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Record W2050854380 · doi:10.1503/jpn.100138

Can Asperger syndrome be distinguished from autism? An anatomic likelihood meta-analysis of MRI studies

2011· review· en· W2050854380 on OpenAlexvenueno aff
Kevin Yu, Charlton Cheung, Siew E. Chua, Gráinne McAlonan

Bibliographic record

VenueJournal of Psychiatry and Neuroscience · 2011
Typereview
Languageen
FieldNeuroscience
TopicAutism Spectrum Disorder Research
Canadian institutionsnot available
Fundersnot available
KeywordsAutismAsperger syndromeMeta-analysisPsychologyNeuroscienceClinical psychologyMedicineDevelopmental psychologyPathology

Abstract

fetched live from OpenAlex

BACKGROUND: The question of whether Asperger syndrome can be distinguished from autism has attracted much debate and may even incur delay in diagnosis and intervention. Accordingly, there has been a proposal for Asperger syndrome to be subsumed under autism in the forthcoming Diagnostic and Statistical Manual of Mental Disorders, fifth edition, in 2013. One approach to resolve this question has been to adopt the criterion of absence of clinically significant language or cognitive delay--essentially, the "absence of language delay." To our knowledge, this is the first meta-analysis of magnetic resonance imaging (MRI) studies of people with autism to compare absence with presence of language delay. It capitalizes on the voxel-based morphometry (VBM) approach to systematically explore the whole brain for anatomic correlates of delay and no delay in language acquisition in people with autism spectrum disorders. METHODS: We conducted a systematic search for VBM MRI studies of grey matter volume in people with autism. Studies with a majority (at least 70%) of participants with autism diagnoses and a history of language delay were assigned to the autism group (n = 151, control n = 190). Those with a majority (at least 70%) of individuals with autism diagnoses and no language delay were assigned to the Asperger syndrome group (n = 149, control n = 214). We entered study coordinates into anatomic likelihood estimation meta-analysis software with sampling size weighting to compare grey matter summary maps driven by Asperger syndrome or autism. RESULTS: The summary autism grey matter map showed lower volumes in the cerebellum, right uncus, dorsal hippocampus and middle temporal gyrus compared with controls; grey matter volumes were greater in the bilateral caudate, prefrontal lobe and ventral temporal lobe. The summary Asperger syndrome map indicated lower grey matter volumes in the bilateral amygdala/hippocampal gyrus and prefrontal lobe, left occipital gyrus, right cerebellum, putamen and precuneus compared with controls; grey matter volumes were greater in more limited regions, including the bilateral inferior parietal lobule and the left fusiform gyrus. Both Asperger syndrome and autism studies reported volume increase in clusters in the ventral temporal lobe of the left hemisphere. LIMITATIONS: We assigned studies to autism and Asperger syndrome groups for separate analyses of the data and did not carry out a direct statistical group comparison. In addition, studies available for analysis did not capture the entire spectrum, therefore we cannot be certain that our findings apply to a wider population than that sampled. CONCLUSION: Whereas grey matter differences in people with Asperger syndrome compared with controls are sparser than those reported in studies of people with autism, the distribution and direction of differences in each category are distinctive.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Meta-analysis · Consensus signal: Meta-analysis
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.423
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0050.003
Bibliometrics0.0020.004
Science and technology studies0.0000.001
Scholarly communication0.0000.001
Open science0.0020.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.193
GPT teacher head0.404
Teacher spread0.211 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designMeta-analysis
Domainnot available
GenreReview

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations108
Published2011
Admission routes1
Has abstractyes

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