Transcript slides - UCSD Cognitive Science

Structural brain imaging is a
window on postnatal brain
development in children
Terry Jernigan
What We Thought We Knew
About Brain Structure
• Neuronal populations and their connections (the
basic architecture of the brain) are established
during the prenatal period.
• Soon after birth oligodendrocytes proliferate,
differentiate, and wrap the connecting axons with
insulating, fatty, sheaths of myelin.
• This more or less completes the development of
the hardware, and little additional growth of the
brain occurs after age 5 or 6.
“Brain structure is adult at approximately age 5.”
Structural MRI of young child from NIH Brain Development Study
Brain Morphometry
Predictions Based on Conventional
Views of Brain Morphology
• Adult brain structure in school-aged children.
• Stable brain morphological characteristics across
childhood, adolescence, and adult years.
• Atrophy of some brain structures in old age.
Age-Related Alterations of
Normalized Cerebral Gray Matter Volume
.8
.75
.7
.65
.6
.55
.5
.45
.4
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Mapping of Cortical Thinning with Longitudinal MRI Data
Gogtay et al., PNAS, 2004
Longitudinal Mapping of Cortical Thickness and
Brain Growth in Normal Children
(Sowell et al.,J. Neurosci., 2004)
Widespread cortical thinning, and focal areas of cortical
thickening observed longitudinally in children over 2
years, from 7 to 9.
Age-Associated Alterations in Volumes of
Subcortical Structures
N.Accumbens
Thalamus
Hippocampus
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Hippocam pus
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N. Accumbens
Th alamu s
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Ag e
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90 100
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Age
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10 20 30 40 50 60 70 80 90 100
Age
Why don’t young brains
appear atrophied?
White Matter Growth Associated with Post-natal Proliferation
of Oligodendrocytes and Myelin Deposition
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Cerebral WM
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Post-Natal Myelination is Well Visualized on MRI :
Myelinating Fiber Tracts from 3 to 12 Months
Summary
• During the first 2-3 decades of life, age-related tissue alterations,
presumably related to brain maturation, can be observed with
morphometry.
• Though the first evidence came in the form of apparent changes in
the morphology of gray matter structures, it was suspected that
much of the change was directly, or indirectly, related to continuing
myelination and fiber tract development.
• However, until recently, further investigation of fiber tract
maturation was limited by the lack of sensitivity to white matter
structure with existing MR methods.
Diffusion imaging provides new
information about brain
connectivity
Diffusion Tensor Imaging
B
A
• Measures diffusion (motion) of protons in water molecules.
• Magnitude and direction of proton motion within a voxel can be
described by a “tensor”.
• Proton diffusion in “free” water (or cerebrospinal fluid) is
isotropic, and also tends to be relatively isotropic in gray matter.
• The linear structure of fiber tracts hinders proton diffusion and
produces anisotropy.
Diffusion Tensor Imaging
Hypothetical Explanation of
Changes on Diffusion
Imaging During Brain
Development
• During development, increasing
myelination and axon calibre,
• result in less extracellular free water,
• which results in lower diffusivity
• and increased fractional anisotropy (FA)
in brain fiber tracts.
Neural Architectural Variability
• Examined closely, the brain exhibits a complex pattern of
age-associated tissue alterations well into adulthood.
• There is substantial variability among individuals at all
ages.
• The questions:
– What is the source of this variability?
• Genetically-mediated difference in patterning of brain (cortical
arealization, strength of connectivity, etc)?
• Genetically-mediated status of maturation?
• Experience-related neuroplastic effects (short term, long term)?
• Interactions between experience and phase of maturation?
– Is this variability functionally meaningful?
Variability in neural architecture is related
to behavioral phenotypic differences,
especially (but not exclusively) in children.
Microstructural Correlates of Infant Functional
Development: Example of the Visual Pathways
(Dubois et al., J. Neurosci, 2008)
Latency of the P1
component of the
Visual Evoked
Potential correlated
with FA in the optic
radiations,
independent of
chronological age, in
5 - 17 week old
infants.
“Double Dissociation” in Correlation Patterns
Standardized
Word ID
Standardized
Digit Recall
Left SCR
0.5
0.5
R2 = 0.406
0.25
0.25
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100
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Bilateral ACR
0.5
0.5
0.25
y = 0.0026x + 0.1966
R2 = 0.4199
0.25
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Niogi, S. & McCandliss, B.D.(2006) Neuropsychologia
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Study of Behavioral Individual Differences in
Danish Children
• 95 school children, 7-13 years old
• sMRI, DTI, cognitive testing
• Inhibitory function measured with
CANTAB Stop Signal Task (SSRT)
• Spatial working memory measured with
CANTAB self-ordered search task
Stop Signal Task
FA in Right IFG and Right pre-SMA Both Contribute to Prediction of Inhibitory
Function in Children
(Madsen SK, Baare WFC, Hansen MV, Skimminge A, Ejersbo LR, Ramsøy TZ, Gerlach
C, Åkeson P, Paulson OB, Jernigan TL ., 2009)
•
Individual differences in children’s inhibitory function is related to FA
differences within the neural circuit previously implicated in SST performance.
Spatial Working Memory Task
Spatial Working Memory Performance Related
to FA in Superior Longitudinal Fasciculus
(Vestergaard M, Madsen KS, Baare WFC, Skimminge A, Ejersbo
LR, Ramsøy TZ, Gerlach C, Åkeson P, Paulson OB, Jernigan TL. .,
Under Review)
Brain microstructural correlates of visuospatial choice
reaction time in children
KS Madsen, WFC Baaré, A Skimminge, M Vestergaard, HR Siebner
and TL Jernigan
Behavioral Variability is Related to Neural Variability
(particularly in brain connections)
• Performance on cognitive tasks across
multiple domains correlates with fiber tract
structural characteristics.
• The associations remain after controlling for
age and global parameters.
• It appears that profiles of behavioral attributes
are reflected in profiles across brain fiber
pathways.
How do we interpret these associations between the
neural and the behavioral differences?
• In children, could they arise because of
differences (among children of similar age) in the
pace of biological development of the brain?
• To what extent do they reflect functional effects
of genetically-mediated differences in neural
connectivity – or in the pace of development of
these neural connections?
• To what extent are they driven by neuroplastic
effects of experience, practice, training, and
other factors that affect neural activity within
brain systems?
Are the associations related to
activity-dependent neuroplastic
changes in the tissue?
Learning to Juggle Produces Transient Change in
Cortical and Tract Morphology
(Draganski et al., Nature, 2004; Scholz et al., 2009)
•
•
•
•
Normal volunteers with no juggling skills were scanned at baseline.
Subjects were taught a simple juggling task to criterion and re-scanned.
After 3 months (Draganski et al.) or 4 week (Scholz et al.) without practice,
jugglers were scanned a third time.
Focal increases in gray matter were observed in middle temporal and left
intraparietal sulcus areas, FA was increased in underlying white matter, and
these were apparently diminished after 3 months.
Altering Cortical Connectivity: Remediation-Induced Changes
in the White Matter of Poor Readers
Timothy A. Keller and Marcel Adam Just, Neuron, 2009
A: Areas where FA
increased in
poor readers
after
remediation.
B: Areas where FA
differed
between good
and poor
readers at
baseline
• 8-10 year old children show apparent interventionlinked alterations of FA in fiber tracts.
Genes Exert Strong Effects on
Brain Morphology
• Twin studies have revealed high heritability of
brain morphology.
• We recently reported that 2 attributes of the
neural architecture, cortical surface area and
cortical thickness are both highly heritable, but
exhibit no genetic association.
• Genetic effects on developmental trajectories
have not yet been examined.
VETSA Study of Twins
Rimol et al. 2010
Pattern of Cortical Arealization Associated with Common
Genetic Variation (in males)
MECP2 SNP rs2239464
(Joyner et al.,PNAS, 2009)
Heritability of Fiber Tract
Structure (Chiang et al., 2009)
• FA in brain fiber tracts exhibits
heritability – i.e., it is more
similar between identical
(monozygotic) twins than
between fraternal (dizygotic)
twins.
Conclusions
• There is evidence that biological development of
brain tissues continues throughout childhood and
adolescence.
• The biological changes can be linked to individual
differences in behavior in developing children.
• There is much left to learn about the meaning of
these associations – i.e., about how genetic
variation, experience, and other environmental
factors interact to influence the developing mental
characteristics (and individuality) of a child.