186 research outputs found
Mapping hybrid functional-structural connectivity traits in the human connectome
One of the crucial questions in neuroscience is how a rich functional
repertoire of brain states relates to its underlying structural organization.
How to study the associations between these structural and functional layers is
an open problem that involves novel conceptual ways of tackling this question.
We here propose an extension of the Connectivity Independent Component Analysis
(connICA) framework, to identify joint structural-functional connectivity
traits. Here, we extend connICA to integrate structural and functional
connectomes by merging them into common hybrid connectivity patterns that
represent the connectivity fingerprint of a subject. We test this extended
approach on the 100 unrelated subjects from the Human Connectome Project. The
method is able to extract main independent structural-functional connectivity
patterns from the entire cohort that are sensitive to the realization of
different tasks. The hybrid connICA extracted two main task-sensitive hybrid
traits. The first, encompassing the within and between connections of dorsal
attentional and visual areas, as well as fronto-parietal circuits. The second,
mainly encompassing the connectivity between visual, attentional, DMN and
subcortical networks. Overall, these findings confirms the potential ofthe
hybrid connICA for the compression of structural/functional connectomes into
integrated patterns from a set of individual brain networks.Comment: article: 34 pages, 4 figures; supplementary material: 5 pages, 5
figure
Centralized and distributed cognitive task processing in the human connectome
A key question in modern neuroscience is how cognitive changes in a human
brain can be quantified and captured by functional connectomes (FC) . A
systematic approach to measure pairwise functional distance at different brain
states is lacking. This would provide a straight-forward way to quantify
differences in cognitive processing across tasks; also, it would help in
relating these differences in task-based FCs to the underlying structural
network. Here we propose a framework, based on the concept of Jensen-Shannon
divergence, to map the task-rest connectivity distance between tasks and
resting-state FC. We show how this information theoretical measure allows for
quantifying connectivity changes in distributed and centralized processing in
functional networks. We study resting-state and seven tasks from the Human
Connectome Project dataset to obtain the most distant links across tasks. We
investigate how these changes are associated to different functional brain
networks, and use the proposed measure to infer changes in the information
processing regimes. Furthermore, we show how the FC distance from resting state
is shaped by structural connectivity, and to what extent this relationship
depends on the task. This framework provides a well grounded mathematical
quantification of connectivity changes associated to cognitive processing in
large-scale brain networks.Comment: 22 pages main, 6 pages supplementary, 6 figures, 5 supplementary
figures, 1 table, 1 supplementary table. arXiv admin note: text overlap with
arXiv:1710.0219
Methods and models for brain connectivity assessment across levels of consciousness
The human brain is one of the most complex and fascinating systems in nature. In the last decades, two events have boosted the investigation of its functional and structural properties. Firstly, the emergence of novel noninvasive neuroimaging modalities, which helped improving the spatial and temporal resolution of the data collected from in vivo human brains. Secondly, the development of advanced mathematical tools in network science and graph theory, which has recently translated into modeling the human brain as a network, giving rise to the area of research so called Brain Connectivity or Connectomics.
In brain network models, nodes correspond to gray-matter regions (based on functional or structural, atlas-based parcellations that constitute a partition), while links or edges correspond either to structural connections as modeled based on white matter fiber-tracts or to the functional coupling between brain regions by computing statistical dependencies between measured brain activity from different nodes.
Indeed, the network approach for studying the brain has several advantages:
1) it eases the study of collective behaviors and interactions between regions;
2) allows to map and study quantitative properties of its anatomical pathways;
3) gives measures to quantify integration and segregation of information processes in the brain, and the flow (i.e. the interacting dynamics) between different cortical and sub-cortical regions.
The main contribution of my PhD work was indeed to develop and implement new models and methods for brain connectivity assessment in the human brain, having as primary application the analysis of neuroimaging data coming from subjects at different levels of consciousness. I have here applied these methods to investigate changes in levels of consciousness, from normal wakefulness (healthy human brains) or drug-induced unconsciousness (i.e. anesthesia) to pathological (i.e. patients with disorders of consciousness)
Predictive Power and Validity of Connectome Predictive Modeling: A Replication and Extension
Neuroimaging, particularly functional magnetic resonance imaging (fMRI), is a rapidly growing research area and has applications ranging from disease classification to understanding neural development. With new advancements in imaging technology, researchers must employ new techniques to accommodate the influx of high resolution data sets. Here, we replicate a new technique: connectome-based predictive modeling (CPM), which constructs a linear predictive model of brain connectivity and behavior. CPM’s advantages over classic machine learning techniques include its relative ease of implementation and transparency compared to “black box” opaqueness and complexity. Is this method efficient, powerful, and reliable in the prediction of behavioral measures from the Human Connectome Project’s resting state fMRI data? Our replication of connectome-based predictive modeling yielded a correlation of approximately r = 0.8 between actual and predicted behavioral measures. However, when the model is given randomly shuffled pairs of subjects and behavior as input data, the prediction succeeds regardless. Applications of various cleaning techniques proved ineffective; further investigation into the legitimacy of connectome-based predictive modeling must be conducted
A morphospace of functional configuration to assess configural breadth based on brain functional networks
The best approach to quantify human brain functional reconfigurations in
response to varying cognitive demands remains an unresolved topic in network
neuroscience. We propose that such functional reconfigurations may be
categorized into three different types: i) Network Configural Breadth, ii)
Task-to-Task transitional reconfiguration, and iii) Within-Task
reconfiguration. In order to quantify these reconfigurations, we propose a
mesoscopic framework focused on functional networks (FNs) or communities. To do
so, we introduce a 2D network morphospace that relies on two novel mesoscopic
metrics, Trapping Efficiency (TE) and Exit Entropy (EE), which capture topology
and integration of information within and between a reference set of FNs. In
this study, we use this framework to quantify the Network Configural Breadth
across different tasks. We show that the metrics defining this morphospace can
differentiate FNs, cognitive tasks and subjects. We also show that network
configural breadth significantly predicts behavioral measures, such as episodic
memory, verbal episodic memory, fluid intelligence and general intelligence. In
essence, we put forth a framework to explore the cognitive space in a
comprehensive manner, for each individual separately, and at different levels
of granularity. This tool that can also quantify the FN reconfigurations that
result from the brain switching between mental states.Comment: main article: 24 pages, 8 figures, 2 tables. supporting information:
11 pages, 5 figure
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