Disruptions in T1-weighted MRI signal trajectories over age in Bipolar Disorder Type-1
Stand-By Time
Tuesday, June 27, 2017: 12:45 PM - 2:45 PMSubmission No:
1203
Submission Type:
Abstract Submission
On Display:
Monday, June 26 & Tuesday, June 27
Authors:
Christopher Rowley1, Manpreet Sehmbi1, Luciano Minuzzi1, Benicio Frey1, Nicholas Bock1
Institutions:
1McMaster University, Hamilton, Canada
First Author:
Introduction:
MRI signals in the cerebral cortex have been shown to follow a quadratic-like trajectory over the lifespan: increasing from childhood into adulthood, and then declining with advanced age [1,2]. This trajectory coincides with cortical myelin development [3]. The changes in MRI signal are believed to be correlated with intracortical myelin levels, as demonstrated by spatially correlated measures of MR parameters and myelin in combined MRI/histology studies [4,5]. Previous research has suggested that there is a loss of intracortical myelin in the dorsal lateral prefrontal cortex in bipolar disorder (BD) using both MRI [6] and histology [7]. The purpose of this study was to use T1-weighted MRI to investigate trajectories in intracortical myelin levels across the whole brain in a large, well-characterized cohort of healthy controls and BD type-1 subjects.
Methods:
T1-weighted, 1mm isotropic images were collected in subjects in the age range of 17-45 years old. Images were collected in 43 subjects diagnosed with BD type-1 and 67 healthy individuals. The T1-weighed sequence used to collect images was previously optimized for intracortical myelin contrast [8]. Processing was performed using CBS High-Res Brain Processing tools (www.nitrc.org/projects/cbs-tools/) in MIPAV (mipav.cit.nih.gov). The T1-weighted signal was analyzed using a surface based approach at the middle depth of the cortex. Each subject's surface was registered to the middle depth of the MNI-152 atlas using a multi-modal surface registration approach [9]. The MarsAtlas was used to parcellate the cortex into 82 regions for analysis. Six ROIs per hemisphere (twelve total) were not analyzed due to poor signal intensity profiles arising from topological errors in segmentations. The remaining regions were fit with general linear models in each group with either linear or quadratic age terms to determine the best model for the signal trajectory.
Results:
Gender was an insignificant predictor in either model (p<0.05), so it was not included in the final analysis. The quadratic model was determined a superior fit in controls over linear fits using Akaike information criterion followed by a chi square test (p<0.05 Bonferonni corrected). Neither model was significant in BD (p>0.05) (see Figure 1). This result signifies that there is no correlation between age and T1-weighted signal in BD type-1 in this age range, suggesting that healthy intracortical myelin trajectories are disrupted in this disorder. Figure 2 illustrates signal changes over the age range in regions that demonstrated reduced cortical thickness in BD in a previous study[10]. In all regions we can see the control fir (blue line) follows a quadratic form, peaking around age 35, the fit for BD (dotted red line) is nearly flat, with no significant correlation with age.
Our data in healthy subjects reiterates findings that suggest that intracortical myelin trajectories follow an inverted 'U' trajectory with age[1,2]. The lack of correlation between T1-weighted signal and age in BD suggests a disruption in the trajectory of intracortical myelin over the age range of 17-45 years old. This disruption in development occurred in all regions we analyzed, which could suggest a global cortical effect on the myelin development in BD type-1. From our data we can see that the difference between subject groups' T1-weighted signal could be maximally identified around 35 years old; this is where a cross sectional study would have the greatest chance of finding differences.
Our data in healthy subjects reiterates findings that suggest that intracortical myelin trajectories follow an inverted 'U' trajectory with age[1,2]. The lack of correlation between T1-weighted signal and age in BD suggests a disruption in the trajectory of intracortical myelin over the age range of 17-45 years old. This disruption in development occurred in all regions we analyzed, which could suggest a global cortical effect on the myelin development in BD type-1. From our data we can see that the difference between subject groups' T1-weighted signal could be maximally identified around 35 years old; this is where a cross sectional study would have the greatest chance of finding differences.
·r2 (top) and p-values (bottom) are mapped across the cortex for linear and quadratic age predictors in controls (left) and BD type-1 (right). Increased r2 and lower p-values suggest improved fit
·Control data significantly fit a quadratic model with age in all four regions. BD did not significantly correlate with age. (ACC: anterior cingulate cortex, MFG: middle frontal gyrus)
Conclusions:
We have shown a global disruption in ICM using T1-weighted signal analysis in type-1 BD. This technique may be useful in investigating other clinical populations with suspected changes in ICM.
Disorders of the Nervous System:
Bipolar Disorder 1
Imaging Methods:
Anatomical MRI
Lifespan Development:
Aging 2
Neuroanatomy:
Cortical Anatomy and Brain Mapping
Cortical Cyto- and Myeloarchitecture
Keywords:
Aging
Cortex
Data analysis
Development
DISORDERS
Myelin
Psychiatric Disorders
STRUCTURAL MRI
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2. Westlye LT (2010) Differentiating maturational and aging-related changes of the cerebral cortex by use of thickness and signal intensity. NeuroImage. 52(1):172-185.
3. Dobbing J (1973) Quantitative growth and development of human brain. Arch Dis Child. 48(10):757-767.
4. Bock NA (2009) Visualizing the entire cortical myelination pattern in marmosets with magnetic resonance imaging. Journal of Neuroscience Methods. 185(1):15-22.
5. Eickhoff S (2005) High-resolution MRI reflects myeloarchitecture and cytoarchitecture of human cerebral cortex. Hum Brain Mapp. 24(3):206-215.
6. Rowley CD (2015) Assessing intracortical myelin in the living human brain using myelinated cortical thickness. Front Neurosci. 9:396.
7. Lake EMR (2016) Altered intracortical myelin staining in the dorsolateral prefrontal cortex in severe mental illness. Eur Arch Psychiatry Clin Neurosci. 1-8.
8. Bock NA (2013) Optimizing T1-weighted imaging of cortical myelin content at 3.0T. NeuroImage. 65:1-12.
9. Tardif CL (2015) Multi-contrast multi-scale surface registration for improved alignment of cortical areas. NeuroImage. 111(C):107-122.
10. Lyoo IK (2006) Regional cerebral cortical thinning in bipolar disorder. Bipolar Disord.;8(1):65-74.