Abstract
Functional interconnections between brain regions define the "connectome" which is of central interest for understanding human brain function. Resting-state functional magnetic resonance (rsfMRI) work has revealed changes in static connectivity related to age, sex, cognitive abilities and psychiatric symptoms, yet little is known how these factors may alter the information flow. The commonly used approach infers functional brain connectivity using stationary coefficients yielding static estimates of the undirected connection strength between brain regions. Dynamic graphical models (DGMs) are a multivariate model with dynamic coefficients reflecting directed temporal associations between nodes, and can yield novel insight into directed functional connectivity. Here, we leveraged this approach to test for associations between edge-wise estimates of direction flow across the functional connectome and age, sex, intellectual abilities and mental health. We applied DGM to investigate patterns of information flow in data from 984 individuals from the Human Connectome Project (HCP) and 10,249 individuals from the UK Biobank. Our analysis yielded patterns of directed connectivity in independent HCP and UK Biobank data similar to those previously reported, including that the cerebellum consistently receives information from other networks. We show robust associations between information flow and age and sex for several connections, with strongest effects of age observed in the sensorimotor network. Visual, auditory and sensorimotor nodes were also linked to mental health. Our findings support the use of DGM as a measure of directed connectivity in rsfMRI data and provide new insight into the shaping of the connectome during aging.
| Original language | English |
|---|---|
| Pages (from-to) | 4173-4186 |
| Number of pages | 14 |
| Journal | Human Brain Mapping |
| Volume | 41 |
| Issue number | 15 |
| DOIs | |
| Publication status | Published - 15 Oct 2020 |
Keywords
- INDEPENDENT COMPONENT ANALYSIS
- HUMAN CONNECTOME PROJECT
- RESTING-STATE FMRI
- QUALITY-CONTROL
- MOTOR CONTROL
- ROBUST
- SCHIZOPHRENIA
- PATTERNS
- OPTIMIZATION
- REGISTRATION
Access to Document
- 10.1002/hbm.25116Licence: CC BY-NC-ND
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In: Human Brain Mapping, Vol. 41, No. 15, 15.10.2020, p. 4173-4186.
Research output: Contribution to journal › Article › Academic › peer-review
TY - JOUR
T1 - Differences in directed functional brain connectivity related to age, sex and mental health
AU - Lund, Martina J.
AU - Alnaes, Dag
AU - Schwab, Simon
AU - van der Meer, Dennis
AU - Andreassen, Ole A.
AU - Westlye, Lars T.
AU - Kaufmann, Tobias
N1 - Funding Information: : The UK Biobank initiative is a large‐scale biobank prospective cohort established by the Medical Research Council and Wellcome Trust (Collins, 2012 ), and funded by the UK Medical Research Council, Wellcome Trust, Department of Health, British Heart Foundation, Diabetes UK, Northwest Regional Development Agency, Scottish Government, and Welsh Assembly Government (Sudlow et al., 2015 ). This population‐based study examines the influence of genetic and environmental factors and the occurrence of disease in participants included in the age range of 40–69 years old, recruited from 2006–2010, and were 45–80 years when they were scanned in the years thereafter (Sudlow et al., 2015 ). The study has recruited 500,000 subjects, where 100,000 are going to be included as an MRI subgroup (Miller et al., 2016 ). Further, participants filled out questionnaires about lifestyle, family, as well as medical history in addition to completing a variety of physical measures (Sudlow et al., 2015 ). In addition, a subset of participants filled in a mental health questionnaire (MHQ) online. All participants provided signed informed consent. UK Biobank was approved by the National Research Ethics Service North West (ref 11/NW/0382, [Health Research Authority, 2016 ]). UK Biobank Funding Information: We thank Tom Nichols for advice and input on this work. This research has been conducted using the UK Biobank Resource (access code 27412, https://www.ukbiobank.ac.uk/) and using data provided by the Human Connectome Project, WU-Minn Consortium (Principal Investigators: David Van Essen and Kamil Ugurbil; 1U54MH091657) funded by the 16 NIH Institutes and Centers that support the NIH Blueprint for Neuroscience Research; Research Council of Norway: #276082 LifespanHealth (T.K.), #223273 NORMENT (O.A.A.), #249795 #298646, #300768 (L.T.W.), #283798 SYNSCHIZ (O.A.A.) Norges Forskningsr?d 249795 LifespanHealth 276082 NORMENT 223273 SYNSCHIZ #283798 H2020 European Research Council: ERC StG #802998 BRAINMINT (L.T.W.) The South-East Norway Regional Health Authority: #(2019101) (L.T.W), #2019107 #2020086 (D.A.). Swiss National Science Foundation SNSF: #171598 (S.S.) and by the McDonnell Center for Systems Neuroscience at Washington University. This work was performed on the TSD (Tjeneste for Sensitive Data) facilities, owned by the University of Oslo, operated and developed by the TSD service group at the University of Oslo, IT-Department (USIT) (tsd-drift@usit.uio.no). Funding Information: : The HCP consortium is funded by the National Institutes of Health (NIH) led by Washington University, University of Minnesota, and Oxford University. HCP is undertaking a systematic effort to map macroscopic human brain circuits and their relationship to behavior in a large population of young healthy adults (Van Essen et al., 2013 ). HCP participants are drawn from a healthy population born in Missouri, where a proportion of the subjects included are adult twins and their nontwin siblings (Van Essen et al., 2013 ). The adult sample consists of 1,200 subjects. Exclusion criteria include having siblings with severe neurodevelopmental disorders, and documented neuropsychiatric or neurologic disorders. Furthermore, individuals with illnesses such as diabetes or high blood pressure and twins born prior to 34 weeks' gestation and nontwins born prior to 37 weeks' gestation were excluded (Van Essen et al., 2013 ). The participants went through an MRI protocol, in addition to extensive behavioral assessment outside the scanner, in the domains of cognitive, emotional, motor, and sensory functions (Van Essen et al., 2013 ). All participants provided signed informed consent. Washington University Institutional Review Board approved the study (Glasser et al., 2016 ). HCP Funding Information: We thank Tom Nichols for advice and input on this work. This research has been conducted using the UK Biobank Resource (access code 27412, https://www.ukbiobank.ac.uk/ ) and using data provided by the Human Connectome Project, WU‐Minn Consortium (Principal Investigators: David Van Essen and Kamil Ugurbil; 1U54MH091657) funded by the 16 NIH Institutes and Centers that support the NIH Blueprint for Neuroscience Research; Research Council of Norway: #276082 LifespanHealth (T.K.), #223273 NORMENT (O.A.A.), #249795 #298646, #300768 (L.T.W.), #283798 SYNSCHIZ (O.A.A.) Norges Forskningsråd 249795 LifespanHealth 276082 NORMENT 223273 SYNSCHIZ #283798 H2020 European Research Council: ERC StG #802998 BRAINMINT (L.T.W.) The South‐East Norway Regional Health Authority: #(2019101) (L.T.W), #2019107 #2020086 (D.A.). Swiss National Science Foundation SNSF: #171598 (S.S.) and by the McDonnell Center for Systems Neuroscience at Washington University. This work was performed on the TSD (Tjeneste for Sensitive Data) facilities, owned by the University of Oslo, operated and developed by the TSD service group at the University of Oslo, IT‐Department (USIT) (tsd‐drift@usit.uio.no). Funding Information: Research Council of Norway, Grant/Award Number: #276082; LifespanHealth, Grant/Award Number: #223273; NORMENT, Grant/Award Numbers: #249795, #298646, #300768; SYNSCHIZ, Grant/Award Number: #283798; H2020 European Research Council: ERC StG BRAINMINT, Grant/Award Number: #802998; The South‐East Norway Regional Health Authority, Grant/Award Numbers: #2019101, #2019107, #2020086; Swiss National Science Foundation, Grant/Award Number: #171598 Funding information Publisher Copyright: © 2020 The Authors. Human Brain Mapping published by Wiley Periodicals LLC.
PY - 2020/10/15
Y1 - 2020/10/15
N2 - Functional interconnections between brain regions define the "connectome" which is of central interest for understanding human brain function. Resting-state functional magnetic resonance (rsfMRI) work has revealed changes in static connectivity related to age, sex, cognitive abilities and psychiatric symptoms, yet little is known how these factors may alter the information flow. The commonly used approach infers functional brain connectivity using stationary coefficients yielding static estimates of the undirected connection strength between brain regions. Dynamic graphical models (DGMs) are a multivariate model with dynamic coefficients reflecting directed temporal associations between nodes, and can yield novel insight into directed functional connectivity. Here, we leveraged this approach to test for associations between edge-wise estimates of direction flow across the functional connectome and age, sex, intellectual abilities and mental health. We applied DGM to investigate patterns of information flow in data from 984 individuals from the Human Connectome Project (HCP) and 10,249 individuals from the UK Biobank. Our analysis yielded patterns of directed connectivity in independent HCP and UK Biobank data similar to those previously reported, including that the cerebellum consistently receives information from other networks. We show robust associations between information flow and age and sex for several connections, with strongest effects of age observed in the sensorimotor network. Visual, auditory and sensorimotor nodes were also linked to mental health. Our findings support the use of DGM as a measure of directed connectivity in rsfMRI data and provide new insight into the shaping of the connectome during aging.
AB - Functional interconnections between brain regions define the "connectome" which is of central interest for understanding human brain function. Resting-state functional magnetic resonance (rsfMRI) work has revealed changes in static connectivity related to age, sex, cognitive abilities and psychiatric symptoms, yet little is known how these factors may alter the information flow. The commonly used approach infers functional brain connectivity using stationary coefficients yielding static estimates of the undirected connection strength between brain regions. Dynamic graphical models (DGMs) are a multivariate model with dynamic coefficients reflecting directed temporal associations between nodes, and can yield novel insight into directed functional connectivity. Here, we leveraged this approach to test for associations between edge-wise estimates of direction flow across the functional connectome and age, sex, intellectual abilities and mental health. We applied DGM to investigate patterns of information flow in data from 984 individuals from the Human Connectome Project (HCP) and 10,249 individuals from the UK Biobank. Our analysis yielded patterns of directed connectivity in independent HCP and UK Biobank data similar to those previously reported, including that the cerebellum consistently receives information from other networks. We show robust associations between information flow and age and sex for several connections, with strongest effects of age observed in the sensorimotor network. Visual, auditory and sensorimotor nodes were also linked to mental health. Our findings support the use of DGM as a measure of directed connectivity in rsfMRI data and provide new insight into the shaping of the connectome during aging.
KW - INDEPENDENT COMPONENT ANALYSIS
KW - HUMAN CONNECTOME PROJECT
KW - RESTING-STATE FMRI
KW - QUALITY-CONTROL
KW - MOTOR CONTROL
KW - ROBUST
KW - SCHIZOPHRENIA
KW - PATTERNS
KW - OPTIMIZATION
KW - REGISTRATION
U2 - 10.1002/hbm.25116
DO - 10.1002/hbm.25116
M3 - Article
C2 - 32613721
SN - 1065-9471
VL - 41
SP - 4173
EP - 4186
JO - Human Brain Mapping
JF - Human Brain Mapping
IS - 15
ER -