Cited by 94Schizophrenia research and treatmentHearing Loss and RehabilitationNeural dynamics and brain function

Mismatch Negativity is a Sensitive and Predictive Biomarker of Perceptual Learning During Auditory Cognitive Training in Schizophrenia

Open access
Perez, Veronica B, Tarasenko, Melissa, Miyakoshi, Makoto, Pianka, Sean T, Makeig, Scott D, Braff, David L, Swerdlow, Neal R, Light, Gregory A·2017·Neuropsychopharmacology

Abstract

Computerized cognitive training is gaining empirical support for use in the treatment of schizophrenia (SZ). Although cognitive training is efficacious for SZ at a group level when delivered in sufficiently intensive doses (eg, 30-50 h), there is variability in individual patient response. The identification of biomarkers sensitive to the neural systems engaged by cognitive training interventions early in the course of treatment could facilitate personalized assignment to treatment. This proof-of-concept study was conducted to determine whether mismatch negativity (MMN), an event-related potential index of auditory sensory discrimination associated with cognitive and psychosocial functioning, would predict gains in auditory perceptual learning and exhibit malleability after initial exposure to the early stages of auditory cognitive training in SZ. MMN was assessed in N=28 SZ patients immediately before and after completing 1 h of a speeded time-order judgment task of two successive frequency-modulated sweeps (Posit Science 'Sound Sweeps' exercise). All SZ patients exhibited the expected improvements in auditory perceptual learning over the 1 h training period (p<0.001), consistent with previous results. Larger MMN amplitudes recorded both before and after the training exercises were associated with greater gains in auditory perceptual learning (r=-0.5 and r=-0.67, respectively, p's<0.01). Significant pretraining vs posttraining MMN amplitude reduction was also observed (p<0.02). MMN is a sensitive index of the neural systems engaged in a single session of auditory cognitive training in SZ. These findings encourage future trials of MMN as a biomarker for individual assignment, prediction, and/or monitoring of patient response to procognitive interventions, including auditory cognitive training in SZ.

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Integrity signals

Registry

Crossref · 10.1038/npp.2017.25

Cites

Statistics

Claims the paper makes

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Claims other papers make about this one

Each is checked against this paper’s own text: does it say what the citing paper says it does?

  • MMN is used as a biomarker for clinical trials.

    “In addition, MMN is used as a biomarker for clinical trials (53, 54).”

    From A Predictive Coding Perspective on Mismatch Negativity Impairment in Schizophrenia (2020) · cites this paper as [53]

    The Introduction describes MMN as a leading candidate biomarker suitable for repeated measurement in intervention studies. The Discussion reports its use in this proof-of-concept training study but proposes further trials; it does not establish that MMN is already used as a biomarker in clinical trials generally. Page numbers were not available in our copy. (LLM, conf=0.82)

    Experimental — accuracy not yet measured

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

The self-check needs the paper’s full text.

The citation lists haven’t been fetched yet

References 54 · Cited by 94

Authors
Perez, V. B., Tarasenko, M., Miyakoshi, M., Pianka, S. T., Makeig, S. D., Braff, D. L., Swerdlow, N. R., & Light, G. A.
Affiliations
  • Alliant University (US)
  • University of California San Diego (US)
Corresponding author
Light, Gregory A
Venue
Neuropsychopharmacology
Published
2017-01-31
Volume
42
Issue
11
Pages
2206-2213
ISSN
  • 0893-133X (ISSN-L)
  • 1740-634X
OpenAlex
W2583119594
Cited by
94
References
54
Citations by year
2026: 3 · 2025: 3 · 2024: 4 · 2023: 3 · 2022: 8 · 2021: 12 · 2020: 22 · 2019: 24 · 2018: 11 · 2017: 4
FWCI
13.68
Type
journal article
Language
en
Open access
Locations
  • Neuropsychopharmacology (publishedVersion)
  • Europe PMC (PubMed Central) (publishedVersion)
  • PubMed Central (publishedVersion)
  • PubMed (publishedVersion)
  • eScholarship (California Digital Library) (submittedVersion)
Funders
  • Brain and Behavior Research Foundation
  • Sidney R. Baer, Jr. Foundation
  • National Alliance for Research on Schizophrenia and Depression
  • Astellas Pharma
  • National Institute of Neurological Disorders and Stroke (R01NS047293)
  • NIH Clinical Center
Topics
  • Health Sciences › Medicine › Psychiatry and Mental health › Schizophrenia research and treatment
  • Life Sciences › Neuroscience › Cognitive Neuroscience › Hearing Loss and Rehabilitation
  • Life Sciences › Neuroscience › Cognitive Neuroscience › Neural dynamics and brain function
Keywords
mismatch negativity, schizophrenia, auditory perceptual learning, biomarkers, treatment response prediction, event-related potentials
MeSH terms
Acoustic Stimulation, Adult, Aged, Analysis of Variance, Auditory Perception, Contingent Negative Variation, Electroencephalography, Evoked Potentials, Auditory, Female, Humans, Male, Middle Aged, Psychiatric Status Rating Scales, Psychophysics, Schizophrenia, Schizophrenic Psychology, Cognitive Behavioral Therapy

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review