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. These include (1) multivariate approaches to decoding the content of brain activity, (2) time-varying approaches to characterising states of brain connectivity, (3) neurobiological modelling of neuroimaging data, and (4) standardisation and big data initiatives.Peer reviewe

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. Functional networks are estimated from short non-overlapping time windows within each “trial,” using a sparse-MVAR (Multi-Variate Auto-Regressive) model. Functional “states” are then identified by partitioning the entire space of functional networks into a small number of groups/symbols via k-means clustering.The multi-trial sequence of symbols is then described by a Markov Model (MM). We show validity of this pipeline on realistic electrode-level simulated EEG data, by demonstrating its ability to discriminate “trials” from two experimental conditions in a range of scenarios. We then apply it to experimental data from two individuals using a Brain-Computer Interface (BCI) via a P300 oddball task. Using just the Markov Model parameters, we obtain statistically significant discrimination between target and non-target trials. The functional networks characterizing each ‘state’ were also highly similar between the two individuals. This work marks the first application of the Markov Model framework to infer time-varying networks from EEG/MEG data. Due to the pre-processing, results from the pipeline are orthogonal to those from conventional ERP averaging or a typical EEG microstate analysis. The results provide powerful proof-of-concept for a Markov model-based approach to analyzing the data, paving the way for its use to track rapid changes in interaction patterns as a task is being performed. MATLAB code for the entire pipeline has been made available.Peer reviewe

Perceptual chunking of spontaneous speech : Validating a new method with non-native listeners

Human perception relies on chunking up an incoming information stream into smaller units to make sense of it. Evidence of chunking has been found across different domains, including visual events, music, and dance movement. It is largely uncontested that language processing must also proceed in smaller chunks of some kind. What these online chunks consist in is much less understood. In this paper, we propose that cognitively relevant chunks can be identified by crowdsourcing listener perceptions of chunk boundaries in real-time speech, even if the listeners are non-native speakers of the language. We present a paradigm in which experiment participants simultaneously listen to short extracts of authentic speech and mark chunk boundaries using a custom-built tablet application. We then test the internal validity of the method by measuring the extent to which fluent L2 listeners agree on chunk boundaries. To do this, we use three datasets collected within the paradigm and a suite of different statistical methods. The external validity of the method is studied in a separate paper and is briefly discussed at the end.Peer reviewe

Graph Ricci curvatures reveal atypical functional connectivity in autism spectrum disorder

Publisher Copyright: © 2022, The Author(s).While standard graph-theoretic measures have been widely used to characterize atypical resting-state functional connectivity in autism spectrum disorder (ASD), geometry-inspired network measures have not been applied. In this study, we apply Forman–Ricci and Ollivier–Ricci curvatures to compare networks of ASD and typically developing individuals (N = 1112) from the Autism Brain Imaging Data Exchange I (ABIDE-I) dataset. We find brain-wide and region-specific ASD-related differences for both Forman–Ricci and Ollivier–Ricci curvatures, with region-specific differences concentrated in Default Mode, Somatomotor and Ventral Attention networks for Forman–Ricci curvature. We use meta-analysis decoding to demonstrate that brain regions with curvature differences are associated to those cognitive domains known to be impaired in ASD. Further, we show that brain regions with curvature differences overlap with those brain regions whose non-invasive stimulation improves ASD-related symptoms. These results suggest the utility of graph Ricci curvatures in characterizing atypical connectivity of clinically relevant regions in ASD and other neurodevelopmental disorders.Peer reviewe

Effects of target expansion on selection performance in older computer users

Point and click interactions using a mouse are an integral part of computer use for current desktop systems. Compared with younger users though, older adults experience greater difficulties performing cursor positioning tasks, and this can present limitations to using a computer easily and effectively. Target expansion is a technique for improving pointing performance, where the target dynamically grows as the cursor approaches. This has the advantage that targets conserve screen real estate in their unexpanded state, yet can still provide the benefits of a larger area to click on. This paper presents two studies of target expansion with older and younger participants, involving multidirectional point-select tasks with a computer mouse. Study 1 compares static versus expanding targets, and Study 2 compares static targets with three alternative techniques for expansion. Results show that expansion can improve times by up to 14%, and reduce error rates by up to 50%. Additionally, expanding targets are beneficial even when the expansion happens late in the movement, i.e. after the cursor has reached the expanded target area or even after it has reached the original target area. Participants’ subjective feedback on the target expansion are generally favorable, and this lends further support for the technique

Comparison of methods to identify modules in noisy or incomplete brain networks

open6siCommunity 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. The four methods are "zeros," "row-column mean," "common neighbors," and "consensus clustering." Using Lancichinetti-Fortunato-Radicchi benchmark-simulated binary and weighted networks, we find that "zeros," "row-column mean," and "common neighbors" approaches perform well with both Louvain and Infomap, whereas "consensus clustering" performs well with Louvain but not Infomap. A similar pattern of results was observed with empirical networks from stereotactical electroencephalography data, except that "consensus clustering" outperforms other approaches on weighted networks with Louvain. Based on these results, we recommend any of the four methods when using Louvain on binary networks, whereas "consensus clustering" is superior with Louvain clustering of weighted networks. When using Infomap, "zeros" or "common neighbors" should be used for both binary and weighted networks. These findings provide a basis to accounting for noisy or missing connections when identifying modules in brain networks.openWilliams N.; Arnulfo G.; Wang S.H.; Nobili L.; Palva S.; Palva J.M.Williams, N.; Arnulfo, G.; Wang, S. H.; Nobili, L.; Palva, S.; Palva, J. M

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

IntroductionGeometry-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.MethodsWe 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 (N = 225).ResultsWe found that both Forman-Ricci curvature and Ollivier-Ricci curvature can capture whole-brain and region-level age-related differences in functional connectivity. Meta-analysis decoding demonstrated that those brain regions with age-related curvature differences were associated with cognitive domains known to manifest age-related changes—movement, affective processing, and somatosensory processing. Moreover, the curvature values of some brain regions showing age-related differences exhibited correlations with behavioral scores of affective processing. Finally, we found an overlap between brain regions showing age-related curvature differences and those brain regions whose non-invasive stimulation resulted in improved movement performance in older adults.DiscussionOur results suggest that both Forman-Ricci curvature and Ollivier-Ricci curvature correctly identify brain regions that are known to be functionally or clinically relevant. Our results add to a growing body of evidence demonstrating the sensitivity of discrete Ricci curvature measures to changes in the organization of functional connectivity networks, both in health and disease

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). In a behavioral experiment, subjects marked chunk boundaries in the excerpts intuitively, which revealed highly consistent chunk boundary markings across the subjects. We next recorded brain activity to investigate whether chunk boundaries with high and medium agreement rates elicit distinct evoked responses compared to non-boundaries. Pauses placed at chunk boundaries elicited a closure positive shift with the sources over bilateral auditory cortices. In contrast, pauses placed within a chunk were perceived as interruptions and elicited a biphasic emitted potential with sources located in the bilateral primary and non-primary auditory areas with right-hemispheric dominance, and in the right inferior frontal cortex. Furthermore, pauses placed at stronger boundaries elicited earlier and more prominent activation over the left hemisphere suggesting that brain responses to chunk boundaries of natural speech can be modulated by the relative strength of different linguistic cues, such as syntactic structure and prosody.Peer reviewe

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

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