Studies comparing diverse groups have shown that many psychiatric diseases involve disruptions across distributed large-scale networks of the brain. There is hope that functional magnetic resonance imaging (fMRI) functional connectivity techniques will shed light on these disruptions, providing prognostic and diagnostic biomarkers as well as targets for therapeutic interventions. However, to date, progress on clinical translation of fMRI methods has been limited. Here, we argue that this limited translation is driven by a combination of intersubject heterogeneity and the relatively low reliability of standard fMRI techniques at the individual level. We review a potential solution to these limitations: the use of new "precision" fMRI approaches that shift the focus of analysis from groups to single individuals through the use of extended data acquisition strategies. We begin by discussing the potential advantages of fMRI functional connectivity methods for improving our understanding of functional neuroanatomy and disruptions in psychiatric disorders. We then discuss the budding field of precision fMRI and findings garnered from this work. We demonstrate that precision fMRI can improve the reliability of functional connectivity measures, while showing high stability and sensitivity to individual differences. We close by discussing the application of these approaches to clinical settings.
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http://dx.doi.org/10.1016/j.biopsych.2019.10.026 | DOI Listing |
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February 2025
Department of Anesthesiology, Faculty of Medicine and Health Sciences, Université de Sherbrooke, Sherbrooke, QC, Canada.
Chronic pain is a pervasive and debilitating condition with increasing implications for public health, affecting millions of individuals worldwide. Despite its high prevalence, the underlying neural mechanisms and pathophysiology remain only partly understood. Since its introduction 35 years ago, brain diffusion magnetic resonance imaging (MRI) has emerged as a powerful tool to investigate changes in white matter microstructure and connectivity associated with chronic pain.
View Article and Find Full Text PDFProc Natl Acad Sci U S A
January 2025
Department of Chemistry and Biochemistry, The Ohio State University, Columbus, OH 43210.
The homo-dodecameric ring-shaped RNA binding attenuation protein (TRAP) from binds up to twelve tryptophan ligands (Trp) and becomes activated to bind a specific sequence in the 5' leader region of the operon mRNA, thereby downregulating biosynthesis of Trp. Thermodynamic measurements of Trp binding have revealed a range of cooperative behavior for different TRAP variants, even if the averaged apparent affinities for Trp have been found to be similar. Proximity between the ligand binding sites, and the ligand-coupled disorder-to-order transition has implicated nearest-neighbor interactions in cooperativity.
View Article and Find Full Text PDFChaos
January 2025
School of Mathematical Science, Jiangsu University, Zhenjiang, Jiangsu 212013, China.
This study explores the impact of stochastic resetting on the random walk dynamics within scale-free (u,v)-flowers. Utilizing the generating function technique, we develop a recursive relationship for the generating function of the first passage time and establish a connection between the mean first passage time with and without resetting. Our investigation spans multiple scenarios, with the random walker starting from various positions and aiming to reach different target nodes, allowing us to identify the optimal resetting probability that minimizes the mean first passage time for each case.
View Article and Find Full Text PDFIndividual choices shape life course trajectories of brain structure and function beyond genes and environment. We hypothesized that individual task engagement in response to a learning program results in individualized learning biographies and connectomics. Genetically identical female mice living in one large shared enclosure freely engaged in self-paced, automatically administered and monitored learning tasks.
View Article and Find Full Text PDFElife
January 2025
Department of Neuroscience, Physiology and Pharmacology, University College London, London, United Kingdom.
Data-driven models of neurons and circuits are important for understanding how the properties of membrane conductances, synapses, dendrites, and the anatomical connectivity between neurons generate the complex dynamical behaviors of brain circuits in health and disease. However, the inherent complexity of these biological processes makes the construction and reuse of biologically detailed models challenging. A wide range of tools have been developed to aid their construction and simulation, but differences in design and internal representation act as technical barriers to those who wish to use data-driven models in their research workflows.
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