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Growth patterns in the developing brain detected by using continuum mechanical tensor maps


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The dynamic nature of growth and degenerative disease processes requires the design of sensitive strategies to detect, track and quantify structural change in the brain in its full spatial and temporal complexity. Although volumes of brain substructures are known to change during development, detailed maps of these dynamic growth processes have been unavailable. Here we report the creation of spatially complex, four-dimensional quantitative maps of growth patterns in the developing human brain, detected using a tensor mapping strategy with greater spatial detail and sensitivity than previously obtainable. By repeatedly scanning children (aged 3-15 years) across time spans of up to four years, a rostro-caudal wave of growth was detected at the corpus callosum, a fibre system that relays information between brain hemispheres. Peak growth rates, in fibres innervating association and language cortices, were attenuated after puberty, and contrasted sharply with a severe, spatially localized loss of subcortical grey matter. Conversely, at ages 3-6 years, the fastest growth rates occurred in frontal networks that regulate the planning of new actions. Local rates, profiles, and principal directions of growth were visualized in each individual child.
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This work was supported by the NIH, the NSF and the Alfred P. Sloan Foundation. We
thank J. DiCarlo, M. Usher and S. Yantis for discussions and J. Lane for technical support.
Correspondence and requests for materials should be addressed to E.N.
letters to nature
VOL 404
9 MARCH 2000
Growth patterns in the developing
brain detected by using
continuum mechanical tensor maps
Paul M. Thompson*, Jay N. Giedd², Roger P. Woods*, David MacDonald³,
Alan C. Evans³& Arthur W. Toga*
*Laboratory of Neuro Imaging, Department of Neurology, Division of Brain
Mapping, UCLA School of Medicine, 710 Westwood Plaza, Los Angeles,
California 90095-1769, USA
²Child Psychiatry Branch, National Institute of Mental Health, NIH,
10 Center Drive, MSC 1600, Bethesda 20982-1600, Maryland, USA
³Montreal Neurological Institute, McGill University, 3801 University Street,
Montreal, Que
Âbec, Canada H3A 2B4
The dynamic nature of growth and degenerative disease processes
requires the design of sensitive strategies to detect, track and
quantify structural change in the brain in its full spatial and
temporal complexity1. Although volumes of brain substructures
are known to change during development2, detailed maps of these
dynamic growth processes have been unavailable. Here we report
the creation of spatially complex, four-dimensional quantitative
maps of growth patterns in the developing human brain, detected
using a tensor mapping strategy with greater spatial detail and
sensitivity than previously obtainable. By repeatedly scanning
children (aged 15 years) across time spans of up to four years, a
rostro-caudal wave of growth was detected at the corpus callosum,
a ®bre system that relays information between brain hemispheres.
Peak growth rates, in ®bres innervating association and language
cortices, were attenuated after puberty, and contrasted sharply
with a severe, spatially localized loss of subcortical grey matter.
Conversely, at ages 3 ±6 years, the fastest growth rates occurred in
frontal networks that regulate the planning of new actions. Local
rates, pro®les, and principal directions of growth were visualized
in each individual child.
Time series of high-resolution three-dimensional magnetic reso-
nance imaging (MRI) scans were acquired across large time spans
from young normal subjects (aged 6, 6±7, 7 ±11, 8±12, 9 ±13 and
11± 15 years) at intervals ranging from two weeks to four years.
Growth patterns were recovered by computing a three-dimensional
elastic deformation ®eld, which recon®gures the anatomy at the
earlier time point into the shape of the anatomy of the later scan.
Maps of local growth rates (Figs 4) revealed the complexity and
regional heterogeneity of the tissue growth, pruning and maturation
processes of late brain development. In subjects aged 6±15 years, the
Figure 1 Growth patterns in the developing human brain detected at ages 3 ±15 years.
A rostro-caudal wave of peak growth rates is detected in young normal subjects scanned
repeatedly across time spans of up to four years. Between ages 3 and 6 years, peak
growth rates (red colours; 60± 80% locally) were detected in the frontal circuits of the
corpus callosum, which sustain mental vigilance and regulate the planning of new actions.
Older children displayed fastest growth at the callosal isthmus, which innervates temporo-
parietal systems supporting spatial association and language function. Between ages
11± 15 years, growth rates still peak at the isthmus, but are attenuated.
Figure 2 Mapping dynamic patterns of brain development: four-dimensional growth
maps. Strikingly similar growth rates were detected in the corpus callosum of ®ve young
normal subjects scanned repeatedly aged 13 years. Peak values throughout the
posterior midbody (red colours) were attenuated after puberty (11 ±15 years). By contrast,
near-zero maps of change were observed between scans acquired over a two-week
interval. Between ages 6 years, extreme growth rates were found in the anterior
interhemispheric ®bre systems that transfer information to sustain mental vigilance and
organize new actions. Tensor maps identify the principal directions of growth rates,
revealing an outward radial tissue expansion in frontal regions.
© 2000 Macmillan Magazines Ltd
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9 MARCH 2000
| 191
highest growth rates were consistently attained in temporo-parietal
systems which are functionally specialized for language, and for
understanding spatial relations (Fig. 2). In contrast to the near-zero
maps of change recovered at short time intervals (`Two-week
interval' in Fig. 2), growth maps spanning large time intervals
showed complex and heterogeneous patterns of change. Between
ages 7 and 11 years (Fig. 2), comparative stability of the splenial and
rostral ®bre systems of the corpus callosum contrasted sharply with
rapid focal growth at the callosal isthmus (up to 80%). Although
global measurements indicated an overall 22.4% increase in mid-
sagittal callosal area during the four-year time span (from
527.6 mm
to 645.6 mm
), these global values disguise the complex-
ity of local growth patterns. Local growth is as high as 80% (Fig. 2), a
feature which may not be apparent with conventional volumetric
Although some individual variation was expected, this focus of
extreme growth at the callosal isthmus was detected consistently in
all subjects tracked between 6 and 15 years (Fig. 2), suggesting that
cortico-cortical networks supporting rapid associative relay and
language functions may myelinate more extensively3and over
longer periods than rostral ®bre systems. In a girl scanned twice
exactly one year apart at ages 6 and 7 years, extreme growth (up to
85%) at the callosal isthmus contrasted with a comparatively
quiescent region in the more rostral systems that innervate frontal
and pre-frontal cortices. When a four-year growth map was gener-
ated for a slightly older child (11± 15 years, Fig. 2), growth rates were
correspondingly reduced in every region. Nonetheless, growth
patterns at the isthmus and splenium (commonly de®ned as the
posterior ®fth of the callosum) were still more rapid (20±25%
locally) than in the more anterior rostrum and genu (near-zero
change). In an analysis of grey matter at the cortex4, we recently
observed a localized grey matter loss in frontal cortex that persists in
normal subjects throughout adolescence even into adulthood. The
gradual quiescence of growth at the rostral callosum around pub-
erty may therefore be a precursor to a prolonged regressive process
of grey matter loss through adolescence into adulthood in the
frontal circuits it innervates.
Several near-zero maps of change were recovered at short time
intervals. Figure 2 shows a typical map from a subject scanned at age
8 years, exactly four years later at age 12 years, and again two weeks
later. Negligible change at short time intervals (`Two-week interval'
in Fig. 2) contrasted with a highly heterogeneous map of growth
across the four-year time span. Growth rates again achieved their
highest rates in the associative and linguistic networks that cross at
the callosal isthmus.
Figure 3 Patterns of cerebral growth. a, In a subject scanned at age 7 years and again
exactly four years later at age 11 years, dramatic growth is found in temporo-parietal
regions (red colours). b, All brain regions are stable in a control experiment (blue colours)
analysing scans acquired two weeks apart. c, Between ages 13 years, growth is also
most pronounced in temporo-parietal regions. d, This was con®rmed by digitally
overlaying models of the cerebral cortex at each time point (arrows 9 and 13). Growth at
the callosal isthmus in subjects aged 11 and 13 years (Fig. 2) is therefore
accompanied by diffuse growth in its (temporo-parietal) lobar projection zones.
Figure 4 Detecting three-dimensional patterns of deep nuclear tissue loss. a,b, Tensor
maps distinguish local growth or brain tissue loss from global displacements (b) of the
adjacent ventricular anatomy, modelled here (a) at ages 7 years (red) and 11 years
(yellow). c,d, Between ages 7 and 11 years, three-dimensional displacement vector
maps show the deformation required to recon®gure earlier models of the caudate head
into their later shape. The caudate tail is stable (blue colours, d). e,f, Local growth (e) and
anatomical displacement (d) of the caudate head are independently recovered, with 50%
tissue loss detected locally (e,f), adjacent to a region of 20± 30% growth throughout the
internal capsule.
© 2000 Macmillan Magazines Ltd
letters to nature
VOL 404
9 MARCH 2000
A subject scanned at ages 3 and 6 years exhibited a focus of peak
growth rates (60±80% locally) throughout the anterior corpus
callosum, in frontal circuits that help to sustain a vigilant mental
state and regulate the organization and planning of new actions. The
extremely rapid rates of local growth are consistent with metabolic
studies using positron emission tomography5, which show an
extraordinary doubling of the rates of glucose metabolism in the
frontal cortex between ages 2 and 4 years, with frontal metabolic
rates remaining at 199% of their adult values throughout the age
range of 8 years. Between ages 3 and 6 years, when language
function and associative thinking are not yet fully developed,
growth rates at the isthmus were more quiescent (Fig. 2; 20%
growth). Later growth foci in the isthmus, found consistently in all
subjects aged 15 years, may re¯ect ®ne tuning of language
functions known to occur late in childhood.
Regressive processes (tissue loss) were also detected at the same
time as rapid growth. In the 11 and 9±13 year old subjects (Fig.
3), maps of lobar growth revealed pronounced (2±6 mm) temporo-
parietal and pre-frontal enlargement. Somatosensory, motor and
occipital brain regions were comparatively stable, with near-zero
change in all brain regions at short time intervals (Fig. 3b). Up to
50% loss of tissue volume was detected at the caudate head (Fig. 4e
and f). This tissue loss was highly localized, and contrasted with a
20± 30% growth of the adjacent internal capsule (for which a
separate surface model was made) and a 10% dilation of the
superior ventricular horn (Fig. 4a). Gross volumetric measures
con®rmed an overall 60 mm
tissue loss at the caudate head,
although these global measures disguise the regional complexity
of the change. This example helps illustrate how tensor maps
distinguish local growth patterns (Fig. 4e) from bulk shifts, such
as global displacements of the adjacent cerebral ventricles (Fig. 4a
and b). Three-dimensional vector displacement maps (Fig. 4b and
d) emphasize that both global and local displacements are required
to match modelled anatomical elements across time. The three-
dimensional deformation ®eld, however, encodes the patterns of
local anatomical dilation and contraction, and its values are
unaffected by global displacements. Maps of local three-dimen-
sional growth are therefore not critically dependent on how well
scans are initially aligned, and can de®ne growth at arbitrary three-
dimensional points in the local anatomy (Fig. 4e). Figure 4f
indicates the anatomical context and regional complexity of these
growth and regressive processes. The foci of tissue loss corroborate
the hypothesis that pruning processes occur during this develop-
mental stage2, suggesting that these processes can be tracked in an
individual child.
We detected striking, spatially complex patterns of growth and
tissue loss in the developing human brain. A rostro-caudal wave of
peak growth rates (Fig. 1) was identi®ed in the corpus callosum.
Fibre systems that mediate language function and associative
thinking grew more rapidly than surrounding regions across time
spans before and during puberty (6±13 years), with growth attenu-
ated shortly afterwards (11±15 years). This temporal pattern coin-
cides with the ending of a well-known critical period for learning
language, consistently noted in studies of second-language acquisi-
tion, including sign language, and in isolated children not exposed
to language during early development6. The ability to learn new
languages declines rapidly after the age of 12 years, as does the
ability to recover language function if linguistic areas in one brain
hemisphere are surgically resected. Peak growth rates in linguistic
callosal regions, as well as their attenuation around puberty, may
re¯ect the conclusion of the critical period for learning language and
for accelerating signal transduction in networks that support both
associative reasoning and language function. We recently found that
the same temporo-parietal ®bre system, crossing at the callosal
isthmus, degenerates fastest in early Alzheimer's disease7, when
progressive neuronal loss and perfusion de®cits begin to occur in
temporo-parietal association cortices and their commissural pro-
jection systems. The sensitivity of the approach may therefore offer
advantages in tracking ®ne-scale effects of therapeutic interventions
in dementia and oncology, mapping the local complexities of
disease processes using dynamic rather than static criteria. M
Magnetic resonance imaging and pre-processing
Three-dimensional (256
´124 ´0.97 mm ´0.97 mm ´1.5 mm resolution) T
fast SPGR (spoiled GRASS (gradient-recalled acquisition in the steady state)) MRI
volumes were acquired from young normal subjects (mean age 8.6 63.1 years) at inter vals
ranging from two weeks to four years. For each scan pair, a radio-frequency bias ®eld
correction algorithm8was applied to both scans to eliminate intensity drifts caused by
scanner ®eld inhomogeneity. The initial scan was then rigidly registered to the target using
automated image registration software9and resampled using chirp-Z (in-plane) and linear
(out-of-plane) interpolation. Registered scans were histogram-matched (that is, their
intensity distributions were equalized) and a preliminary map of differences in MRI signal
intensities between the two scans was constructed1,10. Tensor models of structural change
were then used to calculate rates of tissue dilation, contraction and shearing, mapping
local patterns of change in three dimensions.
Image analysis
A high-resolution surface model of the cortex was automatically extracted11 from each
scan pair, and three-dimensional digital anatomical models, based on parametric surface
meshes12,13, were generated to represent a comprehensive set of deep sulcal, callosal,
caudate and ventricular surfaces at each time point14. Surface models based on manually
digitized data were averaged across multiple trials (N= 6) to minimize error15. These
model surfaces provided anatomic constraints for an elastic image registration
algorithm12,14. For each subject, this algorithm calculated a three-dimensional elastic
deformation vector ®eld, with 384
´256 ´3<0.1 billion degrees of freedom,
recon®guring the anatomy at the earlier time point into the shape of the anatomy of the
later scan. Surface deformations were used to derive a volumetric deformation ®eld from
which local measures of three-dimensional tissue dilation or contraction were quanti®ed.
Landmark points, surfaces, and curved anatomic interfaces were matched up in the pair of
three-dimensional image sets, and the biological validity of the resulting anatomical
transformation was guaranteed by forcing a large system of anatomical surface boundaries
to match exactly. These included multiple structural, functional, and tissue type bound-
aries in three dimensions, including the callosum, caudate, cortex and ventricles. The
deformation ®eld driving the earlier onto the later anatomy was extended to the full
volume using a continuum-mechanical model based on the Cauchy± Navier operator of
linear elasticity14,16±18. The resulting system of 0.1 billion second-order elliptic partial
differential equations was solved by successive over-relaxation methods, with multi-grid
acceleration12,14, on a standard radiologic workstation. Potential artefactual differences due
to differences in how surfaces were parametrized in each scan were compensated for, using
a ®eld of Christoffel symbols to modify the surface differential operators during the
anatomical transformation14.
Tensor map computation
From this transformation, local rates of tissue dilation, contraction and shearing were
calculated. Deformation processes recovered by the image-matching algorithm were
analysed mathematically with vector ®eld operators 14 to produce a variety of tensor maps.
These maps re¯ect the magnitude and principal directions of tissue dilation or contrac-
tion, and the local rates, divergence and gradients of the growth processes detected in the
dynamically changing brain.
Received 27 August 1999; accepted 21 January 2000.
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Alzheimer's disease. Lancet 348, 94±97 (1996).
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3. Yakovlev, P. I. & Lecours, A. R. in Regional Development of the Brain in Early Life (ed. Minkowski, A.)
3±70 (Davis, Philadelphia, 1967).
4. Sowell, E. R., Thompson, P. M.,Holmes, C. J., Jernigan, T. L. & Toga, A. W. In vivo evidence for post-
adolescent brain maturation frontal and striatal regions. Nature Neurosci. 2, 859± 861 (1999).
5. Chugani, H. T., Phelps, M. E. & Mazziotta, J. C. Positronemission tomography study of human brain
functional development. Ann. Neurol. 22, 487±497 (1987).
6. Grimshaw, G. M., Adelstein, A., Bryden, M. P. & MacKinnon, G. E. First-language acquisition in
adolescence: evidence for a critical period for verbal language development. Brain Lang. 63, 237±255
7. Thompson, P. M. et al. Cortical variability and asymmetry in normal aging and Alzheimer's disease.
Cereb. Cortex 8, 492±509 (1998).
8. Zijdenbos, A. P.& Dawant, B. M. Brain segmentation and white matter lesion detection in MR images.
Crit. Rev. Biomed. Eng. 22, 401±465 (1994).
9. Woods, R. P., Cherry, S. R. & Mazziotta, J. C. Rapid automated algorithm for aligning and reslicing
PET images. J. Comp. Assist. Tomogr. 16, 620 ±633 (1992).
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its application to visualizing change in neurodegenerative disorders. J. Comp. Assist. Tomogr. 20,
1012± 1022 (1996).
11. MacDonald, D., Avis, D. & Evans, A. C. in Proc. SPIE Conf. Visualizationin Biomedical Computing (ed.
Robb, R. A.) 2359, 160±169 (1994).
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| 193
12. Thompson, P. M. & Toga, A. W. A surface-based technique for warping 3-dimensional imagesof the
brain. IEEE Trans. Med. Imag. 15, 471±489 (1996).
13. Thompson, P. M. & Toga, A. W. Detection, visualization and animation of abnormal anatomic
structure with a deformable probabilistic brain atlas based on random vector ®eld transformations.
Med. Image Anal. 1, 271±294 (1997).
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15. Thompson, P. M., Schwartz, C., Lin, R. T., Khan, A. A. & Toga, A. W. 3D statistical analysis of sulcal
variability in the human brain. J. Neurosci. 16, 4261±4274 (1996).
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Correspondence and requests for materials should be addressed to P.M.T. (e-mail:
We thank E. Sowell, M. Mega and J. Mazziotta for their advice and support. P.M.T. was
supported by the Howard Hughes Medical Institute, the US Information Agency, and the
US± UK Fulbright Commission. Additional research support was provided by a Human
Brain Project grant to the International Consortium for Brain Mapping, funded jointly by
NIMH and NIDA, by National Institutes of Health intramural funding (J.N.G.), and by
the National Library of Medicine, National Science Foundation, and the NCRR.
A clonogenic common myeloid
progenitor that gives rise
to all myeloid lineages
Koichi Akashi*², David Traver*, Toshihiro Miyamoto
& Irving L. Weissman
Departments of Pathology and Developmental Biology, Stanford University School
of Medicine, Stanford, California 94305, USA
*These authors contributed equally to this work
Haematopoietic stem cells give rise to progeny that progressively
lose self-renewal capacity and become restricted to one lineage1,2.
The points at which haematopoietic stem cell-derived progenitors
commit to each of the various lineages remain mostly unknown.
We have identi®ed a clonogenic common lymphoid progenitor
that can differentiate into T, B and natural killer cells but not
myeloid cells3. Here we report the prospective identi®cation,
puri®cation and characterization, using cell-surface markers
and ¯ow cytometry, of a complementary clonogenic common
myeloid progenitor that gives rise to all myeloid lineages.
Common myeloid progenitors give rise to either megakaryo-
cyte/erythrocyte or granulocyte/macrophage progenitors. Puri-
®ed progenitors were used to provide a ®rst-pass expression
pro®le of various haematopoiesis-related genes. We propose
that the common lymphoid progenitor and common myeloid
progenitor populations re¯ect the earliest branch points between
the lymphoid and myeloid lineages, and that the commitment of
common myeloid progenitors to either the megakaryocyte/
erythrocyte or the granulocyte/macrophage lineages are mutually
exclusive events.
The existence of clonal common lymphoid progenitors (CLPs)3
suggests that complementary progenitors common to all myeloid
cells may also exist. Because the expression of the interleukin-7
receptor a-chain (IL-7Ra) marks the CLPs and other downstream
lymphoid progenitors3,4, we searched the IL-7Ra
fraction of
²Present address: Department of Cancer Immunology and AIDS, Dana-Farber Cancer Institute,
44 Binney Street, Boston, Massachusetts 02115, USA.
murine bone marrow for primitive myeloid progenitor populations.
In steady-state mouse bone marrow, myeloerythroid colony-
forming unit (CFU) activity was found almost exclusively in the
fraction (data not shown). Within this popula-
tion, Sca-1
cells are highly enriched for haematopoietic stem cells
(HSCs)3,5±7. To remove HSCs, Sca-1
cells were excluded. The
fraction was further divided into three
subpopulations according to the expression pro®les of the Fcg
receptor-II/III (FcgR), an important marker for myelomonocytic
cells and a progenitor marker in fetal liver haematopoiesis8, and
CD34, which marks a fraction of haematopoietic stem cells and
progenitors6: the FcgR
, and FcgR
populations (Fig. 1a).
Each of the above populations were cleanly isolatable (Fig. 1b)
and gave rise to distinct colony types in methylcellulose CFU
assays (Figs 1c and 2). In the presence of steel factor (Slf), Flt-3
ligand (FL), IL-11, IL-3, granulocyte/macrophage-colony stimulat-
ing factor (GM-CSF), erythropoietin (Epo) and thrombopoietin
(Tpo), ,80% of single multipotent HSCs randomly committed to
myeloid lineages9, giving rise to various types of myeloid colonies
including CFU-Mix10, burst-forming units-erythroid (BFU-E),
Figure 1 Identi®cation of myeloid progenitors in mouse bone marrow. a, The IL-7Ra
fraction was subdivided into FcgR
, and FcgR
populations (a, b, c respectively as indicated in the right-hand panel). Percentages of each
population relative to whole bone marrow are shown next to each sort gate. b, Re-analysis
of the sorted FcgR
and FcgR
populations. c, Clonogenic
myeloid colony formation in methylcellulose. From each sorted progenitor population, 288
wells receiving a single cell each were scored. FcgR
cells and HSCs formed various
myeloid colonies including CFU-Mix, whereas the FcgR
and FcgR
gave rise only to MegE and GM colonies, respectively (left). Megakaryocyte/erythroid colony
formation from the FcgR
fractions was dependent upon Epo and/or Tpo (right).
© 2000 Macmillan Magazines Ltd
... The finding that the older age group drives the association between CC mid-anterior and central subsection volumes and ToM abilities also complements the hypothesis of an anterior-toposterior maturation gradient (Thompson et al. 2000). According to this hypothesis, the most pronounced growth in anterior sections of the CC occurs in early childhood, around 3-6 years of age. ...
Full-text available
While previous research has demonstrated a link between the corpus callosum (CC) and theory of mind (ToM) abilities in individuals with corpus callosum agenesis (ACC), the relationship between CC volume and ToM remains unclear in healthy children. The present study examined whether CC volume influences children’s performance on ToM tasks that assess their understanding of pretense, emotion recognition, and false beliefs. Forty children aged 6–12 years underwent structural magnetic resonance imaging (MRI) and a cognitive test battery. We found that larger mid-anterior and central subsections of the CC significantly correlated with better ToM abilities. We could also demonstrate age- and sex-related effects, as the CC–ToM relationship differed between younger (6–8 years) and older (9–12 years) children, and between female and male participants. Importantly, the older children drove the association between the CC mid-anterior and central subsection volumes and ToM abilities. This study is the first to demonstrate that CC size is associated with ToM abilities in healthy children, underlining the idea that the CC plays a vital role in their socio-cognitive development. CC subsection volumes may thus not only serve as a measure of heterogeneity in neurodevelopmental populations known to exhibit socio-cognitive deficits, but also in typically developing children.
... Further, according to Huettig et al., both cortical structural imaging studies and functional neuroimaging studies revealed abnormalities in illiterate individuals compared to literate individuals. For example, voxelbased morphometry on structural MRI data revealed greater white matter density in the corpus collosum with the literate participants compared to the illiterate participants, which is thought to be the consequence of "undergoing extensive myelination during the critical period of reding acquisition aged 6-10 ( Thompson et al., 2000). Also, a functional neuroimaging study with PET (Peterson et al., 2007) for example showed that the literate individuals were more left-lateralised in the inferior parietal lobules during reading, while the illiterate individuals showed a more bilateral activations. ...
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Extensive research has shown that phonological awareness including phoneme awareness skills are vital when children acquire literacy skills in alphabetic languages especially in English. Furthermore, research on developmental dyslexia (DD) especially in English has been conducted with research-informed/well-established definitions of DD. This is because compared to other languages, the prevalence of DD in English is high, and thus children with DD form a large minority group. These dyslexia research encompasses cognitive-behavioural, neuroimaging, behavioural and molecular-genetic studies. There seems to be a consensus amongst these researchers that DD manifests itself as a phonological deficit, and thus the phonological deficit hypothesis (as well as naming disfluency) for DD has become prominent in the alphabetic languages, especially in English. This is because print-to-sound or sound-to-print mappings in English are not always one-to-one and thus opaque/inconsistent. Now important questions arise in discussing how children acquire reading skills in non-alphabetic languages especially in Japanese where logographic Kanji and 2-forms of syllabic Kana are used: (i) are phonological awareness skills vital when children learn to read in Japanese? (ii) can the phonological deficit hypothesis explain DD in Japanese? These questions will be addressed in this paper by comparing the behavioural and some neuroimaging studies in alphabetic languages and Japanese Kanji and Kana as well as Chinese, another non-alphabetic languag. It seems that phonological awareness may not be as important for non-alphabetic languages such as Chinese or Japanese at the start of literacy acquisition. Phonological awareness become important skills in Chinese and Japanese only when children are older. Instead of phonological awareness other metalinguistic awareness skills are important for acquisition of reading in Chinese and Japanese such as orthographic or morphological awareness (Chinese), vocabulary size (Japanese), visuo-spatial processing (Chinese and Japanese) and visual-motor integration (Chinese and Japanese) skills. Also available neuroimaging studies will be used to uncover the behavioural dissociation and the neural unity in an English-Japanese bilingual adolescent boy with monolingual dyslexia in English.
... The remodeling of the sensorimotor cortex in response to experience has been demonstrated using imaging techniques, revealing for instance that the cortical representation of the left but not the right hand of string players was larger than in non-players, and that the size increase was correlated with the age at which individuals began to play (Elbert, Pantev, Wienbruch, Rockstroh, & Taub, 1995). This remodeling or in other words neuroplasticity, which is one of the main themes in the pathophysiology of dystonia as reviewed above, has been demonstrated through late childhood, adolescence, and young adulthood (Steen, Ogg, Reddick, & Kingsley, 1997;Thompson et al., 2000). ...
Dystonia syndromes encompass a heterogeneous group of movement disorders which might be differentiated by several clinical-historical features. Among the latter, age-at-onset is probably the most important in predicting the likelihood both for the symptoms to spread from focal to generalized and for a genetic cause to be found. Accordingly, dystonia syndromes are generally stratified into early-onset and late-onset forms, the former having a greater likelihood of being monogenic disorders and the latter to be possibly multifactorial diseases, despite being currently labeled as idiopathic. Nonetheless, there are several similarities between these two groups of dystonia, including shared pathophysiological and biological mechanisms. Moreover, there is also initial evidence of age-related modifiers of early-onset dystonia syndromes and of critical periods of vulnerability of the sensorimotor network, during which a combination of genetic and non-genetic insults is more likely to produce symptoms. Based on these lines of evidence, we reappraise the double-hit hypothesis of dystonia, which would accommodate both similarities and differences between early-onset and late-onset dystonia in a single framework.
... For example, the basal ganglia and the thalamus widen and elongate with increasing age, in addition to many other morphological changes in gray and white matter (Fonov et al., 2011). There is also an uneven growth of brain structures that cannot be entirely represented using an average adult brain template (Thompson et al., 2000;Gogtay et al., 2004). Thus, mapping the effects of DBS on a group level necessitates use of a common pediatric brain template as a reference (Tödt et al., 2022;Dembek et al., 2019;Horn et al., 2017;Al-Fatly et al., 2019), which has never been adopted in neuroimaging analyses of DBS in children (Coblentz et al., 2021;Tambirajoo et al., 2021;Lumsden et al., 2022). ...
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Introduction: Deep brain stimulation (DBS) is an established treatment in patients of various ages with pharmaco-resistant neurological disorders. Surgical targeting and postoperative programming of DBS depend on the spatial location of the stimulating electrodes in relation to the surrounding anatomical structures, and on electrode connectivity to a specific distribution pattern within brain networks. Such information is usually collected using group-level analysis, which relies on the availability of normative imaging resources (atlases and connectomes). Analysis of DBS data in children with debilitating neurological disorders such as dystonia would benefit from such resources, especially given the developmental differences in neuroimaging data between adults and children. We assembled pediatric normative neuroimaging resources from open-access datasets in order to comply with age-related anatomical and functional differences in pediatric DBS populations. We illustrated their utility in a cohort of children with dystonia treated with pallidal DBS. We aimed to derive a local pallidal sweetspot and explore a connectivity fingerprint associated with pallidal stimulation to exemplify the utility of the assembled imaging resources. Methods: An average pediatric brain template (the MNI brain template 4.5-18.5 years) was implemented and used to localize the DBS electrodes in 20 patients from the GEPESTIM registry cohort. A pediatric subcortical atlas, analogous to the DISTAL atlas known in DBS research, was also employed to highlight the anatomical structures of interest. A local pallidal sweetspot was modeled, and its degree of overlap with stimulation volumes was calculated as a correlate of individual clinical outcomes. Additionally, a pediatric functional connectome of 100 neurotypical subjects from the Consortium for Reliability and Reproducibility was built to allow network-based analyses and decipher a connectivity fingerprint responsible for the clinical improvements in our cohort. Results: We successfully implemented a pediatric neuroimaging dataset that will be made available for public use as a tool for DBS analyses. Overlap of stimulation volumes with the identified DBS-sweetspot model correlated significantly with improvement on a local spatial level (R = 0.46, permuted p = 0.019). The functional connectivity fingerprint of DBS outcomes was determined to be a network correlate of therapeutic pallidal stimulation in children with dystonia (R = 0.30, permuted p = 0.003). Conclusions: Local sweetspot and distributed network models provide neuroanatomical substrates for DBS-associated clinical outcomes in dystonia using pediatric neuroimaging surrogate data. Implementation of this pediatric neuroimaging dataset might help to improve the practice and pave the road towards a personalized DBS-neuroimaging analyses in pediatric patients.
... Based on this observation, our present results could be explained by an incomplete maturation of the corpus callosum in children (Giedd et al., 1999;Luders et al., 2010b). In fact, the size of the corpus callosum is known to increase throughout adolescence and up to the middle 20 s (Keshavan et al., 2002), following a posterior-to-anterior gradient of maturation (Danielsen et al., 2020;Giedd et al., 1999;Luders et al., 2010a;Rajapakse et al., 1996;Thompson et al., 2000;Westerhausen et al., 2016) . ...
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Study objectives: Sleep slow wave activity, as measured using EEG delta power (<4 Hz), undergoes significant changes throughout development, mirroring changes in brain function and anatomy. Yet, age-dependent variations in the characteristics of individual slow waves have not been thoroughly investigated. Here we aimed at characterizing individual slow wave properties such as origin, synchronization, and cortical propagation at the transition between childhood and adulthood. Methods: We analyzed overnight high-density (256 electrodes) EEG recordings of healthy typically developing children (N=21, 10.3±1.5 years old) and young healthy adults (N=18, 31.1±4.4 years old). All recordings were preprocessed to reduce artifacts, and NREM slow waves were detected and characterized using validated algorithms. The threshold for statistical significance was set at p=0.05. Results: The slow waves of children were larger and steeper, but less widespread than those of adults. Moreover, they tended to mainly originate from and spread over more posterior brain areas. Relative to those of adults, the slow waves of children also displayed a tendency to more strongly involve and originate from the right than the left hemisphere. The separate analysis of slow waves characterized by high and low synchronization efficiency showed that these waves undergo partially distinct maturation patterns, consistent with their possible dependence on different generation and synchronization mechanisms. Conclusions: Changes in slow wave origin, synchronization, and propagation at the transition between childhood and adulthood are consistent with known modifications in cortico-cortical and subcortico-cortical brain connectivity. In this light, changes in slow-wave properties may provide a valuable yardstick to assess, track, and interpret physiological and pathological development.
... Specifically, each hippocampal or amygdala surface was parameterised into 15,000 vertices (Dong et al., 2019;Yao et al., 2020). And each vertex MMS has two kinds of morphometry features: radial distance (RD) (Pizer et al., 1999;Thompson et al., 2004) and multivariate tensor-based morphometry (mTBM) (Davatzikos, 1996;Thompson et al., 2000;Wang et al., 2010). The RD (a scalar) represents the thickness of the shape at each vertex to the medial axis, which reflects the surface differences along the surface normal directions (Wang et al., 2011). ...
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Mentalising ability, indexed as the ability to understand others' beliefs, feelings, intentions, thoughts and traits, is a pivotal and fundamental component of human social cognition. However, considering the multifaceted nature of mentalising ability, little research has focused on characterising individual differences in different mentalising components. And even less research has been devoted to investigating how the variance in the structural and functional patterns of the amygdala and hippocampus, two vital subcortical regions of the "social brain", are related to inter-individual variability in mentalising ability. Here, as a first step toward filling these gaps, we exploited inter-subject representational similarity analysis (IS-RSA) to assess relationships between amygdala and hippocampal morphometry (surface-based multivariate morphometry statistics, MMS), connectivity (resting-state functional connectivity, rs-FC) and mentalising ability (interactive mentalisation questionnaire [IMQ] scores) across the participants (N = 24). In IS-RSA, we proposed a novel pipeline, that is, computing patching and pooling operations-based surface distance (CPP-SD), to obtain a decent representation for high-dimensional MMS data. On this basis, we found significant correlations (i.e., second-order isomorphisms) between these three distinct modalities, indicating that a trinity existed in idiosyncratic patterns of brain morphometry, connectivity and mentalising ability. Notably, a region-related mentalising specificity emerged from these associations: self-self and self-other mentalisation are more related to the hippocampus, while other-self mentalisation shows a closer link with the amygdala. Furthermore, by utilising the dyadic regression analysis, we observed significant interactions such that subject pairs with similar morphometry had even greater mentalising similarity if they were also similar in rs-FC. Altogether, we demonstrated the feasibility and illustrated the promise of using IS-RSA to study individual differences, deepening our understanding of how individual brains give rise to their mentalising abilities.
... The radial distance (RD) describes the morphological changes along the surface normal direction. 39 Surface tensor-based morphometry (TBM) [40][41][42] examines the spatial derivatives (detJ, where J is the Jacobian matrix of the deformation from the registration) of the deformation maps that register each hippocampal surface to the common template. ...
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Background: Alzheimer's disease (AD) is a neurodegenerative disease characterized by progressive cognitive decline, and mild cognitive impairment (MCI) is associated with a high risk of developing AD. Hippocampal morphometry analysis is believed to be the most robust magnetic resonance imaging (MRI) markers for AD and MCI. Multivariate morphometry statistics (MMS), a quantitative method of surface deformations analysis, is confirmed to have strong statistical power for evaluating hippocampus. Aims: We aimed to test whether surface deformation features in hippocampus can be employed for early classification of AD, MCI, and healthy controls (HC). Methods: We first explored the differences in hippocampus surface deformation among these three groups by using MMS analysis. Additionally, the hippocampal MMS features of selective patches and support vector machine (SVM) were used for the binary classification and triple classification. Results: By the results, we identified significant hippocampal deformation among the three groups, especially in hippocampal CA1. In addition, the binary classification of AD/HC, MCI/HC, AD/MCI showed good performances, and area under curve (AUC) of triple-classification model achieved 0.85. Finally, positive correlations were found between the hippocampus MMS features and cognitive performances. Conclusions: The study revealed significant hippocampal deformation among AD, MCI, and HC. Additionally, we confirmed that hippocampal MMS can be used as a sensitive imaging biomarker for the early diagnosis of AD at the individual level.
Intelligence is a trait of human cognition defined by numerous psychological theories, including Spearman’s two factor theory of intelligence, Gardner’s theory of multiple intelligences, and Robert Sternberg’s triarchic theory of intelligence. Despite the vast amount of literature regarding what causes individuals to score highly on various intelligence metrics, the question remains one of the most debated topics of human cognition. This chapter will neither address which theory most effectively describes intelligence nor what factors cause the wide discrepancies between different individuals’ intelligence scores. Rather we will discuss several replicable magnetic resonance imaging studies and subsequent publications that support the notion that midsagittal corpus callosum thickness is an important anatomical marker of intelligence scores using different metrics—performance and vocabulary intelligence quotient scores.
Visual awareness is a favorable form of consciousness to study neurobiologically. We propose that it takes two forms: a very fast form, linked to iconic memory, that may be difficult to study; and a somewhat slower one involving visual attention and short-term memory. In the slower form an attentional mechanism transiently binds together all those neurons whose activity relates to the relevant features of a single visual object. We suggest this is done by generating coherent semi-synchronous oscillations, probably in the 40-70 Hz range. These oscillations then activate a transient short-term (working) memory. We outfit several lines of experimental work that might advance the understanding of the neural mechanisms involved. The neural basis of very short-term memory especially needs more experimental study.
An iterative algorithm is presented for simultaneous deformation of multiple curves and surfaces to an MRI, with inter-surface constraints and self-intersection avoidance. The resulting robust segmentation, combined with local curvature matching, automatically creates surfaces of MRI datasets with a common mapping to surface parametric space.
This chapter reviews various methods for elastic matching of brain images, and discusses some of their applications, focusing on pathology detection. It emphasizes the advantages and limitations of each approach, while realizing that hybrid strategies will be developed in the future which capitalize on the merits of each. The complexity of anatomic variations, especially at the cortex, mandates the design of specialized approaches to handle neuroanatomic data. Recent years have seen considerable advances in the ability to extract models of neuroanatomic structures in their full spatial complexity. Models which are conveniently parameterized can provide a valuable information source to guide mathematical algorithms which analyze future neuroanatomic data of the same type. One of the most promising applications of warping algorithms is their use as a virtual sensor, creating exquisitely detailed maps of anatomic differences. Maps of anatomical change can also be generated by warping scans acquired from the same subject over time. In many ways, static representations of structure are ill-suited to determine the dynamic effects of disease. Serial scanning of human subjects, however, when combined with a powerful set of registration and modeling algorithms, enables disease and growth processes to be tracked in their full spatial and temporal complexity.
This paper studies mathematical methods in the emerging new discipline of Computational Anatomy. Herein we formalize the Brown/Washington University model of anatomyfollowing the global pattern theory introduced in [1, 2], in which anatomies are represented as deformable templates, collections of 0 � 1 � 2 � 3;dimensional manifolds. Typical structure is carried by the template with the variabilities accommodated via the application of random transformations to the background manifolds. The anatomical model is a quadruple ( � H � I � P), the background space = [ M of 0 � 1 � 2 � 3;dimensional manifolds, the set of di eomorphic transformations on the background space H: $ , the space of idealized medical imagery I, and P the family of probability measures on H. The group of di eomorphic transformations H is chosen to be rich enough so that a large family of shapes may be generated with the topologies of the template maintained. For normal anatomy one deformable template is studied, with ( � H � I) corresponding to a homogeneous space [3], in that it can be completely generated from one of its elements, I = HItemp�Itemp 2I. For disease, a family of templates [ Itemp are introduced of perhaps varying dimensional transformation classes. The complete anatomy is is a collection of homogeneous spaces Itotal = [ (I � H). There are three principal components to computational anatomy studied herein.
From over 100 children studied with 2-deoxy-2{18F}fluoro-D-glucose and positron emission tomography we selected 29 children (aged 5 days to 15.1 years) who had suffered transient neurological events not significantly affecting normal neurodevelopment. These 29 children were reasonably representative of normal children and provided an otherwise unobtainable population in which to study developmental changes in local cerebral metabolic rates for glucose (lCMRGlc). In infants less than 5 weeks old lCMRGlc was highest in sensorimotor cortex, thalamus, brainstem, and cerebellar vermis. By 3 months, lCMRGlc had increased in parietal, temporal, and occipital cortices; basal ganglia; and cerebellar cortex. Frontal and dorsolateral occipital cortical regions displayed a maturational rise in lCMRGlc by approximately 6 to 8 months. Absolute values of lCMRGlc for various grey matter regions were low at birth (13 to 25 μmol/min/100 gm), and rapidly rose to reach adult values (19 to 33 μmol/min/100 gm) by 2 years. lCMRGlc continued to rise until, by 3 to 4 years, it reached values of 49 to 65 μmol/min/100 gm in most regions. These high rates were maintained until approximately 9 years, when they began to Decemberline, and reached adult rates again by the latter part of the second Decemberade. The highest increases of lCMRGlc over adult values occurred in cerebral cortical structures; lesser increases were seen in subcortical structures and in the cerebellum. This time course of lCMRGlc changes matches that describing the process of initial overproduction and subsequent elimination of excessive neurons, synapses, and dendritic spines known to occur in the developing brain. The determination of changing metabolic patterns accompanying normal brain development is a necessary prelude to the study of abnormal brain development with positron emission tomography.
The sexual dimorphism of the human corpus callosum (CC) is currently controversial, possibly because of difficulties in morphometric analysis. We have reinvestigated the issue by using morphometric techniques specially designed to yield objective measurements of CC size and shape. The development of the CC was studied with similar techniques in order to investigate whether its final shape and size might be influenced by axonal elimination, as could be expected from previous animal studies. We have measured the CCs of 32 men and 26 women; 27 male and 19 female CCs were from brain tissue, the others were from magnetic resonance imaging graphs. Women tended to have (1) a smaller cross-sectional callosal area (CCA); (2) a larger fraction of CCA in the posterior fifth of the CC; (3) more slender CCs; and (4) more bulbous splenia. These differences could not be detected by simple inspection but were demonstrated by measurement and statistical analysis. However, CCA was correlated with the other sexually dimorphic parameters, and the sex-related differences in the latter became nonsignificant when variations in CCA were factored out or when male and female populations with similar CCA were compared. In addition, we analyzed CCs of 16 male and 16 female fetuses and of 13 male and 15 female infants and children. This sample ranged in age between 20 weeks of gestation and 14 years but covered in detail the period up to 14 months after birth. CCA increased throughout the latter period but decreased slightly between about 33 weeks of gestation and the beginning of the second postnatal mouth. This decrease coincided with thinning of the CC and a marked increase in bulbosity of the splenium. No sexual dimorphism could be demonstrated until the beginning of the postnatal period. In the age group between birth (at term) and the 14th month, CCA was, as in the adult, larger in males. Unlike in the adults, the CC was longer in males and the bulbosity index was the same in the two sexes. Axonal elimination may play a role in the perinatal pause in CCA growth and in the concomitant changes in callosal shape.