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Record W3006784539 · doi:10.1186/s12916-019-1488-1

Comparison of brain connectomes by MRI and genomics and its implication in Alzheimer’s disease

2020· article· en· W3006784539 on OpenAlexfundno aff
Young Jae Woo, Panos Roussos, Vahram Haroutunian, Pavel Katsel, Sam Gandy, Eric E. Schadt, Jun Zhu

Bibliographic record

VenueBMC Medicine · 2020
Typearticle
Languageen
FieldNeuroscience
TopicFunctional Brain Connectivity Studies
Canadian institutionsnot available
FundersNational Institute of Biomedical Imaging and BioengineeringCanadian Institutes of Health ResearchNational Institutes of HealthGenentechIXICOH. Lundbeck A/SServierEisaiNorthern California Institute for Research and EducationPfizerBiogenBioClinicaF. Hoffmann-La RocheNational Human Genome Research InstituteUniversity of Southern CaliforniaEli Lilly and CompanyU.S. Department of DefenseMeso Scale DiagnosticsAlzheimer's Disease Neuroimaging InitiativeNovartis Pharmaceuticals CorporationBristol-Myers SquibbNational Institute on AgingAlzheimer's AssociationFoundation for the National Institutes of Health
KeywordsConnectomeNeuroscienceWhite matterTranscriptomeNeuroimagingHuman Connectome ProjectGenomicsHuman brainMedicineConnectomicsBiologyGeneMagnetic resonance imagingGeneticsGenomeGene expressionFunctional connectivity

Abstract

fetched live from OpenAlex

Abstract Background The human brain is complex and interconnected structurally. Brain connectome change is associated with Alzheimer’s disease (AD) and other neurodegenerative diseases. Genetics and genomics studies have identified molecular changes in AD; however, the results are often limited to isolated brain regions and are difficult to interpret its findings in respect to brain connectome. The mechanisms of how one brain region impacts the molecular pathways in other regions have not been systematically studied. And how the brain regions susceptible to AD pathology interact with each other at the transcriptome level and how these interactions relate to brain connectome change are unclear. Methods Here, we compared structural brain connectomes defined by probabilistic tracts using diffusion magnetic resonance imaging data in Alzheimer’s Disease Neuroimaging Initiative database and a brain transcriptome dataset covering 17 brain regions. Results We observed that the changes in diffusion measures associated with AD diagnosis status and the associations were replicated in an independent cohort. The result suggests that disease associated white matter changes are focal. Analysis of the brain connectome by genomic data, tissue-tissue transcriptional synchronization between 17 brain regions, indicates that the regions connected by AD-associated tracts were likely connected at the transcriptome level with high number of tissue-to-tissue correlated (TTC) gene pairs (P = 0.03). And genes involved in TTC gene pairs between white matter tract connected brain regions were enriched in signaling pathways (P = 6.08 × 10−9). Further pathway interaction analysis identified ionotropic glutamate receptor pathway and Toll receptor signaling pathways to be important for tissue-tissue synchronization at the transcriptome level. Transcript profile entailing Toll receptor signaling in the blood was significantly associated with diffusion properties of white matter tracts, notable association between fractional anisotropy and bilateral cingulum angular bundles (Ppermutation = 1.0 × 10−2 and 4.9 × 10−4 for left and right respectively). Conclusions In summary, our study suggests that brain connectomes defined by MRI and transcriptome data overlap with each other.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0020.002
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.124
GPT teacher head0.355
Teacher spread0.231 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

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Citations13
Published2020
Admission routes1
Has abstractyes

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