TY - JOUR
T1 - Impact of thresholding on the consistency and sensitivity of diffusion MRI-based brain networks in patients with cerebral small vessel disease
AU - De Brito Robalo, Bruno M
AU - Vlegels, Naomi
AU - Leemans, Alexander
AU - Reijmer, Yael D
AU - Biessels, Geert Jan
N1 - Funding Information:
This work was supported by ZonMw, The Netherlands Organisation for Health Research and Development (VICI grant 91816616 to Geert Jan Biessels). Yael D. Reijmer received funding from Alzheimer Nederland and ZonMw/Deltaplan Dementie (grant #733050503) and a Young Talent Fellowship from the Brain Center Rudolf Magnus, University Medical Center Utrecht. The research of Alexander Leemans is supported by VIDI Grant 639.072.411 from the Netherlands Organization for Scientific Research (NWO). Members of the Utrecht Vascular Cognitive Impairment (VCI) Study group involved in the present study (in alphabetical order by department): University Medical Center Utrecht, The Netherlands, Department of Neurology: E. van den Berg, J. M. Biesbroek, G. J. Biessels, M. Brundel, W. H. Bouvy, L.G. Exalto, C. J. M. Frijns, O. Groeneveld, S. M. Heringa, N. Kalsbeek, L. J. Kappelle, Y. D. Reijmer, J. Verwer; Department of Radiology/Image Sciences Institute: J. de Bresser, H. J. Kuijf, A. Leemans, P. R. Luijten, M. A. Viergever, K. L. Vincken, J. J. M. Zwanenburg; Department of Geriatrics: H. L. Koek; Hospital Diakonessenhuis Zeist, The Netherlands: M. Hamaker, R. Faaij, M. Pleizier, E. Vriens.
Funding Information:
This work was supported by ZonMw, The Netherlands Organisation for Health Research and Development (VICI grant 91816616 to Geert Jan Biessels). Yael D. Reijmer received funding from Alzheimer Nederland and ZonMw/Deltaplan Dementie (grant #733050503) and a Young Talent Fellowship from the Brain Center Rudolf Magnus, University Medical Center Utrecht. The research of Alexander Leemans is supported by VIDI Grant 639.072.411 from the Netherlands Organization for Scientific Research (NWO). Members of the Utrecht Vascular Cognitive Impairment (VCI) Study group involved in the present study (in alphabetical order by department): University Medical Center Utrecht, The Netherlands, Department of Neurology: E. van den Berg, J. M. Biesbroek, G. J. Biessels, M. Brundel, W. H. Bouvy, L.G. Exalto, C. J. M. Frijns, O. Groeneveld, S. M. Heringa, N. Kalsbeek, L. J. Kappelle, Y. D. Reijmer, J. Verwer; Department of Radiology/Image Sciences Institute: J. de Bresser, H. J. Kuijf, A. Leemans, P. R. Luijten, M. A. Viergever, K. L. Vincken, J. J. M. Zwanenburg; Department of Geriatrics: H. L. Koek; Hospital Diakonessenhuis Zeist, The Netherlands: M. Hamaker, R. Faaij, M. Pleizier, E. Vriens.
Publisher Copyright:
© 2022 The Authors. Brain and Behavior published by Wiley Periodicals LLC.
PY - 2022/5
Y1 - 2022/5
N2 - INTRODUCTION: Thresholding of low-weight connections of diffusion MRI-based brain networks has been proposed to remove false-positive connections. It has been previously established that this yields more reproducible scan-rescan network architecture in healthy subjects. In patients with brain disease, network measures are applied to assess inter-individual variation and changes over time. Our aim was to investigate whether thresholding also achieves improved consistency in network architecture in patients, while maintaining sensitivity to disease effects for these applications.METHODS: We applied fixed-density and absolute thresholding on brain networks in patients with cerebral small vessel disease (SVD, n = 86; ≈24 months follow-up), as a clinically relevant exemplar condition. In parallel, we applied the same methods in healthy young subjects (n = 44; scan-rescan interval ≈4 months) as a frame of reference. Consistency of network architecture was assessed with dice similarity of edges and intraclass correlation coefficient (ICC) of edge-weights and hub-scores. Sensitivity to disease effects in patients was assessed by evaluating interindividual variation, changes over time, and differences between those with high and low white matter hyperintensity burden, using correlation analyses and mixed ANOVA.RESULTS: Compared to unthresholded networks, both thresholding methods generated more consistent architecture over time in patients (unthresholded: dice = .70; ICC: .70-.78; thresholded: dice = .77; ICC: .73-.83). However, absolute thresholding created fragmented nodes. Similar observations were made in the reference group. Regarding sensitivity to disease effects in patients, fixed-density thresholds that were optimal in terms of consistency (densities: .10-.30) preserved interindividual variation in global efficiency and node strength as well as the sensitivity to detect effects of time and group. Absolute thresholding produced larger fluctuations of interindividual variation.CONCLUSIONS: Our results indicate that thresholding of low-weight connections, particularly when using fixed-density thresholding, results in more consistent network architecture in patients with longer rescan intervals, while preserving sensitivity to disease effects.
AB - INTRODUCTION: Thresholding of low-weight connections of diffusion MRI-based brain networks has been proposed to remove false-positive connections. It has been previously established that this yields more reproducible scan-rescan network architecture in healthy subjects. In patients with brain disease, network measures are applied to assess inter-individual variation and changes over time. Our aim was to investigate whether thresholding also achieves improved consistency in network architecture in patients, while maintaining sensitivity to disease effects for these applications.METHODS: We applied fixed-density and absolute thresholding on brain networks in patients with cerebral small vessel disease (SVD, n = 86; ≈24 months follow-up), as a clinically relevant exemplar condition. In parallel, we applied the same methods in healthy young subjects (n = 44; scan-rescan interval ≈4 months) as a frame of reference. Consistency of network architecture was assessed with dice similarity of edges and intraclass correlation coefficient (ICC) of edge-weights and hub-scores. Sensitivity to disease effects in patients was assessed by evaluating interindividual variation, changes over time, and differences between those with high and low white matter hyperintensity burden, using correlation analyses and mixed ANOVA.RESULTS: Compared to unthresholded networks, both thresholding methods generated more consistent architecture over time in patients (unthresholded: dice = .70; ICC: .70-.78; thresholded: dice = .77; ICC: .73-.83). However, absolute thresholding created fragmented nodes. Similar observations were made in the reference group. Regarding sensitivity to disease effects in patients, fixed-density thresholds that were optimal in terms of consistency (densities: .10-.30) preserved interindividual variation in global efficiency and node strength as well as the sensitivity to detect effects of time and group. Absolute thresholding produced larger fluctuations of interindividual variation.CONCLUSIONS: Our results indicate that thresholding of low-weight connections, particularly when using fixed-density thresholding, results in more consistent network architecture in patients with longer rescan intervals, while preserving sensitivity to disease effects.
KW - cerebral small vessel disease
KW - diffusion tensor imaging
KW - network density
KW - network reproducibility
KW - network thresholding
UR - https://www.scopus.com/pages/publications/85128066900
U2 - 10.1002/brb3.2523
DO - 10.1002/brb3.2523
M3 - Article
C2 - 35413156
SN - 2162-3279
VL - 12
SP - 1
EP - 16
JO - Brain and Behavior
JF - Brain and Behavior
IS - 5
M1 - e2523
ER -