Dynamic network features of functional and structural brain networks support visual working memory in aging adults.

In this work, we investigated how the relationship between structural connectivity and the dynamics of functional connectivity changes with age to benefit cognitive ability. Visual working memory (VWM) is an important brain function that allows us to maintain a mental representation of the world around us, but its capacity and precision peak by around 20 years old and decrease steadily throughout the rest of our lives. This research examined the functional brain network dynamics associated with VWM throughout the lifespan and found that Default Mode Network and Fronto-Parietal Network states were more well represented in individuals with better VWM.

Examining Relationships between Functional and Structural Brain Network Architecture, Age, and Attention Skills in Early Childhood.

Early childhood is a critical period showing experience-dependent changes in brain structure and function. The complex link between the structural connectivity (SC) and functional connectivity (FC) of the brain is of particular interest. However, its relationship with both age and attention in early childhood is not well understood.

A mechanism for the emergence of low-dimensional structures in brain dynamics.

Recent neuroimaging advancements have led to datasets characterized by an overwhelming number of features. Different dimensionality reduction techniques have been employed to uncover low-dimensional manifold representations underlying cognitive functions, while maintaining the fundamental characteristics of the data. These range from linear algorithms to more intricate non-linear methods for manifold extraction.

The Possibility Space Concept in Neuroscience: Possibilities, Constraints, and Explanations.

Although the brain is often characterized as a complex system, theoretical and philosophical frameworks often struggle to capture this. For example, mainstream mechanistic accounts model neural systems as fixed and static in ways that fail to capture their dynamic nature and large set of possible behaviors. In this paper, we provide a framework for capturing a common type of complex system in neuroscience, which involves two main aspects: (i) constraints on the system and (ii) the system's possibility space of available outcomes.

Advances in physiological and clinical relevance of hiPSC-derived brain models for precision medicine pipelines.

Precision, or personalized, medicine aims to stratify patients based on variable pathogenic signatures to optimize the effectiveness of disease prevention and treatment. This approach is favorable in the context of brain disorders, which are often heterogeneous in their pathophysiological features, patterns of disease progression and treatment response, resulting in limited therapeutic standard-of-care. Here we highlight the transformative role that human induced pluripotent stem cell (hiPSC)-derived neural models are poised to play in advancing precision medicine for brain disorders, particularly emerging innovations that improve the relevance of hiPSC models to human physiology.

Secondary thalamic dysfunction underlies abnormal large-scale neural dynamics in chronic stroke.

Stroke causes pronounced and widespread slowing of neural activity. Despite decades of work exploring these abnormal neural dynamics and their associated functional impairments, their causes remain largely unclear. To close this gap in understanding, we applied a neurophysiological corticothalamic circuit model to simulate magnetoencephalography (MEG) power spectra recorded from chronic stroke patients.

Reorganization of structural connectivity in the brain supports preservation of cognitive ability in healthy aging.

The global population is aging rapidly, and a research question of critical importance is why some older adults suffer tremendous cognitive decline while others are mostly spared. Past aging research has shown that older adults with spared cognitive ability have better local short-range information processing while global long-range processing is less efficient. We took this research a step further to investigate whether the underlying structural connections, measured in vivo using diffusion magnetic resonance imaging (dMRI), show a similar shift to support cognitive ability.

Comparing the stability and reproducibility of brain-behavior relationships found using canonical correlation analysis and partial least squares within the ABCD sample.

Canonical correlation analysis (CCA) and partial least squares correlation (PLS) detect linear associations between two data matrices by computing latent variables (LVs) having maximal correlation (CCA) or covariance (PLS). This study compared the similarity and generalizability of CCA- and PLS-derived brain-behavior relationships. Data were accessed from the baseline Adolescent Brain Cognitive Development (ABCD) dataset ( > 9,000, 9-11 years).

Sex differences in longitudinal changes of episodic memory-related brain activity and cognition in cognitively unimpaired older adults with a family history of Alzheimer's disease.

Episodic memory decline is an early symptom of Alzheimer's disease (AD) - a neurodegenerative disease that has a higher prevalence rate in older females compared to older males. However, little is known about why these sex differences in prevalence rate exist. In the current longitudinal task fMRI study, we explored whether there were sex differences in the patterns of memory decline and brain activity during object-location (spatial context) encoding and retrieval in a large sample of cognitively unimpaired older adults from the Pre-symptomatic Evaluation of Novel or Experimental Treatments for Alzheimer's Disease (PREVENT-AD) program who are at heightened risk of developing AD due to having a family history (+FH) of the disease.

Auditory and reward structures reflect the pleasure of musical expectancies during naturalistic listening.

Enjoying music consistently engages key structures of the neural auditory and reward systems such as the right superior temporal gyrus (R STG) and ventral striatum (VS). Expectations seem to play a central role in this effect, as preferences reliably vary according to listeners' uncertainty about the musical future and surprise about the musical past. Accordingly, VS activity reflects the pleasure of musical surprise, and exhibits stronger correlations with R STG activity as pleasure grows.

Comparing the stability and reproducibility of brain-behaviour relationships found using Canonical Correlation Analysis and Partial Least Squares within the ABCD Sample.

Introduction: Canonical Correlation Analysis (CCA) and Partial Least Squares Correlation (PLS) detect associations between two data matrices based on computing a linear combination between the two matrices (called latent variables; LVs). These LVs maximize correlation (CCA) and covariance (PLS). These different maximization criteria may render one approach more stable and reproducible than the other when working with brain and behavioural data at the population-level.

Integrating psychosocial variables and societal diversity in epidemic models for predicting COVID-19 transmission dynamics.

During the current COVID-19 pandemic, governments must make decisions based on a variety of information including estimations of infection spread, health care capacity, economic and psychosocial considerations. The disparate validity of current short-term forecasts of these factors is a major challenge to governments. By causally linking an established epidemiological spread model with dynamically evolving psychosocial variables, using Bayesian inference we estimate the strength and direction of these interactions for German and Danish data of disease spread, human mobility, and psychosocial factors based on the serial cross-sectional COVID-19 Snapshot Monitoring (COSMO; N = 16,981).

Spectral slowing in chronic stroke reflects abnormalities in both periodic and aperiodic neural dynamics.

Decades of electrophysiological work have demonstrated the presence of "spectral slowing" in stroke patients - a prominent shift in the power spectrum towards lower frequencies, most evident in the vicinity of the lesion itself. Despite the reliability of this slowing as a marker of dysfunctional tissue across patient groups as well as animal models, it has yet to be explained in terms of the pathophysiological processes of stroke. To do so requires clear understanding of the neural dynamics that these differences represent, acknowledging the often overlooked fact that spectral power reflects more than just the amplitude of neural oscillations.

A Robust Modular Automated Neuroimaging Pipeline for Model Inputs to TheVirtualBrain.

TheVirtualBrain, an open-source platform for large-scale network modeling, can be personalized to an individual using a wide range of neuroimaging modalities. With the growing number and scale of neuroimaging data sharing initiatives of both healthy and clinical populations comes an opportunity to create large and heterogeneous sets of dynamic network models to better understand individual differences in network dynamics and their impact on brain health. Here we present TheVirtualBrain-UK Biobank pipeline, a robust, automated and open-source brain image processing solution to address the expanding scope of TheVirtualBrain project.

Brain simulation augments machine-learning-based classification of dementia.

Introduction: Computational brain network modeling using The Virtual Brain (TVB) simulation platform acts synergistically with machine learning (ML) and multi-modal neuroimaging to reveal mechanisms and improve diagnostics in Alzheimer's disease (AD).

Methods: We enhance large-scale whole-brain simulation in TVB with a cause-and-effect model linking local amyloid beta (Aβ) positron emission tomography (PET) with altered excitability. We use PET and magnetic resonance imaging (MRI) data from 33 participants of the Alzheimer's Disease Neuroimaging Initiative (ADNI3) combined with frequency compositions of TVB-simulated local field potentials (LFP) for ML classification.

Exploration of salient risk factors involved in mild cognitive impairment.

Mild cognitive impairment (MCI) is a prevalent and complex condition among older adults that often progresses into Alzheimer's disease (AD). Although MCI affects individuals differently, there are specific indicators of risk commonly associated with the development of MCI. The present study explored the prevalence of seven established MCI risk categories within a large sample of older adults with and without MCI.

EEG variability: Task-driven or subject-driven signal of interest?

Neurons in the brain are seldom perfectly quiet. They continually receive input and generate output, resulting in highly variable patterns of ongoing activity. Yet the functional significance of this variability is not well understood.

Individual Differences in Multisensory Processing Are Related to Broad Differences in the Balance of Local versus Distributed Information.

The brain's ability to extract information from multiple sensory channels is crucial to perception and effective engagement with the environment, but the individual differences observed in multisensory processing lack mechanistic explanation. We hypothesized that, from the perspective of information theory, individuals with more effective multisensory processing will exhibit a higher degree of shared information among distributed neural populations while engaged in a multisensory task, representing more effective coordination of information among regions. To investigate this, healthy young adults completed an audiovisual simultaneity judgment task to measure their temporal binding window (TBW), which quantifies the ability to distinguish fine discrepancies in timing between auditory and visual stimuli.

Brain simulation as a cloud service: The Virtual Brain on EBRAINS.

The Virtual Brain (TVB) is now available as open-source services on the cloud research platform EBRAINS (ebrains.eu). It offers software for constructing, simulating and analysing brain network models including the TVB simulator; magnetic resonance imaging (MRI) processing pipelines to extract structural and functional brain networks; combined simulation of large-scale brain networks with small-scale spiking networks; automatic conversion of user-specified model equations into fast simulation code; simulation-ready brain models of patients and healthy volunteers; Bayesian parameter optimization in epilepsy patient models; data and software for mouse brain simulation; and extensive educational material.

Personalized Connectome-Based Modeling in Patients with Semi-Acute Phase TBI: Relationship to Acute Neuroimaging and 6 Month Follow-Up.

Following traumatic brain injury (TBI), cognitive impairments manifest through interactions between microscopic and macroscopic changes. On the microscale, a neurometabolic cascade alters neurotransmission, while on the macroscale diffuse axonal injury impacts the integrity of long-range connections. Large-scale brain network modeling allows us to make predictions across these spatial scales by integrating neuroimaging data with biophysically based models to investigate how microscale changes invisible to conventional neuroimaging influence large-scale brain dynamics.

Signal complexity indicators of health status in clinical EEG.

Brain signal variability changes across the lifespan in both health and disease, likely reflecting changes in information processing capacity related to development, aging and neurological disorders. While signal complexity, and multiscale entropy (MSE) in particular, has been proposed as a biomarker for neurological disorders, most observations of altered signal complexity have come from studies comparing patients with few to no comorbidities against healthy controls. In this study, we examined whether MSE of brain signals was distinguishable across patient groups in a large and heterogeneous set of clinical-EEG data.

Virtual Connectomic Datasets in Alzheimer's Disease and Aging Using Whole-Brain Network Dynamics Modelling.

Large neuroimaging datasets, including information about structural connectivity (SC) and functional connectivity (FC), play an increasingly important role in clinical research, where they guide the design of algorithms for automated stratification, diagnosis or prediction. A major obstacle is, however, the problem of missing features [e.g.

Bridging Scales in Alzheimer's Disease: Biological Framework for Brain Simulation With The Virtual Brain.

Despite the acceleration of knowledge and data accumulation in neuroscience over the last years, the highly prevalent neurodegenerative disease of AD remains a growing problem. Alzheimer's Disease (AD) is the most common cause of dementia and represents the most prevalent neurodegenerative disease. For AD, disease-modifying treatments are presently lacking, and the understanding of disease mechanisms continues to be incomplete.

A Connectome-Based, Corticothalamic Model of State- and Stimulation-Dependent Modulation of Rhythmic Neural Activity and Connectivity.

Rhythmic activity in the brain fluctuates with behaviour and cognitive state, through a combination of coexisting and interacting frequencies. At large spatial scales such as those studied in human M/EEG, measured oscillatory dynamics are believed to arise primarily from a combination of cortical (intracolumnar) and corticothalamic rhythmogenic mechanisms. Whilst considerable progress has been made in characterizing these two types of neural circuit separately, relatively little work has been done that attempts to unify them into a single consistent picture.

Dynamic Functional Connectivity between order and randomness and its evolution across the human adult lifespan.

Functional Connectivity (FC) during resting-state or task conditions is not static but inherently dynamic. Yet, there is no consensus on whether fluctuations in FC may resemble isolated transitions between discrete FC states rather than continuous changes. This quarrel hampers advancing the study of dynamic FC.