Evidence map›Paper›PMID 32425745›Full record

ArticleFrontiers in neuroscience2020

Correcting Task fMRI Signals for Variability in Baseline CBF Improves BOLD-Behavior Relationships: A Feasibility Study in an Aging Model.

Venkatagiri Krishnamurthy, Lisa C Krishnamurthy, Jonathan H Drucker, Suprateek Kundu, Bing Ji, Kyle Hortman, Simone R Roberts, Kevin Mammino, Stella M Tran, Kaundinya Gopinath and 5 more

Open access · goldAbstract read
In one paragraph

Article in Frontiers in neuroscience, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
14citing papers in PubMed, 1 pooled it
1.3field-weighted citation impact, top 17% 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

14 citing papers in PubMed, 1 synthesis or guideline pooled it, 20 citations in OpenAlex.

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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

15 authors at 3 institutions in 1 country.

Venkatagiri KrishnamurthyDepartment of Neurology, Emory University, Atlanta, GA, United States.
Lisa C KrishnamurthyCenter for Visual and Neurocognitive Rehabilitation, Atlanta Veterans Affairs Medical Center (VAMC), Decatur, GA, United States.
Jonathan H DruckerDepartment of Neurology, Emory University, Atlanta, GA, United States.
Suprateek KunduDepartment of Biostatistics and Bioinformatics, Emory University, Atlanta, GA, United States.
Bing JiDepartment of Neurology, Emory University, Atlanta, GA, United States.
Kyle HortmanCenter for Visual and Neurocognitive Rehabilitation, Atlanta Veterans Affairs Medical Center (VAMC), Decatur, GA, United States.
Simone R RobertsDepartment of Neurology, Emory University, Atlanta, GA, United States.
Kevin MamminoCenter for Visual and Neurocognitive Rehabilitation, Atlanta Veterans Affairs Medical Center (VAMC), Decatur, GA, United States.
Stella M TranDepartment of Psychology, Georgia State University, Atlanta, GA, United States.
Kaundinya GopinathDepartment of Radiology and Imaging Sciences, Emory University, Atlanta, GA, United States.
Keith M McGregorDepartment of Neurology, Emory University, Atlanta, GA, United States.
Amy D RodriguezDepartment of Neurology, Emory University, Atlanta, GA, United States.
Deqiang QiuDepartment of Radiology and Imaging Sciences, Emory University, Atlanta, GA, United States.
Bruce CrossonDepartment of Neurology, Emory University, Atlanta, GA, United States.
Joe R NoceraDepartment of Neurology, Emory University, Atlanta, GA, United States.
Atlanta VA Health Care System · USEmory University · USGeorgia State University · US

Funding

Beyond lesion-language mapping in aphasia: A novel imaging-based prediction modelIK1RX002629 · VA · VETERANS HEALTH ADMINISTRATION · PI KRISHNAMURTHY, LISA C. · 2019 to 2021
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Multimodal Neuroimaging: Advanced Tracking of Longitudinal Aphasia RecoveryIK2RX002934 · VA · VETERANS HEALTH ADMINISTRATION · PI KRISHNAMURTHY, VENKATAGIRI · 2019 to 2025
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RRD VA I01 RX002619RRD VA I01 RX002825RRD VA I50 RX002358RRD VA IK1 RX002629RRD VA IK2 RX002934
6 · The paper itself

Abstract

Blood Oxygen Level Dependent (BOLD) functional MRI is a complex neurovascular signal whose magnitude depends on baseline physiological factors such as cerebral blood flow (CBF). Because baseline CBF varies across the brain and is altered with aging, the interpretation of stand-alone aging-related BOLD changes can be misleading. The primary objective of this study was to develop a methodology that combines task fMRI and arterial spin labeling (ASL) techniques to sensitize task-induced BOLD activity by covarying out the baseline physiology (i.e., CBF) in an aging model. We recruited 11 younger and 13 older healthy participants who underwent ASL and an overt language fMRI task (semantic category member generation). We measured in-scanner language performance to investigate the effect of BOLD sensitization on BOLD-behavior relationships. The results demonstrate that our correction approach is effective at enhancing the specificity and sensitivity of the BOLD signal in both groups. In addition, the correction strengthens the statistical association between task BOLD activity and behavioral performance. Although CBF has inherent age dependence, our results show that retaining the age factor within CBF aides in greater sensitization of task fMRI signals. From a cognitive standpoint, compared to young adults, the older participants showed a delayed domain-general language-related task activity possibly due to compromised vessel compliance. Further, assessment of functional evolution of corrected BOLD activity revealed biphasic BOLD dynamics in both groups where BOLD deactivation may reflect greater semantic demand or increased premium on domain general executive functioning in response to task difficulty. Although it was promising to note that the predictability of behavior using the proposed methodology outperforms other methodologies (i.e., no correction and normalization by division), and provides moderate stability and adequate power, further work with a larger cohort and other task designs is necessary to improve the stability of predicting associated behavior. In summary, we recommend correction of task fMRI signals by covarying out baseline CBF especially when comparing groups with different neurovascular properties. Given that ASL and BOLD fMRI are well established and widely employed techniques, our proposed multi-modal methodology can be readily implemented into data processing pipelines to obtain more accurate BOLD activation maps.

Indexed as

agingBOLD deactivationcerebral blood flowdomain-generallanguage fMRIsemantic fluencysensitization

Identifiers

PMID32425745
PMCPMC7205008
OpenAlexW3023597615

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.