Evidence map›Paper›PMID 35069775›Full record

ArticleBiomedical signal processing and control2022

Reference-Free Adaptive Filtering of Extracellular Neural Signals Recording in Ultra-High Field Magnetic Resonance Imaging Scanners: Removal of Periodic Interferences.

Corey E Cruttenden, Jennifer M Taylor, Mahdi Ahmadi, Yi Zhang, Xiao-Hong Zhu, Wei Chen, Rajesh Rajamani

Abstract read
In one paragraph

Article in Biomedical signal processing and control, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
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

1 citing paper in PubMed.

  1. 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

7 authors.

Corey E CruttendenUniversity of Minnesota Department of Mechanical Engineering, 111 Church St. SE, Minneapolis, MN 55455.
Jennifer M TaylorUniversity of Minnesota Center for Magnetic Resonance Research, 2021 6 St. SE, Minneapolis, MN 55455.
Mahdi AhmadiUniversity of Minnesota Department of Mechanical Engineering, 111 Church St. SE, Minneapolis, MN 55455.
Yi ZhangUniversity of Minnesota Center for Magnetic Resonance Research, 2021 6 St. SE, Minneapolis, MN 55455.
Xiao-Hong ZhuUniversity of Minnesota Center for Magnetic Resonance Research, 2021 6 St. SE, Minneapolis, MN 55455.
Wei ChenUniversity of Minnesota Center for Magnetic Resonance Research, 2021 6 St. SE, Minneapolis, MN 55455.
Rajesh RajamaniUniversity of Minnesota Department of Mechanical Engineering, 111 Church St. SE, Minneapolis, MN 55455.

Funding

TRD4 - Ultrahigh Field Engineering and SafetyP41EB027061 · NIBIB · UNIVERSITY OF MINNESOTA · PI Gregory John Metzger · 2019 to 2026
$11.7M
Integrated fMRI Methods to Study Neurophysiology and Circuit Dynamics at Laminar and Columnar LevelR01MH111413 · NIMH · UNIVERSITY OF MINNESOTA · PI CHEN, WEI · 2016 to 2020
$4.4M
Institutional Center Cores for Advanced NeuroimagingP30NS076408 · NINDS · UNIVERSITY OF MINNESOTA · PI UGURBIL, KAMIL · 2012 to 2020
$4.2M
Ultra High Field (16.4 Tesla) Animal MRI/MRS SystemS10RR025031 · NCRR · UNIVERSITY OF MINNESOTA · PI GARWOOD, MICHAEL · 2008 to 2008
$2.0M
NCRR NIH HHS S10 RR025031NIBIB NIH HHS P41 EB027061NIMH NIH HHS R01 MH111413NINDS NIH HHS P30 NS076408
6 · The paper itself

Abstract

This paper focuses on the removal of periodic artifacts from neural signals recorded in rats in ultra-high field (UHF) MRI scanners, using a reference free adaptive feedforward method. Recording extracellular neural signals in the UHF environment is motivated by the desire to combine neural recording and UHF functional magnetic resonance imaging (fMRI) to better understand brain function. However, the neural signals are found to have extremely high noise artifacts of a periodic nature due to electromagnetic interference and due to small oscillatory motions. In particular, noise at 60 Hz and several harmonics of 60 Hz, sinusoidal noise from a pump, and low frequency breathing motion artifacts are observed. Due to significant overlap between the noise frequencies and the neural frequency region of interest, band pass filters cannot be effectively utilized in this application. Hence, this paper develops adaptive least squares feedforward cancellation filters to remove the periodic artifacts. The interference fundamental frequency is identified precisely using an implementation of k-means in an iterative approach. The paper includes significant animal data from rats recorded in an IACUC-approved procedure in 9.4T and 16.4T MRI machines. For breathing artifacts filtered from 4 rats, the mean signal cancellation values at the harmonic interference frequencies are 5.18, 12.97, and 20.87 dB/Hz for a sliding template subtraction, a single-stage impulse reference method, and the cascaded adaptive filtering approach respectively. For pump artifacts filtered from 2 chronically implanted rats, mean signal cancellation values are 2.85, 9.52 and 12.06 dB/Hz respectively. The experimental results show that periodic noise is very effectively removed by the developed cascaded adaptive least squares feedforward algorithm.

Indexed as

Adaptive filtersartifact removalbreathing artifactsextracellular neural signalsmotion artifactsMRI artifacts

Identifiers

PMID35069775
PMCPMC8782249

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Registered trials

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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.