Hyperedge bundling: Data, source code, and precautions to modeling-accuracy bias to synchrony estimates

Aberrant MEG multi-frequency phase temporal synchronization predicts conversion from mild cognitive impairment-to-Alzheimer's disease

Many neuroimaging studies focus on a frequency-specific or a multi-frequency network analysis showing that functional brain networks are disrupted in patients with Alzheimer's disease (AD). Although those studies enriched our knowledge of the impact of AD in brain's functionality, our goal is to test the effectiveness of combining neuroimaging with network neuroscience to predict with high accuracy subjects with mild cognitive impairment (MCI) that will convert to AD.

In this study, eyes-closed resting-state magnetoencephalography (MEG) recordings from 27 stable MCI (sMCI) and 27 progressive MCI (pMCI) from two scan sessions (baseline and follow-up after approximately 3 years) were projected via beamforming onto an atlas-based set of regions of interest (ROIs). Dynamic functional connectivity networks were constructed independently for the five classical frequency bands while a multivariate phase-based coupling metric was adopted. Thus, computing the distance between the fluctuation of functional strength of every pair of ROIs between the two conditions with dynamic time wrapping (DTW), a large set of features was extracted. A machine learning algorithm revealed 30 DTW-based features in the five frequency bands that can distinguish the sMCI from pMCI with absolute accuracy (100%). Further analysis of the selected links revealed that most of the connected ROIs were part of the default mode network (DMN), the cingulo-opercular (CO), the fronto-parietal and the sensorimotor network.

Overall, our dynamic network multi-frequency analysis approach provides an effective framework of constructing a sensitive MEG-based connectome biomarker for the prediction of conversion from MCI to Alzheimer's disease.

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49 features in the five frequency bands discriminated the two groups with 100% accuracy.

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The analysis revealed most of the links were part of important brain networks as the DMN.

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The alteration of these links, usually affected in AD, is related to synaptic loss.

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Our multi-frequency approach provided a MEG connectome biomarker to predict the conversion to AD.

Dynamic low frequency EEG phase synchronization patterns during proactive control of task switching

Cognitive flexibility is critical for humans living in complex societies with ever-growing multitasking demands. Yet the low-frequency neural dynamics of distinct task-specific and domain-general mechanisms sub-serving mental flexibility are still ill-defined. Here we estimated phase electroencephalogram synchronization by using inter-trial phase coherence (ITPC) at the source space while twenty six young participants were intermittently cued to switch or repeat their perceptual categorization rule of Gabor gratings varying in color and thickness (switch task). Therefore, the aim of this study was to examine whether a proactive control is associated with connectivity only in the frontoparietal theta network, or also involves distinct neural connectivity within the delta band, as distinct neural signatures while preparing to switch or repeat a task set, respectively. To this end, we focused the analysis on late-latencies (from 500 to 800 msec post-cue onset), since they are known to be associated with top-down cognitive control processes. We confirmed that proactive control during a task switch was associated with frontoparietal theta connectivity. But importantly, we also found a distinct role of delta band oscillatory synchronization in proactive control, engaging more posterior frontotemporal regions as opposed to frontoparietal theta connectivity. Additionally, we built a regression model by using the ITPC results in delta and theta bands as predictors, and the behavioral accuracy in the switch task as the criterion, obtaining significant results for both frequency bands. All these findings support the existence of distinct proactive cognitive control processes related to functionally distinct though highly complementary theta and delta frontoparietal and temporoparietal oscillatory networks at late-latency temporal scales.