Evidence map›Paper›PMID 26736751›Full record

ArticleAnnual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference2015

A 3-D admittance-level computational model of a rat hippocampus for improving prosthetic design.

Andrew Gilbert, Kyle Loizos, Anil Kumar RamRakhyani, Phillip Hendrickson, Gianluca Lazzi, Theodore W Berger

Open access · greenAbstract read
In one paragraph

Article in Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
0.1field-weighted citation impact, top 50% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

2 citing papers in PubMed, 3 citations in OpenAlex.

  1. Article
  2. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

6 authors at 2 institutions in 1 country.

Andrew Gilbert
Kyle Loizos
Anil Kumar RamRakhyani
Phillip Hendrickson
Gianluca Lazzi
Theodore W Berger
University of Utah · USUniversity of Southern California · US

Funding

Predictive modeling of bioelectric activity on mammalian multilayered neuronal stU01GM104604 · NIGMS · UNIVERSITY OF UTAH · PI BERGER, THEODORE W., LAZZI, GIANLUCA · 2012 to 2016
$2.8M
NIGMS NIH HHS 1U01GM104604-01NIGMS NIH HHS U01 GM104604
6 · The paper itself

Abstract

Hippocampal prosthetic devices have been developed to bridge the gap between functioning portions of the hippocampus, in order to restore lost memory functionality in those suffering from brain injury or diseases. One approach taken in recent neuroprosthetic design is to use a multi-input, multi-output device that reads data from the CA3 in the hippocampus and electrically stimulates the CA1 in an attempt to mimic the appropriate firing pattern that would occur naturally between the two areas. However, further study needs to be conducted in order to optimize electrode placement, pulse magnitude, and shape for creating the appropriate firing pattern. This paper describes the creation and implementation of an anatomically correct 3D model of the hippocampus to simulate the electric field patterns and axonal activation from electrical stimulation due to an implanted electrode array. The activating function was applied to the voltage results to determine the firing patterns in possible axon locations within the CA1.

Indexed as

HippocampusImaging, Three-DimensionalModels, NeurologicalAnimalsElectric StimulationElectrodes, ImplantedProsthesis DesignRats

Identifiers

PMID26736751
PMCPMC4877026
OpenAlexW2293689016

What OpenQuestion holds

Textmetadata
LicenceTDM
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.