Dynamiceuticals: The Next Stage in Personalized Medicine

Velázquez, José Luis Pérez

doi:10.3389/fnins.2017.00329

Cited by 6 publications

(1 citation statement)

References 32 publications

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“…In addition, our results highlight the need for individualized approaches to systems neuroscience and medicine (65,66) by demonstrating that NNAs contain large individual variations across states (i.e. our transient node class).…”

Section: Individualized Brain Network Are Neededmentioning

confidence: 70%

State-specific individualized functional networks form a predictive signature of brain state

Salehi

Karbasi

et al. 2018

Preprint

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There is extensive evidence that human brain functional organization is dynamic, varying within a subject as the brain switches between tasks demands. This functional organization also varies across subjects, even when they are all engaged in similar tasks. Currently, we lack a comprehensive model that unifies the two dimensions of variation (brain state and subject). Using fMRI data obtained across multiple task-evoked and rest conditions (which we operationally define as brain states) and across multiple subjects, we develop a state-and subject-specific functional network parcellation (the assignment of nodes to networks). Our parcellation approach provides a measure of how node-to-network assignment (NNA) changes across states and across subjects. We demonstrate that the brain's functional networks are not spatially fixed, but reconfigure with brain state. This reconfiguration is robust and reliable to such an extent that it can be used to predict brain state with accuracies up to 97%.Recent studies have made significant progress in characterizing state-evoked changes in functional connectivity elicited by task performance (20-28). However, none of these works explicitly examined the possibility that networks spatially reconfigure: (i) The majority of these studies explicitly assumed that networks remain spatially unchanged across tasks (21,22,29). These analyses were restricted to investigating differences in connectivity between networks, and not whether networks spatially reconfigure across tasks. (ii) Another line of research takes a more abstract perspective by computing global network measures (such as modularity and participation coefficient) and comparing these measures across different states (27,28,(30)(31)(32). Such approaches address the modular reconfiguration of the brain as a whole by defining a new set of networks for each state, but they do not quantify how or whether the same networks change across states. Such studies also typically define a small number of networks (~3-7) whose correspondence to the putative resting-state networks are unclear. These approaches cannot answer how specific networks reorganize as a function of brain state. (iii) Finally, many studies do not directly examine cross-subject variations in network reorganization (20), or treat crosssubject and cross-session changes as similar notions for defining reorganization (33). However, functional organization varies significantly across subjects, yielding large individual differences in network definitions (34,35). There is a clear need to account for both cross-subject and crossstate variability in functional network organization when characterizing brain dynamics.Here, we use a novel approach to dynamically map the brain's functional networks across taskevoked and resting states and across individuals. We operationalize the brain's functional subunits as a set of 268 pre-defined nodes (6). We then apply our recently developed exemplarbased parcellation method (36) to assign a set of pre-defined nodes to individualized, stat...

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Section: Individualized Brain Network Are Neededmentioning

confidence: 70%

State-specific individualized functional networks form a predictive signature of brain state

Salehi

Karbasi

et al. 2018

Preprint

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The Multiscale Wisdom of the Body: Collective Intelligence as a Tractable Interface for Next‐Generation Biomedicine

Levin

2024

BioEssays

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The dominant paradigm in biomedicine focuses on genetically‐specified components of cells and their biochemical dynamics, emphasizing bottom‐up emergence of complexity. Here, I explore the biomedical implications of a complementary emerging field: diverse intelligence. Using tools from behavioral science and multiscale neuroscience, we can study development, regenerative repair, and cancer suppression as behaviors of a collective intelligence of cells navigating the spaces of possible morphologies and transcriptional and physiological states. A focus on the competencies of living material—from molecular to organismal scales—reveals a new landscape for interventions. Such top‐down approaches take advantage of the memories and homeodynamic goal‐seeking behavior of cells and tissues, offering the same massive advantages in biomedicine and bioengineering that reprogrammable hardware has provided information technologies. The bioelectric networks that bind individual cells toward large‐scale anatomical goals are an especially tractable interface to organ‐level plasticity, and tools to modulate them already exist. This suggests a research program to understand and tame the software of life for therapeutic gain by understanding the many examples of basal cognition that operate throughout living bodies.

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