Search bioRxiv⌕ Search

bioRxiv · 10.1101/850545

Gamma coherence mediates interhemispheric integration during multiple object tracking

Abstract

Our ability to track the paths of multiple visual objects moving between the hemifields requires effective integration of information between the two cerebral hemispheres. Coherent neural oscillations in the gamma band (35-70 Hz) are hypothesised to drive this information transfer. Here we manipulated the need for interhemispheric integration using a novel multiple object tracking (MOT) task in which stimuli either moved between the two visual hemifields--requiring interhemispheric integration--or moved within separate visual hemifields. We used electroencephalography (EEG) to measure interhemispheric coherence during the task. Human observers (21 female; 20 male) were poorer at tracking objects between-versus within-hemifields, reflecting a cost of interhemispheric integration. Critically, gamma coherence was greater in trials requiring interhemispheric integration, particularly between sensors over parieto-occipital areas. In approximately half of the participants, the observed cost of integration was associated with a failure of the cerebral hemispheres to become coherent in the gamma band. Moreover, individual differences in this integration cost correlated with endogenous gamma coherence at these same sensors, though with generally opposing relationships for the real and imaginary part of coherence. The real part (capturing synchronisation with a near-zero phase-lag) benefited between-hemifield tracking; imaginary coherence was detrimental. Finally, instantaneous phase-coherence over the tracking period uniquely predicted between-hemifield tracking performance, suggesting that effective integration benefits from sustained interhemispheric synchronisation. Our results show that gamma coherence mediates interhemispheric integration during MOT, and add to a growing body of work demonstrating that coherence drives communication across cortically distributed neural networks.

Source connections

Explore related subjects

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Bland, N. S., Mattingley, J. B., Sale, M. V.. 2019-11-29. Gamma coherence mediates interhemispheric integration during multiple object tracking. https://doi.org/10.1101/850545

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related preprints

Circuit and cell-type mechanisms of the spectrolaminar motif of cortical oscillations

Oscillations are a key feature of cortical activity, coordinating spike timing across areas, layers, and cell types. Recent work suggests these rhythms follow a stereotyped organization across cortical depth, the spectrolaminar (SL) motif, with higher-frequency gamma prominent in superficial layers and lower-frequency alpha/beta in deep layers, yet the mechanisms generating it remain poorly understood. Using a bio-realistic model of mouse primary visual cortex, we reproduced the SL motif and found it preserved across diverse visual stimuli. Systematic cell-type- and layer-resolved perturbations showed that the alpha/beta component was intrinsic to every layer, whereas the 50-80 Hz gamma component was predominantly generated within layer 4 (L4) and propagated network-wide through inter-laminar connections. Pvalb interneurons were the principal regulators of gamma, Sst cells shaped low-frequency power, and Vip cells controlled oscillation magnitude. The temporal properties of L4 Pvalb-to-excitatory synapses were especially influential in generating the SL motif. These models and simulation results thus offer a plausible description of circuit mechanisms underlying cortical oscillations and the SL motif and are publicly available to facilitate further studies.

neuroscience↗

Personalized Prediction of Differences in fMRI Connectivity Estimates Across Preprocessing Pipelines

Longitudinal functional MRI of the human brain is valuable for tracking changes in functional connectivity (FC) associated with disease progression or treatment response. Even so, measured FC values depend on preprocessing, and nuisance regression and temporal filtering affect individuals differently. We examined whether we could use earlier scans to predict how preprocessing would affect a later scan. In 69 ADNI (Alzheimers Disease Neuroimaging Initiative) participants scanned three times, we measured how nuisance regression and filtering, alone and in sequence, changed the measured FC. A ridge regression model combined these responses with baseline FC from the third scan to predict the effect of reversing the processing order. Just four coefficients were shared across 79,800 connections. In leave-one-participant-out testing, mean squared error was 0.012, compared with 0.017 for predicting the same average response for everyone. It outperformed the best of seven alternative predictions chosen for each participant (0.013; normalized gain 0.069, 95% bootstrap interval 0.006-0.134), with lower error in 44 of 69 participants. About half the benefit of using each persons earlier responses, rather than the average across participants, came from which connections changed. This advantage persisted after accounting for acquisition variables. The ADNI-trained model reduced mean error in ten Midnight Scan Club participants without refitting. These results support using personal preprocessing history to predict changes in connectivity estimates on newly acquired scans.

neuroscience↗

Temporal Structure in Resting-State fMRI under Marginal and Cross-Spectral Surrogate Controls

Resting-state functional MRI is commonly analyzed using correlations between regional signals and their frequency spectra, which characterize average connectivity and spectral properties but may miss other aspects of temporal organization. Measures of temporal complexity offer complementary information about the recurrence of signal patterns, how fluctuations vary across timescales, and signal roughness. These properties may reveal disease-related alterations in brain activity, offering opportunities for biomarker discovery and insights into mechanisms of neurological and psychiatric disorders. Here we examined sample entropy (SampEn), multifractal de-trended fluctuation analysis (MFDFA), and Higuchi curve shapes in 732 scans from 270 ADNI participants, including cognitively normal individuals, people with mild cognitive impairment, and people with dementia. Using the seven Yeo resting-state networks (Yeo-7), we compared recordings with randomized signals preserving either each networks amplitude distribution and approximate spectrum (IAAFT), or the complete cross-spectrum across networks. If these properties fully explained temporal complexity, real recordings should resemble their randomized references. We therefore counted how often each real recording was the strongest outlier among itself and 20 references. This occurred in 21.7%, 12.7%, and 44.2% of 5,124 network-visits for SampEn, MFDFA, and Higuchi, versus 5.5%, 5.1%, and 6.3% in surrogate-only controls. Cross-spectrum-preserving references yielded 39.8%, 17.5%, and 49.8%. Simulations showed that changes in sampling or signal processing, including abrupt changes in signal amplitude, can produce similar differences without changing the underlying dynamics. The measures showed repeatability across visits but did not improve retrospective prediction of changes in Clinical Dementia Rating Sum of Boxes (CDR-SB) scores. These findings identify temporal features not reproduced by the tested spectral controls and provide a basis for evaluating their potential as disease biomarkers.

neuroscience↗