Evidence map›Paper›PMID 29526836›Full record

ArticleJournal of neural engineering2018

Beamspace dual signal space projection (bDSSP): a method for selective detection of deep sources in MEG measurements.

Kensuke Sekihara, Yoshiaki Adachi, Hiroshi K Kubota, Chang Cai, Srikantan S Nagarajan

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Journal of neural engineering, 2018. 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

5 authors.

Kensuke SekiharaSignal Analysis Inc., Hachioji, Tokyo, Japan.ORCID 0000-0002-2048-3797
Yoshiaki AdachiApplied Electronics Laboratory, Kanazawa Institute of Technology, Kanazawa, Japan.
Hiroshi K KubotaHealthcare Business Center, Business Development Division, Ricoh Ltd., Tokyo, Japan.
Chang CaiBiomagnetic Imaging Laboratory, University of California, San Francisco, 513 Parnassus Avenue, S362, San Francisco, CA 94143, United States of America.
Srikantan S NagarajanBiomagnetic Imaging Laboratory, University of California, San Francisco, 513 Parnassus Avenue, S362, San Francisco, CA 94143, United States of America.

Funding

Dynamic Brain Imaging of Speech in Primary Progressive AphasiaR01NS100440 · NINDS · UNIVERSITY OF CALIFORNIA, SAN FRANCISCO · PI MARIA LUISA GORNO TEMPINI, John Francis Houde · 2017 to 2026
$3.6M
Imaging sensorimotor adaptation and compensation of speechR01DC013979 · NIDCD · UNIVERSITY OF CALIFORNIA, SAN FRANCISCO · PI HOUDE, JOHN FRANCIS, NAGARAJAN, SRIKANTAN S. · 2014 to 2018
$1.7M
Multimodal modeling framework for fusing structural and functional connectome dataR01EB022717 · NIBIB · WEILL MEDICAL COLL OF CORNELL UNIV · PI NAGARAJAN, SRIKANTAN S., RAJ, ASHISH · 2016 to 2018
$1.6M
Role of auditory feedback in speech motor control in Alzheimer's diseaseR01DC017696 · NIDCD · UNIVERSITY OF CALIFORNIA, SAN FRANCISCO · PI HOUDE, JOHN FRANCIS, NAGARAJAN, SRIKANTAN S. · 2018 to 2020
$1.5M
Timing of Neural activity in Spasmodic DysphoniaR21DC014525 · NIDCD · UNIVERSITY OF CALIFORNIA, SAN FRANCISCO · PI COUREY, MARK S., HOUDE, JOHN FRANCIS · 2015 to 2016
$436k
Fusion of Electromagnetic Brain Imaging and fMRIR21NS076171 · NINDS · UNIVERSITY OF CALIFORNIA, SAN FRANCISCO · PI NAGARAJAN, SRIKANTAN S. · 2011 to 2012
$425k
NIBIB NIH HHS R01 EB022717NIDCD NIH HHS R01 DC013979NIDCD NIH HHS R01 DC017696NIDCD NIH HHS R21 DC014525NINDS NIH HHS R01 NS100440NINDS NIH HHS R21 NS076171
6 · The paper itself

Abstract

objectiveMagnetoencephalography (MEG) has a well-recognized weakness at detecting deeper brain activities. This paper proposes a novel algorithm for selective detection of deep sources by suppressing interference signals from superficial sources in MEG measurements. APPROACH: The proposed algorithm combines the beamspace preprocessing method with the dual signal space projection (DSSP) interference suppression method. A prerequisite of the proposed algorithm is prior knowledge of the location of the deep sources. The proposed algorithm first derives the basis vectors that span a local region just covering the locations of the deep sources. It then estimates the time-domain signal subspace of the superficial sources by using the projector composed of these basis vectors. Signals from the deep sources are extracted by projecting the row space of the data matrix onto the direction orthogonal to the signal subspace of the superficial sources. MAIN

resultsCompared with the previously proposed beamspace signal space separation (SSS) method, the proposed algorithm is capable of suppressing much stronger interference from superficial sources. This capability is demonstrated in our computer simulation as well as experiments using phantom data. SIGNIFICANCE: The proposed bDSSP algorithm can be a powerful tool in studies of physiological functions of midbrain and deep brain structures.

Indexed as

AlgorithmsComputer SimulationModels, NeurologicalSignal Processing, Computer-AssistedBrainHumansMagnetoencephalography

Identifiers

PMID29526836
PMCPMC6287971

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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.