Structural brain imaging is a window on postnatal brain development - - PowerPoint PPT Presentation

Structural brain imaging is a window

• n postnatal brain development in

children

Terry Jernigan

Old (but still widely held) View of Human Brain Structure

• All neurons are present at birth.
• Myelination of fibers occurs rapidly over the first few

years of life.

• Brain structure is “adult” at approximately age 5 (i.e.,

growth is essentially complete).

• Brain morphology is stable during late childhood,

adolescence and adulthood.

• Regressive changes of old age begin after 60.

“Brain structure is adult at approximately age 5.”

Structural MRI of young child from NIH Brain Development Study

Image Analysis for Brain Morphometry

• Stripping (Isolation of Brain

Areas)

• Bias Correction to Reduce

Signal Inhomogeneity

• Tissue Segmentation
• Anatomical Segmentation

(Within-Tissue Segmentation)

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

.4 .45 .5 .55 .6 .65 .7 .75 .8 20 40 60 80 100

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.

Changes in Brain Structure in Maturing Young People

Childhood to Adolescence

(Sowell et al, NeuroImage, 1999)

Adolescence to Adulthood

(Sowell et al, Nature Neuroscience, 1999)

Age-Associated Alterations of Volumes of Subcortical Nuclei

• 2
• 1

1 2 3

Thalamus

10 20 30 40 50 60 70 80 90 100

Age

• 2
• 1

1 2 3

• N. Accumbens

10 20 30 40 50 60 70 80 90 100

Age

Thalamus N.Accumbens

Curvilinear Age-Function for Hippocampal Volume

(Jernigan & Gamst, 2005)

• 3
• 2
• 1

1 2

Hippocampus

10 20 30 40 50 60 70 80 90 100

Age

Hippocampus

from N. Raz - Hedden & Gabrieli, 2005.

.12 .14 .16 .18 .2 .22 .24 .26 .28 .3 20 40 60 80 100

Why don’t young brains appear atrophied?

White Matter Growth Associated with Post-natal Proliferation of Oligodendrocytes and Myelin Deposition

• 3
• 2
• 1

1 2

Cerebral WM

10 20 30 40 50 60 70 80 90 100

Age

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 Tensor Imaging

• Measures diffusion (motion) of protons in water molecules.
• Direction of proton motion within a voxel can be described by a

“tensor”.

• Proton diffusion tends to be relatively isotropic in gray matter.
• The linear structure of fiber tracts constrains proton diffusion and

produces anisotropy.

White Matter Diffusion Properties

Apparent Diffusion Coefficient Tensor size Fractional Anisotropy Tensor shape

Low 1 Highly directional diffusion Isotropic diffusion

FA

White and Gray matter High Cerebrospinal fluid

Mean diffusivity

MD

Slide borrowed from Guido Gerig

Post-Natal Myelination is Well Visualized on MRI : Myelinating Fiber Tracts from 3 to 12 Months

Continued Fiber Tract Development Observable with DTI

(from Hermoye et al., 2006)

Lebel et al., 2007 ISMRM Meeting

Lebel et al., 2007 ISMRM Meeting

Lebel et al., 2007 ISMRM Meeting

Lebel et al., 2007 ISMRM Meeting

Diffusion Tensors and Development

• Tensor size reflects magnitude of diffusion.

– Tensors for voxels in CSF spaces are large and spherical (or isotropic): all 3 eigenvalues the same and all high. – Tensors in gray matter are smaller (less free water) but also isotropic: all 3 eigenvalues the same and all low.

• Tensor shape reflects directionality of diffusion.

– Tensors for voxels in fiber tracts are elongated (or anisotropic) presumably because diffusion of water molecules is higher within axons and along the axonal and myelin surfaces than perpendicular to the fiber tracts: principal eigenvalue (parallel diffusivity) higher than others (perpendicular diffusivity) – high “fractional anisotropy”.

• As fiber tracts mature, axons and their myelin sheaths become

larger and the water in extra-axonal space decreases.

– Less free water reduces all 3 eigenvalues (as in A) – But because axoplasmic flow and diffusion along fiber membranes is preserved or increased, principal eigenvalue (parallel diffusivity) is decreased less than other eigenvalues (perpendicular diffusivity). – Therefore, perpendicular diffusivity and fractional anisotropy are most affected by fiber tract development.

• Alterations of fiber organization (coherence, tortuosity) may

also contribute to anisotropy. A B C

“Absolute eigenvalue diffusion tensor analysis for human brain maturation”

(Suzuki et al., NMR in Biomedicine, 2003)

Contrast Between Age-Related Reductions in Parallel and Perpendicular Diffusivity

(Lebel et al., 2008)

Summary

• Although the changes may be visually subtle,

when examined closely, the brain exhibits a complex pattern of age-associated tissue alterations well into adulthood.

• We are just beginning to understand the biology

and the role that these dynamic changes play in evolving mental functions.

Relationships to Behavior

What is the significance of individual difference variability?

.2 .21 .22 .23 .24 .25 .26 .27 .28 .29 .3 Frontal Lobe Gray Matter 5 10 15 20 25 30 35 Age At Scan

Sowell, Delis, Stiles & Jernigan, 2001

Better memory retrieval was correlated with thinner (more mature) frontal cortex.

Normal Developmental Changes in Inferior Frontal Gray Matter Are Associated with Improvement in Phonological Processing: A Longitudinal MRI Analysis

(Lu et al., Cerebral Cortex, 2007)

Thickening inferior frontal cortex and thinning dorsal prefrontal cortex exhibit distinct functional correlates in the same children across the age range from 7-9.

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

• ptic radiations,

independent of chronological age, in 5 - 17 week old infants.

Imaging Brain Connectivity in Children with Diverse Reading Ability

(Beaulieu et al., 2005)

32 children 8 - 12 years of age Standardized Word ID Scores 72 - 129

Tractography used to identify fiber tracts involved in developing reading fluency

(from Beaulieu et al, 2005)

Maturation of White Matter is Associated with the Development of Cognitive Functions during Childhood

(Nagy et al., 2004)

Voxel clusters in which FA correlated with spatial working memory (A-C) or reading speed (D) in 23 children aged 7 to 19. A D C B

R2 = 0.406

0.25 0.5 70 100 130 0.25 0.5 70 100 130

0.25 0.5 70 100 130

y = 0.0026x + 0.1 R

0.25 0.5 70 100 130

Standardized Word ID Standardized Digit Recall

“Double Dissociation” in Correlation Patterns

Niogi, S. & McCandliss, B.D.(2006) Neuropsychologia Left SCR Bilateral ACR

Study of Response Inhibition in 65 7-13 year old children

Inhibitory Function Measured Using Stop Signal Task

• Principal measure of inhibitory function is the

stop signal reaction time (SSRT), which is an estimate of the time needed to inhibit, or cancel, a prepotent motor response.

• fMRI, lesion studies, and animal studies

suggest that right hemisphere inferior frontal and premotor areas are involved in this function.

Computational Morphometry: Tract-Based Spatial Statistics

(Smith et al., NeuroImage, 2006)

FA in Right IFG and Right pre-SMA account for variability in SSRT

• Individual differences in children’s inhibitory

function is related to FA differences within the neural circuit previously implicated in SST performance.

Summary

• In typically developing children and adolescents,

performance variability on behavioral tasks has been linked to morphological variability in structures within relevant neural systems, and to variability in the microstructure of fiber tracts within these systems.

• Often, the associations with performance have

been shown to remain after controlling for age.

How do we interpret these associations?

Do the relationships in children reflect the effects of variability in fiber tract development? What roles do intrinsic (genetic) factors play relative to extrinsic factors (experience)? Do experiential effects on morphology and fiber tract microstructure vary in magnitude, persistence, or functional impact as a function of age (i.e., across development)?

Cortical morphology and fiber structure exhibit relationships with performance variability in adults as well as children

Genes Exert Strong Effects on Brain Morphology

• Twin studies have revealed high heritability of

brain size, brain shape, and cortical volume.

• Genetic effects on fiber microstructure are

likely but not well characterized.

• Genetic effects on developmental trajectories

have not yet been examined.

There is growing evidence for experience related alteration of brain structure in cortex and fiber tracts

Summary

• Genetic factors undoubtedly have a strong influence
• n neural architectures -
• however, brain structure seems to exhibit subtle

alterations associated with maturation and brain aging, and with dynamic neurobiological responses to experience and training.

• MRI has contributed to an emerging picture of

protracted brain maturation and long-lasting neuroplasticity, persisting into adulthood.

• Given this, traditional structural MRI is giving

way to a new approach to imaging neurobiological as well as behavioral aspects

• f the dynamic processes involved in

development.

I.CAN

Institute for Child and Adolescent Neuroscience

Developmental Studies of Human Behavioral Phenotypic Variability

• Neural function at any point in time reflects

genetic and epigenetic factors, and the influence of both recent and remote extrinsic factors.

• Studies will focus on the evolution of

behavioral differences during development, the roles of genes and experience, and the degree to which behavioral phenotypes can be shaped by interventions.

Thank You