Download - Leiden 17-June-2013
Measuring changes in brain structure across childhood and adolescence
Brain & Development Lab, 17 June 2013
Kate Mills
Thank You!
Sarah-Jayne BlakemoreAnne-Lise Goddings
Jay GieddLiv Clasen
UCL Developmental CognitiveNeuroscience Group
Outline
• Longitudinal studies of structural brain development
• What does structural MRI measure?
• Developmental changes in the structure of the social brain in late childhood and adolescence
• Exploring the developmental mismatch hypothesis with structural brain data
• Assessing brain maturation
Structural Brain Imaging
DTI
Structural Brain Imaging
Roberto Toro Brain Catalogue
Outline
• Longitudinal studies of structural brain development
• What does structural MRI measure?
• Developmental changes in the structure of the social brain in late childhood and adolescence
• Exploring the developmental mismatch hypothesis with structural brain data
• Assessing brain maturation
Longitudinal studies of structural brain development
Giedd et al., 1999: 145 participants 243 scans
Sowell et al., 2004: 45 participants 90 scans
Gogtay et al., 2004: 13 participants 52 scans
Lenroot et al., 2007: 387 participants 829 scans
Raznahan et al., 2011: 647 participants 1250 scans
Urošević et al., 2012: 149 participants 298 scans
van Soelen et al., 2012: 113 participants 226 scans
Pfefferbaum et al., 2013: 47 participants 112 scans
Tamnes et al., 2013: 85 participants 170 scans
Mutlu et al., 2013: 137 participants 209 scans
Auber-Broche et al., 2013: 292 participants 882 scans ...and more each day!
Giedd et al., 1999: 145 participants 243 scans
Sowell et al., 2004: 45 participants 90 scans
Gogtay et al., 2004: 13 participants 52 scans
Lenroot et al., 2007: 387 participants 829 scans
Raznahan et al., 2011: 647 participants 1250 scans
Urošević et al., 2012: 149 participants 298 scans
van Soelen et al., 2012: 113 participants 226 scans
Pfefferbaum et al., 2013: 47 participants 112 scans
Tamnes et al., 2013: 85 participants 170 scans
Mutlu et al., 2013: 137 participants 209 scans
Auber-Broche et al., 2013: 292 participants 882 scans
Child Psychiatry Branch NIMH (Giedd, Rapoport)
Longitudinal studies of structural brain development
• Gray matter density measures the proportion of gray matter in a small region of fixed radius (15 mm) around each cortical point. Similar to cortical thickness.
• Non-linear changes not captured in video.
Gray matter maturation over the cortical surface between ages 4 and 21. The process of gray matter maturation is represented by the blue color. The side bar shows a color representation in units of gray matter volume.
(Gogtay et al., 2004)
Center for the Study of Human Cognition / Lifespan Changes in Brain and Cognition (Walhovd, Fjell) Giedd et al., 1999: 145 participants 243 scans
Sowell et al., 2004: 45 participants 90 scans
Gogtay et al., 2004: 13 participants 52 scans
Lenroot et al., 2007: 387 participants 829 scans
Raznahan et al., 2011: 647 participants 1250 scans
Urošević et al., 2012: 149 participants 298 scans
van Soelen et al., 2012: 113 participants 226 scans
Pfefferbaum et al., 2013: 47 participants 112 scans
Tamnes et al., 2013: 85 participants 170 scans
Mutlu et al., 2013: 137 participants 209 scans
Auber-Broche et al., 2013: 292 participants 882 scans
(Tamnes et al., 2013)
Longitudinal studies of structural brain development
How much does cortical thickness change across development?
Sowell et al., 2004: 5-11 years 0.3 – 0.6mm
Raznahan et al., 2011: 8-22 years ~.25mm
van Soelen et al., 2012: 9-12 years <.24mm
Mills et al., 2013: 9-22 years 0.1 – 0.4mm
Mutlu et al., 2013: 6-30 years ~.5mm
Why longitudinal?
variability between individuals > variability within individuals
Aubert-Broche et al., 2013
Total cerebral volume Total cerebral volume
van Soelen et al., 2013Lenroot et al., 2007
Total cerebral volume
Why longitudinal?
variability between individuals > variability within individuals
Aubert-Broche et al., 2013
Gray Matter Volume Cerebral Cortex Volume
Tamnes et al., 2013
Gray Matter Volume
Raznahan et al., 2011
Steen et al., 2007
Outline
• Longitudinal studies of structural brain development
• What does structural MRI measure?
• Developmental changes in the structure of the social brain in late childhood and adolescence
• Exploring the developmental mismatch hypothesis with structural brain data
• Assessing brain maturation
1mm3 cortical voxel contains:
• 20,000 – 30,000 neurons
• Up to twice as many glial cells
• 0.4 km dendrites
• 4 km axons
• 400,000,000 to 1,000,000,000 synapses
What does structural MRI measure?
(Cragg, 1967; Logothetis, 2008; Sherwood et al., 2006; Pelvig et al., 2008)
What does structural MRI measure?
(Chung et al., 2013)
0.5-mm-thick blocks of BA10 from an autistic patient, stored in formalin for >6 years. Stained for axons with neurofilament protein and myelin basic protein to trace individual fibres.
(Chung et al., 2013)
What does cortical thickness measure?
(Carlo and Stevens, 2013)
Cortical thickness relates to number of glial cells, not number of neurons
Does synaptic number affect cortical thickness?
(Bourgeois and Rakic, 1993)
Does synaptic number affect cortical thickness?
(Bourgeois and Rakic, 1993)
“Changes in the density of synapses affect very little either the volume or the surface of the cortex because the total volume of synaptic boutons (the synaptopil illustrated in Fig. 2) is only a very small fraction of the cortical volume.”
“If we assume that the synaptic contacts are basically spheres (Fig. 2) with this mean diameter, then they would represent only 2% of 1 mm3 of the neuropil or less than 1.5% of the cortical volume, even at this exceptionally high density of synapses. Thus, a decline of synaptic number during puberty should have a rather small effect on the overall volume of the cortex.”
“Neither the overall percentage of neuropil in the cortex nor the volume of the cortex itself changes significantly during puberty (present data; R. Williams, K. Ryder, and P. Rakic, unpublished observations).”
“For example, layers II and III display the highest decrease in synaptic density although these layers have the most steady percentage of neuropil during the life span. In contrast, layer VI, in which we have observed the largest decrease in percentage of neuropil, displays the smallest change in density of synapses.”
What does structural MRI measure?
Gray Matter Volume
White Matter Volume
Cortical Thickness
Surface Area
Gyrification/Folding
Cortical Thickness x Surface AreaRatio of gray relative to white within a sphere
MyelinationAxonal calibre
Synaptic pruningIntracortical myelinationAxonal calibreGlial loss
Number of cortical columns in a functional area
Mechanistic forces and developmental timing
Outline
• Longitudinal studies of structural brain development
• What does structural MRI measure?
• Developmental changes in the structure of the social brain in late childhood and adolescence
• Exploring the developmental mismatch hypothesis with structural brain data
• Assessing brain maturation
Developmental changes in the structure of the social brain in late childhood and adolescence
Mentalizing is the ability to infer the intentions, beliefs and desires of others in order to predict their behavior.
Behavioral and functional neuroimaging studies suggest this ability continues to develop across the second decade.
“Social Brain Network”
mBA10
TPJpSTS
ATC
Defining the temporoparietal junction
(Mars et al., 2011)
Participant Characteristics
Image Processing
(Winkler et al., 2010)
Thickness
White surface
Pial Surface
Surface Area
Gray matter volume
Surface-based Representation
Cortical Thickness × Surface Area = Gray Matter Volume
Statistical Analysis
Mixed-modeling and AIC to determine the best fitting model (cubic, quadratic, or linear)
Gray matter measurement
Age
• Regions of the social brain continue to develop structurally across adolescence.
• Cortical thickness decreases in the TPJ, pSTS, and mBA10 across adolescence, whereas the ATC increases in cortical thickness until late adolescence.
• Surface area for each region followed a cubic trajectory, peaking in early or pre-adolescence before decreasing into the early twenties.
• Sex differences in gray matter volume appear to be driven by surface area.
• Age differences in gray matter volume are a product of changes in surface area and cortical thickness.
Conclusion
red = constantgreen = linearblue = quadraticorange = cubic
Cortical thickness development types
Sex differences in cortical thickness development
Thinning
Outline
• Longitudinal studies of structural brain development
• What does structural MRI measure?
• Developmental changes in the structure of the social brain in late childhood and adolescence
• Exploring the developmental mismatch hypothesis with structural brain data
• Assessing brain maturation
Background
• Regions of the human brain develop at different rates across the first two decades of life.
• Multiple functional imaging studies show heightened recruitment of limbic structures in adolescents compared to adults.
• It has been hypothesized that a mismatch in the timing of maturation between limbic structures (i.e., nucleus accumbens and amygdala) and the prefrontal cortex may underlie some adolescent behaviors.
From Somerville, Jones & Casey, 2010
• Regions of the human brain develop at different rates across the first two decades of life.
• Multiple functional imaging studies show heightened recruitment of limbic structures in adolescents compared to adults.
• It has been hypothesized that a mismatch in the timing of maturation between limbic structures (i.e., nucleus accumbens and amygdala) and the prefrontal cortex may underlie some adolescent behaviors.
• Most support for this hypothesis relies on cross-sectional data.
• It is not known if this pattern can be observed on an individual level.
Background
Participant Characteristics
No. of participants: 33
No. of scans: 152
Age range: 7.01-29.9
Gender: 10 Female, 23 Male
IQ: 118 ± 11
scans
scans
Scans per participant
3456
scans
scans
Methods
Subcortical Segmentation
Surface-basedCorticalReconstruction
• All participants had at least three high quality scans across adolescence.
• FreeSurfer5.3 (!!) longitudinal pipeline.
• Measures of gray matter volume were obtained for amygdala, nucleus accumbens and prefrontal cortex.
• Non-linear mixed-modeling was implemented using the nlme package in R to determine the best fitted model (cubic, quadratic, or linear).
Prefrontal Cortex
Nucleus Accumbens
Amygdala
Regions of Interest
cubic p <.0001
linear p <.0001 quadratic p <.0001
Best fitting models across all participants
Raw volumes for all participants
Models scaled over raw volumes
Findings from other studies
(Tamnes et al., 2013)
Findings from other studies
Early Adolescent9-12 years
Late Adolescent13-17 years
Young Adult18-23 years
(Urošević et al., 2012)
Findings from other studies
(Dennison et al., 2013)
between ages ~12.5 and ~16.5
Findings from other studies
(Mohr and Sisk, 2013)
Pubertally-born neurons and glial cells in the medial amygdala.
(Gee et al., 2013)
Findings from other studies
(Gee et al., 2013)
Findings from other studies
(Christakou et al., 2013)
Findings from other studies
(Costa Dias et al., 2013)
Findings from other studies
Prefrontal cortex subdivisions
Prefrontal cortex subdivisions
cubic p <.0001
cubic p <.02 cubic p <.02
Structural stability as maturational index
(Pfefferbaum et al., 2013)
Prefrontal Cortex
Nucleus Accumbens
Amygdala
age10 16 22 28
160000
180000
200000
Volu
me
10 16 22 280.90
1.00
1.10
Prop
ortio
n of
fina
l tim
epoi
nt v
olum
e
10 16 22 280.90
1.00
1.10
10 16 22 280.90
1.00
1.10
10 16 22 280.90
1.00
1.10
Maturational graphs for each participant
Maturational graphs for each participant
Prefrontal Cortex
Nucleus Accumbens
Amygdala
• The developmental mismatch hypothesis between the nucleus accumbens, amygdala and prefrontal cortex appears to be supported by longitudinal structural data.
• The rate of structural volume change starts to decrease in mid- to late adolescence for the amygdala, whereas the prefrontal cortex and nucleus accumbens show continual change into the mid-twenties.
• This temporal mismatch in structural development is observable to a variable extent between individuals.
Conclusion
Outline
• Longitudinal studies of structural brain development
• What does structural MRI measure?
• Developmental changes in the structure of the social brain in late childhood and adolescence
• Exploring the developmental mismatch hypothesis with structural brain data
• Assessing brain maturation
“Converging neuropsychology, brain imaging and electrical activity evidence suggests that breastfed infants may display preferential myelination and white matter development.”
“improved developmental growth in late maturing white matter association regions”
“While prior imaging studies have shown increased brain volume and cortical thickness in adolescents who were breastfed as infants (Hallowell and Spatz, 2012; Isaacs et al., 2010; Kafouri et al., 2012)”
(Deoni et al., in press)
The age of attaining peak cortical thickness in children with ADHD compared with typically developing children
Structural development, an issue of timing?
• When is “less/more” better?
• What do trajectory rates mean?
• Does cortical thickness mean the same thing at different stages of development?
• How does all of this relate to cognitive/behavioral development?
“We interpret these data to suggest that the NAcc development may precede that of the OFC during adolescence. Protracted development of prefrontal regions, with a transition from diffuse to focal recruitment is consistent with MRI-based neuroanatomical [studies]”
(Dosenbach et al., 2010)
BRAINSTORM