Evidence map›Paper›PMID 33310630›Full record

ArticleNeuroImage. Clinical2021

Classification of evoked responses to inverted faces reveals both spatial and temporal cortical response abnormalities in Autism spectrum disorder.

Adonay S Nunes, Fahimeh Mamashli, Nataliia Kozhemiako, Sheraz Khan, Nicole M McGuiggan, Ainsley Losh, Robert M Joseph, Jyrki Ahveninen, Sam M Doesburg, Matti S Hämäläinen and 1 more

Open access · goldAbstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in NeuroImage. Clinical, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
0.1field-weighted citation impact, top 55% 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

2 citing papers in PubMed, 3 citations in OpenAlex.

  1. Review
  2. 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

11 authors at 2 institutions in 3 countries.

Adonay S NunesDepartment of Neurology, MGH, Harvard Medical School, Boston, MA, USA; Department of Biomedical Physiology and Kinesiology, Simon Fraser University, Vancouver, British Columbia, Canada.
Fahimeh MamashliDepartment of Radiology, MGH, Harvard Medical School, Boston, MA, USA; Athinoula A. Martinos Center for Biomedical Imaging, MGH/HST, Charlestown, MA, USA.
Nataliia KozhemiakoDepartment of Neurology, MGH, Harvard Medical School, Boston, MA, USA; Department of Biomedical Physiology and Kinesiology, Simon Fraser University, Vancouver, British Columbia, Canada.
Sheraz KhanDepartment of Radiology, MGH, Harvard Medical School, Boston, MA, USA; Athinoula A. Martinos Center for Biomedical Imaging, MGH/HST, Charlestown, MA, USA.
Nicole M McGuigganDepartment of Neurology, MGH, Harvard Medical School, Boston, MA, USA; Athinoula A. Martinos Center for Biomedical Imaging, MGH/HST, Charlestown, MA, USA.
Ainsley LoshDepartment of Neurology, MGH, Harvard Medical School, Boston, MA, USA.
Robert M JosephBoston University School of Medicine, Boston, MA, USA.
Jyrki AhveninenDepartment of Radiology, MGH, Harvard Medical School, Boston, MA, USA; Athinoula A. Martinos Center for Biomedical Imaging, MGH/HST, Charlestown, MA, USA.
Sam M DoesburgDepartment of Biomedical Physiology and Kinesiology, Simon Fraser University, Vancouver, British Columbia, Canada; Behavioural and Cognitive Neuroscience Institute, Simon Fraser University, Vancouver, British Columbia, Canada.
Matti S HämäläinenDepartment of Radiology, MGH, Harvard Medical School, Boston, MA, USA; Athinoula A. Martinos Center for Biomedical Imaging, MGH/HST, Charlestown, MA, USA; Department of Neuroscience and Biomedical Engineering, Aalto University School of Science, Espoo, Finland.
Tal KenetDepartment of Neurology, MGH, Harvard Medical School, Boston, MA, USA; Athinoula A. Martinos Center for Biomedical Imaging, MGH/HST, Charlestown, MA, USA. Electronic address: tal@nmr.mgh.harvard.edu.
Harvard University · USSimon Fraser University · CA

Funding

Scalable Software for Distributed Processing and Visualization of Multi-Site MEG/EEG DatasetsR01NS104585 · NINDS · MASSACHUSETTS GENERAL HOSPITAL · PI MATTI HAMALAINEN · 2018 to 2026
$4.3M
Testing the Bottom-Up vs Top-Down Imbalance Hypothesis of ASDR01MH117998 · NIMH · MASSACHUSETTS GENERAL HOSPITAL · PI KENET, TAL · 2018 to 2023
$4.0M
Functional connectivity substrates of social and non-social deficits in ASDR01HD073254 · NICHD · MASSACHUSETTS GENERAL HOSPITAL · PI KENET, TAL · 2013 to 2017
$3.4M
Decoding parametric attributes of auditory working memories from human brain activityR01DC016915 · NIDCD · MASSACHUSETTS GENERAL HOSPITAL · PI AHVENINEN, JYRKI · 2018 to 2022
$3.0M
Non-invasive measures of multisensory cortical feedforward and feedback influencesR01DC016765 · NIDCD · MASSACHUSETTS GENERAL HOSPITAL · PI AHLFORS, SEPPO PENTTI, AHVENINEN, JYRKI · 2018 to 2022
$2.7M
Translating MEG-based biomarkers to EEG-based outcome measures for Autism Spectrum DisordersR21MH116517 · NIMH · MASSACHUSETTS GENERAL HOSPITAL · PI KENET, TAL · 2018 to 2019
$463k
CIHR MOP-136935NICHD NIH HHS R01 HD073254NIDCD NIH HHS R01 DC016765NIDCD NIH HHS R01 DC016915NIMH NIH HHS R01 MH117998NIMH NIH HHS R21 MH116517NINDS NIH HHS R01 NS104585
6 · The paper itself

Abstract

The neurophysiology of face processing has been studied extensively in the context of social impairments associated with autism spectrum disorder (ASD), but the existing studies have concentrated mainly on univariate analyses of responses to upright faces, and, less frequently, inverted faces. The small number of existing studies on neurophysiological responses to inverted face in ASD have used univariate approaches, with divergent results. Here, we used a data-driven, classification-based, multivariate machine learning decoding approach to investigate the temporal and spatial properties of the neurophysiological evoked response for upright and inverted faces, relative to the neurophysiological evoked response for houses, a neutral stimulus. 21 (2 females) ASD and 29 (4 females) TD participants ages 7 to 19 took part in this study. Group level classification accuracies were obtained for each condition, using first the temporal domain of the evoked responses, and then the spatial distribution of the evoked responses on the cortical surface, each separately. We found that classification of responses to inverted neutral faces vs. houses was less accurate in ASD compared to TD, in both the temporal and spatial domains. In contrast, there were no group differences in the classification of evoked responses to upright neutral faces relative to houses. Using the classification in the temporal domain, lower decoding accuracies in ASD were found around 120 ms and 170 ms, corresponding the known components of the evoked responses to faces. Using the classification in the spatial domain, lower decoding accuracies in ASD were found in the right superior marginal gyrus (SMG), intra-parietal sulcus (IPS) and posterior superior temporal sulcus (pSTS), but not in core face processing areas. Importantly, individual classification accuracies from both the temporal and spatial classifiers correlated with ASD severity, confirming the relevance of the results to the ASD phenotype.

Indexed as

Autism Spectrum DisorderFacial RecognitionAdolescentAdultChildFemaleHumansTemporal LobeYoung AdultAutismFacesInverted facesMachine learningMagenetoencephalography

Identifiers

PMID33310630
PMCPMC7734307
OpenAlexW3109385122

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
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.