TMS-evoked potential propagation reflects effective brain connectivity.

Cognition is achieved through communication between brain regions. Consequently, there is considerable interest in measuring effective connectivity. A promising effective connectivity metric is transcranial magnetic stimulation (TMS) evoked potentials (TEPs), an inflection in amplitude of the electroencephalogram recorded from one brain region as a result of TMS applied to another region.

Discrete Ricci curvatures capture age-related changes in human brain functional connectivity networks.

Introduction: Geometry-inspired notions of discrete Ricci curvature have been successfully used as markers of disrupted brain connectivity in neuropsychiatric disorders, but their ability to characterize age-related changes in functional connectivity is unexplored.

Methods: We apply Forman-Ricci curvature and Ollivier-Ricci curvature to compare functional connectivity networks of healthy young and older subjects from the Max Planck Institute Leipzig Study for Mind-Body-Emotion Interactions (MPI-LEMON) dataset ( = 225).

Results: We found that both Forman-Ricci curvature and Ollivier-Ricci curvature can capture whole-brain and region-level age-related differences in functional connectivity.

Event-related responses reflect chunk boundaries in natural speech.

Chunking language has been proposed to be vital for comprehension enabling the extraction of meaning from a continuous stream of speech. However, neurocognitive mechanisms of chunking are poorly understood. The present study investigated neural correlates of chunk boundaries intuitively identified by listeners in natural speech drawn from linguistic corpora using magneto- and electroencephalography (MEEG).

Comparison of Methods to Identify Modules in Noisy or Incomplete Brain Networks.

Community structure, or "modularity," is a fundamentally important aspect in the organization of structural and functional brain networks, but their identification with community detection methods is confounded by noisy or missing connections. Although several methods have been used to account for missing data, the performance of these methods has not been compared quantitatively so far. In this study, we compared four different approaches to account for missing connections when identifying modules in binary and weighted networks using both Louvain and Infomap community detection algorithms.

Markov Model-Based Method to Analyse Time-Varying Networks in EEG Task-Related Data.

The dynamic nature of functional brain networks is being increasingly recognized in cognitive neuroscience, and methods to analyse such time-varying networks in EEG/MEG data are required. In this work, we propose a pipeline to characterize time-varying networks in single-subject EEG task-related data and further, evaluate its validity on both simulated and experimental datasets. Pre-processing is done to remove channel-wise and trial-wise differences in activity.

Neurophysiological signatures of Alzheimer's disease and frontotemporal lobar degeneration: pathology versus phenotype.

The disruption of brain networks is characteristic of neurodegenerative dementias. However, it is controversial whether changes in connectivity reflect only the functional anatomy of disease, with selective vulnerability of brain networks, or the specific neurophysiological consequences of different neuropathologies within brain networks. We proposed that the oscillatory dynamics of cortical circuits reflect the tuning of local neural interactions, such that different pathologies are selective in their impact on the frequency spectrum of oscillations, whereas clinical syndromes reflect the anatomical distribution of pathology and physiological change.

Recent advances in functional neuroimaging analysis for cognitive neuroscience.

Functional magnetic resonance imaging and electro-/magneto-encephalography are some of the main neuroimaging technologies used by cognitive neuroscientists to study how the brain works. However, the methods for analysing the rich spatial and temporal data they provide are constantly evolving, and these new methods in turn allow new scientific questions to be asked about the brain. In this brief review, we highlight a handful of recent analysis developments that promise to further advance our knowledge about the working of the brain.

The Cambridge Centre for Ageing and Neuroscience (Cam-CAN) data repository: Structural and functional MRI, MEG, and cognitive data from a cross-sectional adult lifespan sample.

This paper describes the data repository for the Cambridge Centre for Ageing and Neuroscience (Cam-CAN) initial study cohort. The Cam-CAN Stage 2 repository contains multi-modal (MRI, MEG, and cognitive-behavioural) data from a large (approximately N=700), cross-sectional adult lifespan (18-87years old) population-based sample. The study is designed to characterise age-related changes in cognition and brain structure and function, and to uncover the neurocognitive mechanisms that support healthy cognitive ageing.

The effect of ageing on fMRI: Correction for the confounding effects of vascular reactivity evaluated by joint fMRI and MEG in 335 adults.

In functional magnetic resonance imaging (fMRI) research one is typically interested in neural activity. However, the blood-oxygenation level-dependent (BOLD) signal is a composite of both neural and vascular activity. As factors such as age or medication may alter vascular function, it is essential to account for changes in neurovascular coupling when investigating neurocognitive functioning with fMRI.