Evidence map›Paper›PMID 35347570›Full record

ReviewNeuroinformatics2022

How Machine Learning is Powering Neuroimaging to Improve Brain Health.

Nalini M Singh, Jordan B Harrod, Sandya Subramanian, Mitchell Robinson, Ken Chang, Suheyla Cetin-Karayumak, Adrian Vasile Dalca, Simon Eickhoff, Michael Fox, Loraine Franke and 11 more

Abstract readReview
PubMed Full text at PMC Publisher
In one paragraph

Review in Neuroinformatics, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 26 papers, 4 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
26citing papers in PubMed, 4 pooled it
–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

26 citing papers in PubMed, 4 syntheses or guidelines pooled it.

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  17. Distributed Harmonization: Federated Clustered Batch Effect Adjustment and Generalization.KDD : proceedings. International Conference on Knowledge Discovery & Data Mining · 2024
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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

21 authors.

Nalini M SinghHarvard-MIT Health Sciences and Technology, Massachusetts Institute of Technology, Cambridge, MA, 02139, USA.
Jordan B HarrodHarvard-MIT Health Sciences and Technology, Massachusetts Institute of Technology, Cambridge, MA, 02139, USA.
Sandya SubramanianHarvard-MIT Health Sciences and Technology, Massachusetts Institute of Technology, Cambridge, MA, 02139, USA.
Mitchell RobinsonHarvard-MIT Health Sciences and Technology, Massachusetts Institute of Technology, Cambridge, MA, 02139, USA.
Ken ChangHarvard-MIT Health Sciences and Technology, Massachusetts Institute of Technology, Cambridge, MA, 02139, USA.
Suheyla Cetin-KarayumakDepartment of Psychiatry, Brigham and Women's Hospital and Harvard Medical School, Boston, 02115, USA.
Adrian Vasile DalcaMartinos, Radiology, MGH, MIT, HMS & EECS, Cambridge, 02114, USA.
Simon EickhoffInstitute of Systems Neuroscience, Medical Faculty, Heinrich Heine University Düsseldorf, Düsseldorf, Germany.
Michael FoxCenter for Brain Circuit Therapeutics, Department of Neurology, Psychiatry, and Radiology, Brigham and Women's Hospital and Harvard Medical School, 02115, Boston, USA.
Loraine FrankeUniversity of Massachusetts Boston, Boston, MA, 02125, USA.
Polina GollandComputer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology, Cambridge, MA, 02139, USA.
Daniel HaehnUniversity of Massachusetts Boston, Boston, MA, 02125, USA.
Juan Eugenio IglesiasCentre for Medical Image Computing, University College London, London, UK.
Lauren J O'DonnellDepartment of Radiology, Brigham and Women's Hospital and Harvard Medical School, MA, 02115, Boston, USA.
Yangming OuDepartment of Radiology, Boston Children's Hospital, Harvard Medical School, Boston, MA, 02115, USA.
Yogesh RathiDepartment of Psychiatry, Brigham and Women's Hospital and Harvard Medical School, Boston, 02115, USA.
Shan H SiddiqiDepartment of Psychiatry, Brigham and Women's Hospital and Harvard Medical School, Boston, 02115, USA.
Haoqi SunDepartment of Neurology and McCance Center for Brain Health / Harvard Medical School, Massachusetts General Hospital, Boston, 02114, USA.
M Brandon WestoverDepartment of Neurology and McCance Center for Brain Health / Harvard Medical School, Massachusetts General Hospital, Boston, 02114, USA.
Susan Whitfield-GabrieliDepartment of Psychology, Northeastern University, Boston, 02115, USA.
Randy L GollubDepartment of Psychiatry and Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Boston, MA, 02114, USA. rgollub@partners.org.ORCID 0000-0002-9434-4044

Funding

Investigation of Sleep in the Intensive Care Unit (ICU-SLEEP)R01NS102190 · NINDS · MASSACHUSETTS GENERAL HOSPITAL · PI WESTOVER, MICHAEL BRANDON · 2018 to 2022
$3.2M
Big Data and Deep Learning for the Interictal-Ictal-Injury ContiuumR01NS107291 · NINDS · MASSACHUSETTS GENERAL HOSPITAL · PI WESTOVER, MICHAEL BRANDON · 2018 to 2022
$2.8M
Personalized circuit-based neuromodulation targets for depressionK23MH121657 · NIMH · BRIGHAM AND WOMEN'S HOSPITAL · PI SIDDIQI, SHAN · 2020 to 2024
$978k
NIMH NIH HHS K23 MH121657NIMH NIH HHS L30 MH127717NINDS NIH HHS R01 NS102190NINDS NIH HHS R01 NS107291
6 · The paper itself

Abstract

This report presents an overview of how machine learning is rapidly advancing clinical translational imaging in ways that will aid in the early detection, prediction, and treatment of diseases that threaten brain health. Towards this goal, we aresharing the information presented at a symposium, "Neuroimaging Indicators of Brain Structure and Function - Closing the Gap Between Research and Clinical Application", co-hosted by the McCance Center for Brain Health at Mass General Hospital and the MIT HST Neuroimaging Training Program on February 12, 2021. The symposium focused on the potential for machine learning approaches, applied to increasingly large-scale neuroimaging datasets, to transform healthcare delivery and change the trajectory of brain health by addressing brain care earlier in the lifespan. While not exhaustive, this overview uniquely addresses many of the technical challenges from image formation, to analysis and visualization, to synthesis and incorporation into the clinical workflow. Some of the ethical challenges inherent to this work are also explored, as are some of the regulatory requirements for implementation. We seek to educate, motivate, and inspire graduate students, postdoctoral fellows, and early career investigators to contribute to a future where neuroimaging meaningfully contributes to the maintenance of brain health.

Indexed as

Machine LearningNeuroimagingBrainHumansMagnetic Resonance ImagingBrain healthClinical translational neuroimagingDeep learningEEGMachine learningMRIPETTranscranial magnetic stimulation

Identifiers

PMID35347570
PMCPMC9515245

What OpenQuestion holds

Textmetadata
LicenceCC BY
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.