A multidimensional model of memory complaints in older individuals and the associated hub regions.

Memory complaints are highly prevalent among middle-aged and older adults, and they are frequently reported in individuals experiencing subjective cognitive decline (SCD). SCD has received increasing attention due to its implications for the early detection of dementia. This study aims to advance our comprehension of individuals with SCD by elucidating potential cognitive/psychologic-contributing factors and characterizing cerebral hubs within the brain network. To identify these potential contributing factors, a structural equation modeling approach was employed to investigate the relationships between various factors, such as metacognitive beliefs, personality, anxiety, depression, self-esteem, and resilience, and memory complaints. Our findings revealed that self-esteem and conscientiousness significantly influenced memory complaints. At the cerebral level, analysis of delta and theta electroencephalographic frequency bands recorded during rest was conducted to identify hub regions using a local centrality metric known as betweenness centrality. Notably, our study demonstrated that certain brain regions undergo changes in their hub roles in response to the pathology of SCD. Specifically, the inferior temporal gyrus and the left orbitofrontal area transition into hubs, while the dorsolateral prefrontal cortex and the middle temporal gyrus lose their hub function in the presence of SCD. This rewiring of the neural network may be interpreted as a compensatory response employed by the brain in response to SCD, wherein functional connectivity is maintained or restored by reallocating resources to other regions.

Brain network dynamics in the alpha band during a complex postural control task.

Objective.To decipher brain network dynamic remodeling from electroencephalography (EEG) during a complex postural control (PC) task combining virtual reality and a moving platform.Approach.EEG (64 electrodes) data from 158 healthy subjects were acquired. The experiment is divided into several phases, and visual and motor stimulation is applied progressively. We combined advanced source-space EEG networks with clustering algorithms to decipher the brain networks states (BNSs) that occurred during the task.Main results.The results show that BNS distribution describes the different phases of the experiment with specific transitions between visual, motor, salience, and default mode networks coherently. We also showed that age is a key factor that affects the dynamic transition of BNSs in a healthy cohort.Significance.This study validates an innovative approach, based on a robust methodology and a consequent cohort, to quantify the brain networks dynamics in the BioVRSea paradigm. This work is an important step toward a quantitative evaluation of brain activities during PC and could lay the foundation for developing brain-based biomarkers of PC-related disorders.

HD-EEG for tracking sub-second brain dynamics during cognitive tasks.

This work provides the community with high-density Electroencephalography (HD-EEG, 256 channels) datasets collected during task-free and task-related paradigms. It includes forty-three healthy participants performing visual naming and spelling tasks, visual and auditory naming tasks and a visual working memory task in addition to resting state. The HD-EEG data are furnished in the Brain Imaging Data Structure (BIDS) format. These datasets can be used to (i) track brain networks dynamics and their rapid reconfigurations at sub-second time scale in different conditions, (naming/spelling/rest) and modalities, (auditory/visual) and compare them to each other, (ii) validate several parameters involved in the methods used to estimate cortical brain networks through scalp EEG, such as the open question of optimal number of channels and number of regions of interest and (iii) allow the reproducibility of results obtained so far using HD-EEG. We hope that delivering these datasets will lead to the development of new methods that can be used to estimate brain cortical networks and to better understand the general functioning of the brain during rest and task. Data are freely available from https://openneuro.org .

Identification of epileptogenic networks from dense EEG: A model-based study.

Epilepsy is a network disease. Identifying the epileptogenic networks from noninvasive recordings is a challenging issue. In this context, M/EEG source connectivity is a promising tool to identify brain networks with high temporal and spatial resolution. In this paper, we analyze the impact of the two main factors that intervene in EEG source connectivity processing: i) the algorithm used to solve the EEG inverse problem and ii) the method used to measure the functional connectivity. We evaluate three inverse solutions algorithms (dSPM, wMNE and cMEM) and three connectivity measures (r(2), h(2) and MI) on data simulated from a combined biophysical/physiological model able to generate realistic interictal epileptic spikes reflected in scalp EEG. The performance criterion used here is the similarity between the network identified by each of the inverse/connectivity combination and the original network used in the source model. Results show that the choice of the combination has a high impact on the identified network. Results suggest also that nonlinear methods (nonlinear correlation coefficient and mutual information) for measuring the connectivity are more efficient than the linear one (the cross correlation coefficient). The dSPM as inverse solution shows the lowest performance compared to cMEM and wMNE.

A new algorithm for spatiotemporal analysis of brain functional connectivity.

Specific networks of interacting neuronal assemblies distributed within and across distinct brain regions underlie brain functions. In most cognitive tasks, these interactions are dynamic and take place at the millisecond time scale. Among neuroimaging techniques, magneto/electroencephalography - M/EEG - allows for detection of very short-duration events and offers the single opportunity to follow, in time, the dynamic properties of cognitive processes (sub-millisecond temporal resolution). In this paper, we propose a new algorithm to track the functional brain connectivity dynamics. During a picture naming task, this algorithm aims at segmenting high-resolution EEG signals (hr-EEG) into functional connectivity microstates. The proposed algorithm is based on the K-means clustering of the connectivity graphs obtained from the phase locking value (PLV) method applied on hr-EEG. Results show that the analyzed evoked responses can be divided into six clusters representing distinct networks sequentially involved during the cognitive task, from the picture presentation and recognition to the motor response.