PRESERVING HUMAN LARGE-SCALE BRAIN CONNECTIVITY FINGERPRINT IDENTIFIABILITY WITH RANDOM PROJECTIONS.

The complex etiology of various neurodegenerative diseases and psychiatric disorders, especially at the individual level, has posed unmatched challenges to the advancement of personalized medicine. Recent technical advancements in functional magnetic resonance imaging has enabled researchers to map brain large-scale connectivity at an unprecedented level of subject precision. Nonetheless, along with the early dawn of promises in personalized medicine using various neuroimaging modalities rose the challenge of clinical utility of brain connectomics (e.

Volume-optimal persistence homological scaffolds of hemodynamic networks covary with MEG theta-alpha aperiodic dynamics.

Higher-order properties of functional magnetic resonance imaging (fMRI) induced connectivity have been shown to unravel many exclusive topological and dynamical insights beyond pairwise interactions. Nonetheless, whether these fMRI-induced higher-order properties play a role in disentangling other neuroimaging modalities' insights remains largely unexplored and poorly understood. In this work, by analyzing fMRI data from the Human Connectome Project Young Adult dataset using persistent homology, we discovered that the volume-optimal persistence homological scaffolds of fMRI-based functional connectomes exhibited conservative topological reconfigurations from the resting state to attentional task-positive state.

Tangent space functional reconfigurations in individuals at risk for alcohol use disorder.

Human brain function dynamically adjusts to ever-changing stimuli from the external environment. Studies characterizing brain functional reconfiguration are nevertheless scarce. Here we present a principled mathematical framework to quantify brain functional reconfiguration when engaging and disengaging from a stop signal task (SST).

Longitudinal changes in resting state fMRI brain self-similarity of asymptomatic high school American football athletes.

American football has become the focus of numerous studies highlighting a growing concern that cumulative exposure to repetitive, sports-related head acceleration events (HAEs) may have negative consequences for brain health, even in the absence of a diagnosed concussion. In this longitudinal study, brain functional connectivity was analyzed in a cohort of high school American football athletes over a single play season and compared against participants in non-collision high school sports. Football athletes underwent four resting-state functional magnetic resonance imaging sessions: once before (pre-season), twice during (in-season), and once 34-80 days after the contact activities play season ended (post-season).

Homological landscape of human brain functional sub-circuits.

Human whole-brain functional connectivity networks have been shown to exhibit both local/quasilocal (e.g., set of functional sub-circuits induced by node or edge attributes) and non-local (e.

Matrix-Variate Regression for Sparse, Low-Rank Estimation of Brain Connectivities Associated With a Clinical Outcome.

Objective: We address the problem of finding brain connectivities that are associated with a clinical outcome or phenotype.

Methods: The proposed framework regresses a (scalar) clinical outcome on matrix-variate predictors which arise in the form of brain connectivity matrices. For example, in a large cohort of subjects we estimate those regions of functional connectivities that are associated with neurocognitive scores.

Intra and inter-individual variability in functional connectomes of patients with First Episode of Psychosis.

Patients with Schizophrenia may show different clinical presentations, not only regarding inter-individual comparisons but also in one specific subject over time. In fMRI studies, functional connectomes have been shown to carry valuable individual level information, which can be associated with cognitive and behavioral variables. Moreover, functional connectomes have been used to identify subjects within a group, as if they were fingerprints.

Incorporation of spatial- and connectivity-based cortical brain region information in regularized regression: Application to Human Connectome Project data.

Studying the association of the brain's structure and function with neurocognitive outcomes requires a comprehensive analysis that combines different sources of information from a number of brain-imaging modalities. Recently developed regularization methods provide a novel approach using information about brain structure to improve the estimation of coefficients in the linear regression models. Our proposed method, which is a special case of the Tikhonov regularization, incorporates structural connectivity derived with Diffusion Weighted Imaging and cortical distance information in the penalty term.

A comparison of techniques for deriving clustering and switching scores from verbal fluency word lists.

Objective: To compare techniques for computing clustering and switching scores in terms of agreement, correlation, and empirical value as predictors of incident cognitive impairment (ICI).

Methods: We transcribed animal and letter F fluency recordings on 640 cases of ICI and matched controls from a national epidemiological study, amending each transcription with word timings. We then calculated clustering and switching scores, as well as scores indexing speed of responses, using techniques described in the literature.

Connectivity-informed adaptive regularization for generalized outcomes.

One of the challenging problems in neuroimaging is the principled incorporation of information from different imaging modalities. Data from each modality are frequently analyzed separately using, for instance, dimensionality reduction techniques, which result in a loss of mutual information. We propose a novel regularization method, generalized ridgified Partially Empirical Eigenvectors for Regression (griPEER), to estimate associations between the brain structure features and a scalar outcome within the generalized linear regression framework.

A morphospace of functional configuration to assess configural breadth based on brain functional networks.

The quantification of human brain functional (re)configurations across varying cognitive demands remains an unresolved topic. We propose that such functional configurations may be categorized into three different types: (a) network configural breadth, (b) task-to task transitional reconfiguration, and (c) within-task reconfiguration. Such functional reconfigurations are rather subtle at the whole-brain level.

Toward an information theoretical description of communication in brain networks.

Modeling communication dynamics in the brain is a key challenge in network neuroscience. We present here a framework that combines two measurements for any system where different communication processes are taking place on top of a fixed structural topology: path processing score (PPS) estimates how much the brain signal has changed or has been transformed between any two brain regions (source and target); path broadcasting strength (PBS) estimates the propagation of the signal through edges adjacent to the path being assessed. We use PPS and PBS to explore communication dynamics in large-scale brain networks.

A Structural Connectivity Disruption One Decade before the Typical Age for Dementia: A Study in Healthy Subjects with Family History of Alzheimer's Disease.

The concept of the brain has shifted to a complex system where different subnetworks support the human cognitive functions. Neurodegenerative diseases would affect the interactions among these subnetworks and, the evolution of impairment and the subnetworks involved would be unique for each neurodegenerative disease. In this study, we seek for structural connectivity traits associated with the family history of Alzheimer's disease, that is, early signs of subnetworks impairment due to Alzheimer's disease.

Improving Functional Connectome Fingerprinting with Degree-Normalization.

Functional connectivity quantifies the statistical dependencies between the activity of brain regions, measured using neuroimaging data such as functional magnetic resonance imaging (fMRI) blood-oxygenation-level dependent time series. The network representation of functional connectivity, called a functional connectome (FC), has been shown to contain an individual fingerprint allowing participants identification across consecutive testing sessions. Recently, researchers have focused on the extraction of these fingerprints, with potential applications in personalized medicine.

Geodesic Distance on Optimally Regularized Functional Connectomes Uncovers Individual Fingerprints.

Functional connectomes (FCs) have been shown to provide a reproducible individual fingerprint, which has opened the possibility of personalized medicine for neuro/psychiatric disorders. Thus, developing accurate ways to compare FCs is essential to establish associations with behavior and/or cognition at the individual level. Canonically, FCs are compared using Pearson's correlation coefficient of the entire functional connectivity profiles.

Multi-timescale hybrid components of the functional brain connectome: A bimodal EEG-fMRI decomposition.

Concurrent electroencephalography (EEG) and functional magnetic resonance imaging (fMRI) bridge brain connectivity across timescales. During concurrent EEG-fMRI resting-state recordings, whole-brain functional connectivity (FC) strength is spatially correlated across modalities. However, cross-modal investigations have commonly remained correlational, and joint analysis of EEG-fMRI connectivity is largely unexplored.

GEFF: Graph embedding for functional fingerprinting.

It has been well established that Functional Connectomes (FCs), as estimated from functional MRI (fMRI) data, have an individual fingerprint that can be used to identify an individual from a population (subject-identification). Although identification rate is high when using resting-state FCs, other tasks show moderate to low values. Furthermore, identification rate is task-dependent, and is low when distinct cognitive states, as captured by different fMRI tasks, are compared.

Brain-wide structural connectivity alterations under the control of Alzheimer risk genes.

Background: Alzheimer's disease is the most common form of brain dementia characterized by gradual loss of memory followed by further deterioration of other cognitive function. Large-scale genome-wide association studies have identified and validated more than 20 AD risk genes. However, how these genes are related to the brain-wide breakdown of structural connectivity in AD patients remains unknown.

Functional network connectivity in early-stage schizophrenia.

Schizophrenia is a disorder of altered neural connections resulting in impaired information integration. Whole brain assessment of within- and between-network connections may determine how information processing is disrupted in schizophrenia. Patients with early-stage schizophrenia (n = 56) and a matched control sample (n = 32) underwent resting-state fMRI scans.

The disengaging brain: Dynamic transitions from cognitive engagement and alcoholism risk.

Human functional brain connectivity is usually measured either at "rest" or during cognitive tasks, ignoring life's moments of mental transition. We propose a different approach to understanding brain network transitions. We applied a novel independent component analysis of functional connectivity during motor inhibition (stop signal task) and during the continuous transition to an immediately ensuing rest.

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

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 subjects 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 functional connectomes.