Projects per year
Abstract
Brain structure and many brain functions are known to be genetically controlled, but direct links between neuroimaging measures and their underlying cellular‐level determinants remain largely undiscovered. Here, we adopt a novel computational method for examining potential similarities in high‐dimensional brain imaging data between siblings. We examine oscillatory brain activity measured with magnetoencephalography (MEG) in 201 healthy siblings and apply Bayesian reduced‐rank regression to extract a low‐dimensional representation of familial features in the participants' spectral power structure. Our results show that the structure of the overall spectral power at 1–90 Hz is a highly conspicuous feature that not only relates siblings to each other but also has very high consistency within participants' own data, irrespective of the exact experimental state of the participant. The analysis is extended by seeking genetic associations for low‐dimensional descriptions of the oscillatory brain activity. The observed variability in the MEG spectral power structure was associated with SDK1 (sidekick cell adhesion molecule 1) and suggestively with several other genes that function, for example, in brain development. The current results highlight the potential of sophisticated computational methods in combining molecular and neuroimaging levels for exploring brain functions, even for high‐dimensional data limited to a few hundred participants.
Original language | English |
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Pages (from-to) | 1391-1402 |
Number of pages | 12 |
Journal | Human Brain Mapping |
Volume | 40 |
Issue number | 5 |
Early online date | 2019 |
DOIs | |
Publication status | Published - 1 Apr 2019 |
MoE publication type | A1 Journal article-refereed |
Keywords
- Bayesian reduced-rank regression
- genome-wide association
- GWAS
- heritability
- magnetoencephalography
Fingerprint
Dive into the research topics of 'Discovering heritable modes of MEG spectral power'. Together they form a unique fingerprint.Projects
- 3 Finished
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Individual cortical markers of language function
01/09/2018 → 31/12/2022
Project: Academy of Finland: Other research funding
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Interactive machine learning from multiple biodata sources
01/01/2016 → 31/08/2021
Project: Academy of Finland: Other research funding
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Interactive machine learning from multiple biodata sources
Sundin, I., Kaski, S., Afrabandpey, H., Chen, Y. & Aushev, A.
01/01/2016 → 31/12/2018
Project: Academy of Finland: Other research funding
Equipment
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Aalto Neuroimaging Infrastructure
Veikko Jousmäki (Manager)
School of ScienceFacility/equipment: Facility
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