The quest for identifiability in human functional connectomes

Enrico Amico, Joaquín Goñi

The evaluation of the individual “fingerprint” of a human functional connectome (FC) is becoming a promising avenue for neuroscientific research, due to its enormous potential inherent to drawing single subject inferences from functional connectivity profiles. Here we show that the individual fingerprint of a human functional connectome can be maximized from a reconstruction procedure based on group-wise decomposition in a finite number of brain connectivity modes. We use data from the Human Connectome Project to demonstrate that the optimal reconstruction of the individual FCs through connectivity eigenmodes maximizes subject identifiability across resting-state and all seven tasks evaluated. Read More >

Influences on the Test–Retest Reliability of Functional Connectivity MRI and its Relationship with Behavioral Utility

Stephanie Noble, Marisa N Spann, Fuyuze Tokoglu, Xilin Shen, R Todd Constable, Dustin Scheinost

Best practices are currently being developed for the acquisition and processing of resting-state magnetic resonance imaging data used to estimate brain functional organization—or “functional connectivity.” Standards have been proposed based on test–retest reliability, but open questions remain. These include how amount of data per subject influences whole-brain reliability, the influence of increasing runs versus sessions, the spatial distribution of reliability, the reliability of multivariate methods, and, crucially, how reliability maps onto prediction of behavior. Read More >

Functional connectome fingerprinting and connectome-based predictive modeling (CPM)

Emily S Finn, Xilin Shen, Dustin Scheinost, Monica D Rosenberg, Marvin M Chun, Xenophon Papademetris, R Todd Constable

Functional magnetic resonance imaging (fMRI) studies typically collapse data from many subjects, but brain functional organization varies between individuals. Here we establish that this individual variability is both robust and reliable, using data from the Human Connectome Project to demonstrate that functional connectivity profiles act as a 'fingerprint' that can accurately identify subjects from a large group. Identification was successful across scan sessions and even between task and rest conditions, indicating that an individual's connectivity profile is intrinsic, and can be used to distinguish that individual regardless of how the brain is engaged during imaging. Read More >

Using connectome-based predictive modeling to predict individual behavior from brain connectivity

Xilin Shen, Emily S Finn, Dustin Scheinost, Monica D Rosenberg, Marvin M Chun, Xenophon Papademetris, R Todd Constable

Neuroimaging is a fast-developing research area in which anatomical and functional images of human brains are collected using techniques such as functional magnetic resonance imaging (fMRI), diffusion tensor imaging (DTI), and electroencephalography (EEG). Technical advances and large-scale data sets have allowed for the development of models capable of predicting individual differences in traits and behavior using brain connectivity measures derived from neuroimaging data. Here, we present connectome-based predictive modeling (CPM), a data-driven protocol for developing predictive models of brain–behavior relationships from connectivity data using cross-validation. Read More >

Ten simple rules for predictive modeling of individual differences in neuroimaging

Dustin Scheinost, Stephanie Noble, Corey Horien, Abigail S.Greene, Evelyn MR. Lake, Mehraveh Salehi, Siyuan Gao, Xilin Shen, David O'Connor, Daniel S.Barron, Sarah W.Yip, Monica D.Rosenberg, R. Todd Constable

Establishing brain-behavior associations that map brain organization to phenotypic measures and generalize to novel individuals remains a challenge in neuroimaging. Predictive modeling approaches that define and validate models with independent datasets offer a solution to this problem. While these methods can detect novel and generalizable brain-behavior associations, they can be daunting, which has limited their use by the wider connectivity community. Read More >

A morphospace framework to assess cognitive flexibility based on brain functional networks

Duy Duong-Tran, Enrico Amico, Bernat Corominas-Murtra, Mario Ventresca, Joaquín Goñi

Unfolding how the brain functionally shifts within the cognitive space remains an unresolved question. From a brain connectivity perspective, there exist two main concepts: cognitive shifts and cognitive flexibility. Although the former is the proxy of the latter, the biggest challenge, in terms of bridging these two concepts, lies in the fact that cognitive shifts are governed by topological rules whereas cognitive flexibility is purely numerical. Read More >

Centralized and distributed cognitive task processing in the human connectome

Enrico Amico, Alex Arenas, Joaquín Goñi

A key question in modern neuroscience is how cognitive changes in a human brain can bequantified and captured by functional connectivity (FC). A systematic approach to measurepairwise functional distance at different brain states is lacking. This would provide astraightforward way to quantify differences in cognitive processing across tasks; also, it wouldhelp in relating these differences in task-based FCs to the underlying structural network. Read More >

Modeling communication processes in the human connectome through cooperative learning

Uttara Tipnis, Enrico Amico, Mario Ventresca, Joaquın Goni

Communication processes within the human brain at different cognitive states are neither well understood nor completelycharacterized. We assess communication processes in the human connectome using ant colony-inspired cooperative learningalgorithm, starting from a source with noa prioriinformation about the network topology, and cooperatively searching for the targetthrough a pheromone-inspired model. This framework relies on two parameters, namelypheromone perceptionandedge perception,to define the cognizance and subsequent behaviour of the ants on the network and, overall, the communication processes happeningbetween source and target node Read More >

Towards Subject and Diagnostic Identifiability in the Alzheimer’s Disease Spectrum Based on Functional Connectomes

Diana O. Svaldi, Joaquín Goñi, Apoorva Bharthur Sanjay, Enrico Amico, Shannon L. Risacher, John D. West, Mario Dzemidzic, Andrew Saykin, Liana Apostolova

Alzheimer’s disease (AD) is the only major cause of mortality in the world without an effective disease modifying treatment. Evidence supporting the so called “disconnection hypothesis” suggests that functional connectivity biomarkers may have clinical potential for early detection of AD. However, known issues with low test-retest reliability and signal to noise in functional connectivity may prevent accuracy and subsequent predictive capacity. Read More >

Uncovering multi-site identifiability based on resting-state functional connectomes

Sumra Bari, Enrico Amico, Nicole Vike, Thomas M. Talavage, Joaquín Goñi

Multi-site studies are becoming important to increase statistical power, enhance generalizability, and to improve the likelihood of pooling relevant subgroups together activities which are otherwise limited by the availability of patients or funds at a single site. Even with harmonized imaging sequences, site-dependent variability can mask the advantages of these multi-site studies. The aim of this study was to assess multi-site reproducibility in resting-state functional connectivity fingerprints, and to improve identifiability of obtained functional connectomes. Read More >

Mapping hybrid functional-structural connectivity traits in the human connectome

Enrico Amico, Joaquín Goñi

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. Read More >

Mapping the functional connectome traits of levels of consciousness

Enrico Amico, Daniele Marinazzo, Carol Di Perri, Lizette Heine, Jitka Annen, Charlotte Martial, Mario Dzemidzi, Murielle Kirsch, Vincent Bonhomme, Steven Laureys, Joaquín Goñi

Examining task-free functional connectivity (FC) in the human brain offers insights on how spontaneous integration and segregation of information relate to human cognition, and how this organization may be altered in different conditions, and neurological disorders. This is particularly relevant for patients in disorders of consciousness (DOC) following severe acquired brain damage and coma, one of the most devastating conditions in modern medical care. We present a novel data-driven methodology, connICA, which implements Independent Component Analysis (ICA) for the extraction of robust independent FC patterns (FC-traits) from a set of individual functional connectomes, without imposing any a priori data stratification into groups. Read More >