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Record W2157856429 · doi:10.3389/fnagi.2014.00149

Studying variability in human brain aging in a population-based German cohort—rationale and design of 1000BRAINS

2014· article· en· W2157856429 on OpenAlexaff
Svenja Caspers, Susanne Moebus, Silke Lux, Noreen Pundt, Holger Schütz, Thomas W. Mühleisen, Vincent Gras, Simon B. Eickhoff, Sandro Romanzetti, Tony Stöcker, Rüdiger Stirnberg, Mehmet Eylem Kirlangic, Martina Minnerop, Peter Pieperhoff, Ulrich Mödder, Samir Das, Alan C. Evans, Karl‐Heinz Jöckel, Raimund Erbel, Sven Cichon, Markus M. Nöthen, Dieter Sturma, Andreas Bauer, N. Jon Shah, Karl Zilles, Katrin Amunts

Bibliographic record

VenueFrontiers in Aging Neuroscience · 2014
Typearticle
Languageen
FieldEnvironmental Science
TopicHealth, Environment, Cognitive Aging
Canadian institutionsMcGill UniversityMontreal Neurological Institute and Hospital
FundersHeinz Nixdorf StiftungDeutsche Forschungsgemeinschaft
KeywordsGermanCohortPopulation ageingBrain agingPopulationNeurosciencePsychologyGerontologyMedicineEnvironmental healthGeographyCognitionInternal medicine

Abstract

fetched live from OpenAlex

The ongoing 1000 brains study (1000BRAINS) is an epidemiological and neuroscientific investigation of structural and functional variability in the human brain during aging. The two recruitment sources are the 10-year follow-up cohort of the German Heinz Nixdorf Recall (HNR) Study, and the HNR MultiGeneration Study cohort, which comprises spouses and offspring of HNR subjects. The HNR is a longitudinal epidemiological investigation of cardiovascular risk factors, with a comprehensive collection of clinical, laboratory, socioeconomic, and environmental data from population-based subjects aged 45-75 years on inclusion. HNR subjects underwent detailed assessments in 2000, 2006, and 2011, and completed annual postal questionnaires on health status. 1000BRAINS accesses these HNR data and applies a separate protocol comprising: neuropsychological tests of attention, memory, executive functions and language; examination of motor skills; ratings of personality, life quality, mood and daily activities; analysis of laboratory and genetic data; and state-of-the-art magnetic resonance imaging (MRI, 3 Tesla) of the brain. The latter includes (i) 3D-T1- and 3D-T2-weighted scans for structural analyses and myelin mapping; (ii) three diffusion imaging sequences optimized for diffusion tensor imaging, high-angular resolution diffusion imaging for detailed fiber tracking and for diffusion kurtosis imaging; (iii) resting-state and task-based functional MRI; and (iv) fluid-attenuated inversion recovery and MR angiography for the detection of vascular lesions and the mapping of white matter lesions. The unique design of 1000BRAINS allows: (i) comprehensive investigation of various influences including genetics, environment and health status on variability in brain structure and function during aging; and (ii) identification of the impact of selected influencing factors on specific cognitive subsystems and their anatomical correlates.

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)0.021
metaresearch head score (Gemma)0.017
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 score0.021
Threshold uncertainty score0.110

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0210.017
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0010.002
Bibliometrics0.0020.002
Science and technology studies0.0020.002
Scholarly communication0.0010.001
Open science0.0020.003
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.001

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 head0.022
GPT teacher head0.275
Teacher spread0.253 · 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".

Quick stats

Citations135
Published2014
Admission routes1
Has abstractyes

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