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

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










Auber-Broche et al., 2013: 292 participants 882 scans

Child Psychiatry Branch NIMH (Giedd, Rapoport)


• 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










Auber-Broche et al., 2013: 292 participants 882 scans

(Tamnes et al., 2013)


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






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)


(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.”


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






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






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)


Early Adolescent9-12 years

Late Adolescent13-17 years

Young Adult18-23 years

(Urošević et al., 2012)


(Dennison et al., 2013)

between ages ~12.5 and ~16.5


(Mohr and Sisk, 2013)

Pubertally-born neurons and glial cells in the medial amygdala.

(Gee et al., 2013)


(Christakou et al., 2013)


(Costa Dias et al., 2013)


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






“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

http://vimeo.com/68216338

leiden 17-june-2013

Health & Medicine

brain maturation

structural brain imagingdti

structural mri measure

developmental changes

cortical thickness measure

units of gray matter

proportionof gray matter

gray matter volumeraznahan