original network model for the joint representation of brain structural and functional connectivity properties. This flexible spatio-temporal framework allows the investigation of functional dynamics at multiple temporal scales. Importantly, the investigation of spatio-temporal graphs in healthy subjects have allowed to disclose temporal relationships between local brain activations in resting state recordings, and has highlighted functional communication principles across the brain structural network

Multi-neuroimaging model of identifying neuroplasticity under motor cognitive learning condition: MRI based study.

Motor learning is a fundamental ability and one of the most robust models to study neural plasticity. The majority of human motor learning imaging studies focused on either short-term or long-term learning using one single imaging modality. These studies were thus not able to systematically investigate the dynamic process of motor learning from a multimodal perspective. The current project combined both short-term and long-term motor learning to comprehensively characterize neural plasticity at multiple phenotypic levels of the brain: functional activation, functional connectivity, grey matter volume, and glutamate concentration. To this end, this project involved a cross-sectional and a longitudinal study with multimodal brain imaging techniques (task fMRI, resting-state fMRI, gray matter structural fMRI, pharmacological fMRI, and MRS). Short-term motor learning was significantly correlated with brain network features related to network efficiency. It was also associated with a highly reliable cerebellum-centered network which was significantly modulated by the NMDA antagonist ketamine. Long-term motor learning was associated with increased activation in premotor / SMA and parietal regions and with increased gray matter volume of the SMA and the hippocampus. In addition, long-term motor learning was accompanied by a decrease in the functional connectivity of a network centered on the sensorimotor cortex which was related to handknob glutamate concentration levels and which involved regions that were highlighted by our activation and structural analyses. Taken together, this thesis contributes important evidence to the neurofunctional and neurostructural underpinnings of motor learning and points to the critical roles of the cerebellum, the hippocampus and the relevance of glutamate for motor learning in humans

Automatic classification of medical images based on functional connectivity measurements - methodological exploration

The study of patterns of neuronal activity constitutes a tool of extreme value in the attempt to unveil neural pathological mechanisms. Hence, functional connectivity studies using images from Resting State fMRI (rs-fMRI) are crucial, and there are several metrics which can be used to assess brain connections. Nonetheless, no clear evidence exists that some may be better than others. In this study, in an attempt to discover if certain metrics better characterized certain connections, two different approaches were followed. Data from a public dataset was used - Addiction Connectome Preprocessed Initiative (ACPI) - as well as one toolbox for matrix construction - Multiple Connectivity Analysis (MULAN) - and another for statistical comparison - GraphVar. Both toolboxes run in MATLAB. Metrics under analysis were: correlation, coherence, mutual information, transfer entropy and non-linear correlation. To that end, 116 brain regions were considered. First, considering only healthy subjects, it was done a pairwise comparison between results from different metrics. It was verified that each of them led to different results regarding the same connections. Then, connectivity results between a healthy and a pathological group of subjects with Attention-Deficit/Hyperactivity Disorder (ADHD) were compared. Concerning the differences, several similarities with the known affected areas described amongst the literature were found. However, discrepancies were observed which may be related to differences in sample size and/or the metric used in these studies. In general, it was shown that there is indeed variability between functional metrics and regional specificity. Still, the anatomical and physiological reasons for these differences remain unknown. It was clear that using more than one metric may be important and that the use of more general metrics may have advantages in the study of the pathological brain as it may have more complex dynamics. Furthermore, ensemble tools that have into consideration more than one metric to characterize brain connections may represent invaluable tools for autonomic image classification