Extracting the Groupwise Core Structural Connectivity Network: Bridging Statistical and Graph-Theoretical Approaches

  title={Extracting the Groupwise Core Structural Connectivity Network: Bridging Statistical and Graph-Theoretical Approaches},
  author={Nahuel Lascano and Guillermo Gallardo-Diez and Rachid Deriche and Dorian Mazauric and Demian Wassermann},
Finding the common structural brain connectivity network for a given population is an open problem, crucial for current neuro-science. Recent evidence suggests there's a tightly connected network shared between humans. Obtaining this network will, among many advantages , allow us to focus cognitive and clinical analyses on common connections, thus increasing their statistical power. In turn, knowledge about the common network will facilitate novel analyses to understand the structure-function… 
The visual word form area (VWFA) is part of both language and attention circuitry
These findings support a multiplex model of VWFA function characterized by distinct circuits for integrating language and attention, and point to connectivity-constrained cognition as a key principle of human brain organization.
Modélisation statistique des structures anatomiques de la rétine à partir d'images de fond d'oeil
L’examen non-invasif du fond d’oeil permet d’identifier sur la retine les signes de nombreuses pathologies oculaires qui developpent de graves symptomes pour le patient pouvant entrainer la cecite.


Extracting the Core Structural Connectivity Network: Guaranteeing Network Connectedness Through a Graph-Theoretical Approach
A graph-theoretical algorithm to extract the connected core structural connectivity network of a subject population and guarantees that the extracted core subnetwork is connected agreeing with current evidence that the core structural network is tightly connected.
Complex brain networks: graph theoretical analysis of structural and functional systems
This article reviews studies investigating complex brain networks in diverse experimental modalities and provides an accessible introduction to the basic principles of graph theory and highlights the technical challenges and key questions to be addressed by future developments in this rapidly moving field.
Mapping anatomical connectivity patterns of human cerebral cortex using in vivo diffusion tensor imaging tractography.
This work utilized diffusion tensor imaging deterministic tractography to construct a macroscale anatomical network capturing the underlying common connectivity pattern of human cerebral cortex in a large sample of subjects and further quantitatively analyzed its topological properties with graph theoretical approaches.
Predictive Subnetwork Extraction with Structural Priors for Infant Connectomes
This work employs a new method to identify anatomical subnetworks of the human white matter connectome that are predictive of neurodevelopmental outcomes on a dataset of 168 preterm infant connectomes, generated from diffusion tensor images taken shortly after birth.
Using Diffusion Tractography to Predict Cortical Connection Strength and Distance: A Quantitative Comparison with Tracers in the Monkey
Evaluated tractography's ability to estimate the presence and strength of connections between areas of macaque neocortex is evaluated by comparing its results with published data from retrograde tracer injections, and a novel method for calculating interareal cortical distances is used.
Robust Detection of Dynamic Community Structure in Networks
This work considers the use of statistical null models for facilitating the principled identification of structural modules in semi-decomposable systems and develops a method to construct representative partitions that uses a null model to correct for statistical noise in sets of partitions.
Machine learning for neuroimaging with scikit-learn
It is illustrated how scikit-learn, a Python machine learning library, can be used to perform some key analysis steps and its application to neuroimaging data provides a versatile tool to study the brain.
Circular analysis in systems neuroscience: the dangers of double dipping
It is argued that systems neuroscience needs to adjust some widespread practices to avoid the circularity that can arise from selection, and 'double dipping' the use of the same dataset for selection and selective analysis is suggested.