Individual variability in human brain

Summary

Principal Investigators: Hava Siegelmann, University of Massachusetts Amherst and Lilianne Mujica-Parodi, SUNY Stony Brook
Title: Individual variability in human brain connectivity, modeled using multi-scale dynamics under energy constraint
BRAIN Category: Individuality and Variation

Clinical neuroscience currently lacks the tools for probing how biological constraints imposed upon synapses impact functional connectivity patterns. Our long-range goal is to develop these tools, focusing first upon energy constraints across synaptic-hemodynamic scales.

Principal Investigators: Hava Siegelmann, University of Massachusetts Amherst and Lilianne Mujica-Parodi, SUNY Stony Brook
Title:   Individual variability in human brain connectivity, modeled using multi-scale dynamics under energy constraint
BRAIN Category: Individuality and Variation

Clinical neuroscience currently lacks the tools for probing how biological constraints imposed upon synapses impact functional connectivity patterns.  Our long-range goal is to develop these tools, focusing first upon energy constraints across synaptic-hemodynamic scales.

Image from Lilianne R. Mujica-Parodi's ;ab websote

Image from Lilianne R. Mujica-Parodi’s ;ab websote

Abstract

Award Number: #1533693

Recent years have witnessed an explosion of interest in human brain connectivity and its relationship to brain-based disease. Functional connectivity analyses in neuroimaging have taken three general forms: cross-correlations, weighting of directed connections, and graph-theoretic approaches. Graph-theoretic measures, in particular, provide valuable insights into network features at the time of imaging. Yet, they cannot identify how the brain came to have those features, nor can they inform estimation of future network evolution. Neurological and psychiatric illnesses tend to have degenerative or oscillatory time-courses that range over decades; thus, network evolution will be critical to understanding why two individuals with the same diagnosis show markedly distinct developmental onsets and prognoses.

At the most fundamental level, clinical neuroscience currently lacks the tools for probing how biological constraints imposed upon synapses impact functional connectivity patterns. These constraints include, among others: limited energy resources as per the aggregate energy conversion rate of a finite number of mitochondria, the need to balance excitatory and inhibitory neurotransmitters in order to maintain homeostasis, and neural repair mechanisms (e.g., inflammation, MMP-9). Our long-range goal is to develop these tools, focusing first upon energy constraints across synaptic-hemodynamic scales, for three strategic reasons. 1) Glycemic load is implicated in many neurological diseases, including epilepsy, brain cancer, and dementia. 2) Energy utilization is easy to manipulate experimentally through diet, and to quantify via CO-2 monitoring, with protocols that permit translation to/from animal models for multi-scale modeling. 3) Recent findings link neural connectivity to metabolic expenditure. In the short-term, we focus upon establishing feasibility for three critical principles in preparation for the proposed work. First, we will conduct a pilot neuroimaging study (36 scans; N=12, under three conditions) to establish that our proposed experimental manipulation of energy supply and demand provokes reorganization of brain networks. Second, we aim to bridge scales: to demonstrate how agent-based simulations of point-neurons can incorporate network structure imposed at the level of human neuroimaging, and evolve as a function of changing inputs (energy supply, demand). Third, we propose to develop/adapt methods required to mathematically characterize dynamic networks for both fMRI data and simulations. This fundamental work will position us to conduct future research on modeling of metabolic processes as a function of synapses, glia, and mitochondria, and to use these simulations to predict individual variability of fMRI results as a function of neural energy consumption.

NSF Project Information

Award Number: 1533693

NSF webpage:  nsf.gov/awardsearch/showAward?AWD_ID=1533693&HistoricalAwards

NSF Org: ECCS  Div Of Electrical, Commun & Cyber Sys

Start Date:  August 1, 2015     End Date: July 31, 2017(Estimated)

Awarded Amount to Date: $149,864.00

Investigator(s): Hava Siegelmann hava@cs.umass.edu (Principal Investigator)

NSF Program(s): BIOMEDICAL ENGINEERING, IntgStrat Undst Neurl&Cogn Sys

Program Reference Code(s): 8089, 8091, 8551, 7298

Sponsor: University of Massachusetts Amherst
Research Administration Building
AMHERST, MA 01003-9242 (413)545-0698

NSF Project Information

Award Number: 1533257

NSF webpage:  nsf.gov/awardsearch/showAward?AWD_ID=1533693&HistoricalAwards

NSF Org: ECCS  Div Of Electrical, Commun & Cyber Sys

Start Date: August 1, 2015     End Date: July 31, 2017(Estimated)

Awarded Amount to Date: $150,000.00

Investigator(s): Lilianne Mujica-Parodi lmujicaparodi@gmail.com (Principal Investigator)

NSF Program(s): BIOMEDICAL ENGINEERING, IntgStrat Undst Neurl&Cogn Sys

Program Reference Code(s): 8089, 8091, 8551, 7298

Sponsor: SUNY at Stony Brook
WEST 5510 FRK MEL LIB
STONY BROOK, NY 11794-3362 (631)632-9949

 

 

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