Whole-brain Changes in Longitudinal Relaxation Rate throughout Emerging and Early Middle Adulthood
Poster No:
2144
Submission Type:
Abstract Submission
Authors:
Stella (In Kyung) Heo1, Christopher Rowley2, Kimberly Desmond3,4, Maya Kovacheff1, Mazen Elkhayat1, Rodrigo Mansur4, Roger McIntyre4, Roumen Milev5, Valerie Taylor6, Lakshmi Yatham7, Rudolf Uher8, Luciano Minuzzi9, Benicio Frey9, Nicholas Bock1
Institutions:
1Department of Psychology, Neuroscience and Behaviour, McMaster University, Hamilton, Ontario, 2McConnell Brain Imaging Centre, McGill University, Montreal, Quebec, 3Centre for Addiction and Mental Health (CAMH), Toronto, Ontario, 4Department of Psychiatry, University of Toronto, Toronto, Ontario, 5Department of Psychiatry and Psychology, Queen’s University, Kingston, Ontario, 6Department of Psychiatry, University of Calgary, Calgary, Alberta, 7Department of Psychiatry, University of British Columbia, Vancouver, British Columbia, 8Department of Psychiatry, Dalhousie University, Halifax, Nova Scotia, 9Department of Psychiatry and Behavioural Neurosciences, McMaster University, Hamilton, Ontario
First Author:
Stella (In Kyung) Heo
Department of Psychology, Neuroscience and Behaviour, McMaster University
Hamilton, Ontario
Department of Psychology, Neuroscience and Behaviour, McMaster University
Hamilton, Ontario
Co-Author(s):
Kimberly Desmond
Centre for Addiction and Mental Health (CAMH)|Department of Psychiatry, University of Toronto
Toronto, Ontario|Toronto, Ontario
Centre for Addiction and Mental Health (CAMH)|Department of Psychiatry, University of Toronto
Toronto, Ontario|Toronto, Ontario
Maya Kovacheff
Department of Psychology, Neuroscience and Behaviour, McMaster University
Hamilton, Ontario
Department of Psychology, Neuroscience and Behaviour, McMaster University
Hamilton, Ontario
Mazen Elkhayat
Department of Psychology, Neuroscience and Behaviour, McMaster University
Hamilton, Ontario
Department of Psychology, Neuroscience and Behaviour, McMaster University
Hamilton, Ontario
Luciano Minuzzi
Department of Psychiatry and Behavioural Neurosciences, McMaster University
Hamilton, Ontario
Department of Psychiatry and Behavioural Neurosciences, McMaster University
Hamilton, Ontario
Benicio Frey
Department of Psychiatry and Behavioural Neurosciences, McMaster University
Hamilton, Ontario
Department of Psychiatry and Behavioural Neurosciences, McMaster University
Hamilton, Ontario
Nicholas Bock
Department of Psychology, Neuroscience and Behaviour, McMaster University
Hamilton, Ontario
Department of Psychology, Neuroscience and Behaviour, McMaster University
Hamilton, Ontario
Introduction:
The specific neurobiological alterations underlying age-associated macrostructural changes in the brain remain relatively unknown. With magnetic resonance imaging (MRI), in vivo examinations of the human brain are possible – for instance, longitudinal relaxation rate (R1) is a quantitative metric sensitive to myelin and transition metals (Desmond et al., 2016; Stüber et al., 2014). Both histology stained for intracortical myelin and R1 demonstrate inverted-U trajectories across the lifespan, with both rising during early ages followed by a period of stability in middle adulthood and a progressive degeneration at older ages (Erramuzpe et al., 2020; Lintl & Braak, 1983).
R1 in deep brain structures has not been studied extensively, however. Here, we characterize age trajectories of R1 across the whole brain during emerging and middle adulthood.
R1 in deep brain structures has not been studied extensively, however. Here, we characterize age trajectories of R1 across the whole brain during emerging and middle adulthood.
Methods:
MRI scans were collected across five imaging sites from healthy individuals aged 16-43 years without any neuropsychiatric diagnoses. (N=43F/35M).
Images were acquired on 3T GE or Siemens scanners at isotropic resolution of 1.0mm using 32-channel receive-only head and transmit RF body coils. An inversion-recovery gradient-echo T1-weighted (T1w) image (Anatomical) along with T1w images optimized to maximize (T1wHC) and minimize intracortical contrast (T1wLC) were collected as well as a B1+ map.
Subcortical segmentations and cortical parcellations were created from Anatomical using FreeSurfer and the Human Connectome Project's (HCP) minimal preprocessing pipeline andatlas (Glasser et al., 2016). In total, 28 subcortical and 180 bilateral cortical surface regions of interest (ROIs) were examined. A ratio map between the two T1w images (T1wHC/T1wLC) was calculated and scaled using site- and structure-specific factors to correct for potential inter-site variability. This and the B1+ map were then used to compute R1 maps using look-up tables calculated from Bloch equation simulations.
The association between age and mean R1 was evaluated using linear regression for each ROI in R software, with p values corrected using false discovery rate (FDR). Figures 1 and 2 visualize the slope of regression lines and R2 values.
Images were acquired on 3T GE or Siemens scanners at isotropic resolution of 1.0mm using 32-channel receive-only head and transmit RF body coils. An inversion-recovery gradient-echo T1-weighted (T1w) image (Anatomical) along with T1w images optimized to maximize (T1wHC) and minimize intracortical contrast (T1wLC) were collected as well as a B1+ map.
Subcortical segmentations and cortical parcellations were created from Anatomical using FreeSurfer and the Human Connectome Project's (HCP) minimal preprocessing pipeline andatlas (Glasser et al., 2016). In total, 28 subcortical and 180 bilateral cortical surface regions of interest (ROIs) were examined. A ratio map between the two T1w images (T1wHC/T1wLC) was calculated and scaled using site- and structure-specific factors to correct for potential inter-site variability. This and the B1+ map were then used to compute R1 maps using look-up tables calculated from Bloch equation simulations.
The association between age and mean R1 was evaluated using linear regression for each ROI in R software, with p values corrected using false discovery rate (FDR). Figures 1 and 2 visualize the slope of regression lines and R2 values.
Results:
Significant age effects were found in 52 ROIs, including the right putamen, pallidum and bilateral frontal and parietal cortical areas.
The strongest age association in the cortex was found in the premotor cortex (R^2 = 0.222, B = 0.0022 s-1/year, p = .005), and the smallest age effect was observed in the subgenual area (R^2 < 0.001, B < 0.0001 s-1/year, p = .991).
In subcortical structures, the strongest and weakest age effects were found in the right putamen (R^2 = 0.155, B = 0.0026 s-1/year, p = .022) and the posterior corpus callosum (R^2 < 0.001, B = <0.0001 s-1/year, p = .981), respectively. Overall, the putamen and pallidum showed the strongest age effects, while cerebral white matter and corpus callosum showed the weakest age effects.
The strongest age association in the cortex was found in the premotor cortex (R^2 = 0.222, B = 0.0022 s-1/year, p = .005), and the smallest age effect was observed in the subgenual area (R^2 < 0.001, B < 0.0001 s-1/year, p = .991).
In subcortical structures, the strongest and weakest age effects were found in the right putamen (R^2 = 0.155, B = 0.0026 s-1/year, p = .022) and the posterior corpus callosum (R^2 < 0.001, B = <0.0001 s-1/year, p = .981), respectively. Overall, the putamen and pallidum showed the strongest age effects, while cerebral white matter and corpus callosum showed the weakest age effects.
Conclusions:
Our analysis of age-related changes in R1 across the whole brain allowed for a direct comparison of trends across the cortex and deep brain structures. We found differential age trajectories of R1 across the cortex, with the strongest increases in motor areas and the smallest in the medial frontal cortex. These trends agree with past findings (Grydeland et al., 2019).
In deep brain structures, the strongest age associations were observed in the basal ganglia, while we did not observe a significant age effect in deep white matter in this age range. These results are also in line with previous research (Hallgren & Sourander, 1958; Lebel et al., 2012), exemplifying the possibility of studying whole-brain neurobiology using R1. Whole brain R1 age trajectories in healthy controls in the future could be used as baseline data to detect abnormal trajectories in disease.
In deep brain structures, the strongest age associations were observed in the basal ganglia, while we did not observe a significant age effect in deep white matter in this age range. These results are also in line with previous research (Hallgren & Sourander, 1958; Lebel et al., 2012), exemplifying the possibility of studying whole-brain neurobiology using R1. Whole brain R1 age trajectories in healthy controls in the future could be used as baseline data to detect abnormal trajectories in disease.
Lifespan Development:
Aging 2
Lifespan Development Other
Neuroanatomy, Physiology, Metabolism and Neurotransmission:
Cortical Cyto- and Myeloarchitecture
Normal Development 1
Subcortical Structures
Keywords:
ADULTS
Basal Ganglia
Cortex
MRI
Myelin
NORMAL HUMAN
STRUCTURAL MRI
Sub-Cortical
White Matter
Other - Quantitative MRI
1|2Indicates the priority used for review
Provide references using author date format
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