Interventions using methods such as cognitive training and aerobic exercise have shown potential to enhance cognitive abilities. However, there is often pronounced individual variability in the magnitude of these gains. Here, we propose that brain network modularity, a measure of brain subnetwork segregation, is a unifying biomarker of intervention-related plasticity. We present work from multiple independent studies demonstrating that individual differences in baseline brain modularity predict gains in cognitive control functions across several populations and interventions, spanning healthy adults to patients with clinical deficits and cognitive training to aerobic exercise. We believe that this predictive framework provides a foundation for developing targeted, personalized interventions to improve cognition.
Download full-text PDF |
Source |
---|---|
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6750199 | PMC |
http://dx.doi.org/10.1016/j.tics.2019.01.014 | DOI Listing |
Nat Commun
January 2025
Department of Bioengineering, School of Engineering & Applied Science, University of Pennsylvania, Philadelphia, PA, 19104, USA.
From sequences of discrete events, humans build mental models of their world. Referred to as graph learning, the process produces a model encoding the graph of event-to-event transition probabilities. Recent evidence suggests that some networks are easier to learn than others, but the neural underpinnings of this effect remain unknown.
View Article and Find Full Text PDFAnnu Rev Neurosci
January 2025
1Department of Neuroscience, Karolinska Institutet, Stockholm, Sweden; email:
Locomotion, like all behaviors, possesses an inherent flexibility that allows for the scaling of movement kinematic features, such as speed and vigor, in response to an ever-changing external world and internal drives. This flexibility is embedded in the organization of the spinal locomotor circuits, which encode and decode commands from the brainstem and proprioceptive feedback. This review highlights our current understanding of the modular organization of these locomotor circuits and how this modularity endows them with intrinsic mechanisms to adjust speed and vigor, thereby contributing to the flexibility of locomotor movements.
View Article and Find Full Text PDFProc Natl Acad Sci U S A
January 2025
Ernst Strüngmann Institute, Frankfurt am Main 60528, Germany.
The dynamics of neuronal systems are characterized by hallmark features such as oscillations and synchrony. However, it has remained unclear whether these characteristics are epiphenomena or are exploited for computation. Due to the challenge of selectively interfering with oscillatory network dynamics in neuronal systems, we simulated recurrent networks of damped harmonic oscillators in which oscillatory activity is enforced in each node, a choice well supported by experimental findings.
View Article and Find Full Text PDFFront Neurosci
January 2025
Department of Radiology, Huadong Hospital, Fudan University, Shanghai, China.
Purpose: Tinnitus is considered a neurological disorder affecting both auditory and nonauditory networks. This study aimed to investigate the structural brain covariance network in tinnitus patients and analyze its altered topological properties.
Materials: Fifty three primary tinnitus patients and 67 age- and sex-matched healthy controls (HCs) were included.
PLoS Comput Biol
January 2025
Biorobotics Laboratory, École Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland.
This study introduces a novel neuromechanical model employing a detailed spiking neural network to explore the role of axial proprioceptive sensory feedback, namely stretch feedback, in salamander locomotion. Unlike previous studies that often oversimplified the dynamics of the locomotor networks, our model includes detailed simulations of the classes of neurons that are considered responsible for generating movement patterns. The locomotor circuits, modeled as a spiking neural network of adaptive leaky integrate-and-fire neurons, are coupled to a three-dimensional mechanical model of a salamander with realistic physical parameters and simulated muscles.
View Article and Find Full Text PDFEnter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!