Structural brain imaging is a window on 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 .8 .75 .7 .65 .6 .55 .5 .45 .4 0 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 Adolescence to Adulthood (Sowell et al, NeuroImage, 1999) (Sowell et al, Nature Neuroscience, 1999)
Age-Associated Alterations of Volumes of Subcortical Nuclei N.Accumbens Thalamus 3 3 2 2 N. Accumbens 1 Thalamus 1 0 0 -1 -1 -2 -2 0 10 20 30 40 50 60 70 80 90 100 0 10 20 30 40 50 60 70 80 90 100 Age Age
Curvilinear Age-Function for Hippocampal Volume (Jernigan & Gamst, 2005) 2 1 Hippocampus 0 Hippocampus -1 -2 -3 .3 0 10 20 30 40 50 60 70 80 90 100 .28 Age .26 .24 .22 .2 .18 .16 .14 .12 0 20 40 60 80 100 from N. Raz - Hedden & Gabrieli, 2005 .
Why don’t young brains appear atrophied?
White Matter Growth Associated with Post-natal Proliferation of Oligodendrocytes and Myelin Deposition 2 1 Cerebral WM 0 -1 -2 -3 0 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 Fractional Anisotropy Tensor size Tensor shape Mean diffusivity White and Cerebrospinal Isotropic Highly directional Gray matter fluid diffusion diffusion MD FA Low High 0 1 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. A • 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 B 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 C most affected by fiber tract development. • Alterations of fiber organization (coherence, tortuosity) may also contribute to anisotropy.
“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? Sowell, Delis, Stiles & Jernigan, 2001 .3 .29 .28 .27 Frontal Lobe Gray Matter .26 Better memory .25 retrieval was .24 .23 correlated with thinner .22 (more mature) frontal .21 .2 cortex. 5 10 15 20 25 30 35 Age At Scan
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 optic 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 A B reading speed (D) in 23 children aged 7 to 19. C D
“Double Dissociation” in Correlation Patterns Standardized Standardized Word ID Digit Recall Left SCR 0.5 0.5 R 2 = 0.406 0.25 0.25 70 100 130 70 100 130 Bilateral ACR 0.5 0.5 y = 0.0026x + 0.1 2 = 0.4199 R 0.25 0.25 70 100 130 70 100 130 Niogi, S. & McCandliss, B.D.(2006) Neuropsychologia
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)?
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